diff -Nru python-mne-0.23.4+dfsg/azure-pipelines.yml python-mne-1.1.0+dfsg/azure-pipelines.yml --- python-mne-0.23.4+dfsg/azure-pipelines.yml 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/azure-pipelines.yml 2022-08-03 15:48:14.000000000 +0000 @@ -17,30 +17,29 @@ jobs: - job: Skip pool: - vmImage: 'ubuntu-18.04' + vmImage: 'ubuntu-latest' variables: DECODE_PERCENTS: 'false' RET: 'true' + BUILD_REASON: $(Build.Reason) steps: - bash: | - git_log=`git log --max-count=1 --skip=1 --pretty=format:"%s"` + git_log=`git log --format=oneline -n 1 --skip=1` echo "##vso[task.setvariable variable=log]$git_log" - bash: echo "##vso[task.setvariable variable=RET]false" - condition: or(contains(variables.log, '[skip azp]'), contains(variables.log, '[azp skip]'), contains(variables.log, '[skip ci]'), contains(variables.log, '[ci skip]')) + condition: and(eq(variables.BUILD_REASON, 'PullRequest'), or(contains(variables.log, '[skip azp]'), contains(variables.log, '[azp skip]'), contains(variables.log, '[skip ci]'), contains(variables.log, '[ci skip]'))) - bash: echo "##vso[task.setvariable variable=start_main;isOutput=true]$RET" name: result -- stage: Main - condition: and(succeeded(), eq(dependencies.Check.outputs['Skip.result.start_main'], 'true')) - dependsOn: Check +- stage: Style variables: AZURE_CI: 'true' jobs: - - job: Style + - job: All pool: - vmImage: 'ubuntu-18.04' + vmImage: 'ubuntu-latest' variables: - PYTHON_VERSION: '3.8' + PYTHON_VERSION: '3.9' PYTHON_ARCH: 'x64' steps: - bash: echo $(COMMIT_MSG) @@ -52,10 +51,15 @@ displayName: 'Get Python' - bash: | set -e - python -m pip install --upgrade pip setuptools - python -m pip install numpy scipy matplotlib -r requirements_testing.txt + python -m pip install --progress-bar off --upgrade pip setuptools wheel + python -m pip install --progress-bar off -r requirements_base.txt -r requirements_hdf5.txt -r requirements_testing.txt displayName: Install dependencies - condition: always() + - bash: | + make flake + displayName: make flake + - bash: | + make codespell-error + displayName: make codespell - bash: | make pydocstyle displayName: make pydocstyle @@ -63,6 +67,7 @@ - bash: | make docstring displayName: make docstring + condition: always() - bash: | make nesting displayName: make nesting @@ -76,16 +81,23 @@ displayName: make check-readme condition: always() - - - job: Ultraslow +- stage: Test + condition: and(succeeded(), eq(dependencies.Check.outputs['Skip.result.start_main'], 'true')) + dependsOn: ['Style', 'Check'] + variables: + AZURE_CI: 'true' + jobs: + - job: Ultraslow_PG pool: - vmImage: 'ubuntu-18.04' + vmImage: 'ubuntu-20.04' variables: DISPLAY: ':99' OPENBLAS_NUM_THREADS: '1' steps: - bash: | - sudo apt install libxkbcommon-x11-0 xvfb tcsh libxcb* + set -e + ./tools/setup_xvfb.sh + sudo apt install -yq tcsh displayName: 'Install Ubuntu dependencies' - bash: | source tools/get_minimal_commands.sh @@ -97,18 +109,26 @@ displayName: 'Test minimal commands' - task: UsePythonVersion@0 inputs: - versionSpec: '3.7' + versionSpec: '3.9' architecture: 'x64' addToPath: true displayName: 'Get Python' - bash: | - /sbin/start-stop-daemon --start --quiet --pidfile /tmp/custom_xvfb_99.pid --make-pidfile --background --exec /usr/bin/Xvfb -- :99 -screen 0 1400x900x24 -ac +extension GLX +render -noreset; - displayName: 'Spin up Xvfb' - - bash: | set -e - python -m pip install --upgrade pip setuptools - python -m pip install --upgrade numpy scipy vtk -r requirements.txt -r requirements_testing.txt -r requirements_testing_extra.txt codecov + python -m pip install --progress-bar off --upgrade pip setuptools wheel codecov + python -m pip install --progress-bar off mne-qt-browser[opengl] pyvista scikit-learn pytest-error-for-skips python-picard "PySide6!=6.3.0" qtpy + python -m pip uninstall -yq mne + python -m pip install --progress-bar off --upgrade -e .[test] displayName: 'Install dependencies with pip' + - bash: | + set -e + mne sys_info -pd + mne sys_info -pd | grep "qtpy: .*{PySide6=.*}$" + displayName: Print config + # Uncomment if "xcb not found" Qt errors/segfaults come up again + # - bash: | + # set -e + # LD_DEBUG=libs python -c "from PySide6.QtWidgets import QApplication, QWidget; app = QApplication([]); import matplotlib; matplotlib.use('QtAgg'); import matplotlib.pyplot as plt; plt.figure()" - bash: source tools/get_testing_version.sh displayName: 'Get testing version' - task: Cache@2 @@ -118,40 +138,54 @@ displayName: 'Cache testing data' - script: python -c "import mne; mne.datasets.testing.data_path(verbose=True)" displayName: 'Get test data' - - script: pytest -m "ultraslowtest" --tb=short --cov=mne -vv mne - displayName: 'Run ultraslow tests' - - script: codecov --root $BUILD_REPOSITORY_LOCALPATH -t $CODECOV_TOKEN + - script: pytest --error-for-skips -m "ultraslowtest or pgtest" --tb=short --cov=mne --cov-report=xml --cov-report=html -vv mne + displayName: 'slow and mne-qt-browser tests' + # Coverage + - bash: bash <(curl -s https://codecov.io/bash) displayName: 'Codecov' - env: - CODECOV_TOKEN: $(CODECOV_TOKEN) condition: succeededOrFailed() - task: PublishTestResults@2 inputs: - testResultsFiles: 'junit-*.xml' + testResultsFiles: '**/junit-*.xml' testRunTitle: 'Publish test results for $(Agent.JobName)' failTaskOnFailedTests: true condition: succeededOrFailed() + - task: PublishCodeCoverageResults@1 + inputs: + codeCoverageTool: Cobertura + summaryFileLocation: '$(System.DefaultWorkingDirectory)/**/coverage.xml' + reportDirectory: '$(System.DefaultWorkingDirectory)/**/htmlcov' - - - job: Notebook + - job: Qt pool: - vmImage: 'ubuntu-18.04' + vmImage: 'ubuntu-20.04' + variables: + DISPLAY: ':99' + OPENBLAS_NUM_THREADS: '1' + TEST_OPTIONS: "--tb=short --cov=mne --cov-report=xml --cov-report=html --cov-append -vv mne/viz/_brain mne/viz/backends mne/viz/tests/test_evoked.py mne/gui" steps: + - bash: ./tools/setup_xvfb.sh + displayName: 'Install Ubuntu dependencies' + - task: UsePythonVersion@0 + inputs: + versionSpec: '3.10' + architecture: 'x64' + addToPath: true + displayName: 'Get Python' - bash: | set -e - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh --progress=dot:mega - bash miniconda.sh -b -p ~/miniconda - source ~/miniconda/etc/profile.d/conda.sh - conda activate base - conda env update --file server_environment.yml - pip uninstall -yq mne - pip install -ve . - pip install -r requirements_testing.txt -r requirements_testing_extra.txt codecov - echo "##vso[task.setvariable variable=PATH]${PATH}" - echo "##vso[task.setvariable variable=LD_LIBRARY_PATH]${LD_LIBRARY_PATH}" - displayName: 'Install dependencies' - - script: mne sys_info - displayName: 'Print config and test access to commands' + python -m pip install --progress-bar off --upgrade pip setuptools wheel + python -m pip install --progress-bar off --upgrade --pre --only-binary=\"numpy,scipy,matplotlib,vtk\" numpy scipy matplotlib vtk + python -c "import vtk" + python -m pip install --progress-bar off --upgrade -r requirements.txt -r requirements_testing.txt -r requirements_testing_extra.txt codecov + python -m pip install -e . + displayName: 'Install dependencies with pip' + - bash: | + set -e + which mne + mne sys_info -pd + mne sys_info -pd | grep "^mne: .*dev0.*$" + displayName: Print config - bash: source tools/get_testing_version.sh displayName: 'Get testing version' - task: Cache@2 @@ -161,46 +195,83 @@ displayName: 'Cache testing data' - script: python -c "import mne; mne.datasets.testing.data_path(verbose=True)" displayName: 'Get test data' - - script: pytest --tb=short --cov=mne -vv mne/viz - displayName: 'Run viz tests' - - script: codecov --root $BUILD_REPOSITORY_LOCALPATH -t $CODECOV_TOKEN + - bash: | + set -e + mne sys_info -pd + mne sys_info -pd | grep "qtpy: .*{PySide6=.*}$" + pytest -m "not slowtest" ${TEST_OPTIONS} + python -m pip uninstall -yq PySide6 + displayName: 'PySide6' + - bash: | + set -e + python -m pip install PyQt6 + mne sys_info -pd + mne sys_info -pd | grep "^qtpy: .*{PyQt6=.*}$" + pytest -m "not slowtest" ${TEST_OPTIONS} + python -m pip uninstall -yq PyQt6 PyQt6-sip PyQt6-Qt6 + displayName: 'PyQt6' + - bash: | + set -e + python -m pip install PySide2 + mne sys_info -pd + mne sys_info -pd | grep "^qtpy: .*{PySide2=.*}$" + pytest -m "not slowtest" ${TEST_OPTIONS} + python -m pip uninstall -yq PySide2 + displayName: 'PySide2' + # PyQt5 leaves cruft behind, so run it last + - bash: | + set -e + python -m pip install PyQt5 + mne sys_info -pd + mne sys_info -pd | grep "^qtpy: .*{PyQt5=.*}$" + pytest -m "not slowtest" ${TEST_OPTIONS} + python -m pip uninstall -yq PyQt5 PyQt5-sip PyQt5-Qt5 + displayName: 'PyQt5' + # Coverage + - bash: bash <(curl -s https://codecov.io/bash) displayName: 'Codecov' - env: - CODECOV_TOKEN: $(CODECOV_TOKEN) condition: succeededOrFailed() - task: PublishTestResults@2 inputs: - testResultsFiles: 'junit-*.xml' + testResultsFiles: '**/junit-*.xml' testRunTitle: 'Publish test results for $(Agent.JobName)' failTaskOnFailedTests: true condition: succeededOrFailed() - + - task: PublishCodeCoverageResults@1 + inputs: + codeCoverageTool: Cobertura + summaryFileLocation: '$(System.DefaultWorkingDirectory)/**/coverage.xml' + reportDirectory: '$(System.DefaultWorkingDirectory)/**/htmlcov' - job: Windows pool: - vmImage: 'VS2017-Win2016' + vmImage: 'windows-latest' variables: MNE_LOGGING_LEVEL: 'warning' MNE_FORCE_SERIAL: 'true' OPENBLAS_NUM_THREADS: 1 + OMP_NUM_THREADS: 1 + MKL_NUM_THREADS: 2 + OMP_DYNAMIC: 'false' + MKL_DYNAMIC: 'false' PYTHONUNBUFFERED: 1 PYTHONIOENCODING: 'utf-8' AZURE_CI_WINDOWS: 'true' PYTHON_ARCH: 'x64' + timeoutInMinutes: 70 strategy: maxParallel: 4 matrix: - 3.7 conda: + 3.9 conda: PLATFORM: 'x86-64' TEST_MODE: 'conda' - PYTHON_VERSION: '3.8' - 3.7 pip: + PYTHON_VERSION: '3.9' + 3.9 pip: TEST_MODE: 'pip' - PYTHON_VERSION: '3.7' - 3.8 pip pre: + PYTHON_VERSION: '3.9' + 3.10 pip pre: TEST_MODE: 'pip-pre' - PYTHON_VERSION: '3.8' - OPENBLAS_CORETYPE: 'prescott' # workaround for https://github.com/numpy/numpy/issues/16913 + PYTHON_VERSION: '3.10' steps: - task: UsePythonVersion@0 inputs: @@ -211,35 +282,37 @@ displayName: 'Get Python' # https://docs.microsoft.com/en-us/azure/devops/pipelines/ecosystems/anaconda # https://github.com/MicrosoftDocs/pipelines-anaconda - # Qt 5.9 post-link failure: # https://github.com/ContinuumIO/anaconda-issues/issues/10949 - - script: echo "##vso[task.prependpath]%CONDA%;%CONDA%\condabin;%CONDA%\Scripts;%CONDA%\Library\bin;%SystemRoot%\system32;%SystemRoot%;%SystemRoot%\System32\Wbem;" + - script: | + echo "##vso[task.prependpath]%CONDA%;%CONDA%\condabin;%CONDA%\Scripts;%CONDA%\Library\bin;%PROGRAMFILES%\Git\bin;%SystemRoot%\system32;%SystemRoot%;%SystemRoot%\System32\Wbem;%PROGRAMFILES%\Git\usr\bin" condition: in(variables['TEST_MODE'], 'conda') - displayName: Add conda to PATH, deal with Qt 5.9 bug + displayName: Add conda to PATH, deal with Qt linking bug - bash: | set -e - git clone --depth 1 git://github.com/pyvista/gl-ci-helpers.git + git clone --depth 1 https://github.com/pyvista/gl-ci-helpers.git powershell gl-ci-helpers/appveyor/install_opengl.ps1 displayName: Install OpenGL - bash: | set -e ./tools/azure_dependencies.sh condition: in(variables['TEST_MODE'], 'pip', 'pip-pre') - displayName: 'Install dependencies with pip' - - powershell: | - Set-StrictMode -Version Latest - $ErrorActionPreference = "Stop" - $PSDefaultParameterValues['*:ErrorAction']='Stop' - conda update -n base -c defaults conda - conda env update --name base --file environment.yml - pip uninstall -yq mne - pip install -r requirements_testing.txt -r requirements_testing_extra.txt codecov + displayName: Install dependencies with pip + - script: conda env update --name base --file environment.yml condition: eq(variables['TEST_MODE'], 'conda') - displayName: 'Install dependencies with conda' - - script: python setup.py develop + displayName: Setup MNE environment + - bash: | + set -e + conda remove -c conda-forge --force -yq mne + rm /c/Miniconda/Scripts/mne.exe + condition: eq(variables['TEST_MODE'], 'conda') + displayName: Remove old MNE + - script: pip install -e . displayName: 'Install MNE-Python dev' - - script: mne sys_info - displayName: 'Print config and test access to commands' + - script: pip install --progress-bar off -e .[test] codecov + condition: eq(variables['TEST_MODE'], 'conda') + displayName: Install testing requirements + - script: mne sys_info -pd + displayName: 'Print config' - script: python -c "import numpy; numpy.show_config()" displayName: Print NumPy config - bash: source tools/get_testing_version.sh @@ -251,16 +324,57 @@ displayName: 'Cache testing data' - script: python -c "import mne; mne.datasets.testing.data_path(verbose=True)" displayName: 'Get test data' - - script: pytest -m "not slowtest" --tb=short --cov=mne -vv mne + - script: pytest -m "not (slowtest or pgtest)" --tb=short --cov=mne --cov-report=xml --cov-report=html -vv mne displayName: 'Run tests' - - script: codecov --root %BUILD_REPOSITORY_LOCALPATH% -t %CODECOV_TOKEN% + - bash: bash <(curl -s https://codecov.io/bash) displayName: 'Codecov' - env: - CODECOV_TOKEN: $(CODECOV_TOKEN) condition: succeededOrFailed() - task: PublishTestResults@2 inputs: - testResultsFiles: 'junit-*.xml' + testResultsFiles: '**/junit-*.xml' testRunTitle: 'Publish test results for $(Agent.JobName) $(TEST_MODE) $(PYTHON_VERSION)' failTaskOnFailedTests: true condition: succeededOrFailed() + - task: PublishCodeCoverageResults@1 + inputs: + codeCoverageTool: Cobertura + summaryFileLocation: '$(System.DefaultWorkingDirectory)/**/coverage.xml' + reportDirectory: '$(System.DefaultWorkingDirectory)/**/htmlcov' + + - job: SphinxWindows + pool: + vmImage: 'windows-latest' + variables: + AZURE_CI_WINDOWS: 'true' + PYTHON_ARCH: 'x64' + PLATFORM: 'x86-64' + PYTHON_VERSION: '3.9' + steps: + - bash: | + set -e + git clone --depth 1 https://github.com/pyvista/gl-ci-helpers.git + powershell gl-ci-helpers/appveyor/install_opengl.ps1 + displayName: Install OpenGL + - script: echo "##vso[task.prependpath]%CONDA%;%CONDA%\condabin;%CONDA%\Scripts;%CONDA%\Library\bin;%CONDA%\Library\bin\graphviz;%PROGRAMFILES%\Git\bin;%SystemRoot%\system32;%SystemRoot%;%SystemRoot%\System32\Wbem;%PROGRAMFILES%\Git\usr\bin" + displayName: Add conda to PATH, deal with Qt linking bug + - script: conda update -n base -c defaults conda + displayName: Update conda + - script: conda env update --name base --file environment.yml + displayName: Setup MNE environment + - script: conda install -c conda-forge graphviz + displayName: Add graphviz dot + - bash: | + set -e + conda remove -c conda-forge --force -yq mne + rm /c/Miniconda/Scripts/mne.exe + displayName: Remove old MNE + - script: pip install -e . + displayName: Install dev MNE + - script: pip install --progress-bar off -r requirements_doc.txt + displayName: Install documentation dependencies + - script: mne sys_info -pd + displayName: Print config and test access to commands + - script: python -c "import numpy; numpy.show_config()" + displayName: Print NumPy config + - bash: make -C doc html_dev-noplot + displayName: 'Build doc' diff -Nru python-mne-0.23.4+dfsg/.circleci/config.yml python-mne-1.1.0+dfsg/.circleci/config.yml --- python-mne-0.23.4+dfsg/.circleci/config.yml 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/.circleci/config.yml 2022-08-03 15:48:14.000000000 +0000 @@ -1,22 +1,24 @@ -# Tagging a commit with: -# - [circle front] will build the front page and perform test-doc -# - [circle full] will build everything -# - [circle linkcheck] will run our linkcheck -# - [circle interactive_test] will run our test suite (useful for debugging -# issues using "Rerun with SSH") +# By default, for PRs CircleCI will build only examples that have changed. +# For main commits, builds are skipped entirely, as we only do full builds +# scheduled for one time daily. +# +# Tagging a commit with the following overrides these behaviors: +# - [circle front] will run the front page examples and perform test-doc +# - [circle full] will run all examples and perform test-doc +# - [circle linkcheck] will run our linkcheck job +# - [circle deploy] on a main or maint/* commit will try to immediately build +# and deploy docs rather than waiting for the nightly build version: 2.1 -_xvfb: &xvfb - name: Start Xvfb virtual framebuffer - command: | - echo "export DISPLAY=:99" >> $BASH_ENV - /sbin/start-stop-daemon --start --quiet --pidfile /tmp/custom_xvfb_99.pid --make-pidfile --background --exec /usr/bin/Xvfb -- :99 -screen 0 1280x1024x24 -ac +extension GLX +render -noreset -nolisten tcp -nolisten unix - jobs: build_docs: + parameters: + scheduled: + type: string + default: "false" docker: - - image: circleci/python:3.8.5-buster + - image: cimg/base:stable-20.04 steps: - restore_cache: keys: @@ -25,9 +27,11 @@ - run: name: Complete checkout command: | + set -e if ! git remote -v | grep upstream; then - git remote add upstream git://github.com/mne-tools/mne-python.git + git remote add upstream https://github.com/mne-tools/mne-python.git fi + git remote set-url upstream https://github.com/mne-tools/mne-python.git git fetch upstream - save_cache: key: source-cache @@ -36,45 +40,80 @@ - run: name: Check-skip command: | + set -e export COMMIT_MESSAGE=$(git log --format=oneline -n 1); if [[ -v CIRCLE_PULL_REQUEST ]] && ([[ "$COMMIT_MESSAGE" == *"[skip circle]"* ]] || [[ "$COMMIT_MESSAGE" == *"[circle skip]"* ]]); then echo "Skip detected, exiting job ${CIRCLE_JOB} for PR ${CIRCLE_PULL_REQUEST}." circleci-agent step halt; fi - run: + name: Merge with upstream and triage run + command: | + set -e + echo $(git log -1 --pretty=%B) | tee gitlog.txt + echo ${CI_PULL_REQUEST//*pull\//} | tee merge.txt + if [[ $(cat merge.txt) != "" ]]; then + echo "Merging $(cat merge.txt)"; + git pull --ff-only upstream "refs/pull/$(cat merge.txt)/merge"; + else + if [[ "$CIRCLE_BRANCH" == "main" ]]; then + KIND=dev + else + KIND=stable + fi + export COMMIT_MESSAGE=$(git log --format=oneline -n 1); + if [[ "<< parameters.scheduled >>" == "true" ]]; then + echo "Scheduled full build detected, checking if it's required." + wget https://mne.tools/${KIND}/_version.txt; + REMOTE_VERSION=$(cat _version.txt) + THIS_VERSION=$(git rev-parse HEAD) + echo "Current ${KIND} SHA: ${REMOTE_VERSION}" + echo "This ${KIND} SHA: ${THIS_VERSION}" + if [[ "${THIS_VERSION}" != "${REMOTE_VERSION}" ]]; then + echo "Rebuild required." + else + echo "Rebuild skipped." + circleci-agent step halt; + fi + elif [[ "$COMMIT_MESSAGE" == *"[circle deploy]"* ]]; then + echo "Forced deployed build detected, building and deploying docs"; + else + echo "Waiting until scheduled run to build ${KIND} docs, exiting job ${CIRCLE_JOB}." + circleci-agent step halt; + fi + fi + + - run: name: Set BASH_ENV command: | set -e + ./tools/setup_xvfb.sh + sudo apt install -qq graphviz optipng python3.8-venv python3-venv libxft2 ffmpeg + python3.8 -m venv ~/python_env echo "set -e" >> $BASH_ENV echo "export OPENBLAS_NUM_THREADS=4" >> $BASH_ENV echo "export XDG_RUNTIME_DIR=/tmp/runtime-circleci" >> $BASH_ENV echo "export MNE_FULL_DATE=true" >> $BASH_ENV source tools/get_minimal_commands.sh - echo "export MNE_3D_BACKEND=pyvista" >> $BASH_ENV + echo "export MNE_3D_BACKEND=pyvistaqt" >> $BASH_ENV + echo "export MNE_3D_OPTION_MULTI_SAMPLES=1" >> $BASH_ENV + echo "export MNE_BROWSER_BACKEND=qt" >> $BASH_ENV + echo "export MNE_BROWSER_PRECOMPUTE=false" >> $BASH_ENV echo "export PATH=~/.local/bin/:$PATH" >> $BASH_ENV + echo "export DISPLAY=:99" >> $BASH_ENV + echo "source ~/python_env/bin/activate" >> $BASH_ENV + mkdir -p ~/.local/bin + ln -s ~/python_env/bin/python ~/.local/bin/python echo "BASH_ENV:" cat $BASH_ENV mkdir -p ~/mne_data - touch pattern.txt; + touch pattern.txt - run: name: check neuromag2ft command: | neuromag2ft --version - run: - name: Merge with upstream - command: | - echo $(git log -1 --pretty=%B) | tee gitlog.txt - echo ${CI_PULL_REQUEST//*pull\//} | tee merge.txt - if [[ $(cat merge.txt) != "" ]]; then - echo "Merging $(cat merge.txt)"; - git pull --ff-only upstream "refs/pull/$(cat merge.txt)/merge"; - fi - - - run: - <<: *xvfb - - - run: name: Install fonts needed for diagrams command: | mkdir -p $HOME/.fonts @@ -107,20 +146,20 @@ - ~/.local/bin - run: - name: Check PyQt5 - command: LD_DEBUG=libs python -c "from PyQt5.QtWidgets import QApplication, QWidget; app = QApplication([])" + name: Check Qt + command: LD_DEBUG=libs python -c "from PySide6.QtWidgets import QApplication, QWidget; app = QApplication([])" # Load tiny cache so that ~/.mne does not need to be created below - restore_cache: keys: - - data-cache-tiny-0-23 + - data-cache-tiny-0 # Look at what we have and fail early if there is some library conflict - run: name: Check installation command: | which python - QT_DEBUG_PLUGINS=1 mne sys_info + QT_DEBUG_PLUGINS=1 mne sys_info -pd python -c "import numpy; numpy.show_config()" LIBGL_DEBUG=verbose python -c "import pyvistaqt; pyvistaqt.BackgroundPlotter(show=True)" python -c "import mne; mne.set_config('MNE_USE_CUDA', 'false')" # this is needed for the config tutorial @@ -261,7 +300,7 @@ # Keep these separate, maybe better in terms of size limitations (?) - save_cache: - key: data-cache-tiny-0-23 # < 100 M, might as well combine + key: data-cache-tiny-0 # < 100 M, might as well combine paths: - ~/.mne - ~/mne_data/MNE-kiloword-data # (28 M) @@ -378,7 +417,7 @@ - run: name: Check installation command: | - mne sys_info + mne sys_info -pd - run: name: make linkcheck command: | @@ -394,8 +433,8 @@ deploy: - docker: - - image: circleci/python:3.8.5-buster + machine: + image: ubuntu-2004:202111-01 steps: - attach_workspace: at: /tmp/build @@ -454,73 +493,46 @@ paths: - ~/mne_data/MNE-visual_92_categories-data - - interactive_test: - docker: - - image: circleci/python:3.9.2-buster - steps: - - restore_cache: - keys: - - source-cache - - checkout - - run: - name: Set BASH_ENV - command: | - set -e - echo "set -e" >> $BASH_ENV - echo "export OPENBLAS_NUM_THREADS=1" >> $BASH_ENV - mkdir -p ~/mne_data - - run: - name: Check-skip - command: | - export COMMIT_MESSAGE=$(git log --format=oneline -n 1); - if [[ "$COMMIT_MESSAGE" != *"[circle interactive_test]"* ]]; then - echo "Skip detected, exiting job ${CIRCLE_JOB}." - circleci-agent step halt; - fi - - run: - <<: *xvfb - - restore_cache: - keys: - - pip-cache - - run: - name: Get Python running - command: | - ./tools/circleci_dependencies.sh - - run: - name: Check installation - command: | - mne sys_info - - restore_cache: - keys: - - data-cache-testing - - run: - name: Get data - command: | - python -c "import mne; mne.datasets.testing.data_path(verbose=True)" - - run: - name: pytest - command: | - pytest -m "not slowtest" mne -xv - workflows: default: jobs: - - build_docs - - linkcheck - - interactive_test + - build_docs: + name: build_docs + - linkcheck: + name: linkcheck - deploy: + name: deploy requires: - build_docs filters: branches: only: - main - - maint/0.23 + - maint/1.1 + + main: + jobs: + - build_docs: + scheduled: "true" + name: build_docs_main + - deploy: + name: deploy_main + requires: + - build_docs_main + triggers: + - schedule: + # "At 00:00" (once a day) should be enough "0 0 * * *", + # But for testing at first, let's do once an hour (6 AM GMT) + cron: "0 6 * * *" + filters: + branches: + only: + - main weekly: jobs: - linkcheck: + name: linkcheck_weekly scheduled: "true" triggers: - schedule: diff -Nru python-mne-0.23.4+dfsg/CITATION.cff python-mne-1.1.0+dfsg/CITATION.cff --- python-mne-0.23.4+dfsg/CITATION.cff 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/CITATION.cff 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,655 @@ +cff-version: 1.2.0 +title: "MNE-Python" +message: "If you use this software, please cite both the software itself, and the paper listed in the preferred-citation field." +version: 1.1.0 +date-released: "2022-08-03" +commit: c210dfb4a1cab292c47c892fb2ee3c0b308ec90b +doi: 10.5281/zenodo.592483 +keywords: + - MEG + - magnetoencephalography + - EEG + - electroencephalography + - fNIRS + - "functional near-infrared spectroscopy" + - iEEG + - "intracranial EEG" + - eCoG + - electrocorticography + - DBS + - "deep brain stimulation" +authors: + - family-names: Larson + given-names: Eric + - family-names: Gramfort + given-names: Alexandre + - family-names: Engemann + given-names: Denis A + - family-names: Leppakangas + given-names: Jaakko + - family-names: Brodbeck + given-names: Christian + - family-names: Jas + given-names: Mainak + - family-names: Brooks + given-names: Teon + - family-names: Sassenhagen + given-names: Jona + - family-names: Luessi + given-names: Martin + - family-names: King + given-names: Jean-Remi + - family-names: McCloy + given-names: Daniel + - family-names: Goj + given-names: Roman + - family-names: Favelier + given-names: Guillaume + - family-names: Höchenberger + given-names: Richard + - family-names: Brunner + given-names: Clemens + - family-names: van Vliet + given-names: Marijn + - family-names: Wronkiewicz + given-names: Mark + - family-names: Holdgraf + given-names: Chris + - family-names: Massich + given-names: Joan + - family-names: Bekhti + given-names: Yousra + - family-names: Rockhill + given-names: Alex + - family-names: Appelhoff + given-names: Stefan + - family-names: Leggitt + given-names: Alan + - family-names: Dykstra + given-names: Andrew + - family-names: Luke + given-names: Rob + - family-names: Trachel + given-names: Romain + - family-names: De Santis + given-names: Lorenzo + - family-names: Panda + given-names: Asish + - family-names: Magnuski + given-names: Mikołaj + - family-names: Billinger + given-names: Martin + - family-names: Westner + given-names: Britta + - family-names: Wakeman + given-names: Dan G + - family-names: Strohmeier + given-names: Daniel + - family-names: Bharadwaj + given-names: Hari + - family-names: Linzen + given-names: Tal + - family-names: Barachant + given-names: Alexandre + - family-names: Scheltienne + given-names: Mathieu + - family-names: Ruzich + given-names: Emily + - family-names: Bailey + given-names: Christopher J + - family-names: Li + given-names: Adam + - family-names: Moutard + given-names: Clément + - family-names: Bloy + given-names: Luke + - family-names: Raimondo + given-names: Fede + - family-names: Nurminen + given-names: Jussi + - family-names: Montoya + given-names: Jair + - family-names: Woodman + given-names: Marmaduke + - family-names: Lee + given-names: Ingoo + - family-names: Schulz + given-names: Martin + - family-names: Foti + given-names: Nick + - family-names: Nangini + given-names: Cathy + - family-names: García Alanis + given-names: José C + - family-names: Maddox + given-names: Ross + - family-names: LaPlante + given-names: Roan + - family-names: Drew + given-names: Ashley + - family-names: Dinh + given-names: Christoph + - family-names: Hauk + given-names: Olaf + - family-names: Dumas + given-names: Guillaume + - family-names: Hartmann + given-names: Thomas + - family-names: Benerradi + given-names: Johann + - family-names: Pasler + given-names: Paul + - family-names: Repplinger + given-names: Stefan + - family-names: Rudiuk + given-names: Alexander + - family-names: Radanovic + given-names: Ana + - family-names: Buran + given-names: Brad + - family-names: Massias + given-names: Mathurin + - family-names: Hämäläinen + given-names: Matti + - family-names: Sripad + given-names: Praveen + - family-names: Chirkov + given-names: Valerii + - family-names: Mullins + given-names: Christopher + - family-names: Raimundo + given-names: Félix + - family-names: Alday + given-names: Phillip + - family-names: Pari + given-names: Ram + - family-names: Kornblith + given-names: Simon + - family-names: Halchenko + given-names: Yaroslav + - family-names: Luo + given-names: Yu-Han + - family-names: Ort + given-names: Eduard + - family-names: Kasper + given-names: Johannes + - family-names: Doelling + given-names: Keith + - family-names: Jensen + given-names: Mads + - family-names: Gahlot + given-names: Tanay + - family-names: Nunes + given-names: Adonay + - family-names: Gütlin + given-names: Dirk + - name: kjs + - family-names: Weinstein + given-names: Alejandro + - family-names: Lamus + given-names: Camilo + - family-names: Galván + given-names: Catalina María + - family-names: Moënne-Loccoz + given-names: Cristóbal + - family-names: Houck + given-names: Jon + - family-names: Klein + given-names: Natalie + - family-names: Rantala + given-names: Antti + - family-names: Maess + given-names: Burkhard + - family-names: O'Reilly + given-names: Christian + - family-names: Peterson + given-names: Erica + - family-names: Heinila + given-names: Erkka + - family-names: Kolkhorst + given-names: Henrich + - family-names: Banville + given-names: Hubert + - family-names: Hanna + given-names: Jeff + - family-names: Clarke + given-names: Maggie + - family-names: Anelli + given-names: Matteo + - family-names: Kaneda + given-names: Michiru + - family-names: Bannier + given-names: Pierre-Antoine + - family-names: Choudhary + given-names: Saket + - family-names: Huberty + given-names: Scott + - family-names: Kern + given-names: Simon + - family-names: Forster + given-names: Carina + - family-names: Kim + given-names: Cora + - family-names: Klotzsche + given-names: Felix + - family-names: Wong + given-names: Fu-Te + - family-names: Kojcic + given-names: Ivana + - family-names: Nielsen + given-names: Jesper Duemose + - family-names: Lankinen + given-names: Kaisu + - family-names: Tabavi + given-names: Kambiz + - family-names: Maksymenko + given-names: Kostiantyn + - family-names: Thibault + given-names: Louis + - family-names: Gayraud + given-names: Nathalie + - family-names: Ward + given-names: Nick + - name: dependabot[bot] + - family-names: Gauthier + given-names: Antoine + - family-names: Pinsard + given-names: Basile + - family-names: Welke + given-names: Dominik + - family-names: Stephen + given-names: Emily + - family-names: Hornberger + given-names: Erik + - family-names: Hathaway + given-names: Evan + - family-names: Kalenkovich + given-names: Evgenii + - family-names: Mamashli + given-names: Fahimeh + - family-names: Marinato + given-names: Giorgio + - family-names: Anevar + given-names: Hafeza + - family-names: Zhang + given-names: Jack + - family-names: Sosulski + given-names: Jan + - family-names: Stout + given-names: Jeff + - family-names: Eisenman + given-names: Larry + - family-names: Esch + given-names: Lorenz + - family-names: Barascud + given-names: Nicolas + - family-names: Legrand + given-names: Nicolas + - family-names: Falach + given-names: Rotem + - family-names: Deslauriers-Gauthier + given-names: Samuel + - family-names: Cotroneo + given-names: Silvia + - family-names: Matindi + given-names: Steve + - family-names: Bierer + given-names: Steven + - family-names: Férat + given-names: Victor + - family-names: Peterson + given-names: Victoria + - family-names: Tonin + given-names: Alessandro + - family-names: Kovrig + given-names: Alexander + - family-names: Pascarella + given-names: Annalisa + - family-names: Karekal + given-names: Apoorva + - family-names: Zhao + given-names: Christina + - family-names: Krzemiński + given-names: Dominik + - family-names: Welke + given-names: Dominik + - family-names: Makowski + given-names: Dominique + - family-names: Mikulan + given-names: Ezequiel + - family-names: Schiratti + given-names: Jean-Baptiste + - family-names: Evans + given-names: Jen + - family-names: Drew + given-names: Jordan + - family-names: Teves + given-names: Joshua + - family-names: Mathewson + given-names: Kyle + - family-names: Gwilliams + given-names: Laura + - family-names: Varghese + given-names: Lenny + - name: Lx37 + - family-names: Dovgialo + given-names: Marian + - family-names: Boggess + given-names: Matt + - family-names: Eberlein + given-names: Matthias + - family-names: Sherif + given-names: Mohamed + - family-names: Kozhemiako + given-names: Nataliia + - family-names: Srinivasan + given-names: Naveen + - family-names: Wilming + given-names: Niklas + - family-names: Chapochnikov + given-names: Nikolai + - family-names: Kozynets + given-names: Oleh + - family-names: Ablin + given-names: Pierre + - family-names: Bertrand + given-names: Quentin + - family-names: Shoorangiz + given-names: Reza + - family-names: Hübner + given-names: Rodrigo + - family-names: Sommariva + given-names: Sara + - family-names: Khan + given-names: Sheraz + - family-names: Herbst + given-names: Sophie + - family-names: Datta + given-names: Sumalyo + - family-names: Jochmann + given-names: Thomas + - family-names: Flak + given-names: Tod + - family-names: Dupré la Tour + given-names: Tom + - family-names: Stenner + given-names: Tristan + - family-names: NessAiver + given-names: Tziona + - name: akshay0724 + - name: sviter + - family-names: Hindle + given-names: Abram + - family-names: Koutsou + given-names: Achilleas + - family-names: Fecker + given-names: Adeline + - family-names: Wagner + given-names: Adina + - family-names: Ciok + given-names: Alex + - family-names: Pradhan + given-names: Aniket + - family-names: Padee + given-names: Anna + - family-names: Dubarry + given-names: Anne-Sophie + - family-names: Waniek + given-names: Anton Nikolas + - family-names: Singhal + given-names: Archit + - family-names: Rokem + given-names: Ariel + - family-names: Hurst + given-names: Austin + - family-names: Beasley + given-names: Ben + - family-names: Nicenboim + given-names: Bruno + - family-names: de la Torre + given-names: Carlos + - family-names: Clauss + given-names: Christian + - family-names: Mista + given-names: Christian + - family-names: Li + given-names: Chun-Hui + - family-names: Braboszcz + given-names: Claire + - family-names: Sleiter + given-names: Darin Erat + - family-names: Haslacher + given-names: David + - family-names: Sabbagh + given-names: David + - family-names: Kostas + given-names: Demetres + - family-names: Petkova + given-names: Desislava + - family-names: Altukhov + given-names: Dmitrii + - family-names: Eich + given-names: Eberhard + - family-names: DuPre + given-names: Elizabeth + - family-names: Lau + given-names: Ellen + - family-names: Olivetti + given-names: Emanuele + - family-names: Varano + given-names: Enrico + - family-names: de Montalivet + given-names: Etienne + - family-names: Goldstein + given-names: Evgeny + - family-names: Zamberlan + given-names: Federico + - family-names: Weber + given-names: Frederik D + - family-names: Tan + given-names: Gansheng + - family-names: Brookshire + given-names: Geoff + - family-names: Maymandi + given-names: Hamid + - family-names: Sonntag + given-names: Hermann + - family-names: Ye + given-names: Hongjiang + - family-names: Machairas + given-names: Ilias + - family-names: Kaczmarzyk + given-names: Jakub + - family-names: Zerfowski + given-names: Jan + - family-names: van den Bosch + given-names: Jasper J F + - family-names: Van Der Donckt + given-names: Jeroen + - family-names: van der Meer + given-names: Johan + - family-names: Niediek + given-names: Johannes + - family-names: Veillette + given-names: John + - family-names: Koen + given-names: Josh + - family-names: Bear + given-names: Joshua J + - family-names: Zhu + given-names: Judy D + - family-names: Dammers + given-names: Juergen + - family-names: Galán + given-names: Julia Guiomar Niso + - family-names: Slama + given-names: Katarina + - family-names: Leinweber + given-names: Katrin + - family-names: Grabot + given-names: Laetitia + - family-names: Andersen + given-names: Lau Møller + - family-names: Barbosa + given-names: Leonardo S + - family-names: Hamilton + given-names: Liberty + - family-names: Alfine + given-names: Lorenzo + - family-names: Gemein + given-names: Lukas + - family-names: Hecker + given-names: Lukas + - family-names: Hejtmánek + given-names: Lukáš + - family-names: Kitzbichler + given-names: Manfred + - family-names: Kumar + given-names: Manoj + - family-names: Kadwani + given-names: Manorama + - family-names: Sutela + given-names: Manu + - family-names: Koculak + given-names: Marcin + - family-names: van Harmelen + given-names: Martin + - name: MartinBaBer + - family-names: Courtemanche + given-names: Matt + - family-names: Tucker + given-names: Matt + - family-names: Visconti di Oleggio Castello + given-names: Matteo + - family-names: Dold + given-names: Matthias + - family-names: Toivonen + given-names: Matti + - family-names: Shader + given-names: Maureen + - family-names: Krause + given-names: Michael + - family-names: Rybář + given-names: Milan + - family-names: He + given-names: Mingjian + - family-names: Daneshzand + given-names: Mohammad + - family-names: Gensollen + given-names: Nicolas + - family-names: Proulx + given-names: Nicole + - family-names: Chalas + given-names: Nikolas + - family-names: Sundaram + given-names: Padma + - family-names: Roujansky + given-names: Paul + - family-names: Silva + given-names: Pedro + - family-names: Molfese + given-names: Peter J + - family-names: Li + given-names: Quanliang + - family-names: Nadkarni + given-names: Rahul + - family-names: Gatti + given-names: Ramiro + - family-names: Apariciogarcia + given-names: Ramonapariciog + - family-names: Nasri + given-names: Reza + - family-names: Koehler + given-names: Richard + - family-names: Stargardsky + given-names: Riessarius + - family-names: Oostenveld + given-names: Robert + - family-names: Seymour + given-names: Robert + - family-names: Schirrmeister + given-names: Robin Tibor + - family-names: Law + given-names: Ryan + - family-names: Pai + given-names: Sagun + - family-names: Perry + given-names: Sam + - family-names: Major + given-names: Sebastian + - family-names: Treguer + given-names: Sebastien + - family-names: Castaño + given-names: Sebastián + - family-names: Deng + given-names: Senwen + - family-names: Antopolskiy + given-names: Sergey + - family-names: Wong + given-names: Simeon + - family-names: Poil + given-names: Simon-Shlomo + - family-names: Foslien + given-names: Sondre + - family-names: Singh + given-names: Sourav + - family-names: Chambon + given-names: Stanislas + - family-names: Bethard + given-names: Steven + - family-names: Gutstein + given-names: Steven M + - family-names: Meyer + given-names: Svea Marie + - family-names: Wang + given-names: T + - family-names: Papadopoulo + given-names: Theodore + - family-names: Donoghue + given-names: Thomas + - family-names: Radman + given-names: Thomas + - family-names: Merk + given-names: Timon + - family-names: Gates + given-names: Timothy + - family-names: Clausner + given-names: Tommy + - family-names: Xia + given-names: Xiaokai + - family-names: Zhang + given-names: Zhi + - name: buildqa +preferred-citation: + title: "MEG and EEG Data Analysis with MNE-Python" + journal: "Frontiers in Neuroscience" + type: article + year: 2013 + volume: 7 + issue: 267 + start: 1 + end: 13 + doi: 10.3389/fnins.2013.00267 + authors: + - family-names: Gramfort + given-names: Alexandre + - family-names: Luessi + given-names: Martin + - family-names: Larson + given-names: Eric + - family-names: Engemann + given-names: Denis A. + - family-names: Strohmeier + given-names: Daniel + - family-names: Brodbeck + given-names: Christian + - family-names: Goj + given-names: Roman + - family-names: Jas + given-names: Mainak + - family-names: Brooks + given-names: Teon + - family-names: Parkkonen + given-names: Lauri + - family-names: Hämäläinen + given-names: Matti S. diff -Nru python-mne-0.23.4+dfsg/codemeta.json python-mne-1.1.0+dfsg/codemeta.json --- python-mne-0.23.4+dfsg/codemeta.json 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/codemeta.json 2022-08-03 15:48:14.000000000 +0000 @@ -5,22 +5,28 @@ "codeRepository": "git+https://github.com/mne-tools/mne-python.git", "dateCreated": "2010-12-26", "datePublished": "2014-08-04", - "dateModified": "2021-04-27", - "downloadUrl": "https://github.com/mne-tools/mne-python/archive/v0.23.0.zip", + "dateModified": "2022-08-03", + "downloadUrl": "https://github.com/mne-tools/mne-python/archive/v1.1.0.zip", "issueTracker": "https://github.com/mne-tools/mne-python/issues", "name": "MNE-Python", - "version": "0.23.0", + "version": "1.1.0", "description": "MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data. It provides methods for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, and statistics.", "applicationCategory": "Neuroscience", "developmentStatus": "active", "referencePublication": "https://doi.org/10.3389/fnins.2013.00267", "keywords": [ "MEG", + "magnetoencephalography", "EEG", + "electroencephalography", "fNIRS", - "ECoG", - "sEEG", - "DBS" + "functional near-infrared spectroscopy", + "iEEG", + "intracranial EEG", + "eCoG", + "electrocorticography", + "DBS", + "deep brain stimulation" ], "programmingLanguage": [ "Python" @@ -31,7 +37,7 @@ "macOS" ], "softwareRequirements": [ - "python>=3.6", + "python>=3.7", "numpy>=1.15.4", "scipy>=1.1.0" ], @@ -92,33 +98,39 @@ }, { "@type":"Person", - "email":"jeanremi.kibng+github@gmail.com", + "email":"jeanremi.king+github@gmail.com", "givenName":"Jean-Remi", "familyName": "King" }, { "@type":"Person", - "email":"roman.goj@gmail.com", - "givenName":"Roman", - "familyName": "Goj" - }, - { - "@type":"Person", "email":"dan@mccloy.info", "givenName":"Daniel", "familyName": "McCloy" }, { "@type":"Person", + "email":"roman.goj@gmail.com", + "givenName":"Roman", + "familyName": "Goj" + }, + { + "@type":"Person", "email":"guillaume.favelier@gmail.com", "givenName":"Guillaume", "familyName": "Favelier" }, { "@type":"Person", - "email":"wronk.mark@gmail.com", - "givenName":"Mark", - "familyName": "Wronkiewicz" + "email":"richard.hoechenberger@gmail.com", + "givenName":"Richard", + "familyName": "Höchenberger" + }, + { + "@type":"Person", + "email":"clemens.brunner@gmail.com", + "givenName":"Clemens", + "familyName": "Brunner" }, { "@type":"Person", @@ -128,9 +140,9 @@ }, { "@type":"Person", - "email":"clemens.brunner@gmail.com", - "givenName":"Clemens", - "familyName": "Brunner" + "email":"wronk.mark@gmail.com", + "givenName":"Mark", + "familyName": "Wronkiewicz" }, { "@type":"Person", @@ -152,9 +164,15 @@ }, { "@type":"Person", - "email":"richard.hoechenberger@gmail.com", - "givenName":"Richard", - "familyName": "Höchenberger" + "email":"aprockhill206@gmail.com", + "givenName":"Alex", + "familyName": "Rockhill" + }, + { + "@type":"Person", + "email":"stefan.appelhoff@mailbox.org", + "givenName":"Stefan", + "familyName": "Appelhoff" }, { "@type":"Person", @@ -170,6 +188,12 @@ }, { "@type":"Person", + "email":"code@robertluke.net", + "givenName":"Rob", + "familyName": "Luke" + }, + { + "@type":"Person", "email":"romain.trachel@inria.fr", "givenName":"Romain", "familyName": "Trachel" @@ -188,24 +212,12 @@ }, { "@type":"Person", - "email":"stefan.appelhoff@mailbox.org", - "givenName":"Stefan", - "familyName": "Appelhoff" - }, - { - "@type":"Person", "email":"mmagnuski@swps.edu.pl", "givenName":"Mikołaj", "familyName": "Magnuski" }, { "@type":"Person", - "email":"", - "givenName":"Robert", - "familyName": "Luke" - }, - { - "@type":"Person", "email":"martin.billinger@tugraz.at", "givenName":"Martin", "familyName": "Billinger" @@ -248,6 +260,12 @@ }, { "@type":"Person", + "email":"mathieu.scheltienne@gmail.com", + "givenName":"Mathieu", + "familyName": "Scheltienne" + }, + { + "@type":"Person", "email":"emilyr@nmr.mgh.harvard.edu", "givenName":"Emily", "familyName": "Ruzich" @@ -260,6 +278,12 @@ }, { "@type":"Person", + "email":"adam2392@gmail.com", + "givenName":"Adam", + "familyName": "Li" + }, + { + "@type":"Person", "email":"clement.moutard@gmail.com", "givenName":"Clément", "familyName": "Moutard" @@ -302,6 +326,12 @@ }, { "@type":"Person", + "email":"dev@earthman-music.de", + "givenName":"Martin", + "familyName": "Schulz" + }, + { + "@type":"Person", "email":"nfoti01@gmail.com", "givenName":"Nick", "familyName": "Foti" @@ -320,9 +350,9 @@ }, { "@type":"Person", - "email":"adam2392@gmail.com", - "givenName":"Adam", - "familyName": "Li" + "email":"ross.maddox@rochester.edu", + "givenName":"Ross", + "familyName": "Maddox" }, { "@type":"Person", @@ -332,9 +362,9 @@ }, { "@type":"Person", - "email":"rkmaddox@uw.edu", - "givenName":"Ross", - "familyName": "Maddox" + "email":"", + "givenName":"Ashley", + "familyName": "Drew" }, { "@type":"Person", @@ -356,6 +386,18 @@ }, { "@type":"Person", + "email":"thomas.hartmann@th-ht.de", + "givenName":"Thomas", + "familyName": "Hartmann" + }, + { + "@type":"Person", + "email":"johann.benerradi@gmail.com", + "givenName":"Johann", + "familyName": "Benerradi" + }, + { + "@type":"Person", "email":"paul@ppasler.de", "givenName":"Paul", "familyName": "Pasler" @@ -368,12 +410,6 @@ }, { "@type":"Person", - "email":"thomas.hartmann@th-ht.de", - "givenName":"Thomas", - "familyName": "Hartmann" - }, - { - "@type":"Person", "email":"alxanderr@gmail.com", "givenName":"Alexander", "familyName": "Rudiuk" @@ -392,12 +428,6 @@ }, { "@type":"Person", - "email":"johann.benerradi@gmail.com", - "givenName":"Johann", - "familyName": "Benerradi" - }, - { - "@type":"Person", "email":"mathurin.massias@gmail.com", "givenName":"Mathurin", "familyName": "Massias" @@ -416,9 +446,9 @@ }, { "@type":"Person", - "email":"aprockhill206@gmail.com", - "givenName":"Alex", - "familyName": "Rockhill" + "email":"vagechirkov@gmail.com", + "givenName":"Valerii", + "familyName": "Chirkov" }, { "@type":"Person", @@ -452,12 +482,6 @@ }, { "@type":"Person", - "email":"vagechirkov@gmail.com", - "givenName":"Valerii", - "familyName": "Chirkov" - }, - { - "@type":"Person", "email":"debian@onerussian.com", "givenName":"Yaroslav", "familyName": "Halchenko" @@ -470,6 +494,12 @@ }, { "@type":"Person", + "email":"eduardxort@gmail.com", + "givenName":"Eduard", + "familyName": "Ort" + }, + { + "@type":"Person", "email":"jeythekey@tutanota.com", "givenName":"Johannes", "familyName": "Kasper" @@ -506,12 +536,6 @@ }, { "@type":"Person", - "email":"dev@earthman-music.de", - "givenName":"Martin", - "familyName": "Schulz" - }, - { - "@type":"Person", "email":"kjs@llama", "givenName":"", "familyName": "kjs" @@ -531,8 +555,8 @@ { "@type":"Person", "email":"", - "givenName":"Catalina", - "familyName": "Magalvan" + "givenName":"Catalina María", + "familyName": "Galván" }, { "@type":"Person", @@ -542,9 +566,9 @@ }, { "@type":"Person", - "email":"eduardxort@gmail.com", - "givenName":"Eduard", - "familyName": "Ort" + "email":"jon.houck@gmail.com", + "givenName":"Jon", + "familyName": "Houck" }, { "@type":"Person", @@ -566,6 +590,18 @@ }, { "@type":"Person", + "email":"christian.oreilly@gmail.com", + "givenName":"Christian", + "familyName": "O'Reilly" + }, + { + "@type":"Person", + "email":"nordme@uw.edu", + "givenName":"Erica", + "familyName": "Peterson" + }, + { + "@type":"Person", "email":"erkkahe@gmail.com", "givenName":"Erkka", "familyName": "Heinila" @@ -578,15 +614,15 @@ }, { "@type":"Person", - "email":"jeff.hanna@gmail.com", - "givenName":"Jeff", - "familyName": "Hanna" + "email":"hubert.jbanville@gmail.com", + "givenName":"Hubert", + "familyName": "Banville" }, { "@type":"Person", - "email":"jon.houck@gmail.com", - "givenName":"Jon", - "familyName": "Houck" + "email":"jeff.hanna@gmail.com", + "givenName":"Jeff", + "familyName": "Hanna" }, { "@type":"Person", @@ -602,21 +638,45 @@ }, { "@type":"Person", + "email":"rcmdnk@gmail.com", + "givenName":"Michiru", + "familyName": "Kaneda" + }, + { + "@type":"Person", + "email":"pierreantoine.bannier@gmail.com", + "givenName":"Pierre-Antoine", + "familyName": "Bannier" + }, + { + "@type":"Person", "email":"saketkc@gmail.com", "givenName":"Saket", "familyName": "Choudhary" }, { "@type":"Person", - "email":"christian.oreilly@gmail.com", - "givenName":"Christian", - "familyName": "O'Reilly" + "email":"", + "givenName":"Scott", + "familyName": "Huberty" }, { "@type":"Person", - "email":"nordme@uw.edu", - "givenName":"Erica", - "familyName": "Peterson" + "email":"simon.kern@online.de", + "givenName":"Simon", + "familyName": "Kern" + }, + { + "@type":"Person", + "email":"carinaforster0611@gmail.com", + "givenName":"Carina", + "familyName": "Forster" + }, + { + "@type":"Person", + "email":"", + "givenName":"Cora", + "familyName": "Kim" }, { "@type":"Person", @@ -632,12 +692,6 @@ }, { "@type":"Person", - "email":"hubert.jbanville@gmail.com", - "givenName":"Hubert", - "familyName": "Banville" - }, - { - "@type":"Person", "email":"", "givenName":"Ivana", "familyName": "Kojcic" @@ -686,6 +740,12 @@ }, { "@type":"Person", + "email":"", + "givenName":"", + "familyName": "dependabot[bot]" + }, + { + "@type":"Person", "email":"antoine.gauthier@ensta.fr", "givenName":"Antoine", "familyName": "Gauthier" @@ -698,9 +758,9 @@ }, { "@type":"Person", - "email":"", - "givenName":"Cora", - "familyName": "Kim" + "email":"dominik.welke@web.de", + "givenName":"Dominik", + "familyName": "Welke" }, { "@type":"Person", @@ -716,6 +776,12 @@ }, { "@type":"Person", + "email":"", + "givenName":"Evan", + "familyName": "Hathaway" + }, + { + "@type":"Person", "email":"e.kalenkovich@gmail.com", "givenName":"Evgenii", "familyName": "Kalenkovich" @@ -746,6 +812,18 @@ }, { "@type":"Person", + "email":"mail@jan-sosulski.de", + "givenName":"Jan", + "familyName": "Sosulski" + }, + { + "@type":"Person", + "email":"stoutjd@nih.gov", + "givenName":"Jeff", + "familyName": "Stout" + }, + { + "@type":"Person", "email":"leisenman@wustl.edu", "givenName":"Larry", "familyName": "Eisenman" @@ -788,9 +866,9 @@ }, { "@type":"Person", - "email":"simon.kern@online.de", - "givenName":"Simon", - "familyName": "Kern" + "email":"stevematindi@gmail.com", + "givenName":"Steve", + "familyName": "Matindi" }, { "@type":"Person", @@ -812,6 +890,12 @@ }, { "@type":"Person", + "email":"alessandro.tonin@wysscenter.ch", + "givenName":"Alessandro", + "familyName": "Tonin" + }, + { + "@type":"Person", "email":"alexander.kovrig@gmail.com", "givenName":"Alexander", "familyName": "Kovrig" @@ -848,6 +932,12 @@ }, { "@type":"Person", + "email":"dom.mak19@gmail.com", + "givenName":"Dominique", + "familyName": "Makowski" + }, + { + "@type":"Person", "email":"", "givenName":"Ezequiel", "familyName": "Mikulan" @@ -866,6 +956,18 @@ }, { "@type":"Person", + "email":"", + "givenName":"Jordan", + "familyName": "Drew" + }, + { + "@type":"Person", + "email":"jbtevespro@gmail.com", + "givenName":"Joshua", + "familyName": "Teves" + }, + { + "@type":"Person", "email":"kylemath@gmail.com", "givenName":"Kyle", "familyName": "Mathewson" @@ -890,9 +992,9 @@ }, { "@type":"Person", - "email":"", - "givenName":"Martin", - "familyName": "Schulz" + "email":"mdovgialo@fabrizzio.zfb.fuw.edu.pl", + "givenName":"Marian", + "familyName": "Dovgialo" }, { "@type":"Person", @@ -902,6 +1004,12 @@ }, { "@type":"Person", + "email":"", + "givenName":"Matthias", + "familyName": "Eberlein" + }, + { + "@type":"Person", "email":"molpsychistb@gmail.com", "givenName":"Mohamed", "familyName": "Sherif" @@ -914,6 +1022,12 @@ }, { "@type":"Person", + "email":"", + "givenName":"Naveen", + "familyName": "Srinivasan" + }, + { + "@type":"Person", "email":"niklas.wilming@gmail.com", "givenName":"Niklas", "familyName": "Wilming" @@ -921,6 +1035,12 @@ { "@type":"Person", "email":"", + "givenName":"Nikolai", + "familyName": "Chapochnikov" + }, + { + "@type":"Person", + "email":"", "givenName":"Oleh", "familyName": "Kozynets" }, @@ -938,6 +1058,12 @@ }, { "@type":"Person", + "email":"r.shoorangiz@live.com", + "givenName":"Reza", + "familyName": "Shoorangiz" + }, + { + "@type":"Person", "email":"rhubner@gmail.com", "givenName":"Rodrigo", "familyName": "Hübner" @@ -992,6 +1118,12 @@ }, { "@type":"Person", + "email":"tzionan@mail.tau.ac.il", + "givenName":"Tziona", + "familyName": "NessAiver" + }, + { + "@type":"Person", "email":"akshay0724@gmail.com", "givenName":"", "familyName": "akshay0724" @@ -1016,6 +1148,24 @@ }, { "@type":"Person", + "email":"", + "givenName":"Adeline", + "familyName": "Fecker" + }, + { + "@type":"Person", + "email":"adina.wagner@t-online.de", + "givenName":"Adina", + "familyName": "Wagner" + }, + { + "@type":"Person", + "email":"", + "givenName":"Alex", + "familyName": "Ciok" + }, + { + "@type":"Person", "email":"aniket17133@iiitd.ac.in", "givenName":"Aniket", "familyName": "Pradhan" @@ -1040,6 +1190,12 @@ }, { "@type":"Person", + "email":"", + "givenName":"Archit", + "familyName": "Singhal" + }, + { + "@type":"Person", "email":"arokem@gmail.com", "givenName":"Ariel", "familyName": "Rokem" @@ -1052,6 +1208,12 @@ }, { "@type":"Person", + "email":"code@musicinmybrain.net", + "givenName":"Ben", + "familyName": "Beasley" + }, + { + "@type":"Person", "email":"bruno.nicenboim@gmail.com", "givenName":"Bruno", "familyName": "Nicenboim" @@ -1088,6 +1250,12 @@ }, { "@type":"Person", + "email":"darin.sleiter@gmail.com", + "givenName":"Darin Erat", + "familyName": "Sleiter" + }, + { + "@type":"Person", "email":"haslacherdavid@gmail.com", "givenName":"David", "familyName": "Haslacher" @@ -1149,8 +1317,32 @@ { "@type":"Person", "email":"", - "givenName":"Evan", - "familyName": "Hathaway" + "givenName":"Etienne", + "familyName": "de Montalivet" + }, + { + "@type":"Person", + "email":"", + "givenName":"Evgeny", + "familyName": "Goldstein" + }, + { + "@type":"Person", + "email":"", + "givenName":"Federico", + "familyName": "Zamberlan" + }, + { + "@type":"Person", + "email":"", + "givenName":"Frederik D", + "familyName": "Weber" + }, + { + "@type":"Person", + "email":"", + "givenName":"Gansheng", + "familyName": "Tan" }, { "@type":"Person", @@ -1160,6 +1352,12 @@ }, { "@type":"Person", + "email":"", + "givenName":"Hamid", + "familyName": "Maymandi" + }, + { + "@type":"Person", "email":"hermann.sonntag@gmail.com", "givenName":"Hermann", "familyName": "Sonntag" @@ -1173,20 +1371,26 @@ { "@type":"Person", "email":"", + "givenName":"Ilias", + "familyName": "Machairas" + }, + { + "@type":"Person", + "email":"", "givenName":"Jakub", "familyName": "Kaczmarzyk" }, { "@type":"Person", - "email":"japsai@gmail.com", - "givenName":"Jasper J F", - "familyName": "van den Bosch" + "email":"", + "givenName":"Jan", + "familyName": "Zerfowski" }, { "@type":"Person", - "email":"stoutjd@nih.gov", - "givenName":"Jeff", - "familyName": "Stout" + "email":"japsai@gmail.com", + "givenName":"Jasper J F", + "familyName": "van den Bosch" }, { "@type":"Person", @@ -1208,6 +1412,18 @@ }, { "@type":"Person", + "email":"johnv@uchicago.edu", + "givenName":"John", + "familyName": "Veillette" + }, + { + "@type":"Person", + "email":"koen.joshua@gmail.com", + "givenName":"Josh", + "familyName": "Koen" + }, + { + "@type":"Person", "email":"", "givenName":"Joshua J", "familyName": "Bear" @@ -1226,6 +1442,12 @@ }, { "@type":"Person", + "email":"guiomar.niso@ctb.upm.es", + "givenName":"Julia Guiomar Niso", + "familyName": "Galán" + }, + { + "@type":"Person", "email":"slama@berkeley.edu", "givenName":"Katarina", "familyName": "Slama" @@ -1268,6 +1490,18 @@ }, { "@type":"Person", + "email":"", + "givenName":"Lukas", + "familyName": "Gemein" + }, + { + "@type":"Person", + "email":"", + "givenName":"Lukas", + "familyName": "Hecker" + }, + { + "@type":"Person", "email":"hejtmy@gmail.com", "givenName":"Lukáš", "familyName": "Hejtmánek" @@ -1304,12 +1538,6 @@ }, { "@type":"Person", - "email":"mdovgialo@fabrizzio.zfb.fuw.edu.pl", - "givenName":"Marian", - "familyName": "Dovgialo" - }, - { - "@type":"Person", "email":"", "givenName":"Martin", "familyName": "van Harmelen" @@ -1322,6 +1550,12 @@ }, { "@type":"Person", + "email":"", + "givenName":"Matt", + "familyName": "Courtemanche" + }, + { + "@type":"Person", "email":"matt.tucker@nyu.edu", "givenName":"Matt", "familyName": "Tucker" @@ -1334,6 +1568,24 @@ }, { "@type":"Person", + "email":"", + "givenName":"Matthias", + "familyName": "Dold" + }, + { + "@type":"Person", + "email":"", + "givenName":"Matti", + "familyName": "Toivonen" + }, + { + "@type":"Person", + "email":"", + "givenName":"Maureen", + "familyName": "Shader" + }, + { + "@type":"Person", "email":"krause@mpib-berlin.mpg.de", "givenName":"Michael", "familyName": "Krause" @@ -1347,6 +1599,12 @@ { "@type":"Person", "email":"", + "givenName":"Mingjian", + "familyName": "He" + }, + { + "@type":"Person", + "email":"", "givenName":"Mohammad", "familyName": "Daneshzand" }, @@ -1418,12 +1676,24 @@ }, { "@type":"Person", + "email":"reza@ddpo.ir", + "givenName":"Reza", + "familyName": "Nasri" + }, + { + "@type":"Person", "email":"richard.koehler@outlook.de", "givenName":"Richard", "familyName": "Koehler" }, { "@type":"Person", + "email":"rie.acad@gmail.com", + "givenName":"Riessarius", + "familyName": "Stargardsky" + }, + { + "@type":"Person", "email":"r.oostenveld@gmail.com", "givenName":"Robert", "familyName": "Oostenveld" @@ -1442,6 +1712,12 @@ }, { "@type":"Person", + "email":"ryan.law@mpi.nl", + "givenName":"Ryan", + "familyName": "Law" + }, + { + "@type":"Person", "email":"sagung.pai@gmail.com", "givenName":"Sagun", "familyName": "Pai" @@ -1472,6 +1748,12 @@ }, { "@type":"Person", + "email":"", + "givenName":"Senwen", + "familyName": "Deng" + }, + { + "@type":"Person", "email":"s.antopolsky@gmail.com", "givenName":"Sergey", "familyName": "Antopolskiy" @@ -1490,6 +1772,12 @@ }, { "@type":"Person", + "email":"sondre.foslien@gmail.com", + "givenName":"Sondre", + "familyName": "Foslien" + }, + { + "@type":"Person", "email":"", "givenName":"Sourav", "familyName": "Singh" @@ -1502,12 +1790,6 @@ }, { "@type":"Person", - "email":"stevematindi@gmail.com", - "givenName":"Steve", - "familyName": "Matindi" - }, - { - "@type":"Person", "email":"bethard@email.arizona.edu", "givenName":"Steven", "familyName": "Bethard" @@ -1526,6 +1808,12 @@ }, { "@type":"Person", + "email":"", + "givenName":"T", + "familyName": "Wang" + }, + { + "@type":"Person", "email":"Theodore.Papadopoulo@inria.fr", "givenName":"Theodore", "familyName": "Papadopoulo" @@ -1550,12 +1838,24 @@ }, { "@type":"Person", + "email":"tim.gates@iress.com", + "givenName":"Timothy", + "familyName": "Gates" + }, + { + "@type":"Person", "email":"", "givenName":"Tommy", "familyName": "Clausner" }, { "@type":"Person", + "email":"xia@xiaokai.me", + "givenName":"Xiaokai", + "familyName": "Xia" + }, + { + "@type":"Person", "email":"850734033@qq.com", "givenName":"Zhi", "familyName": "Zhang" @@ -1565,18 +1865,6 @@ "email":"", "givenName":"", "familyName": "buildqa" - }, - { - "@type":"Person", - "email":"", - "givenName":"", - "familyName": "chapochn" - }, - { - "@type":"Person", - "email":"", - "givenName":"", - "familyName": "mshader" } ] } diff -Nru python-mne-0.23.4+dfsg/CONTRIBUTING.md python-mne-1.1.0+dfsg/CONTRIBUTING.md --- python-mne-0.23.4+dfsg/CONTRIBUTING.md 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/CONTRIBUTING.md 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,9 @@ +Contributing to MNE-Python +========================== + +MNE-Python is maintained by a community of scientists and research labs. The project accepts contributions in the form of bug reports, fixes, feature additions, and documentation improvements (including typo corrections). The best way to start contributing is by [opening an issue](https://github.com/mne-tools/mne-python/issues/new/choose) on our GitHub page to discuss ideas for changes or enhancements, or to tell us about behavior that you think might be a bug. For *general troubleshooting* or *usage questions*, please consider posting your questions on our [MNE Forum](https://mne.discourse.group). + +Users and contributors to MNE-Python are expected to follow our [code of conduct](https://github.com/mne-tools/.github/blob/main/CODE_OF_CONDUCT.md). + +The [contributing guide](https://mne.tools/dev/install/contributing.html) has details on the preferred contribution workflow +and the recommended system configuration for a smooth contribution/development experience. diff -Nru python-mne-0.23.4+dfsg/CONTRIBUTING.rst python-mne-1.1.0+dfsg/CONTRIBUTING.rst --- python-mne-0.23.4+dfsg/CONTRIBUTING.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/CONTRIBUTING.rst 1970-01-01 00:00:00.000000000 +0000 @@ -1,21 +0,0 @@ -Contributing to MNE-Python -========================== - -MNE-Python is maintained by a community of scientists and research labs, and -accepts contributions in the form of bug reports, fixes, feature additions, and -documentation improvements (even just typo corrections). The best way to start -contributing is by `opening an issue`_ on our GitHub page to discuss your ideas -for changes or enhancements, or to tell us about behavior that you think might -be a bug in MNE-Python. *For general troubleshooting of scripts that use -MNE-Python*, you should instead post on the `MNE Forum`_. Users and -contributors to MNE-Python are expected to follow our `code of conduct`_. - -The `contributing guide`_ has details on the preferred contribution workflow -and how best to configure your system for a smooth experience contributing to -MNE-Python. - - -.. _`opening an issue`: https://github.com/mne-tools/mne-python/issues/new/choose -.. _`MNE Forum`: https://mne.discourse.group -.. _`code of conduct`: https://github.com/mne-tools/.github/blob/main/CODE_OF_CONDUCT.md -.. _`contributing guide`: https://mne-tools.github.io/dev/install/contributing.html diff -Nru python-mne-0.23.4+dfsg/.coveragerc python-mne-1.1.0+dfsg/.coveragerc --- python-mne-0.23.4+dfsg/.coveragerc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/.coveragerc 2022-08-03 15:48:14.000000000 +0000 @@ -1,9 +1,7 @@ [run] branch = True source = mne -include = */mne/* omit = - */mne/externals/* */bin/* */setup.py */mne/fixes* diff -Nru python-mne-0.23.4+dfsg/debian/changelog python-mne-1.1.0+dfsg/debian/changelog --- python-mne-0.23.4+dfsg/debian/changelog 2021-09-28 08:14:03.000000000 +0000 +++ python-mne-1.1.0+dfsg/debian/changelog 2022-08-04 12:10:02.000000000 +0000 @@ -1,3 +1,14 @@ +python-mne (1.1.0+dfsg-1) unstable; urgency=medium + + * Team upload. + * New upstream version + * Standards-Version: 4.6.1 (routine-update) + * Set upstream metadata fields: Security-Contact. + * Switch from python-vtk7 to python-vtk9 + Closes: #1016602 + + -- Andreas Tille Thu, 04 Aug 2022 14:10:02 +0200 + python-mne (0.23.4+dfsg-1) unstable; urgency=medium * Team upload. diff -Nru python-mne-0.23.4+dfsg/debian/control python-mne-1.1.0+dfsg/debian/control --- python-mne-0.23.4+dfsg/debian/control 2021-09-28 08:14:03.000000000 +0000 +++ python-mne-1.1.0+dfsg/debian/control 2022-08-04 12:10:02.000000000 +0000 @@ -10,7 +10,7 @@ libgl1-mesa-dri, libjs-jquery, libjs-jquery-ui, - python3-vtk7, + python3-vtk9, python3-all, python3-coverage, python3-joblib, @@ -28,7 +28,7 @@ xauth, xvfb, yui-compressor -Standards-Version: 4.6.0 +Standards-Version: 4.6.1 Vcs-Browser: https://salsa.debian.org/med-team/python-mne Vcs-Git: https://salsa.debian.org/med-team/python-mne.git Homepage: https://martinos.org/mne @@ -51,7 +51,7 @@ xvfb, ${misc:Depends}, ${python3:Depends} -Recommends: python3-vtk7, +Recommends: python3-vtk9, python3-nibabel, python3-nose, python3-pytest diff -Nru python-mne-0.23.4+dfsg/debian/patches/deb_paths python-mne-1.1.0+dfsg/debian/patches/deb_paths --- python-mne-0.23.4+dfsg/debian/patches/deb_paths 2021-09-28 08:14:03.000000000 +0000 +++ python-mne-1.1.0+dfsg/debian/patches/deb_paths 1970-01-01 00:00:00.000000000 +0000 @@ -1,18 +0,0 @@ -Description: adjust paths to use system-wide packages resources (.js etc) -Author: Jaakko Leppakangas -Origin: upstream -Forwarded: not-needed -Reviewed-By: Yaroslav O. Halchenko -Last-Update: 2019-09-01 - ---- a/mne/report.py -+++ b/mne/report.py -@@ -1491,7 +1491,7 @@ class Report(object): - @verbose - def _init_render(self, verbose=None): - """Initialize the renderer.""" -- inc_fnames = ['jquery.js', 'jquery-ui.min.js', -+ inc_fnames = ['/usr/share/javascript/jquery/jquery.min.js', '/usr/share/javascript/jquery-ui/jquery-ui.min.js', - 'bootstrap.min.js', 'jquery-ui.min.css', - 'bootstrap.min.css'] - diff -Nru python-mne-0.23.4+dfsg/debian/patches/deb_skip_segfaultingtests python-mne-1.1.0+dfsg/debian/patches/deb_skip_segfaultingtests --- python-mne-0.23.4+dfsg/debian/patches/deb_skip_segfaultingtests 2021-09-28 08:14:03.000000000 +0000 +++ python-mne-1.1.0+dfsg/debian/patches/deb_skip_segfaultingtests 1970-01-01 00:00:00.000000000 +0000 @@ -1,14 +0,0 @@ -Author: Yaroslav Halchenko -Last-Update: 2017-12-04 18:32:49 -0500 -Description: Skip test that's known to segfault - ---- a/mne/gui/tests/test_kit2fiff_gui.py -+++ b/mne/gui/tests/test_kit2fiff_gui.py -@@ -128,6 +128,7 @@ def test_kit2fiff_model(): - @traits_test - def test_kit2fiff_gui(): - """Test Kit2Fiff GUI.""" -+ raise SkipTest("known to segfault") - _check_ci() - home_dir = _TempDir() - os.environ['_MNE_GUI_TESTING_MODE'] = 'true' diff -Nru python-mne-0.23.4+dfsg/debian/patches/python3.patch python-mne-1.1.0+dfsg/debian/patches/python3.patch --- python-mne-0.23.4+dfsg/debian/patches/python3.patch 2021-09-28 08:14:03.000000000 +0000 +++ python-mne-1.1.0+dfsg/debian/patches/python3.patch 1970-01-01 00:00:00.000000000 +0000 @@ -1,24 +0,0 @@ -Description: Fix Python 3 issue -Author: Thomas Goirand , - Jaakko Leppakangas -Last-Update: Sun, 01 Sep 2019 14:38:58 +0200 - ---- a/mne/report.py -+++ b/mne/report.py -@@ -1498,9 +1498,13 @@ class Report(object): - include = list() - for inc_fname in inc_fnames: - logger.info('Embedding : %s' % inc_fname) -- fname = op.join(op.dirname(__file__), 'html', inc_fname) -- with open(fname, 'rb') as fid: -- file_content = fid.read().decode('utf-8') -+ if not os.path.isabs(inc_fname): -+ inc_fname = op.join(op.dirname(__file__), 'html', inc_fname) -+ f = open(inc_fname, 'r') -+ if PY3: -+ file_content = f.read() -+ else: -+ file_content = f.read().decode('UTF-8') - if inc_fname.endswith('.js'): - include.append(u'') diff -Nru python-mne-0.23.4+dfsg/debian/patches/series python-mne-1.1.0+dfsg/debian/patches/series --- python-mne-0.23.4+dfsg/debian/patches/series 2021-09-28 08:14:03.000000000 +0000 +++ python-mne-1.1.0+dfsg/debian/patches/series 2022-08-04 12:10:02.000000000 +0000 @@ -1,4 +1 @@ -#deb_skip_segfaultingtests -deb_paths -python3.patch up_check_numpy diff -Nru python-mne-0.23.4+dfsg/debian/patches/up_check_numpy python-mne-1.1.0+dfsg/debian/patches/up_check_numpy --- python-mne-0.23.4+dfsg/debian/patches/up_check_numpy 2021-09-28 08:14:03.000000000 +0000 +++ python-mne-1.1.0+dfsg/debian/patches/up_check_numpy 2022-08-04 12:10:02.000000000 +0000 @@ -10,12 +10,12 @@ from numpy.fft import rfft, irfft from numpy.testing import assert_array_equal, assert_allclose, assert_equal --from mne.utils import requires_sklearn, run_tests_if_main -+from mne.utils import requires_sklearn, run_tests_if_main, check_version +-from mne.utils import requires_sklearn ++from mne.utils import requires_sklearn, check_version from mne.decoding import ReceptiveField, TimeDelayingRidge from mne.decoding.receptive_field import (_delay_time_series, _SCORERS, _times_to_delays, _delays_to_slice) -@@ -516,6 +516,9 @@ def test_inverse_coef(): +@@ -517,6 +517,9 @@ def test_inverse_coef(): rf = ReceptiveField(tmin, tmax, 1., estimator=estimator, patterns=True) rf.fit(X, y) diff -Nru python-mne-0.23.4+dfsg/debian/upstream/metadata python-mne-1.1.0+dfsg/debian/upstream/metadata --- python-mne-0.23.4+dfsg/debian/upstream/metadata 2021-09-28 08:14:03.000000000 +0000 +++ python-mne-1.1.0+dfsg/debian/upstream/metadata 2022-08-04 12:10:02.000000000 +0000 @@ -3,3 +3,4 @@ Bug-Submit: https://github.com/mne-tools/mne-python/issues/new Repository: https://github.com/mne-tools/mne-python.git Repository-Browse: https://github.com/mne-tools/mne-python +Security-Contact: https://github.com/mne-tools/mne-python/tree/HEAD/SECURITY.md diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.10.inc python-mne-1.1.0+dfsg/doc/changes/0.10.inc --- python-mne-0.23.4+dfsg/doc/changes/0.10.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.10.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ .. _changes_0_10: -Version 0.10 ------------- +Version 0.10 (2015-10-26) +------------------------- Changelog ~~~~~~~~~ @@ -76,7 +76,7 @@ - New gfp parameter in :func:`mne.Evoked.plot` method to display Global Field Power (GFP) by `Eric Larson`_. -- Add :meth:`mne.Report.add_slider_to_section` methods to :class:`mne.Report` by `Teon Brooks`_ +- Add ``mne.Report.add_slider_to_section`` methods to :class:`mne.Report` by `Teon Brooks`_ BUG ~~~ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.11.inc python-mne-1.1.0+dfsg/doc/changes/0.11.inc --- python-mne-0.23.4+dfsg/doc/changes/0.11.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.11.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ .. _changes_0_11: -Version 0.11 ------------- +Version 0.11 (2015-12-24) +------------------------- Changelog ~~~~~~~~~ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.12.inc python-mne-1.1.0+dfsg/doc/changes/0.12.inc --- python-mne-0.23.4+dfsg/doc/changes/0.12.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.12.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ .. _changes_0_12: -Version 0.12 ------------- +Version 0.12 (2016-05-10) +------------------------- Changelog ~~~~~~~~~ @@ -69,7 +69,7 @@ - :func:`mne.Epochs.plot_psd` no longer calls a Welch PSD, and instead uses a Multitaper method which is more appropriate for epochs. Flags for this function are passed to :func:`mne.time_frequency.psd_multitaper` by `Chris Holdgraf`_ -- Time-cropping functions (e.g., :func:`mne.Epochs.crop`, :func:`mne.Evoked.crop`, :func:`mne.io.Raw.crop`, :func:`mne.SourceEstimate.crop`) made consistent with behavior of ``tmin`` and ``tmax`` of :class:`mne.Epochs`, where nearest sample is kept. For example, for MGH data acquired with ``sfreq=600.614990234``, constructing ``Epochs(..., tmin=-1, tmax=1)`` has bounds ``+/-1.00064103``, and now ``epochs.crop(-1, 1)`` will also have these bounds (previously they would have been ``+/-0.99897607``). Time cropping functions also no longer use relative tolerances when determining the boundaries. These changes have minor effects on functions that use cropping under the hood, such as :func:`mne.compute_covariance` and :func:`mne.connectivity.spectral_connectivity`. Changes by `Jaakko Leppakangas`_ and `Eric Larson`_ +- Time-cropping functions (e.g., :func:`mne.Epochs.crop`, :func:`mne.Evoked.crop`, :func:`mne.io.Raw.crop`, :func:`mne.SourceEstimate.crop`) made consistent with behavior of ``tmin`` and ``tmax`` of :class:`mne.Epochs`, where nearest sample is kept. For example, for MGH data acquired with ``sfreq=600.614990234``, constructing ``Epochs(..., tmin=-1, tmax=1)`` has bounds ``+/-1.00064103``, and now ``epochs.crop(-1, 1)`` will also have these bounds (previously they would have been ``+/-0.99897607``). Time cropping functions also no longer use relative tolerances when determining the boundaries. These changes have minor effects on functions that use cropping under the hood, such as :func:`mne.compute_covariance` and ``mne.connectivity.spectral_connectivity``. Changes by `Jaakko Leppakangas`_ and `Eric Larson`_ - Fix EEG spherical spline interpolation code to account for average reference by `Mainak Jas`_ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.13.inc python-mne-1.1.0+dfsg/doc/changes/0.13.inc --- python-mne-0.23.4+dfsg/doc/changes/0.13.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.13.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ .. _changes_0_13: -Version 0.13 ------------- +Version 0.13 (2016-09-27) +------------------------- Changelog ~~~~~~~~~ @@ -140,7 +140,7 @@ - Multiple aspects of FIR filtering in MNE-Python has been refactored: - 1. New recommended defaults for ``l_trans_bandwidth='auto'``, ``h_trans_bandwidth='auto'``, and ``filter_length='auto'``. This should generally reduce filter artifacts at the expense of slight decrease in effective filter stop-band attenuation. For details see :ref:`tut_filtering_in_python`. The default values of ``l_trans_bandwidth=h_trans_bandwidth=0.5`` and ``filter_length='10s'`` will change to ``'auto'`` in 0.14. + 1. New recommended defaults for ``l_trans_bandwidth='auto'``, ``h_trans_bandwidth='auto'``, and ``filter_length='auto'``. This should generally reduce filter artifacts at the expense of slight decrease in effective filter stop-band attenuation. For details see :ref:`tut-filtering-in-python`. The default values of ``l_trans_bandwidth=h_trans_bandwidth=0.5`` and ``filter_length='10s'`` will change to ``'auto'`` in 0.14. 2. The ``filter_length=None`` option (i.e. use ``len(x)``) has been deprecated. diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.14.inc python-mne-1.1.0+dfsg/doc/changes/0.14.inc --- python-mne-0.23.4+dfsg/doc/changes/0.14.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.14.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ .. _changes_0_14: -Version 0.14 ------------- +Version 0.14 (2017-03-24) +------------------------- Changelog ~~~~~~~~~ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.15.inc python-mne-1.1.0+dfsg/doc/changes/0.15.inc --- python-mne-0.23.4+dfsg/doc/changes/0.15.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.15.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ .. _changes_0_15: -Version 0.15 ------------- +Version 0.15 (2017-10-20) +------------------------- Changelog ~~~~~~~~~ @@ -152,7 +152,7 @@ - Fix :func:`mne.io.read_raw_brainvision` to read vectorized data correctly by `Jaakko Leppakangas`_ and `Phillip Alday`_ -- Fix :func:`mne.connectivity.spectral_connectivity` so that if ``n_jobs > 1`` it does not ignore last ``n_epochs % n_jobs`` epochs by `Mikołaj Magnuski`_ +- Fix ``mne.connectivity.spectral_connectivity`` so that if ``n_jobs > 1`` it does not ignore last ``n_epochs % n_jobs`` epochs by `Mikołaj Magnuski`_ - Fix :func:`mne.io.read_raw_edf` to infer sampling rate correctly when reading EDF+ files where tal-channel has a higher sampling frequency by `Jaakko Leppakangas`_ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.16.inc python-mne-1.1.0+dfsg/doc/changes/0.16.inc --- python-mne-0.23.4+dfsg/doc/changes/0.16.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.16.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ .. _changes_0_16: -Version 0.16 ------------- +Version 0.16 (2018-05-01) +------------------------- Changelog ~~~~~~~~~ @@ -135,7 +135,7 @@ - Fix bug in :class:`mne.Epochs` when passing events as list with ``event_id=None`` by `Alex Gramfort`_ -- Fix bug in :meth:`mne.Report.add_figs_to_section` when passing :class:`numpy.ndarray` by `Eric Larson`_ +- Fix bug in ``mne.Report.add_figs_to_section`` when passing :class:`numpy.ndarray` by `Eric Larson`_ - Fix bug in CSS class setting in `mne.Report` BEM section by `Eric Larson`_ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.17.inc python-mne-1.1.0+dfsg/doc/changes/0.17.inc --- python-mne-0.23.4+dfsg/doc/changes/0.17.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.17.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ .. _changes_0_17: -Version 0.17 ------------- +Version 0.17 (2018-11-19) +------------------------- Changelog ~~~~~~~~~ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.18.inc python-mne-1.1.0+dfsg/doc/changes/0.18.inc --- python-mne-0.23.4+dfsg/doc/changes/0.18.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.18.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ .. _changes_0_18: -Version 0.18 ------------- +Version 0.18 (2019-05-19) +------------------------- Changelog ~~~~~~~~~ @@ -48,7 +48,7 @@ - Add data fetchers for polysomnography (PSG) recordings from Physionet (:func:`mne.datasets.sleep_physionet.age.fetch_data` and :func:`mne.datasets.sleep_physionet.temazepam.fetch_data`) by `Alex Gramfort`_ and `Joan Massich`_ -- Add envelope correlation code in :func:`mne.connectivity.envelope_correlation` by `Denis Engemann`_, `Sheraz Khan`_, and `Eric Larson`_ +- Add envelope correlation code in ``mne.connectivity.envelope_correlation`` by `Denis Engemann`_, `Sheraz Khan`_, and `Eric Larson`_ - Add option to toggle all projectors in :meth:`mne.io.Raw.plot` and related functions by `Eric Larson`_ @@ -145,7 +145,7 @@ - Fix :func:`mne.gui.coregistration` and :ref:`mne coreg` crashing with segmentation fault when switching subjects by `Eric Larson`_ -- Fix :func:`mne.io.read_raw_brainvision` to accommodate vmrk files which do not have any annotations by `Alexander Kovrig`_ +- Fix :func:`mne.io.read_raw_brainvision` to accommodate vmrk files which do not have any annotations by Alexander Kovrig - Fix :meth:`mne.io.Raw.plot` and :meth:`mne.Epochs.plot` to auto-scale ``misc`` channel types by default by `Eric Larson`_ @@ -230,7 +230,7 @@ - Fix a bug in :class:`mne.preprocessing.ICA`, where manually setting the attribute ``ICA.exclude`` to an np.array resulted in the removal of random components when later also providing the ``exclude`` argument to any ``apply...``-method, by `jeythekey`_ -- Ascending changed to descending sorting of scores for integer ``..._criterion`` arguments in :func:`mne.preprocessing.ICA.detect_artifacts` and ``mne.preprocessing.run_ica``, as it used to be documented; the docstring in these functions was corrected for float ``..._criterion`` arguments, by `jeythekey`_ +- Ascending changed to descending sorting of scores for integer ``..._criterion`` arguments in ``mne.preprocessing.ICA.detect_artifacts`` and ``mne.preprocessing.run_ica``, as it used to be documented; the docstring in these functions was corrected for float ``..._criterion`` arguments, by `jeythekey`_ API ~~~ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.19.inc python-mne-1.1.0+dfsg/doc/changes/0.19.inc --- python-mne-0.23.4+dfsg/doc/changes/0.19.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.19.inc 2022-08-03 15:48:14.000000000 +0000 @@ -9,8 +9,8 @@ .. _changes_0_19: -Version 0.19 ------------- +Version 0.19 (2019-09-24) +------------------------- Changelog ~~~~~~~~~ @@ -55,7 +55,7 @@ - Butterfly channel plots now possible for :meth:`mne.Epochs.plot_psd` with ``average=False``. Infrastructure for this function now shared with analogous Raw function, found in ``mne.viz.utils`` by `Jeff Hanna`_ -- Add option not to orthogonalize power envelopes with ``orthogonalize=False`` in :func:`mne.connectivity.envelope_correlation` by `Denis Engemann`_ +- Add option not to orthogonalize power envelopes with ``orthogonalize=False`` in ``mne.connectivity.envelope_correlation`` by `Denis Engemann`_ - Accept filenames of raw .fif files that end in ``_meg.fif`` to enable complicance with the Brain Imaging Data Structure by `Stefan Appelhoff`_ @@ -85,7 +85,7 @@ - Add plotting of rank estimates in :func:`mne.viz.plot_cov` by `Eric Larson`_ -- Add a new :func:`mne.viz.plot_sensors_connectivity` function to visualize the sensor connectivity in 3D by `Guillaume Favelier`_ and `Alex Gramfort`_ +- Add a new ``mne.viz.plot_sensors_connectivity`` function to visualize the sensor connectivity in 3D by `Guillaume Favelier`_ and `Alex Gramfort`_ - Add support for ``info['utc_offset']``, ``info['device_info']``, and ``info['helium_info']`` components of :class:`mne.Info` by `Eric Larson`_ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.1.inc python-mne-1.1.0+dfsg/doc/changes/0.1.inc --- python-mne-0.23.4+dfsg/doc/changes/0.1.inc 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.1.inc 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,18 @@ +.. _changes_0_1: + +Version 0.1 (2011-09-23) +------------------------ + +- Initial release (initial commit: 2010-12-26). + +Authors +~~~~~~~ + +The committer list for this release is the following (preceded by number +of commits): + +* 315 Alexandre Gramfort +* 1 Ellen Lau +* 41 Emily Ruzich +* 2 Martin Luessi +* 9 Matti Hämäläinen diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.20.inc python-mne-1.1.0+dfsg/doc/changes/0.20.inc --- python-mne-0.23.4+dfsg/doc/changes/0.20.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.20.inc 2022-08-03 15:48:14.000000000 +0000 @@ -9,8 +9,8 @@ .. _changes_0_20: -Version 0.20 ------------- +Version 0.20 (2020-03-27) +------------------------- Changelog ~~~~~~~~~ @@ -65,7 +65,7 @@ - Add the ability to ``return_event_id`` in :func:`mne.read_events` for use with MNE-C produced ``-annot.fif`` files, by `Eric Larson`_ -- Add option to :func:`mne.connectivity.spectral_connectivity` to compute corrected imaginary PLV by `Adonay Nunes`_ +- Add option to ``mne.connectivity.spectral_connectivity`` to compute corrected imaginary PLV by `Adonay Nunes`_ - Add :func:`mne.SourceEstimate.estimate_snr` to estimate source-space SNR, by `Kaisu Lankinen`_ and `Padma Sundaram`_ @@ -348,7 +348,7 @@ - :meth:`mne.io.Raw.pick_channels`, :meth:`mne.Epochs.pick_channels` and :meth:`mne.Evoked.pick_channels` now have an ``ordered`` parameter to enforce the ordering of the picked channels by `Marijn van Vliet`_ -- New methods :meth:`mne.Forward.pick_channels`, :meth:`mne.Covariance.pick_channels`, :meth:`mne.Info.pick_channels`, :meth:`mne.time_frequency.CrossSpectralDensity.pick_channels` by `Marijn van Vliet`_ +- New methods :meth:`mne.Forward.pick_channels`, :meth:`mne.Covariance.pick_channels`, ``mne.Info.pick_channels``, :meth:`mne.time_frequency.CrossSpectralDensity.pick_channels` by `Marijn van Vliet`_ - New attributes ``mne.Forward.ch_names`` and ``mne.Info.ch_names`` by `Marijn van Vliet`_ @@ -356,7 +356,7 @@ - Drop ``unit`` keyword argument from :func:`mne.channels.read_custom_montage`, as it was unused by `Richard Höchenberger`_ -- Deprecate ``mne.preprocessing.run_ica``, use :meth:`mne.preprocessing.ICA.detect_artifacts` instead by `Richard Höchenberger`_ +- Deprecate ``mne.preprocessing.run_ica``, use ``mne.preprocessing.ICA.detect_artifacts`` instead by `Richard Höchenberger`_ Authors ~~~~~~~ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.21.inc python-mne-1.1.0+dfsg/doc/changes/0.21.inc --- python-mne-0.23.4+dfsg/doc/changes/0.21.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.21.inc 2022-08-03 15:48:14.000000000 +0000 @@ -9,8 +9,8 @@ .. _changes_0_21: -Version 0.21 ------------- +Version 0.21 (2020-09-21) +------------------------- .. |Rahul Nadkarni| replace:: **Rahul Nadkarni** .. |Lau Møller Andersen| replace:: **Lau Møller Andersen** @@ -42,7 +42,7 @@ - Speed up reading of annotations in EDF+ files **by new contributor** |Jeroen Van Der Donckt|_ -- Add head to mri and mri to voxel space transform details to :ref:`plot_source_alignment` tutorial, by `Alex Rockhill`_ +- Add head to mri and mri to voxel space transform details to :ref:`tut-source-alignment` tutorial, by `Alex Rockhill`_ - Improve memory efficiency of :func:`mne.concatenate_epochs` by `Eric Larson`_ @@ -150,7 +150,7 @@ - Change default to ``surfaces='auto'`` from ``surfaces='head'`` to allow :func:`mne.viz.plot_alignment` to work when just passing a :class:`mne.Info` as ``plot_alignment(info)`` by `Eric Larson`_ -- Add ECoG misc EDF dataset to the :ref:`tut_working_with_ecog` tutorial to show snapshots of time-frequency activity by `Adam Li`_ +- Add ECoG misc EDF dataset to the :ref:`tut-working-with-ecog` tutorial to show snapshots of time-frequency activity by `Adam Li`_ - Add :func:`mne.viz.set_3d_options` and ``MNE_3D_OPTION_ANTIALIAS`` environment variable to control full-scene antialiasing (FXAA) in 3D functions like :ref:`mne coreg`, :func:`mne.viz.plot_alignment`, and :func:`mne.viz.plot_source_estimates`; this can be useful on systems where FXAA does not work well, such as some with MESA software rendering, by `Eric Larson`_ @@ -214,7 +214,7 @@ - Add reader for SNIRF NIRS data in :func:`mne.io.read_raw_snirf` by `Robert Luke`_ -- `~mne.Evoked` has gained ``tmin`` and ``tmax`` attributes for more consistency with `~mne.Epochs` by `Richard Höchenberger`_ +- `~mne.Evoked` has gained ``tmin`` and ``tmax`` attributes for more consistency with `~mne.Epochs` by `Richard Höchenberger`_ Bugs ~~~~ @@ -271,7 +271,7 @@ - Fix bug with :func:`mne.channels.read_custom_montage` where ``"headshape"`` points in SFP format were not read properly, by `Eric Larson`_ -- Fix bug with setting HTML classes when using :meth:`mne.Report.add_bem_to_section` by `Eric Larson`_ +- Fix bug with setting HTML classes when using ``mne.Report.add_bem_to_section`` by `Eric Larson`_ - Fix bug with convex-hull based MEG helmet creation where the face area could be covered up, by `Eric Larson`_ @@ -399,7 +399,7 @@ 1. Arguments to clustering functions, such as `mne.stats.permutation_cluster_test`, and 2. Function names for defining adjacency, such as `mne.spatio_temporal_src_adjacency` replacing ``mne.spatio_temporal_src_connectivity``. - + The complete list of changed function names is: - ``mne.channels.find_ch_connectivity`` → `~mne.channels.find_ch_adjacency` @@ -438,7 +438,7 @@ - The ``threshold`` argument in :meth:`mne.preprocessing.ICA.find_bads_ecg` defaults to ``None`` in version 0.21 but will change to ``'auto'`` in 0.22 by `Yu-Han Luo`_ -- The function ``mne.preprocessing.mark_flat`` has been deprecated in favor of :func:`mne.preprocessing.annotate_flat` by `Eric Larson`_ +- The function ``mne.preprocessing.mark_flat`` has been deprecated in favor of ``mne.preprocessing.annotate_flat`` by `Eric Larson`_ - The default argument ``meg=True`` in :func:`mne.pick_types` will change to ``meg=False`` in version 0.22 by `Clemens Brunner`_ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.22.inc python-mne-1.1.0+dfsg/doc/changes/0.22.inc --- python-mne-0.23.4+dfsg/doc/changes/0.22.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.22.inc 2022-08-03 15:48:14.000000000 +0000 @@ -9,8 +9,8 @@ .. _changes_0_22: -Version 0.22.0 --------------- +Version 0.22.0 (2020-12-17) +--------------------------- .. |Austin Hurst| replace:: **Austin Hurst** @@ -113,7 +113,7 @@ Bugs ~~~~ -- Fix orthogonalization of power envelopes in :func:`mne.connectivity.envelope_correlation` (:gh:`8658` **by new contributor** |Qianliang Li|_ and `Eric Larson`_) +- Fix orthogonalization of power envelopes in ``mne.connectivity.envelope_correlation`` (:gh:`8658` **by new contributor** |Qianliang Li|_ and `Eric Larson`_) - Fix data overwrite of cascading simulation operations :`mne.simulation.simulate_raw` (:gh:`8633` **by new contributor** |Jeff Stout|_) @@ -165,7 +165,7 @@ - Fix bug with volumetric rendering alpha in :meth:`mne.VolSourceEstimate.plot_3d` and related functions (:gh:`8663` by `Eric Larson`_) -- Fix missing documentation of :func:`mne.io.read_raw_nihon` in :ref:`tut-imorting-eeg-data` (:gh`8320` by `Adam Li`_) +- Fix missing documentation of :func:`mne.io.read_raw_nihon` in :ref:`tut-imorting-eeg-data` (:gh:`8320` by `Adam Li`_) - Fix bug with :func:`mne.add_reference_channels` when :func:`mne.io.Raw.reorder_channels` or related methods are used afterward (:gh:`8303`, :gh:`#8484` by `Eric Larson`_) diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.23.inc python-mne-1.1.0+dfsg/doc/changes/0.23.inc --- python-mne-0.23.4+dfsg/doc/changes/0.23.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.23.inc 2022-08-03 15:48:14.000000000 +0000 @@ -12,42 +12,10 @@ like this (where xxxx is the pull request number): - description of enhancement/bugfix/API change (:gh:`xxxx` **by new contributor** |Firstname Lastname|_) -.. _changes_0_23_4: - -Version 0.23.4 --------------- - -- Fix test tolerances for ARM (:gh:`9705` by `Eric Larson`_) - -.. _changes_0_23_3: - -Version 0.23.3 --------------- - -- Fix GitHub release diff (by `Eric Larson`_) - -.. _changes_0_23_2: - -Version 0.23.2 --------------- - -- Fix bug in shim for ``contextlib.nullcontext`` for Python 3.6 (by `Eric Larson`_) - -.. _changes_0_23_1: - -Version 0.23.1 --------------- - -- Fix bug in `mne.viz.plot_topomap` (and related methods) where passing ``axes`` that are part of a matplotlib figure that uses a constrained layout would emit warnings (:gh:`9558` by `Eric Larson`_) - -- Fix bugs with deprecated uses of SciPy and matplotlib functionality (:gh:`9550`, :gh:`9561`, :gh:`9557`, :gh:`9590`, and :gh:`9677` by `Eric Larson`_) - .. _changes_0_23_0: -Version 0.23.0 --------------- - -.. |New Contributor| replace:: **New Contributor** +Version 0.23.0 (2021-04-27) +--------------------------- .. |Jack Zhang| replace:: **Jack Zhang** @@ -125,9 +93,9 @@ - Add some preprocessing functions to the EEGLAB migration guide (:gh:`9169` **by new contributor** |Apoorva Karekal|_) -- Add :func:`mne.chpi.extract_chpi_locs_kit` to read cHPI coil locations from KIT/Yokogawa data (:gh:`` **by new contributor** |Matt Sanderson|_, `Robert Seymour`_, and `Eric Larson`_) +- Add :func:`mne.chpi.extract_chpi_locs_kit` to read cHPI coil locations from KIT/Yokogawa data (:gh:`8813` **by new contributor** |Matt Sanderson|_, `Robert Seymour`_, and `Eric Larson`_) -- Add ``match_alias`` parameter to :meth:`mne.io.Raw.set_montage` and related functions to match unrecognized channel location names to known aliases (:gh`8799` **by new contributor** |Zhi Zhang|_) +- Add ``match_alias`` parameter to :meth:`mne.io.Raw.set_montage` and related functions to match unrecognized channel location names to known aliases (:gh:`8799` **by new contributor** |Zhi Zhang|_) - Update the ``notebook`` 3d backend to use ``ipyvtk_simple`` for a better integration within ``Jupyter`` (:gh:`8503` by `Guillaume Favelier`_) @@ -135,7 +103,7 @@ - Add channel-specific annotation support to :class:`mne.Annotations` via ``ch_names`` parameter (:gh:`8896` by `Eric Larson`_) -- Add toggle-all button to :class:`mne.Report` HTML and ``width`` argument to :meth:`mne.Report.add_bem_to_section` (:gh:`8723` by `Eric Larson`_) +- Add toggle-all button to :class:`mne.Report` HTML and ``width`` argument to ``mne.Report.add_bem_to_section`` (:gh:`8723` by `Eric Larson`_) - Add infant template MRI dataset downloader :func:`mne.datasets.fetch_infant_template` (:gh:`8738` by `Eric Larson`_ and `Christian O'Reilly`_) @@ -177,11 +145,11 @@ - Add parameter ``align=True`` to `mne.viz.Brain.show_view` to make views relative to the closest canonical (MNI) axes rather than the native MRI surface RAS coordinates (:gh:`8794` by `Eric Larson`_) -- Add ``auto_close`` to `mne.Report.add_figs_to_section` and `mne.Report.add_slider_to_section` to manage closing figures (:gh`8730` by `Guillaume Favelier`_) +- Add ``auto_close`` to ``mne.Report.add_figs_to_section`` and ``mne.Report.add_slider_to_section`` to manage closing figures (:gh:`8730` by `Guillaume Favelier`_) - Add :func:`mne.write_head_bem` to support writing head surface files (:gh:`8841` by `Yu-Han Luo`_) -- The signal of ``resp`` (respiratory) channels is now assumed to be in the unit Volt (:gh:`8858` by `Richard Höchenberger`_) +- The signal of ``resp`` (respiratory) channels is now assumed to be in the unit Volt (:gh:`8858` by `Richard Höchenberger`_) - Static type checkers like Pylance (comes with VS Code) now display the parameters of many more functions correctly, largely improving overall usability for VS Code users (:gh:`8862` by `Richard Höchenberger`_) @@ -248,7 +216,7 @@ - Fix bug with :func:`mne.io.read_raw_kit` where missing marker coils were not handled (:gh:`8989` **by new contributor** |Judy D Zhu|_) -- Fix bug with `mne.connectivity.spectral_connectivity` where time axis in Epochs data object was dropped. (:gh:`8839` **by new contributor** |Anna Padee|_) +- Fix bug with ``mne.connectivity.spectral_connectivity`` where time axis in Epochs data object was dropped. (:gh:`8839` **by new contributor** |Anna Padee|_) - Fix bug with `mne.io.Raw.resample` to allow passing ``stim_picks='misc'`` (:gh:`8844` **by new contributor** |Enrico Varano|_ and `Eric Larson`_) @@ -260,7 +228,7 @@ - Fix bug with :func:`mne.preprocessing.maxwell_filter` where the eSSS basis had to exactly match the good channels instead of being a superset (:gh:`8675` by `Eric Larson`_) -- Fix bug with :meth:`mne.Report.add_bem_to_section` where ``n_jobs != 1`` would cause ``n_jobs`` subsets of MRI images in some orientations to be flipped (:gh:`8713` by `Eric Larson`_) +- Fix bug with ``mne.Report.add_bem_to_section`` where ``n_jobs != 1`` would cause ``n_jobs`` subsets of MRI images in some orientations to be flipped (:gh:`8713` by `Eric Larson`_) - Fix bug with :meth:`raw.plot() ` where annotations didn't immediately appear when changing window duration (:gh:`8689` by `Daniel McCloy`_) @@ -284,7 +252,7 @@ - Fix bug with :meth:`mne.Annotations.save` where files could be overwritten accidentally, it can now be controlled via the ``overwrite`` argument (:gh:`8896` by `Eric Larson`_) -- Fix bug with ``replace`` argument of :meth:`mne.Report.add_bem_to_section` and :meth:`mne.Report.add_slider_to_section` (:gh:`8723` by `Eric Larson`_) +- Fix bug with ``replace`` argument of ``mne.Report.add_bem_to_section`` and ``mne.Report.add_slider_to_section`` (:gh:`8723` by `Eric Larson`_) - Fix bug with :func:`mne.chpi.compute_chpi_locs` where all cHPI coils being off would lead to an empty array of the wrong dimensionality (:gh:`8956` by `Eric Larson`_) @@ -356,7 +324,7 @@ ~~~~~~~~~~~ - Introduced new ``'auto'`` settings for ``ICA.max_iter``. The old default ``max_iter=200`` will be removed in MNE-Python 0.24 (:gh:`9099` **by new contributor** |Cora Kim|_) -- `mne.viz.plot_sensors_connectivity` now allows setting the colorbar label via the ``cbar_label`` parameter (:gh:`9248` by `Daniel McCloy`_) +- ``mne.viz.plot_sensors_connectivity`` now allows setting the colorbar label via the ``cbar_label`` parameter (:gh:`9248` by `Daniel McCloy`_) - ``mne.read_selection`` has been deprecated in favor of `mne.read_vectorview_selection`. ``mne.read_selection`` will be removed in MNE-Python 0.24 (:gh:`8870` by `Richard Höchenberger`_) diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.24.inc python-mne-1.1.0+dfsg/doc/changes/0.24.inc --- python-mne-0.23.4+dfsg/doc/changes/0.24.inc 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.24.inc 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,425 @@ +.. NOTE: we are now using links to highlight new functions and classes. + Please follow the examples below like :func:`mne.stats.f_mway_rm`, so the + whats_new page will have a link to the function/class documentation. + +.. NOTE: there are 3 separate sections for changes, based on type: + - "Enhancements" for new features + - "Bugs" for bug fixes + - "API changes" for backward-incompatible changes + +.. NOTE: changes from first-time contributors should be added to the TOP of + the relevant section (Enhancements / Bugs / API changes), and should look + like this (where xxxx is the pull request number): + - description of enhancement/bugfix/API change (:gh:`xxxx` **by new contributor** |Firstname Lastname|_) + +.. _changes_0_24_0: + +Version 0.24.0 (2021-11-03) +--------------------------- + +.. |David Julien| replace:: **David Julien** + +.. |Dominique Makowski| replace:: **Dominique Makowski** + +.. |Romain Derollepot| replace:: **Romain Derollepot** + +.. |Jan Sosulski| replace:: **Jan Sosulski** + +.. |Xiaokai Xia| replace:: **Xiaokai Xia** + +.. |Marian Dovgialo| replace:: **Marian Dovgialo** + +.. |Pierre-Antoine Bannier| replace:: **Pierre-Antoine Bannier** + +.. |Darin Erat Sleiter| replace:: **Darin Erat Sleiter** + +.. |Mathieu Scheltienne| replace:: **Mathieu Scheltienne** + +.. |Timothy Gates| replace:: **Timothy Gates** + +.. |Reza Shoorangiz| replace:: **Reza Shoorangiz** + +.. |Evgeny Goldstein| replace:: **Evgeny Goldstein** + +.. |Joshua Teves| replace:: **Joshua Teves** + +.. |Riessarius Stargardsky| replace:: **Riessarius Stargardsky** + +.. |John Samuelsson| replace:: **John Samuelsson** + +Enhancements +~~~~~~~~~~~~ +.. - Add something cool (:gh:`9192` **by new contributor** |New Contributor|_) + +- Add `pooch` to system information reports (:gh:`9801` **by new contributor** |Joshua Teves|_) + +- Get annotation descriptions from the name field of SNIRF stimulus groups when reading SNIRF files via `mne.io.read_raw_snirf` (:gh:`9575` **by new contributor** |Darin Erat Sleiter|_) + +- Add support for NIRSport and NIRSport2 devices to `mne.io.read_raw_nirx` (:gh:`9348` and :gh:`9401` **by new contributor** |David Julien|_, **new contributor** |Romain Derollepot|_, `Robert Luke`_, and `Eric Larson`_) + +- New function :func:`mne.Dipole.to_volume_labels` to get atlas label for MRI coordinates. (:gh:`9376` by **new contributor** |Marian Dovgialo|_) + +- Add support for SURE parameter selection in :func:`mne.inverse_sparse.mixed_norm` and make ``alpha`` parameter now default to ``'sure'`` (:gh:`9430` by **new contributor** |Pierre-Antoine Bannier|_ and `Alex Gramfort`_) + +- Speed up BCD solver in :func:`mne.inverse_sparse.mixed_norm` by adding Anderson acceleration (:gh:`9481` by **new contributor** |Pierre-Antoine Bannier|_ and `Alex Gramfort`_) + +- Add support for list of channel types for EEG/sEEG/ECoG/DBS referencing (:gh:`9637` **by new contributor** |Mathieu Scheltienne|_) + +- Add the ability to set point size and line width in :func:`mne.viz.plot_sensors` and related functions (:gH:`9872` **by new contributor** |Dominique Makowski|_) + +- Add support for changing the default event id used by :func:`mne.make_fixed_length_epochs` (:gh:`9782` **by new contributor** |Mathieu Scheltienne|_) + +- Improve documentation for arguments ``threshold``, ``method`` and ``measure`` of ICA methods `~mne.preprocessing.ICA.find_bads_ecg`, `~mne.preprocessing.ICA.find_bads_ref` and `~mne.preprocessing.ICA.find_bads_eog` (:gh:`9858` **by new contributor** |Mathieu Scheltienne|_) + +- Add support for more than 3 source estimates in :func:`mne.viz.plot_sparse_source_estimates` (:gh:`9640` **by new contributor** |Pierre-Antoine Bannier|_ and `Alex Gramfort`_) + +- Add cortical signal suppression (CSS) :func:`mne.preprocessing.cortical_signal_suppression` (:gh:`6888`, :gh:`9892` **by new contributor** |John Samuelsson|_ and `Eric Larson`_) + +- Show all good channel types and counts when printing a :class:`mne.Info` in the notebook (:gh:`9725` by `Valerii Chirkov`_ and `Eric Larson`_) + +- Speed up point decimation in :func:`mne.io.read_raw_kit` by vectorization and use of :class:`scipy.spatial.cKDTree` (:gh:`9568` by `Jean-Remi King`_ and `Eric Larson`_) + +- Add ability to export EDF+ files using :func:`mne.export.export_raw` (:gh:`9643` by `Adam Li`_) + +- New function :func:`mne.chpi.get_chpi_info` to retrieve basic information about the cHPI system used when recording MEG data (:gh:`9369` by `Richard Höchenberger`_) + +- New namespace `mne.export` created to contain functions (such as `mne.export.export_raw` and `mne.export.export_epochs`) for exporting data to non-FIF formats (:gh:`9427` by `Eric Larson`_) + +- Add support for Hitachi fNIRS devices in `mne.io.read_raw_hitachi` (:gh:`9391` by `Eric Larson`_) + +- Add support for ``picks`` in :func:`mne.stc_near_sensors` (:gh:`9396` by `Eric Larson`_) + +- Add label methods `mne.Label.compute_area` and `mne.Label.distances_to_outside` (:gh:`9424` by `Eric Larson`_) + +- Add projections when printing a :class:`mne.Info` in the notebook (:gh:`9403` by `Alex Gramfort`_) + +- Add new function :func:`mne.bem.make_scalp_surfaces` to compute scalp surfaces for coregistration (:gh:`9463` by `Alex Gramfort`_) + +- Add support for interpolating oxy and deoxyhaemoglobin data types (:gh:`9431` by `Robert Luke`_) + +- Add support for SNIRF files in :class:`mne.Report` (:gh:`9443` by `Robert Luke`_) + +- Add support for exporting MFF evoked files using `mne.export.export_evokeds` and `mne.export.export_evokeds_mff` (:gh:`9406` by `Evan Hathaway`_) + +- :func:`mne.concatenate_raws`, :func:`mne.concatenate_epochs`, and :func:`mne.write_evokeds` gained a new parameter ``on_mismatch``, which controls behavior in case not all of the supplied instances share the same device-to-head transformation (:gh:`9438` by `Richard Höchenberger`_) + +- Add support for multiple datablocks (acquistions with pauses) in :func:`mne.io.read_raw_nihon` (:gh:`9437` by `Federico Raimondo`_) + +- Add new function :func:`mne.preprocessing.annotate_break` to automatically detect and mark "break" periods without any marked experimental events in the continuous data (:gh:`9445` by `Richard Höchenberger`_) + +- Add "time_format" to :meth:`mne.io.Raw.plot` and :meth:`mne.preprocessing.ICA.plot_sources` to display acquisition time on x-axis (:gh:`9419` by `Martin Schulz`_) + +- Speed up :meth:`mne.decoding.TimeDelayingRidge.predict` by switching to FFT-based convolution (:gh:`9458` by `Ross Maddox`_) + +- Show multiple colors and linestyles for excluded components with :class:`mne.Evoked` in :meth:`mne.preprocessing.ICA.plot_sources` (:gh:`9444` by `Martin Schulz`_) + +- Add functions for aligning MRI and CT data `mne.transforms.compute_volume_registration` and `mne.transforms.apply_volume_registration` (:gh:`9503` by `Alex Rockhill`_ and `Eric Larson`_) + +- Add tutorial for how to processes image (CT and MR) files in order to localize electrode contacts for intracranial recordings :ref:`tut-ieeg-localize` (:gh:`9484` by `Alex Rockhill`_) + +- Add vertex number in source space for :meth:`mne.VolSourceEstimate.plot` and :func:`mne.viz.plot_volume_source_estimates` (:gh:`9697` by `Eric Larson`_) + +- Add support for colormap normalization in :func:`mne.viz.plot_topomap` (:gh:`9468` by `Clemens Brunner`_) + +- Update :func:`mne.preprocessing.realign_raw` with Numpy-recommended polynomial fitting method (:gh:`9514` by `Erica Peterson`_) + +- Add a warning to `mne.preprocessing.nirs.beer_lambert_law` when source-detector distances are zero (:gh:`9541` by `Eric Larson`_) + +- Add ``exclude`` parameter to :meth:`mne.io.Raw.plot_psd` and :meth:`mne.Epochs.plot_psd` (:gh:`9519` by `Clemens Brunner`_) + +- Add `mne.Annotations.rename` to rename annotation descriptions (:gh:`9525` by `Robert Luke`_) + +- Add `mne.Annotations.set_durations` to set annotation durations (:gh:`9529` by `Robert Luke`_) + +- The ``exclude`` parameter in `mne.io.read_raw_edf`, `mne.io.read_raw_bdf`, and `mne.io.read_raw_gdf` now also accepts a regular expression (:gh:`9558` by `Clemens Brunner`_) + +- Add :meth:`mne.Evoked.get_data` method to :class:`mne.Evoked` (:gh:`9555` by `Stefan Appelhoff`_) + +- The :meth:`mne.Epochs.get_data` method now has a ``units`` parameter (:gh:`9553` by `Stefan Appelhoff`_) + +- Add :func:`mne.warp_montage_volume` to use an anatomical volume with channel locations at high or low insensities to apply a symmetric diffeomorphic registration to a template (e.g. fsaverage) space (:gh:`9544` by `Alex Rockhill`_) + +- :meth:`mne.io.Raw.get_data`, :meth:`mne.Epochs.get_data`, and :meth:`mne.Evoked.get_data` methods now have ``tmin`` and ``tmax`` parameters to specify data window in seconds to get (:gh:`9556` by `Stefan Appelhoff`_) + +- The :meth:`mne.Evoked.get_data` method now has a ``units`` parameter (:gh:`9578` by `Stefan Appelhoff`_) + +- Add `mne.chpi.compute_chpi_snr` and `mne.viz.plot_chpi_snr` for computing and plotting the time-varying SNR of continuously-active HPI coils (:gh:`9570` by `Daniel McCloy`_ and `Jussi Nurminen`_) + +- Add :func:`mne.get_montage_volume_labels` to find the regions of interest in a Freesurfer atlas anatomical segmentation for an intracranial electrode montage and :func:`mne.viz.plot_channel_labels_circle` to plot them (:gh:`9545` by `Alex Rockhill`_) + +- Add :func:`mne.viz.Brain.add_volume_labels` to plot subcortical surfaces and other regions of interest (:gh:`9540` by `Alex Rockhill`_ and `Eric Larson`_) + +- Add custom cortex curvature colors in :class:`mne.viz.Brain` via the ``cortex`` argument (:gh:`9750` by `Eric Larson`_) + +- Add :meth:`mne.channels.DigMontage.apply_trans` to apply a transform directly to a montage (:gh:`9601` by `Alex Rockhill`_) + +- :meth:`mne.preprocessing.ICA.fit` now emits a warning if any of the ``start``, ``stop``, ``reject``, and ``flat`` parameters are passed when performing ICA on `~mne.Epochs`. These parameters only have an effect on `~mne.io.Raw` data and were previously silently ignored in the case of `~mne.Epochs` (:gh:`9605` by `Richard Höchenberger`_) + +- :func:`mne.viz.plot_alignment` now emits a warning (instead of aborting with an exception) if the surfaces contain topological defects (:gh:`9614` by `Richard Höchenberger`_) + +- The coregistration GUI can now display the head surface even if there are topological defects. Previously, a low-resolution standard head unsuitable for individualized coregistration was displayed (:gh:`9614` by `Richard Höchenberger`_) + +- New :class:`mne.coreg.Coregistration` object to allow coregistration scripting without a dependency on mayavi (:gh:`9516` by `Guillaume Favelier`_). + +- Add :meth:`mne.viz.Brain.add_head` to plot the head surface (:gh:`9618` by `Alex Rockhill`_) + +- Add :func:`mne.channels.read_dig_localite` to read Localite electrode location files (:gh:`9658` by `Clemens Brunner`_) + +- Add :meth:`mne.viz.Brain.add_sensors` to plot sensor locations (:gh:`9585` by `Alex Rockhill`_) + +- Add :func:`mne.coreg.estimate_head_mri_t` to estimate the head->mri transform from fsaverage fiducials (:gh:`9585` by `Alex Rockhill`_) + +- Add :meth:`mne.channels.DigMontage.add_mni_fiducials` to add fiducials to a montage in MNI coordinates (:gh:`9682` by `Alex Rockhill`_) + +- Add :meth:`mne.channels.DigMontage.remove_fiducials` to remove fiducials under rare circumstances when the coordinate frame should not be set to "head" (:gh:`9684` by `Alex Rockhill`_) + +- Add remove methods for mesh objects in :class:`mne.viz.Brain` (:gh:`9688` by `Alex Rockhill`_) + +- Add ``--show-paths`` and ``--developer`` options to :ref:`mne sys_info` (:gh:`9789` by `Eric Larson`_) + +- Add ability to export EDF+ files using :func:`mne.export.export_raw` (:gh:`9643` by `Adam Li`_) + +- Reading EDF files via :func:`mne.io.read_raw_edf` now can infer channel type from the signal label in the EDF header (:gh:`9694` by `Adam Li`_) + +- Add :func:`mne.gui.locate_ieeg` to locate intracranial electrode contacts from a CT, an MRI (with Freesurfer ``recon-all``) and the channel names from an :class:`mne.Info` object (:gh:`9586` by `Alex Rockhill`_) + +- All data fetching code now relies on ``pooch``, which is an added optional requirement for dataset fetchers (:gh:`9742` by `Adam Li`_ and `Daniel McCloy`_) + +- Add phantom FreeSurfer subject fetcher :func:`mne.datasets.fetch_phantom` to allow fetching the Otaniemi phantom (:gh:`9796` by `Eric Larson`_) + +- Add :func:`mne.datasets.fetch_dataset` to allow packages to fetch datasets in a similar fashion to MNE (:gh:`9763` by `Adam Li`_) + +- Add full ECoG dataset to MNE-misc-data and demonstrate its use in :ref:`ex-electrode-pos-2d` and :ref:`tut-ieeg-localize` (:gh:`9784` by `Alex Rockhill`_) + +- Add options ``tol`` and ``accuracy`` to :func:`mne.fit_dipole` to control optimization (:gh:`9810` by `Eric Larson`_) + +- Completely revamp the `~mne.Report` experience: new HTML layout, many new methods, more flexibility; the functionality is demonstrated in :ref:`tut-report` (:gh:`9754`, :gh:`9828`, :gh:`9847`, :gh:`9860`, :gh:`9861`, :gh:`9862`, :gh:`9863`, :gh:`9871`, :gh:`9881`, :gh:`9891`, :gh:`9901` by `Richard Höchenberger`_, `Eric Larson`_, and `Alex Gramfort`_) + +- Add basic HTML representations of `~mne.Forward` and `~mne.minimum_norm.InverseOperator` instances for a nicer Jupyter experience (:gh:`9754` by `Richard Höchenberger`_) + +- `mne.io.read_raw_brainvision` now handles ASCII data with comma-separated values, as may be exported from BrainVision Analyzer (:gh:`9795` by `Richard Höchenberger`_) + +- Add :func:`mne.preprocessing.ieeg.project_sensors_onto_brain` to project ECoG sensors onto the pial surface to compensate for brain shift (:gh:`9800` by `Alex Rockhill`_) + +- Add support for data acquired with NIRx devices using Aurora software version 2021.9 (:gh:`9800` by `Robert Luke`_, and `Eric Larson`_) + +- All functions for reading and writing files should now automatically handle ``~`` (the tilde character) and expand it to the user's home directory. Should you come across any function that doesn't do it, please do let us know! (:gh:`9613`, :gh:`9845` by `Richard Höchenberger`_) + +- All functions accepting a FreeSurfer subjects directory via a ``subjects_dir`` parameter can now consume :py:class:`pathlib.Path` objects too (used to be only strings) (:gh:`9613` by `Richard Höchenberger`_) + +- Add support for colormap normalization in :meth:`mne.time_frequency.AverageTFR.plot` (:gh:`9851` by `Clemens Brunner`_) + +- Add support for BIDS-compatible filenames when splitting big epochs files via the new ``split_naming`` parameter in :meth:`mne.Epochs.save` (:gh:`9869` by `Denis Engemann`_) + +- Add ``by_event_type`` parameter to :meth:`mne.Epochs.average` to create a list containing an :class:`mne.Evoked` object for each event type (:gh:`9859` by `Marijn van Vliet`_) + +- Add pyqtgraph as a new backend for :meth:`mne.io.Raw.plot` (:gh:`9687` by `Martin Schulz`_) + +- Add :func:`mne.viz.set_browser_backend`, :func:`mne.viz.use_browser_backend` and :func:`mne.viz.get_browser_backend` to set matplotlib or pyqtgraph as backend for :meth:`mne.io.Raw.plot` (:gh:`9687` by `Martin Schulz`_) + +Bugs +~~~~ +- Fix bug in :meth:`mne.io.Raw.pick` and related functions when parameter list contains channels which are not in info instance (:gh:`9708` **by new contributor** |Evgeny Goldstein|_) + +- Fix a few typos (:gh:`9706` **by new contributor** |Timothy Gates|_) + +- Fix bug with :meth:`mne.Epochs.crop` and :meth:`mne.Evoked.crop` when ``include_tmax=False``, where the last sample was always cut off, even when ``tmax > epo.times[-1]`` (:gh:`9378` **by new contributor** |Jan Sosulski|_) + +- Fix bug with `mne.io.read_raw_curry` to allow reading Curry 7 and 8 event files with '.ceo' and '.cdt.ceo' extensions (:gh:`9381`, :gh:`9712` by **new contributor** |Xiaokai Xia|_, `Daniel McCloy`_, and **by new contributor** |Reza Shoorangiz|_) + +- Fix bug with `mne.compute_rank`, `mne.cov.compute_whitener` and `mne.preprocessing.ICA.fit` when explicit picks including bad channels are provided (:gh:`9719` **by new contributor** |Mathieu Scheltienne|_) + +- Fix bug with :func:`mne.concatenate_epochs` where the concatenated events could end up in non-chronological order. (:gh:`9765` **by new contributor** |Jan Sosulski|_) + +- Fix bug where :func:`mne.time_frequency.psd_array_welch` and :func:`mne.time_frequency.psd_array_multitaper` failed to handle negative integer for ``n_jobs`` argument (:gh:`9849` **by new contributor** |Riessarius Stargardsky|_) + +- Fix bug where resampling returns empty instances (:gh:`9897` **by new contributor** |Mathieu Scheltienne|_ and `Eric Larson`_) + +- Fix bug with `mne.io.Raw.get_montage` now returning both good and bad channels in the montage. (:gh:`9920` **by new contributor** |Mathieu Scheltienne|_) + +- Fix bug with :func:`mne.io.read_raw_nihon` where latin-1 annotations could not be read (:gh:`9384` by `Alex Gramfort`_) + +- Fix bug when printing a :class:`mne.io.RawArray` in the notebook (:gh:`9404` by `Alex Gramfort`_) + +- Fix bug when computing rank from info for SSS data with only gradiometers or magnetometers (:gh:`9435` by `Alex Gramfort`_) + +- Fix bug with `mne.preprocessing.find_bad_channels_maxwell` where all-flat segments could lead to an error (:gh:`9531` by `Eric Larson`_) + +- Fix bug with `mne.io.Raw.set_montage` and related functions where the channel coordinate frame was not properly set to head (:gh:`9447` by `Eric Larson`_) + +- Fix bug with `mne.io.read_raw_fieldtrip` and `mne.read_epochs_fieldtrip` where channel positions were not set properly (:gh:`9447` by `Eric Larson`_) + +- Fix bug with :func:`mne.io.read_raw_kit` where omitting HPI coils could lead to an :exc:`python:AssertionError` on reading (:gh:`9612` by `Eric Larson`_) + +- Fix bug with :func:`mne.io.read_raw_edf` where reading only stimulus channels would lead to an error (:gh:`9831` by `Eric Larson`_) + +- Fix bug with `mne.preprocessing.nirs.optical_density` where protection against zero values was not guaranteed (:gh:`9522` by `Eric Larson`_) + +- :func:`mne.concatenate_raws` now raises an exception if ``raw.info['dev_head_t']`` differs between files. This behavior can be controlled using the new ``on_mismatch`` parameter (:gh:`9438` by `Richard Höchenberger`_) + +- Fixed bug in :meth:`mne.Epochs.drop_bad` where subsequent rejections failed if they only specified thresholds for a subset of the channel types used in a previous rejection (:gh:`9485` by `Richard Höchenberger`_). + +- Fix bug with `mne.simulation.simulate_evoked`, `mne.apply_forward`, and `mne.apply_forward_raw` where systems with EEG channels that come before MEG channels would have them mixed up in the output evoked or raw object (:gh:`#9513` by `Eric Larson`_) + +- In :func:`mne.viz.plot_ica_scores` and :meth:`mne.preprocessing.ICA.plot_scores`, the figure and axis titles no longer overlap when plotting only a single EOG or ECG channel (:gh:`9489` by `Richard Höchenberger`_). + +- Ensure `mne.io.Raw.get_montage` works with SNIRF data (:gh:`9524` by `Robert Luke`_) + +- Fix bug in :func:`mne.setup_volume_source_space` where non-finite positions could be used in a discrete source space (:gh:`9603` by `Eric Larson`_) + +- Fix bug in :func:`mne.viz.plot_topomap` (and related methods like :meth:`mne.Evoked.plot_topomap`) where large distances between electrodes (higher than head radius) would lead to an error (:gh:`9528` by `Mikołaj Magnuski`_). + +- Fix bug in `mne.viz.plot_topomap` (and related methods) where passing ``axes`` that are part of a matplotlib figure that uses a constrained layout would emit warnings (:gh:`9558` by `Eric Larson`_) + +- Fix bug in :func:`mne.make_forward_solution` where impossible sensor-BEM geometries were not checked (:gh:`9893` by `Eric Larson`_) + +- Fix bug in :func:`mne.concatenate_epochs` when concatenating :class:`mne.Epochs` objects with 0 events (:gh:`9535` by `Marijn van Vliet`_) + +- Fix bug in :func:`mne.viz.Brain.screenshot` where the RGBA mode was not supported (:gh:`9564` by `Guillaume Favelier`_). + +- Fix bug in :func:`mne.io.read_raw_egi` where reading data from a data segment that is part of an acquisition skip would lead to an error (:gh:`9565` by `Eric Larson`_) + +- Prevent :meth:`mne.io.Raw.plot` windows from spawning at sizes too small to properly render (:gh:`9629` by `Daniel McCloy`_) + +- Fix bug where :meth:`mne.io.Raw.plot` raised an error when plotting raw dipole data (:gh:`9771` by `Eric Larson`_) + +- Fix bug in ::meth:`mne.preprocessing.ICA.find_bads_ecg` where passing ``start`` and ``stop`` lead to erroneous data windows depending on the combination of Raw, Epochs, Evoked, and the type (int, float, None) of ``start`` and ``stop`` (:gh:`9556` by `Stefan Appelhoff`_) + +- Fix bug in :func:`mne.viz.set_3d_backend` and :func:`mne.viz.get_3d_backend` where the PyVistaQt-based backend was ambiguously named ``'pyvista'`` instead of ``'pyvistaqt'``; use ``set_3d_backend('pyvistaqt')`` and expect ``'pyvistaqt'`` as the output of :func:`mne.viz.get_3d_backend` instead of ``'pyvista'``, and consider using ``get_3d_backend().startswith('pyvista')`` for example for backward-compatible conditionals (:gh:`9607` by `Guillaume Favelier`_) + +- Fix bug in :func:`mne.viz.plot_compare_evokeds` where confidence bands were not drawn if only one condition was plotted (:gh:`9663` by `Daniel McCloy`_) + +- Fix bug where setting of a montage with fNIRS data got set to "unknown" coordinate frame when it should have been in "head" (:gh:`9630` by `Alex Rockhill`_) + +- Fix bug where "seeg", "ecog", "dbs" and "fnirs" data had coordinate frame unknown upon loading from a file when it should have been in "head" (:gh:`9580` by `Alex Rockhill`_) + +- Raise error when no ``trans`` is provided to :func:`mne.viz.plot_alignment` when required instead of assuming identitiy head->mri transform (:gh:`9585` by `Alex Rockhill`_) + +- Fix bug where :meth:`mne.Epochs.equalize_event_counts` failed when only one good epoch existed for one of the event types (:gh:`9758` by `Daniel McCloy`_) + +- Fix bug where channels with a dollar sign ($) were not being labeled "misc" in :func:`mne.io.read_raw_nihon` (:gh:`9695` by `Adam Li`_) + +- Fix bug where :func:`mne.io.read_raw_persyst` was lower-casing events it found in the ``.lay`` file (:gh:`9746` by `Adam Li`_) + +- Fix bug with Qhull when plotting OPM sensors in :func:`mne.viz.plot_alignment` (:gh:`9799` and :gh:`9804` by `Eric Larson`_) + +- Fix bug where :func:`mne.io.read_raw_snirf` was including the landmark index as a spatial coordinate (:gh:`9777` by `Robert luke`_) + +- Fix bug where `mne.Annotations` were not appending channel names when being added together (:gh:`9780` by `Adam Li`_) + +- `mne.viz.plot_evoked_joint` now correctly handles a ``times='peaks'`` parameter value (:gh:`9754` by `Richard Höchenberger`_) + +- In `mne.viz.plot_compare_evokeds`, slightly adjust the x-axis limit calculation to avoid drawing an axis that is too short (:gh:`9754` by `Richard Höchenberger`_) + +- Fix a bug with :func:`mne.viz.snapshot_brain_montage` with the Mayavi backend where electrodes were shown in the wrong positions, and with the PyVista backend where the snapshot always contained the electrodes but could be missing a hemisphere (:gh:`9933` by `Eric Larson`_) + +- In :meth:`mne.io.Raw.plot_psd` and :meth:`mne.Epochs.plot_psd`, avoid overlap between subplot titles and x-axis tick labels (:gh:`9864` by `Richard Höchenberger`_) + +- In :meth:`mne.Epochs.plot_psd_topomap`, the data is now scaled to match the output of :meth:`mne.Epochs.plot_psd` (:gh:`9873` by `Richard Höchenberger`_) + +- Fix channel locations with ``NaN`` values causing all channel locations not to be plotted in :func:`mne.viz.Brain.add_sensors` (:gh:`9911` by `Alex Rockhill`_) + +- Fix infinite loop bug in :func:`mne.get_montage_volume_labels` (:gh:`9940` by `Alex Rockhill`_) + +API changes +~~~~~~~~~~~ +- The :class:`mne.Info` class is now more strictly checked when attributes are set. Setting most attributes directly will lead to an error in the next release. See the docstring of :class:`mne.Info` for more information (:gh:`9867` **by new contributor** |Mathieu Scheltienne|_) + +- The Mayavi 3D backend has been deprecated (:gh:`9904` by `Eric Larson`_): + + - In 3D plotting functions, use PyVista-based backends (``'pyvistaqt'`` and ``'notebook'``) as PySurfer/Mayavi will be removed in the next version + + - To use the :ref:`mne kit2fiff` command-line GUI for fiducial markers, install the separate ``mne-kit-gui`` module, which can still be launched via ``$ mne kit2fiff`` + + - The ``mne.gui.fiducials`` GUI has been deprecated, use :ref:`mne coreg` or ``mne_kit_gui.fiducials()`` to set MRI fiducials instead + +- In `mne.compute_source_morph`, the ``niter_affine`` and ``niter_sdr`` parameters have been replaced by ``niter`` and ``pipeline`` parameters for more consistent and finer-grained control of registration/warping steps and iteration (:gh:`9505` by `Alex Rockhill`_ and `Eric Larson`_) + +- Split :func:`mne.viz.Brain.show_view` argument ``view`` into ``azimuth``, ``elevation`` and ``focalpoint`` for clearer view setting and make the default for ``row`` and ``col`` apply to all rows and columns (:gh:`9596` by `Alex Rockhill`_) + +- Deprecate ``solver='prox'`` in :func:`mne.inverse_sparse.mixed_norm` in favor of ``solver='cd'`` and ``solver='bcd'`` as coordinate descent solvers consistently outperform proximal gradient descent (:gh:`9608` by `Pierre-Antoine Bannier`_) + +- All ``mne.connectivity`` functions have moved to the ``mne-connectivity`` package; they are deprecated in MNE-Python and will be removed in version 0.25 (:gh:`9493` by `Adam Li`_). + +- :func:`mne.inverse_sparse.mixed_norm` now simply warns when source estimates contain no dipole, e.g. if data are too noisy and alpha is based on SURE (:gh:`9685` by `Alex Gramfort`_) + +- Deprecate functions :samp:`mne.datasets.{DATASET_NAME}.has_{DATASET_NAME}_data()` for these datasets: ``epilepsy_ecog``, ``fnirs_motor``, ``multimodal``, ``opm``, ``phantom_4dbti``, ``refmeg_noise``, ``sample``, ``somato``, and ``ssvep``. Use the generic :func:`mne.datasets.has_dataset` instead (:gh:`9781` by `Daniel McCloy`_ and `Adam Li`_) + +- :class:`mne.Report` modernization has led to multiple deprecations (:gh:`9754` by `Richard Höchenberger`_): + + - ``mne.Report.add_figs_to_section`` and ``mne.Report.add_slider_to_section`` have been deprecated in favor of :meth:`mne.Report.add_figure` + + - ``mne.Report.add_images_to_section`` has been deprecated in favor of :meth:`mne.Report.add_image` + + - ``mne.Report.add_bem_to_section`` has been deprecated in favor of :meth:`mne.Report.add_bem` + + - ``mne.Report.add_htmls_to_section`` has been deprecated in favor of :meth:`mne.Report.add_html` + + - In :meth:`mne.Report.parse_folder`, the ``sort_sections`` parameter has been deprecated in favor of ``sort_content`` + + - In :meth:`mne.Report.remove`, the ``caption`` and ``section`` parameters have been deprecated in favor of ``title`` and ``tags``, respectively + + - The ``mne.Report.sections`` attribute has been deprecated in favor of ``mne.Report.tags`` + + - The ``mne.Report.fnames`` attribute has been deprecated without replacement + +- :meth:`mne.Epochs.plot` and :func:`mne.viz.plot_epochs` gained parameter ``show_scalebars`` analogous to :meth:`mne.viz.plot_raw` (:gh:`9815` by `Daniel McCloy`_) + +- The output folder name for HF_SEF datasets is now ``hf_sef`` instead of ``HF_SEF`` (:gh:`9763` by `Adam Li`_) + +- Deprecate ``mne.viz.utils.center_cmap`` (:gh:`9851` by `Clemens Brunner`_) + +- The default partial pathlength factor of :func:`mne.preprocessing.nirs.beer_lambert_law` will change from 0.1 in 0.24 to 6.0 in the next release (:gh:`9843` by `Robert Luke`_) + +- ``mne.preprocessing.ICA.detect_artifacts`` has been deprecated. Please use `~mne.preprocessing.ICA.find_bads_eog` and `~mne.preprocessing.ICA.find_bads_ecg` instead (:gh:`9909` by `Richard Höchenberger`_) + +Authors +~~~~~~~ +People who contributed to this release in alphabetical order +(people with a + are first time contributors): + +* Adam Li +* Alex Rockhill +* Alexandre Gramfort +* Britta Westner +* Clemens Brunner +* Daniel McCloy +* Darin Erat Sleiter+ +* David Julien+ +* Denis A. Engemann +* Dominique Makowski+ +* Eric Larson +* Erica Peterson +* Evan Hathaway +* Evgeny Goldstein+ +* Fede Raimondo +* Gansheng Tan+ +* Guillaume Favelier +* Hubert Banville +* Jan Sosulski+ +* Jean-Remi King +* Jeff Stout +* Johann Benerradi +* John Samuelsson+ +* Josh Koen+ +* Joshua Teves+ +* Lukas Hecker+ +* Luke Bloy +* Marian Dovgialo+ +* Marijn van Vliet +* Martin Schulz +* Mathieu Scheltienne+ +* Mikołaj Magnuski +* Pierre-Antoine Bannier+ +* Reza Shoorangiz+ +* Richard Höchenberger +* Riessarius Stargardsky+ +* Robert Luke +* Romain Derollepot+ +* Ross Maddox +* Ryan Law+ +* Stefan Appelhoff +* Steve Matindi+ +* Timothy Gates+ +* Valerii Chirkov +* Xiaokai Xia+ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.2.inc python-mne-1.1.0+dfsg/doc/changes/0.2.inc --- python-mne-0.23.4+dfsg/doc/changes/0.2.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.2.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ .. _changes_0_2: -Version 0.2 ------------ +Version 0.2 (2011-11-08) +------------------------ Changelog ~~~~~~~~~ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.3.inc python-mne-1.1.0+dfsg/doc/changes/0.3.inc --- python-mne-0.23.4+dfsg/doc/changes/0.3.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.3.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ .. _changes_0_3: -Version 0.3 ------------ +Version 0.3 (2012-03-23) +------------------------ Changelog ~~~~~~~~~ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.4.inc python-mne-1.1.0+dfsg/doc/changes/0.4.inc --- python-mne-0.23.4+dfsg/doc/changes/0.4.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.4.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ .. _changes_0_4: -Version 0.4 ------------ +Version 0.4 (2012-08-24) +------------------------ Changelog ~~~~~~~~~ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.5.inc python-mne-1.1.0+dfsg/doc/changes/0.5.inc --- python-mne-0.23.4+dfsg/doc/changes/0.5.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.5.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ .. _changes_0_5: -Version 0.5 ------------ +Version 0.5 (2012-12-27) +------------------------ Changelog ~~~~~~~~~ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.6.inc python-mne-1.1.0+dfsg/doc/changes/0.6.inc --- python-mne-0.23.4+dfsg/doc/changes/0.6.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.6.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ .. _changes_0_6: -Version 0.6 ------------ +Version 0.6 (2013-06-15) +------------------------ Changelog ~~~~~~~~~ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.7.inc python-mne-1.1.0+dfsg/doc/changes/0.7.inc --- python-mne-0.23.4+dfsg/doc/changes/0.7.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.7.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ .. _changes_0_7: -Version 0.7 ------------ +Version 0.7 (2013-11-26) +------------------------ Changelog ~~~~~~~~~ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.8.inc python-mne-1.1.0+dfsg/doc/changes/0.8.inc --- python-mne-0.23.4+dfsg/doc/changes/0.8.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.8.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ .. _changes_0_8: -Version 0.8 ------------ +Version 0.8 (2014-06-25) +------------------------ Changelog ~~~~~~~~~ diff -Nru python-mne-0.23.4+dfsg/doc/changes/0.9.inc python-mne-1.1.0+dfsg/doc/changes/0.9.inc --- python-mne-0.23.4+dfsg/doc/changes/0.9.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/0.9.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ .. _changes_0_9: -Version 0.9 ------------ +Version 0.9 (2015-05-22) +------------------------ Changelog ~~~~~~~~~ @@ -88,7 +88,7 @@ - Add ``evoked.as_type`` to allow remapping data in MEG channels to virtual magnetometer or gradiometer channels by `Mainak Jas`_ -- Add :meth:`mne.Report.add_bem_to_section`, :meth:`mne.Report.add_htmls_to_section` methods to :class:`mne.Report` by `Teon Brooks`_ +- Add ``mne.Report.add_bem_to_section``, ``mne.Report.add_htmls_to_section`` methods to :class:`mne.Report` by `Teon Brooks`_ - Add support for KIT epochs files with ``read_epochs_kit`` by `Teon Brooks`_ diff -Nru python-mne-0.23.4+dfsg/doc/changes/1.0.inc python-mne-1.1.0+dfsg/doc/changes/1.0.inc --- python-mne-0.23.4+dfsg/doc/changes/1.0.inc 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/1.0.inc 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,325 @@ +.. NOTE: we use cross-references to highlight new functions and classes. + Please follow the examples below like :func:`mne.stats.f_mway_rm`, so the + whats_new page will have a link to the function/class documentation. + +.. NOTE: there are 3 separate sections for changes, based on type: + - "Enhancements" for new features + - "Bugs" for bug fixes + - "API changes" for backward-incompatible changes + +.. NOTE: changes from first-time contributors should be added to the TOP of + the relevant section (Enhancements / Bugs / API changes), and should look + like this (where xxxx is the pull request number): + + - description of enhancement/bugfix/API change (:gh:`xxxx` by + :newcontrib:`Firstname Lastname`) + + Also add a corresponding entry for yourself in doc/changes/names.inc + +.. _changes_1_0_0: + +Version 1.0.0 (2022-03-21) +-------------------------- + +Enhancements +~~~~~~~~~~~~ + +- Add ``use_orig_time`` option to :meth:`mne.Annotations.crop`. (:gh:`10396` by :newcontrib:`Michiru Kaneda`) + +- Speed up :func:`mne.preprocessing.annotate_muscle_zscore`, :func:`mne.preprocessing.annotate_movement`, and :func:`mne.preprocessing.annotate_nan` through better annotation creation (:gh:`10089` by :newcontrib:`Senwen Deng`) + +- Fix some unused variables in time_frequency_erds.py example (:gh:`10076` by :newcontrib:`Jan Zerfowski`) + +- Document ``BAD_ACQ_SKIP`` annotation behavior (:gh:`10054` by :newcontrib:`Etienne de Montalivet`) + +- Add link to related ``MNE-ARI`` package (:gh:`10174` by :newcontrib:`John Veillette`) + +- :func:`mne.time_frequency.psd_array_multitaper` can now return complex results per-taper when specifying ``output='complex'`` (:gh:`10307` by `Mikołaj Magnuski`_) + +- :func:`mne.time_frequency.tfr_array_multitaper` can now return results for ``output='phase'`` instead of an error (:gh:`10281` by `Mikołaj Magnuski`_) + +- Add show local maxima toggling button to :func:`mne.gui.locate_ieeg` (:gh:`9952` by `Alex Rockhill`_) + +- Show boundaries in :func:`mne.gui.locate_ieeg` (:gh:`10379` by `Eric Larson`_) + +- Add argument ``cval`` to :func:`mne.transforms.apply_volume_registration` to set interpolation values outside the image domain (:gh:`10379` by `Eric Larson`_) + +- Improve docstring of :class:`mne.Info` and add attributes that were not covered (:gh:`9922` by `Mathieu Scheltienne`_) + +- Add an alternate, manual procedure for aligning a CT to an MR procedure to :ref:`tut-ieeg-localize` (:gh:`9978` by `Alex Rockhill`_) + +- Improve docstring of export functions :func:`mne.export.export_raw`, :func:`mne.export.export_epochs`, :func:`mne.export.export_evokeds`, :func:`mne.export.export_evokeds_mff` and issue a warning when there are unapplied projectors (:gh:`9994` by `Mathieu Scheltienne`_) + +- Add support for reading haemoglobin fNIRS data to :func:`mne.io.read_raw_snirf` (:gh:`9929` by `Robert Luke`_) + +- Add ``fill_hole_size`` keyword argument to :func:`mne.viz.Brain.add_volume_labels` to close holes in the mesh (:gh:`10024` by `Alex Rockhill`_) + +- Changed :class:`mne.Epochs` and :class:`mne.Evoked` to have a more concise ``__repr__`` to improve interactive MNE usage in Python Interactive Console, IDEs, and debuggers when many events are handled. (:gh:`10042` by `Jan Sosulski`_) + +- Improve docstring of ``events`` arguments and cross-referencing to :term:`events` (:gh:`10056` by `Mathieu Scheltienne`_) + +- Speed up repeated surface-smoothing operation (e.g., in repeated calls to :meth:`stc.plot() `) (:gh:`10077` by `Eric Larson`_) + +- Add ``verbose`` parameter to :func:`mne.io.Raw.load_bad_channels` and log information on how bad channels are updated (:gh:`10102` by `Stefan Appelhoff`_) + +- Add ``infer_type`` argument to :func:`mne.io.read_raw_edf` and :func:`mne.io.read_raw_bdf` to automatically infer channel types from channel labels (:gh:`10058` by `Clemens Brunner`_) + +- Reduce the time it takes to generate a :class:`mne.io.Raw`, :class:`~mne.Epochs`, or :class:`~mne.preprocessing.ICA` figure if a ``scalings`` parameter is provided (:gh:`10109` by `Richard Höchenberger`_ and `Eric Larson`_) + +- :meth:`mne.Report.add_raw` gained a new ``scalings`` parameter to provide custom data scalings for the butterfly plots (:gh:`10109` by `Richard Höchenberger`_) + +- The ``butterfly`` parameter of :meth:`mne.Report.add_raw` now also accepts numbers to specify how many segments to plot (:gh:`10115` by `Richard Höchenberger`_) + +- The ``psd`` parameter of :meth:`mne.Report.add_epochs` now also accepts numbers to specify the signal duration used for PSD calculation (:gh:`10119` by `Richard Höchenberger`_) + +- Drastically speed up butterfly plot generation in :meth:`mne.Report.add_raw`. We now don't plot annotations anymore; however, we feel that the speed improvements justify this change, also considering the annotations were of limited use in the displayed one-second time slices anyway (:gh:`10114`, :gh:`10116` by `Richard Höchenberger`_) + +- In :class:`mne.Report`, limit the width of automatically generated figures to a maximum of 850 pixels (450 pixels for :class:`mne.SourceEstimate` plots), and the resolution to 100 DPI to reduce file size, memory consumption, and – in some cases like :meth:`mne.Report.add_stc` – processing time (:gh:`10126`, :gh:`10129`, :gh:`10135`, :gh:`10142` by `Richard Höchenberger`_) + +- :class:`~mne.Epochs` metadata tables are now included in :class:`mne.Report` (:gh:`10166` by `Richard Höchenberger`_) + +- :func:`mne.get_head_surf` and :func:`mne.dig_mri_distances` gained a new parameter, ``on_defects``, controlling how to handle surfaces with topological defects (:gh:`10175` by `Richard Höchenberger`_) + +- :meth:`mne.Report.add_epochs` gained a new parameter, ``drop_log_ignore``, to control which drop reasons to omit when creating the drop log plot (:gh:`10182` by `Richard Höchenberger`_) + +- :meth:`mne.Epochs.plot_drop_log` now also includes the absolute number of epochs dropped in the title (:gh:`10186` by `Richard Höchenberger`_) + +- Add a button to show the maximum intensity projection in :func:`mne.gui.locate_ieeg` (:gh:`10185` by `Alex Rockhill`_) + +- Annotations from a :class:`~mne.io.Raw` object are now preserved by the :class:`~mne.Epochs` constructor and are supported when saving Epochs (:gh:`9969` and :gh:`10019` by `Adam Li`_) + +- Add a checkbox to show and hide the MEG helmet in the coregistration GUI (:gh:`10200` by `Guillaume Favelier`_) + +- The coregistration GUI (instantiated via :func:`mne.gui.coregistration`) gained a new attribute, ``coreg``, to access the :class:`mne.coreg.Coregistration` instance used by the GUI (:gh:`10255` by `Richard Höchenberger`_) + +- Add marching cubes display of head if :func:`mne.bem.make_scalp_surfaces` has not computed or the recon-all hasn't finished (:gh:`10202` by `Alex Rockhill`_) + +- The default interaction style of :func:`mne.gui.coregistration` and :func:`mne.viz.plot_alignment` has been changed to ``'terrain'``, which keeps one axis fixed and should make interactions with the 3D scene more predictable (:gh:`9972`, :gh:`10206` by `Richard Höchenberger`_) + +- :func:`mne.gui.coregistration` now uses the proper widget style for push buttons, making for a more native feel of the application (:gh:`10220` by `Richard Höchenberger`_ and `Guillaume Favelier`_) + +- :class:`mne.coreg.Coregistration`, :func:`mne.scale_bem`, and :func:`mne.scale_mri` gained a new parameter, ``on_defects``, controlling how to handle topological defects (:gh:`10230`, :gh:`10249` by `Richard Höchenberger`_) + +- :class:`mne.coreg.Coregistration` gained a new attribute, ``fiducials``, allowing for convenient retrieval of the MRI fiducial points (:gh:`10243`, by `Richard Höchenberger`_) + +- Added plotting points to represent contacts on the max intensity projection plot for :func:`mne.gui.locate_ieeg` (:gh:`10212` by `Alex Rockhill`_) + +- Add lines in 3D and on the maximum intensity projection when more than two electrode contacts are selected to aid in identifying that contact for :func:`mne.gui.locate_ieeg` (:gh:`10212` by `Alex Rockhill`_) + +- Add a ``block`` parameter to :class:`mne.viz.Brain` and the UI of :class:`mne.coreg.Coregistration` to prevent the windows from closing immediately when running in a non-interactive Python session (:gh:`10222` by `Guillaume Favelier`_) + +- All methods of :class:`mne.Report` with a ``tags`` parameter now also accept a single tag passed as a string (previously, you needed to pass a tuple of strings, even for a single tag) (:gh:`10183`, by `Richard Höchenberger`_) + +- :meth:`mne.Report.add_trans` has gained a new parameter, ``alpha``, to control the level of opacity of the rendered head (:gh:`10247`, by `Richard Höchenberger`_) + +- The new convenience function :func:`mne.event.match_event_names` allows for straightforward checking if a specific event name or a group of events is present in a collection of event names (:gh:`10233` by `Richard Höchenberger`_) + +- :meth:`Epochs.plot` is now supported by the pyqtgraph-backend (:gh:`10297` by `Martin Schulz`_) + +- The ``map_surface`` parameter of :meth:`mne.viz.Brain.add_foci` now works and allows you to add foci to a rendering of a brain that are positioned at the vertex of the mesh closest to the given coordinates (:gh:`10299` by `Marijn van Vliet`_) + +- :meth:`mne.preprocessing.ICA.plot_sources()` is now also supported by the ``qt`` backend (:gh:`10330` by `Martin Schulz`_) + +- Added :meth:`mne.viz.Brain.add_dipole` and :meth:`mne.viz.Brain.add_forward` to plot dipoles on a brain as well as :meth:`mne.viz.Brain.remove_dipole` and :meth:`mne.viz.Brain.remove_forward` (:gh:`10373` by `Alex Rockhill`_) + +- Made anterior/posterior slice scrolling in :func:`mne.gui.locate_ieeg` possible for users without page up and page down buttons by allowing angle bracket buttons to be used (:gh:`10384` by `Alex Rockhill`_) + +- Add support for ``theme='auto'`` for automatic dark-mode support in :meth:`raw.plot() ` and related functions and methods when using the ``'qt'`` backend (:gh:`10417` by `Eric Larson`_) + +- Add support for ``MNE_3D_OPTION_THEME`` and ``MNE_BROWSER_THEME`` to use ``['light', 'dark', 'auto']`` or a custom theme path in :class:`mne.viz.Brain` and :meth:`raw.plot() ` (and related functions and methods when using the ``'qt'`` backend(), respectively (:gh:`10418` by `Eric Larson`_) + +- Add support for passing time-frequency data to :func:`mne.stats.spatio_temporal_cluster_test` and :func:`mne.stats.spatio_temporal_cluster_1samp_test` and added an example to :ref:`tut-cluster-spatiotemporal-sensor` (:gh:`10384` by `Alex Rockhill`_) + +- Add support for reading optical density fNIRS data to :func:`mne.io.read_raw_snirf` (:gh:`10408` by `Robert Luke`_) + +- Added :func:`mne.source_space.get_decimated_surfaces` to extract subsurfaces from a SourceSpaces instance or a .fif file (:gh:`10421` by `Joshua Teves`_) + +Bugs +~~~~ +- Fix bug with :func:`mne.io.read_raw_nihon` where latin-1 channels could not be read (:gh:`10429` by :newcontrib:`Matthias Eberlein`) + +- Fix bug with :func:`mne.io.read_raw_nihon` so that it handles duplicates in channel names (:gh:`10431` by :newcontrib:`Matthias Eberlein`) + +- Fix bug in :func:`mne.io.read_raw_egi` where the ``meas_date`` in a ``rawMFF`` object was incorrectly assigned. The function now converts the ``meas_date`` to UTC and provides a UTC offset key, i.e. ``raw.info['utc_offset']``. (:gh:`10304` by :newcontrib:`Scott Huberty`) + +- Fix datetime conversion for tmin/tmax=None cases in :meth:`mne.Annotations.crop`. Allow the use of float and None simultaneously for :meth:`mne.Annotations.crop`. (:gh:`10361` by :newcontrib:`Michiru Kaneda`) + +- Add Shift_JIST mu in :func:`mne.io.read_raw_edf` (:gh:`10356` by :newcontrib:`Michiru Kaneda`) + +- Teach :func:`mne.io.read_raw_bti` to use its ``eog_ch`` parameter (:gh:`10093` by :newcontrib:`Adina Wagner`) + +- Fix use of arguments in :func:`numpy.loadtxt` (:gh:`10189` by :newcontrib:`Federico Zamberlan`) + +- Fix documentation of options in :func:`mne.stc_near_sensors` (:gh:`` by :newcontrib:`Nikolai Chapochnikov`) + +- :func:`mne.time_frequency.tfr_array_multitaper` now returns results per taper when ``output='complex'`` (:gh:`10281` by `Mikołaj Magnuski`_) + +- Fix default of :func:`mne.io.Raw.plot` to be ``use_opengl=None``, which will act like False unless ``MNE_BROWSER_USE_OPENGL=true`` is set in the user configuration (:gh:`9957` by `Eric Larson`_) + +- Fix bug with :class:`mne.Report` where figures were saved with ``bbox_inches='tight'``, which led to inconsistent sizes in sliders (:gh:`9966` by `Eric Larson`_) + +- When opening a saved report and saving it to a different filename again, don't change ``Report.fname`` to avoid a regression when using :func:`~mne.open_report` as a context manager (:gh:`9998` by `Marijn van Vliet`_) + +- Fix bug in :func:`mne.make_forward_solution` where sensor-sphere geometry check was incorrect (:gh:`9968` by `Eric Larson`_) + +- Use single char alphanumeric suffix when renaming long channel names (over 15-characters) when writing to FIF format. (:gh:`10002` by `Luke Bloy`_) + +- Add argument ``overwrite`` to :func:`mne.export.export_raw`, :func:`mne.export.export_epochs`, :func:`mne.export.export_evokeds` and :func:`mne.export.export_evokeds_mff` (:gh:`9975` by `Mathieu Scheltienne`_) + +- :func:`mne.gui.coregistration` and the ``mne coreg`` command didn't respect the ``interaction`` parameter (:gh:`9972` by `Richard Höchenberger`_) + +- Fix incorrect projection of source space onto white matter surface instead of pial in :ref:`tut-working-with-ecog` (:gh:`9980` by `Alex Rockhill`_) + +- Fix channel type support when reading from EEGLAB ``.set`` format with :func:`mne.io.read_raw_eeglab` and :func:`mne.read_epochs_eeglab` (:gh:`9990` by `Mathieu Scheltienne`_) + +- Fix suboptimal alignment using :func:`mne.transforms.compute_volume_registration` (:gh:`9991` by `Alex Rockhill`_) + +- Only warn if header is missing in BrainVision files instead of raising an error (:gh:`10001` by `Clemens Brunner`_) + +- Add argument ``overwrite`` to `mne.preprocessing.ICA.save` to check for existing file (:gh:`10004` by `Mathieu Scheltienne`_) + +- :class:`mne.Report` now raises an exception if invalid tags were passed (:gh:`9970` by `Richard Höchenberger`_) + +- Fix bug in :func:`mne.get_montage_volume_labels` that set the maximum number of voxels to be included too low causing unwanted capping of the included voxel labels (:gh:`10021` by `Alex Rockhill`_) + +- Fix annotation cropping and I/O roundtrip when there is no measurement date available (:gh:`10040` by `Mathieu Scheltienne`_ and `Alex Gramfort`_). + +- Fix bug where :meth:`raw.crop() ` from the beginning of the instance could break annotations and ``raw.first_samp`` (:gh:`10358` by `Eric Larson`_) + +- :func:`~mne.sys_info` output now contains the installed version of ``pooch``, too; this output had been accidentally removed previously (:gh:`10047` by `Richard Höchenberger`_) + +- Fix VTK version extraction in :func:`mne.sys_info` (:gh:`10399` by `Eric Larson`_) + +- Fix automatic channel type detection from channel labels in :func:`mne.io.read_raw_edf` and :func:`mne.io.read_raw_bdf` (and disable this functionality from :func:`mne.io.read_raw_gdf`) (:gh:`10058` by `Clemens Brunner`_) + +- Fix :func:`~mne.stats.permutation_cluster_1samp_test` to properly handle 2-dimensional data in combination with TFCE (:gh:`10073` by `Richard Höchenberger`_) + +- Fix channel grouping error when using "butterfly mode" with :meth:`mne.io.Raw.plot` (:gh:`10087` by `Daniel McCloy`_) + +- Fix inconsistent behavior of ``mne.preprocessing.annotate_*`` functions by making them all return :class:`mne.Annotations` objects with the ``orig_time`` attribute set to ``raw.info["meas_time"]`` (:gh:`10067` and :gh:`10118` by `Stefan Appelhoff`_, `Eric Larson`_, and `Alex Gramfort`_) + +- Fix bug that appears during automatic calculation of the colormap of `mne.viz.Brain` when data values of ``fmin`` and ``fmax`` are too close (:gh:`10074` by `Guillaume Favelier`_) + +- We now display a scrollbar in the tags dropdown of a `~mne.Report` if many tags have been added, granting access to all tags instead of "hiding" them below the bottom of the page (:gh:`10082` by `Richard Höchenberger`_) + +- Creating :class:`mne.Epochs` now provides clearer logging (less ambiguous, no duplicates) when the ``preload`` and/or ``metadata`` parameters are set (:gh:`10112` by `Stefan Appelhoff`_) + +- Fix bug with :class:`mne.Epochs` where save-load round-trip with FIF would cause :meth:`mne.Epochs.apply_baseline` to no longer work (:gh:`10177` by `Eric Larson`_) + +- Fix functions by adding missing ``overwrite`` parameters: :func:`mne.write_events`, :func:`mne.write_cov`, :func:`mne.write_evokeds`, :meth:`mne.SourceEstimate.save`, :func:`mne.minimum_norm.write_inverse_operator`, :func:`mne.write_proj`, and related methods (:gh:`10127` by `Eric Larson`_) + +- Fix bug with :func:`mne.transforms.compute_volume_registration` and :func:`mne.compute_source_morph` (volumetric) where the smoothing factors were not scaled based on ``zooms`` (:gh:`10132` by `Eric Larson`_) + +- Remove repeated logging output when overwriting an existing `~mne.io.Raw` file (:gh:`10095` by `Richard Höchenberger`_ and `Stefan Appelhoff`_) + +- In the plots generated by :meth:`mne.Report.add_stc`, we now only add 5 labels to the color bar to reduce the chance of overlap, which could previously cause the labels to become unreadable (:gh:`10135` by `Richard Höchenberger`_) + +- :meth:`mne.Report.add_trans` now allows you to add sensor alignment plots for head surfaces that have topological defects (:gh:`10175` by `Richard Höchenberger`_) + +- :meth:`mne.Report.add_trans` now also works if no digitization points are present in the data (:gh:`10176` by `Jeff Stout`_) + +- Argument ``verbose`` is now respected by dataset fetching (:gh:`10210` by `Mathieu Scheltienne`_) + +- Fix bug with :func:`mne.io.read_raw_hitachi` where empty ``Comment`` descriptions were not handled properly (:gh:`10235` by `Eric Larson`_) + +- Fix bug with input validation of low-level filtering functions (:gh:`10267` by `Eric Larson`_) + +- :func:`mne.gui.coregistration` now works with surfaces containing topological defects (:gh:`10230`, by `Richard Höchenberger`_) + +- Fix bug with :func:`mne.io.read_raw_nirx` being unable to read measurement dates recorded on systems with German (de_DE), French (fr_FR), and Italian (it_IT) locales (:gh:`10277` by `Eric Larson`_) + +- Fix bug with projector normalization checks that were too sensitive, and improve warning (:gh:`10292` by `Eric Larson`_) + +- Fix bug with :func:`mne.viz.plot_alignment` where head-coordinate source spaces (e.g., from a forward solution) were not properly plotted (:gh:`10309` by `Eric Larson`_) + +- :func:`mne.read_trans` and :func:`mne.io.read_fiducials` now correctly expand ``~`` in the provided path (i.e., to the user's home directory) (:gh:`10265`, :gh:`10415`, by `Richard Höchenberger`_) + +- :func:`mne.find_events` now uses ``first_samp`` and not ``0`` for initial event when using ``initial_value`` (:gh:`10289`, by `Alex Gramfort`_) + +- Fix bug with :func:`mne.channels.make_standard_montage` for ``'standard*'``, ``'mgh*'``, and ``'artinis*'`` montages where the points were incorrectly scaled and fiducials incorrectly set away from the correct values for use with the ``fsaverage`` subject (:gh:`10324` by `Eric Larson`_) + +- Fix bug with :meth:`mne.Report.add_figure` where figures generated externally were closed and possibly resized during render (:gh:`10342` by `Eric Larson`_) + +- Fix bug with :func:`mne.viz.plot_sparse_source_estimates` where the return value was incorrect (:gh:`10347` by `Eric Larson`_) + +- Fix plotting bug in :ref:`ex-electrode-pos-2d` and make view look more natural in :ref:`ex-movement-detect` (:gh:`10313`, by `Alex Rockhill`_) + +- Fix bug with blank 3D rendering with MESA software rendering (:gh:`10400` by `Eric Larson`_) + +- Fix a bug in :func:`mne.gui.locate_ieeg` where 2D lines on slice plots failed to update and were shown when not in maximum projection mode (:gh:`10335`, by `Alex Rockhill`_) + +- Fix misleading color scale in :ref:`tut-cluster-tfr` for the plotting of cluster F-statistics (:gh:`10393` by `Alex Rockhill`_) + +- Fix baseline removal using ``remove_dc=True`` in :meth:`raw.plot() ` for data containing ``np.nan`` (:gh:`10392` by `Clemens Brunner`_) + +- Fix misleading color scale in :ref:`tut-timefreq-twoway-anova` for plotting F-stats (:gh:`10401` by `Alex Rockhill`_) + +- Fix misleading ``T_obs`` return name for :func:`mne.stats.spatio_temporal_cluster_test` when the default returns an F-statistic (:gh:`10401` by `Alex Rockhill`_) + +API changes +~~~~~~~~~~~ +- The default browser for :meth:`raw.plot() `, :meth:`epochs.plot() `, and :meth:`ica.plot_sources() ` has been changed to the ``'qt'`` backend on systems where `mne_qt_browser `__ is installed. To change back to matplotlib within a session, you can use :func:`mne.viz.set_browser_backend('matplotlib') `. To set it permanently on your system, you can use :func:`mne.set_config('MNE_BROWSER_BACKEND', 'matplotlib') ` (:gh:`9960` by `Martin Schulz`_ and `Eric Larson`_) + +- ``mne.Info.pick_channels`` has been deprecated. Use ``inst.pick_channels`` to pick channels from :class:`~mne.io.Raw`, :class:`~mne.Epochs`, and :class:`~mne.Evoked`. Use :func:`mne.pick_info` to pick channels from :class:`mne.Info` (:gh:`10039` by `Mathieu Scheltienne`_) + +- All :func:`data_path ` functions now return :class:`python:pathlib.Path` objects rather than strings. Support for string concatenation with plus (``+``) is thus deprecated and will be removed in 1.2, use the forward-slash ``/`` operator instead (:gh:`10348` by `Eric Larson`_) + +- Argument ``event_list`` has been deprecated in favor of ``events`` in :func:`mne.write_events` (:gh:`10056` by `Mathieu Scheltienne`_) + +- ``mne.preprocessing.annotate_flat`` has been deprecated in favor of :func:`mne.preprocessing.annotate_amplitude`, that covers both minimum and maximum peak-to-peak variation. (:gh:`10143` by `Mathieu Scheltienne`_) + +- The ``max_ori_out`` parameter of :func:`mne.beamformer.apply_lcmv` and related functions is being removed as only signed estimates are supported. ``abs(stc)`` can be used to obtain unsigned estimates (:gh:`10366` by `Eric Larson`_) + +- The ``verbose`` attribute of classes (e.g., :class:`mne.io.Raw`, `mne.Epochs`, etc.) has been deprecated. Explicitly pass ``verbose`` to methods as necessary instead. (:gh:`10267` by `Eric Larson`_) + +- In :func:`mne.viz.set_browser_backend`, the `mne-qt-browser `__-based backend is now called ``'qt'`` rather than ``'pyqtgraph'`` for simplicity (:gh:`10323` by `Eric Larson`_) + +Dependencies +~~~~~~~~~~~~ +Numerous external dependencies that used to be bundled with MNE-Python are now +not shipped with the package anymore and will instead be retrieved +automatically from their official sources when you install MNE-Python. This +simplifies MNE-Python maintenance and keeps the package smaller. The following +new dependencies have been added: + +- `Jinja2`_ (replaces ``Tempita``, which is not maintained anymore; :gh:`10211` by `Richard Höchenberger`_) + + +.. _Jinja2: https://jinja.palletsprojects.com/ + +Authors +~~~~~~~ + +* Adam Li +* Adina Wagner+ +* Alex Rockhill +* Alexandre Gramfort +* Britta Westner +* Clemens Brunner +* Daniel McCloy +* Eduard Ort +* Eric Larson +* Etienne de Montalivet+ +* Federico Zamberlan+ +* Guillaume Favelier +* Jan Sosulski +* Jan Zerfowski+ +* Jeff Stout +* John Veillette+ +* Joshua Teves +* Julia Guiomar Niso Galán +* Luke Bloy +* Marijn van Vliet +* Martin Schulz +* Mathieu Scheltienne +* Matthias Eberlein+ +* Michiru Kaneda+ +* Mikołaj Magnuski +* Nikolai Chapochnikov+ +* Richard Höchenberger +* Robert Luke +* Scott Huberty+ +* Senwen Deng+ +* Stefan Appelhoff +* Steve Matindi +* Thomas Hartmann diff -Nru python-mne-0.23.4+dfsg/doc/changes/1.1.inc python-mne-1.1.0+dfsg/doc/changes/1.1.inc --- python-mne-0.23.4+dfsg/doc/changes/1.1.inc 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/1.1.inc 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,289 @@ +.. NOTE: we use cross-references to highlight new functions and classes. + Please follow the examples below like :func:`mne.stats.f_mway_rm`, so the + whats_new page will have a link to the function/class documentation. + +.. NOTE: there are 3 separate sections for changes, based on type: + - "Enhancements" for new features + - "Bugs" for bug fixes + - "API changes" for backward-incompatible changes + +.. NOTE: changes from first-time contributors should be added to the TOP of + the relevant section (Enhancements / Bugs / API changes), and should look + like this (where xxxx is the pull request number): + + - description of enhancement/bugfix/API change (:gh:`xxxx` by + :newcontrib:`Firstname Lastname`) + + Also add a corresponding entry for yourself in doc/changes/names.inc + +.. _changes_1_1_0: + +Version 1.1.0 (2022-08-03) +-------------------------- + +Enhancements +~~~~~~~~~~~~ +- Added new built-in montage R-Net 128 Ch (Brain Products) (:gh:`10974` by :newcontrib:`Ilias Machairas`) + +- :class:`~mne.channels.DigMontage` objects can now be checked for equality via ``==`` (:gh:`10942` by :newcontrib:`Tziona NessAiver`) + +- Add a note to clarify the inclusion of average referencing gain matrix during whitening (:gh:`10922` by :newcontrib:`Mingjian He`) + +- Add time axis scale bar for :meth:`mne.Epochs.plot` using matplotlib backend (:gh:`10736` by :newcontrib:`Jordan Drew`) + +- Add :func:`mne.bem.distance_to_bem` to find depth of source positions (:gh:`10632` by :newcontrib:`Matt Courtemanche`) + +- Add support for ahdr files in :func:`mne.io.read_raw_brainvision` (:gh:`10515` by :newcontrib:`Alessandro Tonin`) + +- Add support for reading data from Gowerlabs devices to :func:`mne.io.read_raw_snirf` (:gh:`10555` by :newcontrib:`Samuel Powell` and `Robert Luke`_) + +- Add built-in sensor layout files for Geodesic Head Web 130 and 280 devices (:gh:`10627` by `Evan Hathaway`_) + +- Add ``mne-icalabel`` to :func:`mne.sys_info` (:gh:`10615` by `Adam Li`_) + +- Add support for ``overview_mode`` in :meth:`raw.plot() ` and related functions/methods (:gh:`10501` by `Eric Larson`_) + +- Add :meth:`mne.io.Raw.crop_by_annotations` method to get chunks of Raw data based on :class:`mne.Annotations`. (:gh:`10460` by `Alex Gramfort`_) + +- The ``pick_channels`` method gained a ``verbose`` parameter, allowing e.g. to suppress messages about removed projectors (:gh:`10544` by `Richard Höchenberger`_) + +- Add :func:`mne.viz.plot_projs_joint` for joint plotting of projectors and Evoked (:gh:`10720` by `Eric Larson`_) + +- The :func:`mne.make_forward_dipole` function can now take a list of dipoles to make a multi-dipole forward models (:gh:`10464` by `Marijn van Vliet`_) + +- Add ``mode='outlines'`` support to :meth:`mne.Dipole.plot_locations` and :func:`mne.viz.plot_dipole_locations` (:gh:`10699` by `Eric Larson`_) + +- Add :meth:`mne.preprocessing.ICA.find_bads_muscle` to find muscle-related ICA components with an example, :ref:`ex-muscle-ica` (:gh:`10534` by `Alex Rockhill`_) + +- Add example of Xfit-style ECD modeling using multiple dipoles (:gh:`10464` by `Marijn van Vliet`_) + +- Add ``head_source`` argument to :func:`mne.make_field_map` to allow selecting which head source to use (:gh:`10568` by `Eric Larson`_) + +- Add support for ``n_jobs=None`` to support :func:`joblib:joblib.parallel_backend` for more precise control over parallelization (:gh:`10567` by `Eric Larson`_) + +- It is now possible to compute inverse solutions with restricted source orientations using discrete forward models (:gh:`10464` by `Marijn van Vliet`_) + +- The new function :func:`mne.preprocessing.maxwell_filter_prepare_emptyroom` simplifies the preconditioning of an empty-room recording for our Maxwell filtering operations (:gh:`10533` by `Richard Höchenberger`_ and `Eric Larson`_) + +- Add keyboard shortcuts to toggle :meth:`mne.preprocessing.ICA.plot_properties` topomap channel types ('t') and power spectral density log-scale ('l') (:gh:`10557` by `Alex Rockhill`_) + +- Add ``--mri``, and ``--threshold`` options to :ref:`mne make_scalp_surfaces` to improve head surface mesh extraction (:gh:`10591` by `Eric Larson`_) + +- Add :func:`mne.preprocessing.compute_bridged_electrodes` to detect EEG electrodes with shared spatial sources due to a conductive medium connecting two or more electrodes, add :ref:`ex-eeg-bridging` for an example and :func:`mne.viz.plot_bridged_electrodes` to help visualize (:gh:`10571` by `Alex Rockhill`_) + +- Add ``'nearest'`` as an option for the ``image_interp`` argument in :func:`mne.viz.plot_topomap` to plot a topomap without interpolation using a Voronoi parcelation (:gh:`10571` by `Alex Rockhill`_) + +- Add :func:`mne.preprocessing.interpolate_bridged_electrodes` to use the spatially smeared signal to get a better interpolation rather than dropping those channels (:gh:`10587` by `Alex Rockhill`_) + +- Add support for hemoglobin type fNIRS data to temporal derivative distribution repair (TDDR) :func:`mne.preprocessing.nirs.temporal_derivative_distribution_repair` (:gh:`10125` by `Johann Benerradi`_) + +- :func:`mne.viz.plot_evoked_topomap` and :meth:`mne.Evoked.plot_topomap` now display the time range the map was averaged over if ``average`` was passed (:gh:`10606` by `Richard Höchenberger`_) + +- :func:`mne.viz.plot_evoked_topomap` and :meth:`mne.Evoked.plot_topomap` can now average the topographic maps across different time periods for each time point. To do this, pass a list of periods via the ``average`` parameter (:gh:`10610` by `Richard Höchenberger`_) + +- :func:`mne.viz.plot_evoked` and :meth:`mne.Evoked.plot` gained a new parameter, ``highlight``, to visually highlight time periods of interest (:gh:`10614` by `Richard Höchenberger`_) + +- Added fNIRS support to :func:`mne.Info.get_montage` (:gh:`10611` by `Robert Luke`_) + +- Add :func:`mne.viz.plot_ch_adjacency` to visualize and manually edit the channel adjacency matrix (:gh:`10633` by `Mikołaj Magnuski`_) + +- Add :meth:`mne.viz.Brain.get_view` to get the current camera parameters such that they can be passed to :meth:`mne.viz.Brain.show_view` (:gh:`10661` by `Alex Rockhill`_) + +- Added support for Aurora version 2021.9.0.6 to :func:`mne.io.read_raw_nirx` (:gh:`10668` by `Robert Luke`_) + +- Added BrainVision format (``.vhdr``, ``.vmrk``, ``.eeg``) to :func:`mne.export.export_raw` (:gh:`10681` by `Stefan Appelhoff`_) + +- Added ``export`` method to :class:`mne.Evoked` (:gh:`10681` by `Stefan Appelhoff`_) + +- Add timeformat ``YYYY/MM/DD`` to ``mne.io.ctf.info._convert_time`` (:gh:`10674` by `Simon Kern`_) + +- :meth:`mne.Report.add_figure` and :meth:`mne.Report.add_image` gained a new parameter, ``section``, allowing to put multiple figures or images into the same "content block". Previously, the only way to group figures and images was by means of a slider, which sometimes made content hard to discover. The slider remains available as an alternative (:gh:`10694` by `Richard Höchenberger`_) + +- All functions and methods that plot topographic maps for EEG (2D projections of the EEG sensor locations) now accept the parameter value ``sphere='eeglab'`` to lay out the sensors with respect to the head circle in a similar way to how EEGLAB does, i.e., T7/T8 and Fpz/Oz are placed directly on the circle for template montages (:gh:`10572` by `Richard Höchenberger`_) + +- :func:`mne.channels.get_builtin_montages` gained a new parameter, ``descriptions``, which allows to retrieve the descriptions of the montages in addition to their names (:gh:`10373` by `Richard Höchenberger`_) + +- Add ``include`` option to :meth:`mne.io.read_raw_edf`, :meth:`mne.io.read_raw_bdf` and :meth:`mne.io.read_raw_gdf`. (:gh:`10734` by `Michiru Kaneda`_) + +- :func:`mne.gui.coregistration` gained a new parameter, ``fullscreen``, to start the GUI in fullscreen mode (:gh:`10284` by `Richard Höchenberger`_) + +- The built-in FieldTrip channel adjacency matrices, which can be read via :func:`~mne.channels.read_ch_adjacency`, have been synchronized with FieldTrip again. This means that numerous new adjacencies have been added, most notably those for standard 10-05 and 10-20 montages (:gh:`10749` by `Richard Höchenberger`_) + +- :meth:`mne.Evoked.plot_field` gained a new parameter, ``interaction``, to control the rotation axes when interacting with the head (:gh:`10788` by `Richard Höchenberger`_) + +- Add :func:`mne.read_evoked_besa` for reading evokeds from BESA ``.avr`` and ``.mul`` files. (:gh:`10892` by `Marijn van Vliet`_) + +- Add :meth:`mne.time_frequency.EpochsTFR.decimate` to reduce size of time-frequency epochs objects (:gh:`10940` by `Alex Rockhill`_) + +- Add an alias that maps EGI reference channel names (``VREF`` or ``Vertex Reference``) to the electrode name in the standard EGI montages (``Cz``). Now setting standard EGI montages with :meth:`~mne.io.Raw.set_montage` with ``match_alias=True`` will avoid raising an error (:gh:`10823` by `Scott Huberty`_) + +- Add provenance in :func:`mne.time_frequency.EpochsTFR.apply_baseline` (:gh:`10979` by `Alex Rockhill`_) + +Bugs +~~~~ +- Fix bug in ``mne.surface._project_onto_surface`` for the condition ``method != 'accurate' and return_nn`` (:gh:`10930` by `Christian O'Reilly`_) + +- Fix bug in :func:`mne.export.export_raw` to ignore None value in filenames attribute of :class:`mne.io.RawArray` (:gh:`10927` by :newcontrib:`Reza Nasri`) + +- Fix bug in :func:`mne.io.read_raw_edf` to allow reading in all Hypnodyne ZMax EDFs to be read in without issues (:gh:`10754` by :newcontrib:`Frederik Weber`) + +- Fix bug in :func:`mne.Epochs.drop_bad` where data was loaded although no rejection had to be performed (:gh:`10718` by :newcontrib:`Lukas Gemein`) + +- Pick also bad channels when selecting T1T2 magnetometers in :func:`mne.preprocessing.maxwell_filter` (:gh:`10639` by :newcontrib:`Matti Toivonen`) + +- Make ``color`` parameter check in in :func:`mne.viz.plot_evoked_topo` consistent (:gh:`10217` by :newcontrib:`T. Wang` and `Stefan Appelhoff`_) + +- Fix bug in :func:`mne.io.read_raw_brainvision` when BrainVision data are acquired with the Brain Products "V-Amp" amplifier and disabled lowpass filter is marked with value ``0`` (:gh:`10517` by :newcontrib:`Alessandro Tonin`) + +- Fix bug in :func:`mne.pick_types` and related methods where ``csd=True`` was not passed handled properly (:gh:`10470` by :newcontrib:`Matthias Dold`) + +- Fix bug where plots produced using the ``'qt'`` / ``mne_qt_browser`` backend could not be added using :meth:`mne.Report.add_figure` (:gh:`10485` by `Eric Larson`_) + +- Fix bug where ``theme`` was not handled properly in :meth:`mne.io.Raw.plot` (:gh:`10487`, :gh:`10500` by `Mathieu Scheltienne`_ and `Eric Larson`_) + +- Fix bug in :meth:`raw.crop(start, stop) ` that would cause annotations to be erroneously shifted when ``start != 0`` and no measurement date was set. (:gh:`10491` by `Eric Larson`_) + +- Fix bug in :func:`mne.io.read_raw_bti` where unknown electrode locations were not handled properly (:gh:`10662` by `Eric Larson`_) + +- Fix bug in :func:`mne.io.read_raw_ctf` on Windows where large files could not be read (:gh:`10866` by `Eric Larson`_) + +- Fix bug in :func:`mne.io.read_raw_ctf` where invalid measurement dates were not handled properly (:gh:`10957` by `Jean-Remi King`_ and `Eric Larson`_) + +- Rendering issues with recent MESA releases can be avoided by setting the new environment variable``MNE_3D_OPTION_MULTI_SAMPLES=1`` or using :func:`mne.viz.set_3d_options` (:gh:`10513` by `Eric Larson`_) + +- Fix behavior for the ``pyvista`` 3D renderer's ``quiver3D`` function so that default arguments plot a glyph in ``arrow`` mode (:gh:`10493` by `Alex Rockhill`_) + +- Retain epochs metadata when using :func:`mne.channels.combine_channels` (:gh:`10504` by `Clemens Brunner`_) + +- Fix epochs indexing with metadata containing boolean type and missing values (:gh:`10705` by `Clemens Brunner`_ and `Alex Gramfort`_) + +- Fix reading of fiducial locations in :func:`mne.io.read_raw_eeglab` (:gh:`10521` by `Alex Gramfort`_) + +- Prevent creation of montage with invalid ``[x, y, z]`` coordinates with :func:`mne.channels.make_dig_montage` (:gh:`10547` by `Mathieu Scheltienne`_) + +- Fix bug in coregistration GUI that prevented it from starting up if only a high-resolution head model was available (:gh:`10543` by `Richard Höchenberger`_) + +- Fix bug with :class:`mne.Epochs.add_reference_channels` where attributes were not updated properly so subsequent `~mne.Epochs.pick_types` calls were broken (:gh:`10912` by `Eric Larson`_) +- +- Fix bug in the :class:`mne.viz.Brain` tool bar that prevented the buttons to call the corresponding feature (:gh:`10560` by `Guillaume Favelier`_) + +- Fix issue with saving epochs once :func:`~mne.preprocessing.compute_current_source_density` has been used if a rejection threshold was used first (:gh:`10619` by `Alex Rockhill`_ and `Richard Höchenberger`_) + +- Fix bug in :func:`mne.viz.plot_evoked_image` that would cause incorrect sub-titles when using ``group_by`` (:gh:`10618` by `Reza Shoorangiz`_) + +- Reduce memory usage when loading an EDF file with ``preload=False`` (:gh:`10638` by `Clemens Brunner`_) + +- Fix bug in :meth:`mne.io.Raw.get_channel_types` and related methods where ``unique=True`` would return a :class:`python:set` with arbitrary order rather than a :class:`python:list` that preserves the same order as ``inst.ch_names`` (:gh:`10720` by `Eric Larson`_) + +- In :func:`mne.preprocessing.find_bad_channels_maxwell`, do not re-filter the data if a low-pass filter with the requested frequency has already been applied (:gh:`10664` by `Richard Höchenberger`_) + +- Fix a problem in :meth:`mne.Evoked.get_peak`, where under certain circumstances the ``mode`` parameters ``'pos'`` and ``'neg'`` were not honored when ``tmin`` and/or ``tmax`` were passed as well (:gh:`10686` by `Richard Höchenberger`_) + +- :func:`mne.read_evokeds`, :func:`mne.channels.read_custom_montage`, :func:`mne.channels.read_dig_hpts`, :func:`mne.channels.read_dig_polhemus_isotrak`, and :func:`mne.channels.read_polhemus_fastscan` now correctly expand ``~`` in the provided path to the user's home directory (:gh:`10685`, :gh:`10688` by `Richard Höchenberger`_) + +- Fix bug in :func:`mne.io.read_raw_eeglab` and :func:`mne.read_epochs_eeglab`, where the presence of channels without position in the ``.set`` file caused positions incorrectly assigned to all further channels (:gh:`8754` by `Mikołaj Magnuski`_) + +- Combining channels of :class:`mne.Epochs` or :class:`mne.Evoked` objects now properly retains baseline information (:gh:`10703` by `Clemens Brunner`_) + +- In :class:`mne.Report`, some figures would have an undesired border added to the edges; this has now been resolved (:gh:`10730` by `Richard Höchenberger`_) + +- Fix selection of EEG channels and selected sphere when plotting bridged electrodes with :func:`mne.viz.plot_bridged_electrodes` (:gh:`10753` by `Mathieu Scheltienne`_) + +- Fix broken links in :ref:`tut-fix-meshes` (:gh:`10765` by `Alex Rockhill`_) + +- The built-in FieldTrip channel adjacency matrix for ``easycapM1`` had a bug, where the channel ``PO8`` was incorrectly labeled as ``PO6`` This has been resolved by updating the adjacency matrix to the latest version provided by FieldTrip (:gh:`10749` by `Richard Höchenberger`_) + +- Add a YouTube video tutorial for editing Freesurfer surfaces in Blender to :ref:`tut-fix-meshes` (:gh:`10778` by `Alex Rockhill`_) + +- The default interaction style of :meth:`mne.Evoked.plot_field` has been changed such that if behaves like :func:`~mne.viz.plot_alignment` when trying to rotate the head (:gh:`10788` by `Richard Höchenberger`_) + +- Add a video on how to operate the intracranial electrode contact location GUI in :ref:`tut-ieeg-localize` (:gh:`10800` by `Alex Rockhill`_) + +- Add a video on how to operate the coregistration GUI in :ref:`tut-source-alignment` (:gh:`10802` by `Alex Rockhill`_) + +- Add ``show`` and ``block`` arguments to :func:`mne.gui.coregistration` and :func:`mne.gui.locate_ieeg` to pop up the GUIs and halt execution of subsequent code respectively (:gh:`10802` by `Alex Rockhill`_) + +- Correctly report the number of available projections when printing measurement info in a Jupyter notebook (:gh:`10471` by `Clemens Brunner`_) + +- Fix value set in ``raw.orig_format`` for readers of BrainVision (ASCII format), EGI and Artemis123 files (:gh:`10851` by `Mathieu Scheltienne`_) + +- When reading EGI MFF files, channel names are no longer ignored and reference channel information is properly incorporated (:gh:`10898` by `Scott Huberty`_ and `Daniel McCloy`_) + +- Fix bug in :func:`mne.time_frequency.psd_array_welch` and related functions/methods where the bias was not accounted for when ``average='median'`` (:gh:`10990` by `Eric Larson`_) + +- Fix bug in :class:`mne.decoding.TemporalFilter` where filter parameters were not handled properly (:gh:`10968` by `Eric Larson`_) + +- Fix documentation bug in ``ica.plot_sources`` to specify that ``picks`` keyword argument is for picking ICA components to plot (:gh:`10936` by `Adam Li`_) + +- Annotations contained in EDF files are correctly read as UTF-8 according to the EDF specification (:gh:`10963` by `Clemens Brunner`_) + +- Fix bug where complex-valued topoplots caused an error, convert to amplitude instead (:gh:`10978` by `Alex Rockhill`_) + +API and behavior changes +~~~~~~~~~~~~~~~~~~~~~~~~ +- When creating BEM surfaces via :func:`mne.bem.make_watershed_bem` and :func:`mne.bem.make_flash_bem`, the ``copy`` parameter now defaults to ``True``. This means that instead of creating symbolic links inside the FreeSurfer subject's ``bem`` folder, we now create "actual" files. This should avoid troubles when sharing files across different operating systems and file systems (:gh:`10531` by `Richard Höchenberger`_) + +- The ordering of channels returned by :func:`mne.io.read_raw_nirx` is now ordered by channel name, rather than the order provided by the manufacturer. This enables consistent ordering of channels across different file types (:gh:`10555` by `Robert Luke`_) + +- For :func:`mne.viz.plot_topomap`, :func:`mne.viz.plot_evoked_topomap`, :func:`mne.viz.plot_arrowmap`, :func:`mne.viz.plot_ica_components`, :meth:`mne.Covariance.plot_topomap`, :meth:`mne.Evoked.plot_topomap`, :meth:`mne.Evoked.animate_topomap`, :meth:`mne.decoding.CSP.plot_patterns`, :meth:`mne.Projection.plot_topomap` and :meth:`mne.preprocessing.ICA.plot_components` the topomap image interpolation was previously a cubic interpolation but now can be ``'linear'`` and ``'nearest'`` as well. Unless ``image_interp='nearest'`` is passed mne uses a subsequent matplotlib bilinear interpolation to make the interpolated image smoother. Previously, ``'image_interp'`` controlled this second interpolation step and, for the first interpolation, the only option was cubic. To simplify, ``image_interp`` is now responsible for the main interpolation and the subsequent matplotlib image interpolation is bilinear but can be changed afterward using ``im.set_interpolation`` (:gh:`10617` by `Alex Rockhill`_) + +- The FLASH BEM surface generation via :func:`mne.bem.make_flash_bem` or the :ref:`mne flash_bem` command has now been clarified in terms of expected inputs. In :func:`mne.bem.convert_flash_mris` the ``convert`` parameter is now deprecated and the ``flash5`` and ``flash30`` now accept list of file names or nibabel images. One breaking change is that now the flash files are looked for as mef05_*.mgz and mef30_*.mgz in mri/flash folder (:gh:`10715` by `Alex Gramfort`_) + +- :func:`~mne.viz.plot_compare_evokeds` gets a new parameter ``time_unit`` which can be ``"s"`` or ``"ms"`` (:gh:`10950` by `Daniel McCloy`_) + +- The ``to_data_frame`` methods for :meth:`Raw `, :meth:`Epochs `, :meth:`Evoked `, :meth:`SourceEstimate `, :meth:`EpochsTFR `, and :meth:`AverageTFR ` all now have default ``time_format=None`` (keep time in seconds) instead of ``time_format='ms'`` (convert time to milliseconds) (:gh:`10925` by `Daniel McCloy`_) + +- :func:`mne.Evoked.decimate` now aligns to the sample at time=0 (so that the exact sample when the event occurred is preserved) to be consistent with :func:`mne.Epochs.decimate` (:gh:`10945` by `Alex Rockhill`_) + +Authors +~~~~~~~ + +* Adam Li +* Adeline Fecker+ +* Alessandro Tonin+ +* Alex Ciok+ +* Alex Rockhill +* Alexandre Gramfort +* Archit Singhal+ +* Ashley Drew+ +* Ben Beasley+ +* Carina Forster+ +* Christian O'Reilly +* Clemens Brunner +* Cora Kim +* Daniel McCloy +* Dominik Welke +* Dominique Makowski +* Eric Larson +* Evan Hathaway +* Frederik D. Weber+ +* Guillaume Favelier +* Hamid Maymandi+ +* Ilias Machairas+ +* Jean-Remi King +* Johann Benerradi +* Jon Houck +* Jona Sassenhagen +* Jordan Drew+ +* Lukas Gemein+ +* Marijn van Vliet +* Martin Schulz +* Mathieu Scheltienne +* Matt Courtemanche+ +* Matthias Dold+ +* Matti Toivonen+ +* Michiru Kaneda+ +* Mikołaj Magnuski +* Mingjian He+ +* Naveen Srinivasan+ +* Phillip Alday +* Reza Nasri+ +* Reza Shoorangiz +* Richard Höchenberger +* Rob Luke +* Scott Huberty+ +* Simon Kern +* Sondre Foslien+ +* Stefan Appelhoff +* T. Wang+ +* Tziona NessAiver+ diff -Nru python-mne-0.23.4+dfsg/doc/changes/latest.inc.template python-mne-1.1.0+dfsg/doc/changes/latest.inc.template --- python-mne-0.23.4+dfsg/doc/changes/latest.inc.template 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/latest.inc.template 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,34 @@ +.. NOTE: we use cross-references to highlight new functions and classes. + Please follow the examples below like :func:`mne.stats.f_mway_rm`, so the + whats_new page will have a link to the function/class documentation. + +.. NOTE: there are 3 separate sections for changes, based on type: + - "Enhancements" for new features + - "Bugs" for bug fixes + - "API changes" for backward-incompatible changes + +.. NOTE: changes from first-time contributors should be added to the TOP of + the relevant section (Enhancements / Bugs / API changes), and should look + like this (where xxxx is the pull request number): + + - description of enhancement/bugfix/API change (:gh:`xxxx` by + :newcontrib:`Firstname Lastname`) + + Also add a corresponding entry for yourself in doc/changes/names.inc + +.. _current: + +Current (1.1.dev0) +------------------ + +Enhancements +~~~~~~~~~~~~ +- None yet + +Bugs +~~~~ +- None yet + +API changes +~~~~~~~~~~~ +- None yet diff -Nru python-mne-0.23.4+dfsg/doc/changes/names.inc python-mne-1.1.0+dfsg/doc/changes/names.inc --- python-mne-0.23.4+dfsg/doc/changes/names.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/changes/names.inc 2022-08-03 15:48:14.000000000 +0000 @@ -1,391 +1,485 @@ -.. _Alex Gramfort: http://alexandre.gramfort.net +.. _Abram Hindle: http://softwareprocess.es -.. _Martin Luessi: https://github.com/mluessi +.. _Adam Li: http://github.com/adam2392 -.. _Yaroslav Halchenko: http://www.onerussian.com/ +.. _Adeline Fecker: https://github.com/adelinefecker -.. _Daniel Strohmeier: https://github.com/joewalter +.. _Adina Wagner: https://github.com/adswa -.. _Eric Larson: http://larsoner.com +.. _Adonay Nunes: https://github.com/AdoNunes -.. _Denis Engemann: http://denis-engemann.de +.. _Alan Leggitt: https://github.com/leggitta -.. _Christian Brodbeck: https://github.com/christianbrodbeck +.. _Alejandro Weinstein: http://ocam.cl -.. _Simon Kornblith: http://simonster.com +.. _Alessandro Tonin: https://wysscenter.ch/team/alessandro-tonin -.. _Teon Brooks: https://teonbrooks.github.io +.. _Alex Ciok: https://github.com/alexCiok -.. _Mainak Jas: http://ltl.tkk.fi/wiki/Mainak_Jas +.. _Alex Gramfort: http://alexandre.gramfort.net -.. _Roman Goj: http://romanmne.blogspot.co.uk +.. _Alex Rockhill: https://github.com/alexrockhill/ + +.. _Alexander Rudiuk: https://github.com/ARudiuk + +.. _Alexandre Barachant: http://alexandre.barachant.org + +.. _Andrea Brovelli: https://andrea-brovelli.net + +.. _Andreas Hojlund: https://github.com/ahoejlund + +.. _Andres Rodriguez: https://github.com/infinitejest/ .. _Andrew Dykstra: https://github.com/adykstra -.. _Romain Trachel: http://www.lscp.net/braware/trachelBr.html +.. _Aniket Pradhan: https://github.com/Aniket-Pradhan -.. _Christopher Dinh: https://github.com/chdinh +.. _Anna Padee: https://github.com/apadee/ -.. _Nick Ward: http://www.ucl.ac.uk/ion/departments/sobell/Research/NWard +.. _Annalisa Pascarella: http://www.iac.rm.cnr.it/~pasca/ -.. _Tal Linzen: http://tallinzen.net/ +.. _Anne-Sophie Dubarry: https://github.com/annesodub -.. _Roan LaPlante: https://github.com/aestrivex +.. _Antoine Gauthier: https://github.com/Okamille -.. _Mads Jensen: https://github.com/MadsJensen +.. _Antti Rantala: https://github.com/Odingod -.. _Dan Wakeman: https://github.com/dgwakeman +.. _Apoorva Karekal: https://github.com/apoorva6262 -.. _Qunxi Dong: https://github.com/dongqunxi +.. _Archit Singhal: https://github.com/architsinghal-mriirs -.. _Martin Billinger: https://github.com/mbillingr +.. _Ashley Drew: https://github.com/ashdrew -.. _Federico Raimondo: https://github.com/fraimondo +.. _Asish Panda: https://github.com/kaichogami -.. _Cathy Nangini: https://github.com/KatiRG +.. _Austin Hurst: https://github.com/a-hurst -.. _Jean-Remi King: https://github.com/kingjr +.. _Ben Beasley: https://github.com/musicinmybrain -.. _Juergen Dammers: https://github.com/jdammers +.. _Britta Westner: https://github.com/britta-wstnr -.. _Olaf Hauk: http://www.neuroscience.cam.ac.uk/directory/profile.php?olafhauk +.. _Bruno Nicenboim: https://bnicenboim.github.io -.. _Lukas Breuer: http://www.researchgate.net/profile/Lukas_Breuer +.. _buildqa: https://github.com/buildqa -.. _Alan Leggitt: https://github.com/leggitta +.. _Carina Forster: https://github.com/carinafo -.. _Marijn van Vliet: https://github.com/wmvanvliet +.. _Cathy Nangini: https://github.com/KatiRG -.. _Marmaduke Woodman: https://github.com/maedoc +.. _Chris Bailey: https://github.com/cjayb -.. _Jona Sassenhagen: https://github.com/jona-sassenhagen +.. _Chris Holdgraf: https://chrisholdgraf.com -.. _Hari Bharadwaj: http://www.haribharadwaj.com +.. _Chris Mullins: http://crmullins.com -.. _Chris Holdgraf: http://chrisholdgraf.com +.. _Christian Brodbeck: https://github.com/christianbrodbeck -.. _Jaakko Leppakangas: https://github.com/jaeilepp +.. _Christian O'Reilly: https://github.com/christian-oreilly -.. _Yousra Bekhti: https://www.linkedin.com/pub/yousra-bekhti/56/886/421 +.. _Christopher Dinh: https://github.com/chdinh -.. _Mark Wronkiewicz: https://ml.jpl.nasa.gov/people/wronkiewicz/wronkiewicz.html +.. _Chun-Hui Li: https://github.com/iamsc -.. _Sébastien Marti: http://www.researchgate.net/profile/Sebastien_Marti +.. _Clemens Brunner: https://github.com/cbrnr -.. _Chris Bailey: https://github.com/cjayb +.. _Cora Kim: https://github.com/kimcoco -.. _Ross Maddox: https://www.urmc.rochester.edu/labs/maddox-lab.aspx +.. _Cristóbal Moënne-Loccoz: https://github.com/cmmoenne -.. _Alexandre Barachant: http://alexandre.barachant.org +.. _Dan Wakeman: https://github.com/dgwakeman .. _Daniel McCloy: http://dan.mccloy.info -.. _Jair Montoya Martinez: https://github.com/jmontoyam +.. _Daniel Strohmeier: https://github.com/joewalter -.. _Samu Taulu: http://ilabs.washington.edu/institute-faculty/bio/i-labs-samu-taulu-dsc +.. _Darin Erat Sleiter: https://github.com/dsleiter -.. _Lorenzo Desantis: https://github.com/lorenzo-desantis/ +.. _David Haslacher: https://github.com/davidhaslacher -.. _Jukka Nenonen: https://www.linkedin.com/pub/jukka-nenonen/28/b5a/684 +.. _David Julien: https://github.com/Swy7ch -.. _Jussi Nurminen: https://github.com/jjnurminen +.. _David Sabbagh: https://github.com/DavidSabbagh -.. _Clemens Brunner: https://github.com/cbrnr +.. _Demetres Kostas: https://github.com/kostasde -.. _Asish Panda: https://github.com/kaichogami +.. _Denis Engemann: http://denis-engemann.de -.. _Natalie Klein: http://natklein.weebly.com +.. _Dirk Gütlin: https://github.com/DiGyt -.. _Jon Houck: https://www.mrn.org/people/jon-m.-houck/principal-investigators +.. _Dmitrii Altukhov: https://github.com/dmalt -.. _Pablo-Arias: https://github.com/Pablo-Arias +.. _Dominik Welke: https://github.com/dominikwelke/ -.. _Alexander Rudiuk: https://github.com/ARudiuk +.. _Dominique Makowski: https://dominiquemakowski.github.io/ -.. _Mikołaj Magnuski: https://github.com/mmagnuski +.. _Eberhard Eich: https://github.com/ebeich -.. _Felix Raimundo: https://github.com/gamazeps +.. _Eduard Ort: https://github.com/eort -.. _Nick Foti: http://nfoti.github.io +.. _Emily Stephen: http://github.com/emilyps14 -.. _Guillaume Dumas: http://www.extrospection.eu +.. _Enrico Varano: https://github.com/enricovara/ -.. _Chris Mullins: http://crmullins.com +.. _Eric Larson: http://larsoner.com -.. _Phillip Alday: https://palday.bitbucket.io +.. _Erica Peterson: https://github.com/nordme -.. _Andreas Hojlund: https://github.com/ahoejlund +.. _Erkka Heinila: https://github.com/Teekuningas -.. _Johannes Niediek: https://github.com/jniediek +.. _Etienne de Montalivet: https://github.com/etiennedemontalivet -.. _Sheraz Khan: https://github.com/SherazKhan +.. _Evan Hathaway: https://github.com/ephathaway -.. _Antti Rantala: https://github.com/Odingod +.. _Evgeny Goldstein: https://github.com/evgenygoldstein -.. _Keith Doelling: https://github.com/kdoelling1919 +.. _Ezequiel Mikulan: https://github.com/ezemikulan -.. _Paul Pasler: https://github.com/ppasler +.. _Ezequiel Mikulan: https://github.com/ezemikulan -.. _Niklas Wilming: https://github.com/nwilming +.. _Fahimeh Mamashli: https://github.com/fmamashli -.. _Annalisa Pascarella: http://www.iac.rm.cnr.it/~pasca/ +.. _Federico Raimondo: https://github.com/fraimondo -.. _Luke Bloy: https://imaging.research.chop.edu/people/dr-luke-bloy +.. _Federico Zamberlan: https://github.com/fzamberlan -.. _Leonardo Barbosa: https://github.com/noreun +.. _Felix Klotzsche: https://github.com/eioe -.. _Erkka Heinila: https://github.com/Teekuningas +.. _Felix Raimundo: https://github.com/gamazeps -.. _Andrea Brovelli: http://andrea-brovelli.net +.. _Frederik Weber: https://github.com/Frederik-D-Weber -.. _Richard Höchenberger: https://github.com/hoechenberger +.. _Fu-Te Wong: https://github.com/zuxfoucault -.. _Matt Boggess: https://github.com/mattboggess +.. _Geoff Brookshire: https://github.com/gbrookshire -.. _Jean-Baptiste Schiratti: https://github.com/jbschiratti +.. _Guillaume Dumas: http://www.extrospection.eu -.. _Laura Gwilliams: http://lauragwilliams.github.io +.. _Guillaume Favelier: https://github.com/GuillaumeFavelier -.. _Jesper Duemose Nielsen: https://github.com/jdue +.. _Hamid Maymandi: https://github.com/HamidMandi -.. _Mathurin Massias: https://mathurinm.github.io/ +.. _Hari Bharadwaj: http://www.haribharadwaj.com -.. _ramonapariciog: https://github.com/ramonapariciog +.. _Henrich Kolkhorst: https://github.com/hekolk -.. _Britta Westner: https://github.com/britta-wstnr +.. _Hongjiang Ye: https://github.com/rubyyhj -.. _Lukáš Hejtmánek: https://github.com/hejtmy +.. _Hubert Banville: https://github.com/hubertjb -.. _Stefan Repplinger: https://github.com/stfnrpplngr +.. _Ilias Machairas: https://github.com/JungleHippo -.. _Okba Bekhelifi: https://github.com/okbalefthanded +.. _Ivana Kojcic: https://github.com/ikojcic -.. _Nicolas Barascud: https://github.com/nbara +.. _Jaakko Leppakangas: https://github.com/jaeilepp -.. _Alejandro Weinstein: http://ocam.cl +.. _Jack Zhang: https://github.com/jackz314 -.. _Emily Stephen: http://github.com/emilyps14 +.. _Jair Montoya Martinez: https://github.com/jmontoyam -.. _Nathalie Gayraud: https://github.com/ngayraud +.. _Jan Sedivy: https://github.com/honzaseda -.. _Anne-Sophie Dubarry: https://github.com/annesodub +.. _Jan Sosulski: https://jan-sosulski.de -.. _Stefan Appelhoff: http://stefanappelhoff.com +.. _Jan Zerfowski: https://github.com/jzerfowski -.. _Tommy Clausner: https://github.com/TommyClausner +.. _Jasper van den Bosch: https://github.com/ilogue -.. _Pierre Ablin: https://pierreablin.com +.. _Jean-Baptiste Schiratti: https://github.com/jbschiratti -.. _Oleh Kozynets: https://github.com/OlehKSS +.. _Jean-Remi King: https://github.com/kingjr -.. _Susanna Aro: https://www.linkedin.com/in/susanna-aro +.. _Jeff Hanna: https://github.com/jshanna100 + +.. _Jeff Stout: https://megcore.nih.gov/index.php/Staff + +.. _Jeroen Van Der Donckt: https://github.com/jvdd + +.. _Jesper Duemose Nielsen: https://github.com/jdue + +.. _jeythekey: https://github.com/jeythekey .. _Joan Massich: https://github.com/massich -.. _Henrich Kolkhorst: https://github.com/hekolk +.. _Johann Benerradi: https://github.com/HanBnrd -.. _Steven Bethard: https://github.com/bethard +.. _Johannes Niediek: https://github.com/jniediek -.. _Thomas Hartmann: https://github.com/thht +.. _John Samuelsson: https://github.com/johnsam7 -.. _Steven Gutstein: http://vll.cs.utep.edu/team.html +.. _John Veillette: https://psychology.uchicago.edu/directory/john-veillette -.. _Peter Molfese: https://github.com/pmolfese +.. _Jon Houck: https://www.mrn.org/people/jon-m.-houck/principal-investigators -.. _Dirk Gütlin: https://github.com/DiGyt +.. _Jona Sassenhagen: https://github.com/jona-sassenhagen -.. _Jasper van den Bosch: https://github.com/ilogue +.. _Jonathan Kuziek: https://github.com/kuziekj -.. _Ezequiel Mikulan: https://github.com/ezemikulan +.. _Jordan Drew: https://github.com/jadrew43 -.. _Rasmus Zetter: https://people.aalto.fi/rasmus.zetter +.. _Jose Alanis: https://github.com/JoseAlanis -.. _Marcin Koculak: https://github.com/mkoculak +.. _Joshua Bear: https://github.com/joshbear -.. _David Sabbagh: https://github.com/DavidSabbagh +.. _Joshua Teves: https://github.com/jbteves -.. _Hubert Banville: https://github.com/hubertjb +.. _Judy D Zhu: https://github.com/JD-Zhu -.. _buildqa: https://github.com/buildqa +.. _Juergen Dammers: https://github.com/jdammers -.. _jeythekey: https://github.com/jeythekey +.. _Jukka Nenonen: https://www.linkedin.com/pub/jukka-nenonen/28/b5a/684 -.. _Sara Sommariva: http://www.dima.unige.it/~sommariva/ +.. _Jussi Nurminen: https://github.com/jjnurminen -.. _Cristóbal Moënne-Loccoz: https://github.com/cmmoenne +.. _Kaisu Lankinen: http://bishoplab.berkeley.edu/Kaisu.html -.. _David Haslacher: https://github.com/davidhaslacher +.. _kalenkovich: https://github.com/kalenkovich + +.. _Katarina Slama: https://github.com/katarinaslama + +.. _Keith Doelling: https://github.com/kdoelling1919 + +.. _Kostiantyn Maksymenko: https://github.com/makkostya + +.. _Kyle Mathewson: https://github.com/kylemath .. _Larry Eisenman: https://github.com/lneisenman -.. _Stanislas Chambon: https://github.com/Slasnista +.. _Lau Møller Andersen: https://github.com/ualsbombe -.. _Jeff Hanna: https://github.com/jshanna100 +.. _Laura Gwilliams: http://lauragwilliams.github.io -.. _kalenkovich: https://github.com/kalenkovich +.. _Leonardo Barbosa: https://github.com/noreun -.. _Antoine Gauthier: https://github.com/Okamille +.. _Liberty Hamilton: https://github.com/libertyh -.. _Samuel Deslauriers-Gauthier: https://github.com/sdeslauriers +.. _Lorenzo Desantis: https://github.com/lorenzo-desantis/ -.. _Sebastian Castano: https://github.com/jscastanoc +.. _Lukas Breuer: https://www.researchgate.net/profile/Lukas-Breuer-2 -.. _Guillaume Favelier: https://github.com/GuillaumeFavelier +.. _Lukas Gemein: https://github.com/gemeinl -.. _Katarina Slama: https://github.com/katarinaslama +.. _Lukáš Hejtmánek: https://github.com/hejtmy -.. _Bruno Nicenboim: https://bnicenboim.github.io +.. _Luke Bloy: https://www.research.chop.edu/imaging/team -.. _Ivana Kojcic: https://github.com/ikojcic +.. _Lx37: https://github.com/Lx37 -.. _Nikolas Chalas: https://github.com/Nichalas +.. _Mads Jensen: https://github.com/MadsJensen -.. _Quentin Bertrand: https://github.com/QB3 +.. _Maggie Clarke: https://github.com/mdclarke -.. _Alexander Kovrig: https://github.com/OpenSatori +.. _Mainak Jas: https://jasmainak.github.io -.. _Kostiantyn Maksymenko: https://github.com/makkostya +.. _Marcin Koculak: https://github.com/mkoculak -.. _Thomas Radman: https://github.com/tradman +.. _Marian Dovgialo: https://github.com/mdovgialo -.. _Paul Roujansky: https://github.com/paulroujansky +.. _Marijn van Vliet: https://github.com/wmvanvliet -.. _Theodore Papadopoulo: https://github.com/papadop +.. _Mark Wronkiewicz: https://ml.jpl.nasa.gov/people/wronkiewicz/wronkiewicz.html -.. _Milan Rybář: http://milanrybar.cz +.. _Marmaduke Woodman: https://github.com/maedoc -.. _Joshua Bear: https://github.com/joshbear +.. _Martin Billinger: https://github.com/mbillingr -.. _Eberhard Eich: https://github.com/ebeich +.. _Martin Luessi: https://github.com/mluessi -.. _Abram Hindle: http://softwareprocess.es +.. _Martin Schulz: https://github.com/marsipu -.. _Kaisu Lankinen: http://bishoplab.berkeley.edu/Kaisu.html +.. _Mathieu Scheltienne: https://github.com/mscheltienne -.. _Padma Sundaram: https://www.nmr.mgh.harvard.edu/user/8071 +.. _Mathurin Massias: https://mathurinm.github.io/ -.. _Robert Luke: https://github.com/rob-luke +.. _Matt Boggess: https://github.com/mattboggess -.. _Robert Seymour: https://neurofractal.github.io +.. _Matt Courtemanche: https://github.com/mjcourte .. _Matt Sanderson: https://github.com/monkeyman192 -.. _Mohammad Daneshzand: https://github.com/mdaneshzand +.. _Matteo Anelli: https://github.com/matteoanelli -.. _Fahimeh Mamashli: https://github.com/fmamashli +.. _Matthias Dold: https://matthiasdold.de -.. _Jose Alanis: https://github.com/JoseAlanis +.. _Matthias Eberlein: https://github.com/MatthiasEb -.. _Adonay Nunes: https://github.com/AdoNunes +.. _Matti Toivonen: https://github.com/mattitoi -.. _Victor Ferat: https://github.com/vferat +.. _Michiru Kaneda: https://github.com/rcmdnk -.. _Dmitrii Altukhov: https://www.hse.ru/en/org/persons/190873905 +.. _Mikołaj Magnuski: https://github.com/mmagnuski -.. _Demetres Kostas: https://github.com/kostasde +.. _Milan Rybář: http://milanrybar.cz -.. _Alex Rockhill: https://github.com/alexrockhill/ +.. _Mingjian He: https://github.com/mh105 -.. _Chun-Hui Li: https://github.com/iamsc +.. _Mohammad Daneshzand: https://github.com/mdaneshzand -.. _Christian O'Reilly: https://github.com/christian-oreilly +.. _Natalie Klein: https://github.com/natalieklein -.. _Yu-Han Luo: https://github.com/yh-luo +.. _Nathalie Gayraud: https://github.com/ngayraud -.. _Fu-Te Wong: https://github.com/zuxfoucault +.. _Naveen Srinivasan: https://github.com/naveensrinivasan -.. _Sebastian Major: https://github.com/major-s +.. _Nick Foti: http://nfoti.github.io -.. _Geoff Brookshire: https://github.com/gbrookshire +.. _Nick Ward: http://www.ucl.ac.uk/ion/departments/sobell/Research/NWard -.. _Sophie Herbst: https://github.com/SophieHerbst +.. _Nicolas Barascud: https://github.com/nbara -.. _Adam Li: http://github.com/adam2392 +.. _Niklas Wilming: https://github.com/nwilming -.. _Simeon Wong: https://github.com/dtxe +.. _Nikolai Chapochnikov: https://github.com/chapochn -.. _Ramiro Gatti: https://github.com/ragatti +.. _Nikolas Chalas: https://github.com/Nichalas -.. _Liberty Hamilton: https://github.com/libertyh +.. _Okba Bekhelifi: https://github.com/okbalefthanded -.. _Svea Marie Meyer: https://github.com/SveaMeyer13 +.. _Olaf Hauk: http://www.neuroscience.cam.ac.uk/directory/profile.php?olafhauk -.. _Lx37: https://github.com/Lx37 +.. _Oleh Kozynets: https://github.com/OlehKSS -.. _Kyle Mathewson: https://github.com/kylemath +.. _Pablo-Arias: https://github.com/Pablo-Arias -.. _Ezequiel Mikulan: https://github.com/ezemikulan +.. _Padma Sundaram: https://www.nmr.mgh.harvard.edu/user/8071 -.. _Jan Sedivy: https://github.com/honzaseda +.. _Paul Pasler: https://github.com/ppasler -.. _Johann Benerradi: https://github.com/HanBnrd +.. _Paul Roujansky: https://github.com/paulroujansky -.. _Rahul Nadkarni: https://github.com/rahuln +.. _Peter Molfese: https://github.com/pmolfese -.. _Jonathan Kuziek: https://github.com/kuziekj +.. _Phillip Alday: https://palday.bitbucket.io -.. _Lau Møller Andersen: https://github.com/ualsbombe +.. _Pierre Ablin: https://pierreablin.com -.. _Martin Schulz: https://github.com/marsipu +.. _Pierre-Antoine Bannier: https://github.com/PABannier -.. _Jeroen Van Der Donckt: https://github.com/jvdd +.. _Qianliang Li: https://www.dtu.dk/english/service/phonebook/person?id=126774 -.. _Steven Bierer: https://github.com/neurolaunch +.. _Quentin Bertrand: https://github.com/QB3 -.. _Eduard Ort: https://github.com/eort +.. _Qunxi Dong: https://github.com/dongqunxi -.. _Aniket Pradhan: https://github.com/Aniket-Pradhan +.. _Rahul Nadkarni: https://github.com/rahuln -.. _Tod Flak: https://github.com/todflak +.. _Ram Pari: https://github.com/ramkpari -.. _Austin Hurst: https://github.com/a-hurst +.. _Ramiro Gatti: https://github.com/ragatti -.. _Victoria Peterson: https://github.com/vpeterson +.. _ramonapariciog: https://github.com/ramonapariciog -.. _Evan Hathaway: https://github.com/ephathaway +.. _Rasmus Zetter: https://people.aalto.fi/rasmus.zetter -.. _Hongjiang Ye: https://github.com/rubyyhj +.. _Reza Nasri: https://github.com/0reza -.. _Jeff Stout: https://megcore.nih.gov/index.php/Staff +.. _Reza Shoorangiz: https://github.com/rezashr -.. _Qianliang Li: https://www.dtu.dk/english/service/phonebook/person?id=126774 +.. _Richard Höchenberger: https://github.com/hoechenberger .. _Richard Koehler: https://github.com/richardkoehler -.. _Tristan Stenner: https://github.com/tstenner/ +.. _Riessarius Stargardsky: https://github.com/Riessarius -.. _Anna Padee: https://github.com/apadee/ +.. _Roan LaPlante: https://github.com/aestrivex -.. _Andres Rodriguez: https://github.com/infinitejest/ +.. _Robert Luke: https://github.com/rob-luke + +.. _Robert Seymour: https://neurofractal.github.io + +.. _Romain Derollepot: https://github.com/rderollepot + +.. _Romain Trachel: https://fr.linkedin.com/in/trachelr + +.. _Roman Goj: http://romanmne.blogspot.co.uk + +.. _Ross Maddox: https://www.urmc.rochester.edu/labs/maddox-lab.aspx .. _Rotem Falach: https://github.com/Falach -.. _Zhi Zhang: https://github.com/tczhangzhi/ +.. _Samu Taulu: https://phys.washington.edu/people/samu-taulu -.. _Enrico Varano: https://github.com/enricovara/ +.. _Samuel Deslauriers-Gauthier: https://github.com/sdeslauriers -.. _Dominik Welke: https://github.com/dominikwelke/ +.. _Samuel Powell: https://github.com/samuelpowell -.. _Judy D Zhu: https://github.com/JD-Zhu +.. _Sara Sommariva: http://www.dima.unige.it/~sommariva/ -.. _Valerii Chirkov: https://github.com/vagechirkov +.. _Scott Huberty: https://orcid.org/0000-0003-2637-031X -.. _Maggie Clarke: https://github.com/mdclarke +.. _Sebastian Castano: https://github.com/jscastanoc -.. _Apoorva Karekal: https://github.com/apoorva6262 +.. _Sebastian Major: https://github.com/major-s -.. _Matteo Anelli: https://github.com/matteoanelli +.. _Sébastien Marti: https://www.researchgate.net/profile/Sebastien-Marti -.. _Cora Kim: https://github.com/kimcoco +.. _Senwen Deng: https://snwn.de + +.. _Sheraz Khan: https://github.com/SherazKhan .. _Silvia Cotroneo: https://github.com/sfc-neuro -.. _Ram Pari: https://github.com/ramkpari +.. _Simeon Wong: https://github.com/dtxe -.. _Erica Peterson: https://github.com/nordme +.. _Simon Kern: https://github.com/skjerns + +.. _Simon Kornblith: http://simonster.com + +.. _Sondre Foslien: https://github.com/sondrfos + +.. _Sophie Herbst: https://github.com/SophieHerbst + +.. _Stanislas Chambon: https://github.com/Slasnista + +.. _Stefan Appelhoff: http://stefanappelhoff.com + +.. _Stefan Repplinger: https://github.com/stfnrpplngr + +.. _Steven Bethard: https://github.com/bethard + +.. _Steven Bierer: https://github.com/neurolaunch + +.. _Steven Gutstein: https://github.com/smgutstein .. _Sumalyo Datta: https://github.com/Sumalyo -.. _Jack Zhang: https://github.com/jackz314 +.. _Susanna Aro: https://www.linkedin.com/in/susanna-aro -.. _Felix Klotzsche: https://github.com/eioe +.. _Svea Marie Meyer: https://github.com/SveaMeyer13 + +.. _T. Wang: https://github.com/twang5 + +.. _Tal Linzen: http://tallinzen.net/ + +.. _Teon Brooks: https://teonbrooks.com + +.. _Theodore Papadopoulo: https://github.com/papadop + +.. _Thomas Hartmann: https://github.com/thht + +.. _Thomas Radman: https://github.com/tradman + +.. _Timothy Gates: https://au.linkedin.com/in/tim-gates-0528a4199 + +.. _Tod Flak: https://github.com/todflak + +.. _Tommy Clausner: https://github.com/TommyClausner + +.. _Tristan Stenner: https://github.com/tstenner/ + +.. _Tziona NessAiver: https://github.com/TzionaN + +.. _Valerii Chirkov: https://github.com/vagechirkov + +.. _Victor Ferat: https://github.com/vferat + +.. _Victoria Peterson: https://github.com/vpeterson + +.. _Xiaokai Xia: https://github.com/dddd1007 + +.. _Yaroslav Halchenko: http://www.onerussian.com/ + +.. _Yousra Bekhti: https://www.linkedin.com/pub/yousra-bekhti/56/886/421 + +.. _Yu-Han Luo: https://github.com/yh-luo + +.. _Zhi Zhang: https://github.com/tczhangzhi/ diff -Nru python-mne-0.23.4+dfsg/doc/cited.rst python-mne-1.1.0+dfsg/doc/cited.rst --- python-mne-0.23.4+dfsg/doc/cited.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/cited.rst 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ Papers citing MNE-Python ======================== -Estimates provided by Google Scholar as of 27 January 2021: +Estimates provided by Google Scholar as of 02 August 2022: -- `MNE (908) `_ -- `MNE-Python (771) `_ +- `MNE (1270) `_ +- `MNE-Python (1420) `_ diff -Nru python-mne-0.23.4+dfsg/doc/conf.py python-mne-1.1.0+dfsg/doc/conf.py --- python-mne-0.23.4+dfsg/doc/conf.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/conf.py 2022-08-03 15:48:14.000000000 +0000 @@ -6,31 +6,34 @@ # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html +from datetime import datetime, timezone +import faulthandler import gc import os +import subprocess import sys import time import warnings -from datetime import datetime, timezone -from distutils.version import LooseVersion +import numpy as np import matplotlib import sphinx -import sphinx_gallery from sphinx_gallery.sorting import FileNameSortKey, ExplicitOrder from numpydoc import docscrape import mne +from mne.fixes import _compare_version from mne.tests.test_docstring_parameters import error_ignores from mne.utils import (linkcode_resolve, # noqa, analysis:ignore - _assert_no_instances, sizeof_fmt) + _assert_no_instances, sizeof_fmt, run_subprocess) from mne.viz import Brain # noqa -if LooseVersion(sphinx_gallery.__version__) < LooseVersion('0.2'): - raise ImportError('Must have at least version 0.2 of sphinx-gallery, got ' - f'{sphinx_gallery.__version__}') - matplotlib.use('agg') +faulthandler.enable() +os.environ['_MNE_BROWSER_NO_BLOCK'] = 'true' +os.environ['MNE_BROWSER_OVERVIEW_MODE'] = 'hidden' +os.environ['MNE_BROWSER_THEME'] = 'light' +os.environ['MNE_3D_OPTION_THEME'] = 'light' # -- Path setup -------------------------------------------------------------- @@ -87,10 +90,12 @@ 'gen_commands', 'gh_substitutions', 'mne_substitutions', + 'newcontrib_substitutions', 'gen_names', - 'sphinx_bootstrap_divs', 'sphinxcontrib.bibtex', 'sphinx_copybutton', + 'sphinx_design', + 'sphinxcontrib.youtube' ] # Add any paths that contain templates here, relative to this directory. @@ -118,9 +123,6 @@ # documents. default_role = "py:obj" -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'default' - # A list of ignored prefixes for module index sorting. modindex_common_prefix = ['mne.'] @@ -134,14 +136,16 @@ 'python': ('https://docs.python.org/3', None), 'numpy': ('https://numpy.org/devdocs', None), 'scipy': ('https://scipy.github.io/devdocs', None), - 'matplotlib': ('https://matplotlib.org', None), + 'matplotlib': ('https://matplotlib.org/stable', None), 'sklearn': ('https://scikit-learn.org/stable', None), 'numba': ('https://numba.pydata.org/numba-doc/latest', None), 'joblib': ('https://joblib.readthedocs.io/en/latest', None), - 'mayavi': ('http://docs.enthought.com/mayavi/mayavi', None), 'nibabel': ('https://nipy.org/nibabel', None), - 'nilearn': ('http://nilearn.github.io', None), + 'nilearn': ('http://nilearn.github.io/stable', None), + 'nitime': ('https://nipy.org/nitime/', None), 'surfer': ('https://pysurfer.github.io/', None), + 'mne_bids': ('https://mne.tools/mne-bids/stable', None), + 'mne-connectivity': ('https://mne.tools/mne-connectivity/stable', None), 'pandas': ('https://pandas.pydata.org/pandas-docs/stable', None), 'seaborn': ('https://seaborn.pydata.org/', None), 'statsmodels': ('https://www.statsmodels.org/dev', None), @@ -152,32 +156,38 @@ 'picard': ('https://pierreablin.github.io/picard/', None), 'qdarkstyle': ('https://qdarkstylesheet.readthedocs.io/en/latest', None), 'eeglabio': ('https://eeglabio.readthedocs.io/en/latest', None), - 'dipy': ('https://dipy.org/documentation/1.4.0./', - 'https://dipy.org/documentation/1.4.0./objects.inv/'), + 'dipy': ('https://dipy.org/documentation/latest/', + 'https://dipy.org/documentation/latest/objects.inv/'), + 'pooch': ('https://www.fatiando.org/pooch/latest/', None), + 'pybv': ('https://pybv.readthedocs.io/en/latest/', None), } # NumPyDoc configuration ----------------------------------------------------- -# XXX This hack defines what extra methods numpydoc will document +# Define what extra methods numpydoc will document docscrape.ClassDoc.extra_public_methods = mne.utils._doc_special_members numpydoc_class_members_toctree = False +numpydoc_show_inherited_class_members = { + 'mne.SourceSpaces': False, + 'mne.Forward': False, +} numpydoc_attributes_as_param_list = True numpydoc_xref_param_type = True numpydoc_xref_aliases = { # Python 'file-like': ':term:`file-like `', + 'iterator': ':term:`iterator `', + 'path-like': ':term:`path-like`', + 'array-like': ':term:`array-like`', + 'Path': ':class:`python:pathlib.Path`', # Matplotlib 'colormap': ':doc:`colormap `', 'color': ':doc:`color `', - 'collection': ':doc:`collections `', 'Axes': 'matplotlib.axes.Axes', 'Figure': 'matplotlib.figure.Figure', 'Axes3D': 'mpl_toolkits.mplot3d.axes3d.Axes3D', 'ColorbarBase': 'matplotlib.colorbar.ColorbarBase', - # Mayavi - 'mayavi.mlab.Figure': 'mayavi.core.api.Scene', - 'mlab.Figure': 'mayavi.core.api.Scene', # sklearn 'LeaveOneOut': 'sklearn.model_selection.LeaveOneOut', # joblib @@ -222,6 +232,8 @@ 'EMS': 'mne.decoding.EMS', 'CSP': 'mne.decoding.CSP', 'Beamformer': 'mne.beamformer.Beamformer', 'Transform': 'mne.transforms.Transform', + 'Coregistration': 'mne.coreg.Coregistration', + 'Figure3D': 'mne.viz.Figure3D', # dipy 'dipy.align.AffineMap': 'dipy.align.imaffine.AffineMap', 'dipy.align.DiffeomorphicMap': 'dipy.align.imwarp.DiffeomorphicMap', @@ -230,12 +242,12 @@ # words 'instance', 'instances', 'of', 'default', 'shape', 'or', 'with', 'length', 'pair', 'matplotlib', 'optional', 'kwargs', 'in', - 'dtype', 'object', 'self.verbose', + 'dtype', 'object', # shapes 'n_vertices', 'n_faces', 'n_channels', 'm', 'n', 'n_events', 'n_colors', 'n_times', 'obj', 'n_chan', 'n_epochs', 'n_picks', 'n_ch_groups', 'n_dipoles', 'n_ica_components', 'n_pos', 'n_node_names', 'n_tapers', - 'n_signals', 'n_step', 'n_freqs', 'wsize', 'Tx', 'M', 'N', 'p', 'q', + 'n_signals', 'n_step', 'n_freqs', 'wsize', 'Tx', 'M', 'N', 'p', 'q', 'r', 'n_observations', 'n_regressors', 'n_cols', 'n_frequencies', 'n_tests', 'n_samples', 'n_permutations', 'nchan', 'n_points', 'n_features', 'n_parts', 'n_features_new', 'n_components', 'n_labels', 'n_events_in', @@ -244,16 +256,17 @@ 'n_elp', 'n_pts', 'n_tris', 'n_nodes', 'n_nonzero', 'n_events_out', 'n_segments', 'n_orient_inv', 'n_orient_fwd', 'n_orient', 'n_dipoles_lcmv', 'n_dipoles_fwd', 'n_picks_ref', 'n_coords', 'n_meg', 'n_good_meg', - 'n_moments', + 'n_moments', 'n_patterns', 'n_new_events', # Undocumented (on purpose) 'RawKIT', 'RawEximia', 'RawEGI', 'RawEEGLAB', 'RawEDF', 'RawCTF', 'RawBTi', 'RawBrainVision', 'RawCurry', 'RawNIRX', 'RawGDF', 'RawSNIRF', 'RawBOXY', - 'RawPersyst', 'RawNihon', 'RawNedf', + 'RawPersyst', 'RawNihon', 'RawNedf', 'RawHitachi', # sklearn subclasses 'mapping', 'to', 'any', # unlinkable - 'mayavi.mlab.pipeline.surface', - 'CoregFrame', 'Kit2FiffFrame', 'FiducialsFrame', + 'CoregistrationUI', + 'IntracranialElectrodeLocator', + 'mne_qt_browser.figure.MNEQtBrowser', } numpydoc_validate = True numpydoc_validation_checks = {'all'} | set(error_ignores) @@ -269,6 +282,10 @@ r'\.__sub__', r'\.__add__', r'\.__iter__', r'\.__div__', r'\.__neg__', # copied from sklearn r'mne\.utils\.deprecated', + # deprecations + r'mne\.connectivity\.degree', r'mne\.connectivity\.seed_target_indices', + r'mne\.viz\.plot_sensors_connectivity', + r'mne\.viz\.plot_connectivity_circle', } @@ -283,28 +300,76 @@ def __repr__(self): return f'<{self.__class__.__name__}>' - def __call__(self, gallery_conf, fname): + def __call__(self, gallery_conf, fname, when): import matplotlib.pyplot as plt try: from pyvista import Plotter # noqa except ImportError: Plotter = None # noqa + try: + from pyvistaqt import BackgroundPlotter # noqa + except ImportError: + BackgroundPlotter = None # noqa + try: + from vtkmodules.vtkCommonDataModel import vtkPolyData # noqa + except ImportError: + vtkPolyData = None # noqa + try: + from mne_qt_browser._pg_figure import MNEQtBrowser + except ImportError: + MNEQtBrowser = None + from mne.viz.backends.renderer import backend + _Renderer = backend._Renderer if backend is not None else None reset_warnings(gallery_conf, fname) # in case users have interactive mode turned on in matplotlibrc, # turn it off here (otherwise the build can be very slow) plt.ioff() plt.rcParams['animation.embed_limit'] = 30. + plt.rcParams['figure.raise_window'] = False + # neo holds on to an exception, which in turn holds a stack frame, + # which will keep alive the global vars during SG execution + try: + import neo + neo.io.stimfitio.STFIO_ERR = None + except Exception: + pass gc.collect() - # _assert_no_instances(Brain, 'running') # calls gc.collect() - # if Plotter is not None: - # _assert_no_instances(Plotter, 'running') + when = f'mne/conf.py:Resetter.__call__:{when}:{fname}' + # Support stuff like + # MNE_SKIP_INSTANCE_ASSERTIONS="Brain,Plotter,BackgroundPlotter,vtkPolyData,_Renderer" make html_dev-memory # noqa: E501 + # to just test MNEQtBrowser + skips = os.getenv('MNE_SKIP_INSTANCE_ASSERTIONS', '').lower() + prefix = '' + if skips not in ('true', '1', 'all'): + prefix = 'Clean ' + skips = skips.split(',') + if 'brain' not in skips: + _assert_no_instances(Brain, when) # calls gc.collect() + if Plotter is not None and 'plotter' not in skips: + _assert_no_instances(Plotter, when) + if BackgroundPlotter is not None and \ + 'backgroundplotter' not in skips: + _assert_no_instances(BackgroundPlotter, when) + if vtkPolyData is not None and 'vtkpolydata' not in skips: + _assert_no_instances(vtkPolyData, when) + if '_renderer' not in skips: + _assert_no_instances(_Renderer, when) + if MNEQtBrowser is not None and \ + 'mneqtbrowser' not in skips: + # Ensure any manual fig.close() events get properly handled + from mne_qt_browser._pg_figure import QApplication + inst = QApplication.instance() + if inst is not None: + for _ in range(2): + inst.processEvents() + _assert_no_instances(MNEQtBrowser, when) # This will overwrite some Sphinx printing but it's useful # for memory timestamps if os.getenv('SG_STAMP_STARTS', '').lower() == 'true': import psutil process = psutil.Process(os.getpid()) mem = sizeof_fmt(process.memory_info().rss) - print(f'{time.time() - self.t0:6.1f} s : {mem}'.ljust(22)) + print(f'{prefix}{time.time() - self.t0:6.1f} s : {mem}'.ljust(22)) examples_dirs = ['../tutorials', '../examples'] @@ -317,27 +382,35 @@ report_scraper = None else: backend = mne.viz.get_3d_backend() - if backend == 'mayavi': - from traits.api import push_exception_handler - mlab = mne.utils._import_mlab() - # Do not pop up any mayavi windows while running the - # examples. These are very annoying since they steal the focus. - mlab.options.offscreen = True - # hack to initialize the Mayavi Engine - mlab.test_plot3d() - mlab.close() - scrapers += ('mayavi',) - push_exception_handler(reraise_exceptions=True) - elif backend in ('notebook', 'pyvista'): + if backend in ('notebook', 'pyvistaqt'): with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=DeprecationWarning) import pyvista pyvista.OFF_SCREEN = False - brain_scraper = mne.viz._brain._BrainScraper() - scrapers += (brain_scraper, 'pyvista') + scrapers += ( + mne.gui._GUIScraper(), + mne.viz._brain._BrainScraper(), + 'pyvista', + ) report_scraper = mne.report._ReportScraper() scrapers += (report_scraper,) del backend +try: + import mne_qt_browser + _min_ver = _compare_version(mne_qt_browser.__version__, '>=', '0.2') + if mne.viz.get_browser_backend() == 'qt' and _min_ver: + scrapers += (mne.viz._scraper._MNEQtBrowserScraper(),) +except ImportError: + pass + +compress_images = ('images', 'thumbnails') +# let's make things easier on Windows users +# (on Linux and macOS it's easy enough to require this) +if sys.platform.startswith('win'): + try: + subprocess.check_call(['optipng', '--version']) + except Exception: + compress_images = () sphinx_gallery_conf = { 'doc_module': ('mne',), @@ -380,14 +453,15 @@ 'min_reported_time': 1., 'abort_on_example_error': False, 'reset_modules': ('matplotlib', Resetter()), # called w/each script + 'reset_modules_order': 'both', 'image_scrapers': scrapers, - 'show_memory': not sys.platform.startswith('win'), + 'show_memory': not sys.platform.startswith(('win', 'darwin')), 'line_numbers': False, # messes with style 'within_subsection_order': FileNameSortKey, 'capture_repr': ('_repr_html_',), 'junit': os.path.join('..', 'test-results', 'sphinx-gallery', 'junit.xml'), 'matplotlib_animations': True, - 'compress_images': ('images', 'thumbnails'), + 'compress_images': compress_images, 'filename_pattern': '^((?!sgskip).)*$', } # Files were renamed from plot_* with: @@ -437,6 +511,13 @@ 'https://www.nyu.edu/', # noqa Max retries exceeded with url: / (Caused by SSLError(SSLError(1, '[SSL: DH_KEY_TOO_SMALL] dh key too small (_ssl.c:1122)'))) 'https://docs.python.org/3/library/.*', # noqa ('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer')) 'https://hal.archives-ouvertes.fr/hal-01848442.*', # noqa Sometimes: 503 Server Error: Service Unavailable for url: https://hal.archives-ouvertes.fr/hal-01848442/ + 'http://www.cs.ucl.ac.uk/staff/d.barber/brml.*', # noqa Sometimes: Read timed out + 'https://compumedicsneuroscan.com/scan-acquire-configuration-files.*', # noqa SSL certificate error as of 2021/09/28 + 'https://chrisholdgraf.com', # noqa Max retries exceeded sometimes + 'https://www.dtu.dk/english/service/phonebook/person.*', # noqa Too slow + 'https://speakerdeck.com/dengemann/eeg-sensor-covariance-using-cross-validation', # noqa Too slow + 'https://doi.org/10.1002/hbm.10024', # noqa Too slow sometimes + 'https://www.researchgate.net', # noqa As of 2022/05/31 we get "403 Forbidden" errors, might have to do with https://stackoverflow.com/questions/72347165 but not worth the effort to fix ] linkcheck_anchors = False # saves a bit of time linkcheck_timeout = 15 # some can be quite slow @@ -466,24 +547,6 @@ ("py:class", "_FuncT"), # type hint used in @verbose decorator ("py:class", "mne.utils._logging._FuncT"), ] -for key in ('AcqParserFIF', 'BiHemiLabel', 'Dipole', 'DipoleFixed', 'Label', - 'MixedSourceEstimate', 'MixedVectorSourceEstimate', 'Report', - 'SourceEstimate', 'SourceMorph', 'VectorSourceEstimate', - 'VolSourceEstimate', 'VolVectorSourceEstimate', - 'channels.DigMontage', 'channels.Layout', - 'decoding.CSP', 'decoding.EMS', 'decoding.FilterEstimator', - 'decoding.GeneralizingEstimator', 'decoding.LinearModel', - 'decoding.PSDEstimator', 'decoding.ReceptiveField', 'decoding.SSD', - 'decoding.SPoC', 'decoding.Scaler', 'decoding.SlidingEstimator', - 'decoding.TemporalFilter', 'decoding.TimeDelayingRidge', - 'decoding.TimeFrequency', 'decoding.UnsupervisedSpatialFilter', - 'decoding.Vectorizer', - 'preprocessing.ICA', 'preprocessing.Xdawn', - 'simulation.SourceSimulator', - 'time_frequency.CrossSpectralDensity', - 'utils.deprecated', - 'viz.ClickableImage'): - nitpick_ignore.append(('py:obj', f'mne.{key}.__hash__')) suppress_warnings = ['image.nonlocal_uri'] # we intentionally link outside @@ -497,6 +560,7 @@ # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. +switcher_version_match = 'dev' if release.endswith('dev0') else version html_theme_options = { 'icon_links': [ dict(name='GitHub', @@ -516,9 +580,13 @@ 'use_edit_page_button': False, 'navigation_with_keys': False, 'show_toc_level': 1, - 'navbar_end': ['version-switcher', 'navbar-icon-links'], + 'navbar_end': ['theme-switcher', 'version-switcher', 'navbar-icon-links'], 'footer_items': ['copyright'], 'google_analytics_id': 'UA-37225609-1', + 'switcher': { + 'json_url': 'https://mne.tools/dev/_static/versions.json', + 'version_match': switcher_version_match, + } } # The name of an image file (relative to this directory) to place at the top @@ -551,7 +619,7 @@ # Custom sidebar templates, maps document names to template names. html_sidebars = { - 'index': ['search-field.html', 'sidebar-quicklinks.html'], + 'index': ['sidebar-quicklinks.html'], } # If true, links to the reST sources are added to the pages. @@ -571,29 +639,19 @@ # variables to pass to HTML templating engine html_context = { 'build_dev_html': bool(int(os.environ.get('BUILD_DEV_HTML', False))), - 'versions_dropdown': { - 'dev': 'v0.24 (devel)', - 'stable': 'v0.23 (stable)', - '0.22': 'v0.22', - '0.21': 'v0.21', - '0.20': 'v0.20', - '0.19': 'v0.19', - '0.18': 'v0.18', - '0.17': 'v0.17', - '0.16': 'v0.16', - '0.15': 'v0.15', - '0.14': 'v0.14', - '0.13': 'v0.13', - '0.12': 'v0.12', - '0.11': 'v0.11', - }, + 'default_mode': 'auto', + 'pygment_light_style': 'tango', + 'pygment_dark_style': 'native', 'funders': [ - dict(img='nih.png', size='3', title='National Institutes of Health'), + dict(img='nih.svg', size='3', title='National Institutes of Health'), dict(img='nsf.png', size='3.5', title='US National Science Foundation'), - dict(img='erc.svg', size='3.5', title='European Research Council'), + dict(img='erc.svg', size='3.5', title='European Research Council', + klass='only-light'), + dict(img='erc-dark.svg', size='3.5', title='European Research Council', + klass='only-dark'), dict(img='doe.svg', size='3', title='US Department of Energy'), - dict(img='anr.svg', size='4.5', + dict(img='anr.svg', size='3.5', title='Agence Nationale de la Recherche'), dict(img='cds.png', size='2.25', title='Paris-Saclay Center for Data Science'), @@ -619,9 +677,15 @@ url='https://web.mit.edu/', size=md), dict(name='New York University', - img='NYU.png', + img='NYU.svg', url='https://www.nyu.edu/', - size=xs), + size=xs, + klass='only-light'), + dict(name='New York University', + img='NYU-dark.svg', + url='https://www.nyu.edu/', + size=xs, + klass='only-dark'), dict(name='Commissariat à l´énergie atomique et aux énergies alternatives', # noqa E501 img='CEA.png', url='http://www.cea.fr/', @@ -629,15 +693,27 @@ dict(name='Aalto-yliopiston perustieteiden korkeakoulu', img='Aalto.svg', url='https://sci.aalto.fi/', - size=md), + size=md, + klass='only-light'), + dict(name='Aalto-yliopiston perustieteiden korkeakoulu', + img='Aalto-dark.svg', + url='https://sci.aalto.fi/', + size=md, + klass='only-dark'), dict(name='Télécom ParisTech', img='Telecom_Paris_Tech.svg', url='https://www.telecom-paris.fr/', size=md), dict(name='University of Washington', - img='Washington.png', + img='Washington.svg', url='https://www.washington.edu/', - size=md), + size=md, + klass='only-light'), + dict(name='University of Washington', + img='Washington-dark.svg', + url='https://www.washington.edu/', + size=md, + klass='only-dark'), dict(name='Institut du Cerveau et de la Moelle épinière', img='ICM.jpg', url='https://icm-institute.org/', @@ -649,37 +725,108 @@ dict(name='Institut national de la santé et de la recherche médicale', img='Inserm.svg', url='https://www.inserm.fr/', - size=xl), + size=xl, + klass='only-light'), + dict(name='Institut national de la santé et de la recherche médicale', + img='Inserm-dark.svg', + url='https://www.inserm.fr/', + size=xl, + klass='only-dark'), dict(name='Forschungszentrum Jülich', img='Julich.svg', url='https://www.fz-juelich.de/', - size=xl), + size=xl, + klass='only-light'), + dict(name='Forschungszentrum Jülich', + img='Julich-dark.svg', + url='https://www.fz-juelich.de/', + size=xl, + klass='only-dark'), + dict(name='Technische Universität Ilmenau', + img='Ilmenau.svg', + url='https://www.tu-ilmenau.de/', + size=xxl, + klass='only-light'), dict(name='Technische Universität Ilmenau', - img='Ilmenau.gif', + img='Ilmenau-dark.svg', url='https://www.tu-ilmenau.de/', - size=xxl), + size=xxl,\ + klass='only-dark'), dict(name='Berkeley Institute for Data Science', - img='BIDS.png', + img='BIDS.svg', url='https://bids.berkeley.edu/', - size=lg), + size=lg, + klass='only-light'), + dict(name='Berkeley Institute for Data Science', + img='BIDS-dark.svg', + url='https://bids.berkeley.edu/', + size=lg, + klass='only-dark'), dict(name='Institut national de recherche en informatique et en automatique', # noqa E501 img='inria.png', url='https://www.inria.fr/', size=xl), dict(name='Aarhus Universitet', - img='Aarhus.png', + img='Aarhus.svg', url='https://www.au.dk/', - size=xl), + size=xl, + klass='only-light'), + dict(name='Aarhus Universitet', + img='Aarhus-dark.svg', + url='https://www.au.dk/', + size=xl, + klass='only-dark'), dict(name='Karl-Franzens-Universität Graz', - img='Graz.jpg', + img='Graz.svg', url='https://www.uni-graz.at/', size=md), + dict(name='SWPS Uniwersytet Humanistycznospołeczny', + img='SWPS.svg', + url='https://www.swps.pl/', + size=xl, + klass='only-light'), + dict(name='SWPS Uniwersytet Humanistycznospołeczny', + img='SWPS-dark.svg', + url='https://www.swps.pl/', + size=xl, + klass='only-dark'), + dict(name='Max-Planck-Institut für Bildungsforschung', + img='MPIB.svg', + url='https://www.mpib-berlin.mpg.de/', + size=xxl, + klass='only-light'), + dict(name='Max-Planck-Institut für Bildungsforschung', + img='MPIB-dark.svg', + url='https://www.mpib-berlin.mpg.de/', + size=xxl, + klass='only-dark'), + dict(name='Macquarie University', + img='Macquarie.svg', + url='https://www.mq.edu.au/', + size=lg, + klass='only-light'), + dict(name='Macquarie University', + img='Macquarie-dark.svg', + url='https://www.mq.edu.au/', + size=lg, + klass='only-dark'), + dict(name='Children’s Hospital of Philadelphia Research Institute', + img='CHOP.svg', + url='https://www.research.chop.edu/imaging', + size=xxl, + klass='only-light'), + dict(name='Children’s Hospital of Philadelphia Research Institute', + img='CHOP-dark.svg', + url='https://www.research.chop.edu/imaging', + size=xxl, + klass='only-dark'), ], # \u00AD is an optional hyphen (not rendered unless needed) + # If these are changed, the Makefile should be updated, too 'carousel': [ dict(title='Source Estimation', text='Distributed, sparse, mixed-norm, beam\u00ADformers, dipole fitting, and more.', # noqa E501 - url='auto_tutorials/inverse/30_mne_dspm_loreta.html', + url='auto_tutorials/inverse/index.html', img='sphx_glr_30_mne_dspm_loreta_008.gif', alt='dSPM'), dict(title='Machine Learning', @@ -694,18 +841,18 @@ alt='STRF'), dict(title='Statistics', text='Parametric and non-parametric, permutation tests and clustering.', # noqa E501 - url='auto_tutorials/stats-source-space/20_cluster_1samp_spatiotemporal.html', # noqa E501 + url='auto_tutorials/stats-source-space/index.html', img='sphx_glr_20_cluster_1samp_spatiotemporal_001.png', alt='Clusters'), dict(title='Connectivity', text='All-to-all spectral and effective connec\u00ADtivity measures.', # noqa E501 - url='auto_examples/connectivity/mne_inverse_label_connectivity.html', # noqa E501 - img='sphx_glr_mne_inverse_label_connectivity_001.png', + url='https://mne.tools/mne-connectivity/stable/auto_examples/mne_inverse_label_connectivity.html', # noqa E501 + img='https://mne.tools/mne-connectivity/stable/_images/sphx_glr_mne_inverse_label_connectivity_001.png', # noqa E501 alt='Connectivity'), dict(title='Data Visualization', text='Explore your data from multiple perspectives.', url='auto_tutorials/evoked/20_visualize_evoked.html', - img='sphx_glr_20_visualize_evoked_007.png', + img='sphx_glr_20_visualize_evoked_010.png', alt='Visualization'), ] } @@ -729,6 +876,8 @@ # not chapters. latex_toplevel_sectioning = 'part' +_np_print_defaults = np.get_printoptions() + # -- Warnings management ----------------------------------------------------- @@ -754,62 +903,33 @@ warnings.filterwarnings('always', '.*DigMontage is only a subset of.*') warnings.filterwarnings( # xhemi morph (should probably update sample) 'always', '.*does not exist, creating it and saving it.*') - warnings.filterwarnings('default', module='sphinx') # internal warnings - warnings.filterwarnings( - 'always', '.*converting a masked element to nan.*') # matplotlib? + # internal warnings + warnings.filterwarnings('default', module='sphinx') # allow these warnings, but don't show them - warnings.filterwarnings( - 'ignore', '.*OpenSSL\\.rand is deprecated.*') - warnings.filterwarnings('ignore', '.*is currently using agg.*') - warnings.filterwarnings( # SciPy-related warning (maybe 1.2.0 will fix it) - 'ignore', '.*the matrix subclass is not the recommended.*') - warnings.filterwarnings( # some joblib warning - 'ignore', '.*semaphore_tracker: process died unexpectedly.*') - warnings.filterwarnings( # needed until SciPy 1.2.0 is released - 'ignore', '.*will be interpreted as an array index.*', module='scipy') - warnings.filterwarnings( - 'ignore', '.*invalid escape sequence.*', lineno=90) # quantities - warnings.filterwarnings( - 'ignore', '.*"is not" with a literal.*', module='nilearn') - for key in ('HasTraits', r'numpy\.testing', 'importlib', r'np\.loads', - 'Using or importing the ABCs from', # internal modules on 3.7 - r"it will be an error for 'np\.bool_'", # ndimage - "DocumenterBridge requires a state object", # sphinx dev - "'U' mode is deprecated", # sphinx io - r"joblib is deprecated in 0\.21", # nilearn - 'The usage of `cmp` is deprecated and will', # sklearn/pytest - 'scipy.* is deprecated and will be removed in', # dipy - r'Converting `np\.character` to a dtype is deprecated', # vtk - r'sphinx\.util\.smartypants is deprecated', - 'is a deprecated alias for the builtin', # NumPy - 'the old name will be removed', # Jinja, via sphinx - 'rcParams is deprecated', # PyVista rcParams -> global_theme - 'to mean no clipping', - ): + for key in ( + 'The module matplotlib.tight_layout is deprecated', # nilearn + 'invalid version and will not be supported', # pyxdf + 'distutils Version classes are deprecated', # seaborn and neo + '`np.object` is a deprecated alias for the builtin `object`', # pyxdf + # nilearn, should be fixed in > 0.9.1 + 'In future, it will be an error for \'np.bool_\' scalars to', + # sklearn hasn't updated to SciPy's sym_pos dep + 'The \'sym_pos\' keyword is deprecated', + ): warnings.filterwarnings( # deal with other modules having bad imports 'ignore', message=".*%s.*" % key, category=DeprecationWarning) - warnings.filterwarnings( # deal with bootstrap-theme bug - 'ignore', message=".*modify script_files in the theme.*", - category=Warning) - warnings.filterwarnings( # nilearn - 'ignore', message=r'sklearn\.externals\.joblib is deprecated.*', - category=FutureWarning) - warnings.filterwarnings( # nilearn - 'ignore', message=r'The sklearn.* module is.*', category=FutureWarning) - warnings.filterwarnings( # nilearn - 'ignore', message=r'Fetchers from the nilea.*', category=FutureWarning) - warnings.filterwarnings( # deal with other modules having bad imports - 'ignore', message=".*ufunc size changed.*", category=RuntimeWarning) - warnings.filterwarnings( # realtime - 'ignore', message=".*unclosed file.*", category=ResourceWarning) - warnings.filterwarnings('ignore', message='Exception ignored in.*') - # allow this ImportWarning, but don't show it + # xarray/netcdf4 warnings.filterwarnings( - 'ignore', message="can't resolve package from", category=ImportWarning) + 'ignore', message=r'numpy\.ndarray size changed, may indicate.*', + category=RuntimeWarning) + # qdarkstyle warnings.filterwarnings( - 'ignore', message='.*mne-realtime.*', category=DeprecationWarning) - warnings.filterwarnings( # pyvista deprecation - 'ignore', message=r'.*Use of .* is deprecated\. Use .* instead.*') + 'ignore', message=r'.*Setting theme=.*6 in qdarkstyle.*', + category=RuntimeWarning) + + # In case we use np.set_printoptions in any tutorials, we only + # want it to affect those: + np.set_printoptions(**_np_print_defaults) reset_warnings(None, None) @@ -817,31 +937,45 @@ # -- Fontawesome support ----------------------------------------------------- -# here the "b" and "s" refer to "brand" and "solid" (determines which font file -# to look in). "fw-" prefix indicates fixed width. -icons = { - 'apple': 'b', - 'linux': 'b', - 'windows': 'b', - 'hand-paper': 's', - 'question': 's', - 'quote-left': 's', - 'rocket': 's', - 'server': 's', - 'fw-book': 's', - 'fw-code-branch': 's', - 'fw-newspaper': 's', - 'fw-question-circle': 's', - 'fw-quote-left': 's', -} +# here the "fab" and "fas" refer to "brand" and "solid" (determines which font +# file to look in). "fw" indicates fixed width. +brand_icons = ('apple', 'linux', 'windows', 'discourse', 'python') +fixed_icons = ( + # homepage: + 'book', 'code-branch', 'newspaper', 'question-circle', 'quote-left', + # contrib guide: + 'bug', 'comment', 'hand-sparkles', 'magic', 'pencil-alt', 'remove-format', + 'universal-access', 'discourse', 'python', +) +other_icons = ( + 'hand-paper', 'question', 'rocket', 'server', 'code', 'desktop', + 'terminal', 'cloud-download-alt', 'wrench', 'hourglass' +) +icons = dict() +for icon in brand_icons + fixed_icons + other_icons: + font = ('fab' if icon in brand_icons else 'fas',) # brand or solid font + fw = ('fa-fw',) if icon in fixed_icons else () # fixed-width + icons[icon] = font + fw prolog = '' -for icon, cls in icons.items(): - fw = ' fa-fw' if icon.startswith('fw-') else '' +for icon, classes in icons.items(): prolog += f''' .. |{icon}| raw:: html - + +''' + +prolog += ''' +.. |fix-bug| raw:: html + + + + + +''' + +prolog += ''' +.. |ensp| unicode:: U+2002 .. EN SPACE ''' # -- Dependency info ---------------------------------------------------------- @@ -884,7 +1018,7 @@ 'compute_mne_inverse_epochs_in_label.py', 'compute_mne_inverse_raw_in_label.py', 'compute_mne_inverse_volume.py', 'compute_source_psd_epochs.py', 'covariance_whitening_dspm.py', - 'custom_inverse_solver.py', 'cwt_sensor_connectivity.py', + 'custom_inverse_solver.py', 'decoding_csp_eeg.py', 'decoding_csp_timefreq.py', 'decoding_spatio_temporal_source.py', 'decoding_spoc_CMC.py', 'decoding_time_generalization_conditions.py', @@ -901,12 +1035,11 @@ 'left_cerebellum_volume_source.py', 'limo_data.py', 'linear_model_patterns.py', 'linear_regression_raw.py', 'meg_sensors.py', 'mixed_norm_inverse.py', - 'mixed_source_space_connectivity.py', 'mixed_source_space_inverse.py', + 'mixed_source_space_inverse.py', 'mne_cov_power.py', 'mne_helmet.py', 'mne_inverse_coherence_epochs.py', - 'mne_inverse_connectivity_spectrum.py', 'mne_inverse_envelope_correlation.py', 'mne_inverse_envelope_correlation_volume.py', - 'mne_inverse_label_connectivity.py', 'mne_inverse_psi_visual.py', + 'mne_inverse_psi_visual.py', 'morph_surface_stc.py', 'morph_volume_stc.py', 'movement_compensation.py', 'movement_detection.py', 'multidict_reweighted_tfmxne.py', 'muscle_detection.py', 'opm_data.py', 'otp.py', 'parcellation.py', @@ -915,7 +1048,7 @@ 'read_inverse.py', 'read_neo_format.py', 'read_noise_covariance_matrix.py', 'read_stc.py', 'receptive_field_mtrf.py', 'resolution_metrics.py', 'resolution_metrics_eegmeg.py', 'roi_erpimage_by_rt.py', - 'sensor_connectivity.py', 'sensor_noise_level.py', + 'sensor_noise_level.py', 'sensor_permutation_test.py', 'sensor_regression.py', 'shift_evoked.py', 'simulate_evoked_data.py', 'simulate_raw_data.py', 'simulated_raw_data_using_subject_anatomy.py', 'snr_estimate.py', @@ -930,7 +1063,9 @@ 'vector_mne_solution.py', 'virtual_evoked.py', 'xdawn_denoising.py', 'xhemi.py', } +ex = 'auto_examples' co = 'connectivity' +mne_conn = 'https://mne.tools/mne-connectivity/stable' tu = 'auto_tutorials' di = 'discussions' sm = 'source-modeling' @@ -982,12 +1117,21 @@ f'{tu}/{sr}/plot_stats_cluster_spatio_temporal.html': f'{tu}/{sr}/20_cluster_1samp_spatiotemporal.html', # noqa E501 f'{tu}/{sr}/plot_stats_cluster_spatio_temporal_2samp.html': f'{tu}/{sr}/30_cluster_ftest_spatiotemporal.html', # noqa E501 f'{tu}/{sr}/plot_stats_cluster_spatio_temporal_repeated_measures_anova.html': f'{tu}/{sr}/60_cluster_rmANOVA_spatiotemporal.html', # noqa E501 - f'{tu}/{sr}/plot_stats_cluster_time_frequency_repeated_measures_anova.html': f'{tu}/{sr}/70_cluster_rmANOVA_time_freq.html', # noqa E501 + f'{tu}/{sr}/plot_stats_cluster_time_frequency_repeated_measures_anova.html': f'{tu}/{sn}/70_cluster_rmANOVA_time_freq.html', # noqa E501 f'{tu}/{tf}/plot_sensors_time_frequency.html': f'{tu}/{tf}/20_sensors_time_frequency.html', # noqa E501 f'{tu}/{tf}/plot_ssvep.html': f'{tu}/{tf}/50_ssvep.html', f'{tu}/{si}/plot_creating_data_structures.html': f'{tu}/{si}/10_array_objs.html', # noqa E501 f'{tu}/{si}/plot_point_spread.html': f'{tu}/{si}/70_point_spread.html', f'{tu}/{si}/plot_dics.html': f'{tu}/{si}/80_dics.html', + f'{ex}/{co}/mne_inverse_label_connectivity.html': f'{mne_conn}/{ex}/mne_inverse_label_connectivity.html', # noqa E501 + f'{ex}/{co}/cwt_sensor_connectivity.html': f'{mne_conn}/{ex}/cwt_sensor_connectivity.html', # noqa E501 + f'{ex}/{co}/mixed_source_space_connectivity.html': f'{mne_conn}/{ex}/mixed_source_space_connectivity.html', # noqa E501 + f'{ex}/{co}/mne_inverse_coherence_epochs.html': f'{mne_conn}/{ex}/mne_inverse_coherence_epochs.html', # noqa E501 + f'{ex}/{co}/mne_inverse_connectivity_spectrum.html': f'{mne_conn}/{ex}/mne_inverse_connectivity_spectrum.html', # noqa E501 + f'{ex}/{co}/mne_inverse_envelope_correlation_volume.html': f'{mne_conn}/{ex}/mne_inverse_envelope_correlation_volume.html', # noqa E501 + f'{ex}/{co}/mne_inverse_envelope_correlation.html': f'{mne_conn}/{ex}/mne_inverse_envelope_correlation.html', # noqa E501 + f'{ex}/{co}/mne_inverse_psi_visual.html': f'{mne_conn}/{ex}/mne_inverse_psi_visual.html', # noqa E501 + f'{ex}/{co}/sensor_connectivity.html': f'{mne_conn}/{ex}/sensor_connectivity.html', # noqa E501 } @@ -995,7 +1139,9 @@ """Make HTML redirects.""" # https://www.sphinx-doc.org/en/master/extdev/appapi.html # Adapted from sphinxcontrib/redirects (BSD-2-Clause) - if not isinstance(app.builder, sphinx.builders.html.StandaloneHTMLBuilder): + if not (isinstance(app.builder, + sphinx.builders.html.StandaloneHTMLBuilder) and + exception is None): return logger = sphinx.util.logging.getLogger('mne') TEMPLATE = """\ @@ -1038,7 +1184,7 @@ if not to.startswith('http'): assert os.path.isfile(os.path.join(app.outdir, to)), to # handle links to sibling folders - path_parts = to.split(os.path.sep) + path_parts = to.split('/') assert tu in path_parts, path_parts # need to refactor otherwise path_parts = ['..'] + path_parts[(path_parts.index(tu) + 1):] to = os.path.join(*path_parts) @@ -1053,8 +1199,9 @@ line = fid.readline() assert 'Page Redirection' in line, line # handle folders that no longer exist - if fr_path.split(os.path.sep)[-2] in ( - 'misc', 'discussions', 'source-modeling', 'sample-datasets'): + if fr_path.split('/')[-2] in ( + 'misc', 'discussions', 'source-modeling', 'sample-datasets', + 'connectivity'): os.makedirs(os.path.dirname(fr_path), exist_ok=True) with open(fr_path, 'w') as fid: fid.write(TEMPLATE.format(to=to)) @@ -1062,6 +1209,23 @@ f'Added {len(custom_redirects):3d} HTML custom redirects') +def make_version(app, exception): + """Make a text file with the git version.""" + if not (isinstance(app.builder, + sphinx.builders.html.StandaloneHTMLBuilder) and + exception is None): + return + logger = sphinx.util.logging.getLogger('mne') + try: + stdout, _ = run_subprocess(['git', 'rev-parse', 'HEAD'], verbose=False) + except Exception as exc: + logger.warning(f'Failed to write _version.txt: {exc}') + return + with open(os.path.join(app.outdir, '_version.txt'), 'w') as fid: + fid.write(stdout) + logger.info(f'Added "{stdout.rstrip()}" > _version.txt') + + # -- Connect our handlers to the main Sphinx app --------------------------- def setup(app): @@ -1071,4 +1235,9 @@ report_scraper.app = app app.config.rst_prolog = prolog app.connect('builder-inited', report_scraper.copyfiles) + sphinx_logger = sphinx.util.logging.getLogger('mne') + sphinx_logger.info( + f'Building documentation for MNE {release} ({mne.__file__})') + sphinx_logger.info(f'Building with scrapers={scrapers}') app.connect('build-finished', make_redirects) + app.connect('build-finished', make_version) diff -Nru python-mne-0.23.4+dfsg/doc/connectivity.rst python-mne-1.1.0+dfsg/doc/connectivity.rst --- python-mne-0.23.4+dfsg/doc/connectivity.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/connectivity.rst 2022-08-03 15:48:14.000000000 +0000 @@ -2,19 +2,5 @@ Connectivity Estimation ======================= -:py:mod:`mne.connectivity`: - -.. automodule:: mne.connectivity - :no-members: - :no-inherited-members: - -.. currentmodule:: mne.connectivity - -.. autosummary:: - :toctree: generated/ - - degree - envelope_correlation - phase_slope_index - seed_target_indices - spectral_connectivity +As of 0.24, connectivity functionality has been moved to the separate package +:mod:`mne-connectivity:mne_connectivity`. diff -Nru python-mne-0.23.4+dfsg/doc/datasets.rst python-mne-1.1.0+dfsg/doc/datasets.rst --- python-mne-0.23.4+dfsg/doc/datasets.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/datasets.rst 2022-08-03 15:48:14.000000000 +0000 @@ -13,6 +13,8 @@ .. autosummary:: :toctree: generated/ + fetch_dataset + has_dataset brainstorm.bst_auditory.data_path brainstorm.bst_resting.data_path brainstorm.bst_raw.data_path @@ -22,6 +24,7 @@ fetch_fsaverage fetch_hcp_mmp_parcellation fetch_infant_template + fetch_phantom fnirs_motor.data_path hf_sef.data_path kiloword.data_path diff -Nru python-mne-0.23.4+dfsg/doc/events.rst python-mne-1.1.0+dfsg/doc/events.rst --- python-mne-0.23.4+dfsg/doc/events.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/events.rst 2022-08-03 15:48:14.000000000 +0000 @@ -36,6 +36,7 @@ :toctree: generated/ define_target_events + match_event_names shift_time_events :py:mod:`mne.epochs`: diff -Nru python-mne-0.23.4+dfsg/doc/export.rst python-mne-1.1.0+dfsg/doc/export.rst --- python-mne-0.23.4+dfsg/doc/export.rst 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/export.rst 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,19 @@ + +Exporting +================ + +:py:mod:`mne.export`: + +.. automodule:: mne.export + :no-members: + :no-inherited-members: + +.. currentmodule:: mne.export + +.. autosummary:: + :toctree: generated/ + + export_epochs + export_evokeds + export_evokeds_mff + export_raw diff -Nru python-mne-0.23.4+dfsg/doc/file_io.rst python-mne-1.1.0+dfsg/doc/file_io.rst --- python-mne-0.23.4+dfsg/doc/file_io.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/file_io.rst 2022-08-03 15:48:14.000000000 +0000 @@ -24,6 +24,7 @@ read_epochs_fieldtrip read_events read_evokeds + read_evoked_besa read_evoked_fieldtrip read_evokeds_mff read_freesurfer_lut @@ -32,7 +33,6 @@ read_morph_map read_proj read_reject_parameters - read_selection read_source_estimate read_source_spaces read_surface diff -Nru python-mne-0.23.4+dfsg/doc/forward.rst python-mne-1.1.0+dfsg/doc/forward.rst --- python-mne-0.23.4+dfsg/doc/forward.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/forward.rst 2022-08-03 15:48:14.000000000 +0000 @@ -6,9 +6,14 @@ .. autosummary:: :toctree: generated/ + :template: autosummary/class_no_inherited_members.rst Forward SourceSpaces + +.. autosummary:: + :toctree: generated/ + add_source_space_distances apply_forward apply_forward_raw @@ -57,4 +62,6 @@ get_fitting_dig make_watershed_bem make_flash_bem + make_scalp_surfaces convert_flash_mris + distance_to_bem diff -Nru python-mne-0.23.4+dfsg/doc/funding.rst python-mne-1.1.0+dfsg/doc/funding.rst --- python-mne-0.23.4+dfsg/doc/funding.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/funding.rst 2022-08-03 15:48:14.000000000 +0000 @@ -1,5 +1,7 @@ :orphan: +.. _funding: + Funding and other support ========================= @@ -7,44 +9,61 @@ .. rst-class:: list-unstyled funders -- |nih| **National Institutes of Health:** R01-EB009048, R01-EB009048, R01-EB006385, R01-HD40712, R01-NS44319, R01-NS37462, R01-NS104585, P41-EB015896, P41-RR14075-06 -- |nsf| **US National Science Foundation:** 0958669, 1042134 -- |erc| **European Research Council:** YStG-263584, YStG-676943 +- |nih| **National Institutes of Health:** + `R01-EB009048 `_, + `R01-EB006385 `_, + `R01-HD040712 `_, + `R01-NS044319 `_, + `R01-NS037462 `_, + `R01-NS104585 `_, + `P41-EB015896 `_, + `P41-RR014075 `_ +- |nsf| **US National Science Foundation:** + `0958669 `_, + `1042134 `_ +- |erc| |ercdk| **European Research Council:** + `YStG-263584 `_, + `YStG-676943 `_ - |doe| **US Department of Energy:** DE-FG02-99ER62764 (MIND) -- |anr| **Agence Nationale de la Recherche:** `14-NEUC-0002-01 `_, **IDEX** Paris-Saclay `11-IDEX-0003-02 `_ -- |cds| **Paris-Saclay Center for Data Science:** `PARIS-SACLAY `_ -- |goo| **Google:** Summer of code (×6) -- |ama| **Amazon:** AWS Research Grants -- |czi| **Chan Zuckerberg Initiative:** `EOSS2 `_ - - -Additionally, many universities or research institutions have supported their employees’ contributions to MNE-Python as part of normal work duties. These institutions include: - -- `Massachusetts General Hospital `_ -- `Athinoula A. Martinos Center for Biomedical Imaging `_ -- `Harvard Medical School `_ -- `Massachusetts Institute of Technology `_ -- `New York University `_ -- `Commissariat à l’énergie atomique et aux énergies alternatives `_ -- `Aalto-yliopiston perustieteiden korkeakoulu `_ -- `Télécom ParisTech `_ -- `University of Washington `_ -- `Institut du Cerveau et de la Moelle épinière `_ -- `Boston University `_ -- `Institut national de la santé et de la recherche médicale `_ -- `Forschungszentrum Jülich `_ -- `Technische Universität Ilmenau `_ -- `Berkeley Institute for Data Science `_ -- `Institut national de recherche en informatique et en automatique `_ -- `Aarhus Universitet `_ -- `Karl-Franzens-Universität Graz `_ +- |anr| **Agence Nationale de la Recherche:** + `14-NEUC-0002-01 `_, + **IDEX** Paris-Saclay + `11-IDEX-0003-02 `_ +- |cds| **Paris-Saclay Center for Data Science:** + `PARIS-SACLAY `_ +- |goo| **Google:** + Summer of code (×7 years) +- |ama| **Amazon:** + AWS Research Grants +- |czi| **Chan Zuckerberg Initiative:** + `EOSS2`_, + `EOSS4`_ + + +.. _supporting-institutions: + +Institutional partners +---------------------- + +Additionally, many universities or research institutions have supported their +employees’ contributions to MNE-Python as part of normal work duties. These +institutions include: + +.. include:: _includes/institutional-partners.rst + :start-after: institutional-partners-begin-content -.. |nih| image:: _static/funding/nih.png + +.. |nih| image:: _static/funding/nih.svg .. |nsf| image:: _static/funding/nsf.png .. |erc| image:: _static/funding/erc.svg + :class: only-light +.. |ercdk| image:: _static/funding/erc-dark.svg + :class: only-dark .. |doe| image:: _static/funding/doe.svg .. |anr| image:: _static/funding/anr.svg .. |cds| image:: _static/funding/cds.png .. |goo| image:: _static/funding/google.svg .. |ama| image:: _static/funding/amazon.svg .. |czi| image:: _static/funding/czi.svg + +.. include:: links.inc \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/doc/glossary.rst python-mne-1.1.0+dfsg/doc/glossary.rst --- python-mne-0.23.4+dfsg/doc/glossary.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/glossary.rst 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ .. currentmodule:: mne -The Glossary provides short definitions of MNE-Python-specific vocabulary and +The Glossary provides short definitions of vocabulary specific to MNE-Python and general neuroimaging concepts. If you think a term is missing, please consider `creating a new issue`_ or `opening a pull request`_ to add it. @@ -12,175 +12,191 @@ annotations - An annotation is defined by an onset, a duration, and a string - description. It can contain information about the experiments, but - also details on signals marked by a human: bad data segments, - sleep scores, sleep events (spindles, K-complex) etc. - An :class:`Annotations` object is a container of multiple annotations. - See :class:`Annotations` page for the API of the corresponding - object class and :ref:`tut-annotations` - for a tutorial on how to manipulate such objects. + An annotation is defined by an onset, a duration, and a textual + description. It can contain information about the experiment, but + also details on signals marked by a human such as bad data segments, + sleep stages, sleep events (spindles, K-complex), and so on. + An :class:`Annotations` object is a container for multiple annotations, + which is available as the ``annotations`` attribute of :class:`~io.Raw` + objects. See :class:`Annotations` for the class definition and + :ref:`tut-events-vs-annotations` for a short tutorial. + See also :term:`events`. + + array-like + Something that acts like – or can be converted to – a + :class:`NumPy array `. + This includes (but is not limited to) + :class:`arrays `, `lists `, and + `tuples `. beamformer - Beamformer is a popular source estimation approach that uses a set of + A beamformer is a popular source estimation approach that uses a set of spatial filters (beamformer weights) to compute time courses of sources - at predefined coordinates. See :class:`beamformer.Beamformer`. See - also :term:`LCMV`. + at predefined locations. See :class:`beamformer.Beamformer` for the class + definition. See also :term:`LCMV`. BEM boundary element model boundary element method BEM is the acronym for boundary element method or boundary element - model. Both are related to the forward model computation and more - specifically the definion of the conductor model. The - boundary element model consists of surfaces such as the inner skull, - outer skull and outer skin (a.k.a. scalp) that define compartments - of tissues of the head. You can compute the BEM surfaces with + model. Both are related to the definion of the conductor model in the + forward model computation. The boundary element model consists of surfaces + such as the inner skull, outer skull, and outer skin (scalp) that define + compartments of tissues of the head. You can compute the BEM surfaces with :func:`bem.make_watershed_bem` or :func:`bem.make_flash_bem`. - See :ref:`tut-forward` for usage demo. + See :ref:`tut-forward` for a usage demo. channels - Channels refer to MEG sensors, EEG electrodes or any extra electrode - or sensor such as EOG, ECG or sEEG, ECoG etc. Channels usually have - a type, such as gradiometer, and a unit, such as Tesla/Meter that - is used in the code base, e.g. for plotting. See also - :term:`data channels`. + Channels refer to MEG sensors, EEG electrodes or other sensors such as + EOG, ECG, sEEG, ECoG, etc. Channels usually have + a type (such as gradiometer), and a unit (such as T/m) used e.g. for + plotting. See also :term:`data channels`. data channels - Many functions in MNE operate by default on "data channels". These are - channels that typically hold *brain electophysiological* data, - as opposed to other forms of data, such as EOG, ECG, stimulus trigger, + Many functions in MNE-Python operate on "data channels" by default. These + are channels that contain electrophysiological data from the brain, + as opposed to other channel types such as EOG, ECG, stimulus/trigger, or acquisition system status data. The set of channels considered - "data channels" in MNE is (along with their typical scale factors for - plotting, as they are stored in objects in SI units): + "data channels" in MNE contains the following types (together with scale + factors for plotting): .. mne:: data channels list DICS dynamic imaging of coherent sources - Dynamic Imaging of Coherent Sources, a method for computing source - power in different frequency bands. see :ref:`ex-inverse-source-power` - and :func:`beamformer.make_dics`. + Dynamic Imaging of Coherent Sources is a method for computing source + power in different frequency bands. See :ref:`ex-inverse-source-power` + and :func:`beamformer.make_dics` for more details. digitization - Digitization is a procedure of recording the headshape of a subject and - the fiducial coils (or :term:`HPI`) and/or eeg electrodes locations on - the subject’s head. They are represented as a set of points in a 3D space. + Digitization is a procedure of recording the head shape and locations of + fiducial coils (or :term:`HPI`) and/or EEG electrodes on the head. They + are represented as a set of points in 3D space. See :ref:`reading-dig-montages` and :ref:`dig-formats`. dipole ECD equivalent current dipole An equivalent current dipole (ECD) is an approximate representation of - post-synaptic activity in a small region of cortex. The intracellular + post-synaptic activity in a small cortical region. The intracellular currents that give rise to measurable EEG/MEG signals are thought to originate in populations of cortical pyramidal neurons aligned perpendicularly to the cortical surface. Because the length of such current sources is very small relative to the distance between the - cortex and the EEG/MEG sensors, the fields measured by the techniques - are well-approximated by (i.e., "equivalent" to) fields generated by + cortex and the EEG/MEG sensors, the fields measured by these techniques + are well approximated by (i.e., equivalent to) fields generated by idealized point sources (dipoles) located on the cortical surface. dSPM dynamic statistical parametric mapping - Dynamic statistical parametric mapping (abbr. ``dSPM``) gives a noise- - normalized minimum-norm estimate at a given source location. dSPM is - calculated by dividing the activity estimate at each source location by - the baseline standard deviation of the noise. + Dynamic statistical parametric mapping (dSPM) gives a noise-normalized + minimum-norm estimate at a given source location. It is calculated by + dividing the activity estimate at each source location by the baseline + standard deviation of the noise. eLORETA sLORETA eLORETA and sLORETA (exact and standardized low resolution brain - electromagnetic tomography) are linear source estimation techniques, - as are :term:`dSPM` and :term:`MNE`. sLORETA outputs - standardized values (like dSPM does), while eLORETA outputs normalized + electromagnetic tomography) are linear source estimation techniques + like :term:`dSPM` and :term:`MNE`. sLORETA outputs + standardized values (like dSPM), while eLORETA generates normalized current estimates. See :func:`minimum_norm.apply_inverse`, :ref:`tut-inverse-methods`, and :ref:`example-sLORETA`. epochs Epochs (sometimes called "trials" in other software packages) are - equal-length spans of data extracted from raw continuous data. Usually, - epochs are extracted around stimulus events or subject responses, - though sometimes sequential or overlapping epochs are extracted (e.g., + equal-length segments of data extracted from continuous data. Usually, + epochs are extracted around stimulus events or responses, + though sometimes sequential or overlapping epochs are used (e.g., for analysis of resting-state activity). See :class:`Epochs` for the - API of the corresponding object class, and :ref:`tut-epochs-class` for - a narrative overview. + class definition and :ref:`tut-epochs-class` for a narrative overview. events - Events correspond to specific time points in raw data; e.g., - triggers, experimental condition events, etc. MNE represents events with - integers that are stored in numpy arrays of shape (n_events, 3). Such arrays - are classically obtained from a trigger channel, also referred to as - stim channel. + Events correspond to specific time points in raw data, such as triggers, + experimental condition events, etc. MNE-Python represents events with + integers stored in NumPy arrays of shape ``(n_events, 3)``. The first + column contains the event onset (in samples) with :term:`first_samp` + included. The last column contains the event code. The second + column contains the signal value of the immediately preceding sample, + and reflects the fact that event arrays sometimes originate from + analog voltage channels ("trigger channels" or "stim channels"). In + most cases, the second column is all zeros and can be ignored. + Event arrays can be created with :func:`mne.make_fixed_length_events`, + :func:`mne.read_events`, and :func:`mne.find_events`. + See :ref:`tut-events-vs-annotations` for a short tutorial. + See also :term:`annotations`. evoked Evoked data are obtained by averaging epochs. Typically, an evoked object is constructed for each subject and each condition, but it can also be - obtained by averaging a list of evoked over different subjects. - See :class:`EvokedArray` for the API of the corresponding - object class, and :ref:`tut-evoked-class` for a narrative overview. + obtained by averaging a list of evoked objects over different subjects. + See :class:`EvokedArray` for the class definition and + :ref:`tut-evoked-class` for a narrative overview. fiducial fiducial point anatomical landmark Fiducials are objects placed in the field of view of an imaging system - to act as a known spatial reference location that is easy to localize. + to act as known spatial references that are easy to localize. In neuroimaging, fiducials are often placed on anatomical landmarks such as the nasion (NAS) or left/right preauricular points (LPA and RPA). These known reference locations are used to define a coordinate system - used for localization of sensors (hence NAS, LPA and RPA are often + for localizing sensors (hence NAS, LPA and RPA are often called "cardinal points" because they define the cardinal directions of - the "head" coordinate system). The cardinal points are also useful when + the head coordinate system). The cardinal points are also useful when co-registering measurements in different coordinate systems (such as - aligning EEG sensor locations to an MRI of the subject's head). + aligning EEG sensor locations to an MRI of the head). Due to the common neuroimaging practice of placing fiducial objects on - anatomical landmarks, the terms "fiducial", "anatomical landmark" and + anatomical landmarks, the terms "fiducial", "anatomical landmark", and "cardinal point" are often (erroneously) used interchangeably. first_samp The :attr:`~io.Raw.first_samp` attribute of :class:`~io.Raw` objects is an integer representing the number of time samples that passed between the onset of the hardware acquisition system and the - time when data started to be recorded to disk. This approach to sample + time when data recording started. This approach to sample numbering is a peculiarity of VectorView MEG systems, but for consistency it is present in all :class:`~io.Raw` objects regardless of the source of the data. In other words, :attr:`~io.Raw.first_samp` will be ``0`` in :class:`~io.Raw` - objects loaded from non-VectorView data files. + objects loaded from non-VectorView data files. See also + :term:`last_samp`. forward forward solution - The forward solution (abbr. ``fwd``) is a linear operator capturing the + The forward solution is a linear operator capturing the relationship between each dipole location in the :term:`source space` - and the corresponding field distribution measured by the sensors (A.K.A., - the "lead field matrix"). Calculating a forward solution requires a - conductivity model of the head, encapsulating the geometry and - electrical conductivity of the different tissue compartments (see + and the corresponding field distribution measured by the sensors + (the "lead field matrix"). Calculating a forward solution requires a + conductivity model of the head, which encapsulates the geometries and + electrical conductivities of the different tissue compartments (see :term:`boundary element model` and :class:`bem.ConductorModel`). + For information about the Forward object and the data it stores, see + :class:`mne.Forward`. GFP global field power - Global Field Power (abbr. ``GFP``) is a measure of the (non-)uniformity + Global Field Power (GFP) is a measure of the (non-)uniformity of the electromagnetic field at the sensors. It is typically calculated - as the standard deviation of the sensor values at each time point; thus + as the standard deviation of the sensor values at each time point. Thus, it is a one-dimensional time series capturing the spatial variability of the signal across sensor locations. HED hierarchical event descriptors - Hierarchical event descriptors (abbr. ``HED``) are tags that use - keywords separated by '/' to describe different types of - experimental events (for example, stimulus/circle/red/left and - stimulus/circle/blue/left). These tags can be used to group + Hierarchical event descriptors (HED) are tags that use + keywords separated by slashes (/) to describe different types of + experimental events (for example, ``stimulus/circle/red/left`` and + ``stimulus/circle/blue/left``). These tags can be used to group experimental events and select event types for analysis. HPI cHPI head position indicator - Head position indicators (abbr. ``HPI``, or sometimes ``cHPI`` for + Head position indicators (HPI, sometimes cHPI for *continuous* head position indicators) are small coils attached to a subject's head during MEG acquisition. Each coil emits a sinusoidal signal of a different frequency, which is picked up by the MEG sensors @@ -190,9 +206,10 @@ low-pass filtering. See :ref:`tut-head-pos`. info - Also called ``measurement info``, it is a collection of metadata - regarding a :class:`~io.Raw`, :class:`Epochs` or :class:`Evoked` - object, containing channel locations and types, sampling frequency, + measurement info + A "measurement info" (or short "info") object is a collection of metadata + related to :class:`~io.Raw`, :class:`Epochs`, or :class:`Evoked` + objects. It contains channel locations and types, sampling frequency, preprocessing history such as filters, etc. See :ref:`tut-info-class` for a narrative overview. @@ -202,76 +219,97 @@ locations by :math:`N` sensors) that, when applied to the sensor signals, yields estimates of the brain activity that gave rise to the observed sensor signals. Inverse operators are available for the linear - inverse methods MNE, dSPM, sLORETA and eLORETA. - See :func:`minimum_norm.apply_inverse`. + inverse methods :term:`MNE`, :term:`dSPM`, :term:`sLORETA`, and + :term:`eLORETA`. See :func:`minimum_norm.apply_inverse`. label - A :class:`Label` refers to a defined region in the cortex, also often called + A :class:`Label` refers to a defined region in the cortex, often called a region of interest (ROI) in the literature. Labels can be defined - anatomically (based on physical structure of the cortex) or functionally - (based on cortical response to specific stimuli). + anatomically (based on the physical structure of the cortex) or functionally + (based on cortical responses to specific stimuli). See also :term:`ROI`. + + last_samp + The :attr:`~io.Raw.last_samp` attribute of :class:`~io.Raw` + objects is an integer representing the number of time samples that + passed between the start and end of data recording. This approach to sample + numbering is a peculiarity of VectorView MEG systems, but for + consistency it is present in all :class:`~io.Raw` objects + regardless of the source of the data. See also :term:`first_samp`. layout - A :class:`~channels.Layout` gives sensor positions in 2 + A :class:`~channels.Layout` gives sensor positions in two dimensions (defined by ``x``, ``y``, ``width``, and ``height`` values for each sensor). It is primarily used for illustrative purposes (i.e., making - diagrams of approximate sensor positions in top-down diagrams of the head, - so-called topographies or topomaps). + diagrams of approximate sensor positions in cartoons of the head, + so-called topographies or topomaps). See also :term:`montage`. LCMV LCMV beamformer - Linearly constrained minimum variance beamformer, which attempts to + Linearly constrained minimum variance beamformer attempt to estimate activity for a given source while suppressing cross-talk from - other regions, see :func:`beamformer.make_lcmv`. See also + other regions (:func:`beamformer.make_lcmv`). See also :term:`beamformer`. + FreeSurfer LUT + LUT + A FreeSurfer lookup table (LUT) provides a mapping between a given + volumetric atlas or surface label name, its integer value + (e.g., in ``aparc+aseg.mgz``), and its standard color (see the + `FreeSurfer wiki `__ + for more information). Custom LUTs can be also be created from different + surface parcellations, see for example `this comment about HCPMMP + `__. + maximum intensity projection - A method of displaying activity within some volume by, for each pixel, - finding the maximum value along vector from the viewer to the pixel + A method to display pixel-wise activity within some volume by + finding the maximum value along a vector from the viewer to the pixel (i.e., along the vector pependicular to the view plane). MNE minimum-norm estimate minimum-norm estimation - Minimum-norm estimation (abbr. ``MNE``) can be used to generate a distributed - map of activation on a :term:`source space`, usually on a cortical surface. + Minimum-norm estimation (MNE) can be used to generate a distributed + map of activation on a :term:`source space` (usually on a cortical surface). MNE uses a linear :term:`inverse operator` to project sensor measurements into the source space. The :term:`inverse operator` is computed from the :term:`forward solution` for a subject and an estimate of the :term:`noise covariance` of sensor measurements. montage - EEG channel names and the relative positions of the sensor w.r.t. the scalp. - While layout are 2D locations, montages give 3D locations. A montage + EEG channel names and relative positions of sensors on the scalp. + While layouts are 2D locations, montages are 3D locations. A montage can also contain locations for HPI points, fiducial points, or extra head shape points. - See :class:`~channels.DigMontage` for the API of the corresponding object - class. + See :class:`~channels.DigMontage` for the class definition. See also + :term:`layout`. morphing Morphing refers to the operation of transferring source estimates from - one anatomy to another. It is commonly referred as realignment in fMRI - literature. This operation is necessary for group studies (to get the - data in a common space for statistical analysis). + one anatomy to another. It is known as realignment in the fMRI + literature. This operation is necessary for group studies to get the + data into a common space for statistical analysis. See :ref:`ch_morph` for more details. noise covariance - Noise covariance is a matrix that contains the covariance between data + The noise covariance is a matrix that contains the covariance between data channels. It is a square matrix with shape ``n_channels`` :math:`\times` ``n_channels``. It is especially useful when working with multiple sensor - types (e.g. EEG and MEG). It is in - practice estimated from baseline periods or empty room measurements. - The matrix also provides a noise model that can be used for subsequent analysis - like source imaging. + types (e.g. EEG and MEG). In practice, the matrix is estimated from baseline + periods or empty room measurements, and it also provides a noise model + that can be used for subsequent analysis (like source imaging). + + path-like + Something that acts like a path in a file system. This can be a `str` + or a `pathlib.Path`. pick - An integer that is the index of a channel in the measurement info. + An integer that is the index of a channel in the :term:`measurement info`. It allows to obtain the information on a channel in the list of channels available in ``info['chs']``. projector SSP - A projector (abbr. ``proj``), also referred to as Signal Space + A projector, also referred to as Signal Space Projection (SSP), defines a linear operation applied spatially to EEG or MEG data. A matrix multiplication of an SSP projector with the data will reduce the rank of the data by projecting it to a @@ -279,13 +317,24 @@ both the data and the forward operator when performing source localization. Note that EEG average referencing can be done using such a projection operator. Projectors are stored alongside data - in :term:`the measurement info` in the field ``info['projs']``. + in the :term:`measurement info` in the field ``info['projs']``. raw - `~io.Raw` objects hold continuous data (preprocessed or not). One typically - manipulates raw data when reading recordings in a file on disk. - See :class:`~io.RawArray` for the API of the corresponding - object class, and :ref:`tut-raw-class` for a narrative overview. + `~io.Raw` objects hold continuous data (preprocessed or not), typically + obtained from reading recordings stored in a file. + See :class:`~io.RawArray` for the class definition and :ref:`tut-raw-class` + for a narrative overview. + + RAS + Right-Anterior-Superior, denoting the standard way to define coordinate + frames in MNE-Python: + + R + +X is right, -X is left + A + +Y is anterior (front), -Y is posterior (rear) + S + +Z is superior (top), -Z is inferior (bottom) ROI region of interest @@ -303,44 +352,53 @@ source time course Source estimates, commonly referred to as STC (Source Time Courses), are obtained from source localization methods such as :term:`dSPM`, - :term:`sLORETA`, :term:`LCMV` or MxNE. + :term:`sLORETA`, :term:`LCMV`, or MxNE. STCs contain the amplitudes of the neural sources over time. In MNE-Python, :class:`SourceEstimate` objects only store the - amplitudes of activation but not the locations of the sources; the + amplitudes of activation but not the locations of the sources. The locations are stored separately in the :class:`SourceSpaces` object that was used to compute the forward operator. - See :class:`SourceEstimate`, :class:`VolSourceEstimate` - :class:`VectorSourceEstimate`, :class:`MixedSourceEstimate`, - for the API of the corresponding object classes. + See :class:`SourceEstimate`, :class:`VolSourceEstimate`, + :class:`VectorSourceEstimate`, and :class:`MixedSourceEstimate`. source space - A source space (abbr. ``src``) specifies where in the brain one wants - to estimate the - source amplitudes. It corresponds to locations of a set of - candidate :term:`equivalent current dipoles`. MNE mostly works - with source spaces defined on the cortical surfaces estimated + A source space specifies where in the brain source amplitudes are + estimated. It corresponds to locations of a set of + candidate :term:`equivalent current dipoles`. MNE-Python mostly + works with source spaces defined on the cortical surfaces estimated by FreeSurfer from a T1-weighted MRI image. See :ref:`tut-forward` - to read about how to compute a forward operator on a source space. - See :class:`SourceSpaces` for the API of the corresponding - object class. + to read about how to compute a forward operator in a source space. + See :class:`SourceSpaces` for the class definition and information + about the data it contains. stim channel trigger channel - A stim channel, a.k.a. trigger channel, is a channel that encodes - events during the recording. It is typically a channel that is usually + A stim channel or trigger channel is a channel that encodes + events during the recording. It is typically a channel that is always zero and takes positive values when something happens (such as the - onset of a stimulus, or a subject response). Stim channels are often + onset of a stimulus or a subject response). Stim channels are often prefixed with ``STI`` to distinguish them from other channel types. See :ref:`stim-channel-defined` for more details. + template montage + An idealized EEG :term:`montage`, often provided by the manufacturer + of the EEG system or cap. The electrode positions were not actually + measured on the participants' heads, but rather were calculated + assuming optimal theoretical placement on a sphere. + + tfr + A time-frequency representation (TFR) is often a spectrogram (STFT) or + scaleogram (wavelet) showing the frequency content as a function of + time. + trans A coordinate frame affine transformation, usually between the Neuromag head coordinate frame and the MRI Surface RAS coordinate frame used by Freesurfer. whitening A linear operation that transforms data with a known covariance - structure into "whitened data" which has a covariance structure that - is the identity matrix. In other words it creates virtual channels that + structure into "whitened data", which has a covariance structure equal to + the identity matrix. In other words, whitening creates virtual channels that are uncorrelated and have unit variance. This is also known as a sphering transformation. diff -Nru python-mne-0.23.4+dfsg/doc/_includes/forward.rst python-mne-1.1.0+dfsg/doc/_includes/forward.rst --- python-mne-0.23.4+dfsg/doc/_includes/forward.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/_includes/forward.rst 2022-08-03 15:48:14.000000000 +0000 @@ -20,13 +20,14 @@ MEG/EEG and MRI coordinate systems ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. sidebar:: Coordinate systems in MNE-Python +.. note:: Coordinate systems in MNE-Python + :class: sidebar - In some MNE-Python objects (e.g., :class:`~mne.Forward`, - :class:`~mne.SourceSpaces`, etc), information about the coordinate frame is - encoded as a constant integer value. The meaning of those integers is - determined `in the source code - `__. + In some MNE-Python objects (e.g., :class:`~mne.Forward`, + :class:`~mne.SourceSpaces`, etc), information about the coordinate frame is + encoded as a constant integer value. The meaning of those integers is + determined `in the source code + `__. The coordinate systems used in MNE software (and FreeSurfer) and their relationships are depicted in :ref:`coordinate_system_figure`. Except for the @@ -118,7 +119,7 @@ Talairach brain. The differences are larger as you get further from the middle of the brain, towards the outside. The FreeSurfer Talairach coordinates mitigate this problem by additing a an additional - transformation, defined separately for negatice and positive MNI Talairach + transformation, defined separately for negative and positive MNI Talairach :math:`z` coordinates. These two transformations, denoted by :math:`T_-` and :math:`T_+` in :ref:`coordinate_system_figure`, are fixed as discussed in https://imaging.mrc-cbu.cam.ac.uk/imaging/MniTalairach (*Approach 2*). @@ -683,14 +684,15 @@ EEG forward solution in the sphere model ---------------------------------------- -.. sidebar:: Sphere-model examples in MNE-Python +.. note:: Sphere-model examples in MNE-Python + :class: sidebar - For examples of using the sphere model when computing the forward model - (using :func:`mne.make_forward_solution`), see :ref:`Brainstorm CTF phantom - dataset tutorial `, - :ref:`Brainstorm Elekta phantom dataset tutorial - `, and - :ref:`plot_source_alignment_without_mri`. + For examples of using the sphere model when computing the forward model + (using :func:`mne.make_forward_solution`), see :ref:`Brainstorm CTF phantom + dataset tutorial `, + :ref:`Brainstorm Elekta phantom dataset tutorial + `, and + :ref:`tut-source-alignment-without-mri`. When the sphere model is employed, the computation of the EEG solution can be substantially accelerated by using approximation methods described by Mosher diff -Nru python-mne-0.23.4+dfsg/doc/_includes/institutional-partners.rst python-mne-1.1.0+dfsg/doc/_includes/institutional-partners.rst --- python-mne-0.23.4+dfsg/doc/_includes/institutional-partners.rst 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/_includes/institutional-partners.rst 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,41 @@ +:orphan: + +Institutional partners +---------------------- + +.. NOTE: this file is included in doc/funding.rst and doc/overview/people.rst. + Changes here are reflected there. If you want to link to this content, link + to :ref:`supporting-institutions` to link to that section of the funding.rst + page. The next line is a target for :start-after: so we can omit the title + from the include: + institutional-partners-begin-content + +Current partners +~~~~~~~~~~~~~~~~ + +- `Aalto-yliopiston perustieteiden korkeakoulu `_ +- `Aarhus Universitet `_ +- `Athinoula A. Martinos Center for Biomedical Imaging `_ +- `Children’s Hospital of Philadelphia Research Institute `_ +- `Harvard Medical School `_ +- `Institut national de recherche en informatique et en automatique `_ +- `Karl-Franzens-Universität Graz `_ +- `Macquarie University `_ +- `Massachusetts General Hospital `_ +- `Max-Planck-Institut für Bildungsforschung `_ +- `SWPS Uniwersytet Humanistycznospołeczny `_ +- `University of Washington `_ + +Former partners +~~~~~~~~~~~~~~~ + +- `Berkeley Institute for Data Science `_ +- `Boston University `_ +- `Commissariat à l’énergie atomique et aux énergies alternatives `_ +- `Forschungszentrum Jülich `_ +- `Institut du Cerveau et de la Moelle épinière `_ +- `Institut national de la santé et de la recherche médicale `_ +- `Massachusetts Institute of Technology `_ +- `New York University `_ +- `Technische Universität Ilmenau `_ +- `Télécom ParisTech `_ diff -Nru python-mne-0.23.4+dfsg/doc/_includes/inverse.rst python-mne-1.1.0+dfsg/doc/_includes/inverse.rst --- python-mne-0.23.4+dfsg/doc/_includes/inverse.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/_includes/inverse.rst 2022-08-03 15:48:14.000000000 +0000 @@ -150,14 +150,17 @@ :math:`C_0`, is scaled by the number of averages, :math:`L`, *i.e.*, :math:`C = C_0 / L`. +.. note:: + When EEG data are included, the gain matrix :math:`G` needs to be average referenced when computing the linear inverse operator :math:`M`. This is incorporated during creating the spatial whitening operator :math:`C^{-^1/_2}`, which includes any projectors on the data. EEG data average reference (using a projector) is mandatory for source modeling and is checked when calculating the inverse operator. + As shown above, regularization of the inverse solution is equivalent to a change in the variance of the current amplitudes in the Bayesian *a priori* distribution. A convenient choice for the source-covariance matrix :math:`R` is such that :math:`\text{trace}(\tilde{G} R \tilde{G}^\top) / \text{trace}(I) = 1`. With this -choice we can approximate :math:`\lambda^2 \sim 1/SNR`, where SNR is the -(power) signal-to-noise ratio of the whitened data. +choice we can approximate :math:`\lambda^2 \sim 1/\rm{SNR}^2`, where SNR is the +(amplitude) signal-to-noise ratio of the whitened data. .. note:: The definition of the signal to noise-ratio/ :math:`\lambda^2` relationship @@ -167,7 +170,7 @@ have, in general, different units of measure. For example, the MEG data are expressed in T or T/m whereas the unit of EEG is Volts. -See :ref:`tut_compute_covariance` for example of noise covariance computation +See :ref:`tut-compute-covariance` for example of noise covariance computation and whitening. .. _cov_regularization_math: diff -Nru python-mne-0.23.4+dfsg/doc/_includes/memory.rst python-mne-1.1.0+dfsg/doc/_includes/memory.rst --- python-mne-0.23.4+dfsg/doc/_includes/memory.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/_includes/memory.rst 2022-08-03 15:48:14.000000000 +0000 @@ -20,7 +20,7 @@ from mne import io from mne.datasets import sample data_path = sample.data_path() - raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' + raw_fname = data_path / 'MEG' / 'sample' / 'sample_audvis_filt-0-40_raw.fif' raw = io.read_raw_fif(raw_fname, preload=False) .. note:: Filtering, resampling and dropping or selecting channels does not diff -Nru python-mne-0.23.4+dfsg/doc/_includes/morph.rst python-mne-1.1.0+dfsg/doc/_includes/morph.rst --- python-mne-0.23.4+dfsg/doc/_includes/morph.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/_includes/morph.rst 2022-08-03 15:48:14.000000000 +0000 @@ -23,12 +23,13 @@ Why morphing? ~~~~~~~~~~~~~ -.. sidebar:: Morphing examples in MNE-Python +.. note:: Morphing examples in MNE-Python + :class: sidebar - Examples of morphing in MNE-Python include :ref:`this tutorial - ` on surface source estimation or these examples on - :ref:`surface ` and :ref:`volumetric ` - source estimation. + Examples of morphing in MNE-Python include :ref:`this tutorial + ` on surface source estimation or these examples on + :ref:`surface ` and :ref:`volumetric ` + source estimation. Modern neuroimaging techniques, such as source reconstruction or fMRI analyses, make use of advanced mathematical models and hardware to map brain activity diff -Nru python-mne-0.23.4+dfsg/doc/_includes/ssp.rst python-mne-1.1.0+dfsg/doc/_includes/ssp.rst --- python-mne-0.23.4+dfsg/doc/_includes/ssp.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/_includes/ssp.rst 2022-08-03 15:48:14.000000000 +0000 @@ -23,7 +23,7 @@ relies on the fact that the magnetic field distributions generated by the sources in the brain have spatial distributions sufficiently different from those generated by external noise sources. Furthermore, it is implicitly -assumed that the linear space spanned by the significant external noise patters +assumed that the linear space spanned by the significant external noise patterns has a low dimension. Without loss of generality we can always decompose any :math:`n`-channel diff -Nru python-mne-0.23.4+dfsg/doc/index.rst python-mne-1.1.0+dfsg/doc/index.rst --- python-mne-0.23.4+dfsg/doc/index.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/index.rst 2022-08-03 15:48:14.000000000 +0000 @@ -14,9 +14,13 @@ .. image:: _static/mne_logo.svg :alt: MNE-Python - :class: logo + :class: logo, mainlogo, only-light :align: center +.. image:: _static/mne_logo_dark.svg + :alt: MNE-Python + :class: logo, mainlogo, only-dark + :align: center .. rst-class:: h4 text-center font-weight-light my-4 @@ -28,8 +32,8 @@ .. toctree:: :hidden: - Install - Documentation - API Reference - Get help - Development + Install + Documentation + API Reference + Get help + Development diff -Nru python-mne-0.23.4+dfsg/doc/install/advanced.rst python-mne-1.1.0+dfsg/doc/install/advanced.rst --- python-mne-0.23.4+dfsg/doc/install/advanced.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/install/advanced.rst 2022-08-03 15:48:14.000000000 +0000 @@ -5,8 +5,17 @@ Advanced setup ============== -Using with IPython / Jupyter notebooks -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Working with Jupyter Notebooks and JupyterLab +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +If you like using Jupyter notebooks, you should also update the "base" +conda environment to include the ``nb_conda_kernels`` package; this will +make it easier to use MNE-Python in Jupyter Notebooks launched from the +Anaconda GUI: + +.. code-block:: console + + $ conda install --name=base nb_conda_kernels When using MNE-Python within IPython or a Jupyter notebook, we strongly recommend using the Qt matplotlib backend for fast and correct rendering. On @@ -36,7 +45,7 @@ In [1]: %matplotlib inline -but some functionality will be lost. For example, mayavi scenes will still +but some functionality will be lost. For example, PyVista scenes will still pop-up a separate window, but only one window at a time is possible, and interactivity within the scene is limited in non-blocking plot calls. @@ -44,13 +53,11 @@ :class: note If you are using MNE-Python on Windows through IPython or Jupyter, you might - also have to use the IPython magic command ``%gui qt`` after importing - MNE-Python, Mayavi or PySurfer (see `here + also have to use the IPython magic command ``%gui qt`` (see `here `_). For example: .. code-block:: ipython - In [1]: from mayavi import mlab In [2]: %gui qt If you installed the ``nb_conda_kernels`` package into your ``base`` @@ -74,8 +81,19 @@ The notebook 3d backend requires PyVista to be installed along with other packages, -please follow :doc:`mne_python`. +please follow :ref:`standard-instructions`. + +Installing to a headless server +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +With `pyvista`_: +Download the `server environment file`_ and use it to create the conda +environment + +.. code-block:: console + + $ curl --remote-name https://raw.githubusercontent.com/mne-tools/mne-python/main/server_environment.yml + $ conda env create -f server_environment.yml Using the development version ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -120,8 +138,8 @@ .. code-block:: console - curl --remote-name https://raw.githubusercontent.com/mne-tools/mne-python/main/requirements.txt - pip install --user requirements.txt + $ curl --remote-name https://raw.githubusercontent.com/mne-tools/mne-python/main/requirements.txt + $ pip install --user -r requirements.txt Other configurations will probably also work, but we may be unable to offer support if you encounter difficulties related to your particular Python @@ -175,6 +193,12 @@ Also, it's possible that different software rending backends might perform better than others, such as using the ``llvmpipe`` backend rather than ``swr``. +In newer MESA (21+), rendering can be incorrect when using MSAA, so consider +setting: + +.. code-block:: console + + $ export MNE_3D_OPTION_MULTI_SAMPLES=1 MESA also can have trouble with full-screen antialiasing, which you can disable with: @@ -232,7 +256,8 @@ 3D plotting trouble after upgrade on macOS ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -When upgrading MNE-Python from version 0.19 or lower, some macOS users may end up with +When upgrading MNE-Python from version 0.19 or lower, some macOS users may end +up with conflicting versions of some of the 3D plotting dependencies. If you plot using the pyvista 3D backend and find that you can click-drag to rotate the brain, but cannot adjust any of the settings sliders, it is likely that your versions @@ -249,17 +274,10 @@ line for ``pip uninstall -y vtk``. -Trouble using mayavi backend -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. LINKS -If you run into trouble when visualizing source estimates (or anything else) -using mayavi, you can try setting a couple of environment variables at the -beginning of your script, session, or notebook:: - - >>> import os - >>> os.environ['ETS_TOOLKIT'] = 'qt4' - >>> os.environ['QT_API'] = 'pyqt5' - -This will tell mayavi to use Qt backend with PyQt bindings, instead of the -default PySide. For more information, see -http://docs.enthought.com/mayavi/mayavi/building_applications.html#integrating-in-a-qt-application. +.. _environment file: https://raw.githubusercontent.com/mne-tools/mne-python/main/environment.yml +.. _server environment file: https://raw.githubusercontent.com/mne-tools/mne-python/main/server_environment.yml +.. _`pyvista`: https://docs.pyvista.org/ +.. _`X server`: https://en.wikipedia.org/wiki/X_Window_System +.. _`xvfb`: https://en.wikipedia.org/wiki/Xvfb diff -Nru python-mne-0.23.4+dfsg/doc/install/check_installation.rst python-mne-1.1.0+dfsg/doc/install/check_installation.rst --- python-mne-0.23.4+dfsg/doc/install/check_installation.rst 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/install/check_installation.rst 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,78 @@ +.. include:: ../links.inc + +.. _testing-installation: + + +Testing your installation +========================= + +To make sure MNE-Python was installed correctly, type the following command in +a terminal:: + + python -c "import mne; mne.sys_info()" + +.. hint:: + If you installed MNE-Python using one of our installers, enter the above + command in the **Prompt**. + +This should display some system information along with the versions of +MNE-Python and its dependencies. Typical output looks like this:: + + Platform: Linux-5.0.0-1031-gcp-x86_64-with-glibc2.2.5 + Python: 3.8.1 (default, Dec 20 2019, 10:06:11) [GCC 7.4.0] + Executable: /home/travis/virtualenv/python3.8.1/bin/python + CPU: x86_64: 2 cores + Memory: 7.8 GB + + mne: 0.21.dev0 + numpy: 1.19.0.dev0+8dfaa4a {blas=openblas, lapack=openblas} + scipy: 1.5.0.dev0+f614064 + matplotlib: 3.2.1 {backend=QtAgg} + + sklearn: 0.22.2.post1 + numba: 0.49.0 + nibabel: 3.1.0 + cupy: Not found + pandas: 1.0.3 + dipy: 1.1.1 + pyvista: 0.25.2 {pyvistaqt=0.1.0} + vtk: 9.0.0 + qtpy: 2.0.1 {PySide6=6.2.4} + + +.. dropdown:: If you get an error... + :color: danger + :icon: alert-fill + + .. rubric:: If you see an error like: + + :: + + Traceback (most recent call last): + File "", line 1, in + ModuleNotFoundError: No module named 'mne' + + This suggests that your environment containing MNE-Python is not active. + If you followed the setup for 3D plotting/source analysis (i.e., you + installed to a new ``mne`` environment instead of the ``base`` environment) + try running ``conda activate mne`` first, and try again. If this works, + you might want to set your terminal to automatically activate the + ``mne`` environment each time you open a terminal:: + + echo conda activate mne >> ~/.bashrc # for bash shells + echo conda activate mne >> ~/.zprofile # for zsh shells + +If something else went wrong during installation and you can't figure it out, +check out the :ref:`advanced_setup` instructions to see if your problem is +discussed there. If not, the `MNE Forum`_ is a good resources for +troubleshooting installation problems. + +.. highlight:: python + +.. LINKS + +.. _environment file: https://raw.githubusercontent.com/mne-tools/mne-python/main/environment.yml +.. _server environment file: https://raw.githubusercontent.com/mne-tools/mne-python/main/server_environment.yml +.. _`pyvista`: https://docs.pyvista.org/ +.. _`X server`: https://en.wikipedia.org/wiki/X_Window_System +.. _`xvfb`: https://en.wikipedia.org/wiki/Xvfb diff -Nru python-mne-0.23.4+dfsg/doc/install/contributing.rst python-mne-1.1.0+dfsg/doc/install/contributing.rst --- python-mne-0.23.4+dfsg/doc/install/contributing.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/install/contributing.rst 2022-08-03 15:48:14.000000000 +0000 @@ -3,29 +3,70 @@ Contributing guide ================== +.. include:: ../links.inc .. highlight:: console -This page has details on the preferred contribution workflow -and how best to configure your system for a smooth experience contributing to -MNE-Python. - -.. collapse:: |rocket| Want an example to work through? - :class: success - - A great way to learn to contribute is to work through an actual example. - We recommend that you take a look at the `GitHub issues marked "easy"`_, - pick one that looks interesting, and work through it while reading this - guide! +Thanks for taking the time to contribute! MNE-Python is an open-source project +sustained mostly by volunteer effort. We welcome contributions from anyone as +long as they abide by our `Code of Conduct`_. + +There are lots of ways to contribute, such as: + +.. rst-class:: icon-bullets + +- |bug| Use the software, and when you find bugs, tell us about them! We can + only fix the bugs we know about. +- |discourse| Answer questions on `our user forum`_. +- |comment| Tell us about parts of the documentation that you find confusing or + unclear. +- |hand-sparkles| Tell us about things you wish MNE-Python could do, or things + it can do but you wish they were easier. +- |universal-access| Improve the accessibility of our website. +- |fix-bug| Fix bugs. +- |remove-format| Fix mistakes in our function documentation strings. +- |magic| Implement new features. +- |pencil-alt| Improve existing tutorials or write new ones. +- |python| Contribute to one of the many Python packages that MNE-Python + depends on. + +To *report* bugs, *request* new features, or *ask about* confusing +documentation, it's usually best to open a new issue on `our user forum`_ +first; you'll probably get help fastest that way, and it helps keep our GitHub +issue tracker focused on things that we *know* will require changes to our +software (as opposed to problems that can be fixed in the user's code). We may +ultimately ask you to open an issue on GitHub too, but starting on the forum +helps us keep things organized. For fastest results, be sure to include +information about your operating system and MNE-Python version, and (if +applicable) include a reproducible code sample that is as short as possible and +ideally uses one of :ref:`our example datasets `. + +If you want to *fix* bugs, *add* new features, or *improve* our +docstrings/tutorials/website, those kinds of contributions are made through +`our GitHub repository `_. The rest of this page explains +how to set up your workflow to make contributing via GitHub as easy as +possible. + + +.. dropdown:: Want an example to work through? + :color: success + :icon: rocket + + Feel free to just read through the rest of the page, but if you find it + easier to "learn by doing", take a look at our + `GitHub issues marked "easy"`_, pick one that looks interesting, and work + through it while reading this guide! -.. _`GitHub issues marked "easy"`: https://github.com/mne-tools/mne-python/issues?q=is%3Aissue+is%3Aopen+label%3AEASY Overview of contribution process ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +.. note:: Reminder: all contributors are expected to follow our + `code of conduct`_. + Changes to MNE-Python are typically made by `forking`_ the MNE-Python repository, making changes to your fork (usually by `cloning`_ it to your personal computer, making the changes locally, and then `pushing`_ the local -changes up to your fork), and finally creating a `pull request`_ to incorporate +changes up to your fork on GitHub), and finally creating a `pull request`_ to incorporate your changes back into the shared "upstream" version of the codebase. In general you'll be working with three different copies of the MNE-Python @@ -53,7 +94,8 @@ Configuring git ~~~~~~~~~~~~~~~ -.. sidebar:: Git GUI alternative +.. note:: Git GUI alternative + :class: sidebar `GitHub desktop`_ is a GUI alternative to command line git that some users appreciate; it is available for |windows| Windows and |apple| MacOS. @@ -112,30 +154,59 @@ make: *** No targets specified and no makefile found. Stop. -If you don't see this or something similar: +If you don't see this or something similar, you may not have ``make`` installed. + +.. tab-set:: + + .. tab-item:: Linux + :class-content: text-center + + .. button-link:: https://www.gnu.org/software/make/ + :ref-type: ref + :color: primary + :shadow: + :class: font-weight-bold mt-3 + + |cloud-download-alt| |ensp| Get make for Linux + + .. tab-item:: macOS + :class-content: text-center + + .. button-link:: https://www.gnu.org/software/make/ + :ref-type: ref + :color: primary + :shadow: + :class: font-weight-bold mt-3 + + |cloud-download-alt| |ensp| Get make for macOS + + .. tab-item:: Windows -.. sidebar:: If you get: + If you see: :: - *bash: conda: command not found* + bash: make: command not found - you need to add + Install ``make`` for git BASH (which comes with `git for Windows`_): - - :file:`{path_to_Anaconda}` - - :file:`{path_to_Anaconda}\\Scripts` + 1. Download :file:`make-{newest.version}-without-guile-w32-bin.zip` from `ezwinports`_ + 2. Extract zip-folder + 3. Copy the contents into :file:`{path_to_git}\\mingw64\\` (e.g. by merging the + folders with the equivalent ones already inside) + 4. For the first time using git BASH, you need to run once (to be able to + activate your ``mnedev`` environment): :: - to Windows-PATH. + $ conda init bash -- For Linux/MacOS, get `GNU Make`_ -- For Windows, you can install make for git BASH (which comes with `git for Windows`_): + If instead you see an error like: :: - 1. Download :file:`make-{newest.version}-without-guile-w32-bin.zip` from `ezwinports`_ - 2. Extract zip-folder - 3. Copy the contents into :file:`{path_to_git}\\mingw64\\` (e.g. by merging the - folders with the equivalent ones already inside) - 4. For the first time using git BASH, you need to run once (to be able to - activate your mnedev-environment): :: + bash: conda: command not found - $ conda init bash + at the top of your git BASH window, you need to add + + - :file:`{path_to_Anaconda}` + - :file:`{path_to_Anaconda}\\Scripts` + + to Windows-PATH first. Forking the MNE-Python repository @@ -145,7 +216,7 @@ of the codebase, go to the `MNE-Python GitHub`_ page and create a `fork`_ into your GitHub user account. -.. image:: https://help.github.com/assets/images/help/repository/fork_button.jpg +.. image:: https://docs.github.com/assets/cb-28613/images/help/repository/fork_button.png This will create a copy of the MNE-Python codebase inside your GitHub user account (this is called "your fork"). Changes you make to MNE-Python will @@ -162,7 +233,8 @@ Creating the virtual environment ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. sidebar:: Supported Python environments +.. note:: Supported Python environments + :class: sidebar We strongly recommend the `Anaconda`_ or `Miniconda`_ environment managers for Python. Other setups are possible but are not officially supported by @@ -174,7 +246,7 @@ These instructions will set up a Python environment that is separated from your system-level Python and any other managed Python environments on your computer. This lets you switch between different versions of Python (MNE-Python requires -version 3.6 or higher) and also switch between the stable and development +version 3.7 or higher) and also switch between the stable and development versions of MNE-Python (so you can, for example, use the same computer to analyze your data with the stable release, and also work with the latest development version to fix bugs or add new features). Even if you've already @@ -209,23 +281,11 @@ $ cd $INSTALL_LOCATION $ git clone https://github.com/$GITHUB_USERNAME/mne-python.git -.. sidebar:: Remote URLs in git - - Here we use ``git://`` instead of ``https://`` in the URL for the - ``upstream`` remote repository. ``git://`` URLs are read-only, so you can - *pull* changes from ``upstream`` into your local copy (to stay up-to-date - with changes from other contributors) but you cannot *push* changes from - your computer into the ``upstream`` remote. Instead, you must push your - changes to your own remote fork (``origin``) first, and then create a pull - request from your remote into the upstream remote. In :ref:`a later section - ` you'll see a third kind of remote URL for connecting to - GitHub using SSH. - Finally, set up a link between your local clone and the official repository (``upstream``):: $ cd mne-python - $ git remote add upstream git://github.com/mne-tools/mne-python.git + $ git remote add upstream https://github.com/mne-tools/mne-python.git $ git fetch --all Now we'll remove the *stable* version of MNE-Python and replace it with the @@ -233,7 +293,7 @@ the correct environment first (``conda activate mnedev``), and then do:: $ cd $INSTALL_LOCATION/mne-python # make sure we're in the right folder - $ pip uninstall -y mne + $ conda remove --force mne # the --force avoids dependency checking $ pip install -e . The command ``pip install -e .`` installs a python module into the current @@ -269,7 +329,7 @@ - On MacOS, optipng can be installed using Homebrew. - On Windows, unzip :file:`optipng.exe` from the `optipng for Windows`_ archive - into the :file:`doc/` folder. + into the :file:`doc/` folder. This step is optional for Windows users. You can also choose to install some optional linters for reStructuredText:: @@ -311,7 +371,8 @@ $ git merge upstream/main # synchronize local main branch with remote upstream main branch $ git checkout -b new-feature-x # create local branch "new-feature-x" and check it out -.. sidebar:: Alternative +.. note:: Alternative + :class: sidebar You can save some typing by using ``git pull upstream/main`` to replace the ``fetch`` and ``merge`` lines above. @@ -328,7 +389,7 @@ easier for their colleagues to understand and review the changes). For example, you might want to group all the code changes together in one commit, put new unit tests in another commit, and changes to the documentation in a third -commit. Git makes this easy(ish) with something called the `stage`_ (or +commit. Git makes this possible with something called the `stage`_ (or *staging area*). After you've made some changes to the codebase, you'll have what git calls "unstaged changes", which will show up with the `status`_ command:: @@ -450,7 +511,7 @@ Note that any new API elements should be added to the main reference; classes, functions, methods, and attributes cannot be cross-referenced unless -they are included in the :doc:`python_reference` +they are included in the :ref:`api_reference` (:file:`doc/python_reference.rst`). @@ -541,23 +602,21 @@ Welcome to MNE-Python! We're very happy to have you here. 🤗 And to ensure you get proper credit for your work, please add a changelog entry with the -following pattern **at the top** of the respective subsection (bugfix, -new feature etc.): +following pattern **at the top** of the respective subsection (bugs, +enhancements, etc.): .. code-block:: rst - Bug - --- - - .. |Your Name| replace:: **Your Name** + Bugs + ---- - - Short description of the changes (:gh:`0000` **by new contributor** |Your Name|_) + - Short description of the changes (:gh:`0000` by :newcontrib:`Firstname Lastname`) - ... where ``0000`` must be replaced with the respective GitHub pull request (PR) -number. +number, and ``Firstname Lastname`` must be replaced with your full name. It is usually best to wait to add a line to the changelog until your PR is finalized, to avoid merge conflicts (since the changelog is updated with @@ -565,7 +624,7 @@ Lastly, make sure that your name is included in the list of authors in :file:`doc/changes/names.inc`, otherwise the documentation build will fail. -To add an author name, simply append a line with the following pattern (note +To add an author name, append a line with the following pattern (note how the syntax is different from that used in the changelog): .. code-block:: rst @@ -589,7 +648,7 @@ number. Mind the Oxford comma in the case of multiple contributors. Sometimes, changes that shall appear as a single changelog entry are spread out -across multiple PRs. In this case, simply name all relevant PRs, separated by +across multiple PRs. In this case, name all relevant PRs, separated by commas: .. code-block:: rst @@ -786,7 +845,7 @@ All visualization functions must accept a boolean ``show`` parameter and typically return a :class:`matplotlib.figure.Figure` (or a list of :class:`~matplotlib.figure.Figure` objects). 3D visualization functions return -a :class:`mayavi.core.api.Scene`, :class:`surfer.Brain`, or other return type +a :class:`mne.viz.Figure3D`, :class:`mne.viz.Brain`, or other return type as appropriate. Visualization functions should default to the colormap ``RdBu_r`` for signed @@ -799,22 +858,19 @@ Running the test suite ~~~~~~~~~~~~~~~~~~~~~~ -Running the full test suite is as simple as running :: +.. note:: pytest flags + :class: sidebar - $ make test - -.. sidebar:: pytest flags - - The ``-x`` flag exits the pytest run as soon as the first test fails; this - can save some time if you are running an entire file's or module's worth of - tests instead of selecting just a single test as shown here. + The ``-x`` flag exits the pytest run when any test fails; this can speed + up debugging when running all tests in a file or module. The ``--pdb`` flag will automatically start the python debugger upon test failure. -from the ``mne-python`` root folder. Testing the entire module can be quite +The full test suite can be run by calling ``make test`` from the +``mne-python`` root folder. Testing the entire module can be quite slow, however, so to run individual tests while working on a new feature, you -can run, e.g.:: +can run the following line:: $ pytest mne/tests/test_evoked.py::test_io_evoked --verbose @@ -859,12 +915,16 @@ you expect). .. note:: - On Windows, to use the pattern approach, use the following two lines: + If you are using a *Windows command shell*, to use the pattern approach, + use the following two lines: - .. code-block:: python + .. code-block:: doscon - set PATTERN={} - make html_dev-pattern + > set PATTERN= + > make html_dev-pattern + + If you are on Windows but using the `git BASH`_ shell, use the same two + commands but replace ``set`` with ``export``. After either of these commands completes, ``make show`` will open the locally-rendered documentation site in your browser. Additional ``make`` @@ -910,7 +970,7 @@ working on MNE-Python will bear fruit when you contribute to other projects down the road. Here are the guidelines: -- Search the `MNE-Python issues page`_ (both open and closed issues) in case +- Search the `GitHub issues page`_ (both open and closed issues) in case someone else has already started work on the same bugfix or feature. If you don't find anything, `open a new issue`_ to discuss changes with maintainers before starting work on your proposed changes. @@ -932,7 +992,7 @@ for backports or maintenance bugfixes to the current stable version). - Don't forget to include in your PR a brief description of the change in the - :doc:`changelog ` (:file:`doc/whats_new.rst`). + :ref:`changelog ` (:file:`doc/whats_new.rst`). - Our community uses the following commit tags and conventions: @@ -957,13 +1017,13 @@ - ``[skip circle]`` Skip `CircleCI`_, which tests successful building of our documentation. - - ``[skip github]`` Skip our `GitHub Actions`_, which test installation + - ``[skip actions]`` Skip our `GitHub Actions`_, which test installation and execution on Linux and macOS systems. - ``[skip azp]`` Skip `azure`_ which tests installation and execution on Windows systems. - - ``[ci skip]`` is an alias for ``[skip github][skip azp][skip circle]``. + - ``[ci skip]`` is an alias for ``[skip actions][skip azp][skip circle]``. Notice that ``[skip ci]`` is not a valid tag. - ``[circle full]`` triggers a "full" documentation build, i.e., all code @@ -982,15 +1042,14 @@ .. MNE -.. _MNE-Python GitHub: https://github.com/mne-tools/mne-python -.. _MNE-Python issues page: https://github.com/mne-tools/mne-python/issues +.. _`GitHub issues marked "easy"`: https://github.com/mne-tools/mne-python/issues?q=is%3Aissue+is%3Aopen+label%3AEASY .. _open a new issue: https://github.com/mne-tools/mne-python/issues/new/choose .. _This sample pull request: https://github.com/mne-tools/mne-python/pull/6230 +.. _our user forum: https://mne.discourse.group .. git installation .. _the .dmg installer: https://git-scm.com/download/mac -.. _git for Windows: https://gitforwindows.org/ .. _official Linux instructions: https://git-scm.com/download/linux .. _more detailed instructions and alternatives: https://www.atlassian.com/git/tutorials/install-git .. _Windows subsystem for Linux: https://docs.microsoft.com/en-us/windows/wsl/about @@ -1000,8 +1059,6 @@ .. github help pages -.. _GitHub Help: https://help.github.com -.. _GitHub learning lab: https://lab.github.com/ .. _fork: https://help.github.com/en/articles/fork-a-repo .. _clone: https://help.github.com/en/articles/cloning-a-repository .. _push: https://help.github.com/en/articles/pushing-to-a-remote @@ -1025,29 +1082,23 @@ .. git book -.. _pro git book: https://git-scm.com/book/ .. _stage: https://git-scm.com/book/en/v2/Git-Tools-Interactive-Staging .. _configuring git: https://www.git-scm.com/book/en/v2/Customizing-Git-Git-Configuration .. sphinx -.. _sphinx: http://www.sphinx-doc.org -.. _sphinx-gallery: https://sphinx-gallery.github.io -.. _reStructuredText: http://sphinx-doc.org/rest.html -.. _intersphinx: http://www.sphinx-doc.org/en/master/usage/extensions/intersphinx.html +.. _reStructuredText: https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html +.. _intersphinx: https://www.sphinx-doc.org/en/master/usage/extensions/intersphinx.html .. _sphobjinv: https://sphobjinv.readthedocs.io/en/latest/ .. linting -.. _NumPy docstring style guidelines: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt .. _PEP 8: https://www.python.org/dev/peps/pep-0008/ -.. _pep8: https://pypi.org/project/pep8 .. _pyflakes: https://pypi.org/project/pyflakes .. _Flake8: http://flake8.pycqa.org/ .. misc -.. _anaconda: https://www.anaconda.com/distribution/ .. _miniconda: https://conda.io/en/latest/miniconda.html .. _Spyder: https://www.spyder-ide.org/ .. _continuous integration: https://en.wikipedia.org/wiki/Continuous_integration diff -Nru python-mne-0.23.4+dfsg/doc/install/index.rst python-mne-1.1.0+dfsg/doc/install/index.rst --- python-mne-0.23.4+dfsg/doc/install/index.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/install/index.rst 2022-08-03 15:48:14.000000000 +0000 @@ -1,52 +1,80 @@ -.. include:: ../links.inc +.. _install: +.. _quick-start: -Quick start -=========== +Installing MNE-Python +====================== -MNE-Python requires Python version |min_python_version| or higher. If you've -never worked with Python before, skip ahead to the last paragraph of this page. -For users already familiar with Python: -- If you only need 2D plotting capabilities with MNE-Python (i.e., most EEG/ERP - or other sensor-level analyses), you can install MNE-Python using ``pip``: +.. when https://github.com/executablebooks/sphinx-design/issues/66 is fixed, + prepend |cloud-download-alt| |ensp| to the "Download installers" button text + and |wrench| |ensp| to the "Setup instructions" button text - .. code-block:: console +.. grid:: 2 - $ pip install mne + .. grid-item-card:: + :text-align: center - The only hard dependencies are `NumPy`_ and `SciPy`_, though most users will - want to install `Matplotlib`_ too (for plotting). + .. rst-class:: font-weight-bold mb-0 -- If you need MNE-Python's 3D rendering capabilities (e.g., plotting estimated - source activity on a cortical surface) it is a good idea to install - MNE-Python into its own virtual environment. To do this with - `conda `_: + Standalone installers - .. code-block:: console + .. rst-class:: card-subtitle text-muted mt-0 - $ conda create --name=mne --channel=conda-forge mne - $ # ↑↑↑ ↑↑↑ - $ # environment name package name + For Beginners - This will create a new ``conda`` environment called ``mne``. - If you need to convert structural MRI scans into models of the scalp, - inner/outer skull, and cortical surfaces you also need - :doc:`FreeSurfer `. + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + .. image:: ../_static/mne_installer_macOS.png + :alt: MNE-Python Installer + + **New to Python?** Use our standalone installers that include + everything to get you started! + +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + + + .. button-ref:: installers + :ref-type: ref + :color: primary + :shadow: + :class: font-weight-bold + + Download Installers + + + .. grid-item-card:: + :text-align: center + + .. rst-class:: font-weight-bold mb-0 + + Install via ``pip`` or ``conda`` + + .. rst-class:: card-subtitle text-muted mt-0 + + For Advanced Users + + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + .. image:: ../_static/mne_installer_console.png + :alt: Terminal Window + + **Already familiar with Python?** + Follow our advanced setup instructions for ``pip`` and ``conda``! + +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + + .. button-ref:: manual-install + :ref-type: ref + :color: primary + :shadow: + :class: font-weight-bold + + Setup Instructions -For users unfamiliar with Python, the :ref:`standard_instructions` page has detailed instructions for different -operating systems, and there are instructions for :ref:`install-python` -if you don't already have it. The :ref:`advanced_setup` page has additional -tips and tricks for special situations (servers, notebooks, CUDA, installing -the development version, etc). The :ref:`contributing` has additional -installation instructions for (future) contributors to MNE-Python (e.g, extra -dependencies for running our tests and building our docs). .. toctree:: :hidden: - pre_install - install_python - mne_python + installers + manual_install + advanced + check_installation updating freesurfer - advanced + mne_tools_suite diff -Nru python-mne-0.23.4+dfsg/doc/install/installers.rst python-mne-1.1.0+dfsg/doc/install/installers.rst --- python-mne-0.23.4+dfsg/doc/install/installers.rst 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/install/installers.rst 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,133 @@ +.. include:: ../links.inc + +.. _installers: + +MNE-Python installers +===================== + +MNE-Python installers are the easiest way to install MNE-Python and +all dependencies. They also provide many additional +Python packages and tools, including the `Spyder`_ development environment. +Got any questions? Let us know on the `MNE Forum`_! + +.. tab-set:: + :class: platform-selector-tabset + + .. tab-item:: Linux + :class-content: text-center + + .. button-link:: https://github.com/mne-tools/mne-installers/releases/download/v1.0.3/MNE-Python-1.0.3_0-Linux.sh + :ref-type: ref + :color: primary + :shadow: + :class: font-weight-bold mt-3 + + |cloud-download-alt| |ensp| Download for Linux + + **Supported platforms:** Ubuntu 18.04 (Bionic Beaver) and newer + + Run the installer in a terminal via: + + .. code-block:: console + + $ sh ./MNE-Python-1.0.3_0-Linux.sh + + + .. tab-item:: macOS + :class-content: text-center + + .. button-link:: https://github.com/mne-tools/mne-installers/releases/download/v1.0.3/MNE-Python-1.0.3_0-macOS_Intel.pkg + :ref-type: ref + :color: primary + :shadow: + :class: font-weight-bold mt-3 + + |cloud-download-alt| |ensp| Download for macOS + + **Supported platforms:** + macOS 10.15 (Catalina) and newer (Intel and Apple Silicon) + + + .. tab-item:: Windows + :class-content: text-center + + .. button-link:: https://github.com/mne-tools/mne-installers/releases/download/v1.0.3/MNE-Python-1.0.3_0-Windows.exe + :ref-type: ref + :color: primary + :shadow: + :class: font-weight-bold mt-3 + + |cloud-download-alt| |ensp| Download for Windows + + **Supported platforms:** Windows 10 and newer + + +First steps +^^^^^^^^^^^ + +The installer adds menu entries on Linux and Windows, and several application +bundles to the ``Applications`` folder on macOS. + +.. rst-class:: list-unstyled +.. rst-class:: mx-5 +.. rst-class:: mt-4 +.. rst-class:: mb-5 + +- |code| |ensp| Use **Spyder** to start writing your own analysis scripts right away, or to run one of our examples from this website. + + .. rst-class:: mt-3 +- |desktop| |ensp| With **System Info**, list the versions of all installed MNE-Python-related packages. + + .. rst-class:: mt-3 +- |terminal| |ensp| The **Prompt** drops you into a command line interface with a properly activated MNE-Python environment. + + +.. note:: + |hourglass| |ensp| Depending on your system, it may take a little while for these + applications to start, especially on the very first run – which may take + particularly long on Apple Silicon-based computers. Subsequent runs should + usually be much faster. + + +VS Code Setup +^^^^^^^^^^^^^ + +If you want to use MNE-Python with `Visual Studio Code`_, you need to tell the +VS Code Python extension where to find the respective Python executable. To do +so, simply start the **Prompt**. It will display several lines of +information, including a line that will read something like: + +.. code-block:: + + Using Python: /some/directory/mne-python_1.0.0_1/bin/python + +This path is what you need to enter in VS Code when selecting the Python +interpreter. + +.. note:: + This information is currently not displayed on the Windows platform. + + +.. raw:: html + + diff -Nru python-mne-0.23.4+dfsg/doc/install/install_python.rst python-mne-1.1.0+dfsg/doc/install/install_python.rst --- python-mne-0.23.4+dfsg/doc/install/install_python.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/install/install_python.rst 1970-01-01 00:00:00.000000000 +0000 @@ -1,132 +0,0 @@ -.. include:: ../links.inc - -.. _install-python: - -Installing Python -================= - -MNE-Python requires Python and several Python packages. MNE-Python -version |version| requires Python version |min_python_version| or higher. We -recommend the `Anaconda`_ distribution of Python, which comes with more than -250 scientific packages pre-bundled and includes the ``conda`` command line -tool for installing new packages and managing different package sets -("environments") for different projects. - -To get started, follow the `installation instructions for Anaconda`_. -When you are done, if you type the following commands in a command shell, -you should see outputs similar to the following (assuming you installed -conda to ``/home/user/anaconda3``): - -.. collapse:: |linux| Linux - - .. code-block:: console - - $ conda --version && python --version - conda 4.9.2 - Python 3.7.7 :: Anaconda, Inc. - $ which python - /home/user/anaconda3/bin/python - $ which pip - /home/user/anaconda3/bin/pip - - -.. collapse:: |apple| macOS - - .. code-block:: console - - $ conda --version && python --version - conda 4.9.2 - Python 3.7.7 - $ which python - /Users/user/opt/anaconda3/bin/python - $ which pip - /Users/user/opt/anaconda3/bin/pip - - -.. collapse:: |windows| Windows - - Most of our instructions start with ``$``, which indicates - that the commands are designed to be run from a ``bash`` command shell. - - Windows command prompts do not expose the same command-line tools as - ``bash`` shells, so commands like ``which`` will not work. You can test - your installation in Windows ``cmd.exe`` shells with ``where`` instead: - - .. code-block:: doscon - - > where python - C:\Users\user\anaconda3\python.exe - > where pip - C:\Users\user\anaconda3\Scripts\pip.exe - -.. raw:: html - -
- -.. javascript below adapted from nilearn - -.. raw:: html - - - - -.. collapse:: |hand-paper| If you get an error or these look incorrect... - :class: danger - - .. rubric:: If you see something like: - - :: - - conda: command not found - - It means that your ``PATH`` variable (what the system uses to find - programs) is not set properly. In a correct installation, doing:: - - $ echo $PATH - ...:/home/user/anaconda3/bin:... - - Will show the Anaconda binary path (above) somewhere in the output - (probably at or near the beginning), but the ``command not found`` error - suggests that it is missing. - - On Linux or macOS, the installer should have put something - like the following in your ``~/.bashrc`` or ``~/.bash_profile`` (or your - ``.zprofile`` if you're using macOS Catalina or later, where the default - shell is ``zsh``): - - .. code-block:: bash - - # >>> conda initialize >>> - # !! Contents within this block are managed by 'conda init' !! - __conda_setup= ... - ... - # <<< conda initialize <<< - - If this is missing, it is possible that you are not on the same shell that - was used during the installation. You can verify which shell you are on by - using the command:: - - $ echo $SHELL - - If you do not find this line in the configuration file for the shell you - are using (bash, zsh, tcsh, etc.), try running:: - - conda init - - in your command shell. If your shell is not ``cmd.exe`` (Windows) or - ``bash`` (Linux, macOS) you will need to pass the name of the shell to the - ``conda init`` command. See ``conda init --help`` for more info and - supported shells. - - You can also consult the Anaconda documentation and search for - Anaconda install tips (`Stack Overflow`_ results are often helpful) - to fix these or other problems when ``conda`` does not work. diff -Nru python-mne-0.23.4+dfsg/doc/install/manual_install_python.rst python-mne-1.1.0+dfsg/doc/install/manual_install_python.rst --- python-mne-0.23.4+dfsg/doc/install/manual_install_python.rst 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/install/manual_install_python.rst 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,150 @@ +:orphan: + +.. include:: ../links.inc + +.. _install-python: + +Installing Python +^^^^^^^^^^^^^^^^^ + +MNE-Python requires Python and several Python packages. MNE-Python +version |version| requires Python version |min_python_version| or higher. We +recommend the `Anaconda`_ distribution of Python, which comes with more than +250 scientific packages pre-bundled and includes the ``conda`` command line +tool for installing new packages and managing different package sets +("environments") for different projects. + +To get started, follow the `installation instructions for Anaconda`_. +When you are done, if you type the following commands in a command shell, +you should see outputs similar to the following (assuming you installed +conda to ``/home/user/anaconda3``): + + +.. tab-set:: + :class: platform-selector-tabset + + .. tab-item:: Linux + + .. code-block:: console + + $ conda --version && python --version + conda 4.9.2 + Python 3.7.7 :: Anaconda, Inc. + $ which python + /home/user/anaconda3/bin/python + $ which pip + /home/user/anaconda3/bin/pip + + + .. tab-item:: macOS + + .. code-block:: console + + $ conda --version && python --version + conda 4.9.2 + Python 3.7.7 + $ which python + /Users/user/opt/anaconda3/bin/python + $ which pip + /Users/user/opt/anaconda3/bin/pip + + + .. tab-item:: Windows + + Most of our instructions start with ``$``, which indicates + that the commands are designed to be run from a ``bash`` command shell. + + Windows command prompts do not expose the same command-line tools as + ``bash`` shells, so commands like ``which`` will not work. You can test + your installation in Windows ``cmd.exe`` shells with ``where`` instead: + + .. code-block:: doscon + + > where python + C:\Users\user\anaconda3\python.exe + > where pip + C:\Users\user\anaconda3\Scripts\pip.exe + + +.. raw:: html + +
+ + +.. dropdown:: If you get an error... + :color: danger + :icon: alert-fill + + .. rubric:: If you see something like: + + :: + + conda: command not found + + It means that your ``PATH`` variable (what the system uses to find + programs) is not set properly. In a correct installation, doing:: + + $ echo $PATH + ...:/home/user/anaconda3/bin:... + + Will show the Anaconda binary path (above) somewhere in the output + (probably at or near the beginning), but the ``command not found`` error + suggests that it is missing. + + On Linux or macOS, the installer should have put something + like the following in your ``~/.bashrc`` or ``~/.bash_profile`` (or your + ``.zprofile`` if you're using macOS Catalina or later, where the default + shell is ``zsh``): + + .. code-block:: console + + # >>> conda initialize >>> + # !! Contents within this block are managed by 'conda init' !! + __conda_setup= ... + ... + # <<< conda initialize <<< + + If this is missing, it is possible that you are not on the same shell that + was used during the installation. You can verify which shell you are on by + using the command:: + + $ echo $SHELL + + If you do not find this line in the configuration file for the shell you + are using (bash, zsh, tcsh, etc.), try running:: + + conda init + + in your command shell. If your shell is not ``cmd.exe`` (Windows) or + ``bash`` (Linux, macOS) you will need to pass the name of the shell to the + ``conda init`` command. See ``conda init --help`` for more info and + supported shells. + + You can also consult the Anaconda documentation and search for + Anaconda install tips (`Stack Overflow`_ results are often helpful) + to fix these or other problems when ``conda`` does not work. + + +.. raw:: html + + diff -Nru python-mne-0.23.4+dfsg/doc/install/manual_install.rst python-mne-1.1.0+dfsg/doc/install/manual_install.rst --- python-mne-0.23.4+dfsg/doc/install/manual_install.rst 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/install/manual_install.rst 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,140 @@ +.. include:: ../links.inc + +.. _manual-install: +.. _standard-instructions: + +Install via :code:`pip` or :code:`conda` +======================================== + +.. hint:: + If you're unfamiliar with Python, we recommend using our :ref:`installers` + instead. + +MNE-Python requires Python version |min_python_version| or higher. If you +need to install Python, please see :ref:`install-python`. + +Installing MNE-Python with all dependencies +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +We suggest to install MNE-Python into its own ``conda`` environment. + +The dependency stack is large and may take a long time (several tens of +minutes) to resolve on some systems via the default ``conda`` solver. We +therefore highly recommend using `mamba `__ +instead, a ``conda`` replacement that is **much** faster. + +Run in your terminal: + +.. code-block:: console + + $ conda install --channel=conda-forge --name=base mamba + $ mamba create --override-channels --channel=conda-forge --name=mne mne + +This will create a new ``conda`` environment called ``mne`` (you can adjust +this by passing a different name via ``--name``) and install all +dependencies into it. + +If you need to convert structural MRI scans into models +of the scalp, inner/outer skull, and cortical surfaces, you will also need +:doc:`FreeSurfer `. + +Installing a minimal MNE-Python with core functionality only +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +If you only need MNE-Python's core functionality including 2D plotting (but +**without 3D visualization**), install via :code:`pip`: + +.. code-block:: console + + $ pip install mne + +or via :code:`conda`: + +.. code-block:: console + + $ conda create --strict-channel-priority --channel=conda-forge --name=mne mne-base + +This will create a new ``conda`` environment called ``mne`` (you can adjust +this by passing a different name via ``--name``). + +Installing a minimal MNE-Python with EEGLAB I/O support +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +If you plan to use MNE-Python's functions that require **HDF5 I/O** (this +includes :func:`mne.io.read_raw_eeglab`, :meth:`mne.SourceMorph.save`, and +others), you should run via :code:`pip`: + +.. code-block:: console + + $ pip install mne[hdf5] + +or via :code:`conda` + +.. code-block:: console + + $ conda create --strict-channel-priority --channel=conda-forge --name=mne mne-base h5io h5py pymatreader + +This will create a new ``conda`` environment called ``mne`` (you can adjust +this by passing a different name via ``--name``). + +Installing MNE-Python for other scenarios +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +The :ref:`advanced_setup` page has additional +tips and tricks for special situations (servers, notebooks, CUDA, installing +the development version, etc). The :ref:`contributing` has additional +installation instructions for (future) contributors to MNE-Python (e.g, extra +dependencies for running our tests and building our documentation). + +Python IDEs +=========== + +Most users find it convenient to write and run their code in an `Integrated +Development Environment`_ (IDE). Some popular choices for scientific +Python development are: + +- `Visual Studio Code`_ (often shortened to "VS Code" or "vscode") is a + development-focused text editor that supports many programming languages in + addition to Python, includes an integrated terminal console, and has a rich + ecosystem of packages to extend its capabilities. Installing + `Microsoft's Python Extension + `__ is + enough to get most Python users up and running. VS Code is free and + open-source. +- `Spyder`_ is a free and open-source IDE developed by and for scientists who + use Python. It is included by default in the ``base`` environment when you + install Anaconda, and can be started from a terminal with the command + ``spyder`` (or on Windows or macOS, launched from the Anaconda Navigator GUI). + It can also be installed with `dedicated installers `_. + To avoid dependency conflicts with Spyder, you should install ``mne`` in a + separate environment, like explained in the earlier sections. Then, set + Spyder to use the ``mne`` environment as its default interpreter by opening + Spyder and navigating to + :samp:`Tools > Preferences > Python Interpreter > Use the following interpreter`. + There, paste the output of the following terminal commands + + .. code-block:: console + + $ conda activate mne + $ python -c "import sys; print(sys.executable)" + + It should be something like ``C:\Users\user\anaconda3\envs\mne\python.exe`` + (Windows) or ``/Users/user/opt/anaconda3/envs/mne/bin/python`` (macOS). + + If the Spyder console can not start because ``spyder-kernels`` is missing, + install the required version in the ``mne`` environment with the following + commands in the terminal, where you replace ``...`` with the exact version of + ``spyder-kernels`` that Spyder tells you it requires. + + .. code-block:: console + + $ conda activate mne + $ conda install --strict-channel-priority --channel=conda-forge spyder-kernels=... + + Refer to the `Spyder documentation `_ + for more information about ``spyder-kernels`` and the version matching. + + If the Spyder graphic backend is not set to ``inline`` but to e.g. ``Qt5``, + ``PyQt5`` (``pip``) or ``pyqt`` (``conda``) must be installed in the ``mne`` + environment. + +- `PyCharm`_ is an IDE specifically for Python development that provides an + all-in-one installation (no extension packages needed). PyCharm comes in a + free "community" edition and a paid "professional" edition, and is + closed-source. diff -Nru python-mne-0.23.4+dfsg/doc/install/mne_python.rst python-mne-1.1.0+dfsg/doc/install/mne_python.rst --- python-mne-0.23.4+dfsg/doc/install/mne_python.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/install/mne_python.rst 1970-01-01 00:00:00.000000000 +0000 @@ -1,263 +0,0 @@ -.. include:: ../links.inc - -.. _standard_instructions: - -Installing MNE-Python -===================== - -.. highlight:: console - -Once you have Python/Anaconda installed, you have a few choices for how to -install MNE-Python. - -2D plotting and sensor-level analysis -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -If you only need 2D plotting capabilities with MNE-Python (i.e., most EEG/ERP -or other sensor-level analyses), you can install all you need by running -``pip install mne`` in a terminal window (on Windows, use the "Anaconda Prompt" -from the Start menu, or the "CMD.exe prompt" from within the Anaconda Navigator -GUI). This will install MNE-Python into the "base" conda environment, which -should be active by default and should already have the necessary dependencies -(``numpy``, ``scipy``, and ``matplotlib``). - -A second option is to install MNE-Python into its own virtual environment -(instead of installing into conda's "base" environment). This can be done via:: - - $ conda create --name=new_environment_name python=3 - $ conda activate new_environment_name - $ pip install mne - -This approach is a good choice if you want to keep a separate virtual -environment for each project. This helps with reproducibility, since each -project-specific environment will have a record of which versions of the -various software packages are installed in it (accessible with ``conda list``). - -3D plotting and source analysis -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -If you need MNE-Python's 3D rendering capabilities (e.g., plotting estimated -source activity on a cortical surface) it is best to install -MNE-Python into its own virtual environment, so that the extra dependencies -needed for 3D plotting stay in sync (i.e., they only get updated to versions -that are compatible with MNE-Python). See the detailed instructions below for -your operating system. - -.. collapse:: |linux| Linux - - Install MNE-Python from conda-forge:: - - $ conda create --name=mne --channel=conda-forge mne - - .. collapse:: |hand-paper| If you get errors building mayavi... - :class: danger - - Installing `mayavi`_ needs OpenGL support. On debian-like systems this - means installing ``libopengl0``, i.e., ``sudo apt install libopengl0``. - -.. collapse:: |apple| macOS - - Install MNE-Python into a new environment (here called ``mne``, but you can - name the environment whatever you want):: - - $ conda create --name=mne --channel=conda-forge mne - - If you like using Jupyter notebooks, you should also update the "base" - conda environment to include the ``nb_conda_kernels`` package; this will - make it easier to use MNE-Python in Jupyter Notebooks launched from the - Anaconda GUI:: - - $ conda install --name=base nb_conda_kernels - - -.. collapse:: |windows| Windows - - Open an Anaconda command prompt, and run: - - .. code-block:: doscon - - > conda create --name=mne --channel=conda-forge mne - - If you like using Jupyter notebooks, you should also update the "base" - conda environment to include the ``nb_conda_kernels`` package; this will - make it easier to use MNE-Python in Jupyter Notebooks launched from the - Anaconda GUI: - - .. code-block:: doscon - - > conda install --name base nb_conda_kernels - -.. raw:: html - -
- -.. javascript below adapted from nilearn - -.. raw:: html - - - -Installing to a headless server -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -.. collapse:: |server| If you are installing on a headless server... - :class: danger - - With `pyvista`_: - Download the `server environment file`_ and use it to create the conda - environment:: - - $ curl --remote-name https://raw.githubusercontent.com/mne-tools/mne-python/main/server_environment.yml - $ conda create --name=mne --file=server_environment.yml - - With `mayavi`_: - Installing `mayavi`_ requires a running `X server`_. If you are installing - MNE-Python into a computer with no display connected to it, you can try - downloading the :file:`environment.yml` file and removing `mayavi`_ from it - before running :samp:`conda create --name mne --file environment.yml`, then - activating the new environment, and then installing `mayavi`_ using `xvfb`_ - (e.g., :samp:`xvfb-run pip install mayavi`). Be sure to read Mayavi's - instructions on `off-screen rendering`_ and `rendering with a virtual - framebuffer`_. - - Note: if :samp:`xvfb` is not already installed on your server, you will - need administrator privileges to install it. - - -Testing your installation -^^^^^^^^^^^^^^^^^^^^^^^^^ - -To make sure MNE-Python installed itself and its dependencies correctly, -type the following command in a terminal:: - - $ python -c "import mne; mne.sys_info()" - -This should display some system information along with the versions of -MNE-Python and its dependencies. Typical output looks like this:: - - Platform: Linux-5.0.0-1031-gcp-x86_64-with-glibc2.2.5 - Python: 3.8.1 (default, Dec 20 2019, 10:06:11) [GCC 7.4.0] - Executable: /home/travis/virtualenv/python3.8.1/bin/python - CPU: x86_64: 2 cores - Memory: 7.8 GB - - mne: 0.21.dev0 - numpy: 1.19.0.dev0+8dfaa4a {blas=openblas, lapack=openblas} - scipy: 1.5.0.dev0+f614064 - matplotlib: 3.2.1 {backend=Qt5Agg} - - sklearn: 0.22.2.post1 - numba: 0.49.0 - nibabel: 3.1.0 - cupy: Not found - pandas: 1.0.3 - dipy: 1.1.1 - mayavi: 4.7.2.dev0 - pyvista: 0.25.2 {pyvistaqt=0.1.0} - vtk: 9.0.0 - PyQt5: 5.14.1 - - -.. collapse:: |hand-paper| If you get an error... - :class: danger - - .. rubric:: If you see an error like: - - :: - - Traceback (most recent call last): - File "", line 1, in - ModuleNotFoundError: No module named 'mne' - - This suggests that your environment containing MNE-Python is not active. - If you followed the setup for 3D plotting/source analysis (i.e., you - installed to a new ``mne`` environment instead of the ``base`` environment) - try running ``conda activate mne`` first, and try again. If this works, - you might want to set your terminal to automatically activate the - ``mne`` environment each time you open a terminal:: - - $ echo conda activate mne >> ~/.bashrc # for bash shells - $ echo conda activate mne >> ~/.zprofile # for zsh shells - -If something else went wrong during installation and you can't figure it out, -check out the :doc:`advanced` page to see if your problem is discussed there. -If not, the `MNE Forum`_ is a good resources for troubleshooting installation -problems. - - -Python IDEs -^^^^^^^^^^^ - -Most users find it convenient to write and run their code in an `Integrated -Development Environment`_ (IDE). Some popular choices for scientific -Python development are: - -- `Spyder`_ is a free and open-source IDE developed by and for scientists who - use Python. It is included by default in the ``base`` environment when you - install Anaconda, and can be started from a terminal with the command - ``spyder`` (or on Windows or macOS, launched from the Anaconda Navigator GUI). - If you use Spyder, you should *not* install it into the ``mne`` environment; - instead, launch Spyder from the ``base`` environment and set it to use the - ``mne`` environment automatically, by opening Spyder and navigating to - :samp:`Tools > Preferences > Python Interpreter > Use the following interpreter`. - There, paste the output of the following terminal commands:: - - $ conda activate mne - $ python -c "import sys; print(sys.executable)" - - It should be something like ``C:\Users\user\anaconda3\envs\mne\python.exe`` - (Windows) or ``/Users/user/opt/anaconda3/envs/mne/bin/python`` (macOS). -- `Visual Studio Code`_ (often shortened to "VS Code" or "vscode") is a - development-focused text editor that supports many programming languages in - addition to Python, includes an integrated terminal console, and has a rich - ecosystem of packages to extend its capabilities. Installing - `Microsoft's Python Extension - `__ is - enough to get most Python users up and running. VS Code is free and - open-source. -- `Atom`_ is a text editor similar to vscode, with a package ecosystem that - includes a `Python IDE package `__ as - well as `several `__ - `packages `__ - `for `__ - `integrated `__ - `terminals `__. Atom is free and open-source. -- `SublimeText`_ is a general-purpose text editor that is fast and lightweight, - and also has a rich package ecosystem. There is a package called `Terminus`_ - that provides an integrated terminal console, and a (confusingly named) - package called "anaconda" - (`found here `__) that provides - many Python-specific features. SublimeText is free (closed-source shareware). -- `PyCharm`_ is an IDE specifically for Python development that provides an - all-in-one installation (no extension packages needed). PyCharm comes in a - free "community" edition and a paid "professional" edition, and is - closed-source. - -.. highlight:: python - -.. LINKS - -.. _environment file: https://raw.githubusercontent.com/mne-tools/mne-python/main/environment.yml -.. _server environment file: https://raw.githubusercontent.com/mne-tools/mne-python/main/server_environment.yml -.. _`mayavi`: https://docs.enthought.com/mayavi/mayavi/ -.. _`pyvista`: https://docs.pyvista.org/ -.. _`X server`: https://en.wikipedia.org/wiki/X_Window_System -.. _`xvfb`: https://en.wikipedia.org/wiki/Xvfb -.. _`off-screen rendering`: https://docs.enthought.com/mayavi/mayavi/tips.html#off-screen-rendering -.. _`rendering with a virtual framebuffer`: https://docs.enthought.com/mayavi/mayavi/tips.html#rendering-using-the-virtual-framebuffer -.. _`integrated development environment`: https://en.wikipedia.org/wiki/Integrated_development_environment -.. _`spyder`: https://www.spyder-ide.org/ -.. _`visual studio code`: https://code.visualstudio.com/ -.. _`sublimetext`: https://www.sublimetext.com/ -.. _`terminus`: https://packagecontrol.io/packages/Terminus -.. _`pycharm`: https://www.jetbrains.com/pycharm/ -.. _`atom`: https://atom.io/ diff -Nru python-mne-0.23.4+dfsg/doc/install/mne_tools_suite.rst python-mne-1.1.0+dfsg/doc/install/mne_tools_suite.rst --- python-mne-0.23.4+dfsg/doc/install/mne_tools_suite.rst 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/install/mne_tools_suite.rst 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,114 @@ +.. include:: ../links.inc + +Overview of the MNE tools suite +=============================== + +MNE-Python is an open-source Python module for processing, analysis, and +visualization of functional neuroimaging data (EEG, MEG, sEEG, ECoG, and +fNIRS). There are several related or interoperable software packages that you +may also want to install, depending on your analysis needs. + +Related software +^^^^^^^^^^^^^^^^ + +- MNE-C was the initial stage of this project, + providing a set of interrelated command-line and GUI programs focused on + computing cortically constrained Minimum Norm Estimates from MEG and EEG + data. These tools were written in C by Matti Hämäläinen, and are + documented `here `_. See :ref:`install_mne_c` for installation + instructions. + +- MNE-Python reimplements the functionality of MNE-C, extends considerably the + analysis and visualization capabilities, and adds support for additional data + types like functional near-infrared spectroscopy (fNIRS). MNE-Python is + collaboratively developed and has more than 200 contributors. + +- :ref:`MNE MATLAB ` provides a MATLAB interface to the .fif file + format and other MNE data structures, and provides example MATLAB + implementations of some of the core analysis functionality of MNE-C. It is + distributed alongside MNE-C, and can also be downloaded from the `MNE-MATLAB + git repository`_. + +- :ref:`MNE-CPP ` provides core MNE functionality implemented in + C++ and is primarily intended for embedded and real-time applications. + +There is also a growing ecosystem of other Python packages that work alongside +MNE-Python, including packages for: + +.. note:: Something missing? + :class: sidebar + + If you know of a package that is related but not listed here, feel free to + :ref:`make a pull request ` to add it to this list. + +- a graphical user interface for MNE-Python (`MNELAB`_) +- easily importing MEG data from the Human Connectome Project for + use with MNE-Python (`MNE-HCP`_) +- managing MNE projects so that they comply with the `Brain + Imaging Data Structure`_ specification (`MNE-BIDS`_) +- automatic bad channel detection and interpolation (`autoreject`_) +- convolutional sparse dictionary learning and waveform shape estimation + (`alphaCSC`_) +- independent component analysis (ICA) with good performance on real data + (`PICARD`_) +- phase-amplitude coupling (`pactools`_) +- representational similarity analysis (`rsa`_) +- microstate analysis (`microstate`_) +- connectivity analysis using dynamic imaging of coherent sources (DICS) + (`conpy`_) +- general-purpose statistical analysis of M/EEG data (`eelbrain`_) +- post-hoc modification of linear models (`posthoc`_) +- a python implementation of the Preprocessing Pipeline (PREP) for EEG data + (`pyprep`_) +- automatic multi-dipole localization and uncertainty quantification with + the Bayesian algorithm SESAME (`sesameeg`_) +- GLM and group level analysis of near-infrared spectroscopy data (`mne-nirs`_) +- M/EEG inverse solutions using artificial neural networks (`ESINet`_) +- All-Resolutions Inference (ARI) for statistically valid circular inference + and effect localization (`MNE-ARI`_) + + +What should I install? +^^^^^^^^^^^^^^^^^^^^^^ + +If you intend only to perform ERP, ERF, or other sensor-level analyses, +:ref:`MNE-Python ` is all you need. If you prefer to +work with +shell scripts and the Unix command line, or prefer MATLAB over Python, probably +all you need is :doc:`MNE-C ` — the MNE MATLAB toolbox is distributed +with it — although note that the C tools and the MATLAB toolbox are less +actively developed than the MNE-Python module, and hence are considerably less +feature-complete. + +If you want to transform sensor recordings into estimates of localized brain +activity, you will need MNE-Python, plus :doc:`FreeSurfer ` to +convert structural MRI scans into models of the scalp, inner/outer skull, and +cortical surfaces (specifically, for command-line functions +:ref:`mne flash_bem`, :ref:`mne watershed_bem`, and +:ref:`mne make_scalp_surfaces`). + + +Getting help +^^^^^^^^^^^^ + +Help with installation is available through the `MNE Forum`_. See the +:ref:`help` page for more information. + + +.. LINKS: + +.. _MNELAB: https://github.com/cbrnr/mnelab +.. _autoreject: https://autoreject.github.io/ +.. _alphaCSC: https://alphacsc.github.io/ +.. _picard: https://pierreablin.github.io/picard/ +.. _pactools: https://pactools.github.io/ +.. _rsa: https://github.com/wmvanvliet/mne-rsa +.. _microstate: https://github.com/wmvanvliet/mne_microstates +.. _conpy: https://aaltoimaginglanguage.github.io/conpy/ +.. _eelbrain: https://eelbrain.readthedocs.io/en/stable/index.html +.. _posthoc: https://users.aalto.fi/~vanvlm1/posthoc/python/ +.. _pyprep: https://github.com/sappelhoff/pyprep +.. _sesameeg: https://pybees.github.io/sesameeg +.. _mne-nirs: https://github.com/mne-tools/mne-nirs +.. _ESINet: https://github.com/LukeTheHecker/ESINet +.. _MNE-ARI: https://github.com/john-veillette/mne_ari diff -Nru python-mne-0.23.4+dfsg/doc/install/pre_install.rst python-mne-1.1.0+dfsg/doc/install/pre_install.rst --- python-mne-0.23.4+dfsg/doc/install/pre_install.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/install/pre_install.rst 1970-01-01 00:00:00.000000000 +0000 @@ -1,107 +0,0 @@ -.. include:: ../links.inc - -Overview of the MNE tools suite -=============================== - -MNE-Python is an open-source Python module for processing, analysis, and -visualization of functional neuroimaging data (EEG, MEG, sEEG, ECoG, and -fNIRS). There are several related or interoperable software packages that you -may also want to install, depending on your analysis needs. - -Related software -^^^^^^^^^^^^^^^^ - -- MNE-C was the initial stage of this project, - providing a set of interrelated command-line and GUI programs focused on - computing cortically constrained Minimum Norm Estimates from MEG and EEG - data. These tools were written in C by Matti Hämäläinen, and are - documented `here `_. See :ref:`install_mne_c` for installation - instructions. - -- MNE-Python reimplements the functionality of MNE-C, extends considerably the - analysis and visualization capabilities, and adds support for additional data - types like functional near-infrared spectroscopy (fNIRS). MNE-Python is - collaboratively developed and has more than 200 contributors. - -- :ref:`MNE MATLAB ` provides a MATLAB interface to the .fif file - format and other MNE data structures, and provides example MATLAB - implementations of some of the core analysis functionality of MNE-C. It is - distributed alongside MNE-C, and can also be downloaded from the `MNE-MATLAB - git repository`_. - -- :ref:`MNE-CPP ` provides core MNE functionality implemented in - C++ and is primarily intended for embedded and real-time applications. - -There is also a growing ecosystem of other Python packages that work alongside -MNE-Python, including packages for: - -.. sidebar:: Something missing? - - If you know of a package that is related but not listed here, feel free to - :ref:`make a pull request ` to add it to this list. - -- a graphical user interface for MNE-Python (`MNELAB`_) -- easily importing MEG data from the Human Connectome Project for - use with MNE-Python (`MNE-HCP`_) -- managing MNE projects so that they comply with the `Brain - Imaging Data Structure`_ specification (`MNE-BIDS`_) -- automatic bad channel detection and interpolation (`autoreject`_) -- convolutional sparse dictionary learning and waveform shape estimation - (`alphaCSC`_) -- independent component analysis (ICA) with good performance on real data - (`PICARD`_) -- phase-amplitude coupling (`pactools`_) -- representational similarity analysis (`rsa`_) -- microstate analysis (`microstate`_) -- connectivity analysis using dynamic imaging of coherent sources (DICS) - (`conpy`_) -- general-purpose statistical analysis of M/EEG data (`eelbrain`_) -- post-hoc modification of linear models (`posthoc`_) -- a python implementation of the Preprocessing Pipeline (PREP) for EEG data - (`pyprep`_) -- automatic multi-dipole localization and uncertainty quantification with - the Bayesian algorithm SESAME (`sesameeg`_) -- GLM and group level analysis of near-infrared spectroscopy data (`mne-nirs`_) - - -What should I install? -^^^^^^^^^^^^^^^^^^^^^^ - -If you intend only to perform ERP, ERF, or other sensor-level analyses, -:doc:`MNE-Python ` is all you need. If you prefer to work with -shell scripts and the Unix command line, or prefer MATLAB over Python, probably -all you need is :doc:`MNE-C ` — the MNE MATLAB toolbox is distributed -with it — although note that the C tools and the MATLAB toolbox are less -actively developed than the MNE-Python module, and hence are considerably less -feature-complete. - -If you want to transform sensor recordings into estimates of localized brain -activity, you will need MNE-Python, plus :doc:`FreeSurfer ` to -convert structural MRI scans into models of the scalp, inner/outer skull, and -cortical surfaces (specifically, for command-line functions -:ref:`mne flash_bem`, :ref:`mne watershed_bem`, and -:ref:`mne make_scalp_surfaces`). - - -Getting help -^^^^^^^^^^^^ - -Help with installation is available through the `MNE Forum`_. See the -:ref:`help` page for more information. - - -.. LINKS: - -.. _MNELAB: https://github.com/cbrnr/mnelab -.. _autoreject: https://autoreject.github.io/ -.. _alphaCSC: https://alphacsc.github.io/ -.. _picard: https://pierreablin.github.io/picard/ -.. _pactools: https://pactools.github.io/ -.. _rsa: https://github.com/wmvanvliet/rsa -.. _microstate: https://github.com/wmvanvliet/mne_microstates -.. _conpy: https://aaltoimaginglanguage.github.io/conpy/ -.. _eelbrain: https://eelbrain.readthedocs.io/en/stable/index.html -.. _posthoc: https://users.aalto.fi/~vanvlm1/posthoc/python/ -.. _pyprep: https://github.com/sappelhoff/pyprep -.. _sesameeg: https://pybees.github.io/sesameeg -.. _mne-nirs: https://github.com/mne-tools/mne-nirs diff -Nru python-mne-0.23.4+dfsg/doc/install/updating.rst python-mne-1.1.0+dfsg/doc/install/updating.rst --- python-mne-0.23.4+dfsg/doc/install/updating.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/install/updating.rst 2022-08-03 15:48:14.000000000 +0000 @@ -4,6 +4,14 @@ If you want to update MNE-Python to a newer version, there are a few different options, depending on how you originally installed it. +.. hint:: + To update via the :ref:`MNE-Python installers `, simply + download and run the latest installer for your platform. MNE-Python will be + installed in parallel to your existing installation, which you may uninstall + or delete if you don't need it anymore. + +If you're not using the MNE-Python installers, keep reading. + .. warning:: Before performing package upgrade operations, check to make sure that the @@ -64,7 +72,10 @@ Upgrading to the development version ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. warning:: In between releases, function and class APIs can change without +.. warning:: + :class: sidebar + + In between releases, function and class APIs can change without warning. Sometimes, new features or bugfixes become available that are important to your diff -Nru python-mne-0.23.4+dfsg/doc/inverse.rst python-mne-1.1.0+dfsg/doc/inverse.rst --- python-mne-0.23.4+dfsg/doc/inverse.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/inverse.rst 2022-08-03 15:48:14.000000000 +0000 @@ -72,7 +72,6 @@ apply_dics_csd apply_dics_epochs rap_music - tf_dics make_lcmv_resolution_matrix .. currentmodule:: mne diff -Nru python-mne-0.23.4+dfsg/doc/links.inc python-mne-1.1.0+dfsg/doc/links.inc --- python-mne-0.23.4+dfsg/doc/links.inc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/links.inc 2022-08-03 15:48:14.000000000 +0000 @@ -11,18 +11,22 @@ .. mne -.. _`mne-python`: http://mne-tools.github.io/mne-python-intro -.. _`mne-python GitHub`: https://github.com/mne-tools/mne-python -.. _`mne-python sample dataset`: https://s3.amazonaws.com/mne-python/datasets/MNE-sample-data-processed.tar.gz -.. _`mne command line utilities`: http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/MNE_register/ +.. _`MNE-Python`: http://mne.tools/mne-python-intro +.. _`MNE-Python GitHub`: https://github.com/mne-tools/mne-python .. _`mne-scripts`: https://github.com/mne-tools/mne-scripts/ .. _`MNE-C manual`: https://mne.tools/mne-c-manual/MNE-manual-2.7.3.pdf .. _`GitHub issues page`: https://github.com/mne-tools/mne-python/issues/ .. _`MNE Forum`: https://mne.discourse.group -.. _`MNE-BIDS`: https://mne-tools.github.io/mne-bids/ -.. _`MNE-HCP`: http://mne-tools.github.io/mne-hcp/ -.. _`MNE-Realtime`: https://github.com/mne-tools/mne-realtime +.. _`MNE-BIDS`: https://mne.tools/mne-bids +.. _`MNE-BIDS-Pipeline`: https://mne.tools/mne-bids-pipeline +.. _`MNE-HCP`: http://mne.tools/mne-hcp +.. _`MNE-Realtime`: https://mne.tools/mne-realtime .. _`MNE-MATLAB git repository`: https://github.com/mne-tools/mne-matlab +.. _`MNE-Docker`: https://github.com/mne-tools/mne-docker +.. _EOSS2: https://chanzuckerberg.com/eoss/proposals/improving-usability-of-core-neuroscience-analysis-tools-with-mne-python +.. _EOSS4: https://chanzuckerberg.com/eoss/proposals/building-pediatric-and-clinical-data-pipelines-for-mne-python/ +.. _`code of conduct`: https://github.com/mne-tools/.github/blob/main/CODE_OF_CONDUCT.md + .. TUTORIAL LINKS @@ -44,7 +48,6 @@ .. _numpy: http://www.numpy.org .. _`numpy github`: http://github.com/numpy/numpy -.. _`numpy style`: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt .. _`numpy mailing list`: http://mail.scipy.org/mailman/listinfo/numpy-discussion @@ -54,6 +57,12 @@ .. _`scipy github`: http://github.com/scipy/scipy .. _`scipy mailing list`: http://mail.scipy.org/mailman/listinfo/scipy-dev +.. freesurfer + +.. _freesurfer: https://surfer.nmr.mgh.harvard.edu/ +.. _`freesufer github`: https://github.com/freesurfer/freesurfer +.. _`freesurfer mailing list`: https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferSupport + .. nipy @@ -90,19 +99,6 @@ .. _`h5py mailing list`: https://groups.google.com/forum/#!forum/h5py -.. marsbar - -.. _marsbar: http://marsbar.sourceforge.net -.. _`marsbar github`: http://github.com/matthew-brett/marsbar -.. _`MarsBaR mailing list`: https://lists.sourceforge.net/lists/listinfo/marsbar-users - - -.. Astropy - -.. _Astropy: http://www.astropy.org -.. _`Astropy GitHub`: http://github.com/astropy/astropy - - .. Pytest .. _pytest: https://docs.pytest.org/ @@ -118,31 +114,28 @@ .. _pymatreader: https://gitlab.com/obob/pymatreader +.. h5io + +.. _h5io: https://github.com/h5io/h5io + + .. CuPy .. _CuPy: https://cupy.chainer.org/ +.. Dask + +.. _Dask: https://dask.org/ .. git stuff .. _git: https://git-scm.com/ .. _github: https://github.com .. _GitHub Help: https://help.github.com -.. _msysgit: http://code.google.com/p/msysgit/downloads/list -.. _git-osx-installer: http://code.google.com/p/git-osx-installer/downloads/list -.. _subversion: http://subversion.tigris.org/ -.. _git cheat sheet: http://github.com/guides/git-cheat-sheet -.. _pro git book: http://progit.org/ -.. _git svn crash course: http://git-scm.com/course/svn.html -.. _learn.github: http://learn.github.com/ -.. _network graph visualizer: https://github.com/blog/39-say-hello-to-the-network-graph-visualizer -.. _git user manual: https://schacon.github.io/git/user-manual.html -.. _git tutorial: https://schacon.github.io/git/gittutorial.html -.. _git community book: http://book.git-scm.com/ -.. _git ready: http://www.gitready.com/ -.. _git casts: http://www.gitcasts.com/ -.. _git magic: http://www-cs-students.stanford.edu/~blynn/gitmagic/index.html -.. _git concepts: http://www.eecs.harvard.edu/~cduan/technical/git/ +.. _GitHub learning lab: https://lab.github.com/ +.. _pro git book: https://git-scm.com/book/ +.. _git bash: https://gitforwindows.org/ +.. _git for Windows: https://gitforwindows.org/ .. _git clone: http://schacon.github.com/git/git-clone.html .. _git checkout: https://schacon.github.io/git/git-checkout.html .. _git commit: https://schacon.github.io/git/git-commit.html @@ -156,24 +149,10 @@ .. _git remote: https://schacon.github.io/git/git-remote.html .. _git rebase: https://schacon.github.io/git/git-rebase.html .. _git config: https://schacon.github.io/git/git-config.html -.. _git staging area: http://www.gitready.com/beginner/2009/01/18/the-staging-area.html -.. _tangled working copy problem: http://tomayko.com/writings/the-thing-about-git -.. _git management: http://kerneltrap.org/Linux/Git_Management -.. _linux git workflow: http://www.mail-archive.com/dri-devel@lists.sourceforge.net/msg39091.html -.. _git parable: http://tom.preston-werner.com/2009/05/19/the-git-parable.html -.. _git foundation: http://matthew-brett.github.io/pydagogue/foundation.html -.. _deleting master on github: http://matthew-brett.github.io/pydagogue/gh_delete_master.html -.. _rebase without tears: http://matthew-brett.github.io/pydagogue/rebase_without_tears.html -.. _resolving a merge: https://schacon.github.io/git/user-manual.html#resolving-a-merge -.. _ipython git workflow: https://web.archive.org/web/*/http://mail.scipy.org/pipermail/ipython-dev/2010-October/006746.html -.. _why the -a flag?: http://www.gitready.com/beginner/2009/01/18/the-staging-area.html -.. _Fernando's git page: http://www.fperez.org/py4science/git.html .. other stuff .. _python: http://www.python.org -.. _travis: https://travis-ci.org/ -.. _linterflake8: https://atom.io/packages/linter-flake8 .. _Brain Imaging Data Structure: https://bids.neuroimaging.io/ .. python packages @@ -185,25 +164,28 @@ .. _nitime: http://nipy.org/nitime/ .. _joblib: https://pypi.python.org/pypi/joblib .. _scikit-learn: https://scikit-learn.org/stable/ -.. _pysurfer: https://pysurfer.github.io/ .. _pyDICOM: https://pypi.python.org/pypi/pydicom/ .. _matplotlib: https://matplotlib.org/ -.. _sphinx: http://sphinx-doc.org/ -.. _pandas: http://pandas.pydata.org/ +.. _sphinx: https://www.sphinx-doc.org/ +.. _pandas: https://pandas.pydata.org/ .. _PIL: https://pypi.python.org/pypi/PIL +.. _tqdm: https://tqdm.github.io/ +.. _pooch: https://www.fatiando.org/pooch/latest/ .. python editors -.. _atom: https://atom.io/ .. _Spyder: https://www.spyder-ide.org/ -.. _anaconda: https://www.anaconda.com/distribution/ +.. _`integrated development environment`: https://en.wikipedia.org/wiki/Integrated_development_environment +.. _`spyder`: https://www.spyder-ide.org/ +.. _`visual studio code`: https://code.visualstudio.com/ +.. _`pycharm`: https://www.jetbrains.com/pycharm/ + +.. _anaconda: https://www.anaconda.com/products/individual .. _miniconda: https://conda.io/en/latest/miniconda.html .. _miniforge: https://github.com/conda-forge/miniforge .. _installation instructions for Anaconda: http://docs.continuum.io/anaconda/install .. _installation instructions for Miniconda: https://conda.io/projects/conda/en/latest/user-guide/install/index.html .. _Anaconda troubleshooting guide: http://conda.pydata.org/docs/troubleshooting.html -.. _EPD: https://www.enthought.com/products/epd/ -.. _sublime text: http://www.sublimetext.com/ .. installation links @@ -211,9 +193,9 @@ .. _NVIDIA CUDA GPU processing: https://developer.nvidia.com/cuda-zone .. _NVIDIA proprietary drivers: https://www.geforce.com/drivers -.. _Sphinx documentation: http://sphinx-doc.org/rest.html -.. _sphinx gallery documentation: https://sphinx-gallery.github.io -.. _NumPy docstring standard: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt +.. _Sphinx documentation: https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html +.. _sphinx-gallery: https://sphinx-gallery.github.io +.. _NumPy docstring style guidelines: https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard .. _Stack Overflow: https://stackoverflow.com/ .. vim: ft=rst diff -Nru python-mne-0.23.4+dfsg/doc/logging.rst python-mne-1.1.0+dfsg/doc/logging.rst --- python-mne-0.23.4+dfsg/doc/logging.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/logging.rst 2022-08-03 15:48:14.000000000 +0000 @@ -15,6 +15,7 @@ set_config set_cache_dir sys_info + use_log_level verbose :py:mod:`mne.utils`: diff -Nru python-mne-0.23.4+dfsg/doc/Makefile python-mne-1.1.0+dfsg/doc/Makefile --- python-mne-0.23.4+dfsg/doc/Makefile 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/Makefile 2022-08-03 15:48:14.000000000 +0000 @@ -80,7 +80,7 @@ @echo "Build finished. The HTML pages are in _build/html_stable." html_dev-front: - @PATTERN="\(plot_mne_dspm_source_localization.py\|plot_receptive_field.py\|plot_mne_inverse_label_connectivity.py\|plot_sensors_decoding.py\|plot_stats_cluster_spatio_temporal.py\|plot_20_visualize_evoked.py\)" make html_dev-pattern; + @PATTERN="\(30_mne_dspm_loreta.py\|50_decoding.py\|30_strf.py\|20_cluster_1samp_spatiotemporal.py\|20_visualize_evoked.py\)" make html_dev-pattern; dirhtml: $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) _build/dirhtml diff -Nru python-mne-0.23.4+dfsg/doc/mri.rst python-mne-1.1.0+dfsg/doc/mri.rst --- python-mne-0.23.4+dfsg/doc/mri.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/mri.rst 2022-08-03 15:48:14.000000000 +0000 @@ -15,10 +15,24 @@ :toctree: generated/ coreg.get_mni_fiducials + coreg.estimate_head_mri_t + io.read_fiducials + io.write_fiducials + get_montage_volume_labels gui.coregistration - gui.fiducials + gui.locate_ieeg create_default_subject + head_to_mni + head_to_mri + read_freesurfer_lut + read_lta + read_talxfm scale_mri scale_bem scale_labels scale_source_space + transforms.apply_volume_registration + transforms.compute_volume_registration + vertex_to_mni + warp_montage_volume + coreg.Coregistration diff -Nru python-mne-0.23.4+dfsg/doc/overview/cite.rst python-mne-1.1.0+dfsg/doc/overview/cite.rst --- python-mne-0.23.4+dfsg/doc/overview/cite.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/overview/cite.rst 2022-08-03 15:48:14.000000000 +0000 @@ -13,8 +13,9 @@ .. footbibliography:: -.. collapse:: |quote-left| BibTeX for MNE-Python - :class: info +.. dropdown:: BibTeX for MNE-Python + :color: info + :icon: typography .. include:: ../references.bib :code: bibtex @@ -31,8 +32,9 @@ .. footbibliography:: -.. collapse:: |quote-left| BibTeX for inverse algorithms / MNE-C - :class: info +.. dropdown:: BibTeX for inverse algorithms / MNE-C + :color: info + :icon: typography .. include:: ../references.bib :code: bibtex diff -Nru python-mne-0.23.4+dfsg/doc/overview/cookbook.rst python-mne-1.1.0+dfsg/doc/overview/cookbook.rst --- python-mne-0.23.4+dfsg/doc/overview/cookbook.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/overview/cookbook.rst 2022-08-03 15:48:14.000000000 +0000 @@ -87,7 +87,7 @@ distributions generated by the sources in the brain have spatial distributions sufficiently different from those generated by external noise sources. Furthermore, it is implicitly assumed that the linear -space spanned by the significant external noise patters has a low +space spanned by the significant external noise patterns has a low dimension. SSP-based rejection is often done using the @@ -314,7 +314,7 @@ digitization data are employed. The corregistration is stored in ``-trans.fif`` file. If is present, -you can follow :ref:`plot_source_alignment` to validate its correctness. +you can follow :ref:`tut-source-alignment` to validate its correctness. If the ``-trans.fif`` is not present or the alignment is not correct you need to use :func:`mne.gui.coregistration` (or its convenient command line equivalent :ref:`mne coreg`) to generate it. diff -Nru python-mne-0.23.4+dfsg/doc/overview/datasets_index.rst python-mne-1.1.0+dfsg/doc/overview/datasets_index.rst --- python-mne-0.23.4+dfsg/doc/overview/datasets_index.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/overview/datasets_index.rst 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,8 @@ Datasets Overview ################# -.. sidebar:: Contributing datasets to MNE-Python +.. note:: Contributing datasets to MNE-Python + :class: sidebar Do not hesitate to contact MNE-Python developers on the `MNE Forum `_ to discuss the possibility of @@ -121,7 +122,7 @@ .. topic:: Examples - * :ref:`ex-envelope-correlation` + * :ref:`mne-connectivity:ex-envelope-correlation` Median nerve ^^^^^^^^^^^^ @@ -277,7 +278,7 @@ .. topic:: Examples - * :ref:`tut_phantom_4Dbti` + * :ref:`tut-phantom-4Dbti` OPM === @@ -368,19 +369,28 @@ ECoG Dataset ^^^^^^^^^^^^ -:func:`mne.datasets.misc.data_path`. Data exists at ``/ecog/sample_ecog.mat``. +:func:`mne.datasets.misc.data_path`. Data exists at ``/ecog/``. -This dataset contains a sample Electrocorticography (ECoG) dataset. It includes -a single grid of electrodes placed over the temporal lobe during an auditory -listening task. This dataset is primarily used to demonstrate visualization -functions in MNE and does not contain useful metadata for analysis. +This dataset contains a sample electrocorticography (ECoG) dataset. It includes +two grids of electrodes and ten shaft electrodes with simulated motor data (actual data +pending availability). .. topic:: Examples - * :ref:`How to convert 3D electrode positions to a 2D image. - `: Demonstrates - how to project a 3D electrode location onto a 2D image, a common procedure - in electrocorticography. + * :ref:`ex-electrode-pos-2d`: Demonstrates how to project a 3D electrode location onto a 2D image, a common procedure in ECoG analyses. + * :ref:`tut-ieeg-localize`: Demonstrates how to use a graphical user interface to locate electrode contacts as well as warp them to a common atlas. + +sEEG Dataset +^^^^^^^^^^^^ +:func:`mne.datasets.misc.data_path`. Data exists at ``/seeg/``. + +This dataset contains a sample stereoelectroencephalography (sEEG) dataset. +It includes 21 shaft electrodes during a two-choice movement task on a keyboard. + +.. topic:: Examples + + * :ref:`tut-ieeg-localize`: Demonstrates how to use a graphical user interface to locate electrode contacts as well as warp them to a common atlas. + * :ref:`tut-working-with-seeg`: Demonstrates ways to plot sEEG anatomy and results. .. _limo-dataset: diff -Nru python-mne-0.23.4+dfsg/doc/overview/design_philosophy.rst python-mne-1.1.0+dfsg/doc/overview/design_philosophy.rst --- python-mne-0.23.4+dfsg/doc/overview/design_philosophy.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/overview/design_philosophy.rst 2022-08-03 15:48:14.000000000 +0000 @@ -28,10 +28,12 @@ one of `scikit-learn's `_ machine learning pipelines. MNE-Python's 2-D plotting functions also return `matplotlib`_ :class:`~matplotlib.figure.Figure` objects, and the 3D plotting functions -return `mayavi`_ scenes, so you can customize your MNE-Python plots using any -of matplotlib or mayavi's plotting commands. The intent is that MNE-Python will -get most neuroscientists 90% of the way to their desired analysis goal, and -other packages can get them over the finish line. +return :class:`mne.viz.Figure3D` classes with a ``.plotter`` attribute +pointing to :class:`pyvista.Plotter` instances, +so you can customize your MNE-Python plots using any +of matplotlib or PyVista's plotting commands. The intent is that MNE-Python +will get most neuroscientists 90% of the way to their desired analysis goal, +and other packages can get them over the finish line. Submodule-based organization diff -Nru python-mne-0.23.4+dfsg/doc/overview/development.rst python-mne-1.1.0+dfsg/doc/overview/development.rst --- python-mne-0.23.4+dfsg/doc/overview/development.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/overview/development.rst 2022-08-03 15:48:14.000000000 +0000 @@ -1,24 +1,30 @@ - MNE-Python Development ====================== .. NOTE: this first section (up until "overview of contribution process") is - basically a copy/paste of CONTRIBUTING.rst from the repository root, with - one sentence deleted to avoid self-referential linking. Changes made here - should be mirrored there, and vice-versa. - -MNE-Python is maintained by a community of scientists and research labs, and -accepts contributions in the form of bug reports, fixes, feature additions, and -documentation improvements (even just typo corrections). The best way to start -contributing is by `opening an issue`_ on our GitHub page to discuss your ideas -for changes or enhancements, or to tell us about behavior that you think might -be a bug in MNE-Python. *For general troubleshooting of scripts that use -MNE-Python*, you should instead post on the `MNE Forum`_. Users and -contributors to MNE-Python are expected to follow our `code of conduct`_. + basically a copy/paste of CONTRIBUTING.md from the repository root, with one + sentence deleted to avoid self-referential linking. Changes made here should + be mirrored there, and vice-versa. + +MNE-Python is maintained by a community of scientists and research labs. The +project accepts contributions in the form of bug reports, fixes, feature +additions, and documentation improvements (including typo corrections). The +best way to start contributing is by `opening an issue`_ on our GitHub page to +discuss ideas for changes or enhancements, or to tell us about behavior that +you think might be a bug. For *general troubleshooting* or *usage questions*, +please consider posting your questions on our `MNE Forum`_. + +Users and contributors to MNE-Python are expected to follow our +`code of conduct`_. + +The `contributing guide`_ has details on the preferred contribution workflow +and the recommended system configuration for a smooth contribution/development +experience. .. _`opening an issue`: https://github.com/mne-tools/mne-python/issues/new/choose .. _`MNE Forum`: https://mne.discourse.group .. _`code of conduct`: https://github.com/mne-tools/.github/blob/main/CODE_OF_CONDUCT.md +.. _`contributing guide`: https://mne.tools/dev/install/contributing.html .. toctree:: :hidden: @@ -26,3 +32,4 @@ ../install/contributing ../whats_new roadmap + governance diff -Nru python-mne-0.23.4+dfsg/doc/overview/faq.rst python-mne-1.1.0+dfsg/doc/overview/faq.rst --- python-mne-0.23.4+dfsg/doc/overview/faq.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/overview/faq.rst 2022-08-03 15:48:14.000000000 +0000 @@ -15,8 +15,7 @@ Help! I can't get Python and MNE-Python working! ------------------------------------------------ -Check out our installation instructions for :ref:`Python ` and -:ref:`MNE-Python `. +Check out our :ref:`installation instructions `. I still can't get it to work! @@ -25,16 +24,15 @@ See :ref:`help`. -I can't get Mayavi/3D plotting to work under Windows ----------------------------------------------------- +I can't get PyVista/3D plotting to work under Windows +----------------------------------------------------- -If Mayavi plotting in Jupyter Notebooks doesn't work well, using the IPython -magic ``%gui qt`` after importing MNE/Mayavi/PySurfer should `help +If PyVista plotting in Jupyter Notebooks doesn't work well, using the IPython +magic ``%gui qt`` should `help `_. .. code-block:: ipython - from mayavi import mlab %gui qt Python runs on macOS extremely slow even on simple commands! @@ -137,8 +135,7 @@ or to the `GitHub issues page`_. If you want to write your own data to disk (e.g., subject behavioral scores), -we strongly recommend using `h5io `_, which is -based on the `HDF5 format +we strongly recommend using h5io_, which is based on the `HDF5 format `_ and h5py_, to save data in a fast, future-compatible, standard format. @@ -417,7 +414,7 @@ .. _`the most current version`: https://github.com/mne-tools/mne-python/releases/latest .. _`minimal working example`: https://en.wikipedia.org/wiki/Minimal_Working_Example -.. _mri_watershed: https://freesurfer.net/fswiki/mri_watershed +.. _mri_watershed: https://surfer.nmr.mgh.harvard.edu/fswiki/mri_watershed .. _mri_normalize: https://surfer.nmr.mgh.harvard.edu/fswiki/mri_normalize .. _freeview: https://surfer.nmr.mgh.harvard.edu/fswiki/FreeviewGuide/FreeviewIntroduction .. _`FreeSurfer listserv`: https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/ diff -Nru python-mne-0.23.4+dfsg/doc/overview/get_help.rst python-mne-1.1.0+dfsg/doc/overview/get_help.rst --- python-mne-0.23.4+dfsg/doc/overview/get_help.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/overview/get_help.rst 2022-08-03 15:48:14.000000000 +0000 @@ -12,7 +12,7 @@ - The :ref:`faq` page has some troubleshooting tips, and is a good source of general information. There are also some troubleshooting tips built into the :ref:`Python ` and - :ref:`MNE-Python ` installation pages (look for the + :ref:`MNE-Python ` installation pages (look for the |hand-paper| symbols), and some tips related to 3D plotting problems on :ref:`the advanced setup page `. - If you want to request new features or if you're confident that you have diff -Nru python-mne-0.23.4+dfsg/doc/overview/governance.rst python-mne-1.1.0+dfsg/doc/overview/governance.rst --- python-mne-0.23.4+dfsg/doc/overview/governance.rst 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/overview/governance.rst 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,314 @@ +.. _governance: + +================== +Project Governance +================== + +The purpose of this document is to formalize the governance process +used by the MNE-Python project in both ordinary and extraordinary +situations, and to clarify how decisions are made and how the various +elements of our community interact, including the relationship between +open source collaborative development and work that may be funded by +for-profit or non-profit entities. + + +The Project +=========== + +The MNE-Python Project (The Project) is an open source software project. The +goal of The Project is to develop open source software for analysis of +neuroscience data in Python. The Project is released under the BSD (or similar) +open source license, developed openly and is hosted publicly under the +``mne-tools`` GitHub organization. + +The Project is developed by a team of distributed developers, called +Contributors. Contributors are individuals who have contributed code, +documentation, designs, or other work to the Project. Anyone can be a +Contributor. Contributors can be affiliated with any legal entity or +none. Contributors participate in the project by submitting, reviewing, +and discussing GitHub Pull Requests and Issues and participating in open +and public Project discussions on GitHub, Discourse, and other +channels. The foundation of Project participation is openness and +transparency. + +The Project Community consists of all Contributors and Users of the +Project. Contributors work on behalf of and are responsible to the +larger Project Community and we strive to keep the barrier between +Contributors and Users as low as possible. + +The Project is not a legal entity, nor does it currently have any formal +relationships with legal entities. + + +Governance model +================ + +This section describes the governance and leadership model of The +Project. + +The foundations of Project governance are: + +- openness and transparency +- active contribution +- institutional neutrality + + +Traditionally, Project leadership was provided by a subset of Contributors, +informally called Core Developers, whose active and consistent contributions +were rewarded by granting them “commit rights” to the Project GitHub +repositories. In general, all Project decisions are made through consensus among +the Core Developers with input from the Community. + +While this approach has served us well, as the Project grows we see a need for +a more formal governance model. The MNE-Python Core Developers expressed a +preference for a leadership model which includes a BDFL (Benevolent Dictator +for Life). Therefore, moving forward The Project leadership will consist of a +BDFL and Steering Council. + +BDFL +---- + +The Project will have a BDFL (Benevolent Dictator for Life), who is currently +Alexandre Gramfort. As Dictator, the BDFL has the authority to make all final +decisions for The Project. As Benevolent, the BDFL, in practice, chooses to +defer that authority to the consensus of the community discussion channels and +the Steering Council (see below). It is expected, and in the past has been the +case, that the BDFL will only rarely assert their final authority. Because +rarely used, we refer to BDFL’s final authority as a “special” or “overriding” +vote. When it does occur, the BDFL override typically happens in situations +where there is a deadlock in the Steering Council or if the Steering Council +asks the BDFL to make a decision on a specific matter. To ensure the +benevolence of the BDFL, The Project encourages others to fork the project if +they disagree with the overall direction the BDFL is taking. The BDFL may +delegate their authority on a particular decision or set of decisions to +any other Council member at their discretion. + +The BDFL can appoint their successor, but it is expected that the Steering +Council would be consulted on this decision. If the BDFL is unable to appoint a +successor, the Steering Council will make this decision — preferably by +consensus, but if needed, by a majority vote. + +Note that the BDFL can step down at any time, and acting in good faith, will +also listen to serious calls to do so. Also note that the BDFL is more a role +for fallback decision making rather than that of a director/CEO. + +Steering Council +---------------- + +The Project will have a Steering Council that consists of Project Contributors +who have produced contributions that are substantial in quality and quantity, +and sustained over at least one year. The overall role of the Council is to +ensure, through working with the BDFL and taking input from the Community, the +long-term well-being of the project, both technically and as a community. + +During the everyday project activities, Council Members participate in +discussions, code review, and other project activities as peers with all other +Contributors and the Community. In these everyday activities, Council Members +do not have any special power or privilege through their membership on the +Council. However, it is expected that because of the quality and quantity of +their contributions and their expert knowledge of the Project Software and +Services, Council Members will provide useful guidance, both technical and +in terms of project direction, to potentially less experienced contributors. + +The Steering Council and its Members play a special role in certain situations. +In particular, the Council may: + +- Make decisions about the overall scope, vision, and direction of the project. +- Make decisions about strategic collaborations with other organizations or + individuals. +- Make decisions about specific technical issues, features, bugs, and pull + requests. They are the primary mechanism of guiding the code review process + and merging pull requests. +- Make decisions about the Services that are run by The Project and manage + those Services for the benefit of the Project and Community. +- Make decisions when regular community discussion does not produce consensus + on an issue in a reasonable time frame. +- Update policy documents, such as this one. + +Council membership +~~~~~~~~~~~~~~~~~~ + +To become eligible for being a Steering Council Member, an individual must be a +Project Contributor who has produced contributions that are substantial in +quality and quantity, and sustained over at least one year. Potential Council +Members are nominated by existing Council members and voted upon by the +existing Council after asking if the potential Member is interested and willing +to serve in that capacity. The Council will be initially formed from the set of +existing Core Developers who, as of May 2021, have been significantly +active over the last two years. + +When considering potential Members, the Council will look at candidates with a +comprehensive view of their contributions. This will include, but is not limited +to, code, code review, infrastructure work, Discourse participation, +community help/building, education and outreach, design work, etc. We are +deliberately not setting arbitrary quantitative metrics (like “100 commits in +this repo”) to avoid encouraging behavior that plays to the metrics rather than +The Project’s overall well-being. We want to encourage a diverse array of +backgrounds, viewpoints, and talents in our team, which is why we explicitly do +not define code as the sole metric on which council membership will be +evaluated. + +If a Council Member becomes inactive in the project for a period of one year, +they will be considered for removal from the Council. Before removal, inactive +Member will be approached to see if they plan on returning to active +participation. If not, they will be removed immediately upon a Council +vote. If they plan on returning to active participation soon, they will be +given a grace period of one year. If they don’t return to active participation +within that time period they will be removed by vote of the Council without +further grace period. All former Council Members can be considered for +membership again at any time in the future, like any other Project Contributor. +Retired Council Members will be listed on the project website, acknowledging +the period during which they were active in the Council. + +The Council reserves the right to eject current Members, other than the BDFL, +if they are deemed to be actively harmful to the project’s well-being, and +attempts at communication and conflict resolution have failed. + +A list of current Steering Council Members is maintained at the +page :ref:`governance-people`. + +Conflict of interest +~~~~~~~~~~~~~~~~~~~~ + +It is expected that the BDFL and Council Members will be employed at a wide +range of companies, universities, and non-profit organizations. Because of this, +it is possible that Members will have a conflict of interest. Such conflicts of +interest include, but are not limited to: + +- Financial interest, such as investments, employment or contracting work, + outside of The Project that may influence their work on The Project. +- Access to proprietary information of their employer that could potentially + leak into their work with the Project. + +All members of the Council, BDFL included, shall disclose to the rest of the +Council any conflict of interest they may have. Members with a conflict of +interest in a particular issue may participate in Council discussions on that +issue, but must recuse themselves from voting on the issue. If the BDFL has +recused themself for a particular decision, the Council will appoint a +substitute BDFL for that decision. + +Private communications of the Council +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Unless specifically required, all Council discussions and activities will be +public and done in collaboration and discussion with the Project Contributors +and Community. The Council will have a private communication channel that will be used +sparingly and only when a specific matter requires privacy. When private +communications and decisions are needed, the Council will do its best to +summarize those to the Community after removing personal/private/sensitive +information that should not be posted to the public internet. + +Council decision making +~~~~~~~~~~~~~~~~~~~~~~~ + +If it becomes necessary for the Steering Council to produce a formal +decision, then they will use a form of the `Apache Foundation voting +process `_. This is a +formalized version of consensus, in which +1 votes indicate agreement, +-1 votes are vetoes (and must be accompanied with a rationale), +and one can also vote fractionally (e.g. -0.5, +0.5) if one +wishes to express an opinion without registering a full veto. These +numeric votes are also often used informally as a way of getting a +general sense of people's feelings on some issue, and should not +normally be taken as formal votes. A formal vote only occurs if +explicitly declared, and if this does occur, then the vote should be held +open for long enough to give all interested Council Members a chance to +respond — at least one week. + +In practice, we anticipate that for most Steering Council decisions +(e.g., voting in new members) a more informal process will suffice. + + +Institutional Partners and funding +================================== + +The Steering Council is the primary leadership for the project. No +outside institution, individual, or legal entity has the ability to own, +control, usurp, or influence the project other than by participating in +the Project as Contributors and Council Members. However, because +institutions can be an important funding mechanism for the project, it +is important to formally acknowledge institutional participation in the +project. These are Institutional Partners. + +An Institutional Contributor is any individual Project Contributor who +contributes to the project as part of their official duties at an +Institutional Partner. Likewise, an Institutional Council Member is any +Project Steering Council Member who contributes to the project as part +of their official duties at an Institutional Partner. + +With these definitions, an Institutional Partner is any recognized legal +entity in any country that employs at least 1 Institutional Contributor or +Institutional Council Member. Institutional Partners can be for-profit or +non-profit entities. + +Institutions become eligible to become an Institutional Partner by +employing individuals who actively contribute to The Project as part of +their official duties. To state this another way, the only way for a +Partner to influence the project is by actively contributing to the open +development of the project, in equal terms to any other member of the +community of Contributors and Council Members. Merely using Project +Software in institutional context does not allow an entity to become an +Institutional Partner. Financial gifts do not enable an entity to become +an Institutional Partner. Once an institution becomes eligible for +Institutional Partnership, the Steering Council must nominate and +approve the Partnership. + +If, at some point, an existing Institutional Partner stops having any +contributing employees, then a one year grace period commences. If, at +the end of this one-year period, they continue not to have any +contributing employees, then their Institutional Partnership will +lapse, and resuming it will require going through the normal process +for new Partnerships. + +An Institutional Partner is free to pursue funding for their work on The +Project through any legal means. This could involve a non-profit +organization raising money from private foundations and donors or a +for-profit company building proprietary products and services that +leverage Project Software and Services. Funding acquired by +Institutional Partners to work on The Project is called Institutional +Funding. However, no funding obtained by an Institutional Partner can +override the Steering Council. If a Partner has funding to do MNE-Python work +and the Council decides to not pursue that work as a project, the +Partner is free to pursue it on their own. However, in this situation, +that part of the Partner’s work will not be under the MNE-Python umbrella and +cannot use the Project trademarks in any way that suggests a formal +relationship. + +Institutional Partner benefits are: + +- optional acknowledgement on the MNE-Python website and in talks +- ability to acknowledge their own funding sources on the MNE-Python + website and in talks +- ability to influence the project through the participation of their + Council Member +- invitation of the Council Members to MNE-Python Developer Meetings + +A list of current Institutional Partners is maintained at the page +:ref:`supporting-institutions`. + + +Document history +================ + +https://github.com/mne-tools/mne-python/commits/main/doc/overview/governance.rst + + +Acknowledgements +================ + +Substantial portions of this document were adapted from the +`SciPy project's governance document +`_, +which in turn was adapted from +`Jupyter/IPython project's governance document +`_ and +`NumPy's governance document +`_. + +License +======= + +To the extent possible under law, the authors have waived all +copyright and related or neighboring rights to the MNE-Python project +governance document, as per the `CC-0 public domain dedication / license +`_. diff -Nru python-mne-0.23.4+dfsg/doc/overview/index.rst python-mne-1.1.0+dfsg/doc/overview/index.rst --- python-mne-0.23.4+dfsg/doc/overview/index.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/overview/index.rst 2022-08-03 15:48:14.000000000 +0000 @@ -7,10 +7,9 @@ .. note:: - If you haven't already installed Python and MNE-Python, here are the - installation instructions for :ref:`Python ` and - :ref:`MNE-Python `, and some - resources for :doc:`learn_python`. + If you haven't already installed MNE-Python, please take a look + at our :ref:`installation guides`. Please also kindly find some + resources for :doc:`learn_python` if you need to. The documentation for MNE-Python is divided into four main sections: diff -Nru python-mne-0.23.4+dfsg/doc/overview/matlab.rst python-mne-1.1.0+dfsg/doc/overview/matlab.rst --- python-mne-0.23.4+dfsg/doc/overview/matlab.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/overview/matlab.rst 2022-08-03 15:48:14.000000000 +0000 @@ -525,8 +525,9 @@ **measurement info** - Translation of the FIFFB_MEAS_INFO entity, see :ref:`BGBFHDIJ`. This - data structure is returned by fiff_read_meas_info . + Translation of the FIFFB_MEAS_INFO entity, see :ref:`BGBFHDIJ` and + :class:`mne.Info`. This data structure is returned by fiff_read_meas_info, + will not be as complete as :class:`mne.Info`. **surf** @@ -688,7 +689,7 @@ | FIFFV_PROJ_ITEM_NONE | 0 | The nature of this projection item is unknown | +-------------------------------+-------+----------------------------------------------------------+ | FIFFV_PROJ_ITEM_FIELD | 1 | This is projection item is a generic field pattern or | - | | | field patters. | + | | | field patterns. | +-------------------------------+-------+----------------------------------------------------------+ | FIFFV_PROJ_ITEM_DIP_FIX | 2 | This projection item is the field of one dipole | +-------------------------------+-------+----------------------------------------------------------+ diff -Nru python-mne-0.23.4+dfsg/doc/overview/people.rst python-mne-1.1.0+dfsg/doc/overview/people.rst --- python-mne-0.23.4+dfsg/doc/overview/people.rst 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/overview/people.rst 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,46 @@ +:orphan: + +.. include:: ../changes/names.inc + +.. _governance-people: + +Current steering council and institutional partners +=================================================== + +Benevolent Dictator for Life +---------------------------- + +Alexandre Gramfort is the Benevolent Dictator for Life (BDFL) + + +Steering Council +---------------- + +* `Adam Li`_ +* `Alex Gramfort`_ +* `Alex Rockhill`_ +* `Britta Westner`_ +* `Clemens Brunner`_ +* `Daniel McCloy`_ +* `Denis Engemann`_ +* `Eric Larson`_ +* `Guillaume Favelier`_ +* `Luke Bloy`_ +* `Mainak Jas`_ +* `Marijn van Vliet`_ +* `Mikołaj Magnuski`_ +* `Richard Höchenberger`_ +* `Robert Luke`_ +* `Stefan Appelhoff`_ + +Institutional Partners +---------------------- + +.. include:: ../_includes/institutional-partners.rst + :start-after: institutional-partners-begin-content + + +Document history +---------------- + +https://github.com/mne-tools/mne-python/commits/main/doc/overview/people.rst diff -Nru python-mne-0.23.4+dfsg/doc/overview/roadmap.rst python-mne-1.1.0+dfsg/doc/overview/roadmap.rst --- python-mne-0.23.4+dfsg/doc/overview/roadmap.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/overview/roadmap.rst 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,5 @@ +.. include:: ../links.inc + Roadmap ======= @@ -6,70 +8,16 @@ API design considerations. Some of these may be suitable for Google Summer of Code projects, while others require more extensive work. -Clustering statistics API -^^^^^^^^^^^^^^^^^^^^^^^^^ - -The current clustering statistics code has limited functionality. It should be -re-worked to create a new ``cluster_based_statistic`` or similar function. -In particular, the new API should: - -1. Support mixed within- and between-subjects designs, different statistical - functions, etc. This should be done via a ``design`` argument that mirrors - :func:`patsy.dmatrices` or similar community standard (e.g., this is what - is used by :class:`statsmodels.regression.linear_model.OLS`). -2. Have clear tutorials showing how different contrasts can be done (toy data). -3. Have clear tutorials showing some common analyses on real data (time-freq, - sensor space, source space, etc.) -4. Not introduce any significant speed penalty (e.g., < 10% slower) compared - to the existing, more specialized/limited functions. - -More details are in :gh:`4859`. - - -3D visualization -^^^^^^^^^^^^^^^^ - -Historically we have used Mayavi for 3D visualization, but have faced -limitations and challenges with it. We should work to use some other backend -(e.g., PyVista) to get major improvements, such as: - -1. Proper notebook support (through vtkjs) -2. Better interactivity with surface plots -3. Time-frequency plotting (complementary to volume-based - :ref:`time-frequency-viz`) -4. Integration of multiple functions as done in ``mne_analyze``, e.g., - simultaneous source estimate viewing, field map - viewing, head surface display, etc. These are all currently available in - separate functions, but we should be able to combine them in a single plot - as well. - -One such issue for tracking TODO lists for surface plotting is :gh:`7162`. - - -2D visualization -^^^^^^^^^^^^^^^^ - -Our 2D code has a lot of useful functionality, but suffers from several -systemic problems: - -1. It was written by many people for many specific use cases over time, - without ensuring that code was generalized. This means that some functions - are available only in some plotting modes (e.g., grouping by channel types - to obtain a butterfly plot can be done in :func:`mne.viz.plot_raw` but not - :func:`mne.viz.plot_epochs`. -2. The code base has many redundant but not identical pieces of code - (copy-paste-modify rather than generalize-reuse / DRY) that make maintenance - particularly challenging. - -By (extensively) refactoring our code, we would improve the end-user experience -and decrease long-term maintenance costs. +.. contents:: Page contents + :local: +Open +---- .. _time-frequency-viz: Time-frequency visualization ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - We should implement a viewer for interactive visualization of volumetric source-time-frequency (5-D) maps on MRI slices (orthogonal 2D viewer). `NutmegTrip `__ @@ -81,74 +29,26 @@ :target: https://www.youtube.com/watch?v=xKdjZZphdNc :width: 50% +Clustering statistics API +^^^^^^^^^^^^^^^^^^^^^^^^^ +The current clustering statistics code has limited functionality. It should be +re-worked to create a new ``cluster_based_statistic`` or similar function. +In particular, the new API should: -Cluster computing -^^^^^^^^^^^^^^^^^ - -Currently, cloud computing with M/EEG data requires multiple manual steps, -including remote environment setup, data transfer, monitoring of remote jobs, -and retrieval of output data/results. These steps are usually not specific to -the analysis of interest, and thus should be something that can be taken care -of by MNE. Subgoals consist of: - -- Leverage dask and joblib or other libs to allow simple integration with MNE processing steps. Ideally this would be achieved in practice by: - - - One-time (or per-project) setup steps, setting up host keys, access tokens, - etc. - - In code, switch to cloud computing rather than local computing via a simple - change of n_jobs parameter, and/or context manager like with:: - - with use_dask(...): - ... - -- Develop a (short as possible) example that shows people how to run a minimal - task remotely, including setting up access, cluster, nodes, etc. -- Adapt `MNE-study-template `__ - code to use cloud computing (optionally, based on config) rather than local - resources. - -See also :gh:`6086`. - - -Tutorial / example overhaul -^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -We want our tutorials to get users up to speed on: - -1. How to do M/EEG analyses in principle, and -2. How to do M/EEG analyses in MNE-Python in particular - -So far some of our tutorials have been rewritten, but we still have a long way -to go. Relevant tracking issues can be found under the tag :gh:`labels/DOC`. - - -Coregistration / 3D viewer -^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:ref:`mne coreg` is an excellent tool for coregistration, but is limited -by being tied to Mayavi, Traits, and TraitsUI. We should first refactor in -several (mostly) separable steps: - -1. Refactor code to use traitlets -2. GUI elements to use PyQt5 (rather than TraitsUI/pyface) -3. 3D plotting to use our abstracted 3D viz functions rather than Mayavi -4. Refactor distance/fitting classes to public ones to enable the example - from :gh:`6693`. - -Once this is done, we can effectively switch to a PyVista backend. - - -BIDS Integration -^^^^^^^^^^^^^^^^ - -MNE-Python should facilitate analyzing BIDS-compliant datasets thanks to -integration with the MNE-BIDS package. For more -information, see https://github.com/mne-tools/mne-bids. +1. Support mixed within- and between-subjects designs, different statistical + functions, etc. This should be done via a ``design`` argument that mirrors + :func:`patsy.dmatrices` or similar community standard (e.g., this is what + is used by :class:`statsmodels.regression.linear_model.OLS`). +2. Have clear tutorials showing how different contrasts can be done (toy data). +3. Have clear tutorials showing some common analyses on real data (time-freq, + sensor space, source space, etc.) +4. Not introduce any significant speed penalty (e.g., < 10% slower) compared + to the existing, more specialized/limited functions. +More details are in :gh:`4859`. Access to open EEG/MEG databases ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - We should improve the access to open EEG/MEG databases via the :mod:`mne.datasets` module, in other words improve our dataset fetchers. We have physionet, but much more. Having a consistent API to access multiple @@ -170,15 +70,11 @@ Kymata atlas. The participants are healthy human adults listening to the radio and/or watching films, and the data is comprised of (averaged) EEG and MEG sensor data and source current reconstructions. -- `BrainSignals `__ - A website that lists a number of MEG datasets available for download. - `BNCI Horizon `__ BCI datasets. - Integrate OpenMEEG via improved Python bindings ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - `OpenMEEG `__ is a state-of-the art solver for forward modeling in the field of brain imaging with MEG/EEG. It solves numerically partial differential equations (PDE). It is written in C++ with @@ -196,3 +92,124 @@ - Help package OpenMEEG for Debian/Ubuntu - Help manage `the continuous integration system `__ + + +In progress +----------- + +Diversity, Equity, and Inclusion (DEI) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +MNE-Python is committed to recruiting and retaining a diverse pool of +contributors, see :gh:`8221`. + +First-class OPM support +^^^^^^^^^^^^^^^^^^^^^^^ +MNE-Python has support for reading some OPM data formats such as FIF, but +support is still rudimentary. Support should be added for other manufacturers, +and standard (and/or novel) preprocessing routines should be added to deal with +coregistration adjustment, forward modeling, and OPM-specific artifacts. + +Deep source modeling +^^^^^^^^^^^^^^^^^^^^ +Existing source modeling and inverse routines are not explicitly designed to +deal with deep sources. Advanced algorithms exist from MGH for enhancing +deep source localization, and these should be implemented and vetted in +MNE-Python. + +Better sEEG/ECoG/DBS support +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Some support already exists for iEEG electrodes in MNE-Python thanks in part +to standard abstractions. However, iEEG-specific pipeline steps (e.g., +electrode localization) and visualizations (e.g., per-shaft topo plots, +:ref:`time-frequency-viz`) are missing. MNE-Python should work with members of +the ECoG/sEEG community to work with or build in existing tools, and extend +native functionality for depth electrodes. + +Time-frequency classes +^^^^^^^^^^^^^^^^^^^^^^ +Our current codebase implements classes related to :term:`TFRs ` that +remain incomplete. We should implement new classes from the ground up +that can hold frequency data (``Spectrum``), cross-spectral data +(``CrossSpectrum``), multitaper estimates (``MultitaperSpectrum``), and +time-varying estimates (``Spectrogram``). These should work for +continuous, epoched, and averaged sensor data, as well as source-space brain +data. + +See related issues :gh:`6290`, :gh:`7671`, :gh:`8026`, :gh:`8724`, :gh:`9045`, +and PRs :gh:`6609`, :gh:`6629`, :gh:`6672`, :gh:`6673`, :gh:`8397`, and +:gh:`8892`. + +Pediatric and clinical MEG pipelines +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +MNE-Python is in the process of providing automated analysis of BIDS-compliant +datasets, see `MNE-BIDS-Pipeline`_. By incorporating functionality from the +`mnefun `__ pipeline, +which has been used extensively for pediatric data analysis at `I-LABS`_, +better support for pediatric and clinical data processing can be achieved. +Multiple processing steps (e.g., eSSS), sanity checks (e.g., cHPI quality), +and reporting (e.g., SSP joint plots, SNR plots) will be implemented. + +Statistics efficiency +^^^^^^^^^^^^^^^^^^^^^ +A key technique in functional neuroimaging analysis is clustering brain +activity in adjacent regions prior to statistical analysis. An important +clustering algorithm — threshold-free cluster enhancement (TFCE) — currently +relies on computationally expensive permutations for hypothesis testing. +A faster, probabilistic version of TFCE (pTFCE) is available, and we are in the +process of implementing this new algorithm. + +3D visualization +^^^^^^^^^^^^^^^^ +Historically we have used Mayavi for 3D visualization, but have faced +limitations and challenges with it. We should work to use some other backend +(e.g., PyVista) to get major improvements, such as: + +1. *Proper notebook support (through ipyvtklink)* (complete) +2. *Better interactivity with surface plots* (complete) +3. Time-frequency plotting (complementary to volume-based + :ref:`time-frequency-viz`) +4. Integration of multiple functions as done in ``mne_analyze``, e.g., + simultaneous source estimate viewing, field map + viewing, head surface display, etc. These are all currently available in + separate functions, but we should be able to combine them in a single plot + as well. + +The meta-issue for tracking to-do lists for surface plotting is :gh:`7162`. + +.. _documentation-updates: + +Documentation updates +^^^^^^^^^^^^^^^^^^^^^ +Our documentation has many minor issues, which can be found under the tag +:gh:`labels/DOC`. + + +Completed +--------- + +Distributed computing support +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +`MNE-BIDS-Pipeline`_ has been enhanced with support for cloud computing +via `Dask`_ and :doc:`joblib `. +After configuring Dask to use local or remote distributed computing resources, +MNE-BIDS-Pipeline can readily make use of remote workers to parallelize +processing across subjects. + +2D visualization +^^^^^^^^^^^^^^^^ +`This goal `__ +was completed under CZI `EOSS2`_. Some additional enhancements that could also +be implemented are listed in :gh:`7751`. + +Tutorial / example overhaul +^^^^^^^^^^^^^^^^^^^^^^^^^^^ +`This goal `__ +was completed under CZI `EOSS2`_. Ongoing documentation needs are listed in +:ref:`documentation-updates`. + +Cluster computing images +^^^^^^^^^^^^^^^^^^^^^^^^ +As part of `this goal `__, +we created docker images suitable for cloud computing via `MNE-Docker`_. + +.. _I-LABS: http://ilabs.washington.edu/ diff -Nru python-mne-0.23.4+dfsg/doc/preprocessing.rst python-mne-1.1.0+dfsg/doc/preprocessing.rst --- python-mne-0.23.4+dfsg/doc/preprocessing.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/preprocessing.rst 2022-08-03 15:48:14.000000000 +0000 @@ -40,6 +40,7 @@ read_dig_egi read_dig_fif read_dig_hpts + read_dig_localite make_standard_montage read_custom_montage compute_dev_head_t @@ -48,6 +49,7 @@ make_eeg_layout make_grid_layout find_ch_adjacency + get_builtin_ch_adjacencies read_ch_adjacency equalize_channels rename_channels @@ -68,15 +70,19 @@ ICA Xdawn - annotate_flat + annotate_amplitude + annotate_break annotate_movement annotate_muscle_zscore + annotate_nan compute_average_dev_head_t compute_current_source_density + compute_bridged_electrodes compute_fine_calibration compute_maxwell_basis compute_proj_ecg compute_proj_eog + cortical_signal_suppression create_ecg_epochs create_eog_epochs find_bad_channels_maxwell @@ -86,8 +92,10 @@ ica_find_ecg_events ica_find_eog_events infomax + interpolate_bridged_electrodes equalize_bads maxwell_filter + maxwell_filter_prepare_emptyroom oversampled_temporal_projection peak_finder read_ica @@ -116,6 +124,19 @@ scalp_coupling_index temporal_derivative_distribution_repair +:py:mod:`mne.preprocessing.ieeg`: + +.. currentmodule:: mne.preprocessing.ieeg + +.. automodule:: mne.preprocessing.ieeg + :no-members: + :no-inherited-members: + +.. autosummary:: + :toctree: generated/ + + project_sensors_onto_brain + EEG referencing: .. currentmodule:: mne @@ -157,11 +178,13 @@ :toctree: generated/ compute_chpi_amplitudes + compute_chpi_snr compute_chpi_locs compute_head_pos extract_chpi_locs_ctf extract_chpi_locs_kit filter_chpi + get_chpi_info head_pos_to_trans_rot_t read_head_pos write_head_pos diff -Nru python-mne-0.23.4+dfsg/doc/python_reference.rst python-mne-1.1.0+dfsg/doc/python_reference.rst --- python-mne-0.23.4+dfsg/doc/python_reference.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/python_reference.rst 2022-08-03 15:48:14.000000000 +0000 @@ -27,6 +27,7 @@ reading_raw_data file_io creating_from_arrays + export datasets visualization preprocessing diff -Nru python-mne-0.23.4+dfsg/doc/reading_raw_data.rst python-mne-1.1.0+dfsg/doc/reading_raw_data.rst --- python-mne-0.23.4+dfsg/doc/reading_raw_data.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/reading_raw_data.rst 2022-08-03 15:48:14.000000000 +0000 @@ -25,6 +25,7 @@ read_raw_kit read_raw_nedf read_raw_nicolet + read_raw_hitachi read_raw_nirx read_raw_snirf read_raw_eeglab diff -Nru python-mne-0.23.4+dfsg/doc/references.bib python-mne-1.1.0+dfsg/doc/references.bib --- python-mne-0.23.4+dfsg/doc/references.bib 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/references.bib 2022-08-03 15:48:14.000000000 +0000 @@ -47,6 +47,16 @@ year = {2012} } +@article{Alday2019, + title = {How much baseline correction do we need in {ERP} research? Extended {GLM} model can replace baseline correction while lifting its limits}, + author = {Alday, Phillip M.}, + year = {2019}, + volume = {56}, + doi = {10.1111/psyp.13451}, + journal = {Psychophysiology}, + number = {12} +} + @article{ArtoniEtAl2018, author = {Artoni, Fiorenzo and Delorme, Arnaud and Makeig, Scott}, doi = {10.1016/j.neuroimage.2018.03.016}, @@ -101,7 +111,7 @@ @inproceedings{BekhtiEtAl2016, address = {{Trento}}, - author = {Bekhti, Yousra and Strohmeiery, Daniel and Jas, Mainak and Badeau, Roland and Gramfort, Alexandre}, + author = {Bekhti, Yousra and Strohmeier, Daniel and Jas, Mainak and Badeau, Roland and Gramfort, Alexandre}, booktitle = {Proceedings of {{PRNI}}-2016}, doi = {10.1109/PRNI.2016.7552337}, isbn = {978-1-4673-6530-7}, @@ -362,6 +372,23 @@ year = {2006} } +@article{DelormeMakeig2004, + title = {{EEGLAB}: an open source toolbox for analysis of single-trial {EEG} dynamics including independent component analysis}, + volume = {134}, + issn = {0165-0270}, + shorttitle = {{EEGLAB}}, + doi = {10.1016/j.jneumeth.2003.10.009}, + language = {eng}, + number = {1}, + journal = {Journal of Neuroscience Methods}, + author = {Delorme, Arnaud and Makeig, Scott}, + month = mar, + year = {2004}, + pmid = {15102499}, + keywords = {Computer Simulation, Electroencephalography, Evoked Potentials, Software}, + pages = {9--21} +} + @article{DestrieuxEtAl2010, author = {Destrieux, Christophe and Fischl, Bruce and Dale, Anders and Halgren, Eric}, doi = {10.1016/j.neuroimage.2010.06.010}, @@ -373,6 +400,21 @@ year = {2010} } +@inproceedings{DharmapraniEtAl2016, + address = {Orlando, FL, USA}, + title = {A comparison of independent component analysis algorithms and measures to discriminate between {EEG} and artifact components}, + isbn = {978-1-4577-0220-4}, + url = {http://ieeexplore.ieee.org/document/7590828/}, + doi = {10.1109/EMBC.2016.7590828}, + urldate = {2022-04-13}, + booktitle = {2016 38th {Annual} {International} {Conference} of the {IEEE} {Engineering} in {Medicine} and {Biology} {Society} ({EMBC})}, + publisher = {IEEE}, + author = {Dharmaprani, Dhani and Nguyen, Hoang K. and Lewis, Trent W. and DeLosAngeles, Dylan and Willoughby, John O. and Pope, Kenneth J.}, + year = {2016}, + pages = {825--828}, +} + + @article{DufauEtAl2015, author = {Dufau, Stéphane and Grainger, Jonathan and Midgley, Katherine J. and Holcomb, Phillip J.}, doi = {10.1177/0956797615603934}, @@ -564,6 +606,22 @@ year = {2013} } +@article{GreischarEtAl2004, + title = {Effects of electrode density and electrolyte spreading in dense array electroencephalographic recording}, + volume = {115}, + issn = {13882457}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S1388245703003833}, + doi = {10.1016/j.clinph.2003.10.028}, + language = {en}, + number = {3}, + urldate = {2022-04-25}, + journal = {Clinical Neurophysiology}, + author = {Greischar, Lawrence L. and Burghy, Cory A. and van Reekum, Carien M. and Jackson, Daren C. and Pizzagalli, Diego A. and Mueller, Corrina and Davidson, Richard J.}, + month = mar, + year = {2004}, + pages = {710--720} +} + @article{GreveEtAl2013, author = {Greve, Douglas N. and {Van der Haegen}, Lise and Cai, Qing and Stufflebeam, Steven and Sabuncu, Mert R. and Fischl, Bruce and Brysbaert, Marc}, doi = {10.1162/jocn_a_00405}, @@ -656,6 +714,18 @@ year = {1989} } +@article{HamiltonEtAl2017, + author = {Hamilton, Liberty S. and Chang, David L. and Lee, Morgan B. and Chang, Edward F.}, + doi = {10.3389/fninf.2017.00062}, + journal = {Frontiers in Neuroinformatics}, + title = {Semi-automated Anatomical Labeling and Inter-subject Warping of High-Density Intracranial Recording Electrodes in Electrocorticography}, + volume = {11}, + issn = {1662-5196}, + url = {http://journal.frontiersin.org/article/10.3389/fninf.2017.00062/full}, + month = oct, + year = {2017}, +} + @article{HannaEtAl2020, author = {Hanna, Jeff and Kim, Cora and Müller-Voggel, Nadia}, doi = {10.1016/j.jneumeth.2020.108592}, @@ -758,6 +828,16 @@ year = {2016} } +@article{HouckClaus2020, + author = {Houck, Jon M. and Claus, Eric D.}, + doi = {10.1371/journal.pone.0232100}, + journal = {PLOS ONE}, + pages = {e0232100}, + title = {A comparison of automated and manual co-registration for magnetoencephalography}, + volume = {15}, + year = {2020} +} + @article{Hyvarinen1999, author = {Hyvärinen, Aapo}, doi = {10.1109/72.761722}, @@ -1302,7 +1382,8 @@ shorttitle = {Standardized Low-Resolution Brain Electromagnetic Tomography ({{sLORETA}})}, title = {Standardized Low-Resolution Brain Electromagnetic Tomography ({{sLORETA}}): Technical Details}, volume = {24}, - year = {2002} + year = {2002}, + url = {https://pubmed.ncbi.nlm.nih.gov/12575463/} } @article{Pascual-Marqui2011, @@ -1323,7 +1404,7 @@ publisher = {{Cambridge University Press}}, shorttitle = {Spectral Analysis for Physical Applications}, title = {Spectral Analysis for Physical Applications: Multitaper and Conventional Univariate Techniques}, - url = {https://www.cambridge.org/us/academic/subjects/physics/mathematical-methods/spectral-analysis-physical-applications}, + doi = {10.1017/CBO9780511622762}, year = {1993} } @@ -1377,11 +1458,13 @@ @article{pollonini2014auditory, title={Auditory cortex activation to natural speech and simulated cochlear implant speech measured with functional near-infrared spectroscopy}, author={Pollonini, Luca and Olds, Cristen and Abaya, Homer and Bortfeld, Heather and Beauchamp, Michael S and Oghalai, John S}, - journal={Hearing research}, + journal={Hearing Research}, volume={309}, pages={84--93}, year={2014}, - publisher={Elsevier} + publisher={Elsevier}, + pmid={24342740}, + doi={10.1016/j.heares.2013.11.007} } @article{RidgwayEtAl2012, @@ -1418,6 +1501,19 @@ year = {2011} } +@article{RockhillEtAl2022, + doi = {10.21105/joss.03897}, + url = {https://doi.org/10.21105/joss.03897}, + year = {2022}, + publisher = {The Open Journal}, + volume = {7}, + number = {70}, + pages = {3897}, + author = {Alexander P. Rockhill and Eric Larson and Brittany Stedelin and Alessandra Mantovani and Ahmed M. Raslan and Alexandre Gramfort and Nicole C. Swann}, + title = {Intracranial Electrode Location and Analysis in MNE-Python}, + journal = {Journal of Open Source Software} +} + @article{Rousselet2012, author = {Rousselet, Guillaume A.}, doi = {10.3389/fpsyg.2012.00131}, @@ -1465,6 +1561,16 @@ doi = {10.1016/j.neuroimage.2020.116893} } +@article{Samuelsson2019, + author = {Samuelsson, John and Khan, Sheraz and Sundaram, Padma and Peled, Noam and Hämäläinen, Matti}, + title = {Cortical Signal Suppression (CSS) for detection of subcortical activity using MEG and EEG}, + journal = {Brain Topography}, + year = {2019}, + volume = {32}, + pages = {215-228}, + doi = {10.1007/s10548-018-00694-5} +} + @article{Sarvas1987, author = {Sarvas, Jukka}, doi = {10.1088/0031-9155/32/1/004}, @@ -1477,6 +1583,18 @@ year = {1987} } +@article{Sassenhagen2019, +author = {Sassenhagen, Jona and Draschkow, Dejan}, +doi = {10.1111/psyp.13335}, +journal = {Psychophysiology}, +volume = {56}, +number = {6}, +pages = {e13335}, +keywords = {cluster-based permutation test, EEG, MEG, statistics}, +title = {Cluster-based permutation tests of MEG/EEG data do not establish significance of effect latency or location}, +year = {2019} +} + @article{SavitzkyGolay1964, author = {Savitzky, Abraham and Golay, Marcel J. E.}, doi = {10.1021/ac60214a047}, @@ -1697,6 +1815,22 @@ year = {2006} } +@article{TenkeKayser2001, + title = {A convenient method for detecting electrolyte bridges in multichannel electroencephalogram and event-related potential recordings}, + volume = {112}, + issn = {1388-2457}, + doi = {10.1016/s1388-2457(00)00553-8}, + language = {eng}, + number = {3}, + journal = {Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology}, + author = {Tenke, C. E. and Kayser, J.}, + month = mar, + year = {2001}, + pmid = {11222978}, + keywords = {Algorithms, Artifacts, Brain, Electrodes, Electroencephalography, Electrolytes, Evoked Potentials, Humans, Scalp}, + pages = {545--550} +} + @article{TescheEtAl1995, author = {Tesche, Claudia D. and Uusitalo, Mikko A. and Ilmoniemi, Risto J. and Huotilainen, Minna and Kajola, Matti J. and Salonen, Oili L. M.}, doi = {10.1016/0013-4694(95)00064-6}, @@ -1817,6 +1951,19 @@ year = {2010} } +@article{WhithamEtAl2007, + title = {Scalp electrical recording during paralysis: quantitative evidence that {EEG} frequencies above 20 {Hz} are contaminated by {EMG}}, + volume = {118}, + issn = {1388-2457}, + doi = {10.1016/j.clinph.2007.04.027}, + number = {8}, + journal = {Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology}, + author = {Whitham, Emma M. and Pope, Kenneth J. and Fitzgibbon, Sean P. and Lewis, Trent and Clark, C. Richard and Loveless, Stephen and Broberg, Marita and Wallace, Angus and DeLosAngeles, Dylan and Lillie, Peter and Hardy, Andrew and Fronsko, Rik and Pulbrook, Alyson and Willoughby, John O.}, + year = {2007}, + pmid = {17574912}, + pages = {1877--1888}, +} + @article{WidmannEtAl2015, author = {Widmann, Andreas and Schröger, Erich and Maess, Burkhard}, doi = {10.1016/j.jneumeth.2014.08.002}, @@ -2087,3 +2234,63 @@ year = {2019}, pages = {2331216519871395} } + +@article{DeledalleEtAl2014, + author = {Deledalle, Charles-Alban and Vaiter, Samuel and Fadili, Jalal and Peyré, Gabriel}, + title = {Stein Unbiased GrAdient estimator of the Risk (SUGAR) for Multiple Parameter Selection}, + journal = {SIAM Journal on Imaging Sciences}, + volume = {7}, + number = {4}, + pages = {2448-2487}, + year = {2014}, + doi = {10.1137/140968045}, + URL = {https://doi.org/10.1137/140968045} +} + +@InProceedings{BertrandEtAl2020, + title = { Anderson acceleration of coordinate descent }, + author = {Bertrand, Quentin and Massias, Mathurin}, + booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, + pages = {1288--1296}, + year = {2021}, + editor = {Banerjee, Arindam and Fukumizu, Kenji}, + volume = {130}, + series = {Proceedings of Machine Learning Research}, + month = {13--15 Apr}, + publisher = {PMLR}, + pdf = {http://proceedings.mlr.press/v130/bertrand21a/bertrand21a.pdf}, + url = {http://proceedings.mlr.press/v130/bertrand21a.html} +} + +@book{Luck2014, + title = {An Introduction to the Event-Related Potential Technique}, + author = {Luck, Steven J}, + year = {2014}, + edition = {2nd}, + publisher = {The MIT Press}, + address = {Cambridge, MA}, + url = {https://mitpress.mit.edu/books/introduction-event-related-potential-technique-second-edition}, + isbn = {978-0-262-52585-5} +} + +@article{Lopez-CalderonLuck2014, + title = {ERPLAB: An Open-Source Toolbox for the Analysis of Event-Related Potentials}, + author = {Lopez-Calderon, Javier and Luck, Steven J.}, + year = {2014}, + journal = {Frontiers in Human Neuroscience}, + volume = {8}, + issn = {1662-5161}, + doi = {10.3389/fnhum.2014.00213} +} + +@article{LuckGaspelin2017, + title = {How to Get Statistically Significant Effects in Any {{ERP}} Experiment (and Why You Shouldn't)}, + author = {Luck, Steven J. and Gaspelin, Nicholas}, + year = {2017}, + journal = {Psychophysiology}, + volume = {54}, + number = {1}, + pages = {146--157}, + issn = {1469-8986}, + doi = {10.1111/psyp.12639}, +} diff -Nru python-mne-0.23.4+dfsg/doc/source_space.rst python-mne-1.1.0+dfsg/doc/source_space.rst --- python-mne-0.23.4+dfsg/doc/source_space.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/source_space.rst 2022-08-03 15:48:14.000000000 +0000 @@ -17,8 +17,6 @@ VolVectorSourceEstimate SourceMorph compute_source_morph - head_to_mni - head_to_mri extract_label_time_course grade_to_tris grade_to_vertices @@ -33,12 +31,11 @@ read_label read_source_estimate read_source_morph - read_talxfm split_label stc_to_label stc_near_sensors transform_surface_to - vertex_to_mni write_labels_to_annot write_label source_space.compute_distance_to_sensors + source_space.get_decimated_surfaces diff -Nru python-mne-0.23.4+dfsg/doc/sphinxext/gh_substitutions.py python-mne-1.1.0+dfsg/doc/sphinxext/gh_substitutions.py --- python-mne-0.23.4+dfsg/doc/sphinxext/gh_substitutions.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/sphinxext/gh_substitutions.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,12 +4,12 @@ from docutils.parsers.rst.roles import set_classes -# adapted from -# https://doughellmann.com/blog/2010/05/09/defining-custom-roles-in-sphinx/ - - def gh_role(name, rawtext, text, lineno, inliner, options={}, content=[]): - """Link to a GitHub issue.""" + """Link to a GitHub issue. + + adapted from + https://doughellmann.com/blog/2010/05/09/defining-custom-roles-in-sphinx/ + """ try: # issue/PR mode (issues/PR-num will redirect to pull/PR-num) int(text) diff -Nru python-mne-0.23.4+dfsg/doc/sphinxext/newcontrib_substitutions.py python-mne-1.1.0+dfsg/doc/sphinxext/newcontrib_substitutions.py --- python-mne-0.23.4+dfsg/doc/sphinxext/newcontrib_substitutions.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/sphinxext/newcontrib_substitutions.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,26 @@ +# -*- coding: utf-8 -*- + +from docutils.nodes import reference, strong, target + + +def newcontrib_role(name, rawtext, text, lineno, inliner, options={}, + content=[]): + """Create a role to highlight new contributors in changelog entries.""" + newcontrib = f'new contributor {text}' + alias_text = f' <{text}_>' + rawtext = f'`{newcontrib}{alias_text}`_' + refname = text.lower() + strong_node = strong(rawtext, newcontrib) + target_node = target(alias_text, refname=refname, names=[newcontrib]) + target_node.indirect_reference_name = text + options.update(refname=refname, name=newcontrib) + ref_node = reference('', '', strong_node, **options) + ref_node[0].rawsource = rawtext + inliner.document.note_indirect_target(target_node) + inliner.document.note_refname(ref_node) + return [ref_node, target_node], [] + + +def setup(app): + app.add_role('newcontrib', newcontrib_role) + return diff -Nru python-mne-0.23.4+dfsg/doc/sphinxext/sphinx_bootstrap_divs/bootstrap_divs.css python-mne-1.1.0+dfsg/doc/sphinxext/sphinx_bootstrap_divs/bootstrap_divs.css --- python-mne-0.23.4+dfsg/doc/sphinxext/sphinx_bootstrap_divs/bootstrap_divs.css 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/sphinxext/sphinx_bootstrap_divs/bootstrap_divs.css 1970-01-01 00:00:00.000000000 +0000 @@ -1,9 +0,0 @@ -.card-header a { - display: block !important; - text-decoration: none; -} -@media (max-width: 991px) { - .collapse.show{ - display:block !important; - } - } diff -Nru python-mne-0.23.4+dfsg/doc/sphinxext/sphinx_bootstrap_divs/bootstrap_divs.js python-mne-1.1.0+dfsg/doc/sphinxext/sphinx_bootstrap_divs/bootstrap_divs.js --- python-mne-0.23.4+dfsg/doc/sphinxext/sphinx_bootstrap_divs/bootstrap_divs.js 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/sphinxext/sphinx_bootstrap_divs/bootstrap_divs.js 1970-01-01 00:00:00.000000000 +0000 @@ -1,6 +0,0 @@ -$(document).ready(function () { - if(location.hash != null && location.hash != ""){ - $('.collapse').removeClass('show'); - $(location.hash + '.collapse').addClass('show'); - } -}); diff -Nru python-mne-0.23.4+dfsg/doc/sphinxext/sphinx_bootstrap_divs/__init__.py python-mne-1.1.0+dfsg/doc/sphinxext/sphinx_bootstrap_divs/__init__.py --- python-mne-0.23.4+dfsg/doc/sphinxext/sphinx_bootstrap_divs/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/sphinxext/sphinx_bootstrap_divs/__init__.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,196 +0,0 @@ -"""A .. collapse:: directive for sphinx-bootstrap-theme.""" - -import os.path as op -from docutils import nodes -from docutils.parsers.rst.directives import flag, class_option -from docutils.parsers.rst.roles import set_classes -from docutils.statemachine import StringList - -from sphinx.locale import _ -from sphinx.util.docutils import SphinxDirective -from sphinx.util.fileutil import copy_asset - -this_dir = op.dirname(__file__) - -__version__ = '0.1.0.dev0' - - -############################################################################### -# Super classes - -class DivNode(nodes.Body, nodes.Element): - """Generic DivNode class.""" - - def __init__(self, **options): - diff = set(options.keys()).symmetric_difference(set(self.OPTION_KEYS)) - assert len(diff) == 0, (diff, self.__class__.__name__) - self.options = options - super().__init__() - - def visit_node(self, node): - """Visit the node.""" - atts = {} - if node.BASECLASS: - atts['class'] = node.BASECLASS - if node.options.get('class'): - atts['class'] += \ - ' {}-{}'.format(node.BASECLASS, node.options['class']) - self.body.append(self.starttag(node, node.ELEMENT, **atts)) - - def depart_node(self, node): - """Depart the node.""" - self.body.append(''.format(node.ELEMENT)) - - -def _assemble(node, directive): - title_text = directive.arguments[0] - directive.add_name(node) - header = node.HEADER_PRETITLE.format(**node.options).split('\n') - directive.state.nested_parse( - StringList(header), directive.content_offset, node) - - textnodes, messages = directive.state.inline_text( - title_text, directive.lineno) - node += textnodes - node += messages - - header = node.HEADER_POSTTITLE.format(**node.options).split('\n') - directive.state.nested_parse( - StringList(header), directive.content_offset, node) - - directive.state.nested_parse( - directive.content, directive.content_offset, node) - - footer = node.FOOTER.format(**node.options).split('\n') - directive.state.nested_parse( - StringList(footer), directive.content_offset, node) - - -############################################################################### -# .. collapse:: - -class CollapseNode(DivNode): - """Class for .. collapse:: directive.""" - - OPTION_KEYS = ('title', 'id_', 'extra', 'class') - ELEMENT = 'div' - BASECLASS = 'card' - HEADER_PRETITLE = """.. raw:: html - -
-
-
-
""" - FOOTER = """.. raw:: html - -
""" - KNOWN_CLASSES = ( - 'default', 'primary', 'success', 'info', 'warning', 'danger') - - @staticmethod - def _check_class(class_): - if class_ not in CollapseNode.KNOWN_CLASSES: - raise ValueError(':class: option %r must be one of %s' - % (class_, CollapseNode.KNOWN_CLASSES)) - return class_ - - -class CollapseDirective(SphinxDirective): - """Collapse directive.""" - - required_arguments = 1 - optional_arguments = 0 - final_argument_whitespace = True - option_spec = {'open': flag, - 'class': CollapseNode._check_class} - has_content = True - - def run(self): - """Parse.""" - self.assert_has_content() - title_text = _(self.arguments[0]) - extra = _(' show' if 'open' in self.options else '') - class_ = {'class': self.options.get('class', 'default')} - id_ = nodes.make_id(title_text) - node = CollapseNode(title=title_text, id_=id_, extra=extra, **class_) - _assemble(node, self) - return [node] - - -############################################################################### -# .. details:: - -class DetailsNode(DivNode): - """Class for .. details:: directive.""" - - ELEMENT = 'details' - BASECLASS = '' - OPTION_KEYS = ('title', 'class') - HEADER_PRETITLE = """.. raw:: html - - """ - HEADER_POSTTITLE = """.. raw:: html - - """ - FOOTER = """""" - - -class DetailsDirective(SphinxDirective): - """Details directive.""" - - required_arguments = 1 - optional_arguments = 0 - final_argument_whitespace = True - option_spec = {'class': class_option} - has_content = True - - def run(self): - """Parse.""" - set_classes(self.options) - self.assert_has_content() - title_text = _(self.arguments[0]) - class_ = {'class': self.options.get('class', '')} - node = DetailsNode(title=title_text, **class_) - _assemble(node, self) - return [node] - - -############################################################################### -# Generic setup - -def setup(app): - """Set up for Sphinx app.""" - directives = dict( - collapse=CollapseDirective, - details=DetailsDirective, - ) - for key, value in directives.items(): - app.add_directive(key, value) - try: - app.add_css_file('bootstrap_divs.css') - except AttributeError: - app.add_stylesheet('bootstrap_divs.css') - try: - app.add_js_file('bootstrap_divs.js') - except AttributeError: - app.add_javascript('bootstrap_divs.js') - app.connect('build-finished', copy_asset_files) - for node in (CollapseNode, DetailsNode): - app.add_node(node, - html=(node.visit_node, node.depart_node), - latex=(node.visit_node, node.depart_node), - text=(node.visit_node, node.depart_node)) - return dict(version='0.1', parallel_read_safe=True, - parallel_write_safe=True) - - -def copy_asset_files(app, exc): - """Copy static assets.""" - asset_files = ['bootstrap_divs.css', 'bootstrap_divs.js'] - if exc is None: # build succeeded - for path in asset_files: - copy_asset(op.join(this_dir, path), - op.join(app.outdir, '_static')) Binary files 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+ - - - - - + + + + + + diff -Nru python-mne-0.23.4+dfsg/doc/_static/mne_logo.svg python-mne-1.1.0+dfsg/doc/_static/mne_logo.svg --- python-mne-0.23.4+dfsg/doc/_static/mne_logo.svg 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/_static/mne_logo.svg 2022-08-03 15:48:14.000000000 +0000 @@ -1,176 +1,866 @@ - - - - - - image/svg+xml - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + 2022-04-13T15:28:22.434101 + image/svg+xml + + + Matplotlib v3.5.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - + + + + + + + + + Binary files /tmp/tmprw82qwlm/pMK45BCM_h/python-mne-0.23.4+dfsg/doc/_static/pull_button.png and /tmp/tmprw82qwlm/AKapBaGA2j/python-mne-1.1.0+dfsg/doc/_static/pull_button.png differ diff -Nru python-mne-0.23.4+dfsg/doc/_static/style.css python-mne-1.1.0+dfsg/doc/_static/style.css --- python-mne-0.23.4+dfsg/doc/_static/style.css 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/_static/style.css 2022-08-03 15:48:14.000000000 +0000 @@ -5,15 +5,107 @@ --pst-font-family-base: 'Source Sans Pro', var(--pst-font-family-base-system); --pst-font-family-heading: var(--pst-font-family-base); --pst-font-family-monospace: 'Source Code Pro', var(--pst-font-family-monospace-system); - + /* colors that aren't responsive to light/dark mode */ + --mne-color-discord: #5865F2; + --mne-color-twitter: #55acee; + --mne-color-primary: #007bff; + --mne-color-primary-text: #fff; + --mne-color-primary-highlight: #0063cc; + /* font weight */ + --mne-font-weight-semibold: 600; +} + + +html[data-theme="light"] { + /* topbar logo links */ + --mne-color-github: #000; + --mne-color-discourse: #000; + /* code block copy button */ + --copybtn-opacity: 0.75; + /* card header bg color */ + --mne-color-card-header: rgba(0, 0, 0, 0.05); + /* section headings */ + --mne-color-heading: #003e80; + /* pydata-sphinx-theme overrides */ + --pst-color-primary: var(--mne-color-primary); + --pst-color-primary-text: var(--mne-color-primary-text); + --pst-color-primary-highlight: var(--mne-color-primary-highlight); + --pst-color-info: var(--pst-color-primary); + --pst-color-border: #ccc; + --pst-color-background: #fff; + --pst-color-link: var(--pst-color-primary-highlight); + /* sphinx-gallery overrides */ + --sg-download-a-background-color: var(--pst-color-primary); + --sg-download-a-background-image: unset; + --sg-download-a-border-color: var(--pst-color-border); + --sg-download-a-color: #fff; + --sg-download-a-hover-background-color: var(--pst-color-primary-highlight); + --sg-download-a-hover-box-shadow-1: none; + --sg-download-a-hover-box-shadow-2: none; +} +html[data-theme="dark"] { + /* topbar logo links */ + --mne-color-github: rgb(240, 246, 252); /* from their logo SVG */ + --mne-color-discourse: #FFF9AE; /* from their logo SVG */ + /* code block copy button */ + --copybtn-opacity: 0.25; + /* card header bg color */ + --mne-color-card-header: rgba(255, 255, 255, 0.2); + /* section headings */ + --mne-color-heading: #b8cbe0; + /* pydata-sphinx-theme overrides */ + --pst-color-primary: var(--mne-color-primary); + --pst-color-primary-text: var(--mne-color-primary-text); + --pst-color-primary-highlight: var(--mne-color-primary-highlight); + --pst-color-info: var(--pst-color-primary); + --pst-color-border: #333; + --pst-color-background: #000; + --pst-color-link: #66b0ff; + /* sphinx-gallery overrides */ + --sg-download-a-background-color: var(--pst-color-primary); + --sg-download-a-background-image: unset; + --sg-download-a-border-color: var(--pst-color-border); + --sg-download-a-color: #fff; + --sg-download-a-hover-background-color: var(--pst-color-primary-highlight); + --sg-download-a-hover-box-shadow-1: none; + --sg-download-a-hover-box-shadow-2: none; +} +h1, h2, h3, h4, h5, h6 { + color: var(--mne-color-heading); } /* ************************************************************ Sphinx fixes */ + +/* API docs parameter lists */ dl.field-list { grid-template-columns: auto 1fr; } -/* ********************************************************** Sphinx-gallery */ +/* make HTML'd pandas dataframes scrollable */ +table.dataframe { + display: block; + overflow: auto; +} + +/* Long API titles need to wrap for mobile */ +div[id^="mne-"] h1, +div[id^="examples-using-"] h2 { + word-break: break-word; +} + +/* *********************************************** pydata-sphinx-theme fixes */ + +/* this is most critical for the homepage logos, but affects all images */ +html[data-theme="dark"] img { + filter: none; +} + +/* prev/next links */ +.prev-next-area a p.prev-next-title { + color: var(--pst-color-link); +} + +/* **************************************************** sphinx-gallery fixes */ /* backreference links: restore hover decoration that SG removes */ a.sphx-glr-backref-instance:hover { @@ -25,8 +117,8 @@ } /* backreference links: make MNE calls bold and colorful */ a[class^="sphx-glr-backref-module-mne"] { - font-weight: 600; color: rgb(var(--pst-color-link)); + font-weight: var(--mne-font-weight-semibold); } /* suppress redundant note at top of every tutorial and signature at the end */ div.sphx-glr-download-link-note, @@ -38,9 +130,10 @@ } /* script/notebook download buttons */ .sphx-glr-download a.download { - background-image: none; - background-color: rgba(var(--pst-color-info), 0.1); - border-color: rgb(var(--pst-color-info)); + border-radius: 0.5rem; + /* ↓↓↓↓↓↓↓ these two rules copied from sphinx-design */ + box-shadow: 0 .125rem .25rem var(--sd-color-shadow) !important; + transition: color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out; } /* Report embedding */ iframe.sg_report { @@ -50,89 +143,111 @@ display: block; border-style: solid; } -/* gallery thumbnail size */ -.sphx-glr-thumbcontainer { - min-width: 160px; - height: 250px; +/* Disable thumbnail tooltips on hover */ +.sphx-glr-thumbcontainer[tooltip]:hover::before, +.sphx-glr-thumbcontainer[tooltip]:hover::after { + display: none; +} + +/* ***************************************************** sphinx-design fixes */ +p.btn a { + color: unset; +} +/* sphinx-design tabs */ +html .bd-content .sd-tab-set > label:hover, +.bd-content .sd-tab-set > input:not(:checked) + label:hover { + opacity: unset; + color: var(--pst-color-secondary); + border-color: var(--pst-color-secondary); +} + +/* ************************************************************* copy button */ +button.copybtn { + /* always show; https://sphinx-copybutton.readthedocs.io/en/latest/use.html#modify-the-copy-button-s-css */ + opacity: var(--copybtn-opacity); + } + +/* *************************************************** bib reference spacing */ +aside.footnote { + margin-bottom: 0.5rem; +} +aside.footnote:last-child { + margin-bottom: 1rem; } -/* ******************************** make HTML'd pandas dataframes scrollable */ -table.dataframe { - display: block; - overflow: auto; +/* ******************************************************** version dropdown */ +.dropdown-toggle { + font-weight: var(--mne-font-weight-semibold); } -/* ********************************* Long API titles need to wrap for mobile */ -div[id^="mne-"] h1, -div[id^="examples-using-"] h2 { - word-break: break-word; +/* ******************************************************* navbar icon links */ +#navbar-icon-links i.fa-github-square::before { + color: var(--mne-color-github); +} +#navbar-icon-links i.fa-twitter-square::before { + color: var(--mne-color-twitter); +} +#navbar-icon-links i.fa-discourse::before { + color: var(--mne-color-discourse); +} +#navbar-icon-links i.fa-discord::before { + color: var(--mne-color-discord); } -/* ******************************************* in-text sidebar callout boxes */ -div.sidebar, -aside.sidebar { - margin: 0 0 0.5em 1em; - padding: 7px; - width: 40%; - float: right; - clear: right; - overflow-x: auto; - /* above copied from div.sidebar in basic.css; below are our overrides */ - background-color: rgba(var(--pst-color-info), 0.1); - border: 1px solid rgb(var(--pst-color-info)); - border-radius: 4px; +/* ************************************************************ nav elements */ +/* topbar nav inactive */ +.bd-header.navbar-light#navbar-main .navbar-nav li a.nav-link { + color: var(--pst-color-text-muted); +} +/* topbar nav active */ +.bd-header.navbar-light#navbar-main .navbar-nav > li.active > .nav-link { + color: var(--pst-color-link); + font-weight: var(--mne-font-weight-semibold); +} +/* topbar nav hover */ +.bd-header.navbar-light#navbar-main .navbar-nav li a.nav-link:focus, +.bd-header.navbar-light#navbar-main .navbar-nav li a.nav-link:hover { + color: var(--pst-color-secondary); +} +/* sidebar nav */ +nav.bd-links .active > a, +nav.bd-links .active:hover > a, +.toc-entry a.nav-link.active, +.toc-entry a.nav-link.active:hover { + color: var(--pst-color-link); +} +/* sidebar nav hover */ +nav.bd-links li > a:hover, +.toc-entry a.nav-link:hover { + color: var(--pst-color-secondary); } -/* **************************************************************** homepage */ +/* *********************************************************** homepage logo */ img.logo { max-width: 360px; width: 100%; } -/* ************************************* homepage quick links & funders list */ -ul.quicklinks { - font-weight: 600; +/* **************************************************** homepage quick links */ +ul.quicklinks a { + font-weight: var(--mne-font-weight-semibold); + color: var(--pst-color-text-base); } ul.quicklinks a:hover { text-decoration: none; + color: var(--pst-color-secondary); } -ul.funders li { - margin-left: 36px; - text-indent: -36px; - padding-bottom: 9px; -} -ul.funders li img { - width: 30px; - max-height: 24px; - object-fit: contain; -} - -/* these two also affect collapsible divs */ h5.card-header { margin-top: 0px; margin-bottom: 0px; + color: var(--pst-color-text-base); } h5.card-header::before { height: 0px; margin-top: 0px; } -/* ************************************************* dev version warning bar */ -.devbar { - /* body top padding minus navbar height; */ - /*might be possible to calc from theme variables */ - margin-top: -20px; -} - -/* ******************************************************** version dropdown */ -.dropdown { - padding: 0 .5rem; /* match other items in the hamburger menu */ -} -.dropdown-toggle { - font-weight: 600; -} - -/* ***************************************************** front page carousel */ +/* ******************************************************* homepage carousel */ div.frontpage-gallery { overflow: hidden; height: 180px; @@ -161,31 +276,48 @@ opacity: 0.0; transition: 200ms linear; } - -/* ****************************************************** navbar quick links */ - -i.fa-github-square:before { - color: #000; -} -i.fa-twitter-square:before { - color: #55acee; +/* affects the homepage gallery tiles and the homepage sidebar quicklinks card; + needed for dark mode. */ +div.card { + border: 1px solid var(--pst-color-border); + background-color: rgb(var(--pst-color-background)); +} +.card-header { + border-bottom-color: var(--pst-color-border); + background-color: var(--mne-color-card-header); +} + +/* *************************************** homepage funder/institution logos */ +div#funder-logos div.card, +div#institution-logos div.card, +div#funder-logos div.card img, +div#institution-logos div.card img { + background-color: unset; + border: none; + border-radius: unset; } -i.fa-discourse:before { - color: #231e20; + +/* ************************************************************ funders page */ +ul.funders li { + margin-left: 36px; + text-indent: -36px; + padding-bottom: 9px; } -i.fa-discord:before { - color: #7289da; /* 99aab5 is also in-brand for discord */ +ul.funders li img { + width: 30px; + max-height: 24px; + object-fit: contain; + background-color: unset !important; } -/* ************************************************* Previous / Next buttons */ -.prev-next-bottom a.left-prev:before { - content:"❮\00A0" -} -.prev-next-bottom a.right-next:after { - content:"\00A0❯" +/* *********************************************************** contrib guide */ +ul.icon-bullets { + list-style-type: none; + padding-left: 2em; + text-indent: -1.5em; } -.prev-next-bottom a.right-next { - text-align: right; +.small-stack { + font-size: 0.5em; } /* *********************************************************** miscellaneous */ diff -Nru python-mne-0.23.4+dfsg/doc/_static/versions.json python-mne-1.1.0+dfsg/doc/_static/versions.json --- python-mne-0.23.4+dfsg/doc/_static/versions.json 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/_static/versions.json 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,72 @@ +[ + { + "name": "1.2 (devel)", + "version": "dev", + "url": "https://mne.tools/dev/" + }, + { + "name": "1.1 (stable)", + "version": "stable", + "url": "https://mne.tools/stable/" + }, + { + "version": "1.0", + "url": "https://mne.tools/1.0/" + }, + { + "version": "0.24", + "url": "https://mne.tools/0.24/" + }, + { + "version": "0.23", + "url": "https://mne.tools/0.23/" + }, + { + "version": "0.22", + "url": "https://mne.tools/0.22/" + }, + { + "version": "0.21", + "url": "https://mne.tools/0.21/" + }, + { + "version": "0.20", + "url": "https://mne.tools/0.20/" + }, + { + "version": "0.19", + "url": "https://mne.tools/0.19/" + }, + { + "version": "0.18", + "url": "https://mne.tools/0.18/" + }, + { + "version": "0.17", + "url": "https://mne.tools/0.17/" + }, + { + "version": "0.16", + "url": "https://mne.tools/0.16/" + }, + { + "version": "0.15", + "url": "https://mne.tools/0.15/" + }, + { + "version": "0.14", + "url": "https://mne.tools/0.14/" + }, + { + "version": "0.13", + "url": "https://mne.tools/0.13/" + }, + { + "version": "0.12", + "url": "https://mne.tools/0.12/" + }, + { + "version": "0.11", + "url": "https://mne.tools/0.11/" + } +] diff -Nru python-mne-0.23.4+dfsg/doc/_templates/autosummary/class_no_inherited_members.rst python-mne-1.1.0+dfsg/doc/_templates/autosummary/class_no_inherited_members.rst --- python-mne-0.23.4+dfsg/doc/_templates/autosummary/class_no_inherited_members.rst 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/_templates/autosummary/class_no_inherited_members.rst 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,13 @@ +{{ fullname | escape | underline}} + +.. currentmodule:: {{ module }} + +.. autoclass:: {{ objname }} + :special-members: __contains__,__getitem__,__iter__,__len__,__add__,__sub__,__mul__,__div__,__neg__ + :members: + :no-inherited-members: + +.. _sphx_glr_backreferences_{{ fullname }}: + +.. minigallery:: {{ fullname }} + :add-heading: diff -Nru python-mne-0.23.4+dfsg/doc/_templates/autosummary/class.rst python-mne-1.1.0+dfsg/doc/_templates/autosummary/class.rst --- python-mne-0.23.4+dfsg/doc/_templates/autosummary/class.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/_templates/autosummary/class.rst 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ .. currentmodule:: {{ module }} .. autoclass:: {{ objname }} - :special-members: __contains__,__getitem__,__iter__,__len__,__add__,__sub__,__mul__,__div__,__neg__,__hash__ + :special-members: __contains__,__getitem__,__iter__,__len__,__add__,__sub__,__mul__,__div__,__neg__ :members: .. _sphx_glr_backreferences_{{ fullname }}: diff -Nru python-mne-0.23.4+dfsg/doc/_templates/homepage.html python-mne-1.1.0+dfsg/doc/_templates/homepage.html --- python-mne-0.23.4+dfsg/doc/_templates/homepage.html 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/_templates/homepage.html 2022-08-03 15:48:14.000000000 +0000 @@ -5,7 +5,7 @@
- {{ item.alt }} + {{ item.alt }}

{{ item.title }}

{{ item.text }}

@@ -19,11 +19,11 @@

Funders

-
+
{% for item in funders -%} -
+
- {{ item.title }} + {{ item.title }}
{% endfor %} @@ -32,11 +32,11 @@

Supporting institutions

-
+
{% for inst in institutions -%} -
+
- {{ inst.name }} + {{ inst.name }}
{% endfor %} diff -Nru python-mne-0.23.4+dfsg/doc/_templates/layout.html python-mne-1.1.0+dfsg/doc/_templates/layout.html --- python-mne-0.23.4+dfsg/doc/_templates/layout.html 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/_templates/layout.html 2022-08-03 15:48:14.000000000 +0000 @@ -5,29 +5,76 @@ {{ super() }} {% endblock %} - -{% block docs_toc %} -
- {% if meta is defined and not (meta is not none and 'notoc' in meta) %} - {% for toc_item in theme_page_sidebar_items %} -
- {% include toc_item %} +{%- block content %} + + + + + + + + + + + +
+
+
+ {% include "../components/search-field.html" %}
- {% endfor %} - {% endif %} -
-{% endblock %} +
-{% block docs_body %} -
- {% block body %} {% endblock %} - {% if pagename == 'index' %} - {%- include 'homepage.html' -%} - {% endif %} -
-{% endblock %} + {% include "sections/announcement.html" %} + + {% block docs_navbar %} + + {% endblock %} -{%- block scripts_end %} +
+
+ {% include "sections/sidebar-primary.html" %} + + {% if pagename != 'index' %} + {% include "sections/sidebar-secondary.html" %} + {% endif %} + + + {% block docs_main %} +
+ +
+ {% include "sections/header-article.html" %} +
+ + + {% block docs_body %} +
+ {% block body %} {% endblock %} + {% if pagename == 'index' %} + {%- include 'homepage.html' -%} + {% endif %} +
+ {% endblock %} + + + {% if theme_show_prev_next %} +
+ {% include "sections/footer-article.html" %} +
+ {% endif %} +
+ {% endblock %} +
+
+ + {# Scripts placed at the end of the page to cut down on time to first content #} + + {%- block scripts_end %} {{ super() }} + {%- endblock %} + + {%- endblock %} diff -Nru python-mne-0.23.4+dfsg/doc/_templates/version-switcher.html python-mne-1.1.0+dfsg/doc/_templates/version-switcher.html --- python-mne-0.23.4+dfsg/doc/_templates/version-switcher.html 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/_templates/version-switcher.html 1970-01-01 00:00:00.000000000 +0000 @@ -1,11 +0,0 @@ -
- -
- {%- for ver, txt in versions_dropdown.items() %} - {{ txt }} - {%- endfor %} -
-
diff -Nru python-mne-0.23.4+dfsg/doc/visualization.rst python-mne-1.1.0+dfsg/doc/visualization.rst --- python-mne-0.23.4+dfsg/doc/visualization.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/visualization.rst 2022-08-03 15:48:14.000000000 +0000 @@ -15,6 +15,7 @@ Brain ClickableImage + Figure3D add_background_image centers_to_edges compare_fiff @@ -23,8 +24,11 @@ mne_analyze_colormap plot_bem plot_brain_colorbar - plot_connectivity_circle + plot_bridged_electrodes + plot_chpi_snr plot_cov + plot_channel_labels_circle + plot_ch_adjacency plot_csd plot_dipole_amplitudes plot_dipole_locations @@ -52,10 +56,10 @@ plot_layout plot_montage plot_projs_topomap + plot_projs_joint plot_raw plot_raw_psd plot_sensors - plot_sensors_connectivity plot_snr_estimate plot_source_estimates link_brains @@ -75,4 +79,9 @@ set_3d_view set_3d_title create_3d_figure + close_3d_figure + close_all_3d_figures get_brain_class + set_browser_backend + get_browser_backend + use_browser_backend diff -Nru python-mne-0.23.4+dfsg/doc/whats_new.rst python-mne-1.1.0+dfsg/doc/whats_new.rst --- python-mne-0.23.4+dfsg/doc/whats_new.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/doc/whats_new.rst 2022-08-03 15:48:14.000000000 +0000 @@ -5,6 +5,9 @@ .. currentmodule:: mne +.. include:: changes/1.1.inc +.. include:: changes/1.0.inc +.. include:: changes/0.24.inc .. include:: changes/0.23.inc .. include:: changes/0.22.inc .. include:: changes/0.21.inc @@ -27,5 +30,6 @@ .. include:: changes/0.4.inc .. include:: changes/0.3.inc .. include:: changes/0.2.inc +.. include:: changes/0.1.inc .. include:: changes/names.inc .. include:: links.inc diff -Nru python-mne-0.23.4+dfsg/environment.yml python-mne-1.1.0+dfsg/environment.yml --- python-mne-0.23.4+dfsg/environment.yml 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/environment.yml 2022-08-03 15:48:14.000000000 +0000 @@ -7,11 +7,17 @@ - numpy - scipy - matplotlib +- tqdm +- pooch>=1.5 +- decorator +- h5io +- packaging - numba - pandas - xlrd - scikit-learn - h5py +- jinja2 - pillow - statsmodels - jupyter @@ -19,21 +25,26 @@ - psutil - numexpr - imageio -- tqdm - spyder-kernels>=1.10.0 - imageio-ffmpeg>=0.4.1 - vtk>=9.0.1 -- pyvista>=0.24 -- pyvistaqt>=0.2.0 -- mayavi -- PySurfer +- traitlets +- pyvista>=0.32,!=0.35.2 +- pyvistaqt>=0.4 +- qdarkstyle +- darkdetect - dipy - nibabel - nilearn - python-picard -- pyqt!=5.15.3 +- qtpy +- pyqt!=5.15.3,!=5.15.4 - mne - mffpy>=0.5.7 - ipywidgets -- pip: - - ipyvtk-simple +- ipyvtklink +- mne-qt-browser +- pymatreader +- eeglabio +- edflib-python +- pybv diff -Nru python-mne-0.23.4+dfsg/examples/connectivity/cwt_sensor_connectivity.py python-mne-1.1.0+dfsg/examples/connectivity/cwt_sensor_connectivity.py --- python-mne-0.23.4+dfsg/examples/connectivity/cwt_sensor_connectivity.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/connectivity/cwt_sensor_connectivity.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,83 +0,0 @@ -""" -============================================================== -Compute seed-based time-frequency connectivity in sensor space -============================================================== - -Computes the connectivity between a seed-gradiometer close to the visual cortex -and all other gradiometers. The connectivity is computed in the time-frequency -domain using Morlet wavelets and the debiased squared weighted phase lag index -:footcite:`VinckEtAl2011` is used as connectivity metric. -""" -# Author: Martin Luessi -# -# License: BSD (3-clause) - -import numpy as np - -import mne -from mne import io -from mne.connectivity import spectral_connectivity, seed_target_indices -from mne.datasets import sample -from mne.time_frequency import AverageTFR - -print(__doc__) - -############################################################################### -# Set parameters -data_path = sample.data_path() -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' - -# Setup for reading the raw data -raw = io.read_raw_fif(raw_fname) -events = mne.read_events(event_fname) - -# Add a bad channel -raw.info['bads'] += ['MEG 2443'] - -# Pick MEG gradiometers -picks = mne.pick_types(raw.info, meg='grad', eeg=False, stim=False, eog=True, - exclude='bads') - -# Create epochs for left-visual condition -event_id, tmin, tmax = 3, -0.2, 0.5 -epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks, - baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6), - preload=True) - -# Use 'MEG 2343' as seed -seed_ch = 'MEG 2343' -picks_ch_names = [raw.ch_names[i] for i in picks] - -# Create seed-target indices for connectivity computation -seed = picks_ch_names.index(seed_ch) -targets = np.arange(len(picks)) -indices = seed_target_indices(seed, targets) - -# Define wavelet frequencies and number of cycles -cwt_freqs = np.arange(7, 30, 2) -cwt_n_cycles = cwt_freqs / 7. - -# Run the connectivity analysis using 2 parallel jobs -sfreq = raw.info['sfreq'] # the sampling frequency -con, freqs, times, _, _ = spectral_connectivity( - epochs, indices=indices, - method='wpli2_debiased', mode='cwt_morlet', sfreq=sfreq, - cwt_freqs=cwt_freqs, cwt_n_cycles=cwt_n_cycles, n_jobs=1) - -# Mark the seed channel with a value of 1.0, so we can see it in the plot -con[np.where(indices[1] == seed)] = 1.0 - -# Show topography of connectivity from seed -title = 'WPLI2 - Visual - Seed %s' % seed_ch - -layout = mne.find_layout(epochs.info, 'meg') # use full layout - -tfr = AverageTFR(epochs.info, con, times, freqs, len(epochs)) -tfr.plot_topo(fig_facecolor='w', font_color='k', border='k') - - -############################################################################### -# References -# ---------- -# .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/connectivity/mixed_source_space_connectivity.py python-mne-1.1.0+dfsg/examples/connectivity/mixed_source_space_connectivity.py --- python-mne-0.23.4+dfsg/examples/connectivity/mixed_source_space_connectivity.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/connectivity/mixed_source_space_connectivity.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,185 +0,0 @@ -""" -=============================================================================== -Compute mixed source space connectivity and visualize it using a circular graph -=============================================================================== - -This example computes the all-to-all connectivity between 75 regions in a -mixed source space based on dSPM inverse solutions and a FreeSurfer cortical -parcellation. The connectivity is visualized using a circular graph which -is ordered based on the locations of the regions in the axial plane. -""" -# Author: Annalisa Pascarella -# -# License: BSD (3-clause) - -import os.path as op -import numpy as np -import mne -import matplotlib.pyplot as plt - -from mne.datasets import sample -from mne import setup_volume_source_space, setup_source_space -from mne import make_forward_solution -from mne.io import read_raw_fif -from mne.minimum_norm import make_inverse_operator, apply_inverse_epochs -from mne.connectivity import spectral_connectivity -from mne.viz import circular_layout, plot_connectivity_circle - -# Set directories -data_path = sample.data_path() -subject = 'sample' -data_dir = op.join(data_path, 'MEG', subject) -subjects_dir = op.join(data_path, 'subjects') -bem_dir = op.join(subjects_dir, subject, 'bem') - -# Set file names -fname_aseg = op.join(subjects_dir, subject, 'mri', 'aseg.mgz') - -fname_model = op.join(bem_dir, '%s-5120-bem.fif' % subject) -fname_bem = op.join(bem_dir, '%s-5120-bem-sol.fif' % subject) - -fname_raw = data_dir + '/sample_audvis_filt-0-40_raw.fif' -fname_trans = data_dir + '/sample_audvis_raw-trans.fif' -fname_cov = data_dir + '/ernoise-cov.fif' -fname_event = data_dir + '/sample_audvis_filt-0-40_raw-eve.fif' - -# List of sub structures we are interested in. We select only the -# sub structures we want to include in the source space -labels_vol = ['Left-Amygdala', - 'Left-Thalamus-Proper', - 'Left-Cerebellum-Cortex', - 'Brain-Stem', - 'Right-Amygdala', - 'Right-Thalamus-Proper', - 'Right-Cerebellum-Cortex'] - -# Setup a surface-based source space, oct5 is not very dense (just used -# to speed up this example; we recommend oct6 in actual analyses) -src = setup_source_space(subject, subjects_dir=subjects_dir, - spacing='oct5', add_dist=False) - -# Setup a volume source space -# set pos=10.0 for speed, not very accurate; we recommend something smaller -# like 5.0 in actual analyses: -vol_src = setup_volume_source_space( - subject, mri=fname_aseg, pos=10.0, bem=fname_model, - add_interpolator=False, # just for speed, usually use True - volume_label=labels_vol, subjects_dir=subjects_dir) -# Generate the mixed source space -src += vol_src - -# Load data -raw = read_raw_fif(fname_raw) -raw.pick_types(meg=True, eeg=False, eog=True, stim=True).load_data() -events = mne.find_events(raw) -noise_cov = mne.read_cov(fname_cov) - -# compute the fwd matrix -fwd = make_forward_solution(raw.info, fname_trans, src, fname_bem, - mindist=5.0) # ignore sources<=5mm from innerskull -del src - -# Define epochs for left-auditory condition -event_id, tmin, tmax = 1, -0.2, 0.5 -reject = dict(mag=4e-12, grad=4000e-13, eog=150e-6) -epochs = mne.Epochs(raw, events, event_id, tmin, tmax, - reject=reject, preload=False) -del raw - -# Compute inverse solution and for each epoch -snr = 1.0 # use smaller SNR for raw data -inv_method = 'dSPM' -parc = 'aparc' # the parcellation to use, e.g., 'aparc' 'aparc.a2009s' - -lambda2 = 1.0 / snr ** 2 - -# Compute inverse operator -inverse_operator = make_inverse_operator( - epochs.info, fwd, noise_cov, depth=None, fixed=False) -del fwd - -stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, inv_method, - pick_ori=None, return_generator=True) - -# Get labels for FreeSurfer 'aparc' cortical parcellation with 34 labels/hemi -labels_parc = mne.read_labels_from_annot(subject, parc=parc, - subjects_dir=subjects_dir) - -# Average the source estimates within each label of the cortical parcellation -# and each sub-structure contained in the source space. -# When mode = 'mean_flip', this option is used only for the cortical labels. -src = inverse_operator['src'] -label_ts = mne.extract_label_time_course( - stcs, labels_parc, src, mode='mean_flip', allow_empty=True, - return_generator=True) - -# We compute the connectivity in the alpha band and plot it using a circular -# graph layout -fmin = 8. -fmax = 13. -sfreq = epochs.info['sfreq'] # the sampling frequency -con, freqs, times, n_epochs, n_tapers = spectral_connectivity( - label_ts, method='pli', mode='multitaper', sfreq=sfreq, fmin=fmin, - fmax=fmax, faverage=True, mt_adaptive=True, n_jobs=1) - -# We create a list of Label containing also the sub structures -labels_aseg = mne.get_volume_labels_from_src(src, subject, subjects_dir) -labels = labels_parc + labels_aseg - -# read colors -node_colors = [label.color for label in labels] - -# We reorder the labels based on their location in the left hemi -label_names = [label.name for label in labels] -lh_labels = [name for name in label_names if name.endswith('lh')] -rh_labels = [name for name in label_names if name.endswith('rh')] - -# Get the y-location of the label -label_ypos_lh = list() -for name in lh_labels: - idx = label_names.index(name) - ypos = np.mean(labels[idx].pos[:, 1]) - label_ypos_lh.append(ypos) -try: - idx = label_names.index('Brain-Stem') -except ValueError: - pass -else: - ypos = np.mean(labels[idx].pos[:, 1]) - lh_labels.append('Brain-Stem') - label_ypos_lh.append(ypos) - - -# Reorder the labels based on their location -lh_labels = [label for (yp, label) in sorted(zip(label_ypos_lh, lh_labels))] - -# For the right hemi -rh_labels = [label[:-2] + 'rh' for label in lh_labels - if label != 'Brain-Stem' and label[:-2] + 'rh' in rh_labels] - -# Save the plot order -node_order = lh_labels[::-1] + rh_labels - -node_angles = circular_layout(label_names, node_order, start_pos=90, - group_boundaries=[0, len(label_names) // 2]) - - -# Plot the graph using node colors from the FreeSurfer parcellation. We only -# show the 300 strongest connections. -conmat = con[:, :, 0] -fig = plt.figure(num=None, figsize=(8, 8), facecolor='black') -plot_connectivity_circle(conmat, label_names, n_lines=300, - node_angles=node_angles, node_colors=node_colors, - title='All-to-All Connectivity left-Auditory ' - 'Condition (PLI)', fig=fig) - -############################################################################### -# Save the figure (optional) -# -------------------------- -# -# By default matplotlib does not save using the facecolor, even though this was -# set when the figure was generated. If not set via savefig, the labels, title, -# and legend will be cut off from the output png file:: -# -# >>> fname_fig = data_path + '/MEG/sample/plot_mixed_connect.png' -# >>> plt.savefig(fname_fig, facecolor='black') diff -Nru python-mne-0.23.4+dfsg/examples/connectivity/mne_inverse_coherence_epochs.py python-mne-1.1.0+dfsg/examples/connectivity/mne_inverse_coherence_epochs.py --- python-mne-0.23.4+dfsg/examples/connectivity/mne_inverse_coherence_epochs.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/connectivity/mne_inverse_coherence_epochs.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,145 +0,0 @@ -""" -============================================================== -Compute coherence in source space using a MNE inverse solution -============================================================== - -This example computes the coherence between a seed in the left -auditory cortex and the rest of the brain based on single-trial -MNE-dSPM inverse solutions. -""" -# Author: Martin Luessi -# -# License: BSD (3-clause) - -import numpy as np - -import mne -from mne.datasets import sample -from mne.minimum_norm import (apply_inverse, apply_inverse_epochs, - read_inverse_operator) -from mne.connectivity import seed_target_indices, spectral_connectivity - -print(__doc__) - -############################################################################### -# Read the data -# ------------- -# -# First we'll read in the sample MEG data that we'll use for computing -# coherence between channels. We'll convert this into epochs in order to -# compute the event-related coherence. - -data_path = sample.data_path() -subjects_dir = data_path + '/subjects' -fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' -fname_raw = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -fname_event = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' -label_name_lh = 'Aud-lh' -fname_label_lh = data_path + '/MEG/sample/labels/%s.label' % label_name_lh - -event_id, tmin, tmax = 1, -0.2, 0.5 -method = "dSPM" # use dSPM method (could also be MNE or sLORETA) - -# Load data. -inverse_operator = read_inverse_operator(fname_inv) -label_lh = mne.read_label(fname_label_lh) -raw = mne.io.read_raw_fif(fname_raw) -events = mne.read_events(fname_event) - -# Add a bad channel. -raw.info['bads'] += ['MEG 2443'] - -# pick MEG channels. -picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True, - exclude='bads') - -# Read epochs. -epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks, - baseline=(None, 0), - reject=dict(mag=4e-12, grad=4000e-13, eog=150e-6)) - -############################################################################### -# Choose channels for coherence estimation -# ---------------------------------------- -# -# Next we'll calculate our channel sources. Then we'll find the most active -# vertex in the left auditory cortex, which we will later use as seed for the -# connectivity computation. - -snr = 3.0 -lambda2 = 1.0 / snr ** 2 -evoked = epochs.average() -stc = apply_inverse(evoked, inverse_operator, lambda2, method, - pick_ori="normal") - -# Restrict the source estimate to the label in the left auditory cortex. -stc_label = stc.in_label(label_lh) - -# Find number and index of vertex with most power. -src_pow = np.sum(stc_label.data ** 2, axis=1) -seed_vertno = stc_label.vertices[0][np.argmax(src_pow)] -seed_idx = np.searchsorted(stc.vertices[0], seed_vertno) # index in orig stc - -# Generate index parameter for seed-based connectivity analysis. -n_sources = stc.data.shape[0] -indices = seed_target_indices([seed_idx], np.arange(n_sources)) - -############################################################################### -# Compute the inverse solution for each epoch. By using "return_generator=True" -# stcs will be a generator object instead of a list. This allows us so to -# compute the coherence without having to keep all source estimates in memory. - -snr = 1.0 # use lower SNR for single epochs -lambda2 = 1.0 / snr ** 2 -stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method, - pick_ori="normal", return_generator=True) - -############################################################################### -# Compute the coherence between sources -# ------------------------------------- -# -# Now we are ready to compute the coherence in the alpha and beta band. -# fmin and fmax specify the lower and upper freq. for each band, respectively. -# -# To speed things up, we use 2 parallel jobs and use mode='fourier', which -# uses a FFT with a Hanning window to compute the spectra (instead of -# a multitaper estimation, which has a lower variance but is slower). -# By using faverage=True, we directly average the coherence in the alpha and -# beta band, i.e., we will only get 2 frequency bins. - -fmin = (8., 13.) -fmax = (13., 30.) -sfreq = raw.info['sfreq'] # the sampling frequency - -coh, freqs, times, n_epochs, n_tapers = spectral_connectivity( - stcs, method='coh', mode='fourier', indices=indices, - sfreq=sfreq, fmin=fmin, fmax=fmax, faverage=True, n_jobs=1) - -print('Frequencies in Hz over which coherence was averaged for alpha: ') -print(freqs[0]) -print('Frequencies in Hz over which coherence was averaged for beta: ') -print(freqs[1]) - -############################################################################### -# Generate coherence sources and plot -# ----------------------------------- -# -# Finally, we'll generate a SourceEstimate with the coherence. This is simple -# since we used a single seed. For more than one seed we would have to choose -# one of the slices within ``coh``. -# -# .. note:: We use a hack to save the frequency axis as time. -# -# Finally, we'll plot this source estimate on the brain. - -tmin = np.mean(freqs[0]) -tstep = np.mean(freqs[1]) - tmin -coh_stc = mne.SourceEstimate(coh, vertices=stc.vertices, tmin=1e-3 * tmin, - tstep=1e-3 * tstep, subject='sample') - -# Now we can visualize the coherence using the plot method. -brain = coh_stc.plot('sample', 'inflated', 'both', - time_label='Coherence %0.1f Hz', - subjects_dir=subjects_dir, - clim=dict(kind='value', lims=(0.25, 0.4, 0.65))) -brain.show_view('lateral') diff -Nru python-mne-0.23.4+dfsg/examples/connectivity/mne_inverse_connectivity_spectrum.py python-mne-1.1.0+dfsg/examples/connectivity/mne_inverse_connectivity_spectrum.py --- python-mne-0.23.4+dfsg/examples/connectivity/mne_inverse_connectivity_spectrum.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/connectivity/mne_inverse_connectivity_spectrum.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,97 +0,0 @@ -""" -============================================================== -Compute full spectrum source space connectivity between labels -============================================================== - -The connectivity is computed between 4 labels across the spectrum -between 7.5 Hz and 40 Hz. -""" - -# Authors: Alexandre Gramfort -# -# License: BSD (3-clause) - -import matplotlib.pyplot as plt - -import mne -from mne.datasets import sample -from mne.minimum_norm import apply_inverse_epochs, read_inverse_operator -from mne.connectivity import spectral_connectivity - -print(__doc__) - -data_path = sample.data_path() -subjects_dir = data_path + '/subjects' -fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' -fname_raw = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -fname_event = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' - -# Load data -inverse_operator = read_inverse_operator(fname_inv) -raw = mne.io.read_raw_fif(fname_raw) -events = mne.read_events(fname_event) - -# Add a bad channel -raw.info['bads'] += ['MEG 2443'] - -# Pick MEG channels -picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True, - exclude='bads') - -# Define epochs for left-auditory condition -event_id, tmin, tmax = 1, -0.2, 0.5 -epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks, - baseline=(None, 0), reject=dict(mag=4e-12, grad=4000e-13, - eog=150e-6)) - -# Compute inverse solution and for each epoch. By using "return_generator=True" -# stcs will be a generator object instead of a list. -snr = 1.0 # use lower SNR for single epochs -lambda2 = 1.0 / snr ** 2 -method = "dSPM" # use dSPM method (could also be MNE or sLORETA) -stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method, - pick_ori="normal", return_generator=True) - -# Read some labels -names = ['Aud-lh', 'Aud-rh', 'Vis-lh', 'Vis-rh'] -labels = [mne.read_label(data_path + '/MEG/sample/labels/%s.label' % name) - for name in names] - -# Average the source estimates within each label using sign-flips to reduce -# signal cancellations, also here we return a generator -src = inverse_operator['src'] -label_ts = mne.extract_label_time_course(stcs, labels, src, mode='mean_flip', - return_generator=True) - -fmin, fmax = 7.5, 40. -sfreq = raw.info['sfreq'] # the sampling frequency - -con, freqs, times, n_epochs, n_tapers = spectral_connectivity( - label_ts, method='wpli2_debiased', mode='multitaper', sfreq=sfreq, - fmin=fmin, fmax=fmax, mt_adaptive=True, n_jobs=1) - -n_rows, n_cols = con.shape[:2] -fig, axes = plt.subplots(n_rows, n_cols, sharex=True, sharey=True) -for i in range(n_rows): - for j in range(i + 1): - if i == j: - axes[i, j].set_axis_off() - continue - - axes[i, j].plot(freqs, con[i, j, :]) - axes[j, i].plot(freqs, con[i, j, :]) - - if j == 0: - axes[i, j].set_ylabel(names[i]) - axes[0, i].set_title(names[i]) - if i == (n_rows - 1): - axes[i, j].set_xlabel(names[j]) - axes[i, j].set(xlim=[fmin, fmax], ylim=[-0.2, 1]) - axes[j, i].set(xlim=[fmin, fmax], ylim=[-0.2, 1]) - - # Show band limits - for f in [8, 12, 18, 35]: - axes[i, j].axvline(f, color='k') - axes[j, i].axvline(f, color='k') -plt.tight_layout() -plt.show() diff -Nru python-mne-0.23.4+dfsg/examples/connectivity/mne_inverse_envelope_correlation.py python-mne-1.1.0+dfsg/examples/connectivity/mne_inverse_envelope_correlation.py --- python-mne-0.23.4+dfsg/examples/connectivity/mne_inverse_envelope_correlation.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/connectivity/mne_inverse_envelope_correlation.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,106 +0,0 @@ -""" -.. _ex-envelope-correlation: - -============================================= -Compute envelope correlations in source space -============================================= - -Compute envelope correlations of orthogonalized activity -:footcite:`HippEtAl2012,KhanEtAl2018` in source space using resting state -CTF data. -""" - -# Authors: Eric Larson -# Sheraz Khan -# Denis Engemann -# -# License: BSD (3-clause) - -import os.path as op - -import numpy as np -import matplotlib.pyplot as plt - -import mne -from mne.connectivity import envelope_correlation -from mne.minimum_norm import make_inverse_operator, apply_inverse_epochs -from mne.preprocessing import compute_proj_ecg, compute_proj_eog - -data_path = mne.datasets.brainstorm.bst_resting.data_path() -subjects_dir = op.join(data_path, 'subjects') -subject = 'bst_resting' -trans = op.join(data_path, 'MEG', 'bst_resting', 'bst_resting-trans.fif') -src = op.join(subjects_dir, subject, 'bem', subject + '-oct-6-src.fif') -bem = op.join(subjects_dir, subject, 'bem', subject + '-5120-bem-sol.fif') -raw_fname = op.join(data_path, 'MEG', 'bst_resting', - 'subj002_spontaneous_20111102_01_AUX.ds') - -############################################################################## -# Here we do some things in the name of speed, such as crop (which will -# hurt SNR) and downsample. Then we compute SSP projectors and apply them. - -raw = mne.io.read_raw_ctf(raw_fname, verbose='error') -raw.crop(0, 60).pick_types(meg=True, eeg=False).load_data().resample(80) -raw.apply_gradient_compensation(3) -projs_ecg, _ = compute_proj_ecg(raw, n_grad=1, n_mag=2) -projs_eog, _ = compute_proj_eog(raw, n_grad=1, n_mag=2, ch_name='MLT31-4407') -raw.info['projs'] += projs_ecg -raw.info['projs'] += projs_eog -raw.apply_proj() -cov = mne.compute_raw_covariance(raw) # compute before band-pass of interest - -############################################################################## -# Now we band-pass filter our data and create epochs. - -raw.filter(14, 30) -events = mne.make_fixed_length_events(raw, duration=5.) -epochs = mne.Epochs(raw, events=events, tmin=0, tmax=5., - baseline=None, reject=dict(mag=8e-13), preload=True) -del raw - -############################################################################## -# Compute the forward and inverse -# ------------------------------- - -src = mne.read_source_spaces(src) -fwd = mne.make_forward_solution(epochs.info, trans, src, bem) -inv = make_inverse_operator(epochs.info, fwd, cov) -del fwd, src - -############################################################################## -# Compute label time series and do envelope correlation -# ----------------------------------------------------- - -labels = mne.read_labels_from_annot(subject, 'aparc_sub', - subjects_dir=subjects_dir) -epochs.apply_hilbert() # faster to apply in sensor space -stcs = apply_inverse_epochs(epochs, inv, lambda2=1. / 9., pick_ori='normal', - return_generator=True) -label_ts = mne.extract_label_time_course( - stcs, labels, inv['src'], return_generator=True) -corr = envelope_correlation(label_ts, verbose=True) - -# let's plot this matrix -fig, ax = plt.subplots(figsize=(4, 4)) -ax.imshow(corr, cmap='viridis', clim=np.percentile(corr, [5, 95])) -fig.tight_layout() - -############################################################################## -# Compute the degree and plot it -# ------------------------------ - -# sphinx_gallery_thumbnail_number = 2 -threshold_prop = 0.15 # percentage of strongest edges to keep in the graph -degree = mne.connectivity.degree(corr, threshold_prop=threshold_prop) -stc = mne.labels_to_stc(labels, degree) -stc = stc.in_label(mne.Label(inv['src'][0]['vertno'], hemi='lh') + - mne.Label(inv['src'][1]['vertno'], hemi='rh')) -brain = stc.plot( - clim=dict(kind='percent', lims=[75, 85, 95]), colormap='gnuplot', - subjects_dir=subjects_dir, views='dorsal', hemi='both', - smoothing_steps=25, time_label='Beta band') - -############################################################################## -# References -# ---------- -# .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/connectivity/mne_inverse_envelope_correlation_volume.py python-mne-1.1.0+dfsg/examples/connectivity/mne_inverse_envelope_correlation_volume.py --- python-mne-0.23.4+dfsg/examples/connectivity/mne_inverse_envelope_correlation_volume.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/connectivity/mne_inverse_envelope_correlation_volume.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,92 +0,0 @@ -""" -==================================================== -Compute envelope correlations in volume source space -==================================================== - -Compute envelope correlations of orthogonalized activity -:footcite:`HippEtAl2012,KhanEtAl2018` in source space using resting state -CTF data in a volume source space. -""" - -# Authors: Eric Larson -# Sheraz Khan -# Denis Engemann -# -# License: BSD (3-clause) - -import os.path as op - -import mne -from mne.beamformer import make_lcmv, apply_lcmv_epochs -from mne.connectivity import envelope_correlation -from mne.preprocessing import compute_proj_ecg, compute_proj_eog - -data_path = mne.datasets.brainstorm.bst_resting.data_path() -subjects_dir = op.join(data_path, 'subjects') -subject = 'bst_resting' -trans = op.join(data_path, 'MEG', 'bst_resting', 'bst_resting-trans.fif') -bem = op.join(subjects_dir, subject, 'bem', subject + '-5120-bem-sol.fif') -raw_fname = op.join(data_path, 'MEG', 'bst_resting', - 'subj002_spontaneous_20111102_01_AUX.ds') -crop_to = 60. - -############################################################################## -# Here we do some things in the name of speed, such as crop (which will -# hurt SNR) and downsample. Then we compute SSP projectors and apply them. - -raw = mne.io.read_raw_ctf(raw_fname, verbose='error') -raw.crop(0, crop_to).pick_types(meg=True, eeg=False).load_data().resample(80) -raw.apply_gradient_compensation(3) -projs_ecg, _ = compute_proj_ecg(raw, n_grad=1, n_mag=2) -projs_eog, _ = compute_proj_eog(raw, n_grad=1, n_mag=2, ch_name='MLT31-4407') -raw.info['projs'] += projs_ecg -raw.info['projs'] += projs_eog -raw.apply_proj() -cov = mne.compute_raw_covariance(raw) # compute before band-pass of interest - -############################################################################## -# Now we band-pass filter our data and create epochs. - -raw.filter(14, 30) -events = mne.make_fixed_length_events(raw, duration=5.) -epochs = mne.Epochs(raw, events=events, tmin=0, tmax=5., - baseline=None, reject=dict(mag=8e-13), preload=True) -del raw - -############################################################################## -# Compute the forward and inverse -# ------------------------------- - -# This source space is really far too coarse, but we do this for speed -# considerations here -pos = 15. # 1.5 cm is very broad, done here for speed! -src = mne.setup_volume_source_space('bst_resting', pos, bem=bem, - subjects_dir=subjects_dir, verbose=True) -fwd = mne.make_forward_solution(epochs.info, trans, src, bem) -data_cov = mne.compute_covariance(epochs) -filters = make_lcmv(epochs.info, fwd, data_cov, 0.05, cov, - pick_ori='max-power', weight_norm='nai') -del fwd - -############################################################################## -# Compute label time series and do envelope correlation -# ----------------------------------------------------- - -epochs.apply_hilbert() # faster to do in sensor space -stcs = apply_lcmv_epochs(epochs, filters, return_generator=True) -corr = envelope_correlation(stcs, verbose=True) - -############################################################################## -# Compute the degree and plot it -# ------------------------------ - -degree = mne.connectivity.degree(corr, 0.15) -stc = mne.VolSourceEstimate(degree, [src[0]['vertno']], 0, 1, 'bst_resting') -brain = stc.plot( - src, clim=dict(kind='percent', lims=[75, 85, 95]), colormap='gnuplot', - subjects_dir=subjects_dir, mode='glass_brain') - -############################################################################## -# References -# ---------- -# .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/connectivity/mne_inverse_label_connectivity.py python-mne-1.1.0+dfsg/examples/connectivity/mne_inverse_label_connectivity.py --- python-mne-0.23.4+dfsg/examples/connectivity/mne_inverse_label_connectivity.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/connectivity/mne_inverse_label_connectivity.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,179 +0,0 @@ -""" -========================================================================= -Compute source space connectivity and visualize it using a circular graph -========================================================================= - -This example computes the all-to-all connectivity between 68 regions in -source space based on dSPM inverse solutions and a FreeSurfer cortical -parcellation. The connectivity is visualized using a circular graph which -is ordered based on the locations of the regions in the axial plane. -""" -# Authors: Martin Luessi -# Alexandre Gramfort -# Nicolas P. Rougier (graph code borrowed from his matplotlib gallery) -# -# License: BSD (3-clause) - -import numpy as np -import matplotlib.pyplot as plt - -import mne -from mne.datasets import sample -from mne.minimum_norm import apply_inverse_epochs, read_inverse_operator -from mne.connectivity import spectral_connectivity -from mne.viz import circular_layout, plot_connectivity_circle - -print(__doc__) - -############################################################################### -# Load our data -# ------------- -# -# First we'll load the data we'll use in connectivity estimation. We'll use -# the sample MEG data provided with MNE. - -data_path = sample.data_path() -subjects_dir = data_path + '/subjects' -fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' -fname_raw = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -fname_event = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' - -# Load data -inverse_operator = read_inverse_operator(fname_inv) -raw = mne.io.read_raw_fif(fname_raw) -events = mne.read_events(fname_event) - -# Add a bad channel -raw.info['bads'] += ['MEG 2443'] - -# Pick MEG channels -picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True, - exclude='bads') - -# Define epochs for left-auditory condition -event_id, tmin, tmax = 1, -0.2, 0.5 -epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks, - baseline=(None, 0), reject=dict(mag=4e-12, grad=4000e-13, - eog=150e-6)) - -############################################################################### -# Compute inverse solutions and their connectivity -# ------------------------------------------------ -# -# Next, we need to compute the inverse solution for this data. This will return -# the sources / source activity that we'll use in computing connectivity. We'll -# compute the connectivity in the alpha band of these sources. We can specify -# particular frequencies to include in the connectivity with the ``fmin`` and -# ``fmax`` flags. Notice from the status messages how mne-python: -# -# 1. reads an epoch from the raw file -# 2. applies SSP and baseline correction -# 3. computes the inverse to obtain a source estimate -# 4. averages the source estimate to obtain a time series for each label -# 5. includes the label time series in the connectivity computation -# 6. moves to the next epoch. -# -# This behaviour is because we are using generators. Since we only need to -# operate on the data one epoch at a time, using a generator allows us to -# compute connectivity in a computationally efficient manner where the amount -# of memory (RAM) needed is independent from the number of epochs. - -# Compute inverse solution and for each epoch. By using "return_generator=True" -# stcs will be a generator object instead of a list. -snr = 1.0 # use lower SNR for single epochs -lambda2 = 1.0 / snr ** 2 -method = "dSPM" # use dSPM method (could also be MNE or sLORETA) -stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method, - pick_ori="normal", return_generator=True) - -# Get labels for FreeSurfer 'aparc' cortical parcellation with 34 labels/hemi -labels = mne.read_labels_from_annot('sample', parc='aparc', - subjects_dir=subjects_dir) -label_colors = [label.color for label in labels] - -# Average the source estimates within each label using sign-flips to reduce -# signal cancellations, also here we return a generator -src = inverse_operator['src'] -label_ts = mne.extract_label_time_course( - stcs, labels, src, mode='mean_flip', return_generator=True) - -fmin = 8. -fmax = 13. -sfreq = raw.info['sfreq'] # the sampling frequency -con_methods = ['pli', 'wpli2_debiased', 'ciplv'] -con, freqs, times, n_epochs, n_tapers = spectral_connectivity( - label_ts, method=con_methods, mode='multitaper', sfreq=sfreq, fmin=fmin, - fmax=fmax, faverage=True, mt_adaptive=True, n_jobs=1) - -# con is a 3D array, get the connectivity for the first (and only) freq. band -# for each method -con_res = dict() -for method, c in zip(con_methods, con): - con_res[method] = c[:, :, 0] - -############################################################################### -# Make a connectivity plot -# ------------------------ -# -# Now, we visualize this connectivity using a circular graph layout. - -# First, we reorder the labels based on their location in the left hemi -label_names = [label.name for label in labels] - -lh_labels = [name for name in label_names if name.endswith('lh')] - -# Get the y-location of the label -label_ypos = list() -for name in lh_labels: - idx = label_names.index(name) - ypos = np.mean(labels[idx].pos[:, 1]) - label_ypos.append(ypos) - -# Reorder the labels based on their location -lh_labels = [label for (yp, label) in sorted(zip(label_ypos, lh_labels))] - -# For the right hemi -rh_labels = [label[:-2] + 'rh' for label in lh_labels] - -# Save the plot order and create a circular layout -node_order = list() -node_order.extend(lh_labels[::-1]) # reverse the order -node_order.extend(rh_labels) - -node_angles = circular_layout(label_names, node_order, start_pos=90, - group_boundaries=[0, len(label_names) / 2]) - -# Plot the graph using node colors from the FreeSurfer parcellation. We only -# show the 300 strongest connections. -plot_connectivity_circle(con_res['pli'], label_names, n_lines=300, - node_angles=node_angles, node_colors=label_colors, - title='All-to-All Connectivity left-Auditory ' - 'Condition (PLI)') - -############################################################################### -# Make two connectivity plots in the same figure -# ---------------------------------------------- -# -# We can also assign these connectivity plots to axes in a figure. Below we'll -# show the connectivity plot using two different connectivity methods. - -fig = plt.figure(num=None, figsize=(8, 4), facecolor='black') -no_names = [''] * len(label_names) -for ii, method in enumerate(con_methods): - plot_connectivity_circle(con_res[method], no_names, n_lines=300, - node_angles=node_angles, node_colors=label_colors, - title=method, padding=0, fontsize_colorbar=6, - fig=fig, subplot=(1, 3, ii + 1)) - -plt.show() - -############################################################################### -# Save the figure (optional) -# -------------------------- -# -# By default matplotlib does not save using the facecolor, even though this was -# set when the figure was generated. If not set via savefig, the labels, title, -# and legend will be cut off from the output png file. - -# fname_fig = data_path + '/MEG/sample/plot_inverse_connect.png' -# fig.savefig(fname_fig, facecolor='black') diff -Nru python-mne-0.23.4+dfsg/examples/connectivity/mne_inverse_psi_visual.py python-mne-1.1.0+dfsg/examples/connectivity/mne_inverse_psi_visual.py --- python-mne-0.23.4+dfsg/examples/connectivity/mne_inverse_psi_visual.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/connectivity/mne_inverse_psi_visual.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,116 +0,0 @@ -""" -===================================================================== -Compute Phase Slope Index (PSI) in source space for a visual stimulus -===================================================================== - -This example demonstrates how the phase slope index (PSI) -:footcite:`NolteEtAl2008` can be computed -in source space based on single trial dSPM source estimates. In addition, -the example shows advanced usage of the connectivity estimation routines -by first extracting a label time course for each epoch and then combining -the label time course with the single trial source estimates to compute the -connectivity. - -The result clearly shows how the activity in the visual label precedes more -widespread activity (as a postivive PSI means the label time course is -leading). -""" -# Author: Martin Luessi -# -# License: BSD (3-clause) - -import numpy as np - -import mne -from mne.datasets import sample -from mne.minimum_norm import read_inverse_operator, apply_inverse_epochs -from mne.connectivity import seed_target_indices, phase_slope_index - -print(__doc__) - -data_path = sample.data_path() -subjects_dir = data_path + '/subjects' -fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' -fname_raw = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -fname_event = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' -fname_label = data_path + '/MEG/sample/labels/Vis-lh.label' - -event_id, tmin, tmax = 4, -0.2, 0.5 -method = "dSPM" # use dSPM method (could also be MNE or sLORETA) - -# Load data -inverse_operator = read_inverse_operator(fname_inv) -raw = mne.io.read_raw_fif(fname_raw, preload=True) -events = mne.read_events(fname_event) - -# pick MEG channels -picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True, - exclude='bads') - -# Read epochs -epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks, - baseline=(None, 0), reject=dict(mag=4e-12, grad=4000e-13, - eog=150e-6)) - -# Compute inverse solution and for each epoch. Note that since we are passing -# the output to both extract_label_time_course and the phase_slope_index -# functions, we have to use "return_generator=False", since it is only possible -# to iterate over generators once. -snr = 1.0 # use lower SNR for single epochs -lambda2 = 1.0 / snr ** 2 -stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method, - pick_ori="normal", return_generator=True) - -# Now, we generate seed time series by averaging the activity in the left -# visual corex -label = mne.read_label(fname_label) -src = inverse_operator['src'] # the source space used -seed_ts = mne.extract_label_time_course(stcs, label, src, mode='mean_flip', - verbose='error') - -# Combine the seed time course with the source estimates. There will be a total -# of 7500 signals: -# index 0: time course extracted from label -# index 1..7499: dSPM source space time courses -stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method, - pick_ori="normal", return_generator=True) -comb_ts = list(zip(seed_ts, stcs)) - -# Construct indices to estimate connectivity between the label time course -# and all source space time courses -vertices = [src[i]['vertno'] for i in range(2)] -n_signals_tot = 1 + len(vertices[0]) + len(vertices[1]) - -indices = seed_target_indices([0], np.arange(1, n_signals_tot)) - -# Compute the PSI in the frequency range 10Hz-20Hz. We exclude the baseline -# period from the connectivity estimation. -fmin = 10. -fmax = 20. -tmin_con = 0. -sfreq = epochs.info['sfreq'] # the sampling frequency - -psi, _, _, _, _ = phase_slope_index( - comb_ts, mode='multitaper', indices=indices, sfreq=sfreq, - fmin=fmin, fmax=fmax, tmin=tmin_con) - -# Generate a SourceEstimate with the PSI. This is simple since we used a single -# seed (inspect the indices variable to see how the PSI scores are arranged in -# the output) -psi_stc = mne.SourceEstimate(psi, vertices=vertices, tmin=0, tstep=1, - subject='sample') - -# Now we can visualize the PSI using the :meth:`~mne.SourceEstimate.plot` -# method. We use a custom colormap to show signed values -v_max = np.max(np.abs(psi)) -brain = psi_stc.plot(surface='inflated', hemi='lh', - time_label='Phase Slope Index (PSI)', - subjects_dir=subjects_dir, - clim=dict(kind='percent', pos_lims=(95, 97.5, 100))) -brain.show_view('medial') -brain.add_label(fname_label, color='green', alpha=0.7) - -############################################################################### -# References -# ---------- -# .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/connectivity/README.txt python-mne-1.1.0+dfsg/examples/connectivity/README.txt --- python-mne-0.23.4+dfsg/examples/connectivity/README.txt 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/connectivity/README.txt 2022-08-03 15:48:14.000000000 +0000 @@ -4,3 +4,7 @@ Examples demonstrating connectivity analysis in sensor and source space. +.. note:: + Connectivity functionality has moved into the + :mod:`mne-connectivity:mne_connectivity` package. Examples can be found at + :ref:`mne-connectivity:sphx_glr_auto_examples`. diff -Nru python-mne-0.23.4+dfsg/examples/connectivity/sensor_connectivity.py python-mne-1.1.0+dfsg/examples/connectivity/sensor_connectivity.py --- python-mne-0.23.4+dfsg/examples/connectivity/sensor_connectivity.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/connectivity/sensor_connectivity.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,55 +0,0 @@ -""" -=============================================== -Compute all-to-all connectivity in sensor space -=============================================== - -Computes the Phase Lag Index (PLI) between all gradiometers and shows the -connectivity in 3D using the helmet geometry. The left visual stimulation data -are used which produces strong connectvitiy in the right occipital sensors. -""" - -# Author: Martin Luessi -# -# License: BSD (3-clause) - -import mne -from mne.connectivity import spectral_connectivity -from mne.datasets import sample -from mne.viz import plot_sensors_connectivity - -print(__doc__) - -############################################################################### -# Set parameters -data_path = sample.data_path() -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' - -# Setup for reading the raw data -raw = mne.io.read_raw_fif(raw_fname) -events = mne.read_events(event_fname) - -# Add a bad channel -raw.info['bads'] += ['MEG 2443'] - -# Pick MEG gradiometers -picks = mne.pick_types(raw.info, meg='grad', eeg=False, stim=False, eog=True, - exclude='bads') - -# Create epochs for the visual condition -event_id, tmin, tmax = 3, -0.2, 1.5 # need a long enough epoch for 5 cycles -epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks, - baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6)) - -# Compute connectivity for band containing the evoked response. -# We exclude the baseline period: -fmin, fmax = 4., 9. -sfreq = raw.info['sfreq'] # the sampling frequency -tmin = 0.0 # exclude the baseline period -epochs.load_data().pick_types(meg='grad') # just keep MEG and no EOG now -con, freqs, times, n_epochs, n_tapers = spectral_connectivity( - epochs, method='pli', mode='multitaper', sfreq=sfreq, fmin=fmin, fmax=fmax, - faverage=True, tmin=tmin, mt_adaptive=False, n_jobs=1) - -# Now, visualize the connectivity in 3D: -plot_sensors_connectivity(epochs.info, con[:, :, 0]) diff -Nru python-mne-0.23.4+dfsg/examples/datasets/brainstorm_data.py python-mne-1.1.0+dfsg/examples/datasets/brainstorm_data.py --- python-mne-0.23.4+dfsg/examples/datasets/brainstorm_data.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/datasets/brainstorm_data.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-brainstorm-raw: @@ -13,7 +14,9 @@ # Authors: Mainak Jas # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np @@ -28,7 +31,7 @@ data_path = bst_raw.data_path() -raw_path = (data_path + '/MEG/bst_raw/' + +raw_path = (data_path / 'MEG' / 'bst_raw' / 'subj001_somatosensory_20111109_01_AUX-f.ds') # Here we crop to half the length to save memory raw = read_raw_ctf(raw_path).crop(0, 180).load_data() @@ -72,7 +75,7 @@ evoked.plot_topomap(times=np.array([0.016, 0.030, 0.060, 0.070]), time_unit='s') -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/datasets/hf_sef_data.py python-mne-1.1.0+dfsg/examples/datasets/hf_sef_data.py --- python-mne-0.23.4+dfsg/examples/datasets/hf_sef_data.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/datasets/hf_sef_data.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-hf-sef-data: @@ -9,7 +10,9 @@ """ # Author: Jussi Nurminen (jnu@iki.fi) # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne @@ -21,16 +24,16 @@ print(__doc__) -############################################################################### +# %% # Read evoked data evoked = mne.Evoked(fname_evoked) -############################################################################### +# %% # Create a highpass filtered version evoked_hp = evoked.copy() evoked_hp.filter(l_freq=300, h_freq=None) -############################################################################### +# %% # Compare high-pass filtered and unfiltered data on a single channel ch = 'MEG0443' pick = evoked.ch_names.index(ch) diff -Nru python-mne-0.23.4+dfsg/examples/datasets/limo_data.py python-mne-1.1.0+dfsg/examples/datasets/limo_data.py --- python-mne-0.23.4+dfsg/examples/datasets/limo_data.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/datasets/limo_data.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-limo-data: @@ -34,7 +35,9 @@ """ # noqa: E501 # Authors: Jose C. Garcia Alanis # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np import matplotlib.pyplot as plt @@ -50,7 +53,7 @@ # subject to use subj = 1 -############################################################################### +# %% # About the data # -------------- # @@ -78,14 +81,14 @@ # first time. limo_epochs = load_data(subject=subj) -############################################################################### +# %% # Note that the result of the loading process is an # :class:`mne.EpochsArray` containing the data ready to interface # with MNE-Python. print(limo_epochs) -############################################################################### +# %% # Visualize events # ---------------- # @@ -96,7 +99,7 @@ fig = plot_events(limo_epochs.events, event_id=limo_epochs.event_id) fig.suptitle("Distribution of events in LIMO epochs") -############################################################################### +# %% # As it can be seen above, conditions are coded as ``Face/A`` and ``Face/B``. # Information about the phase-coherence of the presented faces is stored in the # epochs metadata. These information can be easily accessed by calling @@ -105,7 +108,7 @@ print(limo_epochs.metadata.head()) -############################################################################### +# %% # Now let's take a closer look at the information in the epochs # metadata. @@ -113,7 +116,7 @@ epochs_summary = limo_epochs.metadata.describe(include='all').round(3) print(epochs_summary) -############################################################################### +# %% # The first column of the summary table above provides more or less the same # information as the ``print(limo_epochs)`` command we ran before. There are # 1055 faces (i.e., epochs), subdivided in 2 conditions (i.e., Face A and @@ -143,7 +146,7 @@ title='Evoked response: Face B', ts_args=ts_args) -############################################################################### +# %% # We can also compute the difference wave contrasting Face A and Face B. # Although, looking at the evoked responses above, we shouldn't expect great # differences among these face-stimuli. @@ -156,7 +159,7 @@ # plot difference wave difference_wave.plot_joint(times=[0.15], title='Difference Face A - Face B') -############################################################################### +# %% # As expected, no clear pattern appears when contrasting # Face A and Face B. However, we could narrow our search a little bit more. # Since this is a "visual paradigm" it might be best to look at electrodes @@ -174,7 +177,7 @@ # compare evoked responses plot_compare_evokeds(evokeds, picks=pick, ylim=dict(eeg=(-15, 7.5))) -############################################################################### +# %% # We do see a difference between Face A and B, but it is pretty small. # # @@ -207,7 +210,7 @@ picks=electrode, cmap=("Phase coherence", "magma")) -############################################################################### +# %% # As shown above, there are some considerable differences between the # activation patterns evoked by stimuli with low vs. high phase-coherence at # the chosen electrodes. @@ -225,7 +228,7 @@ limo_epochs.interpolate_bads(reset_bads=True) limo_epochs.drop_channels(['EXG1', 'EXG2', 'EXG3', 'EXG4']) -############################################################################### +# %% # Define predictor variables and design matrix # -------------------------------------------- # @@ -244,7 +247,7 @@ design['intercept'] = 1 design = design[predictor_vars] -############################################################################### +# %% # Now we can set up the linear model to be used in the analysis using # MNE-Python's func:`~mne.stats.linear_regression` function. @@ -252,7 +255,7 @@ design_matrix=design, names=predictor_vars) -############################################################################### +# %% # Extract regression coefficients # ------------------------------- # @@ -263,7 +266,7 @@ print('predictors are:', list(reg)) print('fields are:', [field for field in getattr(reg['intercept'], '_fields')]) -############################################################################### +# %% # Plot model results # ------------------ # @@ -281,7 +284,7 @@ title='Effect of Phase-coherence', times=[0.23]) -############################################################################### +# %% # We can also plot the corresponding T values. # use unit=False and scale=1 to keep values at their original @@ -297,7 +300,7 @@ times=[0.23]) fig.axes[0].set_ylabel('T-value') -############################################################################### +# %% # Conversely, there appears to be no (or very small) systematic effects when # comparing Face A and Face B stimuli. This is largely consistent with the # difference wave approach presented above. @@ -307,7 +310,7 @@ title='Effect of Face A vs. Face B', times=[0.23]) -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/datasets/opm_data.py python-mne-1.1.0+dfsg/examples/datasets/opm_data.py --- python-mne-0.23.4+dfsg/examples/datasets/opm_data.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/datasets/opm_data.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-opm-somatosensory: @@ -6,27 +7,25 @@ In this dataset, electrical median nerve stimulation was delivered to the left wrist of the subject. Somatosensory evoked fields were measured using -nine QuSpin SERF OPMs placed over the right-hand side somatomotor area. -Here we demonstrate how to localize these custom OPM data in MNE. +nine QuSpin SERF OPMs placed over the right-hand side somatomotor area. Here +we demonstrate how to localize these custom OPM data in MNE. """ # sphinx_gallery_thumbnail_number = 4 -import os.path as op - import numpy as np import mne data_path = mne.datasets.opm.data_path() subject = 'OPM_sample' -subjects_dir = op.join(data_path, 'subjects') -raw_fname = op.join(data_path, 'MEG', 'OPM', 'OPM_SEF_raw.fif') -bem_fname = op.join(subjects_dir, subject, 'bem', - subject + '-5120-5120-5120-bem-sol.fif') -fwd_fname = op.join(data_path, 'MEG', 'OPM', 'OPM_sample-fwd.fif') -coil_def_fname = op.join(data_path, 'MEG', 'OPM', 'coil_def.dat') +subjects_dir = data_path / 'subjects' +raw_fname = data_path / 'MEG' / 'OPM' / 'OPM_SEF_raw.fif' +bem_fname = (subjects_dir / subject / 'bem' / + f'{subject}-5120-5120-5120-bem-sol.fif') +fwd_fname = data_path / 'MEG' / 'OPM' / 'OPM_sample-fwd.fif' +coil_def_fname = data_path / 'MEG' / 'OPM' / 'coil_def.dat' -############################################################################### +# %% # Prepare data for localization # ----------------------------- # First we filter and epoch the data: @@ -54,7 +53,7 @@ cov = mne.compute_covariance(epochs, tmax=0.) del epochs, raw -############################################################################### +# %% # Examine our coordinate alignment for source localization and compute a # forward operator: # @@ -65,7 +64,7 @@ # but should be fine for these analyses. bem = mne.read_bem_solution(bem_fname) -trans = None +trans = mne.transforms.Transform('head', 'mri') # identity transformation # To compute the forward solution, we must # provide our temporary/custom coil definitions, which can be done as:: @@ -73,7 +72,7 @@ # with mne.use_coil_def(coil_def_fname): # fwd = mne.make_forward_solution( # raw.info, trans, src, bem, eeg=False, mindist=5.0, -# n_jobs=1, verbose=True) +# n_jobs=None, verbose=True) fwd = mne.read_forward_solution(fwd_fname) # use fixed orientation here just to save memory later @@ -87,13 +86,13 @@ mne.viz.set_3d_view(figure=fig, azimuth=45, elevation=60, distance=0.4, focalpoint=(0.02, 0, 0.04)) -############################################################################### +# %% # Perform dipole fitting # ---------------------- # Fit dipoles on a subset of time points with mne.use_coil_def(coil_def_fname): - dip_opm, _ = mne.fit_dipole(evoked.copy().crop(0.015, 0.080), + dip_opm, _ = mne.fit_dipole(evoked.copy().crop(0.040, 0.080), cov, bem, trans, verbose=True) idx = np.argmax(dip_opm.gof) print('Best dipole at t=%0.1f ms with %0.1f%% GOF' @@ -103,7 +102,7 @@ dip_opm.plot_locations(trans, subject, subjects_dir, mode='orthoview', idx=idx) -############################################################################### +# %% # Perform minimum-norm localization # --------------------------------- # Due to the small number of sensors, there will be some leakage of activity @@ -124,4 +123,5 @@ # Plot source estimate at time of best dipole fit brain = stc.plot(hemi='rh', views='lat', subjects_dir=subjects_dir, initial_time=dip_opm.times[idx], - clim=dict(kind='percent', lims=[99, 99.9, 99.99])) + clim=dict(kind='percent', lims=[99, 99.9, 99.99]), + size=(400, 300), background='w') diff -Nru python-mne-0.23.4+dfsg/examples/datasets/spm_faces_dataset_sgskip.py python-mne-1.1.0+dfsg/examples/datasets/spm_faces_dataset_sgskip.py --- python-mne-0.23.4+dfsg/examples/datasets/spm_faces_dataset_sgskip.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/datasets/spm_faces_dataset_sgskip.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-spm-faces: @@ -18,7 +19,9 @@ # Authors: Alexandre Gramfort # Denis Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% # sphinx_gallery_thumbnail_number = 10 @@ -33,12 +36,13 @@ print(__doc__) data_path = spm_face.data_path() -subjects_dir = data_path + '/subjects' +subjects_dir = data_path / 'subjects' +spm_path = data_path / 'MEG' / 'spm' -############################################################################### +# %% # Load and filter data, set up epochs -raw_fname = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces%d_3D.ds' +raw_fname = spm_path / 'SPM_CTF_MEG_example_faces%d_3D.ds' raw = io.read_raw_ctf(raw_fname % 1, preload=True) # Take first run # Here to save memory and time we'll downsample heavily -- this is not @@ -90,32 +94,31 @@ noise_cov = mne.compute_covariance(epochs, tmax=0, method='shrunk', rank=None) -############################################################################### +# %% # Visualize fields on MEG helmet # The transformation here was aligned using the dig-montage. It's included in # the spm_faces dataset and is named SPM_dig_montage.fif. -trans_fname = data_path + ('/MEG/spm/SPM_CTF_MEG_example_faces1_3D_' - 'raw-trans.fif') +trans_fname = spm_path / 'SPM_CTF_MEG_example_faces1_3D_raw-trans.fif' maps = mne.make_field_map(evoked[0], trans_fname, subject='spm', - subjects_dir=subjects_dir, n_jobs=1) + subjects_dir=subjects_dir, n_jobs=None) evoked[0].plot_field(maps, time=0.170) -############################################################################### +# %% # Look at the whitened evoked daat evoked[0].plot_white(noise_cov) -############################################################################### +# %% # Compute forward model -src = data_path + '/subjects/spm/bem/spm-oct-6-src.fif' -bem = data_path + '/subjects/spm/bem/spm-5120-5120-5120-bem-sol.fif' +src = subjects_dir / 'spm' / 'bem' / 'spm-oct-6-src.fif' +bem = subjects_dir / 'spm' / 'bem' / 'spm-5120-5120-5120-bem-sol.fif' forward = mne.make_forward_solution(contrast.info, trans_fname, src, bem) -############################################################################### +# %% # Compute inverse solution snr = 3.0 @@ -129,7 +132,7 @@ stc = apply_inverse(contrast, inverse_operator, lambda2, method, pick_ori=None) # stc.save('spm_%s_dSPM_inverse' % contrast.comment) -# Plot contrast in 3D with PySurfer if available +# Plot contrast in 3D with mne.viz.Brain if available brain = stc.plot(hemi='both', subjects_dir=subjects_dir, initial_time=0.170, views=['ven'], clim={'kind': 'value', 'lims': [3., 6., 9.]}) # brain.save_image('dSPM_map.png') diff -Nru python-mne-0.23.4+dfsg/examples/decoding/decoding_csp_eeg.py python-mne-1.1.0+dfsg/examples/decoding/decoding_csp_eeg.py --- python-mne-0.23.4+dfsg/examples/decoding/decoding_csp_eeg.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/decoding/decoding_csp_eeg.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-decoding-csp-eeg: @@ -15,7 +16,9 @@ """ # Authors: Martin Billinger # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np @@ -49,9 +52,6 @@ montage = make_standard_montage('standard_1005') raw.set_montage(montage) -# strip channel names of "." characters -raw.rename_channels(lambda x: x.strip('.')) - # Apply band-pass filter raw.filter(7., 30., fir_design='firwin', skip_by_annotation='edge') @@ -67,7 +67,7 @@ epochs_train = epochs.copy().crop(tmin=1., tmax=2.) labels = epochs.events[:, -1] - 2 -############################################################################### +# %% # Classification with linear discrimant analysis # Define a monte-carlo cross-validation generator (reduce variance): @@ -83,7 +83,7 @@ # Use scikit-learn Pipeline with cross_val_score function clf = Pipeline([('CSP', csp), ('LDA', lda)]) -scores = cross_val_score(clf, epochs_data_train, labels, cv=cv, n_jobs=1) +scores = cross_val_score(clf, epochs_data_train, labels, cv=cv, n_jobs=None) # Printing the results class_balance = np.mean(labels == labels[0]) @@ -96,7 +96,7 @@ csp.plot_patterns(epochs.info, ch_type='eeg', units='Patterns (AU)', size=1.5) -############################################################################### +# %% # Look at performance over time sfreq = raw.info['sfreq'] diff -Nru python-mne-0.23.4+dfsg/examples/decoding/decoding_csp_timefreq.py python-mne-1.1.0+dfsg/examples/decoding/decoding_csp_timefreq.py --- python-mne-0.23.4+dfsg/examples/decoding/decoding_csp_timefreq.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/decoding/decoding_csp_timefreq.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-decoding-csp-eeg-timefreq: @@ -16,7 +17,9 @@ # Alex Barachant # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np @@ -33,7 +36,7 @@ from sklearn.pipeline import make_pipeline from sklearn.preprocessing import LabelEncoder -############################################################################### +# %% # Set parameters and read data event_id = dict(hands=2, feet=3) # motor imagery: hands vs feet subject = 1 @@ -50,15 +53,15 @@ # Assemble the classifier using scikit-learn pipeline clf = make_pipeline(CSP(n_components=4, reg=None, log=True, norm_trace=False), LinearDiscriminantAnalysis()) -n_splits = 5 # how many folds to use for cross-validation +n_splits = 3 # for cross-validation, 5 is better, here we use 3 for speed cv = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42) # Classification & time-frequency parameters tmin, tmax = -.200, 2.000 n_cycles = 10. # how many complete cycles: used to define window size -min_freq = 5. -max_freq = 25. -n_freqs = 8 # how many frequency bins to use +min_freq = 8. +max_freq = 20. +n_freqs = 6 # how many frequency bins to use # Assemble list of frequency range tuples freqs = np.linspace(min_freq, max_freq, n_freqs) # assemble frequencies @@ -72,7 +75,7 @@ # Instantiate label encoder le = LabelEncoder() -############################################################################### +# %% # Loop through frequencies, apply classifier and save scores # init scores @@ -85,7 +88,7 @@ w_size = n_cycles / ((fmax + fmin) / 2.) # in seconds # Apply band-pass filter to isolate the specified frequencies - raw_filter = raw.copy().filter(fmin, fmax, n_jobs=1, fir_design='firwin', + raw_filter = raw.copy().filter(fmin, fmax, fir_design='firwin', skip_by_annotation='edge') # Extract epochs from filtered data, padded by window size @@ -97,11 +100,10 @@ X = epochs.get_data() # Save mean scores over folds for each frequency and time window - freq_scores[freq] = np.mean(cross_val_score(estimator=clf, X=X, y=y, - scoring='roc_auc', cv=cv, - n_jobs=1), axis=0) + freq_scores[freq] = np.mean(cross_val_score( + estimator=clf, X=X, y=y, scoring='roc_auc', cv=cv), axis=0) -############################################################################### +# %% # Plot frequency results plt.bar(freqs[:-1], freq_scores, width=np.diff(freqs)[0], @@ -115,7 +117,7 @@ plt.ylabel('Decoding Scores') plt.title('Frequency Decoding Scores') -############################################################################### +# %% # Loop through frequencies and time, apply classifier and save scores # init scores @@ -128,7 +130,7 @@ w_size = n_cycles / ((fmax + fmin) / 2.) # in seconds # Apply band-pass filter to isolate the specified frequencies - raw_filter = raw.copy().filter(fmin, fmax, n_jobs=1, fir_design='firwin', + raw_filter = raw.copy().filter(fmin, fmax, fir_design='firwin', skip_by_annotation='edge') # Extract epochs from filtered data, padded by window size @@ -148,11 +150,10 @@ X = epochs.copy().crop(w_tmin, w_tmax).get_data() # Save mean scores over folds for each frequency and time window - tf_scores[freq, t] = np.mean(cross_val_score(estimator=clf, X=X, y=y, - scoring='roc_auc', cv=cv, - n_jobs=1), axis=0) + tf_scores[freq, t] = np.mean(cross_val_score( + estimator=clf, X=X, y=y, scoring='roc_auc', cv=cv), axis=0) -############################################################################### +# %% # Plot time-frequency results # Set up time frequency object diff -Nru python-mne-0.23.4+dfsg/examples/decoding/decoding_rsa_sgskip.py python-mne-1.1.0+dfsg/examples/decoding/decoding_rsa_sgskip.py --- python-mne-0.23.4+dfsg/examples/decoding/decoding_rsa_sgskip.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/decoding/decoding_rsa_sgskip.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,5 +1,5 @@ +# -*- coding: utf-8 -*- """ - .. _ex-rsa-noplot: ==================================== @@ -26,9 +26,10 @@ # Jaakko Leppakangas # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% -import os.path as op import numpy as np from pandas import read_csv import matplotlib.pyplot as plt @@ -50,7 +51,7 @@ data_path = visual_92_categories.data_path() # Define stimulus - trigger mapping -fname = op.join(data_path, 'visual_stimuli.csv') +fname = data_path / 'visual_stimuli.csv' conds = read_csv(fname) print(conds.head(5)) @@ -77,9 +78,9 @@ ############################################################################## # Read MEG data n_runs = 4 # 4 for full data (use less to speed up computations) -fname = op.join(data_path, 'sample_subject_%i_tsss_mc.fif') -raws = [read_raw_fif(fname % block, verbose='error') - for block in range(n_runs)] # ignore filename warnings +fnames = [data_path / f'sample_subject_{b}_tsss_mc.fif' for b in range(n_runs)] +raws = [read_raw_fif(fname, verbose='error', on_split_missing='ignore') + for fname in fnames] # ignore filename warnings raw = concatenate_raws(raws) events = mne.find_events(raw, min_duration=.002) diff -Nru python-mne-0.23.4+dfsg/examples/decoding/decoding_spatio_temporal_source.py python-mne-1.1.0+dfsg/examples/decoding/decoding_spatio_temporal_source.py --- python-mne-0.23.4+dfsg/examples/decoding/decoding_spatio_temporal_source.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/decoding/decoding_spatio_temporal_source.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,5 +1,6 @@ +# -*- coding: utf-8 -*- """ -.. _tut_dec_st_source: +.. _ex-dec-st-source: ========================== Decoding source space data @@ -17,7 +18,9 @@ # Jean-Remi King # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np import matplotlib.pyplot as plt @@ -35,16 +38,17 @@ print(__doc__) data_path = mne.datasets.sample.data_path() -fname_fwd = data_path + 'MEG/sample/sample_audvis-meg-oct-6-fwd.fif' -fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif' -subjects_dir = data_path + '/subjects' +meg_path = data_path / 'MEG' / 'sample' +fname_fwd = meg_path / 'sample_audvis-meg-oct-6-fwd.fif' +fname_evoked = meg_path / 'sample_audvis-ave.fif' +subjects_dir = data_path / 'subjects' -############################################################################### +# %% # Set parameters -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' -fname_cov = data_path + '/MEG/sample/sample_audvis-cov.fif' -fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' +event_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif' +fname_cov = meg_path / 'sample_audvis-cov.fif' +fname_inv = meg_path / 'sample_audvis-meg-oct-6-meg-inv.fif' tmin, tmax = -0.2, 0.8 event_id = dict(aud_r=2, vis_r=4) # load contra-lateral conditions @@ -65,7 +69,7 @@ reject=dict(grad=4000e-13, eog=150e-6), decim=5) # decimate to save memory and increase speed -############################################################################### +# %% # Compute inverse solution snr = 3.0 noise_cov = mne.read_cov(fname_cov) @@ -75,7 +79,7 @@ lambda2=1.0 / snr ** 2, verbose=False, method="dSPM", pick_ori="normal") -############################################################################### +# %% # Decoding in sensor space using a logistic regression # Retrieve source space data into an array @@ -89,7 +93,7 @@ time_decod = SlidingEstimator(clf, scoring='roc_auc') # Run cross-validated decoding analyses: -scores = cross_val_multiscore(time_decod, X, y, cv=5, n_jobs=1) +scores = cross_val_multiscore(time_decod, X, y, cv=5, n_jobs=None) # Plot average decoding scores of 5 splits fig, ax = plt.subplots(1) @@ -98,7 +102,7 @@ ax.axvline(0, color='k') plt.legend() -############################################################################### +# %% # To investigate weights, we need to retrieve the patterns of a fitted model # The fitting needs not be cross validated because the weights are based on diff -Nru python-mne-0.23.4+dfsg/examples/decoding/decoding_spoc_CMC.py python-mne-1.1.0+dfsg/examples/decoding/decoding_spoc_CMC.py --- python-mne-0.23.4+dfsg/examples/decoding/decoding_spoc_CMC.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/decoding/decoding_spoc_CMC.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-spoc-cmc: @@ -20,8 +21,9 @@ # Author: Alexandre Barachant # Jean-Remi King # -# License: BSD (3-clause) +# License: BSD-3-Clause +# %% import matplotlib.pyplot as plt import mne @@ -34,25 +36,25 @@ from sklearn.model_selection import KFold, cross_val_predict # Define parameters -fname = data_path() + '/SubjectCMC.ds' +fname = data_path() / 'SubjectCMC.ds' raw = mne.io.read_raw_ctf(fname) -raw.crop(50., 250.) # crop for memory purposes +raw.crop(50., 200.) # crop for memory purposes # Filter muscular activity to only keep high frequencies emg = raw.copy().pick_channels(['EMGlft']).load_data() -emg.filter(20., None, fir_design='firwin') +emg.filter(20., None) # Filter MEG data to focus on beta band raw.pick_types(meg=True, ref_meg=True, eeg=False, eog=False).load_data() -raw.filter(15., 30., fir_design='firwin') +raw.filter(15., 30.) # Build epochs as sliding windows over the continuous raw file -events = mne.make_fixed_length_events(raw, id=1, duration=.250) +events = mne.make_fixed_length_events(raw, id=1, duration=0.75) # Epoch length is 1.5 second -meg_epochs = Epochs(raw, events, tmin=0., tmax=1.500, baseline=None, - detrend=1, decim=8) -emg_epochs = Epochs(emg, events, tmin=0., tmax=1.500, baseline=None) +meg_epochs = Epochs(raw, events, tmin=0., tmax=1.5, baseline=None, + detrend=1, decim=12) +emg_epochs = Epochs(emg, events, tmin=0., tmax=1.5, baseline=None) # Prepare classification X = meg_epochs.get_data() diff -Nru python-mne-0.23.4+dfsg/examples/decoding/decoding_time_generalization_conditions.py python-mne-1.1.0+dfsg/examples/decoding/decoding_time_generalization_conditions.py --- python-mne-0.23.4+dfsg/examples/decoding/decoding_time_generalization_conditions.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/decoding/decoding_time_generalization_conditions.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-linear-sensor-decoding: + ========================================================================= Decoding sensor space data with generalization across time and conditions ========================================================================= @@ -13,7 +16,9 @@ # Alexandre Gramfort # Denis Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import matplotlib.pyplot as plt @@ -30,8 +35,9 @@ # Preprocess data data_path = sample.data_path() # Load and filter data, set up epochs -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -events_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' +events_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif' raw = mne.io.read_raw_fif(raw_fname, preload=True) picks = mne.pick_types(raw.info, meg=True, exclude='bads') # Pick MEG channels raw.filter(1., 30., fir_design='firwin') # Band pass filtering signals @@ -47,11 +53,14 @@ proj=True, picks=picks, baseline=None, preload=True, reject=dict(mag=5e-12), decim=decim, verbose='error') -############################################################################### +# %% # We will train the classifier on all left visual vs auditory trials # and test on all right visual vs auditory trials. -clf = make_pipeline(StandardScaler(), LogisticRegression(solver='lbfgs')) -time_gen = GeneralizingEstimator(clf, scoring='roc_auc', n_jobs=1, +clf = make_pipeline( + StandardScaler(), + LogisticRegression(solver='liblinear') # liblinear is faster than lbfgs +) +time_gen = GeneralizingEstimator(clf, scoring='roc_auc', n_jobs=None, verbose=True) # Fit classifiers on the epochs where the stimulus was presented to the left. @@ -59,12 +68,12 @@ time_gen.fit(X=epochs['Left'].get_data(), y=epochs['Left'].events[:, 2] > 2) -############################################################################### +# %% # Score on the epochs where the stimulus was presented to the right. scores = time_gen.score(X=epochs['Right'].get_data(), y=epochs['Right'].events[:, 2] > 2) -############################################################################### +# %% # Plot fig, ax = plt.subplots(1) im = ax.matshow(scores, vmin=0, vmax=1., cmap='RdBu_r', origin='lower', diff -Nru python-mne-0.23.4+dfsg/examples/decoding/decoding_unsupervised_spatial_filter.py python-mne-1.1.0+dfsg/examples/decoding/decoding_unsupervised_spatial_filter.py --- python-mne-0.23.4+dfsg/examples/decoding/decoding_unsupervised_spatial_filter.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/decoding/decoding_unsupervised_spatial_filter.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-ica-pca-decoding: + ================================================================== Analysis of evoked response using ICA and PCA reduction techniques ================================================================== @@ -11,7 +14,9 @@ # Authors: Jean-Remi King # Asish Panda # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np import matplotlib.pyplot as plt @@ -28,8 +33,9 @@ data_path = sample.data_path() # Load and filter data, set up epochs -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' +event_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif' tmin, tmax = -0.1, 0.3 event_id = dict(aud_l=1, aud_r=2, vis_l=3, vis_r=4) @@ -57,7 +63,8 @@ ############################################################################## # Transform data with ICA computed on the raw epochs (no averaging) -ica = UnsupervisedSpatialFilter(FastICA(30), average=False) +ica = UnsupervisedSpatialFilter( + FastICA(30, whiten='unit-variance'), average=False) ica_data = ica.fit_transform(X) ev1 = mne.EvokedArray(np.mean(ica_data, axis=0), mne.create_info(30, epochs.info['sfreq'], diff -Nru python-mne-0.23.4+dfsg/examples/decoding/decoding_xdawn_eeg.py python-mne-1.1.0+dfsg/examples/decoding/decoding_xdawn_eeg.py --- python-mne-0.23.4+dfsg/examples/decoding/decoding_xdawn_eeg.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/decoding/decoding_xdawn_eeg.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-xdawn-decoding: @@ -12,7 +13,9 @@ """ # Authors: Alexandre Barachant # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np import matplotlib.pyplot as plt @@ -23,7 +26,7 @@ from sklearn.metrics import classification_report, confusion_matrix from sklearn.preprocessing import MinMaxScaler -from mne import io, pick_types, read_events, Epochs, EvokedArray +from mne import io, pick_types, read_events, Epochs, EvokedArray, create_info from mne.datasets import sample from mne.preprocessing import Xdawn from mne.decoding import Vectorizer @@ -33,10 +36,11 @@ data_path = sample.data_path() -############################################################################### +# %% # Set parameters and read data -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' +event_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif' tmin, tmax = -0.1, 0.3 event_id = {'Auditory/Left': 1, 'Auditory/Right': 2, 'Visual/Left': 3, 'Visual/Right': 4} @@ -93,18 +97,18 @@ fig.tight_layout() ax.set(ylabel='True label', xlabel='Predicted label') -############################################################################### +# %% # The ``patterns_`` attribute of a fitted Xdawn instance (here from the last # cross-validation fold) can be used for visualization. fig, axes = plt.subplots(nrows=len(event_id), ncols=n_filter, figsize=(n_filter, len(event_id) * 2)) fitted_xdawn = clf.steps[0][1] -tmp_info = epochs.info.copy() -tmp_info['sfreq'] = 1. +info = create_info(epochs.ch_names, 1, epochs.get_channel_types()) +info.set_montage(epochs.get_montage()) for ii, cur_class in enumerate(sorted(event_id)): cur_patterns = fitted_xdawn.patterns_[cur_class] - pattern_evoked = EvokedArray(cur_patterns[:n_filter].T, tmp_info, tmin=0) + pattern_evoked = EvokedArray(cur_patterns[:n_filter].T, info, tmin=0) pattern_evoked.plot_topomap( times=np.arange(n_filter), time_format='Component %d' if ii == 0 else '', colorbar=False, @@ -112,7 +116,7 @@ axes[ii, 0].set(ylabel=cur_class) fig.tight_layout(h_pad=1.0, w_pad=1.0, pad=0.1) -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/decoding/ems_filtering.py python-mne-1.1.0+dfsg/examples/decoding/ems_filtering.py --- python-mne-0.23.4+dfsg/examples/decoding/ems_filtering.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/decoding/ems_filtering.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-ems-filtering: @@ -13,7 +14,7 @@ filters, surrogate single trials are created in which multi-sensor activity is reduced to one time series which exposes experimental effects, if present. -We will first plot a trials x times image of the single trials and order the +We will first plot a trials × times image of the single trials and order the trials by condition. A second plot shows the average time series for each condition. Finally a topographic plot is created which exhibits the temporal evolution of the spatial filters. @@ -21,7 +22,9 @@ # Author: Denis Engemann # Jean-Remi King # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np import matplotlib.pyplot as plt @@ -37,8 +40,9 @@ data_path = sample.data_path() # Preprocess the data -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' +event_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif' event_ids = {'AudL': 1, 'VisL': 3} # Read data and create epochs diff -Nru python-mne-0.23.4+dfsg/examples/decoding/linear_model_patterns.py python-mne-1.1.0+dfsg/examples/decoding/linear_model_patterns.py --- python-mne-0.23.4+dfsg/examples/decoding/linear_model_patterns.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/decoding/linear_model_patterns.py 2022-08-03 15:48:14.000000000 +0000 @@ -19,7 +19,9 @@ # Romain Trachel # Jean-Remi King # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne from mne import io, EvokedArray @@ -36,12 +38,12 @@ print(__doc__) data_path = sample.data_path() -sample_path = data_path + '/MEG/sample' +sample_path = data_path / 'MEG' / 'sample' -############################################################################### +# %% # Set parameters -raw_fname = sample_path + '/sample_audvis_filt-0-40_raw.fif' -event_fname = sample_path + '/sample_audvis_filt-0-40_raw-eve.fif' +raw_fname = sample_path / 'sample_audvis_filt-0-40_raw.fif' +event_fname = sample_path / 'sample_audvis_filt-0-40_raw-eve.fif' tmin, tmax = -0.1, 0.4 event_id = dict(aud_l=1, vis_l=3) @@ -61,11 +63,11 @@ meg_epochs = epochs.copy().pick_types(meg=True, eeg=False) meg_data = meg_epochs.get_data().reshape(len(labels), -1) -############################################################################### +# %% # Decoding in sensor space using a LogisticRegression classifier # -------------------------------------------------------------- -clf = LogisticRegression(solver='lbfgs') +clf = LogisticRegression(solver='liblinear') # liblinear is faster than lbfgs scaler = StandardScaler() # create a linear model with LogisticRegression @@ -89,7 +91,7 @@ evoked = EvokedArray(coef, meg_epochs.info, tmin=epochs.tmin) evoked.plot_topomap(title='MEG %s' % name, time_unit='s') -############################################################################### +# %% # Let's do the same on EEG data using a scikit-learn pipeline X = epochs.pick_types(meg=False, eeg=True) @@ -99,8 +101,10 @@ clf = make_pipeline( Vectorizer(), # 1) vectorize across time and channels StandardScaler(), # 2) normalize features across trials - LinearModel( - LogisticRegression(solver='lbfgs'))) # 3) fits a logistic regression + LinearModel( # 3) fits a logistic regression + LogisticRegression(solver='liblinear') + ) +) clf.fit(X, y) # Extract and plot patterns and filters @@ -111,7 +115,7 @@ evoked = EvokedArray(coef, epochs.info, tmin=epochs.tmin) evoked.plot_topomap(title='EEG %s' % name[:-1], time_unit='s') -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/decoding/receptive_field_mtrf.py python-mne-1.1.0+dfsg/examples/decoding/receptive_field_mtrf.py --- python-mne-0.23.4+dfsg/examples/decoding/receptive_field_mtrf.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/decoding/receptive_field_mtrf.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-receptive-field-mtrf: @@ -23,7 +24,9 @@ # Eric Larson # Nicolas Barascud # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% # sphinx_gallery_thumbnail_number = 3 import numpy as np @@ -37,7 +40,7 @@ from sklearn.preprocessing import scale -############################################################################### +# %% # Load the data from the publication # ---------------------------------- # @@ -72,7 +75,7 @@ ax.set(title="Sample activity", xlabel="Time (s)") mne.viz.tight_layout() -############################################################################### +# %% # Create and fit a receptive field model # -------------------------------------- # @@ -121,7 +124,7 @@ ax.set(title="Mean prediction score", xlabel="Channel", ylabel="Score ($r$)") mne.viz.tight_layout() -############################################################################### +# %% # Investigate model coefficients # ============================== # Finally, we will look at how the linear coefficients (sometimes @@ -150,7 +153,7 @@ ax.set(title="Topomap of model coefficients\nfor delay %s" % time_plot) mne.viz.tight_layout() -############################################################################### +# %% # Create and fit a stimulus reconstruction model # ---------------------------------------------- # @@ -208,7 +211,7 @@ max_coef = np.abs(mean_coefs).max() max_patterns = np.abs(mean_patterns).max() -############################################################################### +# %% # Visualize stimulus reconstruction # ================================= # @@ -226,7 +229,7 @@ ax.set_xlabel('Time (s)') mne.viz.tight_layout() -############################################################################### +# %% # Investigate model coefficients # ============================== # @@ -258,7 +261,7 @@ % (time_plot[0], time_plot[1])) mne.viz.tight_layout() -############################################################################### +# %% # References # ---------- # diff -Nru python-mne-0.23.4+dfsg/examples/decoding/ssd_spatial_filters.py python-mne-1.1.0+dfsg/examples/decoding/ssd_spatial_filters.py --- python-mne-0.23.4+dfsg/examples/decoding/ssd_spatial_filters.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/decoding/ssd_spatial_filters.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-ssd-spatial-filters: + =========================================================== Compute Spectro-Spatial Decomposition (SSD) spatial filters =========================================================== @@ -13,7 +16,9 @@ """ # Author: Denis A. Engemann # Victoria Peterson -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import matplotlib.pyplot as plt @@ -22,9 +27,9 @@ from mne.datasets.fieldtrip_cmc import data_path from mne.decoding import SSD -############################################################################### +# %% # Define parameters -fname = data_path() + '/SubjectCMC.ds' +fname = data_path() / 'SubjectCMC.ds' # Prepare data raw = mne.io.read_raw_ctf(fname) @@ -47,7 +52,7 @@ ssd.fit(X=raw.get_data()) -############################################################################### +# %% # Let's investigate spatial filter with max power ratio. # We will first inspect the topographies. # According to Nikulin et al. 2011 this is done by either inverting the filters @@ -84,7 +89,7 @@ # was already quite good. However, when using few components only # the sorting might make a difference. -############################################################################### +# %% # Let's also look at the power spectrum of that source and compare it to # to the power spectrum of the source with lowest SNR. @@ -103,7 +108,7 @@ # We can clearly see that the selected component enjoys an SNR that is # way above the average power spectrum. -############################################################################### +# %% # Epoched data # ------------ # Although we suggest to use this method before epoching, there might be some @@ -132,7 +137,7 @@ pattern_epochs = mne.EvokedArray(data=ssd_epochs.patterns_[:4].T, info=ssd_epochs.info) pattern_epochs.plot_topomap(units=dict(mag='A.U.'), time_format='') -############################################################################### +# %% # References # ---------- # diff -Nru python-mne-0.23.4+dfsg/examples/forward/forward_sensitivity_maps.py python-mne-1.1.0+dfsg/examples/forward/forward_sensitivity_maps.py --- python-mne-0.23.4+dfsg/examples/forward/forward_sensitivity_maps.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/forward/forward_sensitivity_maps.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-sensitivity-maps: @@ -14,7 +15,9 @@ # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np import mne @@ -26,10 +29,9 @@ print(__doc__) data_path = sample.data_path() - -fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' - -subjects_dir = data_path + '/subjects' +meg_path = data_path / 'MEG' / 'sample' +fwd_fname = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif' +subjects_dir = data_path / 'subjects' # Read the forward solutions with surface orientation fwd = mne.read_forward_solution(fwd_fname) @@ -37,14 +39,14 @@ leadfield = fwd['sol']['data'] print("Leadfield size : %d x %d" % leadfield.shape) -############################################################################### +# %% # Compute sensitivity maps grad_map = mne.sensitivity_map(fwd, ch_type='grad', mode='fixed') mag_map = mne.sensitivity_map(fwd, ch_type='mag', mode='fixed') eeg_map = mne.sensitivity_map(fwd, ch_type='eeg', mode='fixed') -############################################################################### +# %% # Show gain matrix a.k.a. leadfield matrix with sensitivity map picks_meg = mne.pick_types(fwd['info'], meg=True, eeg=False) @@ -73,7 +75,7 @@ subjects_dir=subjects_dir, clim=dict(lims=[0, 50, 100]), figure=1) brain_sens.add_text(0.1, 0.9, 'Gradiometer sensitivity', 'title', font_size=16) -############################################################################### +# %% # Compare sensitivity map with distribution of source depths # source space with vertices @@ -94,7 +96,7 @@ clim=dict(kind='value', lims=[0, maxdep / 2., maxdep]), figure=2) brain_dep.add_text(0.1, 0.9, 'Source depth (m)', 'title', font_size=16) -############################################################################### +# %% # Sensitivity is likely to co-vary with the distance between sources to # sensors. To determine the strength of this relationship, we can compute the # correlation between source depth and sensitivity values. @@ -102,7 +104,7 @@ print('Correlation between source depth and gradiomter sensitivity values: %f.' % corr) -############################################################################### +# %% # Gradiometer sensitiviy is highest close to the sensors, and decreases rapidly # with inreasing source depth. This is confirmed by the high negative # correlation between the two. diff -Nru python-mne-0.23.4+dfsg/examples/forward/left_cerebellum_volume_source.py python-mne-1.1.0+dfsg/examples/forward/left_cerebellum_volume_source.py --- python-mne-0.23.4+dfsg/examples/forward/left_cerebellum_volume_source.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/forward/left_cerebellum_volume_source.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-cerebellum-source-space: + ============================================== Generate a left cerebellum volume source space ============================================== @@ -6,12 +9,13 @@ Generate a volume source space of the left cerebellum and plot its vertices relative to the left cortical surface source space and the FreeSurfer segmentation file. - """ # Author: Alan Leggitt # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne from mne import setup_source_space, setup_volume_source_space @@ -20,11 +24,11 @@ print(__doc__) data_path = sample.data_path() -subjects_dir = data_path + '/subjects' +subjects_dir = data_path / 'subjects' subject = 'sample' -aseg_fname = subjects_dir + '/sample/mri/aseg.mgz' +aseg_fname = subjects_dir / 'sample' / 'mri' / 'aseg.mgz' -############################################################################### +# %% # Setup the source spaces # setup a cortical surface source space and extract left hemisphere @@ -41,17 +45,16 @@ # Combine the source spaces src = surf + lh_cereb -############################################################################### +# %% # Plot the positions of each source space fig = mne.viz.plot_alignment(subject=subject, subjects_dir=subjects_dir, - surfaces='white', coord_frame='head', + surfaces='white', coord_frame='mri', src=src) -mne.viz.set_3d_view(fig, azimuth=173.78, elevation=101.75, +mne.viz.set_3d_view(fig, azimuth=180, elevation=90, distance=0.30, focalpoint=(-0.03, -0.01, 0.03)) - -############################################################################### +# %% # You can export source positions to a NIfTI file:: # # >>> nii_fname = 'mne_sample_lh-cerebellum-cortex.nii' diff -Nru python-mne-0.23.4+dfsg/examples/forward/source_space_morphing.py python-mne-1.1.0+dfsg/examples/forward/source_space_morphing.py --- python-mne-0.23.4+dfsg/examples/forward/source_space_morphing.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/forward/source_space_morphing.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-source-space-morphing: + ========================= Use source space morphing ========================= @@ -15,21 +18,21 @@ # Authors: Denis A. Engemann # Eric larson # -# License: BSD (3-clause) +# License: BSD-3-Clause -import os.path as op +# %% import mne data_path = mne.datasets.sample.data_path() -subjects_dir = op.join(data_path, 'subjects') -fname_trans = op.join(data_path, 'MEG', 'sample', - 'sample_audvis_raw-trans.fif') -fname_bem = op.join(subjects_dir, 'sample', 'bem', - 'sample-5120-bem-sol.fif') -fname_src_fs = op.join(subjects_dir, 'fsaverage', 'bem', - 'fsaverage-ico-5-src.fif') -raw_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif') +subjects_dir = data_path / 'subjects' +fname_trans = ( + data_path / 'MEG' / 'sample' / 'sample_audvis_raw-trans.fif') +fname_bem = ( + subjects_dir / 'sample' / 'bem' / 'sample-5120-bem-sol.fif') +fname_src_fs = ( + subjects_dir / 'fsaverage' / 'bem' / 'fsaverage-ico-5-src.fif') +raw_fname = data_path / 'MEG' / 'sample' / 'sample_audvis_raw.fif' # Get relevant channel information info = mne.io.read_info(raw_fname) diff -Nru python-mne-0.23.4+dfsg/examples/inverse/compute_mne_inverse_epochs_in_label.py python-mne-1.1.0+dfsg/examples/inverse/compute_mne_inverse_epochs_in_label.py --- python-mne-0.23.4+dfsg/examples/inverse/compute_mne_inverse_epochs_in_label.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/compute_mne_inverse_epochs_in_label.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-dSPM-epochs: + ================================================== Compute MNE-dSPM inverse solution on single epochs ================================================== @@ -8,7 +11,9 @@ """ # Author: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np import matplotlib.pyplot as plt @@ -21,11 +26,12 @@ print(__doc__) data_path = sample.data_path() -fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' -fname_raw = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -fname_event = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' +meg_path = data_path / 'MEG' / 'sample' +fname_inv = meg_path / 'sample_audvis-meg-oct-6-meg-inv.fif' +fname_raw = meg_path / 'sample_audvis_filt-0-40_raw.fif' +fname_event = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif' label_name = 'Aud-lh' -fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name +fname_label = meg_path / 'labels' / f'{label_name}.label' event_id, tmin, tmax = 1, -0.2, 0.5 @@ -83,7 +89,7 @@ # Average over label (not caring to align polarities here) label_mean_evoked = np.mean(stc_evoked_label.data, axis=0) -############################################################################### +# %% # View activation time-series to illustrate the benefit of aligning/flipping times = 1e3 * stcs[0].times # times in ms @@ -98,7 +104,7 @@ plt.ylabel('dSPM value') plt.show() -############################################################################### +# %% # Viewing single trial dSPM and average dSPM for unflipped pooling over label # Compare to (1) Inverse (dSPM) then average, (2) Evoked then dSPM diff -Nru python-mne-0.23.4+dfsg/examples/inverse/compute_mne_inverse_raw_in_label.py python-mne-1.1.0+dfsg/examples/inverse/compute_mne_inverse_raw_in_label.py --- python-mne-0.23.4+dfsg/examples/inverse/compute_mne_inverse_raw_in_label.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/compute_mne_inverse_raw_in_label.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _example-sLORETA: @@ -12,7 +13,9 @@ """ # Author: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import matplotlib.pyplot as plt @@ -23,10 +26,11 @@ print(__doc__) data_path = sample.data_path() -fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' -fname_raw = data_path + '/MEG/sample/sample_audvis_raw.fif' +fname_inv = ( + data_path / 'MEG' / 'sample' / 'sample_audvis-meg-oct-6-meg-inv.fif') +fname_raw = data_path / 'MEG' / 'sample' / 'sample_audvis_raw.fif' label_name = 'Aud-lh' -fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name +fname_label = data_path / 'MEG' / 'sample' / 'labels' / f'{label_name}.label' snr = 1.0 # use smaller SNR for raw data lambda2 = 1.0 / snr ** 2 @@ -45,9 +49,9 @@ start, stop, pick_ori=None) # Save result in stc files -stc.save('mne_%s_raw_inverse_%s' % (method, label_name)) +stc.save('mne_%s_raw_inverse_%s' % (method, label_name), overwrite=True) -############################################################################### +# %% # View activation time-series plt.plot(1e3 * stc.times, stc.data[::100, :].T) plt.xlabel('time (ms)') diff -Nru python-mne-0.23.4+dfsg/examples/inverse/compute_mne_inverse_volume.py python-mne-1.1.0+dfsg/examples/inverse/compute_mne_inverse_volume.py --- python-mne-0.23.4+dfsg/examples/inverse/compute_mne_inverse_volume.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/compute_mne_inverse_volume.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-inverse-volume: @@ -7,11 +8,12 @@ Compute dSPM inverse solution on MNE evoked dataset in a volume source space and stores the solution in a nifti file for visualisation. - """ # Author: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% from nilearn.plotting import plot_stat_map from nilearn.image import index_img @@ -23,8 +25,9 @@ print(__doc__) data_path = sample.data_path() -fname_inv = data_path + '/MEG/sample/sample_audvis-meg-vol-7-meg-inv.fif' -fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif' +meg_path = data_path / 'MEG' / 'sample' +fname_inv = meg_path / 'sample_audvis-meg-vol-7-meg-inv.fif' +fname_evoked = meg_path / 'sample_audvis-ave.fif' snr = 3.0 lambda2 = 1.0 / snr ** 2 @@ -46,8 +49,9 @@ # Save it as a nifti file # nib.save(img, 'mne_%s_inverse.nii.gz' % method) -t1_fname = data_path + '/subjects/sample/mri/T1.mgz' +t1_fname = data_path / 'subjects' / 'sample' / 'mri' / 'T1.mgz' -# Plotting with nilearn ###################################################### -plot_stat_map(index_img(img, 61), t1_fname, threshold=8., +# %% +# Plot with nilearn: +plot_stat_map(index_img(img, 61), str(t1_fname), threshold=8., title='%s (t=%.1f s.)' % (method, stc.times[61])) diff -Nru python-mne-0.23.4+dfsg/examples/inverse/covariance_whitening_dspm.py python-mne-1.1.0+dfsg/examples/inverse/covariance_whitening_dspm.py --- python-mne-0.23.4+dfsg/examples/inverse/covariance_whitening_dspm.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/covariance_whitening_dspm.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,182 +0,0 @@ -""" - -.. _ex-covariance-whitening-dspm: - -=================================================== -Demonstrate impact of whitening on source estimates -=================================================== - -This example demonstrates the relationship between the noise covariance -estimate and the MNE / dSPM source amplitudes. It computes source estimates for -the SPM faces data and compares proper regularization with insufficient -regularization based on the methods described in -:footcite:`EngemannGramfort2015`. This example demonstrates -that improper regularization can lead to overestimation of source amplitudes. -This example makes use of the previous, non-optimized code path that was used -before implementing the suggestions presented in -:footcite:`EngemannGramfort2015`. - -This example does quite a bit of processing, so even on a -fast machine it can take a couple of minutes to complete. - -.. warning:: Please do not copy the patterns presented here for your own - analysis, this is example is purely illustrative. -""" -# Author: Denis A. Engemann -# -# License: BSD (3-clause) - -import numpy as np -import matplotlib.pyplot as plt - -import mne -from mne import io -from mne.datasets import spm_face -from mne.minimum_norm import apply_inverse, make_inverse_operator -from mne.cov import compute_covariance - -print(__doc__) - -############################################################################## -# Get data - -data_path = spm_face.data_path() -subjects_dir = data_path + '/subjects' - -raw_fname = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces%d_3D.ds' - -raw = io.read_raw_ctf(raw_fname % 1) # Take first run -# To save time and memory for this demo, we'll just use the first -# 2.5 minutes (all we need to get 30 total events) and heavily -# resample 480->60 Hz (usually you wouldn't do either of these!) -raw.crop(0, 150.).pick_types(meg=True, stim=True, exclude='bads').load_data() -raw.filter(None, 20.) - -events = mne.find_events(raw, stim_channel='UPPT001') - -event_ids = {"faces": 1, "scrambled": 2} -tmin, tmax = -0.2, 0.5 -baseline = (None, 0) -reject = dict(mag=3e-12) - -# inverse parameters -conditions = 'faces', 'scrambled' -snr = 3.0 -lambda2 = 1.0 / snr ** 2 -clim = dict(kind='value', lims=[0, 2.5, 5]) - -############################################################################### -# Estimate covariances - -samples_epochs = 5, 15, -method = 'empirical', 'shrunk' -colors = 'steelblue', 'red' -epochs = mne.Epochs( - raw, events, event_ids, tmin, tmax, - baseline=baseline, preload=True, reject=reject, decim=8) -del raw - -noise_covs = list() -evokeds = list() -stcs = list() -methods_ordered = list() -for n_train in samples_epochs: - # estimate covs based on a subset of samples - # make sure we have the same number of conditions. - idx = np.sort(np.concatenate([ - np.where(epochs.events[:, 2] == event_ids[cond])[0][:n_train] - for cond in conditions])) - epochs_train = epochs[idx] - epochs_train.equalize_event_counts(event_ids) - assert len(epochs_train) == 2 * n_train - - # We know some of these have too few samples, so suppress warning - # with verbose='error' - noise_covs.append(compute_covariance( - epochs_train, method=method, tmin=None, tmax=0, # baseline only - return_estimators=True, rank=None, verbose='error')) # returns list - # prepare contrast - evokeds.append([epochs_train[k].average() for k in conditions]) - del epochs_train -del epochs - -# Make forward -trans = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces1_3D_raw-trans.fif' -# oct5 and add_dist are just for speed, not recommended in general! -src = mne.setup_source_space( - 'spm', spacing='oct5', subjects_dir=data_path + '/subjects', - add_dist=False) -bem = data_path + '/subjects/spm/bem/spm-5120-5120-5120-bem-sol.fif' -forward = mne.make_forward_solution(evokeds[0][0].info, trans, src, bem) -del src -for noise_covs_, evokeds_ in zip(noise_covs, evokeds): - # do contrast - - # We skip empirical rank estimation that we introduced in response to - # the findings in reference [1] to use the naive code path that - # triggered the behavior described in [1]. The expected true rank is - # 274 for this dataset. Please do not do this with your data but - # rely on the default rank estimator that helps regularizing the - # covariance. - stcs.append(list()) - methods_ordered.append(list()) - for cov in noise_covs_: - inverse_operator = make_inverse_operator( - evokeds_[0].info, forward, cov, loose=0.2, depth=0.8) - assert len(inverse_operator['sing']) == 274 # sanity check - stc_a, stc_b = (apply_inverse(e, inverse_operator, lambda2, "dSPM", - pick_ori=None) for e in evokeds_) - stc = stc_a - stc_b - methods_ordered[-1].append(cov['method']) - stcs[-1].append(stc) - del inverse_operator, cov, stc, stc_a, stc_b -del forward, noise_covs, evokeds # save some memory - - -############################################################################## -# Show the resulting source estimates - -fig, (axes1, axes2) = plt.subplots(2, 3, figsize=(9.5, 5)) - -for ni, (n_train, axes) in enumerate(zip(samples_epochs, (axes1, axes2))): - # compute stc based on worst and best - ax_dynamics = axes[1] - for stc, ax, method, kind, color in zip(stcs[ni], - axes[::2], - methods_ordered[ni], - ['best', 'worst'], - colors): - brain = stc.plot(subjects_dir=subjects_dir, hemi='both', clim=clim, - initial_time=0.175, background='w', foreground='k') - brain.show_view('ven') - im = brain.screenshot() - brain.close() - - ax.axis('off') - ax.get_xaxis().set_visible(False) - ax.get_yaxis().set_visible(False) - ax.imshow(im) - ax.set_title('{0} ({1} epochs)'.format(kind, n_train * 2)) - - # plot spatial mean - stc_mean = stc.data.mean(0) - ax_dynamics.plot(stc.times * 1e3, stc_mean, - label='{0} ({1})'.format(method, kind), - color=color) - # plot spatial std - stc_var = stc.data.std(0) - ax_dynamics.fill_between(stc.times * 1e3, stc_mean - stc_var, - stc_mean + stc_var, alpha=0.2, color=color) - - # signal dynamics worst and best - ax_dynamics.set(title='{0} epochs'.format(n_train * 2), - xlabel='Time (ms)', ylabel='Source Activation (dSPM)', - xlim=(tmin * 1e3, tmax * 1e3), ylim=(-3, 3)) - ax_dynamics.legend(loc='upper left', fontsize=10) - -fig.subplots_adjust(hspace=0.2, left=0.01, right=0.99, wspace=0.03) - -############################################################################### -# References -# ---------- -# .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/inverse/custom_inverse_solver.py python-mne-1.1.0+dfsg/examples/inverse/custom_inverse_solver.py --- python-mne-0.23.4+dfsg/examples/inverse/custom_inverse_solver.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/custom_inverse_solver.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,5 +1,7 @@ # -*- coding: utf-8 -*- """ +.. _ex-custom-inverse: + ================================================ Source localization with a custom inverse solver ================================================ @@ -18,6 +20,8 @@ in order to try out another inverse algorithm. """ +# %% + import numpy as np from scipy import linalg import mne @@ -26,10 +30,11 @@ data_path = sample.data_path() -fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' -ave_fname = data_path + '/MEG/sample/sample_audvis-ave.fif' -cov_fname = data_path + '/MEG/sample/sample_audvis-shrunk-cov.fif' -subjects_dir = data_path + '/subjects' +meg_path = data_path / 'MEG' / 'sample' +fwd_fname = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif' +ave_fname = meg_path / 'sample_audvis-ave.fif' +cov_fname = meg_path / 'sample_audvis-shrunk-cov.fif' +subjects_dir = data_path / 'subjects' condition = 'Left Auditory' # Read noise covariance matrix @@ -43,7 +48,7 @@ forward = mne.read_forward_solution(fwd_fname) -############################################################################### +# %% # Auxiliary function to run the solver def apply_solver(solver, evoked, forward, noise_cov, loose=0.2, depth=0.8): @@ -117,7 +122,7 @@ return stc -############################################################################### +# %% # Define your solver def solver(M, G, n_orient): @@ -160,14 +165,14 @@ return X, active_set -############################################################################### +# %% # Apply your custom solver # loose, depth = 0.2, 0.8 # corresponds to loose orientation loose, depth = 1., 0. # corresponds to free orientation stc = apply_solver(solver, evoked, forward, noise_cov, loose, depth) -############################################################################### +# %% # View in 2D and 3D ("glass" brain like 3D plot) plot_sparse_source_estimates(forward['src'], stc, bgcolor=(1, 1, 1), opacity=0.1) diff -Nru python-mne-0.23.4+dfsg/examples/inverse/dics_source_power.py python-mne-1.1.0+dfsg/examples/inverse/dics_source_power.py --- python-mne-0.23.4+dfsg/examples/inverse/dics_source_power.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/dics_source_power.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-inverse-source-power: @@ -16,8 +17,9 @@ # Denis Engemann # Stefan Appelhoff # -# License: BSD (3-clause) -import os.path as op +# License: BSD-3-Clause + +# %% import numpy as np import mne @@ -27,13 +29,13 @@ print(__doc__) -############################################################################### +# %% # Reading the raw data and creating epochs: data_path = somato.data_path() subject = '01' task = 'somato' -raw_fname = op.join(data_path, 'sub-{}'.format(subject), 'meg', - 'sub-{}_task-{}_meg.fif'.format(subject, task)) +raw_fname = (data_path / f'sub-{subject}' / 'meg' / + f'sub-{subject}_task-{task}_meg.fif') # Use a shorter segment of raw just for speed here raw = mne.io.read_raw_fif(raw_fname) @@ -46,16 +48,17 @@ del raw # Paths to forward operator and FreeSurfer subject directory -fname_fwd = op.join(data_path, 'derivatives', 'sub-{}'.format(subject), - 'sub-{}_task-{}-fwd.fif'.format(subject, task)) -subjects_dir = op.join(data_path, 'derivatives', 'freesurfer', 'subjects') +fname_fwd = (data_path / 'derivatives' / f'sub-{subject}' / + f'sub-{subject}_task-{task}-fwd.fif') + +subjects_dir = data_path / 'derivatives' / 'freesurfer' / 'subjects' -############################################################################### +# %% # We are interested in the beta band. Define a range of frequencies, using a # log scale, from 12 to 30 Hz. freqs = np.logspace(np.log10(12), np.log10(30), 9) -############################################################################### +# %% # Computing the cross-spectral density matrix for the beta frequency band, for # different time intervals. We use a decim value of 20 to speed up the # computation in this example at the loss of accuracy. @@ -66,14 +69,14 @@ info = epochs.info del epochs -############################################################################### +# %% # To compute the source power for a frequency band, rather than each frequency # separately, we average the CSD objects across frequencies. csd = csd.mean() csd_baseline = csd_baseline.mean() csd_ers = csd_ers.mean() -############################################################################### +# %% # Computing DICS spatial filters using the CSD that was computed on the entire # timecourse. fwd = mne.read_forward_solution(fname_fwd) @@ -81,20 +84,20 @@ pick_ori='max-power', reduce_rank=True, real_filter=True) del fwd -############################################################################### +# %% # Applying DICS spatial filters separately to the CSD computed using the # baseline and the CSD computed during the ERS activity. baseline_source_power, freqs = apply_dics_csd(csd_baseline, filters) beta_source_power, freqs = apply_dics_csd(csd_ers, filters) -############################################################################### +# %% # Visualizing source power during ERS activity relative to the baseline power. stc = beta_source_power / baseline_source_power message = 'DICS source power in the 12-30 Hz frequency band' brain = stc.plot(hemi='both', views='axial', subjects_dir=subjects_dir, subject=subject, time_label=message) -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/inverse/evoked_ers_source_power.py python-mne-1.1.0+dfsg/examples/inverse/evoked_ers_source_power.py --- python-mne-0.23.4+dfsg/examples/inverse/evoked_ers_source_power.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/evoked_ers_source_power.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-source-loc-methods: + ===================================================================== Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM ===================================================================== @@ -11,8 +14,9 @@ # Authors: Luke Bloy # Eric Larson # -# License: BSD (3-clause) -import os.path as op +# License: BSD-3-Clause + +# %% import numpy as np import mne @@ -25,13 +29,13 @@ print(__doc__) -############################################################################### +# %% # Reading the raw data and creating epochs: data_path = somato.data_path() subject = '01' task = 'somato' -raw_fname = op.join(data_path, 'sub-{}'.format(subject), 'meg', - 'sub-{}_task-{}_meg.fif'.format(subject, task)) +raw_fname = (data_path / 'sub-{}'.format(subject) / 'meg' / + 'sub-{}_task-{}_meg.fif'.format(subject, task)) # crop to 5 minutes to save memory raw = mne.io.read_raw_fif(raw_fname).crop(0, 300) @@ -49,37 +53,43 @@ preload=True, decim=3) # Read forward operator and point to freesurfer subject directory -fname_fwd = op.join(data_path, 'derivatives', 'sub-{}'.format(subject), - 'sub-{}_task-{}-fwd.fif'.format(subject, task)) -subjects_dir = op.join(data_path, 'derivatives', 'freesurfer', 'subjects') +fname_fwd = (data_path / 'derivatives' / 'sub-{}'.format(subject) / + 'sub-{}_task-{}-fwd.fif'.format(subject, task)) +subjects_dir = data_path / 'derivatives' / 'freesurfer' / 'subjects' fwd = mne.read_forward_solution(fname_fwd) -############################################################################### +# %% # Compute covariances # ------------------- -# ERS activity starts at 0.5 seconds after stimulus onset. +# ERS activity starts at 0.5 seconds after stimulus onset. Because these +# data have been processed by MaxFilter directly (rather than MNE-Python's +# version), we have to be careful to compute the rank with a more conservative +# threshold in order to get the correct data rank (64). Once this is used in +# combination with an advanced covariance estimator like "shrunk", the rank +# will be correctly preserved. +rank = mne.compute_rank(epochs, tol=1e-6, tol_kind='relative') active_win = (0.5, 1.5) baseline_win = (-1, 0) - baseline_cov = compute_covariance(epochs, tmin=baseline_win[0], tmax=baseline_win[1], method='shrunk', - rank=None) + rank=rank, verbose=True) active_cov = compute_covariance(epochs, tmin=active_win[0], tmax=active_win[1], - method='shrunk', rank=None) + method='shrunk', rank=rank, verbose=True) # Weighted averaging is already in the addition of covariance objects. common_cov = baseline_cov + active_cov +mne.viz.plot_cov(baseline_cov, epochs.info) - -############################################################################### +# %% # Compute some source estimates # ----------------------------- # Here we will use DICS, LCMV beamformer, and dSPM. # # See :ref:`ex-inverse-source-power` for more information about DICS. + def _gen_dics(active_win, baseline_win, epochs): freqs = np.logspace(np.log10(12), np.log10(30), 9) csd = csd_morlet(epochs, freqs, tmin=-1, tmax=1.5, decim=20) @@ -88,7 +98,7 @@ csd_ers = csd_morlet(epochs, freqs, tmin=active_win[0], tmax=active_win[1], decim=20) filters = make_dics(epochs.info, fwd, csd.mean(), pick_ori='max-power', - reduce_rank=True, real_filter=True) + reduce_rank=True, real_filter=True, rank=rank) stc_base, freqs = apply_dics_csd(csd_baseline.mean(), filters) stc_act, freqs = apply_dics_csd(csd_ers.mean(), filters) stc_act /= stc_base @@ -121,7 +131,7 @@ stc_lcmv = _gen_lcmv(active_cov, baseline_cov, common_cov) stc_dspm = _gen_mne(active_cov, baseline_cov, common_cov, fwd, epochs.info) -############################################################################### +# %% # Plot source estimates # --------------------- # DICS: @@ -130,14 +140,14 @@ hemi='rh', subjects_dir=subjects_dir, subject=subject, time_label='DICS source power in the 12-30 Hz frequency band') -############################################################################### +# %% # LCMV: brain_lcmv = stc_lcmv.plot( hemi='rh', subjects_dir=subjects_dir, subject=subject, time_label='LCMV source power in the 12-30 Hz frequency band') -############################################################################### +# %% # dSPM: brain_dspm = stc_dspm.plot( diff -Nru python-mne-0.23.4+dfsg/examples/inverse/gamma_map_inverse.py python-mne-1.1.0+dfsg/examples/inverse/gamma_map_inverse.py --- python-mne-0.23.4+dfsg/examples/inverse/gamma_map_inverse.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/gamma_map_inverse.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-gamma-map: + =============================================================================== Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method =============================================================================== @@ -8,7 +11,9 @@ # Author: Martin Luessi # Daniel Strohmeier # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np @@ -21,10 +26,11 @@ print(__doc__) data_path = sample.data_path() -subjects_dir = data_path + '/subjects' -fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' -evoked_fname = data_path + '/MEG/sample/sample_audvis-ave.fif' -cov_fname = data_path + '/MEG/sample/sample_audvis-cov.fif' +subjects_dir = data_path / 'subjects' +meg_path = data_path / 'MEG' / 'sample' +fwd_fname = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif' +evoked_fname = meg_path / 'sample_audvis-ave.fif' +cov_fname = meg_path / 'sample_audvis-cov.fif' # Read the evoked response and crop it condition = 'Left visual' @@ -45,7 +51,7 @@ evoked, forward, cov, alpha, xyz_same_gamma=True, return_residual=True, return_as_dipoles=True) -############################################################################### +# %% # Plot dipole activations plot_dipole_amplitudes(dipoles) @@ -61,7 +67,7 @@ # subjects_dir=subjects_dir, mode='orthoview', # idx='amplitude') -############################################################################### +# %% # Show the evoked response and the residual for gradiometers ylim = dict(grad=[-120, 120]) evoked.pick_types(meg='grad', exclude='bads') @@ -72,11 +78,11 @@ residual.plot(titles=dict(grad='Residuals Gradiometers'), ylim=ylim, proj=True, time_unit='s') -############################################################################### +# %% # Generate stc from dipoles stc = make_stc_from_dipoles(dipoles, forward['src']) -############################################################################### +# %% # View in 2D and 3D ("glass" brain like 3D plot) # Show the sources as spheres scaled by their strength scale_factors = np.max(np.abs(stc.data), axis=1) @@ -87,7 +93,7 @@ modes=['sphere'], opacity=0.1, scale_factors=(scale_factors, None), fig_name="Gamma-MAP") -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/inverse/label_activation_from_stc.py python-mne-1.1.0+dfsg/examples/inverse/label_activation_from_stc.py --- python-mne-0.23.4+dfsg/examples/inverse/label_activation_from_stc.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/label_activation_from_stc.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-label-time-course: + ================================================== Extracting time course from source_estimate object ================================================== @@ -11,9 +14,9 @@ """ # Author: Christian Brodbeck # -# License: BSD (3-clause) +# License: BSD-3-Clause -import os +# %% import mne from mne.datasets import sample @@ -22,15 +25,15 @@ print(__doc__) data_path = sample.data_path() -os.environ['SUBJECTS_DIR'] = data_path + '/subjects' -meg_path = data_path + '/MEG/sample' +subjects_dir = data_path / 'subjects' +meg_path = data_path / 'MEG' / 'sample' # load the stc -stc = mne.read_source_estimate(meg_path + '/sample_audvis-meg') +stc = mne.read_source_estimate(meg_path / 'sample_audvis-meg') # load the labels -aud_lh = mne.read_label(meg_path + '/labels/Aud-lh.label') -aud_rh = mne.read_label(meg_path + '/labels/Aud-rh.label') +aud_lh = mne.read_label(meg_path / 'labels' / 'Aud-lh.label') +aud_rh = mne.read_label(meg_path / 'labels' / 'Aud-rh.label') # extract the time course for different labels from the stc stc_lh = stc.in_label(aud_lh) @@ -38,10 +41,10 @@ stc_bh = stc.in_label(aud_lh + aud_rh) # calculate center of mass and transform to mni coordinates -vtx, _, t_lh = stc_lh.center_of_mass('sample') -mni_lh = mne.vertex_to_mni(vtx, 0, 'sample')[0] -vtx, _, t_rh = stc_rh.center_of_mass('sample') -mni_rh = mne.vertex_to_mni(vtx, 1, 'sample')[0] +vtx, _, t_lh = stc_lh.center_of_mass('sample', subjects_dir=subjects_dir) +mni_lh = mne.vertex_to_mni(vtx, 0, 'sample', subjects_dir=subjects_dir)[0] +vtx, _, t_rh = stc_rh.center_of_mass('sample', subjects_dir=subjects_dir) +mni_rh = mne.vertex_to_mni(vtx, 1, 'sample', subjects_dir=subjects_dir)[0] # plot the activation plt.figure() diff -Nru python-mne-0.23.4+dfsg/examples/inverse/label_from_stc.py python-mne-1.1.0+dfsg/examples/inverse/label_from_stc.py --- python-mne-0.23.4+dfsg/examples/inverse/label_from_stc.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/label_from_stc.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-functional-label: + ================================================= Generate a functional label from source estimates ================================================= @@ -12,7 +15,9 @@ """ # Author: Luke Bloy # Alex Gramfort -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np import matplotlib.pyplot as plt @@ -24,9 +29,10 @@ print(__doc__) data_path = sample.data_path() -fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' -fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif' -subjects_dir = data_path + '/subjects' +fname_inv = ( + data_path / 'MEG' / 'sample' / 'sample_audvis-meg-oct-6-meg-inv.fif') +fname_evoked = data_path / 'MEG' / 'sample' / 'sample_audvis-ave.fif' +subjects_dir = data_path / 'subjects' subject = 'sample' snr = 3.0 @@ -85,7 +91,7 @@ pca_anat *= np.sign(pca_anat[np.argmax(np.abs(pca_anat))]) pca_func *= np.sign(pca_func[np.argmax(np.abs(pca_anat))]) -############################################################################### +# %% # plot the time courses.... plt.figure() plt.plot(1e3 * stc_anat_label.times, pca_anat, 'k', @@ -95,8 +101,8 @@ plt.legend() plt.show() -############################################################################### -# plot brain in 3D with PySurfer if available +# %% +# plot brain in 3D with mne.viz.Brain if available brain = stc_mean.plot(hemi='lh', subjects_dir=subjects_dir) brain.show_view('lateral') diff -Nru python-mne-0.23.4+dfsg/examples/inverse/label_source_activations.py python-mne-1.1.0+dfsg/examples/inverse/label_source_activations.py --- python-mne-0.23.4+dfsg/examples/inverse/label_source_activations.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/label_source_activations.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-label-time-series: + ==================================================== Extracting the time series of activations in a label ==================================================== @@ -12,7 +15,9 @@ # Authors: Alexandre Gramfort # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import matplotlib.pyplot as plt import matplotlib.patheffects as path_effects @@ -25,9 +30,10 @@ data_path = sample.data_path() label = 'Aud-lh' -label_fname = data_path + '/MEG/sample/labels/%s.label' % label -fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' -fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif' +meg_path = data_path / 'MEG' / 'sample' +label_fname = meg_path / 'labels' / f'{label}.label' +fname_inv = meg_path / 'sample_audvis-meg-oct-6-meg-inv.fif' +fname_evoked = meg_path / 'sample_audvis-ave.fif' snr = 3.0 lambda2 = 1.0 / snr ** 2 @@ -38,7 +44,7 @@ inverse_operator = read_inverse_operator(fname_inv) src = inverse_operator['src'] -############################################################################### +# %% # Compute inverse solution # ------------------------ pick_ori = "normal" # Get signed values to see the effect of sign flip @@ -54,7 +60,7 @@ tcs[mode] = stc.extract_label_time_course(label, src, mode=mode) print("Number of vertices : %d" % len(stc_label.data)) -############################################################################### +# %% # View source activations # ----------------------- @@ -73,7 +79,7 @@ xlim=xlim, ylim=ylim) mne.viz.tight_layout() -############################################################################### +# %% # Using vector solutions # ---------------------- # It's also possible to compute label time courses for a diff -Nru python-mne-0.23.4+dfsg/examples/inverse/mixed_norm_inverse.py python-mne-1.1.0+dfsg/examples/inverse/mixed_norm_inverse.py --- python-mne-0.23.4+dfsg/examples/inverse/mixed_norm_inverse.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/mixed_norm_inverse.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-mixed-norm-inverse: + ================================================================ Compute sparse inverse solution with mixed norm: MxNE and irMxNE ================================================================ @@ -11,7 +14,9 @@ # Author: Alexandre Gramfort # Daniel Strohmeier # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% # sphinx_gallery_thumbnail_number = 2 @@ -27,10 +32,11 @@ print(__doc__) data_path = sample.data_path() -fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' -ave_fname = data_path + '/MEG/sample/sample_audvis-ave.fif' -cov_fname = data_path + '/MEG/sample/sample_audvis-shrunk-cov.fif' -subjects_dir = data_path + '/subjects' +meg_path = data_path / 'MEG' / 'sample' +fwd_fname = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif' +ave_fname = meg_path / 'sample_audvis-ave.fif' +cov_fname = meg_path / 'sample_audvis-shrunk-cov.fif' +subjects_dir = data_path / 'subjects' # Read noise covariance matrix cov = mne.read_cov(cov_fname) @@ -41,10 +47,10 @@ # Handling forward solution forward = mne.read_forward_solution(fwd_fname) -############################################################################### -# Run solver -alpha = 55 # regularization parameter between 0 and 100 (100 is high) -loose, depth = 0.2, 0.9 # loose orientation & depth weighting +# %% +# Run solver with SURE criterion :footcite:`DeledalleEtAl2014` +alpha = "sure" # regularization parameter between 0 and 100 or SURE criterion +loose, depth = 0.9, 0.9 # loose orientation & depth weighting n_mxne_iter = 10 # if > 1 use L0.5/L2 reweighted mixed norm solver # if n_mxne_iter > 1 dSPM weighting can be avoided. @@ -58,9 +64,13 @@ # Compute (ir)MxNE inverse solution with dipole output dipoles, residual = mixed_norm( evoked, forward, cov, alpha, loose=loose, depth=depth, maxit=3000, - tol=1e-4, active_set_size=10, debias=True, weights=stc_dspm, + tol=1e-4, active_set_size=10, debias=False, weights=stc_dspm, weights_min=8., n_mxne_iter=n_mxne_iter, return_residual=True, - return_as_dipoles=True, verbose=True) + return_as_dipoles=True, verbose=True, random_state=0, + # for this dataset we know we should use a high alpha, so avoid some + # of the slower (lower) alpha values + sure_alpha_grid=np.linspace(100, 40, 10), +) t = 0.083 tidx = evoked.time_as_index(t) @@ -68,7 +78,7 @@ print(f'Dipole #{di} GOF at {1000 * t:0.1f} ms: ' f'{float(dip.gof[tidx]):0.1f}%') -############################################################################### +# %% # Plot dipole activations plot_dipole_amplitudes(dipoles) @@ -84,7 +94,7 @@ subjects_dir=subjects_dir, mode='orthoview', idx='amplitude') -############################################################################### +# %% # Plot residual ylim = dict(eeg=[-10, 10], grad=[-400, 400], mag=[-600, 600]) evoked.pick_types(meg=True, eeg=True, exclude='bads') @@ -92,31 +102,32 @@ residual.pick_types(meg=True, eeg=True, exclude='bads') residual.plot(ylim=ylim, proj=True, time_unit='s') -############################################################################### +# %% # Generate stc from dipoles stc = make_stc_from_dipoles(dipoles, forward['src']) -############################################################################### +# %% # View in 2D and 3D ("glass" brain like 3D plot) solver = "MxNE" if n_mxne_iter == 1 else "irMxNE" plot_sparse_source_estimates(forward['src'], stc, bgcolor=(1, 1, 1), fig_name="%s (cond %s)" % (solver, condition), opacity=0.1) -############################################################################### +# %% # Morph onto fsaverage brain and view morph = mne.compute_source_morph(stc, subject_from='sample', subject_to='fsaverage', spacing=None, sparse=True, subjects_dir=subjects_dir) stc_fsaverage = morph.apply(stc) -src_fsaverage_fname = subjects_dir + '/fsaverage/bem/fsaverage-ico-5-src.fif' +src_fsaverage_fname = ( + subjects_dir / 'fsaverage' / 'bem' / 'fsaverage-ico-5-src.fif') src_fsaverage = mne.read_source_spaces(src_fsaverage_fname) plot_sparse_source_estimates(src_fsaverage, stc_fsaverage, bgcolor=(1, 1, 1), fig_name="Morphed %s (cond %s)" % (solver, condition), opacity=0.1) -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/inverse/mixed_source_space_inverse.py python-mne-1.1.0+dfsg/examples/inverse/mixed_source_space_inverse.py --- python-mne-0.23.4+dfsg/examples/inverse/mixed_source_space_inverse.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/mixed_source_space_inverse.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-mixed-source-space-inverse: + ===================================================================== Compute MNE inverse solution on evoked data with a mixed source space ===================================================================== @@ -8,9 +11,10 @@ """ # Author: Annalisa Pascarella # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% -import os.path as op import matplotlib.pyplot as plt from nilearn import plotting @@ -21,23 +25,23 @@ # Set dir data_path = mne.datasets.sample.data_path() subject = 'sample' -data_dir = op.join(data_path, 'MEG', subject) -subjects_dir = op.join(data_path, 'subjects') -bem_dir = op.join(subjects_dir, subject, 'bem') +data_dir = data_path / 'MEG' / subject +subjects_dir = data_path / 'subjects' +bem_dir = subjects_dir / subject / 'bem' # Set file names -fname_mixed_src = op.join(bem_dir, '%s-oct-6-mixed-src.fif' % subject) -fname_aseg = op.join(subjects_dir, subject, 'mri', 'aseg.mgz') +fname_mixed_src = bem_dir / f'{subject}-oct-6-mixed-src.fif' +fname_aseg = subjects_dir / subject / 'mri' / 'aseg.mgz' -fname_model = op.join(bem_dir, '%s-5120-bem.fif' % subject) -fname_bem = op.join(bem_dir, '%s-5120-bem-sol.fif' % subject) +fname_model = bem_dir / f'{subject}-5120-bem.fif' +fname_bem = bem_dir / f'{subject}-5120-bem-sol.fif' -fname_evoked = data_dir + '/sample_audvis-ave.fif' -fname_trans = data_dir + '/sample_audvis_raw-trans.fif' -fname_fwd = data_dir + '/sample_audvis-meg-oct-6-mixed-fwd.fif' -fname_cov = data_dir + '/sample_audvis-shrunk-cov.fif' +fname_evoked = data_dir / f'{subject}_audvis-ave.fif' +fname_trans = data_dir / f'{subject}_audvis_raw-trans.fif' +fname_fwd = data_dir / f'{subject}_audvis-meg-oct-6-mixed-fwd.fif' +fname_cov = data_dir / f'{subject}_audvis-shrunk-cov.fif' -############################################################################### +# %% # Set up our source space # ----------------------- # List substructures we are interested in. We select only the @@ -51,13 +55,13 @@ 'Right-Thalamus-Proper', 'Right-Cerebellum-Cortex'] -############################################################################### +# %% # Get a surface-based source space, here with few source points for speed # in this demonstration, in general you should use oct6 spacing! src = mne.setup_source_space(subject, spacing='oct5', add_dist=False, subjects_dir=subjects_dir) -############################################################################### +# %% # Now we create a mixed src space by adding the volume regions specified in the # list labels_vol. First, read the aseg file and the source space bounds # using the inner skull surface (here using 10mm spacing to save time, @@ -74,30 +78,30 @@ print(f"The source space contains {len(src)} spaces and " f"{sum(s['nuse'] for s in src)} vertices") -############################################################################### +# %% # View the source space # --------------------- src.plot(subjects_dir=subjects_dir) -############################################################################### +# %% # We could write the mixed source space with:: # # >>> write_source_spaces(fname_mixed_src, src, overwrite=True) # # We can also export source positions to NIfTI file and visualize it again: -nii_fname = op.join(bem_dir, '%s-mixed-src.nii' % subject) +nii_fname = bem_dir / f'{subject}-mixed-src.nii' src.export_volume(nii_fname, mri_resolution=True, overwrite=True) -plotting.plot_img(nii_fname, cmap='nipy_spectral') +plotting.plot_img(str(nii_fname), cmap='nipy_spectral') -############################################################################### +# %% # Compute the fwd matrix # ---------------------- fwd = mne.make_forward_solution( fname_evoked, fname_trans, src, fname_bem, mindist=5.0, # ignore sources<=5mm from innerskull - meg=True, eeg=False, n_jobs=1) + meg=True, eeg=False, n_jobs=None) del src # save memory leadfield = fwd['sol']['data'] @@ -111,7 +115,7 @@ baseline=(None, 0)) noise_cov = mne.read_cov(fname_cov) -############################################################################### +# %% # Compute inverse solution # ------------------------ snr = 3.0 # use smaller SNR for raw data @@ -129,7 +133,7 @@ pick_ori=None) src = inverse_operator['src'] -############################################################################### +# %% # Plot the mixed source estimate # ------------------------------ @@ -140,21 +144,21 @@ brain = stc_vec.plot( hemi='both', src=inverse_operator['src'], views='coronal', initial_time=initial_time, subjects_dir=subjects_dir, - brain_kwargs=dict(silhouette=True)) + brain_kwargs=dict(silhouette=True), smoothing_steps=7) -############################################################################### +# %% # Plot the surface # ---------------- brain = stc.surface().plot(initial_time=initial_time, - subjects_dir=subjects_dir) -############################################################################### + subjects_dir=subjects_dir, smoothing_steps=7) +# %% # Plot the volume # --------------- fig = stc.volume().plot(initial_time=initial_time, src=src, subjects_dir=subjects_dir) -############################################################################### +# %% # Process labels # -------------- # Average the source estimates within each label of the cortical parcellation diff -Nru python-mne-0.23.4+dfsg/examples/inverse/mne_cov_power.py python-mne-1.1.0+dfsg/examples/inverse/mne_cov_power.py --- python-mne-0.23.4+dfsg/examples/inverse/mne_cov_power.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/mne_cov_power.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-cov-power: + =================================================================== Compute source power estimate by projecting the covariance with MNE =================================================================== @@ -17,9 +20,10 @@ # Author: Denis A. Engemann # Luke Bloy # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% -import os.path as op import numpy as np import mne @@ -27,43 +31,42 @@ from mne.minimum_norm import make_inverse_operator, apply_inverse_cov data_path = sample.data_path() -subjects_dir = data_path + '/subjects' -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' +subjects_dir = data_path / 'subjects' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' raw = mne.io.read_raw_fif(raw_fname) -############################################################################### +# %% # Compute empty-room covariance # ----------------------------- # First we compute an empty-room covariance, which captures noise from the # sensors and environment. -raw_empty_room_fname = op.join( - data_path, 'MEG', 'sample', 'ernoise_raw.fif') +raw_empty_room_fname = data_path / 'MEG' / 'sample' / 'ernoise_raw.fif' raw_empty_room = mne.io.read_raw_fif(raw_empty_room_fname) -raw_empty_room.crop(0, 60) +raw_empty_room.crop(0, 30) # cropped just for speed raw_empty_room.info['bads'] = ['MEG 2443'] -raw_empty_room.info['projs'] = raw.info['projs'] -noise_cov = mne.compute_raw_covariance( - raw_empty_room, method=['empirical', 'shrunk']) +raw_empty_room.add_proj(raw.info['projs']) +noise_cov = mne.compute_raw_covariance(raw_empty_room, method='shrunk') del raw_empty_room -############################################################################### +# %% # Epoch the data # -------------- -raw.info['bads'] = ['MEG 2443', 'EEG 053'] -raw.load_data().filter(4, 12) +raw.pick(['meg', 'stim', 'eog']).load_data().filter(4, 12) +raw.info['bads'] = ['MEG 2443'] events = mne.find_events(raw, stim_channel='STI 014') event_id = dict(aud_l=1, aud_r=2, vis_l=3, vis_r=4) tmin, tmax = -0.2, 0.5 baseline = (None, 0) # means from the first instant to t = 0 reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6) -epochs = mne.Epochs(raw.copy().filter(4, 12), events, event_id, tmin, tmax, +epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=('meg', 'eog'), baseline=None, - reject=reject, preload=True) + reject=reject, preload=True, decim=5, verbose='error') del raw -############################################################################### +# %% # Compute and plot covariances # ---------------------------- # In addition to the empty-room covariance above, we compute two additional @@ -76,17 +79,15 @@ # to noise sources). base_cov = mne.compute_covariance( - epochs, tmin=-0.2, tmax=0, method=['shrunk', 'empirical'], rank=None, - verbose=True) + epochs, tmin=-0.2, tmax=0, method='shrunk', verbose=True) data_cov = mne.compute_covariance( - epochs, tmin=0., tmax=0.2, method=['shrunk', 'empirical'], rank=None, - verbose=True) + epochs, tmin=0., tmax=0.2, method='shrunk', verbose=True) fig_noise_cov = mne.viz.plot_cov(noise_cov, epochs.info, show_svd=False) fig_base_cov = mne.viz.plot_cov(base_cov, epochs.info, show_svd=False) fig_data_cov = mne.viz.plot_cov(data_cov, epochs.info, show_svd=False) -############################################################################### +# %% # We can also look at the covariances using topomaps, here we just show the # baseline and data covariances, followed by the data covariance whitened # by the baseline covariance: @@ -100,21 +101,20 @@ data_cov.plot_topomap(evoked.info, 'grad', noise_cov=noise_cov, title='Whitened data') -############################################################################### +# %% # Apply inverse operator to covariance # ------------------------------------ # Finally, we can construct an inverse using the empty-room noise covariance: # Read the forward solution and compute the inverse operator -fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-oct-6-fwd.fif' +fname_fwd = meg_path / 'sample_audvis-meg-oct-6-fwd.fif' fwd = mne.read_forward_solution(fname_fwd) - # make an MEG inverse operator info = evoked.info inverse_operator = make_inverse_operator(info, fwd, noise_cov, loose=0.2, depth=0.8) -############################################################################### +# %% # Project our data and baseline covariance to source space: stc_data = apply_inverse_cov(data_cov, evoked.info, inverse_operator, @@ -122,11 +122,12 @@ stc_base = apply_inverse_cov(base_cov, evoked.info, inverse_operator, nave=len(epochs), method='dSPM', verbose=True) -############################################################################### +# %% # And visualize power is relative to the baseline: # sphinx_gallery_thumbnail_number = 9 stc_data /= stc_base brain = stc_data.plot(subject='sample', subjects_dir=subjects_dir, - clim=dict(kind='percent', lims=(50, 90, 98))) + clim=dict(kind='percent', lims=(50, 90, 98)), + smoothing_steps=7) diff -Nru python-mne-0.23.4+dfsg/examples/inverse/morph_surface_stc.py python-mne-1.1.0+dfsg/examples/inverse/morph_surface_stc.py --- python-mne-0.23.4+dfsg/examples/inverse/morph_surface_stc.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/morph_surface_stc.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-morph-surface: @@ -25,35 +26,33 @@ """ # Author: Tommy Clausner # -# License: BSD (3-clause) -import os -import os.path as op +# License: BSD-3-Clause +# %% import mne from mne.datasets import sample print(__doc__) -############################################################################### +# %% # Setup paths data_path = sample.data_path() -sample_dir = op.join(data_path, 'MEG', 'sample') -subjects_dir = op.join(data_path, 'subjects') -fname_src = op.join(subjects_dir, 'sample', 'bem', 'sample-oct-6-src.fif') -fname_fwd = op.join(sample_dir, 'sample_audvis-meg-oct-6-fwd.fif') -fname_fsaverage_src = os.path.join(subjects_dir, 'fsaverage', 'bem', - 'fsaverage-ico-5-src.fif') +sample_dir = data_path / 'MEG' / 'sample' +subjects_dir = data_path / 'subjects' +fname_src = subjects_dir / 'sample' / 'bem' / 'sample-oct-6-src.fif' +fname_fwd = sample_dir / 'sample_audvis-meg-oct-6-fwd.fif' +fname_fsaverage_src = (subjects_dir / 'fsaverage' / 'bem' / + 'fsaverage-ico-5-src.fif') +fname_stc = sample_dir / 'sample_audvis-meg' -fname_stc = os.path.join(sample_dir, 'sample_audvis-meg') - -############################################################################### +# %% # Load example data # Read stc from file stc = mne.read_source_estimate(fname_stc, subject='sample') -############################################################################### +# %% # Setting up SourceMorph for SourceEstimate # ----------------------------------------- # @@ -71,7 +70,7 @@ print(fwd['src']) # n_used=3732, 3766 print([len(v) for v in stc.vertices]) -############################################################################### +# %% # We also need to specify the set of vertices to morph to. This can be done # using the ``spacing`` parameter, but for consistency it's better to pass the # ``src_to`` parameter. @@ -93,7 +92,7 @@ subject_to='fsaverage', src_to=src_to, subjects_dir=subjects_dir) -############################################################################### +# %% # Apply morph to (Vector) SourceEstimate # -------------------------------------- # @@ -102,7 +101,7 @@ stc_fsaverage = morph.apply(stc) -############################################################################### +# %% # Plot results # ------------ @@ -120,7 +119,7 @@ brain.add_text(0.1, 0.9, 'Morphed to fsaverage (spherical)', 'title', font_size=16) -############################################################################### +# %% # As inflated surface brain_inf = stc_fsaverage.plot(surface='inflated', **surfer_kwargs) @@ -128,7 +127,7 @@ brain_inf.add_text(0.1, 0.9, 'Morphed to fsaverage (inflated)', 'title', font_size=16) -############################################################################### +# %% # Reading and writing SourceMorph from and to disk # ------------------------------------------------ # @@ -158,7 +157,7 @@ stc_fsaverage = mne.compute_source_morph(stc, subjects_dir=subjects_dir).apply(stc) -############################################################################### +# %% # For more examples, check out :ref:`examples using SourceMorph.apply # `. # diff -Nru python-mne-0.23.4+dfsg/examples/inverse/morph_volume_stc.py python-mne-1.1.0+dfsg/examples/inverse/morph_volume_stc.py --- python-mne-0.23.4+dfsg/examples/inverse/morph_volume_stc.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/morph_volume_stc.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-morph-volume: @@ -22,7 +23,9 @@ """ # Author: Tommy Clausner # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import os import nibabel as nib @@ -33,7 +36,7 @@ print(__doc__) -############################################################################### +# %% # Setup paths sample_dir_raw = sample.data_path() sample_dir = os.path.join(sample_dir_raw, 'MEG', 'sample') @@ -47,7 +50,7 @@ fetch_fsaverage(subjects_dir) # ensure fsaverage src exists fname_src_fsaverage = subjects_dir + '/fsaverage/bem/fsaverage-vol-5-src.fif' -############################################################################### +# %% # Compute example data. For reference see :ref:`ex-inverse-volume`. # # Load data: @@ -60,7 +63,7 @@ # To save time stc.crop(0.09, 0.09) -############################################################################### +# %% # Get a SourceMorph object for VolSourceEstimate # ---------------------------------------------- # @@ -86,7 +89,7 @@ niter_affine=[10, 10, 5], niter_sdr=[10, 10, 5], # just for speed src_to=src_fs, verbose=True) -############################################################################### +# %% # Apply morph to VolSourceEstimate # -------------------------------- # @@ -109,7 +112,7 @@ stc_fsaverage = morph.apply(stc) -############################################################################### +# %% # Convert morphed VolSourceEstimate into NIfTI # -------------------------------------------- # @@ -119,7 +122,7 @@ # Create mri-resolution volume of results img_fsaverage = morph.apply(stc, mri_resolution=2, output='nifti1') -############################################################################### +# %% # Plot results # ------------ @@ -136,7 +139,7 @@ display.add_overlay(img_fsaverage, alpha=0.75) -############################################################################### +# %% # Reading and writing SourceMorph from and to disk # ------------------------------------------------ # diff -Nru python-mne-0.23.4+dfsg/examples/inverse/multidict_reweighted_tfmxne.py python-mne-1.1.0+dfsg/examples/inverse/multidict_reweighted_tfmxne.py --- python-mne-0.23.4+dfsg/examples/inverse/multidict_reweighted_tfmxne.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/multidict_reweighted_tfmxne.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-iterative-reweighted-tf-mxne: + ============================================================================== Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary ============================================================================== @@ -20,9 +23,9 @@ # Daniel Strohmeier # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause -import os.path as op +# %% import mne from mne.datasets import somato @@ -32,16 +35,16 @@ print(__doc__) -############################################################################### +# %% # Load somatosensory MEG data data_path = somato.data_path() subject = '01' task = 'somato' -raw_fname = op.join(data_path, 'sub-{}'.format(subject), 'meg', - 'sub-{}_task-{}_meg.fif'.format(subject, task)) -fwd_fname = op.join(data_path, 'derivatives', 'sub-{}'.format(subject), - 'sub-{}_task-{}-fwd.fif'.format(subject, task)) +raw_fname = (data_path / f'sub-{subject}' / 'meg' / + f'sub-{subject}_task-{task}_meg.fif') +fwd_fname = (data_path / 'derivatives' / f'sub-{subject}' / + f'sub-{subject}_task-{task}-fwd.fif') # Read evoked raw = mne.io.read_raw_fif(raw_fname) @@ -63,7 +66,7 @@ # Handling forward solution forward = mne.read_forward_solution(fwd_fname) -############################################################################### +# %% # Run iterative reweighted multidict TF-MxNE solver alpha, l1_ratio = 20, 0.05 @@ -81,7 +84,7 @@ wsize=wsize, tstep=tstep, return_as_dipoles=True, return_residual=True) -############################################################################### +# %% # Generate stc from dipoles stc = make_stc_from_dipoles(dipoles, forward['src']) @@ -89,7 +92,7 @@ forward['src'], stc, bgcolor=(1, 1, 1), opacity=0.1, fig_name=f"irTF-MxNE (cond {evoked.comment})") -############################################################################### +# %% # Show the evoked response and the residual for gradiometers ylim = dict(grad=[-300, 300]) evoked.copy().pick_types(meg='grad').plot( @@ -97,7 +100,7 @@ residual.copy().pick_types(meg='grad').plot( titles=dict(grad='Residuals: Gradiometers'), ylim=ylim) -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/inverse/multi_dipole_model.py python-mne-1.1.0+dfsg/examples/inverse/multi_dipole_model.py --- python-mne-0.23.4+dfsg/examples/inverse/multi_dipole_model.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/multi_dipole_model.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,168 @@ +# -*- coding: utf-8 -*- +""" +.. _ex-multi-dipole: + +================================================================= +Computing source timecourses with an XFit-like multi-dipole model +================================================================= + +MEGIN's XFit program offers a "guided ECD modeling" interface, where multiple +dipoles can be fitted interactively. By manually selecting subsets of sensors +and time ranges, dipoles can be fitted to specific signal components. Then, +source timecourses can be computed using a multi-dipole model. The advantage of +using a multi-dipole model over fitting each dipole in isolation, is that when +multiple dipoles contribute to the same signal component, the model can make +sure that activity assigned to one dipole is not also assigned to another. This +example shows how to build a multi-dipole model for estimating source +timecourses for evokeds or single epochs. + +The XFit program is the recommended approach for guided ECD modeling, because +it offers a convenient graphical user interface for it. These dipoles can then +be imported into MNE-Python by using the :func:`mne.read_dipole` function for +building and applying the multi-dipole model. In addition, this example will +also demonstrate how to perform guided ECD modeling using only MNE-Python +functionality, which is less convenient than using XFit, but has the benefit of +being reproducible. +""" +# Author: Marijn van Vliet +# +# License: BSD-3-Clause + +############################################################################### +# Importing everything and setting up the data paths for the MNE-Sample +# dataset. +import mne +from mne.datasets import sample +from mne.channels import read_vectorview_selection +from mne.minimum_norm import (make_inverse_operator, apply_inverse, + apply_inverse_epochs) +import matplotlib.pyplot as plt +import numpy as np + +data_path = sample.data_path() +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_raw.fif' +cov_fname = meg_path / 'sample_audvis-shrunk-cov.fif' +bem_dir = data_path / 'subjects' / 'sample' / 'bem' +bem_fname = bem_dir / 'sample-5120-5120-5120-bem-sol.fif' + +############################################################################### +# Read the MEG data from the audvis experiment. Make epochs and evokeds for the +# left and right auditory conditions. +raw = mne.io.read_raw_fif(raw_fname) +raw = raw.pick_types(meg=True, eog=True, stim=True) +info = raw.info + +# Create epochs for auditory events +events = mne.find_events(raw) +event_id = dict(right=1, left=2) +epochs = mne.Epochs(raw, events, event_id, + tmin=-0.1, tmax=0.3, baseline=(None, 0), + reject=dict(mag=4e-12, grad=4000e-13, eog=150e-6)) + +# Create evokeds for left and right auditory stimulation +evoked_left = epochs['left'].average() +evoked_right = epochs['right'].average() + +############################################################################### +# Guided dipole modeling, meaning fitting dipoles to a manually selected subset +# of sensors as a manually chosen time, can now be performed in MEGINs XFit on +# the evokeds we computed above. However, it is possible to do it completely +# in MNE-Python. + +# Setup conductor model +cov = mne.read_cov(cov_fname) +bem = mne.read_bem_solution(bem_fname) + +# Fit two dipoles at t=80ms. The first dipole is fitted using only the sensors +# on the left side of the helmet. The second dipole is fitted using only the +# sensors on the right side of the helmet. +picks_left = read_vectorview_selection('Left', info=info) +evoked_fit_left = evoked_left.copy().crop(0.08, 0.08) +evoked_fit_left.pick_channels(picks_left) +cov_fit_left = cov.copy().pick_channels(picks_left) + +picks_right = read_vectorview_selection('Right', info=info) +evoked_fit_right = evoked_right.copy().crop(0.08, 0.08) +evoked_fit_right.pick_channels(picks_right) +cov_fit_right = cov.copy().pick_channels(picks_right) + +# Any SSS projections that are active on this data need to be re-normalized +# after picking channels. +evoked_fit_left.info.normalize_proj() +evoked_fit_right.info.normalize_proj() +cov_fit_left['projs'] = evoked_fit_left.info['projs'] +cov_fit_right['projs'] = evoked_fit_right.info['projs'] + +# Fit the dipoles with the subset of sensors. +dip_left, _ = mne.fit_dipole(evoked_fit_left, cov_fit_left, bem) +dip_right, _ = mne.fit_dipole(evoked_fit_right, cov_fit_right, bem) + +############################################################################### +# Now that we have the location and orientations of the dipoles, compute the +# full timecourses using MNE, assigning activity to both dipoles at the same +# time while preventing leakage between the two. We use a very low ``lambda`` +# value to ensure both dipoles are fully used. + +fwd, _ = mne.make_forward_dipole([dip_left, dip_right], bem, info) + +# Apply MNE inverse +inv = make_inverse_operator(info, fwd, cov, fixed=True, depth=0) +stc_left = apply_inverse(evoked_left, inv, method='MNE', lambda2=1E-6) +stc_right = apply_inverse(evoked_right, inv, method='MNE', lambda2=1E-6) + +# Plot the timecourses of the resulting source estimate +fig, axes = plt.subplots(nrows=2, sharex=True, sharey=True) +axes[0].plot(stc_left.times, stc_left.data.T) +axes[0].set_title('Left auditory stimulation') +axes[0].legend(['Dipole 1', 'Dipole 2']) +axes[1].plot(stc_right.times, stc_right.data.T) +axes[1].set_title('Right auditory stimulation') +axes[1].set_xlabel('Time (s)') +fig.supylabel('Dipole amplitude') + +############################################################################### +# We can also fit the timecourses to single epochs. Here, we do it for each +# experimental condition separately. + +stcs_left = apply_inverse_epochs(epochs['left'], inv, lambda2=1E-6, + method='MNE') +stcs_right = apply_inverse_epochs(epochs['right'], inv, lambda2=1E-6, + method='MNE') + +############################################################################### +# To summarize and visualize the single-epoch dipole amplitudes, we will create +# a detailed plot of the mean amplitude of the dipoles during different +# experimental conditions. + +# Summarize the single epoch timecourses by computing the mean amplitude from +# 60-90ms. +amplitudes_left = [] +amplitudes_right = [] +for stc in stcs_left: + amplitudes_left.append(stc.crop(0.06, 0.09).mean().data) +for stc in stcs_right: + amplitudes_right.append(stc.crop(0.06, 0.09).mean().data) +amplitudes = np.vstack([amplitudes_left, amplitudes_right]) + +# Visualize the epoch-by-epoch dipole ampltudes in a detailed figure. +n = len(amplitudes) +n_left = len(amplitudes_left) +mean_left = np.mean(amplitudes_left, axis=0) +mean_right = np.mean(amplitudes_right, axis=0) + +fig, ax = plt.subplots(figsize=(8, 4)) +ax.scatter(np.arange(n), amplitudes[:, 0], label='Dipole 1') +ax.scatter(np.arange(n), amplitudes[:, 1], label='Dipole 2') +transition_point = n_left - 0.5 +ax.plot([0, transition_point], [mean_left[0], mean_left[0]], color='C0') +ax.plot([0, transition_point], [mean_left[1], mean_left[1]], color='C1') +ax.plot([transition_point, n], [mean_right[0], mean_right[0]], color='C0') +ax.plot([transition_point, n], [mean_right[1], mean_right[1]], color='C1') +ax.axvline(transition_point, color='black') +ax.set_xlabel('Epochs') +ax.set_ylabel('Dipole amplitude') +ax.legend() +fig.suptitle('Single epoch dipole amplitudes') +fig.text(0.30, 0.9, 'Left auditory stimulation', ha='center') +fig.text(0.70, 0.9, 'Right auditory stimulation', ha='center') diff -Nru python-mne-0.23.4+dfsg/examples/inverse/psf_ctf_label_leakage.py python-mne-1.1.0+dfsg/examples/inverse/psf_ctf_label_leakage.py --- python-mne-0.23.4+dfsg/examples/inverse/psf_ctf_label_leakage.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/psf_ctf_label_leakage.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-source-leakage: + ============================================================ Visualize source leakage among labels using a circular graph ============================================================ @@ -14,7 +17,9 @@ # Alexandre Gramfort # Nicolas P. Rougier (graph code borrowed from his matplotlib gallery) # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np import matplotlib.pyplot as plt @@ -25,11 +30,13 @@ make_inverse_resolution_matrix, get_point_spread) -from mne.viz import circular_layout, plot_connectivity_circle +from mne.viz import circular_layout +from mne_connectivity.viz import plot_connectivity_circle + print(__doc__) -############################################################################### +# %% # Load forward solution and inverse operator # ------------------------------------------ # @@ -37,9 +44,10 @@ # resolution matrices for different methods. data_path = sample.data_path() -subjects_dir = data_path + '/subjects' -fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' -fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-fixed-inv.fif' +subjects_dir = data_path / 'subjects' +meg_path = data_path / 'MEG' / 'sample' +fname_fwd = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif' +fname_inv = meg_path / 'sample_audvis-meg-oct-6-meg-fixed-inv.fif' forward = mne.read_forward_solution(fname_fwd) # Convert forward solution to fixed source orientations mne.convert_forward_solution( @@ -52,7 +60,7 @@ src = inverse_operator['src'] del forward, inverse_operator # save memory -############################################################################### +# %% # Read and organise labels for cortical parcellation # -------------------------------------------------- # @@ -78,7 +86,7 @@ # For the right hemi rh_labels = [label[:-2] + 'rh' for label in lh_labels] -############################################################################### +# %% # Compute point-spread function summaries (PCA) for all labels # ------------------------------------------------------------ # @@ -95,7 +103,7 @@ n_verts = rm_mne.shape[0] del rm_mne -############################################################################### +# %% # We can show the explained variances of principal components per label. Note # how they differ across labels, most likely due to their varying spatial # extent. @@ -104,7 +112,7 @@ for [name, var] in zip(label_names, pca_vars_mne): print(f'{name}: {var.sum():.1f}% {var}') -############################################################################### +# %% # The output shows the summed variance explained by the first five principal # components as well as the explained variances of the individual components. # @@ -129,12 +137,13 @@ group_boundaries=[0, len(label_names) / 2]) # Plot the graph using node colors from the FreeSurfer parcellation. We only # show the 200 strongest connections. -fig = plt.figure(num=None, figsize=(8, 8), facecolor='black') +fig, ax = plt.subplots( + figsize=(8, 8), facecolor='black', subplot_kw=dict(projection='polar')) plot_connectivity_circle(leakage_mne, label_names, n_lines=200, node_angles=node_angles, node_colors=label_colors, - title='MNE Leakage', fig=fig) + title='MNE Leakage', ax=ax) -############################################################################### +# %% # Most leakage occurs for neighbouring regions, but also for deeper regions # across hemispheres. # @@ -145,7 +154,7 @@ # versus for saved figures. Thus when saving you must specify ``facecolor``, # else your labels, title, etc will not be visible:: # -# >>> fname_fig = data_path + '/MEG/sample/plot_label_leakage.png' +# >>> fname_fig = meg_path / 'plot_label_leakage.png' # >>> fig.savefig(fname_fig, facecolor='black') # # Plot PSFs for individual labels @@ -164,7 +173,7 @@ # Maximum for scaling across plots max_val = np.max([stc_lh.data, stc_rh.data]) -############################################################################### +# %% # Point-spread function for the lateral occipital label in the left hemisphere brain_lh = stc_lh.plot(subjects_dir=subjects_dir, subject='sample', @@ -173,7 +182,7 @@ pos_lims=(0, max_val / 2., max_val))) brain_lh.add_text(0.1, 0.9, label_names[idx[0]], 'title', font_size=16) -############################################################################### +# %% # and in the right hemisphere. brain_rh = stc_rh.plot(subjects_dir=subjects_dir, subject='sample', @@ -182,6 +191,6 @@ pos_lims=(0, max_val / 2., max_val))) brain_rh.add_text(0.1, 0.9, label_names[idx[1]], 'title', font_size=16) -############################################################################### +# %% # Both summary PSFs are confined to their respective hemispheres, indicating # that there is indeed low leakage between these two regions. diff -Nru python-mne-0.23.4+dfsg/examples/inverse/psf_ctf_vertices_lcmv.py python-mne-1.1.0+dfsg/examples/inverse/psf_ctf_vertices_lcmv.py --- python-mne-0.23.4+dfsg/examples/inverse/psf_ctf_vertices_lcmv.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/psf_ctf_vertices_lcmv.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,5 +1,7 @@ # -*- coding: utf-8 -*- """ +.. _ex-psf-ctf-lcmv: + ================================================= Compute cross-talk functions for LCMV beamformers ================================================= @@ -10,7 +12,9 @@ """ # Author: Olaf Hauk # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne from mne.datasets import sample @@ -20,11 +24,12 @@ print(__doc__) data_path = sample.data_path() -subjects_dir = data_path + '/subjects/' -fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' -fname_cov = data_path + '/MEG/sample/sample_audvis-cov.fif' -fname_evo = data_path + '/MEG/sample/sample_audvis-ave.fif' -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' +subjects_dir = data_path / 'subjects' +meg_path = data_path / 'MEG' / 'sample' +fname_fwd = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif' +fname_cov = meg_path / 'sample_audvis-cov.fif' +fname_evo = meg_path / 'sample_audvis-ave.fif' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' # Read raw data raw = mne.io.read_raw_fif(raw_fname) @@ -93,18 +98,17 @@ # Compute resolution matrices for the two LCMV beamformers # -------------------------------------------------------- -rm_pre = make_lcmv_resolution_matrix(filters_pre, forward, info) - -rm_post = make_lcmv_resolution_matrix(filters_post, forward, info) - # compute cross-talk functions (CTFs) for one target vertex sources = [3000] - +verttrue = [forward['src'][0]['vertno'][sources[0]]] # pick one vertex +rm_pre = make_lcmv_resolution_matrix(filters_pre, forward, info) stc_pre = get_cross_talk(rm_pre, forward['src'], sources, norm=True) +del rm_pre +############################################################################## +rm_post = make_lcmv_resolution_matrix(filters_post, forward, info) stc_post = get_cross_talk(rm_post, forward['src'], sources, norm=True) -verttrue = [forward['src'][0]['vertno'][sources[0]]] # pick one vertex -del forward +del rm_post ############################################################################## # Visualize @@ -121,7 +125,7 @@ brain_pre.add_foci(verttrue, coords_as_verts=True, scale_factor=1., hemi='lh', color='green') -############################################################################### +# %% # Post: brain_post = stc_post.plot('sample', 'inflated', 'lh', @@ -134,7 +138,7 @@ brain_post.add_foci(verttrue, coords_as_verts=True, scale_factor=1., hemi='lh', color='green') -############################################################################### +# %% # The pre-stimulus beamformer's CTF has lower values in parietal regions # suppressed alpha activity?) but larger values in occipital regions (less # suppression of visual activity?). diff -Nru python-mne-0.23.4+dfsg/examples/inverse/psf_ctf_vertices.py python-mne-1.1.0+dfsg/examples/inverse/psf_ctf_vertices.py --- python-mne-0.23.4+dfsg/examples/inverse/psf_ctf_vertices.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/psf_ctf_vertices.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,5 +1,8 @@ # -*- coding: utf-8 -*- """ +.. _ex-psd-ctf: + +================================================================== Plot point-spread functions (PSFs) and cross-talk functions (CTFs) ================================================================== @@ -8,7 +11,9 @@ # Authors: Olaf Hauk # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne from mne.datasets import sample @@ -18,10 +23,11 @@ print(__doc__) data_path = sample.data_path() -subjects_dir = data_path + '/subjects/' -fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' -fname_cov = data_path + '/MEG/sample/sample_audvis-cov.fif' -fname_evo = data_path + '/MEG/sample/sample_audvis-ave.fif' +subjects_dir = data_path / 'subjects' +meg_path = data_path / 'MEG' / 'sample' +fname_fwd = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif' +fname_cov = meg_path / 'sample_audvis-cov.fif' +fname_evo = meg_path / 'sample_audvis-ave.fif' # read forward solution forward = mne.read_forward_solution(fname_fwd) @@ -56,6 +62,7 @@ stc_psf = get_point_spread(rm_lor, forward['src'], sources, norm=True) stc_ctf = get_cross_talk(rm_lor, forward['src'], sources, norm=True) +del rm_lor ############################################################################## # Visualize @@ -82,7 +89,7 @@ brain_psf.add_foci(vert_max_psf, coords_as_verts=True, scale_factor=1., hemi='lh', color='black') -############################################################################### +# %% # CTF: brain_ctf = stc_ctf.plot('sample', 'inflated', 'lh', subjects_dir=subjects_dir) @@ -96,6 +103,6 @@ hemi='lh', color='black') -############################################################################### +# %% # The green spheres indicate the true source location, and the black # spheres the maximum of the distribution. diff -Nru python-mne-0.23.4+dfsg/examples/inverse/rap_music.py python-mne-1.1.0+dfsg/examples/inverse/rap_music.py --- python-mne-0.23.4+dfsg/examples/inverse/rap_music.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/rap_music.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-rap-music: + ================================ Compute Rap-Music on evoked data ================================ @@ -9,7 +12,9 @@ # Author: Yousra Bekhti # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne @@ -20,10 +25,11 @@ print(__doc__) data_path = sample.data_path() -subjects_dir = data_path + '/subjects' -fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' -evoked_fname = data_path + '/MEG/sample/sample_audvis-ave.fif' -cov_fname = data_path + '/MEG/sample/sample_audvis-cov.fif' +subjects_dir = data_path / 'subjects' +meg_path = data_path / 'MEG' / 'sample' +fwd_fname = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif' +evoked_fname = meg_path / 'sample_audvis-ave.fif' +cov_fname = meg_path / 'sample_audvis-cov.fif' # Read the evoked response and crop it condition = 'Right Auditory' @@ -52,7 +58,7 @@ residual.plot(ylim=dict(grad=[-300, 300], mag=[-800, 800], eeg=[-6, 8]), time_unit='s') -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/inverse/read_inverse.py python-mne-1.1.0+dfsg/examples/inverse/read_inverse.py --- python-mne-0.23.4+dfsg/examples/inverse/read_inverse.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/read_inverse.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-read-inverse: + =========================== Reading an inverse operator =========================== @@ -7,7 +10,9 @@ """ # Author: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne from mne.datasets import sample @@ -17,12 +22,10 @@ print(__doc__) data_path = sample.data_path() -subjects_dir = data_path + '/subjects' - -fname_trans = data_path + '/MEG/sample/sample_audvis_raw-trans.fif' - -inv_fname = data_path -inv_fname += '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' +subjects_dir = data_path / 'subjects' +meg_path = data_path / 'MEG' / 'sample' +fname_trans = meg_path / 'sample_audvis_raw-trans.fif' +inv_fname = meg_path / 'sample_audvis-meg-oct-6-meg-inv.fif' inv = read_inverse_operator(inv_fname) @@ -40,8 +43,8 @@ print("Number of vertices on the right hemisphere: %d" % len(src[1]['rr'])) print("Number of triangles on right hemisphere: %d" % len(src[1]['use_tris'])) -############################################################################### -# Show result on 3D source space +# %% +# Show the 3D source space fig = mne.viz.plot_alignment(subject='sample', subjects_dir=subjects_dir, trans=fname_trans, surfaces='white', src=src) diff -Nru python-mne-0.23.4+dfsg/examples/inverse/read_stc.py python-mne-1.1.0+dfsg/examples/inverse/read_stc.py --- python-mne-0.23.4+dfsg/examples/inverse/read_stc.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/read_stc.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-read-stc: + =================== Reading an STC file =================== @@ -8,7 +11,9 @@ """ # Author: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import matplotlib.pyplot as plt @@ -18,7 +23,8 @@ print(__doc__) data_path = sample.data_path() -fname = data_path + '/MEG/sample/sample_audvis-meg' +meg_path = data_path / 'MEG' / 'sample' +fname = meg_path / 'sample_audvis-meg' stc = mne.read_source_estimate(fname) diff -Nru python-mne-0.23.4+dfsg/examples/inverse/resolution_metrics_eegmeg.py python-mne-1.1.0+dfsg/examples/inverse/resolution_metrics_eegmeg.py --- python-mne-0.23.4+dfsg/examples/inverse/resolution_metrics_eegmeg.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/resolution_metrics_eegmeg.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-res-metrics-meeg: + ============================================================== Compute spatial resolution metrics to compare MEG with EEG+MEG ============================================================== @@ -13,7 +16,9 @@ """ # Author: Olaf Hauk # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne from mne.datasets import sample @@ -23,10 +28,11 @@ print(__doc__) data_path = sample.data_path() -subjects_dir = data_path + '/subjects/' -fname_fwd_emeg = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' -fname_cov = data_path + '/MEG/sample/sample_audvis-cov.fif' -fname_evo = data_path + '/MEG/sample/sample_audvis-ave.fif' +subjects_dir = data_path / 'subjects/' +meg_path = data_path / 'MEG' / 'sample' +fname_fwd_emeg = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif' +fname_cov = meg_path / 'sample_audvis-cov.fif' +fname_evo = meg_path / 'sample_audvis-ave.fif' # read forward solution with EEG and MEG forward_emeg = mne.read_forward_solution(fname_fwd_emeg) @@ -56,7 +62,7 @@ snr = 3.0 lambda2 = 1.0 / snr ** 2 -############################################################################### +# %% # EEGMEG # ------ # Compute resolution matrices, localization error, and spatial deviations @@ -70,7 +76,7 @@ function='psf', metric='sd_ext') del rm_emeg -############################################################################### +# %% # MEG # --- # Do the same for MEG: @@ -83,7 +89,7 @@ function='psf', metric='sd_ext') del rm_meg -############################################################################### +# %% # Visualization # ------------- # Look at peak localisation error (PLE) across the whole cortex for PSF: @@ -94,7 +100,7 @@ brain_ple_emeg.add_text(0.1, 0.9, 'PLE PSF EMEG', 'title', font_size=16) -############################################################################### +# %% # For MEG only: brain_ple_meg = ple_psf_meg.plot('sample', 'inflated', 'lh', @@ -103,7 +109,7 @@ brain_ple_meg.add_text(0.1, 0.9, 'PLE PSF MEG', 'title', font_size=16) -############################################################################### +# %% # Subtract the two distributions and plot this difference: diff_ple = ple_psf_emeg - ple_psf_meg @@ -115,7 +121,7 @@ brain_ple_diff.add_text(0.1, 0.9, 'PLE EMEG-MEG', 'title', font_size=16) -############################################################################### +# %% # These plots show that with respect to peak localization error, adding EEG to # MEG does not bring much benefit. Next let's visualise spatial deviation (SD) # across the whole cortex for PSF: @@ -126,7 +132,7 @@ brain_sd_emeg.add_text(0.1, 0.9, 'SD PSF EMEG', 'title', font_size=16) -############################################################################### +# %% # For MEG only: brain_sd_meg = sd_psf_meg.plot('sample', 'inflated', 'lh', @@ -135,7 +141,7 @@ brain_sd_meg.add_text(0.1, 0.9, 'SD PSF MEG', 'title', font_size=16) -############################################################################### +# %% # Subtract the two distributions and plot this difference: diff_sd = sd_psf_emeg - sd_psf_meg @@ -147,7 +153,7 @@ brain_sd_diff.add_text(0.1, 0.9, 'SD EMEG-MEG', 'title', font_size=16) -############################################################################### +# %% # Adding EEG to MEG decreases the spatial extent of point-spread # functions (lower spatial deviation, blue colors), thus increasing # resolution, especially for deeper source locations. diff -Nru python-mne-0.23.4+dfsg/examples/inverse/resolution_metrics.py python-mne-1.1.0+dfsg/examples/inverse/resolution_metrics.py --- python-mne-0.23.4+dfsg/examples/inverse/resolution_metrics.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/resolution_metrics.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,10 @@ +# -*- coding: utf-8 -*- """ -============================================================== +.. _ex-res-metrics: + +================================================== Compute spatial resolution metrics in source space -============================================================== +================================================== Compute peak localisation error and spatial deviation for the point-spread functions of dSPM and MNE. Plot their distributions and difference of @@ -11,7 +14,9 @@ """ # Author: Olaf Hauk # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne from mne.datasets import sample @@ -21,10 +26,11 @@ print(__doc__) data_path = sample.data_path() -subjects_dir = data_path + '/subjects/' -fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' -fname_cov = data_path + '/MEG/sample/sample_audvis-cov.fif' -fname_evo = data_path + '/MEG/sample/sample_audvis-ave.fif' +subjects_dir = data_path / 'subjects' +meg_path = data_path / 'MEG' / 'sample' +fname_fwd = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif' +fname_cov = meg_path / 'sample_audvis-cov.fif' +fname_evo = meg_path / 'sample_audvis-ave.fif' # read forward solution forward = mne.read_forward_solution(fname_fwd) @@ -48,7 +54,7 @@ snr = 3.0 lambda2 = 1.0 / snr ** 2 -############################################################################### +# %% # MNE # --- # Compute resolution matrices, peak localisation error (PLE) for point spread @@ -62,7 +68,7 @@ function='psf', metric='sd_ext') del rm_mne -############################################################################### +# %% # dSPM # ---- # Do the same for dSPM: @@ -75,7 +81,7 @@ function='psf', metric='sd_ext') del rm_dspm, forward -############################################################################### +# %% # Visualize results # ----------------- # Visualise peak localisation error (PLE) across the whole cortex for MNE PSF: @@ -84,7 +90,7 @@ clim=dict(kind='value', lims=(0, 2, 4))) brain_ple_mne.add_text(0.1, 0.9, 'PLE MNE', 'title', font_size=16) -############################################################################### +# %% # And dSPM: brain_ple_dspm = ple_dspm_psf.plot('sample', 'inflated', 'lh', @@ -92,7 +98,7 @@ clim=dict(kind='value', lims=(0, 2, 4))) brain_ple_dspm.add_text(0.1, 0.9, 'PLE dSPM', 'title', font_size=16) -############################################################################### +# %% # Subtract the two distributions and plot this difference diff_ple = ple_mne_psf - ple_dspm_psf @@ -101,7 +107,7 @@ clim=dict(kind='value', pos_lims=(0., 1., 2.))) brain_ple_diff.add_text(0.1, 0.9, 'PLE MNE-dSPM', 'title', font_size=16) -############################################################################### +# %% # These plots show that dSPM has generally lower peak localization error (red # color) than MNE in deeper brain areas, but higher error (blue color) in more # superficial areas. @@ -114,7 +120,7 @@ clim=dict(kind='value', lims=(0, 2, 4))) brain_sd_mne.add_text(0.1, 0.9, 'SD MNE', 'title', font_size=16) -############################################################################### +# %% # And dSPM: brain_sd_dspm = sd_dspm_psf.plot('sample', 'inflated', 'lh', @@ -122,7 +128,7 @@ clim=dict(kind='value', lims=(0, 2, 4))) brain_sd_dspm.add_text(0.1, 0.9, 'SD dSPM', 'title', font_size=16) -############################################################################### +# %% # Subtract the two distributions and plot this difference: diff_sd = sd_mne_psf - sd_dspm_psf @@ -132,7 +138,7 @@ clim=dict(kind='value', pos_lims=(0., 1., 2.))) brain_sd_diff.add_text(0.1, 0.9, 'SD MNE-dSPM', 'title', font_size=16) -############################################################################### +# %% # These plots show that dSPM has generally higher spatial deviation than MNE # (blue color), i.e. worse performance to distinguish different sources. # diff -Nru python-mne-0.23.4+dfsg/examples/inverse/snr_estimate.py python-mne-1.1.0+dfsg/examples/inverse/snr_estimate.py --- python-mne-0.23.4+dfsg/examples/inverse/snr_estimate.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/snr_estimate.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,5 +1,7 @@ # -*- coding: utf-8 -*- """ +.. _ex-snr-estimate: + ================================== Estimate data SNR using an inverse ================================== @@ -9,9 +11,9 @@ """ # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause -from os import path as op +# %% from mne.datasets.sample import data_path from mne.minimum_norm import read_inverse_operator @@ -20,9 +22,9 @@ print(__doc__) -data_dir = op.join(data_path(), 'MEG', 'sample') -fname_inv = op.join(data_dir, 'sample_audvis-meg-oct-6-meg-inv.fif') -fname_evoked = op.join(data_dir, 'sample_audvis-ave.fif') +data_dir = data_path() / 'MEG' / 'sample' +fname_inv = data_dir / 'sample_audvis-meg-oct-6-meg-inv.fif' +fname_evoked = data_dir / 'sample_audvis-ave.fif' inv = read_inverse_operator(fname_inv) evoked = read_evokeds(fname_evoked, baseline=(None, 0))[0] diff -Nru python-mne-0.23.4+dfsg/examples/inverse/source_space_snr.py python-mne-1.1.0+dfsg/examples/inverse/source_space_snr.py --- python-mne-0.23.4+dfsg/examples/inverse/source_space_snr.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/source_space_snr.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,8 +1,10 @@ # -*- coding: utf-8 -*- """ -=============================== +.. _ex-source-space-snr: + +========================== Computing source space SNR -=============================== +========================== This example shows how to compute and plot source space SNR as in :footcite:`GoldenholzEtAl2009`. @@ -10,7 +12,9 @@ # Author: Padma Sundaram # Kaisu Lankinen # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% # sphinx_gallery_thumbnail_number = 2 @@ -23,14 +27,15 @@ print(__doc__) data_path = sample.data_path() -subjects_dir = data_path + '/subjects' +subjects_dir = data_path / 'subjects' # Read data -fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif' +meg_path = data_path / 'MEG' / 'sample' +fname_evoked = meg_path / 'sample_audvis-ave.fif' evoked = mne.read_evokeds(fname_evoked, condition='Left Auditory', baseline=(None, 0)) -fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' -fname_cov = data_path + '/MEG/sample/sample_audvis-cov.fif' +fname_fwd = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif' +fname_cov = meg_path / 'sample_audvis-cov.fif' fwd = mne.read_forward_solution(fname_fwd) cov = mne.read_cov(fname_cov) @@ -63,7 +68,7 @@ transparent=True, colormap='viridis') brain = snr_stc.plot(**kwargs) -############################################################################### +# %% # EEG # --- # Next we do the same for EEG and plot the result on the cortex: @@ -75,7 +80,7 @@ snr_stc_eeg = stc_eeg.estimate_snr(evoked_eeg.info, fwd, cov) brain = snr_stc_eeg.plot(**kwargs) -############################################################################### +# %% # The same can be done for MEG, which looks more similar to the MEG-EEG case # than the EEG case does. # diff -Nru python-mne-0.23.4+dfsg/examples/inverse/time_frequency_mixed_norm_inverse.py python-mne-1.1.0+dfsg/examples/inverse/time_frequency_mixed_norm_inverse.py --- python-mne-0.23.4+dfsg/examples/inverse/time_frequency_mixed_norm_inverse.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/time_frequency_mixed_norm_inverse.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-tfr-mixed-norm: + ============================================= Compute MxNE with time-frequency sparse prior ============================================= @@ -20,7 +23,9 @@ # Author: Alexandre Gramfort # Daniel Strohmeier # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np @@ -34,10 +39,11 @@ print(__doc__) data_path = sample.data_path() -subjects_dir = data_path + '/subjects' -fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' -ave_fname = data_path + '/MEG/sample/sample_audvis-no-filter-ave.fif' -cov_fname = data_path + '/MEG/sample/sample_audvis-shrunk-cov.fif' +subjects_dir = data_path / 'subjects' +meg_path = data_path / 'MEG' / 'sample' +fwd_fname = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif' +ave_fname = meg_path / 'sample_audvis-no-filter-ave.fif' +cov_fname = meg_path / 'sample_audvis-shrunk-cov.fif' # Read noise covariance matrix cov = mne.read_cov(cov_fname) @@ -53,7 +59,7 @@ # Handling forward solution forward = mne.read_forward_solution(fwd_fname) -############################################################################### +# %% # Run solver # alpha parameter is between 0 and 100 (100 gives 0 active source) @@ -83,11 +89,11 @@ evoked.crop(tmin=-0.05, tmax=0.3) residual.crop(tmin=-0.05, tmax=0.3) -############################################################################### +# %% # Plot dipole activations plot_dipole_amplitudes(dipoles) -############################################################################### +# %% # Plot location of the strongest dipole with MRI slices idx = np.argmax([np.max(np.abs(dip.amplitude)) for dip in dipoles]) plot_dipole_locations(dipoles[idx], forward['mri_head_t'], 'sample', @@ -100,7 +106,7 @@ # subjects_dir=subjects_dir, mode='orthoview', # idx='amplitude') -############################################################################### +# %% # Show the evoked response and the residual for gradiometers ylim = dict(grad=[-120, 120]) evoked.pick_types(meg='grad', exclude='bads') @@ -111,11 +117,11 @@ residual.plot(titles=dict(grad='Residuals: Gradiometers'), ylim=ylim, proj=True, time_unit='s') -############################################################################### +# %% # Generate stc from dipoles stc = make_stc_from_dipoles(dipoles, forward['src']) -############################################################################### +# %% # View in 2D and 3D ("glass" brain like 3D plot) plot_sparse_source_estimates(forward['src'], stc, bgcolor=(1, 1, 1), opacity=0.1, fig_name="TF-MxNE (cond %s)" @@ -129,7 +135,7 @@ brain.add_label("V1", color="yellow", scalar_thresh=.5, borders=True) brain.add_label("V2", color="red", scalar_thresh=.5, borders=True) -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/inverse/vector_mne_solution.py python-mne-1.1.0+dfsg/examples/inverse/vector_mne_solution.py --- python-mne-0.23.4+dfsg/examples/inverse/vector_mne_solution.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/inverse/vector_mne_solution.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,5 +1,5 @@ +# -*- coding: utf-8 -*- """ - .. _ex-vector-mne-solution: ============================================ @@ -22,7 +22,9 @@ """ # Author: Marijn van Vliet # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np import mne @@ -32,14 +34,16 @@ print(__doc__) data_path = sample.data_path() -subjects_dir = data_path + '/subjects' +subjects_dir = data_path / 'subjects' +smoothing_steps = 7 # Read evoked data -fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif' +meg_path = data_path / 'MEG' / 'sample' +fname_evoked = meg_path / 'sample_audvis-ave.fif' evoked = mne.read_evokeds(fname_evoked, condition=0, baseline=(None, 0)) # Read inverse solution -fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' +fname_inv = meg_path / 'sample_audvis-meg-oct-6-meg-inv.fif' inv = read_inverse_operator(fname_inv) # Apply inverse solution, set pick_ori='vector' to obtain a @@ -51,18 +55,19 @@ # Use peak getter to move visualization to the time point of the peak magnitude _, peak_time = stc.magnitude().get_peak(hemi='lh') -############################################################################### +# %% # Plot the source estimate: # sphinx_gallery_thumbnail_number = 2 brain = stc.plot( - initial_time=peak_time, hemi='lh', subjects_dir=subjects_dir) + initial_time=peak_time, hemi='lh', subjects_dir=subjects_dir, + smoothing_steps=smoothing_steps) # You can save a brain movie with: # brain.save_movie(time_dilation=20, tmin=0.05, tmax=0.16, framerate=10, # interpolation='linear', time_viewer=True) -############################################################################### +# %% # Plot the activation in the direction of maximal power for this data: stc_max, directions = stc.project('pca', src=inv['src']) @@ -72,24 +77,25 @@ f'{np.abs(np.sum(directions * inv["source_nn"][2::3], axis=-1)).mean()}') brain_max = stc_max.plot( initial_time=peak_time, hemi='lh', subjects_dir=subjects_dir, - time_label='Max power') + time_label='Max power', smoothing_steps=smoothing_steps) -############################################################################### +# %% # The normal is very similar: brain_normal = stc.project('normal', inv['src'])[0].plot( initial_time=peak_time, hemi='lh', subjects_dir=subjects_dir, - time_label='Normal') + time_label='Normal', smoothing_steps=smoothing_steps) -############################################################################### +# %% # You can also do this with a fixed-orientation inverse. It looks a lot like # the result above because the ``loose=0.2`` orientation constraint keeps # sources close to fixed orientation: fname_inv_fixed = ( - data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-fixed-inv.fif') + meg_path / 'sample_audvis-meg-oct-6-meg-fixed-inv.fif') inv_fixed = read_inverse_operator(fname_inv_fixed) stc_fixed = apply_inverse( evoked, inv_fixed, lambda2, 'dSPM', pick_ori='vector') brain_fixed = stc_fixed.plot( - initial_time=peak_time, hemi='lh', subjects_dir=subjects_dir) + initial_time=peak_time, hemi='lh', subjects_dir=subjects_dir, + smoothing_steps=smoothing_steps) diff -Nru python-mne-0.23.4+dfsg/examples/io/elekta_epochs.py python-mne-1.1.0+dfsg/examples/io/elekta_epochs.py --- python-mne-0.23.4+dfsg/examples/io/elekta_epochs.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/io/elekta_epochs.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-io-ave-fiff: @@ -11,7 +12,9 @@ """ # Author: Jussi Nurminen (jnu@iki.fi) # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne @@ -23,21 +26,21 @@ print(__doc__) -############################################################################### +# %% # Read raw file raw = mne.io.read_raw_fif(fname_raw) -############################################################################### +# %% # Check DACQ defined averaging categories and other info print(raw.acqparser) -############################################################################### +# %% # Extract epochs corresponding to a category cond = raw.acqparser.get_condition(raw, 'Auditory right') epochs = mne.Epochs(raw, **cond) epochs.average().plot_topo(background_color='w') -############################################################################### +# %% # Get epochs from all conditions, average evokeds = [] for cat in raw.acqparser.categories: @@ -52,7 +55,7 @@ # fname_out = 'multimodal-ave.fif' # mne.write_evokeds(fname_out, evokeds) -############################################################################### +# %% # Make a new averaging category newcat = dict() newcat['comment'] = 'Visual lower left, longer epochs' diff -Nru python-mne-0.23.4+dfsg/examples/io/README.txt python-mne-1.1.0+dfsg/examples/io/README.txt --- python-mne-0.23.4+dfsg/examples/io/README.txt 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/io/README.txt 2022-08-03 15:48:14.000000000 +0000 @@ -4,4 +4,4 @@ Recipes for reading and writing files. See also our :ref:`tutorials on reading data from various recording systems ` and our :ref:`tutorial -on manipulating MNE-Python data structures `. +on manipulating MNE-Python data structures `. diff -Nru python-mne-0.23.4+dfsg/examples/io/read_neo_format.py python-mne-1.1.0+dfsg/examples/io/read_neo_format.py --- python-mne-0.23.4+dfsg/examples/io/read_neo_format.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/io/read_neo_format.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,21 +1,25 @@ +# -*- coding: utf-8 -*- """ .. _ex-read-neo: +=============================================== How to use data in neural ensemble (NEO) format -================================================ +=============================================== This example shows how to create an MNE-Python `~mne.io.Raw` object from data -in the `neural ensemble `__ format. For general +in the `neural ensemble `__ format. For general information on creating MNE-Python's data objects from NumPy arrays, see -:ref:`tut_creating_data_structures`. +:ref:`tut-creating-data-structures`. """ +# %% + import neo import mne -############################################################################### +# %% # This example uses NEO's ``ExampleIO`` object for creating fake data. The data -# will be all zeros, so the plot won't be very interesting; but it should +# will be all zeros, so the plot won't be very interesting, but it should # demonstrate the steps to using NEO data. For actual data and different file # formats, consult the NEO documentation. diff -Nru python-mne-0.23.4+dfsg/examples/io/read_noise_covariance_matrix.py python-mne-1.1.0+dfsg/examples/io/read_noise_covariance_matrix.py --- python-mne-0.23.4+dfsg/examples/io/read_noise_covariance_matrix.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/io/read_noise_covariance_matrix.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-read-noise-cov: + ========================================= Reading/Writing a noise covariance matrix ========================================= @@ -7,21 +10,22 @@ """ # Author: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% -from os import path as op import mne from mne.datasets import sample data_path = sample.data_path() -fname_cov = op.join(data_path, 'MEG', 'sample', 'sample_audvis-cov.fif') -fname_evo = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif') +fname_cov = data_path / 'MEG' / 'sample' / 'sample_audvis-cov.fif' +fname_evo = data_path / 'MEG' / 'sample' / 'sample_audvis-ave.fif' cov = mne.read_cov(fname_cov) print(cov) ev_info = mne.io.read_info(fname_evo) -############################################################################### +# %% # Plot covariance cov.plot(ev_info, exclude='bads', show_svd=False) diff -Nru python-mne-0.23.4+dfsg/examples/io/read_xdf.py python-mne-1.1.0+dfsg/examples/io/read_xdf.py --- python-mne-0.23.4+dfsg/examples/io/read_xdf.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/io/read_xdf.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-read-xdf: @@ -13,17 +14,17 @@ # Authors: Clemens Brunner # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause -import os.path as op +# %% import pyxdf import mne from mne.datasets import misc -fname = op.join( - misc.data_path(), 'xdf', +fname = ( + misc.data_path() / 'xdf' / 'sub-P001_ses-S004_task-Default_run-001_eeg_a2.xdf') streams, header = pyxdf.load_xdf(fname) data = streams[0]["time_series"].T @@ -36,7 +37,7 @@ raw = mne.io.RawArray(data, info) raw.plot(scalings=dict(eeg=100e-6), duration=1, start=14) -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/preprocessing/css.py python-mne-1.1.0+dfsg/examples/preprocessing/css.py --- python-mne-0.23.4+dfsg/examples/preprocessing/css.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/preprocessing/css.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,99 @@ +# -*- coding: utf-8 -*- +""" +.. _ex-css: + +================================================================= +Cortical Signal Suppression (CSS) for removal of cortical signals +================================================================= + +This script shows an example of how to use CSS +:footcite:`Samuelsson2019` . CSS suppresses the cortical contribution +to the signal subspace in EEG data using MEG data, facilitating +detection of subcortical signals. We will illustrate how it works by +simulating one cortical and one subcortical oscillation at different +frequencies; 40 Hz and 239 Hz for cortical and subcortical activity, +respectively, then process it with CSS and look at the power spectral +density of the raw and processed data. + +""" +# Author: John G Samuelsson + +import numpy as np +import matplotlib.pyplot as plt + +import mne +from mne.datasets import sample +from mne.simulation import simulate_sparse_stc, simulate_evoked + +############################################################################### +# Load sample subject data +data_path = sample.data_path() +subjects_dir = data_path / 'subjects' +meg_path = data_path / 'MEG' / 'sample' +fwd_fname = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif' +ave_fname = meg_path / 'sample_audvis-no-filter-ave.fif' +cov_fname = meg_path / 'sample_audvis-cov.fif' +trans_fname = meg_path / 'sample_audvis_raw-trans.fif' +bem_fname = subjects_dir / 'sample' / 'bem' / '/sample-5120-bem-sol.fif' + +raw = mne.io.read_raw_fif(meg_path / 'sample_audvis_raw.fif') +fwd = mne.read_forward_solution(fwd_fname) +fwd = mne.convert_forward_solution(fwd, force_fixed=True, surf_ori=True) +fwd = mne.pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads']) +cov = mne.read_cov(cov_fname) + +############################################################################### +# Find patches (labels) to activate +all_labels = mne.read_labels_from_annot(subject='sample', + subjects_dir=subjects_dir) +labels = [] +for select_label in ['parahippocampal-lh', 'postcentral-rh']: + labels.append([lab for lab in all_labels if lab.name in select_label][0]) +hiplab, postcenlab = labels + +############################################################################### +# Simulate one cortical dipole (40 Hz) and one subcortical (239 Hz) + + +def cortical_waveform(times): + """Create a cortical waveform.""" + return 10e-9 * np.cos(times * 2 * np.pi * 40) + + +def subcortical_waveform(times): + """Create a subcortical waveform.""" + return 10e-9 * np.cos(times * 2 * np.pi * 239) + + +times = np.linspace(0, 0.5, int(0.5 * raw.info['sfreq'])) +stc = simulate_sparse_stc(fwd['src'], n_dipoles=2, times=times, + location='center', subjects_dir=subjects_dir, + labels=[postcenlab, hiplab], + data_fun=cortical_waveform) +stc.data[np.where(np.isin(stc.vertices[0], hiplab.vertices))[0], :] = \ + subcortical_waveform(times) +evoked = simulate_evoked(fwd, stc, raw.info, cov, nave=15) + +############################################################################### +# Process with CSS and plot PSD of EEG data before and after processing +evoked_subcortical = mne.preprocessing.cortical_signal_suppression(evoked, + n_proj=6) +chs = mne.pick_types(evoked.info, meg=False, eeg=True) + +psd = np.mean(np.abs(np.fft.rfft(evoked.data))**2, axis=0) +psd_proc = np.mean(np.abs(np.fft.rfft(evoked_subcortical.data))**2, axis=0) +freq = np.arange(0, stop=int(evoked.info['sfreq'] / 2), + step=evoked.info['sfreq'] / (2 * len(psd))) + +fig, ax = plt.subplots() +ax.plot(freq, psd, label='raw') +ax.plot(freq, psd_proc, label='processed') +ax.text(.2, .7, 'cortical', transform=ax.transAxes) +ax.text(.8, .25, 'subcortical', transform=ax.transAxes) +ax.set(ylabel='EEG Power spectral density', xlabel='Frequency (Hz)') +ax.legend() + +# References +# ^^^^^^^^^^ +# +# .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/preprocessing/define_target_events.py python-mne-1.1.0+dfsg/examples/preprocessing/define_target_events.py --- python-mne-0.23.4+dfsg/examples/preprocessing/define_target_events.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/preprocessing/define_target_events.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-tag-events: + ============================================================ Define target events based on time lag, plot evoked response ============================================================ @@ -15,7 +18,9 @@ """ # Authors: Denis Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne from mne import io @@ -27,10 +32,11 @@ data_path = sample.data_path() -############################################################################### +# %% # Set parameters -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' +event_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif' # Setup for reading the raw data raw = io.read_raw_fif(raw_fname) @@ -44,7 +50,7 @@ picks = mne.pick_types(raw.info, meg='mag', eeg=False, stim=False, eog=True, include=include, exclude='bads') -############################################################################### +# %% # Find stimulus event followed by quick button presses reference_id = 5 # presentation of a smiley face @@ -80,7 +86,7 @@ early, late = [epochs[k].average() for k in event_id] -############################################################################### +# %% # View evoked response times = 1e3 * epochs.times # time in milliseconds diff -Nru python-mne-0.23.4+dfsg/examples/preprocessing/eeg_bridging.py python-mne-1.1.0+dfsg/examples/preprocessing/eeg_bridging.py --- python-mne-0.23.4+dfsg/examples/preprocessing/eeg_bridging.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/preprocessing/eeg_bridging.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,359 @@ +# -*- coding: utf-8 -*- +""" +.. _ex-eeg-bridging: + +=============================================== +Identify EEG Electrodes Bridged by too much Gel +=============================================== + +Research-grade EEG often uses a gel based system, and when too much gel is +applied the gel conducting signal from the scalp to the electrode for one +electrode connects with the gel conducting signal from another electrode +"bridging" the two signals. This is undesirable because the signals from the +two (or more) electrodes are not as independent as they would otherwise be; +they are very similar to each other introducting additional +spatial smearing. An algorithm has been developed to detect electrode +bridging :footcite:`TenkeKayser2001`, which has been implemented in EEGLAB +:footcite:`DelormeMakeig2004`. Unfortunately, there is not a lot to be +done about electrode brigding once the data has been collected as far as +preprocessing other than interpolating bridged channels. Therefore, our +recommendation is to check for electrode bridging early in data collection +and address the problem. Or, if the data has already been collected, quantify +the extent of the bridging so as not to introduce bias into the data from this +effect and exclude subjects with bridging that might effect the outcome of a +study. Preventing electrode bridging is ideal but awareness of the problem at +least will mitigate its potential as a confound to a study. This tutorial +follows +https://psychophysiology.cpmc.columbia.edu/software/eBridge/tutorial.html. + +.. _electrodes.tsv: https://bids-specification.readthedocs.io/en/stable/04-modality-specific-files/03-electroencephalography.html#electrodes-description-_electrodestsv +""" # noqa: E501 +# Authors: Alex Rockhill +# +# License: BSD-3-Clause + +# %% + +# sphinx_gallery_thumbnail_number = 2 + +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.colors import LinearSegmentedColormap + +import mne + +print(__doc__) + +# %% +# Compute Electrical Distance Metric +# ---------------------------------- +# First, let's compute electrical distance metrics for a group of example +# subjects from the EEGBCI dataset in order to estimate electrode bridging. +# The electrical distance is just the variance of signals subtracted +# pairwise. Channels with activity that mirror another channel nearly +# exactly will have very low electrical distance. By inspecting the +# distribution of electrical distances, we can look for pairwise distances +# that are consistently near zero which are indicative of bridging. +# +# .. note:: It is likely to be sufficient to run this algorithm on a +# small portion (~3 minutes is probably plenty) of the data but +# that gel might settle over the course of a study causing more +# bridging so using the last segment of the data will +# give the most conservative estimate. + +montage = mne.channels.make_standard_montage('standard_1005') +ed_data = dict() # electrical distance/bridging data +raw_data = dict() # store infos for electrode positions +for sub in range(1, 11): + print(f'Computing electrode bridges for subject {sub}') + raw_fname = mne.datasets.eegbci.load_data(subject=sub, runs=(1,))[0] + raw = mne.io.read_raw(raw_fname, preload=True, verbose=False) + mne.datasets.eegbci.standardize(raw) # set channel names + raw.set_montage(montage, verbose=False) + raw_data[sub] = raw + ed_data[sub] = mne.preprocessing.compute_bridged_electrodes(raw) + + +# %% +# Examine an Electrical Distance Matrix +# ------------------------------------- +# Before we look at the electrical distance distributions across subjects, +# let's look at the distance matrix for one subject and try and understand +# how the algorithm works. We'll use subject 6 as it is a good example of +# bridging. In the zoomed out color scale version on the right, we can see +# that there is a distribution of electrical distances that are specific to +# that subject's head physiology/geometry and brain activity during the +# recording. On the right, when we clip the color range to zoom in, we can +# see several electrical distance outliers that are near zero; +# these indicate bridging. + +bridged_idx, ed_matrix = ed_data[6] + +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4)) +fig.suptitle('Subject 6 Electrical Distance Matrix') + +# take median across epochs, only use upper triangular, lower is NaNs +ed_plot = np.zeros(ed_matrix.shape[1:]) * np.nan +triu_idx = np.triu_indices(ed_plot.shape[0], 1) +for idx0, idx1 in np.array(triu_idx).T: + ed_plot[idx0, idx1] = np.nanmedian(ed_matrix[:, idx0, idx1]) + +# plot full distribution color range +im1 = ax1.imshow(ed_plot, aspect='auto') +cax1 = fig.colorbar(im1, ax=ax1) +cax1.set_label(r'Electrical Distance ($\mu$$V^2$)') + +# plot zoomed in colors +im2 = ax2.imshow(ed_plot, aspect='auto', vmax=5) +cax2 = fig.colorbar(im2, ax=ax2) +cax2.set_label(r'Electrical Distance ($\mu$$V^2$)') +for ax in (ax1, ax2): + ax.set_xlabel('Channel Index') + ax.set_ylabel('Channel Index') + +fig.tight_layout() + +# %% +# Examine the Distribution of Electrical Distances +# ------------------------------------------------ +# Now let's plot a histogram of the electrical distance matrix. Note that the +# electrical distance matrix from the previous plot is upper triangular but +# does not include the diagonal. This means that the pairwise electrical +# distances are not computed between the same channel (which makes sense as +# the differences between a channel and itself would just be zero). The initial +# peak near zero therefore represents pairs of different channels that +# are nearly identical which is indicative of bridging. EEG recordings +# without bridged electrodes do not have a peak near zero. + +fig, ax = plt.subplots(figsize=(5, 5)) +fig.suptitle('Subject 6 Electrical Distance Matrix Distribution') +ax.hist(ed_matrix[~np.isnan(ed_matrix)], bins=np.linspace(0, 500, 51)) +ax.set_xlabel(r'Electrical Distance ($\mu$$V^2$)') +ax.set_ylabel('Count (channel pairs for all epochs)') + +# %% +# Plot Electrical Distances on a Topomap +# -------------------------------------- +# Now, let's look at the topography of the electrical distance matrix and +# see where our bridged channels are and check that their spatial +# arrangement makes sense. Here, we are looking at the minimum electrical +# distance for each channel and taking the median across all epochs +# (the raw data is epoched into 2 second non-overlapping intervals). +# This example is of the subject from the EEGBCI dataset with the most +# bridged channels so there are many light areas and red lines. They are +# generally grouped together and are biased toward horizontal connections +# (this may be because the EEG experimenter usually stands to the side and +# may have inserted the gel syringe tip in too far). + +mne.viz.plot_bridged_electrodes( + raw_data[6].info, bridged_idx, ed_matrix, + title='Subject 6 Bridged Electrodes', topomap_args=dict(vmax=5)) + +# %% +# Plot the Raw Voltage Time Series for Bridged Electrodes +# ------------------------------------------------------- +# Finally, let's do a sanity check and make sure that the bridged electrodes +# are indeed implausibly similar. We'll plot two bridged electrode pairs: +# F2-F4 and FC2-FC4, for subject 6 where they are bridged and subject 1 +# where they are not. As we can see, the pairs are nearly identical for +# subject 6 confirming that they are likely bridged. Interestingly, even +# though the two pairs are adjacent to each other, there are two distinctive +# pairs, meaning that it is unlikely that all four of these electrodes are +# bridged. + +raw = raw_data[6].copy().pick_channels(['F2', 'F4', 'FC2', 'FC4']) +raw.add_channels([mne.io.RawArray( + raw.get_data(ch1) - raw.get_data(ch2), + mne.create_info([f'{ch1}-{ch2}'], raw.info['sfreq'], 'eeg'), + raw.first_samp) for ch1, ch2 in [('F2', 'F4'), ('FC2', 'FC4')]]) +raw.plot(duration=20, scalings=dict(eeg=2e-4)) + +raw = raw_data[1].copy().pick_channels(['F2', 'F4', 'FC2', 'FC4']) +raw.add_channels([mne.io.RawArray( + raw.get_data(ch1) - raw.get_data(ch2), + mne.create_info([f'{ch1}-{ch2}'], raw.info['sfreq'], 'eeg'), + raw.first_samp) for ch1, ch2 in [('F2', 'F4'), ('FC2', 'FC4')]]) +raw.plot(duration=20, scalings=dict(eeg=2e-4)) + +# %% +# Compare Bridging Across Subjects in the EEGBCI Dataset +# ------------------------------------------------------- +# Now, let's look at the histograms of electrical distances for the whole +# EEGBCI dataset. As we can see in the zoomed in insert on the right, +# for subjects 6, 7 and 8 (and to a lesser extent 2 and 4), there is a +# different shape of the distribution of electrical distances around +# 0 :math:`{\mu}V^2` than for the other subjects. These subjects' +# distributions have a peak around 0 :math:`{\mu}V^2` distance +# and a trough around 5 :math:`{\mu}V^2` which is indicative of +# electrode bridging. The rest of the subjects' distributions increase +# monotonically, indicating normal spatial separation of sources. The +# large discrepancy in shapes of distributions is likely driven primarily by +# artifacts such as blinks which are an order of magnitude larger than +# neural data since this data has not been preprocessed but likely +# reflect neural or at least anatomical differences as well (i.e. the +# distance from the sensors to the brain). + +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4)) +fig.suptitle('Electrical Distance Distribution for EEGBCI Subjects') +for ax in (ax1, ax2): + ax.set_ylabel('Count') + ax.set_xlabel(r'Electrical Distance ($\mu$$V^2$)') + +for sub, (bridged_idx, ed_matrix) in ed_data.items(): + # ed_matrix is upper triangular so exclude bottom half of NaNs + hist, edges = np.histogram(ed_matrix[~np.isnan(ed_matrix)].flatten(), + bins=np.linspace(0, 1000, 101)) + centers = (edges[1:] + edges[:-1]) / 2 + ax1.plot(centers, hist) + hist, edges = np.histogram(ed_matrix[~np.isnan(ed_matrix)].flatten(), + bins=np.linspace(0, 30, 21)) + centers = (edges[1:] + edges[:-1]) / 2 + ax2.plot(centers, hist, label=f'Sub {sub} #={len(bridged_idx)}') + +ax1.axvspan(0, 30, color='r', alpha=0.5) +ax2.legend(loc=(1.04, 0)) +fig.subplots_adjust(right=0.725, bottom=0.15, wspace=0.4) + +# %% +# For the group of subjects, let's look at their electrical distances +# and bridging. Especially since this is the same task, the lack of +# low electrical distances in many of the subjects is compelling +# evidence that the low electrical distance is caused by bridging +# and that it is avoidable given more judicious application of gel or +# other conductive electrolyte solution. + +for sub, (bridged_idx, ed_matrix) in ed_data.items(): + mne.viz.plot_bridged_electrodes( + raw_data[sub].info, bridged_idx, ed_matrix, + title=f'Subject {sub} Bridged Electrodes', topomap_args=dict(vmax=5)) + +# %% +# For subjects with many bridged channels like Subject 6 shown in the example +# above, it is advisable to exclude the subject. This because EEG recording +# montage will not be comparable with the other subjects. And, if we tried to +# interpole, the interpolation would depend on other channels which are also +# bridged in that case. However, for subjects with only a few bridged channels, +# those channels can be interpolated. Since the bridged data is still +# biological (i.e. it is recording the subject's brain), it's just spatially +# smeared, we can use :func:`mne.preprocessing.interpolate_bridged_electrodes` +# to make a virtual channel midway between the two bridged channels +# to aid in interpolation. + +# use subject 2, only one bridged electrode pair +bridged_idx = ed_data[2][0] +raw = mne.preprocessing.interpolate_bridged_electrodes( + raw_data[2].copy(), bridged_idx=bridged_idx) + +# %% +# Let's make sure that our virtual channel aided the interpolation. We can do +# this by simulating a bridge to make sure that we recover the original data +# better with the virtual channel method. If we make two channels nearly the +# same to simulate a bridged electrode but save the original data, we can +# compare the two interpolation methods. As we can see, the virtual channel +# recovers the original data more slightly closely. However, as shown in the +# plots, there is still residual signal for both methods implying that it is +# there is still a loss of data compared to unbridged channels. + +raw = raw_data[2].copy() + +# pick two channels to simulate a bridge +idx0, idx1 = 9, 10 +ch0, ch1 = raw.ch_names[idx0], raw.ch_names[idx1] +bridged_idx_simulated = [(idx0, idx1)] +# get the original data to compare to +data_orig = raw_data[2].get_data(picks=(idx0, idx1)) + +# simulate a bridge between the two channels by taking their mean and adding +# some noise +rng = np.random.default_rng(11) # seed for reproducibility +raw_sim = raw.copy() # raw with simulated electrode bridge +# remove channels with original data +raw_sim = raw_sim.drop_channels([ch0, ch1]) +bridged_data = np.tile(np.mean(data_orig, axis=0), (2, 1)) # copy mean +# add separate noise for each channel +bridged_data[0] += 1e-7 * rng.normal(size=raw.times.size) +bridged_data[1] += 1e-7 * rng.normal(size=raw.times.size) +# add back simulated data +raw_sim = raw_sim.add_channels([mne.io.RawArray( + bridged_data, mne.create_info([ch0, ch1], raw.info['sfreq'], 'eeg'), + raw.first_samp)]) +raw_sim.set_montage(montage) # add back channel positions + +# use virtual channel method +raw_virtual = mne.preprocessing.interpolate_bridged_electrodes( + raw_sim.copy(), bridged_idx=bridged_idx_simulated) +data_virtual = raw_virtual.get_data(picks=(idx0, idx1)) + +# set bads to be bridged electrodes to interpolate without a virtual channel +raw_comp = raw_sim.copy() +raw_comp.info['bads'] = [raw_sim.ch_names[idx0], raw_sim.ch_names[idx1]] +raw_comp.interpolate_bads() +data_comp = raw_comp.get_data(picks=(idx0, idx1)) + +# compute variance of residuals +print('Variance of residual (interpolated data - original data)\n\n' + 'With adding virtual channel: {}\n' + 'Compared to interpolation only using other channels: {}' + ''.format(np.mean(np.var(data_virtual - data_orig, axis=1)), + np.mean(np.var(data_comp - data_orig, axis=1)))) + +# plot results +raw = raw.pick_channels([ch0, ch1]) +raw = raw.add_channels([mne.io.RawArray( + np.concatenate([data_virtual, data_virtual - data_orig]), + mne.create_info([f'{ch0} virtual', f'{ch1} virtual', + f'{ch0} virtual diff', f'{ch1} virtual diff'], + raw.info['sfreq'], 'eeg'), raw.first_samp)]) +raw = raw.add_channels([mne.io.RawArray( + np.concatenate([data_comp, data_comp - data_orig]), + mne.create_info([f'{ch0} comp', f'{ch1} comp', + f'{ch0} comp diff', f'{ch1} comp diff'], + raw.info['sfreq'], 'eeg'), raw.first_samp)]) +raw.plot(scalings=dict(eeg=7e-5)) + +# %% +# The Relationship Between Bridging and Impedances +# ------------------------------------------------ +# Electrode bridging is often brought about by inserting more gel in order +# to bring impendances down. Thus it can be helpful to compare bridging +# to impedances in the quest to be an ideal EEG technician! Low +# impedances lead to less noisy data and EEG without bridging is more +# spatially precise. Brain Imaging Data Structure (BIDS) recommends that +# impedances be stored in an EEG dataset in the `electrodes.tsv`_ file. +# Since the impedances are not stored for this dataset, we will fake +# them to demonstrate how they would be plotted. Here, the impedances +# are plotted as is typical at the end of a setup; most channels are +# good but there are a few that need to have their impedance lowered. +# The impedances should ideally all be less than 25 KOhm before +# starting an experiment when using active systems and less than 5 KOhm +# when using a passive system. + +rng = np.random.default_rng(11) # seed for reproducibility +raw = raw_data[1] +# typically impedances < 25 kOhm are acceptable for active systems and +# impedances < 5 kOhm are desirable for a passive system +impedances = rng.random((len(raw.ch_names,))) * 30 +impedances[10] = 80 # set a few bad impendances +impedances[25] = 99 +cmap = LinearSegmentedColormap.from_list(name='impedance_cmap', + colors=['g', 'y', 'r'], N=256) +fig, ax = plt.subplots(figsize=(5, 5)) +im, cn = mne.viz.plot_topomap(impedances, raw.info, axes=ax, + cmap=cmap, vmin=25, vmax=75) +ax.set_title('Electrode Impendances') +cax = fig.colorbar(im, ax=ax) +cax.set_label(r'Impedance (k$\Omega$)') + +# %% +# Summary +# ------- +# In this example, we have shown a dataset where electrical bridging occurred +# during the EEG setup for several subjects. Hopefully this is convincing as to +# the importance of proper technique as well as checking your work to +# learn and improve as an EEG experimenter, and hopefully this tool will help +# us all collect better EEG data in the future. + +# %% +# References +# ---------- +# .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/preprocessing/eeg_csd.py python-mne-1.1.0+dfsg/examples/preprocessing/eeg_csd.py --- python-mne-0.23.4+dfsg/examples/preprocessing/eeg_csd.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/preprocessing/eeg_csd.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-eeg-csd: + ===================================================== Transform EEG data using current source density (CSD) ===================================================== @@ -12,7 +15,9 @@ """ # Authors: Alex Rockhill # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% # sphinx_gallery_thumbnail_number = 6 @@ -26,28 +31,29 @@ data_path = sample.data_path() -############################################################################### +# %% # Load sample subject data -raw = mne.io.read_raw_fif(data_path + '/MEG/sample/sample_audvis_raw.fif') +meg_path = data_path / 'MEG' / 'sample' +raw = mne.io.read_raw_fif(meg_path / 'sample_audvis_raw.fif') raw = raw.pick_types(meg=False, eeg=True, eog=True, ecg=True, stim=True, exclude=raw.info['bads']).load_data() events = mne.find_events(raw) raw.set_eeg_reference(projection=True).apply_proj() -############################################################################### +# %% # Plot the raw data and CSD-transformed raw data: raw_csd = mne.preprocessing.compute_current_source_density(raw) raw.plot() raw_csd.plot() -############################################################################### +# %% # Also look at the power spectral densities: raw.plot_psd() raw_csd.plot_psd() -############################################################################### +# %% # CSD can also be computed on Evoked (averaged) data. # Here we epoch and average the data so we can demonstrate that. @@ -57,7 +63,7 @@ preload=True) evoked = epochs['auditory'].average() -############################################################################### +# %% # First let's look at how CSD affects scalp topography: times = np.array([-0.1, 0., 0.05, 0.1, 0.15]) @@ -65,7 +71,7 @@ evoked.plot_joint(title='Average Reference', show=False) evoked_csd.plot_joint(title='Current Source Density') -############################################################################### +# %% # CSD has parameters ``stiffness`` and ``lambda2`` affecting smoothing and # spline flexibility, respectively. Let's see how they affect the solution: @@ -81,7 +87,7 @@ colorbar=False, show=False) ax[i, j].set_title('stiffness=%i\nλ²=%s' % (m, lambda2)) -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/preprocessing/eog_artifact_histogram.py python-mne-1.1.0+dfsg/examples/preprocessing/eog_artifact_histogram.py --- python-mne-0.23.4+dfsg/examples/preprocessing/eog_artifact_histogram.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/preprocessing/eog_artifact_histogram.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-eog: + ======================== Show EOG artifact timing ======================== @@ -8,7 +11,9 @@ """ # Authors: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np @@ -22,9 +27,10 @@ data_path = sample.data_path() -############################################################################### +# %% # Set parameters -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' # Setup for reading the raw data raw = io.read_raw_fif(raw_fname, preload=True) @@ -44,7 +50,7 @@ data = epochs.get_data()[:, pick_ch, :] data = np.sum((data.astype(int) & eog_event_id) == eog_event_id, axis=0) -############################################################################### +# %% # Plot EOG artifact distribution fig, ax = plt.subplots() ax.stem(1e3 * epochs.times, data, use_line_collection=True) diff -Nru python-mne-0.23.4+dfsg/examples/preprocessing/find_ref_artifacts.py python-mne-1.1.0+dfsg/examples/preprocessing/find_ref_artifacts.py --- python-mne-0.23.4+dfsg/examples/preprocessing/find_ref_artifacts.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/preprocessing/find_ref_artifacts.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-megnoise_processing: @@ -28,7 +29,9 @@ """ # Authors: Jeff Hanna # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne from mne import io @@ -40,13 +43,13 @@ data_path = refmeg_noise.data_path() -############################################################################### +# %% # Read raw data, cropping to 5 minutes to save memory -raw_fname = data_path + '/sample_reference_MEG_noise-raw.fif' +raw_fname = data_path / 'sample_reference_MEG_noise-raw.fif' raw = io.read_raw_fif(raw_fname).crop(300, 600).load_data() -############################################################################### +# %% # Note that even though standard noise removal has already # been applied to these data, much of the noise in the reference channels # (bottom of the plot) can still be seen in the standard channels. @@ -58,11 +61,11 @@ scalings={"mag": 8e-13, "ref_meg": 2e-11}) raw.plot(**plot_kwargs) -############################################################################### +# %% # The PSD of these data show the noise as clear peaks. raw.plot_psd(fmax=30) -############################################################################### +# %% # Run the "together" algorithm. raw_tog = raw.copy() ica_kwargs = dict( @@ -88,11 +91,11 @@ # Remove the components. raw_tog = ica_tog.apply(raw_tog, exclude=bad_comps) -############################################################################### +# %% # Cleaned data: raw_tog.plot_psd(fmax=30) -############################################################################### +# %% # Now try the "separate" algorithm. raw_sep = raw.copy() @@ -127,12 +130,12 @@ # Remove the components. raw_sep = ica_sep.apply(raw_sep, exclude=bad_comps) -############################################################################### +# %% # Cleaned raw data traces: raw_sep.plot(**plot_kwargs) -############################################################################### +# %% # Cleaned raw data PSD: raw_sep.plot_psd(fmax=30) diff -Nru python-mne-0.23.4+dfsg/examples/preprocessing/fnirs_artifact_removal.py python-mne-1.1.0+dfsg/examples/preprocessing/fnirs_artifact_removal.py --- python-mne-0.23.4+dfsg/examples/preprocessing/fnirs_artifact_removal.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/preprocessing/fnirs_artifact_removal.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-fnirs-artifacts: @@ -11,7 +12,9 @@ """ # Authors: Robert Luke # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import os import mne @@ -19,7 +22,7 @@ from mne.preprocessing.nirs import (optical_density, temporal_derivative_distribution_repair) -############################################################################### +# %% # Import data # ----------- # @@ -38,13 +41,13 @@ raw_od.set_annotations(new_annotations) raw_od.plot(n_channels=15, duration=400, show_scrollbars=False) -############################################################################### +# %% # We can see some small artifacts in the above data from movement around 40, # 190 and 240 seconds. However, this data is relatively clean so we will # add some additional artifacts below. -############################################################################### +# %% # Add artificial artifacts to data # -------------------------------- # @@ -68,7 +71,7 @@ corrupted_od.plot(n_channels=15, duration=400, show_scrollbars=False) -############################################################################### +# %% # Apply temporal derivative distribution repair # --------------------------------------------- # @@ -79,13 +82,13 @@ corrected_tddr.plot(n_channels=15, duration=400, show_scrollbars=False) -############################################################################### +# %% # We can see in the data above that the introduced spikes and shifts are # largely removed, but some residual smaller artifact remains. # The same can be said for the artifacts in the original data. -############################################################################### +# %% # References # ---------- # diff -Nru python-mne-0.23.4+dfsg/examples/preprocessing/ica_comparison.py python-mne-1.1.0+dfsg/examples/preprocessing/ica_comparison.py --- python-mne-0.23.4+dfsg/examples/preprocessing/ica_comparison.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/preprocessing/ica_comparison.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-ica-comp: + =========================================== Compare the different ICA algorithms in MNE =========================================== @@ -9,7 +12,9 @@ """ # Authors: Pierre Ablin # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% from time import time @@ -20,23 +25,23 @@ print(__doc__) -############################################################################### +# %% # Read and preprocess the data. Preprocessing consists of: # # - MEG channel selection # - 1-30 Hz band-pass filter data_path = sample.data_path() -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' -raw = mne.io.read_raw_fif(raw_fname, preload=True) +raw = mne.io.read_raw_fif(raw_fname).crop(0, 60).pick('meg').load_data() -picks = mne.pick_types(raw.info, meg=True) reject = dict(mag=5e-12, grad=4000e-13) raw.filter(1, 30, fir_design='firwin') -############################################################################### +# %% # Define a function that runs ICA on the raw MEG data and plots the components @@ -44,24 +49,24 @@ ica = ICA(n_components=20, method=method, fit_params=fit_params, max_iter='auto', random_state=0) t0 = time() - ica.fit(raw, picks=picks, reject=reject) + ica.fit(raw, reject=reject) fit_time = time() - t0 title = ('ICA decomposition using %s (took %.1fs)' % (method, fit_time)) ica.plot_components(title=title) -############################################################################### +# %% # FastICA run_ica('fastica') -############################################################################### +# %% # Picard run_ica('picard') -############################################################################### +# %% # Infomax run_ica('infomax') -############################################################################### +# %% # Extended Infomax run_ica('infomax', fit_params=dict(extended=True)) diff -Nru python-mne-0.23.4+dfsg/examples/preprocessing/interpolate_bad_channels.py python-mne-1.1.0+dfsg/examples/preprocessing/interpolate_bad_channels.py --- python-mne-0.23.4+dfsg/examples/preprocessing/interpolate_bad_channels.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/preprocessing/interpolate_bad_channels.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-interpolate-bad-channels: @@ -16,7 +17,9 @@ # Authors: Denis A. Engemann # Mainak Jas # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% # sphinx_gallery_thumbnail_number = 2 @@ -26,26 +29,26 @@ print(__doc__) data_path = sample.data_path() - -fname = data_path + '/MEG/sample/sample_audvis-ave.fif' +meg_path = data_path / 'MEG' / 'sample' +fname = meg_path / 'sample_audvis-ave.fif' evoked = mne.read_evokeds(fname, condition='Left Auditory', baseline=(None, 0)) # plot with bads evoked.plot(exclude=[], picks=('grad', 'eeg')) -############################################################################### +# %% # Compute interpolation (also works with Raw and Epochs objects) evoked_interp = evoked.copy().interpolate_bads(reset_bads=False) evoked_interp.plot(exclude=[], picks=('grad', 'eeg')) -############################################################################### +# %% # You can also use minimum-norm for EEG as well as MEG evoked_interp_mne = evoked.copy().interpolate_bads( reset_bads=False, method=dict(eeg='MNE'), verbose=True) evoked_interp_mne.plot(exclude=[], picks=('grad', 'eeg')) -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/preprocessing/movement_compensation.py python-mne-1.1.0+dfsg/examples/preprocessing/movement_compensation.py --- python-mne-0.23.4+dfsg/examples/preprocessing/movement_compensation.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/preprocessing/movement_compensation.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,5 +1,6 @@ +# -*- coding: utf-8 -*- """ -.. _example-movement-comp: +.. _ex-movement-comp: ============================================== Maxwell filter data with movement compensation @@ -15,23 +16,22 @@ """ # Authors: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause -from os import path as op +# %% import mne from mne.preprocessing import maxwell_filter print(__doc__) -data_path = op.join(mne.datasets.misc.data_path(verbose=True), 'movement') +data_path = mne.datasets.misc.data_path(verbose=True) / 'movement' -head_pos = mne.chpi.read_head_pos(op.join(data_path, 'simulated_quats.pos')) -raw = mne.io.read_raw_fif(op.join(data_path, 'simulated_movement_raw.fif')) -raw_stat = mne.io.read_raw_fif(op.join(data_path, - 'simulated_stationary_raw.fif')) +head_pos = mne.chpi.read_head_pos(data_path / 'simulated_quats.pos') +raw = mne.io.read_raw_fif(data_path / 'simulated_movement_raw.fif') +raw_stat = mne.io.read_raw_fif(data_path / 'simulated_stationary_raw.fif') -############################################################################### +# %% # Visualize the "subject" head movements. By providing the measurement # information, the distance to the nearest sensor in each direction # (e.g., left/right for the X direction, forward/backward for Y) can @@ -40,13 +40,13 @@ mne.viz.plot_head_positions( head_pos, mode='traces', destination=raw.info['dev_head_t'], info=raw.info) -############################################################################### +# %% # This can also be visualized using a quiver. mne.viz.plot_head_positions( head_pos, mode='field', destination=raw.info['dev_head_t'], info=raw.info) -############################################################################### +# %% # Process our simulated raw data (taking into account head movements). # extract our resulting events @@ -57,12 +57,12 @@ topo_kwargs = dict(times=[0, 0.1, 0.2], ch_type='mag', vmin=-500, vmax=500, time_unit='s') -############################################################################### +# %% # First, take the average of stationary data (bilateral auditory patterns). evoked_stat = mne.Epochs(raw_stat, events, 1, -0.2, 0.8).average() evoked_stat.plot_topomap(title='Stationary', **topo_kwargs) -############################################################################### +# %% # Second, take a naive average, which averages across epochs that have been # simulated to have different head positions and orientations, thereby # spatially smearing the activity. @@ -70,14 +70,14 @@ evoked = epochs.average() evoked.plot_topomap(title='Moving: naive average', **topo_kwargs) -############################################################################### +# %% # Third, use raw movement compensation (restores pattern). raw_sss = maxwell_filter(raw, head_pos=head_pos) evoked_raw_mc = mne.Epochs(raw_sss, events, 1, -0.2, 0.8).average() evoked_raw_mc.plot_topomap(title='Moving: movement compensated (raw)', **topo_kwargs) -############################################################################### +# %% # Fourth, use evoked movement compensation. For these data, which contain # very large rotations, it does not as cleanly restore the pattern. evoked_evo_mc = mne.epochs.average_movements(epochs, head_pos=head_pos) diff -Nru python-mne-0.23.4+dfsg/examples/preprocessing/movement_detection.py python-mne-1.1.0+dfsg/examples/preprocessing/movement_detection.py --- python-mne-0.23.4+dfsg/examples/preprocessing/movement_detection.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/preprocessing/movement_detection.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,10 @@ +# -*- coding: utf-8 -*- """ -====================================================== +.. _ex-movement-detect: + +===================================================== Annotate movement artifacts and reestimate dev_head_t -====================================================== +===================================================== Periods, where the participant moved considerably, are contaminated by low amplitude artifacts. When averaging the magnetic fields, the more spread the @@ -13,13 +16,12 @@ to annotate periods of head movement, then the device to head transformation matrix is estimated from the artifact-free segments. The new head position will be more representative of the actual head position during the recording. - """ # Authors: Adonay Nunes # Luke Bloy -# License: BSD (3-clause) +# License: BSD-3-Clause -import os.path as op +# %% import mne from mne.datasets.brainstorm import bst_auditory @@ -28,20 +30,20 @@ # Load data data_path = bst_auditory.data_path() -data_path_MEG = op.join(data_path, 'MEG') +data_path_MEG = data_path / 'MEG' subject = 'bst_auditory' -subjects_dir = op.join(data_path, 'subjects') -trans_fname = op.join(data_path, 'MEG', 'bst_auditory', - 'bst_auditory-trans.fif') -raw_fname1 = op.join(data_path_MEG, 'bst_auditory', 'S01_AEF_20131218_01.ds') -raw_fname2 = op.join(data_path_MEG, 'bst_auditory', 'S01_AEF_20131218_02.ds') -# read and concatenate two files +subjects_dir = data_path / 'subjects' +trans_fname = data_path / 'MEG' / 'bst_auditory' / 'bst_auditory-trans.fif' +raw_fname1 = data_path_MEG / 'bst_auditory' / 'S01_AEF_20131218_01.ds' +raw_fname2 = data_path_MEG / 'bst_auditory' / 'S01_AEF_20131218_02.ds' +# read and concatenate two files, ignoring device<->head mismatch raw = read_raw_ctf(raw_fname1, preload=False) -mne.io.concatenate_raws([raw, read_raw_ctf(raw_fname2, preload=False)]) +mne.io.concatenate_raws( + [raw, read_raw_ctf(raw_fname2, preload=False)], on_mismatch='ignore') raw.crop(350, 410).load_data() raw.resample(100, npad="auto") -############################################################################### +# %% # Plot continuous head position with respect to the mean recording position # -------------------------------------------------------------------------- @@ -61,7 +63,7 @@ # The green horizontal lines represent the original head position, whereas the # red lines are the new head position averaged over all the time points. -############################################################################### +# %% # Plot raw data with annotated movement # ------------------------------------------------------------------ @@ -76,5 +78,6 @@ # and plot it: new_dev_head_t = compute_average_dev_head_t(raw, head_pos) raw.info['dev_head_t'] = new_dev_head_t -mne.viz.plot_alignment(raw.info, show_axes=True, subject=subject, - trans=trans_fname, subjects_dir=subjects_dir) +fig = mne.viz.plot_alignment(raw.info, show_axes=True, subject=subject, + trans=trans_fname, subjects_dir=subjects_dir) +mne.viz.set_3d_view(fig, azimuth=90, elevation=60) diff -Nru python-mne-0.23.4+dfsg/examples/preprocessing/muscle_detection.py python-mne-1.1.0+dfsg/examples/preprocessing/muscle_detection.py --- python-mne-0.23.4+dfsg/examples/preprocessing/muscle_detection.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/preprocessing/muscle_detection.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,10 @@ +# -*- coding: utf-8 -*- """ -=========================== +.. _ex-muscle-artifacts: + +========================= Annotate muscle artifacts -=========================== +========================= Muscle contractions produce high frequency activity that can mask brain signal of interest. Muscle artifacts can be produced when clenching the jaw, @@ -24,9 +27,10 @@ """ # Authors: Adonay Nunes # Luke Bloy -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% -import os.path as op import matplotlib.pyplot as plt import numpy as np from mne.datasets.brainstorm import bst_auditory @@ -36,14 +40,14 @@ # Load data data_path = bst_auditory.data_path() -raw_fname = op.join(data_path, 'MEG', 'bst_auditory', 'S01_AEF_20131218_01.ds') +raw_fname = data_path / 'MEG' / 'bst_auditory' / 'S01_AEF_20131218_01.ds' raw = read_raw_ctf(raw_fname, preload=False) raw.crop(130, 160).load_data() # just use a fraction of data for speed here raw.resample(300, npad="auto") -############################################################################### +# %% # Notch filter the data: # # .. note:: @@ -51,9 +55,9 @@ # detecting muscle artifacts. See :ref:`tut-section-line-noise` for an # example. -raw.notch_filter([50, 100]) +raw.notch_filter([60, 120]) -############################################################################### +# %% # The threshold is data dependent, check the optimal threshold by plotting # ``scores_muscle``. @@ -64,7 +68,7 @@ raw, ch_type="mag", threshold=threshold_muscle, min_length_good=0.2, filter_freq=[110, 140]) -############################################################################### +# %% # Plot muscle z-scores across recording # -------------------------------------------------------------------------- @@ -72,7 +76,7 @@ ax.plot(raw.times, scores_muscle) ax.axhline(y=threshold_muscle, color='r') ax.set(xlabel='time, (s)', ylabel='zscore', title='Muscle activity') -############################################################################### +# %% # View the annotations # -------------------------------------------------------------------------- order = np.arange(144, 164) diff -Nru python-mne-0.23.4+dfsg/examples/preprocessing/muscle_ica.py python-mne-1.1.0+dfsg/examples/preprocessing/muscle_ica.py --- python-mne-0.23.4+dfsg/examples/preprocessing/muscle_ica.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/preprocessing/muscle_ica.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,120 @@ +# -*- coding: utf-8 -*- +""" +.. _ex-muscle-ica: + +============================== +Removing muscle ICA components +============================== + +Gross movements produce widespread high-frequency activity across all channels +that is usually not recoverable and so the epoch must be rejected as shown in +:ref:`ex-muscle-artifacts`. More ubiquitously than gross movements, muscle +artifact is produced during postural maintenance. This is more appropriately +removed by ICA otherwise there wouldn't be any epochs left! Note that muscle +artifacts of this kind are much more pronounced in EEG than they are in MEG. + +""" +# Authors: Alex Rockhill +# +# License: BSD-3-Clause + +# %% + +import mne + +data_path = mne.datasets.sample.data_path() +raw_fname = data_path / 'MEG' / 'sample' / 'sample_audvis_raw.fif' +raw = mne.io.read_raw_fif(raw_fname) +raw.crop(tmin=100, tmax=130) # take 30 seconds for speed + +# pick only EEG channels, muscle artifact is basically not picked up by MEG +# if you have a simultaneous recording, you may want to do ICA on MEG and EEG +# separately +raw.pick_types(eeg=True) + +# ICA works best with a highpass filter applied +raw.load_data() +raw.filter(l_freq=1., h_freq=None) + +# %% +# Run ICA +ica = mne.preprocessing.ICA( + n_components=15, method='picard', max_iter='auto', random_state=97) +ica.fit(raw) + +# %% +# Remove components with postural muscle artifact using ICA +ica.plot_sources(raw) + +# %% +# By inspection, let's select out the muscle-artifact components based on +# :footcite:`DharmapraniEtAl2016` manually. +# +# The criteria are: +# +# - Positive slope of log-log power spectrum between 7 and 75 Hz +# (here just flat because it's not in log-log) +# - Peripheral focus or dipole/multi-pole foci (the blue and red +# blobs in the topomap are far from the vertex where the most +# muscle is) +# - Single focal point (low spatial smoothness; there is just one focus +# of the topomap compared to components like the first ones that are +# more likely neural which spread across the topomap) +# +# The other attribute worth noting is that the time course in +# :func:`mne.preprocessing.ICA.plot_sources` looks like EMG; you can +# see spikes when each motor unit fires so that the time course looks fuzzy +# and sometimes has large spikes that are often at regular intervals. +# +# ICA component 13 is a textbook example of what muscle artifact looks like. +# The focus of the topomap for this component is right on the temporalis +# muscle near the ears. There is also a minimum in the power spectrum at around +# 10 Hz, then a maximum at around 25 Hz, generally resulting in a positive +# slope in log-log units; this is a very typical pattern for muscle artifact. + +muscle_idx = [6, 7, 8, 9, 10, 11, 12, 13, 14] +ica.plot_properties(raw, picks=muscle_idx, log_scale=True) + +# first, remove blinks and heartbeat to compare +blink_idx = [0] +heartbeat_idx = [5] +ica.apply(raw, exclude=blink_idx + heartbeat_idx) +ica.plot_overlay(raw, exclude=muscle_idx) + +# %% +# Finally, let's try an automated algorithm to find muscle components +# and ensure that it gets the same components we did manually. +muscle_idx_auto, scores = ica.find_bads_muscle(raw) +ica.plot_scores(scores, exclude=muscle_idx_auto) +print(f'Manually found muscle artifact ICA components: {muscle_idx}\n' + f'Automatically found muscle artifact ICA components: {muscle_idx_auto}') + +# %% +# Let's now replicate this on the EEGBCI dataset +# ---------------------------------------------- + +for sub in (1, 2): + raw = mne.io.read_raw_edf( + mne.datasets.eegbci.load_data(subject=sub, runs=(1,))[0], preload=True) + mne.datasets.eegbci.standardize(raw) # set channel names + montage = mne.channels.make_standard_montage('standard_1005') + raw.set_montage(montage) + raw.filter(l_freq=1., h_freq=None) + + # Run ICA + ica = mne.preprocessing.ICA( + n_components=15, method='picard', max_iter='auto', random_state=97) + ica.fit(raw) + ica.plot_sources(raw) + muscle_idx_auto, scores = ica.find_bads_muscle(raw) + ica.plot_properties(raw, picks=muscle_idx_auto, log_scale=True) + ica.plot_scores(scores, exclude=muscle_idx_auto) + + print(f'Manually found muscle artifact ICA components: {muscle_idx}\n' + 'Automatically found muscle artifact ICA components: ' + f'{muscle_idx_auto}') + +# %% +# References +# ---------- +# .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/preprocessing/otp.py python-mne-1.1.0+dfsg/examples/preprocessing/otp.py --- python-mne-0.23.4+dfsg/examples/preprocessing/otp.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/preprocessing/otp.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-otp: + =========================================================== Plot sensor denoising using oversampled temporal projection =========================================================== @@ -8,9 +11,10 @@ """ # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% -import os.path as op import mne import numpy as np @@ -20,7 +24,7 @@ print(__doc__) -############################################################################### +# %% # Plot the phantom data, lowpassed to get rid of high-frequency artifacts. # We also crop to a single 10-second segment for speed. # Notice that there are two large flux jumps on channel 1522 that could @@ -29,13 +33,12 @@ dipole_number = 1 data_path = bst_phantom_elekta.data_path() -raw = read_raw_fif( - op.join(data_path, 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif')) +raw = read_raw_fif(data_path / 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif') raw.crop(40., 50.).load_data() order = list(range(160, 170)) raw.copy().filter(0., 40.).plot(order=order, n_channels=10) -############################################################################### +# %% # Now we can clean the data with OTP, lowpass, and plot. The flux jumps have # been suppressed alongside the random sensor noise. @@ -44,7 +47,7 @@ raw_clean.plot(order=order, n_channels=10) -############################################################################### +# %% # We can also look at the effect on single-trial phantom localization. # See the :ref:`tut-brainstorm-elekta-phantom` # for more information. Here we use a version that does single-trial @@ -63,7 +66,7 @@ idx = epochs.time_as_index(0.036)[0] data = epochs.get_data()[:, :, idx].T evoked = mne.EvokedArray(data, epochs.info, tmin=0.) - dip = fit_dipole(evoked, cov, sphere, n_jobs=1, verbose=False)[0] + dip = fit_dipole(evoked, cov, sphere, n_jobs=None, verbose=False)[0] actual_pos = mne.dipole.get_phantom_dipoles()[0][dipole_number - 1] misses = 1000 * np.linalg.norm(dip.pos - actual_pos, axis=-1) return misses @@ -76,7 +79,7 @@ print('OTP bias: %0.1fmm (worst: %0.1fmm)' % (np.mean(bias_clean), np.max(bias_clean),)) -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/preprocessing/shift_evoked.py python-mne-1.1.0+dfsg/examples/preprocessing/shift_evoked.py --- python-mne-0.23.4+dfsg/examples/preprocessing/shift_evoked.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/preprocessing/shift_evoked.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-shift-evoked: + ================================== Shifting time-scale in evoked data ================================== @@ -6,7 +9,9 @@ """ # Author: Mainak Jas # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import matplotlib.pyplot as plt import mne @@ -16,8 +21,8 @@ print(__doc__) data_path = sample.data_path() - -fname = data_path + '/MEG/sample/sample_audvis-ave.fif' +meg_path = data_path / 'MEG' / 'sample' +fname = meg_path / 'sample_audvis-ave.fif' # Reading evoked data condition = 'Left Auditory' diff -Nru python-mne-0.23.4+dfsg/examples/preprocessing/virtual_evoked.py python-mne-1.1.0+dfsg/examples/preprocessing/virtual_evoked.py --- python-mne-0.23.4+dfsg/examples/preprocessing/virtual_evoked.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/preprocessing/virtual_evoked.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-virtual-evoked: + ======================= Remap MEG channel types ======================= @@ -14,7 +17,9 @@ # Author: Mainak Jas -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne from mne.datasets import sample @@ -23,10 +28,11 @@ # read the evoked data_path = sample.data_path() -fname = data_path + '/MEG/sample/sample_audvis-ave.fif' +meg_path = data_path / 'MEG' / 'sample' +fname = meg_path / 'sample_audvis-ave.fif' evoked = mne.read_evokeds(fname, condition='Left Auditory', baseline=(None, 0)) -############################################################################### +# %% # First, let's call remap gradiometers to magnometers, and plot # the original and remapped topomaps of the magnetometers. @@ -34,13 +40,13 @@ virt_evoked = evoked.as_type('mag') evoked.plot_topomap(ch_type='mag', title='mag (original)', time_unit='s') -############################################################################### +# %% # plot interpolated grad + mag virt_evoked.plot_topomap(ch_type='mag', time_unit='s', title='mag (interpolated from mag + grad)') -############################################################################### +# %% # Now, we remap magnometers to gradiometers, and plot # the original and remapped topomaps of the gradiometers @@ -48,7 +54,7 @@ virt_evoked = evoked.as_type('grad') evoked.plot_topomap(ch_type='grad', title='grad (original)', time_unit='s') -############################################################################### +# %% # plot interpolated grad + mag virt_evoked.plot_topomap(ch_type='grad', time_unit='s', diff -Nru python-mne-0.23.4+dfsg/examples/preprocessing/xdawn_denoising.py python-mne-1.1.0+dfsg/examples/preprocessing/xdawn_denoising.py --- python-mne-0.23.4+dfsg/examples/preprocessing/xdawn_denoising.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/preprocessing/xdawn_denoising.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-xdawn-denoising: @@ -20,7 +21,9 @@ # Authors: Alexandre Barachant # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% from mne import (io, compute_raw_covariance, read_events, pick_types, Epochs) @@ -32,10 +35,11 @@ data_path = sample.data_path() -############################################################################### +# %% # Set parameters and read data -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' +event_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif' tmin, tmax = -0.1, 0.3 event_id = dict(vis_r=4) @@ -55,7 +59,7 @@ # Plot image epoch before xdawn plot_epochs_image(epochs['vis_r'], picks=[230], vmin=-500, vmax=500) -############################################################################### +# %% # Now, we estimate a set of xDAWN filters for the epochs (which contain only # the ``vis_r`` class). @@ -68,7 +72,7 @@ # Fit xdawn xd.fit(epochs) -############################################################################### +# %% # Epochs are denoised by calling ``apply``, which by default keeps only the # signal subspace corresponding to the first ``n_components`` specified in the # ``Xdawn`` constructor above. @@ -77,7 +81,7 @@ # Plot image epoch after Xdawn plot_epochs_image(epochs_denoised['vis_r'], picks=[230], vmin=-500, vmax=500) -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/README.txt python-mne-1.1.0+dfsg/examples/README.txt --- python-mne-0.23.4+dfsg/examples/README.txt 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/README.txt 2022-08-03 15:48:14.000000000 +0000 @@ -7,6 +7,11 @@ examples are a useful way to discover new analysis or plotting ideas, or to see how a particular technique you’ve read about can be applied using MNE-Python. +.. note:: + If example-scripts contain plots and are run locally, using the + interactive interactive flag with ``python -i tutorial_script.py`` + keeps them open. + .. warning:: These examples sometimes use simulations or shortcuts (such as intentionally diff -Nru python-mne-0.23.4+dfsg/examples/simulation/simulated_raw_data_using_subject_anatomy.py python-mne-1.1.0+dfsg/examples/simulation/simulated_raw_data_using_subject_anatomy.py --- python-mne-0.23.4+dfsg/examples/simulation/simulated_raw_data_using_subject_anatomy.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/simulation/simulated_raw_data_using_subject_anatomy.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-sim-raw-sub: + ======================================= Simulate raw data using subject anatomy ======================================= @@ -14,9 +17,9 @@ # Kostiantyn Maksymenko # Samuel Deslauriers-Gauthier -# License: BSD (3-clause) +# License: BSD-3-Clause -import os.path as op +# %% import numpy as np @@ -25,34 +28,41 @@ print(__doc__) +# %% # In this example, raw data will be simulated for the sample subject, so its # information needs to be loaded. This step will download the data if it not # already on your machine. Subjects directory is also set so it doesn't need # to be given to functions. + data_path = sample.data_path() -subjects_dir = op.join(data_path, 'subjects') +subjects_dir = data_path / 'subjects' subject = 'sample' -meg_path = op.join(data_path, 'MEG', subject) +meg_path = data_path / 'MEG' / subject +# %% # First, we get an info structure from the sample subject. -fname_info = op.join(meg_path, 'sample_audvis_raw.fif') + +fname_info = meg_path / 'sample_audvis_raw.fif' info = mne.io.read_info(fname_info) tstep = 1 / info['sfreq'] +# %% # To simulate sources, we also need a source space. It can be obtained from the # forward solution of the sample subject. -fwd_fname = op.join(meg_path, 'sample_audvis-meg-eeg-oct-6-fwd.fif') + +fwd_fname = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif' fwd = mne.read_forward_solution(fwd_fname) src = fwd['src'] +# %% # To simulate raw data, we need to define when the activity occurs using events # matrix and specify the IDs of each event. # Noise covariance matrix also needs to be defined. # Here, both are loaded from the sample dataset, but they can also be specified # by the user. -fname_event = op.join(meg_path, 'sample_audvis_raw-eve.fif') -fname_cov = op.join(meg_path, 'sample_audvis-cov.fif') +fname_event = meg_path / 'sample_audvis_raw-eve.fif' +fname_cov = meg_path / 'sample_audvis-cov.fif' events = mne.read_events(fname_event) noise_cov = mne.read_cov(fname_cov) @@ -66,7 +76,7 @@ # Take only a few events for speed events = events[:80] -############################################################################### +# %% # In order to simulate source time courses, labels of desired active regions # need to be specified for each of the 4 simulation conditions. # Make a dictionary that maps conditions to activation strengths within @@ -105,7 +115,7 @@ for activation in activation_list)) region_names = list(activations.keys()) -############################################################################### +# %% # Create simulated source activity # -------------------------------- # @@ -125,7 +135,7 @@ return 1e-9 * sinusoid * gf -############################################################################### +# %% # Here, :class:`~mne.simulation.SourceSimulator` is used, which allows to # specify where (label), what (source_time_series), and when (events) event # type will occur. @@ -172,7 +182,7 @@ # SourceSimulator class. stc_data = source_simulator.get_stc() -############################################################################### +# %% # Simulate raw data # ----------------- # @@ -197,7 +207,7 @@ # Plot original and simulated raw data. raw_sim.plot(title='Simulated raw data') -############################################################################### +# %% # Extract epochs and compute evoked responsses # -------------------------------------------- # @@ -211,7 +221,7 @@ evoked_aud_left.plot(spatial_colors=True) evoked_vis_right.plot(spatial_colors=True) -############################################################################### +# %% # Reconstruct simulated source time courses using dSPM inverse operator # --------------------------------------------------------------------- # @@ -232,7 +242,7 @@ brain = stc_diff.plot(subjects_dir=subjects_dir, initial_time=0.1, hemi='split', views=['lat', 'med']) -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/simulation/simulate_evoked_data.py python-mne-1.1.0+dfsg/examples/simulation/simulate_evoked_data.py --- python-mne-0.23.4+dfsg/examples/simulation/simulate_evoked_data.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/simulation/simulate_evoked_data.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-sim-evoked: + ============================== Generate simulated evoked data ============================== @@ -8,7 +11,9 @@ # Author: Daniel Strohmeier # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np import matplotlib.pyplot as plt @@ -21,18 +26,18 @@ print(__doc__) -############################################################################### +# %% # Load real data as templates: data_path = sample.data_path() - -raw = mne.io.read_raw_fif(data_path + '/MEG/sample/sample_audvis_raw.fif') -proj = mne.read_proj(data_path + '/MEG/sample/sample_audvis_ecg-proj.fif') -raw.info['projs'] += proj +meg_path = data_path / 'MEG' / 'sample' +raw = mne.io.read_raw_fif(meg_path / 'sample_audvis_raw.fif') +proj = mne.read_proj(meg_path / 'sample_audvis_ecg-proj.fif') +raw.add_proj(proj) raw.info['bads'] = ['MEG 2443', 'EEG 053'] # mark bad channels -fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' -ave_fname = data_path + '/MEG/sample/sample_audvis-no-filter-ave.fif' -cov_fname = data_path + '/MEG/sample/sample_audvis-cov.fif' +fwd_fname = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif' +ave_fname = meg_path / 'sample_audvis-no-filter-ave.fif' +cov_fname = meg_path / 'sample_audvis-cov.fif' fwd = mne.read_forward_solution(fwd_fname) fwd = mne.pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads']) @@ -40,10 +45,10 @@ info = mne.io.read_info(ave_fname) label_names = ['Aud-lh', 'Aud-rh'] -labels = [mne.read_label(data_path + '/MEG/sample/labels/%s.label' % ln) +labels = [mne.read_label(meg_path / 'labels' / f'{ln}.label') for ln in label_names] -############################################################################### +# %% # Generate source time courses from 2 dipoles and the correspond evoked data times = np.arange(300, dtype=np.float64) / raw.info['sfreq'] - 0.1 @@ -59,7 +64,7 @@ stc = simulate_sparse_stc(fwd['src'], n_dipoles=2, times=times, random_state=42, labels=labels, data_fun=data_fun) -############################################################################### +# %% # Generate noisy evoked data picks = mne.pick_types(raw.info, meg=True, exclude='bads') iir_filter = fit_iir_model_raw(raw, order=5, picks=picks, tmin=60, tmax=180)[1] @@ -67,7 +72,7 @@ evoked = simulate_evoked(fwd, stc, info, cov, nave=nave, use_cps=True, iir_filter=iir_filter) -############################################################################### +# %% # Plot plot_sparse_source_estimates(fwd['src'], stc, bgcolor=(1, 1, 1), opacity=0.5, high_resolution=True) diff -Nru python-mne-0.23.4+dfsg/examples/simulation/simulate_raw_data.py python-mne-1.1.0+dfsg/examples/simulation/simulate_raw_data.py --- python-mne-0.23.4+dfsg/examples/simulation/simulate_raw_data.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/simulation/simulate_raw_data.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-sim-raw: + =========================== Generate simulated raw data =========================== @@ -10,7 +13,9 @@ # Mark Wronkiewicz # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np import matplotlib.pyplot as plt @@ -24,8 +29,9 @@ print(__doc__) data_path = sample.data_path() -raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif' -fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_raw.fif' +fwd_fname = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif' # Load real data as the template raw = mne.io.read_raw_fif(raw_fname) diff -Nru python-mne-0.23.4+dfsg/examples/simulation/source_simulator.py python-mne-1.1.0+dfsg/examples/simulation/source_simulator.py --- python-mne-0.23.4+dfsg/examples/simulation/source_simulator.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/simulation/source_simulator.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-sim-source: + ============================== Generate simulated source data ============================== @@ -11,9 +14,9 @@ # Author: Kostiantyn Maksymenko # Samuel Deslauriers-Gauthier # -# License: BSD (3-clause) +# License: BSD-3-Clause -import os.path as op +# %% import numpy as np @@ -22,25 +25,28 @@ print(__doc__) +# %% # For this example, we will be using the information of the sample subject. # This will download the data if it not already on your machine. We also set # the subjects directory so we don't need to give it to functions. data_path = sample.data_path() -subjects_dir = op.join(data_path, 'subjects') +subjects_dir = data_path / 'subjects' subject = 'sample' +# %% # First, we get an info structure from the test subject. -evoked_fname = op.join(data_path, 'MEG', subject, 'sample_audvis-ave.fif') +evoked_fname = data_path / 'MEG' / subject / 'sample_audvis-ave.fif' info = mne.io.read_info(evoked_fname) tstep = 1. / info['sfreq'] +# %% # To simulate sources, we also need a source space. It can be obtained from the # forward solution of the sample subject. -fwd_fname = op.join(data_path, 'MEG', subject, - 'sample_audvis-meg-eeg-oct-6-fwd.fif') +fwd_fname = data_path / 'MEG' / subject / 'sample_audvis-meg-eeg-oct-6-fwd.fif' fwd = mne.read_forward_solution(fwd_fname) src = fwd['src'] +# %% # To select a region to activate, we use the caudal middle frontal to grow # a region of interest. selected_label = mne.read_labels_from_annot( @@ -51,11 +57,13 @@ subject, selected_label, location=location, extent=extent, subjects_dir=subjects_dir) +# %% # Define the time course of the activity for each source of the region to # activate. Here we use a sine wave at 18 Hz with a peak amplitude # of 10 nAm. source_time_series = np.sin(2. * np.pi * 18. * np.arange(100) * tstep) * 10e-9 +# %% # Define when the activity occurs using events. The first column is the sample # of the event, the second is not used, and the third is the event id. Here the # events occur every 200 samples. @@ -64,12 +72,14 @@ events[:, 0] = 100 + 200 * np.arange(n_events) # Events sample. events[:, 2] = 1 # All events have the sample id. +# %% # Create simulated source activity. Here we use a SourceSimulator whose # add_data method is key. It specified where (label), what # (source_time_series), and when (events) an event type will occur. source_simulator = mne.simulation.SourceSimulator(src, tstep=tstep) source_simulator.add_data(label, source_time_series, events) +# %% # Project the source time series to sensor space and add some noise. The source # simulator can be given directly to the simulate_raw function. raw = mne.simulation.simulate_raw(info, source_simulator, forward=fwd) @@ -77,6 +87,7 @@ mne.simulation.add_noise(raw, cov, iir_filter=[0.2, -0.2, 0.04]) raw.plot() +# %% # Plot evoked data to get another view of the simulated raw data. events = mne.find_events(raw) epochs = mne.Epochs(raw, events, 1, tmin=-0.05, tmax=0.2) diff -Nru python-mne-0.23.4+dfsg/examples/stats/cluster_stats_evoked.py python-mne-1.1.0+dfsg/examples/stats/cluster_stats_evoked.py --- python-mne-0.23.4+dfsg/examples/stats/cluster_stats_evoked.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/stats/cluster_stats_evoked.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-cluster-evoked: + ======================================================= Permutation F-test on sensor data with 1D cluster level ======================================================= @@ -10,7 +13,9 @@ """ # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import matplotlib.pyplot as plt @@ -21,11 +26,12 @@ print(__doc__) -############################################################################### +# %% # Set parameters data_path = sample.data_path() -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' +event_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif' tmin = -0.2 tmax = 0.5 @@ -36,7 +42,7 @@ channel = 'MEG 1332' # include only this channel in analysis include = [channel] -############################################################################### +# %% # Read epochs for the channel of interest picks = mne.pick_types(raw.info, meg=False, eog=True, include=include, exclude='bads') @@ -54,35 +60,34 @@ condition1 = condition1[:, 0, :] # take only one channel to get a 2D array condition2 = condition2[:, 0, :] # take only one channel to get a 2D array -############################################################################### +# %% # Compute statistic threshold = 6.0 T_obs, clusters, cluster_p_values, H0 = \ permutation_cluster_test([condition1, condition2], n_permutations=1000, - threshold=threshold, tail=1, n_jobs=1, + threshold=threshold, tail=1, n_jobs=None, out_type='mask') -############################################################################### +# %% # Plot times = epochs1.times -plt.close('all') -plt.subplot(211) -plt.title('Channel : ' + channel) -plt.plot(times, condition1.mean(axis=0) - condition2.mean(axis=0), - label="ERF Contrast (Event 1 - Event 2)") -plt.ylabel("MEG (T / m)") -plt.legend() -plt.subplot(212) +fig, (ax, ax2) = plt.subplots(2, 1, figsize=(8, 4)) +ax.set_title('Channel : ' + channel) +ax.plot(times, condition1.mean(axis=0) - condition2.mean(axis=0), + label="ERF Contrast (Event 1 - Event 2)") +ax.set_ylabel("MEG (T / m)") +ax.legend() + for i_c, c in enumerate(clusters): c = c[0] if cluster_p_values[i_c] <= 0.05: - h = plt.axvspan(times[c.start], times[c.stop - 1], + h = ax2.axvspan(times[c.start], times[c.stop - 1], color='r', alpha=0.3) else: - plt.axvspan(times[c.start], times[c.stop - 1], color=(0.3, 0.3, 0.3), + ax2.axvspan(times[c.start], times[c.stop - 1], color=(0.3, 0.3, 0.3), alpha=0.3) + hf = plt.plot(times, T_obs, 'g') -plt.legend((h, ), ('cluster p-value < 0.05', )) -plt.xlabel("time (ms)") -plt.ylabel("f-values") -plt.show() +ax2.legend((h, ), ('cluster p-value < 0.05', )) +ax2.set_xlabel("time (ms)") +ax2.set_ylabel("f-values") diff -Nru python-mne-0.23.4+dfsg/examples/stats/fdr_stats_evoked.py python-mne-1.1.0+dfsg/examples/stats/fdr_stats_evoked.py --- python-mne-0.23.4+dfsg/examples/stats/fdr_stats_evoked.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/stats/fdr_stats_evoked.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-fdr-evoked: + ======================================= FDR correction on T-test on sensor data ======================================= @@ -10,7 +13,9 @@ """ # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np from scipy import stats @@ -23,11 +28,12 @@ print(__doc__) -############################################################################### +# %% # Set parameters data_path = sample.data_path() -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' +event_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif' event_id, tmin, tmax = 1, -0.2, 0.5 # Setup for reading the raw data @@ -37,7 +43,7 @@ channel = 'MEG 1332' # include only this channel in analysis include = [channel] -############################################################################### +# %% # Read epochs for the channel of interest picks = mne.pick_types(raw.info, meg=False, eog=True, include=include, exclude='bads') @@ -48,7 +54,7 @@ X = epochs.get_data() # as 3D matrix X = X[:, 0, :] # take only one channel to get a 2D array -############################################################################### +# %% # Compute statistic T, pval = stats.ttest_1samp(X, 0) alpha = 0.05 @@ -62,7 +68,7 @@ reject_fdr, pval_fdr = fdr_correction(pval, alpha=alpha, method='indep') threshold_fdr = np.min(np.abs(T)[reject_fdr]) -############################################################################### +# %% # Plot times = 1e3 * epochs.times diff -Nru python-mne-0.23.4+dfsg/examples/stats/linear_regression_raw.py python-mne-1.1.0+dfsg/examples/stats/linear_regression_raw.py --- python-mne-0.23.4+dfsg/examples/stats/linear_regression_raw.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/stats/linear_regression_raw.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-linear-regression-raw: + ======================================== Regression on continuous data (rER[P/F]) ======================================== @@ -19,7 +22,9 @@ """ # Authors: Jona Sassenhagen # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import matplotlib.pyplot as plt @@ -29,7 +34,8 @@ # Load and preprocess data data_path = sample.data_path() -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' raw = mne.io.read_raw_fif(raw_fname) raw.pick_types(meg='grad', stim=True, eeg=False).load_data() raw.filter(1, None, fir_design='firwin') # high-pass diff -Nru python-mne-0.23.4+dfsg/examples/stats/sensor_permutation_test.py python-mne-1.1.0+dfsg/examples/stats/sensor_permutation_test.py --- python-mne-0.23.4+dfsg/examples/stats/sensor_permutation_test.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/stats/sensor_permutation_test.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-perm-test: + ================================= Permutation T-test on sensor data ================================= @@ -10,7 +13,9 @@ """ # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np @@ -21,11 +26,12 @@ print(__doc__) -############################################################################### +# %% # Set parameters data_path = sample.data_path() -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' +event_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif' event_id = 1 tmin = -0.2 tmax = 0.5 @@ -46,7 +52,7 @@ data = np.mean(data[:, :, temporal_mask], axis=2) n_permutations = 50000 -T0, p_values, H0 = permutation_t_test(data, n_permutations, n_jobs=1) +T0, p_values, H0 = permutation_t_test(data, n_permutations, n_jobs=None) significant_sensors = picks[p_values <= 0.05] significant_sensors_names = [raw.ch_names[k] for k in significant_sensors] @@ -54,7 +60,7 @@ print("Number of significant sensors : %d" % len(significant_sensors)) print("Sensors names : %s" % significant_sensors_names) -############################################################################### +# %% # View location of significantly active sensors evoked = mne.EvokedArray(-np.log10(p_values)[:, np.newaxis], diff -Nru python-mne-0.23.4+dfsg/examples/stats/sensor_regression.py python-mne-1.1.0+dfsg/examples/stats/sensor_regression.py --- python-mne-0.23.4+dfsg/examples/stats/sensor_regression.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/stats/sensor_regression.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,10 @@ +# -*- coding: utf-8 -*- """ -============================================================================ +.. _ex-sensor-regression: + +========================================================================= Analysing continuous features with binning and regression in sensor space -============================================================================ +========================================================================= Predict single trial activity from a continuous variable. A single-trial regression is performed in each sensor and timepoint @@ -25,7 +28,9 @@ # Denis A. Engemann # Jona Sassenhagen # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import pandas as pd import mne @@ -34,7 +39,7 @@ from mne.datasets import kiloword # Load the data -path = kiloword.data_path() + '/kword_metadata-epo.fif' +path = kiloword.data_path() / 'kword_metadata-epo.fif' epochs = mne.read_epochs(path) print(epochs.metadata.head()) diff -Nru python-mne-0.23.4+dfsg/examples/time_frequency/compute_csd.py python-mne-1.1.0+dfsg/examples/time_frequency/compute_csd.py --- python-mne-0.23.4+dfsg/examples/time_frequency/compute_csd.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/time_frequency/compute_csd.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,10 @@ +# -*- coding: utf-8 -*- """ -================================================== +.. _ex-csd-matrix: + +============================================= Compute a cross-spectral density (CSD) matrix -================================================== +============================================= A cross-spectral density (CSD) matrix is similar to a covariance matrix, but in the time-frequency domain. It is the first step towards computing @@ -15,7 +18,9 @@ 3. Using Morlet wavelets: :func:`mne.time_frequency.csd_morlet` """ # Author: Marijn van Vliet -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% from matplotlib import pyplot as plt import mne @@ -24,21 +29,22 @@ print(__doc__) -############################################################################### +# %% # In the following example, the computation of the CSD matrices can be # performed using multiple cores. Set ``n_jobs`` to a value >1 to select the # number of cores to use. n_jobs = 1 -############################################################################### +# %% # Loading the sample dataset. data_path = sample.data_path() -fname_raw = data_path + '/MEG/sample/sample_audvis_raw.fif' -fname_event = data_path + '/MEG/sample/sample_audvis_raw-eve.fif' +meg_path = data_path / 'MEG' / 'sample' +fname_raw = meg_path / 'sample_audvis_raw.fif' +fname_event = meg_path / 'sample_audvis_raw-eve.fif' raw = mne.io.read_raw_fif(fname_raw) events = mne.read_events(fname_event) -############################################################################### +# %% # By default, CSD matrices are computed using all MEG/EEG channels. When # interpreting a CSD matrix with mixed sensor types, be aware that the # measurement units, and thus the scalings, differ across sensors. In this @@ -51,13 +57,13 @@ picks=picks, baseline=(None, 0), reject=dict(grad=4000e-13), preload=True) -############################################################################### +# %% # Computing CSD matrices using short-term Fourier transform and (adaptive) # multitapers is straightforward: csd_fft = csd_fourier(epochs, fmin=15, fmax=20, n_jobs=n_jobs) csd_mt = csd_multitaper(epochs, fmin=15, fmax=20, adaptive=True, n_jobs=n_jobs) -############################################################################### +# %% # When computing the CSD with Morlet wavelets, you specify the exact # frequencies at which to compute it. For each frequency, a corresponding # wavelet will be constructed and convolved with the signal, resulting in a @@ -74,7 +80,7 @@ frequencies = [16, 17, 18, 19, 20] csd_wav = csd_morlet(epochs, frequencies, decim=10, n_jobs=n_jobs) -############################################################################### +# %% # The resulting :class:`mne.time_frequency.CrossSpectralDensity` objects have a # plotting function we can use to compare the results of the different methods. # We're plotting the mean CSD across frequencies. diff -Nru python-mne-0.23.4+dfsg/examples/time_frequency/compute_source_psd_epochs.py python-mne-1.1.0+dfsg/examples/time_frequency/compute_source_psd_epochs.py --- python-mne-0.23.4+dfsg/examples/time_frequency/compute_source_psd_epochs.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/time_frequency/compute_source_psd_epochs.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-psd-inverse: + ===================================================================== Compute Power Spectral Density of inverse solution from single epochs ===================================================================== @@ -9,7 +12,9 @@ """ # Author: Martin Luessi # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import matplotlib.pyplot as plt @@ -20,12 +25,13 @@ print(__doc__) data_path = sample.data_path() -fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' -fname_raw = data_path + '/MEG/sample/sample_audvis_raw.fif' -fname_event = data_path + '/MEG/sample/sample_audvis_raw-eve.fif' +meg_path = data_path / 'MEG' / 'sample' +fname_inv = meg_path / 'sample_audvis-meg-oct-6-meg-inv.fif' +fname_raw = meg_path / 'sample_audvis_raw.fif' +fname_event = meg_path / 'sample_audvis_raw-eve.fif' label_name = 'Aud-lh' -fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name -subjects_dir = data_path + '/subjects' +fname_label = meg_path / 'labels' / f'{label_name}.label' +subjects_dir = data_path / 'subjects' event_id, tmin, tmax = 1, -0.2, 0.5 snr = 1.0 # use smaller SNR for raw data @@ -54,7 +60,7 @@ fmin, fmax = 0., 70. bandwidth = 4. # bandwidth of the windows in Hz -############################################################################### +# %% # Compute source space PSD in label # --------------------------------- # @@ -77,7 +83,7 @@ freqs = stc.times # the frequencies are stored here stc.data = psd_avg # overwrite the last epoch's data with the average -############################################################################### +# %% # Visualize the 10 Hz PSD: brain = stc.plot(initial_time=10., hemi='lh', views='lat', # 10 HZ @@ -85,7 +91,7 @@ smoothing_steps=3, subjects_dir=subjects_dir) brain.add_label(label, borders=True, color='k') -############################################################################### +# %% # Visualize the entire spectrum: fig, ax = plt.subplots() diff -Nru python-mne-0.23.4+dfsg/examples/time_frequency/source_label_time_frequency.py python-mne-1.1.0+dfsg/examples/time_frequency/source_label_time_frequency.py --- python-mne-0.23.4+dfsg/examples/time_frequency/source_label_time_frequency.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/time_frequency/source_label_time_frequency.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-source-space-power-phase-locking: + ========================================================= Compute power and phase lock in label of the source space ========================================================= @@ -13,7 +16,9 @@ """ # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np import matplotlib.pyplot as plt @@ -25,13 +30,14 @@ print(__doc__) -############################################################################### +# %% # Set parameters data_path = sample.data_path() -raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif' -fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_raw.fif' +fname_inv = meg_path / 'sample_audvis-meg-oct-6-meg-inv.fif' label_name = 'Aud-rh' -fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name +fname_label = meg_path / 'labels' / f'{label_name}.label' tmin, tmax, event_id = -0.2, 0.5, 2 @@ -70,7 +76,7 @@ # compute the source space power and the inter-trial coherence power, itc = source_induced_power( this_epochs, inverse_operator, freqs, label, baseline=(-0.1, 0), - baseline_mode='percent', n_cycles=n_cycles, n_jobs=1) + baseline_mode='percent', n_cycles=n_cycles, n_jobs=None) power = np.mean(power, axis=0) # average over sources itc = np.mean(itc, axis=0) # average over sources diff -Nru python-mne-0.23.4+dfsg/examples/time_frequency/source_power_spectrum_opm.py python-mne-1.1.0+dfsg/examples/time_frequency/source_power_spectrum_opm.py --- python-mne-0.23.4+dfsg/examples/time_frequency/source_power_spectrum_opm.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/time_frequency/source_power_spectrum_opm.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-opm-resting-state: @@ -8,7 +9,8 @@ Here we compute the resting state from raw for data recorded using a Neuromag VectorView system and a custom OPM system. The pipeline is meant to mostly follow the Brainstorm :footcite:`TadelEtAl2011` -`OMEGA resting tutorial pipeline `_. +`OMEGA resting tutorial pipeline +`__. The steps we use are: 1. Filtering: downsample heavily. @@ -17,43 +19,36 @@ 4. Frequency: power spectral density (Welch), 4 sec window, 50% overlap. 5. Standardize: normalize by relative power for each source. -.. _bst_omega: https://neuroimage.usc.edu/brainstorm/Tutorials/RestingOmega - Preprocessing ------------- """ -# sphinx_gallery_thumbnail_number = 13 - # Authors: Denis Engemann # Luke Bloy # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause -import os.path as op - -from mne.filter import next_fast_len +# %% import mne - +from mne.filter import next_fast_len print(__doc__) data_path = mne.datasets.opm.data_path() subject = 'OPM_sample' -subjects_dir = op.join(data_path, 'subjects') -bem_dir = op.join(subjects_dir, subject, 'bem') -bem_fname = op.join(subjects_dir, subject, 'bem', - subject + '-5120-5120-5120-bem-sol.fif') -src_fname = op.join(bem_dir, '%s-oct6-src.fif' % subject) -vv_fname = data_path + '/MEG/SQUID/SQUID_resting_state.fif' -vv_erm_fname = data_path + '/MEG/SQUID/SQUID_empty_room.fif' -vv_trans_fname = data_path + '/MEG/SQUID/SQUID-trans.fif' -opm_fname = data_path + '/MEG/OPM/OPM_resting_state_raw.fif' -opm_erm_fname = data_path + '/MEG/OPM/OPM_empty_room_raw.fif' -opm_trans_fname = None -opm_coil_def_fname = op.join(data_path, 'MEG', 'OPM', 'coil_def.dat') +subjects_dir = data_path / 'subjects' +bem_dir = subjects_dir / subject / 'bem' +bem_fname = bem_dir / f'{subject}-5120-5120-5120-bem-sol.fif' +src_fname = bem_dir / f'{subject}-oct6-src.fif' +vv_fname = data_path / 'MEG' / 'SQUID' / 'SQUID_resting_state.fif' +vv_erm_fname = data_path / 'MEG' / 'SQUID' / 'SQUID_empty_room.fif' +vv_trans_fname = data_path / 'MEG' / 'SQUID' / 'SQUID-trans.fif' +opm_fname = data_path / 'MEG' / 'OPM' / 'OPM_resting_state_raw.fif' +opm_erm_fname = data_path / 'MEG' / 'OPM' / 'OPM_empty_room_raw.fif' +opm_trans = mne.transforms.Transform('head', 'mri') # use identity transform +opm_coil_def_fname = data_path / 'MEG' / 'OPM' / 'coil_def.dat' ############################################################################## # Load data, resample. We will store the raw objects in dicts with entries @@ -61,7 +56,7 @@ raws = dict() raw_erms = dict() -new_sfreq = 90. # Nyquist frequency (45 Hz) < line noise freq (50 Hz) +new_sfreq = 60. # Nyquist frequency (30 Hz) < line noise freq (50 Hz) raws['vv'] = mne.io.read_raw_fif(vv_fname, verbose='error') # ignore naming raws['vv'].load_data().resample(new_sfreq) raws['vv'].info['bads'] = ['MEG2233', 'MEG1842'] @@ -77,27 +72,9 @@ assert raws['opm'].info['sfreq'] == raws['vv'].info['sfreq'] ############################################################################## -# Do some minimal artifact rejection just for VectorView data - -titles = dict(vv='VectorView', opm='OPM') -ssp_ecg, _ = mne.preprocessing.compute_proj_ecg( - raws['vv'], tmin=-0.1, tmax=0.1, n_grad=1, n_mag=1) -raws['vv'].add_proj(ssp_ecg, remove_existing=True) -# due to how compute_proj_eog works, it keeps the old projectors, so -# the output contains both projector types (and also the original empty-room -# projectors) -ssp_ecg_eog, _ = mne.preprocessing.compute_proj_eog( - raws['vv'], n_grad=1, n_mag=1, ch_name='MEG0112') -raws['vv'].add_proj(ssp_ecg_eog, remove_existing=True) -raw_erms['vv'].add_proj(ssp_ecg_eog) -fig = mne.viz.plot_projs_topomap(raws['vv'].info['projs'][-4:], - info=raws['vv'].info) -fig.suptitle(titles['vv']) -fig.subplots_adjust(0.05, 0.05, 0.95, 0.85) - -############################################################################## # Explore data +titles = dict(vv='VectorView', opm='OPM') kinds = ('vv', 'opm') n_fft = next_fast_len(int(round(4 * new_sfreq))) print('Using n_fft=%d (%0.1f sec)' % (n_fft, n_fft / raws['vv'].info['sfreq'])) @@ -117,6 +94,8 @@ # We only do it here to save a bit of memory, in general this is not required. del src[0]['dist'], src[1]['dist'] bem = mne.read_bem_solution(bem_fname) +# For speed, let's just use a 1-layer BEM +bem = mne.make_bem_solution(bem['surfs'][-1:]) fwd = dict() # check alignment and generate forward for VectorView @@ -134,12 +113,12 @@ with mne.use_coil_def(opm_coil_def_fname): fig = mne.viz.plot_alignment( - raws['opm'].info, trans=opm_trans_fname, subject=subject, + raws['opm'].info, trans=opm_trans, subject=subject, subjects_dir=subjects_dir, dig=False, coord_frame='mri', surfaces=('head', 'white')) mne.viz.set_3d_view(figure=fig, **kwargs) fwd['opm'] = mne.make_forward_solution( - raws['opm'].info, opm_trans_fname, src, bem, eeg=False, verbose=True) + raws['opm'].info, opm_trans, src, bem, eeg=False, verbose=True) del src, bem @@ -149,8 +128,7 @@ # independently. This makes the value of each sensor point and source location # in each frequency band the percentage of the PSD accounted for by that band. -freq_bands = dict( - delta=(2, 4), theta=(5, 7), alpha=(8, 12), beta=(15, 29), gamma=(30, 45)) +freq_bands = dict(alpha=(8, 12), beta=(15, 29)) topos = dict(vv=dict(), opm=dict()) stcs = dict(vv=dict(), opm=dict()) @@ -176,12 +154,12 @@ del fwd, raws, raw_erms -############################################################################### +# %% # Now we can make some plots of each frequency band. Note that the OPM head # coverage is only over right motor cortex, so only localization # of beta is likely to be worthwhile. # -# Theta +# Alpha # ----- def plot_band(kind, band): @@ -195,19 +173,13 @@ time_label=title, title=title, colormap='inferno', time_viewer=False, show_traces=False, clim=dict(kind='percent', lims=(70, 85, 99)), smoothing_steps=10) - brain.show_view(dict(azimuth=0, elevation=0), roll=0) + brain.show_view(azimuth=0, elevation=0, roll=0) return fig, brain -fig_theta, brain_theta = plot_band('vv', 'theta') - -############################################################################### -# Alpha -# ----- - fig_alpha, brain_alpha = plot_band('vv', 'alpha') -############################################################################### +# %% # Beta # ---- # Here we also show OPM data, which shows a profile similar to the VectorView @@ -215,17 +187,13 @@ fig_beta, brain_beta = plot_band('vv', 'beta') -############################################################################### +# %% # Then OPM: -fig_beta_opm, brain_beta_opm = plot_band('opm', 'beta') -############################################################################### -# Gamma -# ----- - -fig_gamma, brain_gamma = plot_band('vv', 'gamma') +# sphinx_gallery_thumbnail_number = 10 +fig_beta_opm, brain_beta_opm = plot_band('opm', 'beta') -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/time_frequency/source_power_spectrum.py python-mne-1.1.0+dfsg/examples/time_frequency/source_power_spectrum.py --- python-mne-0.23.4+dfsg/examples/time_frequency/source_power_spectrum.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/time_frequency/source_power_spectrum.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-label-psd: + ====================================================== Compute source power spectral density (PSD) in a label ====================================================== @@ -8,7 +11,9 @@ """ # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import matplotlib.pyplot as plt @@ -19,12 +24,13 @@ print(__doc__) -############################################################################### +# %% # Set parameters data_path = sample.data_path() -raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif' -fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' -fname_label = data_path + '/MEG/sample/labels/Aud-lh.label' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_raw.fif' +fname_inv = meg_path / 'sample_audvis-meg-oct-6-meg-inv.fif' +fname_label = meg_path / 'labels' / 'Aud-lh.label' # Setup for reading the raw data raw = io.read_raw_fif(raw_fname, verbose=False) @@ -46,9 +52,9 @@ pick_ori="normal", n_fft=n_fft, label=label, dB=True) -stc.save('psd_dSPM') +stc.save('psd_dSPM', overwrite=True) -############################################################################### +# %% # View PSD of sources in label plt.plot(stc.times, stc.data.T) plt.xlabel('Frequency (Hz)') diff -Nru python-mne-0.23.4+dfsg/examples/time_frequency/source_space_time_frequency.py python-mne-1.1.0+dfsg/examples/time_frequency/source_space_time_frequency.py --- python-mne-0.23.4+dfsg/examples/time_frequency/source_space_time_frequency.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/time_frequency/source_space_time_frequency.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-source-space-tfr: + =================================================== Compute induced power in the source space with dSPM =================================================== @@ -10,7 +13,9 @@ """ # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import matplotlib.pyplot as plt @@ -21,11 +26,12 @@ print(__doc__) -############################################################################### +# %% # Set parameters data_path = sample.data_path() -raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif' -fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_raw.fif' +fname_inv = meg_path / 'sample_audvis-meg-oct-6-meg-inv.fif' tmin, tmax, event_id = -0.2, 0.5, 1 # Setup for reading the raw data @@ -52,12 +58,12 @@ bands = dict(alpha=[9, 11], beta=[18, 22]) stcs = source_band_induced_power(epochs, inverse_operator, bands, n_cycles=2, - use_fft=False, n_jobs=1) + use_fft=False, n_jobs=None) for b, stc in stcs.items(): - stc.save('induced_power_%s' % b) + stc.save('induced_power_%s' % b, overwrite=True) -############################################################################### +# %% # plot mean power plt.plot(stcs['alpha'].times, stcs['alpha'].data.mean(axis=0), label='Alpha') plt.plot(stcs['beta'].times, stcs['beta'].data.mean(axis=0), label='Beta') diff -Nru python-mne-0.23.4+dfsg/examples/time_frequency/temporal_whitening.py python-mne-1.1.0+dfsg/examples/time_frequency/temporal_whitening.py --- python-mne-0.23.4+dfsg/examples/time_frequency/temporal_whitening.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/time_frequency/temporal_whitening.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-temporal-whitening: + ================================ Temporal whitening with AR model ================================ @@ -9,7 +12,9 @@ """ # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np from scipy import signal @@ -22,13 +27,13 @@ print(__doc__) data_path = sample.data_path() - -raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif' -proj_fname = data_path + '/MEG/sample/sample_audvis_ecg-proj.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_raw.fif' +proj_fname = meg_path / 'sample_audvis_ecg-proj.fif' raw = mne.io.read_raw_fif(raw_fname) proj = mne.read_proj(proj_fname) -raw.info['projs'] += proj +raw.add_proj(proj) raw.info['bads'] = ['MEG 2443', 'EEG 053'] # mark bad channels # Set up pick list: Gradiometers - bad channels @@ -45,7 +50,7 @@ d_ = signal.lfilter(b, a, innovation) # regenerate the signal d_ = np.r_[d_[0] * np.ones(order), d_] # dummy samples to keep signal length -############################################################################### +# %% # Plot the different time series and PSDs plt.close('all') plt.figure() diff -Nru python-mne-0.23.4+dfsg/examples/time_frequency/time_frequency_erds.py python-mne-1.1.0+dfsg/examples/time_frequency/time_frequency_erds.py --- python-mne-0.23.4+dfsg/examples/time_frequency/time_frequency_erds.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/time_frequency/time_frequency_erds.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-tfr-erds: + =============================== Compute and visualize ERDS maps =============================== @@ -6,31 +9,35 @@ This example calculates and displays ERDS maps of event-related EEG data. ERDS (sometimes also written as ERD/ERS) is short for event-related desynchronization (ERD) and event-related synchronization (ERS) -:footcite:`PfurtschellerLopesdaSilva1999`. -Conceptually, ERD corresponds to a decrease in power in a specific frequency -band relative to a baseline. Similarly, ERS corresponds to an increase in -power. An ERDS map is a time/frequency representation of ERD/ERS over a range -of frequencies :footcite:`GraimannEtAl2002`. ERDS maps are also known as ERSP -(event-related spectral perturbation) :footcite:`Makeig1993`. - -We use a public EEG BCI data set containing two different motor imagery tasks -available at PhysioNet. The two tasks are imagined hand and feet movement. Our -goal is to generate ERDS maps for each of the two tasks. - -First, we load the data and create epochs of 5s length. The data sets contain -multiple channels, but we will only consider the three channels C3, Cz, and C4. -We compute maps containing frequencies ranging from 2 to 35Hz. We map ERD to -red color and ERS to blue color, which is the convention in many ERDS -publications. Finally, we perform cluster-based permutation tests to estimate -significant ERDS values (corrected for multiple comparisons within channels). +:footcite:`PfurtschellerLopesdaSilva1999`. Conceptually, ERD corresponds to a +decrease in power in a specific frequency band relative to a baseline. +Similarly, ERS corresponds to an increase in power. An ERDS map is a +time/frequency representation of ERD/ERS over a range of frequencies +:footcite:`GraimannEtAl2002`. ERDS maps are also known as ERSP (event-related +spectral perturbation) :footcite:`Makeig1993`. + +In this example, we use an EEG BCI data set containing two different motor +imagery tasks (imagined hand and feet movement). Our goal is to generate ERDS +maps for each of the two tasks. + +First, we load the data and create epochs of 5s length. The data set contains +multiple channels, but we will only consider C3, Cz, and C4. We compute maps +containing frequencies ranging from 2 to 35Hz. We map ERD to red color and ERS +to blue color, which is customary in many ERDS publications. Finally, we +perform cluster-based permutation tests to estimate significant ERDS values +(corrected for multiple comparisons within channels). """ # Authors: Clemens Brunner # Felix Klotzsche # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% +# As usual, we import everything we need. import numpy as np import matplotlib.pyplot as plt +from matplotlib.colors import TwoSlopeNorm import pandas as pd import seaborn as sns import mne @@ -38,46 +45,42 @@ from mne.io import concatenate_raws, read_raw_edf from mne.time_frequency import tfr_multitaper from mne.stats import permutation_cluster_1samp_test as pcluster_test -from mne.viz.utils import center_cmap - -# load and preprocess data #################################################### -subject = 1 # use data from subject 1 -runs = [6, 10, 14] # use only hand and feet motor imagery runs -fnames = eegbci.load_data(subject, runs) -raws = [read_raw_edf(f, preload=True) for f in fnames] -raw = concatenate_raws(raws) +# %% +# First, we load and preprocess the data. We use runs 6, 10, and 14 from +# subject 1 (these runs contains hand and feet motor imagery). +fnames = eegbci.load_data(subject=1, runs=(6, 10, 14)) +raw = concatenate_raws([read_raw_edf(f, preload=True) for f in fnames]) raw.rename_channels(lambda x: x.strip('.')) # remove dots from channel names events, _ = mne.events_from_annotations(raw, event_id=dict(T1=2, T2=3)) -want_chs = ['C3', 'Cz', 'C4'] -picks = mne.pick_channels(raw.info["ch_names"], want_chs) - -# epoch data ################################################################## -tmin, tmax = -1, 4 # define epochs around events (in s) +# %% +# Now we can create 5-second epochs around events of interest. +tmin, tmax = -1, 4 event_ids = dict(hands=2, feet=3) # map event IDs to tasks epochs = mne.Epochs(raw, events, event_ids, tmin - 0.5, tmax + 0.5, - picks=picks, baseline=None, preload=True) + picks=('C3', 'Cz', 'C4'), baseline=None, preload=True) -# compute ERDS maps ########################################################### -freqs = np.arange(2, 36, 1) # frequencies from 2-35Hz -n_cycles = freqs # use constant t/f resolution +# %% +# Here we set suitable values for computing ERDS maps. +freqs = np.arange(2, 36) # frequencies from 2-35Hz vmin, vmax = -1, 1.5 # set min and max ERDS values in plot -baseline = [-1, 0] # baseline interval (in s) -cmap = center_cmap(plt.cm.RdBu, vmin, vmax) # zero maps to white +baseline = (-1, 0) # baseline interval (in s) +cnorm = TwoSlopeNorm(vmin=vmin, vcenter=0, vmax=vmax) # min, center & max ERDS + kwargs = dict(n_permutations=100, step_down_p=0.05, seed=1, buffer_size=None, out_type='mask') # for cluster test -# Run TF decomposition overall epochs -tfr = tfr_multitaper(epochs, freqs=freqs, n_cycles=n_cycles, - use_fft=True, return_itc=False, average=False, - decim=2) -tfr.crop(tmin, tmax) -tfr.apply_baseline(baseline, mode="percent") +# %% +# Finally, we perform time/frequency decomposition over all epochs. +tfr = tfr_multitaper(epochs, freqs=freqs, n_cycles=freqs, use_fft=True, + return_itc=False, average=False, decim=2) +tfr.crop(tmin, tmax).apply_baseline(baseline, mode="percent") + for event in event_ids: # select desired epochs for visualization tfr_ev = tfr[event] @@ -85,10 +88,9 @@ gridspec_kw={"width_ratios": [10, 10, 10, 1]}) for ch, ax in enumerate(axes[:-1]): # for each channel # positive clusters - _, c1, p1, _ = pcluster_test(tfr_ev.data[:, ch, ...], tail=1, **kwargs) + _, c1, p1, _ = pcluster_test(tfr_ev.data[:, ch], tail=1, **kwargs) # negative clusters - _, c2, p2, _ = pcluster_test(tfr_ev.data[:, ch, ...], tail=-1, - **kwargs) + _, c2, p2, _ = pcluster_test(tfr_ev.data[:, ch], tail=-1, **kwargs) # note that we keep clusters with p <= 0.05 from the combined clusters # of two independent tests; in this example, we do not correct for @@ -98,8 +100,8 @@ mask = c[..., p <= 0.05].any(axis=-1) # plot TFR (ERDS map with masking) - tfr_ev.average().plot([ch], vmin=vmin, vmax=vmax, cmap=(cmap, False), - axes=ax, colorbar=False, show=False, mask=mask, + tfr_ev.average().plot([ch], cmap="RdBu", cnorm=cnorm, axes=ax, + colorbar=False, show=False, mask=mask, mask_style="mask") ax.set_title(epochs.ch_names[ch], fontsize=10) @@ -107,11 +109,11 @@ if ch != 0: ax.set_ylabel("") ax.set_yticklabels("") - fig.colorbar(axes[0].images[-1], cax=axes[-1]) - fig.suptitle("ERDS ({})".format(event)) - fig.show() + fig.colorbar(axes[0].images[-1], cax=axes[-1]).ax.set_yscale("linear") + fig.suptitle(f"ERDS ({event})") + plt.show() -############################################################################### +# %% # Similar to `~mne.Epochs` objects, we can also export data from # `~mne.time_frequency.EpochsTFR` and `~mne.time_frequency.AverageTFR` objects # to a :class:`Pandas DataFrame `. By default, the `time` @@ -122,7 +124,7 @@ df = tfr.to_data_frame(time_format=None) df.head() -############################################################################### +# %% # This allows us to use additional plotting functions like # :func:`seaborn.lineplot` to plot confidence bands: @@ -144,7 +146,8 @@ df['band'] = df['band'].cat.remove_unused_categories() # Order channels for plotting: -df['channel'] = df['channel'].cat.reorder_categories(want_chs, ordered=True) +df['channel'] = df['channel'].cat.reorder_categories(('C3', 'Cz', 'C4'), + ordered=True) g = sns.FacetGrid(df, row='band', col='channel', margin_titles=True) g.map(sns.lineplot, 'time', 'value', 'condition', n_boot=10) @@ -157,7 +160,7 @@ g.add_legend(ncol=2, loc='lower center') g.fig.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.08) -############################################################################### +# %% # Having the data as a DataFrame also facilitates subsetting, # grouping, and other transforms. # Here, we use seaborn to plot the average ERDS in the motor imagery interval @@ -173,15 +176,14 @@ g = (g.map(sns.violinplot, 'channel', 'value', 'band', n_boot=10, palette='deep', order=['C3', 'Cz', 'C4'], hue_order=freq_bands_of_interest, - linewidth=0.5) - .add_legend(ncol=4, loc='lower center')) + linewidth=0.5).add_legend(ncol=4, loc='lower center')) g.map(plt.axhline, **axline_kw) g.set_axis_labels("", "ERDS (%)") g.set_titles(col_template="{col_name}", row_template="{row_name}") g.fig.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.3) -############################################################################### +# %% # References # ========== # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/time_frequency/time_frequency_global_field_power.py python-mne-1.1.0+dfsg/examples/time_frequency/time_frequency_global_field_power.py --- python-mne-0.23.4+dfsg/examples/time_frequency/time_frequency_global_field_power.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/time_frequency/time_frequency_global_field_power.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-time-freq-global-field-power: @@ -25,7 +26,7 @@ confidence intervals are computed as described in :footcite:`EfronHastie2016` across channels. -The advantage of this method over summarizing the Space x Time x Frequency +The advantage of this method over summarizing the Space × Time × Frequency output of a Morlet Wavelet in frequency bands is relative speed and, more importantly, the clear-cut comparability of the spectral decomposition (the same type of filter is used across all bands). @@ -40,24 +41,24 @@ # Authors: Denis A. Engemann # Stefan Appelhoff # -# License: BSD (3-clause) -import os.path as op +# License: BSD-3-Clause -import numpy as np +# %% import matplotlib.pyplot as plt +import numpy as np import mne -from mne.datasets import somato from mne.baseline import rescale +from mne.datasets import somato from mne.stats import bootstrap_confidence_interval -############################################################################### +# %% # Set parameters data_path = somato.data_path() subject = '01' task = 'somato' -raw_fname = op.join(data_path, 'sub-{}'.format(subject), 'meg', - 'sub-{}_task-{}_meg.fif'.format(subject, task)) +raw_fname = (data_path / f'sub-{subject}' / 'meg' / + f'sub-{subject}_task-{task}_meg.fif') # let's explore some frequency bands iter_freqs = [ @@ -67,7 +68,7 @@ ('Gamma', 30, 45) ] -############################################################################### +# %% # We create average power time courses for each frequency band # set epoching parameters @@ -87,7 +88,7 @@ raw.load_data() # bandpass filter - raw.filter(fmin, fmax, n_jobs=1, # use more jobs to speed up. + raw.filter(fmin, fmax, n_jobs=None, # use more jobs to speed up. l_trans_bandwidth=1, # make sure filter params are the same h_trans_bandwidth=1) # in each band and skip "auto" option. @@ -104,7 +105,7 @@ del epochs del raw -############################################################################### +# %% # Now we can compute the Global Field Power # We can track the emergence of spatial patterns compared to baseline # for each frequency band, with a bootstrapped confidence interval. diff -Nru python-mne-0.23.4+dfsg/examples/time_frequency/time_frequency_simulated.py python-mne-1.1.0+dfsg/examples/time_frequency/time_frequency_simulated.py --- python-mne-0.23.4+dfsg/examples/time_frequency/time_frequency_simulated.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/time_frequency/time_frequency_simulated.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-tfr-comparison: + ====================================================================== Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell) ====================================================================== @@ -13,7 +16,9 @@ # Denis Engemann # Chris Holdgraf # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np from matplotlib import pyplot as plt @@ -26,7 +31,7 @@ print(__doc__) -############################################################################### +# %% # Simulate data # ------------- # @@ -63,7 +68,7 @@ epochs.average().plot() -############################################################################### +# %% # Calculate a time-frequency representation (TFR) # ----------------------------------------------- # @@ -85,7 +90,7 @@ freqs = np.arange(5., 100., 3.) vmin, vmax = -3., 3. # Define our color limits. -############################################################################### +# %% # **(1) Least smoothing (most variance/background fluctuations).** n_cycles = freqs / 2. @@ -96,7 +101,7 @@ power.plot([0], baseline=(0., 0.1), mode='mean', vmin=vmin, vmax=vmax, title='Sim: Least smoothing, most variance') -############################################################################### +# %% # **(2) Less frequency smoothing, more time smoothing.** n_cycles = freqs # Increase time-window length to 1 second. @@ -107,7 +112,7 @@ power.plot([0], baseline=(0., 0.1), mode='mean', vmin=vmin, vmax=vmax, title='Sim: Less frequency smoothing, more time smoothing') -############################################################################### +# %% # **(3) Less time smoothing, more frequency smoothing.** n_cycles = freqs / 2. @@ -138,7 +143,7 @@ ax.set_title('Sim: Using S transform, width = {:0.1f}'.format(width)) plt.tight_layout() -############################################################################### +# %% # Morlet Wavelets # =============== # @@ -158,7 +163,7 @@ ax.set_title('Sim: Using Morlet wavelet, n_cycles = %s' % n_cycles) plt.tight_layout() -############################################################################### +# %% # Calculating a TFR without averaging over epochs # ----------------------------------------------- # @@ -174,7 +179,7 @@ avgpower.plot([0], baseline=(0., 0.1), mode='mean', vmin=vmin, vmax=vmax, title='Using Morlet wavelets and EpochsTFR', show=False) -############################################################################### +# %% # Operating on arrays # ------------------- # diff -Nru python-mne-0.23.4+dfsg/examples/visualization/3d_to_2d.py python-mne-1.1.0+dfsg/examples/visualization/3d_to_2d.py --- python-mne-0.23.4+dfsg/examples/visualization/3d_to_2d.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/3d_to_2d.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,65 +1,66 @@ +# -*- coding: utf-8 -*- """ .. _ex-electrode-pos-2d: -==================================================== -How to convert 3D electrode positions to a 2D image. -==================================================== +=================================================== +How to convert 3D electrode positions to a 2D image +=================================================== Sometimes we want to convert a 3D representation of electrodes into a 2D -image. For example, if we are using electrocorticography it is common to -create scatterplots on top of a brain, with each point representing an -electrode. +image. For example, if we are using electrocorticography it is common to create +scatterplots on top of a brain, with each point representing an electrode. In this example, we'll show two ways of doing this in MNE-Python. First, -if we have the 3D locations of each electrode then we can use Mayavi to +if we have the 3D locations of each electrode then we can use PyVista to take a snapshot of a view of the brain. If we do not have these 3D locations, and only have a 2D image of the electrodes on the brain, we can use the :class:`mne.viz.ClickableImage` class to choose our own electrode positions on the image. """ # Authors: Christopher Holdgraf +# Alex Rockhill # -# License: BSD (3-clause) -from scipy.io import loadmat +# License: BSD-3-Clause + +# %% +from os.path import dirname +from pathlib import Path + import numpy as np from matplotlib import pyplot as plt -from os import path as op import mne -from mne.viz import ClickableImage # noqa -from mne.viz import (plot_alignment, snapshot_brain_montage, - set_3d_view) - - -print(__doc__) - -subjects_dir = mne.datasets.sample.data_path() + '/subjects' -path_data = mne.datasets.misc.data_path() + '/ecog/sample_ecog.mat' +from mne.io.fiff.raw import read_raw_fif +from mne.viz import ClickableImage # noqa: F401 +from mne.viz import plot_alignment, set_3d_view, snapshot_brain_montage + +misc_path = mne.datasets.misc.data_path() +subjects_dir = misc_path / 'ecog' +ecog_data_fname = subjects_dir / 'sample_ecog_ieeg.fif' # We've already clicked and exported -layout_path = op.join(op.dirname(mne.__file__), 'data', 'image') +layout_path = Path(dirname(mne.__file__)) / 'data' / 'image' layout_name = 'custom_layout.lout' -############################################################################### +# %% # Load data # --------- # # First we will load a sample ECoG dataset which we'll use for generating # a 2D snapshot. -mat = loadmat(path_data) -ch_names = mat['ch_names'].tolist() -elec = mat['elec'] # electrode coordinates in meters -# Now we make a montage stating that the sEEG contacts are in head -# coordinate system (although they are in MRI). This is compensated -# by the fact that below we do not specicty a trans file so the Head<->MRI -# transform is the identity. -montage = mne.channels.make_dig_montage(ch_pos=dict(zip(ch_names, elec)), - coord_frame='head') -info = mne.create_info(ch_names, 1000., 'ecog').set_montage(montage) -print('Created %s channel positions' % len(ch_names)) +raw = read_raw_fif(ecog_data_fname) +raw.pick_channels([f'G{i}' for i in range(1, 257)]) # pick just one grid + +# Since we loaded in the ecog data from FIF, the coordinates +# are in 'head' space, but we actually want them in 'mri' space. +# So we will apply the head->mri transform that was used when +# generating the dataset (the estimated head->mri transform). +montage = raw.get_montage() +trans = mne.coreg.estimate_head_mri_t('sample_ecog', subjects_dir) +montage.apply_trans(trans) -############################################################################### +# %% # Project 3D electrodes to a 2D snapshot # -------------------------------------- # @@ -68,25 +69,30 @@ # with the electrode positions on that image. We use this in conjunction with # :func:`mne.viz.plot_alignment`, which visualizes electrode positions. -fig = plot_alignment(info, subject='sample', subjects_dir=subjects_dir, - surfaces=['pial'], meg=False) -set_3d_view(figure=fig, azimuth=200, elevation=70) +fig = plot_alignment(raw.info, trans=trans, subject='sample_ecog', + subjects_dir=subjects_dir, surfaces=dict(pial=0.9)) +set_3d_view(figure=fig, azimuth=20, elevation=80) xy, im = snapshot_brain_montage(fig, montage) # Convert from a dictionary to array to plot -xy_pts = np.vstack([xy[ch] for ch in info['ch_names']]) +xy_pts = np.vstack([xy[ch] for ch in raw.ch_names]) -# Define an arbitrary "activity" pattern for viz -activity = np.linspace(100, 200, xy_pts.shape[0]) +# Compute beta power to visualize +raw.load_data() +beta_power = raw.filter(20, 30).apply_hilbert(envelope=True).get_data() +beta_power = beta_power.max(axis=1) # take maximum over time # This allows us to use matplotlib to create arbitrary 2d scatterplots fig2, ax = plt.subplots(figsize=(10, 10)) ax.imshow(im) -ax.scatter(*xy_pts.T, c=activity, s=200, cmap='coolwarm') +cmap = ax.scatter(*xy_pts.T, c=beta_power, s=100, cmap='coolwarm') +cbar = fig2.colorbar(cmap) +cbar.ax.set_ylabel('Beta Power') ax.set_axis_off() + # fig2.savefig('./brain.png', bbox_inches='tight') # For ClickableImage -############################################################################### +# %% # Manually creating 2D electrode positions # ---------------------------------------- # @@ -113,7 +119,7 @@ # # Generate a layout from our clicks and normalize by the image # print('Generating and saving layout...') # lt = click.to_layout() -# lt.save(op.join(layout_path, layout_name)) # To save if we want +# lt.save(layout_path / layout_name) # save if we want # # We've already got the layout, load it lt = mne.channels.read_layout(layout_name, path=layout_path, scale=False) @@ -121,7 +127,6 @@ y = (1 - lt.pos[:, 1]) * float(im.shape[0]) # Flip the y-position fig, ax = plt.subplots() ax.imshow(im) -ax.scatter(x, y, s=120, color='r') -plt.autoscale(tight=True) +ax.scatter(x, y, s=80, color='r') +fig.tight_layout() ax.set_axis_off() -plt.show() diff -Nru python-mne-0.23.4+dfsg/examples/visualization/brain.py python-mne-1.1.0+dfsg/examples/visualization/brain.py --- python-mne-0.23.4+dfsg/examples/visualization/brain.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/brain.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,131 @@ +# -*- coding: utf-8 -*- +""" +.. _ex-brain: + +=============================== +Plotting with ``mne.viz.Brain`` +=============================== + +In this example, we'll show how to use :class:`mne.viz.Brain`. +""" +# Author: Alex Rockhill +# +# License: BSD-3-Clause + +# %% +# Load data +# --------- +# +# In this example we use the ``sample`` data which is data from a subject +# being presented auditory and visual stimuli to display the functionality +# of :class:`mne.viz.Brain` for plotting data on a brain. + +import matplotlib.pyplot as plt +from matplotlib.cm import ScalarMappable +from matplotlib.colors import Normalize + +import mne +from mne.datasets import sample + +print(__doc__) + +data_path = sample.data_path() +subjects_dir = data_path / 'subjects' +sample_dir = data_path / 'MEG' / 'sample' + +# %% +# Add source information +# ---------------------- +# +# Plot source information. + +brain_kwargs = dict(alpha=0.1, background='white', cortex='low_contrast') +brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, **brain_kwargs) + +stc = mne.read_source_estimate(sample_dir / 'sample_audvis-meg') +stc.crop(0.09, 0.1) + +kwargs = dict(fmin=stc.data.min(), fmax=stc.data.max(), alpha=0.25, + smoothing_steps='nearest', time=stc.times) +brain.add_data(stc.lh_data, hemi='lh', vertices=stc.lh_vertno, **kwargs) +brain.add_data(stc.rh_data, hemi='rh', vertices=stc.rh_vertno, **kwargs) + +# %% +# Modify the view of the brain +# ---------------------------- +# +# You can adjust the view of the brain using ``show_view`` method. + +brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, **brain_kwargs) +brain.show_view(azimuth=190, elevation=70, distance=350, focalpoint=(0, 0, 20)) + +# %% +# Highlight a region on the brain +# ------------------------------- +# +# It can be useful to highlight a region of the brain for analyses. +# To highlight a region on the brain you can use the ``add_label`` method. +# Labels are stored in the Freesurfer label directory from the ``recon-all`` +# for that subject. Labels can also be made following the +# `Freesurfer instructions +# `_ +# Here we will show Brodmann Area 44. +# +# .. note:: The MNE sample dataset contains only a subselection of the +# Freesurfer labels created during the ``recon-all``. + +brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, **brain_kwargs) +brain.add_label('BA44', hemi='lh', color='green', borders=True) +brain.show_view(azimuth=190, elevation=70, distance=350, focalpoint=(0, 0, 20)) + +# %% +# Include the head in the image +# ----------------------------- +# +# Add a head image using the ``add_head`` method. + +brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, **brain_kwargs) +brain.add_head(alpha=0.5) + +# %% +# Add sensors positions +# --------------------- +# +# To put into context the data that generated the source time course, +# the sensor positions can be displayed as well. + +brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, **brain_kwargs) +evoked = mne.read_evokeds(sample_dir / 'sample_audvis-ave.fif')[0] +trans = mne.read_trans(sample_dir / 'sample_audvis_raw-trans.fif') +brain.add_sensors(evoked.info, trans) +brain.show_view(distance=500) # move back to show sensors + +# %% +# Add current dipoles +# ------------------- +# +# Dipole modeling as in :ref:`tut-dipole-orientations` can be plotted on the +# brain as well. + +brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, **brain_kwargs) +dip = mne.read_dipole(sample_dir / 'sample_audvis_set1.dip') +cmap = plt.get_cmap('YlOrRd') +colors = [cmap(gof / dip.gof.max()) for gof in dip.gof] +brain.add_dipole(dip, trans, colors=colors, scales=list(dip.amplitude * 1e8)) +brain.show_view(azimuth=-20, elevation=60, distance=300) +img = brain.screenshot() # for next section + +# %% +# Create a screenshot for exporting the brain image +# ------------------------------------------------- +# Also, we can a static image of the brain using ``screenshot`` (above), +# which will allow us to add a colorbar. This is useful for figures in +# publications. + +fig, ax = plt.subplots() +ax.imshow(img) +ax.axis('off') +cax = fig.add_axes([0.9, 0.1, 0.05, 0.8]) +norm = Normalize(vmin=0, vmax=dip.gof.max()) +fig.colorbar(ScalarMappable(norm=norm, cmap=cmap), cax=cax) +fig.suptitle('Dipole Fits Scaled by Amplitude and Colored by GOF') diff -Nru python-mne-0.23.4+dfsg/examples/visualization/channel_epochs_image.py python-mne-1.1.0+dfsg/examples/visualization/channel_epochs_image.py --- python-mne-0.23.4+dfsg/examples/visualization/channel_epochs_image.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/channel_epochs_image.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-epochs-image: + ========================================= Visualize channel over epochs as an image ========================================= @@ -14,7 +17,9 @@ """ # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np import matplotlib.pyplot as plt @@ -27,10 +32,11 @@ data_path = sample.data_path() -############################################################################### +# %% # Set parameters -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' +event_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif' event_id, tmin, tmax = 1, -0.2, 0.4 # Setup for reading the raw data @@ -45,7 +51,7 @@ picks=('grad', 'eog'), baseline=(None, 0), preload=True, reject=dict(grad=4000e-13, eog=150e-6)) -############################################################################### +# %% # Show event-related fields images # and order with spectral reordering @@ -72,7 +78,7 @@ order=order_func, vmin=-250, vmax=250, overlay_times=plt_times, show=True) -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/visualization/eeglab_head_sphere.py python-mne-1.1.0+dfsg/examples/visualization/eeglab_head_sphere.py --- python-mne-0.23.4+dfsg/examples/visualization/eeglab_head_sphere.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/eeglab_head_sphere.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,122 +0,0 @@ -""" -.. _ex-topomap-eeglab-style: - -======================================== -How to plot topomaps the way EEGLAB does -======================================== - -If you have previous EEGLAB experience you may have noticed that topomaps -(topoplots) generated using MNE-Python look a little different from those -created in EEGLAB. If you prefer the EEGLAB style this example will show you -how to calculate head sphere origin and radius to obtain EEGLAB-like channel -layout in MNE. - -""" -# Authors: Mikołaj Magnuski -# -# License: BSD (3-clause) -import numpy as np -from matplotlib import pyplot as plt - -import mne - - -print(__doc__) - -############################################################################### -# Create fake data -# ---------------- -# -# First we will create a simple evoked object with a single timepoint using -# biosemi 10-20 channel layout. - -biosemi_montage = mne.channels.make_standard_montage('biosemi64') -n_channels = len(biosemi_montage.ch_names) -fake_info = mne.create_info(ch_names=biosemi_montage.ch_names, sfreq=250., - ch_types='eeg') - -rng = np.random.RandomState(0) -data = rng.normal(size=(n_channels, 1)) * 1e-6 -fake_evoked = mne.EvokedArray(data, fake_info) -fake_evoked.set_montage(biosemi_montage) - -############################################################################### -# Calculate sphere origin and radius -# ---------------------------------- -# -# EEGLAB plots head outline at the level where the head circumference is -# measured -# in the 10-20 system (a line going through Fpz, T8/T4, Oz and T7/T3 channels). -# MNE-Python places the head outline lower on the z dimension, at the level of -# the anatomical landmarks :term:`LPA, RPA, and NAS `. -# Therefore to use the EEGLAB layout we -# have to move the origin of the reference sphere (a sphere that is used as a -# reference when projecting channel locations to a 2d plane) a few centimeters -# up. -# -# Instead of approximating this position by eye, as we did in :ref:`the sensor -# locations tutorial `, here we will calculate it using -# the position of Fpz, T8, Oz and T7 channels available in our montage. - -# first we obtain the 3d positions of selected channels -chs = ['Oz', 'Fpz', 'T7', 'T8'] -pos = np.stack([biosemi_montage.get_positions()['ch_pos'][ch] for ch in chs]) - -# now we calculate the radius from T7 and T8 x position -# (we could use Oz and Fpz y positions as well) -radius = np.abs(pos[[2, 3], 0]).mean() - -# then we obtain the x, y, z sphere center this way: -# x: x position of the Oz channel (should be very close to 0) -# y: y position of the T8 channel (should be very close to 0 too) -# z: average z position of Oz, Fpz, T7 and T8 (their z position should be the -# the same, so we could also use just one of these channels), it should be -# positive and somewhere around `0.03` (3 cm) -x = pos[0, 0] -y = pos[-1, 1] -z = pos[:, -1].mean() - -# lets print the values we got: -print([f'{v:0.5f}' for v in [x, y, z, radius]]) - -############################################################################### -# Compare MNE and EEGLAB channel layout -# ------------------------------------- -# -# We already have the required x, y, z sphere center and its radius — we can -# use these values passing them to the ``sphere`` argument of many -# topo-plotting functions (by passing ``sphere=(x, y, z, radius)``). - -# create a two-panel figure with some space for the titles at the top -fig, ax = plt.subplots(ncols=2, figsize=(8, 4), gridspec_kw=dict(top=0.9), - sharex=True, sharey=True) - -# we plot the channel positions with default sphere - the mne way -fake_evoked.plot_sensors(axes=ax[0], show=False) - -# in the second panel we plot the positions using the EEGLAB reference sphere -fake_evoked.plot_sensors(sphere=(x, y, z, radius), axes=ax[1], show=False) - -# add titles -ax[0].set_title('MNE channel projection', fontweight='bold') -ax[1].set_title('EEGLAB channel projection', fontweight='bold') - -############################################################################### -# Topomaps (topoplots) -# -------------------- -# -# As the last step we do the same, but plotting the topomaps. These will not -# be particularly interesting as they will show random data but hopefully you -# will see the difference. - -fig, ax = plt.subplots(ncols=2, figsize=(8, 4), gridspec_kw=dict(top=0.9), - sharex=True, sharey=True) - -mne.viz.plot_topomap(fake_evoked.data[:, 0], fake_evoked.info, axes=ax[0], - show=False) -mne.viz.plot_topomap(fake_evoked.data[:, 0], fake_evoked.info, axes=ax[1], - show=False, sphere=(x, y, z, radius)) - -# add titles -ax[0].set_title('MNE', fontweight='bold') -ax[1].set_title('EEGLAB', fontweight='bold') diff -Nru python-mne-0.23.4+dfsg/examples/visualization/eeg_on_scalp.py python-mne-1.1.0+dfsg/examples/visualization/eeg_on_scalp.py --- python-mne-0.23.4+dfsg/examples/visualization/eeg_on_scalp.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/eeg_on_scalp.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-eeg-on-scalp: @@ -9,7 +10,9 @@ """ # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne from mne.viz import plot_alignment, set_3d_view @@ -17,12 +20,15 @@ print(__doc__) data_path = mne.datasets.sample.data_path() -subjects_dir = data_path + '/subjects' -trans = mne.read_trans(data_path + '/MEG/sample/sample_audvis_raw-trans.fif') -raw = mne.io.read_raw_fif(data_path + '/MEG/sample/sample_audvis_raw.fif') +subjects_dir = data_path / 'subjects' +meg_path = data_path / 'MEG' / 'sample' +trans = mne.read_trans(meg_path / 'sample_audvis_raw-trans.fif') +raw = mne.io.read_raw_fif(meg_path / 'sample_audvis_raw.fif') + # Plot electrode locations on scalp fig = plot_alignment(raw.info, trans, subject='sample', dig=False, eeg=['original', 'projected'], meg=[], coord_frame='head', subjects_dir=subjects_dir) + # Set viewing angle set_3d_view(figure=fig, azimuth=135, elevation=80) diff -Nru python-mne-0.23.4+dfsg/examples/visualization/evoked_arrowmap.py python-mne-1.1.0+dfsg/examples/visualization/evoked_arrowmap.py --- python-mne-0.23.4+dfsg/examples/visualization/evoked_arrowmap.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/evoked_arrowmap.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-arrowmap: + ============================================= Plotting topographic arrowmaps of evoked data ============================================= @@ -17,7 +20,9 @@ # Authors: Sheraz Khan # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np import mne @@ -29,7 +34,7 @@ print(__doc__) path = sample.data_path() -fname = path + '/MEG/sample/sample_audvis-ave.fif' +fname = path / 'MEG' / 'sample' / 'sample_audvis-ave.fif' # load evoked data condition = 'Left Auditory' @@ -37,7 +42,7 @@ evoked_mag = evoked.copy().pick_types(meg='mag') evoked_grad = evoked.copy().pick_types(meg='grad') -############################################################################### +# %% # Plot magnetometer data as an arrowmap along with the topoplot at the time # of the maximum sensor space activity: max_time_idx = np.abs(evoked_mag.data).mean(axis=0).argmax() @@ -50,13 +55,13 @@ # ``info_from`` and ``info_to`` parameters to interpolate from # gradiometer data to magnetometer data. -############################################################################### +# %% # Plot gradiometer data as an arrowmap along with the topoplot at the time # of the maximum sensor space activity: plot_arrowmap(evoked_grad.data[:, max_time_idx], info_from=evoked_grad.info, info_to=evoked_mag.info) -############################################################################### +# %% # Since Vectorview 102 system perform sparse spatial sampling of the magnetic # field, data from the Vectorview (info_from) can be projected to the high # density CTF 272 system (info_to) for visualization @@ -64,8 +69,8 @@ # Plot gradiometer data as an arrowmap along with the topoplot at the time # of the maximum sensor space activity: path = bst_raw.data_path() -raw_fname = path + ('/MEG/bst_raw/' - 'subj001_somatosensory_20111109_01_AUX-f.ds') +raw_fname = (path / 'MEG' / 'bst_raw' / + 'subj001_somatosensory_20111109_01_AUX-f.ds') raw_ctf = mne.io.read_raw_ctf(raw_fname) raw_ctf_info = mne.pick_info( raw_ctf.info, mne.pick_types(raw_ctf.info, meg=True, ref_meg=False)) diff -Nru python-mne-0.23.4+dfsg/examples/visualization/evoked_topomap.py python-mne-1.1.0+dfsg/examples/visualization/evoked_topomap.py --- python-mne-0.23.4+dfsg/examples/visualization/evoked_topomap.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/evoked_topomap.py 2022-08-03 15:48:14.000000000 +0000 @@ -14,8 +14,11 @@ # Denis A. Engeman # Mikołaj Magnuski # Eric Larson +# Alex Rockhill # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% # sphinx_gallery_thumbnail_number = 5 import numpy as np @@ -27,14 +30,14 @@ print(__doc__) path = sample.data_path() -fname = path + '/MEG/sample/sample_audvis-ave.fif' +fname = path / 'MEG' / 'sample' / 'sample_audvis-ave.fif' # load evoked corresponding to a specific condition # from the fif file and subtract baseline condition = 'Left Auditory' evoked = read_evokeds(fname, condition=condition, baseline=(None, 0)) -############################################################################### +# %% # Basic :func:`~mne.viz.plot_topomap` options # ------------------------------------------- # @@ -45,29 +48,29 @@ times = np.arange(0.05, 0.151, 0.02) evoked.plot_topomap(times, ch_type='mag', time_unit='s') -############################################################################### +# %% # If times is set to None at most 10 regularly spaced topographies will be # shown: evoked.plot_topomap(ch_type='mag', time_unit='s') -############################################################################### +# %% # We can use ``nrows`` and ``ncols`` parameter to create multiline plots # with more timepoints. all_times = np.arange(-0.2, 0.5, 0.03) evoked.plot_topomap(all_times, ch_type='mag', time_unit='s', ncols=8, nrows='auto') -############################################################################### +# %% # Instead of showing topographies at specific time points we can compute # averages of 50 ms bins centered on these time points to reduce the noise in # the topographies: evoked.plot_topomap(times, ch_type='mag', average=0.05, time_unit='s') -############################################################################### +# %% # We can plot gradiometer data (plots the RMS for each pair of gradiometers) evoked.plot_topomap(times, ch_type='grad', time_unit='s') -############################################################################### +# %% # Additional :func:`~mne.viz.plot_topomap` options # ------------------------------------------------ # @@ -82,12 +85,12 @@ evoked.plot_topomap(times, ch_type='mag', cmap='Spectral_r', res=32, outlines='skirt', contours=4, time_unit='s') -############################################################################### +# %% # If you look at the edges of the head circle of a single topomap you'll see # the effect of extrapolation. There are three extrapolation modes: # # - ``extrapolate='local'`` extrapolates only to points close to the sensors. -# - ``extrapolate='head'`` extrapolates out to the head head circle. +# - ``extrapolate='head'`` extrapolates out to the head circle. # - ``extrapolate='box'`` extrapolates to a large box stretching beyond the # head circle. # @@ -107,7 +110,7 @@ ax.set_title('%s %s' % (ch_type.upper(), extr), fontsize=14) fig.tight_layout() -############################################################################### +# %% # More advanced usage # ------------------- # @@ -119,7 +122,65 @@ time_unit='s') plt.subplots_adjust(left=0.01, right=0.99, bottom=0.01, top=0.88) -############################################################################### +# %% +# We can also highlight specific channels by adding a mask, to e.g. mark +# channels exceeding a threshold at a given time: + +# Define a threshold and create the mask +mask = evoked.data > 1e-13 + +# Select times and plot +times = (0.09, 0.1, 0.11) +evoked.plot_topomap(times, ch_type='mag', time_unit='s', mask=mask, + mask_params=dict(markersize=10, markerfacecolor='y')) + +# %% +# Or by manually picking the channels to highlight at different times: + +times = (0.09, 0.1, 0.11) +_times = ((np.abs(evoked.times - t)).argmin() for t in times) +significant_channels = [ + ('MEG 0231', 'MEG 1611', 'MEG 1621', 'MEG 1631', 'MEG 1811'), + ('MEG 2411', 'MEG 2421'), + ('MEG 1621')] +_channels = [np.in1d(evoked.ch_names, ch) for ch in significant_channels] + +mask = np.zeros(evoked.data.shape, dtype='bool') +for _chs, _time in zip(_channels, _times): + mask[_chs, _time] = True + +evoked.plot_topomap(times, ch_type='mag', time_unit='s', mask=mask, + mask_params=dict(markersize=10, markerfacecolor='y')) + +# %% +# Interpolating topomaps +# ---------------------- +# We can also look at the effects of interpolating our data. Perhaps, we +# might have data per channel such as the impedance of each electrode that +# we don't want interpolated, or we just want to visualize the data without +# interpolation as a sanity check. We can use ``image_interp='nearest'`` to +# prevent any interpolation or ``image_interp='linear'`` to do a linear +# interpolation instead of the default cubic interpolation. + +# %% +# The default cubic interpolation is the smoothest and is great for +# publications. + +evoked.plot_topomap(times, ch_type='eeg', time_unit='s', image_interp='cubic') + +# %% +# The linear interpolation might be helpful in some cases. + +evoked.plot_topomap(times, ch_type='eeg', time_unit='s', image_interp='linear') + +# %% +# The nearest (Voronoi, no interpolation) interpolation is especially helpful +# for debugging and seeing the values assigned to the topomap unaltered. + +evoked.plot_topomap(times, ch_type='eeg', time_unit='s', + image_interp='nearest', contours=0) + +# %% # Animating the topomap # --------------------- # diff -Nru python-mne-0.23.4+dfsg/examples/visualization/evoked_whitening.py python-mne-1.1.0+dfsg/examples/visualization/evoked_whitening.py --- python-mne-0.23.4+dfsg/examples/visualization/evoked_whitening.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/evoked_whitening.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-evoked-whitening: @@ -19,7 +20,9 @@ # Authors: Alexandre Gramfort # Denis A. Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne @@ -29,15 +32,16 @@ print(__doc__) -############################################################################### +# %% # Set parameters data_path = sample.data_path() -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' +event_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif' raw = io.read_raw_fif(raw_fname, preload=True) -raw.filter(1, 40, n_jobs=1, fir_design='firwin') +raw.filter(1, 40, fir_design='firwin') raw.info['bads'] += ['MEG 2443'] # bads + 1 more events = mne.read_events(event_fname) @@ -51,13 +55,13 @@ # Uncomment next line to use fewer samples and study regularization effects # epochs = epochs[:20] # For your data, use as many samples as you can! -############################################################################### +# %% # Compute covariance using automated regularization method_params = dict(diagonal_fixed=dict(mag=0.01, grad=0.01, eeg=0.01)) noise_covs = compute_covariance(epochs, tmin=None, tmax=0, method='auto', - return_estimators=True, verbose=True, n_jobs=1, + return_estimators=True, n_jobs=None, projs=None, rank=None, - method_params=method_params) + method_params=method_params, verbose=True) # With "return_estimator=True" all estimated covariances sorted # by log-likelihood are returned. @@ -66,14 +70,14 @@ for c in noise_covs: print("%s : %s" % (c['method'], c['loglik'])) -############################################################################### +# %% # Show the evoked data: evoked = epochs.average() evoked.plot(time_unit='s') # plot evoked response -############################################################################### +# %% # We can then show whitening for our various noise covariance estimates. # # Here we should look to see if baseline signals match the diff -Nru python-mne-0.23.4+dfsg/examples/visualization/meg_sensors.py python-mne-1.1.0+dfsg/examples/visualization/meg_sensors.py --- python-mne-0.23.4+dfsg/examples/visualization/meg_sensors.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/meg_sensors.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-plot-meg-sensors: @@ -9,63 +10,68 @@ """ # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause -import os.path as op +# %% + +from pathlib import Path import mne -from mne.io import read_raw_fif, read_raw_ctf, read_raw_bti, read_raw_kit -from mne.io import read_raw_artemis123 from mne.datasets import sample, spm_face, testing +from mne.io import (read_raw_artemis123, read_raw_bti, read_raw_ctf, + read_raw_fif, read_raw_kit) from mne.viz import plot_alignment, set_3d_title print(__doc__) -############################################################################### +root_path = Path(mne.__file__).parent.absolute() + +# %% # Neuromag # -------- kwargs = dict(eeg=False, coord_frame='meg', show_axes=True, verbose=True) -raw = read_raw_fif(sample.data_path() + '/MEG/sample/sample_audvis_raw.fif') +raw = read_raw_fif( + sample.data_path() / 'MEG' / 'sample' / 'sample_audvis_raw.fif') fig = plot_alignment(raw.info, meg=('helmet', 'sensors'), **kwargs) set_3d_title(figure=fig, title='Neuromag') -############################################################################### +# %% # CTF # --- -raw = read_raw_ctf(spm_face.data_path() + - '/MEG/spm/SPM_CTF_MEG_example_faces1_3D.ds') +raw = read_raw_ctf( + spm_face.data_path() / 'MEG' / 'spm' / 'SPM_CTF_MEG_example_faces1_3D.ds') fig = plot_alignment(raw.info, meg=('helmet', 'sensors', 'ref'), **kwargs) set_3d_title(figure=fig, title='CTF 275') -############################################################################### +# %% # BTi # --- -bti_path = op.abspath(op.dirname(mne.__file__)) + '/io/bti/tests/data/' -raw = read_raw_bti(op.join(bti_path, 'test_pdf_linux'), - op.join(bti_path, 'test_config_linux'), - op.join(bti_path, 'test_hs_linux')) +bti_path = root_path / 'io' / 'bti' / 'tests' / 'data' +raw = read_raw_bti(bti_path / 'test_pdf_linux', + bti_path / 'test_config_linux', + bti_path / 'test_hs_linux') fig = plot_alignment(raw.info, meg=('helmet', 'sensors', 'ref'), **kwargs) set_3d_title(figure=fig, title='Magnes 3600wh') -############################################################################### +# %% # KIT # --- -kit_path = op.abspath(op.dirname(mne.__file__)) + '/io/kit/tests/data/' -raw = read_raw_kit(op.join(kit_path, 'test.sqd')) +kit_path = root_path / 'io' / 'kit' / 'tests' / 'data' +raw = read_raw_kit(kit_path / 'test.sqd') fig = plot_alignment(raw.info, meg=('helmet', 'sensors'), **kwargs) set_3d_title(figure=fig, title='KIT') -############################################################################### +# %% # Artemis123 # ---------- -raw = read_raw_artemis123(op.join( - testing.data_path(), 'ARTEMIS123', - 'Artemis_Data_2017-04-14-10h-38m-59s_Phantom_1k_HPI_1s.bin')) +raw = read_raw_artemis123( + testing.data_path() / 'ARTEMIS123' / + 'Artemis_Data_2017-04-14-10h-38m-59s_Phantom_1k_HPI_1s.bin') fig = plot_alignment(raw.info, meg=('helmet', 'sensors', 'ref'), **kwargs) set_3d_title(figure=fig, title='Artemis123') diff -Nru python-mne-0.23.4+dfsg/examples/visualization/mne_helmet.py python-mne-1.1.0+dfsg/examples/visualization/mne_helmet.py --- python-mne-0.23.4+dfsg/examples/visualization/mne_helmet.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/mne_helmet.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,20 +1,24 @@ +# -*- coding: utf-8 -*- """ +.. _ex-mne-helmet: + +============================= Plot the MNE brain and helmet ============================= This tutorial shows how to make the MNE helmet + brain image. """ -import os.path as op +# %% + import mne sample_path = mne.datasets.sample.data_path() -subjects_dir = op.join(sample_path, 'subjects') -fname_evoked = op.join(sample_path, 'MEG', 'sample', 'sample_audvis-ave.fif') -fname_inv = op.join(sample_path, 'MEG', 'sample', - 'sample_audvis-meg-oct-6-meg-inv.fif') -fname_trans = op.join(sample_path, 'MEG', 'sample', - 'sample_audvis_raw-trans.fif') +subjects_dir = sample_path / 'subjects' +fname_evoked = sample_path / 'MEG' / 'sample' / 'sample_audvis-ave.fif' +fname_inv = (sample_path / 'MEG' / 'sample' / + 'sample_audvis-meg-oct-6-meg-inv.fif') +fname_trans = sample_path / 'MEG' / 'sample' / 'sample_audvis_raw-trans.fif' inv = mne.minimum_norm.read_inverse_operator(fname_inv) evoked = mne.read_evokeds(fname_evoked, baseline=(None, 0), proj=True, verbose=False, condition='Left Auditory') diff -Nru python-mne-0.23.4+dfsg/examples/visualization/montage_sgskip.py python-mne-1.1.0+dfsg/examples/visualization/montage_sgskip.py --- python-mne-0.23.4+dfsg/examples/visualization/montage_sgskip.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/montage_sgskip.py 2022-08-03 15:48:14.000000000 +0000 @@ -11,9 +11,12 @@ # Authors: Alexandre Gramfort # Joan Massich # -# License: BSD Style. +# License: BSD-3-Clause + +# %% import os.path as op +import numpy as np import mne from mne.channels.montage import get_builtin_montages @@ -21,7 +24,7 @@ from mne.viz import set_3d_title, set_3d_view -############################################################################### +# %% # Check all montages against a sphere for current_montage in get_builtin_montages(): @@ -33,12 +36,13 @@ fig = mne.viz.plot_alignment( # Plot options show_axes=True, dig='fiducials', surfaces='head', + trans=mne.Transform("head", "mri", trans=np.eye(4)), # identity bem=sphere, info=info) set_3d_view(figure=fig, azimuth=135, elevation=80) set_3d_title(figure=fig, title=current_montage) -############################################################################### +# %% # Check all montages against fsaverage subjects_dir = op.dirname(fetch_fsaverage()) diff -Nru python-mne-0.23.4+dfsg/examples/visualization/parcellation.py python-mne-1.1.0+dfsg/examples/visualization/parcellation.py --- python-mne-0.23.4+dfsg/examples/visualization/parcellation.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/parcellation.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,5 +1,7 @@ # -*- coding: utf-8 -*- """ +.. _ex-parcellation: + ============================ Plot a cortical parcellation ============================ @@ -20,12 +22,14 @@ # Author: Eric Larson # Denis Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne Brain = mne.viz.get_brain_class() -subjects_dir = mne.datasets.sample.data_path() + '/subjects' +subjects_dir = mne.datasets.sample.data_path() / 'subjects' mne.datasets.fetch_hcp_mmp_parcellation(subjects_dir=subjects_dir, verbose=True) @@ -41,21 +45,21 @@ aud_label = [label for label in labels if label.name == 'L_A1_ROI-lh'][0] brain.add_label(aud_label, borders=False) -############################################################################### +# %% # We can also plot a combined set of labels (23 per hemisphere). brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir, cortex='low_contrast', background='white', size=(800, 600)) brain.add_annotation('HCPMMP1_combined') -############################################################################### +# %% # We can add another custom parcellation brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir, cortex='low_contrast', background='white', size=(800, 600)) brain.add_annotation('aparc_sub') -############################################################################### +# %% # References # ---------- # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/examples/visualization/publication_figure.py python-mne-1.1.0+dfsg/examples/visualization/publication_figure.py --- python-mne-0.23.4+dfsg/examples/visualization/publication_figure.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/publication_figure.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ .. _ex-publication-figure: @@ -13,9 +14,9 @@ # Daniel McCloy # Stefan Appelhoff # -# License: BSD (3-clause) +# License: BSD-3-Clause -############################################################################### +# %% # Imports # ------- # We are importing everything we need for this example: @@ -29,7 +30,7 @@ import mne -############################################################################### +# %% # Evoked plot with brain activation # --------------------------------- # @@ -48,7 +49,7 @@ stc = mne.read_source_estimate(fname_stc) -############################################################################### +# %% # During interactive plotting, we might see figures like this: evoked.plot() @@ -57,7 +58,7 @@ subjects_dir=subjects_dir, initial_time=max_t, time_viewer=False, show_traces=False) -############################################################################### +# %% # To make a publication-ready figure, first we'll re-plot the brain on a white # background, take a screenshot of it, and then crop out the white margins. # While we're at it, let's change the colormap, set custom colormap limits and @@ -74,7 +75,7 @@ screenshot = brain.screenshot() brain.close() -############################################################################### +# %% # Now let's crop out the white margins and the white gap between hemispheres. # The screenshot has dimensions ``(h, w, 3)``, with the last axis being R, G, B # values for each pixel, encoded as integers between ``0`` and ``255``. ``(255, @@ -94,7 +95,7 @@ ax.imshow(image) ax.set_title('{} cropping'.format(title)) -############################################################################### +# %% # A lot of figure settings can be adjusted after the figure is created, but # many can also be adjusted in advance by updating the # :data:`~matplotlib.rcParams` dictionary. This is especially useful when your @@ -110,7 +111,7 @@ 'grid.linestyle': ':', }) -############################################################################### +# %% # Now let's create our custom figure. There are lots of ways to do this step. # Here we'll create the figure and the subplot axes in one step, specifying # overall figure size, number and arrangement of subplots, and the ratio of @@ -147,7 +148,8 @@ frameon=True, columnspacing=0.1, labelspacing=0.1, fontsize=8, fancybox=True, handlelength=1.8) # remove the "N_ave" annotation -axes[evoked_idx].texts.clear() +for text in list(axes[evoked_idx].texts): + text.remove() # Remove spines and add grid axes[evoked_idx].grid(True) axes[evoked_idx].set_axisbelow(True) @@ -176,7 +178,7 @@ ax.text(0.03, ax.get_position().ymax, label, transform=fig.transFigure, fontsize=12, fontweight='bold', va='top', ha='left') -############################################################################### +# %% # Custom timecourse with montage inset # ------------------------------------ # @@ -200,7 +202,7 @@ raw.pick_types(meg=False, eeg=True) -############################################################################### +# %% # Let's make a plot. # channels to plot: @@ -221,7 +223,7 @@ # plot some EEG data ax.plot(times, mean) -############################################################################### +# %% # So far so good. Now let's add the smaller figure within the figure to show # exactly, which sensors we used to make the timecourse. # For that, we use an "inset_axes" that we plot into our existing axes. @@ -239,7 +241,7 @@ # so that our raw object stays intact for potential later analysis raw.copy().pick_channels(to_plot).plot_sensors(title="", axes=axins) -############################################################################### +# %% # That looks nice. But the sensor dots are way too big for our taste. Luckily, # all MNE-Python plots use Matplotlib under the hood and we can customize # each and every facet of them. @@ -249,13 +251,13 @@ # If we inspect our axes we find the objects contained in our plot: print(axins.get_children()) -############################################################################### +# %% # That's quite a a lot of objects, but we know that we want to change the # sensor dots, and those are most certainly a "PathCollection" object. # So let's have a look at how many "collections" we have in the axes. print(axins.collections) -############################################################################### +# %% # There is only one! Those must be the sensor dots we were looking for. # We finally found exactly what we needed. Sometimes this can take a bit of # experimentation. diff -Nru python-mne-0.23.4+dfsg/examples/visualization/roi_erpimage_by_rt.py python-mne-1.1.0+dfsg/examples/visualization/roi_erpimage_by_rt.py --- python-mne-0.23.4+dfsg/examples/visualization/roi_erpimage_by_rt.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/roi_erpimage_by_rt.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _roi-erp: + =========================================================== Plot single trial activity, grouped by ROI and sorted by RT =========================================================== @@ -16,7 +19,9 @@ """ # Authors: Jona Sassenhagen # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne from mne.event import define_target_events @@ -24,10 +29,10 @@ print(__doc__) -############################################################################### +# %% # Load EEGLAB example data (a small EEG dataset) data_path = mne.datasets.testing.data_path() -fname = data_path + "/EEGLAB/test_raw.set" +fname = data_path / 'EEGLAB' / 'test_raw.set' event_id = {"rt": 1, "square": 2} # must be specified for str events raw = mne.io.read_raw_eeglab(fname) @@ -48,7 +53,7 @@ events = mne.events_from_annotations(raw, event_id)[0] -############################################################################### +# %% # Create Epochs # define target events: @@ -63,7 +68,7 @@ epochs = mne.Epochs(raw, events=new_events, tmax=tmax + 0.1, event_id={"square": 2}) -############################################################################### +# %% # Plot using :term:`global field power` # Parameters for plotting @@ -76,7 +81,7 @@ overlay_times=rts / 1000., combine='gfp', ts_args=dict(vlines=[0, rts.mean() / 1000.])) -############################################################################### +# %% # Plot using median epochs.plot_image(group_by=selections, order=order, sigma=1.5, diff -Nru python-mne-0.23.4+dfsg/examples/visualization/sensor_noise_level.py python-mne-1.1.0+dfsg/examples/visualization/sensor_noise_level.py --- python-mne-0.23.4+dfsg/examples/visualization/sensor_noise_level.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/sensor_noise_level.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,5 +1,7 @@ # -*- coding: utf-8 -*- """ +.. _ex-noise-level: + ====================================== Show noise levels from empty room data ====================================== @@ -9,7 +11,9 @@ """ # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import os.path as op import mne @@ -19,11 +23,11 @@ raw_erm = mne.io.read_raw_fif(op.join(data_path, 'MEG', 'sample', 'ernoise_raw.fif'), preload=True) -############################################################################### +# %% # We can plot the absolute noise levels: raw_erm.plot_psd(tmax=10., average=True, spatial_colors=False, dB=False, xscale='log') -############################################################################### +# %% # References # ---------- # diff -Nru python-mne-0.23.4+dfsg/examples/visualization/ssp_projs_sensitivity_map.py python-mne-1.1.0+dfsg/examples/visualization/ssp_projs_sensitivity_map.py --- python-mne-0.23.4+dfsg/examples/visualization/ssp_projs_sensitivity_map.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/ssp_projs_sensitivity_map.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-ssp-proj: + ================================== Sensitivity map of SSP projections ================================== @@ -8,7 +11,9 @@ """ # Author: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import matplotlib.pyplot as plt @@ -20,9 +25,10 @@ data_path = sample.data_path() -subjects_dir = data_path + '/subjects' -fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' -ecg_fname = data_path + '/MEG/sample/sample_audvis_ecg-proj.fif' +subjects_dir = data_path / 'subjects' +meg_path = data_path / 'MEG' / 'sample' +fname = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif' +ecg_fname = meg_path / 'sample_audvis_ecg-proj.fif' fwd = read_forward_solution(fname) @@ -33,7 +39,7 @@ # Compute sensitivity map ssp_ecg_map = sensitivity_map(fwd, ch_type='grad', projs=projs, mode='angle') -############################################################################### +# %% # Show sensitivity map plt.hist(ssp_ecg_map.data.ravel()) diff -Nru python-mne-0.23.4+dfsg/examples/visualization/topo_compare_conditions.py python-mne-1.1.0+dfsg/examples/visualization/topo_compare_conditions.py --- python-mne-0.23.4+dfsg/examples/visualization/topo_compare_conditions.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/topo_compare_conditions.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-topo-compare: + ================================================= Compare evoked responses for different conditions ================================================= @@ -12,7 +15,9 @@ # Authors: Denis Engemann # Alexandre Gramfort -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import matplotlib.pyplot as plt @@ -25,10 +30,11 @@ data_path = sample.data_path() -############################################################################### +# %% # Set parameters -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' -event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' +event_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif' tmin = -0.2 tmax = 0.5 @@ -48,7 +54,7 @@ # Generate list of evoked objects from conditions names evokeds = [epochs[name].average() for name in ('left', 'right')] -############################################################################### +# %% # Show topography for two different conditions colors = 'blue', 'red' diff -Nru python-mne-0.23.4+dfsg/examples/visualization/topo_customized.py python-mne-1.1.0+dfsg/examples/visualization/topo_customized.py --- python-mne-0.23.4+dfsg/examples/visualization/topo_customized.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/topo_customized.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ +# -*- coding: utf-8 -*- """ +.. _ex-topo-custom: + ======================================== Plot custom topographies for MEG sensors ======================================== @@ -12,7 +15,9 @@ # Author: Denis A. Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import numpy as np @@ -27,7 +32,8 @@ print(__doc__) data_path = sample.data_path() -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' +meg_path = data_path / 'MEG' / 'sample' +raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif' raw = io.read_raw_fif(raw_fname, preload=True) raw.filter(1, 20, fir_design='firwin') diff -Nru python-mne-0.23.4+dfsg/examples/visualization/xhemi.py python-mne-1.1.0+dfsg/examples/visualization/xhemi.py --- python-mne-0.23.4+dfsg/examples/visualization/xhemi.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/examples/visualization/xhemi.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,5 +1,7 @@ # -*- coding: utf-8 -*- """ +.. _ex-xhemi: + =========================== Cross-hemisphere comparison =========================== @@ -11,13 +13,15 @@ """ # Author: Christian Brodbeck # -# License: BSD (3-clause) +# License: BSD-3-Clause + +# %% import mne data_dir = mne.datasets.sample.data_path() -subjects_dir = data_dir + '/subjects' -stc_path = data_dir + '/MEG/sample/sample_audvis-meg-eeg' +subjects_dir = data_dir / 'subjects' +stc_path = data_dir / 'MEG' / 'sample' / 'sample_audvis-meg-eeg' stc = mne.read_source_estimate(stc_path, 'sample') # First, morph the data to fsaverage_sym, for which we have left_right diff -Nru python-mne-0.23.4+dfsg/.github/CODEOWNERS python-mne-1.1.0+dfsg/.github/CODEOWNERS --- python-mne-0.23.4+dfsg/.github/CODEOWNERS 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/.github/CODEOWNERS 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,22 @@ +# The purpose of this file is to trigger review requests when PRs touch +# particular files. Those reviews are not mandatory, however it's often useful +# to have an expert pinged who is interested in only one part of MNE and +# doesn't follow general development. Having your name in this file does not +# imply ownership of the code -- the CODEOWNERS filename is a misnomer -- but +# rather just serves to indicate that you want to be pinged for potential +# review. +# +# Note that only GitHub handles (whether individuals or teams) with commit +# rights should be added to this file. +# See https://docs.github.com/en/github/creating-cloning-and-archiving-repositories/about-code-owners +# for more details about how CODEOWNERS works. +# This file was adapted from SciPy. + +# Each line is a file pattern followed by one or more owners. + +# IO +/mne/io/brainvision @sappelhoff +/mne/export @sappelhoff + +# Beamforming +/mne/beamformer/ @britta-wstnr diff -Nru python-mne-0.23.4+dfsg/.github/CONTRIBUTING.md python-mne-1.1.0+dfsg/.github/CONTRIBUTING.md --- python-mne-0.23.4+dfsg/.github/CONTRIBUTING.md 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/.github/CONTRIBUTING.md 2022-08-03 15:48:14.000000000 +0000 @@ -2,11 +2,11 @@ First off, thanks for taking the time to contribute! -The following is a quick summary to a set of guidelines for contributing to [MNE-Python](https://github.com/mne-python/mne-python) on GitHub. These are mostly guidelines, not rules. Use your best judgment, and feel free to propose changes to this document in a pull request. +The following is a quick summary to a set of guidelines for contributing to [MNE-Python](https://github.com/mne-tools/mne-python) on GitHub. These are mostly guidelines, not rules. Use your best judgment, and feel free to propose changes to this document in a pull request. ## Code of Conduct -This project and everyone participating in it is governed by the [MNE-Python's Code of Conduct](CODE_OF_CONDUCT.md). By participating, you are expected to uphold this code. Please report unacceptable behavior to [mne-conduct@googlegroups.com](mailto:mne-conduct@googlegroups.com). +This project and everyone participating in it is governed by the [MNE-Python's Code of Conduct](https://github.com/mne-tools/.github/blob/main/CODE_OF_CONDUCT.md). By participating, you are expected to uphold this code. Please report unacceptable behavior to [mne-conduct@googlegroups.com](mailto:mne-conduct@googlegroups.com). ## How to contribute diff -Nru python-mne-0.23.4+dfsg/.github/dependabot.yml python-mne-1.1.0+dfsg/.github/dependabot.yml --- python-mne-0.23.4+dfsg/.github/dependabot.yml 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/.github/dependabot.yml 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,6 @@ +version: 2 +updates: + - package-ecosystem: "github-actions" + directory: "/" + schedule: + interval: "weekly" diff -Nru python-mne-0.23.4+dfsg/.github/ISSUE_TEMPLATE/config.yml python-mne-1.1.0+dfsg/.github/ISSUE_TEMPLATE/config.yml --- python-mne-0.23.4+dfsg/.github/ISSUE_TEMPLATE/config.yml 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/.github/ISSUE_TEMPLATE/config.yml 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,4 @@ -blank_issues_enabled: false +blank_issues_enabled: true contact_links: - name: Forum url: https://mne.discourse.group diff -Nru python-mne-0.23.4+dfsg/.github/ISSUE_TEMPLATE/question.md python-mne-1.1.0+dfsg/.github/ISSUE_TEMPLATE/question.md --- python-mne-0.23.4+dfsg/.github/ISSUE_TEMPLATE/question.md 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/.github/ISSUE_TEMPLATE/question.md 1970-01-01 00:00:00.000000000 +0000 @@ -1,15 +0,0 @@ ---- -name: Question/Support/Other -about: If you have a usage question... -title: '' -labels: USAGE -assignees: '' -# This template is based on the matplotlib template -# https://github.com/matplotlib/matplotlib/blob/d9aee8eb2bd989d6cfbe21c31ff086dab935bde5/.github/ISSUE_TEMPLATE/questions.md ---- - -If your issue is a usage question, you should ask for help on the -[MNE Forum](https://mne.discourse.group). - -Our GitHub issue tracker is only used to report bugs and suggest improvements -to MNE-Python. For any other questions, please use the forum. diff -Nru python-mne-0.23.4+dfsg/.github/workflows/circle_artifacts.yml python-mne-1.1.0+dfsg/.github/workflows/circle_artifacts.yml --- python-mne-0.23.4+dfsg/.github/workflows/circle_artifacts.yml 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/.github/workflows/circle_artifacts.yml 2022-08-03 15:48:14.000000000 +0000 @@ -9,4 +9,5 @@ with: repo-token: ${{ secrets.GITHUB_TOKEN }} artifact-path: 0/dev/index.html - circleci-jobs: build_docs + circleci-jobs: build_docs,build_docs_main + job-title: Check the rendered docs here! diff -Nru python-mne-0.23.4+dfsg/.github/workflows/codespell_and_flake.yml python-mne-1.1.0+dfsg/.github/workflows/codespell_and_flake.yml --- python-mne-0.23.4+dfsg/.github/workflows/codespell_and_flake.yml 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/.github/workflows/codespell_and_flake.yml 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,10 @@ name: 'codespell_and_flake' +# https://docs.github.com/en/actions/reference/workflow-syntax-for-github-actions#concurrency +# https://docs.github.com/en/developers/webhooks-and-events/events/github-event-types#pullrequestevent +# workflow name, PR number (empty on push), push ref (empty on PR) +concurrency: + group: ${{ github.workflow }}-${{ github.event.number }}-${{ github.event.ref }} + cancel-in-progress: true on: push: branches: @@ -9,16 +15,16 @@ jobs: style: - timeout-minutes: 90 + name: 'codespell and flake' runs-on: ubuntu-20.04 env: CODESPELL_DIRS: 'mne/ doc/ tutorials/ examples/' - CODESPELL_SKIPS: 'doc/auto_*,*.fif,*.eve,*.gz,*.tgz,*.zip,*.mat,*.stc,*.label,*.w,*.bz2,*.annot,*.sulc,*.log,*.local-copy,*.orig_avg,*.inflated_avg,*.gii,*.pyc,*.doctree,*.pickle,*.inv,*.png,*.edf,*.touch,*.thickness,*.nofix,*.volume,*.defect_borders,*.mgh,lh.*,rh.*,COR-*,FreeSurferColorLUT.txt,*.examples,.xdebug_mris_calc,bad.segments,BadChannels,*.hist,empty_file,*.orig,*.js,*.map,*.ipynb,searchindex.dat,install_mne_c.rst,plot_*.rst,*.rst.txt,c_EULA.rst*,*.html,gdf_encodes.txt,*.svg,*.bib,' + CODESPELL_SKIPS: 'doc/auto_*,*.fif,*.eve,*.gz,*.tgz,*.zip,*.mat,*.stc,*.label,*.w,*.bz2,*.annot,*.sulc,*.log,*.local-copy,*.orig_avg,*.inflated_avg,*.gii,*.pyc,*.doctree,*.pickle,*.inv,*.png,*.edf,*.touch,*.thickness,*.nofix,*.volume,*.defect_borders,*.mgh,lh.*,rh.*,COR-*,FreeSurferColorLUT.txt,*.examples,.xdebug_mris_calc,bad.segments,BadChannels,*.hist,empty_file,*.orig,*.js,*.map,*.ipynb,searchindex.dat,install_mne_c.rst,plot_*.rst,*.rst.txt,c_EULA.rst*,*.html,gdf_encodes.txt,*.svg,*.bib,*.css' steps: - - uses: actions/checkout@v2 - - uses: actions/setup-python@v2 + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 with: - python-version: '3.8' + python-version: '3.9' architecture: 'x64' - run: | python -m pip install --upgrade pip setuptools wheel diff -Nru python-mne-0.23.4+dfsg/.github/workflows/compat_minimal.yml python-mne-1.1.0+dfsg/.github/workflows/compat_minimal.yml --- python-mne-0.23.4+dfsg/.github/workflows/compat_minimal.yml 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/.github/workflows/compat_minimal.yml 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ name: 'compat / minimal' +concurrency: + group: ${{ github.workflow }}-${{ github.event.number }}-${{ github.event.ref }} + cancel-in-progress: true on: push: branches: @@ -8,26 +11,9 @@ - '*' jobs: - check_skip: - runs-on: ubuntu-20.04 - outputs: - skip: ${{ steps.result_step.outputs.ci-skip }} - steps: - - uses: actions/checkout@v2 - with: - fetch-depth: 0 - - id: result_step - uses: mstachniuk/ci-skip@master - with: - commit-filter: '[skip ci];[ci skip];[skip github]' - commit-filter-separator: ';' - # Minimal (runs with and without testing data) job: - timeout-minutes: 90 - needs: check_skip - if: ${{ needs.check_skip.outputs.skip == 'false' }} - name: 'py3.7' + name: 'minimal 3.7' runs-on: ubuntu-20.04 defaults: run: @@ -40,12 +26,11 @@ MNE_FORCE_SERIAL: true MNE_LOGGING_LEVEL: 'warning' MNE_SKIP_NETWORK_TEST: 1 - MNE_SKIP_TESTING_DATASET_TESTS: true OPENBLAS_NUM_THREADS: '1' PYTHONUNBUFFERED: '1' PYTHON_VERSION: '3.7' steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 with: fetch-depth: 0 - run: ./tools/setup_xvfb.sh @@ -68,7 +53,10 @@ - shell: bash -el {0} run: ./tools/get_testing_version.sh name: 'Get testing version' - - uses: actions/cache@v2 + - shell: bash -el {0} + run: MNE_SKIP_TESTING_DATASET_TESTS=true pytest -m "not (ultraslowtest or pgtest)" --tb=short --cov=mne --cov-report xml -vv -rfE mne/ + name: Run tests with no testing data + - uses: actions/cache@v3 with: key: ${{ env.TESTING_VERSION }} path: ~/mne_data @@ -82,6 +70,6 @@ - shell: bash -el {0} run: ./tools/github_actions_test.sh name: 'Run tests' - - uses: codecov/codecov-action@v1 + - uses: codecov/codecov-action@v3 if: success() name: 'Upload coverage to CodeCov' diff -Nru python-mne-0.23.4+dfsg/.github/workflows/compat_old.yml python-mne-1.1.0+dfsg/.github/workflows/compat_old.yml --- python-mne-0.23.4+dfsg/.github/workflows/compat_old.yml 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/.github/workflows/compat_old.yml 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ name: 'compat / old' +concurrency: + group: ${{ github.workflow }}-${{ github.event.number }}-${{ github.event.ref }} + cancel-in-progress: true on: push: branches: @@ -8,39 +11,22 @@ - '*' jobs: - check_skip: - runs-on: ubuntu-20.04 - outputs: - skip: ${{ steps.result_step.outputs.ci-skip }} - steps: - - uses: actions/checkout@v2 - with: - fetch-depth: 0 - - id: result_step - uses: mstachniuk/ci-skip@master - with: - commit-filter: '[skip ci];[ci skip];[skip github]' - commit-filter-separator: ';' - - # Old dependencies job: - timeout-minutes: 90 - needs: check_skip - if: ${{ needs.check_skip.outputs.skip == 'false' }} - name: 'py3.6' + name: 'old 3.7' runs-on: ubuntu-18.04 defaults: run: shell: bash env: - CONDA_DEPENDENCIES: 'numpy=1.15.4 scipy=1.1 matplotlib=3.0.3 pandas=0.23.4 scikit-learn=0.20.2' + CONDA_DEPENDENCIES: 'numpy=1.18 scipy=1.4 matplotlib=3.1 pandas=1.0 scikit-learn=0.22' DISPLAY: ':99.0' MNE_LOGGING_LEVEL: 'warning' OPENBLAS_NUM_THREADS: '1' PYTHONUNBUFFERED: '1' - PYTHON_VERSION: '3.6' + PYTHON_VERSION: '3.7' + MNE_IGNORE_WARNINGS_IN_TESTS: 'true' steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - run: ./tools/setup_xvfb.sh name: 'Setup xvfb' - uses: conda-incubator/setup-miniconda@v2 @@ -61,7 +47,7 @@ - shell: bash -el {0} run: ./tools/get_testing_version.sh name: 'Get testing version' - - uses: actions/cache@v2 + - uses: actions/cache@v3 with: key: ${{ env.TESTING_VERSION }} path: ~/mne_data @@ -75,6 +61,6 @@ - shell: bash -el {0} run: ./tools/github_actions_test.sh name: 'Run tests' - - uses: codecov/codecov-action@v1 + - uses: codecov/codecov-action@v3 if: success() name: 'Upload coverage to CodeCov' diff -Nru python-mne-0.23.4+dfsg/.github/workflows/linux_conda.yml python-mne-1.1.0+dfsg/.github/workflows/linux_conda.yml --- python-mne-0.23.4+dfsg/.github/workflows/linux_conda.yml 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/.github/workflows/linux_conda.yml 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ name: 'linux / conda' +concurrency: + group: ${{ github.workflow }}-${{ github.event.number }}-${{ github.event.ref }} + cancel-in-progress: true on: push: branches: @@ -8,27 +11,9 @@ - '*' jobs: - check_skip: - runs-on: ubuntu-20.04 - outputs: - skip: ${{ steps.result_step.outputs.ci-skip }} - steps: - - uses: actions/checkout@v2 - with: - fetch-depth: 0 - - id: result_step - uses: mstachniuk/ci-skip@master - with: - commit-filter: '[skip ci];[ci skip];[skip github]' - commit-filter-separator: ';' - - # Linux - job: - timeout-minutes: 90 - needs: check_skip - if: ${{ needs.check_skip.outputs.skip == 'false' }} - name: 'py3.8' + py310: runs-on: ubuntu-20.04 + name: 'linux conda 3.10' defaults: run: shell: bash @@ -38,9 +23,9 @@ MNE_LOGGING_LEVEL: 'warning' MKL_NUM_THREADS: '1' PYTHONUNBUFFERED: '1' - PYTHON_VERSION: '3.8' + PYTHON_VERSION: '3.10' steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - run: ./tools/setup_xvfb.sh name: 'Setup xvfb' - uses: conda-incubator/setup-miniconda@v2 @@ -61,12 +46,16 @@ run: ./tools/github_actions_install.sh name: 'Install MNE' - shell: bash -el {0} + run: | + QT_QPA_PLATFORM=xcb LIBGL_DEBUG=verbose LD_DEBUG=libs python -c "import pyvistaqt; pyvistaqt.BackgroundPlotter(show=True)" + name: 'Check Qt GL' + - shell: bash -el {0} run: ./tools/github_actions_infos.sh name: 'Show infos' - shell: bash -el {0} run: ./tools/get_testing_version.sh name: 'Get testing version' - - uses: actions/cache@v2 + - uses: actions/cache@v3 with: key: ${{ env.TESTING_VERSION }} path: ~/mne_data @@ -80,6 +69,49 @@ - shell: bash -el {0} run: ./tools/github_actions_test.sh name: 'Run tests' - - uses: codecov/codecov-action@v1 + - uses: codecov/codecov-action@v3 + if: success() + name: 'Upload coverage to CodeCov' + + notebook: + timeout-minutes: 90 + runs-on: ubuntu-20.04 + defaults: + run: + shell: bash + env: + CONDA_ENV: 'server_environment.yml' + PYTHON_VERSION: '3.8' + steps: + - uses: actions/checkout@v3 + - uses: conda-incubator/setup-miniconda@v2 + with: + activate-environment: 'mne' + python-version: ${{ env.PYTHON_VERSION }} + environment-file: ${{ env.CONDA_ENV }} + name: 'Setup conda' + - shell: bash -el {0} + run: | + pip uninstall -yq mne + pip install --progress-bar off -ve .[test] + name: 'Install dependencies' + - shell: bash -el {0} + run: ./tools/github_actions_infos.sh + name: 'Show infos' + - shell: bash -el {0} + run: ./tools/get_testing_version.sh + name: 'Get testing version' + - uses: actions/cache@v3 + with: + key: ${{ env.TESTING_VERSION }} + path: ~/mne_data + name: 'Cache testing data' + - shell: bash -el {0} + run: ./tools/github_actions_download.sh + name: 'Download testing data' + - shell: bash -el {0} + run: pytest --tb=short -m "not pgtest" --cov=mne --cov-report=xml --cov-report=html -vv mne/viz + name: 'Run viz tests' + - uses: codecov/codecov-action@v3 if: success() name: 'Upload coverage to CodeCov' diff -Nru python-mne-0.23.4+dfsg/.github/workflows/linux_pip.yml python-mne-1.1.0+dfsg/.github/workflows/linux_pip.yml --- python-mne-0.23.4+dfsg/.github/workflows/linux_pip.yml 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/.github/workflows/linux_pip.yml 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ name: 'linux / pip-pre' +concurrency: + group: ${{ github.workflow }}-${{ github.event.number }}-${{ github.event.ref }} + cancel-in-progress: true on: push: branches: @@ -7,27 +10,13 @@ branches: - '*' -jobs: - check_skip: - runs-on: ubuntu-20.04 - outputs: - skip: ${{ steps.result_step.outputs.ci-skip }} - steps: - - uses: actions/checkout@v2 - with: - fetch-depth: 0 - - id: result_step - uses: mstachniuk/ci-skip@master - with: - commit-filter: '[skip ci];[ci skip];[skip github]' - commit-filter-separator: ';' +permissions: + contents: read +jobs: # PIP-pre + non-default stim channel + log level info job: - timeout-minutes: 90 - needs: check_skip - if: ${{ needs.check_skip.outputs.skip == 'false' }} - name: 'py3.9' + name: 'linux pip 3.10' runs-on: ubuntu-20.04 defaults: run: @@ -38,12 +27,12 @@ MNE_STIM_CHANNEL: 'STI101' OPENBLAS_NUM_THREADS: '1' PYTHONUNBUFFERED: '1' - PYTHON_VERSION: '3.9' + PYTHON_VERSION: '3.10' steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - run: ./tools/setup_xvfb.sh name: 'Setup xvfb' - - uses: actions/setup-python@v2 + - uses: actions/setup-python@v4 with: python-version: ${{ env.PYTHON_VERSION }} name: 'Setup python' @@ -61,10 +50,13 @@ - shell: bash -el {0} run: ./tools/github_actions_infos.sh name: 'Show infos' + # Uncomment if "xcb not found" Qt errors/segfaults come up again + # - shell: bash -el {0} + # run: LD_DEBUG=libs python -c "from PyQt6.QtWidgets import QApplication, QWidget; app = QApplication([]); import matplotlib; matplotlib.use('QtAgg'); import matplotlib.pyplot as plt; plt.figure()" - shell: bash -el {0} run: ./tools/get_testing_version.sh name: 'Get testing version' - - uses: actions/cache@v2 + - uses: actions/cache@v3 with: key: ${{ env.TESTING_VERSION }} path: ~/mne_data @@ -78,6 +70,6 @@ - shell: bash -el {0} run: ./tools/github_actions_test.sh name: 'Run tests' - - uses: codecov/codecov-action@v1 + - uses: codecov/codecov-action@v3 if: success() name: 'Upload coverage to CodeCov' diff -Nru python-mne-0.23.4+dfsg/.github/workflows/macos_conda.yml python-mne-1.1.0+dfsg/.github/workflows/macos_conda.yml --- python-mne-0.23.4+dfsg/.github/workflows/macos_conda.yml 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/.github/workflows/macos_conda.yml 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,7 @@ name: 'macos / conda' +concurrency: + group: ${{ github.workflow }}-${{ github.event.number }}-${{ github.event.ref }} + cancel-in-progress: true on: push: branches: @@ -8,25 +11,8 @@ - '*' jobs: - check_skip: - runs-on: ubuntu-20.04 - outputs: - skip: ${{ steps.result_step.outputs.ci-skip }} - steps: - - uses: actions/checkout@v2 - with: - fetch-depth: 0 - - id: result_step - uses: mstachniuk/ci-skip@master - with: - commit-filter: '[skip ci];[ci skip];[skip github]' - commit-filter-separator: ';' - job: - timeout-minutes: 90 - needs: check_skip - if: ${{ needs.check_skip.outputs.skip == 'false' }} - name: 'py3.8' + name: 'macos 3.8' runs-on: macos-latest defaults: run: @@ -39,7 +25,7 @@ CONDA_ENV: 'environment.yml' CI_OS_NAME: 'osx' steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: conda-incubator/setup-miniconda@v2 with: activate-environment: 'mne' @@ -49,11 +35,16 @@ - shell: bash -el {0} run: | ./tools/github_actions_dependencies.sh - source tools/get_minimal_commands.sh name: 'Install dependencies' - - shell: bash -el {0} - run: mne_surf2bem --version - name: 'Check minimal commands' + # https://github.com/mne-tools/mne-python/issues/10805 + # https://github.com/mne-tools/mne-python/runs/7042965701?check_suite_focus=true + #- shell: bash -el {0} + # run: | + # source tools/get_minimal_commands.sh + # name: 'Install minimal commands' + #- shell: bash -el {0} + # run: mne_surf2bem --version + # name: 'Check minimal commands' - shell: bash -el {0} run: ./tools/github_actions_install.sh name: 'Install MNE' @@ -63,7 +54,7 @@ - shell: bash -el {0} run: ./tools/get_testing_version.sh name: 'Get testing version' - - uses: actions/cache@v2 + - uses: actions/cache@v3 with: key: ${{ env.TESTING_VERSION }} path: ~/mne_data @@ -77,6 +68,6 @@ - shell: bash -el {0} run: ./tools/github_actions_test.sh name: 'Run tests' - - uses: codecov/codecov-action@v1 + - uses: codecov/codecov-action@v3 if: success() name: 'Upload coverage to CodeCov' diff -Nru python-mne-0.23.4+dfsg/.gitignore python-mne-1.1.0+dfsg/.gitignore --- python-mne-0.23.4+dfsg/.gitignore 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/.gitignore 2022-08-03 15:48:14.000000000 +0000 @@ -56,11 +56,14 @@ doc/auto_tutorials/ doc/modules/generated/ doc/sphinxext/cachedir +tutorials/intro/report.h5 +tutorials/intro/report_partial.hdf5 tutorials/misc/report.h5 +tutorials/io/fnirs.csv pip-log.txt .coverage* -tags coverage.xml +tags doc/coverages doc/samples doc/*.dat @@ -87,3 +90,6 @@ .venv/ venv/ *.json +.hypothesis/ + +.ipynb_checkpoints/ diff -Nru python-mne-0.23.4+dfsg/ignore_words.txt python-mne-1.1.0+dfsg/ignore_words.txt --- python-mne-0.23.4+dfsg/ignore_words.txt 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/ignore_words.txt 2022-08-03 15:48:14.000000000 +0000 @@ -35,3 +35,5 @@ master nin trough +recuse +ro diff -Nru python-mne-0.23.4+dfsg/.lgtm.yml python-mne-1.1.0+dfsg/.lgtm.yml --- python-mne-0.23.4+dfsg/.lgtm.yml 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/.lgtm.yml 2022-08-03 15:48:14.000000000 +0000 @@ -1,9 +1,4 @@ extraction: - python: - index: - filters: - - exclude: "mne/externals/*.py" - - exclude: "mne/externals/*/*.py" javascript: index: filters: diff -Nru python-mne-0.23.4+dfsg/LICENSE.txt python-mne-1.1.0+dfsg/LICENSE.txt --- python-mne-0.23.4+dfsg/LICENSE.txt 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/LICENSE.txt 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,4 @@ -Copyright © 2011-2019, authors of MNE-Python +Copyright © 2011-2022, authors of MNE-Python All rights reserved. Redistribution and use in source and binary forms, with or without diff -Nru python-mne-0.23.4+dfsg/logo/generate_mne_logos.py python-mne-1.1.0+dfsg/logo/generate_mne_logos.py --- python-mne-0.23.4+dfsg/logo/generate_mne_logos.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/logo/generate_mne_logos.py 2022-08-03 15:48:14.000000000 +0000 @@ -8,7 +8,7 @@ """ # @author: drmccloy # Created on Mon Jul 20 11:28:16 2015 -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np import os.path as op @@ -17,12 +17,12 @@ from scipy.stats import multivariate_normal from matplotlib.path import Path from matplotlib.text import TextPath -from matplotlib.patches import PathPatch, Ellipse +from matplotlib.patches import PathPatch, Ellipse, FancyBboxPatch, Rectangle from matplotlib.colors import LinearSegmentedColormap # manually set values -dpi = 300. -center_fudge = np.array([15, 0]) # compensate for font bounding box padding +dpi = 300 +center_fudge = np.array([15, 30]) # compensate for font bounding box padding tagline_scale_fudge = 0.97 # to get justification right tagline_offset_fudge = np.array([0, -100.]) @@ -34,7 +34,7 @@ rcParams.update(rcp) # initialize figure (no axes, margins, etc) -fig = plt.figure(1, figsize=(5, 3), frameon=False, dpi=dpi) +fig = plt.figure(1, figsize=(5, 2.25), frameon=False, dpi=dpi) ax = plt.Axes(fig, [0., 0., 1., 1.]) ax.set_axis_off() fig.add_axes(ax) @@ -71,9 +71,12 @@ mne_field_line_cols = LinearSegmentedColormap('mne_line', redbl) # plot gradient and contour lines -im = plt.imshow(Z, cmap=mne_field_grad_cols, aspect='equal') -cs = plt.contour(Z, 9, cmap=mne_field_line_cols, linewidths=1) -plot_dims = np.r_[np.diff(ax.get_xbound()), np.diff(ax.get_ybound())] +im = ax.imshow(Z, cmap=mne_field_grad_cols, aspect='equal', zorder=1) +cs = ax.contour(Z, 9, cmap=mne_field_line_cols, linewidths=1, zorder=1) +xlim, ylim = ax.get_xbound(), ax.get_ybound() +plot_dims = np.r_[np.diff(xlim), np.diff(ylim)] +rect = Rectangle( + [xlim[0], ylim[0]], plot_dims[0], plot_dims[1], facecolor='w', zorder=0.5) # create MNE clipping mask mne_path = TextPath((0, 0), 'MNE') @@ -86,13 +89,15 @@ ax.add_patch(PathPatch(mne_clip, color='w', zorder=0, linewidth=0)) # apply clipping mask to field gradient and lines im.set_clip_path(mne_clip, transform=im.get_transform()) +ax.add_patch(rect) +rect.set_clip_path(mne_clip, transform=im.get_transform()) for coll in cs.collections: coll.set_clip_path(mne_clip, transform=im.get_transform()) # get final position of clipping mask mne_corners = mne_clip.get_extents().corners() # add tagline -rcParams.update({'font.sans-serif': ['Cooper Hewitt'], 'font.weight': 'light'}) +rcParams.update({'font.sans-serif': ['Cooper Hewitt'], 'font.weight': '300'}) tag_path = TextPath((0, 0), 'MEG + EEG ANALYSIS & VISUALIZATION') dims = tag_path.vertices.max(0) - tag_path.vertices.min(0) vert = tag_path.vertices - dims / 2. @@ -113,36 +118,58 @@ static_dir = op.join(op.dirname(__file__), '..', 'doc', '_static') assert op.isdir(static_dir) plt.savefig(op.join(static_dir, 'mne_logo.svg'), transparent=True) +tag_patch.set_facecolor('w') +rect.set_facecolor('0.5') +plt.savefig(op.join(static_dir, 'mne_logo_dark.svg'), transparent=True) +tag_patch.set_facecolor('k') +rect.set_facecolor('w') -# modify to make an icone +# modify to make the splash screen data_dir = op.join(op.dirname(__file__), '..', 'mne', 'icons') -ax.patches.pop(-1) # no tag line for our icon -ax.collections[:] = [] +ax.patches[-1].set_facecolor('w') +for coll in list(ax.collections): + coll.remove() bounds = np.array([ [mne_path.vertices[:, ii].min(), mne_path.vertices[:, ii].max()] for ii in range(2)]) bounds *= (plot_dims / dims) xy = np.mean(bounds, axis=1) - [100, 0] r = np.diff(bounds, axis=1).max() * 1.2 -ax.add_patch(Ellipse(xy, r, r, clip_on=False, zorder=-1, fc='k')) +w, h = r, r * (2 / 3) +box_xy = [xy[0] - w * 0.5, xy[1] - h * (2 / 5)] +ax.set_ylim(box_xy[1] + h * 1.001, box_xy[1] - h * 0.001) +patch = FancyBboxPatch( + box_xy, w, h, clip_on=False, zorder=-1, fc='k', ec='none', alpha=0.75, + boxstyle="round,rounding_size=200.0", mutation_aspect=1) +ax.add_patch(patch) +fig.set_size_inches((512 / dpi, 512 * (h / w) / dpi)) +plt.savefig(op.join(data_dir, 'mne_splash.png'), transparent=True) +patch.remove() + +# modify to make an icon +ax.patches.pop(-1) # no tag line for our icon +patch = Ellipse(xy, r, r, clip_on=False, zorder=-1, fc='k') +ax.add_patch(patch) ax.set_ylim(xy[1] + r / 1.9, xy[1] - r / 1.9) fig.set_size_inches((256 / dpi, 256 / dpi)) # Qt does not support clip paths in SVG rendering so we have to use PNG here # then use "optipng -o7" on it afterward (14% reduction in file size) -plt.savefig(op.join(data_dir, 'mne-circle-black.png'), transparent=True) +plt.savefig(op.join(data_dir, 'mne_default_icon.png'), transparent=True) plt.close() -# 92x22 image -w_px = 92 -h_px = 22 -center_fudge = np.array([20, 0]) +# 188x45 image +dpi = 96 # for SVG it's different +w_px = 188 +h_px = 45 +center_fudge = np.array([60, 0]) scale_fudge = 2.1 rcParams.update({'font.sans-serif': ['Primetime'], 'font.weight': 'black'}) x = np.linspace(-1., 1., w_px // 2) y = np.linspace(-1., 1., h_px // 2) X, Y = np.meshgrid(x, y) # initialize figure (no axes, margins, etc) -fig = plt.figure(1, figsize=(w_px / dpi, h_px / dpi), frameon=False, dpi=dpi) +fig = plt.figure(1, figsize=(w_px / dpi, h_px / dpi), facecolor='k', + frameon=False, dpi=dpi) ax = plt.Axes(fig, [0., 0., 1., 1.]) ax.set_axis_off() fig.add_axes(ax) @@ -160,7 +187,7 @@ offset = np.array([scale_fudge, 1.]) * \ np.array([-dims[0], plot_dims[-1]]) / 2. - center_fudge mne_clip = Path(offset + vert * mult, mne_path.codes) -mne_patch = PathPatch(mne_clip, facecolor='w', edgecolor='none', zorder=10) +mne_patch = PathPatch(mne_clip, facecolor='0.5', edgecolor='none', zorder=10) ax.add_patch(mne_patch) # adjust xlim and ylim mne_corners = mne_clip.get_extents().corners() @@ -173,5 +200,6 @@ ax.set_xlim(xmin - xpad, xl[1] + xpad) ax.set_ylim(ymax + ypad, ymin - ypad) plt.draw() -plt.savefig(op.join(static_dir, 'mne_logo_small.svg'), transparent=True) +plt.savefig(op.join(static_dir, 'mne_logo_small.svg'), + dpi=dpi, transparent=True) plt.close() diff -Nru python-mne-0.23.4+dfsg/logo/LICENSE python-mne-1.1.0+dfsg/logo/LICENSE --- python-mne-0.23.4+dfsg/logo/LICENSE 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/logo/LICENSE 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,397 @@ +Attribution 4.0 International + +Copyright © 2022, MNE-Python Contributors + +======================================================================= + +Creative Commons Corporation ("Creative Commons") is not a law firm and +does not provide legal services or legal advice. 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Except for the limited purpose of indicating that +material is shared under a Creative Commons public license or as +otherwise permitted by the Creative Commons policies published at +creativecommons.org/policies, Creative Commons does not authorize the +use of the trademark "Creative Commons" or any other trademark or logo +of Creative Commons without its prior written consent including, +without limitation, in connection with any unauthorized modifications +to any of its public licenses or any other arrangements, +understandings, or agreements concerning use of licensed material. For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. diff -Nru python-mne-0.23.4+dfsg/logo/README.md python-mne-1.1.0+dfsg/logo/README.md --- python-mne-0.23.4+dfsg/logo/README.md 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/logo/README.md 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,15 @@ +# MNE-Python logos + +The following logos used for MNE-Python are made available under a +[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) +license, see the [LICENSE](./LICENSE) file in this directory: + +- The main MNE-Python logo: [mne_logo.svg](../doc/_static/mne_logo.svg) +- The hexagonal version of the MNE-Python logo: [logo_hex.svg](./logo_hex.svg) +- The small form of the MNE-Python logo: [mne_logo_small.svg](../doc/_static/mne_logo_small.svg) +- The MNE-Python "helmet": [mne_helmet.png](../doc/_static/mne_helmet.png) +- The MNE-Python "helmet" thumbnail: [favicon.ico](../doc/_static/favicon.ico) + +See also the code to create some of these logos: + +- [generate_mne_logos.py](./generate_mne_logos.py) diff -Nru python-mne-0.23.4+dfsg/.mailmap python-mne-1.1.0+dfsg/.mailmap --- python-mne-0.23.4+dfsg/.mailmap 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/.mailmap 2022-08-03 15:48:14.000000000 +0000 @@ -1,230 +1,280 @@ -Alexandre Gramfort Alexandre Gramfort -Alexandre Gramfort Alexandre Gramfort +Adam Li Adam Li +Adam Li Adam Li +Alan Leggitt leggitta +Alessandro Tonin Lychfindel <58313635+Lychfindel@users.noreply.github.com> +Alex Rockhill Alex +Alex Rockhill Alex +Alex Rockhill Alex Rockhill +Alex Rockhill Alex Rockhill +Alexander Rudiuk Alexander Rudiuk +Alexandre Barachant alexandre barachant Alexandre Gramfort Alexandre Gramfort +Alexandre Gramfort Alexandre Gramfort Alexandre Gramfort Alexandre Gramfort +Alexandre Gramfort Alexandre Gramfort Alexandre Gramfort Alexandre Gramfort -Martin Luessi mluessi@nmr.mgh.harvard.edu -Martin Luessi martin -Martin Luessi martin -Matti Hämäläinen Matti Hämäläinen -Matti Hämäläinen Matti Hamalainen -Matti Hämäläinen Matti Hamalainen -Matti Hämäläinen mshamalainen -Christian Brodbeck christianmbrodbeck -Christian Brodbeck Christian Brodbeck -Louis Thibault = -Louis Thibault Louis Thibault -Eric Larson Eric Larson -Eric Larson Eric89GXL -Eric Larson Eric Larson -Eric Larson Eric89GXL -Denis A. Engemann dengemann -Denis A. Engemann denis -Denis A. Engemann dengemann -Denis A. Engemann Denis Engemann -Denis A. Engemann Denis A. Engemann -Denis A. Engemann Denis Engemann -Denis A. Engemann Denis A. Engemann -Denis A. Engemann Denis A. Engemann -Daniel Strohmeier joewalter -Daniel Strohmeier Daniel Strohmeier -Dan G. Wakeman Daniel G. Wakeman -Dan G. Wakeman dgwakeman -Dan G. Wakeman dgwakeman -Dan G. Wakeman Daniel Wakeman -Teon Brooks -Teon Brooks Teon -Teon Brooks Teon Brooks -Teon Brooks -Romain Trachel Romain Trachel -Romain Trachel Romain Trachel -Romain Trachel trachelr -Roman Goj +Ana Radanovic anaradanovic <79697247+anaradanovic@users.noreply.github.com> Andrew Dykstra -Yousra Bekhti Yoursa BEKHTI -Yousra Bekhti Yoursa BEKHTI -Yousra Bekhti yousrabk -Alan Leggitt leggitta -Praveen Sripad prav -Praveen Sripad prav -Martin Billinger kazemakase -Martin Billinger Martin Billinger -Martin Billinger Martin -Martin Billinger mbillingr -Martin van Harmelen <1544429+MPvHarmelen@users.noreply.github.com> Martin <1544429+MPvHarmelen@users.noreply.github.com> -Mainak Jas Mainak -Mainak Jas Mainak Jas -Mainak Jas mainakjas -Mainak Jas Mainak Jas -Mainak Jas Mainak Jas -Marmaduke Woodman maedoc +Anna Padee <44297909+apadee@users.noreply.github.com> apadee <44297909+apadee@users.noreply.github.com> +Anne-Sophie Dubarry annesodub +Archit Singhal <43236121+architsinghal-mriirs@users.noreply.github.com> archit singhal +Ashley Drew <33734402+ashdrew@users.noreply.github.com> ashdrew <33734402+ashdrew@users.noreply.github.com> +Asish Panda kaichogami +Basile Pinsard Brad Buran Brad Buran +Britta Westner britta-wstnr +Burkhard Maess Burkhard Maess +Carina Forster Carina +Carlos de la Torre carlos +Catalina María Galván <79813952+catalinamagalvan@users.noreply.github.com> Catalina Magalvan <79813952+catalinamagalvan@users.noreply.github.com> +Catalina María Galván <79813952+catalinamagalvan@users.noreply.github.com> catalinamagalvan <79813952+catalinamagalvan@users.noreply.github.com> Cathy Nangini CN -Olaf Hauk Olaf Hauk -Olaf Hauk olafhauk -Jean-Remi King kingjr -Jean-Remi King UMR9752 -Jean-Remi King UMR9752 -Jean-Remi King kingjr -Jean-Remi King Jean-Rémi KING -Jean-Remi King kingjr -Roan LaPlante aestrivex -Mark Wronkiewicz wronk -Basile Pinsard +Chris Holdgraf Chris Holdgraf +Chris Holdgraf Christopher Holdgraf +Christian Brodbeck Christian Brodbeck +Christian Brodbeck christianmbrodbeck +Christian Mista cmista <79416030+cmista@users.noreply.github.com> +Christina Zhao ChristinaZhao +Christoph Dinh Christoph Dinh +Christopher J. Bailey Chris Bailey +Claire Braboszcz claire-braboszcz Clément Moutard -Manoj Kumar MechCoder -Ingoo Lee dlsrnsi -Jona Sassenhagen Jona Sassenhagen -Jona Sassenhagen jona-sassenhagen -Jona Sassenhagen jona-sassenhagen@ -Jona Sassenhagen jona-sassenhagen -Jona Sassenhagen jona -Jona Sassenhagen sassenha -Jona Sassenhagen jona.sassenhagen@gmail.com -Yousra Bekhti Yousra BEKHTI -Ross Maddox unknown -Jaakko Leppakangas Jaakko Leppakangas -Jaakko Leppakangas jaeilepp -Jaakko Leppakangas jaeilepp -Jair Montoya jmontoyam -Natalie Klein natalieklein +Cora Kim <41998428+kimcoco@users.noreply.github.com> kimcoco <41998428+kimcoco@users.noreply.github.com> +Cristóbal Moënne-Loccoz Cristóbal +Dan G. Wakeman Daniel G. Wakeman +Dan G. Wakeman Daniel Wakeman +Dan G. Wakeman dgwakeman +Dan G. Wakeman dgwakeman Daniel McCloy Daniel McCloy Daniel McCloy drammock +Daniel Strohmeier Daniel Strohmeier +Daniel Strohmeier joewalter +David Julien David JULIEN +David Sabbagh DavidSabbagh <33925146+DavidSabbagh@users.noreply.github.com> +Demetres Kostas <40433000+kostasde@users.noreply.github.com> kostasde <40433000+kostasde@users.noreply.github.com> +Denis A. Engemann dengemann +Denis A. Engemann dengemann +Denis A. Engemann denis +Denis A. Engemann Denis A. Engemann +Denis A. Engemann Denis A. Engemann +Denis A. Engemann Denis A. Engemann +Denis A. Engemann Denis Engemann +Denis A. Engemann Denis Engemann +Dmitrii Altukhov dmalt +Dominik Krzemiński dokato +Dominik Welke dominikwelke <33089761+dominikwelke@users.noreply.github.com> +Eberhard Eich ebeich +Eduard Ort Eduard Ort +Eduard Ort eort +Eduard Ort examplename +Ellen Lau ellenlau +Emily Stephen Emily P. Stephen +Emily Stephen emilyps14 +Enrico Varano <69973551+enricovara@users.noreply.github.com> enricovara <69973551+enricovara@users.noreply.github.com> +Eric Larson Eric Larson +Eric Larson Eric Larson +Eric Larson Eric Larson +Eric Larson Eric89GXL +Eric Larson Eric89GXL +Erica Peterson nordme <38704848+nordme@users.noreply.github.com> +Erica Peterson nordme +Erik Hornberger er06645810 +Erik Hornberger Erik Hornberger +Erkka Heinila Teekuningas +Etienne de Montalivet +Evgenii Kalenkovich kalenkovich +Evgeny Goldstein <84768107+evgenygoldstein@users.noreply.github.com> evgenygoldstein <84768107+evgenygoldstein@users.noreply.github.com> +Ezequiel Mikulan <39155887+ezemikulan@users.noreply.github.com> ezemikulan <39155887+ezemikulan@users.noreply.github.com> +Fahimeh Mamashli <33672431+fmamashli@users.noreply.github.com> fmamashli <33672431+fmamashli@users.noreply.github.com> Fede Raimondo Fede -Fede Raimondo Fede Raimondo +Fede Raimondo Fede Raimondo Fede Raimondo Fede Raimondo -Fede Raimondo Federico Raimondo +Fede Raimondo Fede Raimondo Fede Raimondo Federico Raimondo -Fede Raimondo Fede Raimondo -Emily Stephen emilyps14 -Emily Stephen Emily P. Stephen -Marian Dovgialo +Fede Raimondo Federico Raimondo +Federico Zamberlan <44038765+fzamberlan@users.noreply.github.com> +Felix Klotzsche eioe +Felix Klotzsche eioe +Félix Raimundo Felix Raimundo +Frederik D. Weber Frederik-D-Weber +Fu-Te Wong foucault +Fu-Te Wong zuxfoucault +Gansheng Tan <49130176+GanshengT@users.noreply.github.com> Gansheng TAN <49130176+GanshengT@users.noreply.github.com> +Giorgio Marinato neurogima <76406896+neurogima@users.noreply.github.com> Guillaume Dumas deep-introspection Guillaume Dumas Guillaume Dumas -Félix Raimundo Felix Raimundo -Asish Panda kaichogami -Mikołaj Magnuski mmagnuski -Mikołaj Magnuski Mikolaj Magnuski -Alexandre Barachant alexandre barachant -Lorenzo Alfine lorrandal -Paul Pasler ppasler -Jon Houck Jon Houck -Cristóbal Moënne-Loccoz Cristóbal -Chris Holdgraf Chris Holdgraf -Chris Holdgraf Christopher Holdgraf -Britta Westner britta-wstnr +Hamid Maymandi <46011104+HamidMandi@users.noreply.github.com> Hamid <46011104+HamidMandi@users.noreply.github.com> +Hongjiang Ye YE Hongjiang +Hubert Banville hubertjb +Ingoo Lee dlsrnsi +Jaakko Leppakangas Jaakko Leppakangas +Jaakko Leppakangas jaeilepp +Jaakko Leppakangas jaeilepp +Jair Montoya jmontoyam +Jan Sosulski jsosulski +Jean-Baptiste Schiratti Jean-Baptiste SCHIRATTI +Jean-Remi King Jean-Rémi KING +Jean-Remi King kingjr +Jean-Remi King kingjr +Jean-Remi King kingjr +Jean-Remi King UMR9752 +Jean-Remi King UMR9752 +Jeff Stout jstout211 Jesper Duemose Nielsen jdue +Joan Massich Joan Massich +Joan Massich massich +Johannes Kasper jeythekey <44215387+jeythekey@users.noreply.github.com> +John Samuelsson johnsam7 +John Veillette +Jon Houck Jon Houck +Jona Sassenhagen jona +Jona Sassenhagen Jona Sassenhagen +Jona Sassenhagen jona-sassenhagen +Jona Sassenhagen jona-sassenhagen +Jona Sassenhagen jona-sassenhagen@ +Jona Sassenhagen jona.sassenhagen@gmail.com +Jona Sassenhagen sassenha +Jordan Drew <39603454+jadrew43@users.noreply.github.com> jadrew43 <39603454+jadrew43@users.noreply.github.com> +Joris Van den Bossche Joris Van den Bossche +José C. García Alanis Jose Alanis +José C. García Alanis Jose C. G. Alanis <12409129+JoseAlanis@users.noreply.github.com> +José C. García Alanis José C. G. Alanis <12409129+JoseAlanis@users.noreply.github.com> +José C. García Alanis José C. García Alanis <12409129+JoseAlanis@users.noreply.github.com> +Joshua J Bear +Joshua Teves Joshua Teves +Kaisu Lankinen <41806798+klankinen@users.noreply.github.com> klankinen <41806798+klankinen@users.noreply.github.com> +Kambiz Tabavi Kambiz Tavabi +Kambiz Tabavi kambysese +Katarina Slama katarinaslama +Kostiantyn Maksymenko kostiantyn maksymenko +Kostiantyn Maksymenko Maksymenko Kostiantyn +Kostiantyn Maksymenko Maksymenko Kostiantyn Laetitia Grabot LaetitiaG -Nicolas Barascud nbara -Nicolas Barascud Nicolas Barascud <10333715+nbara@users.noreply.github.com> -Lukáš Hejtmánek hejtmy -Ramonapariciog Apariciogarcia ramonapariciog -Mathurin Massias mathurinm -Simon Kern skjerns -Simon Kern skjerns <14980558+skjerns@users.noreply.github.com> -Simon Kern Simon Kern <14980558+skjerns@users.noreply.github.com> -Steven M. Gutstein S. M. Gutstein -Steven M. Gutstein smgutstein -Robin Tibor Schirrmeister robintibor -Anne-Sophie Dubarry annesodub -Claire Braboszcz claire-braboszcz Larry Eisenman lneisenman +Lenny Varghese lennyvarghese +Lorenz Esch Lorenz Esch +Lorenzo Alfine lorrandal +Louis Thibault = +Louis Thibault Louis Thibault +Lukas Gemein gemeinl +Lukáš Hejtmánek hejtmy +Mads Jensen mads jensen +Mainak Jas Mainak +Mainak Jas Mainak Jas +Mainak Jas Mainak Jas +Mainak Jas Mainak Jas +Mainak Jas mainakjas +Manoj Kumar MechCoder +Manu Sutela MJAS1 +Marian Dovgialo Marian Dovgialo +Marian Dovgialo mdovgialo +Marijn van Vliet Marijn van Vliet +Mark Wronkiewicz wronk +Marmaduke Woodman maedoc +Martin Billinger kazemakase +Martin Billinger Martin +Martin Billinger Martin Billinger +Martin Billinger mbillingr +Martin Luessi martin +Martin Luessi martin +Martin Luessi mluessi@nmr.mgh.harvard.edu +Martin Schulz Martin Schulz <46245704+marsipu@users.noreply.github.com> +Martin van Harmelen <1544429+MPvHarmelen@users.noreply.github.com> Martin <1544429+MPvHarmelen@users.noreply.github.com> +Mathieu Scheltienne Mathieu Scheltienne <73893616+mscheltienne@users.noreply.github.com> +Mathieu Scheltienne Mathieu Scheltienne Mathurin Massias mathurinm -Pierre Ablin pierreablin -Erik Hornberger er06645810 -Erik Hornberger Erik Hornberger -Kostiantyn Maksymenko Maksymenko Kostiantyn -Kostiantyn Maksymenko kostiantyn maksymenko +Mathurin Massias mathurinm +Matt Sanderson monkeyman192 +Matteo Anelli Matteo Anelli +Matteo Visconti di Oleggio Castello Matteo Visconti dOC +Matthias Dold <62005770+matthiasdold@users.noreply.github.com> matthiasdold <62005770+matthiasdold@users.noreply.github.com> +Matthias Eberlein <41163089+MatthiasEb@users.noreply.github.com> MatthiasEb <41163089+MatthiasEb@users.noreply.github.com> +Matti Hämäläinen Matti Hamalainen +Matti Hämäläinen Matti Hamalainen +Matti Hämäläinen Matti Hämäläinen +Matti Hämäläinen mshamalainen +Matti Toivonen <105695400+mattitoi@users.noreply.github.com> mattitoi <105695400+mattitoi@users.noreply.github.com> +Maureen Shader <55732126+mshader@users.noreply.github.com> mshader <55732126+mshader@users.noreply.github.com> +Michiru Kaneda rcmdnk +Mikołaj Magnuski Mikolaj Magnuski +Mikołaj Magnuski mmagnuski +Mohamed Sherif mohdsherif +Mohammad Daneshzand <55800429+mdaneshzand@users.noreply.github.com> mdaneshzand <55800429+mdaneshzand@users.noreply.github.com> +Natalie Klein natalieklein Nathalie Gayraud Nathalie Nathalie Gayraud Nathalie -Dominik Krzemiński dokato -Ezequiel Mikulan <39155887+ezemikulan@users.noreply.github.com> ezemikulan <39155887+ezemikulan@users.noreply.github.com> -Hubert Banville hubertjb -Sara Sommariva sarasommariva -Manu Sutela MJAS1 -Joan Massich massich -Joan Massich Joan Massich -David Sabbagh DavidSabbagh <33925146+DavidSabbagh@users.noreply.github.com> -Nikolas Chalas Nichalas -Katarina Slama katarinaslama -Evgenii Kalenkovich kalenkovich -Thomas Donoghue Tom -Steve Matindi stevemats -Nicolas Legrand LegrandNico +Naveen Srinivasan <172697+naveensrinivasan@users.noreply.github.com> Naveen <172697+naveensrinivasan@users.noreply.github.com> +Nicolas Barascud nbara +Nicolas Barascud Nicolas Barascud <10333715+nbara@users.noreply.github.com> +Nicolas Gensollen Gensollen Nicolas Legrand Legrand Nicolas +Nicolas Legrand LegrandNico Nicolas Legrand Nicolas Legrand -Johannes Kasper jeythekey <44215387+jeythekey@users.noreply.github.com> -Thomas Radman -Joshua J Bear +Niklas Wilming Niklas Wilming +Nikolai Chapochnikov <23103092+chapochn@users.noreply.github.com> chapochn <23103092+chapochn@users.noreply.github.com> +Nikolai Chapochnikov <23103092+chapochn@users.noreply.github.com> Nikolai M Chapochnikov <23103092+chapochn@users.noreply.github.com> +Nikolas Chalas Nichalas +Olaf Hauk Olaf Hauk +Olaf Hauk olafhauk +Paul Pasler ppasler Paul Roujansky -José C. García Alanis José C. García Alanis <12409129+JoseAlanis@users.noreply.github.com> -José C. García Alanis José C. G. Alanis <12409129+JoseAlanis@users.noreply.github.com> -José C. García Alanis Jose C. G. Alanis <12409129+JoseAlanis@users.noreply.github.com> -José C. García Alanis Jose Alanis -Kostiantyn Maksymenko Maksymenko Kostiantyn -Samuel Deslauriers-Gauthier Samuel Deslauriers-Gauthier -Eberhard Eich ebeich -Dominik Welke dominikwelke <33089761+dominikwelke@users.noreply.github.com> -Alex Rockhill Alex Rockhill -Alex Rockhill Alex -Alex Rockhill Alex Rockhill -Carlos de la Torre carlos -Fahimeh Mamashli <33672431+fmamashli@users.noreply.github.com> fmamashli <33672431+fmamashli@users.noreply.github.com> -Fu-Te Wong foucault -Fu-Te Wong zuxfoucault -Kaisu Lankinen <41806798+klankinen@users.noreply.github.com> klankinen <41806798+klankinen@users.noreply.github.com> -Demetres Kostas <40433000+kostasde@users.noreply.github.com> kostasde <40433000+kostasde@users.noreply.github.com> -Mohammad Daneshzand <55800429+mdaneshzand@users.noreply.github.com> mdaneshzand <55800429+mdaneshzand@users.noreply.github.com> -Mohamed Sherif mohdsherif -Dmitrii Altukhov dmalt -Jeff Stout jstout211 -Eduard Ort examplename -Eduard Ort eort -Eduard Ort Eduard Ort -Tod Flak <45362686+todflak@users.noreply.github.com> todflak <45362686+todflak@users.noreply.github.com> -Hongjiang Ye YE Hongjiang -Victoria Peterson vpeterson +Pedro Silva pbnsilva Phillip Alday Phillip Alday Phillip Alday Phillip Alday -Niklas Wilming Niklas Wilming -Ellen Lau ellenlau -Kambiz Tabavi kambysese -Kambiz Tabavi Kambiz Tavabi -Lenny Varghese lennyvarghese -Mads Jensen mads jensen -Christoph Dinh Christoph Dinh -Matteo Visconti di Oleggio Castello Matteo Visconti dOC -Marijn van Vliet Marijn van Vliet -Jean-Baptiste Schiratti Jean-Baptiste SCHIRATTI -Pedro Silva pbnsilva -Christopher J. Bailey Chris Bailey -Alexander Rudiuk Alexander Rudiuk -Tanay Gahlot Tanay -Erkka Heinila Teekuningas -Burkhard Maess Burkhard Maess -Lorenz Esch Lorenz Esch -Joris Van den Bossche Joris Van den Bossche -Christina Zhao ChristinaZhao -Rotem Falach Falach -Nicolas Gensollen Gensollen -Matteo Anelli Matteo Anelli +Pierre Ablin pierreablin +Pierre-Antoine Bannier PAB +Pierre-Antoine Bannier Pierre-Antoine Bannier +Pierre-Antoine Bannier Pierre-Antoine Bannier +Pierre-Antoine Bannier Pierre-Antoine Bannier +Pierre-Antoine Bannier Pierre-Antoine Bannier +Praveen Sripad prav +Praveen Sripad prav Ram Pari Ram +Ramonapariciog Apariciogarcia ramonapariciog +Reza Nasri Reza +Reza Nasri RezaNasri +Roan LaPlante aestrivex +Rob Luke Robert Luke <748691+rob-luke@users.noreply.github.com> +Robin Tibor Schirrmeister robintibor +Romain Trachel Romain Trachel +Romain Trachel Romain Trachel +Romain Trachel trachelr +Roman Goj +Ross Maddox rkmaddox +Ross Maddox Ross Maddox +Ross Maddox unknown +Rotem Falach Falach +Ryan Law Ryan Law +Samuel Deslauriers-Gauthier Samuel Deslauriers-Gauthier +Sara Sommariva sarasommariva +Sebastien Treguer DataFox +Senwen Deng <36327760+snwnde@users.noreply.github.com> Senwen DENG <36327760+snwnde@users.noreply.github.com> +Silvia Cotroneo <78911192+sfc-neuro@users.noreply.github.com> sfc-neuro <78911192+sfc-neuro@users.noreply.github.com> +Simon Kern Simon Kern <14980558+skjerns@users.noreply.github.com> +Simon Kern skjerns <14980558+skjerns@users.noreply.github.com> +Simon Kern skjerns +Sondre Foslien sondrfos +Steve Matindi stevemats Steven Bierer Steven Bierer <40672003+NeuroLaunch@users.noreply.github.com> -Valerii Chirkov Valerii <42982039+vagechirkov@users.noreply.github.com> -Zhi Zhang <850734033@qq.com> ZHANG Zhi <850734033@qq.com> -Ana Radanovic anaradanovic <79697247+anaradanovic@users.noreply.github.com> -Catalina Magalvan <79813952+catalinamagalvan@users.noreply.github.com> catalinamagalvan <79813952+catalinamagalvan@users.noreply.github.com> -Christian Mista cmista <79416030+cmista@users.noreply.github.com> -Felix Klotzsche eioe -Felix Klotzsche eioe -Enrico Varano <69973551+enricovara@users.noreply.github.com> enricovara <69973551+enricovara@users.noreply.github.com> -Giorgio Marinato neurogima <76406896+neurogima@users.noreply.github.com> -Erica Peterson nordme -Erica Peterson nordme <38704848+nordme@users.noreply.github.com> +Steven M. Gutstein S. M. Gutstein +Steven M. Gutstein smgutstein +T. Wang <81429617+twang5@users.noreply.github.com> twang5 <81429617+twang5@users.noreply.github.com> +Tanay Gahlot Tanay +Teon Brooks +Teon Brooks +Teon Brooks Teon +Teon Brooks Teon Brooks +Thomas Donoghue Tom +Thomas Radman Timon Merk <38216460+timonmerk@users.noreply.github.com> timonmerk <38216460+timonmerk@users.noreply.github.com> -Anna Padee <44297909+apadee@users.noreply.github.com> apadee <44297909+apadee@users.noreply.github.com> -Cora Kim <41998428+kimcoco@users.noreply.github.com> kimcoco <41998428+kimcoco@users.noreply.github.com> -Matt Sanderson monkeyman192 -Silvia Cotroneo <78911192+sfc-neuro@users.noreply.github.com> sfc-neuro <78911192+sfc-neuro@users.noreply.github.com> -Sebastien Treguer DataFox +Timothy Gates Tim Gates +Tod Flak <45362686+todflak@users.noreply.github.com> todflak <45362686+todflak@users.noreply.github.com> Tristan Stenner Tristan Stenner +Tziona NessAiver TzionaN +Valerii Chirkov Valerii <42982039+vagechirkov@users.noreply.github.com> +Victoria Peterson vpeterson +Yousra Bekhti Yoursa BEKHTI +Yousra Bekhti Yoursa BEKHTI +Yousra Bekhti Yousra BEKHTI +Yousra Bekhti yousrabk +Zhi Zhang <850734033@qq.com> ZHANG Zhi <850734033@qq.com> diff -Nru python-mne-0.23.4+dfsg/Makefile python-mne-1.1.0+dfsg/Makefile --- python-mne-0.23.4+dfsg/Makefile 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/Makefile 2022-08-03 15:48:14.000000000 +0000 @@ -1,11 +1,9 @@ # simple makefile to simplify repetetive build env management tasks under posix -# caution: testing won't work on windows, see README - PYTHON ?= python PYTESTS ?= py.test CTAGS ?= ctags -CODESPELL_SKIPS ?= "doc/auto_*,*.fif,*.eve,*.gz,*.tgz,*.zip,*.mat,*.stc,*.label,*.w,*.bz2,*.annot,*.sulc,*.log,*.local-copy,*.orig_avg,*.inflated_avg,*.gii,*.pyc,*.doctree,*.pickle,*.inv,*.png,*.edf,*.touch,*.thickness,*.nofix,*.volume,*.defect_borders,*.mgh,lh.*,rh.*,COR-*,FreeSurferColorLUT.txt,*.examples,.xdebug_mris_calc,bad.segments,BadChannels,*.hist,empty_file,*.orig,*.js,*.map,*.ipynb,searchindex.dat,install_mne_c.rst,plot_*.rst,*.rst.txt,c_EULA.rst*,*.html,gdf_encodes.txt,*.svg,references.bib" +CODESPELL_SKIPS ?= "doc/auto_*,*.fif,*.eve,*.gz,*.tgz,*.zip,*.mat,*.stc,*.label,*.w,*.bz2,*.annot,*.sulc,*.log,*.local-copy,*.orig_avg,*.inflated_avg,*.gii,*.pyc,*.doctree,*.pickle,*.inv,*.png,*.edf,*.touch,*.thickness,*.nofix,*.volume,*.defect_borders,*.mgh,lh.*,rh.*,COR-*,FreeSurferColorLUT.txt,*.examples,.xdebug_mris_calc,bad.segments,BadChannels,*.hist,empty_file,*.orig,*.js,*.map,*.ipynb,searchindex.dat,install_mne_c.rst,plot_*.rst,*.rst.txt,c_EULA.rst*,*.html,gdf_encodes.txt,*.svg,references.bib,*.css" CODESPELL_DIRS ?= mne/ doc/ tutorials/ examples/ all: clean inplace test test-doc @@ -73,7 +71,7 @@ $(PYTESTS) --cov=mne --cov-report html:coverage test-doc: sample_data testing_data - $(PYTESTS) --doctest-modules --doctest-ignore-import-errors --doctest-glob='*.rst' ./doc/ --ignore=./doc/auto_examples --ignore=./doc/auto_tutorials --ignore=./doc/_build --fulltrace + $(PYTESTS) --doctest-modules --doctest-ignore-import-errors --doctest-glob='*.rst' ./doc/ --ignore=./doc/auto_examples --ignore=./doc/auto_tutorials --ignore=./doc/_build --ignore=./doc/conf.py --ignore=doc/sphinxext --fulltrace test-coverage: testing_data rm -rf coverage .coverage diff -Nru python-mne-0.23.4+dfsg/MANIFEST.in python-mne-1.1.0+dfsg/MANIFEST.in --- python-mne-0.23.4+dfsg/MANIFEST.in 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/MANIFEST.in 2022-08-03 15:48:14.000000000 +0000 @@ -2,6 +2,8 @@ include LICENSE.txt include SECURITY.md include requirements.txt +include requirements_base.txt +include requirements_hdf5.txt include requirements_testing.txt include requirements_testing_extra.txt include requirements_doc.txt @@ -21,6 +23,11 @@ include mne/datasets/_fsaverage/root.txt include mne/datasets/_fsaverage/bem.txt include mne/datasets/_infant/*.txt +include mne/datasets/_phantom/*.txt +include mne/data/dataset_checksums.txt +include mne/data/eegbci_checksums.txt + +recursive-include mne/html_templates *.html.jinja recursive-include mne/channels/data/layouts * recursive-include mne/channels/data/montages * @@ -31,12 +38,13 @@ recursive-include mne/html *.js recursive-include mne/html *.css +recursive-include mne/report * + recursive-include mne/io/artemis123/resources * recursive-include mne mne/datasets *.csv include mne/io/edf/gdf_encodes.txt include mne/datasets/sleep_physionet/SHA1SUMS -include mne/externals/tqdm/_tqdm/tqdm.1 ### Exclude @@ -52,11 +60,13 @@ exclude ignore_words.txt exclude .mailmap exclude codemeta.json +exclude CITATION.cff recursive-exclude mne *.pyc recursive-exclude doc * recursive-exclude logo * +exclude CONTRIBUTING.md exclude CODE_OF_CONDUCT.md exclude .github exclude .github/CONTRIBUTING.md @@ -69,6 +79,7 @@ # Test files recursive-exclude mne/io/tests/data * +recursive-exclude mne/io/besa/tests/data * recursive-exclude mne/io/bti/tests/data * recursive-exclude mne/io/edf/tests/data * recursive-exclude mne/io/kit/tests/data * diff -Nru python-mne-0.23.4+dfsg/mne/annotations.py python-mne-1.1.0+dfsg/mne/annotations.py --- python-mne-0.23.4+dfsg/mne/annotations.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/annotations.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors: Jaakko Leppakangas # Robert Luke # -# License: BSD (3-clause) +# License: BSD-3-Clause from collections import OrderedDict from datetime import datetime, timedelta, timezone @@ -20,7 +20,7 @@ _check_pandas_installed, _mask_to_onsets_offsets, _DefaultEventParser, _check_dt, _stamp_to_dt, _dt_to_stamp, _check_fname, int_like, _check_option, fill_doc, - _on_missing) + _on_missing, _is_numeric, _check_dict_keys) from .io.write import (start_block, end_block, write_float, write_name_list, write_double, start_file, write_string) @@ -111,11 +111,11 @@ starting time of annotation acquisition. If None (default), starting time is determined from beginning of raw data acquisition. In general, ``raw.info['meas_date']`` (or None) can be used for syncing - the annotations with raw data if their acquisiton is started at the + the annotations with raw data if their acquisition is started at the same time. If it is a string, it should conform to the ISO8601 format. More precisely to this '%%Y-%%m-%%d %%H:%%M:%%S.%%f' particular case of the ISO8601 format where the delimiter between date and time is ' '. - %(annot_ch_names)s + %(ch_names_annot)s .. versionadded:: 0.23 @@ -230,6 +230,20 @@ n | | e +------+ orig_time onset[0]' + + .. warning:: + This means that when ``raw.info['meas_date'] is None``, doing + ``raw.set_annotations(raw.annotations)`` will not alter ``raw`` if and + only if ``raw.first_samp == 0``. When it's non-zero, + ``raw.set_annotations`` will assume that the "new" annotations refer to + the original data (with ``first_samp==0``), and will be re-referenced to + the new time offset! + + **Specific annotation** + + ``BAD_ACQ_SKIP`` annotation leads to specific reading/writing file + behaviours. See :meth:`mne.io.read_raw_fif` and + :meth:`Raw.save() ` notes for details. """ # noqa: E501 def __init__(self, onset, duration, description, @@ -292,7 +306,8 @@ "add/concatenate 2 annotations " "(got %s != %s)" % (self.orig_time, other.orig_time)) - return self.append(other.onset, other.duration, other.description) + return self.append(other.onset, other.duration, other.description, + other.ch_names) def __iter__(self): """Iterate over the annotations.""" @@ -331,7 +346,7 @@ description : str | array-like Description for the annotation. To reject epochs, use description starting with keyword 'bad'. - %(annot_ch_names)s + %(ch_names_annot)s .. versionadded:: 0.23 @@ -448,6 +463,14 @@ .. versionadded:: 0.23 %(verbose)s + + Notes + ----- + The format of the information stored in the saved annotation objects + depends on the chosen file format. :file:`.csv` files store the onset + as timestamps (e.g., ``2002-12-03 19:01:56.676071``), + whereas :file:`.txt` files store onset as seconds since start of the + recording (e.g., ``45.95597082905339``). """ check_fname(fname, 'annotations', ('-annot.fif', '-annot.fif.gz', '_annot.fif', '_annot.fif.gz', @@ -473,7 +496,8 @@ self.ch_names = self.ch_names[order] @verbose - def crop(self, tmin=None, tmax=None, emit_warning=False, verbose=None): + def crop(self, tmin=None, tmax=None, emit_warning=False, + use_orig_time=True, verbose=None): """Remove all annotation that are outside of [tmin, tmax]. The method operates inplace. @@ -487,7 +511,10 @@ emit_warning : bool Whether to emit warnings when limiting or omitting annotations. Defaults to False. - %(verbose_meth)s + use_orig_time : bool + Whether to use orig_time as an offset. + Defaults to True. + %(verbose)s Returns ------- @@ -496,41 +523,46 @@ """ if len(self) == 0: return self # no annotations, nothing to do - if self.orig_time is None: + if not use_orig_time or self.orig_time is None: offset = _handle_meas_date(0) else: offset = self.orig_time if tmin is None: - tmin = timedelta(self.onset.min()) + offset + tmin = timedelta(seconds=self.onset.min()) + offset if tmax is None: - tmax = timedelta((self.onset + self.duration).max()) + offset + tmax = timedelta( + seconds=(self.onset + self.duration).max()) + offset for key, val in [('tmin', tmin), ('tmax', tmax)]: _validate_type(val, ('numeric', _datetime), key, 'numeric, datetime, or None') - if tmin > tmax: - raise ValueError('tmax should be greater than or equal to tmin ' - '(%s < %s).' % (tmax, tmin)) - logger.debug('Cropping annotations %s - %s' % (tmin, tmax)) absolute_tmin = _handle_meas_date(tmin) absolute_tmax = _handle_meas_date(tmax) del tmin, tmax + if absolute_tmin > absolute_tmax: + raise ValueError('tmax should be greater than or equal to tmin ' + '(%s < %s).' % (absolute_tmin, absolute_tmax)) + logger.debug('Cropping annotations %s - %s' % (absolute_tmin, + absolute_tmax)) onsets, durations, descriptions, ch_names = [], [], [], [] out_of_bounds, clip_left_elem, clip_right_elem = [], [], [] - for onset, duration, description, ch in zip( - self.onset, self.duration, self.description, self.ch_names): + for idx, (onset, duration, description, ch) in enumerate(zip( + self.onset, self.duration, self.description, self.ch_names)): # if duration is NaN behave like a zero if np.isnan(duration): duration = 0. # convert to absolute times - absolute_onset = timedelta(0, onset) + offset - absolute_offset = absolute_onset + timedelta(0, duration) + absolute_onset = timedelta(seconds=onset) + offset + absolute_offset = absolute_onset + timedelta(seconds=duration) out_of_bounds.append( absolute_onset > absolute_tmax or absolute_offset < absolute_tmin) if out_of_bounds[-1]: clip_left_elem.append(False) clip_right_elem.append(False) + logger.debug( + f' [{idx}] Dropping ' + f'({absolute_onset} - {absolute_offset}: {description})') else: # clip the left side clip_left_elem.append(absolute_onset < absolute_tmin) @@ -546,8 +578,13 @@ durations.append(duration) onsets.append( (absolute_onset - offset).total_seconds()) + logger.debug( + f' [{idx}] Keeping ' + f'({absolute_onset} - {absolute_offset} -> ' + f'{onset} - {onset + duration})') descriptions.append(description) ch_names.append(ch) + logger.debug(f'Cropping complete (kept {len(onsets)})') self.onset = np.array(onsets, float) self.duration = np.array(durations, float) assert (self.duration >= 0).all() @@ -567,9 +604,286 @@ return self + @verbose + def set_durations(self, mapping, verbose=None): + """Set annotation duration(s). Operates inplace. + + Parameters + ---------- + mapping : dict | float + A dictionary mapping the annotation description to a duration in + seconds e.g. ``{'ShortStimulus' : 3, 'LongStimulus' : 12}``. + Alternatively, if a number is provided, then all annotations + durations are set to the single provided value. + %(verbose)s + + Returns + ------- + self : mne.Annotations + The modified Annotations object. + + Notes + ----- + .. versionadded:: 0.24.0 + """ + _validate_type(mapping, (int, float, dict)) + + if isinstance(mapping, dict): + _check_dict_keys(mapping, self.description, + valid_key_source="data", + key_description="Annotation description(s)") + for stim in mapping: + map_idx = [desc == stim for desc in self.description] + self.duration[map_idx] = mapping[stim] + + elif _is_numeric(mapping): + self.duration = np.ones(self.description.shape) * mapping + + else: + raise ValueError("Setting durations requires the mapping of " + "descriptions to times to be provided as a dict. " + f"Instead {type(mapping)} was provided.") + + return self + + @verbose + def rename(self, mapping, verbose=None): + """Rename annotation description(s). Operates inplace. + + Parameters + ---------- + mapping : dict + A dictionary mapping the old description to a new description, + e.g. {'1.0' : 'Control', '2.0' : 'Stimulus'}. + %(verbose)s + + Returns + ------- + self : mne.Annotations + The modified Annotations object. + + Notes + ----- + .. versionadded:: 0.24.0 + """ + _validate_type(mapping, dict) + _check_dict_keys(mapping, self.description, valid_key_source="data", + key_description="Annotation description(s)") + + for old, new in mapping.items(): + self.description = [d.replace(old, new) for d in self.description] + + self.description = np.array(self.description) + return self + + +class EpochAnnotationsMixin: + """Mixin class for Annotations in Epochs.""" + + @property + def annotations(self): # noqa: D102 + return self._annotations + + @verbose + def set_annotations(self, annotations, on_missing='raise', *, + verbose=None): + """Setter for Epoch annotations from Raw. + + This method does not handle offsetting the times based + on first_samp or measurement dates, since that is expected + to occur in Raw.set_annotations(). + + Parameters + ---------- + annotations : instance of mne.Annotations | None + Annotations to set. + %(on_missing_ch_names)s + %(verbose)s + + Returns + ------- + self : instance of Epochs + The epochs object with annotations. + + Notes + ----- + Annotation onsets and offsets are stored as time in seconds (not as + sample numbers). + + If you have an ``-epo.fif`` file saved to disk created before 1.0, + annotations can be added correctly only if no decimation or + resampling was performed. We thus suggest to regenerate your + :class:`mne.Epochs` from raw and re-save to disk with 1.0+ if you + want to safely work with :class:`~mne.Annotations` in epochs. + + Since this method does not handle offsetting the times based + on first_samp or measurement dates, the recommended way to add + Annotations is:: + + raw.set_annotations(annotations) + annotations = raw.annotations + epochs.set_annotations(annotations) + + .. versionadded:: 1.0 + """ + _validate_type(annotations, (Annotations, None), 'annotations') + if annotations is None: + self._annotations = None + else: + if getattr(self, '_unsafe_annot_add', False): + warn('Adding annotations to Epochs created (and saved to ' + 'disk) before 1.0 will yield incorrect results if ' + 'decimation or resampling was performed on the instance, ' + 'we recommend regenerating the Epochs and re-saving them ' + 'to disk') + new_annotations = annotations.copy() + new_annotations._prune_ch_names(self.info, on_missing) + self._annotations = new_annotations + return self + + def get_annotations_per_epoch(self): + """Get a list of annotations that occur during each epoch. + + Returns + ------- + epoch_annots : list + A list of lists (with length equal to number of epochs) where each + inner list contains any annotations that overlap the corresponding + epoch. Annotations are stored as a :class:`tuple` of onset, + duration, description (not as a :class:`~mne.Annotations` object), + where the onset is now relative to time=0 of the epoch, rather than + time=0 of the original continuous (raw) data. + """ + # create a list of annotations for each epoch + epoch_annot_list = [[] for _ in range(len(self.events))] + + # check if annotations exist + if self.annotations is None: + return epoch_annot_list + + # when each epoch and annotation starts/stops + # no need to account for first_samp here... + epoch_tzeros = self.events[:, 0] / self._raw_sfreq + epoch_starts, epoch_stops = np.atleast_2d( + epoch_tzeros) + np.atleast_2d(self.times[[0, -1]]).T + # ... because first_samp isn't accounted for here either + annot_starts = self._annotations.onset + annot_stops = annot_starts + self._annotations.duration + + # the first two cases (annot_straddles_epoch_{start|end}) will both + # (redundantly) capture cases where an annotation fully encompasses + # an epoch (e.g., annot from 1-4s, epoch from 2-3s). The redundancy + # doesn't matter because results are summed and then cast to bool (all + # we care about is presence/absence of overlap). + annot_straddles_epoch_start = np.logical_and( + np.atleast_2d(epoch_starts) >= np.atleast_2d(annot_starts).T, + np.atleast_2d(epoch_starts) < np.atleast_2d(annot_stops).T) + + annot_straddles_epoch_end = np.logical_and( + np.atleast_2d(epoch_stops) > np.atleast_2d(annot_starts).T, + np.atleast_2d(epoch_stops) <= np.atleast_2d(annot_stops).T) + + # this captures the only remaining case we care about: annotations + # fully contained within an epoch (or exactly coextensive with it). + annot_fully_within_epoch = np.logical_and( + np.atleast_2d(epoch_starts) <= np.atleast_2d(annot_starts).T, + np.atleast_2d(epoch_stops) >= np.atleast_2d(annot_stops).T) + + # combine all cases to get array of shape (n_annotations, n_epochs). + # Nonzero entries indicate overlap between the corresponding + # annotation (row index) and epoch (column index). + all_cases = (annot_straddles_epoch_start + + annot_straddles_epoch_end + + annot_fully_within_epoch) + + # for each Epoch-Annotation overlap occurrence: + for annot_ix, epo_ix in zip(*np.nonzero(all_cases)): + this_annot = self._annotations[annot_ix] + this_tzero = epoch_tzeros[epo_ix] + # adjust annotation onset to be relative to epoch tzero... + annot = (this_annot['onset'] - this_tzero, + this_annot['duration'], + this_annot['description']) + # ...then add it to the correct sublist of `epoch_annot_list` + epoch_annot_list[epo_ix].append(annot) + return epoch_annot_list + + def add_annotations_to_metadata(self, overwrite=False): + """Add raw annotations into the Epochs metadata data frame. + + Adds three columns to the ``metadata`` consisting of a list + in each row: + - ``annot_onset``: the onset of each Annotation within + the Epoch relative to the start time of the Epoch (in seconds). + - ``annot_duration``: the duration of each Annotation + within the Epoch in seconds. + - ``annot_description``: the free-form text description of each + Annotation. + + Parameters + ---------- + overwrite : bool + Whether to overwrite existing columns in metadata or not. + Default is False. + + Returns + ------- + self : instance of Epochs + The modified instance (instance is also modified inplace). + + Notes + ----- + .. versionadded:: 1.0 + """ + pd = _check_pandas_installed() + + # check if annotations exist + if self.annotations is None: + warn(f'There were no Annotations stored in {self}, so ' + 'metadata was not modified.') + return self + + # get existing metadata DataFrame or instantiate an empty one + if self._metadata is not None: + metadata = self._metadata + else: + data = np.empty((len(self.events), 0)) + metadata = pd.DataFrame(data=data) + + if any(name in metadata.columns for name in + ['annot_onset', 'annot_duration', 'annot_description']) and \ + not overwrite: + raise RuntimeError( + 'Metadata for Epochs already contains columns ' + '"annot_onset", "annot_duration", or "annot_description".') + + # get the Epoch annotations, then convert to separate lists for + # onsets, durations, and descriptions + epoch_annot_list = self.get_annotations_per_epoch() + onset, duration, description = [], [], [] + for epoch_annot in epoch_annot_list: + for ix, annot_prop in enumerate((onset, duration, description)): + entry = [annot[ix] for annot in epoch_annot] + + # round onset and duration to avoid IO round trip mismatch + if ix < 2: + entry = np.round(entry, decimals=12).tolist() + + annot_prop.append(entry) + + # Create a new Annotations column that is instantiated as an empty + # list per Epoch. + metadata['annot_onset'] = pd.Series(onset) + metadata['annot_duration'] = pd.Series(duration) + metadata['annot_description'] = pd.Series(description) + + # reset the metadata + self.metadata = metadata + return self + def _combine_annotations(one, two, one_n_samples, one_first_samp, - two_first_samp, sfreq, meas_date): + two_first_samp, sfreq): """Combine a tuple of annotations.""" assert one is not None assert two is not None @@ -711,15 +1025,14 @@ if 'ch_names' in annot: annot['ch_names'] = [ _prep_name_list(ch, 'write') for ch in annot['ch_names']] - annot.to_csv(fname) + annot.to_csv(fname, index=False) def _write_annotations_txt(fname, annot): content = "# MNE-Annotations\n" if annot.orig_time is not None: # for backward compat, we do not write tzinfo (assumed UTC) - content += ("# orig_time : %s \n" - % annot.orig_time.replace(tzinfo=None)) + content += f"# orig_time : {annot.orig_time.replace(tzinfo=None)}\n" content += "# onset, duration, description" data = [annot.onset, annot.duration, annot.description] if annot._any_ch_names(): @@ -738,8 +1051,8 @@ def read_annotations(fname, sfreq='auto', uint16_codec=None): r"""Read annotations from a file. - This function reads a .fif, .fif.gz, .vrmk, .edf, .txt, .csv .cnt, .cef, - or .set file and makes an :class:`mne.Annotations` object. + This function reads a .fif, .fif.gz, .vmrk, .amrk, .edf, .txt, .csv, .cnt, + .cef, or .set file and makes an :class:`mne.Annotations` object. Parameters ---------- @@ -747,13 +1060,14 @@ The filename. sfreq : float | 'auto' The sampling frequency in the file. This parameter is necessary for - \*.vmrk and \*.cef files as Annotations are expressed in seconds and - \*.vmrk/\*.cef files are in samples. For any other file format, - ``sfreq`` is omitted. If set to 'auto' then the ``sfreq`` is taken - from the respective info file of the same name with according file - extension (\*.vhdr for brainvision; \*.dap for Curry 7; \*.cdt.dpa for - Curry 8). So data.vrmk looks for sfreq in data.vhdr, data.cef looks in - data.dap and data.cdt.cef looks in data.cdt.dpa. + \*.vmrk, \*.amrk, and \*.cef files as Annotations are expressed in + seconds and \*.vmrk/\*.amrk/\*.cef files are in samples. For any other + file format, ``sfreq`` is omitted. If set to 'auto' then the ``sfreq`` + is taken from the respective info file of the same name with according + file extension (\*.vhdr/\*.ahdr for brainvision; \*.dap for Curry 7; + \*.cdt.dpa for Curry 8). So data.vmrk/amrk looks for sfreq in + data.vhdr/ahdr, data.cef looks in data.dap and data.cdt.cef looks in + data.cdt.dpa. uint16_codec : str | None This parameter is only used in EEGLAB (\*.set) and omitted otherwise. If your \*.set file contains non-ascii characters, sometimes reading @@ -797,7 +1111,7 @@ description=description, orig_time=orig_time, ch_names=ch_names) - elif name.endswith('vmrk'): + elif name.endswith(('vmrk', 'amrk')): annotations = _read_annotations_brainvision(fname, sfreq=sfreq) elif name.endswith('csv'): @@ -882,7 +1196,7 @@ starting time of annotation acquisition. If None (default), starting time is determined from beginning of raw data acquisition. In general, ``raw.info['meas_date']`` (or None) can be used for syncing - the annotations with raw data if their acquisiton is started at the + the annotations with raw data if their acquisition is started at the same time. Returns @@ -1094,7 +1408,7 @@ regexp=r'^(?![Bb][Aa][Dd]|[Ee][Dd][Gg][Ee]).*$', use_rounding=True, chunk_duration=None, verbose=None): - """Get events and event_id from an Annotations object. + """Get :term:`events` and ``event_id`` from an Annotations object. Parameters ---------- @@ -1139,10 +1453,9 @@ Returns ------- - events : ndarray, shape (n_events, 3) - The events. + %(events)s event_id : dict - The event_id variable that can be passed to Epochs. + The event_id variable that can be passed to :class:`~mne.Epochs`. See Also -------- @@ -1267,3 +1580,11 @@ orig_time=orig_time) return annots + + +def _adjust_onset_meas_date(annot, raw): + """Adjust the annotation onsets based on raw meas_date.""" + # If there is a non-None meas date, then the onset should take into + # account the first_samp / first_time. + if raw.info['meas_date'] is not None: + annot.onset += raw.first_time diff -Nru python-mne-0.23.4+dfsg/mne/baseline.py python-mne-1.1.0+dfsg/mne/baseline.py --- python-mne-0.23.4+dfsg/mne/baseline.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/baseline.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np @@ -32,7 +32,7 @@ dimension should be time. times : 1D array Time instants is seconds. - %(rescale_baseline)s + %(baseline_rescale)s mode : 'mean' | 'ratio' | 'logratio' | 'percent' | 'zscore' | 'zlogratio' Perform baseline correction by diff -Nru python-mne-0.23.4+dfsg/mne/beamformer/_compute_beamformer.py python-mne-1.1.0+dfsg/mne/beamformer/_compute_beamformer.py --- python-mne-0.23.4+dfsg/mne/beamformer/_compute_beamformer.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/beamformer/_compute_beamformer.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,7 +4,7 @@ # Roman Goj # Britta Westner # -# License: BSD (3-clause) +# License: BSD-3-Clause from copy import deepcopy @@ -16,11 +16,10 @@ from ..minimum_norm.inverse import _get_vertno, _prepare_forward from ..source_space import label_src_vertno_sel from ..utils import (verbose, check_fname, _reg_pinv, _check_option, logger, - _pl, _check_src_normal, check_version, _sym_mat_pow, warn) + _pl, _check_src_normal, check_version, _sym_mat_pow, warn, + _import_h5io_funcs) from ..time_frequency.csd import CrossSpectralDensity -from ..externals.h5io import read_hdf5, write_hdf5 - def _check_proj_match(proj, filters): """Check whether SSP projections in data and spatial filter match.""" @@ -386,7 +385,6 @@ return W, max_power_ori -# TODO: Eventually we can @jit() this to make it faster def _compute_power(Cm, W, n_orient): """Use beamformer filters to compute source power. @@ -404,10 +402,9 @@ """ n_sources = W.shape[0] // n_orient - source_power = np.zeros(n_sources) - for k in range(n_sources): - Wk = W[n_orient * k: n_orient * k + n_orient] - source_power[k] = np.trace(Wk @ Cm @ Wk.conj().T).real + Wk = W.reshape(n_sources, n_orient, W.shape[1]) + source_power = np.trace((Wk @ Cm @ Wk.conj().transpose(0, 2, 1)).real, + axis1=1, axis2=2) return source_power @@ -462,6 +459,8 @@ %(overwrite)s %(verbose)s """ + _, write_hdf5 = _import_h5io_funcs() + ending = '-%s.h5' % (self['kind'].lower(),) check_fname(fname, self['kind'], (ending,)) csd_orig = None @@ -488,6 +487,7 @@ filter : instance of Beamformer The beamformer filter. """ + read_hdf5, _ = _import_h5io_funcs() beamformer = read_hdf5(fname, title='mnepython') if 'csd' in beamformer: beamformer['csd'] = CrossSpectralDensity(**beamformer['csd']) diff -Nru python-mne-0.23.4+dfsg/mne/beamformer/_dics.py python-mne-1.1.0+dfsg/mne/beamformer/_dics.py --- python-mne-0.23.4+dfsg/mne/beamformer/_dics.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/beamformer/_dics.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,19 +5,18 @@ # Susanna Aro # Roman Goj # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np from ..channels import equalize_channels from ..io.pick import pick_info, pick_channels -from ..utils import (logger, verbose, warn, _check_one_ch_type, +from ..utils import (logger, verbose, _check_one_ch_type, _check_channels_spatial_filter, _check_rank, - _check_option, _validate_type, deprecated) + _check_option, _validate_type) from ..forward import _subject_from_forward from ..minimum_norm.inverse import combine_xyz, _check_reference, _check_depth from ..rank import compute_rank from ..source_estimate import _make_stc, _get_src_type -from ..time_frequency import csd_fourier, csd_multitaper, csd_morlet from ._compute_beamformer import (_prepare_beamformer_input, _compute_beamformer, _check_src_type, Beamformer, _compute_power, @@ -27,23 +26,22 @@ @verbose def make_dics(info, forward, csd, reg=0.05, noise_csd=None, label=None, pick_ori=None, rank=None, weight_norm=None, - reduce_rank=False, depth=1., real_filter=None, + reduce_rank=False, depth=1., real_filter=True, inversion='matrix', verbose=None): """Compute a Dynamic Imaging of Coherent Sources (DICS) spatial filter. This is a beamformer filter that can be used to estimate the source power at a specific frequency range :footcite:`GrossEtAl2001`. It does this by - constructing a spatial - filter for each source point. The computation of these filters is very - similar to those of the LCMV beamformer (:func:`make_lcmv`), but instead of - operating on a covariance matrix, the CSD matrix is used. When applying - these filters to a CSD matrix (see :func:`apply_dics_csd`), the source - power can be estimated for each source point. + constructing a spatial filter for each source point. + The computation of these filters is very similar to those of the LCMV + beamformer (:func:`make_lcmv`), but instead of operating on a covariance + matrix, the CSD matrix is used. When applying these filters to a CSD matrix + (see :func:`apply_dics_csd`), the source power can be estimated for each + source point. Parameters ---------- - info : instance of Info - Measurement info, e.g. ``epochs.info``. + %(info_not_none)s forward : instance of Forward Forward operator. csd : instance of CrossSpectralDensity @@ -63,8 +61,8 @@ .. versionadded:: 0.20 label : Label | None Restricts the solution to a given label. - %(bf_pick_ori)s - %(rank_None)s + %(pick_ori_bf)s + %(rank_none)s .. versionadded:: 0.17 %(weight_norm)s @@ -77,9 +75,9 @@ matrices to compute real filters. .. versionchanged:: 0.23 - Version 0.23 deprecated ``False`` as default for ``real_filter``. - With version 0.24, ``True`` will be the new default. - %(bf_inversion)s + Version 0.23 an earlier used ``real_filter=False`` as the default, + as of version 0.24 ``True`` is the default. + %(inversion_bf)s .. versionchanged:: 0.21 Default changed to ``'matrix'``. @@ -132,7 +130,7 @@ See Also -------- - apply_dics_csd, tf_dics + apply_dics_csd Notes ----- @@ -177,14 +175,6 @@ info = pick_info(info, pick_channels(info['ch_names'], [], info['bads'])) info, forward, csd = equalize_channels([info, forward, csd]) - if real_filter is None: - depr_message = ('The current default of real_filter=False is ' - 'deprecated and will be changed to real_filter=True ' - 'in version 0.24. Set real_filter explicitly to ' - 'avoid this warning.') - warn(depr_message, DeprecationWarning) - real_filter = False - csd, noise_csd = _prepare_noise_csd(csd, noise_csd, real_filter) depth = _check_depth(depth, 'depth_sparse') @@ -503,328 +493,3 @@ src_type=filters['src_type'], tmin=0., tstep=1., subject=subject, warn_text=warn_text), frequencies) - - -@deprecated( - 'tf_dics is deprecated and will be removed in 0.24, use LCMV with ' - 'covariances matrices computed on band-passed data or DICS instead.') -@verbose -def tf_dics(epochs, forward, noise_csds, tmin, tmax, tstep, win_lengths, - subtract_evoked=False, mode='fourier', freq_bins=None, - frequencies=None, n_ffts=None, mt_bandwidths=None, - mt_adaptive=False, mt_low_bias=True, cwt_n_cycles=7, decim=1, - reg=0.05, label=None, pick_ori=None, rank=None, inversion='single', - weight_norm=None, depth=1., real_filter=False, - reduce_rank=False, verbose=None): - """5D time-frequency beamforming based on DICS. - - Calculate source power in time-frequency windows using a spatial filter - based on the Dynamic Imaging of Coherent Sources (DICS) beamforming - approach :footcite:`DalalEtAl2008`. For each time window and frequency bin - combination, cross-spectral density (CSD) is computed and used to create a - DICS beamformer spatial filter. - - Parameters - ---------- - epochs : Epochs - Single trial epochs. - forward : dict - Forward operator. - noise_csds : list of instances of CrossSpectralDensity | None - Noise cross-spectral density for each frequency bin. If these are - specified, the DICS filters will be applied to both the signal and - noise CSDs. The source power estimates for each frequency bin will be - scaled by the estimated noise power (signal / noise). - Specifying ``None`` will disable performing noise normalization. - tmin : float - Minimum time instant to consider. - tmax : float - Maximum time instant to consider. - tstep : float - Spacing between consecutive time windows, should be smaller than or - equal to the shortest time window length. - win_lengths : list of float - Time window lengths in seconds. One time window length should be - provided for each frequency bin. - subtract_evoked : bool - If True, subtract the averaged evoked response prior to computing the - tf source grid. Defaults to False. - mode : 'fourier' | 'multitaper' | 'cwt_morlet' - Spectrum estimation mode. Defaults to 'fourier'. - freq_bins : list of tuple of float - Start and end point of frequency bins of interest. - Only used in 'multitaper' or 'fourier' mode. For 'cwt_morlet' mode, use - the ``frequencies`` parameter instead. - frequencies : list of float | list of list of float - The frequencies to compute the source power for. If you want to compute - the average power for multiple frequency bins, specify a list of - lists: each list containing the frequencies for the corresponding bin. - Only used in 'cwt_morlet' mode. In other modes, use the ``freq_bins`` - parameter instead. - n_ffts : list | None - Length of the FFT for each frequency bin. If ``None`` (the default), - the exact number of samples between ``tmin`` and ``tmax`` will be used. - Only used in 'multitaper' or 'fourier' mode. - mt_bandwidths : list of float - The bandwidths of the multitaper windowing function in Hz. Only used in - 'multitaper' mode. One value should be provided for each frequency bin. - Defaults to None. - mt_adaptive : bool - Use adaptive weights to combine the tapered spectra into CSD. Only used - in 'multitaper' mode. Defaults to False. - mt_low_bias : bool - Only use tapers with more than 90%% spectral concentration within - bandwidth. Only used in 'multitaper' mode. Defaults to True. - cwt_n_cycles : float | list of float | None - Number of cycles to use when constructing Morlet wavelets. Fixed number - or one per frequency. Defaults to 7. - Only used in 'cwt_morlet' mode. - decim : int | slice - To reduce memory usage, decimation factor during time-frequency - decomposition. Defaults to 1 (no decimation). - Only used in 'cwt_morlet' mode. - - If `int`, uses tfr[..., ::decim]. - If `slice`, uses tfr[..., decim]. - reg : float - Regularization to use for the DICS beamformer computation. - Defaults to 0.05. - label : Label | None - Restricts the solution to a given label. Defaults to None. - pick_ori : None | 'normal' | 'max-power' - The source orientation to estimate source power for: - - ``None`` : - orientations are pooled. (Default) - 'normal' : - filters are computed for the orientation tangential to the - cortical surface - 'max-power' : - filters are computer for the orientation that maximizes - spectral power. - - Defaults to ``None``. - - rank : None | int | 'full' - This controls the effective rank of the covariance matrix when - computing the inverse. The rank can be set explicitly by specifying an - integer value. If ``None``, the rank will be automatically estimated. - Since applying regularization will always make the covariance matrix - full rank, the rank is estimated before regularization in this case. If - 'full', the rank will be estimated after regularization and hence - will mean using the full rank, unless ``reg=0`` is used. - The default is None. - - .. versionadded:: 0.17 - inversion : 'single' | 'matrix' - This determines how the beamformer deals with source spaces in "free" - orientation. Such source spaces define three orthogonal dipoles at each - source point. When ``inversion='single'``, each dipole is considered - as an individual source and the corresponding spatial filter is - computed for each dipole separately. When ``inversion='matrix'``, all - three dipoles at a source vertex are considered as a group and the - spatial filters are computed jointly using a matrix inversion. While - ``inversion='single'`` is more stable, ``inversion='matrix'`` is more - precise. See Notes of :func:`make_dics`. Defaults to 'single'. - weight_norm : None | 'unit-noise-gain' - How to normalize the beamformer weights. None means no normalization is - performed. If 'unit-noise-gain', the unit-noise gain minimum variance - beamformer will be computed (Borgiotti-Kaplan beamformer) - :footcite:`SekiharaNagarajan2008`. Defaults to ``None``. - %(depth)s - real_filter : bool - If ``True``, take only the real part of the cross-spectral-density - matrices to compute real filters. Defaults to ``False``. - %(reduce_rank)s - %(verbose)s - - Returns - ------- - stcs : list of SourceEstimate | VolSourceEstimate - Source power at each time window. One SourceEstimate object is returned - for each frequency bin. - - Notes - ----- - Dalal et al. :footcite:`DalalEtAl2008` used a synthetic aperture - magnetometry beamformer (SAM) in each time-frequency window instead of - DICS. - - An alternative to using noise CSDs is to normalize the forward solution - (``depth``) or the beamformer weights (``weight_norm``). In - this case, ``noise_csds`` may be set to ``None``. - - References - ---------- - .. footbibliography:: - """ - _check_reference(epochs) - rank = _check_rank(rank) - - if mode == 'cwt_morlet' and frequencies is None: - raise ValueError('In "cwt_morlet" mode, the "frequencies" parameter ' - 'should be used.') - elif mode != 'cwt_morlet' and freq_bins is None: - raise ValueError('In "%s" mode, the "freq_bins" parameter should be ' - 'used.' % mode) - - if frequencies is not None: - # Make sure frequencies are always in the form of a list of lists - frequencies = [np.atleast_1d(f) for f in frequencies] - n_freq_bins = len(frequencies) - else: - n_freq_bins = len(freq_bins) - - if len(win_lengths) != n_freq_bins: - raise ValueError('One time window length expected per frequency bin') - if any(win_length < tstep for win_length in win_lengths): - raise ValueError('Time step should not be larger than any of the ' - 'window lengths') - - if noise_csds is not None and len(noise_csds) != n_freq_bins: - raise ValueError('One noise CSD object expected per frequency bin') - - if n_ffts is not None and len(n_ffts) != n_freq_bins: - raise ValueError('When specifying number of FFT samples, one value ' - 'must be provided per frequency bin') - if mt_bandwidths is not None and len(mt_bandwidths) != n_freq_bins: - raise ValueError('When using multitaper mode and specifying ' - 'multitaper transform bandwidth, one value must be ' - 'provided per frequency bin') - if isinstance(cwt_n_cycles, (int, float)): - # create a list out of single values to match n_freq_bins - n_cyc = cwt_n_cycles - cwt_n_cycles = [n_cyc] * n_freq_bins - - # Multiplying by 1e3 to avoid numerical issues, e.g. 0.3 // 0.05 == 5 - n_time_steps = int(((tmax - tmin) * 1e3) // (tstep * 1e3)) - - # Subtract evoked response - if subtract_evoked: - epochs = epochs.copy().subtract_evoked() - - sol_final = [] - - # Compute source power for each frequency bin - for i_freq in range(n_freq_bins): - win_length = win_lengths[i_freq] - n_overlap = int((win_length * 1e3) // (tstep * 1e3)) - - # Scale noise CSD to allow data and noise CSDs to have different length - if noise_csds is not None: - noise_csd = noise_csds[i_freq].copy() - noise_csd._data /= noise_csd.n_fft - - if mode == 'cwt_morlet': - freq_bin = frequencies[i_freq] - fmin = np.min(freq_bin) - fmax = np.max(freq_bin) - n_cycles = cwt_n_cycles[i_freq] - else: - fmin, fmax = freq_bins[i_freq] - if n_ffts is None: - n_fft = None - else: - n_fft = n_ffts[i_freq] - if mt_bandwidths is None: - mt_bandwidth = None - else: - mt_bandwidth = mt_bandwidths[i_freq] - - sol_single = [] - sol_overlap = [] - for i_time in range(n_time_steps): - win_tmin = tmin + i_time * tstep - win_tmax = win_tmin + win_length - - # If in the last step the last time point was not covered in - # previous steps and will not be covered now, a solution needs to - # be calculated for an additional time window - if (i_time == n_time_steps - 1 and - win_tmax - tstep < tmax and - win_tmax >= tmax + (epochs.times[-1] - epochs.times[-2])): - warn('Adding a time window to cover last time points') - win_tmin = tmax - win_length - win_tmax = tmax - - if win_tmax < tmax + (epochs.times[-1] - epochs.times[-2]): - # Counteracts unsafe floating point arithmetic ensuring all - # relevant samples will be taken into account when selecting - # data in time windows - logger.info( - 'Computing time-frequency DICS beamformer for time ' - 'window %d to %d ms, in frequency range %d to %d Hz' % - (win_tmin * 1e3, win_tmax * 1e3, fmin, fmax) - ) - - # Calculating data CSD in current time window - if mode == 'fourier': - csd = csd_fourier( - epochs, fmin=fmin, fmax=fmax, tmin=win_tmin, - tmax=win_tmax, n_fft=n_fft, verbose=False) - elif mode == 'multitaper': - csd = csd_multitaper( - epochs, fmin=fmin, fmax=fmax, tmin=win_tmin, - tmax=win_tmax, n_fft=n_fft, bandwidth=mt_bandwidth, - low_bias=mt_low_bias, verbose=False) - elif mode == 'cwt_morlet': - csd = csd_morlet( - epochs, frequencies=freq_bin, tmin=win_tmin, - tmax=win_tmax, n_cycles=n_cycles, decim=decim, - verbose=False) - else: - raise ValueError('Invalid mode, choose either ' - "'fourier' or 'multitaper'") - - csd = csd.sum() - - # Scale data CSD to allow data and noise CSDs to have different - # length - csd._data /= csd.n_fft - - filters = make_dics(epochs.info, forward, csd, reg=reg, - label=label, pick_ori=pick_ori, - rank=rank, inversion=inversion, - weight_norm=weight_norm, depth=depth, - reduce_rank=reduce_rank, - real_filter=real_filter, verbose=False) - stc, _ = apply_dics_csd(csd, filters, verbose=False) - - if noise_csds is not None: - # Scale signal power by noise power - noise_stc, _ = apply_dics_csd(noise_csd, filters, - verbose=False) - stc /= noise_stc - - sol_single.append(stc.data[:, 0]) - - # Average over all time windows that contain the current time - # point, which is the current time window along with - # n_overlap - 1 previous ones - if i_time - n_overlap < 0: - curr_sol = np.mean(sol_single[0:i_time + 1], axis=0) - else: - curr_sol = np.mean(sol_single[i_time - n_overlap + 1: - i_time + 1], axis=0) - - # The final result for the current time point in the current - # frequency bin - sol_overlap.append(curr_sol) - - # Gathering solutions for all time points for current frequency bin - sol_final.append(sol_overlap) - - sol_final = np.array(sol_final) - - # Creating stc objects containing all time points for each frequency bin - stcs = [] - # compatibility with 0.16, add src_type as None if not present: - filters, warn_text = _check_src_type(filters) - - for i_freq in range(n_freq_bins): - stc = _make_stc(sol_final[i_freq, :, :].T, vertices=stc.vertices, - src_type=filters['src_type'], tmin=tmin, tstep=tstep, - subject=stc.subject, warn_text=warn_text) - stcs.append(stc) - - return stcs diff -Nru python-mne-0.23.4+dfsg/mne/beamformer/__init__.py python-mne-1.1.0+dfsg/mne/beamformer/__init__.py --- python-mne-0.23.4+dfsg/mne/beamformer/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/beamformer/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,8 +2,7 @@ from ._lcmv import (make_lcmv, apply_lcmv, apply_lcmv_epochs, apply_lcmv_raw, apply_lcmv_cov) -from ._dics import (make_dics, apply_dics, apply_dics_epochs, apply_dics_csd, - tf_dics) +from ._dics import make_dics, apply_dics, apply_dics_epochs, apply_dics_csd from ._rap_music import rap_music from ._compute_beamformer import Beamformer, read_beamformer from .resolution_matrix import make_lcmv_resolution_matrix diff -Nru python-mne-0.23.4+dfsg/mne/beamformer/_lcmv.py python-mne-1.1.0+dfsg/mne/beamformer/_lcmv.py --- python-mne-0.23.4+dfsg/mne/beamformer/_lcmv.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/beamformer/_lcmv.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,7 +4,7 @@ # Roman Goj # Britta Westner # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np from ..rank import compute_rank @@ -13,8 +13,8 @@ from ..forward import _subject_from_forward from ..minimum_norm.inverse import combine_xyz, _check_reference, _check_depth from ..source_estimate import _make_stc, _get_src_type -from ..utils import logger, verbose, _check_channels_spatial_filter -from ..utils import _check_one_ch_type, _check_info_inv +from ..utils import (logger, verbose, _check_channels_spatial_filter, + _check_one_ch_type, _check_info_inv) from ._compute_beamformer import ( _prepare_beamformer_input, _compute_power, _compute_beamformer, _check_src_type, Beamformer, _proj_whiten_data) @@ -29,9 +29,9 @@ Parameters ---------- - info : instance of Info - The measurement info to specify the channels to include. - Bad channels in info['bads'] are not used. + %(info_not_none)s + Specifies the channels to include. Bad channels (in ``info['bads']``) + are not used. forward : instance of Forward Forward operator. data_cov : instance of Covariance @@ -44,7 +44,7 @@ gradiometers with magnetometers or EEG with MEG. label : instance of Label Restricts the LCMV solution to a given label. - %(bf_pick_ori)s + %(pick_ori_bf)s - ``'vector'`` Keeps the currents for each direction separate @@ -56,7 +56,7 @@ %(depth)s .. versionadded:: 0.18 - %(bf_inversion)s + %(inversion_bf)s .. versionadded:: 0.21 %(verbose)s @@ -200,12 +200,8 @@ return filters -def _apply_lcmv(data, filters, info, tmin, max_ori_out): +def _apply_lcmv(data, filters, info, tmin): """Apply LCMV spatial filter to data for source reconstruction.""" - if max_ori_out != 'signed': - raise ValueError('max_ori_out must be "signed", got %s' - % (max_ori_out,)) - if isinstance(data, np.ndarray) and data.ndim == 2: data = [data] return_single = True @@ -239,8 +235,6 @@ sol = (W, M) else: sol = np.dot(W, M) - if filters['pick_ori'] == 'max-power' and max_ori_out == 'abs': - sol = np.abs(sol) tstep = 1.0 / info['sfreq'] @@ -256,7 +250,7 @@ @verbose -def apply_lcmv(evoked, filters, max_ori_out='signed', verbose=None): +def apply_lcmv(evoked, filters, *, verbose=None): """Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights. Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights @@ -269,8 +263,6 @@ filters : instance of Beamformer LCMV spatial filter (beamformer weights). Filter weights returned from :func:`make_lcmv`. - max_ori_out : 'signed' - Specify in case of pick_ori='max-power'. %(verbose)s Returns @@ -296,14 +288,14 @@ data = data[sel] stc = _apply_lcmv(data=data, filters=filters, info=info, - tmin=tmin, max_ori_out=max_ori_out) + tmin=tmin) return next(stc) @verbose -def apply_lcmv_epochs(epochs, filters, max_ori_out='signed', - return_generator=False, verbose=None): +def apply_lcmv_epochs(epochs, filters, *, return_generator=False, + verbose=None): """Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights. Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights @@ -316,8 +308,6 @@ filters : instance of Beamformer LCMV spatial filter (beamformer weights) Filter weights returned from :func:`make_lcmv`. - max_ori_out : 'signed' - Specify in case of pick_ori='max-power'. return_generator : bool Return a generator object instead of a list. This allows iterating over the stcs without having to keep them all in memory. @@ -340,7 +330,7 @@ sel = _check_channels_spatial_filter(epochs.ch_names, filters) data = epochs.get_data()[:, sel, :] stcs = _apply_lcmv(data=data, filters=filters, info=info, - tmin=tmin, max_ori_out=max_ori_out) + tmin=tmin) if not return_generator: stcs = [s for s in stcs] @@ -349,8 +339,7 @@ @verbose -def apply_lcmv_raw(raw, filters, start=None, stop=None, max_ori_out='signed', - verbose=None): +def apply_lcmv_raw(raw, filters, start=None, stop=None, *, verbose=None): """Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights. Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights @@ -367,8 +356,6 @@ Index of first time sample (index not time is seconds). stop : int Index of first time sample not to include (index not time is seconds). - max_ori_out : 'signed' - Specify in case of pick_ori='max-power'. %(verbose)s Returns @@ -388,8 +375,7 @@ data, times = raw[sel, start:stop] tmin = times[0] - stc = _apply_lcmv(data=data, filters=filters, info=info, - tmin=tmin, max_ori_out=max_ori_out) + stc = _apply_lcmv(data=data, filters=filters, info=info, tmin=tmin) return next(stc) @@ -433,7 +419,7 @@ # compatibility with 0.16, add src_type as None if not present: filters, warn_text = _check_src_type(filters) - return(_make_stc(source_power, vertices=filters['vertices'], + return _make_stc(source_power, vertices=filters['vertices'], src_type=filters['src_type'], tmin=0., tstep=1., subject=filters['subject'], - source_nn=filters['source_nn'], warn_text=warn_text)) + source_nn=filters['source_nn'], warn_text=warn_text) diff -Nru python-mne-0.23.4+dfsg/mne/beamformer/_rap_music.py python-mne-1.1.0+dfsg/mne/beamformer/_rap_music.py --- python-mne-0.23.4+dfsg/mne/beamformer/_rap_music.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/beamformer/_rap_music.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Authors: Yousra Bekhti # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np @@ -11,11 +11,12 @@ from ..io.pick import pick_channels_evoked, pick_info, pick_channels_forward from ..inverse_sparse.mxne_inverse import _make_dipoles_sparse from ..minimum_norm.inverse import _log_exp_var -from ..utils import logger, verbose, _check_info_inv +from ..utils import logger, verbose, _check_info_inv, fill_doc from ..dipole import Dipole from ._compute_beamformer import _prepare_beamformer_input +@fill_doc def _apply_rap_music(data, info, times, forward, noise_cov, n_dipoles=2, picks=None): """RAP-MUSIC for evoked data. @@ -24,8 +25,7 @@ ---------- data : array, shape (n_channels, n_times) Evoked data. - info : dict - Measurement info. + %(info_not_none)s times : array Times. forward : instance of Forward diff -Nru python-mne-0.23.4+dfsg/mne/beamformer/resolution_matrix.py python-mne-1.1.0+dfsg/mne/beamformer/resolution_matrix.py --- python-mne-0.23.4+dfsg/mne/beamformer/resolution_matrix.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/beamformer/resolution_matrix.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,15 +2,16 @@ """Compute resolution matrix for beamformers.""" # Authors: olaf.hauk@mrc-cbu.cam.ac.uk # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np from ..io.pick import pick_channels, pick_info, pick_channels_forward from ..evoked import EvokedArray -from ..utils import logger +from ..utils import logger, fill_doc from ._lcmv import apply_lcmv +@fill_doc def make_lcmv_resolution_matrix(filters, forward, info): """Compute resolution matrix for LCMV beamformer. @@ -21,8 +22,7 @@ (see mne.beamformer.make_lcmv). forward : instance of Forward Forward Solution with leadfield matrix. - info : instance of Info - Measurement info used to compute LCMV filters. + %(info_not_none)s Used to compute LCMV filters. Returns ------- @@ -59,24 +59,9 @@ return resmat -def _get_matrix_from_lcmv(filters, forward, info, max_ori_out='signed', - verbose=None): +def _get_matrix_from_lcmv(filters, forward, info, verbose=None): """Get inverse matrix for LCMV beamformer. - Parameters - ---------- - filters : instance of Beamformer - LCMV spatial filter. - forward : instance of Forward - The forward solution. - info : instance of Info - Measurement info used to compute the LCMV filters. - max_ori_out : str - As for beamformer.apply_lcmv(). Default 'signed'. - verbose : bool, str, int, or None - If not None, override default verbose level (see mne.verbose() and - Logging documentation for more). - Returns ------- invmat : array, shape (n_dipoles, n_channels) @@ -95,8 +80,7 @@ evo_ident = EvokedArray(id_mat, info=info, tmin=0.) # apply beamformer to identity matrix - stc_lcmv = apply_lcmv(evo_ident, filters, max_ori_out='signed', - verbose=verbose) + stc_lcmv = apply_lcmv(evo_ident, filters, verbose=verbose) # turn source estimate into numpsy array invmat = stc_lcmv.data diff -Nru python-mne-0.23.4+dfsg/mne/beamformer/tests/test_dics.py python-mne-1.1.0+dfsg/mne/beamformer/tests/test_dics.py --- python-mne-0.23.4+dfsg/mne/beamformer/tests/test_dics.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/beamformer/tests/test_dics.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,29 +1,30 @@ # Authors: Marijn van Vliet # Britta Westner # -# License: BSD 3 clause +# License: BSD-3-Clause import copy as cp import os.path as op import pytest -from numpy.testing import assert_array_equal, assert_allclose +from numpy.testing import (assert_array_equal, assert_allclose, + assert_array_less) import numpy as np import mne -from mne.datasets import testing from mne.beamformer import (make_dics, apply_dics, apply_dics_epochs, - apply_dics_csd, tf_dics, read_beamformer, - Beamformer) + apply_dics_csd, read_beamformer, Beamformer) from mne.beamformer._compute_beamformer import _prepare_beamformer_input from mne.beamformer._dics import _prepare_noise_csd -from mne.time_frequency import csd_morlet -from mne.utils import object_diff, requires_h5py, catch_logging +from mne.beamformer.tests.test_lcmv import _assert_weight_norm +from mne.datasets import testing +from mne.io.constants import FIFF from mne.proj import compute_proj_evoked, make_projector from mne.surface import _compute_nearest -from mne.beamformer.tests.test_lcmv import _assert_weight_norm -from mne.time_frequency import CrossSpectralDensity +from mne.time_frequency import CrossSpectralDensity, csd_morlet from mne.time_frequency.csd import _sym_mat_to_vector +from mne.transforms import invert_transform, apply_trans +from mne.utils import object_diff, requires_version, catch_logging data_path = testing.data_path(download=False) fname_raw = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif') @@ -73,7 +74,8 @@ # Create an info object that holds information about the sensors info = mne.create_info(fwd['info']['ch_names'], sfreq, ch_types='grad') - info.update(fwd['info']) # Merge in sensor position information + with info._unlock(): + info.update(fwd['info']) # Merge in sensor position information # heavily decimate sensors to make it much faster info = mne.pick_info(info, np.arange(info['nchan'])[::5]) fwd = mne.pick_channels_forward(fwd, info['ch_names']) @@ -140,17 +142,15 @@ return noise_csd, rank -@pytest.mark.filterwarnings('ignore:.*real_filter.*will be changed.*:' - 'DeprecationWarning') @pytest.mark.slowtest @testing.requires_testing_data -@requires_h5py +@requires_version('h5io') @idx_param @pytest.mark.parametrize('whiten', [ pytest.param(False, marks=pytest.mark.slowtest), True, ]) -def test_make_dics(tmpdir, _load_forward, idx, whiten): +def test_make_dics(tmp_path, _load_forward, idx, whiten): """Test making DICS beamformer filters.""" # We only test proper handling of parameters here. Testing the results is # done in test_apply_dics_timeseries and test_apply_dics_csd. @@ -213,7 +213,7 @@ weight_norm = 'unit-noise-gain' inversion = 'single' filters = make_dics(epochs.info, fwd_surf, csd, label=label, pick_ori=None, - weight_norm=weight_norm, depth=None, + weight_norm=weight_norm, depth=None, real_filter=False, noise_csd=noise_csd, inversion=inversion) assert filters['weights'].shape == (n_freq, n_verts * n_orient, n_channels) assert np.iscomplexobj(filters['weights']) @@ -319,7 +319,7 @@ # Test whether spatial filter contains src_type assert 'src_type' in filters - fname = op.join(str(tmpdir), 'filters-dics.h5') + fname = op.join(str(tmp_path), 'filters-dics.h5') filters.save(fname) filters_read = read_beamformer(fname) assert isinstance(filters, Beamformer) @@ -336,8 +336,6 @@ return np.linalg.norm(rr_got - rr_want) -@pytest.mark.filterwarnings('ignore:.*real_filter.*will be changed.*:' - 'DeprecationWarning') @idx_param @pytest.mark.parametrize('inversion, weight_norm', [ ('single', None), @@ -370,8 +368,6 @@ assert power.data[source_ind, 1] > power.data[source_ind, 0] -@pytest.mark.filterwarnings('ignore:.*real_filter.*will be changed.*:' - 'DeprecationWarning') @pytest.mark.parametrize('pick_ori', [None, 'normal', 'max-power']) @pytest.mark.parametrize('inversion', ['single', 'matrix']) @idx_param @@ -423,8 +419,6 @@ fwd['src'][0]['rr'][vertices][source_ind][np.newaxis])[0] -@pytest.mark.filterwarnings('ignore:.*real_filter.*will be changed.*:' - 'DeprecationWarning') @idx_param def test_real(_load_forward, idx): """Test using a real-valued filter.""" @@ -437,9 +431,7 @@ real_filter=True, inversion='single') # Also test here that no warings are thrown - implemented to check whether # src should not be None warning occurs: - with pytest.warns(None) as w: - power, f = apply_dics_csd(csd, filters_real) - assert len(w) == 0 + power, f = apply_dics_csd(csd, filters_real) assert f == [10, 20] dist = _fwd_dist(power, fwd_surf, vertices, source_ind) @@ -475,8 +467,6 @@ apply_dics_csd(csd, filters_vol) -@pytest.mark.filterwarnings('ignore:.*real_filter.*will be changed.*:' - 'DeprecationWarning') @pytest.mark.filterwarnings("ignore:The use of several sensor types with the" ":RuntimeWarning") @idx_param @@ -514,9 +504,7 @@ # Sanity checks on the resulting STC after applying DICS on epochs. # Also test here that no warnings are thrown - implemented to check whether # src should not be None warning occurs - with pytest.warns(None) as w: - stcs = apply_dics_epochs(epochs, filters) - assert len(w) == 0 + stcs = apply_dics_epochs(epochs, filters) assert isinstance(stcs, list) assert len(stcs) == 1 @@ -547,7 +535,7 @@ evoked_proj = evoked.copy() p = compute_proj_evoked(evoked_proj, n_grad=1, n_mag=0, n_eeg=0) proj_matrix = make_projector(p, evoked_proj.ch_names)[0] - evoked_proj.info['projs'] += p + evoked_proj.add_proj(p) filters_proj = make_dics(evoked_proj.info, fwd_surf, csd20, label=label) assert_array_equal(filters_proj['proj'], proj_matrix) stc_proj = apply_dics(evoked_proj, filters_proj) @@ -582,139 +570,6 @@ apply_dics_epochs(epochs, filters_vol) -@pytest.mark.slowtest -@testing.requires_testing_data -@pytest.mark.filterwarnings('ignore:.*tf_dics is dep.*:DeprecationWarning') -@pytest.mark.filterwarnings('ignore:.*real_filter.*will be changed.*:' - 'DeprecationWarning') -def test_tf_dics(_load_forward): - """Test 5D time-frequency beamforming based on DICS.""" - fwd_free, fwd_surf, fwd_fixed, _ = _load_forward - # idx isn't really used so let's just simulate one - epochs, _, _, source_vertno, label, vertices, source_ind = \ - _simulate_data(fwd_fixed, idx=0) - reg = 1 # Lots of regularization for our toy dataset - - tmin = 0 - tmax = 9 - tstep = 4 - win_lengths = [5, 5] - frequencies = [10, 20] - freq_bins = [(8, 12), (18, 22)] - - with pytest.raises(ValueError, match='several sensor types'): - stcs = tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep, win_lengths, - freq_bins=freq_bins, frequencies=frequencies, - decim=10, reg=reg, label=label) - epochs.pick_types(meg='grad') - # Compute DICS for two time windows and two frequencies - for mode in ['fourier', 'multitaper', 'cwt_morlet']: - stcs = tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep, win_lengths, - mode=mode, freq_bins=freq_bins, frequencies=frequencies, - decim=10, reg=reg, label=label) - - # Did we find the true source at 20 Hz? - dist = _fwd_dist(stcs[1], fwd_surf, vertices, source_ind, tidx=0) - assert dist == 0 - dist = _fwd_dist(stcs[1], fwd_surf, vertices, source_ind, tidx=1) - assert dist == 0 - - # 20 Hz power should decrease over time - assert stcs[1].data[source_ind, 0] > stcs[1].data[source_ind, 1] - - # 20 Hz power should be more than 10 Hz power at the true source - assert stcs[1].data[source_ind, 0] > stcs[0].data[source_ind, 0] - - # Manually compute source power and compare with the last tf_dics result. - source_power = [] - time_windows = [(0, 5), (4, 9)] - for time_window in time_windows: - csd = csd_morlet(epochs, frequencies=[frequencies[1]], - tmin=time_window[0], tmax=time_window[1], decim=10) - csd = csd.sum() - csd._data /= csd.n_fft - filters = make_dics(epochs.info, fwd_surf, csd, reg=reg, label=label, - inversion='single') - stc_source_power, _ = apply_dics_csd(csd, filters) - source_power.append(stc_source_power.data) - - # Comparing tf_dics results with dics_source_power results - assert_allclose(stcs[1].data, np.array(source_power).squeeze().T, atol=0) - - # Test using noise csds. We're going to use identity matrices. That way, - # since we're using unit-noise-gain weight normalization, there should be - # no effect. - stcs = tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep, win_lengths, - mode='cwt_morlet', frequencies=frequencies, decim=10, - reg=reg, label=label, depth=None, - weight_norm='unit-noise-gain') - noise_csd = csd.copy() - inds = np.triu_indices(csd.n_channels) - # Using [:, :] syntax for in-place broadcasting - noise_csd._data[:, :] = 2 * np.eye(csd.n_channels)[inds][:, np.newaxis] - noise_csd.n_fft = 2 # Dividing by n_fft should yield an identity CSD - noise_csds = [noise_csd, noise_csd] # Two frequency bins - stcs_norm = tf_dics(epochs, fwd_surf, noise_csds, tmin, tmax, tstep, - win_lengths, mode='cwt_morlet', - frequencies=frequencies, decim=10, reg=reg, - label=label, depth=None, - weight_norm='unit-noise-gain') - assert_allclose(3 * stcs_norm[0].data, stcs[0].data, atol=0) - assert_allclose(3 * stcs_norm[1].data, stcs[1].data, atol=0) - - # Test invalid parameter combinations - with pytest.raises(ValueError, match='fourier.*freq_bins" parameter'): - tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep, win_lengths, - mode='fourier', freq_bins=None) - with pytest.raises(ValueError, match='cwt_morlet.*frequencies" param'): - tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep, win_lengths, - mode='cwt_morlet', frequencies=None) - - # Test if incorrect number of noise CSDs is detected - with pytest.raises(ValueError, match='One noise CSD object expected per'): - tf_dics(epochs, fwd_surf, [noise_csds[0]], tmin, tmax, tstep, - win_lengths, freq_bins=freq_bins) - - # Test if freq_bins and win_lengths incompatibility is detected - with pytest.raises(ValueError, match='One time window length expected'): - tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep, - win_lengths=[0, 1, 2], freq_bins=freq_bins) - - # Test if time step exceeding window lengths is detected - with pytest.raises(ValueError, match='Time step should not be larger'): - tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep=0.15, - win_lengths=[0.2, 0.1], freq_bins=freq_bins) - - # Test if incorrect number of n_ffts is detected - with pytest.raises(ValueError, match='When specifying number of FFT'): - tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep, - win_lengths, freq_bins=freq_bins, n_ffts=[1]) - - # Test if incorrect number of mt_bandwidths is detected - with pytest.raises(ValueError, match='When using multitaper mode and'): - tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep, - win_lengths=win_lengths, freq_bins=freq_bins, - mode='multitaper', mt_bandwidths=[20]) - - # Test if 'cwt_morlet' mode works with both fixed cycle numbers and lists - # of cycle numbers - tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep, - win_lengths, frequencies=frequencies, mode='cwt_morlet', - cwt_n_cycles=7) - tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep, - win_lengths, frequencies=frequencies, mode='cwt_morlet', - cwt_n_cycles=[5., 7.]) - - # Test if subtracting evoked responses yields NaN's, since we only have one - # epoch. Suppress division warnings. - assert len(epochs) == 1, len(epochs) - with np.errstate(invalid='ignore'): - stcs = tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep, win_lengths, - mode='cwt_morlet', frequencies=frequencies, - subtract_evoked=True, reg=reg, label=label, decim=20) - assert np.all(np.isnan(stcs[0].data)) - - def _cov_as_csd(cov, info): rng = np.random.RandomState(0) assert cov['data'].ndim == 2 @@ -759,17 +614,62 @@ if not use_cov: evoked.pick_types(meg='grad') noise_csd = None - loc = apply_dics(evoked, make_dics( + filters = make_dics( evoked.info, fwd, data_csd, reg, noise_csd, pick_ori=pick_ori, - weight_norm=weight_norm, depth=depth, real_filter=real_filter)).data + weight_norm=weight_norm, depth=depth, real_filter=real_filter) + loc = apply_dics(evoked, filters).data loc = np.linalg.norm(loc, axis=1) if pick_ori == 'vector' else np.abs(loc) # Compute the percentage of sources for which there is no loc bias: perc = (want == np.argmax(loc, axis=0)).mean() * 100 assert lower <= perc <= upper -@pytest.mark.filterwarnings('ignore:.*real_filter.*will be changed.*:' - 'DeprecationWarning') +@pytest.mark.parametrize( + 'weight_norm, lower, upper, lower_ori, upper_ori, real_filter', [ + ('unit-noise-gain-invariant', 57, 58, 0.60, 0.61, False), + ('unit-noise-gain', 57, 58, 0.60, 0.61, False), + ('unit-noise-gain', 57, 58, 0.60, 0.61, True), + (None, 27, 28, 0.56, 0.57, False), + ]) +def test_orientation_max_power(bias_params_fixed, bias_params_free, + weight_norm, lower, upper, lower_ori, upper_ori, + real_filter): + """Test orientation selection for bias for max-power DICS.""" + # we simulate data for the fixed orientation forward and beamform using + # the free orientation forward, and check the orientation match at the end + evoked, _, noise_cov, data_cov, want = bias_params_fixed + noise_csd = _cov_as_csd(noise_cov, evoked.info) + data_csd = _cov_as_csd(data_cov, evoked.info) + del data_cov, noise_cov + fwd = bias_params_free[1] + filters = make_dics(evoked.info, fwd, data_csd, 0.05, noise_csd, + pick_ori='max-power', weight_norm=weight_norm, + depth=None, real_filter=real_filter) + loc = np.abs(apply_dics(evoked, filters).data) + ori = filters['max_power_ori'][0] + assert ori.shape == (246, 3) + loc = np.abs(loc) + # Compute the percentage of sources for which there is no loc bias: + max_idx = np.argmax(loc, axis=0) + mask = want == max_idx # ones that localized properly + perc = mask.mean() * 100 + assert lower <= perc <= upper + # Compute the dot products of our forward normals and + # assert we get some hopefully reasonable agreement + assert fwd['coord_frame'] == FIFF.FIFFV_COORD_HEAD + nn = np.concatenate( + [s['nn'][v] for s, v in zip(fwd['src'], filters['vertices'])]) + nn = nn[want] + nn = apply_trans(invert_transform(fwd['mri_head_t']), nn, move=False) + assert_allclose(np.linalg.norm(nn, axis=1), 1, atol=1e-6) + assert_allclose(np.linalg.norm(ori, axis=1), 1, atol=1e-12) + dots = np.abs((nn[mask] * ori[mask]).sum(-1)) + assert_array_less(dots, 1) + assert_array_less(0, dots) + got = np.mean(dots) + assert lower_ori < got < upper_ori + + @testing.requires_testing_data @idx_param @pytest.mark.parametrize('whiten', (False, True)) @@ -808,7 +708,7 @@ assert f'Computing rank from covariance with rank={use_rank}' in log, log stc_2, _ = apply_dics_csd(csd, filters_2) corr = np.corrcoef(stc_2.data.ravel(), stc.data.ravel())[0, 1] - assert 0.8 < corr < 0.99999 + assert 0.8 < corr < 0.999999 # degenerate conditions if whiten: diff -Nru python-mne-0.23.4+dfsg/mne/beamformer/tests/test_external.py python-mne-1.1.0+dfsg/mne/beamformer/tests/test_external.py --- python-mne-0.23.4+dfsg/mne/beamformer/tests/test_external.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/beamformer/tests/test_external.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Britta Westner # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op @@ -10,12 +10,10 @@ from scipy.io import savemat import mne -from mne.datasets import testing from mne.beamformer import make_lcmv, apply_lcmv, apply_lcmv_cov from mne.beamformer.tests.test_lcmv import _get_data -from mne.externals.pymatreader import read_mat -from mne.utils import run_tests_if_main - +from mne.datasets import testing +from mne.utils import requires_version data_path = testing.data_path(download=False) ft_data_path = op.join(data_path, 'fieldtrip', 'beamformer') @@ -69,6 +67,7 @@ # beamformer types to be tested: unit-gain (vector and scalar) and # unit-noise-gain (time series and power output [apply_lcmv_cov]) +@requires_version('pymatreader') @pytest.mark.parametrize('bf_type, weight_norm, pick_ori, pwr', [ ['ug_scal', None, 'max-power', False], ['ung', 'unit-noise-gain', 'max-power', False], @@ -78,6 +77,7 @@ ]) def test_lcmv_fieldtrip(_get_bf_data, bf_type, weight_norm, pick_ori, pwr): """Test LCMV vs fieldtrip output.""" + from pymatreader import read_mat evoked, data_cov, fwd = _get_bf_data # run the MNE-Python beamformer @@ -106,6 +106,3 @@ assert_allclose(np.linalg.norm(stc_mne.data, axis=1), np.linalg.norm(stc_ft_data, axis=1), rtol=1e-6) assert_allclose(stc_mne.data, stc_ft_data, rtol=1e-6) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/beamformer/tests/test_lcmv.py python-mne-1.1.0+dfsg/mne/beamformer/tests/test_lcmv.py --- python-mne-0.23.4+dfsg/mne/beamformer/tests/test_lcmv.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/beamformer/tests/test_lcmv.py 2022-08-03 15:48:14.000000000 +0000 @@ -24,7 +24,8 @@ from mne.minimum_norm import make_inverse_operator, apply_inverse from mne.minimum_norm.tests.test_inverse import _assert_free_ori_match from mne.simulation import simulate_evoked -from mne.utils import object_diff, requires_h5py, catch_logging +from mne.utils import (object_diff, requires_version, catch_logging, + _record_warnings) data_path = testing.data_path(download=False) @@ -37,6 +38,7 @@ fname_event = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw-eve.fif') fname_label = op.join(data_path, 'MEG', 'sample', 'labels', 'Aud-lh.label') +ctf_fname = op.join(data_path, 'CTF', 'somMDYO-18av.ds') reject = dict(grad=4000e-13, mag=4e-12) @@ -117,6 +119,7 @@ forward_surf_ori, forward_fixed, forward_vol +@pytest.mark.slowtest @testing.requires_testing_data def test_lcmv_vector(): """Test vector LCMV solutions.""" @@ -125,7 +128,8 @@ # For speed and for rank-deficiency calculation simplicity, # just use grads info = mne.pick_info(info, mne.pick_types(info, meg='grad', exclude=())) - info.update(bads=[], projs=[]) + with info._unlock(): + info.update(bads=[], projs=[]) forward = mne.read_forward_solution(fname_fwd) forward = mne.pick_channels_forward(forward, info['ch_names']) @@ -204,7 +208,7 @@ @pytest.mark.slowtest -@requires_h5py +@requires_version('h5io') @testing.requires_testing_data @pytest.mark.parametrize('reg, proj, kind', [ (0.01, True, 'volume'), @@ -212,7 +216,7 @@ (0.01, False, 'surface'), (0., True, 'surface'), ]) -def test_make_lcmv_bem(tmpdir, reg, proj, kind): +def test_make_lcmv_bem(tmp_path, reg, proj, kind): """Test LCMV with evoked data and single trials.""" raw, epochs, evoked, data_cov, noise_cov, label, forward,\ forward_surf_ori, forward_fixed, forward_vol = _get_data(proj=proj) @@ -225,7 +229,7 @@ filters = make_lcmv(evoked.info, fwd, data_cov, reg=reg, noise_cov=noise_cov) - stc = apply_lcmv(evoked, filters, max_ori_out='signed') + stc = apply_lcmv(evoked, filters) stc.crop(0.02, None) stc_pow = np.sum(np.abs(stc.data), axis=1) @@ -241,7 +245,7 @@ filters = make_lcmv(evoked.info, forward_surf_ori, data_cov, reg=reg, noise_cov=noise_cov, pick_ori='normal', weight_norm=None) - stc_normal = apply_lcmv(evoked, filters, max_ori_out='signed') + stc_normal = apply_lcmv(evoked, filters) stc_normal.crop(0.02, None) stc_pow = np.sum(np.abs(stc_normal.data), axis=1) @@ -262,7 +266,7 @@ # Test picking source orientation maximizing output source power filters = make_lcmv(evoked.info, fwd, data_cov, reg=reg, noise_cov=noise_cov, pick_ori='max-power') - stc_max_power = apply_lcmv(evoked, filters, max_ori_out='signed') + stc_max_power = apply_lcmv(evoked, filters) stc_max_power.crop(0.02, None) stc_pow = np.sum(np.abs(stc_max_power.data), axis=1) idx = np.argmax(stc_pow) @@ -302,7 +306,7 @@ assert 'rank %s' % rank in repr(filters) # I/O - fname = op.join(str(tmpdir), 'filters.h5') + fname = op.join(str(tmp_path), 'filters.h5') with pytest.warns(RuntimeWarning, match='-lcmv.h5'): filters.save(fname) filters_read = read_beamformer(fname) @@ -344,8 +348,6 @@ evoked_ch.pick_channels(evoked_ch.ch_names[1:]) filters = make_lcmv(evoked.info, forward_vol, data_cov, reg=0.01, noise_cov=noise_cov) - pytest.raises(ValueError, apply_lcmv, evoked_ch, filters, - max_ori_out='signed') # Test if discrepancies in channel selection of data and fwd model are # handled correctly in apply_lcmv @@ -356,18 +358,18 @@ # this channel from the data # also test here that no warnings are thrown - implemented to check whether # src should not be None warning occurs - stc = apply_lcmv(evoked, filters, max_ori_out='signed') + stc = apply_lcmv(evoked, filters) # the result should be equal to applying this filter to a dataset without # this channel: - stc_ch = apply_lcmv(evoked_ch, filters, max_ori_out='signed') + stc_ch = apply_lcmv(evoked_ch, filters) assert_array_almost_equal(stc.data, stc_ch.data) # Test if non-matching SSP projection is detected in application of filter if proj: raw_proj = raw.copy().del_proj() with pytest.raises(ValueError, match='do not match the projections'): - apply_lcmv_raw(raw_proj, filters, max_ori_out='signed') + apply_lcmv_raw(raw_proj, filters) # Test apply_lcmv_raw use_raw = raw.copy().crop(0, 1) @@ -382,15 +384,14 @@ del filters['src_type'] # emulate 0.16 behaviour to cause warning with pytest.warns(RuntimeWarning, match='spatial filter does not contain ' 'src_type'): - apply_lcmv(evoked, filters, max_ori_out='signed') + apply_lcmv(evoked, filters) # Now test single trial using fixed orientation forward solution # so we can compare it to the evoked solution filters = make_lcmv(epochs.info, forward_fixed, data_cov, reg=0.01, noise_cov=noise_cov) - stcs = apply_lcmv_epochs(epochs, filters, max_ori_out='signed') - stcs_ = apply_lcmv_epochs(epochs, filters, return_generator=True, - max_ori_out='signed') + stcs = apply_lcmv_epochs(epochs, filters) + stcs_ = apply_lcmv_epochs(epochs, filters, return_generator=True) assert_array_equal(stcs[0].data, next(stcs_).data) epochs.drop_bad() @@ -405,14 +406,14 @@ # compare it to the solution using evoked with fixed orientation filters = make_lcmv(evoked.info, forward_fixed, data_cov, reg=0.01, noise_cov=noise_cov) - stc_fixed = apply_lcmv(evoked, filters, max_ori_out='signed') + stc_fixed = apply_lcmv(evoked, filters) assert_array_almost_equal(stc_avg, stc_fixed.data) # use a label so we have few source vertices and delayed computation is # not used filters = make_lcmv(epochs.info, forward_fixed, data_cov, reg=0.01, noise_cov=noise_cov, label=label) - stcs_label = apply_lcmv_epochs(epochs, filters, max_ori_out='signed') + stcs_label = apply_lcmv_epochs(epochs, filters) assert_array_almost_equal(stcs_label[0].data, stcs[0].in_label(label).data) @@ -467,7 +468,7 @@ filters = make_lcmv(evoked.info, fwd_sphere, data_cov, reg=0.1, noise_cov=noise_cov, weight_norm=weight_norm, pick_ori=pick_ori, reduce_rank=True) - stc_sphere = apply_lcmv(evoked, filters, max_ori_out='signed') + stc_sphere = apply_lcmv(evoked, filters) if isinstance(stc_sphere, VolVectorSourceEstimate): stc_sphere = stc_sphere.magnitude() else: @@ -520,9 +521,7 @@ @testing.requires_testing_data def test_lcmv_ctf_comp(): """Test interpolation with compensated CTF data.""" - ctf_dir = op.join(testing.data_path(download=False), 'CTF') - raw_fname = op.join(ctf_dir, 'somMDYO-18av.ds') - raw = mne.io.read_raw_ctf(raw_fname, preload=True) + raw = mne.io.read_raw_ctf(ctf_fname, preload=True) raw.pick(raw.ch_names[:70]) events = mne.make_fixed_length_events(raw, duration=0.2)[:2] @@ -545,12 +544,13 @@ make_lcmv(info_comp, fwd, data_cov) +@pytest.mark.slowtest @testing.requires_testing_data @pytest.mark.parametrize('proj, weight_norm', [ (True, 'unit-noise-gain'), (False, 'unit-noise-gain'), - pytest.param(True, None, marks=pytest.mark.slowtest), - pytest.param(True, 'nai', marks=pytest.mark.slowtest), + (True, None), + (True, 'nai'), ]) def test_lcmv_reg_proj(proj, weight_norm): """Test LCMV with and without proj.""" @@ -674,8 +674,8 @@ # no reg (0.00, 'vector', None, True, None, 23, 24, 0.96, 0.97), (0.00, 'vector', 'unit-noise-gain-invariant', True, None, 52, 54, 0.95, 0.96), # noqa: E501 - (0.00, 'vector', 'unit-noise-gain', True, None, 44, 46, 0.97, 0.98), - (0.00, 'vector', 'nai', True, None, 44, 46, 0.97, 0.98), + (0.00, 'vector', 'unit-noise-gain', True, None, 44, 48, 0.97, 0.99), + (0.00, 'vector', 'nai', True, None, 44, 48, 0.97, 0.99), (0.00, 'max-power', None, True, None, 14, 15, 0, 0), (0.00, 'max-power', 'unit-noise-gain-invariant', True, None, 35, 37, 0, 0), # noqa: E501 (0.00, 'max-power', 'unit-noise-gain', True, None, 35, 37, 0, 0), @@ -689,7 +689,7 @@ if not use_cov: evoked.pick_types(meg='grad') noise_cov = None - with pytest.warns(None): # rank deficiency of data_cov + with _record_warnings(): # rank deficiency of data_cov filters = make_lcmv(evoked.info, fwd, data_cov, reg, noise_cov, pick_ori=pick_ori, weight_norm=weight_norm, @@ -712,16 +712,16 @@ # orientation values @pytest.mark.parametrize( 'reg, weight_norm, use_cov, depth, lower, upper, lower_ori, upper_ori', [ - (0.05, 'unit-noise-gain-invariant', False, None, 38, 40, 0.52, 0.54), - (0.05, 'unit-noise-gain', False, None, 38, 40, 0.52, 0.54), - (0.05, 'nai', True, None, 56, 57, 0.56, 0.58), - (0.05, None, True, None, 27, 28, 0.54, 0.56), - (0.05, None, True, 0.8, 42, 43, 0.54, 0.56), + (0.05, 'unit-noise-gain-invariant', False, None, 38, 40, 0.54, 0.55), + (0.05, 'unit-noise-gain', False, None, 38, 40, 0.54, 0.55), + (0.05, 'nai', True, None, 56, 57, 0.59, 0.61), + (0.05, None, True, None, 27, 28, 0.56, 0.57), + (0.05, None, True, 0.8, 42, 43, 0.56, 0.57), # no reg - (0.00, None, True, None, 50, 51, 0.57, 0.58), - (0.00, 'unit-noise-gain-invariant', True, None, 73, 75, 0.57, 0.58), - (0.00, 'unit-noise-gain', True, None, 73, 75, 0.57, 0.58), - (0.00, 'nai', True, None, 73, 75, 0.57, 0.58), + (0.00, None, True, None, 50, 51, 0.58, 0.59), + (0.00, 'unit-noise-gain-invariant', True, None, 73, 75, 0.59, 0.61), + (0.00, 'unit-noise-gain', True, None, 73, 75, 0.59, 0.61), + (0.00, 'nai', True, None, 73, 75, 0.59, 0.61), ]) def test_orientation_max_power(bias_params_fixed, bias_params_free, reg, weight_norm, use_cov, depth, lower, upper, @@ -734,17 +734,18 @@ if not use_cov: evoked.pick_types(meg='grad') noise_cov = None - with pytest.warns(None): # rank deficiency of data_cov - filters = make_lcmv(evoked.info, fwd, data_cov, reg, - noise_cov, pick_ori='max-power', - weight_norm=weight_norm, - depth=depth) + filters = make_lcmv(evoked.info, fwd, data_cov, reg, + noise_cov, pick_ori='max-power', + weight_norm=weight_norm, + depth=depth) loc = apply_lcmv(evoked, filters).data ori = filters['max_power_ori'] + assert ori.shape == (246, 3) loc = np.abs(loc) # Compute the percentage of sources for which there is no loc bias: max_idx = np.argmax(loc, axis=0) - perc = (want == max_idx).mean() * 100 + mask = want == max_idx # ones that localized properly + perc = mask.mean() * 100 assert lower <= perc <= upper # Compute the dot products of our forward normals and assert fwd['coord_frame'] == FIFF.FIFFV_COORD_HEAD @@ -754,7 +755,7 @@ nn = apply_trans(invert_transform(fwd['mri_head_t']), nn, move=False) assert_allclose(np.linalg.norm(nn, axis=1), 1, atol=1e-6) assert_allclose(np.linalg.norm(ori, axis=1), 1, atol=1e-12) - dots = np.abs((nn * ori).sum(-1)) + dots = np.abs((nn[mask] * ori[mask]).sum(-1)) assert_array_less(dots, 1) assert_array_less(0, dots) got = np.mean(dots) diff -Nru python-mne-0.23.4+dfsg/mne/beamformer/tests/test_rap_music.py python-mne-1.1.0+dfsg/mne/beamformer/tests/test_rap_music.py --- python-mne-0.23.4+dfsg/mne/beamformer/tests/test_rap_music.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/beamformer/tests/test_rap_music.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,12 +1,13 @@ # Authors: Yousra Bekhti # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op + +import pytest import numpy as np from scipy import linalg - from numpy.testing import assert_allclose import mne @@ -29,7 +30,8 @@ # Read evoked evoked = mne.read_evokeds(fname_ave, 0, baseline=(None, 0)) evoked.info['bads'] = ['MEG 2443'] - evoked.info['lowpass'] = 16 # fake for decim + with evoked.info._unlock(): + evoked.info['lowpass'] = 16 # fake for decim evoked.decimate(12) evoked.crop(0.0, 0.3) picks = mne.pick_types(evoked.info, meg=True, eeg=False) @@ -153,6 +155,7 @@ _check_dipoles(dipoles, forward_fixed, stc, sim_evoked, residual) +@pytest.mark.slowtest @testing.requires_testing_data def test_rap_music_sphere(): """Test RAP-MUSIC with real data, sphere model, MEG only.""" diff -Nru python-mne-0.23.4+dfsg/mne/beamformer/tests/test_resolution_matrix.py python-mne-1.1.0+dfsg/mne/beamformer/tests/test_resolution_matrix.py --- python-mne-0.23.4+dfsg/mne/beamformer/tests/test_resolution_matrix.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/beamformer/tests/test_resolution_matrix.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # Authors: Olaf Hauk # -# License: BSD (3-clause) +# License: BSD-3-Clause """ Test computation of resolution matrix for LCMV beamformers. diff -Nru python-mne-0.23.4+dfsg/mne/bem.py python-mne-1.1.0+dfsg/mne/bem.py --- python-mne-0.23.4+dfsg/mne/bem.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/bem.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Eric Larson # Lorenzo De Santis # -# License: BSD (3-clause) +# License: BSD-3-Clause # The computations in this code were primarily derived from Matti Hämäläinen's # C code. @@ -13,6 +13,7 @@ import glob import os import os.path as op +from pathlib import Path import shutil from copy import deepcopy @@ -20,22 +21,21 @@ from .io.constants import FIFF, FWD from .io._digitization import _dig_kind_dict, _dig_kind_rev, _dig_kind_ints -from .io.write import (start_file, start_block, write_float, write_int, - write_float_matrix, write_int_matrix, end_block, - end_file) +from .io.write import (start_and_end_file, start_block, write_float, write_int, + write_float_matrix, write_int_matrix, end_block) from .io.tag import find_tag from .io.tree import dir_tree_find from .io.open import fiff_open from .surface import (read_surface, write_surface, complete_surface_info, _compute_nearest, _get_ico_surface, read_tri, - _fast_cross_nd_sum, _get_solids, _complete_sphere_surf) + _fast_cross_nd_sum, _get_solids, _complete_sphere_surf, + decimate_surface) from .transforms import _ensure_trans, apply_trans, Transform from .utils import (verbose, logger, run_subprocess, get_subjects_dir, warn, _pl, _validate_type, _TempDir, _check_freesurfer_home, _check_fname, has_nibabel, _check_option, path_like, - _on_missing) -from .fixes import einsum -from .externals.h5io import write_hdf5, read_hdf5 + _on_missing, _import_h5io_funcs, _ensure_int, + _path_like) # ############################################################################ @@ -104,9 +104,9 @@ l2 = np.linalg.norm(v2, axis=1) l3 = np.linalg.norm(v3, axis=1) ss = l1 * l2 * l3 - ss += einsum('ij,ij,i->i', v1, v2, l3) - ss += einsum('ij,ij,i->i', v1, v3, l2) - ss += einsum('ij,ij,i->i', v2, v3, l1) + ss += np.einsum('ij,ij,i->i', v1, v2, l3) + ss += np.einsum('ij,ij,i->i', v1, v3, l2) + ss += np.einsum('ij,ij,i->i', v2, v3, l1) solids = np.arctan2(triples, ss) # We *could* subselect the good points from v1, v2, v3, triples, solids, @@ -263,14 +263,14 @@ surf = complete_surface_info(surf, copy=copy, verbose=False) fewer = np.where([len(t) < 3 for t in surf['neighbor_tri']])[0] if len(fewer) > 0: + fewer = list(fewer) + fewer = (fewer[:80] + ['...']) if len(fewer) > 80 else fewer + fewer = ', '.join(str(f) for f in fewer) msg = ('Surface {} has topological defects: {:.0f} / {:.0f} vertices ' 'have fewer than three neighboring triangles [{}]{}' - .format(_bem_surf_name[surf['id']], len(fewer), surf['ntri'], - ', '.join(str(f) for f in fewer), extra)) - if incomplete == 'raise': - raise RuntimeError(msg) - else: - warn(msg) + .format(_bem_surf_name[surf['id']], len(fewer), len(surf['rr']), + fewer, extra)) + _on_missing(on_missing=incomplete, msg=msg, name='on_defects') return surf @@ -485,7 +485,7 @@ 'number of elements (1 or 3)') for si, surf in enumerate(surfs): if isinstance(surf, str): - surfs[si] = read_surface(surf, return_dict=True)[-1] + surfs[si] = surf = read_surface(surf, return_dict=True)[-1] # Downsampling if the surface is isomorphic with a subdivided icosahedron if ico is not None: for si, surf in enumerate(surfs): @@ -653,8 +653,8 @@ def _one_step(mu, u): """Evaluate the residual sum of squares fit for one set of mu values.""" - if np.abs(mu).max() > 1.0: - return 1.0 + if np.abs(mu).max() >= 1.0: + return 100.0 # Compose the data for the linear fitting, compute SVD, then residuals y, uu, sing, vv = _compose_linear_fitting_data(mu, u) @@ -684,13 +684,13 @@ # Do the nonlinear minimization, constraining mu to the interval [-1, +1] mu_0 = np.zeros(3) fun = partial(_one_step, u=u) - max_ = 1. - 2e-4 # adjust for fmin_cobyla "catol" that not all scipy have - cons = list() - for ii in range(nfit): - def mycon(x, ii=ii): - return max_ - np.abs(x[ii]) - cons.append(mycon) - mu = fmin_cobyla(fun, mu_0, cons, rhobeg=0.5, rhoend=1e-5, disp=0) + catol = 1e-6 + max_ = 1. - 2 * catol + + def cons(x): + return max_ - np.abs(x) + + mu = fmin_cobyla(fun, mu_0, [cons], rhobeg=0.5, rhoend=1e-5, catol=catol) # (6) Do the final step: calculation of the linear parameters rv, lambda_ = _compute_linear_parameters(mu, u) @@ -719,9 +719,7 @@ If float, compute spherical shells for EEG using the given radius. If 'auto', estimate an appropriate radius from the dig points in Info, If None, exclude shells (single layer sphere model). - info : instance of Info | None - Measurement info. Only needed if ``r0`` or ``head_radius`` are - ``'auto'``. + %(info)s Only needed if ``r0`` or ``head_radius`` are ``'auto'``. relative_radii : array-like Relative radii for the spherical shells. sigmas : array-like @@ -827,8 +825,7 @@ Parameters ---------- - info : instance of Info - Measurement info. + %(info_not_none)s %(dig_kinds)s units : str Can be "m" (default) or "mm". @@ -868,8 +865,7 @@ Parameters ---------- - info : instance of Info - The measurement info. + %(info_not_none)s %(dig_kinds)s %(exclude_frontal)s Default is True. @@ -1027,7 +1023,7 @@ @verbose def make_watershed_bem(subject, subjects_dir=None, overwrite=False, volume='T1', atlas=False, gcaatlas=False, preflood=None, - show=False, copy=False, T1=None, brainmask='ws.mgz', + show=False, copy=True, T1=None, brainmask='ws.mgz', verbose=None): """Create BEM surfaces using the FreeSurfer watershed algorithm. @@ -1051,10 +1047,11 @@ .. versionadded:: 0.12 copy : bool - If True (default False), use copies instead of symlinks for surfaces + If True (default), use copies instead of symlinks for surfaces (if they do not already exist). .. versionadded:: 0.18 + .. versionchanged:: 1.1 Use copies instead of symlinks. T1 : bool | None If True, pass the ``-T1`` flag. By default (None), this takes the same value as ``gcaatlas``. @@ -1258,6 +1255,7 @@ def _read_bem_surfaces_h5(fname, s_id): + read_hdf5, _ = _import_h5io_funcs() bem = read_hdf5(fname) try: [s['id'] for s in bem['surfs']] @@ -1350,7 +1348,7 @@ if tag is None: raise ValueError('Vertex data not found') - res['rr'] = tag.data.astype(np.float64) # XXX : double because of mayavi + res['rr'] = tag.data.astype(np.float64) if res['rr'].shape[0] != res['np']: raise ValueError('Vertex information is incorrect') @@ -1360,7 +1358,7 @@ if tag is None: res['nn'] = None else: - res['nn'] = tag.data.copy() + res['nn'] = tag.data.astype(np.float64) if res['nn'].shape[0] != res['np']: raise ValueError('Vertex normal information is incorrect') @@ -1400,6 +1398,7 @@ fname = _check_fname(fname, 'read', True, 'fname') # mirrors fwd_bem_load_surfaces from fwd_bem_model.c if fname.endswith('.h5'): + read_hdf5, _ = _import_h5io_funcs() logger.info('Loading surfaces and solution...') bem = read_hdf5(fname) else: @@ -1565,19 +1564,19 @@ fname = _check_fname(fname, overwrite=overwrite, name='fname') if fname.endswith('.h5'): + _, write_hdf5 = _import_h5io_funcs() write_hdf5(fname, dict(surfs=surfs), overwrite=True) else: - with start_file(fname) as fid: + with start_and_end_file(fname) as fid: start_block(fid, FIFF.FIFFB_BEM) write_int(fid, FIFF.FIFF_BEM_COORD_FRAME, surfs[0]['coord_frame']) _write_bem_surfaces_block(fid, surfs) end_block(fid, FIFF.FIFFB_BEM) - end_file(fid) @verbose def write_head_bem(fname, rr, tris, on_defects='raise', overwrite=False, - verbose=None): + *, verbose=None): """Write a head surface to a fiff file. Parameters @@ -1636,6 +1635,7 @@ """ fname = _check_fname(fname, overwrite=overwrite, name='fname') if fname.endswith('.h5'): + _, write_hdf5 = _import_h5io_funcs() bem = {k: bem[k] for k in ('surfs', 'solution', 'bem_method')} write_hdf5(fname, bem, overwrite=True) else: @@ -1644,7 +1644,7 @@ def _write_bem_solution_fif(fname, bem): _check_bem_size(bem['surfs']) - with start_file(fname) as fid: + with start_and_end_file(fname) as fid: start_block(fid, FIFF.FIFFB_BEM) # Coordinate frame (mainly for backward compatibility) write_int(fid, FIFF.FIFF_BEM_COORD_FRAME, @@ -1659,7 +1659,6 @@ write_float_matrix(fid, FIFF.FIFF_BEM_POT_SOLUTION, bem['solution']) end_block(fid, FIFF.FIFFB_BEM) - end_file(fid) # ############################################################################# @@ -1690,62 +1689,112 @@ return env, mri_dir, bem_dir +def _write_echos(mri_dir, flash_echos, angle): + import nibabel as nib + from nibabel.spatialimages import SpatialImage + if _path_like(flash_echos): + flash_echos = nib.load(flash_echos) + if isinstance(flash_echos, SpatialImage): + flash_echo_imgs = [] + data = np.asanyarray(flash_echos.dataobj) + affine = flash_echos.affine + if data.ndim == 3: + data = data[..., np.newaxis] + for echo_idx in range(data.shape[3]): + this_echo_img = flash_echos.__class__( + data[..., echo_idx], affine=affine, + header=deepcopy(flash_echos.header) + ) + flash_echo_imgs.append(this_echo_img) + flash_echos = flash_echo_imgs + del flash_echo_imgs + for idx, flash_echo in enumerate(flash_echos, 1): + if _path_like(flash_echo): + flash_echo = nib.load(flash_echo) + nib.save(flash_echo, + op.join(mri_dir, 'flash', f'mef{angle}_{idx:03d}.mgz')) + + @verbose def convert_flash_mris(subject, flash30=True, convert=True, unwarp=False, - subjects_dir=None, verbose=None): - """Convert DICOM files for use with make_flash_bem. + subjects_dir=None, flash5=True, verbose=None): + """Synthetize the flash 5 files for use with make_flash_bem. + + This function aims to produce a synthesized flash 5 MRI from + multiecho flash (MEF) MRI data. This function can use MEF data + with 5 or 30 flip angles. If flash5 (and flash30) images are not + explicitly provided, it will assume that the different echos are available + in the mri/flash folder of the subject with the following naming + convention "mef_.mgz", e.g. "mef05_001.mgz" + or "mef30_001.mgz". Parameters ---------- subject : str Subject name. - flash30 : bool - Use 30-degree flip angle data. + flash30 : bool | list of SpatialImage or path-like | SpatialImage | path-like + If False do not use 30-degree flip angle data. + The list of flash 5 echos to use. If True it will look for files + named mef30_*.mgz in the subject's mri/flash directory and if not False + the list of flash 5 echos images will be written to the mri/flash + folder with convention mef05_.mgz. If a SpatialImage object + each frame of the image will be interpreted as an echo. convert : bool Assume that the Flash MRI images have already been converted to mgz files. + + DEPRECATED: This option is deprecated and will be removed in 1.2. + Set it explicitly to False to silence the deprecation warning + and use the flash5_echos and flash30_echos parameters to specify + the images to use. unwarp : bool Run grad_unwarp with -unwarp option on each of the converted data sets. It requires FreeSurfer's MATLAB toolbox to be properly installed. %(subjects_dir)s + flash5 : list of SpatialImage or path-like | SpatialImage | path-like | True + The list of flash 5 echos to use. If True it will look for files + named mef05_*.mgz in the subject's mri/flash directory and if not None + the list of flash 5 echos images will be written to the mri/flash + folder with convention mef05_.mgz. If a SpatialImage object + each frame of the image will be interpreted as an echo. %(verbose)s + Returns + ------- + flash5_img : path-like + The path the synthesized flash 5 MRI. + Notes ----- - Before running this script do the following: - (unless convert=False is specified) - - 1. Copy all of your FLASH images in a single directory and - create a directory to hold the output of mne_organize_dicom - 2. cd to and run - $ mne_organize_dicom - to create an appropriate directory structure - 3. Create symbolic links to make flash05 and flash30 point to the - appropriate series: - $ ln -s flash05 - $ ln -s flash30 - Some partition formats (e.g. FAT32) do not support symbolic links. - In this case, copy the file to the appropriate series: - $ cp flash05 - $ cp flash30 - 4. cd to the directory where flash05 and flash30 links are - 5. Set SUBJECTS_DIR and SUBJECT environment variables appropriately - 6. Run this script - This function assumes that the Freesurfer segmentation of the subject has been completed. In particular, the T1.mgz and brain.mgz MRI volumes should be, as usual, in the subject's mri directory. - """ + """ # noqa: E501 env, mri_dir = _prepare_env(subject, subjects_dir)[:2] tempdir = _TempDir() # fsl and Freesurfer create some random junk in CWD run_subprocess_env = partial(run_subprocess, env=env, cwd=tempdir) + + mri_dir = Path(mri_dir) # Step 1a : Data conversion to mgz format - if not op.exists(op.join(mri_dir, 'flash', 'parameter_maps')): - os.makedirs(op.join(mri_dir, 'flash', 'parameter_maps')) + flash_dir = mri_dir / "flash" + pm_dir = flash_dir / 'parameter_maps' + pm_dir.mkdir(parents=True, exist_ok=True) echos_done = 0 + + if convert and not isinstance(flash5, bool): + raise ValueError("When passing flash5 images, convert must be False.") + if convert and not isinstance(flash30, bool): + raise ValueError("When passing flash30 images, convert must be False.") + if convert: + warn("The convert parameter is deprecated and will be removed in " + "1.2. You can now pass the Flash 5 and 30 echos as nibabel " + "images to the function via the flash5_echos and flash30_echos" + "parameters.", + DeprecationWarning) + logger.info("\n---- Converting Flash images ----") echos = ['001', '002', '003', '004', '005', '006', '007', '008'] if flash30: @@ -1772,8 +1821,7 @@ raise RuntimeError("Directory %s is missing." % op.join('flash' + flash, echo)) sample_file = glob.glob(op.join('flash' + flash, echo, '*'))[0] - dest_file = op.join(mri_dir, 'flash', - 'mef' + flash + '_' + echo + '.mgz') + dest_file = op.join(mri_dir, 'flash', f'mef{flash}_{echo}.mgz') # do not redo if already present if op.isfile(dest_file): logger.info("The file %s is already there") @@ -1781,10 +1829,15 @@ cmd = ['mri_convert', sample_file, dest_file] run_subprocess_env(cmd) echos_done += 1 + else: + if not isinstance(flash5, bool): + _write_echos(mri_dir, flash5, angle='05') + if not isinstance(flash30, bool): + _write_echos(mri_dir, flash30, angle='30') + # Step 1b : Run grad_unwarp on converted files - flash_dir = op.join(mri_dir, "flash") - template = op.join(flash_dir, "mef*.mgz") - files = glob.glob(template) + template = op.join(flash_dir, "mef*_*.mgz") + files = sorted(glob.glob(template)) if len(files) == 0: raise ValueError('No suitable source files found (%s)' % template) if unwarp: @@ -1795,56 +1848,51 @@ 'true'] run_subprocess_env(cmd) # Clear parameter maps if some of the data were reconverted - pm_dir = op.join(flash_dir, 'parameter_maps') - if echos_done > 0 and op.exists(pm_dir): + if echos_done > 0 and pm_dir.exists(): shutil.rmtree(pm_dir) logger.info("\nParameter maps directory cleared") - if not op.exists(pm_dir): - os.makedirs(pm_dir) + if not pm_dir.exists(): + pm_dir.mkdir(parents=True, exist_ok=True) # Step 2 : Create the parameter maps if flash30: logger.info("\n---- Creating the parameter maps ----") if unwarp: - files = glob.glob(op.join(flash_dir, "mef05*u.mgz")) + files = sorted(glob.glob(op.join(flash_dir, "mef05_*u.mgz"))) if len(os.listdir(pm_dir)) == 0: - cmd = (['mri_ms_fitparms'] + - files + - [op.join(flash_dir, 'parameter_maps')]) + cmd = (['mri_ms_fitparms'] + files + [str(pm_dir)]) run_subprocess_env(cmd) else: logger.info("Parameter maps were already computed") # Step 3 : Synthesize the flash 5 images logger.info("\n---- Synthesizing flash 5 images ----") - if not op.exists(op.join(pm_dir, 'flash5.mgz')): + if not (pm_dir / 'flash5.mgz').exists(): cmd = ['mri_synthesize', '20', '5', '5', - op.join(pm_dir, 'T1.mgz'), - op.join(pm_dir, 'PD.mgz'), - op.join(pm_dir, 'flash5.mgz') + (pm_dir / 'T1.mgz'), + (pm_dir / 'PD.mgz'), + (pm_dir / 'flash5.mgz') ] run_subprocess_env(cmd) - os.remove(op.join(pm_dir, 'flash5_reg.mgz')) + (pm_dir / 'flash5_reg.mgz').unlink() else: logger.info("Synthesized flash 5 volume is already there") else: logger.info("\n---- Averaging flash5 echoes ----") - template = op.join(flash_dir, - "mef05*u.mgz" if unwarp else "mef05*.mgz") - files = glob.glob(template) + template = "mef05_*u.mgz" if unwarp else "mef05_*.mgz" + files = sorted(flash_dir.glob(template)) if len(files) == 0: raise ValueError('No suitable source files found (%s)' % template) - cmd = (['mri_average', '-noconform'] + - files + - [op.join(pm_dir, 'flash5.mgz')]) + cmd = (['mri_average', '-noconform'] + files + [pm_dir / 'flash5.mgz']) run_subprocess_env(cmd) - if op.exists(op.join(pm_dir, 'flash5_reg.mgz')): - os.remove(op.join(pm_dir, 'flash5_reg.mgz')) + (pm_dir / 'flash5_reg.mgz').unlink(missing_ok=True) del tempdir # finally done running subprocesses - assert op.isfile(op.join(pm_dir, 'flash5.mgz')) + assert (pm_dir / 'flash5.mgz').exists() + return pm_dir / 'flash5.mgz' @verbose def make_flash_bem(subject, overwrite=False, show=True, subjects_dir=None, - flash_path=None, copy=False, verbose=None): + flash_path=None, copy=True, *, flash5_img=None, + register=True, verbose=None): """Create 3-Layer BEM model from prepared flash MRI images. Parameters @@ -1857,15 +1905,31 @@ Show surfaces to visually inspect all three BEM surfaces (recommended). %(subjects_dir)s flash_path : str | None + [DEPRECATED] Use the flash5_img parameter instead. + Path to the flash images. If None (default), mri/flash/parameter_maps within the subject reconstruction is used. .. versionadded:: 0.13.0 + .. versionchanged:: 1.1 Deprecated copy : bool - If True (default False), use copies instead of symlinks for surfaces + If True (default), use copies instead of symlinks for surfaces (if they do not already exist). .. versionadded:: 0.18 + .. versionchanged:: 1.1 Use copies instead of symlinks. + flash5_img : None | path-like | Nifti1Image + The path to the synthesized flash 5 MRI image or the image itself. If + None (default), the path defaults to flash5.mgz within the flash_path + folder. If not present the image is copied or written to the + flash_path folder as flash5.mgz. + + .. versionadded:: 1.1.0 + register : bool + Register the flash 5 image with T1.mgz file. If False, we assume + that the images are already coregistered. + + .. versionadded:: 1.1.0 %(verbose)s See Also @@ -1877,8 +1941,8 @@ This program assumes that FreeSurfer is installed and sourced properly. This function extracts the BEM surfaces (outer skull, inner skull, and - outer skin) from multiecho FLASH MRI data with spin angles of 5 and 30 - degrees, in mgz format. + outer skin) from a FLASH 5 MRI image synthesized from multiecho FLASH + images acquired with spin angles of 5 and 30 degrees. """ from .viz.misc import plot_bem @@ -1887,65 +1951,86 @@ run_subprocess_env = partial(run_subprocess, env=env, cwd=tempdir) + mri_dir = Path(mri_dir) + bem_dir = Path(bem_dir) if flash_path is None: - flash_path = op.join(mri_dir, 'flash', 'parameter_maps') + flash_path = mri_dir / 'flash' / 'parameter_maps' else: - flash_path = op.abspath(flash_path) + warn("The flash_path parameter is deprecated and will be removed " + "in version 1.1. Use the flash5_img parameter instead.") + flash_path = Path(flash_path).resolve() subjects_dir = env['SUBJECTS_DIR'] + flash_path.mkdir(exist_ok=True, parents=True) logger.info('\nProcessing the flash MRI data to produce BEM meshes with ' 'the following parameters:\n' 'SUBJECTS_DIR = %s\n' 'SUBJECT = %s\n' 'Result dir = %s\n' % (subjects_dir, subject, - op.join(bem_dir, 'flash'))) + bem_dir / 'flash')) # Step 4 : Register with MPRAGE - logger.info("\n---- Registering flash 5 with MPRAGE ----") - flash5 = op.join(flash_path, 'flash5.mgz') - flash5_reg = op.join(flash_path, 'flash5_reg.mgz') - if not op.exists(flash5_reg): - if op.exists(op.join(mri_dir, 'T1.mgz')): - ref_volume = op.join(mri_dir, 'T1.mgz') - else: - ref_volume = op.join(mri_dir, 'T1') - cmd = ['fsl_rigid_register', '-r', ref_volume, '-i', flash5, - '-o', flash5_reg] + flash5 = flash_path / 'flash5.mgz' + + if _path_like(flash5_img): + logger.info(f"Copying flash 5 image {flash5_img} to {flash5}") + cmd = ['mri_convert', Path(flash5_img).resolve(), flash5] run_subprocess_env(cmd) + elif flash5_img is None: + if not flash5.exists(): + raise ValueError(f'Flash 5 image cannot be found at {flash5}.') else: - logger.info("Registered flash 5 image is already there") + logger.info(f"Writing flash 5 image at {flash5}") + import nibabel as nib + nib.save(flash5_img, flash5) + + if register: + logger.info("\n---- Registering flash 5 with T1 MPRAGE ----") + flash5_reg = flash_path / 'flash5_reg.mgz' + if not flash5_reg.exists(): + if (mri_dir / 'T1.mgz').exists(): + ref_volume = mri_dir / 'T1.mgz' + else: + ref_volume = mri_dir / 'T1' + cmd = ['fsl_rigid_register', '-r', str(ref_volume), '-i', + str(flash5), '-o', str(flash5_reg)] + run_subprocess_env(cmd) + else: + logger.info("Registered flash 5 image is already there") + else: + flash5_reg = flash5 + # Step 5a : Convert flash5 into COR logger.info("\n---- Converting flash5 volume into COR format ----") - flash5_dir = op.join(mri_dir, 'flash5') + flash5_dir = mri_dir / 'flash5' shutil.rmtree(flash5_dir, ignore_errors=True) - os.makedirs(flash5_dir) - cmd = ['mri_convert', flash5_reg, op.join(mri_dir, 'flash5')] + flash5_dir.mkdir(exist_ok=True, parents=True) + cmd = ['mri_convert', flash5_reg, flash5_dir] run_subprocess_env(cmd) # Step 5b and c : Convert the mgz volumes into COR convert_T1 = False - T1_dir = op.join(mri_dir, 'T1') - if not op.isdir(T1_dir) or len(glob.glob(op.join(T1_dir, 'COR*'))) == 0: + T1_dir = mri_dir / 'T1' + if not T1_dir.is_dir() or next(T1_dir.glob('COR*')) is None: convert_T1 = True convert_brain = False - brain_dir = op.join(mri_dir, 'brain') - if not op.isdir(brain_dir) or \ - len(glob.glob(op.join(brain_dir, 'COR*'))) == 0: + brain_dir = mri_dir / 'brain' + if not brain_dir.is_dir() or next(brain_dir.glob('COR*')) is None: convert_brain = True logger.info("\n---- Converting T1 volume into COR format ----") if convert_T1: - T1_fname = op.join(mri_dir, 'T1.mgz') - if not op.isfile(T1_fname): + T1_fname = mri_dir / 'T1.mgz' + if not T1_fname.is_file(): raise RuntimeError("Both T1 mgz and T1 COR volumes missing.") - os.makedirs(T1_dir) + T1_dir.mkdir(exist_ok=True, parents=True) cmd = ['mri_convert', T1_fname, T1_dir] run_subprocess_env(cmd) else: logger.info("T1 volume is already in COR format") logger.info("\n---- Converting brain volume into COR format ----") if convert_brain: - brain_fname = op.join(mri_dir, 'brain.mgz') - if not op.isfile(brain_fname): + brain_fname = mri_dir / 'brain.mgz' + if not brain_fname.is_file(): raise RuntimeError("Both brain mgz and brain COR volumes missing.") - os.makedirs(brain_dir) + brain_dir.mkdir(exist_ok=True, parents=True) cmd = ['mri_convert', brain_fname, brain_dir] run_subprocess_env(cmd) else: @@ -1957,13 +2042,12 @@ del tempdir # ran our last subprocess; clean up directory logger.info("\n---- Converting the tri files into surf files ----") - flash_bem_dir = op.join(bem_dir, 'flash') - if not op.exists(flash_bem_dir): - os.makedirs(flash_bem_dir) + flash_bem_dir = bem_dir / 'flash' + flash_bem_dir.mkdir(exist_ok=True, parents=True) surfs = ['inner_skull', 'outer_skull', 'outer_skin'] for surf in surfs: - out_fname = op.join(flash_bem_dir, surf + '.tri') - shutil.move(op.join(bem_dir, surf + '.tri'), out_fname) + out_fname = flash_bem_dir / (surf + '.tri') + shutil.move(bem_dir / (surf + '.tri'), out_fname) nodes, tris = read_tri(out_fname, swap=True) # Do not write volume info here because the tris are already in # standard Freesurfer coords @@ -1972,8 +2056,7 @@ # Cleanup section logger.info("\n---- Cleaning up ----") - os.remove(op.join(bem_dir, 'inner_skull_tmp.tri')) - # os.chdir(mri_dir) + (bem_dir / 'inner_skull_tmp.tri').unlink() if convert_T1: shutil.rmtree(T1_dir) logger.info("Deleted the T1 COR volume") @@ -1986,18 +2069,18 @@ logger.info("\n---- Creating symbolic links ----") # os.chdir(bem_dir) for surf in surfs: - surf = op.join(bem_dir, surf + '.surf') - if not overwrite and op.exists(surf): + surf = bem_dir / (surf + '.surf') + if not overwrite and surf.exists(): skip_symlink = True else: - if op.exists(surf): - os.remove(surf) - _symlink(op.join(flash_bem_dir, op.basename(surf)), surf, copy) + if surf.exists(): + surf.unlink() + _symlink(flash_bem_dir / surf.name, surf, copy) skip_symlink = False if skip_symlink: logger.info("Unable to create all symbolic links to .surf files " "in bem folder. Use --overwrite option to recreate them.") - dest = op.join(bem_dir, 'flash') + dest = bem_dir / 'flash' else: logger.info("Symbolic links to .surf files created in bem folder") dest = bem_dir @@ -2062,3 +2145,192 @@ bem['surfs'][-1]['id'] = id_ return bem + + +def _check_file(fname, overwrite): + """Prevent overwrites.""" + if op.isfile(fname) and not overwrite: + raise IOError(f'File {fname} exists, use --overwrite to overwrite it') + + +_tri_levels = dict( + medium=30000, + sparse=2500, +) + + +@verbose +def make_scalp_surfaces(subject, subjects_dir=None, force=True, + overwrite=False, no_decimate=False, *, + threshold=20, mri='T1.mgz', verbose=None): + """Create surfaces of the scalp and neck. + + The scalp surfaces are required for using the MNE coregistration GUI, and + allow for a visualization of the alignment between anatomy and channel + locations. + + Parameters + ---------- + %(subject)s + %(subjects_dir)s + force : bool + Force creation of the surface even if it has some topological defects. + Defaults to ``True``. See :ref:`tut-fix-meshes` for ideas on how to + fix problematic meshes. + %(overwrite)s + no_decimate : bool + Disable the "medium" and "sparse" decimations. In this case, only + a "dense" surface will be generated. Defaults to ``False``, i.e., + create surfaces for all three types of decimations. + threshold : int + The threshold to use with the MRI in the call to ``mkheadsurf``. + The default is 20. + + .. versionadded:: 1.1 + mri : str + The MRI to use. Should exist in ``$SUBJECTS_DIR/$SUBJECT/mri``. + + .. versionadded:: 1.1 + %(verbose)s + """ + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + incomplete = 'warn' if force else 'raise' + subj_path = op.join(subjects_dir, subject) + if not op.exists(subj_path): + raise RuntimeError('%s does not exist. Please check your subject ' + 'directory path.' % subj_path) + + # Backward compat for old FreeSurfer (?) + _validate_type(mri, str, 'mri') + if mri == 'T1.mgz': + mri = mri if op.exists(op.join(subj_path, 'mri', mri)) else 'T1' + + logger.info('1. Creating a dense scalp tessellation with mkheadsurf...') + + def check_seghead(surf_path=op.join(subj_path, 'surf')): + surf = None + for k in ['lh.seghead', 'lh.smseghead']: + this_surf = op.join(surf_path, k) + if op.exists(this_surf): + surf = this_surf + break + return surf + + my_seghead = check_seghead() + threshold = _ensure_int(threshold, 'threshold') + if my_seghead is None: + this_env = deepcopy(os.environ) + this_env['SUBJECTS_DIR'] = subjects_dir + this_env['SUBJECT'] = subject + this_env['subjdir'] = subjects_dir + '/' + subject + if 'FREESURFER_HOME' not in this_env: + raise RuntimeError( + 'The FreeSurfer environment needs to be set up to use ' + 'make_scalp_surfaces to create the outer skin surface ' + 'lh.seghead') + run_subprocess([ + 'mkheadsurf', '-subjid', subject, '-srcvol', mri, + '-thresh1', str(threshold), + '-thresh2', str(threshold)], env=this_env) + + surf = check_seghead() + if surf is None: + raise RuntimeError('mkheadsurf did not produce the standard output ' + 'file.') + + bem_dir = op.join(subjects_dir, subject, 'bem') + if not op.isdir(bem_dir): + os.mkdir(bem_dir) + fname_template = op.join(bem_dir, '%s-head-{}.fif' % subject) + dense_fname = fname_template.format('dense') + logger.info('2. Creating %s ...' % dense_fname) + _check_file(dense_fname, overwrite) + # Helpful message if we get a topology error + msg = ('\n\nConsider using pymeshfix directly to fix the mesh, or --force ' + 'to ignore the problem.') + surf = _surfaces_to_bem( + [surf], [FIFF.FIFFV_BEM_SURF_ID_HEAD], [1], + incomplete=incomplete, extra=msg)[0] + write_bem_surfaces(dense_fname, surf, overwrite=overwrite) + if os.getenv('_MNE_TESTING_SCALP', 'false') == 'true': + tris = [len(surf['tris'])] # don't actually decimate + for ii, (level, n_tri) in enumerate(_tri_levels.items(), 3): + if no_decimate: + break + logger.info(f'{ii}. Creating {level} tessellation...') + logger.info(f'{ii}.1 Decimating the dense tessellation ' + f'({len(surf["tris"])} -> {n_tri} triangles)...') + points, tris = decimate_surface(points=surf['rr'], + triangles=surf['tris'], + n_triangles=n_tri) + dec_fname = fname_template.format(level) + logger.info('%i.2 Creating %s' % (ii, dec_fname)) + _check_file(dec_fname, overwrite) + dec_surf = _surfaces_to_bem( + [dict(rr=points, tris=tris)], + [FIFF.FIFFV_BEM_SURF_ID_HEAD], [1], rescale=False, + incomplete=incomplete, extra=msg) + write_bem_surfaces(dec_fname, dec_surf, overwrite=overwrite) + logger.info('[done]') + + +@verbose +def distance_to_bem(pos, bem, trans=None, verbose=None): + """Calculate the distance of positions to inner skull surface. + + Parameters + ---------- + pos : array, shape (..., 3) + Position(s) in m, in head coordinates. + bem : instance of ConductorModel + Conductor model. + %(trans)s If None (default), assumes bem is in head coordinates. + %(verbose)s + + Returns + ------- + distances : float | array, shape (...) + The computed distance(s). A float is returned if pos is + an array of shape (3,) corresponding to a single position. + + Notes + ----- + .. versionadded:: 1.1 + """ + ndim = pos.ndim + if ndim == 1: + pos = pos[np.newaxis, :] + + n = pos.shape[0] + distance = np.zeros((n,)) + + logger.info( + 'Computing distance to inner skull surface for ' + + f'{n} position{_pl(n)}...' + ) + + if bem['is_sphere']: + center = bem['r0'] + + if trans: + center = apply_trans(trans, center, move=True) + radius = bem['layers'][0]['rad'] + + distance = np.abs(radius - np.linalg.norm( + pos - center, axis=1 + )) + + else: # is BEM + surface_points = bem['surfs'][0]['rr'] + + if trans: + surface_points = apply_trans( + trans, surface_points, move=True + ) + + _, distance = _compute_nearest(surface_points, pos, return_dists=True) + + if ndim == 1: + distance = distance[0] # return just a float if one pos is passed + + return distance diff -Nru python-mne-0.23.4+dfsg/mne/channels/channels.py python-mne-1.1.0+dfsg/mne/channels/channels.py --- python-mne-0.23.4+dfsg/mne/channels/channels.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/channels/channels.py 2022-08-03 15:48:14.000000000 +0000 @@ -6,36 +6,36 @@ # Teon Brooks # Daniel McCloy # -# License: BSD (3-clause) +# License: BSD-3-Clause -import os import os.path as op +from pathlib import Path import sys from collections import OrderedDict +from dataclasses import dataclass from copy import deepcopy from functools import partial +import string +from typing import Union import numpy as np from ..defaults import HEAD_SIZE_DEFAULT, _handle_default -from ..transforms import _frame_to_str from ..utils import (verbose, logger, warn, _check_preload, _validate_type, fill_doc, _check_option, - _get_stim_channel, _check_fname) -from ..io.compensator import get_current_comp + _get_stim_channel, _check_fname, _check_dict_keys) from ..io.constants import FIFF from ..io.meas_info import (anonymize_info, Info, MontageMixin, create_info, _rename_comps) from ..io.pick import (channel_type, pick_info, pick_types, _picks_by_type, _check_excludes_includes, _contains_ch_type, channel_indices_by_type, pick_channels, _picks_to_idx, - _get_channel_types, get_channel_type_constants, + get_channel_type_constants, _pick_data_channels) from ..io.tag import _rename_list from ..io.write import DATE_NONE from ..io.proj import setup_proj -from ..io._digitization import _get_data_as_dict_from_dig def _get_meg_system(info): @@ -171,9 +171,14 @@ for inst in instances: # Only perform picking when needed if inst.ch_names != common_channels: - if copy: - inst = inst.copy() - inst.pick_channels(common_channels, ordered=True) + if isinstance(inst, Info): + sel = pick_channels(inst.ch_names, common_channels, exclude=[], + ordered=True) + inst = pick_info(inst, sel, copy=copy, verbose=False) + else: + if copy: + inst = inst.copy() + inst.pick_channels(common_channels, ordered=True) if len(inst.ch_names) == len(common_channels): reordered = True equalized_instances.append(inst) @@ -186,102 +191,6 @@ return equalized_instances -class ContainsMixin(object): - """Mixin class for Raw, Evoked, Epochs.""" - - def __contains__(self, ch_type): - """Check channel type membership. - - Parameters - ---------- - ch_type : str - Channel type to check for. Can be e.g. 'meg', 'eeg', 'stim', etc. - - Returns - ------- - in : bool - Whether or not the instance contains the given channel type. - - Examples - -------- - Channel type membership can be tested as:: - - >>> 'meg' in inst # doctest: +SKIP - True - >>> 'seeg' in inst # doctest: +SKIP - False - - """ - if ch_type == 'meg': - has_ch_type = (_contains_ch_type(self.info, 'mag') or - _contains_ch_type(self.info, 'grad')) - else: - has_ch_type = _contains_ch_type(self.info, ch_type) - return has_ch_type - - @property - def compensation_grade(self): - """The current gradient compensation grade.""" - return get_current_comp(self.info) - - @fill_doc - def get_channel_types(self, picks=None, unique=False, only_data_chs=False): - """Get a list of channel type for each channel. - - Parameters - ---------- - %(picks_all)s - unique : bool - Whether to return only unique channel types. Default is ``False``. - only_data_chs : bool - Whether to ignore non-data channels. Default is ``False``. - - Returns - ------- - channel_types : list - The channel types. - """ - return _get_channel_types(self.info, picks=picks, unique=unique, - only_data_chs=only_data_chs) - - @fill_doc - def get_montage(self): - """Get a DigMontage from instance. - - Returns - ------- - %(montage)s - """ - from ..channels.montage import make_dig_montage - if self.info['dig'] is None: - return None - # obtain coord_frame, and landmark coords - # (nasion, lpa, rpa, hsp, hpi) from DigPoints - montage_bunch = _get_data_as_dict_from_dig(self.info['dig']) - coord_frame = _frame_to_str.get(montage_bunch.coord_frame) - - # get the channel names and chs data structure - ch_names, chs = self.info['ch_names'], self.info['chs'] - picks = pick_types(self.info, meg=False, eeg=True, - seeg=True, ecog=True, dbs=True) - - # channel positions from dig do not match ch_names one to one, - # so use loc[:3] instead - ch_pos = {ch_names[ii]: chs[ii]['loc'][:3] for ii in picks} - - # create montage - montage = make_dig_montage( - ch_pos=ch_pos, - coord_frame=coord_frame, - nasion=montage_bunch.nasion, - lpa=montage_bunch.lpa, - rpa=montage_bunch.rpa, - hsp=montage_bunch.hsp, - hpi=montage_bunch.hpi, - ) - return montage - - channel_type_constants = get_channel_type_constants() _human2fiff = {k: v.get('kind', FIFF.FIFFV_COIL_NONE) for k, v in channel_type_constants.items()} @@ -322,11 +231,11 @@ Parameters ---------- - %(set_eeg_reference_ref_channels)s - %(set_eeg_reference_projection)s - %(set_eeg_reference_ch_type)s - %(set_eeg_reference_forward)s - %(verbose_meth)s + %(ref_channels_set_eeg_reference)s + %(projection_set_eeg_reference)s + %(ch_type_set_eeg_reference)s + %(forward_set_eeg_reference)s + %(verbose)s Returns ------- @@ -402,7 +311,7 @@ mapping : dict A dictionary mapping a channel to a sensor type (str), e.g., ``{'EEG061': 'eog'}``. - %(verbose_meth)s + %(verbose)s Returns ------- @@ -480,8 +389,8 @@ Parameters ---------- - %(rename_channels_mapping_duplicates)s - %(verbose_meth)s + %(mapping_rename_channels_duplicates)s + %(verbose)s Returns ------- @@ -567,8 +476,8 @@ .. versionadded:: 0.13.0 show : bool Show figure if True. Defaults to True. - %(topomap_sphere_auto)s - %(verbose_meth)s + %(sphere_topomap_auto)s + %(verbose)s Returns ------- @@ -584,7 +493,7 @@ Notes ----- This function plots the sensor locations from the info structure using - matplotlib. For drawing the sensors using mayavi see + matplotlib. For drawing the sensors using PyVista see :func:`mne.viz.plot_alignment`. .. versionadded:: 0.12.0 @@ -601,7 +510,8 @@ Parameters ---------- - %(anonymize_info_parameters)s + %(daysback_anonymize_info)s + %(keep_his_anonymize_info)s %(verbose)s Returns @@ -652,7 +562,8 @@ """ from ..annotations import _handle_meas_date meas_date = _handle_meas_date(meas_date) - self.info['meas_date'] = meas_date + with self.info._unlock(): + self.info['meas_date'] = meas_date # clear file_id and meas_id if needed if meas_date is None: @@ -745,7 +656,7 @@ in ``info['bads']``. selection : list of str Restrict sensor channels (MEG, EEG) to this list of channel names. - %(verbose_meth)s + %(verbose)s Returns ------- @@ -764,8 +675,8 @@ self.info, meg=meg, eeg=eeg, stim=stim, eog=eog, ecg=ecg, emg=emg, ref_meg=ref_meg, misc=misc, resp=resp, chpi=chpi, exci=exci, ias=ias, syst=syst, seeg=seeg, dipole=dipole, gof=gof, bio=bio, - ecog=ecog, fnirs=fnirs, dbs=dbs, include=include, exclude=exclude, - selection=selection) + ecog=ecog, fnirs=fnirs, csd=csd, dbs=dbs, include=include, + exclude=exclude, selection=selection) self._pick_drop_channels(idx) @@ -784,7 +695,8 @@ return self - def pick_channels(self, ch_names, ordered=False): + @verbose + def pick_channels(self, ch_names, ordered=False, *, verbose=None): """Pick some channels. Parameters @@ -796,6 +708,9 @@ the modified instance matches the order of ``ch_names``. .. versionadded:: 0.20.0 + %(verbose)s + + .. versionadded:: 1.1 Returns ------- @@ -819,8 +734,8 @@ picks = pick_channels(self.info['ch_names'], ch_names, ordered=ordered) return self._pick_drop_channels(picks) - @fill_doc - def pick(self, picks, exclude=()): + @verbose + def pick(self, picks, exclude=(), *, verbose=None): """Pick a subset of channels. Parameters @@ -829,6 +744,9 @@ exclude : list | str Set of channels to exclude, only used when picking based on types (e.g., exclude="bads" when picks="meg"). + %(verbose)s + + .. versionadded:: 0.24.0 Returns ------- @@ -920,7 +838,8 @@ idx = np.setdiff1d(np.arange(len(self.ch_names)), bad_idx) return self._pick_drop_channels(idx) - def _pick_drop_channels(self, idx): + @verbose + def _pick_drop_channels(self, idx, *, verbose=None): # avoid circular imports from ..io import BaseRaw from ..time_frequency import AverageTFR, EpochsTFR @@ -1146,7 +1065,7 @@ exclude : list | tuple The channels to exclude from interpolation. If excluded a bad channel will stay in bads. - %(verbose_meth)s + %(verbose)s Returns ------- @@ -1196,9 +1115,8 @@ Parameters ---------- - info : dict - Measurement info to modify. - %(rename_channels_mapping_duplicates)s + %(info_not_none)s Note: modified in place. + %(mapping_rename_channels_duplicates)s %(verbose)s """ _validate_type(info, Info, 'info') @@ -1208,11 +1126,8 @@ # first check and assemble clean mappings of index and name if isinstance(mapping, dict): - orig_names = sorted(list(mapping.keys())) - missing = [orig_name not in ch_names for orig_name in orig_names] - if any(missing): - raise ValueError("Channel name(s) in mapping missing from info: " - "%s" % np.array(orig_names)[np.array(missing)]) + _check_dict_keys(mapping, ch_names, key_description="channel name(s)", + valid_key_source="info") new_names = [(ch_names.index(ch_name), new_name) for ch_name, new_name in mapping.items()] elif callable(mapping): @@ -1261,9 +1176,300 @@ return cell +@dataclass +class _BuiltinChannelAdjacency: + name: str + description: str + fname: str + source_url: Union[str, None] + + +_ft_neighbor_url_t = string.Template( + 'https://github.com/fieldtrip/fieldtrip/raw/master/' + 'template/neighbours/$fname' +) + +_BUILTIN_CHANNEL_ADJACENCIES = [ + _BuiltinChannelAdjacency( + name='biosemi16', + description='Biosemi 16-electrode cap', + fname='biosemi16_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='biosemi16_neighb.mat'), + ), + _BuiltinChannelAdjacency( + name='biosemi32', + description='Biosemi 32-electrode cap', + fname='biosemi32_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='biosemi32_neighb.mat'), + ), + _BuiltinChannelAdjacency( + name='biosemi64', + description='Biosemi 64-electrode cap', + fname='biosemi64_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='biosemi64_neighb.mat'), + ), + _BuiltinChannelAdjacency( + name='bti148', + description='BTI 148-channel system', + fname='bti148_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='bti148_neighb.mat'), + ), + _BuiltinChannelAdjacency( + name='bti248', + description='BTI 248-channel system', + fname='bti248_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='bti248_neighb.mat'), + ), + _BuiltinChannelAdjacency( + name='bti248grad', + description='BTI 248 gradiometer system', + fname='bti248grad_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='bti248grad_neighb.mat'), # noqa: E501 + ), + _BuiltinChannelAdjacency( + name='ctf64', + description='CTF 64 axial gradiometer', + fname='ctf64_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='ctf64_neighb.mat'), + ), + _BuiltinChannelAdjacency( + name='ctf151', + description='CTF 151 axial gradiometer', + fname='ctf151_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='ctf151_neighb.mat'), + ), + _BuiltinChannelAdjacency( + name='ctf275', + description='CTF 275 axial gradiometer', + fname='ctf275_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='ctf275_neighb.mat'), + ), + _BuiltinChannelAdjacency( + name='easycap32ch-avg', + description='', + fname='easycap32ch-avg_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='easycap32ch-avg_neighb.mat'), # noqa: E501 + ), + _BuiltinChannelAdjacency( + name='easycap64ch-avg', + description='', + fname='easycap64ch-avg_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='easycap64ch-avg_neighb.mat'), # noqa: E501 + ), + _BuiltinChannelAdjacency( + name='easycap128ch-avg', + description='', + fname='easycap128ch-avg_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='easycap128ch-avg_neighb.mat'), # noqa: E501 + ), + _BuiltinChannelAdjacency( + name='easycapM1', + description='Easycap M1', + fname='easycapM1_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='easycapM1_neighb.mat'), + ), + _BuiltinChannelAdjacency( + name='easycapM11', + description='Easycap M11', + fname='easycapM11_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='easycapM11_neighb.mat'), # noqa: E501 + ), + _BuiltinChannelAdjacency( + name='easycapM14', + description='Easycap M14', + fname='easycapM14_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='easycapM14_neighb.mat'), # noqa: E501 + ), + _BuiltinChannelAdjacency( + name='easycapM15', + description='Easycap M15', + fname='easycapM15_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='easycapM15_neighb.mat'), # noqa: E501 + ), + _BuiltinChannelAdjacency( + name='KIT-157', + description='', + fname='KIT-157_neighb.mat', + source_url=None, + ), + _BuiltinChannelAdjacency( + name='KIT-208', + description='', + fname='KIT-208_neighb.mat', + source_url=None, + ), + _BuiltinChannelAdjacency( + name='KIT-NYU-2019', + description='', + fname='KIT-NYU-2019_neighb.mat', + source_url=None, + ), + _BuiltinChannelAdjacency( + name='KIT-UMD-1', + description='', + fname='KIT-UMD-1_neighb.mat', + source_url=None, + ), + _BuiltinChannelAdjacency( + name='KIT-UMD-2', + description='', + fname='KIT-UMD-2_neighb.mat', + source_url=None, + ), + _BuiltinChannelAdjacency( + name='KIT-UMD-3', + description='', + fname='KIT-UMD-3_neighb.mat', + source_url=None, + ), + _BuiltinChannelAdjacency( + name='KIT-UMD-4', + description='', + fname='KIT-UMD-4_neighb.mat', + source_url=None, + ), + _BuiltinChannelAdjacency( + name='neuromag306mag', + description='Neuromag306, only magnetometers', + fname='neuromag306mag_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='neuromag306mag_neighb.mat'), # noqa: E501 + ), + _BuiltinChannelAdjacency( + name='neuromag306planar', + description='Neuromag306, only planar gradiometers', + fname='neuromag306planar_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='neuromag306planar_neighb.mat'), # noqa: E501 + ), + _BuiltinChannelAdjacency( + name='neuromag122cmb', + description='Neuromag122, only combined planar gradiometers', + fname='neuromag122cmb_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='neuromag122cmb_neighb.mat'), # noqa: E501 + ), + _BuiltinChannelAdjacency( + name='neuromag306cmb', + description='Neuromag306, only combined planar gradiometers', + fname='neuromag306cmb_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='neuromag306cmb_neighb.mat'), # noqa: E501 + ), + _BuiltinChannelAdjacency( + name='ecog256', + description='ECOG 256channels, average referenced', + fname='ecog256_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='ecog256_neighb.mat'), # noqa: E501 + ), + _BuiltinChannelAdjacency( + name='ecog256bipolar', + description='ECOG 256channels, bipolar referenced', + fname='ecog256bipolar_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='ecog256bipolar_neighb.mat'), # noqa: E501 + ), + _BuiltinChannelAdjacency( + name='eeg1010_neighb', + description='', + fname='eeg1010_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='eeg1010_neighb.mat'), + ), + _BuiltinChannelAdjacency( + name='elec1005', + description='Standard 10-05 system', + fname='elec1005_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='elec1005_neighb.mat'), + ), + _BuiltinChannelAdjacency( + name='elec1010', + description='Standard 10-10 system', + fname='elec1010_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='elec1010_neighb.mat'), + ), + _BuiltinChannelAdjacency( + name='elec1020', + description='Standard 10-20 system', + fname='elec1020_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='elec1020_neighb.mat'), + ), + _BuiltinChannelAdjacency( + name='itab28', + description='ITAB 28-channel system', + fname='itab28_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='itab28_neighb.mat'), + ), + _BuiltinChannelAdjacency( + name='itab153', + description='ITAB 153-channel system', + fname='itab153_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='itab153_neighb.mat'), + ), + _BuiltinChannelAdjacency( + name='language29ch-avg', + description='MPI for Psycholinguistic: Averaged 29-channel cap', + fname='language29ch-avg_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='language29ch-avg_neighb.mat'), # noqa: E501 + ), + _BuiltinChannelAdjacency( + name='mpi_59_channels', + description='MPI for Psycholinguistic: 59-channel cap', + fname='mpi_59_channels_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='mpi_59_channels_neighb.mat'), # noqa: E501 + ), + _BuiltinChannelAdjacency( + name='yokogawa160', + description='', + fname='yokogawa160_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='yokogawa160_neighb.mat'), # noqa: E501 + ), + _BuiltinChannelAdjacency( + name='yokogawa440', + description='', + fname='yokogawa440_neighb.mat', + source_url=_ft_neighbor_url_t.substitute(fname='yokogawa440_neighb.mat'), # noqa: E501 + ), +] + + +@fill_doc +def get_builtin_ch_adjacencies(*, descriptions=False): + """Get a list of all FieldTrip neighbor definitions shipping with MNE. + + The names of the these neighbor definitions can be passed to + :func:`read_ch_adjacency`. + + Parameters + ---------- + descriptions : bool + Whether to return not only the neighbor definition names, but also + their corresponding descriptions. If ``True``, a list of tuples is + returned, where the first tuple element is the neighbor definition name + and the second is the description. If ``False`` (default), only the + names are returned. + + Returns + ------- + neighbor_name : list of str | list of tuple + If ``descriptions=False``, the names of all builtin FieldTrip neighbor + definitions that can be loaded directly via :func:`read_ch_adjacency`. + + If ``descriptions=True``, a list of tuples ``(name, description)``. + + Notes + ----- + .. versionadded:: 1.1 + """ + if descriptions: + return sorted( + [(m.name, m.description) for m in _BUILTIN_CHANNEL_ADJACENCIES], + key=lambda x: x[0].casefold() # only sort based on name + ) + else: + return sorted( + [m.name for m in _BUILTIN_CHANNEL_ADJACENCIES], + key=str.casefold + ) + + @fill_doc def read_ch_adjacency(fname, picks=None): - """Parse FieldTrip neighbors .mat file. + """Read a channel adjacency ("neighbors") file that ships with MNE. More information on these neighbor definitions can be found on the related `FieldTrip documentation pages @@ -1272,10 +1478,15 @@ Parameters ---------- fname : str - The file name. Example: 'neuromag306mag', 'neuromag306planar', - 'ctf275', 'biosemi64', etc. + The path to the file to load, or the name of a channel adjacency + matrix that ships with MNE-Python. + + .. note:: + You can retrieve the names of all + built-in channel adjacencies via + :func:`mne.channels.get_builtin_ch_adjacencies`. %(picks_all)s - Picks Must match the template. + Picks must match the template. Returns ------- @@ -1286,31 +1497,50 @@ See Also -------- + get_builtin_ch_adjacencies + mne.viz.plot_ch_adjacency find_ch_adjacency + mne.stats.combine_adjacency Notes ----- - This function is closely related to :func:`find_ch_adjacency`. If you - don't know the correct file for the neighbor definitions, - :func:`find_ch_adjacency` can compute the adjacency matrix from 2d - sensor locations. + If the neighbor definition you need is not shipped by MNE-Python, + you may use :func:`find_ch_adjacency` to compute the + adjacency matrix based on your 2D sensor locations. + + Note that depending on your use case, you may need to additionally use + :func:`mne.stats.combine_adjacency` to prepare a final "adjacency" + to pass to the eventual function. """ from scipy.io import loadmat - if not op.isabs(fname): - templates_dir = op.realpath(op.join(op.dirname(__file__), - 'data', 'neighbors')) - templates = os.listdir(templates_dir) - for f in templates: - if f == fname: - break - if f == fname + '_neighb.mat': - fname += '_neighb.mat' - break - else: - raise ValueError('I do not know about this neighbor ' - 'template: "{}"'.format(fname)) - - fname = op.join(templates_dir, fname) + if op.isabs(fname): + fname = _check_fname( + fname=fname, + overwrite='read', + must_exist=True + ) + else: # built-in FieldTrip neighbors + ch_adj_name = fname + del fname + if ch_adj_name.endswith('_neighb.mat'): # backward-compat + ch_adj_name = ch_adj_name.replace('_neighb.mat', '') + + if ch_adj_name not in get_builtin_ch_adjacencies(): + raise ValueError( + f'No built-in channel adjacency matrix found with name: ' + f'{ch_adj_name}. Valid names are: ' + f'{", ".join(get_builtin_ch_adjacencies())}' + ) + + ch_adj = [a for a in _BUILTIN_CHANNEL_ADJACENCIES + if a.name == ch_adj_name][0] + fname = ch_adj.fname + templates_dir = Path(__file__).resolve().parent / 'data' / 'neighbors' + fname = _check_fname( # only needed to convert to a string + fname=templates_dir / fname, + overwrite='read', + must_exist=True + ) nb = loadmat(fname)['neighbours'] ch_names = _recursive_flatten(nb['label'], str) @@ -1322,6 +1552,13 @@ # picking before constructing matrix is buggy adjacency = adjacency[picks][:, picks] ch_names = [ch_names[p] for p in picks] + + # make sure MEG channel names contain space after "MEG" + for idx, ch_name in enumerate(ch_names): + if ch_name.startswith('MEG') and not ch_name[3] == ' ': + ch_name = ch_name.replace('MEG', 'MEG ') + ch_names[idx] = ch_name + return adjacency, ch_names @@ -1339,7 +1576,7 @@ Returns ------- - ch_adjacency : scipy.sparse matrix + ch_adjacency : scipy.sparse.spmatrix The adjacency matrix. """ from scipy import sparse @@ -1364,21 +1601,21 @@ return ch_adjacency +@fill_doc def find_ch_adjacency(info, ch_type): """Find the adjacency matrix for the given channels. This function tries to infer the appropriate adjacency matrix template for the given channels. If a template is not found, the adjacency matrix - is computed using Delaunay triangulation based on 2d sensor locations. + is computed using Delaunay triangulation based on 2D sensor locations. Parameters ---------- - info : instance of Info - The measurement info. + %(info_not_none)s ch_type : str | None The channel type for computing the adjacency matrix. Currently - supports 'mag', 'grad', 'eeg' and None. If None, the info must contain - only one channel type. + supports ``'mag'``, ``'grad'``, ``'eeg'`` and ``None``. + If ``None``, the info must contain only one channel type. Returns ------- @@ -1389,6 +1626,9 @@ See Also -------- + mne.viz.plot_ch_adjacency + mne.stats.combine_adjacency + get_builtin_ch_adjacencies read_ch_adjacency Notes @@ -1400,6 +1640,10 @@ is always computed for EEG data and never loaded from a template file. If you want to load a template for a given montage use :func:`read_ch_adjacency` directly. + + Note that depending on your use case, you may need to additionally use + :func:`mne.stats.combine_adjacency` to prepare a final "adjacency" + to pass to the eventual function. """ if ch_type is None: picks = channel_indices_by_type(info) @@ -1443,27 +1687,27 @@ conn_name = KIT_NEIGHBORS.get(info['kit_system_id']) if conn_name is not None: - logger.info('Reading adjacency matrix for %s.' % conn_name) + logger.info(f'Reading adjacency matrix for {conn_name}.') return read_ch_adjacency(conn_name) logger.info('Could not find a adjacency matrix for the data. ' 'Computing adjacency based on Delaunay triangulations.') return _compute_ch_adjacency(info, ch_type) +@fill_doc def _compute_ch_adjacency(info, ch_type): """Compute channel adjacency matrix using Delaunay triangulations. Parameters ---------- - info : instance of mne.measuerment_info.Info - The measurement info. + %(info_not_none)s ch_type : str The channel type for computing the adjacency matrix. Currently supports 'mag', 'grad' and 'eeg'. Returns ------- - ch_adjacency : scipy.sparse matrix, shape (n_channels, n_channels) + ch_adjacency : scipy.sparse.csr_matrix, shape (n_channels, n_channels) The adjacency matrix. ch_names : list The list of channel names present in adjacency matrix. @@ -1510,13 +1754,13 @@ return ch_adjacency, ch_names +@fill_doc def fix_mag_coil_types(info, use_cal=False): """Fix magnetometer coil types. Parameters ---------- - info : dict - The info dict to correct. Corrections are done in-place. + %(info_not_none)s Corrections are done in-place. use_cal : bool If True, further refine the check for old coil types by checking ``info['chs'][ii]['cal']``. @@ -1546,13 +1790,14 @@ for ii in old_mag_inds: info['chs'][ii]['coil_type'] = FIFF.FIFFV_COIL_VV_MAG_T3 logger.info('%d of %d magnetometer types replaced with T3.' % - (len(old_mag_inds), len(pick_types(info, meg='mag')))) + (len(old_mag_inds), + len(pick_types(info, meg='mag', exclude=[])))) info._check_consistency() def _get_T1T2_mag_inds(info, use_cal=False): """Find T1/T2 magnetometer coil types.""" - picks = pick_types(info, meg='mag') + picks = pick_types(info, meg='mag', exclude=[]) old_mag_inds = [] # From email exchanges, systems with the larger T2 coil only use the cal # value of 2.09e-11. Newer T3 magnetometers use 4.13e-11 or 1.33e-10 @@ -1614,6 +1859,7 @@ has_csd_coils) +@fill_doc def make_1020_channel_selections(info, midline="z"): """Return dict mapping from ROI names to lists of picks for 10/20 setups. @@ -1628,11 +1874,9 @@ Parameters ---------- - info : instance of Info - Where to obtain the channel names from. The picks will - be in relation to the position in ``info["ch_names"]``. If possible, - this lists will be sorted by y value position of the channel locations, - i.e., from back to front. + %(info_not_none)s If possible, the channel lists will be sorted + posterior-to-anterior; otherwise they default to the order specified in + ``info["ch_names"]``. midline : str Names ending in any of these characters are stored under the ``Midline`` key. Defaults to 'z'. Note that capitalization is ignored. @@ -1798,15 +2042,17 @@ new_data = np.swapaxes(new_data, 0, ch_axis) info = create_info(sfreq=inst.info['sfreq'], ch_names=new_ch_names, ch_types=new_ch_types) + # create new instances and make sure to copy important attributes if isinstance(inst, BaseRaw): - combined_inst = RawArray(new_data, info, first_samp=inst.first_samp, - verbose=inst.verbose) + combined_inst = RawArray(new_data, info, first_samp=inst.first_samp) elif isinstance(inst, BaseEpochs): combined_inst = EpochsArray(new_data, info, events=inst.events, - tmin=inst.times[0], verbose=inst.verbose) + tmin=inst.times[0], baseline=inst.baseline) + if inst.metadata is not None: + combined_inst.metadata = inst.metadata.copy() elif isinstance(inst, Evoked): combined_inst = EvokedArray(new_data, info, tmin=inst.times[0], - verbose=inst.verbose) + baseline=inst.baseline) return combined_inst @@ -1903,10 +2149,9 @@ fname : str Filename of the selection file (if ``None``, built-in selections are used). - info : instance of Info - Measurement info file, which will be used to determine the spacing - of channel names to return, e.g. ``'MEG 0111'`` for old Neuromag - systems and ``'MEG0111'`` for new ones. + %(info)s Used to determine which channel naming convention to use, e.g. + ``'MEG 0111'`` (with space) for old Neuromag systems and ``'MEG0111'`` + (without space) for new ones. %(verbose)s Returns diff -Nru python-mne-0.23.4+dfsg/mne/channels/data/layouts/GeodesicHeadWeb-130.lout python-mne-1.1.0+dfsg/mne/channels/data/layouts/GeodesicHeadWeb-130.lout --- python-mne-0.23.4+dfsg/mne/channels/data/layouts/GeodesicHeadWeb-130.lout 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/channels/data/layouts/GeodesicHeadWeb-130.lout 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,132 @@ + -42.19 43.52 -41.70 28.71 +001 0.50 0.74 0.02 0.02 E1 +002 0.53 0.79 0.02 0.02 E2 +003 0.55 0.73 0.02 0.02 E3 +004 0.50 0.69 0.02 0.02 E4 +005 0.45 0.73 0.02 0.02 E5 +006 0.46 0.79 0.02 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I should be: - # dig_ch_pos['REF'] = coordinates - # Fiducials elif kind == 2: fid_name = fid_name_map[name] @@ -72,9 +66,7 @@ return Bunch( # EGI stuff nasion=fids['nasion'], lpa=fids['lpa'], rpa=fids['rpa'], - dig_ch_pos=dig_ch_pos, coord_frame='unknown', - # not EGI stuff - hsp=None, hpi=None, elp=None, point_names=None, + ch_pos=dig_ch_pos, coord_frame='unknown', ) diff -Nru python-mne-0.23.4+dfsg/mne/channels/__init__.py python-mne-1.1.0+dfsg/mne/channels/__init__.py --- python-mne-0.23.4+dfsg/mne/channels/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/channels/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -11,13 +11,13 @@ read_dig_egi, read_dig_captrak, read_dig_fif, read_dig_polhemus_isotrak, read_polhemus_fastscan, compute_dev_head_t, make_standard_montage, - read_custom_montage, read_dig_hpts, + read_custom_montage, read_dig_hpts, read_dig_localite, compute_native_head_t) from .channels import (equalize_channels, rename_channels, fix_mag_coil_types, read_ch_adjacency, _get_ch_type, find_ch_adjacency, make_1020_channel_selections, combine_channels, read_vectorview_selection, _SELECTIONS, _EEG_SELECTIONS, - _divide_to_regions) + _divide_to_regions, get_builtin_ch_adjacencies) __all__ = [ # Data Structures @@ -29,8 +29,8 @@ # Readers 'read_ch_adjacency', 'read_dig_captrak', 'read_dig_dat', - 'read_dig_egi', 'read_dig_fif', 'read_dig_montage', - 'read_dig_polhemus_isotrak', 'read_layout', 'read_montage', + 'read_dig_egi', 'read_dig_fif', 'read_dig_localite', + 'read_dig_polhemus_isotrak', 'read_layout', 'read_polhemus_fastscan', 'read_custom_montage', 'read_dig_hpts', # Helpers diff -Nru python-mne-0.23.4+dfsg/mne/channels/interpolation.py python-mne-1.1.0+dfsg/mne/channels/interpolation.py --- python-mne-0.23.4+dfsg/mne/channels/interpolation.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/channels/interpolation.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Denis Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np from numpy.polynomial.legendre import legval @@ -205,15 +205,13 @@ @verbose def _interpolate_bads_nirs(inst, method='nearest', exclude=(), verbose=None): from scipy.spatial.distance import pdist, squareform - from mne.preprocessing.nirs import _channel_frequencies,\ - _check_channels_ordered + from mne.preprocessing.nirs import _validate_nirs_info - # Returns pick of all nirs and ensures channels are correctly ordered - freqs = np.unique(_channel_frequencies(inst.info)) - picks_nirs = _check_channels_ordered(inst.info, freqs) - if len(picks_nirs) == 0: + if len(pick_types(inst.info, fnirs=True, exclude=())) == 0: return + # Returns pick of all nirs and ensures channels are correctly ordered + picks_nirs = _validate_nirs_info(inst.info) nirs_ch_names = [inst.info['ch_names'][p] for p in picks_nirs] nirs_ch_names = [ch for ch in nirs_ch_names if ch not in exclude] bads_nirs = [ch for ch in inst.info['bads'] if ch in nirs_ch_names] diff -Nru python-mne-0.23.4+dfsg/mne/channels/layout.py python-mne-1.1.0+dfsg/mne/channels/layout.py --- python-mne-0.23.4+dfsg/mne/channels/layout.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/channels/layout.py 2022-08-03 15:48:14.000000000 +0000 @@ -28,8 +28,9 @@ class Layout(object): """Sensor layouts. - Layouts are typically loaded from a file using read_layout. Only use this - class directly if you're constructing a new layout. + Layouts are typically loaded from a file using + :func:`~mne.channels.read_layout`. Only use this class directly if you're + constructing a new layout. Parameters ---------- @@ -184,6 +185,57 @@ See Also -------- Layout.save + + Notes + ----- + Valid ``kind`` arguments are: + + .. table:: + :widths: auto + + +----------------------+ + | Kind | + +======================+ + | biosemi | + +----------------------+ + | CTF151 | + +----------------------+ + | CTF275 | + +----------------------+ + | CTF-275 | + +----------------------+ + | EEG1005 | + +----------------------+ + | EGI256 | + +----------------------+ + | GeodesicHeadWeb-130 | + +----------------------+ + | GeodesicHeadWeb-280 | + +----------------------+ + | KIT-125 | + +----------------------+ + | KIT-157 | + +----------------------+ + | KIT-160 | + +----------------------+ + | KIT-AD | + +----------------------+ + | KIT-AS-2008 | + +----------------------+ + | KIT-UMD-3 | + +----------------------+ + | magnesWH3600 | + +----------------------+ + | Neuromag_122 | + +----------------------+ + | Vectorview-all | + +----------------------+ + | Vectorview-grad | + +----------------------+ + | Vectorview-grad_norm | + +----------------------+ + | Vectorview-mag | + +----------------------+ """ if path is None: path = op.join(op.dirname(__file__), 'data', 'layouts') @@ -217,14 +269,14 @@ return Layout(box=box, pos=pos, names=names, kind=kind, ids=ids) +@fill_doc def make_eeg_layout(info, radius=0.5, width=None, height=None, exclude='bads', csd=False): """Create .lout file from EEG electrode digitization. Parameters ---------- - info : instance of Info - Measurement info (e.g., raw.info). + %(info_not_none)s radius : float Viewport radius as a fraction of main figure height. Defaults to 0.5. width : float | None @@ -303,9 +355,7 @@ Parameters ---------- - info : instance of Info | None - Measurement info (e.g., raw.info). If None, default names will be - employed. + %(info_not_none)s %(picks_base)s all good misc channels. n_col : int | None Number of columns to generate. If None, a square grid will be produced. @@ -370,13 +420,13 @@ return layout +@fill_doc def find_layout(info, ch_type=None, exclude='bads'): """Choose a layout based on the channels in the info 'chs' field. Parameters ---------- - info : instance of Info - The measurement info. + %(info_not_none)s ch_type : {'mag', 'grad', 'meg', 'eeg'} | None The channel type for selecting single channel layouts. Defaults to None. Note, this argument will only be considered for @@ -460,13 +510,13 @@ return layout +@fill_doc def _find_kit_layout(info, n_grads): """Determine the KIT layout. Parameters ---------- - info : Info - Info object. + %(info_not_none)s n_grads : int Number of KIT-gradiometers in the info. @@ -595,14 +645,14 @@ return width, height +@fill_doc def _find_topomap_coords(info, picks, layout=None, ignore_overlap=False, to_sphere=True, sphere=None): """Guess the E/MEG layout and return appropriate topomap coordinates. Parameters ---------- - info : instance of Info - Measurement info. + %(info_not_none)s picks : str | list | slice | None None will choose all channels. layout : None | instance of Layout @@ -631,6 +681,7 @@ return pos +@fill_doc def _auto_topomap_coords(info, picks, ignore_overlap, to_sphere, sphere): """Make a 2 dimensional sensor map from sensor positions in an info dict. @@ -640,8 +691,7 @@ Parameters ---------- - info : instance of Info - The measurement info. + %(info_not_none)s picks : list | str | slice | None None will pick all channels. ignore_overlap : bool @@ -669,7 +719,7 @@ locs3d = np.array([ch['loc'][:3] for ch in chs]) # If electrode locations are not available, use digization points - if not _check_ch_locs(chs): + if not _check_ch_locs(info=info, picks=picks): logging.warning('Did not find any electrode locations (in the info ' 'object), will attempt to use digitization points ' 'instead. However, if digitization points do not ' @@ -774,14 +824,14 @@ return np.column_stack([xs, ys, zs]) +@fill_doc def _pair_grad_sensors(info, layout=None, topomap_coords=True, exclude='bads', raise_error=True): """Find the picks for pairing grad channels. Parameters ---------- - info : instance of Info - An info dictionary containing channel information. + %(info_not_none)s layout : Layout | None The layout if available. Defaults to None. topomap_coords : bool diff -Nru python-mne-0.23.4+dfsg/mne/channels/montage.py python-mne-1.1.0+dfsg/mne/channels/montage.py --- python-mne-0.23.4+dfsg/mne/channels/montage.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/channels/montage.py 2022-08-03 15:48:14.000000000 +0000 @@ -12,20 +12,20 @@ # License: Simplified BSD from collections import OrderedDict +from dataclasses import dataclass from copy import deepcopy -from functools import partial import os.path as op import re import numpy as np from ..defaults import HEAD_SIZE_DEFAULT -from ..source_space import get_mni_fiducials +from .._freesurfer import get_mni_fiducials from ..viz import plot_montage from ..transforms import (apply_trans, get_ras_to_neuromag_trans, _sph_to_cart, _topo_to_sph, _frame_to_str, Transform, _verbose_frames, _fit_matched_points, - _quat_to_affine) + _quat_to_affine, _ensure_trans) from ..io._digitization import (_count_points_by_type, _get_dig_eeg, _make_dig_points, write_dig, _read_dig_fif, _format_dig_points, @@ -33,27 +33,141 @@ _get_data_as_dict_from_dig) from ..io.meas_info import create_info from ..io.open import fiff_open -from ..io.pick import pick_types, _picks_to_idx +from ..io.pick import pick_types, _picks_to_idx, channel_type from ..io.constants import FIFF, CHANNEL_LOC_ALIASES from ..utils import (warn, copy_function_doc_to_method_doc, _pl, verbose, _check_option, _validate_type, _check_fname, _on_missing, - fill_doc) + fill_doc, _docdict) from ._dig_montage_utils import _read_dig_montage_egi from ._dig_montage_utils import _parse_brainvision_dig_montage -_BUILT_IN_MONTAGES = [ - 'EGI_256', - 'GSN-HydroCel-128', 'GSN-HydroCel-129', 'GSN-HydroCel-256', - 'GSN-HydroCel-257', 'GSN-HydroCel-32', 'GSN-HydroCel-64_1.0', - 'GSN-HydroCel-65_1.0', - 'biosemi128', 'biosemi16', 'biosemi160', 'biosemi256', - 'biosemi32', 'biosemi64', - 'easycap-M1', 'easycap-M10', - 'mgh60', 'mgh70', - 'standard_1005', 'standard_1020', 'standard_alphabetic', - 'standard_postfixed', 'standard_prefixed', 'standard_primed', - 'artinis-octamon', 'artinis-brite23' + +@dataclass +class _BuiltinStandardMontage: + name: str + description: str + + +_BUILTIN_STANDARD_MONTAGES = [ + _BuiltinStandardMontage( + name='standard_1005', + description='Electrodes are named and positioned according to the ' + 'international 10-05 system (343+3 locations)', + ), + _BuiltinStandardMontage( + name='standard_1020', + description='Electrodes are named and positioned according to the ' + 'international 10-20 system (94+3 locations)', + ), + _BuiltinStandardMontage( + name='standard_alphabetic', + description='Electrodes are named with LETTER-NUMBER combinations ' + '(A1, B2, F4, …) (65+3 locations)', + ), + _BuiltinStandardMontage( + name='standard_postfixed', + description='Electrodes are named according to the international ' + '10-20 system using postfixes for intermediate positions ' + '(100+3 locations)', + ), + _BuiltinStandardMontage( + name='standard_prefixed', + description='Electrodes are named according to the international ' + '10-20 system using prefixes for intermediate positions ' + '(74+3 locations)', + ), + _BuiltinStandardMontage( + name='standard_primed', + description="Electrodes are named according to the international " + "10-20 system using prime marks (' and '') for " + "intermediate positions (100+3 locations)", + ), + _BuiltinStandardMontage( + name='biosemi16', + description='BioSemi cap with 16 electrodes (16+3 locations)', + ), + _BuiltinStandardMontage( + name='biosemi32', + description='BioSemi cap with 32 electrodes (32+3 locations)', + ), + _BuiltinStandardMontage( + name='biosemi64', + description='BioSemi cap with 64 electrodes (64+3 locations)', + ), + _BuiltinStandardMontage( + name='biosemi128', + description='BioSemi cap with 128 electrodes (128+3 locations)', + ), + _BuiltinStandardMontage( + name='biosemi160', + description='BioSemi cap with 160 electrodes (160+3 locations)', + ), + _BuiltinStandardMontage( + name='biosemi256', + description='BioSemi cap with 256 electrodes (256+3 locations)', + ), + _BuiltinStandardMontage( + name='easycap-M1', + description='EasyCap with 10-05 electrode names (74 locations)', + ), + _BuiltinStandardMontage( + name='easycap-M10', + description='EasyCap with numbered electrodes (61 locations)', + ), + _BuiltinStandardMontage( + name='EGI_256', + description='Geodesic Sensor Net (256 locations)', + ), + _BuiltinStandardMontage( + name='GSN-HydroCel-32', + description='HydroCel Geodesic Sensor Net and Cz (33+3 locations)', + ), + _BuiltinStandardMontage( + name='GSN-HydroCel-64_1.0', + description='HydroCel Geodesic Sensor Net (64+3 locations)', + ), + _BuiltinStandardMontage( + name='GSN-HydroCel-65_1.0', + description='HydroCel Geodesic Sensor Net and Cz (65+3 locations)', + ), + _BuiltinStandardMontage( + name='GSN-HydroCel-128', + description='HydroCel Geodesic Sensor Net (128+3 locations)', + ), + _BuiltinStandardMontage( + name='GSN-HydroCel-129', + description='HydroCel Geodesic Sensor Net and Cz (129+3 locations)', + ), + _BuiltinStandardMontage( + name='GSN-HydroCel-256', + description='HydroCel Geodesic Sensor Net (256+3 locations)', + ), + _BuiltinStandardMontage( + name='GSN-HydroCel-257', + description='HydroCel Geodesic Sensor Net and Cz (257+3 locations)', + ), + _BuiltinStandardMontage( + name='mgh60', + description='The (older) 60-channel cap used at MGH (60+3 locations)', + ), + _BuiltinStandardMontage( + name='mgh70', + description='The (newer) 70-channel BrainVision cap used at MGH ' + '(70+3 locations)', + ), + _BuiltinStandardMontage( + name='artinis-octamon', + description='Artinis OctaMon fNIRS (8 sources, 2 detectors)', + ), + _BuiltinStandardMontage( + name='artinis-brite23', + description='Artinis Brite23 fNIRS (11 sources, 7 detectors)', + ), + _BuiltinStandardMontage( + name='brainproducts-RNP-BA-128', + description='Brain Products with 10-10 electrode names (128 channels)', + ) ] @@ -66,16 +180,36 @@ return _frame_to_str[dig_coord_frames.pop()] if dig_coord_frames else None -def get_builtin_montages(): - """Get a list of all builtin montages. +def get_builtin_montages(*, descriptions=False): + """Get a list of all standard montages shipping with MNE-Python. + + The names of the montages can be passed to :func:`make_standard_montage`. + + Parameters + ---------- + descriptions : bool + Whether to return not only the montage names, but also their + corresponding descriptions. If ``True``, a list of tuples is returned, + where the first tuple element is the montage name and the second is + the montage description. If ``False`` (default), only the names are + returned. + + .. versionadded:: 1.1 Returns ------- - montages : list - Names of all builtin montages that can be used by - :func:`make_standard_montage`. + montages : list of str | list of tuple + If ``descriptions=False``, the names of all builtin montages that can + be used by :func:`make_standard_montage`. + + If ``descriptions=True``, a list of tuples ``(name, description)``. """ - return _BUILT_IN_MONTAGES + if descriptions: + return [ + (m.name, m.description) for m in _BUILTIN_STANDARD_MONTAGES + ] + else: + return [m.name for m in _BUILTIN_STANDARD_MONTAGES] def make_dig_montage(ch_pos=None, nasion=None, lpa=None, rpa=None, @@ -105,8 +239,15 @@ space. They only necessary if computation of a ``compute_dev_head_t`` is True. coord_frame : str - The coordinate frame of the points. Usually this is "unknown" + The coordinate frame of the points. Usually this is ``'unknown'`` for native digitizer space. + Other valid values are: ``'head'``, ``'meg'``, ``'mri'``, + ``'mri_voxel'``, ``'mri_tal'``, ``'ras'``, ``'fs_tal'``, + ``'ctf_head'``, and ``'ctf_meg'``. + + .. note:: + For custom montages without fiducials, this parameter must be set + to ``'head'``. Returns ------- @@ -119,13 +260,8 @@ read_dig_captrak read_dig_egi read_dig_fif + read_dig_localite read_dig_polhemus_isotrak - - Notes - ----- - Valid ``coord_frame`` arguments are 'meg', 'mri', 'mri_voxel', 'head', - 'mri_tal', 'ras', 'fs_tal', 'ctf_head', 'ctf_meg', 'unknown'. For custom - montages without fiducials this parameter has to be set to 'head'. """ _validate_type(ch_pos, (dict, None), 'ch_pos') if ch_pos is None: @@ -149,8 +285,6 @@ Parameters ---------- - dev_head_t : array, shape (4, 4) - A Device-to-Head transformation matrix. dig : list of dict The object containing all the dig points. ch_names : list of str @@ -163,6 +297,7 @@ read_dig_egi read_dig_fif read_dig_hpts + read_dig_localite read_dig_polhemus_isotrak make_dig_montage @@ -171,10 +306,7 @@ .. versionadded:: 0.9.0 """ - def __init__(self, dev_head_t=None, dig=None, ch_names=None): - # XXX: dev_head_t now is np.array, we should add dev_head_transform - # (being instance of Transformation) and move the parameter to the - # end of the call. + def __init__(self, *, dig=None, ch_names=None): dig = list() if dig is None else dig _validate_type(item=dig, types=list, item_name='dig') ch_names = list() if ch_names is None else ch_names @@ -185,7 +317,6 @@ ' of channel names provided (%d)' % (n_eeg, len(ch_names)) ) - self.dev_head_t = dev_head_t self.dig = dig self.ch_names = ch_names @@ -208,7 +339,7 @@ Parameters ---------- - %(rename_channels_mapping_duplicates)s + %(mapping_rename_channels_duplicates)s Returns ------- @@ -220,16 +351,19 @@ rename_channels(temp_info, mapping, allow_duplicates) self.ch_names = temp_info['ch_names'] - def save(self, fname): + @verbose + def save(self, fname, *, overwrite=False, verbose=None): """Save digitization points to FIF. Parameters ---------- - fname : str + fname : path-like The filename to use. Should end in .fif or .fif.gz. + %(overwrite)s + %(verbose)s """ coord_frame = _check_get_coord_frame(self.dig) - write_dig(fname, self.dig, coord_frame) + write_dig(fname, self.dig, coord_frame, overwrite=overwrite) def __iadd__(self, other): """Add two DigMontages in place. @@ -241,7 +375,7 @@ system and location values. """ def is_fid_defined(fid): - return not( + return not ( fid.nasion is None and fid.lpa is None and fid.rpa is None ) @@ -298,6 +432,15 @@ out += other return out + def __eq__(self, other): + """Compare different DigMontage objects for equality. + + Returns + ------- + Boolean output from comparison of .dig + """ + return self.dig == other.dig and self.ch_names == other.ch_names + def _get_ch_pos(self): pos = [d['r'] for d in _get_dig_eeg(self.dig)] assert len(self.ch_names) == len(pos) @@ -357,6 +500,23 @@ return positions @verbose + def apply_trans(self, trans, verbose=None): + """Apply a transformation matrix to the montage. + + Parameters + ---------- + trans : instance of mne.transforms.Transform + The transformation matrix to be applied. + %(verbose)s + """ + _validate_type(trans, Transform, 'trans') + coord_frame = self.get_positions()['coord_frame'] + trans = _ensure_trans(trans, fro=coord_frame, to=trans['to']) + for d in self.dig: + d['r'] = apply_trans(trans, d['r']) + d['coord_frame'] = trans['to'] + + @verbose def add_estimated_fiducials(self, subject, subjects_dir=None, verbose=None): """Estimate fiducials based on FreeSurfer ``fsaverage`` subject. @@ -380,7 +540,7 @@ See Also -------- - :ref:`plot_source_alignment` + :ref:`tut-source-alignment` Notes ----- @@ -388,17 +548,17 @@ coordinate frame. Any coordinate frame can be transformed to ``head`` if the fiducials (i.e. LPA, RPA and Nasion) are defined. One can use this function to estimate those fiducials - and then use ``montage.get_native_head_t()`` to get the - head <-> MRI transform. + and then use ``mne.channels.compute_native_head_t(montage)`` + to get the head <-> MRI transform. """ # get coordframe and fiducial coordinates montage_bunch = _get_data_as_dict_from_dig(self.dig) - # get the coordinate frame as a string and check that it's MRI + # get the coordinate frame and check that it's MRI if montage_bunch.coord_frame != FIFF.FIFFV_COORD_MRI: raise RuntimeError( - f'Montage should be in mri coordinate frame to call ' - f'`add_estimated_fiducials`. The current coordinate ' + f'Montage should be in the "mri" coordinate frame ' + f'to use `add_estimated_fiducials`. The current coordinate ' f'frame is {montage_bunch.coord_frame}') # estimate LPA, nasion, RPA from FreeSurfer fsaverage @@ -408,6 +568,74 @@ self.dig = fids_mri + self.dig return self + @verbose + def add_mni_fiducials(self, subjects_dir=None, verbose=None): + """Add fiducials to a montage in MNI space. + + Parameters + ---------- + %(subjects_dir)s + %(verbose)s + + Returns + ------- + inst : instance of DigMontage + The instance, modified in-place. + + Notes + ----- + ``fsaverage`` is in MNI space and so its fiducials can be + added to a montage in "mni_tal". MNI is an ACPC-aligned + coordinate system (the posterior commissure is the origin) + so since BIDS requires channel locations for ECoG, sEEG and + DBS to be in ACPC space, this function can be used to allow + those coordinate to be transformed to "head" space (origin + between LPA and RPA). + """ + montage_bunch = _get_data_as_dict_from_dig(self.dig) + + # get the coordinate frame and check that it's MNI TAL + if montage_bunch.coord_frame != FIFF.FIFFV_MNE_COORD_MNI_TAL: + raise RuntimeError( + f'Montage should be in the "mni_tal" coordinate frame ' + f'to use `add_estimated_fiducials`. The current coordinate ' + f'frame is {montage_bunch.coord_frame}') + + fids_mni = get_mni_fiducials('fsaverage', subjects_dir) + for fid in fids_mni: + # "mri" and "mni_tal" are equivalent for fsaverage + assert fid['coord_frame'] == FIFF.FIFFV_COORD_MRI + fid['coord_frame'] = FIFF.FIFFV_MNE_COORD_MNI_TAL + self.dig = fids_mni + self.dig + return self + + @verbose + def remove_fiducials(self, verbose=None): + """Remove the fiducial points from a montage. + + Parameters + ---------- + %(verbose)s + + Returns + ------- + inst : instance of DigMontage + The instance, modified in-place. + + Notes + ----- + MNE will transform a montage to the internal "head" coordinate + frame if the fiducials are present. Under most circumstances, this + is ideal as it standardizes the coordinate frame for things like + plotting. However, in some circumstances, such as saving a ``raw`` + with intracranial data to BIDS format, the coordinate frame + should not be changed by removing fiducials. + """ + for d in self.dig.copy(): + if d['kind'] == FIFF.FIFFV_POINT_CARDINAL: + self.dig.remove(d) + return self + VALID_SCALES = dict(mm=1e-3, cm=1e-2, m=1) @@ -475,6 +703,7 @@ read_dig_egi read_dig_fif read_dig_hpts + read_dig_localite read_dig_polhemus_isotrak make_dig_montage @@ -541,6 +770,7 @@ read_dig_captrak read_dig_polhemus_isotrak read_dig_hpts + read_dig_localite make_dig_montage """ _check_fname(fname, overwrite='read', must_exist=True) @@ -563,7 +793,7 @@ Parameters ---------- - fname : str + fname : path-like The filepath of .hpts file. unit : 'm' | 'cm' | 'mm' Unit of the positions. Defaults to 'mm'. @@ -580,6 +810,7 @@ read_dig_dat read_dig_egi read_dig_fif + read_dig_localite read_dig_polhemus_isotrak make_dig_montage @@ -626,6 +857,7 @@ ... """ from ._standard_montage_utils import _str_names, _str + fname = _check_fname(fname, overwrite='read', must_exist=True) _scale = _check_unit_and_get_scaling(unit) out = np.genfromtxt(fname, comments='#', @@ -669,6 +901,7 @@ read_dig_dat read_dig_fif read_dig_hpts + read_dig_localite read_dig_polhemus_isotrak make_dig_montage """ @@ -679,12 +912,6 @@ _scaling=1., _all_data_kwargs_are_none=True ) - - # XXX: to change to the new naming in v.0.20 (all this block should go) - data.pop('point_names') - data['hpi'] = data.pop('elp') - data['ch_pos'] = data.pop('dig_ch_pos') - return make_dig_montage(**data) @@ -709,6 +936,7 @@ read_dig_egi read_dig_fif read_dig_hpts + read_dig_localite read_dig_polhemus_isotrak make_dig_montage """ @@ -718,6 +946,53 @@ return make_dig_montage(**data) +def read_dig_localite(fname, nasion=None, lpa=None, rpa=None): + """Read Localite .csv file. + + Parameters + ---------- + fname : path-like + File name. + nasion : str | None + Name of nasion fiducial point. + lpa : str | None + Name of left preauricular fiducial point. + rpa : str | None + Name of right preauricular fiducial point. + + Returns + ------- + montage : instance of DigMontage + The montage. + + See Also + -------- + DigMontage + read_dig_captrak + read_dig_dat + read_dig_egi + read_dig_fif + read_dig_hpts + read_dig_polhemus_isotrak + make_dig_montage + """ + ch_pos = {} + with open(fname) as f: + f.readline() # skip first row + for row in f: + _, name, x, y, z = row.split(",") + ch_pos[name] = np.array((float(x), float(y), float(z))) / 1000 + + if nasion is not None: + nasion = ch_pos.pop(nasion) + if lpa is not None: + lpa = ch_pos.pop(lpa) + if rpa is not None: + rpa = ch_pos.pop(rpa) + + return make_dig_montage(ch_pos, nasion, lpa, rpa) + + def _get_montage_in_head(montage): coords = set([d['coord_frame'] for d in montage.dig]) if len(coords) == 1 and coords.pop() == FIFF.FIFFV_COORD_HEAD: @@ -753,11 +1028,11 @@ info['chs'][ch_idx]['loc'][6:9] = detector_pos midpoint = (source_pos + detector_pos) / 2 info['chs'][ch_idx]['loc'][:3] = midpoint + info['chs'][ch_idx]['coord_frame'] = FIFF.FIFFV_COORD_HEAD # Modify info['dig'] in place - info['dig'] = montage.dig - - return info + with info._unlock(): + info['dig'] = montage.dig @fill_doc @@ -773,8 +1048,7 @@ Parameters ---------- - info : instance of Info - The measurement info to update. + %(info_not_none)s %(montage)s %(match_case)s %(match_alias)s @@ -784,167 +1058,190 @@ ----- This function will change the info variable in place. """ - _validate_type(montage, types=(DigMontage, type(None), str), - item_name='montage') - + _validate_type(montage, (DigMontage, None, str), 'montage') + if montage is None: + # Next line modifies info['dig'] in place + with info._unlock(): + info['dig'] = None + for ch in info['chs']: + # Next line modifies info['chs'][#]['loc'] in place + ch['loc'] = np.full(12, np.nan) + return if isinstance(montage, str): # load builtin montage - _check_option('montage', montage, _BUILT_IN_MONTAGES) + _check_option( + parameter='montage', value=montage, + allowed_values=[m.name for m in _BUILTIN_STANDARD_MONTAGES] + ) montage = make_standard_montage(montage) - if isinstance(montage, DigMontage): - mnt_head = _get_montage_in_head(montage) - del montage - - def _backcompat_value(pos, ref_pos): - if any(np.isnan(pos)): - return np.full(6, np.nan) - else: - return np.concatenate((pos, ref_pos)) - - # get the channels in the montage in head - ch_pos = mnt_head._get_ch_pos() - - # only get the eeg, seeg, dbs, ecog channels - _pick_chs = partial( - pick_types, exclude=[], eeg=True, seeg=True, dbs=True, ecog=True, - meg=False) - - # get the reference position from the loc[3:6] - chs = info['chs'] - ref_pos = [chs[ii]['loc'][3:6] for ii in _pick_chs(info)] - - # keep reference location from EEG/ECoG/SEEG/DBS channels if they - # already exist and are all the same. - custom_eeg_ref_dig = False - # Note: ref position is an empty list for fieldtrip data - if ref_pos: - if all([np.equal(ref_pos[0], pos).all() for pos in ref_pos]) \ - and not np.equal(ref_pos[0], [0, 0, 0]).all(): - eeg_ref_pos = ref_pos[0] - # since we have an EEG reference position, we have - # to add it into the info['dig'] as EEG000 - custom_eeg_ref_dig = True - if not custom_eeg_ref_dig: - refs = set(ch_pos) & {'EEG000', 'REF'} - assert len(refs) <= 1 - eeg_ref_pos = np.zeros(3) if not(refs) else ch_pos.pop(refs.pop()) - - # This raises based on info being subset/superset of montage - info_names = [info['ch_names'][ii] for ii in _pick_chs(info)] - dig_names = mnt_head._get_dig_names() - ref_names = [None, 'EEG000', 'REF'] - - if match_case: - ch_pos_use = ch_pos - info_names_use = info_names - dig_names_use = dig_names + mnt_head = _get_montage_in_head(montage) + del montage + + def _backcompat_value(pos, ref_pos): + if any(np.isnan(pos)): + return np.full(6, np.nan) else: - ch_pos_use = OrderedDict( - (name.lower(), pos) for name, pos in ch_pos.items()) - info_names_use = [name.lower() for name in info_names] - dig_names_use = [name.lower() if name is not None else name - for name in dig_names] - ref_names = [name.lower() if name is not None else name - for name in ref_names] - n_dup = len(ch_pos) - len(ch_pos_use) - if n_dup: - raise ValueError('Cannot use match_case=False as %s montage ' - 'name(s) require case sensitivity' % n_dup) - n_dup = len(info_names_use) - len(set(info_names_use)) - if n_dup: - raise ValueError('Cannot use match_case=False as %s channel ' - 'name(s) require case sensitivity' % n_dup) - - # use lookup table to match unrecognized channel names to known aliases - if match_alias: - alias_dict = (match_alias if isinstance(match_alias, dict) else - CHANNEL_LOC_ALIASES) - if not match_case: - alias_dict = { - ch_name.lower(): ch_alias.lower() - for ch_name, ch_alias in alias_dict.items() - } + return np.concatenate((pos, ref_pos)) + + # get the channels in the montage in head + ch_pos = mnt_head._get_ch_pos() - # excluded ch_alias not in info, to prevent unnecessary mapping and - # warning messages based on aliases. + # only get the eeg, seeg, dbs, ecog channels + picks = pick_types( + info, meg=False, eeg=True, seeg=True, dbs=True, ecog=True, + exclude=()) + non_picks = np.setdiff1d(np.arange(info['nchan']), picks) + + # get the reference position from the loc[3:6] + chs = [info['chs'][ii] for ii in picks] + non_names = [info['chs'][ii]['ch_name'] for ii in non_picks] + del picks + ref_pos = [ch['loc'][3:6] for ch in chs] + + # keep reference location from EEG-like channels if they + # already exist and are all the same. + custom_eeg_ref_dig = False + # Note: ref position is an empty list for fieldtrip data + if ref_pos: + if all([np.equal(ref_pos[0], pos).all() for pos in ref_pos]) \ + and not np.equal(ref_pos[0], [0, 0, 0]).all(): + eeg_ref_pos = ref_pos[0] + # since we have an EEG reference position, we have + # to add it into the info['dig'] as EEG000 + custom_eeg_ref_dig = True + if not custom_eeg_ref_dig: + refs = set(ch_pos) & {'EEG000', 'REF'} + assert len(refs) <= 1 + eeg_ref_pos = np.zeros(3) if not refs else ch_pos.pop(refs.pop()) + + # This raises based on info being subset/superset of montage + info_names = [ch['ch_name'] for ch in chs] + dig_names = mnt_head._get_dig_names() + ref_names = [None, 'EEG000', 'REF'] + + if match_case: + info_names_use = info_names + dig_names_use = dig_names + non_names_use = non_names + else: + ch_pos_use = OrderedDict( + (name.lower(), pos) for name, pos in ch_pos.items()) + info_names_use = [name.lower() for name in info_names] + dig_names_use = [name.lower() if name is not None else name + for name in dig_names] + non_names_use = [name.lower() for name in non_names] + ref_names = [name.lower() if name is not None else name + for name in ref_names] + n_dup = len(ch_pos) - len(ch_pos_use) + if n_dup: + raise ValueError('Cannot use match_case=False as %s montage ' + 'name(s) require case sensitivity' % n_dup) + n_dup = len(info_names_use) - len(set(info_names_use)) + if n_dup: + raise ValueError('Cannot use match_case=False as %s channel ' + 'name(s) require case sensitivity' % n_dup) + ch_pos = ch_pos_use + del ch_pos_use + del dig_names + + # use lookup table to match unrecognized channel names to known aliases + if match_alias: + alias_dict = (match_alias if isinstance(match_alias, dict) else + CHANNEL_LOC_ALIASES) + if not match_case: alias_dict = { - ch_name: ch_alias + ch_name.lower(): ch_alias.lower() for ch_name, ch_alias in alias_dict.items() - if ch_alias in ch_pos_use } - info_names_use = [ - alias_dict.get(ch_name, ch_name) for ch_name in info_names_use - ] - - # warn user if there is not a full overlap of montage with info_chs - not_in_montage = [name for name, use in zip(info_names, info_names_use) - if use not in ch_pos_use] - if len(not_in_montage): # DigMontage is subset of info - missing_coord_msg = ( - 'DigMontage is only a subset of info. There are ' - f'{len(not_in_montage)} channel position{_pl(not_in_montage)} ' - 'not present in the DigMontage. The required channels are:\n\n' - f'{not_in_montage}.\n\nConsider using inst.set_channel_types ' - 'if these are not EEG channels, or use the on_missing ' - 'parameter if the channel positions are allowed to be unknown ' - 'in your analyses.' - ) - _on_missing(on_missing, missing_coord_msg) - - # set ch coordinates and names from digmontage or nan coords - ch_pos_use = dict( - (name, ch_pos_use.get(name, [np.nan] * 3)) - for name in info_names) # order does not matter here - for name, use in zip(info_names, info_names_use): - _loc_view = info['chs'][info['ch_names'].index(name)]['loc'] - # Next line modifies info['chs'][#]['loc'] in place - _loc_view[:6] = _backcompat_value(ch_pos_use[use], eeg_ref_pos) - - del ch_pos_use + # excluded ch_alias not in info, to prevent unnecessary mapping and + # warning messages based on aliases. + alias_dict = { + ch_name: ch_alias + for ch_name, ch_alias in alias_dict.items() + } + info_names_use = [ + alias_dict.get(ch_name, ch_name) for ch_name in info_names_use + ] + non_names_use = [ + alias_dict.get(ch_name, ch_name) for ch_name in non_names_use + ] + + # warn user if there is not a full overlap of montage with info_chs + missing = np.where([use not in ch_pos for use in info_names_use])[0] + if len(missing): # DigMontage is subset of info + missing_names = [info_names[ii] for ii in missing] + missing_coord_msg = ( + 'DigMontage is only a subset of info. There are ' + f'{len(missing)} channel position{_pl(missing)} ' + 'not present in the DigMontage. The required channels are:\n\n' + f'{missing_names}.\n\nConsider using inst.set_channel_types ' + 'if these are not EEG channels, or use the on_missing ' + 'parameter if the channel positions are allowed to be unknown ' + 'in your analyses.' + ) + _on_missing(on_missing, missing_coord_msg) - # XXX this is probably wrong as it uses the order from the montage - # rather than the order of our info['ch_names'] ... - digpoints = [ - mnt_head.dig[ii] for ii, name in enumerate(dig_names_use) - if name in (info_names_use + ref_names)] - - # get a copy of the old dig - if info['dig'] is not None: - old_dig = info['dig'].copy() - else: - old_dig = [] + # set ch coordinates and names from digmontage or nan coords + for ii in missing: + ch_pos[info_names_use[ii]] = [np.nan] * 3 + del info_names + + assert len(non_names_use) == len(non_names) + # There are no issues here with fNIRS being in non_names_use because + # these names are like "D1_S1_760" and the ch_pos for a fNIRS montage + # will have entries "D1" and "S1". + extra = np.where([non in ch_pos for non in non_names_use])[0] + if len(extra): + types = '/'.join(sorted(set( + channel_type(info, non_picks[ii]) for ii in extra))) + names = [non_names[ii] for ii in extra] + warn(f'Not setting position{_pl(extra)} of {len(extra)} {types} ' + f'channel{_pl(extra)} found in montage:\n{names}\n' + 'Consider setting the channel types to be of ' + f'{_docdict["montage_types"]} ' + 'using inst.set_channel_types before calling inst.set_montage, ' + 'or omit these channels when creating your montage.') + + for ch, use in zip(chs, info_names_use): + # Next line modifies info['chs'][#]['loc'] in place + if use in ch_pos: + ch['loc'][:6] = _backcompat_value(ch_pos[use], eeg_ref_pos) + ch['coord_frame'] = FIFF.FIFFV_COORD_HEAD + del ch_pos + + # XXX this is probably wrong as it uses the order from the montage + # rather than the order of our info['ch_names'] ... + digpoints = [ + mnt_head.dig[ii] for ii, name in enumerate(dig_names_use) + if name in (info_names_use + ref_names)] + + # get a copy of the old dig + if info['dig'] is not None: + old_dig = info['dig'].copy() + else: + old_dig = [] - # determine if needed to add an extra EEG REF DigPoint - if custom_eeg_ref_dig: - # ref_name = 'EEG000' if match_case else 'eeg000' - ref_dig_dict = {'kind': FIFF.FIFFV_POINT_EEG, - 'r': eeg_ref_pos, - 'ident': 0, - 'coord_frame': info['dig'].pop()['coord_frame']} - ref_dig_point = _format_dig_points([ref_dig_dict])[0] - # only append the reference dig point if it was already - # in the old dig - if ref_dig_point in old_dig: - digpoints.append(ref_dig_point) - # Next line modifies info['dig'] in place + # determine if needed to add an extra EEG REF DigPoint + if custom_eeg_ref_dig: + # ref_name = 'EEG000' if match_case else 'eeg000' + ref_dig_dict = {'kind': FIFF.FIFFV_POINT_EEG, + 'r': eeg_ref_pos, + 'ident': 0, + 'coord_frame': info['dig'].pop()['coord_frame']} + ref_dig_point = _format_dig_points([ref_dig_dict])[0] + # only append the reference dig point if it was already + # in the old dig + if ref_dig_point in old_dig: + digpoints.append(ref_dig_point) + # Next line modifies info['dig'] in place + with info._unlock(): info['dig'] = _format_dig_points(digpoints, enforce_order=True) - if mnt_head.dev_head_t is not None: - # Next line modifies info['dev_head_t'] in place - info['dev_head_t'] = Transform('meg', 'head', mnt_head.dev_head_t) - - # Handle fNIRS with source, detector and channel - fnirs_picks = _picks_to_idx(info, 'fnirs', allow_empty=True) - if len(fnirs_picks) > 0: - info = _set_montage_fnirs(info, mnt_head) - - else: # None case - # Next line modifies info['dig'] in place - info['dig'] = None - for ch in info['chs']: - # Next line modifies info['chs'][#]['loc'] in place - ch['loc'] = np.full(12, np.nan) + # Handle fNIRS with source, detector and channel + fnirs_picks = _picks_to_idx(info, 'fnirs', allow_empty=True) + if len(fnirs_picks) > 0: + _set_montage_fnirs(info, mnt_head) def _read_isotrak_elp_points(fname): @@ -1024,7 +1321,7 @@ Parameters ---------- - fname : str + fname : path-like The filepath of Polhemus ISOTrak formatted file. File extension is expected to be '.hsp', '.elp' or '.eeg'. ch_names : None | list of str @@ -1050,8 +1347,10 @@ read_dig_dat read_dig_egi read_dig_fif + read_dig_localite """ VALID_FILE_EXT = ('.hsp', '.elp', '.eeg') + fname = _check_fname(fname, overwrite='read', must_exist=True) _scale = _check_unit_and_get_scaling(unit) _, ext = op.splitext(fname) @@ -1106,8 +1405,8 @@ Parameters ---------- - fname : str - The filepath of .txt Polhemus FastSCAN file. + fname : path-like + The path of .txt Polhemus FastSCAN file. unit : 'm' | 'cm' | 'mm' Unit of the digitizer file. Polhemus FastSCAN systems data is usually exported in millimeters. Defaults to 'mm'. @@ -1125,6 +1424,7 @@ make_dig_montage """ VALID_FILE_EXT = ['.txt'] + fname = _check_fname(fname, overwrite='read', must_exist=True) _scale = _check_unit_and_get_scaling(unit) _, ext = op.splitext(fname) @@ -1154,7 +1454,7 @@ Parameters ---------- - fname : str + fname : path-like File extension is expected to be: '.loc' or '.locs' or '.eloc' (for EEGLAB files), '.sfp' (BESA/EGI files), '.csd', @@ -1202,11 +1502,12 @@ 'matlab': ('.csd', ), 'asa electrode': ('.elc', ), 'generic (Theta-phi in degrees)': ('.txt', ), - 'standard BESA spherical': ('.elp', ), # XXX: not same as polhemus elp + 'standard BESA spherical': ('.elp', ), # NB: not same as polhemus elp 'brainvision': ('.bvef', ), 'xyz': ('.csv', '.tsv', '.xyz'), } + fname = _check_fname(fname, overwrite='read', must_exist=True) _, ext = op.splitext(fname) _check_option('fname', ext, list(sum(SUPPORTED_FILE_EXT.values(), ()))) @@ -1333,16 +1634,23 @@ return Transform(coord_frame, 'head', native_head_t) -def make_standard_montage(kind, head_size=HEAD_SIZE_DEFAULT): - """Read a generic (built-in) montage. +def make_standard_montage(kind, head_size='auto'): + """Read a generic (built-in) standard montage that ships with MNE-Python. Parameters ---------- kind : str - The name of the montage to use. See notes for valid kinds. - head_size : float + The name of the montage to use. + + .. note:: + You can retrieve the names of all + built-in montages via :func:`mne.channels.get_builtin_montages`. + head_size : float | None | str The head size (radius, in meters) to use for spherical montages. - Defaults to 95mm. + Can be None to not scale the read sizes. ``'auto'`` (default) will + use 95mm for all montages except the ``'standard*'``, ``'mgh*'``, and + ``'artinis*'``, which are already in fsaverage's MRI coordinates + (same as MNI). Returns ------- @@ -1351,7 +1659,7 @@ See Also -------- - DigMontage + get_builtin_montages make_dig_montage read_custom_montage @@ -1360,63 +1668,23 @@ Individualized (digitized) electrode positions should be read in using :func:`read_dig_captrak`, :func:`read_dig_dat`, :func:`read_dig_egi`, :func:`read_dig_fif`, :func:`read_dig_polhemus_isotrak`, - :func:`read_dig_hpts` or made with :func:`make_dig_montage`. - - Valid ``kind`` arguments are: - - =================== ===================================================== - Kind Description - =================== ===================================================== - standard_1005 Electrodes are named and positioned according to the - international 10-05 system (343+3 locations) - standard_1020 Electrodes are named and positioned according to the - international 10-20 system (94+3 locations) - standard_alphabetic Electrodes are named with LETTER-NUMBER combinations - (A1, B2, F4, ...) (65+3 locations) - standard_postfixed Electrodes are named according to the international - 10-20 system using postfixes for intermediate - positions (100+3 locations) - standard_prefixed Electrodes are named according to the international - 10-20 system using prefixes for intermediate - positions (74+3 locations) - standard_primed Electrodes are named according to the international - 10-20 system using prime marks (' and '') for - intermediate positions (100+3 locations) - - biosemi16 BioSemi cap with 16 electrodes (16+3 locations) - biosemi32 BioSemi cap with 32 electrodes (32+3 locations) - biosemi64 BioSemi cap with 64 electrodes (64+3 locations) - biosemi128 BioSemi cap with 128 electrodes (128+3 locations) - biosemi160 BioSemi cap with 160 electrodes (160+3 locations) - biosemi256 BioSemi cap with 256 electrodes (256+3 locations) - - easycap-M1 EasyCap with 10-05 electrode names (74 locations) - easycap-M10 EasyCap with numbered electrodes (61 locations) - - EGI_256 Geodesic Sensor Net (256 locations) - - GSN-HydroCel-32 HydroCel Geodesic Sensor Net and Cz (33+3 locations) - GSN-HydroCel-64_1.0 HydroCel Geodesic Sensor Net (64+3 locations) - GSN-HydroCel-65_1.0 HydroCel Geodesic Sensor Net and Cz (65+3 locations) - GSN-HydroCel-128 HydroCel Geodesic Sensor Net (128+3 locations) - GSN-HydroCel-129 HydroCel Geodesic Sensor Net and Cz (129+3 locations) - GSN-HydroCel-256 HydroCel Geodesic Sensor Net (256+3 locations) - GSN-HydroCel-257 HydroCel Geodesic Sensor Net and Cz (257+3 locations) - - mgh60 The (older) 60-channel cap used at - MGH (60+3 locations) - mgh70 The (newer) 70-channel BrainVision cap used at - MGH (70+3 locations) - - artinis-octamon Artinis OctaMon fNIRS (8 sources, 2 detectors) - - artinis-brite23 Artinis Brite23 fNIRS (11 sources, 7 detectors) - =================== ===================================================== + :func:`read_dig_hpts`, or manually made with :func:`make_dig_montage`. .. versionadded:: 0.19.0 """ from ._standard_montage_utils import standard_montage_look_up_table - _check_option('kind', kind, _BUILT_IN_MONTAGES) + _validate_type(kind, str, 'kind') + _check_option( + parameter='kind', value=kind, + allowed_values=[m.name for m in _BUILTIN_STANDARD_MONTAGES] + ) + _validate_type(head_size, ('numeric', str, None), 'head_size') + if isinstance(head_size, str): + _check_option('head_size', head_size, ('auto',), extra='when str') + if kind.startswith(('standard', 'mgh', 'artinis')): + head_size = None + else: + head_size = HEAD_SIZE_DEFAULT return standard_montage_look_up_table[kind](head_size=head_size) diff -Nru python-mne-0.23.4+dfsg/mne/channels/_standard_montage_utils.py python-mne-1.1.0+dfsg/mne/channels/_standard_montage_utils.py --- python-mne-0.23.4+dfsg/mne/channels/_standard_montage_utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/channels/_standard_montage_utils.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors: Joan Massich # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause from collections import OrderedDict import csv @@ -12,6 +12,7 @@ import xml.etree.ElementTree as ElementTree from .montage import make_dig_montage +from .._freesurfer import get_mni_fiducials from ..transforms import _sph_to_cart from ..utils import warn, _pl from . import __file__ as _CHANNELS_INIT_FILE @@ -93,12 +94,20 @@ break ch_names_.append(line.strip(' ').strip('\n')) - pos = np.array(pos) + pos = np.array(pos) / 1000. ch_pos = _check_dupes_odict(ch_names_, pos) nasion, lpa, rpa = [ch_pos.pop(n) for n in fid_names] - scale = head_size / np.median(np.linalg.norm(pos, axis=1)) + if head_size is None: + scale = 1. + else: + scale = head_size / np.median(np.linalg.norm(pos, axis=1)) for value in ch_pos.values(): value *= scale + # if we are in MRI/MNI coordinates, we need to replace nasion, LPA, and RPA + # with those of fsaverage for ``trans='fsaverage'`` to work + if coord_frame == 'mri': + lpa, nasion, rpa = [ + x['r'].copy() for x in get_mni_fiducials('fsaverage')] nasion *= scale lpa *= scale rpa *= scale @@ -130,24 +139,32 @@ 'biosemi32': partial(_biosemi, basename='biosemi32.txt'), 'biosemi64': partial(_biosemi, basename='biosemi64.txt'), - 'mgh60': partial(_mgh_or_standard, basename='mgh60.elc'), - 'mgh70': partial(_mgh_or_standard, basename='mgh70.elc'), + 'mgh60': partial(_mgh_or_standard, basename='mgh60.elc', + coord_frame='mri'), + 'mgh70': partial(_mgh_or_standard, basename='mgh70.elc', + coord_frame='mri'), 'standard_1005': partial(_mgh_or_standard, - basename='standard_1005.elc'), + basename='standard_1005.elc', coord_frame='mri'), 'standard_1020': partial(_mgh_or_standard, - basename='standard_1020.elc'), + basename='standard_1020.elc', coord_frame='mri'), 'standard_alphabetic': partial(_mgh_or_standard, - basename='standard_alphabetic.elc'), + basename='standard_alphabetic.elc', + coord_frame='mri'), 'standard_postfixed': partial(_mgh_or_standard, - basename='standard_postfixed.elc'), + basename='standard_postfixed.elc', + coord_frame='mri'), 'standard_prefixed': partial(_mgh_or_standard, - basename='standard_prefixed.elc'), + basename='standard_prefixed.elc', + coord_frame='mri'), 'standard_primed': partial(_mgh_or_standard, - basename='standard_primed.elc'), + basename='standard_primed.elc', + coord_frame='mri'), 'artinis-octamon': partial(_mgh_or_standard, coord_frame='mri', basename='artinis-octamon.elc'), 'artinis-brite23': partial(_mgh_or_standard, coord_frame='mri', basename='artinis-brite23.elc'), + 'brainproducts-RNP-BA-128': partial( + _easycap, basename='brainproducts-RNP-BA-128.txt') } @@ -293,10 +310,7 @@ def _read_elp_besa(fname, head_size): # This .elp is not the same as polhemus elp. see _read_isotrak_elp_points dtype = np.dtype('S8, S8, f8, f8, f8') - try: - data = np.loadtxt(fname, dtype=dtype, skip_header=1) - except TypeError: - data = np.loadtxt(fname, dtype=dtype, skiprows=1) + data = np.loadtxt(fname, dtype=dtype) ch_names = data['f1'].astype(str).tolist() az = data['f2'] diff -Nru python-mne-0.23.4+dfsg/mne/channels/tests/test_channels.py python-mne-1.1.0+dfsg/mne/channels/tests/test_channels.py --- python-mne-0.23.4+dfsg/mne/channels/tests/test_channels.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/channels/tests/test_channels.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,12 +1,13 @@ # Author: Daniel G Wakeman # Denis A. Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op - +from pathlib import Path from copy import deepcopy from functools import partial +import hashlib import pytest import numpy as np @@ -15,16 +16,20 @@ from mne.channels import (rename_channels, read_ch_adjacency, combine_channels, find_ch_adjacency, make_1020_channel_selections, - read_custom_montage, equalize_channels) -from mne.channels.channels import (_ch_neighbor_adjacency, - _compute_ch_adjacency) + read_custom_montage, equalize_channels, + get_builtin_ch_adjacencies) +from mne.channels.channels import ( + _ch_neighbor_adjacency, _compute_ch_adjacency, + _BUILTIN_CHANNEL_ADJACENCIES, _BuiltinChannelAdjacency +) from mne.io import (read_info, read_raw_fif, read_raw_ctf, read_raw_bti, read_raw_eeglab, read_raw_kit, RawArray) from mne.io.constants import FIFF -from mne.utils import _TempDir, run_tests_if_main from mne import (pick_types, pick_channels, EpochsArray, EvokedArray, make_ad_hoc_cov, create_info, read_events, Epochs) from mne.datasets import testing +from mne.utils import requires_pandas, requires_version +from mne.parallel import parallel_func io_dir = op.join(op.dirname(__file__), '..', '..', 'io') base_dir = op.join(io_dir, 'tests', 'data') @@ -32,6 +37,8 @@ eve_fname = op.join(base_dir, 'test-eve.fif') fname_kit_157 = op.join(io_dir, 'kit', 'tests', 'data', 'test.sqd') +testing_path = testing.data_path(download=False) + @pytest.mark.parametrize('preload', (True, False)) @pytest.mark.parametrize('proj', (True, False)) @@ -178,9 +185,21 @@ pytest.raises(ValueError, raw.set_channel_types, ch_types) -def test_read_ch_adjacency(): +def test_get_builtin_ch_adjacencies(): + """Test retrieving the names of all built-in FieldTrip neighbors.""" + names = get_builtin_ch_adjacencies() + assert names + assert len(names) == len(set(names)) # no duplicates + assert len(names) == len(_BUILTIN_CHANNEL_ADJACENCIES) + + names_and_descriptions = get_builtin_ch_adjacencies(descriptions=True) + for name_and_description in names_and_descriptions: + assert len(name_and_description) == 2 + + +def test_read_ch_adjacency(tmp_path): """Test reading channel adjacency templates.""" - tempdir = _TempDir() + tempdir = str(tmp_path) a = partial(np.array, dtype=' np.std(raw_od._data[bad_0]) - - -run_tests_if_main() + raw_haemo = beer_lambert_law(raw_od, ppf=6) + raw_haemo.info['bads'] = raw_haemo.ch_names[2:4] + assert raw_haemo.info['bads'] == ['S1_D2 hbo', 'S1_D2 hbr'] + raw_haemo.interpolate_bads() + assert raw_haemo.info['bads'] == [] diff -Nru python-mne-0.23.4+dfsg/mne/channels/tests/test_layout.py python-mne-1.1.0+dfsg/mne/channels/tests/test_layout.py --- python-mne-0.23.4+dfsg/mne/channels/tests/test_layout.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/channels/tests/test_layout.py 2022-08-03 15:48:14.000000000 +0000 @@ -18,11 +18,9 @@ find_layout, HEAD_SIZE_DEFAULT) from mne.channels.layout import (_box_size, _find_topomap_coords, generate_2d_layout) -from mne.utils import run_tests_if_main from mne import pick_types, pick_info from mne.io import read_raw_kit, _empty_info, read_info from mne.io.constants import FIFF -from mne.utils import _TempDir io_dir = op.join(op.dirname(__file__), '..', '..', 'io') fif_fname = op.join(io_dir, 'tests', 'data', 'test_raw.fif') @@ -48,14 +46,15 @@ {'cal': 0.002142000012099743, 'ch_name': 'EOG 061', 'coil_type': 1, 'coord_frame': 0, 'kind': 202, 'loc': loc.copy(), 'logno': 61, 'range': 1.0, 'scanno': 376, 'unit': 107, 'unit_mul': 0}] + test_info._unlocked = False test_info._update_redundant() test_info._check_consistency() return test_info -def test_io_layout_lout(): +def test_io_layout_lout(tmp_path): """Test IO with .lout files.""" - tempdir = _TempDir() + tempdir = str(tmp_path) layout = read_layout('Vectorview-all', scale=False) layout.save(op.join(tempdir, 'foobar.lout')) layout_read = read_layout(op.join(tempdir, 'foobar.lout'), path='./', @@ -65,9 +64,9 @@ print(layout) # test repr -def test_io_layout_lay(): +def test_io_layout_lay(tmp_path): """Test IO with .lay files.""" - tempdir = _TempDir() + tempdir = str(tmp_path) layout = read_layout('CTF151', scale=False) layout.save(op.join(tempdir, 'foobar.lay')) layout_read = read_layout(op.join(tempdir, 'foobar.lay'), path='./', @@ -100,7 +99,7 @@ for z_pt in ((HEAD_SIZE_DEFAULT, 0., 0.), (0., HEAD_SIZE_DEFAULT, 0.)): - info['dig'][-1]['r'] = z_pt + info['dig'][-1]['r'] = np.array(z_pt) l1 = _find_topomap_coords(info, picks, **kwargs) assert_allclose(l1[-1], z_pt[:2], err_msg='Z=0 point moved', atol=1e-6) @@ -115,6 +114,7 @@ _find_topomap_coords(info, picks, **kwargs) # Test function with too little EEG digitization points: it should fail + info._unlocked = True info['dig'] = info['dig'][:-2] with pytest.raises(ValueError, match='Number of EEG digitization points'): _find_topomap_coords(info, picks, **kwargs) @@ -125,7 +125,8 @@ _find_topomap_coords(info, picks, **kwargs) # Test function without EEG digitization points: it should fail - info['dig'] = [d for d in info['dig'] if d['kind'] != FIFF.FIFFV_POINT_EEG] + info['dig'] = [d for d in info['dig'] + if d['kind'] != FIFF.FIFFV_POINT_EEG] with pytest.raises(RuntimeError, match='Did not find any digitization'): _find_topomap_coords(info, picks, **kwargs) @@ -138,9 +139,9 @@ _find_topomap_coords(info, picks, **kwargs) -def test_make_eeg_layout(): +def test_make_eeg_layout(tmp_path): """Test creation of EEG layout.""" - tempdir = _TempDir() + tempdir = str(tmp_path) tmp_name = 'foo' lout_name = 'test_raw' lout_orig = read_layout(kind=lout_name, path=lout_path) @@ -164,9 +165,9 @@ pytest.raises(ValueError, make_eeg_layout, info, height=1.1) -def test_make_grid_layout(): +def test_make_grid_layout(tmp_path): """Test creation of grid layout.""" - tempdir = _TempDir() + tempdir = str(tmp_path) tmp_name = 'bar' lout_name = 'test_ica' lout_orig = read_layout(kind=lout_name, path=lout_path) @@ -262,7 +263,8 @@ assert_equal(lout.kind, 'KIT-157') # fallback for missing IDs for val in (35, 52, 54, 1001): - raw_kit.info['kit_system_id'] = val + with raw_kit.info._unlock(): + raw_kit.info['kit_system_id'] = val lout = find_layout(raw_kit.info) assert lout.kind == 'custom' @@ -367,6 +369,3 @@ # Make sure background image normalizing is correct lt_bg = generate_2d_layout(xy, bg_image=bg_image) assert_allclose(lt_bg.pos[:, :2].max(), xy.max() / float(sbg)) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/channels/tests/test_montage.py python-mne-1.1.0+dfsg/mne/channels/tests/test_montage.py --- python-mne-0.23.4+dfsg/mne/channels/tests/test_montage.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/channels/tests/test_montage.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,11 +1,13 @@ # Author: Teon Brooks # Stefan Appelhoff # -# License: BSD (3-clause) +# License: BSD-3-Clause +from contextlib import nullcontext from itertools import chain -import os +from pathlib import Path import os.path as op +import shutil import pytest @@ -18,28 +20,31 @@ import matplotlib.pyplot as plt from mne import __file__ as _mne_file, create_info, read_evokeds, pick_types -from mne.fixes import nullcontext +from mne.source_space import get_mni_fiducials from mne.utils._testing import assert_object_equal from mne.channels import (get_builtin_montages, DigMontage, read_dig_dat, read_dig_egi, read_dig_captrak, read_dig_fif, make_standard_montage, read_custom_montage, compute_dev_head_t, make_dig_montage, read_dig_polhemus_isotrak, compute_native_head_t, - read_polhemus_fastscan, + read_polhemus_fastscan, read_dig_localite, read_dig_hpts) -from mne.channels.montage import transform_to_head, _check_get_coord_frame -from mne.utils import run_tests_if_main, assert_dig_allclose +from mne.channels.montage import ( + transform_to_head, _check_get_coord_frame, _BUILTIN_STANDARD_MONTAGES +) +from mne.utils import assert_dig_allclose, _record_warnings from mne.bem import _fit_sphere from mne.io.constants import FIFF from mne.io._digitization import (_format_dig_points, _get_fid_coords, _get_dig_eeg, _count_points_by_type) -from mne.transforms import _ensure_trans +from mne.transforms import (_ensure_trans, apply_trans, invert_transform, + _get_trans) from mne.viz._3d import _fiducial_coords from mne.io.kit import read_mrk from mne.io import (read_raw_brainvision, read_raw_egi, read_raw_fif, - read_fiducials, __file__ as _MNE_IO_FILE) + read_fiducials, __file__ as _MNE_IO_FILE, read_raw_nirx) from mne.io import RawArray from mne.datasets import testing @@ -61,6 +66,9 @@ bdf_fname2 = op.join(data_path, 'BDF', 'test_bdf_stim_channel.bdf') egi_fname1 = op.join(data_path, 'EGI', 'test_egi.mff') cnt_fname = op.join(data_path, 'CNT', 'scan41_short.cnt') +fnirs_dname = op.join(data_path, 'NIRx', 'nirscout', + 'nirx_15_2_recording_w_short') +subjects_dir = op.join(data_path, 'subjects') io_dir = op.dirname(_MNE_IO_FILE) kit_dir = op.join(io_dir, 'kit', 'tests', 'data') @@ -102,8 +110,8 @@ return np.array([d['r'] for d in _get_dig_eeg(montage.dig)]) -def test_dig_montage_trans(tmpdir): - """Test getting a trans from montage.""" +def test_dig_montage_trans(tmp_path): + """Test getting a trans from and applying a trans to a montage.""" nasion, lpa, rpa, *ch_pos = np.random.RandomState(0).randn(10, 3) ch_pos = {f'EEG{ii:3d}': pos for ii, pos in enumerate(ch_pos, 1)} montage = make_dig_montage(ch_pos, nasion=nasion, lpa=lpa, rpa=rpa, @@ -111,8 +119,15 @@ trans = compute_native_head_t(montage) _ensure_trans(trans) # ensure that we can save and load it, too - fname = tmpdir.join('temp-mon.fif') + fname = tmp_path / 'temp-mon.fif' _check_roundtrip(montage, fname, 'mri') + # test applying a trans + position1 = montage.get_positions() + montage.apply_trans(trans) + assert montage.get_positions()['coord_frame'] == 'head' + montage.apply_trans(invert_transform(trans)) + position2 = montage.get_positions() + assert str(position1) == str(position2) # exactly equal def test_fiducials(): @@ -135,27 +150,14 @@ def test_documented(): """Test that standard montages are documented.""" - docs = make_standard_montage.__doc__ - lines = [line[4:] for line in docs.splitlines()] - start = stop = None - for li, line in enumerate(lines): - if line.startswith('====') and li < len(lines) - 2 and \ - lines[li + 1].startswith('Kind') and\ - lines[li + 2].startswith('===='): - start = li + 3 - elif start is not None and li > start and line.startswith('===='): - stop = li - break - assert (start is not None) - assert (stop is not None) - kinds = [line.split(' ')[0] for line in lines[start:stop]] - kinds = [kind for kind in kinds if kind != ''] - montages = os.listdir(op.join(op.dirname(_mne_file), 'channels', 'data', - 'montages')) - montages = sorted(op.splitext(m)[0] for m in montages) - assert_equal(len(set(montages)), len(montages)) - assert_equal(len(set(kinds)), len(kinds), err_msg=str(sorted(kinds))) - assert_equal(set(montages), set(kinds)) + montage_dir = Path(_mne_file).parent / 'channels' / 'data' / 'montages' + montage_files = Path(montage_dir).glob('*') + montage_names = [f.stem for f in montage_files] + + assert len(montage_names) == len(_BUILTIN_STANDARD_MONTAGES) + assert set(montage_names) == set( + [m.name for m in _BUILTIN_STANDARD_MONTAGES] + ) @pytest.mark.parametrize('reader, file_content, expected_dig, ext, warning', [ @@ -286,8 +288,7 @@ pytest.param( partial(read_custom_montage, head_size=None), - ('346\n' # XXX: this should actually race an error 346 != 4 - 'FID\t LPA\t -120.03\t 0\t 85\n' + ('FID\t LPA\t -120.03\t 0\t 85\n' 'FID\t RPA\t 120.03\t 0\t 85\n' 'FID\t Nz\t 114.03\t 90\t 85\n' 'EEG\t F3\t -62.027\t -50.053\t 85\n' @@ -427,10 +428,10 @@ id='brainvision'), ]) def test_montage_readers( - reader, file_content, expected_dig, ext, warning, tmpdir + reader, file_content, expected_dig, ext, warning, tmp_path ): """Test that we have an equivalent of read_montage for all file formats.""" - fname = op.join(str(tmpdir), 'test.{ext}'.format(ext=ext)) + fname = op.join(str(tmp_path), 'test.{ext}'.format(ext=ext)) with open(fname, 'w') as fid: fid.write(file_content) @@ -452,7 +453,7 @@ for key in ('coord_frame', 'ident', 'kind'): assert isinstance(d1[key], int) assert isinstance(d2[key], int) - with pytest.warns(None) as w: + with _record_warnings() as w: xform = compute_native_head_t(dig_montage) assert xform['to'] == FIFF.FIFFV_COORD_HEAD assert xform['from'] == FIFF.FIFFV_COORD_UNKNOWN @@ -476,7 +477,7 @@ ) -def test_read_dig_dat(tmpdir): +def test_read_dig_dat(tmp_path): """Test reading *.dat electrode locations.""" rows = [ ['Nasion', 78, 0.00, 1.00, 0.00], @@ -487,7 +488,7 @@ ['Centroid', 67, 0.00, 0.00, 0.00], ] # write mock test.dat file - temp_dir = str(tmpdir) + temp_dir = str(tmp_path) fname_temp = op.join(temp_dir, 'test.dat') with open(fname_temp, 'w') as fid: for row in rows: @@ -548,11 +549,11 @@ assert repr(montage) == ( '' - ) # XXX: is this wrong? extra is not in headspace, is it? + ) assert set([d['coord_frame'] for d in montage.dig]) == { FIFF.FIFFV_COORD_UNKNOWN - } # XXX: so far we build everything in 'unknown' + } EXPECTED_FID_IN_POLHEMUS = { 'nasion': [0.001393, 0.0131613, -0.0046967], @@ -565,21 +566,24 @@ assert_allclose(val, EXPECTED_FID_IN_POLHEMUS[kk]) -def test_read_dig_montage_using_polhemus_fastscan_error_handling(tmpdir): +def test_read_dig_montage_using_polhemus_fastscan_error_handling(tmp_path): """Test reading Polhemus FastSCAN errors.""" with open(op.join(kit_dir, 'test_elp.txt')) as fid: content = fid.read().replace('FastSCAN', 'XxxxXXXX') - fname = str(tmpdir.join('faulty_FastSCAN.txt')) + fname = tmp_path / 'faulty_FastSCAN.txt' with open(fname, 'w') as fid: fid.write(content) with pytest.raises(ValueError, match='not contain.*Polhemus FastSCAN'): _ = read_polhemus_fastscan(fname) + fname = tmp_path / 'faulty_FastSCAN.bar' + with open(fname, 'w') as fid: + fid.write(content) EXPECTED_ERR_MSG = "allowed value is '.txt', but got '.bar' instead" with pytest.raises(ValueError, match=EXPECTED_ERR_MSG): - _ = read_polhemus_fastscan(fname=tmpdir.join('foo.bar')) + _ = read_polhemus_fastscan(fname=fname) def test_read_dig_polhemus_isotrak_hsp(): @@ -624,13 +628,13 @@ @pytest.fixture(scope='module') -def isotrak_eeg(tmpdir_factory): +def isotrak_eeg(tmp_path_factory): """Mock isotrak file with EEG positions.""" _SEED = 42 N_ROWS, N_COLS = 5, 3 content = np.random.RandomState(_SEED).randn(N_ROWS, N_COLS) - fname = tmpdir_factory.mktemp('data').join('test.eeg') + fname = tmp_path_factory.mktemp('data') / 'test.eeg' with open(str(fname), 'w') as fid: fid.write(( '3 200\n' @@ -686,7 +690,7 @@ assert dig_point['coord_frame'] == FIFF.FIFFV_COORD_UNKNOWN -def test_read_dig_polhemus_isotrak_error_handling(isotrak_eeg, tmpdir): +def test_read_dig_polhemus_isotrak_error_handling(isotrak_eeg, tmp_path): """Test errors in reading Polhemus IsoTrak files. 1 - matching ch_names and number of points in isotrak file. @@ -702,7 +706,9 @@ ) # Check fname extensions - fname = op.join(tmpdir, 'foo.bar') + fname = op.join(tmp_path, 'test.bar') + shutil.copyfile(isotrak_eeg, fname) + with pytest.raises( ValueError, match="Allowed val.*'.hsp', '.elp', and '.eeg', but got '.bar' instead" @@ -848,12 +854,12 @@ @testing.requires_testing_data -def test_fif_dig_montage(tmpdir): +def test_fif_dig_montage(tmp_path): """Test FIF dig montage support.""" dig_montage = read_dig_fif(fif_dig_montage_fname) # test round-trip IO - temp_dir = str(tmpdir) + temp_dir = str(tmp_path) fname_temp = op.join(temp_dir, 'test.fif') _check_roundtrip(dig_montage, fname_temp) @@ -910,7 +916,7 @@ @testing.requires_testing_data -def test_egi_dig_montage(tmpdir): +def test_egi_dig_montage(tmp_path): """Test EGI MFF XML dig montage support.""" dig_montage = read_dig_egi(egi_dig_montage_fname) fid, coord = _get_fid_coords(dig_montage.dig) @@ -948,7 +954,7 @@ ) # test round-trip IO - fname_temp = tmpdir.join('egi_test.fif') + fname_temp = tmp_path / 'egi_test.fif' _check_roundtrip(dig_montage, fname_temp, 'unknown') _check_roundtrip(dig_montage_in_head, fname_temp) @@ -965,7 +971,7 @@ @testing.requires_testing_data -def test_read_dig_captrak(tmpdir): +def test_read_dig_captrak(tmp_path): """Test reading a captrak montage file.""" EXPECTED_CH_NAMES_OLD = [ 'AF3', 'AF4', 'AF7', 'AF8', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'CP1', @@ -997,7 +1003,8 @@ ) montage = transform_to_head(montage) # transform_to_head has to be tested - _check_roundtrip(montage=montage, fname=str(tmpdir.join('bvct_test.fif'))) + _check_roundtrip(montage=montage, + fname=str(tmp_path / 'bvct_test.fif')) fid, _ = _get_fid_coords(montage.dig) assert_allclose( @@ -1041,6 +1048,7 @@ orig_pos = np.array([raw.info['chs'][pick]['loc'][:3] for pick in eeg_picks]) atol = 1e-6 + mon = None if rename == 'raw': raw.rename_channels(lambda x: x.replace('EEG ', 'EEG')) raw.set_montage('mgh60') # test loading with string argument @@ -1050,10 +1058,11 @@ assert [raw.ch_names[pick] for pick in eeg_picks] == mon.ch_names raw.set_montage(mon) else: - atol = 3e-3 # XXX old defs here apparently (maybe not realistic)? + atol = 3e-3 # different subsets of channel locations assert rename == 'custom' assert len(_MGH60) == 60 mon = make_standard_montage('standard_1020') + assert len(mon._get_ch_pos()) == 94 def renamer(x): try: @@ -1064,21 +1073,35 @@ mon.rename_channels(renamer) raw.set_montage(mon) + if mon is not None: + # first two are 'Fz' and 'F2', take them from standard_1020.elc -- + # they should not be changed on load! + want_pos = [[0.3122, 58.5120, 66.4620], [29.5142, 57.6019, 59.5400]] + got_pos = [mon.get_positions()['ch_pos'][f'EEG {x:03d}'] * 1000 + for x in range(1, 3)] + assert_allclose(want_pos, got_pos) + assert mon.dig[0]['coord_frame'] == FIFF.FIFFV_COORD_MRI + trans = compute_native_head_t(mon) + trans_2 = _get_trans('fsaverage', 'mri', 'head')[0] + assert trans['to'] == trans_2['to'] + assert trans['from'] == trans_2['from'] + assert_allclose(trans['trans'], trans_2['trans'], atol=1e-6) + new_pos = np.array([ch['loc'][:3] for ch in raw.info['chs'] if ch['ch_name'].startswith('EEG')]) assert ((orig_pos != new_pos).all()) r0 = _fit_sphere(new_pos)[1] - assert_allclose(r0, [0.000775, 0.006881, 0.047398], atol=1e-3) + assert_allclose(r0, [-0.001021, 0.014554, 0.041404], atol=1e-4) # spot check - assert_allclose(new_pos[:2], [[0.000273, 0.084920, 0.105838], - [0.028822, 0.083529, 0.099164]], atol=atol) + assert_allclose(new_pos[:2], [[-0.001229, 0.093274, 0.102639], + [0.027968, 0.09187, 0.09578]], atol=atol) # XXX: this does not check ch_names + it cannot work because of write_dig def _check_roundtrip(montage, fname, coord_frame='head'): """Check roundtrip writing.""" - montage.save(fname) + montage.save(fname, overwrite=True) montage_read = read_dig_fif(fname=fname) assert_equal(repr(montage), repr(montage_read)) @@ -1345,6 +1368,44 @@ ) +@testing.requires_testing_data +@pytest.mark.parametrize('ch_type', ('eeg', 'ecog', 'seeg', 'dbs')) +def test_montage_head_frame(ch_type): + """Test that head frame is set properly.""" + # gh-9446 + data = np.random.randn(2, 100) + info = create_info(['a', 'b'], 512, ch_type) + for ch in info['chs']: + assert ch['coord_frame'] == FIFF.FIFFV_COORD_HEAD + raw = RawArray(data, info) + ch_pos = dict(a=[-0.00250136, 0.04913788, 0.05047056], + b=[-0.00528394, 0.05066484, 0.05061559]) + lpa, nasion, rpa = get_mni_fiducials( + 'fsaverage', subjects_dir=subjects_dir) + lpa, nasion, rpa = lpa['r'], nasion['r'], rpa['r'] + montage = make_dig_montage( + ch_pos, coord_frame='mri', nasion=nasion, lpa=lpa, rpa=rpa) + mri_head_t = compute_native_head_t(montage) + raw.set_montage(montage) + pos = apply_trans(mri_head_t, np.array(list(ch_pos.values()))) + for p, ch in zip(pos, raw.info['chs']): + assert ch['coord_frame'] == FIFF.FIFFV_COORD_HEAD + assert_allclose(p, ch['loc'][:3]) + + # Also test that including channels in the montage that will not have their + # positions set will emit a warning + raw.set_channel_types(dict(a='misc')) + with pytest.warns(RuntimeWarning, match='Not setting .*of 1 misc channel'): + raw.set_montage(montage) + + # and with a bunch of bad types + raw = read_raw_fif(fif_fname) + ch_pos = {ch_name: np.zeros(3) for ch_name in raw.ch_names} + mon = make_dig_montage(ch_pos, coord_frame='head') + with pytest.warns(RuntimeWarning, match='316 eog/grad/mag/stim channels'): + raw.set_montage(mon) + + def test_set_montage_with_missing_coordinates(): """Test set montage with missing coordinates.""" N_CHANNELS, NaN = 3, np.nan @@ -1475,7 +1536,8 @@ assert_dig_allclose(raw_bv2.info, raw_bv.info) # if dig is not set in the info, then montage returns None - raw.info['dig'] = None + with raw.info._unlock(): + raw.info['dig'] = None assert raw.get_montage() is None # the montage should fulfill a roundtrip with make_dig_montage @@ -1498,8 +1560,15 @@ def test_get_builtin_montages(): """Test help function to obtain builtin montages.""" - EXPECTED_NUM = 26 - assert len(get_builtin_montages()) == EXPECTED_NUM + EXPECTED_COUNT = 27 + + montages = get_builtin_montages() + assert len(montages) == EXPECTED_COUNT + + montages_with_descriptions = get_builtin_montages(descriptions=True) + assert len(montages_with_descriptions) == EXPECTED_COUNT + for montage_with_description in montages_with_descriptions: + assert len(montage_with_description) == 2 @testing.requires_testing_data @@ -1511,8 +1580,22 @@ plt.close('all') +def test_montage_equality(): + """Test montage equality.""" + rng = np.random.RandomState(0) + fiducials = dict(zip(('nasion', 'lpa', 'rpa'), rng.rand(3, 3))) + + # hsp positions are [1X, 1X, 1X] + hsp1 = make_dig_montage(**fiducials, hsp=np.full((2, 3), 11.)) + hsp2 = make_dig_montage(**fiducials, hsp=np.full((2, 3), 12.)) + hsp2_identical = make_dig_montage(**fiducials, hsp=np.full((2, 3), 12.)) + + assert hsp1 != hsp2 + assert hsp2 == hsp2_identical + + @testing.requires_testing_data -def test_montage_add_estimated_fiducials(): +def test_montage_add_fiducials(): """Test montage can add estimated fiducials for rpa, lpa, nas.""" # get the fiducials from test file subjects_dir = op.join(data_path, 'subjects') @@ -1527,6 +1610,11 @@ montage = make_dig_montage(ch_pos=test_ch_pos, coord_frame='mri') montage.add_estimated_fiducials(subject=subject, subjects_dir=subjects_dir) + # check that adding MNI fiducials fails because we're in MRI + with pytest.raises(RuntimeError, match='Montage should be in the ' + '"mni_tal" coordinate frame'): + montage.add_mni_fiducials(subjects_dir=subjects_dir) + # check that these fiducials are close to the estimated fiducials ch_pos = montage.get_positions() fids_est = [ch_pos['lpa'], ch_pos['nasion'], ch_pos['rpa']] @@ -1537,10 +1625,85 @@ # an error should be raised if the montage is not in `mri` coord_frame # which is the FreeSurfer RAS montage = make_dig_montage(ch_pos=test_ch_pos, coord_frame='mni_tal') - with pytest.raises(RuntimeError, match='Montage should be in mri ' - 'coordinate frame'): + with pytest.raises(RuntimeError, match='Montage should be in the ' + '"mri" coordinate frame'): montage.add_estimated_fiducials(subject=subject, subjects_dir=subjects_dir) + # test that adding MNI fiducials works + montage.add_mni_fiducials(subjects_dir=subjects_dir) + test_fids = get_mni_fiducials('fsaverage', subjects_dir=subjects_dir) + for fid, test_fid in zip(montage.dig[:3], test_fids): + assert_array_equal(fid['r'], test_fid['r']) + + # test remove fiducials + montage.remove_fiducials() + assert all([d['kind'] != FIFF.FIFFV_POINT_CARDINAL for d in montage.dig]) + + +def test_read_dig_localite(tmp_path): + """Test reading Localite .csv file.""" + contents = """#,id,x,y,z + 1,Nasion,-2.016253511,6.243001715,34.63167712 + 2,LPA,71.96698724,-29.88835576,113.6703679 + 3,RPA,-82.77279316,-22.45928121,116.4005828 + 4,ch01,53.62814378,-91.37837488,29.69071863 + 5,ch02,54.02504821,-59.96228146,23.21714217 + 6,ch03,47.93261613,-29.99373786,24.56468867 + 7,ch04,29.04824633,-86.60006321,13.5073523 + 8,ch05,25.76285783,-58.1658606,3.854848377 + 9,ch06,25.39636794,-27.28186717,9.78490351 + 10,ch07,-5.181242819,-85.52115113,7.201882904 + 11,ch08,-4.995704801,-60.47053977,0.998486757 + 12,ch09,-2.680020493,-31.14357171,6.114621138 + 13,ch10,-33.65019131,-92.34198454,13.2326512 + 14,ch11,-36.22420417,-61.23822776,6.028649571 + 15,ch12,-33.21551039,-31.21772978,8.458854072 + 16,ch13,-61.38400606,-92.67546012,29.5783456 + 17,ch14,-61.16539571,-61.86866187,26.23986153 + 18,ch15,-55.82855386,-34.77319103,25.8083942""" + + fname = tmp_path / 'localite.csv' + with open(fname, 'w') as f: + for row in contents.split('\n'): + f.write(f'{row.lstrip()}\n') + montage = read_dig_localite(fname, nasion="Nasion", lpa="LPA", rpa="RPA") + s = '' + assert repr(montage) == s + assert montage.ch_names == [f'ch{i:02}' for i in range(1, 16)] + + +def test_make_wrong_dig_montage(): + """Test that a montage with non numeric is not possible.""" + make_dig_montage(ch_pos={'A1': ['0', '0', '0']}) # converted to floats + with pytest.raises(ValueError, match="could not convert string to float"): + make_dig_montage(ch_pos={'A1': ['a', 'b', 'c']}) + with pytest.raises(TypeError, match="instance of ndarray, list, or tuple"): + make_dig_montage(ch_pos={'A1': 5}) + -run_tests_if_main() +@testing.requires_testing_data +def test_fnirs_montage(): + """Ensure fNIRS montages can be get and set.""" + raw = read_raw_nirx(fnirs_dname) + info_orig = raw.copy().info + mtg = raw.get_montage() + + num_sources = np.sum(["S" in optode for optode in mtg.ch_names]) + num_detectors = np.sum(["D" in optode for optode in mtg.ch_names]) + assert num_sources == 5 + assert num_detectors == 13 + + # Make a change to the montage before setting + raw.info['chs'][2]['loc'][:3] = [1., 2, 3] + # Set montage back to original + raw.set_montage(mtg) + + for ch in range(len(raw.ch_names)): + assert_array_equal(info_orig['chs'][ch]['loc'], + raw.info['chs'][ch]['loc']) + + # Mixed channel types not supported yet + raw.set_channel_types({ch_name: 'eeg' for ch_name in raw.ch_names[-2:]}) + with pytest.raises(ValueError, match='mix of fNIRS'): + raw.get_montage() diff -Nru python-mne-0.23.4+dfsg/mne/channels/tests/test_standard_montage.py python-mne-1.1.0+dfsg/mne/channels/tests/test_standard_montage.py --- python-mne-0.23.4+dfsg/mne/channels/tests/test_standard_montage.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/channels/tests/test_standard_montage.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors: Joan Massich # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import pytest @@ -29,10 +29,11 @@ for k, v in fids.items(): assert v is not None, k for d in montage.dig: - if kind == 'artinis-octamon' or kind == 'artinis-brite23': - assert d['coord_frame'] == FIFF.FIFFV_COORD_MRI + if kind.startswith(('artinis', 'standard', 'mgh')): + want = FIFF.FIFFV_COORD_MRI else: - assert d['coord_frame'] == FIFF.FIFFV_COORD_UNKNOWN + want = FIFF.FIFFV_COORD_UNKNOWN + assert d['coord_frame'] == want def test_standard_montage_errors(): @@ -53,6 +54,7 @@ ['biosemi256', 1e-8], ['biosemi32', 1e-8], ['biosemi64', 1e-8], + ['brainproducts-RNP-BA-128', 1e-8] ]) def test_standard_montages_on_sphere(kind, tol, head_size): """Test some standard montage are on sphere.""" @@ -142,12 +144,11 @@ assert trans['from'] == trans_fs['from'] translation = 1000 * np.linalg.norm(trans['trans'][:3, 3] - trans_fs['trans'][:3, 3]) - # TODO: This is actually quite big... - assert 15 < translation < 18 # mm + assert 0 < translation < 1 # mm rotation = np.rad2deg( _angle_between_quats(rot_to_quat(trans['trans'][:3, :3]), rot_to_quat(trans_fs['trans'][:3, :3]))) - assert 3 < rotation < 7 # degrees + assert 0 < rotation < 1 # degrees def test_set_montage_artinis_basic(): @@ -161,7 +162,7 @@ old_info_od = raw_od.info.copy() raw.set_montage(montage_octamon) raw_od.set_montage(montage_octamon) - raw_hb = beer_lambert_law(raw_od) # montage needed for Beer Lambert law + raw_hb = beer_lambert_law(raw_od, ppf=6) # montage needed for BLL # Check that the montage was actually modified assert_raises(AssertionError, assert_array_almost_equal, old_info['chs'][0]['loc'][:9], @@ -172,15 +173,15 @@ # Check a known location assert_array_almost_equal(raw.info['chs'][0]['loc'][:3], - [0.0616, 0.075398, 0.07347]) + [0.054243, 0.081884, 0.054544]) assert_array_almost_equal(raw.info['chs'][8]['loc'][:3], - [-0.033875, 0.101276, 0.077291]) + [-0.03013, 0.105097, 0.055894]) assert_array_almost_equal(raw.info['chs'][12]['loc'][:3], - [-0.062749, 0.080417, 0.074884]) + [-0.055681, 0.086566, 0.055858]) assert_array_almost_equal(raw_od.info['chs'][12]['loc'][:3], - [-0.062749, 0.080417, 0.074884]) + [-0.055681, 0.086566, 0.055858]) assert_array_almost_equal(raw_hb.info['chs'][12]['loc'][:3], - [-0.062749, 0.080417, 0.074884]) + [-0.055681, 0.086566, 0.055858]) # Check that locations are identical for a pair of channels (all elements # except the 10th which is the wavelength if not hbo and hbr type) assert_array_almost_equal(raw.info['chs'][0]['loc'][:9], @@ -200,11 +201,11 @@ raw.info['chs'][0]['loc'][:9]) # Check a known location assert_array_almost_equal(raw.info['chs'][0]['loc'][:3], - [0.085583, 0.036275, 0.089426]) + [0.068931, 0.046201, 0.072055]) assert_array_almost_equal(raw.info['chs'][8]['loc'][:3], - [0.069555, 0.078579, 0.069305]) + [0.055196, 0.082757, 0.052165]) assert_array_almost_equal(raw.info['chs'][12]['loc'][:3], - [0.044861, 0.100952, 0.065175]) + [0.033592, 0.102607, 0.047423]) # Check that locations are identical for a pair of channels (all elements # except the 10th which is the wavelength if not hbo and hbr type) assert_array_almost_equal(raw.info['chs'][0]['loc'][:9], diff -Nru python-mne-0.23.4+dfsg/mne/chpi.py python-mne-1.1.0+dfsg/mne/chpi.py --- python-mne-0.23.4+dfsg/mne/chpi.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/chpi.py 2022-08-03 15:48:14.000000000 +0000 @@ -17,7 +17,7 @@ # Authors: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause from functools import partial @@ -30,7 +30,7 @@ from .io.kit.kit import RawKIT as _RawKIT from .io.meas_info import _simplify_info, Info from .io.pick import (pick_types, pick_channels, pick_channels_regexp, - pick_info) + pick_info, _picks_to_idx) from .io.proj import Projection, setup_proj from .io.constants import FIFF from .io.ctf.trans import _make_ctf_coord_trans_set @@ -45,7 +45,8 @@ quat_to_rot, rot_to_quat, _fit_matched_points, _quat_to_affine, als_ras_trans) from .utils import (verbose, logger, use_log_level, _check_fname, warn, - _validate_type, ProgressBar, _check_option, _pl) + _validate_type, ProgressBar, _check_option, _pl, + _on_missing) # Eventually we should add: # hpicons @@ -295,16 +296,42 @@ # ############################################################################ # Estimate positions from data + @verbose -def _get_hpi_info(info, allow_empty=False, verbose=None): - """Get HPI information from raw.""" +def get_chpi_info(info, on_missing='raise', verbose=None): + """Retrieve cHPI information from the data. + + Parameters + ---------- + %(info_not_none)s + %(on_missing_chpi)s + %(verbose)s + + Returns + ------- + hpi_freqs : array, shape (n_coils,) + The frequency used for each individual cHPI coil. + hpi_pick : int | None + The index of the ``STIM`` channel containing information about when + which cHPI coils were switched on. + hpi_on : array, shape (n_coils,) + The values coding for the "on" state of each individual cHPI coil. + + Notes + ----- + .. versionadded:: 0.24 + """ + _validate_type(item=info, item_name='info', types=Info) + _check_option(parameter='on_missing', value=on_missing, + allowed_values=['ignore', 'raise', 'warn']) + if len(info['hpi_meas']) == 0 or \ ('coil_freq' not in info['hpi_meas'][0]['hpi_coils'][0]): - if allow_empty: - return np.empty(0), None, np.empty(0) - raise RuntimeError('Appropriate cHPI information not found in' - 'info["hpi_meas"] and info["hpi_subsystem"], ' - 'cannot process cHPI') + _on_missing(on_missing, + msg='No appropriate cHPI information found in ' + 'info["hpi_meas"] and info["hpi_subsystem"]') + return np.empty(0), None, np.empty(0) + hpi_coils = sorted(info['hpi_meas'][-1]['hpi_coils'], key=lambda x: x['number']) # ascending (info) order @@ -525,7 +552,9 @@ ext_order=1, allow_empty=False, verbose=None): """Generate HPI structure for HPI localization.""" # grab basic info. - hpi_freqs, hpi_pick, hpi_ons = _get_hpi_info(info, allow_empty=allow_empty) + on_missing = 'raise' if not allow_empty else 'ignore' + hpi_freqs, hpi_pick, hpi_ons = get_chpi_info(info, on_missing=on_missing) + _validate_type(t_window, (str, 'numeric'), 't_window') if info['line_freq'] is not None: line_freqs = np.arange(info['line_freq'], info['sfreq'] / 3., @@ -560,30 +589,35 @@ if t_window <= 0: raise ValueError('t_window (%s) must be > 0' % (t_window,)) logger.info('Using time window: %0.1f ms' % (1000 * t_window,)) - model_n_window = int(round(float(t_window) * info['sfreq'])) - # build model to extract sinusoidal amplitudes. - slope = np.linspace(-0.5, 0.5, model_n_window)[:, np.newaxis] - rps = np.arange(model_n_window)[:, np.newaxis].astype(float) - rps *= 2 * np.pi / info['sfreq'] # radians/sec - f_t = hpi_freqs[np.newaxis, :] * rps - l_t = line_freqs[np.newaxis, :] * rps - model = [np.sin(f_t), np.cos(f_t)] # hpi freqs - model += [np.sin(l_t), np.cos(l_t)] # line freqs - model += [slope, np.ones(slope.shape)] - model = np.concatenate(model, axis=1) + window_nsamp = np.rint(t_window * info['sfreq']).astype(int) + model = _setup_hpi_glm(hpi_freqs, line_freqs, info['sfreq'], window_nsamp) inv_model = np.linalg.pinv(model) inv_model_reord = _reorder_inv_model(inv_model, len(hpi_freqs)) proj, proj_op, meg_picks = _setup_ext_proj(info, ext_order) - + # include mag and grad picks separately, for SNR computations + mag_picks = _picks_to_idx(info, 'mag', allow_empty=True) + grad_picks = _picks_to_idx(info, 'grad', allow_empty=True) # Set up magnetic dipole fits - hpi = dict(meg_picks=meg_picks, hpi_pick=hpi_pick, - model=model, inv_model=inv_model, t_window=t_window, - inv_model_reord=inv_model_reord, - on=hpi_ons, n_window=model_n_window, proj=proj, proj_op=proj_op, - freqs=hpi_freqs, line_freqs=line_freqs) + hpi = dict( + meg_picks=meg_picks, mag_picks=mag_picks, grad_picks=grad_picks, + hpi_pick=hpi_pick, model=model, inv_model=inv_model, t_window=t_window, + inv_model_reord=inv_model_reord, on=hpi_ons, n_window=window_nsamp, + proj=proj, proj_op=proj_op, freqs=hpi_freqs, line_freqs=line_freqs) return hpi +def _setup_hpi_glm(hpi_freqs, line_freqs, sfreq, window_nsamp): + """Initialize a general linear model for HPI amplitude estimation.""" + slope = np.linspace(-0.5, 0.5, window_nsamp)[:, np.newaxis] + radians_per_sec = 2 * np.pi * np.arange(window_nsamp, dtype=float) / sfreq + f_t = hpi_freqs[np.newaxis, :] * radians_per_sec[:, np.newaxis] + l_t = line_freqs[np.newaxis, :] * radians_per_sec[:, np.newaxis] + model = [np.sin(f_t), np.cos(f_t), # hpi freqs + np.sin(l_t), np.cos(l_t), # line freqs + slope, np.ones_like(slope)] # drift, DC + return np.hstack(model) + + @jit() def _reorder_inv_model(inv_model, n_freqs): # Reorder for faster computation @@ -610,7 +644,8 @@ kind=FIFF.FIFFV_PROJ_ITEM_HOMOG_FIELD, desc='SSS', active=False, data=dict(data=ext, ncol=info['nchan'], col_names=info['ch_names'], nrow=len(ext))) - info['projs'] = [proj] + with info._unlock(): + info['projs'] = [proj] proj_op, _ = setup_proj( info, add_eeg_ref=False, activate=False, verbose=False) assert proj_op.shape == (len(meg_picks),) * 2 @@ -622,14 +657,16 @@ return (' t=%0.3f:' % fit_time).ljust(17) -def _fit_chpi_amplitudes(raw, time_sl, hpi): +def _fit_chpi_amplitudes(raw, time_sl, hpi, snr=False): """Fit amplitudes for each channel from each of the N cHPI sinusoids. Returns ------- - sin_fit : ndarray, shape (n_freqs, n_channels)) + sin_fit : ndarray, shape (n_freqs, n_channels) The sin amplitudes matching each cHPI frequency. Will be all nan if this time window should be skipped. + snr : ndarray, shape (n_freqs, 2) + Estimated SNR for this window, separately for mag and grad channels. """ # No need to detrend the data because our model has a DC term with use_log_level(False): @@ -647,6 +684,10 @@ n_on = ons.all(axis=-1).sum(axis=0) if not (n_on >= 3).all(): return None + if snr: + return _fast_fit_snr( + this_data, len(hpi['freqs']), hpi['model'], hpi['inv_model'], + hpi['mag_picks'], hpi['grad_picks']) return _fast_fit(this_data, hpi['proj_op'], len(hpi['freqs']), hpi['model'], hpi['inv_model_reord']) @@ -657,8 +698,8 @@ if this_data.shape[1] != model.shape[0]: model = model[:this_data.shape[1]] inv_model_reord = _reorder_inv_model(np.linalg.pinv(model), n_freqs) - proj_data = np.dot(proj, this_data) - X = np.dot(inv_model_reord, proj_data.T) + proj_data = proj @ this_data + X = inv_model_reord @ proj_data.T sin_fit = np.zeros((n_freqs, X.shape[1])) for fi in range(n_freqs): @@ -670,6 +711,34 @@ return sin_fit +@jit() +def _fast_fit_snr(this_data, n_freqs, model, inv_model, mag_picks, grad_picks): + # first or last window + if this_data.shape[1] != model.shape[0]: + model = model[:this_data.shape[1]] + inv_model = np.linalg.pinv(model) + coefs = np.ascontiguousarray(inv_model) @ np.ascontiguousarray(this_data.T) + # average sin & cos terms (special property of sinusoids: power=A²/2) + hpi_power = (coefs[:n_freqs] ** 2 + coefs[n_freqs:(2 * n_freqs)] ** 2) / 2 + resid = this_data - np.ascontiguousarray((model @ coefs).T) + # can't use np.var(..., axis=1) with Numba, so do it manually: + resid_mean = np.atleast_2d(resid.sum(axis=1) / resid.shape[1]).T + squared_devs = np.abs(resid - resid_mean) ** 2 + resid_var = squared_devs.sum(axis=1) / squared_devs.shape[1] + # output array will be (n_freqs, 3 * n_ch_types). The 3 columns for each + # channel type are the SNR, the mean cHPI power and the residual variance + # (which gets tiled to shape (n_freqs,) because it's a scalar). + snrs = np.empty((n_freqs, 0)) + # average power & compute residual variance separately for each ch type + for _picks in (mag_picks, grad_picks): + if len(_picks): + avg_power = hpi_power[:, _picks].sum(axis=1) / len(_picks) + avg_resid = resid_var[_picks].mean() * np.ones(n_freqs) + snr = 10 * np.log10(avg_power / avg_resid) + snrs = np.hstack((snrs, np.stack((snr, avg_power, avg_resid), 1))) + return snrs + + def _check_chpi_param(chpi_, name): if name == 'chpi_locs': want_ndims = dict(times=1, rrs=3, moments=3, gofs=2) @@ -728,8 +797,7 @@ Parameters ---------- - info : instance of Info - Measurement information. + %(info_not_none)s %(chpi_locs)s Typically obtained by :func:`~mne.chpi.compute_chpi_locs` or :func:`~mne.chpi.extract_chpi_locs_ctf`. @@ -737,7 +805,7 @@ Minimum distance (m) to accept for coil position fitting. gof_limit : float Minimum goodness of fit to accept for each coil. - %(chpi_adjust_dig)s + %(adjust_dig_chpi)s %(verbose)s Returns @@ -886,6 +954,43 @@ @verbose +def compute_chpi_snr(raw, t_step_min=0.01, t_window='auto', ext_order=1, + tmin=0, tmax=None, verbose=None): + """Compute time-varying estimates of cHPI SNR. + + Parameters + ---------- + raw : instance of Raw + Raw data with cHPI information. + t_step_min : float + Minimum time step to use. + %(t_window_chpi_t)s + %(ext_order_chpi)s + %(tmin_raw)s + %(tmax_raw)s + %(verbose)s + + Returns + ------- + chpi_snrs : dict + The time-varying cHPI SNR estimates, with entries "times", "freqs", + "snr_mag", "power_mag", and "resid_mag" (and/or "snr_grad", + "power_grad", and "resid_grad", depending on which channel types are + present in ``raw``). + + See Also + -------- + mne.chpi.compute_chpi_locs, mne.chpi.compute_chpi_amplitudes + + Notes + ----- + .. versionadded:: 0.24 + """ + return _compute_chpi_amp_or_snr(raw, t_step_min, t_window, ext_order, + tmin, tmax, verbose, snr=True) + + +@verbose def compute_chpi_amplitudes(raw, t_step_min=0.01, t_window='auto', ext_order=1, tmin=0, tmax=None, verbose=None): """Compute time-varying cHPI amplitudes. @@ -895,12 +1000,11 @@ raw : instance of Raw Raw data with cHPI information. t_step_min : float - Minimum time step to use. If correlations are sufficiently high, - t_step_max will be used. - %(chpi_t_window)s - %(chpi_ext_order)s - %(raw_tmin)s - %(raw_tmax)s + Minimum time step to use. + %(t_window_chpi_t)s + %(ext_order_chpi)s + %(tmin_raw)s + %(tmax_raw)s %(verbose)s Returns @@ -909,7 +1013,7 @@ See Also -------- - mne.chpi.compute_chpi_locs + mne.chpi.compute_chpi_locs, mne.chpi.compute_chpi_snr Notes ----- @@ -934,6 +1038,19 @@ .. versionadded:: 0.20 """ + return _compute_chpi_amp_or_snr(raw, t_step_min, t_window, ext_order, + tmin, tmax, verbose) + + +def _compute_chpi_amp_or_snr(raw, t_step_min=0.01, t_window='auto', + ext_order=1, tmin=0, tmax=None, verbose=None, + snr=False): + """Compute cHPI amplitude or SNR. + + See compute_chpi_amplitudes for parameter descriptions. One additional + boolean parameter ``snr`` signals whether to return SNR instead of + amplitude. + """ hpi = _setup_hpi_amplitude_fitting(raw.info, t_window, ext_order=ext_order) tmin, tmax = raw._tmin_tmax_to_start_stop(tmin, tmax) tmin = tmin / raw.info['sfreq'] @@ -946,14 +1063,26 @@ % (len(hpi['freqs']), len(fit_idxs), tmax - tmin)) del tmin, tmax sin_fits = dict() + sin_fits['proj'] = hpi['proj'] sin_fits['times'] = np.round(fit_idxs + raw.first_samp - hpi['n_window'] / 2.) / raw.info['sfreq'] - sin_fits['proj'] = hpi['proj'] - sin_fits['slopes'] = np.empty( - (len(sin_fits['times']), - len(hpi['freqs']), - len(sin_fits['proj']['data']['col_names']))) - for mi, midpt in enumerate(ProgressBar(fit_idxs, mesg='cHPI amplitudes')): + n_times = len(sin_fits['times']) + n_freqs = len(hpi['freqs']) + n_chans = len(sin_fits['proj']['data']['col_names']) + if snr: + del sin_fits['proj'] + sin_fits['freqs'] = hpi['freqs'] + ch_types = raw.get_channel_types() + grad_offset = 3 if 'mag' in ch_types else 0 + for ch_type in ('mag', 'grad'): + if ch_type in ch_types: + for key in ('snr', 'power', 'resid'): + cols = 1 if key == 'resid' else n_freqs + sin_fits[f'{ch_type}_{key}'] = np.empty((n_times, cols)) + else: + sin_fits['slopes'] = np.empty((n_times, n_freqs, n_chans)) + message = f"cHPI {'SNRs' if snr else 'amplitudes'}" + for mi, midpt in enumerate(ProgressBar(fit_idxs, mesg=message)): # # 0. determine samples to fit. # @@ -964,7 +1093,24 @@ # # 1. Fit amplitudes for each channel from each of the N sinusoids # - sin_fits['slopes'][mi] = _fit_chpi_amplitudes(raw, time_sl, hpi) + amps_or_snrs = _fit_chpi_amplitudes(raw, time_sl, hpi, snr) + if snr: + # unpack the SNR estimates. mag & grad are returned in one array + # (because of Numba) so take care with which column is which. + # note that mean residual is a scalar (same for all HPI freqs) but + # is returned as a (tiled) vector (again, because Numba) so that's + # why below we take amps_or_snrs[0, 2] instead of [:, 2] + ch_types = raw.get_channel_types() + if 'mag' in ch_types: + sin_fits['mag_snr'][mi] = amps_or_snrs[:, 0] # SNR + sin_fits['mag_power'][mi] = amps_or_snrs[:, 1] # mean power + sin_fits['mag_resid'][mi] = amps_or_snrs[0, 2] # mean resid + if 'grad' in ch_types: + sin_fits['grad_snr'][mi] = amps_or_snrs[:, grad_offset] + sin_fits['grad_power'][mi] = amps_or_snrs[:, grad_offset + 1] + sin_fits['grad_resid'][mi] = amps_or_snrs[0, grad_offset + 2] + else: + sin_fits['slopes'][mi] = amps_or_snrs return sin_fits @@ -975,8 +1121,7 @@ Parameters ---------- - info : instance of Info - The measurement information. + %(info_not_none)s %(chpi_amplitudes)s Typically obtained by :func:`mne.chpi.compute_chpi_amplitudes`. t_step_max : float @@ -984,7 +1129,7 @@ too_close : str How to handle HPI positions too close to the sensors, can be 'raise' (default), 'warning', or 'info'. - %(chpi_adjust_dig)s + %(adjust_dig_chpi)s %(verbose)s Returns @@ -1025,7 +1170,8 @@ meg_picks = pick_channels( info['ch_names'], proj['data']['col_names'], ordered=True) info = pick_info(info, meg_picks) # makes a copy - info['projs'] = [proj] + with info._unlock(): + info['projs'] = [proj] del meg_picks, proj meg_coils = _concatenate_coils(_create_meg_coils(info['chs'], 'accurate')) @@ -1129,8 +1275,8 @@ If True, also filter line noise. t_step : float Time step to use for estimation, default is 0.01 (10 ms). - %(chpi_t_window)s - %(chpi_ext_order)s + %(t_window_chpi_t)s + %(ext_order_chpi)s allow_line_only : bool If True, allow filtering line noise only. The default is False, which only allows the function to run when cHPI information is present. diff -Nru python-mne-0.23.4+dfsg/mne/commands/mne_browse_raw.py python-mne-1.1.0+dfsg/mne/commands/mne_browse_raw.py --- python-mne-0.23.4+dfsg/mne/commands/mne_browse_raw.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/commands/mne_browse_raw.py 2022-08-03 15:48:14.000000000 +0000 @@ -21,7 +21,6 @@ def run(): """Run command.""" - import matplotlib.pyplot as plt from mne.commands.utils import get_optparser, _add_verbose_flag from mne.viz import _RAW_CLIP_DEF @@ -135,8 +134,8 @@ raw.plot(duration=duration, start=start, n_channels=n_channels, group_by=group_by, show_options=show_options, events=events, highpass=highpass, lowpass=lowpass, filtorder=filtorder, - clipping=clipping, proj=not proj_off, verbose=verbose) - plt.show(block=True) + clipping=clipping, proj=not proj_off, verbose=verbose, + show=True, block=True) mne.utils.run_command_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/commands/mne_compute_proj_ecg.py python-mne-1.1.0+dfsg/mne/commands/mne_compute_proj_ecg.py --- python-mne-0.23.4+dfsg/mne/commands/mne_compute_proj_ecg.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/commands/mne_compute_proj_ecg.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,7 +5,7 @@ -------- .. code-block:: console - $ mne compute_proj_ecg -i sample_audvis_raw.fif -c "MEG 1531" \ + $ mne compute_proj_ecg -i sample_audvis_raw.fif -c "MEG 1531" -a \ --l-freq 1 --h-freq 100 \ --rej-grad 3000 --rej-mag 4000 --rej-eeg 100 @@ -61,7 +61,7 @@ default=True) parser.add_option("-a", "--average", dest="average", action="store_true", help="Compute SSP after averaging", - default=False) # XXX: change to default=True in 0.17 + default=False) parser.add_option("--proj", dest="proj", help="Use SSP projections from a fif file.", default=None) @@ -182,7 +182,7 @@ else: raw_event = raw - flat = None # XXX : not exposed to the user + flat = None projs, events = mne.preprocessing.compute_proj_ecg( raw, raw_event, tmin, tmax, n_grad, n_mag, n_eeg, l_freq, h_freq, average, filter_length, n_jobs, ch_name, reject, flat, bads, avg_ref, diff -Nru python-mne-0.23.4+dfsg/mne/commands/mne_compute_proj_eog.py python-mne-1.1.0+dfsg/mne/commands/mne_compute_proj_eog.py --- python-mne-0.23.4+dfsg/mne/commands/mne_compute_proj_eog.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/commands/mne_compute_proj_eog.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,7 +5,7 @@ -------- .. code-block:: console - $ mne compute_proj_eog -i sample_audvis_raw.fif \ + $ mne compute_proj_eog -i sample_audvis_raw.fif -a \ --l-freq 1 --h-freq 35 \ --rej-grad 3000 --rej-mag 4000 --rej-eeg 100 @@ -13,7 +13,7 @@ .. code-block:: console - $ mne compute_proj_eog -i sample_audvis_raw.fif \ + $ mne compute_proj_eog -i sample_audvis_raw.fif -a \ --l-freq 1 --h-freq 35 \ --rej-grad 3000 --rej-mag 4000 --rej-eeg 100 \ --proj sample_audvis_ecg-proj.fif @@ -65,7 +65,7 @@ "save memory)", default=True) parser.add_option("-a", "--average", dest="average", action="store_true", help="Compute SSP after averaging", - default=False) # XXX: change to default=True in 0.17 + default=False) parser.add_option("--proj", dest="proj", help="Use SSP projections from a fif file.", default=None) @@ -166,7 +166,7 @@ else: raw_event = raw - flat = None # XXX : not exposed to the user + flat = None projs, events = mne.preprocessing.compute_proj_eog( raw=raw, raw_event=raw_event, tmin=tmin, tmax=tmax, n_grad=n_grad, n_mag=n_mag, n_eeg=n_eeg, l_freq=l_freq, h_freq=h_freq, diff -Nru python-mne-0.23.4+dfsg/mne/commands/mne_coreg.py python-mne-1.1.0+dfsg/mne/commands/mne_coreg.py --- python-mne-0.23.4+dfsg/mne/commands/mne_coreg.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/commands/mne_coreg.py 2022-08-03 15:48:14.000000000 +0000 @@ -14,7 +14,6 @@ import os.path as op import mne -from mne.utils import ETSContext def run(): @@ -50,23 +49,6 @@ help="Use a low-resolution head surface.") parser.add_option('--trans', dest='trans', default=None, help='Head<->MRI transform FIF file ("-trans.fif")') - parser.add_option('--project-eeg', dest='project_eeg', - action='store_true', default=None, - help="Project EEG electrodes to the head surface (" - "for visualization purposes only)") - parser.add_option('--orient-to-surface', - action='store_true', default=None, - dest='orient_to_surface', - help='Orient points to the surface.') - parser.add_option('--scale-by-distance', - action='store_true', default=None, - dest='scale_by_distance', - help='Scale points by distance from the surface.') - parser.add_option('--mark-inside', - action='store_true', default=None, - dest='mark_inside', - help='Mark points inside the head using a different ' - 'color.') parser.add_option('--interaction', type=str, default=None, dest='interaction', help='Interaction style to use, can be "trackball" or ' @@ -98,24 +80,19 @@ trans = options.trans if trans is not None: trans = op.expanduser(trans) - try: - import faulthandler - faulthandler.enable() - except ImportError: - pass # old Python2 - with ETSContext(): - mne.gui.coregistration( - options.tabbed, inst=options.inst, subject=options.subject, - subjects_dir=subjects_dir, - guess_mri_subject=options.guess_mri_subject, - head_opacity=options.head_opacity, head_high_res=head_high_res, - trans=trans, scrollable=True, project_eeg=options.project_eeg, - orient_to_surface=options.orient_to_surface, - scale_by_distance=options.scale_by_distance, - mark_inside=options.mark_inside, interaction=options.interaction, - scale=options.scale, - advanced_rendering=options.advanced_rendering, - verbose=options.verbose) + import faulthandler + faulthandler.enable() + mne.gui.coregistration( + options.tabbed, inst=options.inst, subject=options.subject, + subjects_dir=subjects_dir, + guess_mri_subject=options.guess_mri_subject, + head_opacity=options.head_opacity, head_high_res=head_high_res, + trans=trans, scrollable=True, + interaction=options.interaction, + scale=options.scale, + advanced_rendering=options.advanced_rendering, + show=True, block=True, + verbose=options.verbose) mne.utils.run_command_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/commands/mne_flash_bem.py python-mne-1.1.0+dfsg/mne/commands/mne_flash_bem.py --- python-mne-0.23.4+dfsg/mne/commands/mne_flash_bem.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/commands/mne_flash_bem.py 2022-08-03 15:48:14.000000000 +0000 @@ -6,6 +6,8 @@ .. code-block:: console $ mne flash_bem --subject=sample + $ mne flash_bem -s sample -n --registered -5 sample/mri/mef05.mgz -3 sample/mri/mef30.mgz + $ mne flash_bem -s sample -n --registered -5 sample/mri/flash/mef05_*.mgz -3 sample/mri/flash/mef30_*.mgz Notes ----- @@ -14,37 +16,35 @@ This function extracts the BEM surfaces (outer skull, inner skull, and outer skin) from multiecho FLASH MRI data with spin angles of 5 and 30 -degrees. The multiecho FLASH data are inputted in DICOM format. +degrees. The multiecho FLASH data can be input as .mgz or .nii files. This function assumes that the Freesurfer segmentation of the subject has been completed. In particular, the T1.mgz and brain.mgz MRI volumes should be, as usual, in the subject's mri directory. -Before running this script do the following: -(unless the --noconvert option is specified) - -1. Copy all of your FLASH images in a single directory and - create a directory to hold the output of mne_organize_dicom -2. cd to and run - $ mne_organize_dicom - to create an appropriate directory structure -3. Create symbolic links to make flash05 and flash30 point to the - appropriate series: - $ ln -s flash05 - $ ln -s flash30 - Some partition formats (e.g. FAT32) do not support symbolic links. - In this case, copy the file to the appropriate series: - $ cp flash05 - $ cp flash30 -4. cd to the directory where flash05 and flash30 links are -5. Set SUBJECTS_DIR and SUBJECT environment variables appropriately -6. Run this script -""" +""" # noqa E501 # Authors: Lorenzo De Santis +# Alexandre Gramfort import mne from mne.bem import convert_flash_mris, make_flash_bem +def _vararg_callback(option, opt_str, value, parser): + assert value is None + value = [] + + for arg in parser.rargs: + # stop on --foo like options + if arg[:2] == "--" and len(arg) > 2: + break + if arg[:1] == "-" and len(arg) > 1: + break + value.append(arg) + + del parser.rargs[:len(value)] + setattr(parser.values, option.dest, value) + + def run(): """Run command.""" from mne.commands.utils import get_optparser @@ -55,17 +55,27 @@ help="Subject name", default=None) parser.add_option("-d", "--subjects-dir", dest="subjects_dir", help="Subjects directory", default=None) - parser.add_option("-3", "--noflash30", dest="noflash30", + parser.add_option("-3", "--flash30", "--noflash30", dest="flash30", + action="callback", callback=_vararg_callback, + help=("The 30-degree flip angle data. If no argument do " + "not use flash30. If arguments are given, them as " + "file names.")) + parser.add_option("-5", "--flash5", dest="flash5", + action="callback", callback=_vararg_callback, + help=("Path to the multiecho flash 5 images. " + "Can be one file or one per echo."),) + parser.add_option("-r", "--registered", dest="registered", action="store_true", default=False, - help=("Skip the 30-degree flip angle data"),) + help=("Set if the Flash MRI images have already " + "been registered with the T1.mgz file.")) parser.add_option("-n", "--noconvert", dest="noconvert", action="store_true", default=False, - help=("Assume that the Flash MRI images have already " - "been converted to mgz files")) + help=("[DEPRECATED] Assume that the Flash MRI images " + "have already been converted to mgz files")) parser.add_option("-u", "--unwarp", dest="unwarp", action="store_true", default=False, - help=("Run grad_unwarp with -unwarp option on " - "each of the converted data sets")) + help=("Run grad_unwarp with -unwarp " + "option on each of the converted data sets")) parser.add_option("-o", "--overwrite", dest="overwrite", action="store_true", default=False, help="Write over existing .surf files in bem folder") @@ -77,16 +87,24 @@ action="store_true") parser.add_option("-p", "--flash-path", dest="flash_path", default=None, - help="The directory containing flash05.mgz and " - "flash30.mgz files (defaults to " + help="[DEPRECATED] The directory containing flash5.mgz " + "files (defaults to " "$SUBJECTS_DIR/$SUBJECT/mri/flash/parameter_maps") - options, args = parser.parse_args() + options, _ = parser.parse_args() subject = options.subject subjects_dir = options.subjects_dir - flash30 = not options.noflash30 + flash5 = options.flash5 + if flash5 is None or len(flash5) == 0: + flash5 = True + flash30 = options.flash30 + if flash30 is None: + flash30 = True + elif len(flash30) == 0: + flash30 = False convert = not options.noconvert + register = not options.registered unwarp = options.unwarp overwrite = options.overwrite show = options.show @@ -97,12 +115,14 @@ parser.print_help() raise RuntimeError('The subject argument must be set') - convert_flash_mris(subject=subject, subjects_dir=subjects_dir, - flash30=flash30, convert=convert, unwarp=unwarp, - verbose=True) + flash5_img = convert_flash_mris( + subject=subject, subjects_dir=subjects_dir, flash5=flash5, + flash30=flash30, convert=convert, unwarp=unwarp, verbose=True + ) make_flash_bem(subject=subject, subjects_dir=subjects_dir, overwrite=overwrite, show=show, flash_path=flash_path, - copy=copy, verbose=True) + copy=copy, register=register, flash5_img=flash5_img, + verbose=True) mne.utils.run_command_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/commands/mne_kit2fiff.py python-mne-1.1.0+dfsg/mne/commands/mne_kit2fiff.py --- python-mne-0.23.4+dfsg/mne/commands/mne_kit2fiff.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/commands/mne_kit2fiff.py 2022-08-03 15:48:14.000000000 +0000 @@ -21,7 +21,6 @@ import mne from mne.io import read_raw_kit -from mne.utils import ETSContext def run(): @@ -60,8 +59,13 @@ input_fname = options.input_fname if input_fname is None: - with ETSContext(): - mne.gui.kit2fiff() + try: + from mne_kit_gui import kit2fiff # noqa + except ImportError: + raise ImportError( + 'The mne_kit_gui package is required, install it using ' + 'conda or pip') from None + kit2fiff() sys.exit(0) hsp_fname = options.hsp_fname diff -Nru python-mne-0.23.4+dfsg/mne/commands/mne_make_scalp_surfaces.py python-mne-1.1.0+dfsg/mne/commands/mne_make_scalp_surfaces.py --- python-mne-0.23.4+dfsg/mne/commands/mne_make_scalp_surfaces.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/commands/mne_make_scalp_surfaces.py 2022-08-03 15:48:14.000000000 +0000 @@ -15,21 +15,11 @@ $ mne make_scalp_surfaces --overwrite --subject sample """ -import copy import os -import os.path as op import sys import mne -from mne.utils import (run_subprocess, verbose, logger, ETSContext, - get_subjects_dir) - - -def _check_file(fname, overwrite): - """Prevent overwrites.""" - if op.isfile(fname) and not overwrite: - raise IOError('File %s exists, use --overwrite to overwrite it' - % fname) +from mne.bem import make_scalp_surfaces def run(): @@ -44,8 +34,13 @@ help='Overwrite previously computed surface') parser.add_option('-s', '--subject', dest='subject', help='The name of the subject', type='str') + parser.add_option('-m', '--mri', dest='mri', type='str', default='T1.mgz', + help='The MRI file to process using mkheadsurf.') parser.add_option('-f', '--force', dest='force', action='store_true', - help='Force transformation of surface into bem.') + help='Force creation of the surface even if it has ' + 'some topological defects.') + parser.add_option('-t', '--threshold', dest='threshold', type='int', + default=20, help='Threshold value to use with the MRI.') parser.add_option("-d", "--subjects-dir", dest="subjects_dir", help="Subjects directory", default=subjects_dir) parser.add_option("-n", "--no-decimate", dest="no_decimate", @@ -59,79 +54,15 @@ if subject is None or subjects_dir is None: parser.print_help() sys.exit(1) - _run(subjects_dir, subject, options.force, options.overwrite, - options.no_decimate, options.verbose) - - -@verbose -def _run(subjects_dir, subject, force, overwrite, no_decimate, verbose=None): - this_env = copy.deepcopy(os.environ) - subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) - this_env['SUBJECTS_DIR'] = subjects_dir - this_env['SUBJECT'] = subject - if 'FREESURFER_HOME' not in this_env: - raise RuntimeError('The FreeSurfer environment needs to be set up ' - 'for this script') - incomplete = 'warn' if force else 'raise' - subj_path = op.join(subjects_dir, subject) - if not op.exists(subj_path): - raise RuntimeError('%s does not exist. Please check your subject ' - 'directory path.' % subj_path) - - mri = 'T1.mgz' if op.exists(op.join(subj_path, 'mri', 'T1.mgz')) else 'T1' - - logger.info('1. Creating a dense scalp tessellation with mkheadsurf...') - - def check_seghead(surf_path=op.join(subj_path, 'surf')): - surf = None - for k in ['lh.seghead', 'lh.smseghead']: - this_surf = op.join(surf_path, k) - if op.exists(this_surf): - surf = this_surf - break - return surf - - my_seghead = check_seghead() - if my_seghead is None: - run_subprocess(['mkheadsurf', '-subjid', subject, '-srcvol', mri], - env=this_env) - - surf = check_seghead() - if surf is None: - raise RuntimeError('mkheadsurf did not produce the standard output ' - 'file.') - - bem_dir = op.join(subjects_dir, subject, 'bem') - if not op.isdir(bem_dir): - os.mkdir(bem_dir) - dense_fname = op.join(bem_dir, '%s-head-dense.fif' % subject) - logger.info('2. Creating %s ...' % dense_fname) - _check_file(dense_fname, overwrite) - # Helpful message if we get a topology error - msg = '\n\nConsider using --force as an additional input parameter.' - surf = mne.bem._surfaces_to_bem( - [surf], [mne.io.constants.FIFF.FIFFV_BEM_SURF_ID_HEAD], [1], - incomplete=incomplete, extra=msg)[0] - mne.write_bem_surfaces(dense_fname, surf, overwrite=overwrite) - levels = 'medium', 'sparse' - tris = [] if no_decimate else [30000, 2500] - if os.getenv('_MNE_TESTING_SCALP', 'false') == 'true': - tris = [len(surf['tris'])] # don't actually decimate - for ii, (n_tri, level) in enumerate(zip(tris, levels), 3): - logger.info('%i. Creating %s tessellation...' % (ii, level)) - logger.info('%i.1 Decimating the dense tessellation...' % ii) - with ETSContext(): - points, tris = mne.decimate_surface(points=surf['rr'], - triangles=surf['tris'], - n_triangles=n_tri) - dec_fname = dense_fname.replace('dense', level) - logger.info('%i.2 Creating %s' % (ii, dec_fname)) - _check_file(dec_fname, overwrite) - dec_surf = mne.bem._surfaces_to_bem( - [dict(rr=points, tris=tris)], - [mne.io.constants.FIFF.FIFFV_BEM_SURF_ID_HEAD], [1], rescale=False, - incomplete=incomplete, extra=msg) - mne.write_bem_surfaces(dec_fname, dec_surf, overwrite=overwrite) + make_scalp_surfaces( + subject=subject, + subjects_dir=subjects_dir, + force=options.force, + overwrite=options.overwrite, + no_decimate=options.no_decimate, + threshold=options.threshold, + mri=options.mri, + verbose=options.verbose) mne.utils.run_command_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/commands/mne_surf2bem.py python-mne-1.1.0+dfsg/mne/commands/mne_surf2bem.py --- python-mne-0.23.4+dfsg/mne/commands/mne_surf2bem.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/commands/mne_surf2bem.py 2022-08-03 15:48:14.000000000 +0000 @@ -12,7 +12,7 @@ """ # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import sys diff -Nru python-mne-0.23.4+dfsg/mne/commands/mne_sys_info.py python-mne-1.1.0+dfsg/mne/commands/mne_sys_info.py --- python-mne-0.23.4+dfsg/mne/commands/mne_sys_info.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/commands/mne_sys_info.py 2022-08-03 15:48:14.000000000 +0000 @@ -18,12 +18,18 @@ def run(): """Run command.""" parser = mne.commands.utils.get_optparser(__file__, usage='mne sys_info') + parser.add_option('-p', '--show-paths', dest='show_paths', + help='Show module paths', action='store_true') + parser.add_option('-d', '--developer', dest='developer', + help='Show additional developer module information', + action='store_true') options, args = parser.parse_args() + dependencies = 'developer' if options.developer else 'user' if len(args) != 0: parser.print_help() sys.exit(1) - mne.sys_info() + mne.sys_info(show_paths=options.show_paths, dependencies=dependencies) mne.utils.run_command_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/commands/tests/test_commands.py python-mne-1.1.0+dfsg/mne/commands/tests/test_commands.py --- python-mne-0.23.4+dfsg/mne/commands/tests/test_commands.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/commands/tests/test_commands.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,16 +1,18 @@ # -*- coding: utf-8 -*- +import glob import os from os import path as op import shutil -import glob +from pathlib import Path import numpy as np import pytest from numpy.testing import assert_equal, assert_allclose +import mne from mne import (concatenate_raws, read_bem_surfaces, read_surface, read_source_spaces, read_bem_solution) -from mne.bem import ConductorModel +from mne.bem import ConductorModel, convert_flash_mris from mne.commands import (mne_browse_raw, mne_bti2fiff, mne_clean_eog_ecg, mne_compute_proj_ecg, mne_compute_proj_eog, mne_coreg, mne_kit2fiff, @@ -22,15 +24,17 @@ mne_prepare_bem_model, mne_sys_info) from mne.datasets import testing from mne.io import read_raw_fif, read_info -from mne.utils import (run_tests_if_main, requires_mne, - requires_mayavi, requires_vtk, requires_freesurfer, - requires_nibabel, traits_test, ArgvSetter, modified_env, - _stamp_to_dt) +from mne.utils import (requires_mne, requires_freesurfer, + requires_nibabel, ArgvSetter, + _stamp_to_dt, _record_warnings) base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data') raw_fname = op.join(base_dir, 'test_raw.fif') -subjects_dir = op.join(testing.data_path(download=False), 'subjects') +testing_path = testing.data_path(download=False) +subjects_dir = op.join(testing_path, 'subjects') +bem_model_fname = op.join(testing_path, 'subjects', + 'sample', 'bem', 'sample-320-320-320-bem.fif') def check_usage(module, force_help=False): @@ -49,7 +53,7 @@ """Test mne browse_raw.""" check_usage(mne_browse_raw) with ArgvSetter(('--raw', raw_fname)): - with pytest.warns(None): # mpl show warning sometimes + with _record_warnings(): # mpl show warning mne_browse_raw.run() @@ -81,10 +85,10 @@ @requires_mne -def test_clean_eog_ecg(tmpdir): +def test_clean_eog_ecg(tmp_path): """Test mne clean_eog_ecg.""" check_usage(mne_clean_eog_ecg) - tempdir = str(tmpdir) + tempdir = str(tmp_path) raw = concatenate_raws([read_raw_fif(f) for f in [raw_fname, raw_fname, raw_fname]]) raw.info['bads'] = ['MEG 2443'] @@ -99,10 +103,10 @@ @pytest.mark.slowtest @pytest.mark.parametrize('fun', (mne_compute_proj_ecg, mne_compute_proj_eog)) -def test_compute_proj_exg(tmpdir, fun): +def test_compute_proj_exg(tmp_path, fun): """Test mne compute_proj_ecg/eog.""" check_usage(fun) - tempdir = str(tmpdir) + tempdir = str(tmp_path) use_fname = op.join(tempdir, op.basename(raw_fname)) bad_fname = op.join(tempdir, 'bads.txt') with open(bad_fname, 'w') as fid: @@ -110,7 +114,7 @@ shutil.copyfile(raw_fname, use_fname) with ArgvSetter(('-i', use_fname, '--bad=' + bad_fname, '--rej-eeg', '150')): - with pytest.warns(None): # samples, sometimes + with _record_warnings(): # samples, sometimes fun.run() fnames = glob.glob(op.join(tempdir, '*proj.fif')) assert len(fnames) == 1 @@ -129,36 +133,41 @@ check_usage(mne_kit2fiff, force_help=True) -@pytest.mark.slowtest # slow on Travis OSX -@requires_vtk +@pytest.mark.slowtest +@pytest.mark.ultraslowtest @testing.requires_testing_data -def test_make_scalp_surfaces(tmpdir): +def test_make_scalp_surfaces(tmp_path, monkeypatch): """Test mne make_scalp_surfaces.""" + pytest.importorskip('nibabel') + pytest.importorskip('pyvista') check_usage(mne_make_scalp_surfaces) has = 'SUBJECTS_DIR' in os.environ # Copy necessary files to avoid FreeSurfer call - tempdir = str(tmpdir) + tempdir = str(tmp_path) surf_path = op.join(subjects_dir, 'sample', 'surf') surf_path_new = op.join(tempdir, 'sample', 'surf') os.mkdir(op.join(tempdir, 'sample')) os.mkdir(surf_path_new) subj_dir = op.join(tempdir, 'sample', 'bem') os.mkdir(subj_dir) - shutil.copy(op.join(surf_path, 'lh.seghead'), surf_path_new) cmd = ('-s', 'sample', '--subjects-dir', tempdir) - with modified_env(**{'_MNE_TESTING_SCALP': 'true'}): - dense_fname = op.join(subj_dir, 'sample-head-dense.fif') - medium_fname = op.join(subj_dir, 'sample-head-medium.fif') - with ArgvSetter(cmd, disable_stdout=False, disable_stderr=False): - with modified_env(FREESURFER_HOME=None): - pytest.raises(RuntimeError, mne_make_scalp_surfaces.run) - with modified_env(FREESURFER_HOME=tempdir): - mne_make_scalp_surfaces.run() - assert op.isfile(dense_fname) - assert op.isfile(medium_fname) - with pytest.raises(IOError, match='overwrite'): - mne_make_scalp_surfaces.run() + monkeypatch.setattr( + mne.bem, 'decimate_surface', + lambda points, triangles, n_triangles: (points, triangles)) + dense_fname = op.join(subj_dir, 'sample-head-dense.fif') + medium_fname = op.join(subj_dir, 'sample-head-medium.fif') + with ArgvSetter(cmd, disable_stdout=False, disable_stderr=False): + monkeypatch.delenv('FREESURFER_HOME', None) + with pytest.raises(RuntimeError, match='The FreeSurfer environ'): + mne_make_scalp_surfaces.run() + shutil.copy(op.join(surf_path, 'lh.seghead'), surf_path_new) + monkeypatch.setenv('FREESURFER_HOME', tempdir) + mne_make_scalp_surfaces.run() + assert op.isfile(dense_fname) + assert op.isfile(medium_fname) + with pytest.raises(IOError, match='overwrite'): + mne_make_scalp_surfaces.run() # actually check the outputs head_py = read_bem_surfaces(dense_fname) assert_equal(len(head_py), 1) @@ -187,18 +196,17 @@ @pytest.mark.slowtest -@requires_mayavi -@traits_test @testing.requires_testing_data -def test_report(tmpdir): +def test_report(tmp_path): """Test mne report.""" + pytest.importorskip('nibabel') check_usage(mne_report) - tempdir = str(tmpdir) + tempdir = str(tmp_path) use_fname = op.join(tempdir, op.basename(raw_fname)) shutil.copyfile(raw_fname, use_fname) with ArgvSetter(('-p', tempdir, '-i', use_fname, '-d', subjects_dir, '-s', 'sample', '--no-browser', '-m', '30')): - with pytest.warns(None): # contour levels + with _record_warnings(): # contour levels mne_report.run() fnames = glob.glob(op.join(tempdir, '*.html')) assert len(fnames) == 1 @@ -215,14 +223,14 @@ @requires_nibabel() @requires_freesurfer('mri_watershed') @testing.requires_testing_data -def test_watershed_bem(tmpdir): +def test_watershed_bem(tmp_path): """Test mne watershed bem.""" check_usage(mne_watershed_bem) # from T1.mgz Mdc = np.array([[-1, 0, 0], [0, 0, -1], [0, 1, 0]]) Pxyz_c = np.array([-5.273613, 9.039085, -27.287964]) # Copy necessary files to tempdir - tempdir = str(tmpdir) + tempdir = str(tmp_path) mridata_path = op.join(subjects_dir, 'sample', 'mri') subject_path_new = op.join(tempdir, 'sample') mridata_path_new = op.join(subject_path_new, 'mri') @@ -256,35 +264,61 @@ assert_allclose(vol_info['cras'], Pxyz_c, **kwargs) -@pytest.mark.timeout(120) # took ~70 sec locally +@pytest.mark.timeout(180) # took ~70 sec locally @pytest.mark.slowtest @pytest.mark.ultraslowtest @requires_freesurfer @testing.requires_testing_data -def test_flash_bem(tmpdir): +def test_flash_bem(tmp_path): """Test mne flash_bem.""" check_usage(mne_flash_bem, force_help=True) # Copy necessary files to tempdir - tempdir = str(tmpdir) - mridata_path = op.join(subjects_dir, 'sample', 'mri') - subject_path_new = op.join(tempdir, 'sample') - mridata_path_new = op.join(subject_path_new, 'mri') - os.makedirs(op.join(mridata_path_new, 'flash')) - os.makedirs(op.join(subject_path_new, 'bem')) + tempdir = Path(str(tmp_path)) + mridata_path = Path(subjects_dir) / 'sample' / 'mri' + subject_path_new = tempdir / 'sample' + mridata_path_new = subject_path_new / 'mri' + flash_path = mridata_path_new / 'flash' + flash_path.mkdir(parents=True, exist_ok=True) + bem_path = mridata_path_new / 'bem' + bem_path.mkdir(parents=True, exist_ok=True) shutil.copyfile(op.join(mridata_path, 'T1.mgz'), op.join(mridata_path_new, 'T1.mgz')) shutil.copyfile(op.join(mridata_path, 'brain.mgz'), op.join(mridata_path_new, 'brain.mgz')) # Copy the available mri/flash/mef*.mgz files from the dataset - flash_path = op.join(mridata_path_new, 'flash') for kind in (5, 30): - in_fname = op.join(mridata_path, 'flash', 'mef%02d.mgz' % kind) - shutil.copyfile(in_fname, op.join(flash_path, op.basename(in_fname))) + in_fname = mridata_path / "flash" / f'mef{kind:02d}.mgz' + in_fname_echo = flash_path / f'mef{kind:02d}_001.mgz' + shutil.copyfile(in_fname, flash_path / in_fname_echo.name) # Test mne flash_bem with --noconvert option # (since there are no DICOM Flash images in dataset) for s in ('outer_skin', 'outer_skull', 'inner_skull'): - assert not op.isfile(op.join(subject_path_new, 'bem', '%s.surf' % s)) - with ArgvSetter(('-d', tempdir, '-s', 'sample', '-n'), + assert not op.isfile(subject_path_new / 'bem' / f'{s}.surf') + + # First test without flash30 + with ArgvSetter(('-d', tempdir, '-s', 'sample', '-n', '-r', '-3'), + disable_stdout=False, disable_stderr=False): + mne_flash_bem.run() + for s in ('outer_skin', 'outer_skull', 'inner_skull'): + surf_path = subject_path_new / 'bem' / f'{s}.surf' + assert surf_path.exists() + surf_path.unlink() # cleanup + shutil.rmtree(flash_path / "parameter_maps") # remove old files + + # Test synthesize flash5 with MEF flash5 and flash30 default locations + flash5_img = convert_flash_mris( + subject="sample", subjects_dir=tempdir, convert=False, + unwarp=False + ) + assert flash5_img == (flash_path / "parameter_maps" / "flash5.mgz") + assert flash5_img.exists() + shutil.rmtree(flash_path / "parameter_maps") # remove old files + + # Test with flash5 and flash30 + shutil.rmtree(flash_path) # first remove old files + with ArgvSetter(('-d', tempdir, '-s', 'sample', '-n', + '-3', str(mridata_path / "flash" / 'mef30.mgz'), + '-5', str(mridata_path / "flash" / 'mef05.mgz')), disable_stdout=False, disable_stderr=False): mne_flash_bem.run() @@ -303,12 +337,11 @@ @testing.requires_testing_data -def test_setup_source_space(tmpdir): +def test_setup_source_space(tmp_path): """Test mne setup_source_space.""" check_usage(mne_setup_source_space, force_help=True) # Using the sample dataset - subjects_dir = op.join(testing.data_path(download=False), 'subjects') - use_fname = op.join(tmpdir, "sources-src.fif") + use_fname = op.join(tmp_path, "sources-src.fif") # Test command with ArgvSetter(('--src', use_fname, '-d', subjects_dir, '-s', 'sample', '--morph', 'sample', @@ -333,12 +366,11 @@ @pytest.mark.slowtest @testing.requires_testing_data -def test_setup_forward_model(tmpdir): +def test_setup_forward_model(tmp_path): """Test mne setup_forward_model.""" check_usage(mne_setup_forward_model, force_help=True) # Using the sample dataset - subjects_dir = op.join(testing.data_path(download=False), 'subjects') - use_fname = op.join(tmpdir, "model-bem.fif") + use_fname = op.join(tmp_path, "model-bem.fif") # Test command with ArgvSetter(('--model', use_fname, '-d', subjects_dir, '--homog', '-s', 'sample', '--ico', '3', '--verbose')): @@ -351,13 +383,11 @@ @pytest.mark.slowtest @testing.requires_testing_data -def test_mne_prepare_bem_model(tmpdir): +def test_mne_prepare_bem_model(tmp_path): """Test mne setup_source_space.""" check_usage(mne_prepare_bem_model, force_help=True) # Using the sample dataset - bem_model_fname = op.join(testing.data_path(download=False), 'subjects', - 'sample', 'bem', 'sample-320-320-320-bem.fif') - bem_solution_fname = op.join(tmpdir, "bem_solution-bem-sol.fif") + bem_solution_fname = op.join(tmp_path, "bem_solution-bem-sol.fif") # Test command with ArgvSetter(('--bem', bem_model_fname, '--sol', bem_solution_fname, '--verbose')): @@ -384,15 +414,12 @@ assert 'numpy' in out.stdout.getvalue() -def test_anonymize(tmpdir): +def test_anonymize(tmp_path): """Test mne anonymize.""" check_usage(mne_anonymize) - out_fname = op.join(tmpdir, 'anon_test_raw.fif') + out_fname = op.join(tmp_path, 'anon_test_raw.fif') with ArgvSetter(('-f', raw_fname, '-o', out_fname)): mne_anonymize.run() info = read_info(out_fname) - assert(op.exists(out_fname)) + assert op.exists(out_fname) assert info['meas_date'] == _stamp_to_dt((946684800, 0)) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/commands/utils.py python-mne-1.1.0+dfsg/mne/commands/utils.py --- python-mne-0.23.4+dfsg/mne/commands/utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/commands/utils.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Authors: Yaroslav Halchenko # Stefan Appelhoff # -# License: BSD (3-clause) +# License: BSD-3-Clause import glob import importlib diff -Nru python-mne-0.23.4+dfsg/mne/conftest.py python-mne-1.1.0+dfsg/mne/conftest.py --- python-mne-0.23.4+dfsg/mne/conftest.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/conftest.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,10 +1,11 @@ # -*- coding: utf-8 -*- # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause from contextlib import contextmanager -from distutils.version import LooseVersion +import inspect +from textwrap import dedent import gc import os import os.path as op @@ -13,14 +14,23 @@ import sys import warnings import pytest +from unittest import mock import numpy as np import mne +from mne import read_events, pick_types, Epochs +from mne.channels import read_layout +from mne.coreg import create_default_subject from mne.datasets import testing -from mne.fixes import has_numba +from mne.fixes import has_numba, _compare_version +from mne.io import read_raw_fif, read_raw_ctf, read_raw_nirx, read_raw_snirf from mne.stats import cluster_level -from mne.utils import _pl, _assert_no_instances, numerics +from mne.utils import (_pl, _assert_no_instances, numerics, Bunch, + _check_qt_version, _TempDir) + +# data from sample dataset +from mne.viz._figure import use_browser_backend test_path = testing.data_path(download=False) s_path = op.join(test_path, 'MEG', 'sample') @@ -33,77 +43,83 @@ fname_aseg = op.join(test_path, 'subjects', 'sample', 'mri', 'aseg.mgz') subjects_dir = op.join(test_path, 'subjects') fname_src = op.join(bem_path, 'sample-oct-4-src.fif') -subjects_dir = op.join(test_path, 'subjects') -fname_cov = op.join(s_path, 'sample_audvis_trunc-cov.fif') fname_trans = op.join(s_path, 'sample_audvis_trunc-trans.fif') +ctf_dir = op.join(test_path, 'CTF') +fname_ctf_continuous = op.join(ctf_dir, 'testdata_ctf.ds') + +nirx_path = test_path / 'NIRx' +snirf_path = test_path / 'SNIRF' +nirsport2 = nirx_path / 'nirsport_v2' / 'aurora_recording _w_short_and_acc' +nirsport2_snirf = ( + snirf_path / 'NIRx' / 'NIRSport2' / '1.0.3' / + '2021-05-05_001.snirf') +nirsport2_2021_9 = nirx_path / 'nirsport_v2' / 'aurora_2021_9' +nirsport2_20219_snirf = ( + snirf_path / 'NIRx' / 'NIRSport2' / '2021.9' / + '2021-10-01_002.snirf') + +# data from mne.io.tests.data +base_dir = op.join(op.dirname(__file__), 'io', 'tests', 'data') +fname_raw_io = op.join(base_dir, 'test_raw.fif') +fname_event_io = op.join(base_dir, 'test-eve.fif') +fname_cov_io = op.join(base_dir, 'test-cov.fif') +fname_evoked_io = op.join(base_dir, 'test-ave.fif') +event_id, tmin, tmax = 1, -0.1, 1.0 +vv_layout = read_layout('Vectorview-all') + +collect_ignore = [ + 'export/_brainvision.py', + 'export/_eeglab.py', + 'export/_edf.py'] + def pytest_configure(config): """Configure pytest options.""" # Markers - for marker in ('slowtest', 'ultraslowtest'): + for marker in ('slowtest', 'ultraslowtest', 'pgtest'): config.addinivalue_line('markers', marker) # Fixtures - for fixture in ('matplotlib_config',): + for fixture in ('matplotlib_config', 'close_all', 'check_verbose', + 'qt_config', 'protect_config'): config.addinivalue_line('usefixtures', fixture) + # pytest-qt uses PYTEST_QT_API, but let's make it respect qtpy's QT_API + # if present + if os.getenv('PYTEST_QT_API') is None and os.getenv('QT_API') is not None: + os.environ['PYTEST_QT_API'] = os.environ['QT_API'] + # Warnings # - Once SciPy updates not to have non-integer and non-tuple errors (1.2.0) # we should remove them from here. # - This list should also be considered alongside reset_warnings in # doc/conf.py. + if os.getenv('MNE_IGNORE_WARNINGS_IN_TESTS', '') != 'true': + first_kind = 'error' + else: + first_kind = 'always' warning_lines = r""" - error:: - ignore:.*deprecated and ignored since IPython.*:DeprecationWarning - ignore::ImportWarning - ignore:the matrix subclass:PendingDeprecationWarning - ignore:numpy.dtype size changed:RuntimeWarning - ignore:.*HasTraits.trait_.*:DeprecationWarning - ignore:.*takes no parameters:DeprecationWarning - ignore:joblib not installed:RuntimeWarning - ignore:Using a non-tuple sequence for multidimensional indexing:FutureWarning - ignore:using a non-integer number instead of an integer will result in an error:DeprecationWarning - ignore:Importing from numpy.testing.decorators is deprecated:DeprecationWarning - ignore:np.loads is deprecated, use pickle.loads instead:DeprecationWarning - ignore:The oldnumeric module will be dropped:DeprecationWarning - ignore:Collection picker None could not be converted to float:UserWarning - ignore:covariance is not positive-semidefinite:RuntimeWarning - ignore:Can only plot ICA components:RuntimeWarning - ignore:Matplotlib is building the font cache using fc-list:UserWarning - ignore:Using or importing the ABCs from 'collections':DeprecationWarning - ignore:`formatargspec` is deprecated:DeprecationWarning - # This is only necessary until sklearn updates their wheels for NumPy 1.16 - ignore:numpy.ufunc size changed:RuntimeWarning - ignore:.*mne-realtime.*:DeprecationWarning - ignore:.*imp.*:DeprecationWarning - ignore:Exception creating Regex for oneOf.*:SyntaxWarning - ignore:scipy\.gradient is deprecated.*:DeprecationWarning - ignore:sklearn\.externals\.joblib is deprecated.*:FutureWarning - ignore:The sklearn.*module.*deprecated.*:FutureWarning - ignore:.*trait.*handler.*deprecated.*:DeprecationWarning - ignore:.*rich_compare.*metadata.*deprecated.*:DeprecationWarning - ignore:.*In future, it will be an error for 'np.bool_'.*:DeprecationWarning - ignore:.*`np.bool` is a deprecated alias.*:DeprecationWarning - ignore:.*`np.int` is a deprecated alias.*:DeprecationWarning - ignore:.*`np.float` is a deprecated alias.*:DeprecationWarning - ignore:.*`np.object` is a deprecated alias.*:DeprecationWarning - ignore:.*`np.long` is a deprecated alias:DeprecationWarning - ignore:.*Converting `np\.character` to a dtype is deprecated.*:DeprecationWarning - ignore:.*sphinx\.util\.smartypants is deprecated.*: - ignore:.*pandas\.util\.testing is deprecated.*: - ignore:.*tostring.*is deprecated.*:DeprecationWarning - ignore:.*QDesktopWidget\.availableGeometry.*:DeprecationWarning - ignore:Unable to enable faulthandler.*:UserWarning - ignore:Fetchers from the nilearn.*:FutureWarning - ignore:SelectableGroups dict interface is deprecated\. Use select\.:DeprecationWarning - ignore:Call to deprecated class vtk.*:DeprecationWarning - ignore:Call to deprecated method.*Deprecated since.*:DeprecationWarning - always:.*get_data.* is deprecated in favor of.*:DeprecationWarning - ignore:.*rcParams is deprecated.*global_theme.*:DeprecationWarning - ignore:.*distutils\.sysconfig module is deprecated.*:DeprecationWarning - always::ResourceWarning - """ # noqa: E501 + {0}:: + # matplotlib->traitlets (notebook) + ignore:Passing unrecognized arguments to super.*:DeprecationWarning + # notebook tests + ignore:There is no current event loop:DeprecationWarning + ignore:unclosed = LooseVersion('2.1'): - args += (exception_handler,) - super(CallbackRegistryReraise, self).__init__(*args) + def __init__(self, exception_handler=None, signals=None): + super(CallbackRegistryReraise, self).__init__(exception_handler) cbook.CallbackRegistry = CallbackRegistryReraise @@ -206,6 +229,79 @@ pytest.skip('Skipping GUI tests on MacOS CIs and Azure Windows') +@pytest.fixture(scope='function') +def raw_orig(): + """Get raw data without any change to it from mne.io.tests.data.""" + raw = read_raw_fif(fname_raw_io, preload=True) + return raw + + +@pytest.fixture(scope='function') +def raw(): + """ + Get raw data and pick channels to reduce load for testing. + + (from mne.io.tests.data) + """ + raw = read_raw_fif(fname_raw_io, preload=True) + # Throws a warning about a changed unit. + with pytest.warns(RuntimeWarning, match='unit'): + raw.set_channel_types({raw.ch_names[0]: 'ias'}) + raw.pick_channels(raw.ch_names[:9]) + raw.info.normalize_proj() # Fix projectors after subselection + return raw + + +@pytest.fixture(scope='function') +def raw_ctf(): + """Get ctf raw data from mne.io.tests.data.""" + raw_ctf = read_raw_ctf(fname_ctf_continuous, preload=True) + return raw_ctf + + +@pytest.fixture(scope='function') +def events(): + """Get events from mne.io.tests.data.""" + return read_events(fname_event_io) + + +def _get_epochs(stop=5, meg=True, eeg=False, n_chan=20): + """Get epochs.""" + raw = read_raw_fif(fname_raw_io) + events = read_events(fname_event_io) + picks = pick_types(raw.info, meg=meg, eeg=eeg, stim=False, + ecg=False, eog=False, exclude='bads') + # Use a subset of channels for plotting speed + picks = np.round(np.linspace(0, len(picks) + 1, n_chan)).astype(int) + with pytest.warns(RuntimeWarning, match='projection'): + epochs = Epochs(raw, events[:stop], event_id, tmin, tmax, picks=picks, + proj=False, preload=False) + epochs.info.normalize_proj() # avoid warnings + return epochs + + +@pytest.fixture() +def epochs(): + """ + Get minimal, pre-loaded epochs data suitable for most tests. + + (from mne.io.tests.data) + """ + return _get_epochs().load_data() + + +@pytest.fixture() +def epochs_unloaded(): + """Get minimal, unloaded epochs data from mne.io.tests.data.""" + return _get_epochs() + + +@pytest.fixture() +def epochs_full(): + """Get full, preloaded epochs from mne.io.tests.data.""" + return _get_epochs(None).load_data() + + @pytest.fixture(scope='session', params=[testing._pytest_param()]) def _evoked(): # This one is session scoped, so be sure not to modify it (use evoked @@ -228,6 +324,12 @@ return mne.read_cov(fname_cov) +@pytest.fixture +def noise_cov_io(): + """Get noise-covariance (from mne.io.tests.data).""" + return mne.read_cov(fname_cov_io) + + @pytest.fixture(scope='function') def bias_params_free(evoked, noise_cov): """Provide inputs for free bias functions.""" @@ -279,44 +381,113 @@ gc.collect() -@pytest.fixture(params=["mayavi", "pyvista"]) -def renderer(request, garbage_collect): +@pytest.fixture +def mpl_backend(garbage_collect): + """Use for epochs/ica when not implemented with pyqtgraph yet.""" + with use_browser_backend('matplotlib') as backend: + yield backend + backend._close_all() + + +# Skip functions or modules for mne-qt-browser < 0.2.0 +pre_2_0_skip_modules = ['mne.viz.tests.test_epochs', + 'mne.viz.tests.test_ica'] +pre_2_0_skip_funcs = ['test_plot_raw_white', + 'test_plot_raw_selection'] + + +def _check_pyqtgraph(request): + # Check Qt + qt_version, api = _check_qt_version(return_api=True) + if (not qt_version) or _compare_version(qt_version, '<', '5.12'): + pytest.skip(f'Qt API {api} has version {qt_version} ' + f'but pyqtgraph needs >= 5.12!') + try: + import mne_qt_browser # noqa: F401 + # Check mne-qt-browser version + lower_2_0 = _compare_version(mne_qt_browser.__version__, '<', '0.2.0') + m_name = request.function.__module__ + f_name = request.function.__name__ + if lower_2_0 and m_name in pre_2_0_skip_modules: + pytest.skip(f'Test-Module "{m_name}" was skipped for' + f' mne-qt-browser < 0.2.0') + elif lower_2_0 and f_name in pre_2_0_skip_funcs: + pytest.skip(f'Test "{f_name}" was skipped for ' + f'mne-qt-browser < 0.2.0') + except Exception: + pytest.skip('Requires mne_qt_browser') + else: + ver = mne_qt_browser.__version__ + if api != 'PyQt5' and _compare_version(ver, '<=', '0.2.6'): + pytest.skip(f'mne_qt_browser {ver} requires PyQt5, API is {api}') + + +@pytest.mark.pgtest +@pytest.fixture +def pg_backend(request, garbage_collect): + """Use for pyqtgraph-specific test-functions.""" + _check_pyqtgraph(request) + with use_browser_backend('qt') as backend: + backend._close_all() + yield backend + backend._close_all() + # This shouldn't be necessary, but let's make sure nothing is stale + import mne_qt_browser + mne_qt_browser._browser_instances.clear() + + +@pytest.fixture(params=[ + 'matplotlib', + pytest.param('qt', marks=pytest.mark.pgtest), +]) +def browser_backend(request, garbage_collect, monkeypatch): + """Parametrizes the name of the browser backend.""" + backend_name = request.param + if backend_name == 'qt': + _check_pyqtgraph(request) + with use_browser_backend(backend_name) as backend: + backend._close_all() + monkeypatch.setenv('MNE_BROWSE_RAW_SIZE', '10,10') + yield backend + backend._close_all() + if backend_name == 'qt': + # This shouldn't be necessary, but let's make sure nothing is stale + import mne_qt_browser + mne_qt_browser._browser_instances.clear() + + +@pytest.fixture(params=["pyvistaqt"]) +def renderer(request, options_3d, garbage_collect): """Yield the 3D backends.""" with _use_backend(request.param, interactive=False) as renderer: yield renderer -@pytest.fixture(params=["pyvista"]) -def renderer_pyvista(request, garbage_collect): +@pytest.fixture(params=["pyvistaqt"]) +def renderer_pyvistaqt(request, options_3d, garbage_collect): """Yield the PyVista backend.""" with _use_backend(request.param, interactive=False) as renderer: yield renderer @pytest.fixture(params=["notebook"]) -def renderer_notebook(request): +def renderer_notebook(request, options_3d): """Yield the 3D notebook renderer.""" with _use_backend(request.param, interactive=False) as renderer: yield renderer -@pytest.fixture(scope="module", params=["pyvista"]) -def renderer_interactive_pyvista(request): +@pytest.fixture(scope="module", params=["pyvistaqt"]) +def renderer_interactive_pyvistaqt(request, options_3d): """Yield the interactive PyVista backend.""" with _use_backend(request.param, interactive=True) as renderer: yield renderer -@pytest.fixture(scope="module", params=["pyvista", "mayavi"]) -def renderer_interactive(request): +@pytest.fixture(scope="module", params=["pyvistaqt"]) +def renderer_interactive(request, options_3d): """Yield the interactive 3D backends.""" with _use_backend(request.param, interactive=True) as renderer: - if renderer._get_3d_backend() == 'mayavi': - with warnings.catch_warnings(record=True): - try: - from surfer import Brain # noqa: 401 analysis:ignore - except Exception: - pytest.skip('Requires PySurfer') yield renderer @@ -333,30 +504,27 @@ def _check_skip_backend(name): - from mne.viz.backends.tests._utils import (has_mayavi, has_pyvista, - has_pyqt5, has_imageio_ffmpeg) - check_pyvista = name in ('pyvista', 'notebook') - check_pyqt5 = name in ('mayavi', 'pyvista') - if name == 'mayavi': - if not has_mayavi(): - pytest.skip("Test skipped, requires mayavi.") - elif name == 'pyvista': + from mne.viz.backends.tests._utils import (has_pyvista, + has_imageio_ffmpeg, + has_pyvistaqt) + if name in ('pyvistaqt', 'notebook'): + if not has_pyvista(): + pytest.skip("Test skipped, requires pyvista.") if not has_imageio_ffmpeg(): pytest.skip("Test skipped, requires imageio-ffmpeg") - if check_pyvista and not has_pyvista(): - pytest.skip("Test skipped, requires pyvista.") - if check_pyqt5 and not has_pyqt5(): - pytest.skip("Test skipped, requires PyQt5.") + if name == 'pyvistaqt' and not _check_qt_version(): + pytest.skip("Test skipped, requires Qt.") + if name == 'pyvistaqt' and not has_pyvistaqt(): + pytest.skip("Test skipped, requires pyvistaqt") @pytest.fixture(scope='session') def pixel_ratio(): """Get the pixel ratio.""" - from mne.viz.backends.tests._utils import (has_mayavi, has_pyvista, - has_pyqt5) - if not (has_mayavi() or has_pyvista()) or not has_pyqt5(): + from mne.viz.backends.tests._utils import has_pyvista + if not has_pyvista() or not _check_qt_version(): return 1. - from PyQt5.QtWidgets import QApplication, QMainWindow + from qtpy.QtWidgets import QApplication, QMainWindow _ = QApplication.instance() or QApplication([]) window = QMainWindow() ratio = float(window.devicePixelRatio()) @@ -365,11 +533,28 @@ @pytest.fixture(scope='function', params=[testing._pytest_param()]) -def subjects_dir_tmp(tmpdir): +def subjects_dir_tmp(tmp_path): """Copy MNE-testing-data subjects_dir to a temp dir for manipulation.""" for key in ('sample', 'fsaverage'): - shutil.copytree(op.join(subjects_dir, key), str(tmpdir.join(key))) - return str(tmpdir) + shutil.copytree(op.join(subjects_dir, key), str(tmp_path / key)) + return str(tmp_path) + + +@pytest.fixture(params=[testing._pytest_param()]) +def subjects_dir_tmp_few(tmp_path): + """Copy fewer files to a tmp_path.""" + subjects_path = tmp_path / 'subjects' + os.mkdir(subjects_path) + # add fsaverage + create_default_subject(subjects_dir=subjects_path, fs_home=test_path, + verbose=True) + # add sample (with few files) + sample_path = subjects_path / 'sample' + os.makedirs(sample_path / 'bem') + for dirname in ('mri', 'surf'): + shutil.copytree( + test_path / 'subjects' / 'sample' / dirname, sample_path / dirname) + return subjects_path # Scoping these as session will make things faster, but need to make sure @@ -449,7 +634,7 @@ invs = dict() for kind, fwd in _all_src_types_fwd.items(): assert fwd['src'].kind == kind - with pytest.warns(RuntimeWarning, match='has magnitude'): + with pytest.warns(RuntimeWarning, match='has been reduced'): invs[kind] = mne.minimum_norm.make_inverse_operator( evoked.info, fwd, cov) return invs, evoked @@ -478,10 +663,11 @@ """Create a 7mm source space with labels.""" pytest.importorskip('nibabel') volume_labels = mne.get_volume_labels_from_aseg(fname_aseg) - src = mne.setup_volume_source_space( - 'sample', 7., mri='aseg.mgz', volume_label=volume_labels, - add_interpolator=False, bem=fname_bem, - subjects_dir=subjects_dir) + with pytest.warns(RuntimeWarning, match='Found no usable.*Left-vessel.*'): + src = mne.setup_volume_source_space( + 'sample', 7., mri='aseg.mgz', volume_label=volume_labels, + add_interpolator=False, bem=fname_bem, + subjects_dir=subjects_dir) lut, _ = mne.read_freesurfer_lut() assert len(volume_labels) == 46 assert volume_labels[0] == 'Unknown' @@ -490,13 +676,39 @@ def _fail(*args, **kwargs): + __tracebackhide__ = True raise AssertionError('Test should not download') @pytest.fixture(scope='function') def download_is_error(monkeypatch): """Prevent downloading by raising an error when it's attempted.""" - monkeypatch.setattr(mne.utils.fetching, '_get_http', _fail) + import pooch + monkeypatch.setattr(pooch, 'retrieve', _fail) + + +# We can't use monkeypatch because its scope (function-level) conflicts with +# the requests fixture (module-level), so we live with a module-scoped version +# that uses mock +@pytest.fixture(scope='module') +def options_3d(): + """Disable advanced 3d rendering.""" + with mock.patch.dict( + os.environ, { + "MNE_3D_OPTION_ANTIALIAS": "false", + "MNE_3D_OPTION_DEPTH_PEELING": "false", + "MNE_3D_OPTION_SMOOTH_SHADING": "false", + } + ): + yield + + +@pytest.fixture(scope='session') +def protect_config(): + """Protect ~/.mne.""" + temp = _TempDir() + with mock.patch.dict(os.environ, {"_MNE_FAKE_HOME_DIR": temp}): + yield @pytest.fixture() @@ -504,25 +716,21 @@ """Ensure that brain can be properly garbage collected.""" keys = ( 'renderer_interactive', - 'renderer_interactive_pyvista', - 'renderer_interactive_pysurfer', + 'renderer_interactive_pyvistaqt', 'renderer', - 'renderer_pyvista', + 'renderer_pyvistaqt', 'renderer_notebook', ) assert set(request.fixturenames) & set(keys) != set() for key in keys: if key in request.fixturenames: - is_pv = request.getfixturevalue(key)._get_3d_backend() == 'pyvista' + is_pv = \ + request.getfixturevalue(key)._get_3d_backend() == 'pyvistaqt' close_func = request.getfixturevalue(key).backend._close_all break if not is_pv: yield return - import pyvista - if LooseVersion(pyvista.__version__) <= LooseVersion('0.26.1'): - yield - return from mne.viz import Brain ignore = set(id(o) for o in gc.get_objects()) yield @@ -535,7 +743,7 @@ if outcome != 'passed': return _assert_no_instances(Brain, 'after') - # We only check VTK for PyVista -- Mayavi/PySurfer is not as strict + # Check VTK objs = gc.get_objects() bad = list() for o in objs: @@ -571,7 +779,8 @@ # split mne/tests/test_whatever.py into separate categories since these # are essentially submodule-level tests. Keeping just [:3] works, # except for mne/viz where we want level-4 granulatity - parts = parts[:4 if parts[:2] == ('mne', 'viz') else 3] + split_submodules = (('mne', 'viz'), ('mne', 'preprocessing')) + parts = parts[:4 if parts[:2] in split_submodules else 3] if not parts[-1].endswith('.py'): parts = parts + ('',) file_key = '/'.join(parts) @@ -607,3 +816,95 @@ if request.param == 'Numba' and not has_numba: pytest.skip('Numba not installed') yield request.param + + +# Create one nbclient and reuse it +@pytest.fixture(scope='session') +def _nbclient(): + try: + import nbformat + from jupyter_client import AsyncKernelManager + from nbclient import NotebookClient + from ipywidgets import Button # noqa + import ipyvtklink # noqa + except Exception as exc: + return pytest.skip(f'Skipping Notebook test: {exc}') + km = AsyncKernelManager(config=None) + nb = nbformat.reads(""" +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata":{}, + "outputs": [], + "source":[] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version":3}, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}""", as_version=4) + client = NotebookClient(nb, km=km) + yield client + client._cleanup_kernel() + + +@pytest.fixture(scope='function') +def nbexec(_nbclient): + """Execute Python code in a notebook.""" + # Adapted/simplified from nbclient/client.py (BSD-3-Clause) + _nbclient._cleanup_kernel() + + def execute(code, reset=False): + _nbclient.reset_execution_trackers() + with _nbclient.setup_kernel(): + assert _nbclient.kc is not None + cell = Bunch(cell_type='code', metadata={}, source=dedent(code)) + _nbclient.execute_cell(cell, 0, execution_count=0) + _nbclient.set_widgets_metadata() + + yield execute + + +def pytest_runtest_call(item): + """Run notebook code written in Python.""" + if 'nbexec' in getattr(item, 'fixturenames', ()): + nbexec = item.funcargs['nbexec'] + code = inspect.getsource(getattr(item.module, item.name.split('[')[0])) + code = code.splitlines() + ci = 0 + for ci, c in enumerate(code): + if c.startswith(' '): # actual content + break + code = '\n'.join(code[ci:]) + + def run(nbexec=nbexec, code=code): + nbexec(code) + + item.runtest = run + return + + +@pytest.mark.filterwarnings('ignore:.*Extraction of measurement.*:') +@pytest.fixture(params=( + [nirsport2, nirsport2_snirf, testing._pytest_param()], + [nirsport2_2021_9, nirsport2_20219_snirf, testing._pytest_param()], +)) +def nirx_snirf(request): + """Return a (raw_nirx, raw_snirf) matched pair.""" + pytest.importorskip('h5py') + return (read_raw_nirx(request.param[0], preload=True), + read_raw_snirf(request.param[1], preload=True)) diff -Nru python-mne-0.23.4+dfsg/mne/connectivity/effective.py python-mne-1.1.0+dfsg/mne/connectivity/effective.py --- python-mne-0.23.4+dfsg/mne/connectivity/effective.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/connectivity/effective.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,160 +0,0 @@ -# Authors: Martin Luessi -# -# License: BSD (3-clause) - -import copy - -import numpy as np - -from ..utils import logger, verbose -from .spectral import spectral_connectivity - - -@verbose -def phase_slope_index(data, indices=None, sfreq=2 * np.pi, - mode='multitaper', fmin=None, fmax=np.inf, - tmin=None, tmax=None, mt_bandwidth=None, - mt_adaptive=False, mt_low_bias=True, - cwt_freqs=None, cwt_n_cycles=7, block_size=1000, - n_jobs=1, verbose=None): - """Compute the Phase Slope Index (PSI) connectivity measure. - - The PSI is an effective connectivity measure, i.e., a measure which can - give an indication of the direction of the information flow (causality). - For two time series, and one computes the PSI between the first and the - second time series as follows - - indices = (np.array([0]), np.array([1])) - psi = phase_slope_index(data, indices=indices, ...) - - A positive value means that time series 0 is ahead of time series 1 and - a negative value means the opposite. - - The PSI is computed from the coherency (see spectral_connectivity), details - can be found in [1]. - - Parameters - ---------- - data : array-like, shape=(n_epochs, n_signals, n_times) - Can also be a list/generator of array, shape =(n_signals, n_times); - list/generator of SourceEstimate; or Epochs. - The data from which to compute connectivity. Note that it is also - possible to combine multiple signals by providing a list of tuples, - e.g., data = [(arr_0, stc_0), (arr_1, stc_1), (arr_2, stc_2)], - corresponds to 3 epochs, and arr_* could be an array with the same - number of time points as stc_*. - indices : tuple of array | None - Two arrays with indices of connections for which to compute - connectivity. If None, all connections are computed. - sfreq : float - The sampling frequency. - mode : str - Spectrum estimation mode can be either: 'multitaper', 'fourier', or - 'cwt_morlet'. - fmin : float | tuple of float - The lower frequency of interest. Multiple bands are defined using - a tuple, e.g., (8., 20.) for two bands with 8Hz and 20Hz lower freq. - If None the frequency corresponding to an epoch length of 5 cycles - is used. - fmax : float | tuple of float - The upper frequency of interest. Multiple bands are dedined using - a tuple, e.g. (13., 30.) for two band with 13Hz and 30Hz upper freq. - tmin : float | None - Time to start connectivity estimation. - tmax : float | None - Time to end connectivity estimation. - mt_bandwidth : float | None - The bandwidth of the multitaper windowing function in Hz. - Only used in 'multitaper' mode. - mt_adaptive : bool - Use adaptive weights to combine the tapered spectra into PSD. - Only used in 'multitaper' mode. - mt_low_bias : bool - Only use tapers with more than 90%% spectral concentration within - bandwidth. Only used in 'multitaper' mode. - cwt_freqs : array - Array of frequencies of interest. Only used in 'cwt_morlet' mode. - cwt_n_cycles : float | array of float - Number of cycles. Fixed number or one per frequency. Only used in - 'cwt_morlet' mode. - block_size : int - How many connections to compute at once (higher numbers are faster - but require more memory). - n_jobs : int - How many epochs to process in parallel. - %(verbose)s - - Returns - ------- - psi : array - Computed connectivity measure(s). The shape of each array is either - (n_signals, n_signals, n_bands) mode: 'multitaper' or 'fourier' - (n_signals, n_signals, n_bands, n_times) mode: 'cwt_morlet' - when "indices" is None, or - (n_con, n_bands) mode: 'multitaper' or 'fourier' - (n_con, n_bands, n_times) mode: 'cwt_morlet' - when "indices" is specified and "n_con = len(indices[0])". - freqs : array - Frequency points at which the connectivity was computed. - times : array - Time points for which the connectivity was computed. - n_epochs : int - Number of epochs used for computation. - n_tapers : int - The number of DPSS tapers used. Only defined in 'multitaper' mode. - Otherwise None is returned. - - References - ---------- - [1] Nolte et al. "Robustly Estimating the Flow Direction of Information in - Complex Physical Systems", Physical Review Letters, vol. 100, no. 23, - pp. 1-4, Jun. 2008. - """ - logger.info('Estimating phase slope index (PSI)') - # estimate the coherency - cohy, freqs_, times, n_epochs, n_tapers = spectral_connectivity( - data, method='cohy', indices=indices, sfreq=sfreq, mode=mode, - fmin=fmin, fmax=fmax, fskip=0, faverage=False, tmin=tmin, tmax=tmax, - mt_bandwidth=mt_bandwidth, mt_adaptive=mt_adaptive, - mt_low_bias=mt_low_bias, cwt_freqs=cwt_freqs, - cwt_n_cycles=cwt_n_cycles, block_size=block_size, n_jobs=n_jobs, - verbose=verbose) - - logger.info('Computing PSI from estimated Coherency') - # compute PSI in the requested bands - if fmin is None: - fmin = -np.inf # set it to -inf, so we can adjust it later - - bands = list(zip(np.asarray((fmin,)).ravel(), np.asarray((fmax,)).ravel())) - n_bands = len(bands) - - freq_dim = -2 if mode == 'cwt_morlet' else -1 - - # allocate space for output - out_shape = list(cohy.shape) - out_shape[freq_dim] = n_bands - psi = np.zeros(out_shape, dtype=np.float64) - - # allocate accumulator - acc_shape = copy.copy(out_shape) - acc_shape.pop(freq_dim) - acc = np.empty(acc_shape, dtype=np.complex128) - - freqs = list() - idx_fi = [slice(None)] * cohy.ndim - idx_fj = [slice(None)] * cohy.ndim - for band_idx, band in enumerate(bands): - freq_idx = np.where((freqs_ > band[0]) & (freqs_ < band[1]))[0] - freqs.append(freqs_[freq_idx]) - - acc.fill(0.) - for fi, fj in zip(freq_idx, freq_idx[1:]): - idx_fi[freq_dim] = fi - idx_fj[freq_dim] = fj - acc += np.conj(cohy[tuple(idx_fi)]) * cohy[tuple(idx_fj)] - - idx_fi[freq_dim] = band_idx - psi[tuple(idx_fi)] = np.imag(acc) - logger.info('[PSI Estimation Done]') - - return psi, freqs, times, n_epochs, n_tapers diff -Nru python-mne-0.23.4+dfsg/mne/connectivity/envelope.py python-mne-1.1.0+dfsg/mne/connectivity/envelope.py --- python-mne-0.23.4+dfsg/mne/connectivity/envelope.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/connectivity/envelope.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,152 +0,0 @@ -# Authors: Eric Larson -# Sheraz Khan -# Denis Engemann -# -# License: BSD (3-clause) - -import numpy as np - -from ..filter import next_fast_len -from ..source_estimate import _BaseSourceEstimate -from ..utils import verbose, _check_combine, _check_option - - -@verbose -def envelope_correlation(data, combine='mean', orthogonalize="pairwise", - log=False, absolute=True, verbose=None): - """Compute the envelope correlation. - - Parameters - ---------- - data : array-like, shape=(n_epochs, n_signals, n_times) | generator - The data from which to compute connectivity. - The array-like object can also be a list/generator of array, - each with shape (n_signals, n_times), or a :class:`~mne.SourceEstimate` - object (and ``stc.data`` will be used). If it's float data, - the Hilbert transform will be applied; if it's complex data, - it's assumed the Hilbert has already been applied. - combine : 'mean' | callable | None - How to combine correlation estimates across epochs. - Default is 'mean'. Can be None to return without combining. - If callable, it must accept one positional input. - For example:: - - combine = lambda data: np.median(data, axis=0) - - orthogonalize : 'pairwise' | False - Whether to orthogonalize with the pairwise method or not. - Defaults to 'pairwise'. Note that when False, - the correlation matrix will not be returned with - absolute values. - - .. versionadded:: 0.19 - log : bool - If True (default False), square and take the log before orthonalizing - envelopes or computing correlations. - - .. versionadded:: 0.22 - absolute : bool - If True (default), then take the absolute value of correlation - coefficients before making each epoch's correlation matrix - symmetric (and thus before combining matrices across epochs). - Only used when ``orthogonalize=True``. - - .. versionadded:: 0.22 - %(verbose)s - - Returns - ------- - corr : ndarray, shape ([n_epochs, ]n_nodes, n_nodes) - The pairwise orthogonal envelope correlations. - This matrix is symmetric. If combine is None, the array - with have three dimensions, the first of which is ``n_epochs``. - - Notes - ----- - This function computes the power envelope correlation between - orthogonalized signals :footcite:`HippEtAl2012,KhanEtAl2018`. - - .. versionchanged:: 0.22 - Computations fixed for ``orthogonalize=True`` and diagonal entries are - set explicitly to zero. - - References - ---------- - .. footbibliography:: - """ - _check_option('orthogonalize', orthogonalize, (False, 'pairwise')) - from scipy.signal import hilbert - n_nodes = None - if combine is not None: - fun = _check_combine(combine, valid=('mean',)) - else: # None - fun = np.array - - corrs = list() - # Note: This is embarassingly parallel, but the overhead of sending - # the data to different workers is roughly the same as the gain of - # using multiple CPUs. And we require too much GIL for prefer='threading' - # to help. - for ei, epoch_data in enumerate(data): - if isinstance(epoch_data, _BaseSourceEstimate): - epoch_data = epoch_data.data - if epoch_data.ndim != 2: - raise ValueError('Each entry in data must be 2D, got shape %s' - % (epoch_data.shape,)) - n_nodes, n_times = epoch_data.shape - if ei > 0 and n_nodes != corrs[0].shape[0]: - raise ValueError('n_nodes mismatch between data[0] and data[%d], ' - 'got %s and %s' - % (ei, n_nodes, corrs[0].shape[0])) - # Get the complex envelope (allowing complex inputs allows people - # to do raw.apply_hilbert if they want) - if epoch_data.dtype in (np.float32, np.float64): - n_fft = next_fast_len(n_times) - epoch_data = hilbert(epoch_data, N=n_fft, axis=-1)[..., :n_times] - - if epoch_data.dtype not in (np.complex64, np.complex128): - raise ValueError('data.dtype must be float or complex, got %s' - % (epoch_data.dtype,)) - data_mag = np.abs(epoch_data) - data_conj_scaled = epoch_data.conj() - data_conj_scaled /= data_mag - if log: - data_mag *= data_mag - np.log(data_mag, out=data_mag) - # subtract means - data_mag_nomean = data_mag - np.mean(data_mag, axis=-1, keepdims=True) - # compute variances using linalg.norm (square, sum, sqrt) since mean=0 - data_mag_std = np.linalg.norm(data_mag_nomean, axis=-1) - data_mag_std[data_mag_std == 0] = 1 - corr = np.empty((n_nodes, n_nodes)) - for li, label_data in enumerate(epoch_data): - if orthogonalize is False: # the new code - label_data_orth = data_mag[li] - label_data_orth_std = data_mag_std[li] - else: - label_data_orth = (label_data * data_conj_scaled).imag - np.abs(label_data_orth, out=label_data_orth) - # protect against invalid value -- this will be zero - # after (log and) mean subtraction - label_data_orth[li] = 1. - if log: - label_data_orth *= label_data_orth - np.log(label_data_orth, out=label_data_orth) - label_data_orth -= np.mean(label_data_orth, axis=-1, - keepdims=True) - label_data_orth_std = np.linalg.norm(label_data_orth, axis=-1) - label_data_orth_std[label_data_orth_std == 0] = 1 - # correlation is dot product divided by variances - corr[li] = np.sum(label_data_orth * data_mag_nomean, axis=1) - corr[li] /= data_mag_std - corr[li] /= label_data_orth_std - if orthogonalize is not False: - # Make it symmetric (it isn't at this point) - if absolute: - corr = np.abs(corr) - corr = (corr.T + corr) / 2. - corrs.append(corr) - del corr - - corr = fun(corrs) - return corr diff -Nru python-mne-0.23.4+dfsg/mne/connectivity/__init__.py python-mne-1.1.0+dfsg/mne/connectivity/__init__.py --- python-mne-0.23.4+dfsg/mne/connectivity/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/connectivity/__init__.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,6 +0,0 @@ -"""Spectral and effective connectivity measures.""" - -from .utils import seed_target_indices, degree -from .spectral import spectral_connectivity -from .effective import phase_slope_index -from .envelope import envelope_correlation diff -Nru python-mne-0.23.4+dfsg/mne/connectivity/spectral.py python-mne-1.1.0+dfsg/mne/connectivity/spectral.py --- python-mne-0.23.4+dfsg/mne/connectivity/spectral.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/connectivity/spectral.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,1093 +0,0 @@ -# Authors: Martin Luessi -# Denis A. Engemann -# -# License: BSD (3-clause) - -from functools import partial -from inspect import getmembers - -import numpy as np - -from .utils import check_indices -from ..utils import _check_option -from ..fixes import _get_args, _import_fft -from ..parallel import parallel_func -from ..source_estimate import _BaseSourceEstimate -from ..epochs import BaseEpochs -from ..time_frequency.multitaper import (_mt_spectra, _compute_mt_params, - _psd_from_mt, _csd_from_mt, - _psd_from_mt_adaptive) -from ..time_frequency.tfr import morlet, cwt -from ..utils import logger, verbose, _time_mask, warn, _arange_div - -######################################################################## -# Various connectivity estimators - - -class _AbstractConEstBase(object): - """ABC for connectivity estimators.""" - - def start_epoch(self): - raise NotImplementedError('start_epoch method not implemented') - - def accumulate(self, con_idx, csd_xy): - raise NotImplementedError('accumulate method not implemented') - - def combine(self, other): - raise NotImplementedError('combine method not implemented') - - def compute_con(self, con_idx, n_epochs): - raise NotImplementedError('compute_con method not implemented') - - -class _EpochMeanConEstBase(_AbstractConEstBase): - """Base class for methods that estimate connectivity as mean epoch-wise.""" - - def __init__(self, n_cons, n_freqs, n_times): - self.n_cons = n_cons - self.n_freqs = n_freqs - self.n_times = n_times - - if n_times == 0: - self.csd_shape = (n_cons, n_freqs) - else: - self.csd_shape = (n_cons, n_freqs, n_times) - - self.con_scores = None - - def start_epoch(self): # noqa: D401 - """Called at the start of each epoch.""" - pass # for this type of con. method we don't do anything - - def combine(self, other): - """Include con. accumated for some epochs in this estimate.""" - self._acc += other._acc - - -class _CohEstBase(_EpochMeanConEstBase): - """Base Estimator for Coherence, Coherency, Imag. Coherence.""" - - def __init__(self, n_cons, n_freqs, n_times): - super(_CohEstBase, self).__init__(n_cons, n_freqs, n_times) - - # allocate space for accumulation of CSD - self._acc = np.zeros(self.csd_shape, dtype=np.complex128) - - def accumulate(self, con_idx, csd_xy): - """Accumulate CSD for some connections.""" - self._acc[con_idx] += csd_xy - - -class _CohEst(_CohEstBase): - """Coherence Estimator.""" - - name = 'Coherence' - - def compute_con(self, con_idx, n_epochs, psd_xx, psd_yy): # lgtm - """Compute final con. score for some connections.""" - if self.con_scores is None: - self.con_scores = np.zeros(self.csd_shape) - csd_mean = self._acc[con_idx] / n_epochs - self.con_scores[con_idx] = np.abs(csd_mean) / np.sqrt(psd_xx * psd_yy) - - -class _CohyEst(_CohEstBase): - """Coherency Estimator.""" - - name = 'Coherency' - - def compute_con(self, con_idx, n_epochs, psd_xx, psd_yy): # lgtm - """Compute final con. score for some connections.""" - if self.con_scores is None: - self.con_scores = np.zeros(self.csd_shape, - dtype=np.complex128) - csd_mean = self._acc[con_idx] / n_epochs - self.con_scores[con_idx] = csd_mean / np.sqrt(psd_xx * psd_yy) - - -class _ImCohEst(_CohEstBase): - """Imaginary Coherence Estimator.""" - - name = 'Imaginary Coherence' - - def compute_con(self, con_idx, n_epochs, psd_xx, psd_yy): # lgtm - """Compute final con. score for some connections.""" - if self.con_scores is None: - self.con_scores = np.zeros(self.csd_shape) - csd_mean = self._acc[con_idx] / n_epochs - self.con_scores[con_idx] = np.imag(csd_mean) / np.sqrt(psd_xx * psd_yy) - - -class _PLVEst(_EpochMeanConEstBase): - """PLV Estimator.""" - - name = 'PLV' - - def __init__(self, n_cons, n_freqs, n_times): - super(_PLVEst, self).__init__(n_cons, n_freqs, n_times) - - # allocate accumulator - self._acc = np.zeros(self.csd_shape, dtype=np.complex128) - - def accumulate(self, con_idx, csd_xy): - """Accumulate some connections.""" - self._acc[con_idx] += csd_xy / np.abs(csd_xy) - - def compute_con(self, con_idx, n_epochs): - """Compute final con. score for some connections.""" - if self.con_scores is None: - self.con_scores = np.zeros(self.csd_shape) - plv = np.abs(self._acc / n_epochs) - self.con_scores[con_idx] = plv - - -class _ciPLVEst(_EpochMeanConEstBase): - """corrected imaginary PLV Estimator.""" - - name = 'ciPLV' - - def __init__(self, n_cons, n_freqs, n_times): - super(_ciPLVEst, self).__init__(n_cons, n_freqs, n_times) - - # allocate accumulator - self._acc = np.zeros(self.csd_shape, dtype=np.complex128) - - def accumulate(self, con_idx, csd_xy): - """Accumulate some connections.""" - self._acc[con_idx] += csd_xy / np.abs(csd_xy) - - def compute_con(self, con_idx, n_epochs): - """Compute final con. score for some connections.""" - if self.con_scores is None: - self.con_scores = np.zeros(self.csd_shape) - imag_plv = np.abs(np.imag(self._acc)) / n_epochs - real_plv = np.real(self._acc) / n_epochs - real_plv = np.clip(real_plv, -1, 1) # bounded from -1 to 1 - mask = (np.abs(real_plv) == 1) # avoid division by 0 - real_plv[mask] = 0 - corrected_imag_plv = imag_plv / np.sqrt(1 - real_plv ** 2) - self.con_scores[con_idx] = corrected_imag_plv - - -class _PLIEst(_EpochMeanConEstBase): - """PLI Estimator.""" - - name = 'PLI' - - def __init__(self, n_cons, n_freqs, n_times): - super(_PLIEst, self).__init__(n_cons, n_freqs, n_times) - - # allocate accumulator - self._acc = np.zeros(self.csd_shape) - - def accumulate(self, con_idx, csd_xy): - """Accumulate some connections.""" - self._acc[con_idx] += np.sign(np.imag(csd_xy)) - - def compute_con(self, con_idx, n_epochs): - """Compute final con. score for some connections.""" - if self.con_scores is None: - self.con_scores = np.zeros(self.csd_shape) - pli_mean = self._acc[con_idx] / n_epochs - self.con_scores[con_idx] = np.abs(pli_mean) - - -class _PLIUnbiasedEst(_PLIEst): - """Unbiased PLI Square Estimator.""" - - name = 'Unbiased PLI Square' - - def compute_con(self, con_idx, n_epochs): - """Compute final con. score for some connections.""" - if self.con_scores is None: - self.con_scores = np.zeros(self.csd_shape) - pli_mean = self._acc[con_idx] / n_epochs - - # See Vinck paper Eq. (30) - con = (n_epochs * pli_mean ** 2 - 1) / (n_epochs - 1) - - self.con_scores[con_idx] = con - - -class _WPLIEst(_EpochMeanConEstBase): - """WPLI Estimator.""" - - name = 'WPLI' - - def __init__(self, n_cons, n_freqs, n_times): - super(_WPLIEst, self).__init__(n_cons, n_freqs, n_times) - - # store both imag(csd) and abs(imag(csd)) - acc_shape = (2,) + self.csd_shape - self._acc = np.zeros(acc_shape) - - def accumulate(self, con_idx, csd_xy): - """Accumulate some connections.""" - im_csd = np.imag(csd_xy) - self._acc[0, con_idx] += im_csd - self._acc[1, con_idx] += np.abs(im_csd) - - def compute_con(self, con_idx, n_epochs): - """Compute final con. score for some connections.""" - if self.con_scores is None: - self.con_scores = np.zeros(self.csd_shape) - - num = np.abs(self._acc[0, con_idx]) - denom = self._acc[1, con_idx] - - # handle zeros in denominator - z_denom = np.where(denom == 0.) - denom[z_denom] = 1. - - con = num / denom - - # where we had zeros in denominator, we set con to zero - con[z_denom] = 0. - - self.con_scores[con_idx] = con - - -class _WPLIDebiasedEst(_EpochMeanConEstBase): - """Debiased WPLI Square Estimator.""" - - name = 'Debiased WPLI Square' - - def __init__(self, n_cons, n_freqs, n_times): - super(_WPLIDebiasedEst, self).__init__(n_cons, n_freqs, n_times) - # store imag(csd), abs(imag(csd)), imag(csd)^2 - acc_shape = (3,) + self.csd_shape - self._acc = np.zeros(acc_shape) - - def accumulate(self, con_idx, csd_xy): - """Accumulate some connections.""" - im_csd = np.imag(csd_xy) - self._acc[0, con_idx] += im_csd - self._acc[1, con_idx] += np.abs(im_csd) - self._acc[2, con_idx] += im_csd ** 2 - - def compute_con(self, con_idx, n_epochs): - """Compute final con. score for some connections.""" - if self.con_scores is None: - self.con_scores = np.zeros(self.csd_shape) - - # note: we use the trick from fieldtrip to compute the - # the estimate over all pairwise epoch combinations - sum_im_csd = self._acc[0, con_idx] - sum_abs_im_csd = self._acc[1, con_idx] - sum_sq_im_csd = self._acc[2, con_idx] - - denom = sum_abs_im_csd ** 2 - sum_sq_im_csd - - # handle zeros in denominator - z_denom = np.where(denom == 0.) - denom[z_denom] = 1. - - con = (sum_im_csd ** 2 - sum_sq_im_csd) / denom - - # where we had zeros in denominator, we set con to zero - con[z_denom] = 0. - - self.con_scores[con_idx] = con - - -class _PPCEst(_EpochMeanConEstBase): - """Pairwise Phase Consistency (PPC) Estimator.""" - - name = 'PPC' - - def __init__(self, n_cons, n_freqs, n_times): - super(_PPCEst, self).__init__(n_cons, n_freqs, n_times) - - # store csd / abs(csd) - self._acc = np.zeros(self.csd_shape, dtype=np.complex128) - - def accumulate(self, con_idx, csd_xy): - """Accumulate some connections.""" - denom = np.abs(csd_xy) - z_denom = np.where(denom == 0.) - denom[z_denom] = 1. - this_acc = csd_xy / denom - this_acc[z_denom] = 0. # handle division by zero - - self._acc[con_idx] += this_acc - - def compute_con(self, con_idx, n_epochs): - """Compute final con. score for some connections.""" - if self.con_scores is None: - self.con_scores = np.zeros(self.csd_shape) - - # note: we use the trick from fieldtrip to compute the - # the estimate over all pairwise epoch combinations - con = ((self._acc[con_idx] * np.conj(self._acc[con_idx]) - n_epochs) / - (n_epochs * (n_epochs - 1.))) - - self.con_scores[con_idx] = np.real(con) - - -############################################################################### -def _epoch_spectral_connectivity(data, sig_idx, tmin_idx, tmax_idx, sfreq, - mode, window_fun, eigvals, wavelets, - freq_mask, mt_adaptive, idx_map, block_size, - psd, accumulate_psd, con_method_types, - con_methods, n_signals, n_times, - accumulate_inplace=True): - """Estimate connectivity for one epoch (see spectral_connectivity).""" - n_cons = len(idx_map[0]) - - if wavelets is not None: - n_times_spectrum = n_times - n_freqs = len(wavelets) - else: - n_times_spectrum = 0 - n_freqs = np.sum(freq_mask) - - if not accumulate_inplace: - # instantiate methods only for this epoch (used in parallel mode) - con_methods = [mtype(n_cons, n_freqs, n_times_spectrum) - for mtype in con_method_types] - - _check_option('mode', mode, ('cwt_morlet', 'multitaper', 'fourier')) - if len(sig_idx) == n_signals: - # we use all signals: use a slice for faster indexing - sig_idx = slice(None, None) - - # compute tapered spectra - x_t = list() - this_psd = list() - for this_data in data: - if mode in ('multitaper', 'fourier'): - if isinstance(this_data, _BaseSourceEstimate): - _mt_spectra_partial = partial(_mt_spectra, dpss=window_fun, - sfreq=sfreq) - this_x_t = this_data.transform_data( - _mt_spectra_partial, idx=sig_idx, tmin_idx=tmin_idx, - tmax_idx=tmax_idx) - else: - this_x_t, _ = _mt_spectra( - this_data[sig_idx, tmin_idx:tmax_idx], - window_fun, sfreq) - - if mt_adaptive: - # compute PSD and adaptive weights - _this_psd, weights = _psd_from_mt_adaptive( - this_x_t, eigvals, freq_mask, return_weights=True) - - # only keep freqs of interest - this_x_t = this_x_t[:, :, freq_mask] - else: - # do not use adaptive weights - this_x_t = this_x_t[:, :, freq_mask] - if mode == 'multitaper': - weights = np.sqrt(eigvals)[np.newaxis, :, np.newaxis] - else: - # hack to so we can sum over axis=-2 - weights = np.array([1.])[:, None, None] - - if accumulate_psd: - _this_psd = _psd_from_mt(this_x_t, weights) - else: # mode == 'cwt_morlet' - if isinstance(this_data, _BaseSourceEstimate): - cwt_partial = partial(cwt, Ws=wavelets, use_fft=True, - mode='same') - this_x_t = this_data.transform_data( - cwt_partial, idx=sig_idx, tmin_idx=tmin_idx, - tmax_idx=tmax_idx) - else: - this_x_t = cwt(this_data[sig_idx, tmin_idx:tmax_idx], - wavelets, use_fft=True, mode='same') - _this_psd = (this_x_t * this_x_t.conj()).real - - x_t.append(this_x_t) - if accumulate_psd: - this_psd.append(_this_psd) - - x_t = np.concatenate(x_t, axis=0) - if accumulate_psd: - this_psd = np.concatenate(this_psd, axis=0) - - # accumulate or return psd - if accumulate_psd: - if accumulate_inplace: - psd += this_psd - else: - psd = this_psd - else: - psd = None - - # tell the methods that a new epoch starts - for method in con_methods: - method.start_epoch() - - # accumulate connectivity scores - if mode in ['multitaper', 'fourier']: - for i in range(0, n_cons, block_size): - con_idx = slice(i, i + block_size) - if mt_adaptive: - csd = _csd_from_mt(x_t[idx_map[0][con_idx]], - x_t[idx_map[1][con_idx]], - weights[idx_map[0][con_idx]], - weights[idx_map[1][con_idx]]) - else: - csd = _csd_from_mt(x_t[idx_map[0][con_idx]], - x_t[idx_map[1][con_idx]], - weights, weights) - - for method in con_methods: - method.accumulate(con_idx, csd) - else: # mode == 'cwt_morlet' # reminder to add alternative TFR methods - for i_block, i in enumerate(range(0, n_cons, block_size)): - con_idx = slice(i, i + block_size) - # this codes can be very slow - csd = (x_t[idx_map[0][con_idx]] * - x_t[idx_map[1][con_idx]].conjugate()) - - for method in con_methods: - method.accumulate(con_idx, csd) - # future estimator types need to be explicitly handled here - - return con_methods, psd - - -def _get_n_epochs(epochs, n): - """Generate lists with at most n epochs.""" - epochs_out = list() - for epoch in epochs: - if not isinstance(epoch, (list, tuple)): - epoch = (epoch,) - epochs_out.append(epoch) - if len(epochs_out) >= n: - yield epochs_out - epochs_out = list() - if 0 < len(epochs_out) < n: - yield epochs_out - - -def _check_method(method): - """Test if a method implements the required interface.""" - interface_members = [m[0] for m in getmembers(_AbstractConEstBase) - if not m[0].startswith('_')] - method_members = [m[0] for m in getmembers(method) - if not m[0].startswith('_')] - - for member in interface_members: - if member not in method_members: - return False, member - return True, None - - -def _get_and_verify_data_sizes(data, sfreq, n_signals=None, n_times=None, - times=None, warn_times=True): - """Get and/or verify the data sizes and time scales.""" - if not isinstance(data, (list, tuple)): - raise ValueError('data has to be a list or tuple') - n_signals_tot = 0 - # Sometimes data can be (ndarray, SourceEstimate) groups so in the case - # where ndarray comes first, don't use it for times - times_inferred = False - for this_data in data: - this_n_signals, this_n_times = this_data.shape - if n_times is not None: - if this_n_times != n_times: - raise ValueError('all input time series must have the same ' - 'number of time points') - else: - n_times = this_n_times - n_signals_tot += this_n_signals - - if hasattr(this_data, 'times'): - assert isinstance(this_data, _BaseSourceEstimate) - this_times = this_data.times - if times is not None and not times_inferred: - if warn_times and not np.allclose(times, this_times): - with np.printoptions(threshold=4, linewidth=120): - warn('time scales of input time series do not match:\n' - f'{this_times}\n{times}') - warn_times = False - else: - times = this_times - elif times is None: - times_inferred = True - times = _arange_div(n_times, sfreq) - - if n_signals is not None: - if n_signals != n_signals_tot: - raise ValueError('the number of time series has to be the same in ' - 'each epoch') - n_signals = n_signals_tot - - return n_signals, n_times, times, warn_times - - -# map names to estimator types -_CON_METHOD_MAP = {'coh': _CohEst, 'cohy': _CohyEst, 'imcoh': _ImCohEst, - 'plv': _PLVEst, 'ciplv': _ciPLVEst, 'ppc': _PPCEst, - 'pli': _PLIEst, 'pli2_unbiased': _PLIUnbiasedEst, - 'wpli': _WPLIEst, 'wpli2_debiased': _WPLIDebiasedEst} - - -def _check_estimators(method, mode): - """Check construction of connectivity estimators.""" - n_methods = len(method) - con_method_types = list() - for this_method in method: - if this_method in _CON_METHOD_MAP: - con_method_types.append(_CON_METHOD_MAP[this_method]) - elif isinstance(this_method, str): - raise ValueError('%s is not a valid connectivity method' % - this_method) - else: - # support for custom class - method_valid, msg = _check_method(this_method) - if not method_valid: - raise ValueError('The supplied connectivity method does ' - 'not have the method %s' % msg) - con_method_types.append(this_method) - - # determine how many arguments the compute_con_function needs - n_comp_args = [len(_get_args(mtype.compute_con)) - for mtype in con_method_types] - - # we currently only support 3 arguments - if any(n not in (3, 5) for n in n_comp_args): - raise ValueError('The .compute_con method needs to have either ' - '3 or 5 arguments') - # if none of the comp_con functions needs the PSD, we don't estimate it - accumulate_psd = any(n == 5 for n in n_comp_args) - return con_method_types, n_methods, accumulate_psd, n_comp_args - - -@verbose -def spectral_connectivity(data, method='coh', indices=None, sfreq=2 * np.pi, - mode='multitaper', fmin=None, fmax=np.inf, - fskip=0, faverage=False, tmin=None, tmax=None, - mt_bandwidth=None, mt_adaptive=False, - mt_low_bias=True, cwt_freqs=None, - cwt_n_cycles=7, block_size=1000, n_jobs=1, - verbose=None): - """Compute frequency- and time-frequency-domain connectivity measures. - - The connectivity method(s) are specified using the "method" parameter. - All methods are based on estimates of the cross- and power spectral - densities (CSD/PSD) Sxy and Sxx, Syy. - - Parameters - ---------- - data : array-like, shape=(n_epochs, n_signals, n_times) | Epochs - The data from which to compute connectivity. Note that it is also - possible to combine multiple signals by providing a list of tuples, - e.g., data = [(arr_0, stc_0), (arr_1, stc_1), (arr_2, stc_2)], - corresponds to 3 epochs, and arr_* could be an array with the same - number of time points as stc_*. The array-like object can also - be a list/generator of array, shape =(n_signals, n_times), - or a list/generator of SourceEstimate or VolSourceEstimate objects. - method : str | list of str - Connectivity measure(s) to compute. - indices : tuple of array | None - Two arrays with indices of connections for which to compute - connectivity. If None, all connections are computed. - sfreq : float - The sampling frequency. - mode : str - Spectrum estimation mode can be either: 'multitaper', 'fourier', or - 'cwt_morlet'. - fmin : float | tuple of float - The lower frequency of interest. Multiple bands are defined using - a tuple, e.g., (8., 20.) for two bands with 8Hz and 20Hz lower freq. - If None the frequency corresponding to an epoch length of 5 cycles - is used. - fmax : float | tuple of float - The upper frequency of interest. Multiple bands are dedined using - a tuple, e.g. (13., 30.) for two band with 13Hz and 30Hz upper freq. - fskip : int - Omit every "(fskip + 1)-th" frequency bin to decimate in frequency - domain. - faverage : bool - Average connectivity scores for each frequency band. If True, - the output freqs will be a list with arrays of the frequencies - that were averaged. - tmin : float | None - Time to start connectivity estimation. Note: when "data" is an array, - the first sample is assumed to be at time 0. For other types - (Epochs, etc.), the time information contained in the object is used - to compute the time indices. - tmax : float | None - Time to end connectivity estimation. Note: when "data" is an array, - the first sample is assumed to be at time 0. For other types - (Epochs, etc.), the time information contained in the object is used - to compute the time indices. - mt_bandwidth : float | None - The bandwidth of the multitaper windowing function in Hz. - Only used in 'multitaper' mode. - mt_adaptive : bool - Use adaptive weights to combine the tapered spectra into PSD. - Only used in 'multitaper' mode. - mt_low_bias : bool - Only use tapers with more than 90%% spectral concentration within - bandwidth. Only used in 'multitaper' mode. - cwt_freqs : array - Array of frequencies of interest. Only used in 'cwt_morlet' mode. - cwt_n_cycles : float | array of float - Number of cycles. Fixed number or one per frequency. Only used in - 'cwt_morlet' mode. - block_size : int - How many connections to compute at once (higher numbers are faster - but require more memory). - n_jobs : int - How many epochs to process in parallel. - %(verbose)s - - Returns - ------- - con : array | list of array - Computed connectivity measure(s). The shape of each array is either - (n_signals, n_signals, n_freqs) mode: 'multitaper' or 'fourier' - (n_signals, n_signals, n_freqs, n_times) mode: 'cwt_morlet' - when "indices" is None, or - (n_con, n_freqs) mode: 'multitaper' or 'fourier' - (n_con, n_freqs, n_times) mode: 'cwt_morlet' - when "indices" is specified and "n_con = len(indices[0])". - freqs : array - Frequency points at which the connectivity was computed. - times : array - Time points for which the connectivity was computed. - n_epochs : int - Number of epochs used for computation. - n_tapers : int - The number of DPSS tapers used. Only defined in 'multitaper' mode. - Otherwise None is returned. - - Notes - ----- - The spectral densities can be estimated using a multitaper method with - digital prolate spheroidal sequence (DPSS) windows, a discrete Fourier - transform with Hanning windows, or a continuous wavelet transform using - Morlet wavelets. The spectral estimation mode is specified using the - "mode" parameter. - - By default, the connectivity between all signals is computed (only - connections corresponding to the lower-triangular part of the - connectivity matrix). If one is only interested in the connectivity - between some signals, the "indices" parameter can be used. For example, - to compute the connectivity between the signal with index 0 and signals - "2, 3, 4" (a total of 3 connections) one can use the following:: - - indices = (np.array([0, 0, 0]), # row indices - np.array([2, 3, 4])) # col indices - - con_flat = spectral_connectivity(data, method='coh', - indices=indices, ...) - - In this case con_flat.shape = (3, n_freqs). The connectivity scores are - in the same order as defined indices. - - **Supported Connectivity Measures** - - The connectivity method(s) is specified using the "method" parameter. The - following methods are supported (note: ``E[]`` denotes average over - epochs). Multiple measures can be computed at once by using a list/tuple, - e.g., ``['coh', 'pli']`` to compute coherence and PLI. - - 'coh' : Coherence given by:: - - | E[Sxy] | - C = --------------------- - sqrt(E[Sxx] * E[Syy]) - - 'cohy' : Coherency given by:: - - E[Sxy] - C = --------------------- - sqrt(E[Sxx] * E[Syy]) - - 'imcoh' : Imaginary coherence :footcite:`NolteEtAl2004` given by:: - - Im(E[Sxy]) - C = ---------------------- - sqrt(E[Sxx] * E[Syy]) - - 'plv' : Phase-Locking Value (PLV) :footcite:`LachauxEtAl1999` given - by:: - - PLV = |E[Sxy/|Sxy|]| - - 'ciplv' : corrected imaginary PLV (icPLV) - :footcite:`BrunaEtAl2018` given by:: - - |E[Im(Sxy/|Sxy|)]| - ciPLV = ------------------------------------ - sqrt(1 - |E[real(Sxy/|Sxy|)]| ** 2) - - 'ppc' : Pairwise Phase Consistency (PPC), an unbiased estimator - of squared PLV :footcite:`VinckEtAl2010`. - - 'pli' : Phase Lag Index (PLI) :footcite:`StamEtAl2007` given by:: - - PLI = |E[sign(Im(Sxy))]| - - 'pli2_unbiased' : Unbiased estimator of squared PLI - :footcite:`VinckEtAl2011`. - - 'wpli' : Weighted Phase Lag Index (WPLI) :footcite:`VinckEtAl2011` - given by:: - - |E[Im(Sxy)]| - WPLI = ------------------ - E[|Im(Sxy)|] - - 'wpli2_debiased' : Debiased estimator of squared WPLI - :footcite:`VinckEtAl2011`. - - References - ---------- - .. footbibliography:: - """ - if n_jobs != 1: - parallel, my_epoch_spectral_connectivity, _ = \ - parallel_func(_epoch_spectral_connectivity, n_jobs, - verbose=verbose) - - # format fmin and fmax and check inputs - if fmin is None: - fmin = -np.inf # set it to -inf, so we can adjust it later - - fmin = np.array((fmin,), dtype=float).ravel() - fmax = np.array((fmax,), dtype=float).ravel() - if len(fmin) != len(fmax): - raise ValueError('fmin and fmax must have the same length') - if np.any(fmin > fmax): - raise ValueError('fmax must be larger than fmin') - - n_bands = len(fmin) - - # assign names to connectivity methods - if not isinstance(method, (list, tuple)): - method = [method] # make it a list so we can iterate over it - - # handle connectivity estimators - (con_method_types, n_methods, accumulate_psd, - n_comp_args) = _check_estimators(method=method, mode=mode) - - if isinstance(data, BaseEpochs): - times_in = data.times # input times for Epochs input type - sfreq = data.info['sfreq'] - else: - times_in = None - - # loop over data; it could be a generator that returns - # (n_signals x n_times) arrays or SourceEstimates - epoch_idx = 0 - logger.info('Connectivity computation...') - warn_times = True - for epoch_block in _get_n_epochs(data, n_jobs): - if epoch_idx == 0: - # initialize everything times and frequencies - (n_cons, times, n_times, times_in, n_times_in, tmin_idx, - tmax_idx, n_freqs, freq_mask, freqs, freqs_bands, freq_idx_bands, - n_signals, indices_use, warn_times) = _prepare_connectivity( - epoch_block=epoch_block, times_in=times_in, - tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax, sfreq=sfreq, - indices=indices, mode=mode, fskip=fskip, n_bands=n_bands, - cwt_freqs=cwt_freqs, faverage=faverage) - - # get the window function, wavelets, etc for different modes - (spectral_params, mt_adaptive, n_times_spectrum, - n_tapers) = _assemble_spectral_params( - mode=mode, n_times=n_times, mt_adaptive=mt_adaptive, - mt_bandwidth=mt_bandwidth, sfreq=sfreq, - mt_low_bias=mt_low_bias, cwt_n_cycles=cwt_n_cycles, - cwt_freqs=cwt_freqs, freqs=freqs, freq_mask=freq_mask) - - # unique signals for which we actually need to compute PSD etc. - sig_idx = np.unique(np.r_[indices_use[0], indices_use[1]]) - - # map indices to unique indices - idx_map = [np.searchsorted(sig_idx, ind) for ind in indices_use] - - # allocate space to accumulate PSD - if accumulate_psd: - if n_times_spectrum == 0: - psd_shape = (len(sig_idx), n_freqs) - else: - psd_shape = (len(sig_idx), n_freqs, n_times_spectrum) - psd = np.zeros(psd_shape) - else: - psd = None - - # create instances of the connectivity estimators - con_methods = [mtype(n_cons, n_freqs, n_times_spectrum) - for mtype in con_method_types] - - sep = ', ' - metrics_str = sep.join([meth.name for meth in con_methods]) - logger.info(' the following metrics will be computed: %s' - % metrics_str) - - # check dimensions and time scale - for this_epoch in epoch_block: - _, _, _, warn_times = _get_and_verify_data_sizes( - this_epoch, sfreq, n_signals, n_times_in, times_in, - warn_times=warn_times) - - call_params = dict( - sig_idx=sig_idx, tmin_idx=tmin_idx, - tmax_idx=tmax_idx, sfreq=sfreq, mode=mode, - freq_mask=freq_mask, idx_map=idx_map, block_size=block_size, - psd=psd, accumulate_psd=accumulate_psd, - mt_adaptive=mt_adaptive, - con_method_types=con_method_types, - con_methods=con_methods if n_jobs == 1 else None, - n_signals=n_signals, n_times=n_times, - accumulate_inplace=True if n_jobs == 1 else False) - call_params.update(**spectral_params) - - if n_jobs == 1: - # no parallel processing - for this_epoch in epoch_block: - logger.info(' computing connectivity for epoch %d' - % (epoch_idx + 1)) - # con methods and psd are updated inplace - _epoch_spectral_connectivity(data=this_epoch, **call_params) - epoch_idx += 1 - else: - # process epochs in parallel - logger.info(' computing connectivity for epochs %d..%d' - % (epoch_idx + 1, epoch_idx + len(epoch_block))) - - out = parallel(my_epoch_spectral_connectivity( - data=this_epoch, **call_params) - for this_epoch in epoch_block) - # do the accumulation - for this_out in out: - for method, parallel_method in zip(con_methods, this_out[0]): - method.combine(parallel_method) - if accumulate_psd: - psd += this_out[1] - - epoch_idx += len(epoch_block) - - # normalize - n_epochs = epoch_idx - if accumulate_psd: - psd /= n_epochs - - # compute final connectivity scores - con = list() - for method, n_args in zip(con_methods, n_comp_args): - # future estimators will need to be handled here - if n_args == 3: - # compute all scores at once - method.compute_con(slice(0, n_cons), n_epochs) - elif n_args == 5: - # compute scores block-wise to save memory - for i in range(0, n_cons, block_size): - con_idx = slice(i, i + block_size) - psd_xx = psd[idx_map[0][con_idx]] - psd_yy = psd[idx_map[1][con_idx]] - method.compute_con(con_idx, n_epochs, psd_xx, psd_yy) - else: - raise RuntimeError('This should never happen.') - - # get the connectivity scores - this_con = method.con_scores - - if this_con.shape[0] != n_cons: - raise ValueError('First dimension of connectivity scores must be ' - 'the same as the number of connections') - if faverage: - if this_con.shape[1] != n_freqs: - raise ValueError('2nd dimension of connectivity scores must ' - 'be the same as the number of frequencies') - con_shape = (n_cons, n_bands) + this_con.shape[2:] - this_con_bands = np.empty(con_shape, dtype=this_con.dtype) - for band_idx in range(n_bands): - this_con_bands[:, band_idx] =\ - np.mean(this_con[:, freq_idx_bands[band_idx]], axis=1) - this_con = this_con_bands - - con.append(this_con) - - if indices is None: - # return all-to-all connectivity matrices - logger.info(' assembling connectivity matrix') - con_flat = con - con = list() - for this_con_flat in con_flat: - this_con = np.zeros((n_signals, n_signals) + - this_con_flat.shape[1:], - dtype=this_con_flat.dtype) - this_con[indices_use] = this_con_flat - con.append(this_con) - - logger.info('[Connectivity computation done]') - - if n_methods == 1: - # for a single method return connectivity directly - con = con[0] - - if faverage: - # for each band we return the frequencies that were averaged - freqs = freqs_bands - - return con, freqs, times, n_epochs, n_tapers - - -def _prepare_connectivity(epoch_block, times_in, tmin, tmax, - fmin, fmax, sfreq, indices, - mode, fskip, n_bands, - cwt_freqs, faverage): - """Check and precompute dimensions of results data.""" - rfftfreq = _import_fft('rfftfreq') - first_epoch = epoch_block[0] - - # get the data size and time scale - n_signals, n_times_in, times_in, warn_times = _get_and_verify_data_sizes( - first_epoch, sfreq, times=times_in) - - n_times_in = len(times_in) - - if tmin is not None and tmin < times_in[0]: - warn('start time tmin=%0.2f s outside of the time scope of the data ' - '[%0.2f s, %0.2f s]' % (tmin, times_in[0], times_in[-1])) - if tmax is not None and tmax > times_in[-1]: - warn('stop time tmax=%0.2f s outside of the time scope of the data ' - '[%0.2f s, %0.2f s]' % (tmax, times_in[0], times_in[-1])) - - mask = _time_mask(times_in, tmin, tmax, sfreq=sfreq) - tmin_idx, tmax_idx = np.where(mask)[0][[0, -1]] - tmax_idx += 1 - tmin_true = times_in[tmin_idx] - tmax_true = times_in[tmax_idx - 1] # time of last point used - - times = times_in[tmin_idx:tmax_idx] - n_times = len(times) - - if indices is None: - logger.info('only using indices for lower-triangular matrix') - # only compute r for lower-triangular region - indices_use = np.tril_indices(n_signals, -1) - else: - indices_use = check_indices(indices) - - # number of connectivities to compute - n_cons = len(indices_use[0]) - - logger.info(' computing connectivity for %d connections' - % n_cons) - logger.info(' using t=%0.3fs..%0.3fs for estimation (%d points)' - % (tmin_true, tmax_true, n_times)) - - # get frequencies of interest for the different modes - if mode in ('multitaper', 'fourier'): - # fmin fmax etc is only supported for these modes - # decide which frequencies to keep - freqs_all = rfftfreq(n_times, 1. / sfreq) - elif mode == 'cwt_morlet': - # cwt_morlet mode - if cwt_freqs is None: - raise ValueError('define frequencies of interest using ' - 'cwt_freqs') - else: - cwt_freqs = cwt_freqs.astype(np.float64) - if any(cwt_freqs > (sfreq / 2.)): - raise ValueError('entries in cwt_freqs cannot be ' - 'larger than Nyquist (sfreq / 2)') - freqs_all = cwt_freqs - else: - raise ValueError('mode has an invalid value') - - # check that fmin corresponds to at least 5 cycles - dur = float(n_times) / sfreq - five_cycle_freq = 5. / dur - if len(fmin) == 1 and fmin[0] == -np.inf: - # we use the 5 cycle freq. as default - fmin = np.array([five_cycle_freq]) - else: - if np.any(fmin < five_cycle_freq): - warn('fmin=%0.3f Hz corresponds to %0.3f < 5 cycles ' - 'based on the epoch length %0.3f sec, need at least %0.3f ' - 'sec epochs or fmin=%0.3f. Spectrum estimate will be ' - 'unreliable.' % (np.min(fmin), dur * np.min(fmin), dur, - 5. / np.min(fmin), five_cycle_freq)) - - # create a frequency mask for all bands - freq_mask = np.zeros(len(freqs_all), dtype=bool) - for f_lower, f_upper in zip(fmin, fmax): - freq_mask |= ((freqs_all >= f_lower) & (freqs_all <= f_upper)) - - # possibly skip frequency points - for pos in range(fskip): - freq_mask[pos + 1::fskip + 1] = False - - # the frequency points where we compute connectivity - freqs = freqs_all[freq_mask] - n_freqs = len(freqs) - - # get the freq. indices and points for each band - freq_idx_bands = [np.where((freqs >= fl) & (freqs <= fu))[0] - for fl, fu in zip(fmin, fmax)] - freqs_bands = [freqs[freq_idx] for freq_idx in freq_idx_bands] - - # make sure we don't have empty bands - for i, n_f_band in enumerate([len(f) for f in freqs_bands]): - if n_f_band == 0: - raise ValueError('There are no frequency points between ' - '%0.1fHz and %0.1fHz. Change the band ' - 'specification (fmin, fmax) or the ' - 'frequency resolution.' - % (fmin[i], fmax[i])) - if n_bands == 1: - logger.info(' frequencies: %0.1fHz..%0.1fHz (%d points)' - % (freqs_bands[0][0], freqs_bands[0][-1], - n_freqs)) - else: - logger.info(' computing connectivity for the bands:') - for i, bfreqs in enumerate(freqs_bands): - logger.info(' band %d: %0.1fHz..%0.1fHz ' - '(%d points)' % (i + 1, bfreqs[0], - bfreqs[-1], len(bfreqs))) - if faverage: - logger.info(' connectivity scores will be averaged for ' - 'each band') - - return (n_cons, times, n_times, times_in, n_times_in, tmin_idx, - tmax_idx, n_freqs, freq_mask, freqs, freqs_bands, freq_idx_bands, - n_signals, indices_use, warn_times) - - -def _assemble_spectral_params(mode, n_times, mt_adaptive, mt_bandwidth, sfreq, - mt_low_bias, cwt_n_cycles, cwt_freqs, - freqs, freq_mask): - """Prepare time-frequency decomposition.""" - spectral_params = dict( - eigvals=None, window_fun=None, wavelets=None) - n_tapers = None - n_times_spectrum = 0 - if mode == 'multitaper': - window_fun, eigvals, mt_adaptive = _compute_mt_params( - n_times, sfreq, mt_bandwidth, mt_low_bias, mt_adaptive) - spectral_params.update(window_fun=window_fun, eigvals=eigvals) - elif mode == 'fourier': - logger.info(' using FFT with a Hanning window to estimate ' - 'spectra') - spectral_params.update(window_fun=np.hanning(n_times), eigvals=1.) - elif mode == 'cwt_morlet': - logger.info(' using CWT with Morlet wavelets to estimate ' - 'spectra') - - # reformat cwt_n_cycles if we have removed some frequencies - # using fmin, fmax, fskip - cwt_n_cycles = np.array((cwt_n_cycles,), dtype=float).ravel() - if len(cwt_n_cycles) > 1: - if len(cwt_n_cycles) != len(cwt_freqs): - raise ValueError('cwt_n_cycles must be float or an ' - 'array with the same size as cwt_freqs') - cwt_n_cycles = cwt_n_cycles[freq_mask] - - # get the Morlet wavelets - spectral_params.update( - wavelets=morlet(sfreq, freqs, - n_cycles=cwt_n_cycles, zero_mean=True)) - n_times_spectrum = n_times - else: - raise ValueError('mode has an invalid value') - return spectral_params, mt_adaptive, n_times_spectrum, n_tapers diff -Nru python-mne-0.23.4+dfsg/mne/connectivity/tests/test_effective.py python-mne-1.1.0+dfsg/mne/connectivity/tests/test_effective.py --- python-mne-0.23.4+dfsg/mne/connectivity/tests/test_effective.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/connectivity/tests/test_effective.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,39 +0,0 @@ -import numpy as np -from numpy.testing import assert_array_almost_equal - -from mne.connectivity import phase_slope_index - - -def test_psi(): - """Test Phase Slope Index (PSI) estimation.""" - sfreq = 50. - n_signals = 3 - n_epochs = 10 - n_times = 500 - rng = np.random.RandomState(42) - data = rng.randn(n_epochs, n_signals, n_times) - - # simulate time shifts - for i in range(n_epochs): - data[i, 1, 10:] = data[i, 0, :-10] # signal 0 is ahead - data[i, 2, :-10] = data[i, 0, 10:] # signal 2 is ahead - - psi, freqs, times, n_epochs, n_tapers = phase_slope_index( - data, mode='fourier', sfreq=sfreq) - assert psi[1, 0, 0] < 0 - assert psi[2, 0, 0] > 0 - - indices = (np.array([0]), np.array([1])) - psi_2, freqs, times, n_epochs, n_tapers = phase_slope_index( - data, mode='fourier', sfreq=sfreq, indices=indices) - - # the measure is symmetric (sign flip) - assert_array_almost_equal(psi_2[0, 0], -psi[1, 0, 0]) - - cwt_freqs = np.arange(5., 20, 0.5) - psi_cwt, freqs, times, n_epochs, n_tapers = phase_slope_index( - data, mode='cwt_morlet', sfreq=sfreq, cwt_freqs=cwt_freqs, - indices=indices) - - assert np.all(psi_cwt > 0) - assert psi_cwt.shape[-1] == n_times diff -Nru python-mne-0.23.4+dfsg/mne/connectivity/tests/test_envelope.py python-mne-1.1.0+dfsg/mne/connectivity/tests/test_envelope.py --- python-mne-0.23.4+dfsg/mne/connectivity/tests/test_envelope.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/connectivity/tests/test_envelope.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,100 +0,0 @@ -# Authors: Eric Larson -# Sheraz Khan -# Denis Engemann -# -# License: BSD (3-clause) - -import numpy as np -from numpy.testing import assert_allclose -import pytest -from scipy.signal import hilbert - -from mne.connectivity import envelope_correlation - - -def _compute_corrs_orig(data): - # This is the version of the code by Sheraz and Denis. - # For this version (epochs, labels, time) must be -> (labels, time, epochs) - n_epochs, n_labels, _ = data.shape - corr = np.zeros((n_labels, n_labels)) - for epoch_data in data: - for ii in range(n_labels): - for jj in range(n_labels): - # Get timeseries for each pair - x, y = epoch_data[ii], epoch_data[jj] - x_mag = np.abs(x) - x_conj_scaled = x.conj() - x_conj_scaled /= x_mag - # Calculate orthogonalization - y_orth_x = (y * x_conj_scaled).imag - y_orth_x_mag = np.abs(y_orth_x) - # Estimate correlation - corr[ii, jj] += np.abs(np.corrcoef(x_mag, y_orth_x_mag)[0, 1]) - corr = (corr + corr.T) / (2. * n_epochs) - corr.flat[::n_labels + 1] = 0. - return corr - - -def test_envelope_correlation(): - """Test the envelope correlation function.""" - rng = np.random.RandomState(0) - data = rng.randn(2, 4, 64) - data_hilbert = hilbert(data, axis=-1) - corr_orig = _compute_corrs_orig(data_hilbert) - assert (0 <= corr_orig).all() - assert (corr_orig < 1).all() - # using complex data - corr = envelope_correlation(data_hilbert) - assert_allclose(corr, corr_orig) - # using callable - corr = envelope_correlation(data_hilbert, - combine=lambda data: np.mean(data, axis=0)) - assert_allclose(corr, corr_orig) - # do Hilbert internally, and don't combine - corr = envelope_correlation(data, combine=None) - assert corr.shape == (data.shape[0],) + corr_orig.shape - corr = np.mean(corr, axis=0) - assert_allclose(corr, corr_orig) - # degenerate - with pytest.raises(ValueError, match='float'): - envelope_correlation(data.astype(int)) - with pytest.raises(ValueError, match='entry in data must be 2D'): - envelope_correlation(data[np.newaxis]) - with pytest.raises(ValueError, match='n_nodes mismatch'): - envelope_correlation([rng.randn(2, 8), rng.randn(3, 8)]) - with pytest.raises(ValueError, match='mean or callable'): - envelope_correlation(data, 1.) - with pytest.raises(ValueError, match='Combine option'): - envelope_correlation(data, 'foo') - with pytest.raises(ValueError, match='Invalid value.*orthogonalize.*'): - envelope_correlation(data, orthogonalize='foo') - - corr_plain = envelope_correlation(data, combine=None, orthogonalize=False) - assert corr_plain.shape == (data.shape[0],) + corr_orig.shape - assert np.min(corr_plain) < 0 - corr_plain_mean = np.mean(corr_plain, axis=0) - assert_allclose(np.diag(corr_plain_mean), 1) - np_corr = np.array([np.corrcoef(np.abs(x)) for x in data_hilbert]) - assert_allclose(corr_plain, np_corr) - - # check against FieldTrip, which uses the square-log-norm version - # from scipy.io import savemat - # savemat('data.mat', dict(data_hilbert=data_hilbert)) - # matlab - # load data - # ft_connectivity_powcorr_ortho(reshape(data_hilbert(1,:,:), [4, 64])) - # ft_connectivity_powcorr_ortho(reshape(data_hilbert(2,:,:), [4, 64])) - ft_vals = np.array([ - [[np.nan, 0.196734553900236, 0.063173148355451, -0.242638384630448], - [0.196734553900236, np.nan, 0.041799775495150, -0.088205187548542], - [0.063173148355451, 0.041799775495150, np.nan, 0.090331428512317], - [-0.242638384630448, -0.088205187548542, 0.090331428512317, np.nan]], - [[np.nan, -0.013270857462890, 0.185200598081295, 0.140284351572544], - [-0.013270857462890, np.nan, 0.150981508043722, -0.000671809276372], - [0.185200598081295, 0.150981508043722, np.nan, 0.137460244313337], - [0.140284351572544, -0.000671809276372, 0.137460244313337, np.nan]], - ], float) - ft_vals[np.isnan(ft_vals)] = 0 - corr_log = envelope_correlation( - data, combine=None, log=True, absolute=False) - assert_allclose(corr_log, ft_vals) diff -Nru python-mne-0.23.4+dfsg/mne/connectivity/tests/test_spectral.py python-mne-1.1.0+dfsg/mne/connectivity/tests/test_spectral.py --- python-mne-0.23.4+dfsg/mne/connectivity/tests/test_spectral.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/connectivity/tests/test_spectral.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,272 +0,0 @@ -import numpy as np -from numpy.testing import assert_array_almost_equal, assert_allclose -import pytest - -from mne import EpochsArray, SourceEstimate, create_info -from mne.connectivity import spectral_connectivity -from mne.connectivity.spectral import _CohEst, _get_n_epochs -from mne.filter import filter_data - - -def _stc_gen(data, sfreq, tmin, combo=False): - """Simulate a SourceEstimate generator.""" - vertices = [np.arange(data.shape[1]), np.empty(0)] - for d in data: - if not combo: - stc = SourceEstimate(data=d, vertices=vertices, - tmin=tmin, tstep=1 / float(sfreq)) - yield stc - else: - # simulate a combination of array and source estimate - arr = d[0] - stc = SourceEstimate(data=d[1:], vertices=vertices, - tmin=tmin, tstep=1 / float(sfreq)) - yield (arr, stc) - - -@pytest.mark.parametrize('method', ['coh', 'cohy', 'imcoh', 'plv', - ['ciplv', 'ppc', 'pli', 'pli2_unbiased', - 'wpli', 'wpli2_debiased', 'coh']]) -@pytest.mark.parametrize('mode', ['multitaper', 'fourier', 'cwt_morlet']) -def test_spectral_connectivity(method, mode): - """Test frequency-domain connectivity methods.""" - # Use a case known to have no spurious correlations (it would bad if - # tests could randomly fail): - rng = np.random.RandomState(0) - trans_bandwidth = 2. - - sfreq = 50. - n_signals = 3 - n_epochs = 8 - n_times = 256 - - tmin = 0. - tmax = (n_times - 1) / sfreq - data = rng.randn(n_signals, n_epochs * n_times) - times_data = np.linspace(tmin, tmax, n_times) - # simulate connectivity from 5Hz..15Hz - fstart, fend = 5.0, 15.0 - data[1, :] = filter_data(data[0, :], sfreq, fstart, fend, - filter_length='auto', fir_design='firwin2', - l_trans_bandwidth=trans_bandwidth, - h_trans_bandwidth=trans_bandwidth) - # add some noise, so the spectrum is not exactly zero - data[1, :] += 1e-2 * rng.randn(n_times * n_epochs) - data = data.reshape(n_signals, n_epochs, n_times) - data = np.transpose(data, [1, 0, 2]) - - # First we test some invalid parameters: - pytest.raises(ValueError, spectral_connectivity, data, method='notamethod') - pytest.raises(ValueError, spectral_connectivity, data, - mode='notamode') - - # test invalid fmin fmax settings - pytest.raises(ValueError, spectral_connectivity, data, fmin=10, - fmax=10 + 0.5 * (sfreq / float(n_times))) - pytest.raises(ValueError, spectral_connectivity, data, fmin=10, fmax=5) - pytest.raises(ValueError, spectral_connectivity, data, fmin=(0, 11), - fmax=(5, 10)) - pytest.raises(ValueError, spectral_connectivity, data, fmin=(11,), - fmax=(12, 15)) - - # define some frequencies for cwt - cwt_freqs = np.arange(3, 24.5, 1) - - if method == 'coh' and mode == 'multitaper': - # only check adaptive estimation for coh to reduce test time - check_adaptive = [False, True] - else: - check_adaptive = [False] - - if method == 'coh' and mode == 'cwt_morlet': - # so we also test using an array for num cycles - cwt_n_cycles = 7. * np.ones(len(cwt_freqs)) - else: - cwt_n_cycles = 7. - - for adaptive in check_adaptive: - - if adaptive: - mt_bandwidth = 1. - else: - mt_bandwidth = None - - con, freqs, times, n, _ = spectral_connectivity( - data, method=method, mode=mode, indices=None, sfreq=sfreq, - mt_adaptive=adaptive, mt_low_bias=True, - mt_bandwidth=mt_bandwidth, cwt_freqs=cwt_freqs, - cwt_n_cycles=cwt_n_cycles) - - assert (n == n_epochs) - assert_array_almost_equal(times_data, times) - - if mode == 'multitaper': - upper_t = 0.95 - lower_t = 0.5 - else: # mode == 'fourier' or mode == 'cwt_morlet' - # other estimates have higher variance - upper_t = 0.8 - lower_t = 0.75 - - # test the simulated signal - gidx = np.searchsorted(freqs, (fstart, fend)) - bidx = np.searchsorted(freqs, - (fstart - trans_bandwidth * 2, - fend + trans_bandwidth * 2)) - if method == 'coh': - assert np.all(con[1, 0, gidx[0]:gidx[1]] > upper_t), \ - con[1, 0, gidx[0]:gidx[1]].min() - # we see something for zero-lag - assert (np.all(con[1, 0, :bidx[0]] < lower_t)) - assert np.all(con[1, 0, bidx[1]:] < lower_t), \ - con[1, 0, bidx[1:]].max() - elif method == 'cohy': - # imaginary coh will be zero - check = np.imag(con[1, 0, gidx[0]:gidx[1]]) - assert np.all(check < lower_t), check.max() - # we see something for zero-lag - assert np.all(np.abs(con[1, 0, gidx[0]:gidx[1]]) > upper_t) - assert np.all(np.abs(con[1, 0, :bidx[0]]) < lower_t) - assert np.all(np.abs(con[1, 0, bidx[1]:]) < lower_t) - elif method == 'imcoh': - # imaginary coh will be zero - assert np.all(con[1, 0, gidx[0]:gidx[1]] < lower_t) - assert np.all(con[1, 0, :bidx[0]] < lower_t) - assert np.all(con[1, 0, bidx[1]:] < lower_t), \ - con[1, 0, bidx[1]:].max() - - # compute a subset of connections using indices and 2 jobs - indices = (np.array([2, 1]), np.array([0, 0])) - - if not isinstance(method, list): - test_methods = (method, _CohEst) - else: - test_methods = method - - stc_data = _stc_gen(data, sfreq, tmin) - con2, freqs2, times2, n2, _ = spectral_connectivity( - stc_data, method=test_methods, mode=mode, indices=indices, - sfreq=sfreq, mt_adaptive=adaptive, mt_low_bias=True, - mt_bandwidth=mt_bandwidth, tmin=tmin, tmax=tmax, - cwt_freqs=cwt_freqs, cwt_n_cycles=cwt_n_cycles) - - assert isinstance(con2, list) - assert len(con2) == len(test_methods) - - if method == 'coh': - assert_array_almost_equal(con2[0], con2[1]) - - if not isinstance(method, list): - con2 = con2[0] # only keep the first method - - # we get the same result for the probed connections - assert_array_almost_equal(freqs, freqs2) - assert_array_almost_equal(con[indices], con2) - assert (n == n2) - assert_array_almost_equal(times_data, times2) - else: - # we get the same result for the probed connections - assert (len(con) == len(con2)) - for c, c2 in zip(con, con2): - assert_array_almost_equal(freqs, freqs2) - assert_array_almost_equal(c[indices], c2) - assert (n == n2) - assert_array_almost_equal(times_data, times2) - - # compute same connections for two bands, fskip=1, and f. avg. - fmin = (5., 15.) - fmax = (15., 30.) - con3, freqs3, times3, n3, _ = spectral_connectivity( - data, method=method, mode=mode, indices=indices, - sfreq=sfreq, fmin=fmin, fmax=fmax, fskip=1, faverage=True, - mt_adaptive=adaptive, mt_low_bias=True, - mt_bandwidth=mt_bandwidth, cwt_freqs=cwt_freqs, - cwt_n_cycles=cwt_n_cycles) - - assert (isinstance(freqs3, list)) - assert (len(freqs3) == len(fmin)) - for i in range(len(freqs3)): - assert np.all((freqs3[i] >= fmin[i]) & - (freqs3[i] <= fmax[i])) - - # average con2 "manually" and we get the same result - if not isinstance(method, list): - for i in range(len(freqs3)): - freq_idx = np.searchsorted(freqs2, freqs3[i]) - con2_avg = np.mean(con2[:, freq_idx], axis=1) - assert_array_almost_equal(con2_avg, con3[:, i]) - else: - for j in range(len(con2)): - for i in range(len(freqs3)): - freq_idx = np.searchsorted(freqs2, freqs3[i]) - con2_avg = np.mean(con2[j][:, freq_idx], axis=1) - assert_array_almost_equal(con2_avg, con3[j][:, i]) - # test _get_n_epochs - full_list = list(range(10)) - out_lens = np.array([len(x) for x in _get_n_epochs(full_list, 4)]) - assert ((out_lens == np.array([4, 4, 2])).all()) - out_lens = np.array([len(x) for x in _get_n_epochs(full_list, 11)]) - assert (len(out_lens) > 0) - assert (out_lens[0] == 10) - - -@pytest.mark.parametrize('kind', ('epochs', 'ndarray', 'stc', 'combo')) -def test_epochs_tmin_tmax(kind): - """Test spectral.spectral_connectivity with epochs and arrays.""" - rng = np.random.RandomState(0) - n_epochs, n_chs, n_times, sfreq, f = 10, 2, 2000, 1000., 20. - data = rng.randn(n_epochs, n_chs, n_times) - sig = np.sin(2 * np.pi * f * np.arange(1000) / sfreq) * np.hanning(1000) - data[:, :, 500:1500] += sig - info = create_info(n_chs, sfreq, 'eeg') - if kind == 'epochs': - tmin = -1 - X = EpochsArray(data, info, tmin=tmin) - elif kind == 'stc': - tmin = -1 - X = [SourceEstimate(d, [[0], [0]], tmin, 1. / sfreq) for d in data] - elif kind == 'combo': - tmin = -1 - X = [(d[[0]], SourceEstimate(d[[1]], [[0], []], tmin, 1. / sfreq)) - for d in data] - else: - assert kind == 'ndarray' - tmin = 0 - X = data - want_times = np.arange(n_times) / sfreq + tmin - - # Parameters for computing connectivity - fmin, fmax = f - 2, f + 2 - kwargs = {'method': 'coh', 'mode': 'multitaper', 'sfreq': sfreq, - 'fmin': fmin, 'fmax': fmax, 'faverage': True, - 'mt_adaptive': False, 'n_jobs': 1} - - # Check the entire interval - conn = spectral_connectivity(X, **kwargs) - assert 0.89 < conn[0][1, 0] < 0.91 - assert_allclose(conn[2], want_times) - # Check a time interval before the sinusoid - conn = spectral_connectivity(X, tmax=tmin + 0.5, **kwargs) - assert 0 < conn[0][1, 0] < 0.15 - # Check a time during the sinusoid - conn = spectral_connectivity(X, tmin=tmin + 0.5, tmax=tmin + 1.5, **kwargs) - assert 0.93 < conn[0][1, 0] <= 0.94 - # Check a time interval after the sinusoid - conn = spectral_connectivity(X, tmin=tmin + 1.5, tmax=tmin + 1.9, **kwargs) - assert 0 < conn[0][1, 0] < 0.15 - - # Check for warning if tmin, tmax is outside of the time limits of data - with pytest.warns(RuntimeWarning, match='start time tmin'): - spectral_connectivity(X, **kwargs, tmin=tmin - 0.1) - - with pytest.warns(RuntimeWarning, match='stop time tmax'): - spectral_connectivity(X, **kwargs, tmax=tmin + 2.5) - - # make one with mismatched times - if kind != 'combo': - return - X = [(SourceEstimate(d[[0]], [[0], []], tmin - 1, 1. / sfreq), - SourceEstimate(d[[1]], [[0], []], tmin, 1. / sfreq)) for d in data] - with pytest.warns(RuntimeWarning, match='time scales of input') as w: - spectral_connectivity(X, **kwargs) - assert len(w) == 1 # just one even though there were multiple epochs diff -Nru python-mne-0.23.4+dfsg/mne/connectivity/tests/test_utils.py python-mne-1.1.0+dfsg/mne/connectivity/tests/test_utils.py --- python-mne-0.23.4+dfsg/mne/connectivity/tests/test_utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/connectivity/tests/test_utils.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,56 +0,0 @@ -import numpy as np -from numpy.testing import assert_array_equal -import pytest - -from mne.connectivity import seed_target_indices, degree - - -def test_indices(): - """Test connectivity indexing methods.""" - n_seeds_test = [1, 3, 4] - n_targets_test = [2, 3, 200] - rng = np.random.RandomState(42) - for n_seeds in n_seeds_test: - for n_targets in n_targets_test: - idx = rng.permutation(np.arange(n_seeds + n_targets)) - seeds = idx[:n_seeds] - targets = idx[n_seeds:] - indices = seed_target_indices(seeds, targets) - assert len(indices) == 2 - assert len(indices[0]) == len(indices[1]) - assert len(indices[0]) == n_seeds * n_targets - for seed in seeds: - assert np.sum(indices[0] == seed) == n_targets - for target in targets: - assert np.sum(indices[1] == target) == n_seeds - - -def test_degree(): - """Test degree function.""" - # degenerate conditions - with pytest.raises(ValueError, match='threshold'): - degree(np.eye(3), 2.) - # a simple one - corr = np.eye(10) - assert_array_equal(degree(corr), np.zeros(10)) - # more interesting - corr = np.array([[0.5, 0.7, 0.4], - [0.1, 0.3, 0.6], - [0.2, 0.8, 0.9]]) - deg = degree(corr, 1) - assert_array_equal(deg, [2, 2, 2]) - - # The values for assert_array_equal below were obtained with: - # - # >>> import bct - # >>> bct.degrees_und(bct.utils.threshold_proportional(corr, 0.25) > 0) - # - # But they can also be figured out just from the structure. - - # Asymmetric (6 usable nodes) - assert_array_equal(degree(corr, 0.33), [0, 2, 0]) - assert_array_equal(degree(corr, 0.5), [0, 2, 1]) - # Symmetric (3 usable nodes) - corr = (corr + corr.T) / 2. - assert_array_equal(degree(corr, 0.33), [0, 1, 1]) - assert_array_equal(degree(corr, 0.66), [1, 2, 1]) diff -Nru python-mne-0.23.4+dfsg/mne/connectivity/utils.py python-mne-1.1.0+dfsg/mne/connectivity/utils.py --- python-mne-0.23.4+dfsg/mne/connectivity/utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/connectivity/utils.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,93 +0,0 @@ -# Authors: Martin Luessi -# -# License: BSD (3-clause) -import numpy as np - - -def check_indices(indices): - """Check indices parameter.""" - if not isinstance(indices, tuple) or len(indices) != 2: - raise ValueError('indices must be a tuple of length 2') - - if len(indices[0]) != len(indices[1]): - raise ValueError('Index arrays indices[0] and indices[1] must ' - 'have the same length') - - return indices - - -def seed_target_indices(seeds, targets): - """Generate indices parameter for seed based connectivity analysis. - - Parameters - ---------- - seeds : array of int | int - Seed indices. - targets : array of int | int - Indices of signals for which to compute connectivity. - - Returns - ------- - indices : tuple of array - The indices parameter used for connectivity computation. - """ - # make them arrays - seeds = np.asarray((seeds,)).ravel() - targets = np.asarray((targets,)).ravel() - - n_seeds = len(seeds) - n_targets = len(targets) - - indices = (np.concatenate([np.tile(i, n_targets) for i in seeds]), - np.tile(targets, n_seeds)) - - return indices - - -def degree(connectivity, threshold_prop=0.2): - """Compute the undirected degree of a connectivity matrix. - - Parameters - ---------- - connectivity : ndarray, shape (n_nodes, n_nodes) - The connectivity matrix. - threshold_prop : float - The proportion of edges to keep in the graph before - computing the degree. The value should be between 0 - and 1. - - Returns - ------- - degree : ndarray, shape (n_nodes,) - The computed degree. - - Notes - ----- - During thresholding, the symmetry of the connectivity matrix is - auto-detected based on :func:`numpy.allclose` of it with its transpose. - """ - connectivity = np.array(connectivity) - if connectivity.ndim != 2 or \ - connectivity.shape[0] != connectivity.shape[1]: - raise ValueError('connectivity must be have shape (n_nodes, n_nodes), ' - 'got %s' % (connectivity.shape,)) - n_nodes = len(connectivity) - if np.allclose(connectivity, connectivity.T): - split = 2. - connectivity[np.tril_indices(n_nodes)] = 0 - else: - split = 1. - threshold_prop = float(threshold_prop) - if not 0 < threshold_prop <= 1: - raise ValueError('threshold must be 0 <= threshold < 1, got %s' - % (threshold_prop,)) - degree = connectivity.ravel() # no need to copy because np.array does - degree[::n_nodes + 1] = 0. - n_keep = int(round((degree.size - len(connectivity)) * - threshold_prop / split)) - degree[np.argsort(degree)[:-n_keep]] = 0 - degree.shape = connectivity.shape - if split == 2: - degree += degree.T # normally unsafe, but we know where our zeros are - degree = np.sum(degree > 0, axis=0) - return degree diff -Nru python-mne-0.23.4+dfsg/mne/coreg.py python-mne-1.1.0+dfsg/mne/coreg.py --- python-mne-0.23.4+dfsg/mne/coreg.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/coreg.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Authors: Christian Brodbeck # -# License: BSD (3-clause) +# License: BSD-3-Clause import configparser import fnmatch @@ -20,18 +20,28 @@ from .io import read_fiducials, write_fiducials, read_info from .io.constants import FIFF +from .io.meas_info import Info +from .io._digitization import _get_data_as_dict_from_dig +# keep get_mni_fiducials for backward compat (no burden to keep in this +# namespace, too) +from ._freesurfer import (_read_mri_info, get_mni_fiducials, # noqa: F401 + estimate_head_mri_t) # noqa: F401 from .label import read_label, Label from .source_space import (add_source_space_distances, read_source_spaces, # noqa: E501,F401 - write_source_spaces, _read_mri_info, - get_mni_fiducials) -from .surface import read_surface, write_surface, _normalize_vectors + write_source_spaces) +from .surface import (read_surface, write_surface, _normalize_vectors, + complete_surface_info, decimate_surface, + _DistanceQuery) from .bem import read_bem_surfaces, write_bem_surfaces from .transforms import (rotation, rotation3d, scaling, translation, Transform, _read_fs_xfm, _write_fs_xfm, invert_transform, combine_transforms, _quat_to_euler, - _fit_matched_points) + _fit_matched_points, apply_trans, + rot_to_quat, _angle_between_quats) +from .channels import make_dig_montage from .utils import (get_config, get_subjects_dir, logger, pformat, verbose, - warn, has_nibabel) + warn, has_nibabel, fill_doc, _validate_type, + _check_subject, _check_option) from .viz._3d import _fiducial_coords # some path templates @@ -43,16 +53,31 @@ surf_dirname = os.path.join(subject_dirname, 'surf') bem_fname = os.path.join(bem_dirname, "{subject}-{name}.fif") head_bem_fname = pformat(bem_fname, name='head') +head_sparse_fname = pformat(bem_fname, name='head-sparse') fid_fname = pformat(bem_fname, name='fiducials') fid_fname_general = os.path.join(bem_dirname, "{head}-fiducials.fif") src_fname = os.path.join(bem_dirname, '{subject}-{spacing}-src.fif') _head_fnames = (os.path.join(bem_dirname, 'outer_skin.surf'), + head_sparse_fname, head_bem_fname) _high_res_head_fnames = (os.path.join(bem_dirname, '{subject}-head-dense.fif'), os.path.join(surf_dirname, 'lh.seghead'), os.path.join(surf_dirname, 'lh.smseghead')) +def _map_fid_name_to_idx(name: str) -> int: + """Map a fiducial name to its index in the DigMontage.""" + name = name.lower() + + if name == 'lpa': + return 0 + elif name == 'nasion': + return 1 + else: + assert name == 'rpa' + return 2 + + def _make_writable(fname): """Make a file writable.""" os.chmod(fname, stat.S_IMODE(os.lstat(fname)[stat.ST_MODE]) | 128) # write @@ -77,13 +102,13 @@ return path +@fill_doc def coregister_fiducials(info, fiducials, tol=0.01): """Create a head-MRI transform by aligning 3 fiducial points. Parameters ---------- - info : Info - Measurement info object with fiducials in head coordinate space. + %(info_not_none)s fiducials : str | list of dict Fiducials in MRI coordinate space (either path to a ``*-fiducials.fif`` file or list of fiducials as returned by :func:`read_fiducials`. @@ -92,6 +117,9 @@ ------- trans : Transform The device-MRI transform. + + .. note:: The :class:`mne.Info` object fiducials must be in the + head coordinate space. """ if isinstance(info, str): info = read_info(info) @@ -226,46 +254,49 @@ zax = np.arange(zmin, zmax, res) # find voxels containing one or more point - H, _ = np.histogramdd(pts, bins=(xax, yax, zax), normed=False) - - # for each voxel, select one point - X, Y, Z = pts.T - out = np.empty((np.sum(H > 0), 3)) - for i, (xbin, ybin, zbin) in enumerate(zip(*np.nonzero(H))): - x = xax[xbin] - y = yax[ybin] - z = zax[zbin] - xi = np.logical_and(X >= x, X < x + res) - yi = np.logical_and(Y >= y, Y < y + res) - zi = np.logical_and(Z >= z, Z < z + res) - idx = np.logical_and(zi, np.logical_and(yi, xi)) - ipts = pts[idx] - - mid = np.array([x, y, z]) + res / 2. - dist = cdist(ipts, [mid]) - i_min = np.argmin(dist) - ipt = ipts[i_min] - out[i] = ipt + H, _ = np.histogramdd(pts, bins=(xax, yax, zax), density=False) + xbins, ybins, zbins = np.nonzero(H) + x = xax[xbins] + y = yax[ybins] + z = zax[zbins] + mids = np.c_[x, y, z] + res / 2. + + # each point belongs to at most one voxel center, so figure those out + # (cKDTree faster than BallTree for these small problems) + tree = _DistanceQuery(mids, method='cKDTree') + _, mid_idx = tree.query(pts) + + # then figure out which to actually use based on proximity + # (take advantage of sorting the mid_idx to get our mapping of + # pts to nearest voxel midpoint) + sort_idx = np.argsort(mid_idx) + bounds = np.cumsum( + np.concatenate([[0], np.bincount(mid_idx, minlength=len(mids))])) + assert len(bounds) == len(mids) + 1 + out = list() + for mi, mid in enumerate(mids): + # Now we do this: + # + # use_pts = pts[mid_idx == mi] + # + # But it's faster for many points than making a big boolean indexer + # over and over (esp. since each point can only belong to a single + # voxel). + use_pts = pts[sort_idx[bounds[mi]:bounds[mi + 1]]] + if not len(use_pts): + out.append([np.inf] * 3) + else: + out.append( + use_pts[np.argmin(cdist(use_pts, mid[np.newaxis])[:, 0])]) + out = np.array(out, float).reshape(-1, 3) + out = out[np.abs(out - mids).max(axis=1) < res / 2.] + # """ return out def _trans_from_params(param_info, params): - """Convert transformation parameters into a transformation matrix. - - Parameters - ---------- - param_info : tuple, len = 3 - Tuple describing the parameters in x (do_translate, do_rotate, - do_scale). - params : tuple - The transformation parameters. - - Returns - ------- - trans : array, shape = (4, 4) - Transformation matrix. - """ + """Convert transformation parameters into a transformation matrix.""" do_rotate, do_translate, do_scale = param_info i = 0 trans = [] @@ -359,6 +390,8 @@ if param_info in ((True, True, 0), (True, True, 1)) and _ALLOW_ANALITICAL: src_pts = np.asarray(src_pts, float) tgt_pts = np.asarray(tgt_pts, float) + if weights is not None: + weights = np.asarray(weights, float) x, s = _fit_matched_points( src_pts, tgt_pts, weights, bool(param_info[2])) x[:3] = _quat_to_euler(x[:3]) @@ -798,7 +831,7 @@ @verbose def scale_bem(subject_to, bem_name, subject_from=None, scale=None, - subjects_dir=None, verbose=None): + subjects_dir=None, *, on_defects='raise', verbose=None): """Scale a bem file. Parameters @@ -817,6 +850,9 @@ otherwise it is read from subject_to's config file. subjects_dir : None | str Override the SUBJECTS_DIR environment variable. + %(on_defects)s + + .. versionadded:: 1.0 %(verbose)s """ subjects_dir, subject_from, scale, uniform = \ @@ -830,7 +866,7 @@ if os.path.exists(dst): raise IOError("File already exists: %s" % dst) - surfs = read_bem_surfaces(src) + surfs = read_bem_surfaces(src, on_defects=on_defects) for surf in surfs: surf['rr'] *= scale if not uniform: @@ -898,7 +934,7 @@ @verbose def scale_mri(subject_from, subject_to, scale, overwrite=False, subjects_dir=None, skip_fiducials=False, labels=True, - annot=False, verbose=None): + annot=False, *, on_defects='raise', verbose=None): """Create a scaled copy of an MRI subject. Parameters @@ -920,12 +956,22 @@ Also scale all labels (default True). annot : bool Copy ``*.annot`` files to the new location (default False). + %(on_defects)s + + .. versionadded:: 1.0 %(verbose)s See Also -------- + scale_bem : Add a scaled BEM to a scaled MRI. scale_labels : Add labels to a scaled MRI. scale_source_space : Add a source space to a scaled MRI. + + Notes + ----- + This function will automatically call :func:`scale_bem`, + :func:`scale_labels`, and :func:`scale_source_space` based on expected + filename patterns in the subject directory. """ subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) paths = _find_mri_paths(subject_from, skip_fiducials, subjects_dir) @@ -966,7 +1012,7 @@ logger.debug('BEM files [in m]') for bem_name in paths['bem']: scale_bem(subject_to, bem_name, subject_from, scale, subjects_dir, - verbose=False) + on_defects=on_defects, verbose=False) logger.debug('fiducials [in m]') for fname in paths['fid']: @@ -976,7 +1022,7 @@ for pt in pts: pt['r'] = pt['r'] * scale dest = fname.format(subject=subject_to, subjects_dir=subjects_dir) - write_fiducials(dest, pts, cframe, verbose=False) + write_fiducials(dest, pts, cframe, overwrite=True, verbose=False) logger.debug('MRIs [nibabel]') os.mkdir(mri_dirname.format(subjects_dir=subjects_dir, @@ -1026,7 +1072,7 @@ @verbose def scale_source_space(subject_to, src_name, subject_from=None, scale=None, - subjects_dir=None, n_jobs=1, verbose=None): + subjects_dir=None, n_jobs=None, verbose=None): """Scale a source space for an mri created with scale_mri(). Parameters @@ -1219,3 +1265,798 @@ F_mri_ras, 'ras', 'ras'), F_ras_mni, 'ras', 'mni_tal') _write_fs_xfm(fname_to, T_ras_mni['trans'], kind) + + +def _read_surface(filename, *, on_defects): + bem = dict() + if filename is not None and op.exists(filename): + if filename.endswith('.fif'): + bem = read_bem_surfaces( + filename, on_defects=on_defects, verbose=False + )[0] + else: + try: + bem = read_surface(filename, return_dict=True)[2] + bem['rr'] *= 1e-3 + complete_surface_info(bem, copy=False) + except Exception: + raise ValueError( + "Error loading surface from %s (see " + "Terminal for details)." % filename) + return bem + + +@fill_doc +class Coregistration(object): + """Class for MRI<->head coregistration. + + Parameters + ---------- + info : instance of Info | None + The measurement info. + %(subject)s + %(subjects_dir)s + %(fiducials)s + %(on_defects)s + + .. versionadded:: 1.0 + + Attributes + ---------- + fiducials : instance of DigMontage + A montage containing the MRI fiducials. + trans : instance of Transform + MRI<->Head coordinate transformation. + + See Also + -------- + mne.scale_mri + + Notes + ----- + Internal computation quantities parameters are in the following units: + + - rotation are in radians + - translation are in m + - scale are in scale proportion + + If using a scale mode, the :func:`~mne.scale_mri` should be used + to create a surrogate MRI subject with the proper scale factors. + """ + + def __init__(self, info, subject, subjects_dir=None, fiducials='auto', *, + on_defects='raise'): + _validate_type(info, (Info, None), 'info') + self._info = info + self._subject = _check_subject(subject, subject) + self._subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + self._scale_mode = None + self._on_defects = on_defects + + self._rot_trans = None + self._default_parameters = \ + np.array([0., 0., 0., 0., 0., 0., 1., 1., 1.]) + + self._rotation = self._default_parameters[:3] + self._translation = self._default_parameters[3:6] + self._scale = self._default_parameters[6:9] + self._icp_iterations = 20 + self._icp_angle = 0.2 + self._icp_distance = 0.2 + self._icp_scale = 0.2 + self._icp_fid_matches = ('nearest', 'matched') + self._icp_fid_match = self._icp_fid_matches[0] + self._lpa_weight = 1. + self._nasion_weight = 10. + self._rpa_weight = 1. + self._hsp_weight = 1. + self._eeg_weight = 1. + self._hpi_weight = 1. + self._extra_points_filter = None + + self._setup_digs() + self._setup_bem() + + self._fid_filename = None + self._setup_fiducials(fiducials) + self.reset() + + def _setup_digs(self): + if self._info is None: + self._dig_dict = dict( + hpi=np.zeros((1, 3)), + dig_ch_pos_location=np.zeros((1, 3)), + hsp=np.zeros((1, 3)), + rpa=np.zeros((1, 3)), + nasion=np.zeros((1, 3)), + lpa=np.zeros((1, 3)), + ) + else: + self._dig_dict = _get_data_as_dict_from_dig( + dig=self._info['dig'], + exclude_ref_channel=False + ) + # adjustments: + # set weights to 0 for None input + # convert fids to float arrays + for k, w_atr in zip(['nasion', 'lpa', 'rpa', 'hsp', 'hpi'], + ['_nasion_weight', '_lpa_weight', + '_rpa_weight', '_hsp_weight', '_hpi_weight']): + if self._dig_dict[k] is None: + self._dig_dict[k] = np.zeros((0, 3)) + setattr(self, w_atr, 0) + elif k in ['rpa', 'nasion', 'lpa']: + self._dig_dict[k] = np.array([self._dig_dict[k]], float) + + def _setup_bem(self): + # find high-res head model (if possible) + high_res_path = _find_head_bem(self._subject, self._subjects_dir, + high_res=True) + low_res_path = _find_head_bem(self._subject, self._subjects_dir, + high_res=False) + if high_res_path is None and low_res_path is None: + raise RuntimeError("No standard head model was " + f"found for subject {self._subject}") + if high_res_path is not None: + self._bem_high_res = _read_surface( + high_res_path, on_defects=self._on_defects + ) + logger.info(f'Using high resolution head model in {high_res_path}') + else: + self._bem_high_res = _read_surface( + low_res_path, on_defects=self._on_defects + ) + logger.info(f'Using low resolution head model in {low_res_path}') + if low_res_path is None: + # This should be very rare! + warn('No low-resolution head found, decimating high resolution ' + 'mesh (%d vertices): %s' % (len(self._bem_high_res['rr']), + high_res_path,)) + # Create one from the high res one, which we know we have + rr, tris = decimate_surface(self._bem_high_res['rr'], + self._bem_high_res['tris'], + n_triangles=5120) + # directly set the attributes of bem_low_res + self._bem_low_res = complete_surface_info( + dict(rr=rr, tris=tris), copy=False, verbose=False) + else: + self._bem_low_res = _read_surface( + low_res_path, on_defects=self._on_defects + ) + + def _setup_fiducials(self, fids): + _validate_type(fids, (str, dict, list)) + # find fiducials file + fid_accurate = None + if fids == 'auto': + fid_files = _find_fiducials_files(self._subject, + self._subjects_dir) + if len(fid_files) > 0: + # Read fiducials from disk + fid_filename = fid_files[0].format( + subjects_dir=self._subjects_dir, subject=self._subject) + logger.info(f'Using fiducials from: {fid_filename}.') + fids, _ = read_fiducials(fid_filename) + fid_accurate = True + self._fid_filename = fid_filename + else: + fids = 'estimated' + + if fids == 'estimated': + logger.info('Estimating fiducials from fsaverage.') + fid_accurate = False + fids = get_mni_fiducials(self._subject, self._subjects_dir) + + fid_accurate = True if fid_accurate is None else fid_accurate + if isinstance(fids, list): + fid_coords = _fiducial_coords(fids) + else: + assert isinstance(fids, dict) + fid_coords = np.array([fids['lpa'], fids['nasion'], fids['rpa']], + dtype=float) + + self._fid_points = fid_coords + self._fid_accurate = fid_accurate + + # does not seem to happen by itself ... so hard code it: + self._reset_fiducials() + + def _reset_fiducials(self): + dig_montage = make_dig_montage( + lpa=self._fid_points[0], + nasion=self._fid_points[1], + rpa=self._fid_points[2], + coord_frame='mri' + ) + self.fiducials = dig_montage + + def _update_params(self, rot=None, tra=None, sca=None, + force_update=False): + if force_update and tra is None: + tra = self._translation + rot_changed = False + if rot is not None: + rot_changed = True + self._last_rotation = self._rotation.copy() + self._rotation = rot + tra_changed = False + if rot_changed or tra is not None: + if tra is None: + tra = self._translation + tra_changed = True + self._last_translation = self._translation.copy() + self._translation = tra + self._head_mri_t = rotation(*self._rotation).T + self._head_mri_t[:3, 3] = \ + -np.dot(self._head_mri_t[:3, :3], tra) + self._transformed_dig_hpi = \ + apply_trans(self._head_mri_t, self._dig_dict['hpi']) + self._transformed_dig_eeg = \ + apply_trans( + self._head_mri_t, self._dig_dict['dig_ch_pos_location']) + self._transformed_dig_extra = \ + apply_trans(self._head_mri_t, + self._filtered_extra_points) + self._transformed_orig_dig_extra = \ + apply_trans(self._head_mri_t, self._dig_dict['hsp']) + self._mri_head_t = rotation(*self._rotation) + self._mri_head_t[:3, 3] = np.array(tra) + if tra_changed or sca is not None: + if sca is None: + sca = self._scale + self._last_scale = self._scale.copy() + self._scale = sca + self._mri_trans = np.eye(4) + self._mri_trans[:, :3] *= sca + self._transformed_high_res_mri_points = \ + apply_trans(self._mri_trans, + self._processed_high_res_mri_points) + self._update_nearest_calc() + + if tra_changed: + self._nearest_transformed_high_res_mri_idx_orig_hsp = \ + self._nearest_calc.query(self._transformed_orig_dig_extra)[1] + self._nearest_transformed_high_res_mri_idx_hpi = \ + self._nearest_calc.query(self._transformed_dig_hpi)[1] + self._nearest_transformed_high_res_mri_idx_eeg = \ + self._nearest_calc.query(self._transformed_dig_eeg)[1] + self._nearest_transformed_high_res_mri_idx_rpa = \ + self._nearest_calc.query( + apply_trans(self._head_mri_t, self._dig_dict['rpa']))[1] + self._nearest_transformed_high_res_mri_idx_nasion = \ + self._nearest_calc.query( + apply_trans(self._head_mri_t, self._dig_dict['nasion']))[1] + self._nearest_transformed_high_res_mri_idx_lpa = \ + self._nearest_calc.query( + apply_trans(self._head_mri_t, self._dig_dict['lpa']))[1] + + def set_scale_mode(self, scale_mode): + """Select how to fit the scale parameters. + + Parameters + ---------- + scale_mode : None | str + The scale mode can be 'uniform', '3-axis' or disabled. + Defaults to None. + + * 'uniform': 1 scale factor is recovered. + * '3-axis': 3 scale factors are recovered. + * None: do not scale the MRI. + + Returns + ------- + self : Coregistration + The modified Coregistration object. + """ + self._scale_mode = scale_mode + return self + + def set_grow_hair(self, value): + """Compensate for hair on the digitizer head shape. + + Parameters + ---------- + value : float + Move the back of the MRI head outwards by ``value`` (mm). + + Returns + ------- + self : Coregistration + The modified Coregistration object. + """ + self._grow_hair = value + self._update_params(force_update=True) + return self + + def set_rotation(self, rot): + """Set the rotation parameter. + + Parameters + ---------- + rot : array, shape (3,) + The rotation parameter (in radians). + + Returns + ------- + self : Coregistration + The modified Coregistration object. + """ + self._update_params(rot=np.array(rot)) + return self + + def set_translation(self, tra): + """Set the translation parameter. + + Parameters + ---------- + tra : array, shape (3,) + The translation parameter (in m.). + + Returns + ------- + self : Coregistration + The modified Coregistration object. + """ + self._update_params(tra=np.array(tra)) + return self + + def set_scale(self, sca): + """Set the scale parameter. + + Parameters + ---------- + sca : array, shape (3,) + The scale parameter. + + Returns + ------- + self : Coregistration + The modified Coregistration object. + """ + self._update_params(sca=np.array(sca)) + return self + + def _update_nearest_calc(self): + self._nearest_calc = _DistanceQuery( + self._processed_high_res_mri_points * self._scale) + + @property + def _filtered_extra_points(self): + if self._extra_points_filter is None: + return self._dig_dict['hsp'] + else: + return self._dig_dict['hsp'][self._extra_points_filter] + + @property + def _parameters(self): + return np.concatenate((self._rotation, self._translation, self._scale)) + + @property + def _last_parameters(self): + return np.concatenate((self._last_rotation, + self._last_translation, self._last_scale)) + + @property + def _changes(self): + move = np.linalg.norm(self._last_translation - self._translation) * 1e3 + angle = np.rad2deg(_angle_between_quats( + rot_to_quat(rotation(*self._rotation)[:3, :3]), + rot_to_quat(rotation(*self._last_rotation)[:3, :3]))) + percs = 100 * (self._scale - self._last_scale) / self._last_scale + return move, angle, percs + + @property + def _nearest_transformed_high_res_mri_idx_hsp(self): + return self._nearest_calc.query( + apply_trans(self._head_mri_t, self._filtered_extra_points))[1] + + @property + def _has_hsp_data(self): + return (self._has_mri_data and + len(self._nearest_transformed_high_res_mri_idx_hsp) > 0) + + @property + def _has_hpi_data(self): + return (self._has_mri_data and + len(self._nearest_transformed_high_res_mri_idx_hpi) > 0) + + @property + def _has_eeg_data(self): + return (self._has_mri_data and + len(self._nearest_transformed_high_res_mri_idx_eeg) > 0) + + @property + def _has_lpa_data(self): + mri_point = self.fiducials.dig[_map_fid_name_to_idx('lpa')] + assert mri_point['ident'] == FIFF.FIFFV_POINT_LPA + has_mri_data = np.any(mri_point['r']) + has_head_data = np.any(self._dig_dict['lpa']) + return has_mri_data and has_head_data + + @property + def _has_nasion_data(self): + mri_point = self.fiducials.dig[_map_fid_name_to_idx('nasion')] + assert mri_point['ident'] == FIFF.FIFFV_POINT_NASION + has_mri_data = np.any(mri_point['r']) + has_head_data = np.any(self._dig_dict['nasion']) + return has_mri_data and has_head_data + + @property + def _has_rpa_data(self): + mri_point = self.fiducials.dig[_map_fid_name_to_idx('rpa')] + assert mri_point['ident'] == FIFF.FIFFV_POINT_RPA + has_mri_data = np.any(mri_point['r']) + has_head_data = np.any(self._dig_dict['rpa']) + return has_mri_data and has_head_data + + @property + def _processed_high_res_mri_points(self): + return self._get_processed_mri_points('high') + + @property + def _processed_low_res_mri_points(self): + return self._get_processed_mri_points('low') + + def _get_processed_mri_points(self, res): + bem = self._bem_low_res if res == 'low' else self._bem_high_res + points = bem['rr'].copy() + if self._grow_hair: + assert len(bem['nn']) # should be guaranteed by _read_surface + scaled_hair_dist = (1e-3 * self._grow_hair / + np.array(self._scale)) + hair = points[:, 2] > points[:, 1] + points[hair] += bem['nn'][hair] * scaled_hair_dist + return points + + @property + def _has_mri_data(self): + return len(self._transformed_high_res_mri_points) > 0 + + @property + def _has_dig_data(self): + return (self._has_mri_data and + len(self._nearest_transformed_high_res_mri_idx_hsp) > 0) + + @property + def _orig_hsp_point_distance(self): + mri_points = self._transformed_high_res_mri_points[ + self._nearest_transformed_high_res_mri_idx_orig_hsp] + hsp_points = self._transformed_orig_dig_extra + return np.linalg.norm(mri_points - hsp_points, axis=-1) + + def _log_dig_mri_distance(self, prefix): + errs_nearest = self.compute_dig_mri_distances() + logger.info(f'{prefix} median distance: ' + f'{np.median(errs_nearest * 1000):6.2f} mm') + + @property + def scale(self): + """Get the current scale factor. + + Returns + ------- + scale : ndarray, shape (3,) + The scale factors. + """ + return self._scale.copy() + + @verbose + def fit_fiducials(self, lpa_weight=1., nasion_weight=10., rpa_weight=1., + verbose=None): + """Find rotation and translation to fit all 3 fiducials. + + Parameters + ---------- + lpa_weight : float + Relative weight for LPA. The default value is 1. + nasion_weight : float + Relative weight for nasion. The default value is 10. + rpa_weight : float + Relative weight for RPA. The default value is 1. + %(verbose)s + + Returns + ------- + self : Coregistration + The modified Coregistration object. + """ + logger.info('Aligning using fiducials') + self._log_dig_mri_distance('Start') + n_scale_params = self._n_scale_params + if n_scale_params == 3: + # enfore 1 even for 3-axis here (3 points is not enough) + logger.info("Enforcing 1 scaling parameter for fit " + "with fiducials.") + n_scale_params = 1 + self._lpa_weight = lpa_weight + self._nasion_weight = nasion_weight + self._rpa_weight = rpa_weight + + head_pts = np.vstack((self._dig_dict['lpa'], + self._dig_dict['nasion'], + self._dig_dict['rpa'])) + mri_pts = np.vstack( + (self.fiducials.dig[0]['r'], # LPA + self.fiducials.dig[1]['r'], # Nasion + self.fiducials.dig[2]['r']) # RPA + ) + weights = [lpa_weight, nasion_weight, rpa_weight] + + if n_scale_params == 0: + mri_pts *= self._scale # not done in fit_matched_points + x0 = self._parameters + x0 = x0[:6 + n_scale_params] + est = fit_matched_points(mri_pts, head_pts, x0=x0, out='params', + scale=n_scale_params, weights=weights) + if n_scale_params == 0: + self._update_params(rot=est[:3], tra=est[3:6]) + else: + assert est.size == 7 + est = np.concatenate([est, [est[-1]] * 2]) + assert est.size == 9 + self._update_params(rot=est[:3], tra=est[3:6], sca=est[6:9]) + self._log_dig_mri_distance('End ') + return self + + def _setup_icp(self, n_scale_params): + head_pts = list() + mri_pts = list() + weights = list() + if self._has_dig_data and self._hsp_weight > 0: # should be true + head_pts.append(self._filtered_extra_points) + mri_pts.append(self._processed_high_res_mri_points[ + self._nearest_transformed_high_res_mri_idx_hsp]) + weights.append(np.full(len(head_pts[-1]), self._hsp_weight)) + for key in ('lpa', 'nasion', 'rpa'): + if getattr(self, f'_has_{key}_data'): + head_pts.append(self._dig_dict[key]) + if self._icp_fid_match == 'matched': + idx = _map_fid_name_to_idx(name=key) + p = self.fiducials.dig[idx]['r'].reshape(1, -1) + mri_pts.append(p) + else: + assert self._icp_fid_match == 'nearest' + mri_pts.append(self._processed_high_res_mri_points[ + getattr( + self, + '_nearest_transformed_high_res_mri_idx_%s' + % (key,))]) + weights.append(np.full(len(mri_pts[-1]), + getattr(self, '_%s_weight' % key))) + if self._has_eeg_data and self._eeg_weight > 0: + head_pts.append(self._dig_dict['dig_ch_pos_location']) + mri_pts.append(self._processed_high_res_mri_points[ + self._nearest_transformed_high_res_mri_idx_eeg]) + weights.append(np.full(len(mri_pts[-1]), self._eeg_weight)) + if self._has_hpi_data and self._hpi_weight > 0: + head_pts.append(self._dig_dict['hpi']) + mri_pts.append(self._processed_high_res_mri_points[ + self._nearest_transformed_high_res_mri_idx_hpi]) + weights.append(np.full(len(mri_pts[-1]), self._hpi_weight)) + head_pts = np.concatenate(head_pts) + mri_pts = np.concatenate(mri_pts) + weights = np.concatenate(weights) + if n_scale_params == 0: + mri_pts *= self._scale # not done in fit_matched_points + return head_pts, mri_pts, weights + + def set_fid_match(self, match): + """Set the strategy for fitting anatomical landmark (fiducial) points. + + Parameters + ---------- + match : 'nearest' | 'matched' + Alignment strategy; ``'nearest'`` aligns anatomical landmarks to + any point on the head surface; ``'matched'`` aligns to the fiducial + points in the MRI. + + Returns + ------- + self : Coregistration + The modified Coregistration object. + """ + _check_option('match', match, self._icp_fid_matches) + self._icp_fid_match = match + return self + + @verbose + def fit_icp(self, n_iterations=20, lpa_weight=1., nasion_weight=10., + rpa_weight=1., hsp_weight=1., eeg_weight=1., hpi_weight=1., + callback=None, verbose=None): + """Find MRI scaling, translation, and rotation to match HSP. + + Parameters + ---------- + n_iterations : int + Maximum number of iterations. + lpa_weight : float + Relative weight for LPA. The default value is 1. + nasion_weight : float + Relative weight for nasion. The default value is 10. + rpa_weight : float + Relative weight for RPA. The default value is 1. + hsp_weight : float + Relative weight for HSP. The default value is 1. + eeg_weight : float + Relative weight for EEG. The default value is 1. + hpi_weight : float + Relative weight for HPI. The default value is 1. + callback : callable | None + A function to call on each iteration. Useful for status message + updates. It will be passed the keyword arguments ``iteration`` + and ``n_iterations``. + %(verbose)s + + Returns + ------- + self : Coregistration + The modified Coregistration object. + """ + logger.info('Aligning using ICP') + self._log_dig_mri_distance('Start ') + n_scale_params = self._n_scale_params + self._lpa_weight = lpa_weight + self._nasion_weight = nasion_weight + self._rpa_weight = rpa_weight + self._hsp_weight = hsp_weight + self._eeg_weight = eeg_weight + self._hsp_weight = hpi_weight + + # Initial guess (current state) + est = self._parameters + est = est[:[6, 7, None, 9][n_scale_params]] + + # Do the fits, assigning and evaluating at each step + for iteration in range(n_iterations): + head_pts, mri_pts, weights = self._setup_icp(n_scale_params) + est = fit_matched_points(mri_pts, head_pts, scale=n_scale_params, + x0=est, out='params', weights=weights) + if n_scale_params == 0: + self._update_params(rot=est[:3], tra=est[3:6]) + elif n_scale_params == 1: + est = np.array(list(est) + [est[-1]] * 2) + self._update_params(rot=est[:3], tra=est[3:6], sca=est[6:9]) + else: + self._update_params(rot=est[:3], tra=est[3:6], sca=est[6:9]) + angle, move, scale = self._changes + self._log_dig_mri_distance(f' ICP {iteration + 1:2d} ') + if callback is not None: + callback(iteration, n_iterations) + if angle <= self._icp_angle and move <= self._icp_distance and \ + all(scale <= self._icp_scale): + break + self._log_dig_mri_distance('End ') + return self + + @property + def _n_scale_params(self): + if self._scale_mode is None: + n_scale_params = 0 + elif self._scale_mode == 'uniform': + n_scale_params = 1 + else: + n_scale_params = 3 + return n_scale_params + + def omit_head_shape_points(self, distance): + """Exclude head shape points that are far away from the MRI head. + + Parameters + ---------- + distance : float + Exclude all points that are further away from the MRI head than + this distance (in m.). A value of distance <= 0 excludes nothing. + + Returns + ------- + self : Coregistration + The modified Coregistration object. + """ + distance = float(distance) + if distance <= 0: + return + + # find the new filter + mask = self._orig_hsp_point_distance <= distance + n_excluded = np.sum(~mask) + logger.info("Coregistration: Excluding %i head shape points with " + "distance >= %.3f m.", n_excluded, distance) + # set the filter + self._extra_points_filter = mask + self._update_params(force_update=True) + return self + + def compute_dig_mri_distances(self): + """Compute distance between head shape points and MRI skin surface. + + Returns + ------- + dist : array, shape (n_points,) + The distance of the head shape points to the MRI skin surface. + + See Also + -------- + mne.dig_mri_distances + """ + # we don't use `dig_mri_distances` here because it should be much + # faster to use our already-determined nearest points + hsp_points, mri_points, _ = self._setup_icp(0) + hsp_points = apply_trans(self._head_mri_t, hsp_points) + return np.linalg.norm(mri_points - hsp_points, axis=-1) + + @property + def trans(self): + """The head->mri :class:`~mne.transforms.Transform`.""" + return Transform('head', 'mri', self._head_mri_t) + + def reset(self): + """Reset all the parameters affecting the coregistration. + + Returns + ------- + self : Coregistration + The modified Coregistration object. + """ + self._grow_hair = 0. + self.set_rotation(self._default_parameters[:3]) + self.set_translation(self._default_parameters[3:6]) + self.set_scale(self._default_parameters[6:9]) + self._extra_points_filter = None + self._update_nearest_calc() + return self + + def _get_fiducials_distance(self): + distance = dict() + for key in ('lpa', 'nasion', 'rpa'): + idx = _map_fid_name_to_idx(name=key) + fid = self.fiducials.dig[idx]['r'].reshape(1, -1) + + transformed_mri = apply_trans(self._mri_trans, fid) + transformed_hsp = apply_trans( + self._head_mri_t, self._dig_dict[key]) + distance[key] = np.linalg.norm( + np.ravel(transformed_mri - transformed_hsp)) + return np.array(list(distance.values())) * 1e3 + + def _get_fiducials_distance_str(self): + dists = self._get_fiducials_distance() + return f"Fiducials: {dists[0]:.1f}, {dists[1]:.1f}, {dists[2]:.1f} mm" + + def _get_point_distance(self): + mri_points = list() + hsp_points = list() + if self._hsp_weight > 0 and self._has_hsp_data: + mri_points.append(self._transformed_high_res_mri_points[ + self._nearest_transformed_high_res_mri_idx_hsp]) + hsp_points.append(self._transformed_dig_extra) + assert len(mri_points[-1]) == len(hsp_points[-1]) + if self._eeg_weight > 0 and self._has_eeg_data: + mri_points.append(self._transformed_high_res_mri_points[ + self._nearest_transformed_high_res_mri_idx_eeg]) + hsp_points.append(self._transformed_dig_eeg) + assert len(mri_points[-1]) == len(hsp_points[-1]) + if self._hpi_weight > 0 and self._has_hpi_data: + mri_points.append(self._transformed_high_res_mri_points[ + self._nearest_transformed_high_res_mri_idx_hpi]) + hsp_points.append(self._transformed_dig_hpi) + assert len(mri_points[-1]) == len(hsp_points[-1]) + if all(len(h) == 0 for h in hsp_points): + return None + mri_points = np.concatenate(mri_points) + hsp_points = np.concatenate(hsp_points) + return np.linalg.norm(mri_points - hsp_points, axis=-1) + + def _get_point_distance_str(self): + point_distance = self._get_point_distance() + if point_distance is None: + return "" + dists = 1e3 * point_distance + av_dist = np.mean(dists) + std_dist = np.std(dists) + kinds = [kind for kind, check in + (('HSP', self._hsp_weight > 0 and self._has_hsp_data), + ('EEG', self._eeg_weight > 0 and self._has_eeg_data), + ('HPI', self._hpi_weight > 0 and self._has_hpi_data)) + if check] + kinds = '+'.join(kinds) + return f"{len(dists)} {kinds}: {av_dist:.1f} ± {std_dist:.1f} mm" diff -Nru python-mne-0.23.4+dfsg/mne/cov.py python-mne-1.1.0+dfsg/mne/cov.py --- python-mne-0.23.4+dfsg/mne/cov.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/cov.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,18 +2,17 @@ # Matti Hämäläinen # Denis A. Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause from copy import deepcopy -from distutils.version import LooseVersion import itertools as itt from math import log -import os import numpy as np -from .defaults import _EXTRAPOLATE_DEFAULT, _BORDER_DEFAULT, DEFAULTS -from .io.write import start_file, end_file +from .defaults import (_INTERPOLATION_DEFAULT, _EXTRAPOLATE_DEFAULT, + _BORDER_DEFAULT, DEFAULTS) +from .io.write import start_and_end_file from .io.proj import (make_projector, _proj_equal, activate_proj, _check_projs, _needs_eeg_average_ref_proj, _has_eeg_average_ref_proj, _read_proj, _write_proj) @@ -38,7 +37,7 @@ warn, copy_function_doc_to_method_doc, _pl, _undo_scaling_cov, _scaled_array, _validate_type, _check_option, eigh, fill_doc, _on_missing, - _check_on_missing) + _check_on_missing, _check_fname) from . import viz from .fixes import (BaseEstimator, EmpiricalCovariance, _logdet, @@ -93,7 +92,7 @@ The method used to compute the covariance. loglik : float The log likelihood. - %(verbose_meth)s + %(verbose)s Attributes ---------- @@ -114,8 +113,9 @@ read_cov """ + @verbose def __init__(self, data, names, bads, projs, nfree, eig=None, eigvec=None, - method=None, loglik=None, verbose=None): + method=None, loglik=None, *, verbose=None): """Init of covariance.""" diag = (data.ndim == 1) projs = _check_projs(projs) @@ -126,7 +126,6 @@ self['method'] = method if loglik is not None: self['loglik'] = loglik - self.verbose = verbose @property def data(self): @@ -143,27 +142,24 @@ """Number of degrees of freedom.""" return self['nfree'] - def save(self, fname): + @verbose + def save(self, fname, *, overwrite=False, verbose=None): """Save covariance matrix in a FIF file. Parameters ---------- fname : str Output filename. + %(overwrite)s + + .. versionadded:: 1.0 + %(verbose)s """ check_fname(fname, 'covariance', ('-cov.fif', '-cov.fif.gz', '_cov.fif', '_cov.fif.gz')) - - fid = start_file(fname) - - try: + fname = _check_fname(fname=fname, overwrite=overwrite) + with start_and_end_file(fname) as fid: _write_cov(fid, self) - except Exception: - fid.close() - os.remove(fname) - raise - - end_file(fid) def copy(self): """Copy the Covariance object. @@ -262,39 +258,37 @@ size=1, cbar_fmt="%3.1f", proj=False, show=True, show_names=False, title=None, mask=None, mask_params=None, outlines='head', - contours=6, image_interp='bilinear', + contours=6, image_interp=_INTERPOLATION_DEFAULT, axes=None, extrapolate=_EXTRAPOLATE_DEFAULT, sphere=None, - border=_BORDER_DEFAULT, - noise_cov=None, verbose=None): + border=_BORDER_DEFAULT, noise_cov=None, verbose=None): """Plot a topomap of the covariance diagonal. Parameters ---------- - info : instance of Info - The measurement information. - %(topomap_ch_type)s - %(topomap_vmin_vmax)s - %(topomap_cmap)s - %(topomap_sensors)s - %(topomap_colorbar)s - %(topomap_scalings)s - %(topomap_units)s - %(topomap_res)s - %(topomap_size)s - %(topomap_cbar_fmt)s - %(plot_proj)s + %(info_not_none)s + %(ch_type_topomap)s + %(vmin_vmax_topomap)s + %(cmap_topomap)s + %(sensors_topomap)s + %(colorbar_topomap)s + %(scalings_topomap)s + %(units_topomap)s + %(res_topomap)s + %(size_topomap)s + %(cbar_fmt_topomap)s + %(proj_plot)s %(show)s - %(topomap_show_names)s - %(title_None)s - %(topomap_mask)s - %(topomap_mask_params)s - %(topomap_outlines)s - %(topomap_contours)s - %(topomap_image_interp)s - %(topomap_axes)s - %(topomap_extrapolate)s - %(topomap_sphere_auto)s - %(topomap_border)s + %(show_names_topomap)s + %(title_none)s + %(mask_topomap)s + %(mask_params_topomap)s + %(outlines_topomap)s + %(contours_topomap)s + %(image_interp_topomap)s + %(axes_topomap)s + %(extrapolate_topomap)s + %(sphere_topomap_auto)s + %(border_topomap)s noise_cov : instance of Covariance | None If not None, whiten the instance with ``noise_cov`` before plotting. @@ -385,6 +379,7 @@ """ check_fname(fname, 'covariance', ('-cov.fif', '-cov.fif.gz', '_cov.fif', '_cov.fif.gz')) + fname = _check_fname(fname=fname, must_exist=True, overwrite='read') f, tree = fiff_open(fname)[:2] with f as fid: return Covariance(**_read_cov(fid, tree, FIFF.FIFFV_MNE_NOISE_COV, @@ -400,8 +395,7 @@ Parameters ---------- - info : instance of Info - Measurement info. + %(info_not_none)s std : dict of float | None Standard_deviation of the diagonal elements. If dict, keys should be ``'grad'`` for gradiometers, ``'mag'`` for magnetometers and ``'eeg'`` @@ -445,9 +439,9 @@ @verbose def compute_raw_covariance(raw, tmin=0, tmax=None, tstep=0.2, reject=None, flat=None, picks=None, method='empirical', - method_params=None, cv=3, scalings=None, n_jobs=1, - return_estimators=False, reject_by_annotation=True, - rank=None, verbose=None): + method_params=None, cv=3, scalings=None, + n_jobs=None, return_estimators=False, + reject_by_annotation=True, rank=None, verbose=None): """Estimate noise covariance matrix from a continuous segment of raw data. It is typically useful to estimate a noise covariance from empty room @@ -520,7 +514,7 @@ %(reject_by_annotation_epochs)s .. versionadded:: 0.14 - %(rank_None)s + %(rank_none)s .. versionadded:: 0.17 @@ -684,8 +678,9 @@ @verbose def compute_covariance(epochs, keep_sample_mean=True, tmin=None, tmax=None, projs=None, method='empirical', method_params=None, - cv=3, scalings=None, n_jobs=1, return_estimators=False, - on_mismatch='raise', rank=None, verbose=None): + cv=3, scalings=None, n_jobs=None, + return_estimators=False, on_mismatch='raise', + rank=None, verbose=None): """Estimate noise covariance matrix from epochs. The noise covariance is typically estimated on pre-stimulus periods @@ -772,7 +767,7 @@ unstable results in covariance calculation, e.g. when data have been processed with Maxwell filtering but not transformed to the same head position. - %(rank_None)s + %(rank_none)s .. versionadded:: 0.17 @@ -1017,14 +1012,6 @@ return eig, eigvec -def _get_iid_kwargs(): - import sklearn - kwargs = dict() - if LooseVersion(sklearn.__version__) < LooseVersion('0.22'): - kwargs['iid'] = False - return kwargs - - def _compute_covariance_auto(data, method, info, method_params, cv, scalings, n_jobs, stop_early, picks_list, rank): """Compute covariance auto mode.""" @@ -1104,16 +1091,13 @@ del sc elif method_ == 'shrunk': - try: - from sklearn.model_selection import GridSearchCV - except Exception: # support sklearn < 0.18 - from sklearn.grid_search import GridSearchCV + from sklearn.model_selection import GridSearchCV from sklearn.covariance import ShrunkCovariance shrinkage = mp.pop('shrinkage') tuned_parameters = [{'shrinkage': shrinkage}] shrinkages = [] gs = GridSearchCV(ShrunkCovariance(**mp), - tuned_parameters, cv=cv, **_get_iid_kwargs()) + tuned_parameters, cv=cv) for ch_type, picks in sub_picks_list: gs.fit(data_[:, picks]) shrinkages.append((ch_type, gs.best_estimator_.shrinkage, @@ -1183,11 +1167,7 @@ def _cross_val(data, est, cv, n_jobs): """Compute cross validation.""" - try: - from sklearn.model_selection import cross_val_score - except ImportError: - # XXX support sklearn < 0.18 - from sklearn.cross_validation import cross_val_score + from sklearn.model_selection import cross_val_score return np.mean(cross_val_score(est, data, cv=cv, n_jobs=n_jobs, scoring=_gaussian_loglik_scorer)) @@ -1383,7 +1363,8 @@ ############################################################################### # Writing -def write_cov(fname, cov): +@verbose +def write_cov(fname, cov, *, overwrite=False, verbose=None): """Write a noise covariance matrix. Parameters @@ -1392,12 +1373,16 @@ The name of the file. It should end with -cov.fif or -cov.fif.gz. cov : Covariance The noise covariance matrix. + %(overwrite)s + + .. versionadded:: 1.0 + %(verbose)s See Also -------- read_cov """ - cov.save(fname) + cov.save(fname, overwrite=overwrite, verbose=verbose) ############################################################################### @@ -1440,12 +1425,11 @@ ---------- noise_cov : instance of Covariance The noise covariance to process. - info : dict - The measurement info (used to get channel types and bad channels). + %(info_not_none)s (Used to get channel types and bad channels). ch_names : list | None The channel names to be considered. Can be None to use ``info['ch_names']``. - %(rank_None)s + %(rank_none)s .. versionadded:: 0.18 Support for 'info' mode. @@ -1529,7 +1513,7 @@ eigvec = np.zeros((n_chan, n_chan), dtype) mask = np.zeros(n_chan, bool) for ch_type, picks in _picks_by_type(info, meg_combined=True, - ref_meg=False, exclude='bads'): + ref_meg=False, exclude=[]): if len(picks) == 0: continue this_C = C[np.ix_(picks, picks)] @@ -1577,8 +1561,7 @@ ---------- cov : Covariance The noise covariance matrix. - info : dict - The measurement info (used to get channel types and bad channels). + %(info_not_none)s (Used to get channel types and bad channels). mag : float (default 0.1) Regularization factor for MEG magnetometers. grad : float (default 0.1) @@ -1611,7 +1594,7 @@ Regularization factor for EEG-CSD signals. dbs : float (default 0.1) Regularization factor for DBS signals. - %(rank_None)s + %(rank_none)s .. versionadded:: 0.17 @@ -1766,7 +1749,7 @@ scalings = _handle_default('scalings_cov_rank', None) cov = _compute_covariance_auto( data.T, method=method, method_params=method_params, - info=info, cv=None, n_jobs=1, stop_early=True, + info=info, cv=None, n_jobs=None, stop_early=True, picks_list=picks_list, scalings=scalings, rank=rank)[reg]['data'] return cov @@ -1783,11 +1766,10 @@ ---------- noise_cov : Covariance The noise covariance. - info : dict | None - The measurement info. Can be None if ``noise_cov`` has already been + %(info)s Can be None if ``noise_cov`` has already been prepared with :func:`prepare_noise_cov`. %(picks_good_data_noref)s - %(rank_None)s + %(rank_none)s .. versionadded:: 0.18 Support for 'info' mode. @@ -1899,7 +1881,7 @@ %(picks_good_data)s diag : bool (default False) If True, whiten using only the diagonal of the covariance. - %(rank_None)s + %(rank_none)s .. versionadded:: 0.18 Support for 'info' mode. @@ -2095,3 +2077,12 @@ # Done! end_block(fid, FIFF.FIFFB_MNE_COV) + + +@verbose +def _ensure_cov(cov, name='cov', *, verbose=None): + _validate_type(cov, ('path-like', Covariance), name) + logger.info('Noise covariance : %s' % (cov,)) + if not isinstance(cov, Covariance): + cov = read_cov(cov, verbose=False) + return cov diff -Nru python-mne-0.23.4+dfsg/mne/cuda.py python-mne-1.1.0+dfsg/mne/cuda.py --- python-mne-0.23.4+dfsg/mne/cuda.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/cuda.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,12 +1,11 @@ # Authors: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np -from .fixes import _import_fft from .utils import (sizeof_fmt, logger, get_config, warn, _explain_exception, - verbose) + verbose, fill_doc, _check_option) _cuda_capable = False @@ -154,10 +153,11 @@ ----- This function is designed to be used with fft_multiply_repeated(). """ - rfft, irfft = _import_fft(('rfft', 'irfft')) + from scipy.fft import rfft, irfft cuda_dict = dict(n_fft=n_fft, rfft=rfft, irfft=irfft, h_fft=rfft(h, n=n_fft)) - if n_jobs == 'cuda': + if isinstance(n_jobs, str): + _check_option('n_jobs', n_jobs, ('cuda',)) n_jobs = 1 init_cuda() if _cuda_capable: @@ -169,13 +169,13 @@ except Exception as exp: logger.info('CUDA not used, could not instantiate memory ' '(arrays may be too large: "%s"), falling back to ' - 'n_jobs=1' % str(exp)) + 'n_jobs=None' % str(exp)) cuda_dict.update(h_fft=h_fft, rfft=_cuda_upload_rfft, irfft=_cuda_irfft_get) else: logger.info('CUDA not used, CUDA could not be initialized, ' - 'falling back to n_jobs=1') + 'falling back to n_jobs=None') return n_jobs, cuda_dict @@ -247,14 +247,15 @@ ----- This function is designed to be used with fft_resample(). """ - rfft, irfft = _import_fft(('rfft', 'irfft')) + from scipy.fft import rfft, irfft cuda_dict = dict(use_cuda=False, rfft=rfft, irfft=irfft) rfft_len_x = len(W) // 2 + 1 # fold the window onto inself (should be symmetric) and truncate W = W.copy() W[1:rfft_len_x] = (W[1:rfft_len_x] + W[::-1][:rfft_len_x - 1]) / 2. W = W[:rfft_len_x] - if n_jobs == 'cuda': + if isinstance(n_jobs, str): + _check_option('n_jobs', n_jobs, ('cuda',)) n_jobs = 1 init_cuda() if _cuda_capable: @@ -266,14 +267,14 @@ except Exception: logger.info('CUDA not used, could not instantiate memory ' '(arrays may be too large), falling back to ' - 'n_jobs=1') + 'n_jobs=None') else: cuda_dict.update(use_cuda=True, rfft=_cuda_upload_rfft, irfft=_cuda_irfft_get) else: logger.info('CUDA not used, CUDA could not be initialized, ' - 'falling back to n_jobs=1') + 'falling back to n_jobs=None') cuda_dict['W'] = W return n_jobs, cuda_dict @@ -290,6 +291,7 @@ return cupy.fft.irfft(x, n=n, axis=axis).get() +@fill_doc def _fft_resample(x, new_len, npads, to_removes, cuda_dict=None, pad='reflect_limited'): """Do FFT resampling with a filter function (possibly using CUDA). @@ -307,11 +309,8 @@ Number of samples to remove after resampling. cuda_dict : dict Dictionary constructed using setup_cuda_multiply_repeated(). - pad : str - The type of padding to use. Supports all :func:`np.pad` ``mode`` - options. Can also be "reflect_limited" (default), which pads with a - reflected version of each vector mirrored on the first and last values - of the vector, followed by zeros. + %(pad)s + The default is ``'reflect_limited'``. .. versionadded:: 0.15 diff -Nru python-mne-0.23.4+dfsg/mne/data/coil_def.dat python-mne-1.1.0+dfsg/mne/data/coil_def.dat --- python-mne-0.23.4+dfsg/mne/data/coil_def.dat 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/data/coil_def.dat 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # # Copyright 2005 - 2019 # -# Matti Hämäläinen +# Matti Hamalainen # Athinoula A. Martinos Center for Biomedical Imaging # Charlestown, MA, USA # @@ -33,7 +33,7 @@ # # Produced with: # -# mne_list_coil_def version 1.14 compiled at Sep 20 2019 14:43:57 +# mne_list_coil_def version 1.14 compiled at May 15 2021 07:58:54 # 3 2 0 2 2.789e-02 1.620e-02 "Neuromag-122 planar gradiometer size = 27.89 mm base = 16.20 mm" 61.7284 8.100e-03 0.000e+00 0.000e+00 0.000 0.000 1.000 @@ -650,16 +650,16 @@ -0.1250 -4.150e-03 4.150e-03 4.850e-02 0.000 0.000 1.000 -0.1250 -4.150e-03 -4.150e-03 4.850e-02 0.000 0.000 1.000 -0.1250 4.150e-03 -4.150e-03 4.850e-02 0.000 0.000 1.000 -1 8001 0 1 7.000e-04 0.000e+00 "OPM sensor Gen1 size = 0.70 mm" +1 8001 0 1 7.000e-04 0.000e+00 "QSpin OPM sensor Gen1 size = 0.70 mm" 1.0000 0.000e+00 0.000e+00 0.000e+00 0.000 0.000 1.000 -1 8001 1 6 7.000e-04 0.000e+00 "OPM sensor Gen1 size = 0.70 mm" +1 8001 1 6 7.000e-04 0.000e+00 "QSpin OPM sensor Gen1 size = 0.70 mm" 0.1667 -1.250e-03 0.000e+00 0.000e+00 0.000 0.000 1.000 0.1667 -7.500e-04 0.000e+00 0.000e+00 0.000 0.000 1.000 0.1667 -2.500e-04 0.000e+00 0.000e+00 0.000 0.000 1.000 0.1667 2.500e-04 0.000e+00 0.000e+00 0.000 0.000 1.000 0.1667 7.500e-04 0.000e+00 0.000e+00 0.000 0.000 1.000 0.1667 1.250e-03 0.000e+00 0.000e+00 0.000 0.000 1.000 -1 8001 2 24 7.000e-04 0.000e+00 "OPM sensor Gen1 size = 0.70 mm" +1 8001 2 24 7.000e-04 0.000e+00 "QSpin OPM sensor Gen1 size = 0.70 mm" 0.0417 -1.250e-03 1.750e-04 1.750e-04 0.000 0.000 1.000 0.0417 -1.250e-03 -1.750e-04 1.750e-04 0.000 0.000 1.000 0.0417 -1.250e-03 -1.750e-04 -1.750e-04 0.000 0.000 1.000 @@ -684,16 +684,16 @@ 0.0417 1.250e-03 -1.750e-04 1.750e-04 0.000 0.000 1.000 0.0417 1.250e-03 -1.750e-04 -1.750e-04 0.000 0.000 1.000 0.0417 1.250e-03 1.750e-04 -1.750e-04 0.000 0.000 1.000 -1 8002 0 1 7.000e-04 0.000e+00 "OPM sensor Gen2 size = 0.70 mm" +1 8002 0 1 7.000e-04 0.000e+00 "QSpin OPM sensor Gen2 size = 0.70 mm" 1.0000 0.000e+00 0.000e+00 0.000e+00 0.000 0.000 1.000 -1 8002 1 6 7.000e-04 0.000e+00 "OPM sensor Gen2 size = 0.70 mm" +1 8002 1 6 7.000e-04 0.000e+00 "QSpin OPM sensor Gen2 size = 0.70 mm" 0.1667 -1.250e-03 0.000e+00 0.000e+00 0.000 0.000 1.000 0.1667 -7.500e-04 0.000e+00 0.000e+00 0.000 0.000 1.000 0.1667 -2.500e-04 0.000e+00 0.000e+00 0.000 0.000 1.000 0.1667 2.500e-04 0.000e+00 0.000e+00 0.000 0.000 1.000 0.1667 7.500e-04 0.000e+00 0.000e+00 0.000 0.000 1.000 0.1667 1.250e-03 0.000e+00 0.000e+00 0.000 0.000 1.000 -1 8002 2 24 7.000e-04 0.000e+00 "OPM sensor Gen2 size = 0.70 mm" +1 8002 2 24 7.000e-04 0.000e+00 "QSpin OPM sensor Gen2 size = 0.70 mm" 0.0417 -1.250e-03 1.750e-04 1.750e-04 0.000 0.000 1.000 0.0417 -1.250e-03 -1.750e-04 1.750e-04 0.000 0.000 1.000 0.0417 -1.250e-03 -1.750e-04 -1.750e-04 0.000 0.000 1.000 @@ -718,3 +718,59 @@ 0.0417 1.250e-03 -1.750e-04 1.750e-04 0.000 0.000 1.000 0.0417 1.250e-03 -1.750e-04 -1.750e-04 0.000 0.000 1.000 0.0417 1.250e-03 1.750e-04 -1.750e-04 0.000 0.000 1.000 +1 8101 0 1 2.000e-03 0.000e+00 "FieldLine OPM sensor Gen1 size = 2.00 mm" + 1.0000 0.000e+00 0.000e+00 0.000e+00 0.000 0.000 1.000 +1 8101 1 6 2.000e-03 0.000e+00 "FieldLine OPM sensor Gen1 size = 2.00 mm" + 0.1667 -1.250e-03 0.000e+00 0.000e+00 0.000 0.000 1.000 + 0.1667 -7.500e-04 0.000e+00 0.000e+00 0.000 0.000 1.000 + 0.1667 -2.500e-04 0.000e+00 0.000e+00 0.000 0.000 1.000 + 0.1667 2.500e-04 0.000e+00 0.000e+00 0.000 0.000 1.000 + 0.1667 7.500e-04 0.000e+00 0.000e+00 0.000 0.000 1.000 + 0.1667 1.250e-03 0.000e+00 0.000e+00 0.000 0.000 1.000 +1 8101 2 24 2.000e-03 0.000e+00 "FieldLine OPM sensor Gen1 size = 2.00 mm" + 0.0417 -1.250e-03 5.000e-04 5.000e-04 0.000 0.000 1.000 + 0.0417 -1.250e-03 -5.000e-04 5.000e-04 0.000 0.000 1.000 + 0.0417 -1.250e-03 -5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.0417 -1.250e-03 5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.0417 -7.500e-04 5.000e-04 5.000e-04 0.000 0.000 1.000 + 0.0417 -7.500e-04 -5.000e-04 5.000e-04 0.000 0.000 1.000 + 0.0417 -7.500e-04 -5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.0417 -7.500e-04 5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.0417 -2.500e-04 5.000e-04 5.000e-04 0.000 0.000 1.000 + 0.0417 -2.500e-04 -5.000e-04 5.000e-04 0.000 0.000 1.000 + 0.0417 -2.500e-04 -5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.0417 -2.500e-04 5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.0417 2.500e-04 5.000e-04 5.000e-04 0.000 0.000 1.000 + 0.0417 2.500e-04 -5.000e-04 5.000e-04 0.000 0.000 1.000 + 0.0417 2.500e-04 -5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.0417 2.500e-04 5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.0417 7.500e-04 5.000e-04 5.000e-04 0.000 0.000 1.000 + 0.0417 7.500e-04 -5.000e-04 5.000e-04 0.000 0.000 1.000 + 0.0417 7.500e-04 -5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.0417 7.500e-04 5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.0417 1.250e-03 5.000e-04 5.000e-04 0.000 0.000 1.000 + 0.0417 1.250e-03 -5.000e-04 5.000e-04 0.000 0.000 1.000 + 0.0417 1.250e-03 -5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.0417 1.250e-03 5.000e-04 -5.000e-04 0.000 0.000 1.000 +1 8201 0 1 2.000e-03 0.000e+00 "Kernel OPM sensor Gen1 size = 2.00 mm" + 1.0000 0.000e+00 0.000e+00 0.000e+00 0.000 0.000 1.000 +1 8201 1 9 2.000e-03 0.000e+00 "Kernel OPM sensor Gen1 size = 2.00 mm" + 0.1111 0.000e+00 0.000e+00 0.000e+00 0.000 0.000 1.000 + 0.1111 -5.000e-04 -5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.1111 5.000e-04 -5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.1111 -5.000e-04 5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.1111 5.000e-04 5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.1111 -5.000e-04 -5.000e-04 5.000e-04 0.000 0.000 1.000 + 0.1111 5.000e-04 -5.000e-04 5.000e-04 0.000 0.000 1.000 + 0.1111 -5.000e-04 5.000e-04 5.000e-04 0.000 0.000 1.000 + 0.1111 5.000e-04 5.000e-04 5.000e-04 0.000 0.000 1.000 +1 8201 2 9 2.000e-03 0.000e+00 "Kernel OPM sensor Gen1 size = 2.00 mm" + 0.1111 0.000e+00 0.000e+00 0.000e+00 0.000 0.000 1.000 + 0.1111 -5.000e-04 -5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.1111 5.000e-04 -5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.1111 -5.000e-04 5.000e-04 -5.000e-04 0.000 0.000 1.000 + 0.1111 5.000e-04 5.000e-04 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- - {{if meas_date is not None}} - - {{else}}{{endif}} - - - - {{if info['experimenter'] is not None}} - - {{else}}{{endif}} - - - {{if info['subject_info'] is not None}} - {{if 'his_id' in info['subject_info'].keys()}} - - {{endif}} - {{else}}{{endif}} - - - - {{if info['dig'] is not None}} - - {{else}} - - {{endif}} - - - - - - - - {{if info['bads'] is not None}} - - {{else}}{{endif}} - - - - - - - - - - - - - - - - - - - - -
Measurement date{{meas_date}}Unknown
Experimenter{{info['experimenter']}}Unknown
Participant{{info['subject_info']['his_id']}}Unknown
Digitized points{{len(info['dig'])}} pointsNot available
Good channels{{n_mag}} magnetometer, {{n_grad}} gradiometer, - and {{n_eeg}} EEG channels
Bad channels{{', '.join(info['bads'])}}None
EOG channels{{eog}}
ECG channels{{ecg}}
Sampling frequency{{u'%0.2f' % info['sfreq']}} Hz
Highpass{{u'%0.2f' % info['highpass']}} Hz
Lowpass{{u'%0.2f' % info['lowpass']}} Hz
-""") - -raw_template = Template(""" -{{info_repr[:-9]}} - - Filenames - {{', '.join(filenames)}} - - - Duration - {{duration}} (HH:MM:SS) - - -""") - -epochs_template = Template(""" - - - - - - - - {{if events is not None}} - - {{else}} - - {{endif}} - - - - - - - - - -
Number of events{{len(epochs.events)}}
Events{{events}}Not available
Time range{{f'{epochs.tmin:.3f} – {epochs.tmax:.3f} sec'}}
Baseline{{baseline}}
-""") diff -Nru python-mne-0.23.4+dfsg/mne/data/image/custom_layout.lout python-mne-1.1.0+dfsg/mne/data/image/custom_layout.lout --- python-mne-0.23.4+dfsg/mne/data/image/custom_layout.lout 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/data/image/custom_layout.lout 2022-08-03 15:48:14.000000000 +0000 @@ -1,23 +1,257 @@ - 0.00 0.00 0.03 0.03 -000 0.49 0.74 0.07 0.05 0 -001 0.50 0.77 0.07 0.05 1 -002 0.53 0.79 0.07 0.05 2 -003 0.56 0.75 0.07 0.05 3 -004 0.62 0.76 0.07 0.05 4 -005 0.61 0.72 0.07 0.05 5 -006 0.58 0.70 0.07 0.05 6 -007 0.56 0.65 0.07 0.05 7 -008 0.53 0.60 0.07 0.05 8 -009 0.50 0.55 0.07 0.05 9 -010 0.58 0.57 0.07 0.05 10 -011 0.61 0.49 0.07 0.05 11 -012 0.63 0.55 0.07 0.05 12 -013 0.65 0.60 0.07 0.05 13 -014 0.74 0.41 0.07 0.05 14 -015 0.70 0.45 0.07 0.05 15 -016 0.65 0.48 0.07 0.05 16 -017 0.63 0.42 0.07 0.05 17 -018 0.67 0.39 0.07 0.05 18 -019 0.60 0.36 0.07 0.05 19 -020 0.64 0.33 0.07 0.05 20 -021 0.69 0.32 0.07 0.05 21 + 0.00 0.00 0.01 0.02 +000 0.79 0.46 0.07 0.05 0 +001 0.78 0.48 0.07 0.05 1 +002 0.76 0.51 0.07 0.05 2 +003 0.74 0.53 0.07 0.05 3 +004 0.72 0.55 0.07 0.05 4 +005 0.71 0.57 0.07 0.05 5 +006 0.69 0.59 0.07 0.05 6 +007 0.67 0.62 0.07 0.05 7 +008 0.66 0.64 0.07 0.05 8 +009 0.64 0.66 0.07 0.05 9 +010 0.62 0.68 0.07 0.05 10 +011 0.61 0.69 0.07 0.05 11 +012 0.59 0.71 0.07 0.05 12 +013 0.58 0.73 0.07 0.05 13 +014 0.56 0.75 0.07 0.05 14 +015 0.54 0.77 0.07 0.05 15 +016 0.77 0.44 0.07 0.05 16 +017 0.75 0.46 0.07 0.05 17 +018 0.73 0.49 0.07 0.05 18 +019 0.72 0.51 0.07 0.05 19 +020 0.70 0.54 0.07 0.05 20 +021 0.68 0.56 0.07 0.05 21 +022 0.66 0.58 0.07 0.05 22 +023 0.65 0.60 0.07 0.05 23 +024 0.63 0.62 0.07 0.05 24 +025 0.62 0.64 0.07 0.05 25 +026 0.60 0.66 0.07 0.05 26 +027 0.58 0.68 0.07 0.05 27 +028 0.57 0.70 0.07 0.05 28 +029 0.55 0.71 0.07 0.05 29 +030 0.53 0.73 0.07 0.05 30 +031 0.52 0.75 0.07 0.05 31 +032 0.75 0.42 0.07 0.05 32 +033 0.73 0.45 0.07 0.05 33 +034 0.71 0.47 0.07 0.05 34 +035 0.69 0.50 0.07 0.05 35 +036 0.68 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0.32 0.07 0.05 245 +246 0.33 0.34 0.07 0.05 246 +247 0.32 0.37 0.07 0.05 247 +248 0.30 0.39 0.07 0.05 248 +249 0.28 0.41 0.07 0.05 249 +250 0.27 0.43 0.07 0.05 250 +251 0.25 0.46 0.07 0.05 251 +252 0.24 0.48 0.07 0.05 252 +253 0.23 0.50 0.07 0.05 253 +254 0.21 0.52 0.07 0.05 254 +255 0.20 0.54 0.07 0.05 255 diff -Nru python-mne-0.23.4+dfsg/mne/datasets/brainstorm/bst_auditory.py python-mne-1.1.0+dfsg/mne/datasets/brainstorm/bst_auditory.py --- python-mne-0.23.4+dfsg/mne/datasets/brainstorm/bst_auditory.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/brainstorm/bst_auditory.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,17 +1,11 @@ # Authors: Mainak Jas # -# License: BSD (3-clause) - -from functools import partial - +# License: BSD-3-Clause from ...utils import verbose -from ..utils import (has_dataset, _data_path, _get_version, _version_doc, - _data_path_doc_accept) - -has_brainstorm_data = partial(has_dataset, name='brainstorm.bst_auditory') - +from ..utils import (_get_version, _version_doc, + _data_path_doc_accept, _download_mne_dataset) -_description = u""" +_description = """ URL: http://neuroimage.usc.edu/brainstorm/DatasetAuditory - One subject, two acquisition runs of 6 minutes each - Subject stimulated binaurally with intra-aural earphones @@ -28,12 +22,12 @@ @verbose -def data_path(path=None, force_update=False, update_path=True, download=True, - *, accept=False, verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='brainstorm', - download=download, archive_name='bst_auditory.tar.gz', - accept=accept) +def data_path(path=None, force_update=False, update_path=True, + download=True, accept=False, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='bst_auditory', processor='nested_untar', path=path, + force_update=force_update, update_path=update_path, + download=download, accept=accept) _data_path_doc = _data_path_doc_accept.format( @@ -44,7 +38,7 @@ def get_version(): # noqa: D103 - return _get_version('brainstorm.bst_auditory') + return _get_version('bst_auditory') get_version.__doc__ = _version_doc.format(name='brainstorm') diff -Nru python-mne-0.23.4+dfsg/mne/datasets/brainstorm/bst_phantom_ctf.py python-mne-1.1.0+dfsg/mne/datasets/brainstorm/bst_phantom_ctf.py --- python-mne-0.23.4+dfsg/mne/datasets/brainstorm/bst_phantom_ctf.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/brainstorm/bst_phantom_ctf.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,28 +1,22 @@ # Authors: Eric Larson # -# License: BSD (3-clause) - -from functools import partial - +# License: BSD-3-Clause from ...utils import verbose -from ..utils import (has_dataset, _data_path, _get_version, _version_doc, - _data_path_doc_accept) - -has_brainstorm_data = partial(has_dataset, name='brainstorm.bst_phantom_ctf') - +from ..utils import (_get_version, _version_doc, + _data_path_doc_accept, _download_mne_dataset) -_description = u""" +_description = """ URL: http://neuroimage.usc.edu/brainstorm/Tutorials/PhantomCtf """ @verbose -def data_path(path=None, force_update=False, update_path=True, download=True, - *, accept=False, verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='brainstorm', - download=download, archive_name='bst_phantom_ctf.tar.gz', - accept=accept) +def data_path(path=None, force_update=False, update_path=True, + download=True, accept=False, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='bst_phantom_ctf', processor='nested_untar', path=path, + force_update=force_update, update_path=update_path, + download=download, accept=accept) _data_path_doc = _data_path_doc_accept.format( @@ -33,7 +27,7 @@ def get_version(): # noqa: D103 - return _get_version('brainstorm.bst_phantom_ctf') + return _get_version('bst_phantom_ctf') get_version.__doc__ = _version_doc.format(name='brainstorm') diff -Nru python-mne-0.23.4+dfsg/mne/datasets/brainstorm/bst_phantom_elekta.py python-mne-1.1.0+dfsg/mne/datasets/brainstorm/bst_phantom_elekta.py --- python-mne-0.23.4+dfsg/mne/datasets/brainstorm/bst_phantom_elekta.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/brainstorm/bst_phantom_elekta.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,16 +1,9 @@ # Authors: Eric Larson # -# License: BSD (3-clause) - -from functools import partial - +# License: BSD-3-Clause from ...utils import verbose -from ..utils import (has_dataset, _data_path, _get_version, _version_doc, - _data_path_doc_accept) - -has_brainstorm_data = partial(has_dataset, - name='brainstorm.bst_phantom_elekta') - +from ..utils import (_get_version, _version_doc, + _data_path_doc_accept, _download_mne_dataset) _description = u""" URL: http://neuroimage.usc.edu/brainstorm/Tutorials/PhantomElekta @@ -18,13 +11,12 @@ @verbose -def data_path(path=None, force_update=False, update_path=True, download=True, - *, accept=False, verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='brainstorm', - download=download, - archive_name='bst_phantom_elekta.tar.gz', - accept=accept) +def data_path(path=None, force_update=False, update_path=True, + download=True, accept=False, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='bst_phantom_elekta', processor='nested_untar', path=path, + force_update=force_update, update_path=update_path, + download=download, accept=accept) _data_path_doc = _data_path_doc_accept.format( @@ -36,7 +28,7 @@ def get_version(): # noqa: D103 - return _get_version('brainstorm.bst_phantom_elekta') + return _get_version('bst_phantom_elekta') get_version.__doc__ = _version_doc.format(name='brainstorm') diff -Nru python-mne-0.23.4+dfsg/mne/datasets/brainstorm/bst_raw.py python-mne-1.1.0+dfsg/mne/datasets/brainstorm/bst_raw.py --- python-mne-0.23.4+dfsg/mne/datasets/brainstorm/bst_raw.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/brainstorm/bst_raw.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,16 +1,16 @@ # Authors: Mainak Jas # -# License: BSD (3-clause) - +# License: BSD-3-Clause from functools import partial from ...utils import verbose, get_config -from ..utils import (has_dataset, _data_path, _get_version, _version_doc, - _data_path_doc_accept) +from ..utils import (has_dataset, _get_version, _version_doc, + _data_path_doc_accept, _download_mne_dataset) + -has_brainstorm_data = partial(has_dataset, name='brainstorm.bst_raw') +has_brainstorm_data = partial(has_dataset, name='bst_raw') -_description = u""" +_description = """ URL: http://neuroimage.usc.edu/brainstorm/DatasetMedianNerveCtf - One subject, one acquisition run of 6 minutes - Subject stimulated using Digitimer Constant Current Stimulator @@ -26,12 +26,12 @@ @verbose -def data_path(path=None, force_update=False, update_path=True, download=True, - *, accept=False, verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='brainstorm', - download=download, archive_name='bst_raw.tar.gz', - accept=accept) +def data_path(path=None, force_update=False, update_path=True, + download=True, accept=False, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='bst_raw', processor='nested_untar', path=path, + force_update=force_update, update_path=update_path, + download=download, accept=accept) _data_path_doc = _data_path_doc_accept.format( @@ -42,7 +42,7 @@ def get_version(): # noqa: D103 - return _get_version('brainstorm.bst_raw') + return _get_version('bst_raw') get_version.__doc__ = _version_doc.format(name='brainstorm') diff -Nru python-mne-0.23.4+dfsg/mne/datasets/brainstorm/bst_resting.py python-mne-1.1.0+dfsg/mne/datasets/brainstorm/bst_resting.py --- python-mne-0.23.4+dfsg/mne/datasets/brainstorm/bst_resting.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/brainstorm/bst_resting.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,16 +1,11 @@ # Authors: Mainak Jas # -# License: BSD (3-clause) - -from functools import partial - +# License: BSD-3-Clause from ...utils import verbose -from ..utils import (has_dataset, _data_path, _get_version, _version_doc, - _data_path_doc_accept) - -has_brainstorm_data = partial(has_dataset, name='brainstorm.bst_resting') +from ..utils import (_get_version, _version_doc, + _data_path_doc_accept, _download_mne_dataset) -_description = u""" +_description = """ URL: http://neuroimage.usc.edu/brainstorm/DatasetResting - One subject - Two runs of 10 min of resting state recordings @@ -19,12 +14,12 @@ @verbose -def data_path(path=None, force_update=False, update_path=True, download=True, - *, accept=False, verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='brainstorm', - download=download, archive_name='bst_resting.tar.gz', - accept=accept) +def data_path(path=None, force_update=False, update_path=True, + download=True, accept=False, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='bst_resting', processor='nested_untar', path=path, + force_update=force_update, update_path=update_path, + download=download, accept=accept) _data_path_doc = _data_path_doc_accept.format( @@ -35,7 +30,7 @@ def get_version(): # noqa: D103 - return _get_version('brainstorm.bst_resting') + return _get_version('bst_resting') get_version.__doc__ = _version_doc.format(name='brainstorm') diff -Nru python-mne-0.23.4+dfsg/mne/datasets/config.py python-mne-1.1.0+dfsg/mne/datasets/config.py --- python-mne-0.23.4+dfsg/mne/datasets/config.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/config.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,329 @@ +# Authors: Adam Li +# Daniel McCloy +# +# License: BSD Style. + + +_bst_license_text = """ +License +------- +This tutorial dataset (EEG and MRI data) remains a property of the MEG Lab, +McConnell Brain Imaging Center, Montreal Neurological Institute, +McGill University, Canada. Its use and transfer outside the Brainstorm +tutorial, e.g. for research purposes, is prohibited without written consent +from the MEG Lab. + +If you reference this dataset in your publications, please: + + 1) acknowledge its authors: Elizabeth Bock, Esther Florin, Francois Tadel + and Sylvain Baillet, and + 2) cite Brainstorm as indicated on the website: + http://neuroimage.usc.edu/brainstorm + +For questions, please contact Francois Tadel (francois.tadel@mcgill.ca). +""" + +_hcp_mmp_license_text = """ +License +------- +I request access to data collected by the Washington University - University +of Minnesota Consortium of the Human Connectome Project (WU-Minn HCP), and +I agree to the following: + +1. I will not attempt to establish the identity of or attempt to contact any + of the included human subjects. + +2. I understand that under no circumstances will the code that would link + these data to Protected Health Information be given to me, nor will any + additional information about individual human subjects be released to me + under these Open Access Data Use Terms. + +3. I will comply with all relevant rules and regulations imposed by my + institution. This may mean that I need my research to be approved or + declared exempt by a committee that oversees research on human subjects, + e.g. my IRB or Ethics Committee. The released HCP data are not considered + de-identified, insofar as certain combinations of HCP Restricted Data + (available through a separate process) might allow identification of + individuals. Different committees operate under different national, state + and local laws and may interpret regulations differently, so it is + important to ask about this. If needed and upon request, the HCP will + provide a certificate stating that you have accepted the HCP Open Access + Data Use Terms. + +4. I may redistribute original WU-Minn HCP Open Access data and any derived + data as long as the data are redistributed under these same Data Use Terms. + +5. I will acknowledge the use of WU-Minn HCP data and data derived from + WU-Minn HCP data when publicly presenting any results or algorithms + that benefitted from their use. + + 1. Papers, book chapters, books, posters, oral presentations, and all + other printed and digital presentations of results derived from HCP + data should contain the following wording in the acknowledgments + section: "Data were provided [in part] by the Human Connectome + Project, WU-Minn Consortium (Principal Investigators: David Van Essen + and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and + Centers that support the NIH Blueprint for Neuroscience Research; and + by the McDonnell Center for Systems Neuroscience at Washington + University." + + 2. Authors of publications or presentations using WU-Minn HCP data + should cite relevant publications describing the methods used by the + HCP to acquire and process the data. The specific publications that + are appropriate to cite in any given study will depend on what HCP + data were used and for what purposes. An annotated and appropriately + up-to-date list of publications that may warrant consideration is + available at http://www.humanconnectome.org/about/acknowledgehcp.html + + 3. The WU-Minn HCP Consortium as a whole should not be included as an + author of publications or presentations if this authorship would be + based solely on the use of WU-Minn HCP data. + +6. Failure to abide by these guidelines will result in termination of my + privileges to access WU-Minn HCP data. +""" + +# To update the `testing` or `misc` datasets, push or merge commits to their +# respective repos, and make a new release of the dataset on GitHub. Then +# update the checksum in the MNE_DATASETS dict below, and change version +# here: ↓↓↓↓↓ ↓↓↓ +RELEASES = dict(testing='0.138', misc='0.23') +TESTING_VERSIONED = f'mne-testing-data-{RELEASES["testing"]}' +MISC_VERSIONED = f'mne-misc-data-{RELEASES["misc"]}' + +# To update any other dataset besides `testing` or `misc`, upload the new +# version of the data archive itself (e.g., to https://osf.io or wherever) and +# then update the corresponding checksum in the MNE_DATASETS dict entry below. +MNE_DATASETS = dict() + +# MANDATORY KEYS: +# - archive_name : the name of the compressed file that is downloaded +# - hash : the checksum type followed by a colon and then the checksum value +# (examples: "sha256:19uheid...", "md5:upodh2io...") +# - url : URL from which the file can be downloaded +# - folder_name : the subfolder within the MNE data folder in which to save and +# uncompress (if needed) the file(s) +# +# OPTIONAL KEYS: +# - config_key : key to use with `mne.set_config` to store the on-disk location +# of the downloaded dataset (ex: "MNE_DATASETS_EEGBCI_PATH"). + +# Testing and misc are at the top as they're updated most often +MNE_DATASETS['testing'] = dict( + archive_name=f'{TESTING_VERSIONED}.tar.gz', + hash='md5:9aaaef3481a92cbdb04a9ada8ddfe1cd', + url=('https://codeload.github.com/mne-tools/mne-testing-data/' + f'tar.gz/{RELEASES["testing"]}'), + # In case we ever have to resort to osf.io again... + # archive_name='mne-testing-data.tar.gz', + # hash='md5:c805a5fed8ca46f723e7eec828d90824', + # url='https://osf.io/dqfgy/download?version=1', # 0.136 + folder_name='MNE-testing-data', + config_key='MNE_DATASETS_TESTING_PATH', +) +MNE_DATASETS['misc'] = dict( + archive_name=f'{MISC_VERSIONED}.tar.gz', # 'mne-misc-data', + hash='md5:01e409d82ff11ca8b19a27c4f7ee6794', + url=('https://codeload.github.com/mne-tools/mne-misc-data/tar.gz/' + f'{RELEASES["misc"]}'), + folder_name='MNE-misc-data', + config_key='MNE_DATASETS_MISC_PATH' +) + +MNE_DATASETS['fnirs_motor'] = dict( + archive_name='MNE-fNIRS-motor-data.tgz', + hash='md5:c4935d19ddab35422a69f3326a01fef8', + url='https://osf.io/dj3eh/download?version=1', + folder_name='MNE-fNIRS-motor-data', + config_key='MNE_DATASETS_FNIRS_MOTOR_PATH', +) + +MNE_DATASETS['kiloword'] = dict( + archive_name='MNE-kiloword-data.tar.gz', + hash='md5:3a124170795abbd2e48aae8727e719a8', + url='https://osf.io/qkvf9/download?version=1', + folder_name='MNE-kiloword-data', + config_key='MNE_DATASETS_KILOWORD_PATH', +) + +MNE_DATASETS['multimodal'] = dict( + archive_name='MNE-multimodal-data.tar.gz', + hash='md5:26ec847ae9ab80f58f204d09e2c08367', + url='https://ndownloader.figshare.com/files/5999598', + folder_name='MNE-multimodal-data', + config_key='MNE_DATASETS_MULTIMODAL_PATH', +) + +MNE_DATASETS['opm'] = dict( + archive_name='MNE-OPM-data.tar.gz', + hash='md5:370ad1dcfd5c47e029e692c85358a374', + url='https://osf.io/p6ae7/download?version=2', + folder_name='MNE-OPM-data', + config_key='MNE_DATASETS_OPM_PATH', +) + +MNE_DATASETS['phantom_4dbti'] = dict( + archive_name='MNE-phantom-4DBTi.zip', + hash='md5:938a601440f3ffa780d20a17bae039ff', + url='https://osf.io/v2brw/download?version=2', + folder_name='MNE-phantom-4DBTi', + config_key='MNE_DATASETS_PHANTOM_4DBTI_PATH', +) + +MNE_DATASETS['sample'] = dict( + archive_name='MNE-sample-data-processed.tar.gz', + hash='md5:e8f30c4516abdc12a0c08e6bae57409c', + url='https://osf.io/86qa2/download?version=6', + folder_name='MNE-sample-data', + config_key='MNE_DATASETS_SAMPLE_PATH', +) + +MNE_DATASETS['somato'] = dict( + archive_name='MNE-somato-data.tar.gz', + hash='md5:32fd2f6c8c7eb0784a1de6435273c48b', + url='https://osf.io/tp4sg/download?version=7', + folder_name='MNE-somato-data', + config_key='MNE_DATASETS_SOMATO_PATH' +) + +MNE_DATASETS['spm'] = dict( + archive_name='MNE-spm-face.tar.gz', + hash='md5:9f43f67150e3b694b523a21eb929ea75', + url='https://osf.io/je4s8/download?version=2', + folder_name='MNE-spm-face', + config_key='MNE_DATASETS_SPM_FACE_PATH', +) + +# Visual 92 categories has the dataset split into 2 files. +# We define a dictionary holding the items with the same +# value across both files: folder name and configuration key. +MNE_DATASETS['visual_92_categories'] = dict( + folder_name='MNE-visual_92_categories-data', + config_key='MNE_DATASETS_VISUAL_92_CATEGORIES_PATH', +) +MNE_DATASETS['visual_92_categories_1'] = dict( + archive_name='MNE-visual_92_categories-data-part1.tar.gz', + hash='md5:74f50bbeb65740903eadc229c9fa759f', + url='https://osf.io/8ejrs/download?version=1', + folder_name='MNE-visual_92_categories-data', + config_key='MNE_DATASETS_VISUAL_92_CATEGORIES_PATH', +) +MNE_DATASETS['visual_92_categories_2'] = dict( + archive_name='MNE-visual_92_categories-data-part2.tar.gz', + hash='md5:203410a98afc9df9ae8ba9f933370e20', + url='https://osf.io/t4yjp/download?version=1', + folder_name='MNE-visual_92_categories-data', + config_key='MNE_DATASETS_VISUAL_92_CATEGORIES_PATH', +) + +MNE_DATASETS['mtrf'] = dict( + archive_name='mTRF_1.5.zip', + hash='md5:273a390ebbc48da2c3184b01a82e4636', + url='https://osf.io/h85s2/download?version=1', + folder_name='mTRF_1.5', + config_key='MNE_DATASETS_MTRF_PATH' +) +MNE_DATASETS['refmeg_noise'] = dict( + archive_name='sample_reference_MEG_noise-raw.zip', + hash='md5:779fecd890d98b73a4832e717d7c7c45', + url='https://osf.io/drt6v/download?version=1', + folder_name='MNE-refmeg-noise-data', + config_key='MNE_DATASETS_REFMEG_NOISE_PATH' +) + +MNE_DATASETS['ssvep'] = dict( + archive_name='ssvep_example_data.zip', + hash='md5:af866bbc0f921114ac9d683494fe87d6', + url='https://osf.io/z8h6k/download?version=5', + folder_name='ssvep-example-data', + config_key='MNE_DATASETS_SSVEP_PATH' +) + +MNE_DATASETS['erp_core'] = dict( + archive_name='MNE-ERP-CORE-data.tar.gz', + hash='md5:5866c0d6213bd7ac97f254c776f6c4b1', + url='https://osf.io/rzgba/download?version=1', + folder_name='MNE-ERP-CORE-data', + config_key='MNE_DATASETS_ERP_CORE_PATH', +) + +MNE_DATASETS['epilepsy_ecog'] = dict( + archive_name='MNE-epilepsy-ecog-data.tar.gz', + hash='md5:ffb139174afa0f71ec98adbbb1729dea', + url='https://osf.io/z4epq/download?version=1', + folder_name='MNE-epilepsy-ecog-data', + config_key='MNE_DATASETS_EPILEPSY_ECOG_PATH', +) + +# Fieldtrip CMC dataset +MNE_DATASETS['fieldtrip_cmc'] = dict( + archive_name='SubjectCMC.zip', + hash='md5:6f9fd6520f9a66e20994423808d2528c', + url='https://osf.io/j9b6s/download?version=1', + folder_name='MNE-fieldtrip_cmc-data', + config_key='MNE_DATASETS_FIELDTRIP_CMC_PATH' +) + +# brainstorm datasets: +MNE_DATASETS['bst_auditory'] = dict( + archive_name='bst_auditory.tar.gz', + hash='md5:fa371a889a5688258896bfa29dd1700b', + url='https://osf.io/5t9n8/download?version=1', + folder_name='MNE-brainstorm-data', + config_key='MNE_DATASETS_BRAINSTORM_PATH', +) +MNE_DATASETS['bst_phantom_ctf'] = dict( + archive_name='bst_phantom_ctf.tar.gz', + hash='md5:80819cb7f5b92d1a5289db3fb6acb33c', + url='https://osf.io/sxr8y/download?version=1', + folder_name='MNE-brainstorm-data', + config_key='MNE_DATASETS_BRAINSTORM_PATH', +) +MNE_DATASETS['bst_phantom_elekta'] = dict( + archive_name='bst_phantom_elekta.tar.gz', + hash='md5:1badccbe17998d18cc373526e86a7aaf', + url='https://osf.io/dpcku/download?version=1', + folder_name='MNE-brainstorm-data', + config_key='MNE_DATASETS_BRAINSTORM_PATH', +) +MNE_DATASETS['bst_raw'] = dict( + archive_name='bst_raw.tar.gz', + hash='md5:fa2efaaec3f3d462b319bc24898f440c', + url='https://osf.io/9675n/download?version=2', + folder_name='MNE-brainstorm-data', + config_key='MNE_DATASETS_BRAINSTORM_PATH', +) +MNE_DATASETS['bst_resting'] = dict( + archive_name='bst_resting.tar.gz', + hash='md5:70fc7bf9c3b97c4f2eab6260ee4a0430', + url='https://osf.io/m7bd3/download?version=3', + folder_name='MNE-brainstorm-data', + config_key='MNE_DATASETS_BRAINSTORM_PATH', +) + +# HF-SEF +MNE_DATASETS['hf_sef_raw'] = dict( + archive_name='hf_sef_raw.tar.gz', + hash='md5:33934351e558542bafa9b262ac071168', + url='https://zenodo.org/record/889296/files/hf_sef_raw.tar.gz', + folder_name='hf_sef', + config_key='MNE_DATASETS_HF_SEF_PATH', +) +MNE_DATASETS['hf_sef_evoked'] = dict( + archive_name='hf_sef_evoked.tar.gz', + hash='md5:13d34cb5db584e00868677d8fb0aab2b', + url=('https://zenodo.org/record/3523071/files/' + 'hf_sef_evoked.tar.gz'), + folder_name='hf_sef', + config_key='MNE_DATASETS_HF_SEF_PATH', +) + +# "fake" dataset (for testing) +MNE_DATASETS['fake'] = dict( + archive_name='foo.tgz', + hash='md5:3194e9f7b46039bb050a74f3e1ae9908', + url=('https://github.com/mne-tools/mne-testing-data/raw/master/' + 'datasets/foo.tgz'), + folder_name='foo', + config_key='MNE_DATASETS_FAKE_PATH' +) diff -Nru python-mne-0.23.4+dfsg/mne/datasets/eegbci/eegbci.py python-mne-1.1.0+dfsg/mne/datasets/eegbci/eegbci.py --- python-mne-0.23.4+dfsg/mne/datasets/eegbci/eegbci.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/eegbci/eegbci.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,18 +1,22 @@ # Author: Martin Billinger +# Adam Li +# Daniel McCloy # License: BSD Style. import os from os import path as op +import pkg_resources +import re -from ..utils import _get_path, _do_path_update -from ...utils import _fetch_file, _url_to_local_path, verbose +from ..utils import _get_path, _do_path_update, _mne_path +from ...utils import _url_to_local_path, verbose EEGMI_URL = 'https://physionet.org/files/eegmmidb/1.0.0/' @verbose -def data_path(url, path=None, force_update=False, update_path=None, +def data_path(url, path=None, force_update=False, update_path=None, *, verbose=None): """Get path to local copy of EEGMMI dataset URL. @@ -39,7 +43,7 @@ Returns ------- - path : list of str + path : list of Path Local path to the given data file. This path is contained inside a list of length one, for compatibility. @@ -59,10 +63,13 @@ ---------- .. footbibliography:: """ # noqa: E501 + import pooch + key = 'MNE_DATASETS_EEGBCI_PATH' name = 'EEGBCI' path = _get_path(path, key, name) - destination = _url_to_local_path(url, op.join(path, 'MNE-eegbci-data')) + fname = 'MNE-eegbci-data' + destination = _url_to_local_path(url, op.join(path, fname)) destinations = [destination] # Fetch the file @@ -71,10 +78,16 @@ os.remove(destination) if not op.isdir(op.dirname(destination)): os.makedirs(op.dirname(destination)) - _fetch_file(url, destination, print_destination=False) + pooch.retrieve( + # URL to one of Pooch's test files + url=url, + path=destination, + fname=fname + ) # Offer to update the path _do_path_update(path, update_path, key, name) + destinations = [_mne_path(dest) for dest in destinations] return destinations @@ -142,15 +155,49 @@ ---------- .. footbibliography:: """ # noqa: E501 + import pooch + if not hasattr(runs, '__iter__'): runs = [runs] - data_paths = [] - for r in runs: - url = '{u}S{s:03d}/S{s:03d}R{r:02d}.edf'.format(u=base_url, - s=subject, r=r) - data_paths.extend(data_path(url, path, force_update, update_path)) + # get local storage path + config_key = 'MNE_DATASETS_EEGBCI_PATH' + folder = 'MNE-eegbci-data' + name = 'EEGBCI' + path = _get_path(path, config_key, name) + # extract path parts + pattern = r'(?:https?://.*)(files)/(eegmmidb)/(\d+\.\d+\.\d+)/?' + match = re.compile(pattern).match(base_url) + if match is None: + raise ValueError('base_url does not match the expected EEGMI folder ' + 'structure. Please notify MNE-Python developers.') + base_path = op.join(path, folder, *match.groups()) + + # create the download manager + fetcher = pooch.create( + path=base_path, + base_url=base_url, + version=None, # Data versioning is decoupled from MNE-Python version. + registry=None, # Registry is loaded from file, below. + retry_if_failed=2 # 2 retries = 3 total attempts + ) + + # load the checksum registry + registry = pkg_resources.resource_stream( + 'mne', op.join('data', 'eegbci_checksums.txt')) + fetcher.load_registry(registry) + + # fetch the file(s) + data_paths = [] + for run in runs: + file_part = f'S{subject:03d}/S{subject:03d}R{run:02d}.edf' + destination = op.join(base_path, file_part) + if force_update and op.isfile(destination): + os.remove(destination) + data_paths.append(fetcher.fetch(file_part)) + # update path in config if desired + _do_path_update(path, update_path, config_key, name) return data_paths diff -Nru python-mne-0.23.4+dfsg/mne/datasets/epilepsy_ecog/_data.py python-mne-1.1.0+dfsg/mne/datasets/epilepsy_ecog/_data.py --- python-mne-0.23.4+dfsg/mne/datasets/epilepsy_ecog/_data.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/epilepsy_ecog/_data.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,22 +2,18 @@ # Alex Rockhill # License: BSD Style. -from functools import partial - from ...utils import verbose -from ..utils import (has_dataset, _data_path, _data_path_doc, - _get_version, _version_doc) - -has_epilepsy_ecog_data = partial(has_dataset, name='epilepsy_ecog') +from ..utils import (_data_path_doc, _get_version, _version_doc, + _download_mne_dataset) @verbose -def data_path( - path=None, force_update=False, update_path=True, - download=True, verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='epilepsy_ecog', - download=download) +def data_path(path=None, force_update=False, update_path=True, + download=True, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='epilepsy_ecog', processor='untar', path=path, + force_update=force_update, update_path=update_path, + download=download) data_path.__doc__ = _data_path_doc.format( diff -Nru python-mne-0.23.4+dfsg/mne/datasets/epilepsy_ecog/__init__.py python-mne-1.1.0+dfsg/mne/datasets/epilepsy_ecog/__init__.py --- python-mne-0.23.4+dfsg/mne/datasets/epilepsy_ecog/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/epilepsy_ecog/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,3 @@ """Clinical epilepsy datasets.""" -from ._data import data_path, has_epilepsy_ecog_data, get_version +from ._data import data_path, get_version diff -Nru python-mne-0.23.4+dfsg/mne/datasets/erp_core/erp_core.py python-mne-1.1.0+dfsg/mne/datasets/erp_core/erp_core.py --- python-mne-0.23.4+dfsg/mne/datasets/erp_core/erp_core.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/erp_core/erp_core.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,18 +1,15 @@ -from functools import partial - from ...utils import verbose -from ..utils import (has_dataset, _data_path, _data_path_doc, - _get_version, _version_doc) - -has_erp_core_data = partial(has_dataset, name='erp_core') +from ..utils import (_data_path_doc, + _get_version, _version_doc, _download_mne_dataset) @verbose def data_path(path=None, force_update=False, update_path=True, - download=True, verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='erp_core', - download=download) + download=True, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='erp_core', processor='untar', path=path, + force_update=force_update, update_path=update_path, + download=download) data_path.__doc__ = _data_path_doc.format(name='erp_core', diff -Nru python-mne-0.23.4+dfsg/mne/datasets/_fake/_fake.py python-mne-1.1.0+dfsg/mne/datasets/_fake/_fake.py --- python-mne-0.23.4+dfsg/mne/datasets/_fake/_fake.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/_fake/_fake.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,16 +4,17 @@ # License: BSD Style. from ...utils import verbose -from ..utils import (_data_path, _data_path_doc, +from ..utils import (_data_path_doc, _download_mne_dataset, _get_version, _version_doc) @verbose def data_path(path=None, force_update=False, update_path=False, - download=True, verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='fake', - download=download) + download=True, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='fake', processor='untar', path=path, + force_update=force_update, update_path=update_path, + download=download) data_path.__doc__ = _data_path_doc.format(name='fake', diff -Nru python-mne-0.23.4+dfsg/mne/datasets/_fetch.py python-mne-1.1.0+dfsg/mne/datasets/_fetch.py --- python-mne-0.23.4+dfsg/mne/datasets/_fetch.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/_fetch.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,292 @@ +# Authors: Adam Li +# +# License: BSD Style. + +import logging +import sys +import os +import os.path as op +from shutil import rmtree + +from .. import __version__ as mne_version +from ..utils import logger, warn, _safe_input +from .config import ( + _bst_license_text, + RELEASES, + TESTING_VERSIONED, + MISC_VERSIONED, +) +from .utils import _dataset_version, _do_path_update, _get_path, _mne_path +from ..fixes import _compare_version + + +_FAKE_VERSION = None # used for monkeypatching while testing versioning + + +def fetch_dataset( + dataset_params, + processor=None, + path=None, + force_update=False, + update_path=True, + download=True, + check_version=False, + return_version=False, + accept=False, + auth=None, + token=None, +): + """Fetch an MNE-compatible dataset using pooch. + + Parameters + ---------- + dataset_params : list of dict | dict + The dataset name(s) and corresponding parameters to download the + dataset(s). The dataset parameters that contains the following keys: + ``archive_name``, ``url``, ``folder_name``, ``hash``, + ``config_key`` (optional). See Notes. + processor : None | "unzip" | "untar" | instance of pooch.Unzip | instance of pooch.Untar + What to do after downloading the file. ``"unzip"`` and ``"untar"`` will + decompress the downloaded file in place; for custom extraction (e.g., + only extracting certain files from the archive) pass an instance of + :class:`pooch.Unzip` or :class:`pooch.Untar`. If ``None`` (the + default), the files are left as-is. + path : None | str + Directory in which to put the dataset. If ``None``, the dataset + location is determined by first checking whether + ``dataset_params['config_key']`` is defined, and if so, whether that + config key exists in the MNE-Python config file. If so, the configured + path is used; if not, the location is set to the value of the + ``MNE_DATA`` config key (if it exists), or ``~/mne_data`` otherwise. + force_update : bool + Force update of the dataset even if a local copy exists. + Default is False. + update_path : bool | None + If True (default), set the mne-python config to the given + path. If None, the user is prompted. + download : bool + If False and the dataset has not been downloaded yet, it will not be + downloaded and the path will be returned as ``''`` (empty string). This + is mostly used for testing purposes and can be safely ignored by most + users. + check_version : bool + Whether to check the version of the dataset or not. Each version + of the dataset is stored in the root with a ``version.txt`` file. + return_version : bool + Whether or not to return the version of the dataset or not. + Defaults to False. + accept : bool + Some MNE-supplied datasets require acceptance of an additional license. + Default is ``False``. + auth : tuple | None + Optional authentication tuple containing the username and + password/token, passed to :class:`pooch.HTTPDownloader` (e.g., + ``auth=('foo', 012345)``). + token : str | None + Optional authentication token passed to :class:`pooch.HTTPDownloader`. + + Returns + ------- + data_path : instance of Path + The path to the fetched dataset. + version : str + Only returned if ``return_version`` is True. + + See Also + -------- + mne.get_config + mne.set_config + mne.datasets.has_dataset + + Notes + ----- + The ``dataset_params`` argument must contain the following keys: + + - ``archive_name``: The name of the (possibly compressed) file to download + - ``url``: URL from which the file can be downloaded + - ``folder_name``: the subfolder within the ``MNE_DATA`` folder in which to + save and uncompress (if needed) the file(s) + - ``hash``: the cryptographic hash type of the file followed by a colon and + then the hash value (examples: "sha256:19uheid...", "md5:upodh2io...") + - ``config_key`` (optional): key passed to :func:`mne.set_config` to store + the on-disk location of the downloaded dataset (e.g., + ``"MNE_DATASETS_EEGBCI_PATH"``). This will only work for the provided + datasets listed :ref:`here `; do not use for user-defined + datasets. + + An example would look like:: + + {'dataset_name': 'sample', + 'archive_name': 'MNE-sample-data-processed.tar.gz', + 'hash': 'md5:12b75d1cb7df9dfb4ad73ed82f61094f', + 'url': 'https://osf.io/86qa2/download?version=5', + 'folder_name': 'MNE-sample-data', + 'config_key': 'MNE_DATASETS_SAMPLE_PATH'} + + For datasets where a single (possibly compressed) file must be downloaded, + pass a single :class:`dict` as ``dataset_params``. For datasets where + multiple files must be downloaded and (optionally) uncompressed separately, + pass a list of dicts. + """ # noqa E501 + import pooch + + if auth is not None: + if len(auth) != 2: + raise RuntimeError( + "auth should be a 2-tuple consisting " + "of a username and password/token." + ) + + # processor to uncompress files + if processor == "untar": + processor = pooch.Untar(extract_dir=path) + elif processor == "unzip": + processor = pooch.Unzip(extract_dir=path) + + if isinstance(dataset_params, dict): + dataset_params = [dataset_params] + + # extract configuration parameters + names = [params["dataset_name"] for params in dataset_params] + name = names[0] + dataset_dict = dataset_params[0] + config_key = dataset_dict.get('config_key', None) + folder_name = dataset_dict["folder_name"] + + # get download path for specific dataset + path = _get_path(path=path, key=config_key, name=name) + + # get the actual path to each dataset folder name + final_path = op.join(path, folder_name) + + # handle BrainStorm datasets with nested folders for datasets + if name.startswith("bst_"): + final_path = op.join(final_path, name) + + final_path = _mne_path(final_path) + + # additional condition: check for version.txt and parse it + # check if testing or misc data is outdated; if so, redownload it + want_version = RELEASES.get(name, None) + want_version = _FAKE_VERSION if name == "fake" else want_version + + # get the version of the dataset and then check if the version is outdated + data_version = _dataset_version(final_path, name) + outdated = (want_version is not None and + _compare_version(want_version, '>', data_version)) + + if outdated: + logger.info( + f"Dataset {name} version {data_version} out of date, " + f"latest version is {want_version}" + ) + empty = _mne_path("") + + # return empty string if outdated dataset and we don't want to download + if (not force_update) and outdated and not download: + logger.info( + 'Dataset out of date but force_update=False and download=False, ' + 'returning empty data_path') + return (empty, data_version) if return_version else empty + + # reasons to bail early (hf_sef has separate code for this): + if ( + (not force_update) + and (not outdated) + and (not name.startswith("hf_sef_")) + ): + # ...if target folder exists (otherwise pooch downloads every + # time because we don't save the archive files after unpacking, so + # pooch can't check its checksum) + if op.isdir(final_path): + if config_key is not None: + _do_path_update(path, update_path, config_key, name) + return (final_path, data_version) if return_version else final_path + # ...if download=False (useful for debugging) + elif not download: + return (empty, data_version) if return_version else empty + # ...if user didn't accept the license + elif name.startswith("bst_"): + if accept or "--accept-brainstorm-license" in sys.argv: + answer = "y" + else: + # If they don't have stdin, just accept the license + # https://github.com/mne-tools/mne-python/issues/8513#issuecomment-726823724 # noqa: E501 + answer = _safe_input( + "%sAgree (y/[n])? " % _bst_license_text, use="y") + if answer.lower() != "y": + raise RuntimeError( + "You must agree to the license to use this " "dataset" + ) + # downloader & processors + download_params = dict(progressbar=logger.level <= logging.INFO) + if name == "fake": + download_params["progressbar"] = False + if auth is not None: + download_params["auth"] = auth + if token is not None: + download_params["headers"] = {"Authorization": f"token {token}"} + downloader = pooch.HTTPDownloader(**download_params) + + # make mappings from archive names to urls and to checksums + urls = dict() + registry = dict() + for idx, this_name in enumerate(names): + this_dataset = dataset_params[idx] + archive_name = this_dataset["archive_name"] + dataset_url = this_dataset["url"] + dataset_hash = this_dataset["hash"] + urls[archive_name] = dataset_url + registry[archive_name] = dataset_hash + + # create the download manager + fetcher = pooch.create( + path=str(final_path) if processor is None else path, + base_url="", # Full URLs are given in the `urls` dict. + version=None, # Data versioning is decoupled from MNE-Python version. + urls=urls, + registry=registry, + retry_if_failed=2, # 2 retries = 3 total attempts + ) + + # use our logger level for pooch's logger too + pooch.get_logger().setLevel(logger.getEffectiveLevel()) + + for idx in range(len(names)): + # fetch and unpack the data + archive_name = dataset_params[idx]["archive_name"] + fetcher.fetch( + fname=archive_name, downloader=downloader, processor=processor + ) + # after unpacking, remove the archive file + if processor is not None: + os.remove(op.join(path, archive_name)) + + # remove version number from "misc" and "testing" datasets folder names + if name == "misc": + rmtree(final_path, ignore_errors=True) + os.replace(op.join(path, MISC_VERSIONED), final_path) + elif name == "testing": + rmtree(final_path, ignore_errors=True) + os.replace(op.join(path, TESTING_VERSIONED), final_path) + + # maybe update the config + if config_key is not None: + old_name = "brainstorm" if name.startswith("bst_") else name + _do_path_update(path, update_path, config_key, old_name) + + # compare the version of the dataset and mne + data_version = _dataset_version(path, name) + # 0.7 < 0.7.git should be False, therefore strip + if check_version and ( + _compare_version(data_version, '<', mne_version.strip(".git")) + ): + warn( + "The {name} dataset (version {current}) is older than " + "mne-python (version {newest}). If the examples fail, " + "you may need to update the {name} dataset by using " + "mne.datasets.{name}.data_path(force_update=True)".format( + name=name, current=data_version, newest=mne_version + ) + ) + return (final_path, data_version) if return_version else final_path diff -Nru python-mne-0.23.4+dfsg/mne/datasets/fieldtrip_cmc/fieldtrip_cmc.py python-mne-1.1.0+dfsg/mne/datasets/fieldtrip_cmc/fieldtrip_cmc.py --- python-mne-0.23.4+dfsg/mne/datasets/fieldtrip_cmc/fieldtrip_cmc.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/fieldtrip_cmc/fieldtrip_cmc.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,33 +2,26 @@ # Alexandre Barachant # # License: BSD Style. - -from functools import partial - from ...utils import verbose -from ..utils import (has_dataset, _data_path, _data_path_doc, - _get_version, _version_doc) - - -data_name = "fieldtrip_cmc" -conf_name = "MNE_DATASETS_FIELDTRIP_CMC_PATH" -has_mtrf_data = partial(has_dataset, name=data_name) +from ..utils import (_data_path_doc, + _get_version, _version_doc, _download_mne_dataset) @verbose -def data_path(path=None, force_update=False, update_path=True, download=True, - verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name=data_name, - download=download) +def data_path(path=None, force_update=False, update_path=True, + download=True, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='fieldtrip_cmc', processor='nested_unzip', path=path, + force_update=force_update, update_path=update_path, + download=download) -data_path.__doc__ = _data_path_doc.format(name=data_name, - conf=conf_name) +data_path.__doc__ = _data_path_doc.format( + name='fieldtrip_cmc', conf='MNE_DATASETS_FIELDTRIP_CMC_PATH') def get_version(): # noqa: D103 - return _get_version(data_name) + return _get_version('fieldtrip_cmc') -get_version.__doc__ = _version_doc.format(name=data_name) +get_version.__doc__ = _version_doc.format(name='fieldtrip_cmc') diff -Nru python-mne-0.23.4+dfsg/mne/datasets/fnirs_motor/fnirs_motor.py python-mne-1.1.0+dfsg/mne/datasets/fnirs_motor/fnirs_motor.py --- python-mne-0.23.4+dfsg/mne/datasets/fnirs_motor/fnirs_motor.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/fnirs_motor/fnirs_motor.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,22 +1,18 @@ # Authors: Eric Larson # License: BSD Style. -from functools import partial - from ...utils import verbose -from ..utils import (has_dataset, _data_path, _data_path_doc, - _get_version, _version_doc) - - -has_fnirs_motor_data = partial(has_dataset, name='fnirs_motor') +from ..utils import (_data_path_doc, _get_version, _version_doc, + _download_mne_dataset) @verbose -def data_path(path=None, force_update=False, update_path=True, download=True, - verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='fnirs_motor', - download=download) +def data_path(path=None, force_update=False, update_path=True, + download=True, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='fnirs_motor', processor='untar', path=path, + force_update=force_update, update_path=update_path, + download=download) data_path.__doc__ = _data_path_doc.format(name='fnirs_motor', diff -Nru python-mne-0.23.4+dfsg/mne/datasets/fnirs_motor/__init__.py python-mne-1.1.0+dfsg/mne/datasets/fnirs_motor/__init__.py --- python-mne-0.23.4+dfsg/mne/datasets/fnirs_motor/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/fnirs_motor/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,3 @@ """fNIRS motor dataset.""" -from .fnirs_motor import data_path, has_fnirs_motor_data, get_version +from .fnirs_motor import data_path, get_version diff -Nru python-mne-0.23.4+dfsg/mne/datasets/_fsaverage/base.py python-mne-1.1.0+dfsg/mne/datasets/_fsaverage/base.py --- python-mne-0.23.4+dfsg/mne/datasets/_fsaverage/base.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/_fsaverage/base.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,15 +5,14 @@ import os import os.path as op - from ..utils import _manifest_check_download, _get_path -from ...utils import (verbose, get_subjects_dir, set_config) +from ...utils import verbose, get_subjects_dir, set_config FSAVERAGE_MANIFEST_PATH = op.dirname(__file__) @verbose -def fetch_fsaverage(subjects_dir=None, verbose=None): +def fetch_fsaverage(subjects_dir=None, *, verbose=None): """Fetch and update fsaverage. Parameters @@ -66,19 +65,19 @@ # fid.write('\n'.join(names)) # subjects_dir = _set_montage_coreg_path(subjects_dir) - subjects_dir = op.abspath(subjects_dir) + subjects_dir = op.abspath(op.expanduser(subjects_dir)) fs_dir = op.join(subjects_dir, 'fsaverage') os.makedirs(fs_dir, exist_ok=True) _manifest_check_download( manifest_path=op.join(FSAVERAGE_MANIFEST_PATH, 'root.txt'), destination=op.join(subjects_dir), - url='https://osf.io/3bxqt/download?revision=2', + url='https://osf.io/3bxqt/download?version=2', hash_='5133fe92b7b8f03ae19219d5f46e4177', ) _manifest_check_download( manifest_path=op.join(FSAVERAGE_MANIFEST_PATH, 'bem.txt'), destination=op.join(subjects_dir, 'fsaverage'), - url='https://osf.io/7ve8g/download?revision=4', + url='https://osf.io/7ve8g/download?version=4', hash_='b31509cdcf7908af6a83dc5ee8f49fb1', ) return fs_dir diff -Nru python-mne-0.23.4+dfsg/mne/datasets/hf_sef/hf_sef.py python-mne-1.1.0+dfsg/mne/datasets/hf_sef/hf_sef.py --- python-mne-0.23.4+dfsg/mne/datasets/hf_sef/hf_sef.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/hf_sef/hf_sef.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,16 +4,16 @@ # License: BSD Style. -import tarfile import os.path as op import os -from ...utils import _fetch_file, verbose, _check_option -from ..utils import _get_path, logger, _do_path_update +from ...utils import verbose, _check_option +from ..utils import _get_path, _do_path_update, _download_mne_dataset +from ..config import MNE_DATASETS @verbose def data_path(dataset='evoked', path=None, force_update=False, - update_path=True, verbose=None): + update_path=True, *, verbose=None): u"""Get path to local copy of the high frequency SEF dataset. Gets a local copy of the high frequency SEF MEG dataset @@ -47,54 +47,33 @@ ---------- .. footbibliography:: """ - key = 'MNE_DATASETS_HF_SEF_PATH' - name = 'HF_SEF' - path = _get_path(path, key, name) - destdir = op.join(path, 'HF_SEF') - - urls = {'evoked': - 'https://zenodo.org/record/3523071/files/hf_sef_evoked.tar.gz', - 'raw': - 'https://zenodo.org/record/889296/files/hf_sef_raw.tar.gz'} - hashes = {'evoked': '13d34cb5db584e00868677d8fb0aab2b', - 'raw': '33934351e558542bafa9b262ac071168'} - _check_option('dataset', dataset, sorted(urls.keys())) - url = urls[dataset] - hash_ = hashes[dataset] - fn = url.split('/')[-1] # pick the filename from the url - archive = op.join(destdir, fn) - - # check for existence of evoked and raw sets - has = dict() - subjdir = op.join(destdir, 'subjects') - megdir_a = op.join(destdir, 'MEG', 'subject_a') - has['evoked'] = op.isdir(destdir) and op.isdir(subjdir) - has['raw'] = op.isdir(megdir_a) and any(['raw' in fn_ for fn_ in - os.listdir(megdir_a)]) - - if not has[dataset] or force_update: - if not op.isdir(destdir): - os.mkdir(destdir) - _fetch_file(url, archive, hash_=hash_) - - with tarfile.open(archive) as tar: - logger.info('Decompressing %s' % archive) - for member in tar.getmembers(): - # strip the leading dirname 'hf_sef/' from the archive paths - # this should be fixed when making next version of archives - member.name = member.name[7:] - try: - tar.extract(member, destdir) - except IOError: - # check whether file exists but could not be overwritten - fn_full = op.join(destdir, member.name) - if op.isfile(fn_full): - os.remove(fn_full) - tar.extract(member, destdir) - else: # some more sinister cause for IOError - raise - - os.remove(archive) - - _do_path_update(path, update_path, key, name) - return destdir + _check_option('dataset', dataset, ('evoked', 'raw')) + if dataset == 'raw': + data_dict = MNE_DATASETS['hf_sef_raw'] + data_dict['dataset_name'] = 'hf_sef_raw' + else: + data_dict = MNE_DATASETS['hf_sef_evoked'] + data_dict['dataset_name'] = 'hf_sef_evoked' + config_key = data_dict['config_key'] + folder_name = data_dict['folder_name'] + + # get download path for specific dataset + path = _get_path(path=path, key=config_key, name=folder_name) + final_path = op.join(path, folder_name) + megdir = op.join(final_path, 'MEG', 'subject_a') + has_raw = (dataset == 'raw' and op.isdir(megdir) and + any('raw' in filename for filename in os.listdir(megdir))) + has_evoked = (dataset == 'evoked' and + op.isdir(op.join(final_path, 'subjects'))) + # data not there, or force_update requested: + if has_raw or has_evoked and not force_update: + _do_path_update(path, update_path, config_key, + folder_name) + return final_path + + # instantiate processor that unzips file + data_path = _download_mne_dataset(name=data_dict['dataset_name'], + processor='untar', path=path, + force_update=force_update, + update_path=update_path, download=True) + return data_path diff -Nru python-mne-0.23.4+dfsg/mne/datasets/_infant/base.py python-mne-1.1.0+dfsg/mne/datasets/_infant/base.py --- python-mne-0.23.4+dfsg/mne/datasets/_infant/base.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/_infant/base.py 2022-08-03 15:48:14.000000000 +0000 @@ -31,7 +31,7 @@ @verbose -def fetch_infant_template(age, subjects_dir=None, verbose=None): +def fetch_infant_template(age, subjects_dir=None, *, verbose=None): """Fetch and update an infant MRI template. Parameters diff -Nru python-mne-0.23.4+dfsg/mne/datasets/__init__.py python-mne-1.1.0+dfsg/mne/datasets/__init__.py --- python-mne-0.23.4+dfsg/mne/datasets/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -26,10 +26,12 @@ from . import ssvep from . import erp_core from . import epilepsy_ecog +from ._fetch import fetch_dataset from .utils import (_download_all_example_data, fetch_hcp_mmp_parcellation, - fetch_aparc_sub_parcellation) + fetch_aparc_sub_parcellation, has_dataset) from ._fsaverage.base import fetch_fsaverage from ._infant.base import fetch_infant_template +from ._phantom.base import fetch_phantom __all__ = [ '_download_all_example_data', '_fake', 'brainstorm', 'eegbci', @@ -37,5 +39,7 @@ 'fetch_hcp_mmp_parcellation', 'fieldtrip_cmc', 'hf_sef', 'kiloword', 'misc', 'mtrf', 'multimodal', 'opm', 'phantom_4dbti', 'sample', 'sleep_physionet', 'somato', 'spm_face', 'ssvep', 'testing', - 'visual_92_categories', 'limo', 'erp_core', 'epilepsy_ecog' + 'visual_92_categories', 'limo', 'erp_core', 'epilepsy_ecog', + 'fetch_dataset', 'fetch_phantom', 'has_dataset', 'refmeg_noise', + 'fnirs_motor' ] diff -Nru python-mne-0.23.4+dfsg/mne/datasets/kiloword/kiloword.py python-mne-1.1.0+dfsg/mne/datasets/kiloword/kiloword.py --- python-mne-0.23.4+dfsg/mne/datasets/kiloword/kiloword.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/kiloword/kiloword.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,12 +1,12 @@ # License: BSD Style. from ...utils import verbose -from ..utils import _data_path, _get_version, _version_doc +from ..utils import (_get_version, _version_doc, _download_mne_dataset) @verbose -def data_path(path=None, force_update=False, update_path=True, download=True, - verbose=None): +def data_path(path=None, force_update=False, update_path=True, + download=True, *, verbose=None): """Get path to local copy of the kiloword dataset. This is the dataset from :footcite:`DufauEtAl2015`. @@ -35,7 +35,7 @@ Returns ------- - path : list of str + path : list of Path Local path to the given data file. This path is contained inside a list of length one, for compatibility. @@ -43,9 +43,10 @@ ---------- .. footbibliography:: """ - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='kiloword', - download=download) + return _download_mne_dataset( + name='kiloword', processor='untar', path=path, + force_update=force_update, update_path=update_path, + download=download) def get_version(): diff -Nru python-mne-0.23.4+dfsg/mne/datasets/limo/limo.py python-mne-1.1.0+dfsg/mne/datasets/limo/limo.py --- python-mne-0.23.4+dfsg/mne/datasets/limo/limo.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/limo/limo.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,50 +1,25 @@ # Authors: Jose C. Garcia Alanis # -# License: BSD (3-clause) +# License: BSD-3-Clause import os import os.path as op -import shutil -import zipfile -from sys import stdout import numpy as np from ...channels import make_standard_montage from ...epochs import EpochsArray from ...io.meas_info import create_info -from ...utils import _fetch_file, _check_pandas_installed, verbose -from ..utils import _get_path, _do_path_update +from ...utils import _check_pandas_installed, verbose +from ..utils import _get_path, _do_path_update, logger + # root url for LIMO files root_url = 'https://files.de-1.osf.io/v1/resources/52rea/providers/osfstorage/' -# subject identifier -subject_ids = {'S1': '5cde823c8d6e050018595862', - 'S2': '5cde825e23fec40017e0561a', - 'S3': '5cf7eedee650a2001ad560f2', - 'S4': '5cf7eee7d4c7d700193defcb', - 'S5': '5cf7eeece650a20017d5b153', - - 'S6': '5cf8300fe650a20018d59cef', - 'S7': '5cf83018a542b8001bc7c75f', - 'S8': '5cf8301ea542b8001ac7cc47', - 'S9': '5cf830243a4d9500178a692b', - 'S10': '5cf83029e650a20017d600b1', - - 'S11': '5cf834bfa542b8001bc7cae0', - 'S12': '5cf834c53a4d9500188a6311', - 'S13': '5cf834caa542b8001cc8149b', - 'S14': '5cf834cf3a4d9500178a6c6c', - 'S15': '5cf834d63a4d9500168ae5d6', - - 'S16': '5cf834dbe650a20018d5a123', - 'S17': '5cf834e23a4d9500198a911f', - 'S18': '5cf834e73a4d9500198a9122'} - @verbose -def data_path(subject, path=None, force_update=False, update_path=None, +def data_path(subject, path=None, force_update=False, update_path=None, *, verbose=None): """Get path to local copy of LIMO dataset URL. @@ -55,7 +30,8 @@ Parameters ---------- subject : int - Subject to download. Must be of class ìnt in the range from 1 to 18. + Subject to download. Must be of :class:`ìnt` in the range from 1 + to 18 (inclusive). path : None | str Location of where to look for the LIMO data storing directory. If None, the environment variable or config parameter @@ -90,46 +66,117 @@ ---------- .. footbibliography:: """ # noqa: E501 - # set destination path for download - key = 'MNE_DATASETS_LIMO_PATH' - name = 'LIMO' - path = _get_path(path, key, name) - limo_dir = op.join(path, 'MNE-limo-data') - subject_id = 'S%s' % subject - destination = op.join(limo_dir, '%s.zip') % subject_id - - # url for subject in question - url = root_url + subject_ids[subject_id] + '/?zip=' - - # check if LIMO directory exists; update if desired - if not op.isdir(limo_dir) or force_update: - if op.isdir(limo_dir): - shutil.rmtree(limo_dir) - if not op.isdir(limo_dir): - os.makedirs(limo_dir) - - # check if subject in question exists - if not op.isdir(op.join(limo_dir, subject_id)): - os.makedirs(op.join(limo_dir, subject_id)) - _fetch_file(url, destination, print_destination=False) - - # check if download is a zip-folder - if any(group.endswith(".zip") for group in op.splitext(destination)): - if not op.isdir(op.join(limo_dir, subject_id)): - os.makedirs(op.join(limo_dir, subject_id)) - with zipfile.ZipFile(destination) as z1: - files = [op.join(limo_dir, file) for file in z1.namelist()] - stdout.write('Decompressing %g files from\n' - '"%s" ...' % (len(files), destination)) - z1.extractall(op.join(limo_dir, subject_id)) - stdout.write(' [done]\n') - z1.close() - os.remove(destination) + import pooch - # update path if desired - _do_path_update(path, update_path, key, name) + downloader = pooch.HTTPDownloader(progressbar=True) # use tqdm - return limo_dir + # local storage patch + config_key = 'MNE_DATASETS_LIMO_PATH' + name = 'LIMO' + subj = f'S{subject}' + path = _get_path(path, config_key, name) + base_path = op.join(path, 'MNE-limo-data') + subject_path = op.join(base_path, subj) + # the remote URLs are in the form of UUIDs: + urls = dict( + S18={'Yr.mat': '5cf839833a4d9500178a6ff8', + 'LIMO.mat': '5cf83907e650a2001ad592e4'}, + S17={'Yr.mat': '5cf838e83a4d9500168aeb76', + 'LIMO.mat': '5cf83867a542b80019c87602'}, + S16={'Yr.mat': '5cf83857e650a20019d5778f', + 'LIMO.mat': '5cf837dc3a4d9500188a64fe'}, + S15={'Yr.mat': '5cf837cce650a2001ad591e8', + 'LIMO.mat': '5cf83758a542b8001ac7d11d'}, + S14={'Yr.mat': '5cf837493a4d9500198a938f', + 'LIMO.mat': '5cf836e4a542b8001bc7cc53'}, + S13={'Yr.mat': '5cf836d23a4d9500178a6df7', + 'LIMO.mat': '5cf836543a4d9500168ae7cb'}, + S12={'Yr.mat': '5cf83643d4c7d700193e5954', + 'LIMO.mat': '5cf835193a4d9500178a6c92'}, + S11={'Yr.mat': '5cf8356ea542b8001cc81517', + 'LIMO.mat': '5cf834f7d4c7d700163daab8'}, + S10={'Yr.mat': '5cf833b0e650a20019d57454', + 'LIMO.mat': '5cf83204e650a20018d59eb2'}, + S9={'Yr.mat': '5cf83201a542b8001cc811cf', + 'LIMO.mat': '5cf8316c3a4d9500168ae13b'}, + S8={'Yr.mat': '5cf8326ce650a20017d60373', + 'LIMO.mat': '5cf8316d3a4d9500198a8dc5'}, + S7={'Yr.mat': '5cf834a03a4d9500168ae59b', + 'LIMO.mat': '5cf83069e650a20017d600d7'}, + S6={'Yr.mat': '5cf830e6a542b80019c86a70', + 'LIMO.mat': '5cf83057a542b80019c869ca'}, + S5={'Yr.mat': '5cf8115be650a20018d58041', + 'LIMO.mat': '5cf80c0bd4c7d700193e213c'}, + S4={'Yr.mat': '5cf810c9a542b80019c8450a', + 'LIMO.mat': '5cf80bf83a4d9500198a6eb4'}, + S3={'Yr.mat': '5cf80c55d4c7d700163d8f52', + 'LIMO.mat': '5cf80bdea542b80019c83cab'}, + S2={'Yr.mat': '5cde827123fec40019e01300', + 'LIMO.mat': '5cde82682a50c4001677c259'}, + S1={'Yr.mat': '5d6d3071536cf5001a8b0c78', + 'LIMO.mat': '5d6d305f6f41fc001a3151d8'}, + ) + # these can't be in the registry file (mne/data/dataset_checksums.txt) + # because of filename duplication + hashes = dict( + S18={'Yr.mat': 'md5:87f883d442737971a80fc0a35d057e51', + 'LIMO.mat': 'md5:8b4879646f65d7876fa4adf2e40162c5'}, + S17={'Yr.mat': 'md5:7b667ec9eefd7a9996f61ae270e295ee', + 'LIMO.mat': 'md5:22eaca4e6fad54431fd61b307fc426b8'}, + S16={'Yr.mat': 'md5:c877afdb4897426421577e863a45921a', + 'LIMO.mat': 'md5:86672d7afbea1e8c39305bc3f852c8c2'}, + S15={'Yr.mat': 'md5:eea9e0140af598fefc08c886a6f05de5', + 'LIMO.mat': 'md5:aed5cb71ddbfd27c6a3ac7d3e613d07f'}, + S14={'Yr.mat': 'md5:8bd842cfd8588bd5d32e72fdbe70b66e', + 'LIMO.mat': 'md5:1e07d1f36f2eefad435a77530daf2680'}, + S13={'Yr.mat': 'md5:d7925d2af7288b8a5186dfb5dbb63d34', + 'LIMO.mat': 'md5:ba891015d2f9e447955fffa9833404ca'}, + S12={'Yr.mat': 'md5:0e1d05beaa4bf2726e0d0671b78fe41e', + 'LIMO.mat': 'md5:423fd479d71097995b6614ecb11df9ad'}, + S11={'Yr.mat': 'md5:1b0016fb9832e43b71f79c1992fcbbb1', + 'LIMO.mat': 'md5:1a281348c2a41ee899f42731d30cda70'}, + S10={'Yr.mat': 'md5:13c66f60e241b9a9cc576eaf1b55a417', + 'LIMO.mat': 'md5:3c4b41e221eb352a21bbef1a7e006f06'}, + S9={'Yr.mat': 'md5:3ae1d9c3a1d9325deea2f2dddd1ab507', + 'LIMO.mat': 'md5:5e204e2a4bcfe4f535b4b1af469b37f7'}, + S8={'Yr.mat': 'md5:7e9adbca4e03d8d7ce8ea07ccecdc8fd', + 'LIMO.mat': 'md5:88313c21d34428863590e586b2bc3408'}, + S7={'Yr.mat': 'md5:6b5290a6725ecebf1022d5d2789b186d', + 'LIMO.mat': 'md5:8c769219ebc14ce3f595063e84bfc0a9'}, + S6={'Yr.mat': 'md5:420c858a8340bf7c28910b7b0425dc5d', + 'LIMO.mat': 'md5:9cf4e1a405366d6bd0cc6d996e32fd63'}, + S5={'Yr.mat': 'md5:946436cfb474c8debae56ffb1685ecf3', + 'LIMO.mat': 'md5:241fac95d3a79d2cea081391fb7078bd'}, + S4={'Yr.mat': 'md5:c8216af78ac87b739e86e57b345cafdd', + 'LIMO.mat': 'md5:8e10ef36c2e075edc2f787581ba33459'}, + S3={'Yr.mat': 'md5:ff02e885b65b7b807146f259a30b1b5e', + 'LIMO.mat': 'md5:59b5fb3a9749003133608b5871309e2c'}, + S2={'Yr.mat': 'md5:a4329022e57fd07ceceb7d1735fd2718', + 'LIMO.mat': 'md5:98b284b567f2dd395c936366e404f2c6'}, + S1={'Yr.mat': 'md5:076c0ae78fb71d43409c1877707df30e', + 'LIMO.mat': 'md5:136c8cf89f8f111a11f531bd9fa6ae69'}, + ) + # create the download manager + fetcher = pooch.create( + path=subject_path, + base_url='', + version=None, # Data versioning is decoupled from MNE-Python version. + registry=hashes[subj], + urls={key: f'{root_url}{uuid}' for key, uuid in urls[subj].items()}, + retry_if_failed=2 # 2 retries = 3 total attempts + ) + # use our logger level for pooch's logger too + pooch.get_logger().setLevel(logger.getEffectiveLevel()) + # fetch the data + for fname in ('LIMO.mat', 'Yr.mat'): + destination = op.join(subject_path, fname) + if force_update and op.isfile(destination): + os.remove(destination) + # fetch the remote file (if local file missing or has hash mismatch) + fetcher.fetch(fname=fname, downloader=downloader) + # update path in config if desired + _do_path_update(path, update_path, config_key, name) + return base_path @verbose diff -Nru python-mne-0.23.4+dfsg/mne/datasets/misc/__init__.py python-mne-1.1.0+dfsg/mne/datasets/misc/__init__.py --- python-mne-0.23.4+dfsg/mne/datasets/misc/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/misc/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,3 @@ """MNE misc dataset.""" -from ._misc import data_path +from ._misc import data_path, _pytest_mark diff -Nru python-mne-0.23.4+dfsg/mne/datasets/misc/_misc.py python-mne-1.1.0+dfsg/mne/datasets/misc/_misc.py --- python-mne-0.23.4+dfsg/mne/datasets/misc/_misc.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/misc/_misc.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,15 +4,22 @@ # License: BSD Style. from ...utils import verbose -from ..utils import _data_path, _data_path_doc +from ..utils import has_dataset, _data_path_doc, _download_mne_dataset @verbose def data_path(path=None, force_update=False, update_path=True, - download=True, verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='misc', - download=download) + download=True, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='misc', processor='untar', path=path, + force_update=force_update, update_path=update_path, + download=download) + + +def _pytest_mark(): + import pytest + return pytest.mark.skipif( + not has_dataset(name='misc'), reason='Requires misc dataset') data_path.__doc__ = _data_path_doc.format(name='misc', diff -Nru python-mne-0.23.4+dfsg/mne/datasets/mtrf/mtrf.py python-mne-1.1.0+dfsg/mne/datasets/mtrf/mtrf.py --- python-mne-0.23.4+dfsg/mne/datasets/mtrf/mtrf.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/mtrf/mtrf.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,23 +2,21 @@ # # License: BSD Style. -from functools import partial - from ...utils import verbose -from ..utils import (has_dataset, _data_path, _data_path_doc, - _get_version, _version_doc) +from ..utils import (_data_path_doc, + _get_version, _version_doc, _download_mne_dataset) data_name = 'mtrf' -has_mtrf_data = partial(has_dataset, name=data_name) @verbose -def data_path(path=None, force_update=False, update_path=True, download=True, - verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name=data_name, - download=download) +def data_path(path=None, force_update=False, update_path=True, + download=True, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name=data_name, processor='unzip', path=path, + force_update=force_update, update_path=update_path, + download=download) data_path.__doc__ = _data_path_doc.format(name=data_name, diff -Nru python-mne-0.23.4+dfsg/mne/datasets/multimodal/__init__.py python-mne-1.1.0+dfsg/mne/datasets/multimodal/__init__.py --- python-mne-0.23.4+dfsg/mne/datasets/multimodal/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/multimodal/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,3 @@ """Multimodal dataset.""" -from .multimodal import data_path, has_multimodal_data, get_version +from .multimodal import data_path, get_version diff -Nru python-mne-0.23.4+dfsg/mne/datasets/multimodal/multimodal.py python-mne-1.1.0+dfsg/mne/datasets/multimodal/multimodal.py --- python-mne-0.23.4+dfsg/mne/datasets/multimodal/multimodal.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/multimodal/multimodal.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,22 +3,18 @@ # Eric Larson # License: BSD Style. -from functools import partial - from ...utils import verbose -from ..utils import (has_dataset, _data_path, _data_path_doc, - _get_version, _version_doc) - - -has_multimodal_data = partial(has_dataset, name='multimodal') +from ..utils import (_data_path_doc, _get_version, _version_doc, + _download_mne_dataset) @verbose -def data_path(path=None, force_update=False, update_path=True, download=True, - verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='multimodal', - download=download) +def data_path(path=None, force_update=False, update_path=True, + download=True, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='multimodal', processor='untar', path=path, + force_update=force_update, update_path=update_path, + download=download) data_path.__doc__ = _data_path_doc.format(name='multimodal', diff -Nru python-mne-0.23.4+dfsg/mne/datasets/opm/__init__.py python-mne-1.1.0+dfsg/mne/datasets/opm/__init__.py --- python-mne-0.23.4+dfsg/mne/datasets/opm/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/opm/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,3 @@ """OPM dataset.""" -from .opm import data_path, has_opm_data, get_version +from .opm import data_path, get_version diff -Nru python-mne-0.23.4+dfsg/mne/datasets/opm/opm.py python-mne-1.1.0+dfsg/mne/datasets/opm/opm.py --- python-mne-0.23.4+dfsg/mne/datasets/opm/opm.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/opm/opm.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,22 +3,18 @@ # Eric Larson # License: BSD Style. -from functools import partial - from ...utils import verbose -from ..utils import (has_dataset, _data_path, _data_path_doc, - _get_version, _version_doc) - - -has_opm_data = partial(has_dataset, name='opm') +from ..utils import (_data_path_doc, _get_version, _version_doc, + _download_mne_dataset) @verbose -def data_path(path=None, force_update=False, update_path=True, download=True, - verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='opm', - download=download) +def data_path(path=None, force_update=False, update_path=True, + download=True, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='opm', processor='untar', path=path, + force_update=force_update, update_path=update_path, + download=download) data_path.__doc__ = _data_path_doc.format(name='opm', diff -Nru python-mne-0.23.4+dfsg/mne/datasets/_phantom/base.py python-mne-1.1.0+dfsg/mne/datasets/_phantom/base.py --- python-mne-0.23.4+dfsg/mne/datasets/_phantom/base.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/_phantom/base.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,62 @@ +# -*- coding: utf-8 -*- +# Authors: Eric Larson +# License: BSD Style. + +import os +import os.path as op + +from ..utils import _manifest_check_download +from ...utils import verbose, get_subjects_dir, _check_option, _validate_type + +PHANTOM_MANIFEST_PATH = op.dirname(__file__) + + +@verbose +def fetch_phantom(kind, subjects_dir=None, *, verbose=None): + """Fetch and update a phantom subject. + + Parameters + ---------- + kind : str + The kind of phantom to fetch. Can only be ``'otaniemi'`` (default). + %(subjects_dir)s + %(verbose)s + + Returns + ------- + subject_dir : str + The resulting phantom subject directory. + + See Also + -------- + mne.dipole.get_phantom_dipoles + + Notes + ----- + This function is designed to provide a head surface and T1.mgz for + the 32-dipole Otaniemi phantom. The VectorView/TRIUX phantom has the same + basic outside geometry, but different internal dipole positions. + + Unlike most FreeSurfer subjects, the Otaniemi phantom scan was aligned + to the "head" coordinate frame, so an identity head<->MRI :term:`trans` + is appropriate. + + .. versionadded:: 0.24 + """ + phantoms = dict( + otaniemi=dict(url='https://osf.io/j5czy/download?version=1', + hash='42d17db5b1db3e30327ffb4cf2649de8'), + ) + _validate_type(kind, str, 'kind') + _check_option('kind', kind, list(phantoms)) + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + subject = f'phantom_{kind}' + subject_dir = op.join(subjects_dir, subject) + os.makedirs(subject_dir, exist_ok=True) + _manifest_check_download( + manifest_path=op.join(PHANTOM_MANIFEST_PATH, f'{subject}.txt'), + destination=subjects_dir, + url=phantoms[kind]['url'], + hash_=phantoms[kind]['hash'], + ) + return subject_dir diff -Nru python-mne-0.23.4+dfsg/mne/datasets/_phantom/phantom_otaniemi.txt python-mne-1.1.0+dfsg/mne/datasets/_phantom/phantom_otaniemi.txt --- python-mne-0.23.4+dfsg/mne/datasets/_phantom/phantom_otaniemi.txt 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/_phantom/phantom_otaniemi.txt 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,3 @@ +phantom_otaniemi/bem/phantom_otaniemi-fiducials.fif +phantom_otaniemi/mri/T1.mgz +phantom_otaniemi/surf/lh.seghead diff -Nru python-mne-0.23.4+dfsg/mne/datasets/phantom_4dbti/__init__.py python-mne-1.1.0+dfsg/mne/datasets/phantom_4dbti/__init__.py --- python-mne-0.23.4+dfsg/mne/datasets/phantom_4dbti/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/phantom_4dbti/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,3 @@ """Multimodal dataset.""" -from .phantom_4dbti import data_path, has_phantom_4dbti_data, get_version +from .phantom_4dbti import data_path, get_version diff -Nru python-mne-0.23.4+dfsg/mne/datasets/phantom_4dbti/phantom_4dbti.py python-mne-1.1.0+dfsg/mne/datasets/phantom_4dbti/phantom_4dbti.py --- python-mne-0.23.4+dfsg/mne/datasets/phantom_4dbti/phantom_4dbti.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/phantom_4dbti/phantom_4dbti.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,22 +2,18 @@ # # License: BSD Style. -from functools import partial - from ...utils import verbose -from ..utils import (has_dataset, _data_path, _data_path_doc, - _get_version, _version_doc) - - -has_phantom_4dbti_data = partial(has_dataset, name='phantom_4dbti') +from ..utils import (_data_path_doc, _get_version, _version_doc, + _download_mne_dataset) @verbose -def data_path(path=None, force_update=False, update_path=True, download=True, - verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='phantom_4dbti', - download=download) +def data_path(path=None, force_update=False, update_path=True, + download=True, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='phantom_4dbti', processor='unzip', path=path, + force_update=force_update, update_path=update_path, + download=download) data_path.__doc__ = _data_path_doc.format( diff -Nru python-mne-0.23.4+dfsg/mne/datasets/refmeg_noise/__init__.py python-mne-1.1.0+dfsg/mne/datasets/refmeg_noise/__init__.py --- python-mne-0.23.4+dfsg/mne/datasets/refmeg_noise/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/refmeg_noise/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,3 @@ """MEG reference-noise data set.""" -from .refmeg_noise import data_path, has_refmeg_noise_data, get_version +from .refmeg_noise import data_path, get_version diff -Nru python-mne-0.23.4+dfsg/mne/datasets/refmeg_noise/refmeg_noise.py python-mne-1.1.0+dfsg/mne/datasets/refmeg_noise/refmeg_noise.py --- python-mne-0.23.4+dfsg/mne/datasets/refmeg_noise/refmeg_noise.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/refmeg_noise/refmeg_noise.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,26 +1,22 @@ # Authors: Jeff Hanna # License: BSD Style. -from functools import partial - from ...utils import verbose -from ..utils import (has_dataset, _data_path, _data_path_doc, - _get_version, _version_doc) - - -has_refmeg_noise_data = partial(has_dataset, name='refmeg_noise') +from ..utils import (_data_path_doc, _get_version, _version_doc, + _download_mne_dataset) @verbose -def data_path(path=None, force_update=False, update_path=True, download=True, - verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='refmeg_noise', - download=download) +def data_path(path=None, force_update=False, update_path=True, + download=True, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='refmeg_noise', processor='unzip', path=path, + force_update=force_update, update_path=update_path, + download=download) -data_path.__doc__ = _data_path_doc.format(name='refmeg_noise', - conf='MNE_DATASETS_REFMEG_NOISE_PATH') # noqa +data_path.__doc__ = _data_path_doc.format( + name='refmeg_noise', conf='MNE_DATASETS_REFMEG_NOISE_PATH') def get_version(): # noqa: D103 diff -Nru python-mne-0.23.4+dfsg/mne/datasets/sample/__init__.py python-mne-1.1.0+dfsg/mne/datasets/sample/__init__.py --- python-mne-0.23.4+dfsg/mne/datasets/sample/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/sample/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,3 @@ """MNE sample dataset.""" -from .sample import data_path, has_sample_data, get_version +from .sample import data_path, get_version diff -Nru python-mne-0.23.4+dfsg/mne/datasets/sample/sample.py python-mne-1.1.0+dfsg/mne/datasets/sample/sample.py --- python-mne-0.23.4+dfsg/mne/datasets/sample/sample.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/sample/sample.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,22 +3,18 @@ # Eric Larson # License: BSD Style. -from functools import partial - from ...utils import verbose -from ..utils import (has_dataset, _data_path, _data_path_doc, - _get_version, _version_doc) - - -has_sample_data = partial(has_dataset, name='sample') +from ..utils import (_data_path_doc, _get_version, _version_doc, + _download_mne_dataset) @verbose -def data_path(path=None, force_update=False, update_path=True, download=True, - verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='sample', - download=download) +def data_path(path=None, force_update=False, update_path=True, + download=True, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='sample', processor='untar', path=path, + force_update=force_update, update_path=update_path, + download=download) data_path.__doc__ = _data_path_doc.format(name='sample', diff -Nru python-mne-0.23.4+dfsg/mne/datasets/sleep_physionet/age.py python-mne-1.1.0+dfsg/mne/datasets/sleep_physionet/age.py --- python-mne-0.23.4+dfsg/mne/datasets/sleep_physionet/age.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/sleep_physionet/age.py 2022-08-03 15:48:14.000000000 +0000 @@ -17,8 +17,7 @@ @verbose def fetch_data(subjects, recording=(1, 2), path=None, force_update=False, - update_path=None, base_url=BASE_URL, on_missing='raise', - verbose=None): # noqa: D301 + base_url=BASE_URL, on_missing='raise', *, verbose=None): # noqa: D301, E501 """Get paths to local copies of PhysioNet Polysomnography dataset files. This will fetch data from the publicly available subjects from PhysioNet's @@ -44,15 +43,12 @@ path : None | str Location of where to look for the PhysioNet data storing location. If None, the environment variable or config parameter - ``MNE_DATASETS_PHYSIONET_SLEEP_PATH`` is used. If it doesn't exist, the - "~/mne_data" directory is used. If the Polysomnography dataset - is not found under the given path, the data - will be automatically downloaded to the specified folder. + ``PHYSIONET_SLEEP_PATH`` is used. If it doesn't exist, the "~/mne_data" + directory is used. If the Polysomnography dataset is not found under + the given path, the data will be automatically downloaded to the + specified folder. force_update : bool Force update of the dataset even if a local copy exists. - update_path : bool | None - If True, set the MNE_DATASETS_EEGBCI_PATH in mne-python - config to the given path. If None, the user is prompted. base_url : str The URL root. on_missing : 'raise' | 'warn' | 'ignore' @@ -95,7 +91,7 @@ psg_records = records[np.where(records['type'] == b'PSG')] hyp_records = records[np.where(records['type'] == b'Hypnogram')] - path = data_path(path=path, update_path=update_path) + path = data_path(path=path) params = [path, force_update, base_url] _check_subjects( diff -Nru python-mne-0.23.4+dfsg/mne/datasets/sleep_physionet/temazepam.py python-mne-1.1.0+dfsg/mne/datasets/sleep_physionet/temazepam.py --- python-mne-0.23.4+dfsg/mne/datasets/sleep_physionet/temazepam.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/sleep_physionet/temazepam.py 2022-08-03 15:48:14.000000000 +0000 @@ -16,8 +16,8 @@ @verbose -def fetch_data(subjects, *, path=None, force_update=False, - update_path=None, base_url=BASE_URL, verbose=None): +def fetch_data(subjects, path=None, force_update=False, base_url=BASE_URL, *, + verbose=None): """Get paths to local copies of PhysioNet Polysomnography dataset files. This will fetch data from the publicly available subjects from PhysioNet's @@ -36,15 +36,12 @@ path : None | str Location of where to look for the PhysioNet data storing location. If None, the environment variable or config parameter - ``MNE_DATASETS_PHYSIONET_SLEEP_PATH`` is used. If it doesn't exist, - the "~/mne_data" directory is used. If the Polysomnography dataset - is not found under the given path, the data - will be automatically downloaded to the specified folder. + ``PHYSIONET_SLEEP_PATH`` is used. If it doesn't exist, the "~/mne_data" + directory is used. If the Polysomnography dataset is not found under + the given path, the data will be automatically downloaded to the + specified folder. force_update : bool Force update of the dataset even if a local copy exists. - update_path : bool | None - If True, set the MNE_DATASETS_EEGBCI_PATH in mne-python - config to the given path. If None, the user is prompted. base_url : str The base URL to download from. %(verbose)s @@ -83,7 +80,7 @@ _check_subjects(subjects, 22) - path = data_path(path=path, update_path=update_path) + path = data_path(path=path) params = [path, force_update, base_url] fnames = [] diff -Nru python-mne-0.23.4+dfsg/mne/datasets/sleep_physionet/tests/test_physionet.py python-mne-1.1.0+dfsg/mne/datasets/sleep_physionet/tests/test_physionet.py --- python-mne-0.23.4+dfsg/mne/datasets/sleep_physionet/tests/test_physionet.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/sleep_physionet/tests/test_physionet.py 2022-08-03 15:48:14.000000000 +0000 @@ -8,8 +8,8 @@ import pytest from numpy.testing import assert_array_equal +import pooch -import mne from mne.utils import requires_good_network from mne.utils import requires_pandas, requires_version from mne.datasets.sleep_physionet import age, temazepam @@ -20,9 +20,9 @@ @pytest.fixture(scope='session') -def physionet_tmpdir(tmpdir_factory): +def physionet_tmpdir(tmp_path_factory): """Fixture exposing a temporary directory for testing.""" - return str(tmpdir_factory.mktemp('physionet_files')) + return str(tmp_path_factory.mktemp('physionet_files')) class _FakeFetch: @@ -60,12 +60,13 @@ # Check it has been called with the right parameters in the right # order. for idx, current in enumerate(call_fname_hash_pairs): - call_args, call_kwargs = mocked_func.call_args_list[idx] - assert call_args[0] == _get_expected_url(current['name']) - assert call_args[1] == _get_expected_path(base_path, current['name']) - assert call_kwargs['hash_'] == current['hash'] - assert call_kwargs['hash_type'] == 'sha1' - assert call_kwargs['print_destination'] is False + _, call_kwargs = mocked_func.call_args_list[idx] + hash_type, hash = call_kwargs['known_hash'].split(':') + assert call_kwargs['url'] == _get_expected_url(current['name']) + assert op.join(call_kwargs['path'], call_kwargs['fname']) == \ + _get_expected_path(base_path, current['name']) + assert hash == current['hash'] + assert hash_type == 'sha1' @pytest.mark.timeout(60) @@ -73,10 +74,10 @@ @requires_good_network @requires_pandas @requires_version('xlrd', '0.9') -def test_run_update_age_records(tmpdir): +def test_run_update_age_records(tmp_path): """Test Sleep Physionet URL handling.""" import pandas as pd - fname = op.join(str(tmpdir), "records.csv") + fname = op.join(str(tmp_path), "records.csv") _update_sleep_age_records(fname) data = pd.read_csv(fname) pd.testing.assert_frame_equal(data, pd.read_csv(AGE_SLEEP_RECORDS)) @@ -86,18 +87,19 @@ def test_sleep_physionet_age_missing_subjects(physionet_tmpdir, subject, download_is_error): """Test handling of missing subjects in Sleep Physionet age fetcher.""" - params = {'path': physionet_tmpdir, 'update_path': False} - with pytest.raises( ValueError, match='This dataset contains subjects 0 to 82'): age.fetch_data( - subjects=[subject], recording=[1], on_missing='raise', **params) + subjects=[subject], recording=[1], on_missing='raise', + path=physionet_tmpdir) with pytest.warns(RuntimeWarning, match='This dataset contains subjects 0 to 82'): age.fetch_data( - subjects=[subject], recording=[1], on_missing='warn', **params) + subjects=[subject], recording=[1], on_missing='warn', + path=physionet_tmpdir) paths = age.fetch_data( - subjects=[subject], recording=[1], on_missing='ignore', **params) + subjects=[subject], recording=[1], on_missing='ignore', + path=physionet_tmpdir) assert paths == [] @@ -105,42 +107,40 @@ def test_sleep_physionet_age_missing_recordings(physionet_tmpdir, subject, recording, download_is_error): """Test handling of missing recordings in Sleep Physionet age fetcher.""" - params = {'path': physionet_tmpdir, 'update_path': False} - with pytest.raises( ValueError, match=f'Requested recording {recording} for subject'): age.fetch_data(subjects=[subject], recording=[recording], - on_missing='raise', **params) + on_missing='raise', path=physionet_tmpdir) with pytest.warns(RuntimeWarning, match=f'Requested recording {recording} for subject'): age.fetch_data(subjects=[subject], recording=[recording], - on_missing='warn', **params) + on_missing='warn', path=physionet_tmpdir) paths = age.fetch_data(subjects=[subject], recording=[recording], - on_missing='ignore', **params) + on_missing='ignore', path=physionet_tmpdir) assert paths == [] def test_sleep_physionet_age(physionet_tmpdir, monkeypatch, download_is_error): """Test Sleep Physionet URL handling.""" # check download_is_error patching - params = {'path': physionet_tmpdir, 'update_path': False} with pytest.raises(AssertionError, match='Test should not download'): - age.fetch_data(subjects=[0], recording=[1], **params) + age.fetch_data(subjects=[0], recording=[1], path=physionet_tmpdir) # then patch my_func = _FakeFetch() - monkeypatch.setattr( - mne.datasets.sleep_physionet._utils, '_fetch_file', my_func) + monkeypatch.setattr(pooch, 'retrieve', my_func) - paths = age.fetch_data(subjects=[0], recording=[1], **params) + paths = age.fetch_data(subjects=[0], recording=[1], path=physionet_tmpdir) assert_array_equal(_keep_basename_only(paths), [['SC4001E0-PSG.edf', 'SC4001EC-Hypnogram.edf']]) - paths = age.fetch_data(subjects=[0, 1], recording=[1], **params) + paths = age.fetch_data(subjects=[0, 1], recording=[1], + path=physionet_tmpdir) assert_array_equal(_keep_basename_only(paths), [['SC4001E0-PSG.edf', 'SC4001EC-Hypnogram.edf'], ['SC4011E0-PSG.edf', 'SC4011EH-Hypnogram.edf']]) - paths = age.fetch_data(subjects=[0], recording=[1, 2], **params) + paths = age.fetch_data(subjects=[0], recording=[1, 2], + path=physionet_tmpdir) assert_array_equal(_keep_basename_only(paths), [['SC4001E0-PSG.edf', 'SC4001EC-Hypnogram.edf'], ['SC4002E0-PSG.edf', 'SC4002EC-Hypnogram.edf']]) @@ -174,10 +174,10 @@ @requires_good_network @requires_pandas @requires_version('xlrd', '0.9') -def test_run_update_temazepam_records(tmpdir): +def test_run_update_temazepam_records(tmp_path): """Test Sleep Physionet URL handling.""" import pandas as pd - fname = op.join(str(tmpdir), "records.csv") + fname = op.join(str(tmp_path), "records.csv") _update_sleep_temazepam_records(fname) data = pd.read_csv(fname) @@ -188,12 +188,9 @@ def test_sleep_physionet_temazepam(physionet_tmpdir, monkeypatch): """Test Sleep Physionet URL handling.""" my_func = _FakeFetch() - monkeypatch.setattr( - mne.datasets.sleep_physionet._utils, '_fetch_file', my_func) - - params = {'path': physionet_tmpdir, 'update_path': False} + monkeypatch.setattr(pooch, 'retrieve', my_func) - paths = temazepam.fetch_data(subjects=[0], **params) + paths = temazepam.fetch_data(subjects=[0], path=physionet_tmpdir) assert_array_equal(_keep_basename_only(paths), [['ST7011J0-PSG.edf', 'ST7011JP-Hypnogram.edf']]) @@ -207,4 +204,4 @@ with pytest.raises( ValueError, match='This dataset contains subjects 0 to 21'): - paths = temazepam.fetch_data(subjects=[22], **params) + paths = temazepam.fetch_data(subjects=[22], path=physionet_tmpdir) diff -Nru python-mne-0.23.4+dfsg/mne/datasets/sleep_physionet/_utils.py python-mne-1.1.0+dfsg/mne/datasets/sleep_physionet/_utils.py --- python-mne-0.23.4+dfsg/mne/datasets/sleep_physionet/_utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/sleep_physionet/_utils.py 2022-08-03 15:48:14.000000000 +0000 @@ -6,10 +6,10 @@ import os import os.path as op + import numpy as np -from distutils.version import LooseVersion -from ...utils import (_fetch_file, verbose, _TempDir, _check_pandas_installed, +from ...utils import (verbose, _TempDir, _check_pandas_installed, _on_missing) from ..utils import _get_path @@ -27,6 +27,7 @@ def _fetch_one(fname, hashsum, path, force_update, base_url): + import pooch # Fetch the file url = base_url + '/' + fname destination = op.join(path, fname) @@ -35,13 +36,17 @@ os.remove(destination) if not op.isdir(op.dirname(destination)): os.makedirs(op.dirname(destination)) - _fetch_file(url, destination, print_destination=False, - hash_=hashsum, hash_type='sha1') + pooch.retrieve( + url=url, + known_hash=f"sha1:{hashsum}", + path=path, + fname=fname + ) return destination @verbose -def _data_path(path=None, force_update=False, update_path=None, verbose=None): +def _data_path(path=None, verbose=None): """Get path to local copy of EEG Physionet age Polysomnography dataset URL. This is a low-level function useful for getting a local copy of a @@ -53,20 +58,14 @@ path : None | str Location of where to look for the data storing location. If None, the environment variable or config parameter - ``MNE_DATASETS_PHYSIONET_SLEEP_PATH`` is used. If it doesn't exist, the - "~/mne_data" directory is used. If the dataset - is not found under the given path, the data - will be automatically downloaded to the specified folder. - force_update : bool - Force update of the dataset even if a local copy exists. - update_path : bool | None - If True, set the MNE_DATASETS_PHYSIONET_SLEEP_PATH in mne-python - config to the given path. If None, the user is prompted. + ``PHYSIONET_SLEEP_PATH`` is used. If it doesn't exist, the "~/mne_data" + directory is used. If the dataset is not found under the given path, + the data will be automatically downloaded to the specified folder. %(verbose)s Returns ------- - path : list of str + path : list of Path Local path to the given data file. This path is contained inside a list of length one, for compatibility. @@ -82,15 +81,19 @@ def _update_sleep_temazepam_records(fname=TEMAZEPAM_SLEEP_RECORDS): """Help function to download Physionet's temazepam dataset records.""" + import pooch + pd = _check_pandas_installed() tmp = _TempDir() # Download subjects info. subjects_fname = op.join(tmp, 'ST-subjects.xls') - _fetch_file(url=TEMAZEPAM_RECORDS_URL, - file_name=subjects_fname, - hash_=TEMAZEPAM_RECORDS_URL_SHA1, - hash_type='sha1') + pooch.retrieve( + url=TEMAZEPAM_RECORDS_URL, + known_hash=f"sha1:{TEMAZEPAM_RECORDS_URL_SHA1}", + path=tmp, + fname=op.basename(subjects_fname) + ) # Load and Massage the checksums. sha1_df = pd.read_csv(sha1sums_fname, sep=' ', header=None, @@ -102,8 +105,7 @@ # Load and massage the data. data = pd.read_excel(subjects_fname, header=[0, 1]) - if LooseVersion(pd.__version__) >= LooseVersion('0.24.0'): - data = data.set_index(('Subject - age - sex', 'Nr')) + data = data.set_index(('Subject - age - sex', 'Nr')) data.index.name = 'subject' data.columns.names = [None, None] data = (data.set_index([('Subject - age - sex', 'Age'), @@ -124,10 +126,7 @@ data = data.set_index(['id', 'subject', 'age', 'sex', 'drug', 'lights off', 'night nr', 'record type']).unstack() data.columns = [l1 + '_' + l2 for l1, l2 in data.columns] - if LooseVersion(pd.__version__) < LooseVersion('0.21.0'): - data = data.reset_index().drop(labels=['id'], axis=1) - else: - data = data.reset_index().drop(columns=['id']) + data = data.reset_index().drop(columns=['id']) data['sex'] = (data.sex.astype('category') .cat.rename_categories({1: 'male', 2: 'female'})) @@ -142,15 +141,18 @@ def _update_sleep_age_records(fname=AGE_SLEEP_RECORDS): """Help function to download Physionet's age dataset records.""" + import pooch pd = _check_pandas_installed() tmp = _TempDir() # Download subjects info. subjects_fname = op.join(tmp, 'SC-subjects.xls') - _fetch_file(url=AGE_RECORDS_URL, - file_name=subjects_fname, - hash_=AGE_RECORDS_URL_SHA1, - hash_type='sha1') + pooch.retrieve( + url=AGE_RECORDS_URL, + known_hash=f"sha1:{AGE_RECORDS_URL_SHA1}", + path=tmp, + fname=op.basename(subjects_fname) + ) # Load and Massage the checksums. sha1_df = pd.read_csv(sha1sums_fname, sep=' ', header=None, @@ -176,10 +178,7 @@ .str.split('.', expand=True)[0] .astype('category')) - if LooseVersion(pd.__version__) < LooseVersion('0.21.0'): - data = data.reset_index().drop(labels=['id'], axis=1) - else: - data = data.reset_index().drop(columns=['id']) + data = data.reset_index().drop(columns=['id']) data = data[['subject', 'night', 'record type', 'age', 'sex', 'lights off', 'sha', 'fname']] diff -Nru python-mne-0.23.4+dfsg/mne/datasets/somato/__init__.py python-mne-1.1.0+dfsg/mne/datasets/somato/__init__.py --- python-mne-0.23.4+dfsg/mne/datasets/somato/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/somato/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,3 @@ """Somatosensory dataset.""" -from .somato import data_path, has_somato_data, get_version +from .somato import data_path, get_version diff -Nru python-mne-0.23.4+dfsg/mne/datasets/somato/somato.py python-mne-1.1.0+dfsg/mne/datasets/somato/somato.py --- python-mne-0.23.4+dfsg/mne/datasets/somato/somato.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/somato/somato.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,22 +3,18 @@ # Eric Larson # License: BSD Style. -from functools import partial - from ...utils import verbose -from ..utils import (has_dataset, _data_path, _data_path_doc, - _get_version, _version_doc) - - -has_somato_data = partial(has_dataset, name='somato') +from ..utils import (_data_path_doc, _get_version, _version_doc, + _download_mne_dataset) @verbose -def data_path(path=None, force_update=False, update_path=True, download=True, - verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='somato', - download=download) +def data_path(path=None, force_update=False, update_path=True, + download=True, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='somato', processor='untar', path=path, + force_update=force_update, update_path=update_path, + download=download) data_path.__doc__ = _data_path_doc.format(name='somato', diff -Nru python-mne-0.23.4+dfsg/mne/datasets/spm_face/spm_data.py python-mne-1.1.0+dfsg/mne/datasets/spm_face/spm_data.py --- python-mne-0.23.4+dfsg/mne/datasets/spm_face/spm_data.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/spm_face/spm_data.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,19 +5,20 @@ from functools import partial from ...utils import verbose, get_config -from ..utils import (has_dataset, _data_path, _data_path_doc, - _get_version, _version_doc) +from ..utils import (has_dataset, _data_path_doc, _get_version, + _version_doc, _download_mne_dataset) has_spm_data = partial(has_dataset, name='spm') @verbose -def data_path(path=None, force_update=False, update_path=True, download=True, - verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='spm', - download=download) +def data_path(path=None, force_update=False, update_path=True, + download=True, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='spm', processor='untar', path=path, + force_update=force_update, update_path=update_path, + download=download) data_path.__doc__ = _data_path_doc.format(name='spm', diff -Nru python-mne-0.23.4+dfsg/mne/datasets/ssvep/__init__.py python-mne-1.1.0+dfsg/mne/datasets/ssvep/__init__.py --- python-mne-0.23.4+dfsg/mne/datasets/ssvep/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/ssvep/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,3 @@ """SSVEP dataset.""" -from .ssvep import data_path, has_ssvep_data, get_version +from .ssvep import data_path, get_version diff -Nru python-mne-0.23.4+dfsg/mne/datasets/ssvep/ssvep.py python-mne-1.1.0+dfsg/mne/datasets/ssvep/ssvep.py --- python-mne-0.23.4+dfsg/mne/datasets/ssvep/ssvep.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/ssvep/ssvep.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,22 +1,18 @@ # Authors: Dominik Welke # License: BSD Style. -from functools import partial - from ...utils import verbose -from ..utils import (has_dataset, _data_path, _data_path_doc, - _get_version, _version_doc) - -has_ssvep_data = partial(has_dataset, name='ssvep') +from ..utils import (_data_path_doc, _get_version, _version_doc, + _download_mne_dataset) @verbose -def data_path( - path=None, force_update=False, update_path=True, - download=True, verbose=None): # noqa: D103 - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='ssvep', - download=download) +def data_path(path=None, force_update=False, update_path=True, + download=True, *, verbose=None): # noqa: D103 + return _download_mne_dataset( + name='ssvep', processor='unzip', path=path, + force_update=force_update, update_path=update_path, + download=download) data_path.__doc__ = _data_path_doc.format(name='ssvep', diff -Nru python-mne-0.23.4+dfsg/mne/datasets/testing/_testing.py python-mne-1.1.0+dfsg/mne/datasets/testing/_testing.py --- python-mne-0.23.4+dfsg/mne/datasets/testing/_testing.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/testing/_testing.py 2022-08-03 15:48:14.000000000 +0000 @@ -6,23 +6,24 @@ from functools import partial from ...utils import verbose, get_config -from ..utils import (has_dataset, _data_path, _data_path_doc, - _get_version, _version_doc) - +from ..utils import (has_dataset, _data_path_doc, _get_version, + _version_doc, _download_mne_dataset) has_testing_data = partial(has_dataset, name='testing') @verbose def data_path(path=None, force_update=False, update_path=True, - download=True, verbose=None): # noqa: D103 + download=True, *, verbose=None): # noqa: D103 # Make sure we don't do something stupid if download and \ get_config('MNE_SKIP_TESTING_DATASET_TESTS', 'false') == 'true': raise RuntimeError('Cannot download data if skipping is forced') - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='testing', - download=download) + + return _download_mne_dataset( + name='testing', processor='untar', path=path, + force_update=force_update, update_path=update_path, + download=download) data_path.__doc__ = _data_path_doc.format(name='testing', @@ -51,13 +52,14 @@ def _pytest_param(*args, **kwargs): - if len(args) == len(kwargs) == 0: + if len(args) == 0: args = ('testing_data',) import pytest # turn anything that uses testing data into an auto-skipper by # setting params=[testing._pytest_param()], or by parametrizing functions # with testing._pytest_param(whatever) - return pytest.param(*args, **kwargs, marks=_pytest_mark()) + kwargs['marks'] = kwargs.get('marks', list()) + [_pytest_mark()] + return pytest.param(*args, **kwargs) def _pytest_mark(): diff -Nru python-mne-0.23.4+dfsg/mne/datasets/tests/test_datasets.py python-mne-1.1.0+dfsg/mne/datasets/tests/test_datasets.py --- python-mne-0.23.4+dfsg/mne/datasets/tests/test_datasets.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/tests/test_datasets.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,27 +1,30 @@ +from functools import partial import os from os import path as op import re import shutil import zipfile -import sys +import pooch import pytest from mne import datasets, read_labels_from_annot, write_labels_to_annot -from mne.datasets import testing, fetch_infant_template -from mne.datasets._infant import base as infant_base +from mne.datasets import (testing, fetch_infant_template, fetch_phantom, + fetch_dataset) from mne.datasets._fsaverage.base import _set_montage_coreg_path -from mne.datasets.utils import _manifest_check_download +from mne.datasets._infant import base as infant_base +from mne.datasets._phantom import base as phantom_base +from mne.datasets.utils import _manifest_check_download, _mne_path from mne.utils import (requires_good_network, get_subjects_dir, ArgvSetter, _pl, use_log_level, catch_logging, hashfunc) -subjects_dir = op.join(testing.data_path(download=False), 'subjects') +subjects_dir = testing.data_path(download=False) / 'subjects' -def test_datasets_basic(tmpdir, monkeypatch): +def test_datasets_basic(tmp_path, monkeypatch): """Test simple dataset functions.""" # XXX 'hf_sef' and 'misc' do not conform to these standards for dname in ('sample', 'somato', 'spm_face', 'testing', 'opm', @@ -31,85 +34,93 @@ 'visual_92_categories', 'fieldtrip_cmc'): if dname.startswith('bst'): dataset = getattr(datasets.brainstorm, dname) - check_name = 'brainstorm.%s' % (dname,) else: dataset = getattr(datasets, dname) - check_name = dname - if dataset.data_path(download=False) != '': + if str(dataset.data_path(download=False)) != '.': assert isinstance(dataset.get_version(), str) - assert datasets.utils.has_dataset(check_name) + assert datasets.has_dataset(dname) else: assert dataset.get_version() is None - assert not datasets.utils.has_dataset(check_name) - print('%s: %s' % (dname, datasets.utils.has_dataset(check_name))) - tempdir = str(tmpdir) + assert not datasets.has_dataset(dname) + print('%s: %s' % (dname, datasets.has_dataset(dname))) + tempdir = str(tmp_path) + # Explicitly test one that isn't preset (given the config) + monkeypatch.setenv('MNE_DATASETS_SAMPLE_PATH', tempdir) + dataset = datasets.sample + assert str(dataset.data_path(download=False)) == '.' + assert dataset.get_version() != '' + assert dataset.get_version() is None # don't let it read from the config file to get the directory, # force it to look for the default monkeypatch.setenv('_MNE_FAKE_HOME_DIR', tempdir) monkeypatch.delenv('SUBJECTS_DIR', raising=False) - assert (datasets.utils._get_path(None, 'foo', 'bar') == + assert (str(datasets.utils._get_path(None, 'foo', 'bar')) == op.join(tempdir, 'mne_data')) assert get_subjects_dir(None) is None _set_montage_coreg_path() sd = get_subjects_dir() assert sd.endswith('MNE-fsaverage-data') - monkeypatch.setenv('MNE_DATA', str(tmpdir.join('foo'))) + monkeypatch.setenv('MNE_DATA', str(tmp_path / 'foo')) with pytest.raises(FileNotFoundError, match='as specified by MNE_DAT'): testing.data_path(download=False) @requires_good_network -def test_downloads(tmpdir, monkeypatch, capsys): +def test_downloads(tmp_path, monkeypatch, capsys): """Test dataset URL and version handling.""" # Try actually downloading a dataset - kwargs = dict(path=str(tmpdir), verbose=True) - path = datasets._fake.data_path(update_path=False, **kwargs) - out, _ = capsys.readouterr() - assert 'Downloading' in out + kwargs = dict(path=str(tmp_path), verbose=True) + # XXX we shouldn't need to disable capsys here, but there's a pytest bug + # that we're hitting (https://github.com/pytest-dev/pytest/issues/5997) + # now that we use pooch + with capsys.disabled(): + path = datasets._fake.data_path(update_path=False, **kwargs) assert op.isdir(path) assert op.isfile(op.join(path, 'bar')) - assert not datasets.utils.has_dataset('fake') # not in the desired path + assert not datasets.has_dataset('fake') # not in the desired path assert datasets._fake.get_version() is None assert datasets.utils._get_version('fake') is None - monkeypatch.setenv('_MNE_FAKE_HOME_DIR', str(tmpdir)) + monkeypatch.setenv('_MNE_FAKE_HOME_DIR', str(tmp_path)) with pytest.warns(RuntimeWarning, match='non-standard config'): new_path = datasets._fake.data_path(update_path=True, **kwargs) assert path == new_path out, _ = capsys.readouterr() assert 'Downloading' not in out # No version: shown as existing but unknown version - assert datasets.utils.has_dataset('fake') + assert datasets.has_dataset('fake') # XXX logic bug, should be "unknown" - assert datasets._fake.get_version() == '0.7' + assert datasets._fake.get_version() == '0.0' # With a version but no required one: shown as existing and gives version - fname = tmpdir / 'foo' / 'version.txt' + fname = tmp_path / 'foo' / 'version.txt' with open(fname, 'w') as fid: fid.write('0.1') - assert datasets.utils.has_dataset('fake') + assert datasets.has_dataset('fake') assert datasets._fake.get_version() == '0.1' datasets._fake.data_path(download=False, **kwargs) out, _ = capsys.readouterr() assert 'out of date' not in out # With the required version: shown as existing with the required version - monkeypatch.setattr(datasets.utils, '_FAKE_VERSION', '0.1') - assert datasets.utils.has_dataset('fake') + monkeypatch.setattr(datasets._fetch, '_FAKE_VERSION', '0.1') + assert datasets.has_dataset('fake') assert datasets._fake.get_version() == '0.1' datasets._fake.data_path(download=False, **kwargs) out, _ = capsys.readouterr() assert 'out of date' not in out - monkeypatch.setattr(datasets.utils, '_FAKE_VERSION', '0.2') + monkeypatch.setattr(datasets._fetch, '_FAKE_VERSION', '0.2') # With an older version: # 1. Marked as not actually being present - assert not datasets.utils.has_dataset('fake') + assert not datasets.has_dataset('fake') # 2. Will try to update when `data_path` gets called, with logged message want_msg = 'Correctly trying to download newer version' - def _error_download(url, full_name, print_destination, hash_, hash_type): + def _error_download(self, fname, downloader, processor): + url = self.get_url(fname) + full_path = self.abspath / fname assert 'foo.tgz' in url - assert str(tmpdir) in full_name + assert str(tmp_path) in str(full_path) raise RuntimeError(want_msg) - monkeypatch.setattr(datasets.utils, '_fetch_file', _error_download) + monkeypatch.setattr(pooch.Pooch, 'fetch', _error_download) with pytest.raises(RuntimeError, match=want_msg): datasets._fake.data_path(**kwargs) out, _ = capsys.readouterr() @@ -119,9 +130,9 @@ @pytest.mark.slowtest @testing.requires_testing_data @requires_good_network -def test_fetch_parcellations(tmpdir): +def test_fetch_parcellations(tmp_path): """Test fetching parcellations.""" - this_subjects_dir = str(tmpdir) + this_subjects_dir = str(tmp_path) os.mkdir(op.join(this_subjects_dir, 'fsaverage')) os.mkdir(op.join(this_subjects_dir, 'fsaverage', 'label')) os.mkdir(op.join(this_subjects_dir, 'fsaverage', 'surf')) @@ -156,7 +167,8 @@ _zip_fnames = ['foo/foo.txt', 'foo/bar.txt', 'foo/baz.txt'] -def _fake_zip_fetch(url, fname, hash_): +def _fake_zip_fetch(url, path, fname, known_hash): + fname = op.join(path, fname) with zipfile.ZipFile(fname, 'w') as zipf: with zipf.open('foo/', 'w'): pass @@ -165,14 +177,12 @@ pass -@pytest.mark.skipif(sys.version_info < (3, 6), - reason="writing zip files requires python3.6 or higher") @pytest.mark.parametrize('n_have', range(len(_zip_fnames))) -def test_manifest_check_download(tmpdir, n_have, monkeypatch): +def test_manifest_check_download(tmp_path, n_have, monkeypatch): """Test our manifest downloader.""" - monkeypatch.setattr(datasets.utils, '_fetch_file', _fake_zip_fetch) - destination = op.join(str(tmpdir), 'empty') - manifest_path = op.join(str(tmpdir), 'manifest.txt') + monkeypatch.setattr(pooch, 'retrieve', _fake_zip_fetch) + destination = op.join(str(tmp_path), 'empty') + manifest_path = op.join(str(tmp_path), 'manifest.txt') with open(manifest_path, 'w') as fid: for fname in _zip_fnames: fid.write('%s\n' % fname) @@ -188,7 +198,8 @@ pass with catch_logging() as log: with use_log_level(True): - url = hash_ = '' # we mock the _fetch_file so these are not used + # we mock the pooch.retrieve so these are not used + url = hash_ = '' _manifest_check_download(manifest_path, destination, url, hash_) log = log.getvalue() n_missing = 3 - n_have @@ -203,17 +214,67 @@ assert op.isfile(op.join(destination, fname)) -def _fake_mcd(manifest_path, destination, url, hash_): - name = url.split('/')[-1].split('.')[0] +def _fake_mcd(manifest_path, destination, url, hash_, name=None, + fake_files=False): + if name is None: + name = url.split('/')[-1].split('.')[0] + assert name in url + assert name in destination assert name in manifest_path - assert name in destination - assert name in url assert len(hash_) == 32 + if fake_files: + with open(manifest_path) as fid: + for path in fid: + path = path.strip() + if not path: + continue + fname = op.join(destination, path) + os.makedirs(op.dirname(fname), exist_ok=True) + with open(fname, 'wb'): + pass -def test_infant(tmpdir, monkeypatch): +def test_infant(tmp_path, monkeypatch): """Test fetch_infant_template.""" monkeypatch.setattr(infant_base, '_manifest_check_download', _fake_mcd) - fetch_infant_template('12mo', subjects_dir=tmpdir) + fetch_infant_template('12mo', subjects_dir=tmp_path) with pytest.raises(ValueError, match='Invalid value for'): - fetch_infant_template('0mo', subjects_dir=tmpdir) + fetch_infant_template('0mo', subjects_dir=tmp_path) + + +def test_phantom(tmp_path, monkeypatch): + """Test phantom data downloading.""" + # The Otaniemi file is only ~6MB, so in principle maybe we could test + # an actual download here. But it doesn't seem worth it given that + # CircleCI will at least test the VectorView one, and this file should + # not change often. + monkeypatch.setattr(phantom_base, '_manifest_check_download', + partial(_fake_mcd, name='phantom_otaniemi', + fake_files=True)) + fetch_phantom('otaniemi', subjects_dir=tmp_path) + assert op.isfile(tmp_path / 'phantom_otaniemi' / 'mri' / 'T1.mgz') + + +def test_fetch_uncompressed_file(tmp_path): + """Test downloading an uncompressed file with our fetch function.""" + dataset_dict = dict( + dataset_name='license', + url=('https://raw.githubusercontent.com/mne-tools/mne-python/main/' + 'LICENSE.txt'), + archive_name='LICENSE.foo', + folder_name=op.join(tmp_path, 'foo'), + hash=None) + fetch_dataset(dataset_dict, path=None, force_update=True) + assert (tmp_path / 'foo' / 'LICENSE.foo').is_file() + + +def test_mne_path(): + """Test our Path wrapping.""" + path = _mne_path("") + assert str(path) == '.' + with pytest.deprecated_call(match=r'pathlib\.Path object'): + assert path + 'me' == f'.{op.sep}me' + assert str(path / 'me') == 'me' + assert str('me' / path) == 'me' + with pytest.raises(TypeError, match='can only concatenate str'): + 'me' + path # our paths were absolute, so this should raise an error diff -Nru python-mne-0.23.4+dfsg/mne/datasets/utils.py python-mne-1.1.0+dfsg/mne/datasets/utils.py --- python-mne-0.23.4+dfsg/mne/datasets/utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/utils.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,31 +3,28 @@ # Eric Larson # Denis Egnemann # Stefan Appelhoff +# Adam Li +# Daniel McCloy +# # License: BSD Style. from collections import OrderedDict import os import os.path as op -import shutil -import tarfile -import stat +from pathlib import WindowsPath, PosixPath import sys import zipfile import tempfile -from distutils.version import LooseVersion import numpy as np -from .. import __version__ as mne_version +from .config import _hcp_mmp_license_text, MNE_DATASETS from ..label import read_labels_from_annot, Label, write_labels_to_annot -from ..utils import (get_config, set_config, _fetch_file, logger, warn, - verbose, get_subjects_dir, hashfunc, _pl, _safe_input) -from ..utils.docs import docdict -from ..externals.doccer import docformat +from ..utils import (get_config, set_config, logger, _validate_type, warn, + verbose, get_subjects_dir, _pl, _safe_input) +from ..utils.docs import docdict, _docformat -_FAKE_VERSION = None # used for monkeypatching while testing versioning - _data_path_doc = """Get path to local copy of {name} dataset. Parameters @@ -41,8 +38,9 @@ will be automatically downloaded to the specified folder. force_update : bool Force update of the {name} dataset even if a local copy exists. + Default is False. update_path : bool | None - If True, set the ``{conf}`` in mne-python + If True (default), set the ``{conf}`` in mne-python config to the given path. If None, the user is prompted. download : bool If False and the {name} dataset has not been downloaded yet, @@ -53,15 +51,15 @@ Returns ------- - path : str + path : instance of Path Path to {name} dataset directory. """ _data_path_doc_accept = _data_path_doc.split('%(verbose)s') _data_path_doc_accept[-1] = '%(verbose)s' + _data_path_doc_accept[-1] _data_path_doc_accept.insert(1, ' %(accept)s') _data_path_doc_accept = ''.join(_data_path_doc_accept) -_data_path_doc = docformat(_data_path_doc, docdict) -_data_path_doc_accept = docformat(_data_path_doc_accept, docdict) +_data_path_doc = _docformat(_data_path_doc, docdict) +_data_path_doc_accept = _docformat(_data_path_doc_accept, docdict) _version_doc = """Get version of the local {name} dataset. @@ -73,86 +71,6 @@ """ -_bst_license_text = """ -License -------- -This tutorial dataset (EEG and MRI data) remains a property of the MEG Lab, -McConnell Brain Imaging Center, Montreal Neurological Institute, -McGill University, Canada. Its use and transfer outside the Brainstorm -tutorial, e.g. for research purposes, is prohibited without written consent -from the MEG Lab. - -If you reference this dataset in your publications, please: - - 1) acknowledge its authors: Elizabeth Bock, Esther Florin, Francois Tadel - and Sylvain Baillet, and - 2) cite Brainstorm as indicated on the website: - http://neuroimage.usc.edu/brainstorm - -For questions, please contact Francois Tadel (francois.tadel@mcgill.ca). -""" - -_hcp_mmp_license_text = """ -License -------- -I request access to data collected by the Washington University - University -of Minnesota Consortium of the Human Connectome Project (WU-Minn HCP), and -I agree to the following: - -1. I will not attempt to establish the identity of or attempt to contact any - of the included human subjects. - -2. I understand that under no circumstances will the code that would link - these data to Protected Health Information be given to me, nor will any - additional information about individual human subjects be released to me - under these Open Access Data Use Terms. - -3. I will comply with all relevant rules and regulations imposed by my - institution. This may mean that I need my research to be approved or - declared exempt by a committee that oversees research on human subjects, - e.g. my IRB or Ethics Committee. The released HCP data are not considered - de-identified, insofar as certain combinations of HCP Restricted Data - (available through a separate process) might allow identification of - individuals. Different committees operate under different national, state - and local laws and may interpret regulations differently, so it is - important to ask about this. If needed and upon request, the HCP will - provide a certificate stating that you have accepted the HCP Open Access - Data Use Terms. - -4. I may redistribute original WU-Minn HCP Open Access data and any derived - data as long as the data are redistributed under these same Data Use Terms. - -5. I will acknowledge the use of WU-Minn HCP data and data derived from - WU-Minn HCP data when publicly presenting any results or algorithms - that benefitted from their use. - - 1. Papers, book chapters, books, posters, oral presentations, and all - other printed and digital presentations of results derived from HCP - data should contain the following wording in the acknowledgments - section: "Data were provided [in part] by the Human Connectome - Project, WU-Minn Consortium (Principal Investigators: David Van Essen - and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and - Centers that support the NIH Blueprint for Neuroscience Research; and - by the McDonnell Center for Systems Neuroscience at Washington - University." - - 2. Authors of publications or presentations using WU-Minn HCP data - should cite relevant publications describing the methods used by the - HCP to acquire and process the data. The specific publications that - are appropriate to cite in any given study will depend on what HCP - data were used and for what purposes. An annotated and appropriately - up-to-date list of publications that may warrant consideration is - available at http://www.humanconnectome.org/about/acknowledgehcp.html - - 3. The WU-Minn HCP Consortium as a whole should not be included as an - author of publications or presentations if this authorship would be - based solely on the use of WU-Minn HCP data. - -6. Failure to abide by these guidelines will result in termination of my - privileges to access WU-Minn HCP data. -""" - - def _dataset_version(path, name): """Get the version of the dataset.""" ver_fname = op.join(path, 'version.txt') @@ -160,30 +78,30 @@ with open(ver_fname, 'r') as fid: version = fid.readline().strip() # version is on first line else: + logger.debug(f'Version file missing: {ver_fname}') # Sample dataset versioning was introduced after 0.3 # SPM dataset was introduced with 0.7 - version = '0.3' if name == 'sample' else '0.7' - + versions = dict(sample='0.7', spm='0.3') + version = versions.get(name, '0.0') return version def _get_path(path, key, name): """Get a dataset path.""" # 1. Input + _validate_type(path, ('path-like', None), path) if path is not None: - if not isinstance(path, str): - raise ValueError('path must be a string or None') return path - # 2. get_config(key) + # 2. get_config(key) — unless key is None or "" (special get_config values) # 3. get_config('MNE_DATA') - path = get_config(key, get_config('MNE_DATA')) + path = get_config(key or 'MNE_DATA', get_config('MNE_DATA')) if path is not None: if not op.exists(path): msg = (f"Download location {path} as specified by MNE_DATA does " f"not exist. Either create this directory manually and try " f"again, or set MNE_DATA to an existing directory.") raise FileNotFoundError(msg) - return path + return _mne_path(path) # 4. ~/mne_data (but use a fake home during testing so we don't # unnecessarily create ~/mne_data) logger.info('Using default location ~/mne_data for %s...' % name) @@ -199,7 +117,7 @@ "argument to data_path() where user has " "write permissions, for ex:data_path" "('/home/xyz/me2/')" % (path)) - return path + return _mne_path(path) def _do_path_update(path, update_path, key, name): @@ -220,385 +138,103 @@ update_path = False if update_path: - set_config(key, path, set_env=False) + set_config(key, str(path), set_env=False) return path -def _data_path(path=None, force_update=False, update_path=True, download=True, - name=None, check_version=False, return_version=False, - archive_name=None, accept=False): - """Aux function.""" - key = { - 'fake': 'MNE_DATASETS_FAKE_PATH', - 'misc': 'MNE_DATASETS_MISC_PATH', - 'sample': 'MNE_DATASETS_SAMPLE_PATH', - 'spm': 'MNE_DATASETS_SPM_FACE_PATH', - 'somato': 'MNE_DATASETS_SOMATO_PATH', - 'brainstorm': 'MNE_DATASETS_BRAINSTORM_PATH', - 'testing': 'MNE_DATASETS_TESTING_PATH', - 'multimodal': 'MNE_DATASETS_MULTIMODAL_PATH', - 'fnirs_motor': 'MNE_DATASETS_FNIRS_MOTOR_PATH', - 'opm': 'MNE_DATASETS_OPM_PATH', - 'visual_92_categories': 'MNE_DATASETS_VISUAL_92_CATEGORIES_PATH', - 'kiloword': 'MNE_DATASETS_KILOWORD_PATH', - 'mtrf': 'MNE_DATASETS_MTRF_PATH', - 'fieldtrip_cmc': 'MNE_DATASETS_FIELDTRIP_CMC_PATH', - 'phantom_4dbti': 'MNE_DATASETS_PHANTOM_4DBTI_PATH', - 'limo': 'MNE_DATASETS_LIMO_PATH', - 'refmeg_noise': 'MNE_DATASETS_REFMEG_NOISE_PATH', - 'ssvep': 'MNE_DATASETS_SSVEP_PATH', - 'erp_core': 'MNE_DATASETS_ERP_CORE_PATH', - 'epilepsy_ecog': 'MNE_DATASETS_EPILEPSY_ECOG_PATH', - }[name] - - path = _get_path(path, key, name) - # To update the testing or misc dataset, push commits, then make a new - # release on GitHub. Then update the "releases" variable: - releases = dict(testing='0.118', misc='0.9') - # And also update the "md5_hashes['testing']" variable below. - # To update any other dataset, update the data archive itself (upload - # an updated version) and update the md5 hash. - - # try to match url->archive_name->folder_name - urls = dict( # the URLs to use - brainstorm=dict( - bst_auditory='https://osf.io/5t9n8/download?version=1', - bst_phantom_ctf='https://osf.io/sxr8y/download?version=1', - bst_phantom_elekta='https://osf.io/dpcku/download?version=1', - bst_raw='https://osf.io/9675n/download?version=2', - bst_resting='https://osf.io/m7bd3/download?version=3'), - fake='https://github.com/mne-tools/mne-testing-data/raw/master/' - 'datasets/foo.tgz', - misc='https://codeload.github.com/mne-tools/mne-misc-data/' - 'tar.gz/%s' % releases['misc'], - sample='https://osf.io/86qa2/download?version=5', - somato='https://osf.io/tp4sg/download?version=7', - spm='https://osf.io/je4s8/download?version=2', - testing='https://codeload.github.com/mne-tools/mne-testing-data/' - 'tar.gz/%s' % releases['testing'], - multimodal='https://ndownloader.figshare.com/files/5999598', - fnirs_motor='https://osf.io/dj3eh/download?version=1', - opm='https://osf.io/p6ae7/download?version=2', - visual_92_categories=[ - 'https://osf.io/8ejrs/download?version=1', - 'https://osf.io/t4yjp/download?version=1'], - mtrf='https://osf.io/h85s2/download?version=1', - kiloword='https://osf.io/qkvf9/download?version=1', - fieldtrip_cmc='https://osf.io/j9b6s/download?version=1', - phantom_4dbti='https://osf.io/v2brw/download?version=2', - refmeg_noise='https://osf.io/drt6v/download?version=1', - ssvep='https://osf.io/z8h6k/download?version=5', - erp_core='https://osf.io/rzgba/download?version=1', - epilepsy_ecog='https://osf.io/z4epq/download?revision=1', - ) - # filename of the resulting downloaded archive (only needed if the URL - # name does not match resulting filename) - archive_names = dict( - fieldtrip_cmc='SubjectCMC.zip', - kiloword='MNE-kiloword-data.tar.gz', - misc='mne-misc-data-%s.tar.gz' % releases['misc'], - mtrf='mTRF_1.5.zip', - multimodal='MNE-multimodal-data.tar.gz', - fnirs_motor='MNE-fNIRS-motor-data.tgz', - opm='MNE-OPM-data.tar.gz', - sample='MNE-sample-data-processed.tar.gz', - somato='MNE-somato-data.tar.gz', - spm='MNE-spm-face.tar.gz', - testing='mne-testing-data-%s.tar.gz' % releases['testing'], - visual_92_categories=['MNE-visual_92_categories-data-part1.tar.gz', - 'MNE-visual_92_categories-data-part2.tar.gz'], - phantom_4dbti='MNE-phantom-4DBTi.zip', - refmeg_noise='sample_reference_MEG_noise-raw.zip', - ssvep='ssvep_example_data.zip', - erp_core='MNE-ERP-CORE-data.tar.gz', - epilepsy_ecog='MNE-epilepsy-ecog-data.tar.gz', - ) - # original folder names that get extracted (only needed if the - # archive does not extract the right folder name; e.g., usually GitHub) - folder_origs = dict( # not listed means None (no need to move) - misc='mne-misc-data-%s' % releases['misc'], - testing='mne-testing-data-%s' % releases['testing'], - ssvep='ssvep-example-data' - ) - # finally, where we want them to extract to (only needed if the folder name - # is not the same as the last bit of the archive name without the file - # extension) - folder_names = dict( - brainstorm='MNE-brainstorm-data', - fake='foo', - misc='MNE-misc-data', - mtrf='mTRF_1.5', - sample='MNE-sample-data', - testing='MNE-testing-data', - visual_92_categories='MNE-visual_92_categories-data', - fieldtrip_cmc='MNE-fieldtrip_cmc-data', - phantom_4dbti='MNE-phantom-4DBTi', - refmeg_noise='MNE-refmeg-noise-data', - ssvep='ssvep-example-data', - erp_core='MNE-ERP-CORE-data', - ) - md5_hashes = dict( - brainstorm=dict( - bst_auditory='fa371a889a5688258896bfa29dd1700b', - bst_phantom_ctf='80819cb7f5b92d1a5289db3fb6acb33c', - bst_phantom_elekta='1badccbe17998d18cc373526e86a7aaf', - bst_raw='fa2efaaec3f3d462b319bc24898f440c', - bst_resting='70fc7bf9c3b97c4f2eab6260ee4a0430'), - fake='3194e9f7b46039bb050a74f3e1ae9908', - misc='f832ce9c4c27e83396cc977b293b0aa9', - sample='12b75d1cb7df9dfb4ad73ed82f61094f', - somato='32fd2f6c8c7eb0784a1de6435273c48b', - spm='9f43f67150e3b694b523a21eb929ea75', - testing='0ca196c6dc4966570e14151cdf7ad4a5', - multimodal='26ec847ae9ab80f58f204d09e2c08367', - fnirs_motor='c4935d19ddab35422a69f3326a01fef8', - opm='370ad1dcfd5c47e029e692c85358a374', - visual_92_categories=['74f50bbeb65740903eadc229c9fa759f', - '203410a98afc9df9ae8ba9f933370e20'], - kiloword='3a124170795abbd2e48aae8727e719a8', - mtrf='273a390ebbc48da2c3184b01a82e4636', - fieldtrip_cmc='6f9fd6520f9a66e20994423808d2528c', - phantom_4dbti='938a601440f3ffa780d20a17bae039ff', - refmeg_noise='779fecd890d98b73a4832e717d7c7c45', - ssvep='af866bbc0f921114ac9d683494fe87d6', - erp_core='5866c0d6213bd7ac97f254c776f6c4b1', - epilepsy_ecog='ffb139174afa0f71ec98adbbb1729dea', - ) - assert set(md5_hashes) == set(urls) - url = urls[name] - hash_ = md5_hashes[name] - folder_orig = folder_origs.get(name, None) - if name == 'brainstorm': - assert archive_name is not None - url = [url[archive_name.split('.')[0]]] - folder_path = [op.join(path, folder_names[name], - archive_name.split('.')[0])] - hash_ = [hash_[archive_name.split('.')[0]]] - archive_name = [archive_name] - else: - url = [url] if not isinstance(url, list) else url - hash_ = [hash_] if not isinstance(hash_, list) else hash_ - archive_name = archive_names.get(name) - if archive_name is None: - archive_name = [u.split('/')[-1] for u in url] - if not isinstance(archive_name, list): - archive_name = [archive_name] - folder_path = [op.join(path, folder_names.get(name, a.split('.')[0])) - for a in archive_name] - if not isinstance(folder_orig, list): - folder_orig = [folder_orig] * len(url) - folder_path = [op.abspath(f) for f in folder_path] - assert hash_ is not None - assert all(isinstance(x, list) for x in (url, archive_name, hash_, - folder_path)) - assert len(url) == len(archive_name) == len(hash_) == len(folder_path) - logger.debug('URL: %s' % (url,)) - logger.debug('archive_name: %s' % (archive_name,)) - logger.debug('hash: %s' % (hash_,)) - logger.debug('folder_path: %s' % (folder_path,)) - - need_download = any(not op.exists(f) for f in folder_path) - # additional condition: check for version.txt and parse it - want_version = releases.get(name, None) - want_version = _FAKE_VERSION if name == 'fake' else want_version - if not need_download and want_version is not None: - data_version = _dataset_version(folder_path[0], name) - need_download = LooseVersion(data_version) < LooseVersion(want_version) - if need_download: - logger.info(f'Dataset {name} version {data_version} out of date, ' - f'latest version is {want_version}') - if need_download and not download: - return '' - - if need_download or force_update: - logger.debug('Downloading: need_download=%s, force_update=%s' - % (need_download, force_update)) - for f in folder_path: - logger.debug(' Exists: %s: %s' % (f, op.exists(f))) - if name == 'brainstorm': - if accept or '--accept-brainstorm-license' in sys.argv: - answer = 'y' - else: - # If they don't have stdin, just accept the license - # https://github.com/mne-tools/mne-python/issues/8513#issuecomment-726823724 # noqa: E501 - answer = _safe_input( - '%sAgree (y/[n])? ' % _bst_license_text, use='y') - if answer.lower() != 'y': - raise RuntimeError('You must agree to the license to use this ' - 'dataset') - assert len(url) == len(hash_) - assert len(url) == len(archive_name) - assert len(url) == len(folder_orig) - assert len(url) == len(folder_path) - assert len(url) > 0 - # 1. Get all the archives - full_name = list() - for u, an, h, fo in zip(url, archive_name, hash_, folder_orig): - remove_archive, full = _download(path, u, an, h) - full_name.append(full) - del archive_name - # 2. Extract all of the files - remove_dir = True - for u, fp, an, h, fo in zip(url, folder_path, full_name, hash_, - folder_orig): - _extract(path, name, fp, an, fo, remove_dir) - remove_dir = False # only do on first iteration - # 3. Remove all of the archives - if remove_archive: - for an in full_name: - os.remove(op.join(path, an)) - - logger.info('Successfully extracted to: %s' % folder_path) - - _do_path_update(path, update_path, key, name) - path = folder_path[0] - - # compare the version of the dataset and mne - data_version = _dataset_version(path, name) - # 0.7 < 0.7.git should be False, therefore strip - if check_version and (LooseVersion(data_version) < - LooseVersion(mne_version.strip('.git'))): - warn('The {name} dataset (version {current}) is older than ' - 'mne-python (version {newest}). If the examples fail, ' - 'you may need to update the {name} dataset by using ' - 'mne.datasets.{name}.data_path(force_update=True)'.format( - name=name, current=data_version, newest=mne_version)) - return (path, data_version) if return_version else path - - -def _download(path, url, archive_name, hash_, hash_type='md5'): - """Download and extract an archive, completing the filename.""" - martinos_path = '/cluster/fusion/sample_data/' + archive_name - neurospin_path = '/neurospin/tmp/gramfort/' + archive_name - remove_archive = False - if op.exists(martinos_path): - full_name = martinos_path - elif op.exists(neurospin_path): - full_name = neurospin_path - else: - full_name = op.join(path, archive_name) - remove_archive = True - fetch_archive = True - if op.exists(full_name): - logger.info('Archive exists (%s), checking hash %s.' - % (archive_name, hash_,)) - fetch_archive = False - if hashfunc(full_name, hash_type=hash_type) != hash_: - if input('Archive already exists but the hash does not match: ' - '%s\nOverwrite (y/[n])?' - % (archive_name,)).lower() == 'y': - os.remove(full_name) - fetch_archive = True - if fetch_archive: - logger.info('Downloading archive %s to %s' % (archive_name, path)) - _fetch_file(url, full_name, print_destination=False, - hash_=hash_, hash_type=hash_type) - return remove_archive, full_name - - -def _extract(path, name, folder_path, archive_name, folder_orig, remove_dir): - if op.exists(folder_path) and remove_dir: - logger.info('Removing old directory: %s' % (folder_path,)) - - def onerror(func, path, exc_info): - """Deal with access errors (e.g. testing dataset read-only).""" - # Is the error an access error ? - do = False - if not os.access(path, os.W_OK): - perm = os.stat(path).st_mode | stat.S_IWUSR - os.chmod(path, perm) - do = True - if not os.access(op.dirname(path), os.W_OK): - dir_perm = (os.stat(op.dirname(path)).st_mode | - stat.S_IWUSR) - os.chmod(op.dirname(path), dir_perm) - do = True - if do: - func(path) - else: - raise exc_info[1] - shutil.rmtree(folder_path, onerror=onerror) - - logger.info('Decompressing the archive: %s' % archive_name) - logger.info('(please be patient, this can take some time)') - if name == 'fieldtrip_cmc': - extract_path = folder_path - elif name == 'brainstorm': - extract_path = op.join(*op.split(folder_path)[:-1]) - else: - extract_path = path - if archive_name.endswith('.zip'): - with zipfile.ZipFile(archive_name, 'r') as ff: - ff.extractall(extract_path) +def _download_mne_dataset(name, processor, path, force_update, + update_path, download, accept=False): + """Aux function for downloading internal MNE datasets.""" + import pooch + from mne.datasets._fetch import fetch_dataset + + # import pooch library for handling the dataset downloading + dataset_params = MNE_DATASETS[name] + dataset_params['dataset_name'] = name + config_key = MNE_DATASETS[name]['config_key'] + folder_name = MNE_DATASETS[name]['folder_name'] + + # get download path for specific dataset + path = _get_path(path=path, key=config_key, name=name) + + # instantiate processor that unzips file + if processor == 'nested_untar': + processor_ = pooch.Untar(extract_dir=op.join(path, folder_name)) + elif processor == 'nested_unzip': + processor_ = pooch.Unzip(extract_dir=op.join(path, folder_name)) else: - if archive_name.endswith('.bz2'): - ext = 'bz2' - else: - ext = 'gz' - with tarfile.open(archive_name, 'r:%s' % ext) as tf: - tf.extractall(path=extract_path) + processor_ = processor - if folder_orig is not None: - shutil.move(op.join(path, folder_orig), folder_path) + # handle case of multiple sub-datasets with different urls + if name == 'visual_92_categories': + dataset_params = [] + for name in ['visual_92_categories_1', 'visual_92_categories_2']: + this_dataset = MNE_DATASETS[name] + this_dataset['dataset_name'] = name + dataset_params.append(this_dataset) + + return fetch_dataset(dataset_params=dataset_params, processor=processor_, + path=path, force_update=force_update, + update_path=update_path, download=download, + accept=accept) def _get_version(name): """Get a dataset version.""" + from mne.datasets._fetch import fetch_dataset + if not has_dataset(name): return None - if name.startswith('brainstorm'): - name, archive_name = name.split('.') - else: - archive_name = None - return _data_path(name=name, archive_name=archive_name, - return_version=True)[1] + dataset_params = MNE_DATASETS[name] + dataset_params['dataset_name'] = name + config_key = MNE_DATASETS[name]['config_key'] + + # get download path for specific dataset + path = _get_path(path=None, key=config_key, name=name) + + return fetch_dataset(dataset_params, path=path, + return_version=True)[1] def has_dataset(name): - """Check for dataset presence. + """Check for presence of a dataset. Parameters ---------- - name : str - The dataset name. - For brainstorm datasets, should be formatted like - "brainstorm.bst_raw". + name : str | dict + The dataset to check. Strings refer to one of the supported datasets + listed :ref:`here `. A :class:`dict` can be used to check for + user-defined datasets (see the Notes section of :func:`fetch_dataset`), + and must contain keys ``dataset_name``, ``archive_name``, ``url``, + ``folder_name``, ``hash``. Returns ------- has : bool True if the dataset is present. """ - name = 'spm' if name == 'spm_face' else name - if name.startswith('brainstorm'): - name, archive_name = name.split('.') - endswith = archive_name + from mne.datasets._fetch import fetch_dataset + + if isinstance(name, dict): + dataset_name = name['dataset_name'] + dataset_params = name + else: + dataset_name = 'spm' if name == 'spm_face' else name + dataset_params = MNE_DATASETS[dataset_name] + dataset_params['dataset_name'] = dataset_name + + config_key = dataset_params['config_key'] + + # get download path for specific dataset + path = _get_path(path=None, key=config_key, name=dataset_name) + + dp = fetch_dataset(dataset_params, path=path, download=False, + check_version=False) + if dataset_name.startswith('bst_'): + check = dataset_name else: - archive_name = None - # XXX eventually should be refactored with data_path - endswith = { - 'fieldtrip_cmc': 'MNE-fieldtrip_cmc-data', - 'fake': 'foo', - 'misc': 'MNE-misc-data', - 'sample': 'MNE-sample-data', - 'somato': 'MNE-somato-data', - 'spm': 'MNE-spm-face', - 'multimodal': 'MNE-multimodal-data', - 'fnirs_motor': 'MNE-fNIRS-motor-data', - 'opm': 'MNE-OPM-data', - 'testing': 'MNE-testing-data', - 'visual_92_categories': 'MNE-visual_92_categories-data', - 'kiloword': 'MNE-kiloword-data', - 'phantom_4dbti': 'MNE-phantom-4DBTi', - 'mtrf': 'mTRF_1.5', - 'refmeg_noise': 'MNE-refmeg-noise-data', - 'ssvep': 'ssvep-example-data', - 'erp_core': 'MNE-ERP-CORE-data', - 'epilepsy_ecog': 'MNE-epilepsy-ecog-data' - }[name] - dp = _data_path(download=False, name=name, check_version=False, - archive_name=archive_name) - return dp.endswith(endswith) + check = MNE_DATASETS[dataset_name]['folder_name'] + return str(dp).endswith(check) @verbose @@ -617,7 +253,8 @@ eegbci, multimodal, opm, hf_sef, mtrf, fieldtrip_cmc, kiloword, phantom_4dbti, sleep_physionet, limo, fnirs_motor, refmeg_noise, fetch_infant_template, - fetch_fsaverage, ssvep, erp_core, epilepsy_ecog) + fetch_fsaverage, ssvep, erp_core, epilepsy_ecog, + fetch_phantom) sample_path = sample.data_path() testing.data_path() misc.data_path() @@ -637,19 +274,19 @@ brainstorm.bst_raw.data_path(accept=True) brainstorm.bst_auditory.data_path(accept=True) brainstorm.bst_resting.data_path(accept=True) - brainstorm.bst_phantom_elekta.data_path(accept=True) + phantom_path = brainstorm.bst_phantom_elekta.data_path(accept=True) + fetch_phantom('otaniemi', subjects_dir=phantom_path) brainstorm.bst_phantom_ctf.data_path(accept=True) eegbci.load_data(1, [6, 10, 14], update_path=True) for subj in range(4): eegbci.load_data(subj + 1, runs=[3], update_path=True) - sleep_physionet.age.fetch_data(subjects=[0, 1], recording=[1], - update_path=True) + sleep_physionet.age.fetch_data(subjects=[0, 1], recording=[1]) # If the user has SUBJECTS_DIR, respect it, if not, set it to the EEG one # (probably on CircleCI, or otherwise advanced user) fetch_fsaverage(None) fetch_infant_template('6mo') fetch_hcp_mmp_parcellation( - subjects_dir=sample_path + '/subjects', accept=True) + subjects_dir=sample_path / 'subjects', accept=True) limo.load_data(subject=1, update_path=True) erp_core.data_path() @@ -674,6 +311,8 @@ ---------- .. footbibliography:: """ + import pooch + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) destination = op.join(subjects_dir, 'fsaverage', 'label') urls = dict(lh='https://osf.io/p92yb/download', @@ -681,9 +320,15 @@ hashes = dict(lh='9e4d8d6b90242b7e4b0145353436ef77', rh='dd6464db8e7762d969fc1d8087cd211b') for hemi in ('lh', 'rh'): - fname = op.join(destination, '%s.aparc_sub.annot' % hemi) - if not op.isfile(fname): - _fetch_file(urls[hemi], fname, hash_=hashes[hemi]) + fname = f'{hemi}.aparc_sub.annot' + fpath = op.join(destination, fname) + if not op.isfile(fpath): + pooch.retrieve( + url=urls[hemi], + known_hash=f"md5:{hashes[hemi]}", + path=destination, + fname=fname + ) @verbose @@ -716,6 +361,8 @@ ---------- .. footbibliography:: """ + import pooch + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) destination = op.join(subjects_dir, 'fsaverage', 'label') fnames = [op.join(destination, '%s.HCPMMP1.annot' % hemi) @@ -732,9 +379,16 @@ if answer.lower() != 'y': raise RuntimeError('You must agree to the license to use this ' 'dataset') - for hemi, fname in zip(('lh', 'rh'), fnames): - if not op.isfile(fname): - _fetch_file(urls[hemi], fname, hash_=hashes[hemi]) + for hemi, fpath in zip(('lh', 'rh'), fnames): + if not op.isfile(fpath): + fname = op.basename(fpath) + pooch.retrieve( + url=urls[hemi], + known_hash=f"md5:{hashes[hemi]}", + path=destination, + fname=fname + ) + if combine: fnames = [op.join(destination, '%s.HCPMMP1_combined.annot' % hemi) for hemi in ('lh', 'rh')] @@ -836,6 +490,8 @@ def _manifest_check_download(manifest_path, destination, url, hash_): + import pooch + with open(manifest_path, 'r') as fid: names = [name.strip() for name in fid.readlines()] manifest_path = op.basename(manifest_path) @@ -850,7 +506,13 @@ logger.info('Downloading missing files remotely') fname_path = op.join(path, 'temp.zip') - _fetch_file(url, fname_path, hash_=hash_) + pooch.retrieve( + url=url, + known_hash=f"md5:{hash_}", + path=path, + fname=op.basename(fname_path) + ) + logger.info('Extracting missing file%s' % (_pl(need),)) with zipfile.ZipFile(fname_path, 'r') as ff: members = set(f for f in ff.namelist() if not f.endswith('/')) @@ -862,3 +524,33 @@ ff.extract(name, path=destination) logger.info('Successfully extracted %d file%s' % (len(need), _pl(need))) + + +# Adapted from pathlib.Path.__new__ +def _mne_path(path): + klass = MNEWindowsPath if os.name == 'nt' else MNEPosixPath + out = klass._from_parts((path,)) + if not out._flavour.is_supported: + raise NotImplementedError("cannot instantiate %r on your system" + % (klass.__name__,)) + return out + + +class _PathAdd: + + def __add__(self, other): + if isinstance(other, str): + warn('data_path functions now return pathlib.Path objects which ' + 'do not natively support the plus (+) operator, switch to ' + 'using forward slash (/) instead. Support for plus will be ' + 'removed in 1.2.', DeprecationWarning) + return f'{str(self)}{op.sep}{other}' + raise NotImplementedError + + +class MNEWindowsPath(_PathAdd, WindowsPath): # noqa: D101 + pass + + +class MNEPosixPath(_PathAdd, PosixPath): # noqa: D101 + pass diff -Nru python-mne-0.23.4+dfsg/mne/datasets/visual_92_categories/visual_92_categories.py python-mne-1.1.0+dfsg/mne/datasets/visual_92_categories/visual_92_categories.py --- python-mne-0.23.4+dfsg/mne/datasets/visual_92_categories/visual_92_categories.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/datasets/visual_92_categories/visual_92_categories.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,12 +1,13 @@ # License: BSD Style. from ...utils import verbose -from ..utils import _data_path, _data_path_doc, _get_version, _version_doc +from ..utils import (_download_mne_dataset, _data_path_doc, _get_version, + _version_doc) @verbose -def data_path(path=None, force_update=False, update_path=True, download=True, - verbose=None): +def data_path(path=None, force_update=False, update_path=True, + download=True, *, verbose=None): """ Get path to local copy of visual_92_categories dataset. @@ -32,9 +33,8 @@ Returns ------- - path : list of str - Local path to the given data file. This path is contained inside a list - of length one, for compatibility. + path : instance of Path + Local path to the given data file. Notes ----- @@ -42,9 +42,10 @@ Radoslaw M. Cichy, Dimitrios Pantazis, Aude Oliva (2014) Resolving human object recognition in space and time. doi: 10.1038/NN.3635 """ - return _data_path(path=path, force_update=force_update, - update_path=update_path, name='visual_92_categories', - download=download) + return _download_mne_dataset( + name='visual_92_categories', processor='untar', path=path, + force_update=force_update, update_path=update_path, + download=download) data_path.__doc__ = _data_path_doc.format( diff -Nru python-mne-0.23.4+dfsg/mne/decoding/base.py python-mne-1.1.0+dfsg/mne/decoding/base.py --- python-mne-0.23.4+dfsg/mne/decoding/base.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/base.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,14 +4,14 @@ # Alexandre Gramfort # Jean-Remi King # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np -import time +import datetime as dt import numbers from ..parallel import parallel_func from ..fixes import BaseEstimator, is_classifier, _get_check_scoring -from ..utils import logger, warn, fill_doc +from ..utils import warn, verbose class LinearModel(BaseEstimator): @@ -231,12 +231,8 @@ cv = check_cv(cv=cv, y=y, classifier=est_is_classifier) # Extract train and test set to retrieve them at predict time - if hasattr(cv, 'split'): - cv_splits = [(train, test) for train, test in - cv.split(X=np.zeros_like(y), y=y)] - else: - # XXX support sklearn.cross_validation cv - cv_splits = [(train, test) for train, test in cv] + cv_splits = [(train, test) for train, test in + cv.split(X=np.zeros_like(y), y=y)] if not np.all([len(train) for train, _ in cv_splits]): raise ValueError('Some folds do not have any train epochs.') @@ -278,7 +274,7 @@ # and remove it from the transformers. if terminal: last_is_estimator = inverse_func[-1] is False - all_invertible = not(False in inverse_func[:-1]) + all_invertible = False not in inverse_func[:-1] if last_is_estimator and all_invertible: # keep all inverse transformation and remove last estimation inverse_func = inverse_func[:-1] @@ -355,9 +351,9 @@ return coef -@fill_doc +@verbose def cross_val_multiscore(estimator, X, y=None, groups=None, scoring=None, - cv=None, n_jobs=1, verbose=0, fit_params=None, + cv=None, n_jobs=None, verbose=None, fit_params=None, pre_dispatch='2*n_jobs'): """Evaluate a score by cross-validation. @@ -386,7 +382,7 @@ Determines the cross-validation splitting strategy. Possible inputs for cv are: - - None, to use the default 3-fold cross validation, + - None, to use the default 5-fold cross validation, - integer, to specify the number of folds in a ``(Stratified)KFold``, - An object to be used as a cross-validation generator. - An iterable yielding train, test splits. @@ -396,8 +392,7 @@ :class:`sklearn.model_selection.StratifiedKFold` is used. In all other cases, :class:`sklearn.model_selection.KFold` is used. %(n_jobs)s - verbose : int, optional - The verbosity level. + %(verbose)s fit_params : dict, optional Parameters to pass to the fit method of the estimator. pre_dispatch : int, or str, optional @@ -437,13 +432,16 @@ # Note: this parallelization is implemented using MNE Parallel parallel, p_func, n_jobs = parallel_func(_fit_and_score, n_jobs, pre_dispatch=pre_dispatch) - scores = parallel(p_func(clone(estimator), X, y, scorer, train, test, - 0, None, fit_params) - for train, test in cv_iter) + scores = parallel( + p_func( + estimator=clone(estimator), X=X, y=y, scorer=scorer, train=train, + test=test, parameters=None, fit_params=fit_params + ) for train, test in cv_iter + ) return np.array(scores)[:, 0, ...] # flatten over joblib output. -def _fit_and_score(estimator, X, y, scorer, train, test, verbose, +def _fit_and_score(estimator, X, y, scorer, train, test, parameters, fit_params, return_train_score=False, return_parameters=False, return_n_test_samples=False, return_times=False, error_score='raise'): @@ -453,14 +451,6 @@ from sklearn.utils.metaestimators import _safe_split from sklearn.utils.validation import _num_samples - if verbose > 1: - if parameters is None: - msg = '' - else: - msg = '%s' % (', '.join('%s=%s' % (k, v) - for k, v in parameters.items())) - print("[CV] %s %s" % (msg, (64 - len(msg)) * '.')) - # Adjust length of sample weights fit_params = fit_params if fit_params is not None else {} fit_params = _check_fit_params(X, fit_params, train) @@ -468,7 +458,7 @@ if parameters is not None: estimator.set_params(**parameters) - start_time = time.time() + start_time = dt.datetime.now() X_train, y_train = _safe_split(estimator, X, y, train) X_test, y_test = _safe_split(estimator, X, y, test, train) @@ -481,8 +471,8 @@ except Exception as e: # Note fit time as time until error - fit_time = time.time() - start_time - score_time = 0.0 + fit_duration = dt.datetime.now() - start_time + score_duration = dt.timedelta(0) if error_score == 'raise': raise elif isinstance(error_score, numbers.Number): @@ -498,25 +488,21 @@ " make sure that it has been spelled correctly.)") else: - fit_time = time.time() - start_time + fit_duration = dt.datetime.now() - start_time test_score = _score(estimator, X_test, y_test, scorer) - score_time = time.time() - start_time - fit_time + score_duration = dt.datetime.now() - start_time - fit_duration if return_train_score: train_score = _score(estimator, X_train, y_train, scorer) - if verbose > 2: - msg += ", score=%f" % test_score - if verbose > 1: - total_time = score_time + fit_time - end_msg = "%s, total=%s" % (msg, logger.short_format_time(total_time)) - print("[CV] %s %s" % ((64 - len(end_msg)) * '.', end_msg)) - ret = [train_score, test_score] if return_train_score else [test_score] if return_n_test_samples: ret.append(_num_samples(X_test)) if return_times: - ret.extend([fit_time, score_time]) + ret.extend([ + fit_duration.total_seconds(), + score_duration.total_seconds() + ]) if return_parameters: ret.append(parameters) return ret diff -Nru python-mne-0.23.4+dfsg/mne/decoding/csp.py python-mne-1.1.0+dfsg/mne/decoding/csp.py --- python-mne-0.23.4+dfsg/mne/decoding/csp.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/csp.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,7 +5,7 @@ # Clemens Brunner # Jean-Remi King # -# License: BSD (3-clause) +# License: BSD-3-Clause import copy as cp @@ -14,8 +14,9 @@ from .base import BaseEstimator from .mixin import TransformerMixin from ..cov import _regularized_covariance +from ..defaults import _INTERPOLATION_DEFAULT from ..fixes import pinv -from ..utils import fill_doc, _check_option, _validate_type +from ..utils import fill_doc, _check_option, _validate_type, copy_doc @fill_doc @@ -62,7 +63,7 @@ Parameters to pass to :func:`mne.compute_covariance`. .. versionadded:: 0.16 - %(rank_None)s + %(rank_none)s .. versionadded:: 0.17 component_order : 'mutual_info' | 'alternate' (default 'mutual_info') @@ -231,6 +232,10 @@ X /= self.std_ return X + @copy_doc(TransformerMixin.fit_transform) + def fit_transform(self, X, y, **fit_params): # noqa: D102 + return super().fit_transform(X, y=y, **fit_params) + @fill_doc def plot_patterns(self, info, components=None, ch_type=None, vmin=None, vmax=None, cmap='RdBu_r', sensors=True, @@ -238,7 +243,7 @@ size=1, cbar_fmt='%3.1f', name_format='CSP%01d', show=True, show_names=False, title=None, mask=None, mask_params=None, outlines='head', contours=6, - image_interp='bilinear', average=None, + image_interp=_INTERPOLATION_DEFAULT, average=None, sphere=None): """Plot topographic patterns of components. @@ -247,9 +252,8 @@ Parameters ---------- - info : instance of Info - Info dictionary of the epochs used for fitting. - If not possible, consider using ``create_info``. + %(info_not_none)s Used for fitting. If not available, consider using + :func:`mne.create_info`. components : float | array of float | None The patterns to plot. If None, n_components will be shown. ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None @@ -309,31 +313,22 @@ only significant sensors will be shown. title : str | None Title. If None (default), no title is displayed. - mask : ndarray of bool, shape (n_channels, n_times) | None - The channels to be marked as significant at a given time point. - Indices set to `True` will be considered. Defaults to None. - mask_params : dict | None - Additional plotting parameters for plotting significant sensors. - Default (None) equals:: - - dict(marker='o', markerfacecolor='w', markeredgecolor='k', - linewidth=0, markersize=4) - %(topomap_outlines)s + %(mask_patterns_topomap)s + %(mask_params_topomap)s + %(outlines_topomap)s contours : int | array of float The number of contour lines to draw. If 0, no contours will be drawn. When an integer, matplotlib ticker locator is used to find suitable values for the contour thresholds (may sometimes be inaccurate, use array for accuracy). If an array, the values represent the levels for the contours. Defaults to 6. - image_interp : str - The image interpolation to be used. - All matplotlib options are accepted. + %(image_interp_topomap)s average : float | None The time window around a given time to be used for averaging (seconds). For example, 0.01 would translate into window that starts 5 ms before and ends 5 ms after a given time point. Defaults to None, which means no averaging. - %(topomap_sphere_auto)s + %(sphere_topomap_auto)s Returns ------- @@ -346,7 +341,8 @@ # set sampling frequency to have 1 component per time point info = cp.deepcopy(info) - info['sfreq'] = 1. + with info._unlock(): + info['sfreq'] = 1. # create an evoked patterns = EvokedArray(self.patterns_.T, info, tmin=0) # the call plot_topomap @@ -367,7 +363,7 @@ size=1, cbar_fmt='%3.1f', name_format='CSP%01d', show=True, show_names=False, title=None, mask=None, mask_params=None, outlines='head', contours=6, - image_interp='bilinear', average=None): + image_interp=_INTERPOLATION_DEFAULT, average=None): """Plot topographic filters of components. The filters are used to extract discriminant neural sources from @@ -375,9 +371,8 @@ Parameters ---------- - info : instance of Info - Info dictionary of the epochs used for fitting. - If not possible, consider using ``create_info``. + %(info_not_none)s Used for fitting. If not available, consider using + :func:`mne.create_info`. components : float | array of float | None The patterns to plot. If None, n_components will be shown. ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None @@ -446,16 +441,14 @@ dict(marker='o', markerfacecolor='w', markeredgecolor='k', linewidth=0, markersize=4) - %(topomap_outlines)s + %(outlines_topomap)s contours : int | array of float The number of contour lines to draw. If 0, no contours will be drawn. When an integer, matplotlib ticker locator is used to find suitable values for the contour thresholds (may sometimes be inaccurate, use array for accuracy). If an array, the values represent the levels for the contours. Defaults to 6. - image_interp : str - The image interpolation to be used. - All matplotlib options are accepted. + %(image_interp_topomap)s average : float | None The time window around a given time to be used for averaging (seconds). For example, 0.01 would translate into window that @@ -473,7 +466,8 @@ # set sampling frequency to have 1 component per time point info = cp.deepcopy(info) - info['sfreq'] = 1. + with info._unlock(): + info['sfreq'] = 1. # create an evoked filters = EvokedArray(self.filters_.T, info, tmin=0) # the call plot_topomap @@ -709,7 +703,7 @@ Parameters to pass to :func:`mne.compute_covariance`. .. versionadded:: 0.16 - %(rank_None)s + %(rank_none)s .. versionadded:: 0.17 diff -Nru python-mne-0.23.4+dfsg/mne/decoding/ems.py python-mne-1.1.0+dfsg/mne/decoding/ems.py --- python-mne-0.23.4+dfsg/mne/decoding/ems.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/ems.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Alexandre Gramfort # Jean-Remi King # -# License: BSD (3-clause) +# License: BSD-3-Clause from collections import Counter @@ -92,7 +92,7 @@ @verbose -def compute_ems(epochs, conditions=None, picks=None, n_jobs=1, cv=None, +def compute_ems(epochs, conditions=None, picks=None, n_jobs=None, cv=None, verbose=None): """Compute event-matched spatial filter on epochs. @@ -189,7 +189,7 @@ y = epochs.events[:, 2] _, cv_splits = _set_cv(cv, 'classifier', X=y, y=y) - parallel, p_func, _ = parallel_func(_run_ems, n_jobs=n_jobs) + parallel, p_func, n_jobs = parallel_func(_run_ems, n_jobs=n_jobs) # FIXME this parallelization should be removed. # 1) it's numpy computation so it's already efficient, # 2) it duplicates the data in RAM, diff -Nru python-mne-0.23.4+dfsg/mne/decoding/mixin.py python-mne-1.1.0+dfsg/mne/decoding/mixin.py --- python-mne-0.23.4+dfsg/mne/decoding/mixin.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/mixin.py 2022-08-03 15:48:14.000000000 +0000 @@ -6,17 +6,17 @@ def fit_transform(self, X, y=None, **fit_params): """Fit to data, then transform it. - Fits transformer to X and y with optional parameters fit_params - and returns a transformed version of X. + Fits transformer to ``X`` and ``y`` with optional parameters + ``fit_params``, and returns a transformed version of ``X``. Parameters ---------- X : array, shape (n_samples, n_features) Training set. y : array, shape (n_samples,) - Target values. + Target values or class labels. **fit_params : dict - Additional fitting parameters passed to ``self.fit``. + Additional fitting parameters passed to the ``fit`` method.. Returns ------- diff -Nru python-mne-0.23.4+dfsg/mne/decoding/receptive_field.py python-mne-1.1.0+dfsg/mne/decoding/receptive_field.py --- python-mne-0.23.4+dfsg/mne/decoding/receptive_field.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/receptive_field.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Authors: Chris Holdgraf # Eric Larson -# License: BSD (3-clause) +# License: BSD-3-Clause import numbers @@ -11,9 +11,10 @@ from .base import get_coef, BaseEstimator, _check_estimator from .time_delaying_ridge import TimeDelayingRidge from ..fixes import is_regressor -from ..utils import _validate_type, verbose +from ..utils import _validate_type, verbose, fill_doc +@fill_doc class ReceptiveField(BaseEstimator): """Fit a receptive field model. @@ -66,10 +67,7 @@ duration. Only used if ``estimator`` is float or None. .. versionadded:: 0.18 - verbose : bool, str, int, or None - If not None, override default verbose level (see - :func:`mne.verbose` and :ref:`Logging documentation ` - for more). + %(verbose)s Attributes ---------- @@ -108,7 +106,7 @@ @verbose def __init__(self, tmin, tmax, sfreq, feature_names=None, estimator=None, fit_intercept=None, scoring='r2', patterns=False, - n_jobs=1, edge_correction=True, verbose=None): + n_jobs=None, edge_correction=True, verbose=None): self.feature_names = feature_names self.sfreq = float(sfreq) self.tmin = tmin @@ -119,7 +117,6 @@ self.patterns = patterns self.n_jobs = n_jobs self.edge_correction = edge_correction - self.verbose = verbose def __repr__(self): # noqa: D105 s = "tmin, tmax : (%.3f, %.3f), " % (self.tmin, self.tmax) @@ -153,7 +150,6 @@ y = y.reshape(-1, y.shape[-1], order='F') return X, y - @verbose def fit(self, X, y): """Fit a receptive field model. diff -Nru python-mne-0.23.4+dfsg/mne/decoding/search_light.py python-mne-1.1.0+dfsg/mne/decoding/search_light.py --- python-mne-0.23.4+dfsg/mne/decoding/search_light.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/search_light.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Author: Jean-Remi King # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np @@ -8,7 +8,7 @@ from .base import BaseEstimator, _check_estimator from ..fixes import _get_check_scoring from ..parallel import parallel_func -from ..utils import (_validate_type, array_split_idx, ProgressBar, +from ..utils import (array_split_idx, ProgressBar, verbose, fill_doc) @@ -22,18 +22,9 @@ Parameters ---------- - base_estimator : object - The base estimator to iteratively fit on a subset of the dataset. - scoring : callable, str, default None - Score function (or loss function) with signature - ``score_func(y, y_pred, **kwargs)``. - Note that the predict_method is automatically identified if scoring is - a string (e.g. scoring="roc_auc" calls predict_proba) but is not - automatically set if scoring is a callable (e.g. - scoring=sklearn.metrics.roc_auc_score). + %(base_estimator)s + %(scoring)s %(n_jobs)s - The number of jobs to run in parallel for both `fit` and `predict`. - If -1, then the number of jobs is set to the number of cores. %(verbose)s Attributes @@ -42,16 +33,14 @@ List of fitted scikit-learn estimators (one per task). """ - def __init__(self, base_estimator, scoring=None, n_jobs=1, + @verbose + def __init__(self, base_estimator, scoring=None, n_jobs=None, *, verbose=None): # noqa: D102 _check_estimator(base_estimator) self._estimator_type = getattr(base_estimator, "_estimator_type", None) self.base_estimator = base_estimator self.n_jobs = n_jobs self.scoring = scoring - self.verbose = verbose - - _validate_type(self.n_jobs, 'int', 'n_jobs') def __repr__(self): # noqa: D105 repr_str = '<' + super(SlidingEstimator, self).__repr__() @@ -60,7 +49,6 @@ repr_str += ', fitted with %i estimators' % len(self.estimators_) return repr_str + '>' - @verbose # to use class value def fit(self, X, y, **fit_params): """Fit a series of independent estimators to the dataset. @@ -82,12 +70,11 @@ Return self. """ self._check_Xy(X, y) + parallel, p_func, n_jobs = parallel_func( + _sl_fit, self.n_jobs, max_jobs=X.shape[-1], verbose=False) self.estimators_ = list() self.fit_params = fit_params # For fitting, the parallelization is across estimators. - parallel, p_func, n_jobs = parallel_func(_sl_fit, self.n_jobs, - verbose=False) - n_jobs = min(n_jobs, X.shape[-1]) mesg = 'Fitting %s' % (self.__class__.__name__,) with ProgressBar(X.shape[-1], mesg=mesg) as pb: estimators = parallel( @@ -129,7 +116,6 @@ """ # noqa: E501 return self.fit(X, y, **fit_params).transform(X) - @verbose # to use the class value def _transform(self, X, method): """Aux. function to make parallel predictions/transformation.""" self._check_Xy(X) @@ -139,10 +125,9 @@ 'X.shape[-1]') # For predictions/transforms the parallelization is across the data and # not across the estimators to avoid memory load. - mesg = 'Transforming %s' % (self.__class__.__name__,) parallel, p_func, n_jobs = parallel_func( - _sl_transform, self.n_jobs, verbose=False) - n_jobs = min(n_jobs, X.shape[-1]) + _sl_transform, self.n_jobs, max_jobs=X.shape[-1], verbose=False) + mesg = 'Transforming %s' % (self.__class__.__name__,) X_splits = np.array_split(X, n_jobs, axis=-1) idx, est_splits = zip(*array_split_idx(self.estimators_, n_jobs)) with ProgressBar(X.shape[-1], mesg=mesg) as pb: @@ -285,8 +270,8 @@ # For predictions/transforms the parallelization is across the data and # not across the estimators to avoid memory load. - parallel, p_func, n_jobs = parallel_func(_sl_score, self.n_jobs) - n_jobs = min(n_jobs, X.shape[-1]) + parallel, p_func, n_jobs = parallel_func( + _sl_score, self.n_jobs, max_jobs=X.shape[-1]) X_splits = np.array_split(X, n_jobs, axis=-1) est_splits = np.array_split(self.estimators_, n_jobs) score = parallel(p_func(est, scoring, x, y) @@ -304,6 +289,7 @@ return self.estimators_[0].classes_ +@fill_doc def _sl_fit(estimator, X, y, pb, **fit_params): """Aux. function to fit SlidingEstimator in parallel. @@ -311,8 +297,7 @@ Parameters ---------- - base_estimator : object - The base estimator to iteratively fit on a subset of the dataset. + %(base_estimator)s X : array, shape (n_samples, nd_features, n_estimators) The target data. The feature dimension can be multidimensional e.g. X.shape = (n_samples, n_features_1, n_features_2, n_estimators) @@ -427,18 +412,9 @@ Parameters ---------- - base_estimator : object - The base estimator to iteratively fit on a subset of the dataset. - scoring : callable | str | None - Score function (or loss function) with signature - ``score_func(y, y_pred, **kwargs)``. - Note that the predict_method is automatically identified if scoring is - a string (e.g. scoring="roc_auc" calls predict_proba) but is not - automatically set if scoring is a callable (e.g. - scoring=sklearn.metrics.roc_auc_score). + %(base_estimator)s + %(scoring)s %(n_jobs)s - The number of jobs to run in parallel for both `fit` and `predict`. - If -1, then the number of jobs is set to the number of cores. %(verbose)s """ @@ -449,15 +425,13 @@ repr_str += ', fitted with %i estimators>' % len(self.estimators_) return repr_str - @verbose # use class value def _transform(self, X, method): """Aux. function to make parallel predictions/transformation.""" self._check_Xy(X) method = _check_method(self.base_estimator, method) mesg = 'Transforming %s' % (self.__class__.__name__,) parallel, p_func, n_jobs = parallel_func( - _gl_transform, self.n_jobs, verbose=False) - n_jobs = min(n_jobs, X.shape[-1]) + _gl_transform, self.n_jobs, max_jobs=X.shape[-1], verbose=False) with ProgressBar(X.shape[-1] * len(self.estimators_), mesg=mesg) as pb: y_pred = parallel( p_func(self.estimators_, x_split, method, pb.subset(pb_idx)) @@ -521,7 +495,7 @@ Notes ----- - This requires base_estimator to have a `predict_proba` method. + This requires ``base_estimator`` to have a ``predict_proba`` method. """ # noqa: E501 return self._transform(X, 'predict_proba') @@ -544,11 +518,11 @@ Notes ----- - This requires base_estimator to have a ``decision_function`` method. + This requires ``base_estimator`` to have a ``decision_function`` + method. """ # noqa: E501 return self._transform(X, 'decision_function') - @verbose # to use class value def score(self, X, y): """Score each of the estimators on the tested dimensions. @@ -573,9 +547,8 @@ # For predictions/transforms the parallelization is across the data and # not across the estimators to avoid memory load. mesg = 'Scoring %s' % (self.__class__.__name__,) - parallel, p_func, n_jobs = parallel_func(_gl_score, self.n_jobs, - verbose=False) - n_jobs = min(n_jobs, X.shape[-1]) + parallel, p_func, n_jobs = parallel_func( + _gl_score, self.n_jobs, max_jobs=X.shape[-1], verbose=False) scoring = check_scoring(self.base_estimator, self.scoring) y = _fix_auc(scoring, y) with ProgressBar(X.shape[-1] * len(self.estimators_), mesg=mesg) as pb: diff -Nru python-mne-0.23.4+dfsg/mne/decoding/ssd.py python-mne-1.1.0+dfsg/mne/decoding/ssd.py --- python-mne-0.23.4+dfsg/mne/decoding/ssd.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/ssd.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Author: Denis A. Engemann # Victoria Peterson -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np @@ -27,9 +27,7 @@ Parameters ---------- - info : instance of mne.Info - The info object containing the channel and sampling information. - It must match the input data. + %(info_not_none)s Must match the input data. filt_params_signal : dict Filtering for the frequencies of interest. filt_params_noise : dict diff -Nru python-mne-0.23.4+dfsg/mne/decoding/tests/test_base.py python-mne-1.1.0+dfsg/mne/decoding/tests/test_base.py --- python-mne-0.23.4+dfsg/mne/decoding/tests/test_base.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/tests/test_base.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Author: Jean-Remi King, # Marijn van Vliet, # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np from numpy.testing import (assert_array_equal, assert_array_almost_equal, @@ -75,11 +75,11 @@ parameters = {'kernel': ['linear'], 'C': [1, 10]} lm_gs_classification = LinearModel( - GridSearchCV(svm.SVC(), parameters, cv=2, refit=True, n_jobs=1)) + GridSearchCV(svm.SVC(), parameters, cv=2, refit=True, n_jobs=None)) assert (is_classifier(lm_gs_classification)) lm_gs_regression = LinearModel( - GridSearchCV(svm.SVR(), parameters, cv=2, refit=True, n_jobs=1)) + GridSearchCV(svm.SVR(), parameters, cv=2, refit=True, n_jobs=None)) assert (is_regressor(lm_gs_regression)) # Define a classifier, an invertible transformer and an non-invertible one. @@ -333,7 +333,7 @@ from sklearn.model_selection import GridSearchCV parameters = {'kernel': ['linear'], 'C': [1, 10]} clf = LinearModel( - GridSearchCV(svm.SVC(), parameters, cv=2, refit=True, n_jobs=1)) + GridSearchCV(svm.SVC(), parameters, cv=2, refit=True, n_jobs=None)) clf.fit(X, y) assert_equal(clf.filters_.shape, (n_features,)) assert_equal(clf.patterns_.shape, (n_features,)) @@ -345,7 +345,7 @@ n_targets = 1 Y = rng.rand(n, n_targets) clf = LinearModel( - GridSearchCV(svm.SVR(), parameters, cv=2, refit=True, n_jobs=1)) + GridSearchCV(svm.SVR(), parameters, cv=2, refit=True, n_jobs=None)) clf.fit(X, y) assert_equal(clf.filters_.shape, (n_features, )) assert_equal(clf.patterns_.shape, (n_features, )) @@ -402,7 +402,7 @@ pytest.raises(ValueError, cross_val_multiscore, clf, X, y, cv=cv, scoring='roc_auc') clf = SlidingEstimator(logreg, scoring='roc_auc') - scores_auc = cross_val_multiscore(clf, X, y, cv=cv, n_jobs=1) + scores_auc = cross_val_multiscore(clf, X, y, cv=cv, n_jobs=None) scores_auc_manual = list() for train, test in cv.split(X, y): clf.fit(X[train], y[train]) diff -Nru python-mne-0.23.4+dfsg/mne/decoding/tests/test_csp.py python-mne-1.1.0+dfsg/mne/decoding/tests/test_csp.py --- python-mne-0.23.4+dfsg/mne/decoding/tests/test_csp.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/tests/test_csp.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Alexandre Barachant # Jean-Remi King # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op diff -Nru python-mne-0.23.4+dfsg/mne/decoding/tests/test_ems.py python-mne-1.1.0+dfsg/mne/decoding/tests/test_ems.py --- python-mne-0.23.4+dfsg/mne/decoding/tests/test_ems.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/tests/test_ems.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Author: Denis A. Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op import numpy as np @@ -8,7 +8,7 @@ import pytest from mne import io, Epochs, read_events, pick_types -from mne.utils import requires_sklearn, run_tests_if_main +from mne.utils import requires_sklearn from mne.decoding import compute_ems, EMS data_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data') @@ -82,6 +82,3 @@ assert_equal(ems.__repr__(), '') assert_array_almost_equal(filters, np.mean(coefs, axis=0)) assert_array_almost_equal(surrogates, np.vstack(Xt)) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/decoding/tests/test_receptive_field.py python-mne-1.1.0+dfsg/mne/decoding/tests/test_receptive_field.py --- python-mne-0.23.4+dfsg/mne/decoding/tests/test_receptive_field.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/tests/test_receptive_field.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Chris Holdgraf # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op import pytest @@ -10,7 +10,7 @@ from numpy.fft import rfft, irfft from numpy.testing import assert_array_equal, assert_allclose, assert_equal -from mne.utils import requires_sklearn, run_tests_if_main +from mne.utils import requires_sklearn from mne.decoding import ReceptiveField, TimeDelayingRidge from mne.decoding.receptive_field import (_delay_time_series, _SCORERS, _times_to_delays, _delays_to_slice) @@ -143,6 +143,7 @@ assert_array_equal(X_delayed[:ii, :, idx], 0.) +@pytest.mark.slowtest # slow on Azure @pytest.mark.parametrize('n_jobs', n_jobs_test) @requires_sklearn def test_receptive_field_basic(n_jobs): @@ -542,6 +543,3 @@ with pytest.warns((RuntimeWarning, UserWarning), match='[Singular|scipy.linalg.solve]'): rf.fit(y, X) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/decoding/tests/test_search_light.py python-mne-1.1.0+dfsg/mne/decoding/tests/test_search_light.py --- python-mne-0.23.4+dfsg/mne/decoding/tests/test_search_light.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/tests/test_search_light.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,12 +1,12 @@ # Author: Jean-Remi King, # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np from numpy.testing import assert_array_equal, assert_equal import pytest -from mne.utils import requires_sklearn +from mne.utils import requires_sklearn, _record_warnings from mne.fixes import _get_args from mne.decoding.search_light import SlidingEstimator, GeneralizingEstimator from mne.decoding.transformer import Vectorizer @@ -30,7 +30,7 @@ from sklearn.linear_model import Ridge, LogisticRegression from sklearn.pipeline import make_pipeline from sklearn.metrics import roc_auc_score, make_scorer - with pytest.warns(None): # NumPy module import + with _record_warnings(): # NumPy module import from sklearn.ensemble import BaggingClassifier from sklearn.base import is_classifier @@ -121,7 +121,7 @@ assert_array_equal(score_manual, score_sl) # n_jobs - sl = SlidingEstimator(logreg, n_jobs=1, scoring='roc_auc') + sl = SlidingEstimator(logreg, n_jobs=None, scoring='roc_auc') score_1job = sl.fit(X, y).score(X, y) sl.n_jobs = 2 score_njobs = sl.fit(X, y).score(X, y) @@ -133,19 +133,14 @@ sl.predict(X[..., [0]]) # pipeline - class _LogRegTransformer(LogisticRegression): - # XXX needs transformer in pipeline to get first proba only - def __init__(self): - super(_LogRegTransformer, self).__init__() - self.multi_class = 'ovr' - self.random_state = 0 - self.solver = 'liblinear' - def transform(self, X): return super(_LogRegTransformer, self).predict_proba(X)[..., 1] - pipe = make_pipeline(SlidingEstimator(_LogRegTransformer()), + logreg_transformer = _LogRegTransformer( + random_state=0, multi_class='ovr', solver='liblinear' + ) + pipe = make_pipeline(SlidingEstimator(logreg_transformer), logreg) pipe.fit(X, y) pipe.predict(X) diff -Nru python-mne-0.23.4+dfsg/mne/decoding/tests/test_ssd.py python-mne-1.1.0+dfsg/mne/decoding/tests/test_ssd.py --- python-mne-0.23.4+dfsg/mne/decoding/tests/test_ssd.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/tests/test_ssd.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Author: Denis A. Engemann # Victoria Peterson -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np import pytest @@ -292,7 +292,7 @@ def test_return_filtered(): """Test return filtered option.""" # Check return_filtered - # Simulated more noise data and with broader freqquency than the desired + # Simulated more noise data and with broader frequency than the desired X, _, _ = simulate_data(SNR=0.9, freqs_sig=[4, 13]) sf = 250 n_channels = X.shape[0] diff -Nru python-mne-0.23.4+dfsg/mne/decoding/tests/test_time_frequency.py python-mne-1.1.0+dfsg/mne/decoding/tests/test_time_frequency.py --- python-mne-0.23.4+dfsg/mne/decoding/tests/test_time_frequency.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/tests/test_time_frequency.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Author: Jean-Remi King, # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np diff -Nru python-mne-0.23.4+dfsg/mne/decoding/tests/test_transformer.py python-mne-1.1.0+dfsg/mne/decoding/tests/test_transformer.py --- python-mne-0.23.4+dfsg/mne/decoding/tests/test_transformer.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/tests/test_transformer.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Author: Mainak Jas # Romain Trachel # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op import numpy as np @@ -14,7 +14,7 @@ from mne.decoding import (Scaler, FilterEstimator, PSDEstimator, Vectorizer, UnsupervisedSpatialFilter, TemporalFilter) from mne.defaults import DEFAULTS -from mne.utils import requires_sklearn, run_tests_if_main, check_version +from mne.utils import requires_sklearn, check_version, use_log_level tmin, tmax = -0.2, 0.5 event_id = dict(aud_l=1, vis_l=3) @@ -239,7 +239,13 @@ X = np.random.rand(101, 500) filt = TemporalFilter(l_freq=25., h_freq=50., sfreq=1000., filter_length=150, fir_design='firwin2') - assert_equal(filt.fit_transform(X).shape, X.shape) + with use_log_level('error'): # warning about transition bandwidth + assert_equal(filt.fit_transform(X).shape, X.shape) -run_tests_if_main() +def test_bad_triage(): + """Test for gh-10924.""" + filt = TemporalFilter(l_freq=8, h_freq=60, sfreq=160.) + # Used to fail with "ValueError: Effective band-stop frequency (135.0) is + # too high (maximum based on Nyquist is 80.0)" + filt.fit_transform(np.zeros((1, 1, 481))) diff -Nru python-mne-0.23.4+dfsg/mne/decoding/time_delaying_ridge.py python-mne-1.1.0+dfsg/mne/decoding/time_delaying_ridge.py --- python-mne-0.23.4+dfsg/mne/decoding/time_delaying_ridge.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/time_delaying_ridge.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Authors: Eric Larson # Ross Maddox # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np @@ -11,11 +11,10 @@ from ..cuda import _setup_cuda_fft_multiply_repeated from ..filter import next_fast_len from ..fixes import jit -from ..parallel import check_n_jobs from ..utils import warn, ProgressBar, logger -def _compute_corrs(X, y, smin, smax, n_jobs=1, fit_intercept=False, +def _compute_corrs(X, y, smin, smax, n_jobs=None, fit_intercept=False, edge_correction=True): """Compute auto- and cross-correlations.""" if fit_intercept: @@ -42,8 +41,9 @@ n_fft = next_fast_len(2 * X.shape[0] - 1) - n_jobs, cuda_dict = _setup_cuda_fft_multiply_repeated( + _, cuda_dict = _setup_cuda_fft_multiply_repeated( n_jobs, [1.], n_fft, 'correlation calculations') + del n_jobs # only used to set as CUDA # create our Toeplitz indexer ij = np.empty((len_trf, len_trf), int) @@ -212,7 +212,7 @@ # Note: we must use overwrite_a=False in order to be able to # use the fall-back solution below in case a LinAlgError # is raised - w = linalg.solve(mat, x_y, sym_pos=True, overwrite_a=False) + w = linalg.solve(mat, x_y, overwrite_a=False, assume_a='pos') except np.linalg.LinAlgError: warn('Singular matrix in solving dual problem. Using ' 'least-squares solution instead.') @@ -272,7 +272,7 @@ _estimator_type = "regressor" def __init__(self, tmin, tmax, sfreq, alpha=0., reg_type='ridge', - fit_intercept=True, n_jobs=1, edge_correction=True): + fit_intercept=True, n_jobs=None, edge_correction=True): if tmin > tmax: raise ValueError('tmin must be <= tmax, got %s and %s' % (tmin, tmax)) @@ -314,12 +314,11 @@ else: assert X.ndim == 2 and y.ndim == 2 assert X.shape[0] == y.shape[0] - n_jobs = check_n_jobs(self.n_jobs, allow_cuda=True) # These are split into two functions because it's possible that we # might want to allow people to do them separately (e.g., to test # different regularization parameters). self.cov_, x_y_, n_ch_x, X_offset, y_offset = _compute_corrs( - X, y, self._smin, self._smax, n_jobs, self.fit_intercept, + X, y, self._smin, self._smax, self.n_jobs, self.fit_intercept, self.edge_correction) self.coef_ = _fit_corrs(self.cov_, x_y_, n_ch_x, self.reg_type, self.alpha, n_ch_x) @@ -343,6 +342,8 @@ X : ndarray The predicted response. """ + from scipy.signal import fftconvolve + if X.ndim == 2: X = X[:, np.newaxis, :] singleton = True @@ -354,7 +355,7 @@ for ei in range(X.shape[1]): for oi in range(self.coef_.shape[0]): for fi in range(self.coef_.shape[1]): - temp = np.convolve(X[:, ei, fi], self.coef_[oi, fi]) + temp = fftconvolve(X[:, ei, fi], self.coef_[oi, fi]) temp = temp[max(-smin, 0):][:len(out) - offset] out[offset:len(temp) + offset, ei, oi] += temp out += self.intercept_ diff -Nru python-mne-0.23.4+dfsg/mne/decoding/time_frequency.py python-mne-1.1.0+dfsg/mne/decoding/time_frequency.py --- python-mne-0.23.4+dfsg/mne/decoding/time_frequency.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/time_frequency.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,12 +1,12 @@ # Author: Jean-Remi King # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np from .mixin import TransformerMixin from .base import BaseEstimator from ..time_frequency.tfr import _compute_tfr, _check_tfr_param -from ..utils import fill_doc, _check_option +from ..utils import fill_doc, _check_option, verbose @fill_doc @@ -59,10 +59,10 @@ mne.time_frequency.tfr_multitaper """ + @verbose def __init__(self, freqs, sfreq=1.0, method='morlet', n_cycles=7.0, time_bandwidth=None, use_fft=True, decim=1, output='complex', - n_jobs=1, verbose=None): # noqa: D102 - """Init TimeFrequency transformer.""" + n_jobs=None, *, verbose=None): # noqa: D102 freqs, sfreq, _, n_cycles, time_bandwidth, decim = \ _check_tfr_param(freqs, sfreq, method, True, n_cycles, time_bandwidth, use_fft, decim, output) @@ -77,7 +77,6 @@ self.output = _check_option('output', output, ['complex', 'power', 'phase']) self.n_jobs = n_jobs - self.verbose = verbose def fit_transform(self, X, y=None): """Time-frequency transform of times series along the last axis. @@ -138,8 +137,7 @@ # Compute time-frequency Xt = _compute_tfr(X, self.freqs, self.sfreq, self.method, self.n_cycles, True, self.time_bandwidth, - self.use_fft, self.decim, self.output, self.n_jobs, - self.verbose) + self.use_fft, self.decim, self.output, self.n_jobs) # Back to original shape if not shape: diff -Nru python-mne-0.23.4+dfsg/mne/decoding/transformer.py python-mne-1.1.0+dfsg/mne/decoding/transformer.py --- python-mne-0.23.4+dfsg/mne/decoding/transformer.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/decoding/transformer.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Alexandre Gramfort # Romain Trachel # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np @@ -11,9 +11,9 @@ from .base import BaseEstimator from .. import pick_types -from ..filter import filter_data, _triage_filter_params +from ..filter import filter_data from ..time_frequency.psd import psd_array_multitaper -from ..utils import fill_doc, _check_option, _validate_type +from ..utils import fill_doc, _check_option, _validate_type, verbose from ..io.pick import (pick_info, _pick_data_channels, _picks_by_type, _picks_to_idx) from ..cov import _check_scalings_user @@ -65,6 +65,7 @@ return X +@fill_doc class Scaler(TransformerMixin, BaseEstimator): u"""Standardize channel data. @@ -77,9 +78,7 @@ Parameters ---------- - info : instance of Info | None - The measurement info. Only necessary if ``scalings`` is a dict or - None. + %(info)s Only necessary if ``scalings`` is a dict or None. scalings : dict, str, default None Scaling method to be applied to data channel wise. @@ -351,10 +350,7 @@ bandwidth. n_jobs : int Number of parallel jobs to use (only used if adaptive=True). - normalization : str - Either "full" or "length" (default). If "full", the PSD will - be normalized by the sampling rate as well as the length of - the signal (as in nitime). + %(normalization)s %(verbose)s See Also @@ -362,9 +358,10 @@ mne.time_frequency.psd_multitaper """ + @verbose def __init__(self, sfreq=2 * np.pi, fmin=0, fmax=np.inf, bandwidth=None, - adaptive=False, low_bias=True, n_jobs=1, - normalization='length', verbose=None): # noqa: D102 + adaptive=False, low_bias=True, n_jobs=None, + normalization='length', *, verbose=None): # noqa: D102 self.sfreq = sfreq self.fmin = fmin self.fmax = fmax @@ -372,7 +369,6 @@ self.adaptive = adaptive self.low_bias = low_bias self.n_jobs = n_jobs - self.verbose = verbose self.normalization = normalization def fit(self, epochs_data, y): @@ -440,8 +436,7 @@ Parameters ---------- - info : instance of Info - Measurement info. + %(info_not_none)s %(l_freq)s %(h_freq)s %(picks_good_data)s @@ -474,9 +469,9 @@ """ def __init__(self, info, l_freq, h_freq, picks=None, filter_length='auto', - l_trans_bandwidth='auto', h_trans_bandwidth='auto', n_jobs=1, - method='fir', iir_params=None, fir_design='firwin', - verbose=None): # noqa: D102 + l_trans_bandwidth='auto', h_trans_bandwidth='auto', + n_jobs=None, method='fir', iir_params=None, + fir_design='firwin', *, verbose=None): # noqa: D102 self.info = info self.l_freq = l_freq self.h_freq = h_freq @@ -526,14 +521,16 @@ self.l_freq < self.h_freq) and self.h_freq < self.info['lowpass']): - self.info['lowpass'] = self.h_freq + with self.info._unlock(): + self.info['lowpass'] = self.h_freq if self.info['highpass'] is None or (self.l_freq is not None and (self.h_freq is None or self.l_freq < self.h_freq) and self.l_freq > self.info['highpass']): - self.info['highpass'] = self.l_freq + with self.info._unlock(): + self.info['highpass'] = self.l_freq return self @@ -769,11 +766,12 @@ mne.filter.filter_data """ + @verbose def __init__(self, l_freq=None, h_freq=None, sfreq=1.0, filter_length='auto', l_trans_bandwidth='auto', - h_trans_bandwidth='auto', n_jobs=1, method='fir', + h_trans_bandwidth='auto', n_jobs=None, method='fir', iir_params=None, fir_window='hamming', fir_design='firwin', - verbose=None): # noqa: D102 + *, verbose=None): # noqa: D102 self.l_freq = l_freq self.h_freq = h_freq self.sfreq = sfreq @@ -785,7 +783,6 @@ self.iir_params = iir_params self.fir_window = fir_window self.fir_design = fir_design - self.verbose = verbose if not isinstance(self.n_jobs, int) and self.n_jobs == 'cuda': raise ValueError('n_jobs must be int or "cuda", got %s instead.' @@ -831,21 +828,11 @@ shape = X.shape X = X.reshape(-1, shape[-1]) - (X, self.sfreq, self.l_freq, self.h_freq, self.l_trans_bandwidth, - self.h_trans_bandwidth, self.filter_length, _, self.fir_window, - self.fir_design) = \ - _triage_filter_params(X, self.sfreq, self.l_freq, self.h_freq, - self.l_trans_bandwidth, - self.h_trans_bandwidth, self.filter_length, - self.method, phase='zero', - fir_window=self.fir_window, - fir_design=self.fir_design) X = filter_data(X, self.sfreq, self.l_freq, self.h_freq, filter_length=self.filter_length, l_trans_bandwidth=self.l_trans_bandwidth, h_trans_bandwidth=self.h_trans_bandwidth, n_jobs=self.n_jobs, method=self.method, iir_params=self.iir_params, copy=False, - fir_window=self.fir_window, fir_design=self.fir_design, - verbose=self.verbose) + fir_window=self.fir_window, fir_design=self.fir_design) return X.reshape(shape) diff -Nru python-mne-0.23.4+dfsg/mne/defaults.py python-mne-1.1.0+dfsg/mne/defaults.py --- python-mne-0.23.4+dfsg/mne/defaults.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/defaults.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Denis A. Engemann # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause from copy import deepcopy @@ -12,23 +12,25 @@ exci='k', ias='k', syst='k', seeg='saddlebrown', dbs='seagreen', dipole='k', gof='k', bio='k', ecog='k', hbo='#AA3377', hbr='b', fnirs_cw_amplitude='k', fnirs_fd_ac_amplitude='k', - fnirs_fd_phase='k', fnirs_od='k', csd='k'), + fnirs_fd_phase='k', fnirs_od='k', csd='k', whitened='k'), si_units=dict(mag='T', grad='T/m', eeg='V', eog='V', ecg='V', emg='V', misc='AU', seeg='V', dbs='V', dipole='Am', gof='GOF', bio='V', ecog='V', hbo='M', hbr='M', ref_meg='T', fnirs_cw_amplitude='V', fnirs_fd_ac_amplitude='V', - fnirs_fd_phase='rad', fnirs_od='V', csd='V/m²'), + fnirs_fd_phase='rad', fnirs_od='V', csd='V/m²', + whitened='Z'), units=dict(mag='fT', grad='fT/cm', eeg='µV', eog='µV', ecg='µV', emg='µV', misc='AU', seeg='mV', dbs='µV', dipole='nAm', gof='GOF', bio='µV', ecog='µV', hbo='µM', hbr='µM', ref_meg='fT', fnirs_cw_amplitude='V', fnirs_fd_ac_amplitude='V', - fnirs_fd_phase='rad', fnirs_od='V', csd='mV/m²'), + fnirs_fd_phase='rad', fnirs_od='V', csd='mV/m²', + whitened='Z'), # scalings for the units scalings=dict(mag=1e15, grad=1e13, eeg=1e6, eog=1e6, emg=1e6, ecg=1e6, misc=1.0, seeg=1e3, dbs=1e6, ecog=1e6, dipole=1e9, gof=1.0, bio=1e6, hbo=1e6, hbr=1e6, ref_meg=1e15, fnirs_cw_amplitude=1.0, fnirs_fd_ac_amplitude=1.0, - fnirs_fd_phase=1., fnirs_od=1.0, csd=1e3), + fnirs_fd_phase=1., fnirs_od=1.0, csd=1e3, whitened=1.), # rough guess for a good plot scalings_plot_raw=dict(mag=1e-12, grad=4e-11, eeg=20e-6, eog=150e-6, ecg=5e-4, emg=1e-3, ref_meg=1e-12, misc='auto', @@ -36,7 +38,8 @@ seeg=1e-4, dbs=1e-4, bio=1e-6, ecog=1e-4, hbo=10e-6, hbr=10e-6, whitened=10., fnirs_cw_amplitude=2e-2, fnirs_fd_ac_amplitude=2e-2, fnirs_fd_phase=2e-1, - fnirs_od=2e-2, csd=200e-4), + fnirs_od=2e-2, csd=200e-4, + dipole=1e-7, gof=1e2), scalings_cov_rank=dict(mag=1e12, grad=1e11, eeg=1e5, # ~100x scalings seeg=1e1, dbs=1e4, ecog=1e4, hbo=1e4, hbr=1e4), ylim=dict(mag=(-600., 600.), grad=(-200., 200.), eeg=(-200., 200.), @@ -51,7 +54,9 @@ fnirs_fd_ac_amplitude='fNIRS (FD AC amplitude)', fnirs_fd_phase='fNIRS (FD phase)', fnirs_od='fNIRS (OD)', hbr='Deoxyhemoglobin', - gof='Goodness of fit', csd='Current source density'), + gof='Goodness of fit', csd='Current source density', + stim='Stimulus', + ), mask_params=dict(marker='o', markerfacecolor='w', markeredgecolor='k', @@ -72,11 +77,13 @@ fnirs_scale=5e-3, source_scale=5e-3, detector_scale=5e-3, - hpi_scale=15e-3, + hpi_scale=4e-3, head_color=(0.988, 0.89, 0.74), hpi_color=(1., 0., 1.), extra_color=(1., 1., 1.), + meg_color=(0., 0.25, 0.5), ref_meg_color=(0.5, 0.5, 0.5), + helmet_color=(0.0, 0.0, 0.6), eeg_color=(1., 0.596, 0.588), eegp_color=(0.839, 0.15, 0.16), ecog_color=(1., 1., 1.), dbs_color=(0.82, 0.455, 0.659), @@ -99,7 +106,68 @@ alpha=None, resolution=1., surface_alpha=None, blending='mip', silhouette_alpha=None, silhouette_linewidth=2.), prefixes={'': 1e0, 'd': 1e1, 'c': 1e2, 'm': 1e3, 'µ': 1e6, 'u': 1e6, - 'n': 1e9, 'p': 1e12, 'f': 1e15} + 'n': 1e9, 'p': 1e12, 'f': 1e15}, + transform_zooms=dict( + translation=None, rigid=None, affine=None, sdr=None), + transform_niter=dict( + translation=(10000, 1000, 100), + rigid=(10000, 1000, 100), + affine=(10000, 1000, 100), + sdr=(10, 10, 5)), + volume_label_indices=( + # Left and middle + 4, # Left-Lateral-Ventricle + 5, # Left-Inf-Lat-Vent + + 8, # Left-Cerebellum-Cortex + + 10, # Left-Thalamus-Proper + 11, # Left-Caudate + 12, # Left-Putamen + 13, # Left-Pallidum + 14, # 3rd-Ventricle + 15, # 4th-Ventricle + 16, # Brain-Stem + 17, # Left-Hippocampus + 18, # Left-Amygdala + + 26, # Left-Accumbens-area + + 28, # Left-VentralDC + + # Right + 43, # Right-Lateral-Ventricle + 44, # Right-Inf-Lat-Vent + + 47, # Right-Cerebellum-Cortex + + 49, # Right-Thalamus-Proper + 50, # Right-Caudate + 51, # Right-Putamen + 52, # Right-Pallidum + 53, # Right-Hippocampus + 54, # Right-Amygdala + + 58, # Right-Accumbens-area + + 60, # Right-VentralDC + ), + report_stc_plot_kwargs=dict( + views=('lateral', 'medial'), + hemi='split', + backend='pyvistaqt', + time_viewer=False, + show_traces=False, + size=(450, 450), + background='white', + time_label=None, + add_data_kwargs={ + 'colorbar_kwargs': { + 'label_font_size': 12, + 'n_labels': 5 + } + } + ) ) @@ -123,4 +191,5 @@ HEAD_SIZE_DEFAULT = 0.095 # in [m] _BORDER_DEFAULT = 'mean' +_INTERPOLATION_DEFAULT = 'cubic' _EXTRAPOLATE_DEFAULT = 'auto' diff -Nru python-mne-0.23.4+dfsg/mne/dipole.py python-mne-1.1.0+dfsg/mne/dipole.py --- python-mne-0.23.4+dfsg/mne/dipole.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/dipole.py 2022-08-03 15:48:14.000000000 +0000 @@ -13,15 +13,17 @@ import numpy as np -from .cov import read_cov, compute_whitener +from .cov import compute_whitener, _ensure_cov from .io.constants import FIFF from .io.pick import pick_types from .io.proj import make_projector, _needs_eeg_average_ref_proj from .bem import _fit_sphere from .evoked import _read_evoked, _aspect_rev, _write_evokeds from .fixes import pinvh +from ._freesurfer import read_freesurfer_lut, _get_aseg from .transforms import _print_coord_trans, _coord_frame_name, apply_trans from .viz.evoked import _plot_evoked +from ._freesurfer import head_to_mni, head_to_mri from .forward._make_forward import (_get_trans, _setup_bem, _prep_meg_channels, _prep_eeg_channels) from .forward._compute_forward import (_compute_forwards_meeg, @@ -30,15 +32,17 @@ from .surface import (transform_surface_to, _compute_nearest, _points_outside_surface) from .bem import _bem_find_surface, _bem_surf_name -from .source_space import _make_volume_source_space, SourceSpaces, head_to_mni +from .source_space import _make_volume_source_space, SourceSpaces from .parallel import parallel_func from .utils import (logger, verbose, _time_mask, warn, _check_fname, - check_fname, _pl, fill_doc, _check_option, ShiftTimeMixin, - _svd_lwork, _repeated_svd, _get_blas_funcs) + check_fname, _pl, fill_doc, _check_option, + _svd_lwork, _repeated_svd, _get_blas_funcs, _validate_type, + copy_function_doc_to_method_doc, TimeMixin) +from .viz import plot_dipole_locations @fill_doc -class Dipole(object): +class Dipole(TimeMixin): u"""Dipole class for sequential dipole fits. .. note:: This class should usually not be instantiated directly, @@ -97,8 +101,8 @@ @verbose def __init__(self, times, pos, amplitude, ori, gof, name=None, conf=None, khi2=None, nfree=None, - verbose=None): # noqa: D102 - self.times = np.array(times) + *, verbose=None): # noqa: D102 + self._set_times(np.array(times)) self.pos = np.array(pos) self.amplitude = np.array(amplitude) self.ori = np.array(ori) @@ -110,7 +114,6 @@ self.conf[key] = np.array(value) self.khi2 = np.array(khi2) if khi2 is not None else None self.nfree = np.array(nfree) if nfree is not None else None - self.verbose = verbose def __repr__(self): # noqa: D105 s = "n_times : %s" % len(self.times) @@ -129,7 +132,7 @@ %(overwrite)s .. versionadded:: 0.20 - %(verbose_meth)s + %(verbose)s Notes ----- @@ -143,8 +146,8 @@ else: _write_dipole_text(fname, self) - @fill_doc - def crop(self, tmin=None, tmax=None, include_tmax=True): + @verbose + def crop(self, tmin=None, tmax=None, include_tmax=True, verbose=None): """Crop data to a given time interval. Parameters @@ -154,6 +157,7 @@ tmax : float | None End time of selection in seconds. %(include_tmax)s + %(verbose)s Returns ------- @@ -165,8 +169,8 @@ sfreq = 1. / np.median(np.diff(self.times)) mask = _time_mask(self.times, tmin, tmax, sfreq=sfreq, include_tmax=include_tmax) - for attr in ('times', 'pos', 'gof', 'amplitude', 'ori', - 'khi2', 'nfree'): + self._set_times(self.times[mask]) + for attr in ('pos', 'gof', 'amplitude', 'ori', 'khi2', 'nfree'): if getattr(self, attr) is not None: setattr(self, attr, getattr(self, attr)[mask]) for key in self.conf.keys(): @@ -184,95 +188,44 @@ return deepcopy(self) @verbose + @copy_function_doc_to_method_doc(plot_dipole_locations) def plot_locations(self, trans, subject, subjects_dir=None, mode='orthoview', coord_frame='mri', idx='gof', show_all=True, ax=None, block=False, show=True, - scale=5e-3, color=(1.0, 0.0, 0.0), fig=None, - verbose=None, title=None): - """Plot dipole locations in 3d. + scale=None, color=None, *, highlight_color='r', + fig=None, title=None, head_source='seghead', + surf='pial', width=None, verbose=None): + return plot_dipole_locations( + self, trans, subject, subjects_dir, mode, coord_frame, idx, + show_all, ax, block, show, scale=scale, color=color, + highlight_color=highlight_color, fig=fig, title=title, + head_source=head_source, surf=surf, width=width) + + @verbose + def to_mni(self, subject, trans, subjects_dir=None, + verbose=None): + """Convert dipole location from head to MNI coordinates. Parameters ---------- - trans : dict - The mri to head trans. - subject : str - The subject name corresponding to FreeSurfer environment - variable SUBJECT. + %(subject)s + %(trans_not_none)s %(subjects_dir)s - mode : str - Can be ``'arrow'``, ``'sphere'`` or ``'orthoview'``. - - .. versionadded:: 0.14.0 - coord_frame : str - Coordinate frame to use, 'head' or 'mri'. Defaults to 'mri'. - - .. versionadded:: 0.14.0 - idx : int | 'gof' | 'amplitude' - Index of the initially plotted dipole. Can also be 'gof' to plot - the dipole with highest goodness of fit value or 'amplitude' to - plot the dipole with the highest amplitude. The dipoles can also be - browsed through using up/down arrow keys or mouse scroll. Defaults - to 'gof'. Only used if mode equals 'orthoview'. - - .. versionadded:: 0.14.0 - show_all : bool - Whether to always plot all the dipoles. If True (default), the - active dipole is plotted as a red dot and it's location determines - the shown MRI slices. The the non-active dipoles are plotted as - small blue dots. If False, only the active dipole is plotted. - Only used if mode equals 'orthoview'. - - .. versionadded:: 0.14.0 - ax : instance of matplotlib Axes3D | None - Axes to plot into. If None (default), axes will be created. - Only used if mode equals 'orthoview'. - - .. versionadded:: 0.14.0 - block : bool - Whether to halt program execution until the figure is closed. - Defaults to False. Only used if mode equals 'orthoview'. - - .. versionadded:: 0.14.0 - show : bool - Show figure if True. Defaults to True. - Only used if mode equals 'orthoview'. - - scale : float - The scale of the dipoles if ``mode`` is 'arrow' or 'sphere'. - color : tuple - The color of the dipoles if ``mode`` is 'arrow' or 'sphere'. - fig : mayavi.mlab.Figure | None - Mayavi Scene in which to plot the alignment. - If ``None``, creates a new 600x600 pixel figure with black - background. - - .. versionadded:: 0.14.0 - %(verbose_meth)s - %(dipole_locs_fig_title)s - - .. versionadded:: 0.21.0 + %(verbose)s Returns ------- - fig : instance of mayavi.mlab.Figure or matplotlib.figure.Figure - The mayavi figure or matplotlib Figure. - - Notes - ----- - .. versionadded:: 0.9.0 + pos_mni : array, shape (n_pos, 3) + The MNI coordinates (in mm) of pos. """ - _check_option('mode', mode, [None, 'arrow', 'sphere', 'orthoview']) - - from .viz import plot_dipole_locations - return plot_dipole_locations( - self, trans, subject, subjects_dir, mode, coord_frame, idx, - show_all, ax, block, show, scale=scale, color=color, fig=fig, - title=title) + mri_head_t, trans = _get_trans(trans) + return head_to_mni(self.pos, subject, mri_head_t, + subjects_dir=subjects_dir, verbose=verbose) @verbose - def to_mni(self, subject, trans, subjects_dir=None, + def to_mri(self, subject, trans, subjects_dir=None, verbose=None): - """Convert dipole location from head coordinate system to MNI coordinates. + """Convert dipole location from head to MRI surface RAS coordinates. Parameters ---------- @@ -283,13 +236,52 @@ Returns ------- - pos_mni : array, shape (n_pos, 3) - The MNI coordinates (in mm) of pos. + pos_mri : array, shape (n_pos, 3) + The Freesurfer surface RAS coordinates (in mm) of pos. """ mri_head_t, trans = _get_trans(trans) - return head_to_mni(self.pos, subject, mri_head_t, + return head_to_mri(self.pos, subject, mri_head_t, subjects_dir=subjects_dir, verbose=verbose) + @verbose + def to_volume_labels(self, trans, subject='fsaverage', aseg='aparc+aseg', + subjects_dir=None, verbose=None): + """Find an ROI in atlas for the dipole positions. + + Parameters + ---------- + %(trans)s + %(subject)s + %(aseg)s + %(subjects_dir)s + %(verbose)s + + Returns + ------- + labels : list + List of anatomical region names from anatomical segmentation atlas. + + Notes + ----- + .. versionadded:: 0.24 + """ + aseg_img, aseg_data = _get_aseg(aseg, subject, subjects_dir) + mri_vox_t = np.linalg.inv(aseg_img.header.get_vox2ras_tkr()) + + # Load freesurface atlas LUT + lut_inv = read_freesurfer_lut()[0] + lut = {v: k for k, v in lut_inv.items()} + + # transform to voxel space from head space + pos = self.to_mri(subject, trans, subjects_dir=subjects_dir, + verbose=verbose) + pos = apply_trans(mri_vox_t, pos) + pos = np.rint(pos).astype(int) + + # Get voxel value and label from LUT + labels = [lut.get(aseg_data[tuple(coord)], 'Unknown') for coord in pos] + return labels + def plot_amplitudes(self, color='k', show=True): """Plot the dipole amplitudes as a function of time. @@ -366,7 +358,7 @@ @fill_doc -class DipoleFixed(ShiftTimeMixin): +class DipoleFixed(TimeMixin): """Dipole class for fixed-position dipole fits. .. note:: This class should usually not be instantiated directly, @@ -374,8 +366,7 @@ Parameters ---------- - info : instance of Info - The measurement info. + %(info_not_none)s data : array, shape (n_channels, n_times) The dipole data. times : array, shape (n_times,) @@ -405,15 +396,14 @@ @verbose def __init__(self, info, data, times, nave, aspect_kind, - comment='', verbose=None): # noqa: D102 + comment='', *, verbose=None): # noqa: D102 self.info = info self.nave = nave self._aspect_kind = aspect_kind self.kind = _aspect_rev.get(aspect_kind, 'unknown') self.comment = comment - self.times = times + self._set_times(np.array(times)) self.data = data - self.verbose = verbose self.preload = True self._update_first_last() @@ -452,7 +442,7 @@ The name of the .fif file. Must end with ``'.fif'`` or ``'.fif.gz'`` to make it explicit that the file contains dipole information in FIF format. - %(verbose_meth)s + %(verbose)s """ check_fname(fname, 'DipoleFixed', ('-dip.fif', '-dip.fif.gz', '_dip.fif', '_dip.fif.gz',), @@ -498,8 +488,7 @@ Returns ------- - dipole : instance of Dipole or DipoleFixed - The dipole. + %(dipole)s See Also -------- @@ -730,7 +719,7 @@ # ############################################################################# # Fitting -def _dipole_forwards(fwd_data, whitener, rr, n_jobs=1): +def _dipole_forwards(fwd_data, whitener, rr, n_jobs=None): """Compute the forward solution and do other nice stuff.""" B = _compute_forwards_meeg(rr, fwd_data, n_jobs, silent=True) B = np.concatenate(B, axis=1) @@ -751,7 +740,7 @@ @verbose -def _make_guesses(surf, grid, exclude, mindist, n_jobs=1, verbose=None): +def _make_guesses(surf, grid, exclude, mindist, n_jobs=None, verbose=None): """Make a guess space inside a sphere or BEM surface.""" if 'rr' in surf: logger.info('Guess surface (%s) is in %s coordinates' @@ -838,14 +827,14 @@ def _fit_dipoles(fun, min_dist_to_inner_skull, data, times, guess_rrs, - guess_data, fwd_data, whitener, ori, n_jobs, rank): + guess_data, fwd_data, whitener, ori, n_jobs, rank, rhoend): """Fit a single dipole to the given whitened, projected data.""" from scipy.optimize import fmin_cobyla - parallel, p_fun, _ = parallel_func(fun, n_jobs) + parallel, p_fun, n_jobs = parallel_func(fun, n_jobs) # parallel over time points res = parallel(p_fun(min_dist_to_inner_skull, B, t, guess_rrs, guess_data, fwd_data, whitener, - fmin_cobyla, ori, rank) + fmin_cobyla, ori, rank, rhoend) for B, t in zip(data.T, times)) pos = np.array([r[0] for r in res]) amp = np.array([r[1] for r in res]) @@ -1039,7 +1028,8 @@ def _fit_dipole(min_dist_to_inner_skull, B_orig, t, guess_rrs, - guess_data, fwd_data, whitener, fmin_cobyla, ori, rank): + guess_data, fwd_data, whitener, fmin_cobyla, ori, rank, + rhoend): """Fit a single bit of data.""" B = np.dot(whitener, B_orig) @@ -1073,7 +1063,7 @@ # function we can use to ensure we stay inside the inner skull / # smallest sphere rd_final = fmin_cobyla(fun, x0, (constraint,), consargs=(), - rhobeg=5e-2, rhoend=5e-5, disp=False) + rhobeg=5e-2, rhoend=rhoend, disp=False) # simplex = _make_tetra_simplex() + x0 # _simplex_minimize(simplex, 1e-4, 2e-4, fun) @@ -1103,7 +1093,7 @@ def _fit_dipole_fixed(min_dist_to_inner_skull, B_orig, t, guess_rrs, guess_data, fwd_data, whitener, - fmin_cobyla, ori, rank): + fmin_cobyla, ori, rank, rhoend): """Fit a data using a fixed position.""" B = np.dot(whitener, B_orig) B2 = np.dot(B, B) @@ -1128,8 +1118,9 @@ @verbose -def fit_dipole(evoked, cov, bem, trans=None, min_dist=5., n_jobs=1, - pos=None, ori=None, rank=None, verbose=None): +def fit_dipole(evoked, cov, bem, trans=None, min_dist=5., n_jobs=None, + pos=None, ori=None, rank=None, accuracy='normal', tol=5e-5, + verbose=None): """Fit a dipole. Parameters @@ -1166,9 +1157,19 @@ for each time instant. .. versionadded:: 0.12 - %(rank_None)s + %(rank_none)s .. versionadded:: 0.20 + accuracy : str + Can be "normal" (default) or "accurate", which gives the most accurate + coil definition but is typically not necessary for real-world data. + + .. versionadded:: 0.24 + tol : float + Final accuracy of the optimization (see ``rhoend`` argument of + :func:`scipy.optimize.fmin_cobyla`). + + .. versionadded:: 0.24 %(verbose)s Returns @@ -1196,6 +1197,8 @@ # are what is needed: evoked = evoked.copy() + _validate_type(accuracy, str, 'accuracy') + _check_option('accuracy', accuracy, ('accurate', 'normal')) # Determine if a list of projectors has an average EEG ref if _needs_eeg_average_ref_proj(evoked.info): @@ -1261,7 +1264,6 @@ % (1000 * r0[0], 1000 * r0[1], 1000 * r0[2], kind, R)) inner_skull = dict(R=R, r0=r0) # NB sphere model defined in head frame del R, r0 - accurate = False # can be an option later (shouldn't make big diff) # Deal with DipoleFixed cases here if pos is not None: @@ -1301,12 +1303,9 @@ if guess_exclude > 0: logger.info('Guess exclude : %6.1f mm' % (1000 * guess_exclude,)) - logger.info('Using %s MEG coil definitions.' - % ("accurate" if accurate else "standard")) + logger.info(f'Using {accuracy} MEG coil definitions.') fit_n_jobs = n_jobs - if isinstance(cov, str): - logger.info('Noise covariance : %s' % (cov,)) - cov = read_cov(cov, verbose=False) + cov = _ensure_cov(cov) logger.info('') _print_coord_trans(mri_head_t) @@ -1321,7 +1320,7 @@ if 'grad' in ch_types or 'mag' in ch_types: megcoils, compcoils, megnames, meg_info = \ _prep_meg_channels(info, exclude='bads', - accurate=accurate, verbose=verbose) + accuracy=accuracy, verbose=verbose) if 'eeg' in ch_types: eegels, eegnames = _prep_eeg_channels(info, exclude='bads', verbose=verbose) @@ -1401,13 +1400,14 @@ fun = _fit_dipole_fixed if fixed_position else _fit_dipole out = _fit_dipoles( fun, min_dist_to_inner_skull, data, times, guess_src['rr'], - guess_data, fwd_data, whitener, ori, n_jobs, rank) + guess_data, fwd_data, whitener, ori, n_jobs, rank, tol) assert len(out) == 8 if fixed_position and ori is not None: # DipoleFixed data = np.array([out[1], out[3]]) out_info = deepcopy(info) loc = np.concatenate([pos, ori, np.zeros(6)]) + out_info._unlocked = True out_info['chs'] = [ dict(ch_name='dip 01', loc=loc, kind=FIFF.FIFFV_DIPOLE_WAVE, coord_frame=FIFF.FIFFV_COORD_UNKNOWN, unit=FIFF.FIFF_UNIT_AM, @@ -1424,6 +1424,7 @@ 'experimenter', 'hpi_subsystem', 'proj_id', 'proj_name', 'subject_info']: out_info[key] = None + out_info._unlocked = False out_info['bads'] = [] out_info._update_redundant() out_info._check_consistency() @@ -1458,6 +1459,10 @@ ori : ndarray, shape (n_dipoles, 3) The dipole orientations. + See Also + -------- + mne.datasets.fetch_phantom + Notes ----- The Elekta phantoms have a radius of 79.5mm, and HPI coil locations diff -Nru python-mne-0.23.4+dfsg/mne/epochs.py python-mne-1.1.0+dfsg/mne/epochs.py --- python-mne-0.23.4+dfsg/mne/epochs.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/epochs.py 2022-08-03 15:48:14.000000000 +0000 @@ -9,7 +9,7 @@ # Mainak Jas # Stefan Appelhoff # -# License: BSD (3-clause) +# License: BSD-3-Clause from functools import partial from collections import Counter @@ -20,13 +20,14 @@ import numpy as np -from .io.utils import _get_als_coords_from_chs -from .io.write import (start_file, start_block, end_file, end_block, +from .io.utils import _construct_bids_filename +from .io.write import (start_and_end_file, start_block, end_block, write_int, write_float, write_float_matrix, write_double_matrix, write_complex_float_matrix, write_complex_double_matrix, write_id, write_string, _get_split_size, _NEXT_FILE_BUFFER, INT32_MAX) -from .io.meas_info import read_meas_info, write_meas_info, _merge_info +from .io.meas_info import (read_meas_info, write_meas_info, _merge_info, + _ensure_infos_match, ContainsMixin) from .io.open import fiff_open, _get_next_fname from .io.tree import dir_tree_find from .io.tag import read_tag, read_tag_info @@ -35,34 +36,36 @@ from .io.pick import (channel_indices_by_type, channel_type, pick_channels, pick_info, _pick_data_channels, _DATA_CH_TYPES_SPLIT, _picks_to_idx) -from .io.proj import setup_proj, ProjMixin, _proj_equal -from .io.base import BaseRaw, TimeMixin +from .io.proj import setup_proj, ProjMixin +from .io.base import BaseRaw, _get_ch_factors from .bem import _check_origin -from .evoked import EvokedArray, _check_decim +from .evoked import EvokedArray from .baseline import rescale, _log_rescale, _check_baseline -from .channels.channels import (ContainsMixin, UpdateChannelsMixin, +from .channels.channels import (UpdateChannelsMixin, SetChannelsMixin, InterpolationMixin) from .filter import detrend, FilterMixin, _check_fun from .parallel import parallel_func -from .event import _read_events_fif, make_fixed_length_events -from .fixes import _get_args, rng_uniform +from .event import (_read_events_fif, make_fixed_length_events, + match_event_names) +from .fixes import rng_uniform from .viz import (plot_epochs, plot_epochs_psd, plot_epochs_psd_topomap, plot_epochs_image, plot_topo_image_epochs, plot_drop_log) from .utils import (_check_fname, check_fname, logger, verbose, - _time_mask, check_random_state, warn, _pl, + check_random_state, warn, _pl, sizeof_fmt, SizeMixin, copy_function_doc_to_method_doc, - _check_pandas_installed, _check_eeglabio_installed, - _check_preload, GetEpochsMixin, + _check_pandas_installed, + _check_preload, GetEpochsMixin, TimeMixin, _prepare_read_metadata, _prepare_write_metadata, _check_event_id, _gen_events, _check_option, - _check_combine, ShiftTimeMixin, _build_data_frame, + _check_combine, _build_data_frame, _check_pandas_index_arguments, _convert_times, _scale_dataframe_data, _check_time_format, object_size, _on_missing, _validate_type, _ensure_events, - _infer_check_export_fmt) + _path_like) from .utils.docs import fill_doc -from .data.html_templates import epochs_template +from .annotations import (_write_annotations, _read_annotations_fif, + EpochAnnotationsMixin) def _pack_reject_params(epochs): @@ -74,28 +77,37 @@ return reject_params -def _save_split(epochs, fname, part_idx, n_parts, fmt): +def _save_split(epochs, fname, part_idx, n_parts, fmt, split_naming, + overwrite): """Split epochs. Anything new added to this function also needs to be added to BaseEpochs.save to account for new file sizes. """ # insert index in filename - path, base = op.split(fname) - idx = base.find('.') + base, ext = op.splitext(fname) if part_idx > 0: - fname = op.join(path, '%s-%d.%s' % (base[:idx], part_idx, - base[idx + 1:])) + if split_naming == 'neuromag': + fname = '%s-%d%s' % (base, part_idx, ext) + else: + assert split_naming == 'bids' + fname = _construct_bids_filename(base, ext, part_idx, + validate=False) + _check_fname(fname, overwrite=overwrite) next_fname = None if part_idx < n_parts - 1: - next_fname = op.join(path, '%s-%d.%s' % (base[:idx], part_idx + 1, - base[idx + 1:])) + if split_naming == 'neuromag': + next_fname = '%s-%d%s' % (base, part_idx + 1, ext) + else: + assert split_naming == 'bids' + next_fname = _construct_bids_filename(base, ext, part_idx + 1, + validate=False) next_idx = part_idx + 1 else: next_idx = None - with start_file(fname) as fid: + with start_and_end_file(fname) as fid: _save_part(fid, epochs, fmt, n_parts, next_fname, next_idx) @@ -131,6 +143,12 @@ elif fmt == 'double': write_function = write_double_matrix + # Epoch annotations are written if there are any + annotations = getattr(epochs, 'annotations', []) + if annotations is not None and len(annotations): + _write_annotations(fid, annotations) + + # write Epoch event windows start_block(fid, FIFF.FIFFB_MNE_EVENTS) write_int(fid, FIFF.FIFF_MNE_EVENT_LIST, epochs.events.T) write_string(fid, FIFF.FIFF_DESCRIPTION, _event_id_string(epochs.event_id)) @@ -149,6 +167,9 @@ write_int(fid, FIFF.FIFF_FIRST_SAMPLE, first) write_int(fid, FIFF.FIFF_LAST_SAMPLE, last) + # write raw original sampling rate + write_float(fid, FIFF.FIFF_MNE_EPOCHS_RAW_SFREQ, epochs._raw_sfreq) + # save baseline if epochs.baseline is not None: bmin, bmax = epochs.baseline @@ -192,7 +213,6 @@ end_block(fid, FIFF.FIFFB_MNE_EPOCHS) end_block(fid, FIFF.FIFFB_PROCESSED_DATA) end_block(fid, FIFF.FIFFB_MEAS) - end_file(fid) def _event_id_string(event_id): @@ -318,9 +338,9 @@ @fill_doc -class BaseEpochs(ProjMixin, ContainsMixin, UpdateChannelsMixin, ShiftTimeMixin, +class BaseEpochs(ProjMixin, ContainsMixin, UpdateChannelsMixin, SetChannelsMixin, InterpolationMixin, FilterMixin, - TimeMixin, SizeMixin, GetEpochsMixin): + TimeMixin, SizeMixin, GetEpochsMixin, EpochAnnotationsMixin): """Abstract base class for `~mne.Epochs`-type classes. .. warning:: This class provides basic functionality and should never be @@ -328,25 +348,25 @@ Parameters ---------- - info : dict - A copy of the `~mne.Info` dictionary from the raw object. + %(info_not_none)s data : ndarray | None If ``None``, data will be read from the Raw object. If ndarray, must be of shape (n_epochs, n_channels, n_times). - %(epochs_events_event_id)s + %(events_epochs)s + %(event_id)s %(epochs_tmin_tmax)s %(baseline_epochs)s Defaults to ``(None, 0)``, i.e. beginning of the the data until time point zero. - %(epochs_raw)s + %(raw_epochs)s %(picks_all)s %(reject_epochs)s %(flat)s %(decim)s %(epochs_reject_tmin_tmax)s - %(epochs_detrend)s + %(detrend_epochs)s %(proj_epochs)s - %(epochs_on_missing)s + %(on_missing_epochs)s preload_at_end : bool %(epochs_preload)s selection : iterable | None @@ -357,9 +377,14 @@ be ignored. filename : str | None The filename (if the epochs are read from disk). - %(epochs_metadata)s - %(epochs_event_repeated)s + %(metadata_epochs)s + %(event_repeated_epochs)s %(verbose)s + raw_sfreq : float + The original Raw object sampling rate. If None, then it is set to + ``info['sfreq']``. + annotations : instance of mne.Annotations | None + Annotations to set. Notes ----- @@ -369,15 +394,15 @@ """ @verbose - def __init__(self, info, data, events, event_id=None, tmin=-0.2, tmax=0.5, + def __init__(self, info, data, events, event_id=None, + tmin=-0.2, tmax=0.5, baseline=(None, 0), raw=None, picks=None, reject=None, flat=None, decim=1, reject_tmin=None, reject_tmax=None, detrend=None, proj=True, on_missing='raise', preload_at_end=False, selection=None, drop_log=None, filename=None, metadata=None, event_repeated='error', - verbose=None): # noqa: D102 - self.verbose = verbose - + *, verbose=None, raw_sfreq=None, + annotations=None): # noqa: D102 if events is not None: # RtEpochs can have events=None events = _ensure_events(events) events_max = events.max() @@ -399,7 +424,10 @@ # ensure metadata matches original events size self.selection = np.arange(len(events)) self.events = events - self.metadata = metadata + + # same as self.metadata = metadata, but suppress log in favor + # of logging below (after setting self.selection) + GetEpochsMixin.metadata.fset(self, metadata, verbose=False) del events values = list(self.event_id.values()) @@ -433,6 +461,11 @@ metadata = [metadata[s] for s in sub] elif metadata is not None: metadata = metadata.iloc[sub] + + # Remove temporarily set metadata from above, and set + # again to get the correct log ("adding metadata", instead of + # "replacing existing metadata") + GetEpochsMixin.metadata.fset(self, None, verbose=False) self.metadata = metadata del metadata @@ -559,7 +592,11 @@ for ii, epoch in enumerate(self._data): self._data[ii] = np.dot(self._projector, epoch) self._filename = str(filename) if filename is not None else filename + if raw_sfreq is None: + raw_sfreq = self.info['sfreq'] + self._raw_sfreq = raw_sfreq self._check_consistency() + self.set_annotations(annotations) def _check_consistency(self): """Check invariants of epochs object.""" @@ -614,59 +651,6 @@ return self @verbose - def decimate(self, decim, offset=0, verbose=None): - """Decimate the epochs. - - Parameters - ---------- - %(decim)s - %(decim_offset)s - %(verbose_meth)s - - Returns - ------- - epochs : instance of Epochs - The decimated Epochs object. - - See Also - -------- - mne.Evoked.decimate - mne.Epochs.resample - mne.io.Raw.resample - - Notes - ----- - %(decim_notes)s - - If ``decim`` is 1, this method does not copy the underlying data. - - .. versionadded:: 0.10.0 - - References - ---------- - .. footbibliography:: - """ - decim, offset, new_sfreq = _check_decim(self.info, decim, offset) - start_idx = int(round(-self._raw_times[0] * (self.info['sfreq'] * - self._decim))) - self._decim *= decim - i_start = start_idx % self._decim + offset - decim_slice = slice(i_start, None, self._decim) - self.info['sfreq'] = new_sfreq - if self.preload: - if decim != 1: - self._data = self._data[:, :, decim_slice].copy() - self._raw_times = self._raw_times[decim_slice].copy() - else: - self._data = np.ascontiguousarray(self._data) - self._decim_slice = slice(None) - self._decim = 1 - else: - self._decim_slice = decim_slice - self._set_times(self._raw_times[self._decim_slice]) - return self - - @verbose def apply_baseline(self, baseline=(None, 0), *, verbose=None): """Baseline correct epochs. @@ -675,7 +659,7 @@ %(baseline_epochs)s Defaults to ``(None, 0)``, i.e. beginning of the the data until time point zero. - %(verbose_meth)s + %(verbose)s Returns ------- @@ -703,7 +687,10 @@ self._do_baseline = False else: # logging happens in "rescale" in "if" branch logger.info(_log_rescale(baseline)) - assert self._do_baseline is True + # For EpochsArray and Epochs, this is already True: + # assert self._do_baseline is True + # ... but for EpochsFIF it's not, so let's set it explicitly + self._do_baseline = True self.baseline = baseline return self @@ -745,18 +732,26 @@ bad_msg = ('{kind}["{key}"] == {new} {op} {old} (old value), new ' '{kind} values must be at least as stringent as ' 'previous ones') - for key in set(reject.keys()).union(old_reject.keys()): - old = old_reject.get(key, np.inf) - new = reject.get(key, np.inf) - if new > old: - raise ValueError(bad_msg.format(kind='reject', key=key, - new=new, old=old, op='>')) - for key in set(flat.keys()).union(old_flat.keys()): - old = old_flat.get(key, -np.inf) - new = flat.get(key, -np.inf) - if new < old: - raise ValueError(bad_msg.format(kind='flat', key=key, - new=new, old=old, op='<')) + + # copy thresholds for channel types that were used previously, but not + # passed this time + for key in set(old_reject) - set(reject): + reject[key] = old_reject[key] + # make sure new thresholds are at least as stringent as the old ones + for key in reject: + if key in old_reject and reject[key] > old_reject[key]: + raise ValueError( + bad_msg.format(kind='reject', key=key, new=reject[key], + old=old_reject[key], op='>')) + + # same for flat thresholds + for key in set(old_flat) - set(flat): + flat[key] = old_flat[key] + for key in flat: + if key in old_flat and flat[key] < old_flat[key]: + raise ValueError( + bad_msg.format(kind='flat', key=key, new=flat[key], + old=old_flat[key], op='<')) # after validation, set parameters self._bad_dropped = False @@ -934,7 +929,7 @@ return self @fill_doc - def average(self, picks=None, method="mean"): + def average(self, picks=None, method="mean", by_event_type=False): """Compute an average over epochs. Parameters @@ -948,11 +943,11 @@ (n_channels, n_time). Note that due to file type limitations, the kind for all these will be "average". + %(by_event_type)s Returns ------- - evoked : instance of Evoked | dict of Evoked - The averaged epochs. + %(evoked_by_event_type_returns)s Notes ----- @@ -974,22 +969,32 @@ This would compute the trimmed mean. """ - return self._compute_aggregate(picks=picks, mode=method) + if by_event_type: + evokeds = list() + for event_type in self.event_id.keys(): + ev = self[event_type]._compute_aggregate(picks=picks, + mode=method) + ev.comment = event_type + evokeds.append(ev) + else: + evokeds = self._compute_aggregate(picks=picks, mode=method) + return evokeds @fill_doc - def standard_error(self, picks=None): + def standard_error(self, picks=None, by_event_type=False): """Compute standard error over epochs. Parameters ---------- %(picks_all_data)s + %(by_event_type)s Returns ------- - evoked : instance of Evoked - The standard error over epochs. + %(std_err_by_event_type_returns)s """ - return self._compute_aggregate(picks, "std") + return self.average(picks=picks, method="std", + by_event_type=by_event_type) def _compute_aggregate(self, picks, mode='mean'): """Compute the mean, median, or std over epochs and return Evoked.""" @@ -1072,12 +1077,12 @@ info = deepcopy(info) # don't apply baseline correction; we'll set evoked.baseline manually evoked = EvokedArray(data, info, tmin=self.times[0], comment=comment, - nave=n_events, kind=kind, baseline=None, - verbose=self.verbose) + nave=n_events, kind=kind, baseline=None) evoked.baseline = self.baseline - # XXX: above constructor doesn't recreate the times object precisely - evoked.times = self.times.copy() + # the above constructor doesn't recreate the times object precisely + # due to numerical precision issues + evoked._set_times(self.times.copy()) # pick channels picks = _picks_to_idx(self.info, picks, 'data_or_ica', ()) @@ -1101,25 +1106,30 @@ def plot(self, picks=None, scalings=None, n_epochs=20, n_channels=20, title=None, events=None, event_color=None, order=None, show=True, block=False, decim='auto', noise_cov=None, - butterfly=False, show_scrollbars=True, epoch_colors=None, - event_id=None, group_by='type'): + butterfly=False, show_scrollbars=True, show_scalebars=True, + epoch_colors=None, event_id=None, group_by='type', + precompute=None, use_opengl=None, *, theme=None, + overview_mode=None): return plot_epochs(self, picks=picks, scalings=scalings, n_epochs=n_epochs, n_channels=n_channels, title=title, events=events, event_color=event_color, order=order, show=show, block=block, decim=decim, noise_cov=noise_cov, butterfly=butterfly, show_scrollbars=show_scrollbars, + show_scalebars=show_scalebars, epoch_colors=epoch_colors, event_id=event_id, - group_by=group_by) + group_by=group_by, precompute=precompute, + use_opengl=use_opengl, theme=theme, + overview_mode=overview_mode) @copy_function_doc_to_method_doc(plot_epochs_psd) def plot_psd(self, fmin=0, fmax=np.inf, tmin=None, tmax=None, proj=False, bandwidth=None, adaptive=False, low_bias=True, normalization='length', picks=None, ax=None, color='black', xscale='linear', area_mode='std', area_alpha=0.33, - dB=True, estimate='auto', show=True, n_jobs=1, + dB=True, estimate='auto', show=True, n_jobs=None, average=False, line_alpha=None, spatial_colors=True, - sphere=None, verbose=None): + sphere=None, exclude='bads', verbose=None): return plot_epochs_psd(self, fmin=fmin, fmax=fmax, tmin=tmin, tmax=tmax, proj=proj, bandwidth=bandwidth, adaptive=adaptive, low_bias=low_bias, @@ -1129,14 +1139,14 @@ show=show, n_jobs=n_jobs, average=average, line_alpha=line_alpha, spatial_colors=spatial_colors, sphere=sphere, - verbose=verbose) + exclude=exclude, verbose=verbose) @copy_function_doc_to_method_doc(plot_epochs_psd_topomap) def plot_psd_topomap(self, bands=None, tmin=None, tmax=None, proj=False, bandwidth=None, adaptive=False, low_bias=True, normalization='length', ch_type=None, cmap=None, agg_fun=None, dB=True, - n_jobs=1, normalize=False, cbar_fmt='auto', + n_jobs=None, normalize=False, cbar_fmt='auto', outlines='head', axes=None, show=True, sphere=None, vlim=(None, None), verbose=None): return plot_epochs_psd_topomap( @@ -1170,11 +1180,15 @@ from disk. To avoid reading epochs from disk multiple times, use :meth:`mne.Epochs.load_data()`. + .. note:: To constrain the time period used for estimation of signal + quality, set ``epochs.reject_tmin`` and + ``epochs.reject_tmax``, respectively. + Parameters ---------- %(reject_drop_bad)s %(flat_drop_bad)s - %(verbose_meth)s + %(verbose)s Returns ------- @@ -1222,7 +1236,7 @@ return _drop_log_stats(self.drop_log, ignore) @copy_function_doc_to_method_doc(plot_drop_log) - def plot_drop_log(self, threshold=0, n_max_plot=20, subject='Unknown subj', + def plot_drop_log(self, threshold=0, n_max_plot=20, subject=None, color=(0.9, 0.9, 0.9), width=0.8, ignore=('IGNORED',), show=True): if not self._bad_dropped: @@ -1269,7 +1283,7 @@ reason : str Reason for dropping the epochs ('ECG', 'timeout', 'blink' etc). Default: 'USER'. - %(verbose_meth)s + %(verbose)s Returns ------- @@ -1313,7 +1327,8 @@ return epoch @verbose - def _get_data(self, out=True, picks=None, item=None, verbose=None): + def _get_data(self, out=True, picks=None, item=None, *, units=None, + tmin=None, tmax=None, verbose=None): """Load all data, dropping bad epochs along the way. Parameters @@ -1322,8 +1337,31 @@ Return the data. Setting this to False is used to reject bad epochs without caching all the data, which saves memory. %(picks_all)s - %(verbose_meth)s + item : slice | array-like | str | list | None + See docstring of get_data method. + %(units)s + tmin : int | float | None + Start time of data to get in seconds. + tmax : int | float | None + End time of data to get in seconds. + %(verbose)s """ + # if called with 'out=False', the call came from 'drop_bad()' + # if no reasons to drop, just declare epochs as good and return + if not out: + # make sure first and last epoch not out of bounds of raw + in_bounds = self.preload or ( + self._get_epoch_from_raw(idx=0) is not None and + self._get_epoch_from_raw(idx=-1) is not None) + # might be BaseEpochs or Epochs, only the latter has the attribute + reject_by_annotation = getattr(self, 'reject_by_annotation', False) + if (self.reject is None and self.flat is None and in_bounds and + self._reject_time is None and not reject_by_annotation): + logger.debug('_get_data is a noop, returning') + self._bad_dropped = True + return None + start, stop = self._handle_tmin_tmax(tmin, tmax) + if item is None: item = slice(None) elif not self._bad_dropped: @@ -1340,19 +1378,37 @@ else: # we start out with an empty array, allocate only if necessary data = np.empty((0, len(self.info['ch_names']), len(self.times))) - logger.info('Loading data for %s events and %s original time ' - 'points ...' % (n_events, len(self._raw_times))) + msg = (f'for {n_events} events and {len(self._raw_times)} ' + 'original time points') + if self._decim > 1: + msg += ' (prior to decimation)' + if getattr(self._raw, "preload", False): + logger.info(f'Using data from preloaded Raw {msg} ...') + else: + logger.info(f'Loading data {msg} ...') + + orig_picks = picks + if orig_picks is None: + picks = _picks_to_idx(self.info, picks, "all", exclude=()) + else: + picks = _picks_to_idx(self.info, picks) + + # handle units param only if we are going to return data (out==True) + if (units is not None) and out: + ch_factors = _get_ch_factors(self, units, picks) if self._bad_dropped: if not out: return if self.preload: data = data[select] - if picks is None: - return data - else: - picks = _picks_to_idx(self.info, picks) - return data[:, picks] + if orig_picks is not None: + data = data[:, picks] + if units is not None: + data *= ch_factors[:, np.newaxis] + if start != 0 or stop != self.times.size: + data = data[..., start:stop] + return data # we need to load from disk, drop, and return data detrend_picks = self._detrend_picks @@ -1431,11 +1487,13 @@ select_data=False) if out: - if picks is None: - return data - else: - picks = _picks_to_idx(self.info, picks) - return data[:, picks] + if orig_picks is not None: + data = data[:, picks] + if units is not None: + data *= ch_factors[:, np.newaxis] + if start != 0 or stop != self.times.size: + data = data[..., start:stop] + return data else: return None @@ -1448,7 +1506,8 @@ return [] @fill_doc - def get_data(self, picks=None, item=None): + def get_data(self, picks=None, item=None, units=None, tmin=None, + tmax=None): """Get all epochs as a 3D array. Parameters @@ -1462,16 +1521,28 @@ None (default) is an alias for ``slice(None)``. .. versionadded:: 0.20 + %(units)s + + .. versionadded:: 0.24 + tmin : int | float | None + Start time of data to get in seconds. + + .. versionadded:: 0.24.0 + tmax : int | float | None + End time of data to get in seconds. + + .. versionadded:: 0.24.0 Returns ------- data : array of shape (n_epochs, n_channels, n_times) A view on epochs data. """ - return self._get_data(picks=picks, item=item) + return self._get_data(picks=picks, item=item, units=units, tmin=tmin, + tmax=tmax) @verbose - def apply_function(self, fun, picks=None, dtype=None, n_jobs=1, + def apply_function(self, fun, picks=None, dtype=None, n_jobs=None, channel_wise=True, verbose=None, **kwargs): """Apply a function to a subset of channels. @@ -1479,13 +1550,13 @@ Parameters ---------- - %(applyfun_fun)s + %(fun_applyfun)s %(picks_all_data_noref)s - %(applyfun_dtype)s + %(dtype_applyfun)s %(n_jobs)s - %(applyfun_chwise_epo)s - %(verbose_meth)s - %(kwarg_fun)s + %(channel_wise_applyfun_epo)s + %(verbose)s + %(kwargs_fun)s Returns ------- @@ -1503,6 +1574,7 @@ self._data = self._data.astype(dtype) if channel_wise: + parallel, p_fun, n_jobs = parallel_func(_check_fun, n_jobs) if n_jobs == 1: _fun = partial(_check_fun, fun, **kwargs) # modify data inplace to save memory @@ -1511,7 +1583,6 @@ _fun, -1, data_in[:, idx, :]) else: # use parallel function - parallel, p_fun, _ = parallel_func(_check_fun, n_jobs) data_picks_new = parallel(p_fun( fun, data_in[:, p, :], **kwargs) for p in picks) for pp, p in enumerate(picks): @@ -1522,32 +1593,10 @@ return self @property - def times(self): - """Time vector in seconds.""" - return self._times_readonly - - def _set_times(self, times): - """Set self._times_readonly (and make it read only).""" - # naming used to indicate that it shouldn't be - # changed directly, but rather via this method - self._times_readonly = times.copy() - self._times_readonly.flags['WRITEABLE'] = False - - @property - def tmin(self): - """First time point.""" - return self.times[0] - - @property def filename(self): """The filename.""" return self._filename - @property - def tmax(self): - """Last time point.""" - return self.times[-1] - def __repr__(self): """Build string representation.""" s = ' %s events ' % len(self.events) @@ -1566,15 +1615,20 @@ s += ', ~%s' % (sizeof_fmt(self._size),) s += ', data%s loaded' % ('' if self.preload else ' not') s += ', with metadata' if self.metadata is not None else '' + max_events = 10 counts = ['%r: %i' % (k, sum(self.events[:, 2] == v)) - for k, v in sorted(self.event_id.items())] + for k, v in list(self.event_id.items())[:max_events]] if len(self.event_id) > 0: s += ',' + '\n '.join([''] + counts) + if len(self.event_id) > max_events: + not_shown_events = len(self.event_id) - max_events + s += f"\n and {not_shown_events} more events ..." class_name = self.__class__.__name__ class_name = 'Epochs' if class_name == 'BaseEpochs' else class_name return '<%s | %s>' % (class_name, s) def _repr_html_(self): + from .html_templates import repr_templates_env if self.baseline is None: baseline = 'off' else: @@ -1582,22 +1636,24 @@ baseline = f'{baseline[0]} – {baseline[1]} sec' if isinstance(self.event_id, dict): - events = '' + event_strings = [] for k, v in sorted(self.event_id.items()): n_events = sum(self.events[:, 2] == v) - events += f'{k}: {n_events}
' + event_strings.append(f'{k}: {n_events}') elif isinstance(self.event_id, list): - events = '' + event_strings = [] for k in self.event_id: n_events = sum(self.events[:, 2] == k) - events += f'{k}: {n_events}
' + event_strings.append(f'{k}: {n_events}') elif isinstance(self.event_id, int): n_events = len(self.events[:, 2]) - events = f'{self.event_id}: {n_events}
' + event_strings = [f'{self.event_id}: {n_events}'] else: - events = None - return epochs_template.substitute(epochs=self, baseline=baseline, - events=events) + event_strings = None + + t = repr_templates_env.get_template('epochs.html.jinja') + t = t.render(epochs=self, baseline=baseline, events=event_strings) + return t @verbose def crop(self, tmin=None, tmax=None, include_tmax=True, verbose=None): @@ -1610,7 +1666,7 @@ tmax : float | None End time of selection in seconds. %(include_tmax)s - %(verbose_meth)s + %(verbose)s Returns ------- @@ -1624,25 +1680,7 @@ # XXX this could be made to work on non-preloaded data... _check_preload(self, 'Modifying data of epochs') - if tmin is None: - tmin = self.tmin - elif tmin < self.tmin: - warn('tmin is not in epochs time interval. tmin is set to ' - 'epochs.tmin') - tmin = self.tmin - - if tmax is None: - tmax = self.tmax - elif tmax > self.tmax: - warn('tmax is not in epochs time interval. tmax is set to ' - 'epochs.tmax') - tmax = self.tmax - - tmask = _time_mask(self.times, tmin, tmax, sfreq=self.info['sfreq'], - include_tmax=include_tmax) - self._set_times(self.times[tmask]) - self._raw_times = self._raw_times[tmask] - self._data = self._data[:, :, tmask] + super().crop(tmin=tmin, tmax=tmax, include_tmax=include_tmax) # Adjust rejection period if self.reject_tmin is not None and self.reject_tmin < self.tmin: @@ -1683,14 +1721,14 @@ @verbose def save(self, fname, split_size='2GB', fmt='single', overwrite=False, - verbose=True): + split_naming='neuromag', verbose=True): """Save epochs in a fif file. Parameters ---------- fname : str - The name of the file, which should end with -epo.fif or - -epo.fif.gz. + The name of the file, which should end with ``-epo.fif`` or + ``-epo.fif.gz``. split_size : str | int Large raw files are automatically split into multiple pieces. This parameter specifies the maximum size of each piece. If the @@ -1713,7 +1751,10 @@ but will change to False in 0.19. .. versionadded:: 0.18 - %(verbose_meth)s + %(split_naming)s + + .. versionadded:: 0.24 + %(verbose)s Notes ----- @@ -1722,8 +1763,8 @@ check_fname(fname, 'epochs', ('-epo.fif', '-epo.fif.gz', '_epo.fif', '_epo.fif.gz')) - # check for file existence - _check_fname(fname, overwrite) + # check for file existence and expand `~` if present + fname = _check_fname(fname=fname, overwrite=overwrite) split_size_bytes = _get_split_size(split_size) @@ -1811,56 +1852,35 @@ this_epochs = self[epoch_idx] if n_parts > 1 else self # avoid missing event_ids in splits this_epochs.event_id = self.event_id - _save_split(this_epochs, fname, part_idx, n_parts, fmt) + _save_split(this_epochs, fname, part_idx, n_parts, fmt, + split_naming, overwrite) @verbose - def export(self, fname, fmt='auto', verbose=None): + def export(self, fname, fmt='auto', *, overwrite=False, verbose=None): """Export Epochs to external formats. - Supported formats: EEGLAB (set, uses :mod:`eeglabio`) + %(export_fmt_support_epochs)s + %(export_warning)s Parameters ---------- - %(export_params_fname)s - %(export_params_fmt)s + %(fname_export_params)s + %(export_fmt_params_epochs)s + %(overwrite)s + + .. versionadded:: 0.24.1 %(verbose)s Notes ----- + .. versionadded:: 0.24 + + %(export_warning_note_epochs)s %(export_eeglab_note)s """ - supported_export_formats = { - 'eeglab': ('set',), - 'edf': ('edf',), - 'brainvision': ('eeg', 'vmrk', 'vhdr',) - } - fmt = _infer_check_export_fmt(fmt, fname, supported_export_formats) - - if fmt == 'eeglab': - _check_eeglabio_installed() - import eeglabio.epochs - # load data first - self.load_data() - - # remove extra epoc and STI channels - drop_chs = ['epoc', 'STI 014'] - ch_names = [ch for ch in self.ch_names if ch not in drop_chs] - cart_coords = _get_als_coords_from_chs(self.info['chs'], - drop_chs) - - eeglabio.epochs.export_set(fname, - data=self.get_data(picks=ch_names), - sfreq=self.info['sfreq'], - events=self.events, - tmin=self.tmin, tmax=self.tmax, - ch_names=ch_names, - event_id=self.event_id, - ch_locs=cart_coords) - elif fmt == 'edf': - raise NotImplementedError('Export to EDF format not implemented.') - elif fmt == 'brainvision': - raise NotImplementedError('Export to BrainVision not implemented.') + from .export import export_epochs + export_epochs(fname, self, fmt, overwrite=overwrite, verbose=verbose) def equalize_event_counts(self, event_ids=None, method='mintime'): """Equalize the number of trials in each condition. @@ -1974,8 +1994,8 @@ for id_ in event_ids] for ii, id_ in enumerate(event_ids): if len(id_) == 0: - raise KeyError(orig_ids[ii] + "not found in the " - "epoch object's event_id.") + raise KeyError(f"{orig_ids[ii]} not found in the epoch " + "object's event_id.") elif len({sub_id in ids for sub_id in id_}) != 1: err = ("Don't mix hierarchical and regular event_ids" " like in \'%s\'." % ", ".join(id_)) @@ -2001,10 +2021,10 @@ # actually remove the indices return self, indices - @fill_doc + @verbose def to_data_frame(self, picks=None, index=None, scalings=None, copy=True, long_format=False, - time_format='ms'): + time_format=None, *, verbose=None): """Export data in tabular structure as a pandas DataFrame. Channels are converted to columns in the DataFrame. By default, @@ -2016,15 +2036,16 @@ Parameters ---------- %(picks_all)s - %(df_index_epo)s + %(index_df_epo)s Valid string values are 'time', 'epoch', and 'condition'. Defaults to ``None``. - %(df_scalings)s - %(df_copy)s - %(df_longform_epo)s - %(df_time_format)s + %(scalings_df)s + %(copy_df)s + %(long_format_df_epo)s + %(time_format_df)s .. versionadded:: 0.20 + %(verbose)s Returns ------- @@ -2236,7 +2257,7 @@ The time window used for metadata generation need not correspond to the time window used to create the `~mne.Epochs`, to which the metadata will be attached; it may well be much shorter or longer, or not overlap at all, - if desired. The can be useful, for example, to include events that ccurred + if desired. The can be useful, for example, to include events that occurred before or after an epoch, e.g. during the inter-trial interval. .. versionadded:: 0.23 @@ -2245,7 +2266,6 @@ ---------- .. footbibliography:: """ - from .utils.mixin import _hid_match pd = _check_pandas_installed() _validate_type(event_id, types=(dict,), item_name='event_id') @@ -2282,7 +2302,7 @@ keep_last=keep_last).items(): for first_last_event_name in values: try: - _hid_match(event_id, [first_last_event_name]) + match_event_names(event_id, [first_last_event_name]) except KeyError: raise ValueError( f'Event "{first_last_event_name}", specified in ' @@ -2378,7 +2398,9 @@ # Handle keep_first and keep_last event aggregation for event_group_name in keep_first + keep_last: - if event_name not in _hid_match(event_id, [event_group_name]): + if event_name not in match_event_names( + event_id, [event_group_name] + ): continue if event_group_name in keep_first: @@ -2426,8 +2448,9 @@ Parameters ---------- - %(epochs_raw)s - %(epochs_events_event_id)s + %(raw_epochs)s + %(events_epochs)s + %(event_id)s %(epochs_tmin_tmax)s %(baseline_epochs)s Defaults to ``(None, 0)``, i.e. beginning of the the data until @@ -2440,17 +2463,16 @@ %(proj_epochs)s %(decim)s %(epochs_reject_tmin_tmax)s - %(epochs_detrend)s - %(epochs_on_missing)s + %(detrend_epochs)s + %(on_missing_epochs)s %(reject_by_annotation_epochs)s - %(epochs_metadata)s - %(epochs_event_repeated)s + %(metadata_epochs)s + %(event_repeated_epochs)s %(verbose)s Attributes ---------- - info : instance of Info - Measurement info. + %(info_not_none)s event_id : dict Names of conditions corresponding to event_ids. ch_names : list of string @@ -2482,7 +2504,6 @@ Time vector in seconds. Goes from ``tmin`` to ``tmax``. Time interval between consecutive time samples is equal to the inverse of the sampling frequency. - %(verbose)s See Also -------- @@ -2510,6 +2531,19 @@ For example with the event_id ``{'aud': 1, 'vis': 2}`` and the events ``[[0, 0, 1], [0, 0, 2]]``, the "merge" behavior will update both event_id and events to be: ``{'aud/vis': 3}`` and ``[[0, 0, 3]]`` respectively. + + There is limited support for :class:`~mne.Annotations` in the + :class:`~mne.Epochs` class. Currently annotations that are present in the + :class:`~mne.io.Raw` object will be preserved in the resulting + :class:`~mne.Epochs` object, but: + + 1. It is not yet possible to add annotations + to the Epochs object programmatically (via code) or interactively + (through the plot window) + 2. Concatenating :class:`~mne.Epochs` objects + that contain annotations is not supported, and any annotations will + be dropped when concatenating. + 3. Annotations will be lost on save. """ @verbose @@ -2528,14 +2562,19 @@ proj = proj or raw.proj self.reject_by_annotation = reject_by_annotation + + # keep track of original sfreq (needed for annotations) + raw_sfreq = raw.info['sfreq'] + # call BaseEpochs constructor super(Epochs, self).__init__( - info, None, events, event_id, tmin, tmax, metadata=metadata, - baseline=baseline, raw=raw, picks=picks, reject=reject, - flat=flat, decim=decim, reject_tmin=reject_tmin, + info, None, events, event_id, tmin, tmax, + metadata=metadata, baseline=baseline, raw=raw, picks=picks, + reject=reject, flat=flat, decim=decim, reject_tmin=reject_tmin, reject_tmax=reject_tmax, detrend=detrend, proj=proj, on_missing=on_missing, preload_at_end=preload, - event_repeated=event_repeated, verbose=verbose) + event_repeated=event_repeated, verbose=verbose, + raw_sfreq=raw_sfreq, annotations=raw.annotations) @verbose def _get_epoch_from_raw(self, idx, verbose=None): @@ -2591,9 +2630,8 @@ data : array, shape (n_epochs, n_channels, n_times) The channels' time series for each epoch. See notes for proper units of measure. - info : instance of Info - Info dictionary. Consider using ``create_info`` to populate - this structure. + %(info_not_none)s Consider using :func:`mne.create_info` to populate this + structure. events : None | array of int, shape (n_events, 3) The events typically returned by the read_events function. If some events don't match the events of interest as specified @@ -2651,6 +2689,11 @@ * M: hbo, hbr * Am: dipole * AU: misc + + EpochsArray does not set `Annotations`. If you would like to create + simulated data with Annotations that are then preserved in the Epochs + object, you would use `mne.io.RawArray` first and then create an + `mne.Epochs` object. """ @verbose @@ -2673,11 +2716,13 @@ events = _gen_events(n_epochs) info = info.copy() # do not modify original info tmax = (data.shape[2] - 1) / info['sfreq'] + tmin + super(EpochsArray, self).__init__( - info, data, events, event_id, tmin, tmax, baseline, reject=reject, - flat=flat, reject_tmin=reject_tmin, reject_tmax=reject_tmax, - decim=1, metadata=metadata, selection=selection, proj=proj, - on_missing=on_missing) + info, data, events, event_id, tmin, tmax, baseline, + reject=reject, flat=flat, reject_tmin=reject_tmin, + reject_tmax=reject_tmax, decim=1, metadata=metadata, + selection=selection, proj=proj, on_missing=on_missing, + verbose=verbose) if self.baseline is not None: self._do_baseline = True if len(events) != np.in1d(self.events[:, 2], @@ -2812,16 +2857,17 @@ """Find a boolean mask to minimize timing differences.""" from scipy.interpolate import interp1d keep = np.ones((len(t_longer)), dtype=bool) - if len(t_shorter) == 0: + # special case: length zero or one + if len(t_shorter) < 2: # interp1d won't work keep.fill(False) + if len(t_shorter) == 1: + idx = np.argmin(np.abs(t_longer - t_shorter)) + keep[idx] = True return keep scores = np.ones((len(t_longer))) x1 = np.arange(len(t_shorter)) # The first set of keep masks to test - kwargs = dict(copy=False, bounds_error=False) - # this is a speed tweak, only exists for certain versions of scipy - if 'assume_sorted' in _get_args(interp1d.__init__): - kwargs['assume_sorted'] = True + kwargs = dict(copy=False, bounds_error=False, assume_sorted=True) shorter_interp = interp1d(x1, t_shorter, fill_value=t_shorter[-1], **kwargs) for ii in range(len(t_longer) - len(t_shorter)): @@ -2894,6 +2940,8 @@ # Read the measurement info info, meas = read_meas_info(fid, tree, clean_bads=True) + # read in the Annotations if they exist + annotations = _read_annotations_fif(fid, tree) events, mappings = _read_events_fif(fid, tree) # Metadata @@ -2933,6 +2981,7 @@ baseline = None selection = None drop_log = None + raw_sfreq = None reject_params = {} for k in range(my_epochs['nent']): kind = my_epochs['directory'][k].kind @@ -2968,6 +3017,9 @@ elif kind == FIFF.FIFF_MNE_EPOCHS_REJECT_FLAT: tag = read_tag(fid, pos) reject_params = json.loads(tag.data) + elif kind == FIFF.FIFF_MNE_EPOCHS_RAW_SFREQ: + tag = read_tag(fid, pos) + raw_sfreq = tag.data if bmin is not None or bmax is not None: baseline = (bmin, bmax) @@ -3031,7 +3083,7 @@ return (info, data, data_tag, events, event_id, metadata, tmin, tmax, baseline, selection, drop_log, epoch_shape, cals, reject_params, - fmt) + fmt, annotations, raw_sfreq) @verbose @@ -3040,14 +3092,11 @@ Parameters ---------- - fname : str | file-like - The epochs filename to load. Filename should end with -epo.fif or - -epo.fif.gz. If a file-like object is provided, preloading must be - used. + %(fname_epochs)s %(proj_epochs)s preload : bool - If True, read all epochs from disk immediately. If False, epochs will - be read on demand. + If True, read all epochs from disk immediately. If ``False``, epochs + will be read on demand. %(verbose)s Returns @@ -3081,9 +3130,7 @@ Parameters ---------- - fname : str | file-like - The name of the file, which should end with -epo.fif or -epo.fif.gz. If - a file-like object is provided, preloading must be used. + %(fname_epochs)s %(proj_epochs)s preload : bool If True, read all epochs from disk immediately. If False, epochs will @@ -3100,9 +3147,13 @@ @verbose def __init__(self, fname, proj=True, preload=True, verbose=None): # noqa: D102 - if isinstance(fname, str): - check_fname(fname, 'epochs', ('-epo.fif', '-epo.fif.gz', - '_epo.fif', '_epo.fif.gz')) + if _path_like(fname): + check_fname( + fname=fname, filetype='epochs', + endings=('-epo.fif', '-epo.fif.gz', '_epo.fif', '_epo.fif.gz') + ) + fname = _check_fname(fname=fname, must_exist=True, + overwrite='read') elif not preload: raise ValueError('preload must be used with file-like objects') @@ -3116,7 +3167,7 @@ next_fname = _get_next_fname(fid, fname, tree) (info, data, data_tag, events, event_id, metadata, tmin, tmax, baseline, selection, drop_log, epoch_shape, cals, - reject_params, fmt) = \ + reject_params, fmt, annotations, raw_sfreq) = \ _read_one_epoch_file(fid, tree, preload) if (events[:, 0] < 0).any(): @@ -3134,7 +3185,7 @@ baseline=None, metadata=metadata, on_missing='ignore', selection=selection, drop_log=drop_log, - proj=False, verbose=False) + proj=False, verbose=False, raw_sfreq=raw_sfreq) epoch.baseline = baseline epoch._do_baseline = False # might be superfluous but won't hurt ep_list.append(epoch) @@ -3148,8 +3199,9 @@ if next_fname is not None: fnames.append(next_fname) - (info, data, events, event_id, tmin, tmax, metadata, baseline, - selection, drop_log, _) = \ + unsafe_annot_add = raw_sfreq is None + (info, data, raw_sfreq, events, event_id, tmin, tmax, metadata, + baseline, selection, drop_log) = \ _concatenate_epochs(ep_list, with_data=preload, add_offset=False) # we need this uniqueness for non-preloaded data to work properly if len(np.unique(events[:, 0])) != len(events): @@ -3171,15 +3223,20 @@ # again, ensure we're retaining the baseline period originally loaded # from disk without trying to re-apply baseline correction super(EpochsFIF, self).__init__( - info, data, events, event_id, tmin, tmax, baseline=None, raw=raw, + info, data, events, event_id, tmin, tmax, + baseline=None, raw=raw, proj=proj, preload_at_end=False, on_missing='ignore', selection=selection, drop_log=drop_log, filename=fname_rep, - metadata=metadata, verbose=verbose, **reject_params) + metadata=metadata, verbose=verbose, raw_sfreq=raw_sfreq, + annotations=annotations, **reject_params) self.baseline = baseline self._do_baseline = False # use the private property instead of drop_bad so that epochs # are not all read from disk for preload=False self._bad_dropped = True + # private property to suggest that people re-save epochs if they add + # annotations + self._unsafe_annot_add = unsafe_annot_add @verbose def _get_epoch_from_raw(self, idx, verbose=None): @@ -3284,7 +3341,7 @@ raise ValueError('All epochs must be preloaded.') info = _merge_info([epochs.info for epochs in epochs_list]) - data = [epochs.get_data() for epochs in epochs_list] + data = [epochs._data for epochs in epochs_list] _check_merge_epochs(epochs_list) for d in data: if len(d) != len(data[0]): @@ -3304,13 +3361,9 @@ proj = any(e.proj for e in epochs_list) - if verbose is None: - verbose = any(e.verbose for e in epochs_list) - epochs = epochs_list[0].copy() epochs.info = info epochs.picks = None - epochs.verbose = verbose epochs.events = events epochs.preload = True epochs._bad_dropped = True @@ -3320,60 +3373,32 @@ return epochs -def _compare_epochs_infos(info1, info2, name): - """Compare infos.""" - if not isinstance(name, str): # passed epochs index - name = f'epochs[{name:d}]' - info1._check_consistency() - info2._check_consistency() - if info1['nchan'] != info2['nchan']: - raise ValueError(f'{name}.info[\'nchan\'] must match') - if set(info1['bads']) != set(info2['bads']): - raise ValueError(f'{name}.info[\'bads\'] must match') - if info1['sfreq'] != info2['sfreq']: - raise ValueError(f'{name}.info[\'sfreq\'] must match') - if set(info1['ch_names']) != set(info2['ch_names']): - raise ValueError(f'{name}.info[\'ch_names\'] must match') - if len(info2['projs']) != len(info1['projs']): - raise ValueError(f'SSP projectors in {name} must be the same') - if any(not _proj_equal(p1, p2) for p1, p2 in - zip(info2['projs'], info1['projs'])): - raise ValueError(f'SSP projectors in {name} must be the same') - if (info1['dev_head_t'] is None) != (info2['dev_head_t'] is None) or \ - (info1['dev_head_t'] is not None and not - np.allclose(info1['dev_head_t']['trans'], - info2['dev_head_t']['trans'], rtol=1e-6)): - raise ValueError(f'{name}.info[\'dev_head_t\'] must match. The ' - 'instances probably come from different runs, and ' - 'are therefore associated with different head ' - 'positions. Manually change info[\'dev_head_t\'] to ' - 'avoid this message but beware that this means the ' - 'MEG sensors will not be properly spatially aligned. ' - 'See mne.preprocessing.maxwell_filter to realign the ' - 'runs to a common head position.') - - -def _update_offset(offset, events, shift): - if offset == 0: - return offset - offset = 0 if offset is None else offset - offset = np.int64(offset) + np.max(events[:, 0]) + shift - if offset > INT32_MAX: - warn(f'Event number greater than {INT32_MAX} created, events[:, 0] ' - 'will be assigned consecutive increasing integer values') - offset = 0 - return offset - - -def _concatenate_epochs(epochs_list, with_data=True, add_offset=True): +def _concatenate_epochs(epochs_list, with_data=True, add_offset=True, *, + on_mismatch='raise'): """Auxiliary function for concatenating epochs.""" if not isinstance(epochs_list, (list, tuple)): raise TypeError('epochs_list must be a list or tuple, got %s' % (type(epochs_list),)) + + # to make warning messages only occur once during concatenation + warned = False + for ei, epochs in enumerate(epochs_list): if not isinstance(epochs, BaseEpochs): raise TypeError('epochs_list[%d] must be an instance of Epochs, ' 'got %s' % (ei, type(epochs))) + + if (getattr(epochs, 'annotations', None) is not None and + len(epochs.annotations) > 0 and + not warned): + warned = True + warn('Concatenation of Annotations within Epochs is not supported ' + 'yet. All annotations will be dropped.') + + # create a copy, so that the Annotations are not modified in place + # from the original object + epochs = epochs.copy() + epochs.set_annotations(None) out = epochs_list[0] offsets = [0] if with_data: @@ -3382,22 +3407,30 @@ events = [out.events] metadata = [out.metadata] baseline, tmin, tmax = out.baseline, out.tmin, out.tmax + raw_sfreq = out._raw_sfreq info = deepcopy(out.info) - verbose = out.verbose drop_log = out.drop_log event_id = deepcopy(out.event_id) selection = out.selection # offset is the last epoch + tmax + 10 second shift = int((10 + tmax) * out.info['sfreq']) - events_offset = _update_offset(None, out.events, shift) + events_offset = int(np.max(events[0][:, 0])) + shift + events_overflow = False + warned = False for ii, epochs in enumerate(epochs_list[1:], 1): - _compare_epochs_infos(epochs.info, info, ii) + _ensure_infos_match(epochs.info, info, f'epochs[{ii}]', + on_mismatch=on_mismatch) if not np.allclose(epochs.times, epochs_list[0].times): raise ValueError('Epochs must have same times') if epochs.baseline != baseline: raise ValueError('Baseline must be same for all epochs') + if epochs._raw_sfreq != raw_sfreq and not warned: + warned = True + warn('The original raw sampling rate of the Epochs does not ' + 'match for all Epochs. Please proceed cautiously.') + # compare event_id common_keys = list(set(event_id).intersection(set(epochs.event_id))) for key in common_keys: @@ -3412,11 +3445,20 @@ epochs.drop_bad() offsets.append(len(epochs)) evs = epochs.events.copy() - # add offset - if add_offset: + if len(epochs.events) == 0: + warn('One of the Epochs objects to concatenate was empty.') + elif add_offset: + # We need to cast to a native Python int here to detect an + # overflow of a numpy int32 (which is the default on windows) + max_timestamp = int(np.max(evs[:, 0])) evs[:, 0] += events_offset - # Update offset for the next iteration. - events_offset = _update_offset(events_offset, epochs.events, shift) + events_offset += max_timestamp + shift + if events_offset > INT32_MAX: + warn(f'Event number greater than {INT32_MAX} created, ' + 'events[:, 0] will be assigned consecutive increasing ' + 'integer values') + events_overflow = True + add_offset = False # we no longer need to add offset events.append(evs) selection = np.concatenate((selection, epochs.selection)) drop_log = drop_log + epochs.drop_log @@ -3424,7 +3466,7 @@ metadata.append(epochs.metadata) events = np.concatenate(events, axis=0) # check to see if we exceeded our maximum event offset - if events_offset == 0: + if events_overflow: events[:, 0] = np.arange(1, len(events) + 1) # Create metadata object (or make it None) @@ -3452,46 +3494,56 @@ (offsets[-1], len(out.ch_names), len(out.times)), dtype=this_data.dtype) data[start:stop] = this_data - return (info, data, events, event_id, tmin, tmax, metadata, baseline, - selection, drop_log, verbose) + return (info, data, raw_sfreq, events, event_id, tmin, tmax, metadata, + baseline, selection, drop_log) -def _finish_concat(info, data, events, event_id, tmin, tmax, metadata, - baseline, selection, drop_log, verbose): +def _finish_concat(info, data, raw_sfreq, events, event_id, tmin, tmax, + metadata, baseline, selection, drop_log): """Finish concatenation for epochs not read from disk.""" selection = np.where([len(d) == 0 for d in drop_log])[0] out = BaseEpochs( info, data, events, event_id, tmin, tmax, baseline=baseline, selection=selection, drop_log=drop_log, proj=False, - on_missing='ignore', metadata=metadata, verbose=verbose) + on_missing='ignore', metadata=metadata, raw_sfreq=raw_sfreq) out.drop_bad() return out -def concatenate_epochs(epochs_list, add_offset=True): - """Concatenate a list of epochs into one epochs object. +@verbose +def concatenate_epochs(epochs_list, add_offset=True, *, on_mismatch='raise', + verbose=None): + """Concatenate a list of `~mne.Epochs` into one `~mne.Epochs` object. + + .. note:: Unlike `~mne.concatenate_raws`, this function does **not** + modify any of the input data. Parameters ---------- epochs_list : list - List of Epochs instances to concatenate (in order). + List of `~mne.Epochs` instances to concatenate (in that order). add_offset : bool If True, a fixed offset is added to the event times from different Epochs sets, such that they are easy to distinguish after the concatenation. If False, the event times are unaltered during the concatenation. + %(on_mismatch_info)s + %(verbose)s + + .. versionadded:: 0.24 Returns ------- epochs : instance of Epochs - The result of the concatenation (first Epochs instance passed in). + The result of the concatenation. Notes ----- .. versionadded:: 0.9.0 """ return _finish_concat(*_concatenate_epochs(epochs_list, - add_offset=add_offset)) + add_offset=add_offset, + on_mismatch=on_mismatch)) @verbose @@ -3505,24 +3557,24 @@ ---------- epochs : instance of Epochs The epochs to operate on. - %(maxwell_pos)s + %(head_pos_maxwell)s orig_sfreq : float | None The original sample frequency of the data (that matches the event sample numbers in ``epochs.events``). Can be ``None`` if data have not been decimated or resampled. %(picks_all_data)s - %(maxwell_origin)s + %(origin_maxwell)s weight_all : bool If True, all channels are weighted by the SSS basis weights. If False, only MEG channels are weighted, other channels receive uniform weight per epoch. - %(maxwell_int)s - %(maxwell_ext)s - %(maxwell_dest)s - %(maxwell_ref)s + %(int_order_maxwell)s + %(ext_order_maxwell)s + %(destination_maxwell_dest)s + %(ignore_ref_maxwell)s return_mapping : bool If True, return the mapping matrix. - %(maxwell_mag)s + %(mag_scale_maxwell)s .. versionadded:: 0.13 %(verbose)s @@ -3679,7 +3731,7 @@ @verbose def make_fixed_length_epochs(raw, duration=1., preload=False, reject_by_annotation=True, proj=True, overlap=0., - verbose=None): + id=1, verbose=None): """Divide continuous raw data into equal-sized consecutive epochs. Parameters @@ -3700,6 +3752,10 @@ ``0 <= overlap < duration``. Default is 0, i.e., no overlap. .. versionadded:: 0.23.0 + id : int + The id to use (default 1). + + .. versionadded:: 0.24.0 %(verbose)s Returns @@ -3711,10 +3767,10 @@ ----- .. versionadded:: 0.20 """ - events = make_fixed_length_events(raw, 1, duration=duration, + events = make_fixed_length_events(raw, id=id, duration=duration, overlap=overlap) delta = 1. / raw.info['sfreq'] - return Epochs(raw, events, event_id=[1], tmin=0, tmax=duration - delta, + return Epochs(raw, events, event_id=[id], tmin=0, tmax=duration - delta, baseline=None, preload=preload, reject_by_annotation=reject_by_annotation, proj=proj, verbose=verbose) diff -Nru python-mne-0.23.4+dfsg/mne/event.py python-mne-1.1.0+dfsg/mne/event.py --- python-mne-0.23.4+dfsg/mne/event.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/event.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,29 +5,31 @@ # Teon Brooks # Clement Moutard # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op -import numpy as np +from collections.abc import Sequence +import numpy as np from .utils import (check_fname, logger, verbose, _get_stim_channel, warn, - _validate_type, _check_option) + _validate_type, _check_option, fill_doc, _check_fname, + _on_missing, _check_on_missing) from .io.constants import FIFF from .io.tree import dir_tree_find from .io.tag import read_tag from .io.open import fiff_open -from .io.write import write_int, start_block, start_file, end_block, end_file +from .io.write import write_int, start_block, start_and_end_file, end_block from .io.pick import pick_channels +@fill_doc def pick_events(events, include=None, exclude=None, step=False): - """Select some events. + """Select some :term:`events`. Parameters ---------- - events : ndarray - Array as returned by mne.find_events. + %(events)s include : int | list | None A event id to include or a list of them. If None all events are included. @@ -191,7 +193,7 @@ @verbose def read_events(filename, include=None, exclude=None, mask=None, mask_type='and', return_event_id=False, verbose=None): - """Read events from fif or text file. + """Read :term:`events` from fif or text file. See :ref:`tut-events-vs-annotations` and :ref:`tut-event-arrays` for more information about events. @@ -229,8 +231,7 @@ Returns ------- - events: array, shape (n_events, 3) - The list of events. + %(events)s event_id : dict Dictionary of ``{str: int}`` mappings of event IDs. @@ -299,8 +300,9 @@ return out -def write_events(filename, event_list): - """Write events to file. +@verbose +def write_events(filename, events, *, overwrite=False, verbose=None): + """Write :term:`events` to file. Parameters ---------- @@ -311,32 +313,29 @@ .txt) events are written as plain text. Note that new format event files do not contain the "time" column (used to be the second column). - event_list : array, shape (n_events, 3) - The list of events. + %(events)s + %(overwrite)s + %(verbose)s See Also -------- read_events """ + filename = _check_fname(filename, overwrite=overwrite) check_fname(filename, 'events', ('.eve', '-eve.fif', '-eve.fif.gz', '-eve.lst', '-eve.txt', '_eve.fif', '_eve.fif.gz', '_eve.lst', '_eve.txt')) - ext = op.splitext(filename)[1].lower() - if ext == '.fif' or ext == '.gz': + if ext in ('.fif', '.gz'): # Start writing... - fid = start_file(filename) - - start_block(fid, FIFF.FIFFB_MNE_EVENTS) - write_int(fid, FIFF.FIFF_MNE_EVENT_LIST, event_list.T) - end_block(fid, FIFF.FIFFB_MNE_EVENTS) - - end_file(fid) + with start_and_end_file(filename) as fid: + start_block(fid, FIFF.FIFFB_MNE_EVENTS) + write_int(fid, FIFF.FIFF_MNE_EVENT_LIST, events.T) + end_block(fid, FIFF.FIFFB_MNE_EVENTS) else: - f = open(filename, 'w') - for e in event_list: - f.write('%6d %6d %3d\n' % tuple(e)) - f.close() + with open(filename, 'w') as f: + for e in events: + f.write('%6d %6d %3d\n' % tuple(e)) def _find_stim_steps(data, first_samp, pad_start=None, pad_stop=None, merge=0): @@ -466,7 +465,8 @@ initial_value = data[0, 0] if initial_value != 0: if initial_event: - events = np.insert(events, 0, [0, 0, initial_value], axis=0) + events = np.insert( + events, 0, [first_samp, 0, initial_value], axis=0) else: logger.info('Trigger channel has a non-zero initial value of {} ' '(consider using initial_event=True to detect this ' @@ -538,7 +538,7 @@ consecutive='increasing', min_duration=0, shortest_event=2, mask=None, uint_cast=False, mask_type='and', initial_event=False, verbose=None): - """Find events from raw file. + """Find :term:`events` from raw file. See :ref:`tut-events-vs-annotations` and :ref:`tut-event-arrays` for more information about events. @@ -597,13 +597,7 @@ Returns ------- - events : array, shape = (n_events, 3) - All events that were found. The first column contains the event time - in samples and the third column contains the event id. For output = - 'onset' or 'step', the second column contains the value of the stim - channel immediately before the event/step. For output = 'offset', - the second column contains the value of the stim channel after the - event offset. + %(events)s See Also -------- @@ -757,7 +751,7 @@ def merge_events(events, ids, new_id, replace_events=True): - """Merge a set of events. + """Merge a set of :term:`events`. Parameters ---------- @@ -817,13 +811,13 @@ return events_out +@fill_doc def shift_time_events(events, ids, tshift, sfreq): - """Shift an event. + """Shift a set of :term:`events`. Parameters ---------- - events : array, shape=(n_events, 3) - The events. + %(events)s ids : ndarray of int | None The ids of events to shift. tshift : float @@ -834,7 +828,7 @@ Returns ------- - new_events : array + new_events : array of int, shape (n_new_events, 3) The new events. """ events = events.copy() @@ -847,9 +841,10 @@ return events +@fill_doc def make_fixed_length_events(raw, id=1, start=0, stop=None, duration=1., first_samp=True, overlap=0.): - """Make a set of events separated by a fixed duration. + """Make a set of :term:`events` separated by a fixed duration. Parameters ---------- @@ -865,10 +860,10 @@ duration : float The duration to separate events by (in seconds). first_samp : bool - If True (default), times will have raw.first_samp added to them, as + If True (default), times will have :term:`first_samp` added to them, as in :func:`mne.find_events`. This behavior is not desirable if the returned events will be combined with event times that already - have ``raw.first_samp`` added to them, e.g. event times that come + have :term:`first_samp` added to them, e.g. event times that come from :func:`mne.find_events`. overlap : float The overlap between events (in seconds). @@ -878,8 +873,7 @@ Returns ------- - new_events : array - The new events. + %(events)s """ from .io.base import BaseRaw _validate_type(raw, BaseRaw, "raw") @@ -925,7 +919,7 @@ Parameters ---------- events : list of array - List of event arrays, typically each extracted from a + List of :term:`events` arrays, typically each extracted from a corresponding raw file that is being concatenated. first_samps : list or array of int First sample numbers of the raw files concatenated. @@ -961,6 +955,7 @@ return events_out +@fill_doc class AcqParserFIF(object): """Parser for Elekta data acquisition settings. @@ -972,8 +967,7 @@ Parameters ---------- - info : Info - An instance of Info where the DACQ parameters will be taken from. + %(info_not_none)s This is where the DACQ parameters will be taken from. Attributes ---------- @@ -1422,3 +1416,86 @@ conds_data.append(dict(events=cat_t0, event_id=cat_id, tmin=tmin, tmax=tmax)) return conds_data[0] if len(conds_data) == 1 else conds_data + + +def match_event_names(event_names, keys, *, on_missing='raise'): + """Search a collection of event names for matching (sub-)groups of events. + + This function is particularly helpful when using grouped event names + (i.e., event names containing forward slashes ``/``). Please see the + Examples section below for a working example. + + Parameters + ---------- + event_names : array-like of str | dict + Either a collection of event names, or the ``event_id`` dictionary + mapping event names to event codes. + keys : array-like of str | str + One or multiple event names or groups to search for in ``event_names``. + on_missing : 'raise' | 'warn' | 'ignore' + How to handle situations when none of the ``keys`` can be found in + ``event_names``. If ``'warn'`` or ``'ignore'``, an empty list will be + returned. + + Returns + ------- + matches : list of str + All event names that match any of the ``keys`` provided. + + Notes + ----- + .. versionadded:: 1.0 + + Examples + -------- + Assuming the following grouped event names in the data, you could easily + query for all ``auditory`` and ``left`` event names:: + + >>> event_names = [ + ... 'auditory/left', + ... 'auditory/right', + ... 'visual/left', + ... 'visual/right' + ... ] + >>> match_event_names( + ... event_names=event_names, + ... keys=['auditory', 'left'] + ... ) + ['auditory/left', 'auditory/right', 'visual/left'] + """ + _check_on_missing(on_missing) + + if isinstance(event_names, dict): + event_names = list(event_names) + + # ensure we have a list of `keys` + if ( + isinstance(keys, (Sequence, np.ndarray)) and + not isinstance(keys, str) + ): + keys = list(keys) + else: + keys = [keys] + + matches = [] + + # form the hierarchical event name mapping + for key in keys: + if not isinstance(key, str): + raise ValueError(f'keys must be strings, got {type(key)} ({key})') + + matches.extend( + name for name in event_names + if set(key.split('/')).issubset(name.split('/')) + ) + + if not matches: + _on_missing( + on_missing=on_missing, + msg=f'Event name "{key}" could not be found. The following events ' + f'are present in the data: {", ".join(event_names)}', + error_klass=KeyError + ) + + matches = sorted(set(matches)) # deduplicate if necessary + return matches diff -Nru python-mne-0.23.4+dfsg/mne/evoked.py python-mne-1.1.0+dfsg/mne/evoked.py --- python-mne-0.23.4+dfsg/mne/evoked.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/evoked.py 2022-08-03 15:48:14.000000000 +0000 @@ -6,23 +6,24 @@ # Mads Jensen # Jona Sassenhagen # -# License: BSD (3-clause) +# License: BSD-3-Clause from copy import deepcopy import numpy as np from .baseline import rescale, _log_rescale, _check_baseline -from .channels.channels import (ContainsMixin, UpdateChannelsMixin, +from .channels.channels import (UpdateChannelsMixin, SetChannelsMixin, InterpolationMixin) from .channels.layout import _merge_ch_data, _pair_grad_sensors -from .defaults import _EXTRAPOLATE_DEFAULT, _BORDER_DEFAULT +from .defaults import (_INTERPOLATION_DEFAULT, _EXTRAPOLATE_DEFAULT, + _BORDER_DEFAULT) from .filter import detrend, FilterMixin, _check_fun -from .utils import (check_fname, logger, verbose, _time_mask, warn, sizeof_fmt, +from .utils import (check_fname, logger, verbose, warn, sizeof_fmt, SizeMixin, copy_function_doc_to_method_doc, _validate_type, - fill_doc, _check_option, ShiftTimeMixin, _build_data_frame, + fill_doc, _check_option, _build_data_frame, _check_pandas_installed, _check_pandas_index_arguments, _convert_times, _scale_dataframe_data, _check_time_format, - _check_preload) + _check_preload, _check_fname, TimeMixin) from .viz import (plot_evoked, plot_evoked_topomap, plot_evoked_field, plot_evoked_image, plot_evoked_topo) from .viz.evoked import plot_evoked_white, plot_evoked_joint @@ -33,13 +34,14 @@ from .io.tag import read_tag from .io.tree import dir_tree_find from .io.pick import pick_types, _picks_to_idx, _FNIRS_CH_TYPES_SPLIT -from .io.meas_info import (read_meas_info, write_meas_info, - _read_extended_ch_info, _rename_list) +from .io.meas_info import (ContainsMixin, read_meas_info, write_meas_info, + _read_extended_ch_info, _rename_list, + _ensure_infos_match) from .io.proj import ProjMixin -from .io.write import (start_file, start_block, end_file, end_block, +from .io.write import (start_and_end_file, start_block, end_block, write_int, write_string, write_float_matrix, write_id, write_float, write_complex_float_matrix) -from .io.base import TimeMixin, _check_maxshield +from .io.base import _check_maxshield, _get_ch_factors from .parallel import parallel_func _aspect_dict = { @@ -60,8 +62,7 @@ @fill_doc class Evoked(ProjMixin, ContainsMixin, UpdateChannelsMixin, SetChannelsMixin, - InterpolationMixin, FilterMixin, TimeMixin, SizeMixin, - ShiftTimeMixin): + InterpolationMixin, FilterMixin, TimeMixin, SizeMixin): """Evoked data. Parameters @@ -87,8 +88,7 @@ Attributes ---------- - info : dict - Measurement info. + %(info_not_none)s ch_names : list of str List of channels' names. nave : int @@ -114,7 +114,6 @@ baseline : None | tuple of length 2 This attribute reflects whether the data has been baseline-corrected (it will be a ``tuple`` then) or not (it will be ``None``). - %(verbose)s Notes ----- @@ -123,19 +122,24 @@ @verbose def __init__(self, fname, condition=None, proj=True, - kind='average', allow_maxshield=False, + kind='average', allow_maxshield=False, *, verbose=None): # noqa: D102 _validate_type(proj, bool, "'proj'") # Read the requested data - self.info, self.nave, self._aspect_kind, self.comment, self.times, \ + fname = _check_fname(fname=fname, must_exist=True, overwrite='read') + self.info, self.nave, self._aspect_kind, self.comment, times, \ self.data, self.baseline = _read_evoked(fname, condition, kind, allow_maxshield) + self._set_times(times) + self._raw_times = self.times.copy() + self._decim = 1 + self._update_first_last() - self.verbose = verbose self.preload = True # project and baseline correct if proj: self.apply_proj() + self.filename = fname @property def kind(self): @@ -157,8 +161,42 @@ """Set the data matrix.""" self._data = data + @fill_doc + def get_data(self, picks=None, units=None, tmin=None, tmax=None): + """Get evoked data as 2D array. + + Parameters + ---------- + %(picks_all)s + %(units)s + tmin : float | None + Start time of data to get in seconds. + tmax : float | None + End time of data to get in seconds. + + Returns + ------- + data : ndarray, shape (n_channels, n_times) + A view on evoked data. + + Notes + ----- + .. versionadded:: 0.24 + """ + picks = _picks_to_idx(self.info, picks, "all", exclude=()) + + start, stop = self._handle_tmin_tmax(tmin, tmax) + + data = self.data[picks, start:stop] + + if units is not None: + ch_factors = _get_ch_factors(self, units, picks) + data *= ch_factors[:, np.newaxis] + + return data + @verbose - def apply_function(self, fun, picks=None, dtype=None, n_jobs=1, + def apply_function(self, fun, picks=None, dtype=None, n_jobs=None, verbose=None, **kwargs): """Apply a function to a subset of channels. @@ -166,12 +204,12 @@ Parameters ---------- - %(applyfun_fun_evoked)s + %(fun_applyfun_evoked)s %(picks_all_data_noref)s - %(applyfun_dtype)s + %(dtype_applyfun)s %(n_jobs)s - %(verbose_meth)s - %(kwarg_fun)s + %(verbose)s + %(kwargs_fun)s Returns ------- @@ -191,13 +229,13 @@ # check the dimension of the incoming evoked data _check_option('evoked.ndim', self._data.ndim, [2]) + parallel, p_fun, n_jobs = parallel_func(_check_fun, n_jobs) if n_jobs == 1: # modify data inplace to save memory for idx in picks: self._data[idx, :] = _check_fun(fun, data_in[idx, :], **kwargs) else: # use parallel function - parallel, p_fun, _ = parallel_func(_check_fun, n_jobs) data_picks_new = parallel(p_fun( fun, data_in[p, :], **kwargs) for p in picks) for pp, p in enumerate(picks): @@ -214,7 +252,7 @@ %(baseline_evoked)s Defaults to ``(None, 0)``, i.e. beginning of the the data until time point zero. - %(verbose_meth)s + %(verbose)s Returns ------- @@ -242,14 +280,17 @@ return self - def save(self, fname): - """Save dataset to file. + @verbose + def save(self, fname, *, overwrite=False, verbose=None): + """Save evoked data to a file. Parameters ---------- fname : str The name of the file, which should end with ``-ave.fif(.gz)`` or ``_ave.fif(.gz)``. + %(overwrite)s + %(verbose)s Notes ----- @@ -260,10 +301,40 @@ Information on baseline correction will be stored with the data, and will be restored when reading again via `mne.read_evokeds`. """ - write_evokeds(fname, self) + write_evokeds(fname, self, overwrite=overwrite) + + @verbose + def export(self, fname, fmt='auto', *, overwrite=False, verbose=None): + """Export Evoked to external formats. + + %(export_fmt_support_evoked)s + + %(export_warning)s + + Parameters + ---------- + %(fname_export_params)s + %(export_fmt_params_evoked)s + %(overwrite)s + %(verbose)s + + Notes + ----- + .. versionadded:: 1.1 + + %(export_warning_note_evoked)s + """ + from .export import export_evokeds + export_evokeds(fname, self, fmt, overwrite=overwrite, verbose=verbose) def __repr__(self): # noqa: D105 - s = "'%s' (%s, N=%s)" % (self.comment, self.kind, self.nave) + max_comment_length = 1000 + if len(self.comment) > max_comment_length: + comment = self.comment[:max_comment_length] + comment += "..." + else: + comment = self.comment + s = "'%s' (%s, N=%s)" % (comment, self.kind, self.nave) s += ", %0.5g – %0.5g sec" % (self.times[0], self.times[-1]) s += ', baseline ' if self.baseline is None: @@ -283,116 +354,21 @@ """Channel names.""" return self.info['ch_names'] - @property - def tmin(self): - """First time point. - - .. versionadded:: 0.21 - """ - return self.times[0] - - @property - def tmax(self): - """Last time point. - - .. versionadded:: 0.21 - """ - return self.times[-1] - - @fill_doc - def crop(self, tmin=None, tmax=None, include_tmax=True, verbose=None): - """Crop data to a given time interval. - - Parameters - ---------- - tmin : float | None - Start time of selection in seconds. - tmax : float | None - End time of selection in seconds. - %(include_tmax)s - %(verbose_meth)s - - Returns - ------- - evoked : instance of Evoked - The cropped Evoked object, modified in-place. - - Notes - ----- - %(notes_tmax_included_by_default)s - """ - if tmin is None: - tmin = self.tmin - elif tmin < self.tmin: - warn(f'tmin is not in Evoked time interval. tmin is set to ' - f'evoked.tmin ({self.tmin:g} sec)') - tmin = self.tmin - - if tmax is None: - tmax = self.tmax - elif tmax > self.tmax: - warn(f'tmax is not in Evoked time interval. tmax is set to ' - f'evoked.tmax ({self.tmax:g} sec)') - tmax = self.tmax - - mask = _time_mask(self.times, tmin, tmax, sfreq=self.info['sfreq'], - include_tmax=include_tmax) - self.times = self.times[mask] - self._update_first_last() - self.data = self.data[:, mask] - - return self - - @verbose - def decimate(self, decim, offset=0, verbose=None): - """Decimate the evoked data. - - Parameters - ---------- - %(decim)s - %(decim_offset)s - %(verbose_meth)s - - Returns - ------- - evoked : instance of Evoked - The decimated Evoked object. - - See Also - -------- - Epochs.decimate - Epochs.resample - mne.io.Raw.resample - - Notes - ----- - %(decim_notes)s - - .. versionadded:: 0.13.0 - """ - decim, offset, new_sfreq = _check_decim(self.info, decim, offset) - start_idx = int(round(self.times[0] * (self.info['sfreq'] * decim))) - i_start = start_idx % decim + offset - decim_slice = slice(i_start, None, decim) - self.info['sfreq'] = new_sfreq - self.data = self.data[:, decim_slice].copy() - self.times = self.times[decim_slice].copy() - self._update_first_last() - return self - @copy_function_doc_to_method_doc(plot_evoked) def plot(self, picks=None, exclude='bads', unit=True, show=True, ylim=None, xlim='tight', proj=False, hline=None, units=None, scalings=None, titles=None, axes=None, gfp=False, window_title=None, spatial_colors=False, zorder='unsorted', selectable=True, - noise_cov=None, time_unit='s', sphere=None, verbose=None): + noise_cov=None, time_unit='s', sphere=None, *, highlight=None, + verbose=None): return plot_evoked( self, picks=picks, exclude=exclude, unit=unit, show=show, ylim=ylim, proj=proj, xlim=xlim, hline=hline, units=units, scalings=scalings, titles=titles, axes=axes, gfp=gfp, window_title=window_title, spatial_colors=spatial_colors, zorder=zorder, selectable=selectable, noise_cov=noise_cov, - time_unit=time_unit, sphere=sphere, verbose=verbose) + time_unit=time_unit, sphere=sphere, highlight=highlight, + verbose=verbose) @copy_function_doc_to_method_doc(plot_evoked_image) def plot_image(self, picks=None, exclude='bads', unit=True, show=True, @@ -437,8 +413,9 @@ time_unit='s', time_format=None, proj=False, show=True, show_names=False, title=None, mask=None, mask_params=None, outlines='head', - contours=6, image_interp='bilinear', average=None, - axes=None, extrapolate=_EXTRAPOLATE_DEFAULT, sphere=None, + contours=6, image_interp=_INTERPOLATION_DEFAULT, + average=None, axes=None, + extrapolate=_EXTRAPOLATE_DEFAULT, sphere=None, border=_BORDER_DEFAULT, nrows=1, ncols='auto'): return plot_evoked_topomap( self, times=times, ch_type=ch_type, vmin=vmin, @@ -448,17 +425,18 @@ time_format=time_format, proj=proj, show=show, show_names=show_names, title=title, mask=mask, mask_params=mask_params, outlines=outlines, contours=contours, - image_interp=image_interp, average=average, - axes=axes, extrapolate=extrapolate, sphere=sphere, border=border, + image_interp=image_interp, average=average, axes=axes, + extrapolate=extrapolate, sphere=sphere, border=border, nrows=nrows, ncols=ncols) @copy_function_doc_to_method_doc(plot_evoked_field) def plot_field(self, surf_maps, time=None, time_label='t = %0.0f ms', - n_jobs=1, fig=None, vmax=None, n_contours=21, verbose=None): + n_jobs=None, fig=None, vmax=None, n_contours=21, + *, interaction='terrain', verbose=None): return plot_evoked_field(self, surf_maps, time=time, time_label=time_label, n_jobs=n_jobs, fig=fig, vmax=vmax, n_contours=n_contours, - verbose=verbose) + interaction=interaction, verbose=verbose) @copy_function_doc_to_method_doc(plot_evoked_white) def plot_white(self, noise_cov, show=True, rank=None, time_unit='s', @@ -478,8 +456,8 @@ @fill_doc def animate_topomap(self, ch_type=None, times=None, frame_rate=None, butterfly=False, blit=True, show=True, time_unit='s', - sphere=None, *, extrapolate=_EXTRAPOLATE_DEFAULT, - verbose=None): + sphere=None, *, image_interp=_INTERPOLATION_DEFAULT, + extrapolate=_EXTRAPOLATE_DEFAULT, verbose=None): """Make animation of evoked data as topomap timeseries. The animation can be paused/resumed with left mouse button. @@ -515,11 +493,12 @@ or "s" (will become the default in 0.17). .. versionadded:: 0.16 - %(topomap_sphere_auto)s - %(topomap_extrapolate)s + %(sphere_topomap_auto)s + %(image_interp_topomap)s + %(extrapolate_topomap)s .. versionadded:: 0.22 - %(verbose_meth)s + %(verbose)s Returns ------- @@ -535,7 +514,8 @@ return _topomap_animation( self, ch_type=ch_type, times=times, frame_rate=frame_rate, butterfly=butterfly, blit=blit, show=show, time_unit=time_unit, - sphere=sphere, extrapolate=extrapolate, verbose=verbose) + sphere=sphere, image_interp=image_interp, + extrapolate=extrapolate, verbose=verbose) def as_type(self, ch_type='grad', mode='fast'): """Compute virtual evoked using interpolated fields. @@ -627,16 +607,15 @@ Parameters ---------- ch_type : str | None - The channel type to use. Defaults to None. If more than one sensor - Type is present in the data the channel type has to be explicitly - set. + The channel type to use. Defaults to None. If more than one channel + type is present in the data, this value **must** be provided. tmin : float | None The minimum point in time to be considered for peak getting. If None (default), the beginning of the data is used. tmax : float | None The maximum point in time to be considered for peak getting. If None (default), the end of the data is used. - mode : {'pos', 'neg', 'abs'} + mode : 'pos' | 'neg' | 'abs' How to deal with the sign of the data. If 'pos' only positive values will be considered. If 'neg' only negative values will be considered. If 'abs' absolute values will be considered. @@ -670,17 +649,19 @@ _check_option('ch_type', str(ch_type), supported) if ch_type is not None and ch_type not in types_used: - raise ValueError('Channel type `{ch_type}` not found in this ' - 'evoked object.'.format(ch_type=ch_type)) + raise ValueError( + f'Channel type "{ch_type}" not found in this evoked object.' + ) elif len(types_used) > 1 and ch_type is None: - raise RuntimeError('More than one sensor type found. `ch_type` ' - 'must not be `None`, pass a sensor type ' - 'value instead') + raise RuntimeError( + 'Multiple data channel types found. Please pass the "ch_type" ' + 'parameter.' + ) if merge_grads: if ch_type != 'grad': - raise ValueError('Channel type must be grad for merge_grads') + raise ValueError('Channel type must be "grad" for merge_grads') elif mode == 'neg': raise ValueError('Negative mode (mode=neg) does not make ' 'sense with merge_grads=True') @@ -731,10 +712,10 @@ return out - @fill_doc + @verbose def to_data_frame(self, picks=None, index=None, scalings=None, copy=True, long_format=False, - time_format='ms'): + time_format=None, *, verbose=None): """Export data in tabular structure as a pandas DataFrame. Channels are converted to columns in the DataFrame. By default, @@ -744,14 +725,15 @@ Parameters ---------- %(picks_all)s - %(df_index_evk)s + %(index_df_evk)s Defaults to ``None``. - %(df_scalings)s - %(df_copy)s - %(df_longform_raw)s - %(df_time_format)s + %(scalings_df)s + %(copy_df)s + %(long_format_df_raw)s + %(time_format_df)s .. versionadded:: 0.20 + %(verbose)s Returns ------- @@ -782,30 +764,6 @@ return df -def _check_decim(info, decim, offset): - """Check decimation parameters.""" - if decim < 1 or decim != int(decim): - raise ValueError('decim must be an integer > 0') - decim = int(decim) - new_sfreq = info['sfreq'] / float(decim) - lowpass = info['lowpass'] - if decim > 1 and lowpass is None: - warn('The measurement information indicates data is not low-pass ' - 'filtered. The decim=%i parameter will result in a sampling ' - 'frequency of %g Hz, which can cause aliasing artifacts.' - % (decim, new_sfreq)) - elif decim > 1 and new_sfreq < 3 * lowpass: - warn('The measurement information indicates a low-pass frequency ' - 'of %g Hz. The decim=%i parameter will result in a sampling ' - 'frequency of %g Hz, which can cause aliasing artifacts.' - % (lowpass, decim, new_sfreq)) # > 50% nyquist lim - offset = int(offset) - if not 0 <= offset < decim: - raise ValueError('decim must be at least 0 and less than %s, got ' - '%s' % (decim, offset)) - return decim, offset, new_sfreq - - @fill_doc class EvokedArray(Evoked): """Evoked object from numpy array. @@ -814,9 +772,8 @@ ---------- data : array of shape (n_channels, n_times) The channels' evoked response. See notes for proper units of measure. - info : instance of Info - Info dictionary. Consider using ``create_info`` to populate - this structure. + %(info_not_none)s Consider using :func:`mne.create_info` to populate this + structure. tmin : float Start time before event. Defaults to 0. comment : str @@ -848,7 +805,7 @@ @verbose def __init__(self, data, info, tmin=0., comment='', nave=1, kind='average', - baseline=None, verbose=None): # noqa: D102 + baseline=None, *, verbose=None): # noqa: D102 dtype = np.complex128 if np.iscomplexobj(data) else np.float64 data = np.asanyarray(data, dtype=dtype) @@ -865,14 +822,15 @@ self.first = int(round(tmin * info['sfreq'])) self.last = self.first + np.shape(data)[-1] - 1 - self.times = np.arange(self.first, self.last + 1, - dtype=np.float64) / info['sfreq'] + self._set_times(np.arange(self.first, self.last + 1, + dtype=np.float64) / info['sfreq']) + self._raw_times = self.times.copy() + self._decim = 1 self.info = info.copy() # do not modify original info self.nave = nave self.kind = kind self.comment = comment self.picks = None - self.verbose = verbose self.preload = True self._projector = None _validate_type(self.kind, "str", "kind") @@ -883,7 +841,7 @@ self.baseline = baseline if self.baseline is not None: # omit log msg if not baselining - self.apply_baseline(self.baseline, verbose=self.verbose) + self.apply_baseline(self.baseline) def _get_entries(fid, evoked_node, allow_maxshield=False): @@ -1059,8 +1017,8 @@ Parameters ---------- - fname : str - The file name, which should end with -ave.fif or -ave.fif.gz. + fname : path-like + The filename, which should end with ``-ave.fif`` or ``-ave.fif.gz``. condition : int or str | list of int or str | None The index or list of indices of the evoked dataset to read. FIF files can contain multiple datasets. If None, all datasets are returned as a @@ -1108,6 +1066,7 @@ saving, this will be reflected in their ``baseline`` attribute after reading. """ + fname = _check_fname(fname, overwrite='read', must_exist=True) check_fname(fname, 'evoked', ('-ave.fif', '-ave.fif.gz', '_ave.fif', '_ave.fif.gz')) logger.info('Reading %s ...' % fname) @@ -1189,7 +1148,9 @@ my_evoked = evoked_node[condition] # Identify the aspects - my_aspect, info['maxshield'] = _get_aspect(my_evoked, allow_maxshield) + with info._unlock(): + my_aspect, info['maxshield'] = _get_aspect(my_evoked, + allow_maxshield) # Now find the data in the evoked block nchan = 0 @@ -1325,7 +1286,9 @@ return info, nave, aspect_kind, comment, times, data, baseline -def write_evokeds(fname, evoked): +@verbose +def write_evokeds(fname, evoked, *, on_mismatch='raise', overwrite=False, + verbose=None): """Write an evoked dataset to a file. Parameters @@ -1336,6 +1299,13 @@ The evoked dataset, or list of evoked datasets, to save in one file. Note that the measurement info from the first evoked instance is used, so be sure that information matches. + %(on_mismatch_info)s + %(overwrite)s + + .. versionadded:: 1.0 + %(verbose)s + + .. versionadded:: 0.24 See Also -------- @@ -1348,24 +1318,25 @@ `~mne.Evoked` object, and will be restored when reading the data again via `mne.read_evokeds`. """ - _write_evokeds(fname, evoked) + _write_evokeds(fname, evoked, on_mismatch=on_mismatch, overwrite=overwrite) -def _write_evokeds(fname, evoked, check=True): +def _write_evokeds(fname, evoked, check=True, *, on_mismatch='raise', + overwrite=False): """Write evoked data.""" - from .epochs import _compare_epochs_infos from .dipole import DipoleFixed # avoid circular import + fname = _check_fname(fname=fname, overwrite=overwrite) if check: check_fname(fname, 'evoked', ('-ave.fif', '-ave.fif.gz', '_ave.fif', '_ave.fif.gz')) - if not isinstance(evoked, list): + if not isinstance(evoked, (list, tuple)): evoked = [evoked] warned = False # Create the file and save the essentials - with start_file(fname) as fid: + with start_and_end_file(fname) as fid: start_block(fid, FIFF.FIFFB_MEAS) write_id(fid, FIFF.FIFF_BLOCK_ID) @@ -1379,7 +1350,9 @@ start_block(fid, FIFF.FIFFB_PROCESSED_DATA) for ei, e in enumerate(evoked): if ei: - _compare_epochs_infos(evoked[0].info, e.info, f'evoked[{ei}]') + _ensure_infos_match(info1=evoked[0].info, info2=e.info, + name=f'evoked[{ei}]', + on_mismatch=on_mismatch) start_block(fid, FIFF.FIFFB_EVOKED) # Comment is optional @@ -1432,7 +1405,6 @@ end_block(fid, FIFF.FIFFB_PROCESSED_DATA) end_block(fid, FIFF.FIFFB_MEAS) - end_file(fid) def _get_peak(data, times, tmin=None, tmax=None, mode='abs'): @@ -1473,14 +1445,20 @@ if tmax is None: tmax = times[-1] - if tmin < times.min(): - raise ValueError('The tmin value is out of bounds. It must be ' - 'within {} and {}'.format(times.min(), times.max())) - if tmax > times.max(): - raise ValueError('The tmax value is out of bounds. It must be ' - 'within {} and {}'.format(times.min(), times.max())) - if tmin > tmax: - raise ValueError('The tmin must be smaller or equal to tmax') + if tmin < times.min() or tmax > times.max(): + if tmin < times.min(): + param_name = 'tmin' + param_val = tmin + else: + param_name = 'tmax' + param_val = tmax + + raise ValueError( + f'{param_name} ({param_val}) is out of bounds. It must be ' + f'between {times.min()} and {times.max()}' + ) + elif tmin > tmax: + raise ValueError(f'tmin ({tmin}) must be <= tmax ({tmax})') time_win = (times >= tmin) & (times <= tmax) mask = np.ones_like(data).astype(bool) @@ -1488,11 +1466,11 @@ maxfun = np.argmax if mode == 'pos': - if not np.any(data > 0): + if not np.any(data[~mask] > 0): raise ValueError('No positive values encountered. Cannot ' 'operate in pos mode.') elif mode == 'neg': - if not np.any(data < 0): + if not np.any(data[~mask] < 0): raise ValueError('No negative values encountered. Cannot ' 'operate in neg mode.') maxfun = np.argmin diff -Nru python-mne-0.23.4+dfsg/mne/export/_brainvision.py python-mne-1.1.0+dfsg/mne/export/_brainvision.py --- python-mne-0.23.4+dfsg/mne/export/_brainvision.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/export/_brainvision.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,19 @@ +# -*- coding: utf-8 -*- +# Authors: MNE Developers +# +# License: BSD-3-Clause + +import os + +from ..utils import _check_pybv_installed +_check_pybv_installed() +from pybv._export import _export_mne_raw # noqa: E402 + + +def _export_raw(fname, raw, overwrite): + """Export Raw object to BrainVision via pybv.""" + fname = str(fname) + ext = os.path.splitext(fname)[-1] + if ext != ".vhdr": + fname = fname.replace(ext, ".vhdr") + _export_mne_raw(raw=raw, fname=fname, overwrite=overwrite) diff -Nru python-mne-0.23.4+dfsg/mne/export/_edf.py python-mne-1.1.0+dfsg/mne/export/_edf.py --- python-mne-0.23.4+dfsg/mne/export/_edf.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/export/_edf.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,281 @@ +# -*- coding: utf-8 -*- +# Authors: MNE Developers +# +# License: BSD-3-Clause + +from contextlib import contextmanager +import numpy as np + +from ..utils import _check_edflib_installed, warn +_check_edflib_installed() +from EDFlib.edfwriter import EDFwriter # noqa: E402 + + +def _try_to_set_value(header, key, value, channel_index=None): + """Set key/value pairs in EDF header.""" + # all EDFLib set functions are set + # for example "setPatientName()" + func_name = f'set{key}' + func = getattr(header, func_name) + + # some setter functions are indexed by channels + if channel_index is None: + return_val = func(value) + else: + return_val = func(channel_index, value) + + # a nonzero return value indicates an error + if return_val != 0: + raise RuntimeError(f"Setting {key} with {value} " + f"returned an error value " + f"{return_val}.") + + +@contextmanager +def _auto_close(fid): + # try to close the handle no matter what + try: + yield fid + finally: + try: + fid.close() + except Exception: + pass # we did our best + + +def _export_raw(fname, raw, physical_range, add_ch_type): + """Export Raw objects to EDF files. + + TODO: if in future the Info object supports transducer or + technician information, allow writing those here. + """ + # scale to save data in EDF + phys_dims = 'uV' + + # get EEG-related data in uV + units = dict(eeg='uV', ecog='uV', seeg='uV', eog='uV', ecg='uV', emg='uV', + bio='uV', dbs='uV') + + digital_min = -32767 + digital_max = 32767 + file_type = EDFwriter.EDFLIB_FILETYPE_EDFPLUS + + # load data first + raw.load_data() + + # remove extra STI channels + orig_ch_types = raw.get_channel_types() + drop_chs = [] + if 'stim' in orig_ch_types: + stim_index = np.argwhere(np.array(orig_ch_types) == 'stim') + stim_index = np.atleast_1d(stim_index.squeeze()).tolist() + drop_chs.extend([raw.ch_names[idx] for idx in stim_index]) + + # Add warning if any channel types are not voltage based. + # Users are expected to only export data that is voltage based, + # such as EEG, ECoG, sEEG, etc. + # Non-voltage channels are dropped by the export function. + # Note: we can write these other channels, such as 'misc' + # but these are simply a "catch all" for unknown or undesired + # channels. + voltage_types = list(units) + ['stim', 'misc'] + non_voltage_ch = [ch not in voltage_types for ch in orig_ch_types] + if any(non_voltage_ch): + warn(f"Non-voltage channels detected: {non_voltage_ch}. MNE-Python's " + 'EDF exporter only supports voltage-based channels, because the ' + 'EDF format cannot accommodate much of the accompanying data ' + 'necessary for channel types like MEG and fNIRS (channel ' + 'orientations, coordinate frame transforms, etc). You can ' + 'override this restriction by setting those channel types to ' + '"misc" but no guarantees are made of the fidelity of that ' + 'approach.') + + ch_names = [ch for ch in raw.ch_names if ch not in drop_chs] + ch_types = np.array(raw.get_channel_types(picks=ch_names)) + n_channels = len(ch_names) + n_times = raw.n_times + + # Sampling frequency in EDF only supports integers, so to allow for + # float sampling rates from Raw, we adjust the output sampling rate + # for all channels and the data record duration. + sfreq = raw.info['sfreq'] + if float(sfreq).is_integer(): + out_sfreq = int(sfreq) + data_record_duration = None + else: + out_sfreq = np.floor(sfreq).astype(int) + data_record_duration = int(np.around( + out_sfreq / sfreq, decimals=6) * 1e6) + + warn(f'Data has a non-integer sampling rate of {sfreq}; writing to ' + 'EDF format may cause a small change to sample times.') + + # get any filter information applied to the data + lowpass = raw.info['lowpass'] + highpass = raw.info['highpass'] + linefreq = raw.info['line_freq'] + filter_str_info = f"HP:{highpass}Hz LP:{lowpass}Hz N:{linefreq}Hz" + + # get the entire dataset in uV + data = raw.get_data(units=units, picks=ch_names) + + if physical_range == 'auto': + # get max and min for each channel type data + ch_types_phys_max = dict() + ch_types_phys_min = dict() + + for _type in np.unique(ch_types): + _picks = np.nonzero(ch_types == _type)[0] + _data = raw.get_data(units=units, picks=_picks) + ch_types_phys_max[_type] = _data.max() + ch_types_phys_min[_type] = _data.min() + else: + # get the physical min and max of the data in uV + # Physical ranges of the data in uV is usually set by the manufacturer + # and properties of the electrode. In general, physical max and min + # should be the clipping levels of the ADC input and they should be + # the same for all channels. For example, Nihon Kohden uses +3200 uV + # and -3200 uV for all EEG channels (which are the actual clipping + # levels of their input amplifiers & ADC). + # For full discussion, see: https://github.com/sccn/eeglab/issues/246 + pmin, pmax = physical_range[0], physical_range[1] + + # check that physical min and max is not exceeded + if data.max() > pmax: + raise RuntimeError(f'The maximum μV of the data {data.max()} is ' + f'more than the physical max passed in {pmax}.') + if data.min() < pmin: + raise RuntimeError(f'The minimum μV of the data {data.min()} is ' + f'less than the physical min passed in {pmin}.') + + # create instance of EDF Writer + with _auto_close(EDFwriter(fname, file_type, n_channels)) as hdl: + # set channel data + for idx, ch in enumerate(ch_names): + ch_type = ch_types[idx] + signal_label = f'{ch_type.upper()} {ch}' if add_ch_type else ch + if len(signal_label) > 16: + raise RuntimeError(f'Signal label for {ch} ({ch_type}) is ' + f'longer than 16 characters, which is not ' + f'supported in EDF. Please shorten the ' + f'channel name before exporting to EDF.') + + if physical_range == 'auto': + # take the channel type minimum and maximum + pmin = ch_types_phys_min[ch_type] + pmax = ch_types_phys_max[ch_type] + for key, val in [('PhysicalMaximum', pmax), + ('PhysicalMinimum', pmin), + ('DigitalMaximum', digital_max), + ('DigitalMinimum', digital_min), + ('PhysicalDimension', phys_dims), + ('SampleFrequency', out_sfreq), + ('SignalLabel', signal_label), + ('PreFilter', filter_str_info)]: + _try_to_set_value(hdl, key, val, channel_index=idx) + + # set patient info + subj_info = raw.info.get('subject_info') + if subj_info is not None: + birthday = subj_info.get('birthday') + + # get the full name of subject if available + first_name = subj_info.get('first_name') + last_name = subj_info.get('last_name') + first_name = first_name or '' + last_name = last_name or '' + joiner = '' + if len(first_name) and len(last_name): + joiner = ' ' + name = joiner.join([first_name, last_name]) + + hand = subj_info.get('hand') + sex = subj_info.get('sex') + + if birthday is not None: + if hdl.setPatientBirthDate(birthday[0], birthday[1], + birthday[2]) != 0: + raise RuntimeError( + f"Setting patient birth date to {birthday} " + f"returned an error") + for key, val in [('PatientName', name), + ('PatientGender', sex), + ('AdditionalPatientInfo', f'hand={hand}')]: + _try_to_set_value(hdl, key, val) + + # set measurement date + meas_date = raw.info['meas_date'] + if meas_date: + subsecond = int(meas_date.microsecond / 100) + if hdl.setStartDateTime(year=meas_date.year, month=meas_date.month, + day=meas_date.day, hour=meas_date.hour, + minute=meas_date.minute, + second=meas_date.second, + subsecond=subsecond) != 0: + raise RuntimeError(f"Setting start date time {meas_date} " + f"returned an error") + + device_info = raw.info.get('device_info') + if device_info is not None: + device_type = device_info.get('type') + _try_to_set_value(hdl, 'Equipment', device_type) + + # set data record duration + if data_record_duration is not None: + _try_to_set_value(hdl, 'DataRecordDuration', data_record_duration) + + # compute number of data records to loop over + n_blocks = np.ceil(n_times / out_sfreq).astype(int) + + # increase the number of annotation signals if necessary + annots = raw.annotations + if annots is not None: + n_annotations = len(raw.annotations) + n_annot_chans = int(n_annotations / n_blocks) + if np.mod(n_annotations, n_blocks): + n_annot_chans += 1 + if n_annot_chans > 1: + hdl.setNumberOfAnnotationSignals(n_annot_chans) + + # Write each data record sequentially + for idx in range(n_blocks): + end_samp = (idx + 1) * out_sfreq + if end_samp > n_times: + end_samp = n_times + start_samp = idx * out_sfreq + + # then for each datarecord write each channel + for jdx in range(n_channels): + # create a buffer with sampling rate + buf = np.zeros(out_sfreq, np.float64, "C") + + # get channel data for this block + ch_data = data[jdx, start_samp:end_samp] + + # assign channel data to the buffer and write to EDF + buf[:len(ch_data)] = ch_data + err = hdl.writeSamples(buf) + if err != 0: + raise RuntimeError( + f"writeSamples() for channel{ch_names[jdx]} " + f"returned error: {err}") + + # there was an incomplete datarecord + if len(ch_data) != len(buf): + warn(f'EDF format requires equal-length data blocks, ' + f'so {(len(buf) - len(ch_data)) / sfreq} seconds of ' + 'zeros were appended to all channels when writing the ' + 'final block.') + + # write annotations + if annots is not None: + for desc, onset, duration in zip(raw.annotations.description, + raw.annotations.onset, + raw.annotations.duration): + # annotations are written in terms of 100 microseconds + onset = onset * 10000 + duration = duration * 10000 + if hdl.writeAnnotation(onset, duration, desc) != 0: + raise RuntimeError(f'writeAnnotation() returned an error ' + f'trying to write {desc} at {onset} ' + f'for {duration} seconds.') diff -Nru python-mne-0.23.4+dfsg/mne/export/_eeglab.py python-mne-1.1.0+dfsg/mne/export/_eeglab.py --- python-mne-0.23.4+dfsg/mne/export/_eeglab.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/export/_eeglab.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,70 @@ +# -*- coding: utf-8 -*- +# Authors: MNE Developers +# +# License: BSD-3-Clause + +import numpy as np + +from ..utils import _check_eeglabio_installed +_check_eeglabio_installed() +import eeglabio.raw # noqa: E402 +import eeglabio.epochs # noqa: E402 + + +def _export_raw(fname, raw): + # load data first + raw.load_data() + + # remove extra epoc and STI channels + drop_chs = ['epoc'] + # filenames attribute of RawArray is filled with None + if raw.filenames[0] and not (raw.filenames[0].endswith('.fif')): + drop_chs.append('STI 014') + + ch_names = [ch for ch in raw.ch_names if ch not in drop_chs] + cart_coords = _get_als_coords_from_chs(raw.info['chs'], drop_chs) + + annotations = [raw.annotations.description, + raw.annotations.onset, + raw.annotations.duration] + eeglabio.raw.export_set( + fname, data=raw.get_data(picks=ch_names), sfreq=raw.info['sfreq'], + ch_names=ch_names, ch_locs=cart_coords, annotations=annotations) + + +def _export_epochs(fname, epochs): + _check_eeglabio_installed() + # load data first + epochs.load_data() + + # remove extra epoc and STI channels + drop_chs = ['epoc', 'STI 014'] + ch_names = [ch for ch in epochs.ch_names if ch not in drop_chs] + cart_coords = _get_als_coords_from_chs(epochs.info['chs'], drop_chs) + + eeglabio.epochs.export_set( + fname, data=epochs.get_data(picks=ch_names), + sfreq=epochs.info['sfreq'], events=epochs.events, + tmin=epochs.tmin, tmax=epochs.tmax, ch_names=ch_names, + event_id=epochs.event_id, ch_locs=cart_coords) + + +def _get_als_coords_from_chs(chs, drop_chs=None): + """Extract channel locations in ALS format (x, y, z) from a chs instance. + + Returns + ------- + None if no valid coordinates are found (all zeros) + """ + if drop_chs is None: + drop_chs = [] + cart_coords = np.array([d['loc'][:3] for d in chs + if d['ch_name'] not in drop_chs]) + if cart_coords.any(): # has coordinates + # (-y x z) to (x y z) + cart_coords[:, 0] = -cart_coords[:, 0] # -y to y + # swap x (1) and y (0) + cart_coords[:, [0, 1]] = cart_coords[:, [1, 0]] + else: + cart_coords = None + return cart_coords diff -Nru python-mne-0.23.4+dfsg/mne/export/_egimff.py python-mne-1.1.0+dfsg/mne/export/_egimff.py --- python-mne-0.23.4+dfsg/mne/export/_egimff.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/export/_egimff.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,171 @@ +# -*- coding: utf-8 -*- +# Authors: MNE Developers +# +# License: BSD-3-Clause + +import os +import shutil +import datetime +import os.path as op + +import numpy as np + +from ..io.egi.egimff import _import_mffpy +from ..io.pick import pick_types, pick_channels +from ..utils import verbose, warn, _check_fname + + +@verbose +def export_evokeds_mff(fname, evoked, history=None, *, overwrite=False, + verbose=None): + """Export evoked dataset to MFF. + + %(export_warning)s + + Parameters + ---------- + %(fname_export_params)s + evoked : list of Evoked instances + List of evoked datasets to export to one file. Note that the + measurement info from the first evoked instance is used, so be sure + that information matches. + history : None (default) | list of dict + Optional list of history entries (dictionaries) to be written to + history.xml. This must adhere to the format described in + mffpy.xml_files.History.content. If None, no history.xml will be + written. + %(overwrite)s + + .. versionadded:: 0.24.1 + %(verbose)s + + Notes + ----- + .. versionadded:: 0.24 + + %(export_warning_note_evoked)s + + Only EEG channels are written to the output file. + ``info['device_info']['type']`` must be a valid MFF recording device + (e.g. 'HydroCel GSN 256 1.0'). This field is automatically populated when + using MFF read functions. + """ + mffpy = _import_mffpy('Export evokeds to MFF.') + import pytz + info = evoked[0].info + if np.round(info['sfreq']) != info['sfreq']: + raise ValueError('Sampling frequency must be a whole number. ' + f'sfreq: {info["sfreq"]}') + sampling_rate = int(info['sfreq']) + + # check for unapplied projectors + if any(not proj['active'] for proj in evoked[0].info['projs']): + warn('Evoked instance has unapplied projectors. Consider applying ' + 'them before exporting with evoked.apply_proj().') + + # Initialize writer + # Future changes: conditions based on version or mffpy requirement if + # https://github.com/BEL-Public/mffpy/pull/92 is merged and released. + fname = _check_fname(fname, overwrite=overwrite) + if op.exists(fname): + os.remove(fname) if op.isfile(fname) else shutil.rmtree(fname) + writer = mffpy.Writer(fname) + current_time = pytz.utc.localize(datetime.datetime.utcnow()) + writer.addxml('fileInfo', recordTime=current_time) + try: + device = info['device_info']['type'] + except (TypeError, KeyError): + raise ValueError('No device type. Cannot determine sensor layout.') + writer.add_coordinates_and_sensor_layout(device) + + # Add EEG data + eeg_channels = pick_types(info, eeg=True, exclude=[]) + eeg_bin = mffpy.bin_writer.BinWriter(sampling_rate) + for ave in evoked: + # Signals are converted to µV + block = (ave.data[eeg_channels] * 1e6).astype(np.float32) + eeg_bin.add_block(block, offset_us=0) + writer.addbin(eeg_bin) + + # Add categories + categories_content = _categories_content_from_evokeds(evoked) + writer.addxml('categories', categories=categories_content) + + # Add history + if history: + writer.addxml('historyEntries', entries=history) + + writer.write() + + +def _categories_content_from_evokeds(evoked): + """Return categories.xml content for evoked dataset.""" + content = dict() + begin_time = 0 + for ave in evoked: + # Times are converted to microseconds + sfreq = ave.info['sfreq'] + duration = np.round(len(ave.times) / sfreq * 1e6).astype(int) + end_time = begin_time + duration + event_time = begin_time - np.round(ave.tmin * 1e6).astype(int) + eeg_bads = _get_bad_eeg_channels(ave.info) + content[ave.comment] = [ + _build_segment_content(begin_time, end_time, event_time, eeg_bads, + name='Average', nsegs=ave.nave) + ] + begin_time += duration + return content + + +def _get_bad_eeg_channels(info): + """Return a list of bad EEG channels formatted for categories.xml. + + Given a list of only the EEG channels in file, return the indices of this + list (starting at 1) that correspond to bad channels. + """ + if len(info['bads']) == 0: + return [] + eeg_channels = pick_types(info, eeg=True, exclude=[]) + bad_channels = pick_channels(info['ch_names'], info['bads']) + bads_elementwise = np.isin(eeg_channels, bad_channels) + return list(np.flatnonzero(bads_elementwise) + 1) + + +def _build_segment_content(begin_time, end_time, event_time, eeg_bads, + status='unedited', name=None, pns_bads=None, + nsegs=None): + """Build content for a single segment in categories.xml. + + Segments are sorted into categories in categories.xml. In a segmented MFF + each category can contain multiple segments, but in an averaged MFF each + category only contains one segment (the average). + """ + channel_status = [{ + 'signalBin': 1, + 'exclusion': 'badChannels', + 'channels': eeg_bads + }] + if pns_bads: + channel_status.append({ + 'signalBin': 2, + 'exclusion': 'badChannels', + 'channels': pns_bads + }) + content = { + 'status': status, + 'beginTime': begin_time, + 'endTime': end_time, + 'evtBegin': event_time, + 'evtEnd': event_time, + 'channelStatus': channel_status, + } + if name: + content['name'] = name + if nsegs: + content['keys'] = { + '#seg': { + 'type': 'long', + 'data': nsegs + } + } + return content diff -Nru python-mne-0.23.4+dfsg/mne/export/_export.py python-mne-1.1.0+dfsg/mne/export/_export.py --- python-mne-0.23.4+dfsg/mne/export/_export.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/export/_export.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,200 @@ +# -*- coding: utf-8 -*- +# Authors: MNE Developers +# +# License: BSD-3-Clause + +import os.path as op + +from ._egimff import export_evokeds_mff +from ..utils import logger, verbose, warn, _check_fname, _validate_type + + +@verbose +def export_raw(fname, raw, fmt='auto', physical_range='auto', + add_ch_type=False, *, overwrite=False, verbose=None): + """Export Raw to external formats. + + %(export_fmt_support_raw)s + + %(export_warning)s + + Parameters + ---------- + %(fname_export_params)s + raw : instance of Raw + The raw instance to export. + %(export_fmt_params_raw)s + %(physical_range_export_params)s + %(add_ch_type_export_params)s + %(overwrite)s + + .. versionadded:: 0.24.1 + %(verbose)s + + Notes + ----- + .. versionadded:: 0.24 + + %(export_warning_note_raw)s + %(export_eeglab_note)s + %(export_edf_note)s + """ + fname = _check_fname(fname, overwrite=overwrite) + supported_export_formats = { # format : (extensions,) + 'eeglab': ('set',), + 'edf': ('edf',), + 'brainvision': ('eeg', 'vmrk', 'vhdr',) + } + fmt = _infer_check_export_fmt(fmt, fname, supported_export_formats) + + # check for unapplied projectors + if any(not proj['active'] for proj in raw.info['projs']): + warn('Raw instance has unapplied projectors. Consider applying ' + 'them before exporting with raw.apply_proj().') + + if fmt == 'eeglab': + from ._eeglab import _export_raw + _export_raw(fname, raw) + elif fmt == 'edf': + from ._edf import _export_raw + _export_raw(fname, raw, physical_range, add_ch_type) + elif fmt == 'brainvision': + from ._brainvision import _export_raw + _export_raw(fname, raw, overwrite) + + +@verbose +def export_epochs(fname, epochs, fmt='auto', *, overwrite=False, verbose=None): + """Export Epochs to external formats. + + %(export_fmt_support_epochs)s + + %(export_warning)s + + Parameters + ---------- + %(fname_export_params)s + epochs : instance of Epochs + The epochs to export. + %(export_fmt_params_epochs)s + %(overwrite)s + + .. versionadded:: 0.24.1 + %(verbose)s + + Notes + ----- + .. versionadded:: 0.24 + + %(export_warning_note_epochs)s + %(export_eeglab_note)s + """ + fname = _check_fname(fname, overwrite=overwrite) + supported_export_formats = { + 'eeglab': ('set',), + } + fmt = _infer_check_export_fmt(fmt, fname, supported_export_formats) + + # check for unapplied projectors + if any(not proj['active'] for proj in epochs.info['projs']): + warn('Epochs instance has unapplied projectors. Consider applying ' + 'them before exporting with epochs.apply_proj().') + + if fmt == 'eeglab': + from ._eeglab import _export_epochs + _export_epochs(fname, epochs) + + +@verbose +def export_evokeds(fname, evoked, fmt='auto', *, overwrite=False, + verbose=None): + """Export evoked dataset to external formats. + + This function is a wrapper for format-specific export functions. The export + function is selected based on the inferred file format. For additional + options, use the format-specific functions. + + %(export_fmt_support_evoked)s + + %(export_warning)s + + Parameters + ---------- + %(fname_export_params)s + evoked : Evoked instance, or list of Evoked instances + The evoked dataset, or list of evoked datasets, to export to one file. + Note that the measurement info from the first evoked instance is used, + so be sure that information matches. + %(export_fmt_params_evoked)s + %(overwrite)s + + .. versionadded:: 0.24.1 + %(verbose)s + + See Also + -------- + mne.write_evokeds + mne.export.export_evokeds_mff + + Notes + ----- + .. versionadded:: 0.24 + + %(export_warning_note_evoked)s + """ + fname = _check_fname(fname, overwrite=overwrite) + supported_export_formats = { + 'mff': ('mff',), + } + fmt = _infer_check_export_fmt(fmt, fname, supported_export_formats) + + if not isinstance(evoked, list): + evoked = [evoked] + + logger.info(f'Exporting evoked dataset to {fname}...') + + if fmt == 'mff': + export_evokeds_mff(fname, evoked, overwrite=overwrite) + + +def _infer_check_export_fmt(fmt, fname, supported_formats): + """Infer export format from filename extension if auto. + + Raises error if fmt is auto and no file extension found, + then checks format against supported formats, raises error if format is not + supported. + + Parameters + ---------- + fmt : str + Format of the export, will only infer the format from filename if fmt + is auto. + fname : str + Name of the target export file, only used when fmt is auto. + supported_formats : dict of str : tuple/list + Dictionary containing supported formats (as keys) and each format's + corresponding file extensions in a tuple (e.g., {'eeglab': ('set',)}) + """ + _validate_type(fmt, str, 'fmt') + fmt = fmt.lower() + if fmt == "auto": + fmt = op.splitext(fname)[1] + if fmt: + fmt = fmt[1:].lower() + # find fmt in supported formats dict's tuples + fmt = next((k for k, v in supported_formats.items() if fmt in v), + fmt) # default to original fmt for raising error later + else: + raise ValueError(f"Couldn't infer format from filename {fname}" + " (no extension found)") + + if fmt not in supported_formats: + supported = [] + for format, extensions in supported_formats.items(): + ext_str = ', '.join(f'*.{ext}' for ext in extensions) + supported.append(f'{format} ({ext_str})') + + supported_str = ', '.join(supported) + raise ValueError(f"Format '{fmt}' is not supported. " + f"Supported formats are {supported_str}.") + return fmt diff -Nru python-mne-0.23.4+dfsg/mne/export/__init__.py python-mne-1.1.0+dfsg/mne/export/__init__.py --- python-mne-0.23.4+dfsg/mne/export/__init__.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/export/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,2 @@ +from ._export import export_raw, export_epochs, export_evokeds +from ._egimff import export_evokeds_mff diff -Nru python-mne-0.23.4+dfsg/mne/export/tests/test_export.py python-mne-1.1.0+dfsg/mne/export/tests/test_export.py --- python-mne-0.23.4+dfsg/mne/export/tests/test_export.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/export/tests/test_export.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,475 @@ +# -*- coding: utf-8 -*- +"""Test exporting functions.""" +# Authors: MNE Developers +# +# License: BSD-3-Clause + +from datetime import datetime, timezone +from mne.io import RawArray +from mne.io.meas_info import create_info +from pathlib import Path +import os.path as op + +import pytest +import numpy as np +from numpy.testing import (assert_allclose, assert_array_almost_equal, + assert_array_equal) + +from mne import (read_epochs_eeglab, Epochs, read_evokeds, read_evokeds_mff, + Annotations) +from mne.datasets import testing, misc +from mne.export import export_evokeds, export_evokeds_mff +from mne.io import (read_raw_fif, read_raw_eeglab, read_raw_edf, + read_raw_brainvision) +from mne.utils import (_check_eeglabio_installed, requires_version, + object_diff, _check_edflib_installed, _resource_path, + _check_pybv_installed) +from mne.tests.test_epochs import _get_data + +fname_evoked = _resource_path('mne.io.tests.data', 'test-ave.fif') +fname_raw = _resource_path('mne.io.tests.data', 'test_raw.fif') + +data_path = testing.data_path(download=False) +egi_evoked_fname = op.join(data_path, 'EGI', 'test_egi_evoked.mff') +misc_path = misc.data_path(download=False) + + +@pytest.mark.skipif(not _check_pybv_installed(strict=False), + reason='pybv not installed') +@pytest.mark.parametrize( + ['meas_date', 'orig_time', 'ext'], [ + [None, None, '.vhdr'], + [datetime(2022, 12, 3, 19, 1, 10, 720100, tzinfo=timezone.utc), + None, + '.eeg'], + ]) +def test_export_raw_pybv(tmp_path, meas_date, orig_time, ext): + """Test saving a Raw instance to BrainVision format via pybv.""" + raw = read_raw_fif(fname_raw, preload=True) + raw.apply_proj() + + raw.set_meas_date(meas_date) + + # add some annotations + annots = Annotations( + onset=[3, 6, 9, 12, 14], # seconds + duration=[1, 1, 0.5, 0.25, 9], # seconds + description=[ + "Stimulus/S 1", + "Stimulus/S2.50", + "Response/R101", + "Look at this", + "Comment/And at this", + ], + ch_names=[(), (), (), ("EEG 001",), ("EEG 001", "EEG 002")], + orig_time=orig_time, + ) + raw.set_annotations(annots) + + temp_fname = tmp_path / ('test' + ext) + with pytest.warns(RuntimeWarning, match="'short' format. Converting"): + raw.export(temp_fname) + raw_read = read_raw_brainvision(str(temp_fname).replace('.eeg', '.vhdr')) + assert raw.ch_names == raw_read.ch_names + assert_allclose(raw.times, raw_read.times) + assert_allclose(raw.get_data(), raw_read.get_data()) + + +@requires_version('pymatreader') +@pytest.mark.skipif(not _check_eeglabio_installed(strict=False), + reason='eeglabio not installed') +def test_export_raw_eeglab(tmp_path): + """Test saving a Raw instance to EEGLAB's set format.""" + raw = read_raw_fif(fname_raw, preload=True) + raw.apply_proj() + temp_fname = op.join(str(tmp_path), 'test.set') + raw.export(temp_fname) + raw.drop_channels([ch for ch in ['epoc'] + if ch in raw.ch_names]) + raw_read = read_raw_eeglab(temp_fname, preload=True) + assert raw.ch_names == raw_read.ch_names + cart_coords = np.array([d['loc'][:3] for d in raw.info['chs']]) # just xyz + cart_coords_read = np.array([d['loc'][:3] for d in raw_read.info['chs']]) + assert_allclose(cart_coords, cart_coords_read) + assert_allclose(raw.times, raw_read.times) + assert_allclose(raw.get_data(), raw_read.get_data()) + + # test overwrite + with pytest.raises(FileExistsError, match='Destination file exists'): + raw.export(temp_fname, overwrite=False) + raw.export(temp_fname, overwrite=True) + + # test pathlib.Path files + raw.export(Path(temp_fname), overwrite=True) + + # test warning with unapplied projectors + raw = read_raw_fif(fname_raw, preload=True) + with pytest.warns(RuntimeWarning, + match='Raw instance has unapplied projectors.'): + raw.export(temp_fname, overwrite=True) + + +@pytest.mark.skipif(not _check_edflib_installed(strict=False), + reason='edflib-python not installed') +def test_double_export_edf(tmp_path): + """Test exporting an EDF file multiple times.""" + rng = np.random.RandomState(123456) + format = 'edf' + ch_types = ['eeg', 'eeg', 'stim', 'ecog', 'ecog', 'seeg', 'eog', 'ecg', + 'emg', 'dbs', 'bio'] + info = create_info(len(ch_types), sfreq=1000, ch_types=ch_types) + data = rng.random(size=(len(ch_types), 1000)) * 1e-5 + + # include subject info and measurement date + info['subject_info'] = dict(first_name='mne', last_name='python', + birthday=(1992, 1, 20), sex=1, hand=3) + raw = RawArray(data, info) + + # export once + temp_fname = tmp_path / f'test.{format}' + raw.export(temp_fname, add_ch_type=True) + raw_read = read_raw_edf(temp_fname, infer_types=True, preload=True) + + # export again + raw_read.load_data() + raw_read.export(temp_fname, add_ch_type=True, overwrite=True) + raw_read = read_raw_edf(temp_fname, infer_types=True, preload=True) + + # stim channel should be dropped + raw.drop_channels('2') + + assert raw.ch_names == raw_read.ch_names + # only compare the original length, since extra zeros are appended + orig_raw_len = len(raw) + assert_array_almost_equal( + raw.get_data(), raw_read.get_data()[:, :orig_raw_len], decimal=4) + assert_allclose( + raw.times, raw_read.times[:orig_raw_len], rtol=0, atol=1e-5) + + # check channel types except for 'bio', which loses its type + orig_ch_types = raw.get_channel_types() + read_ch_types = raw_read.get_channel_types() + assert_array_equal(orig_ch_types, read_ch_types) + + +@pytest.mark.skipif(not _check_edflib_installed(strict=False), + reason='edflib-python not installed') +def test_export_edf_annotations(tmp_path): + """Test that exporting EDF preserves annotations.""" + rng = np.random.RandomState(123456) + format = 'edf' + ch_types = ['eeg', 'eeg', 'stim', 'ecog', 'ecog', 'seeg', + 'eog', 'ecg', 'emg', 'dbs', 'bio'] + ch_names = np.arange(len(ch_types)).astype(str).tolist() + info = create_info(ch_names, sfreq=1000, + ch_types=ch_types) + data = rng.random(size=(len(ch_names), 2000)) * 1.e-5 + raw = RawArray(data, info) + + annotations = Annotations( + onset=[0.01, 0.05, 0.90, 1.05], duration=[0, 1, 0, 0], + description=['test1', 'test2', 'test3', 'test4']) + raw.set_annotations(annotations) + + # export + temp_fname = op.join(str(tmp_path), f'test.{format}') + raw.export(temp_fname) + + # read in the file + raw_read = read_raw_edf(temp_fname, preload=True) + assert_array_equal(raw.annotations.onset, raw_read.annotations.onset) + assert_array_equal(raw.annotations.duration, raw_read.annotations.duration) + assert_array_equal(raw.annotations.description, + raw_read.annotations.description) + + +@pytest.mark.skipif(not _check_edflib_installed(strict=False), + reason='edflib-python not installed') +def test_rawarray_edf(tmp_path): + """Test saving a Raw array with integer sfreq to EDF.""" + rng = np.random.RandomState(12345) + format = 'edf' + ch_types = ['eeg', 'eeg', 'stim', 'ecog', 'seeg', 'eog', 'ecg', 'emg', + 'dbs', 'bio'] + ch_names = np.arange(len(ch_types)).astype(str).tolist() + info = create_info(ch_names, sfreq=1000, + ch_types=ch_types) + data = rng.random(size=(len(ch_names), 1000)) * 1e-5 + + # include subject info and measurement date + subject_info = dict(first_name='mne', last_name='python', + birthday=(1992, 1, 20), sex=1, hand=3) + info['subject_info'] = subject_info + raw = RawArray(data, info) + time_now = datetime.now() + meas_date = datetime(year=time_now.year, month=time_now.month, + day=time_now.day, hour=time_now.hour, + minute=time_now.minute, second=time_now.second, + tzinfo=timezone.utc) + raw.set_meas_date(meas_date) + temp_fname = op.join(str(tmp_path), f'test.{format}') + + raw.export(temp_fname, add_ch_type=True) + raw_read = read_raw_edf(temp_fname, infer_types=True, preload=True) + + # stim channel should be dropped + raw.drop_channels('2') + + assert raw.ch_names == raw_read.ch_names + # only compare the original length, since extra zeros are appended + orig_raw_len = len(raw) + assert_array_almost_equal( + raw.get_data(), raw_read.get_data()[:, :orig_raw_len], decimal=4) + assert_allclose( + raw.times, raw_read.times[:orig_raw_len], rtol=0, atol=1e-5) + + # check channel types except for 'bio', which loses its type + orig_ch_types = raw.get_channel_types() + read_ch_types = raw_read.get_channel_types() + assert_array_equal(orig_ch_types, read_ch_types) + assert raw.info['meas_date'] == raw_read.info['meas_date'] + + # channel name can't be longer than 16 characters with the type added + raw_bad = raw.copy() + raw_bad.rename_channels({'1': 'abcdefghijklmnopqrstuvwxyz'}) + with pytest.raises(RuntimeError, match='Signal label'), \ + pytest.warns(RuntimeWarning, match='Data has a non-integer'): + raw_bad.export(temp_fname, overwrite=True) + + # include bad birthday that is non-EDF compliant + bad_info = info.copy() + bad_info['subject_info']['birthday'] = (1700, 1, 20) + raw = RawArray(data, bad_info) + with pytest.raises(RuntimeError, match='Setting patient birth date'): + raw.export(temp_fname, overwrite=True) + + # include bad measurement date that is non-EDF compliant + raw = RawArray(data, info) + meas_date = datetime(year=1984, month=1, day=1, tzinfo=timezone.utc) + raw.set_meas_date(meas_date) + with pytest.raises(RuntimeError, match='Setting start date time'): + raw.export(temp_fname, overwrite=True) + + # test that warning is raised if there are non-voltage based channels + raw = RawArray(data, info) + with pytest.warns(RuntimeWarning, match='The unit'): + raw.set_channel_types({'9': 'hbr'}) + with pytest.warns(RuntimeWarning, match='Non-voltage channels'): + raw.export(temp_fname, overwrite=True) + + # data should match up to the non-accepted channel + raw_read = read_raw_edf(temp_fname, preload=True) + orig_raw_len = len(raw) + assert_array_almost_equal( + raw.get_data()[:-1, :], raw_read.get_data()[:, :orig_raw_len], + decimal=4) + assert_allclose( + raw.times, raw_read.times[:orig_raw_len], rtol=0, atol=1e-5) + + # the data should still match though + raw_read = read_raw_edf(temp_fname, preload=True) + raw.drop_channels('2') + assert raw.ch_names == raw_read.ch_names + orig_raw_len = len(raw) + assert_array_almost_equal( + raw.get_data(), raw_read.get_data()[:, :orig_raw_len], decimal=4) + assert_allclose( + raw.times, raw_read.times[:orig_raw_len], rtol=0, atol=1e-5) + + +@pytest.mark.skipif(not _check_edflib_installed(strict=False), + reason='edflib-python not installed') +@pytest.mark.parametrize( + ['dataset', 'format'], [ + ['test', 'edf'], + pytest.param('misc', 'edf', marks=[pytest.mark.slowtest, + misc._pytest_mark()]), + ]) +def test_export_raw_edf(tmp_path, dataset, format): + """Test saving a Raw instance to EDF format.""" + if dataset == 'test': + raw = read_raw_fif(fname_raw) + elif dataset == 'misc': + fname = op.join(misc_path, 'ecog', 'sample_ecog_ieeg.fif') + raw = read_raw_fif(fname) + + # only test with EEG channels + raw.pick_types(eeg=True, ecog=True, seeg=True) + raw.load_data() + orig_ch_names = raw.ch_names + temp_fname = op.join(str(tmp_path), f'test.{format}') + + # test runtime errors + with pytest.raises(RuntimeError, match='The maximum'), \ + pytest.warns(RuntimeWarning, match='Data has a non-integer'): + raw.export(temp_fname, physical_range=(-1e6, 0)) + with pytest.raises(RuntimeError, match='The minimum'), \ + pytest.warns(RuntimeWarning, match='Data has a non-integer'): + raw.export(temp_fname, physical_range=(0, 1e6)) + + if dataset == 'test': + with pytest.warns(RuntimeWarning, match='Data has a non-integer'): + raw.export(temp_fname) + elif dataset == 'misc': + with pytest.warns(RuntimeWarning, match='EDF format requires'): + raw.export(temp_fname) + + if 'epoc' in raw.ch_names: + raw.drop_channels(['epoc']) + + raw_read = read_raw_edf(temp_fname, preload=True) + assert orig_ch_names == raw_read.ch_names + # only compare the original length, since extra zeros are appended + orig_raw_len = len(raw) + + # assert data and times are not different + # Due to the physical range of the data, reading and writing is + # not lossless. For example, a physical min/max of -/+ 3200 uV + # will result in a resolution of 0.09 uV. This resolution + # though is acceptable for most EEG manufacturers. + assert_array_almost_equal( + raw.get_data(), raw_read.get_data()[:, :orig_raw_len], decimal=4) + + # Due to the data record duration limitations of EDF files, one + # cannot store arbitrary float sampling rate exactly. Usually this + # results in two sampling rates that are off by very low number of + # decimal points. This for practical purposes does not matter + # but will result in an error when say the number of time points + # is very very large. + assert_allclose( + raw.times, raw_read.times[:orig_raw_len], rtol=0, atol=1e-5) + + +@requires_version('pymatreader') +@pytest.mark.skipif(not _check_eeglabio_installed(strict=False), + reason='eeglabio not installed') +@pytest.mark.parametrize('preload', (True, False)) +def test_export_epochs_eeglab(tmp_path, preload): + """Test saving an Epochs instance to EEGLAB's set format.""" + raw, events = _get_data()[:2] + raw.load_data() + epochs = Epochs(raw, events, preload=preload) + temp_fname = op.join(str(tmp_path), 'test.set') + epochs.export(temp_fname) + epochs.drop_channels([ch for ch in ['epoc', 'STI 014'] + if ch in epochs.ch_names]) + epochs_read = read_epochs_eeglab(temp_fname) + assert epochs.ch_names == epochs_read.ch_names + cart_coords = np.array([d['loc'][:3] + for d in epochs.info['chs']]) # just xyz + cart_coords_read = np.array([d['loc'][:3] + for d in epochs_read.info['chs']]) + assert_allclose(cart_coords, cart_coords_read) + assert_array_equal(epochs.events[:, 0], + epochs_read.events[:, 0]) # latency + assert epochs.event_id.keys() == epochs_read.event_id.keys() # just keys + assert_allclose(epochs.times, epochs_read.times) + assert_allclose(epochs.get_data(), epochs_read.get_data()) + + # test overwrite + with pytest.raises(FileExistsError, match='Destination file exists'): + epochs.export(temp_fname, overwrite=False) + epochs.export(temp_fname, overwrite=True) + + # test pathlib.Path files + epochs.export(Path(temp_fname), overwrite=True) + + # test warning with unapplied projectors + epochs = Epochs(raw, events, preload=preload, proj=False) + with pytest.warns(RuntimeWarning, + match='Epochs instance has unapplied projectors.'): + epochs.export(Path(temp_fname), overwrite=True) + + +@pytest.mark.filterwarnings('ignore::FutureWarning') +@requires_version('mffpy', '0.5.7') +@testing.requires_testing_data +@pytest.mark.parametrize('fmt', ('auto', 'mff')) +@pytest.mark.parametrize('do_history', (True, False)) +def test_export_evokeds_to_mff(tmp_path, fmt, do_history): + """Test exporting evoked dataset to MFF.""" + evoked = read_evokeds_mff(egi_evoked_fname) + export_fname = op.join(str(tmp_path), 'evoked.mff') + history = [ + { + 'name': 'Test Segmentation', + 'method': 'Segmentation', + 'settings': ['Setting 1', 'Setting 2'], + 'results': ['Result 1', 'Result 2'] + }, + { + 'name': 'Test Averaging', + 'method': 'Averaging', + 'settings': ['Setting 1', 'Setting 2'], + 'results': ['Result 1', 'Result 2'] + } + ] + if do_history: + export_evokeds_mff(export_fname, evoked, history=history) + else: + export_evokeds(export_fname, evoked, fmt=fmt) + # Drop non-EEG channels + evoked = [ave.drop_channels(['ECG', 'EMG']) for ave in evoked] + evoked_exported = read_evokeds_mff(export_fname) + assert len(evoked) == len(evoked_exported) + for ave, ave_exported in zip(evoked, evoked_exported): + # Compare infos + assert object_diff(ave_exported.info, ave.info) == '' + # Compare data + assert_allclose(ave_exported.data, ave.data) + # Compare properties + assert ave_exported.nave == ave.nave + assert ave_exported.kind == ave.kind + assert ave_exported.comment == ave.comment + assert_allclose(ave_exported.times, ave.times) + + # test overwrite + with pytest.raises(FileExistsError, match='Destination file exists'): + if do_history: + export_evokeds_mff(export_fname, evoked, history=history, + overwrite=False) + else: + export_evokeds(export_fname, evoked, overwrite=False) + + if do_history: + export_evokeds_mff(export_fname, evoked, history=history, + overwrite=True) + else: + export_evokeds(export_fname, evoked, overwrite=True) + + # test export from evoked directly + evoked[0].export(export_fname, overwrite=True) + + +@pytest.mark.filterwarnings('ignore::FutureWarning') +@requires_version('mffpy', '0.5.7') +@testing.requires_testing_data +def test_export_to_mff_no_device(): + """Test no device type throws ValueError.""" + evoked = read_evokeds_mff(egi_evoked_fname, condition='Category 1') + evoked.info['device_info'] = None + with pytest.raises(ValueError, match='No device type.'): + export_evokeds('output.mff', evoked) + + +@pytest.mark.filterwarnings('ignore::FutureWarning') +@requires_version('mffpy', '0.5.7') +def test_export_to_mff_incompatible_sfreq(): + """Test non-whole number sampling frequency throws ValueError.""" + evoked = read_evokeds(fname_evoked) + with pytest.raises(ValueError, match=f'sfreq: {evoked[0].info["sfreq"]}'): + export_evokeds('output.mff', evoked) + + +@pytest.mark.parametrize('fmt,ext', [ + ('EEGLAB', 'set'), + ('EDF', 'edf'), + ('BrainVision', 'vhdr'), + ('auto', 'vhdr') +]) +def test_export_evokeds_unsupported_format(fmt, ext): + """Test exporting evoked dataset to non-supported formats.""" + evoked = read_evokeds(fname_evoked) + errstr = fmt.lower() if fmt != "auto" else "vhdr" + with pytest.raises(ValueError, match=f"Format '{errstr}' is not .*"): + export_evokeds(f'output.{ext}', evoked, fmt=fmt) diff -Nru python-mne-0.23.4+dfsg/mne/externals/decorator.py python-mne-1.1.0+dfsg/mne/externals/decorator.py --- python-mne-0.23.4+dfsg/mne/externals/decorator.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/decorator.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,454 +0,0 @@ -# ######################### LICENSE ############################ # - -# Copyright (c) 2005-2018, Michele Simionato -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are -# met: - -# Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# Redistributions in bytecode form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in -# the documentation and/or other materials provided with the -# distribution. - -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -# HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -""" -Decorator module, see http://pypi.python.org/pypi/decorator -for the documentation. -""" -from __future__ import print_function - -import re -import sys -import inspect -import operator -import itertools -import collections - -__version__ = '4.4.2' - -if sys.version_info >= (3,): - from inspect import getfullargspec - - def get_init(cls): - return cls.__init__ -else: - FullArgSpec = collections.namedtuple( - 'FullArgSpec', 'args varargs varkw defaults ' - 'kwonlyargs kwonlydefaults annotations') - - def getfullargspec(f): - "A quick and dirty replacement for getfullargspec for Python 2.X" - return FullArgSpec._make(inspect.getargspec(f) + ([], None, {})) - - def get_init(cls): - return cls.__init__.__func__ - -try: - iscoroutinefunction = inspect.iscoroutinefunction -except AttributeError: - # let's assume there are no coroutine functions in old Python - def iscoroutinefunction(f): - return False -try: - from inspect import isgeneratorfunction -except ImportError: - # assume no generator function in old Python versions - def isgeneratorfunction(caller): - return False - - -DEF = re.compile(r'\s*def\s*([_\w][_\w\d]*)\s*\(') - - -# basic functionality -class FunctionMaker(object): - """ - An object with the ability to create functions with a given signature. - It has attributes name, doc, module, signature, defaults, dict and - methods update and make. - """ - - # Atomic get-and-increment provided by the GIL - _compile_count = itertools.count() - - # make pylint happy - args = varargs = varkw = defaults = kwonlyargs = kwonlydefaults = () - - def __init__(self, func=None, name=None, signature=None, - defaults=None, doc=None, module=None, funcdict=None): - self.shortsignature = signature - if func: - # func can be a class or a callable, but not an instance method - self.name = func.__name__ - if self.name == '': # small hack for lambda functions - self.name = '_lambda_' - self.doc = func.__doc__ - self.module = func.__module__ - if inspect.isfunction(func): - argspec = getfullargspec(func) - self.annotations = getattr(func, '__annotations__', {}) - for a in ('args', 'varargs', 'varkw', 'defaults', 'kwonlyargs', - 'kwonlydefaults'): - setattr(self, a, getattr(argspec, a)) - for i, arg in enumerate(self.args): - setattr(self, 'arg%d' % i, arg) - allargs = list(self.args) - allshortargs = list(self.args) - if self.varargs: - allargs.append('*' + self.varargs) - allshortargs.append('*' + self.varargs) - elif self.kwonlyargs: - allargs.append('*') # single star syntax - for a in self.kwonlyargs: - allargs.append('%s=None' % a) - allshortargs.append('%s=%s' % (a, a)) - if self.varkw: - allargs.append('**' + self.varkw) - allshortargs.append('**' + self.varkw) - self.signature = ', '.join(allargs) - self.shortsignature = ', '.join(allshortargs) - self.dict = func.__dict__.copy() - # func=None happens when decorating a caller - if name: - self.name = name - if signature is not None: - self.signature = signature - if defaults: - self.defaults = defaults - if doc: - self.doc = doc - if module: - self.module = module - if funcdict: - self.dict = funcdict - # check existence required attributes - assert hasattr(self, 'name') - if not hasattr(self, 'signature'): - raise TypeError('You are decorating a non function: %s' % func) - - def update(self, func, **kw): - "Update the signature of func with the data in self" - func.__name__ = self.name - func.__doc__ = getattr(self, 'doc', None) - func.__dict__ = getattr(self, 'dict', {}) - func.__defaults__ = self.defaults - func.__kwdefaults__ = self.kwonlydefaults or None - func.__annotations__ = getattr(self, 'annotations', None) - try: - frame = sys._getframe(3) - except AttributeError: # for IronPython and similar implementations - callermodule = '?' - else: - callermodule = frame.f_globals.get('__name__', '?') - func.__module__ = getattr(self, 'module', callermodule) - func.__dict__.update(kw) - - def make(self, src_templ, evaldict=None, addsource=False, **attrs): - "Make a new function from a given template and update the signature" - src = src_templ % vars(self) # expand name and signature - evaldict = evaldict or {} - mo = DEF.search(src) - if mo is None: - raise SyntaxError('not a valid function template\n%s' % src) - name = mo.group(1) # extract the function name - names = set([name] + [arg.strip(' *') for arg in - self.shortsignature.split(',')]) - for n in names: - if n in ('_func_', '_call_'): - raise NameError('%s is overridden in\n%s' % (n, src)) - - if not src.endswith('\n'): # add a newline for old Pythons - src += '\n' - - # Ensure each generated function has a unique filename for profilers - # (such as cProfile) that depend on the tuple of (, - # , ) being unique. - filename = '' % next(self._compile_count) - try: - code = compile(src, filename, 'single') - exec(code, evaldict) - except Exception: - print('Error in generated code:', file=sys.stderr) - print(src, file=sys.stderr) - raise - func = evaldict[name] - if addsource: - attrs['__source__'] = src - self.update(func, **attrs) - return func - - @classmethod - def create(cls, obj, body, evaldict, defaults=None, - doc=None, module=None, addsource=True, **attrs): - """ - Create a function from the strings name, signature and body. - evaldict is the evaluation dictionary. If addsource is true an - attribute __source__ is added to the result. The attributes attrs - are added, if any. - """ - if isinstance(obj, str): # "name(signature)" - name, rest = obj.strip().split('(', 1) - signature = rest[:-1] # strip a right parens - func = None - else: # a function - name = None - signature = None - func = obj - self = cls(func, name, signature, defaults, doc, module) - ibody = '\n'.join(' ' + line for line in body.splitlines()) - caller = evaldict.get('_call_') # when called from `decorate` - if caller and iscoroutinefunction(caller): - body = ('async def %(name)s(%(signature)s):\n' + ibody).replace( - 'return', 'return await') - else: - body = 'def %(name)s(%(signature)s):\n' + ibody - return self.make(body, evaldict, addsource, **attrs) - - -def decorate(func, caller, extras=()): - """ - decorate(func, caller) decorates a function using a caller. - If the caller is a generator function, the resulting function - will be a generator function. - """ - evaldict = dict(_call_=caller, _func_=func) - es = '' - for i, extra in enumerate(extras): - ex = '_e%d_' % i - evaldict[ex] = extra - es += ex + ', ' - - if '3.5' <= sys.version < '3.6': - # with Python 3.5 isgeneratorfunction returns True for all coroutines - # however we know that it is NOT possible to have a generator - # coroutine in python 3.5: PEP525 was not there yet - generatorcaller = isgeneratorfunction( - caller) and not iscoroutinefunction(caller) - else: - generatorcaller = isgeneratorfunction(caller) - if generatorcaller: - fun = FunctionMaker.create( - func, "for res in _call_(_func_, %s%%(shortsignature)s):\n" - " yield res" % es, evaldict, __wrapped__=func) - else: - fun = FunctionMaker.create( - func, "return _call_(_func_, %s%%(shortsignature)s)" % es, - evaldict, __wrapped__=func) - if hasattr(func, '__qualname__'): - fun.__qualname__ = func.__qualname__ - return fun - - -def decorator(caller, _func=None): - """decorator(caller) converts a caller function into a decorator""" - if _func is not None: # return a decorated function - # this is obsolete behavior; you should use decorate instead - return decorate(_func, caller) - # else return a decorator function - defaultargs, defaults = '', () - if inspect.isclass(caller): - name = caller.__name__.lower() - doc = 'decorator(%s) converts functions/generators into ' \ - 'factories of %s objects' % (caller.__name__, caller.__name__) - elif inspect.isfunction(caller): - if caller.__name__ == '': - name = '_lambda_' - else: - name = caller.__name__ - doc = caller.__doc__ - nargs = caller.__code__.co_argcount - ndefs = len(caller.__defaults__ or ()) - defaultargs = ', '.join(caller.__code__.co_varnames[nargs-ndefs:nargs]) - if defaultargs: - defaultargs += ',' - defaults = caller.__defaults__ - else: # assume caller is an object with a __call__ method - name = caller.__class__.__name__.lower() - doc = caller.__call__.__doc__ - evaldict = dict(_call=caller, _decorate_=decorate) - dec = FunctionMaker.create( - '%s(func, %s)' % (name, defaultargs), - 'if func is None: return lambda func: _decorate_(func, _call, (%s))\n' - 'return _decorate_(func, _call, (%s))' % (defaultargs, defaultargs), - evaldict, doc=doc, module=caller.__module__, __wrapped__=caller) - if defaults: - dec.__defaults__ = (None,) + defaults - return dec - - -# ####################### contextmanager ####################### # - -try: # Python >= 3.2 - from contextlib import _GeneratorContextManager -except ImportError: # Python >= 2.5 - from contextlib import GeneratorContextManager as _GeneratorContextManager - - -class ContextManager(_GeneratorContextManager): - def __call__(self, func): - """Context manager decorator""" - return FunctionMaker.create( - func, "with _self_: return _func_(%(shortsignature)s)", - dict(_self_=self, _func_=func), __wrapped__=func) - - -init = getfullargspec(_GeneratorContextManager.__init__) -n_args = len(init.args) -if n_args == 2 and not init.varargs: # (self, genobj) Python 2.7 - def __init__(self, g, *a, **k): - return _GeneratorContextManager.__init__(self, g(*a, **k)) - ContextManager.__init__ = __init__ -elif n_args == 2 and init.varargs: # (self, gen, *a, **k) Python 3.4 - pass -elif n_args == 4: # (self, gen, args, kwds) Python 3.5 - def __init__(self, g, *a, **k): - return _GeneratorContextManager.__init__(self, g, a, k) - ContextManager.__init__ = __init__ - -_contextmanager = decorator(ContextManager) - - -def contextmanager(func): - # Enable Pylint config: contextmanager-decorators=decorator.contextmanager - return _contextmanager(func) - - -# ############################ dispatch_on ############################ # - -def append(a, vancestors): - """ - Append ``a`` to the list of the virtual ancestors, unless it is already - included. - """ - add = True - for j, va in enumerate(vancestors): - if issubclass(va, a): - add = False - break - if issubclass(a, va): - vancestors[j] = a - add = False - if add: - vancestors.append(a) - - -# inspired from simplegeneric by P.J. Eby and functools.singledispatch -def dispatch_on(*dispatch_args): - """ - Factory of decorators turning a function into a generic function - dispatching on the given arguments. - """ - assert dispatch_args, 'No dispatch args passed' - dispatch_str = '(%s,)' % ', '.join(dispatch_args) - - def check(arguments, wrong=operator.ne, msg=''): - """Make sure one passes the expected number of arguments""" - if wrong(len(arguments), len(dispatch_args)): - raise TypeError('Expected %d arguments, got %d%s' % - (len(dispatch_args), len(arguments), msg)) - - def gen_func_dec(func): - """Decorator turning a function into a generic function""" - - # first check the dispatch arguments - argset = set(getfullargspec(func).args) - if not set(dispatch_args) <= argset: - raise NameError('Unknown dispatch arguments %s' % dispatch_str) - - typemap = {} - - def vancestors(*types): - """ - Get a list of sets of virtual ancestors for the given types - """ - check(types) - ras = [[] for _ in range(len(dispatch_args))] - for types_ in typemap: - for t, type_, ra in zip(types, types_, ras): - if issubclass(t, type_) and type_ not in t.mro(): - append(type_, ra) - return [set(ra) for ra in ras] - - def ancestors(*types): - """ - Get a list of virtual MROs, one for each type - """ - check(types) - lists = [] - for t, vas in zip(types, vancestors(*types)): - n_vas = len(vas) - if n_vas > 1: - raise RuntimeError( - 'Ambiguous dispatch for %s: %s' % (t, vas)) - elif n_vas == 1: - va, = vas - mro = type('t', (t, va), {}).mro()[1:] - else: - mro = t.mro() - lists.append(mro[:-1]) # discard t and object - return lists - - def register(*types): - """ - Decorator to register an implementation for the given types - """ - check(types) - - def dec(f): - check(getfullargspec(f).args, operator.lt, ' in ' + f.__name__) - typemap[types] = f - return f - return dec - - def dispatch_info(*types): - """ - An utility to introspect the dispatch algorithm - """ - check(types) - lst = [] - for anc in itertools.product(*ancestors(*types)): - lst.append(tuple(a.__name__ for a in anc)) - return lst - - def _dispatch(dispatch_args, *args, **kw): - types = tuple(type(arg) for arg in dispatch_args) - try: # fast path - f = typemap[types] - except KeyError: - pass - else: - return f(*args, **kw) - combinations = itertools.product(*ancestors(*types)) - next(combinations) # the first one has been already tried - for types_ in combinations: - f = typemap.get(types_) - if f is not None: - return f(*args, **kw) - - # else call the default implementation - return func(*args, **kw) - - return FunctionMaker.create( - func, 'return _f_(%s, %%(shortsignature)s)' % dispatch_str, - dict(_f_=_dispatch), register=register, default=func, - typemap=typemap, vancestors=vancestors, ancestors=ancestors, - dispatch_info=dispatch_info, __wrapped__=func) - - gen_func_dec.__name__ = 'dispatch_on' + dispatch_str - return gen_func_dec diff -Nru python-mne-0.23.4+dfsg/mne/externals/doccer.py python-mne-1.1.0+dfsg/mne/externals/doccer.py --- python-mne-0.23.4+dfsg/mne/externals/doccer.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/doccer.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,149 +0,0 @@ -# Trimmed from SciPy's version - -''' Utilities to allow inserting docstring fragments for common -parameters into function and method docstrings''' - -from __future__ import division, print_function, absolute_import - -import sys - -__all__ = ['docformat', 'indentcount_lines', - 'filldoc', 'unindent_dict', 'unindent_string'] - - -def docformat(docstring, docdict=None, funcname=None): - ''' Fill a function docstring from variables in dictionary - - Adapt the indent of the inserted docs - - Parameters - ---------- - docstring : string - docstring from function, possibly with dict formatting strings - docdict : dict, optional - dictionary with keys that match the dict formatting strings - and values that are docstring fragments to be inserted. The - indentation of the inserted docstrings is set to match the - minimum indentation of the ``docstring`` by adding this - indentation to all lines of the inserted string, except the - first - - Returns - ------- - outstring : string - string with requested ``docdict`` strings inserted - - Examples - -------- - >>> docformat(' Test string with %(value)s', {'value':'inserted value'}) - ' Test string with inserted value' - >>> docstring = 'First line\\n Second line\\n %(value)s' - >>> inserted_string = "indented\\nstring" - >>> docdict = {'value': inserted_string} - >>> docformat(docstring, docdict) - 'First line\\n Second line\\n indented\\n string' - ''' - if not docstring: - return docstring - if docdict is None: - docdict = {} - if not docdict: - return docstring - lines = docstring.expandtabs().splitlines() - # Find the minimum indent of the main docstring, after first line - if len(lines) < 2: - icount = 0 - else: - icount = indentcount_lines(lines[1:]) - indent = ' ' * icount - # Insert this indent to dictionary docstrings - indented = {} - for name, dstr in docdict.items(): - lines = dstr.expandtabs().splitlines() - try: - newlines = [lines[0]] - for line in lines[1:]: - newlines.append(indent+line) - indented[name] = '\n'.join(newlines) - except IndexError: - indented[name] = dstr - funcname = docstring.split('\n')[0] if funcname is None else funcname - try: - return docstring % indented - except (TypeError, ValueError, KeyError) as exp: - raise RuntimeError('Error documenting %s:\n%s' - % (funcname, str(exp))) - - -def indentcount_lines(lines): - ''' Minimum indent for all lines in line list - - >>> lines = [' one', ' two', ' three'] - >>> indentcount_lines(lines) - 1 - >>> lines = [] - >>> indentcount_lines(lines) - 0 - >>> lines = [' one'] - >>> indentcount_lines(lines) - 1 - >>> indentcount_lines([' ']) - 0 - ''' - indentno = sys.maxsize - for line in lines: - stripped = line.lstrip() - if stripped: - indentno = min(indentno, len(line) - len(stripped)) - if indentno == sys.maxsize: - return 0 - return indentno - - -def filldoc(docdict, unindent_params=True): - ''' Return docstring decorator using docdict variable dictionary - - Parameters - ---------- - docdict : dictionary - dictionary containing name, docstring fragment pairs - unindent_params : {False, True}, boolean, optional - If True, strip common indentation from all parameters in - docdict - - Returns - ------- - decfunc : function - decorator that applies dictionary to input function docstring - - ''' - if unindent_params: - docdict = unindent_dict(docdict) - - def decorate(f): - f.__doc__ = docformat(f.__doc__, docdict, f.__name__) - return f - return decorate - - -def unindent_dict(docdict): - ''' Unindent all strings in a docdict ''' - can_dict = {} - for name, dstr in docdict.items(): - can_dict[name] = unindent_string(dstr) - return can_dict - - -def unindent_string(docstring): - ''' Set docstring to minimum indent for all lines, including first - - >>> unindent_string(' two') - 'two' - >>> unindent_string(' two\\n three') - 'two\\n three' - ''' - lines = docstring.expandtabs().splitlines() - icount = indentcount_lines(lines) - if icount == 0: - return docstring - return '\n'.join([line[icount:] for line in lines]) diff -Nru python-mne-0.23.4+dfsg/mne/externals/FieldTrip.py python-mne-1.1.0+dfsg/mne/externals/FieldTrip.py --- python-mne-0.23.4+dfsg/mne/externals/FieldTrip.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/FieldTrip.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,508 +0,0 @@ -""" -FieldTrip buffer (V1) client in pure Python - -(C) 2010 S. Klanke -""" - -# We need socket, struct, and numpy -import socket -import struct -import numpy - -VERSION = 1 -PUT_HDR = 0x101 -PUT_DAT = 0x102 -PUT_EVT = 0x103 -PUT_OK = 0x104 -PUT_ERR = 0x105 -GET_HDR = 0x201 -GET_DAT = 0x202 -GET_EVT = 0x203 -GET_OK = 0x204 -GET_ERR = 0x205 -FLUSH_HDR = 0x301 -FLUSH_DAT = 0x302 -FLUSH_EVT = 0x303 -FLUSH_OK = 0x304 -FLUSH_ERR = 0x305 -WAIT_DAT = 0x402 -WAIT_OK = 0x404 -WAIT_ERR = 0x405 - -DATATYPE_CHAR = 0 -DATATYPE_UINT8 = 1 -DATATYPE_UINT16 = 2 -DATATYPE_UINT32 = 3 -DATATYPE_UINT64 = 4 -DATATYPE_INT8 = 5 -DATATYPE_INT16 = 6 -DATATYPE_INT32 = 7 -DATATYPE_INT64 = 8 -DATATYPE_FLOAT32 = 9 -DATATYPE_FLOAT64 = 10 -DATATYPE_UNKNOWN = 0xFFFFFFFF - -CHUNK_UNSPECIFIED = 0 -CHUNK_CHANNEL_NAMES = 1 -CHUNK_CHANNEL_FLAGS = 2 -CHUNK_RESOLUTIONS = 3 -CHUNK_ASCII_KEYVAL = 4 -CHUNK_NIFTI1 = 5 -CHUNK_SIEMENS_AP = 6 -CHUNK_CTF_RES4 = 7 -CHUNK_NEUROMAG_FIF = 8 - -# List for converting FieldTrip datatypes to Numpy datatypes -numpyType = ['int8', 'uint8', 'uint16', 'uint32', 'uint64', - 'int8', 'int16', 'int32', 'int64', 'float32', 'float64'] -# Corresponding word sizes -wordSize = [1, 1, 2, 4, 8, 1, 2, 4, 8, 4, 8] -# FieldTrip data type as indexed by numpy dtype.num -# this goes 0 => nothing, 1..4 => int8, uint8, int16, uint16, 7..10 => -# int32, uint32, int64, uint64 11..12 => float32, float64 -dataType = [-1, 5, 1, 6, 2, -1, -1, 7, 3, 8, 4, 9, 10] - - -def serialize(A): - """ - Returns Fieldtrip data type and string representation of the given - object, if possible. - """ - if isinstance(A, str): - return (0, A) - - if isinstance(A, numpy.ndarray): - dt = A.dtype - if not(dt.isnative) or dt.num < 1 or dt.num >= len(dataType): - return (DATATYPE_UNKNOWN, None) - - ft = dataType[dt.num] - if ft == -1: - return (DATATYPE_UNKNOWN, None) - - if A.flags['C_CONTIGUOUS']: - # great, just use the array's buffer interface - return (ft, str(A.data)) - - # otherwise, we need a copy to C order - AC = A.copy('C') - return (ft, str(AC.data)) - - if isinstance(A, int): - return (DATATYPE_INT32, struct.pack('i', A)) - - if isinstance(A, float): - return (DATATYPE_FLOAT64, struct.pack('d', A)) - - return (DATATYPE_UNKNOWN, None) - - -class Chunk: - - def __init__(self): - self.type = 0 - self.size = 0 - self.buf = '' - - -class Header: - - """Class for storing header information in the FieldTrip buffer format""" - - def __init__(self): - self.nChannels = 0 - self.nSamples = 0 - self.nEvents = 0 - self.fSample = 0.0 - self.dataType = 0 - self.chunks = {} - self.labels = [] - - def __str__(self): - return ('Channels.: %i\nSamples..: %i\nEvents...: %i\nSampFreq.: ' - '%f\nDataType.: %s\n' - % (self.nChannels, self.nSamples, self.nEvents, - self.fSample, numpyType[self.dataType])) - - -class Event: - """Class for storing events in the FieldTrip buffer format""" - - def __init__(self, S=None): - if S is None: - self.type = '' - self.value = '' - self.sample = 0 - self.offset = 0 - self.duration = 0 - else: - self.deserialize(S) - - def __str__(self): - return ('Type.....: %s\nValue....: %s\nSample...: %i\nOffset...: ' - '%i\nDuration.: %i\n' % (str(self.type), str(self.value), - self.sample, self.offset, - self.duration)) - - def deserialize(self, buf): - bufsize = len(buf) - if bufsize < 32: - return 0 - - (type_type, type_numel, value_type, value_numel, sample, - offset, duration, bsiz) = struct.unpack('IIIIIiiI', buf[0:32]) - - self.sample = sample - self.offset = offset - self.duration = duration - - st = type_numel * wordSize[type_type] - sv = value_numel * wordSize[value_type] - - if bsiz + 32 > bufsize or st + sv > bsiz: - raise IOError( - 'Invalid event definition -- does not fit in given buffer') - - raw_type = buf[32:32 + st] - raw_value = buf[32 + st:32 + st + sv] - - if type_type == 0: - self.type = raw_type - else: - self.type = numpy.ndarray( - (type_numel), dtype=numpyType[type_type], buffer=raw_type) - - if value_type == 0: - self.value = raw_value - else: - self.value = numpy.ndarray( - (value_numel), dtype=numpyType[value_type], buffer=raw_value) - - return bsiz + 32 - - def serialize(self): - """ - Returns the contents of this event as a string, ready to - send over the network, or None in case of conversion problems. - """ - type_type, type_buf = serialize(self.type) - if type_type == DATATYPE_UNKNOWN: - return None - type_size = len(type_buf) - type_numel = type_size / wordSize[type_type] - - value_type, value_buf = serialize(self.value) - if value_type == DATATYPE_UNKNOWN: - return None - value_size = len(value_buf) - value_numel = value_size / wordSize[value_type] - - bufsize = type_size + value_size - - S = struct.pack('IIIIIiiI', type_type, type_numel, value_type, - value_numel, int(self.sample), int(self.offset), - int(self.duration), bufsize) - return S + type_buf + value_buf - - -class Client: - - """Class for managing a client connection to a FieldTrip buffer.""" - - def __init__(self): - self.isConnected = False - self.sock = [] - - def connect(self, hostname, port=1972): - """ - connect(hostname [, port]) -- make a connection, default port is - 1972. - """ - self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - self.sock.connect((hostname, port)) - self.sock.setblocking(True) - self.isConnected = True - - def disconnect(self): - """disconnect() -- close a connection.""" - if self.isConnected: - self.sock.close() - self.sock = [] - self.isConnected = False - - def sendRaw(self, request): - """Send all bytes of the string 'request' out to socket.""" - if not(self.isConnected): - raise IOError('Not connected to FieldTrip buffer') - - N = len(request) - nw = self.sock.send(request) - while nw < N: - nw += self.sock.send(request[nw:]) - - def sendRequest(self, command, payload=None): - if payload is None: - request = struct.pack('HHI', VERSION, command, 0) - else: - request = struct.pack( - 'HHI', VERSION, command, len(payload)) + payload - self.sendRaw(request) - - def receiveResponse(self, minBytes=0): - """ - Receive response from server on socket 's' and return it as - (status,bufsize,payload). - """ - - resp_hdr = self.sock.recv(8) - while len(resp_hdr) < 8: - resp_hdr += self.sock.recv(8 - len(resp_hdr)) - - (version, command, bufsize) = struct.unpack('HHI', resp_hdr) - - if version != VERSION: - self.disconnect() - raise IOError('Bad response from buffer server - disconnecting') - - if bufsize > 0: - payload = self.sock.recv(bufsize) - while len(payload) < bufsize: - payload += self.sock.recv(bufsize - len(payload)) - else: - payload = None - return (command, bufsize, payload) - - def getHeader(self): - """ - getHeader() -- grabs header information from the buffer an returns - it as a Header object. - """ - - self.sendRequest(GET_HDR) - (status, bufsize, payload) = self.receiveResponse() - - if status == GET_ERR: - return None - - if status != GET_OK: - self.disconnect() - raise IOError('Bad response from buffer server - disconnecting') - - if bufsize < 24: - self.disconnect() - raise IOError('Invalid HEADER packet received (too few bytes) - ' - 'disconnecting') - - (nchans, nsamp, nevt, fsamp, dtype, - bfsiz) = struct.unpack('IIIfII', payload[0:24]) - - H = Header() - H.nChannels = nchans - H.nSamples = nsamp - H.nEvents = nevt - H.fSample = fsamp - H.dataType = dtype - - if bfsiz > 0: - offset = 24 - while offset + 8 < bufsize: - (chunk_type, chunk_len) = struct.unpack( - 'II', payload[offset:offset + 8]) - offset += 8 - if offset + chunk_len > bufsize: - break - H.chunks[chunk_type] = payload[offset:offset + chunk_len] - offset += chunk_len - - if CHUNK_CHANNEL_NAMES in H.chunks: - L = H.chunks[CHUNK_CHANNEL_NAMES].split(b'\0') - numLab = len(L) - if numLab >= H.nChannels: - H.labels = [x.decode('utf-8') for x in L[0:H.nChannels]] - - return H - - def putHeader(self, nChannels, fSample, dataType, labels=None, - chunks=None): - haveLabels = False - extras = '' - if not(labels is None): - serLabels = '' - try: - for n in range(0, nChannels): - serLabels += labels[n] + '\0' - except: - raise ValueError('Channels names (labels), if given,' - ' must be a list of N=numChannels strings') - - extras = struct.pack('II', CHUNK_CHANNEL_NAMES, - len(serLabels)) + serLabels - haveLabels = True - - if not(chunks is None): - for chunk_type, chunk_data in chunks: - if haveLabels and chunk_type == CHUNK_CHANNEL_NAMES: - # ignore channel names chunk in case we got labels - continue - extras += struct.pack('II', chunk_type, - len(chunk_data)) + chunk_data - - sizeChunks = len(extras) - - hdef = struct.pack('IIIfII', nChannels, 0, 0, - fSample, dataType, sizeChunks) - request = struct.pack('HHI', VERSION, PUT_HDR, - sizeChunks + len(hdef)) + hdef + extras - self.sendRaw(request) - (status, bufsize, resp_buf) = self.receiveResponse() - if status != PUT_OK: - raise IOError('Header could not be written') - - def getData(self, index=None): - """ - getData([indices]) -- retrieve data samples and return them as a - Numpy array, samples in rows(!). The 'indices' argument is optional, - and if given, must be a tuple or list with inclusive, zero-based - start/end indices. - """ - - if index is None: - request = struct.pack('HHI', VERSION, GET_DAT, 0) - else: - indS = int(index[0]) - indE = int(index[1]) - request = struct.pack('HHIII', VERSION, GET_DAT, 8, indS, indE) - self.sendRaw(request) - - (status, bufsize, payload) = self.receiveResponse() - if status == GET_ERR: - return None - - if status != GET_OK: - self.disconnect() - raise IOError('Bad response from buffer server - disconnecting') - - if bufsize < 16: - self.disconnect() - raise IOError('Invalid DATA packet received (too few bytes)') - - (nchans, nsamp, datype, bfsiz) = struct.unpack('IIII', payload[0:16]) - - if bfsiz < bufsize - 16 or datype >= len(numpyType): - raise IOError('Invalid DATA packet received') - - raw = payload[16:bfsiz + 16] - D = numpy.ndarray((nsamp, nchans), dtype=numpyType[datype], buffer=raw) - - return D - - def getEvents(self, index=None): - """ - getEvents([indices]) -- retrieve events and return them as a list - of Event objects. The 'indices' argument is optional, and if given, - must be a tuple or list with inclusive, zero-based start/end indices. - The 'type' and 'value' fields of the event will be converted to strings - or Numpy arrays. - """ - - if index is None: - request = struct.pack('HHI', VERSION, GET_EVT, 0) - else: - indS = int(index[0]) - indE = int(index[1]) - request = struct.pack('HHIII', VERSION, GET_EVT, 8, indS, indE) - self.sendRaw(request) - - (status, bufsize, resp_buf) = self.receiveResponse() - if status == GET_ERR: - return [] - - if status != GET_OK: - self.disconnect() - raise IOError('Bad response from buffer server - disconnecting') - - offset = 0 - E = [] - while 1: - e = Event() - nextOffset = e.deserialize(resp_buf[offset:]) - if nextOffset == 0: - break - E.append(e) - offset = offset + nextOffset - - return E - - def putEvents(self, E): - """ - putEvents(E) -- writes a single or multiple events, depending on - whether an 'Event' object, or a list of 'Event' objects is - given as an argument. - """ - if isinstance(E, Event): - buf = E.serialize() - else: - buf = '' - num = 0 - for e in E: - if not(isinstance(e, Event)): - raise 'Element %i in given list is not an Event' % num - buf = buf + e.serialize() - num = num + 1 - - self.sendRequest(PUT_EVT, buf) - (status, bufsize, resp_buf) = self.receiveResponse() - - if status != PUT_OK: - raise IOError('Events could not be written.') - - def putData(self, D): - """ - putData(D) -- writes samples that must be given as a NUMPY array, - samples x channels. The type of the samples (D) and the number of - channels must match the corresponding quantities in the FieldTrip - buffer. - """ - - if not(isinstance(D, numpy.ndarray)) or len(D.shape) != 2: - raise ValueError( - 'Data must be given as a NUMPY array (samples x channels)') - - nSamp = D.shape[0] - nChan = D.shape[1] - - (dataType, dataBuf) = serialize(D) - - dataBufSize = len(dataBuf) - - request = struct.pack('HHI', VERSION, PUT_DAT, 16 + dataBufSize) - dataDef = struct.pack('IIII', nChan, nSamp, dataType, dataBufSize) - self.sendRaw(request + dataDef + dataBuf) - - (status, bufsize, resp_buf) = self.receiveResponse() - if status != PUT_OK: - raise IOError('Samples could not be written.') - - def poll(self): - - request = struct.pack('HHIIII', VERSION, WAIT_DAT, 12, 0, 0, 0) - self.sendRaw(request) - - (status, bufsize, resp_buf) = self.receiveResponse() - - if status != WAIT_OK or bufsize < 8: - raise IOError('Polling failed.') - - return struct.unpack('II', resp_buf[0:8]) - - def wait(self, nsamples, nevents, timeout): - request = struct.pack('HHIIII', VERSION, WAIT_DAT, - 12, int(nsamples), int(nevents), int(timeout)) - self.sendRaw(request) - - (status, bufsize, resp_buf) = self.receiveResponse() - - if status != WAIT_OK or bufsize < 8: - raise IOError('Wait request failed.') - - return struct.unpack('II', resp_buf[0:8]) diff -Nru python-mne-0.23.4+dfsg/mne/externals/h5io/_h5io.py python-mne-1.1.0+dfsg/mne/externals/h5io/_h5io.py --- python-mne-0.23.4+dfsg/mne/externals/h5io/_h5io.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/h5io/_h5io.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,746 +0,0 @@ -# -*- coding: utf-8 -*- -# Authors: Eric Larson -# -# License: BSD (3-clause) - -from datetime import datetime, timezone, timedelta -import json -import sys -import tempfile -from shutil import rmtree -from os import path as op - -import numpy as np - -# Adapted from six -PY3 = sys.version_info[0] == 3 -text_type = str if PY3 else unicode # noqa -string_types = str if PY3 else basestring # noqa - -special_chars = {'{FWDSLASH}': '/'} -tab_str = '----' - - -def _import_sparse(): - try: - from scipy import sparse - except ImportError: - sparse = None - return sparse - - -############################################################################## -# WRITING - -def _check_h5py(): - """Helper to check if h5py is installed""" - try: - import h5py - except ImportError: - raise ImportError('the h5py module is required to use HDF5 I/O') - return h5py - - -def _create_titled_group(root, key, title): - """Helper to create a titled group in h5py""" - out = root.create_group(key) - out.attrs['TITLE'] = title - return out - - -def _create_titled_dataset(root, key, title, data, comp_kw=None): - """Helper to create a titled dataset in h5py""" - comp_kw = {} if comp_kw is None else comp_kw - out = root.create_dataset(key, data=data, **comp_kw) - out.attrs['TITLE'] = title - return out - - -def _create_pandas_dataset(fname, root, key, title, data): - h5py = _check_h5py() - rootpath = '/'.join([root, key]) - data.to_hdf(fname, rootpath) - with h5py.File(fname, mode='a') as fid: - fid[rootpath].attrs['TITLE'] = 'pd_dataframe' - - -def write_hdf5(fname, data, overwrite=False, compression=4, - title='h5io', slash='error', use_json=False): - """Write python object to HDF5 format using h5py. - - Parameters - ---------- - fname : str - Filename to use. - data : object - Object to write. Can be of any of these types: - - {ndarray, dict, list, tuple, int, float, str, Datetime} - - Note that dict objects must only have ``str`` keys. It is recommended - to use ndarrays where possible, as it is handled most efficiently. - overwrite : True | False | 'update' - If True, overwrite file (if it exists). If 'update', appends the title - to the file (or replace value if title exists). - compression : int - Compression level to use (0-9) to compress data using gzip. - title : str - The top-level directory name to use. Typically it is useful to make - this your package name, e.g. ``'mnepython'``. - slash : 'error' | 'replace' - Whether to replace forward-slashes ('/') in any key found nested within - keys in data. This does not apply to the top level name (title). - If 'error', '/' is not allowed in any lower-level keys. - use_json : bool - To accelerate the read and write performance of small dictionaries and - lists they can be combined to JSON objects and stored as strings. - """ - h5py = _check_h5py() - mode = 'w' - if op.isfile(fname): - if isinstance(overwrite, string_types): - if overwrite != 'update': - raise ValueError('overwrite must be "update" or a bool') - mode = 'a' - elif not overwrite: - raise IOError('file "%s" exists, use overwrite=True to overwrite' - % fname) - if not isinstance(title, string_types): - raise ValueError('title must be a string') - comp_kw = dict() - if compression > 0: - comp_kw = dict(compression='gzip', compression_opts=compression) - with h5py.File(fname, mode=mode) as fid: - if title in fid: - del fid[title] - cleanup_data = [] - _triage_write(title, data, fid, comp_kw, str(type(data)), - cleanup_data, slash=slash, title=title, - use_json=use_json) - - # Will not be empty if any extra data to be written - for data in cleanup_data: - # In case different extra I/O needs different inputs - title = list(data.keys())[0] - if title in ['pd_dataframe', 'pd_series']: - rootname, key, value = data[title] - _create_pandas_dataset(fname, rootname, key, title, value) - - -def _triage_write(key, value, root, comp_kw, where, - cleanup_data, slash='error', title=None, - use_json=False): - sparse = _import_sparse() - if key != title and '/' in key: - if slash == 'error': - raise ValueError('Found a key with "/", ' - 'this is not allowed if slash == error') - elif slash == 'replace': - # Auto-replace keys with proper values - for key_spec, val_spec in special_chars.items(): - key = key.replace(val_spec, key_spec) - else: - raise ValueError("slash must be one of ['error', 'replace'") - - if use_json and isinstance(value, (list, dict)) and \ - _json_compatible(value, slash=slash): - value = np.frombuffer(json.dumps(value).encode('utf-8'), np.uint8) - _create_titled_dataset(root, key, 'json', value, comp_kw) - elif isinstance(value, dict): - sub_root = _create_titled_group(root, key, 'dict') - for key, sub_value in value.items(): - if not isinstance(key, string_types): - raise TypeError('All dict keys must be strings') - _triage_write( - 'key_{0}'.format(key), sub_value, sub_root, comp_kw, - where + '["%s"]' % key, cleanup_data=cleanup_data, slash=slash) - elif isinstance(value, (list, tuple)): - title = 'list' if isinstance(value, list) else 'tuple' - sub_root = _create_titled_group(root, key, title) - for vi, sub_value in enumerate(value): - _triage_write( - 'idx_{0}'.format(vi), sub_value, sub_root, comp_kw, - where + '[%s]' % vi, cleanup_data=cleanup_data, slash=slash) - elif isinstance(value, type(None)): - _create_titled_dataset(root, key, 'None', [False]) - elif isinstance(value, (int, float)): - if isinstance(value, int): - title = 'int' - else: # isinstance(value, float): - title = 'float' - _create_titled_dataset(root, key, title, np.atleast_1d(value)) - elif isinstance(value, datetime): - title = 'datetime' - value = np.frombuffer(value.isoformat().encode('utf-8'), np.uint8) - _create_titled_dataset(root, key, title, value) - elif isinstance(value, (np.integer, np.floating, np.bool_)): - title = 'np_{0}'.format(value.__class__.__name__) - _create_titled_dataset(root, key, title, np.atleast_1d(value)) - elif isinstance(value, string_types): - if isinstance(value, text_type): # unicode - value = np.frombuffer(value.encode('utf-8'), np.uint8) - title = 'unicode' - else: - value = np.frombuffer(value.encode('ASCII'), np.uint8) - title = 'ascii' - _create_titled_dataset(root, key, title, value, comp_kw) - elif isinstance(value, np.ndarray): - if not (value.dtype == np.dtype('object') and - len(set([sub.dtype for sub in value])) == 1): - _create_titled_dataset(root, key, 'ndarray', value) - else: - ma_index, ma_data = multiarray_dump(value) - sub_root = _create_titled_group(root, key, 'multiarray') - _create_titled_dataset(sub_root, 'index', 'ndarray', ma_index) - _create_titled_dataset(sub_root, 'data', 'ndarray', ma_data) - elif sparse is not None and isinstance(value, sparse.csc_matrix): - sub_root = _create_titled_group(root, key, 'csc_matrix') - _triage_write('data', value.data, sub_root, comp_kw, - where + '.csc_matrix_data', cleanup_data=cleanup_data, - slash=slash) - _triage_write('indices', value.indices, sub_root, comp_kw, - where + '.csc_matrix_indices', cleanup_data=cleanup_data, - slash=slash) - _triage_write('indptr', value.indptr, sub_root, comp_kw, - where + '.csc_matrix_indptr', cleanup_data=cleanup_data, - slash=slash) - elif sparse is not None and isinstance(value, sparse.csr_matrix): - sub_root = _create_titled_group(root, key, 'csr_matrix') - _triage_write('data', value.data, sub_root, comp_kw, - where + '.csr_matrix_data', cleanup_data=cleanup_data, - slash=slash) - _triage_write('indices', value.indices, sub_root, comp_kw, - where + '.csr_matrix_indices', cleanup_data=cleanup_data, - slash=slash) - _triage_write('indptr', value.indptr, sub_root, comp_kw, - where + '.csr_matrix_indptr', cleanup_data=cleanup_data, - slash=slash) - _triage_write('shape', value.shape, sub_root, comp_kw, - where + '.csr_matrix_shape', cleanup_data=cleanup_data, - slash=slash) - else: - try: - from pandas import DataFrame, Series - except ImportError: - pass - else: - if isinstance(value, (DataFrame, Series)): - if isinstance(value, DataFrame): - title = 'pd_dataframe' - else: - title = 'pd_series' - rootname = root.name - cleanup_data.append({title: (rootname, key, value)}) - return - - err_str = 'unsupported type %s (in %s)' % (type(value), where) - raise TypeError(err_str) - -############################################################################## -# READING - - -def read_hdf5(fname, title='h5io', slash='ignore'): - """Read python object from HDF5 format using h5py - - Parameters - ---------- - fname : str - File to load. - title : str - The top-level directory name to use. Typically it is useful to make - this your package name, e.g. ``'mnepython'``. - slash : 'ignore' | 'replace' - Whether to replace the string {FWDSLASH} with the value /. This does - not apply to the top level name (title). If 'ignore', nothing will be - replaced. - - Returns - ------- - data : object - The loaded data. Can be of any type supported by ``write_hdf5``. - """ - h5py = _check_h5py() - if not op.isfile(fname): - raise IOError('file "%s" not found' % fname) - if not isinstance(title, string_types): - raise ValueError('title must be a string') - with h5py.File(fname, mode='r') as fid: - if title not in fid: - raise ValueError('no "%s" data found' % title) - if isinstance(fid[title], h5py.Group): - if 'TITLE' not in fid[title].attrs: - raise ValueError('no "%s" data found' % title) - data = _triage_read(fid[title], slash=slash) - return data - - -def _triage_read(node, slash='ignore'): - if slash not in ['ignore', 'replace']: - raise ValueError("slash must be one of 'replace', 'ignore'") - h5py = _check_h5py() - sparse = _import_sparse() - type_str = node.attrs['TITLE'] - if isinstance(type_str, bytes): - type_str = type_str.decode() - if isinstance(node, h5py.Group): - if type_str == 'dict': - data = dict() - for key, subnode in node.items(): - if slash == 'replace': - for key_spec, val_spec in special_chars.items(): - key = key.replace(key_spec, val_spec) - data[key[4:]] = _triage_read(subnode, slash=slash) - elif type_str in ['list', 'tuple']: - data = list() - ii = 0 - while True: - subnode = node.get('idx_{0}'.format(ii), None) - if subnode is None: - break - data.append(_triage_read(subnode, slash=slash)) - ii += 1 - assert len(data) == ii - data = tuple(data) if type_str == 'tuple' else data - return data - elif type_str == 'csc_matrix': - if sparse is None: - raise RuntimeError('scipy must be installed to read this data') - data = sparse.csc_matrix((_triage_read(node['data'], slash=slash), - _triage_read(node['indices'], - slash=slash), - _triage_read(node['indptr'], - slash=slash))) - elif type_str == 'csr_matrix': - if sparse is None: - raise RuntimeError('scipy must be installed to read this data') - data = sparse.csr_matrix((_triage_read(node['data'], slash=slash), - _triage_read(node['indices'], - slash=slash), - _triage_read(node['indptr'], - slash=slash)), - shape=_triage_read(node['shape'])) - elif type_str in ['pd_dataframe', 'pd_series']: - from pandas import read_hdf, HDFStore - rootname = node.name - filename = node.file.filename - with HDFStore(filename, 'r') as tmpf: - data = read_hdf(tmpf, rootname) - elif type_str == 'multiarray': - ma_index = _triage_read(node.get('index', None), slash=slash) - ma_data = _triage_read(node.get('data', None), slash=slash) - data = multiarray_load(ma_index, ma_data) - else: - raise NotImplementedError('Unknown group type: {0}' - ''.format(type_str)) - elif type_str == 'ndarray': - data = np.array(node) - elif type_str in ('int', 'float'): - cast = int if type_str == 'int' else float - data = cast(np.array(node)[0]) - elif type_str == 'datetime': - data = text_type(np.array(node).tobytes().decode('utf-8')) - data = fromisoformat(data) - elif type_str.startswith('np_'): - np_type = type_str.split('_')[1] - cast = getattr(np, np_type) - data = cast(np.array(node)[0]) - elif type_str in ('unicode', 'ascii', 'str'): # 'str' for backward compat - decoder = 'utf-8' if type_str == 'unicode' else 'ASCII' - cast = text_type if type_str == 'unicode' else str - data = cast(np.array(node).tobytes().decode(decoder)) - elif type_str == 'json': - node_unicode = str(np.array(node).tobytes().decode('utf-8')) - data = json.loads(node_unicode) - elif type_str == 'None': - data = None - else: - raise TypeError('Unknown node type: {0}'.format(type_str)) - return data - - -# ############################################################################ -# UTILITIES - -def _sort_keys(x): - """Sort and return keys of dict""" - keys = list(x.keys()) # note: not thread-safe - idx = np.argsort([str(k) for k in keys]) - keys = [keys[ii] for ii in idx] - return keys - - -def object_diff(a, b, pre=''): - """Compute all differences between two python variables - - Parameters - ---------- - a : object - Currently supported: dict, list, tuple, ndarray, int, str, bytes, - float. - b : object - Must be same type as x1. - pre : str - String to prepend to each line. - - Returns - ------- - diffs : str - A string representation of the differences. - """ - sparse = _import_sparse() - try: - from pandas import DataFrame, Series - except ImportError: - DataFrame = Series = type(None) - - out = '' - if type(a) != type(b): - out += pre + ' type mismatch (%s, %s)\n' % (type(a), type(b)) - elif isinstance(a, dict): - k1s = _sort_keys(a) - k2s = _sort_keys(b) - m1 = set(k2s) - set(k1s) - if len(m1): - out += pre + ' x1 missing keys %s\n' % (m1) - for key in k1s: - if key not in k2s: - out += pre + ' x2 missing key %s\n' % key - else: - out += object_diff(a[key], b[key], pre + 'd1[%s]' % repr(key)) - elif isinstance(a, (list, tuple)): - if len(a) != len(b): - out += pre + ' length mismatch (%s, %s)\n' % (len(a), len(b)) - else: - for xx1, xx2 in zip(a, b): - out += object_diff(xx1, xx2, pre='') - elif isinstance(a, (string_types, int, float, bytes)): - if a != b: - out += pre + ' value mismatch (%s, %s)\n' % (a, b) - elif a is None: - pass # b must be None due to our type checking - elif isinstance(a, np.ndarray): - if not np.array_equal(a, b): - out += pre + ' array mismatch\n' - elif sparse is not None and sparse.isspmatrix(a): - # sparsity and sparse type of b vs a already checked above by type() - if b.shape != a.shape: - out += pre + (' sparse matrix a and b shape mismatch' - '(%s vs %s)' % (a.shape, b.shape)) - else: - c = a - b - c.eliminate_zeros() - if c.nnz > 0: - out += pre + (' sparse matrix a and b differ on %s ' - 'elements' % c.nnz) - elif isinstance(a, (DataFrame, Series)): - if b.shape != a.shape: - out += pre + (' pandas values a and b shape mismatch' - '(%s vs %s)' % (a.shape, b.shape)) - else: - c = a.values - b.values - nzeros = np.sum(c != 0) - if nzeros > 0: - out += pre + (' pandas values a and b differ on %s ' - 'elements' % nzeros) - else: - raise RuntimeError(pre + ': unsupported type %s (%s)' % (type(a), a)) - return out - - -class _TempDir(str): - """Class for creating and auto-destroying temp dir - - This is designed to be used with testing modules. Instances should be - defined inside test functions. Instances defined at module level can not - guarantee proper destruction of the temporary directory. - - When used at module level, the current use of the __del__() method for - cleanup can fail because the rmtree function may be cleaned up before this - object (an alternative could be using the atexit module instead). - """ - def __new__(self): - new = str.__new__(self, tempfile.mkdtemp()) - return new - - def __init__(self): - self._path = self.__str__() - - def __del__(self): - rmtree(self._path, ignore_errors=True) - - -def _list_file_contents(h5file): - if 'h5io' not in h5file.keys(): - raise ValueError('h5file must contain h5io data') - - # Set up useful variables for later - h5file = h5file['h5io'] - root_title = h5file.attrs['TITLE'] - n_space = np.max([(len(key), len(val.attrs['TITLE'])) - for key, val in h5file.items()]) + 2 - - # Create print strings - strs = ['Root type: %s | Items: %s\n' % (root_title, len(h5file))] - for key, data in h5file.items(): - type_str = data.attrs['TITLE'] - str_format = '%%-%ss' % n_space - if type_str == 'ndarray': - desc = 'Shape: %s' - desc_val = data.shape - elif type_str in ['pd_dataframe', 'pd_series']: - desc = 'Shape: %s' - desc_val = data['values'].shape - elif type_str in ('unicode', 'ascii', 'str'): - desc = 'Text: %s' - decoder = 'utf-8' if type_str == 'unicode' else 'ASCII' - cast = text_type if type_str == 'unicode' else str - data = cast(np.array(data).tobytes().decode(decoder)) - desc_val = data[:10] + '...' if len(data) > 10 else data - else: - desc = 'Items: %s' - desc_val = len(data) - this_str = ('%%s Key: %s | Type: %s | ' + desc) % ( - str_format, str_format, str_format) - this_str = this_str % (tab_str, key, type_str, desc_val) - strs.append(this_str) - out_str = '\n'.join(strs) - print(out_str) - - -def list_file_contents(h5file): - """List the contents of an h5io file. - - This will list the root and one-level-deep contents of the file. - - Parameters - ---------- - h5file : str - The path to an h5io hdf5 file. - """ - h5py = _check_h5py() - err = 'h5file must be an h5py File object, not {0}' - if isinstance(h5file, str): - with h5py.File(h5file, 'r') as f: - _list_file_contents(f) - else: - if not isinstance(h5file, h5py.File): - raise TypeError(err.format(type(h5file))) - _list_file_contents(h5file) - - -def _json_compatible(obj, slash='error'): - if isinstance(obj, (string_types, int, float, bool, type(None))): - return True - elif isinstance(obj, list): - return all([_json_compatible(item) for item in obj]) - elif isinstance(obj, dict): - _check_keys_in_dict(obj, slash=slash) - return all([_json_compatible(item) for item in obj.values()]) - else: - return False - - -def _check_keys_in_dict(obj, slash='error'): - repl = list() - for key in obj.keys(): - if '/' in key: - key_prev = key - if slash == 'error': - raise ValueError('Found a key with "/", ' - 'this is not allowed if slash == error') - elif slash == 'replace': - # Auto-replace keys with proper values - for key_spec, val_spec in special_chars.items(): - key = key.replace(val_spec, key_spec) - repl.append((key, key_prev)) - else: - raise ValueError("slash must be one of ['error', 'replace'") - for key, key_prev in repl: - obj[key] = obj.pop(key_prev) - - -############################################################################## -# Arrays with mixed dimensions -def _validate_object_array(array): - if not (array.dtype == np.dtype('object') and - len(set([sub.dtype for sub in array])) == 1): - raise TypeError('unsupported array type') - - -def _shape_list(array): - return [np.shape(sub) for sub in array] - - -def _validate_sub_shapes(shape_lst): - if not all([shape_lst[0][1:] == t[1:] for t in shape_lst]): - raise ValueError('shape does not match!') - - -def _array_index(shape_lst): - return [t[0] for t in shape_lst] - - -def _index_sum(index_lst): - index_sum_lst = [] - for step in index_lst: - if index_sum_lst != []: - index_sum_lst.append(index_sum_lst[-1] + step) - else: - index_sum_lst.append(step) - return index_sum_lst - - -def _merge_array(array): - merged_lst = [] - for sub in array: - merged_lst += sub.tolist() - return np.array(merged_lst) - - -def multiarray_dump(array): - _validate_object_array(array) - shape_lst = _shape_list(array) - _validate_sub_shapes(shape_lst=shape_lst) - index_sum = _index_sum(index_lst=_array_index(shape_lst=shape_lst)) - return index_sum, _merge_array(array=array) - - -def multiarray_load(index, array_merged): - array_restore = [] - i_prev = 0 - for i in index[:-1]: - array_restore.append(array_merged[i_prev:i]) - i_prev = i - array_restore.append(array_merged[i_prev:]) - return np.array(array_restore) - - -############################################################################### -# BACKPORTS - -try: - fromisoformat = datetime.fromisoformat -except AttributeError: # Python < 3.7 - # Code adapted from CPython - # https://github.com/python/cpython/blob/master/Lib/datetime.py - - def _parse_hh_mm_ss_ff(tstr): - # Parses things of the form HH[:MM[:SS[.fff[fff]]]] - len_str = len(tstr) - - time_comps = [0, 0, 0, 0] - pos = 0 - for comp in range(0, 3): - if (len_str - pos) < 2: - raise ValueError('Incomplete time component') - - time_comps[comp] = int(tstr[pos:pos + 2]) - - pos += 2 - next_char = tstr[pos:pos + 1] - - if not next_char or comp >= 2: - break - - if next_char != ':': - raise ValueError('Invalid time separator: %c' % next_char) - - pos += 1 - - if pos < len_str: - if tstr[pos] != '.': - raise ValueError('Invalid microsecond component') - else: - pos += 1 - - len_remainder = len_str - pos - if len_remainder not in (3, 6): - raise ValueError('Invalid microsecond component') - - time_comps[3] = int(tstr[pos:]) - if len_remainder == 3: - time_comps[3] *= 1000 - - return time_comps - - def fromisoformat(date_string): - """Construct a datetime from the output of datetime.isoformat().""" - if not isinstance(date_string, str): - raise TypeError('fromisoformat: argument must be str') - - # Split this at the separator - dstr = date_string[0:10] - tstr = date_string[11:] - - try: - date_components = _parse_isoformat_date(dstr) - except ValueError: - raise ValueError( - 'Invalid isoformat string: {!r}'.format(date_string)) - - if tstr: - try: - time_components = _parse_isoformat_time(tstr) - except ValueError: - raise ValueError( - 'Invalid isoformat string: {!r}'.format(date_string)) - else: - time_components = [0, 0, 0, 0, None] - - return datetime(*(date_components + time_components)) - - def _parse_isoformat_date(dtstr): - # It is assumed that this function will only be called with a - # string of length exactly 10, and (though this is not used) ASCII-only - year = int(dtstr[0:4]) - if dtstr[4] != '-': - raise ValueError('Invalid date separator: %s' % dtstr[4]) - - month = int(dtstr[5:7]) - - if dtstr[7] != '-': - raise ValueError('Invalid date separator') - - day = int(dtstr[8:10]) - - return [year, month, day] - - def _parse_isoformat_time(tstr): - # Format supported is HH[:MM[:SS[.fff[fff]]]][+HH:MM[:SS[.ffffff]]] - len_str = len(tstr) - if len_str < 2: - raise ValueError('Isoformat time too short') - - # This is equivalent to re.search('[+-]', tstr), but faster - tz_pos = (tstr.find('-') + 1 or tstr.find('+') + 1) - timestr = tstr[:tz_pos - 1] if tz_pos > 0 else tstr - - time_comps = _parse_hh_mm_ss_ff(timestr) - - tzi = None - if tz_pos > 0: - tzstr = tstr[tz_pos:] - - # Valid time zone strings are: - # HH:MM len: 5 - # HH:MM:SS len: 8 - # HH:MM:SS.ffffff len: 15 - - if len(tzstr) not in (5, 8, 15): - raise ValueError('Malformed time zone string') - - tz_comps = _parse_hh_mm_ss_ff(tzstr) - if all(x == 0 for x in tz_comps): - tzi = timezone.utc - else: - tzsign = -1 if tstr[tz_pos - 1] == '-' else 1 - - td = timedelta(hours=tz_comps[0], minutes=tz_comps[1], - seconds=tz_comps[2], microseconds=tz_comps[3]) - - tzi = timezone(tzsign * td) - - time_comps.append(tzi) - - return time_comps diff -Nru python-mne-0.23.4+dfsg/mne/externals/h5io/__init__.py python-mne-1.1.0+dfsg/mne/externals/h5io/__init__.py --- python-mne-0.23.4+dfsg/mne/externals/h5io/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/h5io/__init__.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,6 +0,0 @@ -"""Python Objects Onto HDF5 -""" - -__version__ = '0.1.dev0' - -from ._h5io import read_hdf5, write_hdf5, _TempDir, object_diff # noqa, analysis:ignore diff -Nru python-mne-0.23.4+dfsg/mne/externals/pymatreader/__init__.py python-mne-1.1.0+dfsg/mne/externals/pymatreader/__init__.py --- python-mne-0.23.4+dfsg/mne/externals/pymatreader/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/pymatreader/__init__.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,32 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2018, Dirk Gütlin & Thomas Hartmann -# All rights reserved. -# -# This file is part of the pymatreader Project, see: -# https://gitlab.com/obob/pymatreader -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# * Redistributions of source code must retain the above copyright notice, this -# list of conditions and the following disclaimer. -# -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE -# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -# POSSIBILITY OF SUCH DAMAGE. - -from .pymatreader import read_mat - -__all__ = ['read_mat'] diff -Nru python-mne-0.23.4+dfsg/mne/externals/pymatreader/pymatreader.py python-mne-1.1.0+dfsg/mne/externals/pymatreader/pymatreader.py --- python-mne-0.23.4+dfsg/mne/externals/pymatreader/pymatreader.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/pymatreader/pymatreader.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,100 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2018, Dirk Gütlin & Thomas Hartmann -# All rights reserved. -# -# This file is part of the pymatreader Project, see: -# https://gitlab.com/obob/pymatreader -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# * Redistributions of source code must retain the above copyright notice, this -# list of conditions and the following disclaimer. -# -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE -# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -# POSSIBILITY OF SUCH DAMAGE. - - -import scipy.io -import os - -from scipy.io.matlab.miobase import get_matfile_version - -from .utils import _import_h5py, _hdf5todict, _check_for_scipy_mat_struct - -__all__ = 'read_mat' - -""" -This is a small module intended to facilitate reading .mat files containing -large data structures into python, disregarding of the underlying .mat -file version. -""" - - -def read_mat(filename, variable_names=None, ignore_fields=None, - uint16_codec=None): - """This function reads .mat files of version <7.3 or 7.3 and returns the - contained data structure as a dictionary of nested substructure similar - to scipy.io.loadmat style. - - Parameters - ---------- - filename: str - Path and filename of the .mat file containing the data. - variable_names: list of strings, optional - Reads only the data contained in the specified dict key or - variable name. Default is None. - ignore_fields: list of strings, optional - Ignores every dict key/variable name specified in the list within the - entire structure. Only works for .mat files v 7.3. Default is []. - uint16_codec : str | None - If your file contains non-ascii characters, sometimes reading - it may fail and give rise to error message stating that "buffer is - too small". ``uint16_codec`` allows to specify what codec (for example: - 'latin1' or 'utf-8') should be used when reading character arrays and - can therefore help you solve this problem. - - Returns - ------- - dict - A structure of nested dictionaries, with variable names as keys and - variable data as values. - """ - - if not os.path.exists(filename): - raise IOError('The file %s does not exist.' % (filename,)) - - if ignore_fields is None: - ignore_fields = [] - try: - with open(filename, 'rb') as fid: # avoid open file warnings on error - mjv, _ = get_matfile_version(fid) - extra_kwargs = {} - if mjv == 1: - extra_kwargs['uint16_codec'] = uint16_codec - - raw_data = scipy.io.loadmat(fid, struct_as_record=True, - squeeze_me=True, mat_dtype=False, - variable_names=variable_names, - **extra_kwargs) - data = _check_for_scipy_mat_struct(raw_data) - except NotImplementedError: - ignore_fields.append('#refs#') - h5py = _import_h5py() - with h5py.File(filename, 'r') as hdf5_file: - data = _hdf5todict(hdf5_file, variable_names=variable_names, - ignore_fields=ignore_fields) - return data diff -Nru python-mne-0.23.4+dfsg/mne/externals/pymatreader/utils.py python-mne-1.1.0+dfsg/mne/externals/pymatreader/utils.py --- python-mne-0.23.4+dfsg/mne/externals/pymatreader/utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/pymatreader/utils.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,259 +0,0 @@ -# -*- coding: UTF-8 -*- -# Copyright (c) 2018, Dirk Gütlin & Thomas Hartmann -# All rights reserved. -# -# This file is part of the pymatreader Project, see: -# https://gitlab.com/obob/pymatreader -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# * Redistributions of source code must retain the above copyright notice, this -# list of conditions and the following disclaimer. -# -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE -# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -# POSSIBILITY OF SUCH DAMAGE. -import types -import sys -from warnings import warn - -import numpy -import scipy.io - -if sys.version_info <= (2, 7): - chr = unichr # noqa This is needed for python 2 and 3 compatibility - -standard_matlab_classes = ('char', 'cell', 'float', 'double', 'int', - 'int8', 'int16', 'int32', - 'int64', 'uint', 'uint8', 'uint16', 'logical', - 'uint32', 'uint64', 'struct', 'unknown') - - -def _import_h5py(): - try: - import h5py - except Exception as exc: - raise ImportError('h5py is required to read MATLAB files >= v7.3 ' - '(%s)' % (exc,)) - return h5py - - -def _hdf5todict(hdf5_object, variable_names=None, ignore_fields=None): - """ - Recursively converts a hdf5 object to a python dictionary, - converting all types as well. - - Parameters - ---------- - hdf5_object: Union[h5py.Group, h5py.Dataset] - Object to convert. Can be a h5py File, Group or Dataset - variable_names: iterable, optional - Tuple or list of variables to include. If set to none, all - variable are read. - ignore_fields: iterable, optional - Tuple or list of fields to ignore. If set to none, all fields will - be read. - - Returns - ------- - dict - Python dictionary - """ - - h5py = _import_h5py() - - if isinstance(hdf5_object, h5py.Group): - return _handle_hdf5_group(hdf5_object, variable_names=variable_names, - ignore_fields=ignore_fields) - - elif isinstance(hdf5_object, h5py.Dataset): - return _handle_hdf5_dataset(hdf5_object) - elif isinstance(hdf5_object, (list, types.GeneratorType)): - return [_hdf5todict(item) for item in hdf5_object] - - raise TypeError('Unknown type in hdf5 file') - - -def _handle_hdf5_group(hdf5_object, variable_names=None, ignore_fields=None): - all_keys = set(hdf5_object.keys()) - if ignore_fields: - all_keys = all_keys - set(ignore_fields) - - if variable_names: - all_keys = all_keys & set(variable_names) - - return_dict = dict() - - for key in all_keys: - return_dict[key] = _hdf5todict(hdf5_object[key], - variable_names=None, - ignore_fields=ignore_fields) - - return return_dict - - -def _handle_hdf5_dataset(hdf5_object): - if 'MATLAB_empty' in hdf5_object.attrs.keys(): - data = numpy.empty((0,)) - else: - # this used to be just hdf5_object.value, but this is deprecated - data = hdf5_object[()] - - matlab_class = hdf5_object.attrs.get('MATLAB_class', b'unknown').decode() - - if matlab_class not in standard_matlab_classes: - warn('Complex objects (like classes) are not supported. ' - 'They are imported on a best effort base ' - 'but your mileage will vary.') - - if isinstance(data, numpy.ndarray) and \ - data.dtype == numpy.dtype('object'): - - data = [hdf5_object.file[cur_data] for cur_data in data.flatten()] - if len(data) == 1 and matlab_class == 'cell': - data = data[0] - matlab_class = data.attrs.get('MATLAB_class', - matlab_class).decode() - data = data[()] - return _assign_types(data, matlab_class) - - data = _hdf5todict(data) - - return _assign_types(data, matlab_class) - - -def _convert_string_hdf5(values): - if values.size > 1: - assigned_values = u''.join(chr(c) for c in values.flatten()) - else: - try: - assigned_values = chr(values) - except TypeError: - assigned_values = numpy.array([]) - - return assigned_values - - -def _assign_types(values, matlab_class): - """private function, which assigns correct types to h5py extracted values - from _browse_dataset()""" - if matlab_class == 'char': - values = numpy.squeeze(values).T - assigned_values = _handle_hdf5_strings(values) - elif type(values) == numpy.ndarray: - assigned_values = _handle_ndarray(values) - elif type(values) == numpy.float64: - assigned_values = float(values) - else: - assigned_values = values - return assigned_values - - -def _handle_ndarray(values): - """Handle conversion of ndarrays.""" - values = numpy.squeeze(values).T - if (isinstance(values, numpy.ndarray) and - values.dtype.names == ('real', 'imag')): - values = numpy.array(values.view(numpy.complex)) - - if isinstance(values, numpy.ndarray) and \ - values.size == 1: - - values = values.item() - - return values - - -def _handle_hdf5_strings(values): - if values.ndim in (0, 1): - values = _convert_string_hdf5(values) - elif values.ndim == 2: - values = [_convert_string_hdf5(cur_val) - for cur_val in values] - else: - raise RuntimeError('String arrays with more than 2 dimensions' - 'are not supported at the moment.') - - return values - - -def _check_for_scipy_mat_struct(data): - """ - Private function to check all entries of data for occurrences of - scipy.io.matlab.mio5_params.mat_struct and convert them. - - Parameters - ========== - data: any - data to be checked - - Returns - ========= - object - checked and converted data - """ - if isinstance(data, dict): - for key in data: - data[key] = _check_for_scipy_mat_struct(data[key]) - - if isinstance(data, scipy.io.matlab.mio5_params.MatlabOpaque): - warn('Complex objects (like classes) are not supported. ' - 'They are imported on a best effort base ' - 'but your mileage will vary.') - - if isinstance(data, numpy.ndarray): - data = _handle_scipy_ndarray(data) - - return data - - -def _handle_scipy_ndarray(data): - if data.dtype == numpy.dtype('object') and not \ - isinstance(data, scipy.io.matlab.mio5.MatlabFunction): - as_list = [] - for element in data: - as_list.append(_check_for_scipy_mat_struct(element)) - data = as_list - elif isinstance(data.dtype.names, tuple): - data = _todict_from_np_struct(data) - data = _check_for_scipy_mat_struct(data) - - if isinstance(data, numpy.ndarray): - data = numpy.array(data) - - return data - - -def _todict_from_np_struct(data): - data_dict = dict() - - for cur_field_name in data.dtype.names: - try: - n_items = len(data[cur_field_name]) - cur_list = list() - - for idx in numpy.arange(n_items): - cur_value = data[cur_field_name].item(idx) - cur_value = _check_for_scipy_mat_struct(cur_value) - cur_list.append(cur_value) - - data_dict[cur_field_name] = cur_list - except TypeError: - cur_value = data[cur_field_name].item(0) - cur_value = _check_for_scipy_mat_struct(cur_value) - data_dict[cur_field_name] = cur_value - - return data_dict diff -Nru python-mne-0.23.4+dfsg/mne/externals/tempita/compat3.py python-mne-1.1.0+dfsg/mne/externals/tempita/compat3.py --- python-mne-0.23.4+dfsg/mne/externals/tempita/compat3.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tempita/compat3.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,45 +0,0 @@ -import sys - -__all__ = ['PY3', 'b', 'basestring_', 'bytes', 'next', 'is_unicode'] - -PY3 = True if sys.version_info[0] == 3 else False - -if sys.version_info[0] < 3: - b = bytes = str - basestring_ = basestring -else: - - def b(s): - if isinstance(s, str): - return s.encode('latin1') - return bytes(s) - basestring_ = (bytes, str) - bytes = bytes -text = str - -if sys.version_info[0] < 3: - - def next(obj): - return obj.next() -else: - next = next - - -def is_unicode(obj): - if sys.version_info[0] < 3: - return isinstance(obj, unicode) - else: - return isinstance(obj, str) - - -def coerce_text(v): - if not isinstance(v, basestring_): - if sys.version_info[0] < 3: - attr = '__unicode__' - else: - attr = '__str__' - if hasattr(v, attr): - return unicode(v) - else: - return bytes(v) - return v diff -Nru python-mne-0.23.4+dfsg/mne/externals/tempita/__init__.py python-mne-1.1.0+dfsg/mne/externals/tempita/__init__.py --- python-mne-0.23.4+dfsg/mne/externals/tempita/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tempita/__init__.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,1309 +0,0 @@ -""" -A small templating language - -This implements a small templating language. This language implements -if/elif/else, for/continue/break, expressions, and blocks of Python -code. The syntax is:: - - {{any expression (function calls etc)}} - {{any expression | filter}} - {{for x in y}}...{{endfor}} - {{if x}}x{{elif y}}y{{else}}z{{endif}} - {{py:x=1}} - {{py: - def foo(bar): - return 'baz' - }} - {{default var = default_value}} - {{# comment}} - -You use this with the ``Template`` class or the ``sub`` shortcut. -The ``Template`` class takes the template string and the name of -the template (for errors) and a default namespace. Then (like -``string.Template``) you can call the ``tmpl.substitute(**kw)`` -method to make a substitution (or ``tmpl.substitute(a_dict)``). - -``sub(content, **kw)`` substitutes the template immediately. You -can use ``__name='tmpl.html'`` to set the name of the template. - -If there are syntax errors ``TemplateError`` will be raised. -""" - -import warnings -import re -import sys -import cgi -from urllib.parse import quote as url_quote -import os -import tokenize -from io import StringIO -from ._looper import looper -from .compat3 import PY3, bytes, basestring_, next, is_unicode, coerce_text - -__all__ = ['TemplateError', 'Template', 'sub', 'HTMLTemplate', - 'sub_html', 'html', 'bunch'] - -in_re = re.compile(r'\s+in\s+') -var_re = re.compile(r'^[a-z_][a-z0-9_]*$', re.I) - -try: - from html import escape as html_escape # python 3.8+ -except ImportError: - from cgi import escape as html_escape - - -class TemplateError(Exception): - """Exception raised while parsing a template - """ - - def __init__(self, message, position, name=None): - Exception.__init__(self, message) - self.position = position - self.name = name - - def __str__(self): - msg = ' '.join(self.args) - if self.position: - msg = '%s at line %s column %s' % ( - msg, self.position[0], self.position[1]) - if self.name: - msg += ' in %s' % self.name - return msg - - -class _TemplateContinue(Exception): - pass - - -class _TemplateBreak(Exception): - pass - - -def get_file_template(name, from_template): - path = os.path.join(os.path.dirname(from_template.name), name) - return from_template.__class__.from_filename( - path, namespace=from_template.namespace, - get_template=from_template.get_template) - - -class Template(object): - - default_namespace = { - 'start_braces': '{{', - 'end_braces': '}}', - 'looper': looper, - } - - default_encoding = 'utf8' - default_inherit = None - - def __init__(self, content, name=None, namespace=None, stacklevel=None, - get_template=None, default_inherit=None, line_offset=0, - delimeters=None): - self.content = content - - # set delimeters - if delimeters is None: - delimeters = (self.default_namespace['start_braces'], - self.default_namespace['end_braces']) - else: - assert len(delimeters) == 2 and all( - [isinstance(delimeter, basestring) - for delimeter in delimeters]) - self.default_namespace = self.__class__.default_namespace.copy() - self.default_namespace['start_braces'] = delimeters[0] - self.default_namespace['end_braces'] = delimeters[1] - self.delimeters = delimeters - - self._unicode = is_unicode(content) - if name is None and stacklevel is not None: - try: - caller = sys._getframe(stacklevel) - except ValueError: - pass - else: - globals = caller.f_globals - lineno = caller.f_lineno - if '__file__' in globals: - name = globals['__file__'] - if name.endswith('.pyc') or name.endswith('.pyo'): - name = name[:-1] - elif '__name__' in globals: - name = globals['__name__'] - else: - name = '' - if lineno: - name += ':%s' % lineno - self.name = name - self._parsed = parse( - content, name=name, line_offset=line_offset, - delimeters=self.delimeters) - if namespace is None: - namespace = {} - self.namespace = namespace - self.get_template = get_template - if default_inherit is not None: - self.default_inherit = default_inherit - - def from_filename(cls, filename, namespace=None, encoding=None, - default_inherit=None, get_template=get_file_template): - f = open(filename, 'rb') - c = f.read() - f.close() - if encoding: - c = c.decode(encoding) - return cls(content=c, name=filename, namespace=namespace, - default_inherit=default_inherit, get_template=get_template) - - from_filename = classmethod(from_filename) - - def __repr__(self): - return '<%s %s name=%r>' % ( - self.__class__.__name__, - hex(id(self))[2:], self.name) - - def substitute(self, *args, **kw): - if args: - if kw: - raise TypeError( - "You can only give positional *or* keyword arguments") - if len(args) > 1: - raise TypeError( - "You can only give one positional argument") - if not hasattr(args[0], 'items'): - raise TypeError( - ("If you pass in a single argument, you must pass in a ", - "dict-like object (with a .items() method); you gave %r") - % (args[0],)) - kw = args[0] - ns = kw - ns['__template_name__'] = self.name - if self.namespace: - ns.update(self.namespace) - result, defs, inherit = self._interpret(ns) - if not inherit: - inherit = self.default_inherit - if inherit: - result = self._interpret_inherit(result, defs, inherit, ns) - return result - - def _interpret(self, ns): - # __traceback_hide__ = True - parts = [] - defs = {} - self._interpret_codes(self._parsed, ns, out=parts, defs=defs) - if '__inherit__' in defs: - inherit = defs.pop('__inherit__') - else: - inherit = None - return ''.join(parts), defs, inherit - - def _interpret_inherit(self, body, defs, inherit_template, ns): - # __traceback_hide__ = True - if not self.get_template: - raise TemplateError( - 'You cannot use inheritance without passing in get_template', - position=None, name=self.name) - templ = self.get_template(inherit_template, self) - self_ = TemplateObject(self.name) - for name, value in defs.items(): - setattr(self_, name, value) - self_.body = body - ns = ns.copy() - ns['self'] = self_ - return templ.substitute(ns) - - def _interpret_codes(self, codes, ns, out, defs): - # __traceback_hide__ = True - for item in codes: - if isinstance(item, basestring_): - out.append(item) - else: - self._interpret_code(item, ns, out, defs) - - def _interpret_code(self, code, ns, out, defs): - # __traceback_hide__ = True - name, pos = code[0], code[1] - if name == 'py': - self._exec(code[2], ns, pos) - elif name == 'continue': - raise _TemplateContinue() - elif name == 'break': - raise _TemplateBreak() - elif name == 'for': - vars, expr, content = code[2], code[3], code[4] - expr = self._eval(expr, ns, pos) - self._interpret_for(vars, expr, content, ns, out, defs) - elif name == 'cond': - parts = code[2:] - self._interpret_if(parts, ns, out, defs) - elif name == 'expr': - parts = code[2].split('|') - base = self._eval(parts[0], ns, pos) - for part in parts[1:]: - func = self._eval(part, ns, pos) - base = func(base) - out.append(self._repr(base, pos)) - elif name == 'default': - var, expr = code[2], code[3] - if var not in ns: - result = self._eval(expr, ns, pos) - ns[var] = result - elif name == 'inherit': - expr = code[2] - value = self._eval(expr, ns, pos) - defs['__inherit__'] = value - elif name == 'def': - name = code[2] - signature = code[3] - parts = code[4] - ns[name] = defs[name] = TemplateDef( - self, name, signature, body=parts, ns=ns, pos=pos) - elif name == 'comment': - return - else: - assert 0, "Unknown code: %r" % name - - def _interpret_for(self, vars, expr, content, ns, out, defs): - # __traceback_hide__ = True - for item in expr: - if len(vars) == 1: - ns[vars[0]] = item - else: - if len(vars) != len(item): - raise ValueError( - 'Need %i items to unpack (got %i items)' - % (len(vars), len(item))) - for name, value in zip(vars, item): - ns[name] = value - try: - self._interpret_codes(content, ns, out, defs) - except _TemplateContinue: - continue - except _TemplateBreak: - break - - def _interpret_if(self, parts, ns, out, defs): - # __traceback_hide__ = True - # @@: if/else/else gets through - for part in parts: - assert not isinstance(part, basestring_) - name, pos = part[0], part[1] - if name == 'else': - result = True - else: - result = self._eval(part[2], ns, pos) - if result: - self._interpret_codes(part[3], ns, out, defs) - break - - def _eval(self, code, ns, pos): - # __traceback_hide__ = True - try: - try: - value = eval(code, self.default_namespace, ns) - except SyntaxError as e: - raise SyntaxError( - 'invalid syntax in expression: %s' % code) - return value - except: - exc_info = sys.exc_info() - e = exc_info[1] - if getattr(e, 'args', None): - arg0 = e.args[0] - else: - arg0 = coerce_text(e) - e.args = (self._add_line_info(arg0, pos),) - raise (exc_info[1], e, exc_info[2]) - - def _exec(self, code, ns, pos): - # __traceback_hide__ = True - try: - exec(code, self.default_namespace, ns) - except: - exc_info = sys.exc_info() - e = exc_info[1] - if e.args: - e.args = (self._add_line_info(e.args[0], pos),) - else: - e.args = (self._add_line_info(None, pos),) - raise(exc_info[1], e, exc_info[2]) - - def _repr(self, value, pos): - # __traceback_hide__ = True - try: - if value is None: - return '' - if self._unicode: - try: - if not is_unicode(value): - value = str(value) - if not is_unicode(value): - value = value.decode('utf-8') - except UnicodeDecodeError: - value = bytes(value) - else: - if not isinstance(value, basestring_): - value = coerce_text(value) - if (is_unicode(value) and self.default_encoding): - value = value.encode(self.default_encoding) - except: - exc_info = sys.exc_info() - e = exc_info[1] - e.args = (self._add_line_info(e.args[0], pos),) - # raise(exc_info[1], e, exc_info[2]) - raise(e) - else: - if self._unicode and isinstance(value, bytes): - if not self.default_encoding: - raise UnicodeDecodeError( - 'Cannot decode bytes value %r into unicode ' - '(no default_encoding provided)' % value) - try: - value = value.decode(self.default_encoding) - except UnicodeDecodeError as e: - raise UnicodeDecodeError( - e.encoding, - e.object, - e.start, - e.end, - e.reason + ' in string %r' % value) - elif not self._unicode and is_unicode(value): - if not self.default_encoding: - raise UnicodeEncodeError( - 'Cannot encode unicode value %r into bytes ' - '(no default_encoding provided)' % value) - value = value.encode(self.default_encoding) - return value - - def _add_line_info(self, msg, pos): - msg = "%s at line %s column %s" % ( - msg, pos[0], pos[1]) - if self.name: - msg += " in file %s" % self.name - return msg - - -def sub(content, delimeters=None, **kw): - name = kw.get('__name') - tmpl = Template(content, name=name, delimeters=delimeters) - return tmpl.substitute(kw) - - -def paste_script_template_renderer(content, vars, filename=None): - tmpl = Template(content, name=filename) - return tmpl.substitute(vars) - - -class bunch(dict): - - def __init__(self, **kw): - for name, value in kw.items(): - setattr(self, name, value) - - def __setattr__(self, name, value): - self[name] = value - - def __getattr__(self, name): - try: - return self[name] - except KeyError: - raise AttributeError(name) - - def __getitem__(self, key): - if 'default' in self: - try: - return dict.__getitem__(self, key) - except KeyError: - return dict.__getitem__(self, 'default') - else: - return dict.__getitem__(self, key) - - def __repr__(self): - items = [ - (k, v) for k, v in self.items()] - items.sort() - return '<%s %s>' % ( - self.__class__.__name__, - ' '.join(['%s=%r' % (k, v) for k, v in items])) - -############################################################ -## HTML Templating -############################################################ - - -class html(object): - - def __init__(self, value): - self.value = value - - def __str__(self): - return self.value - - def __html__(self): - return self.value - - def __repr__(self): - return '<%s %r>' % ( - self.__class__.__name__, self.value) - - -def html_quote(value, force=True): - if not force and hasattr(value, '__html__'): - return value.__html__() - if value is None: - return '' - if not isinstance(value, basestring_): - value = coerce_text(value) - if sys.version >= "3" and isinstance(value, bytes): - value = html_escape(value.decode('latin1'), 1) - value = value.encode('latin1') - else: - with warnings.catch_warnings(record=True): # annoying - value = html_escape(value, 1) - if sys.version < "3": - if is_unicode(value): - value = value.encode('ascii', 'xmlcharrefreplace') - return value - - -def url(v): - v = coerce_text(v) - if is_unicode(v): - v = v.encode('utf8') - return url_quote(v) - - -def attr(**kw): - kw = list(kw.items()) - kw.sort() - parts = [] - for name, value in kw: - if value is None: - continue - if name.endswith('_'): - name = name[:-1] - parts.append('%s="%s"' % (html_quote(name), html_quote(value))) - return html(' '.join(parts)) - - -class HTMLTemplate(Template): - - default_namespace = Template.default_namespace.copy() - default_namespace.update(dict( - html=html, - attr=attr, - url=url, - html_quote=html_quote)) - - def _repr(self, value, pos): - if hasattr(value, '__html__'): - value = value.__html__() - quote = False - else: - quote = True - plain = Template._repr(self, value, pos) - if quote: - return html_quote(plain) - else: - return plain - - -def sub_html(content, **kw): - name = kw.get('__name') - tmpl = HTMLTemplate(content, name=name) - return tmpl.substitute(kw) - - -class TemplateDef(object): - def __init__(self, template, func_name, func_signature, - body, ns, pos, bound_self=None): - self._template = template - self._func_name = func_name - self._func_signature = func_signature - self._body = body - self._ns = ns - self._pos = pos - self._bound_self = bound_self - - def __repr__(self): - return '' % ( - self._func_name, self._func_signature, - self._template.name, self._pos) - - def __str__(self): - return self() - - def __call__(self, *args, **kw): - values = self._parse_signature(args, kw) - ns = self._ns.copy() - ns.update(values) - if self._bound_self is not None: - ns['self'] = self._bound_self - out = [] - subdefs = {} - self._template._interpret_codes(self._body, ns, out, subdefs) - return ''.join(out) - - def __get__(self, obj, type=None): - if obj is None: - return self - return self.__class__( - self._template, self._func_name, self._func_signature, - self._body, self._ns, self._pos, bound_self=obj) - - def _parse_signature(self, args, kw): - values = {} - sig_args, var_args, var_kw, defaults = self._func_signature - extra_kw = {} - for name, value in kw.items(): - if not var_kw and name not in sig_args: - raise TypeError( - 'Unexpected argument %s' % name) - if name in sig_args: - values[sig_args] = value - else: - extra_kw[name] = value - args = list(args) - sig_args = list(sig_args) - while args: - while sig_args and sig_args[0] in values: - sig_args.pop(0) - if sig_args: - name = sig_args.pop(0) - values[name] = args.pop(0) - elif var_args: - values[var_args] = tuple(args) - break - else: - raise TypeError( - 'Extra position arguments: %s' - % ', '.join(repr(v) for v in args)) - for name, value_expr in defaults.items(): - if name not in values: - values[name] = self._template._eval( - value_expr, self._ns, self._pos) - for name in sig_args: - if name not in values: - raise TypeError( - 'Missing argument: %s' % name) - if var_kw: - values[var_kw] = extra_kw - return values - - -class TemplateObject(object): - - def __init__(self, name): - self.__name = name - self.get = TemplateObjectGetter(self) - - def __repr__(self): - return '<%s %s>' % (self.__class__.__name__, self.__name) - - -class TemplateObjectGetter(object): - - def __init__(self, template_obj): - self.__template_obj = template_obj - - def __getattr__(self, attr): - return getattr(self.__template_obj, attr, Empty) - - def __repr__(self): - return '<%s around %r>' % ( - self.__class__.__name__, self.__template_obj) - - -class _Empty(object): - def __call__(self, *args, **kw): - return self - - def __str__(self): - return '' - - def __repr__(self): - return 'Empty' - - def __unicode__(self): - if PY3: - return str('') - else: - return unicode('') - - def __iter__(self): - return iter(()) - - def __bool__(self): - return False - - if sys.version < "3": - __nonzero__ = __bool__ - -Empty = _Empty() -del _Empty - -############################################################ -## Lexing and Parsing -############################################################ - - -def lex(s, name=None, trim_whitespace=True, line_offset=0, delimeters=None): - if delimeters is None: - delimeters = (Template.default_namespace['start_braces'], - Template.default_namespace['end_braces']) - in_expr = False - chunks = [] - last = 0 - last_pos = (line_offset + 1, 1) - token_re = re.compile(r'%s|%s' % (re.escape(delimeters[0]), - re.escape(delimeters[1]))) - for match in token_re.finditer(s): - expr = match.group(0) - pos = find_position(s, match.end(), last, last_pos) - if expr == delimeters[0] and in_expr: - raise TemplateError('%s inside expression' % delimeters[0], - position=pos, - name=name) - elif expr == delimeters[1] and not in_expr: - raise TemplateError('%s outside expression' % delimeters[1], - position=pos, - name=name) - if expr == delimeters[0]: - part = s[last:match.start()] - if part: - chunks.append(part) - in_expr = True - else: - chunks.append((s[last:match.start()], last_pos)) - in_expr = False - last = match.end() - last_pos = pos - if in_expr: - raise TemplateError('No %s to finish last expression' % delimeters[1], - name=name, position=last_pos) - part = s[last:] - if part: - chunks.append(part) - if trim_whitespace: - chunks = trim_lex(chunks) - return chunks - -lex.__doc__ = """ -Lex a string into chunks: - - >>> lex('hey') - ['hey'] - >>> lex('hey {{you}}') - ['hey ', ('you', (1, 7))] - >>> lex('hey {{') - Traceback (most recent call last): - ... - mne.externals.tempita.TemplateError: No }} to finish last expression at line 1 column 7 - >>> lex('hey }}') - Traceback (most recent call last): - ... - mne.externals.tempita.TemplateError: }} outside expression at line 1 column 7 - >>> lex('hey {{ {{') - Traceback (most recent call last): - ... - mne.externals.tempita.TemplateError: {{ inside expression at line 1 column 10 - -""" if PY3 else """ -Lex a string into chunks: - - >>> lex('hey') - ['hey'] - >>> lex('hey {{you}}') - ['hey ', ('you', (1, 7))] - >>> lex('hey {{') - Traceback (most recent call last): - ... - TemplateError: No }} to finish last expression at line 1 column 7 - >>> lex('hey }}') - Traceback (most recent call last): - ... - TemplateError: }} outside expression at line 1 column 7 - >>> lex('hey {{ {{') - Traceback (most recent call last): - ... - TemplateError: {{ inside expression at line 1 column 10 - -""" - -statement_re = re.compile(r'^(?:if |elif |for |def |inherit |default |py:)') -single_statements = ['else', 'endif', 'endfor', 'enddef', 'continue', 'break'] -trail_whitespace_re = re.compile(r'\n\r?[\t ]*$') -lead_whitespace_re = re.compile(r'^[\t ]*\n') - - -def trim_lex(tokens): - last_trim = None - for i in range(len(tokens)): - current = tokens[i] - if isinstance(tokens[i], basestring_): - # we don't trim this - continue - item = current[0] - if not statement_re.search(item) and item not in single_statements: - continue - if not i: - prev = '' - else: - prev = tokens[i - 1] - if i + 1 >= len(tokens): - next_chunk = '' - else: - next_chunk = tokens[i + 1] - if (not - isinstance(next_chunk, basestring_) - or not isinstance(prev, basestring_)): - continue - prev_ok = not prev or trail_whitespace_re.search(prev) - if i == 1 and not prev.strip(): - prev_ok = True - if last_trim is not None and last_trim + 2 == i and not prev.strip(): - prev_ok = 'last' - if (prev_ok - and (not next_chunk or lead_whitespace_re.search(next_chunk) - or (i == len(tokens) - 2 and not next_chunk.strip()))): - if prev: - if ((i == 1 and not prev.strip()) or prev_ok == 'last'): - tokens[i - 1] = '' - else: - m = trail_whitespace_re.search(prev) - # +1 to leave the leading \n on: - prev = prev[:m.start() + 1] - tokens[i - 1] = prev - if next_chunk: - last_trim = i - if i == len(tokens) - 2 and not next_chunk.strip(): - tokens[i + 1] = '' - else: - m = lead_whitespace_re.search(next_chunk) - next_chunk = next_chunk[m.end():] - tokens[i + 1] = next_chunk - return tokens - -trim_lex.__doc__ = r""" - Takes a lexed set of tokens, and removes whitespace when there is - a directive on a line by itself: - - >>> tokens = lex('{{if x}}\nx\n{{endif}}\ny', trim_whitespace=False) - >>> tokens - [('if x', (1, 3)), '\nx\n', ('endif', (3, 3)), '\ny'] - >>> trim_lex(tokens) - [('if x', (1, 3)), 'x\n', ('endif', (3, 3)), 'y'] - """ if PY3 else r""" - Takes a lexed set of tokens, and removes whitespace when there is - a directive on a line by itself: - - >>> tokens = lex('{{if x}}\nx\n{{endif}}\ny', trim_whitespace=False) - >>> tokens - [('if x', (1, 3)), '\nx\n', ('endif', (3, 3)), '\ny'] - >>> trim_lex(tokens) - [('if x', (1, 3)), 'x\n', ('endif', (3, 3)), 'y'] - """ - - -def find_position(string, index, last_index, last_pos): - """ - Given a string and index, return (line, column) - """ - lines = string.count('\n', last_index, index) - if lines > 0: - column = index - string.rfind('\n', last_index, index) - else: - column = last_pos[1] + (index - last_index) - return (last_pos[0] + lines, column) - - -def parse(s, name=None, line_offset=0, delimeters=None): - - if delimeters is None: - delimeters = (Template.default_namespace['start_braces'], - Template.default_namespace['end_braces']) - tokens = lex(s, name=name, line_offset=line_offset, delimeters=delimeters) - result = [] - while tokens: - next_chunk, tokens = parse_expr(tokens, name) - result.append(next_chunk) - return result - -parse.__doc__ = r""" - Parses a string into a kind of AST - - >>> parse('{{x}}') - [('expr', (1, 3), 'x')] - >>> parse('foo') - ['foo'] - >>> parse('{{if x}}test{{endif}}') - [('cond', (1, 3), ('if', (1, 3), 'x', ['test']))] - >>> parse( - ... 'series->{{for x in y}}x={{x}}{{endfor}}' - ... ) #doctest: +NORMALIZE_WHITESPACE - ['series->', - ('for', (1, 11), ('x',), 'y', ['x=', ('expr', (1, 27), 'x')])] - >>> parse('{{for x, y in z:}}{{continue}}{{endfor}}') - [('for', (1, 3), ('x', 'y'), 'z', [('continue', (1, 21))])] - >>> parse('{{py:x=1}}') - [('py', (1, 3), 'x=1')] - >>> parse( - ... '{{if x}}a{{elif y}}b{{else}}c{{endif}}' - ... ) #doctest: +NORMALIZE_WHITESPACE - [('cond', (1, 3), ('if', (1, 3), 'x', ['a']), - ('elif', (1, 12), 'y', ['b']), ('else', (1, 23), None, ['c']))] - - Some exceptions:: - - >>> parse('{{continue}}') - Traceback (most recent call last): - ... - mne.externals.tempita.TemplateError: continue outside of for loop at line 1 column 3 - >>> parse('{{if x}}foo') - Traceback (most recent call last): - ... - mne.externals.tempita.TemplateError: No {{endif}} at line 1 column 3 - >>> parse('{{else}}') - Traceback (most recent call last): - ... - mne.externals.tempita.TemplateError: else outside of an if block at line 1 column 3 - >>> parse('{{if x}}{{for x in y}}{{endif}}{{endfor}}') - Traceback (most recent call last): - ... - mne.externals.tempita.TemplateError: Unexpected endif at line 1 column 25 - >>> parse('{{if}}{{endif}}') - Traceback (most recent call last): - ... - mne.externals.tempita.TemplateError: if with no expression at line 1 column 3 - >>> parse('{{for x y}}{{endfor}}') - Traceback (most recent call last): - ... - mne.externals.tempita.TemplateError: Bad for (no "in") in 'x y' at line 1 column 3 - >>> parse('{{py:x=1\ny=2}}') #doctest: +NORMALIZE_WHITESPACE - Traceback (most recent call last): - ... - mne.externals.tempita.TemplateError: Multi-line py blocks must start - with a newline at line 1 column 3 - """ if PY3 else r""" - Parses a string into a kind of AST - - >>> parse('{{x}}') - [('expr', (1, 3), 'x')] - >>> parse('foo') - ['foo'] - >>> parse('{{if x}}test{{endif}}') - [('cond', (1, 3), ('if', (1, 3), 'x', ['test']))] - >>> parse( - ... 'series->{{for x in y}}x={{x}}{{endfor}}' - ... ) #doctest: +NORMALIZE_WHITESPACE - ['series->', - ('for', (1, 11), ('x',), 'y', ['x=', ('expr', (1, 27), 'x')])] - >>> parse('{{for x, y in z:}}{{continue}}{{endfor}}') - [('for', (1, 3), ('x', 'y'), 'z', [('continue', (1, 21))])] - >>> parse('{{py:x=1}}') - [('py', (1, 3), 'x=1')] - >>> parse( - ... '{{if x}}a{{elif y}}b{{else}}c{{endif}}' - ... ) #doctest: +NORMALIZE_WHITESPACE - [('cond', (1, 3), ('if', (1, 3), 'x', ['a']), - ('elif', (1, 12), 'y', ['b']), ('else', (1, 23), None, ['c']))] - - Some exceptions:: - - >>> parse('{{continue}}') - Traceback (most recent call last): - ... - TemplateError: continue outside of for loop at line 1 column 3 - >>> parse('{{if x}}foo') - Traceback (most recent call last): - ... - TemplateError: No {{endif}} at line 1 column 3 - >>> parse('{{else}}') - Traceback (most recent call last): - ... - TemplateError: else outside of an if block at line 1 column 3 - >>> parse('{{if x}}{{for x in y}}{{endif}}{{endfor}}') - Traceback (most recent call last): - ... - TemplateError: Unexpected endif at line 1 column 25 - >>> parse('{{if}}{{endif}}') - Traceback (most recent call last): - ... - TemplateError: if with no expression at line 1 column 3 - >>> parse('{{for x y}}{{endfor}}') - Traceback (most recent call last): - ... - TemplateError: Bad for (no "in") in 'x y' at line 1 column 3 - >>> parse('{{py:x=1\ny=2}}') #doctest: +NORMALIZE_WHITESPACE - Traceback (most recent call last): - ... - TemplateError: Multi-line py blocks must start - with a newline at line 1 column 3 - """ - - -def parse_expr(tokens, name, context=()): - if isinstance(tokens[0], basestring_): - return tokens[0], tokens[1:] - expr, pos = tokens[0] - expr = expr.strip() - if expr.startswith('py:'): - expr = expr[3:].lstrip(' \t') - if expr.startswith('\n') or expr.startswith('\r'): - expr = expr.lstrip('\r\n') - if '\r' in expr: - expr = expr.replace('\r\n', '\n') - expr = expr.replace('\r', '') - expr += '\n' - else: - if '\n' in expr: - raise TemplateError( - 'Multi-line py blocks must start with a newline', - position=pos, name=name) - return ('py', pos, expr), tokens[1:] - elif expr in ('continue', 'break'): - if 'for' not in context: - raise TemplateError( - 'continue outside of for loop', - position=pos, name=name) - return (expr, pos), tokens[1:] - elif expr.startswith('if '): - return parse_cond(tokens, name, context) - elif (expr.startswith('elif ') - or expr == 'else'): - raise TemplateError( - '%s outside of an if block' % expr.split()[0], - position=pos, name=name) - elif expr in ('if', 'elif', 'for'): - raise TemplateError( - '%s with no expression' % expr, - position=pos, name=name) - elif expr in ('endif', 'endfor', 'enddef'): - raise TemplateError( - 'Unexpected %s' % expr, - position=pos, name=name) - elif expr.startswith('for '): - return parse_for(tokens, name, context) - elif expr.startswith('default '): - return parse_default(tokens, name, context) - elif expr.startswith('inherit '): - return parse_inherit(tokens, name, context) - elif expr.startswith('def '): - return parse_def(tokens, name, context) - elif expr.startswith('#'): - return ('comment', pos, tokens[0][0]), tokens[1:] - return ('expr', pos, tokens[0][0]), tokens[1:] - - -def parse_cond(tokens, name, context): - start = tokens[0][1] - pieces = [] - context = context + ('if',) - while 1: - if not tokens: - raise TemplateError( - 'Missing {{endif}}', - position=start, name=name) - if (isinstance(tokens[0], tuple) and tokens[0][0] == 'endif'): - return ('cond', start) + tuple(pieces), tokens[1:] - next_chunk, tokens = parse_one_cond(tokens, name, context) - pieces.append(next_chunk) - - -def parse_one_cond(tokens, name, context): - (first, pos), tokens = tokens[0], tokens[1:] - content = [] - if first.endswith(':'): - first = first[:-1] - if first.startswith('if '): - part = ('if', pos, first[3:].lstrip(), content) - elif first.startswith('elif '): - part = ('elif', pos, first[5:].lstrip(), content) - elif first == 'else': - part = ('else', pos, None, content) - else: - assert 0, "Unexpected token %r at %s" % (first, pos) - while 1: - if not tokens: - raise TemplateError( - 'No {{endif}}', - position=pos, name=name) - if (isinstance(tokens[0], tuple) - and (tokens[0][0] == 'endif' - or tokens[0][0].startswith('elif ') - or tokens[0][0] == 'else')): - return part, tokens - next_chunk, tokens = parse_expr(tokens, name, context) - content.append(next_chunk) - - -def parse_for(tokens, name, context): - first, pos = tokens[0] - tokens = tokens[1:] - context = ('for',) + context - content = [] - assert first.startswith('for ') - if first.endswith(':'): - first = first[:-1] - first = first[3:].strip() - match = in_re.search(first) - if not match: - raise TemplateError( - 'Bad for (no "in") in %r' % first, - position=pos, name=name) - vars = first[:match.start()] - if '(' in vars: - raise TemplateError( - 'You cannot have () in the variable section of a for loop (%r)' - % vars, position=pos, name=name) - vars = tuple([ - v.strip() for v in first[:match.start()].split(',') - if v.strip()]) - expr = first[match.end():] - while 1: - if not tokens: - raise TemplateError( - 'No {{endfor}}', - position=pos, name=name) - if (isinstance(tokens[0], tuple) and tokens[0][0] == 'endfor'): - return ('for', pos, vars, expr, content), tokens[1:] - next_chunk, tokens = parse_expr(tokens, name, context) - content.append(next_chunk) - - -def parse_default(tokens, name, context): - first, pos = tokens[0] - assert first.startswith('default ') - first = first.split(None, 1)[1] - parts = first.split('=', 1) - if len(parts) == 1: - raise TemplateError( - "Expression must be {{default var=value}}; no = found in %r" % - first, position=pos, name=name) - var = parts[0].strip() - if ',' in var: - raise TemplateError( - "{{default x, y = ...}} is not supported", - position=pos, name=name) - if not var_re.search(var): - raise TemplateError( - "Not a valid variable name for {{default}}: %r" - % var, position=pos, name=name) - expr = parts[1].strip() - return ('default', pos, var, expr), tokens[1:] - - -def parse_inherit(tokens, name, context): - first, pos = tokens[0] - assert first.startswith('inherit ') - expr = first.split(None, 1)[1] - return ('inherit', pos, expr), tokens[1:] - - -def parse_def(tokens, name, context): - first, start = tokens[0] - tokens = tokens[1:] - assert first.startswith('def ') - first = first.split(None, 1)[1] - if first.endswith(':'): - first = first[:-1] - if '(' not in first: - func_name = first - sig = ((), None, None, {}) - elif not first.endswith(')'): - raise TemplateError("Function definition doesn't end with ): %s" % - first, position=start, name=name) - else: - first = first[:-1] - func_name, sig_text = first.split('(', 1) - sig = parse_signature(sig_text, name, start) - context = context + ('def',) - content = [] - while 1: - if not tokens: - raise TemplateError( - 'Missing {{enddef}}', - position=start, name=name) - if (isinstance(tokens[0], tuple) and tokens[0][0] == 'enddef'): - return ('def', start, func_name, sig, content), tokens[1:] - next_chunk, tokens = parse_expr(tokens, name, context) - content.append(next_chunk) - - -def parse_signature(sig_text, name, pos): - tokens = tokenize.generate_tokens(StringIO(sig_text).readline) - sig_args = [] - var_arg = None - var_kw = None - defaults = {} - - def get_token(pos=False): - try: - tok_type, tok_string, (srow, scol), (erow, ecol), line = next( - tokens) - except StopIteration: - return tokenize.ENDMARKER, '' - if pos: - return tok_type, tok_string, (srow, scol), (erow, ecol) - else: - return tok_type, tok_string - while 1: - var_arg_type = None - tok_type, tok_string = get_token() - if tok_type == tokenize.ENDMARKER: - break - if tok_type == tokenize.OP and ( - tok_string == '*' or tok_string == '**'): - var_arg_type = tok_string - tok_type, tok_string = get_token() - if tok_type != tokenize.NAME: - raise TemplateError('Invalid signature: (%s)' % sig_text, - position=pos, name=name) - var_name = tok_string - tok_type, tok_string = get_token() - if tok_type == tokenize.ENDMARKER or ( - tok_type == tokenize.OP and tok_string == ','): - if var_arg_type == '*': - var_arg = var_name - elif var_arg_type == '**': - var_kw = var_name - else: - sig_args.append(var_name) - if tok_type == tokenize.ENDMARKER: - break - continue - if var_arg_type is not None: - raise TemplateError('Invalid signature: (%s)' % sig_text, - position=pos, name=name) - if tok_type == tokenize.OP and tok_string == '=': - nest_type = None - unnest_type = None - nest_count = 0 - start_pos = end_pos = None - parts = [] - while 1: - tok_type, tok_string, s, e = get_token(True) - if start_pos is None: - start_pos = s - end_pos = e - if tok_type == tokenize.ENDMARKER and nest_count: - raise TemplateError('Invalid signature: (%s)' % sig_text, - position=pos, name=name) - if (not nest_count and - (tok_type == tokenize.ENDMARKER or - (tok_type == tokenize.OP and tok_string == ','))): - default_expr = isolate_expression( - sig_text, start_pos, end_pos) - defaults[var_name] = default_expr - sig_args.append(var_name) - break - parts.append((tok_type, tok_string)) - if nest_count \ - and tok_type == tokenize.OP \ - and tok_string == nest_type: - nest_count += 1 - elif nest_count \ - and tok_type == tokenize.OP \ - and tok_string == unnest_type: - nest_count -= 1 - if not nest_count: - nest_type = unnest_type = None - elif not nest_count \ - and tok_type == tokenize.OP \ - and tok_string in ('(', '[', '{'): - nest_type = tok_string - nest_count = 1 - unnest_type = {'(': ')', '[': ']', '{': '}'}[nest_type] - return sig_args, var_arg, var_kw, defaults - - -def isolate_expression(string, start_pos, end_pos): - srow, scol = start_pos - srow -= 1 - erow, ecol = end_pos - erow -= 1 - lines = string.splitlines(True) - if srow == erow: - return lines[srow][scol:ecol] - parts = [lines[srow][scol:]] - parts.extend(lines[srow + 1:erow]) - if erow < len(lines): - # It'll sometimes give (end_row_past_finish, 0) - parts.append(lines[erow][:ecol]) - return ''.join(parts) - -_fill_command_usage = """\ -%prog [OPTIONS] TEMPLATE arg=value - -Use py:arg=value to set a Python value; otherwise all values are -strings. -""" - - -def fill_command(args=None): - import sys - import optparse - import pkg_resources - import os - if args is None: - args = sys.argv[1:] - dist = pkg_resources.get_distribution('Paste') - parser = optparse.OptionParser( - version=coerce_text(dist), - usage=_fill_command_usage) - parser.add_option( - '-o', '--output', - dest='output', - metavar="FILENAME", - help="File to write output to (default stdout)") - parser.add_option( - '--html', - dest='use_html', - action='store_true', - help="Use HTML style filling (including automatic HTML quoting)") - parser.add_option( - '--env', - dest='use_env', - action='store_true', - help="Put the environment in as top-level variables") - options, args = parser.parse_args(args) - if len(args) < 1: - print('You must give a template filename') - sys.exit(2) - template_name = args[0] - args = args[1:] - vars = {} - if options.use_env: - vars.update(os.environ) - for value in args: - if '=' not in value: - print('Bad argument: %r' % value) - sys.exit(2) - name, value = value.split('=', 1) - if name.startswith('py:'): - name = name[:3] - value = eval(value) - vars[name] = value - if template_name == '-': - template_content = sys.stdin.read() - template_name = '' - else: - f = open(template_name, 'rb') - template_content = f.read() - f.close() - if options.use_html: - TemplateClass = HTMLTemplate - else: - TemplateClass = Template - template = TemplateClass(template_content, name=template_name) - result = template.substitute(vars) - if options.output: - f = open(options.output, 'wb') - f.write(result) - f.close() - else: - sys.stdout.write(result) - -if __name__ == '__main__': - fill_command() diff -Nru python-mne-0.23.4+dfsg/mne/externals/tempita/_looper.py python-mne-1.1.0+dfsg/mne/externals/tempita/_looper.py --- python-mne-0.23.4+dfsg/mne/externals/tempita/_looper.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tempita/_looper.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,163 +0,0 @@ -""" -Helper for looping over sequences, particular in templates. - -Often in a loop in a template it's handy to know what's next up, -previously up, if this is the first or last item in the sequence, etc. -These can be awkward to manage in a normal Python loop, but using the -looper you can get a better sense of the context. Use like:: - - >>> for loop, item in looper(['a', 'b', 'c']): - ... print("%d %s" % (loop.number, item)) - ... if not loop.last: - ... print('---') - 1 a - --- - 2 b - --- - 3 c - -""" - -import sys -from .compat3 import basestring_ - -__all__ = ['looper'] - - -class looper(object): - """ - Helper for looping (particularly in templates) - - Use this like:: - - for loop, item in looper(seq): - if loop.first: - ... - """ - - def __init__(self, seq): - self.seq = seq - - def __iter__(self): - return looper_iter(self.seq) - - def __repr__(self): - return '<%s for %r>' % ( - self.__class__.__name__, self.seq) - - -class looper_iter(object): - - def __init__(self, seq): - self.seq = list(seq) - self.pos = 0 - - def __iter__(self): - return self - - def __next__(self): - if self.pos >= len(self.seq): - raise StopIteration - result = loop_pos(self.seq, self.pos), self.seq[self.pos] - self.pos += 1 - return result - - if sys.version < "3": - next = __next__ - - -class loop_pos(object): - - def __init__(self, seq, pos): - self.seq = seq - self.pos = pos - - def __repr__(self): - return '' % ( - self.seq[self.pos], self.pos) - - def index(self): - return self.pos - index = property(index) - - def number(self): - return self.pos + 1 - number = property(number) - - def item(self): - return self.seq[self.pos] - item = property(item) - - def __next__(self): - try: - return self.seq[self.pos + 1] - except IndexError: - return None - __next__ = property(__next__) - - if sys.version < "3": - next = __next__ - - def previous(self): - if self.pos == 0: - return None - return self.seq[self.pos - 1] - previous = property(previous) - - def odd(self): - return not self.pos % 2 - odd = property(odd) - - def even(self): - return self.pos % 2 - even = property(even) - - def first(self): - return self.pos == 0 - first = property(first) - - def last(self): - return self.pos == len(self.seq) - 1 - last = property(last) - - def length(self): - return len(self.seq) - length = property(length) - - def first_group(self, getter=None): - """ - Returns true if this item is the start of a new group, - where groups mean that some attribute has changed. The getter - can be None (the item itself changes), an attribute name like - ``'.attr'``, a function, or a dict key or list index. - """ - if self.first: - return True - return self._compare_group(self.item, self.previous, getter) - - def last_group(self, getter=None): - """ - Returns true if this item is the end of a new group, - where groups mean that some attribute has changed. The getter - can be None (the item itself changes), an attribute name like - ``'.attr'``, a function, or a dict key or list index. - """ - if self.last: - return True - return self._compare_group(self.item, self.__next__, getter) - - def _compare_group(self, item, other, getter): - if getter is None: - return item != other - elif (isinstance(getter, basestring_) - and getter.startswith('.')): - getter = getter[1:] - if getter.endswith('()'): - getter = getter[:-2] - return getattr(item, getter)() != getattr(other, getter)() - else: - return getattr(item, getter) != getattr(other, getter) - elif hasattr(getter, '__call__'): - return getter(item) != getter(other) - else: - return item[getter] != other[getter] diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/__init__.py python-mne-1.1.0+dfsg/mne/externals/tqdm/__init__.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/__init__.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,12 +0,0 @@ -from distutils.version import LooseVersion - -try: - from tqdm import __version__ - if LooseVersion(__version__) < LooseVersion('4.36'): - raise ImportError -except ImportError: # use our copy - from ._tqdm import * - from ._tqdm import auto -else: # use the system copy - from tqdm import * - from tqdm import auto diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/autonotebook.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/autonotebook.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/autonotebook.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/autonotebook.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,18 +0,0 @@ -import os - -try: - from IPython import get_ipython - if 'IPKernelApp' not in get_ipython().config: # pragma: no cover - raise ImportError("console") - if 'VSCODE_PID' in os.environ: # pragma: no cover - raise ImportError("vscode") -except: - from .std import tqdm, trange -else: # pragma: no cover - from .notebook import tqdm, trange - from .std import TqdmExperimentalWarning - from warnings import warn - warn("Using `tqdm.autonotebook.tqdm` in notebook mode." - " Use `tqdm.tqdm` instead to force console mode" - " (e.g. in jupyter console)", TqdmExperimentalWarning, stacklevel=2) -__all__ = ["tqdm", "trange"] diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/auto.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/auto.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/auto.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/auto.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,6 +0,0 @@ -import warnings -from .std import TqdmExperimentalWarning -with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=TqdmExperimentalWarning) - from .autonotebook import tqdm, trange -__all__ = ["tqdm", "trange"] diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/cli.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/cli.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/cli.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/cli.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,228 +0,0 @@ -from .std import tqdm, TqdmTypeError, TqdmKeyError -from ._version import __version__ # NOQA -import sys -import re -import logging -__all__ = ["main"] - - -def cast(val, typ): - log = logging.getLogger(__name__) - log.debug((val, typ)) - if " or " in typ: - for t in typ.split(" or "): - try: - return cast(val, t) - except TqdmTypeError: - pass - raise TqdmTypeError(val + ' : ' + typ) - - # sys.stderr.write('\ndebug | `val:type`: `' + val + ':' + typ + '`.\n') - if typ == 'bool': - if (val == 'True') or (val == ''): - return True - elif val == 'False': - return False - else: - raise TqdmTypeError(val + ' : ' + typ) - try: - return eval(typ + '("' + val + '")') - except: - if typ == 'chr': - return chr(ord(eval('"' + val + '"'))) - else: - raise TqdmTypeError(val + ' : ' + typ) - - -def posix_pipe(fin, fout, delim='\n', buf_size=256, - callback=lambda int: None # pragma: no cover - ): - """ - Params - ------ - fin : file with `read(buf_size : int)` method - fout : file with `write` (and optionally `flush`) methods. - callback : function(int), e.g.: `tqdm.update` - """ - fp_write = fout.write - - # tmp = '' - if not delim: - while True: - tmp = fin.read(buf_size) - - # flush at EOF - if not tmp: - getattr(fout, 'flush', lambda: None)() # pragma: no cover - return - - fp_write(tmp) - callback(len(tmp)) - # return - - buf = '' - # n = 0 - while True: - tmp = fin.read(buf_size) - - # flush at EOF - if not tmp: - if buf: - fp_write(buf) - callback(1 + buf.count(delim)) # n += 1 + buf.count(delim) - getattr(fout, 'flush', lambda: None)() # pragma: no cover - return # n - - while True: - try: - i = tmp.index(delim) - except ValueError: - buf += tmp - break - else: - fp_write(buf + tmp[:i + len(delim)]) - callback(1) # n += 1 - buf = '' - tmp = tmp[i + len(delim):] - - -# ((opt, type), ... ) -RE_OPTS = re.compile(r'\n {8}(\S+)\s{2,}:\s*([^,]+)') -# better split method assuming no positional args -RE_SHLEX = re.compile(r'\s*(? : \2', d) - split = RE_OPTS.split(d) - opt_types_desc = zip(split[1::3], split[2::3], split[3::3]) - d = ''.join('\n --{0}=<{0}> : {1}{2}'.format(*otd) - for otd in opt_types_desc if otd[0] not in UNSUPPORTED_OPTS) - - d = """Usage: - tqdm [--help | options] - -Options: - -h, --help Print this help and exit - -v, --version Print version and exit - -""" + d.strip('\n') + '\n' - - # opts = docopt(d, version=__version__) - if any(v in argv for v in ('-v', '--version')): - sys.stdout.write(__version__ + '\n') - sys.exit(0) - elif any(v in argv for v in ('-h', '--help')): - sys.stdout.write(d + '\n') - sys.exit(0) - - argv = RE_SHLEX.split(' '.join(["tqdm"] + argv)) - opts = dict(zip(argv[1::3], argv[3::3])) - - log.debug(opts) - opts.pop('log', True) - - tqdm_args = {'file': fp} - try: - for (o, v) in opts.items(): - try: - tqdm_args[o] = cast(v, opt_types[o]) - except KeyError as e: - raise TqdmKeyError(str(e)) - log.debug('args:' + str(tqdm_args)) - except: - fp.write('\nError:\nUsage:\n tqdm [--help | options]\n') - for i in sys.stdin: - sys.stdout.write(i) - raise - else: - buf_size = tqdm_args.pop('buf_size', 256) - delim = tqdm_args.pop('delim', '\n') - delim_per_char = tqdm_args.pop('bytes', False) - manpath = tqdm_args.pop('manpath', None) - stdin = getattr(sys.stdin, 'buffer', sys.stdin) - stdout = getattr(sys.stdout, 'buffer', sys.stdout) - if manpath is not None: - from os import path - from shutil import copyfile - from pkg_resources import resource_filename, Requirement - fi = resource_filename(Requirement.parse('tqdm'), 'tqdm/tqdm.1') - fo = path.join(manpath, 'tqdm.1') - copyfile(fi, fo) - log.info("written:" + fo) - sys.exit(0) - if delim_per_char: - tqdm_args.setdefault('unit', 'B') - tqdm_args.setdefault('unit_scale', True) - tqdm_args.setdefault('unit_divisor', 1024) - log.debug(tqdm_args) - with tqdm(**tqdm_args) as t: - posix_pipe(stdin, stdout, '', buf_size, t.update) - elif delim == '\n': - log.debug(tqdm_args) - for i in tqdm(stdin, **tqdm_args): - stdout.write(i) - else: - log.debug(tqdm_args) - with tqdm(**tqdm_args) as t: - posix_pipe(stdin, stdout, delim, buf_size, t.update) diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/contrib/__init__.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/contrib/__init__.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/contrib/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/contrib/__init__.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,10 +0,0 @@ -from tqdm import tqdm -from tqdm.utils import ObjectWrapper - - -class DummyTqdmFile(ObjectWrapper): - """Dummy file-like that will write to tqdm""" - def write(self, x, nolock=False): - # Avoid print() second call (useless \n) - if len(x.rstrip()) > 0: - tqdm.write(x, file=self._wrapped, nolock=nolock) diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/gui.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/gui.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/gui.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/gui.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,321 +0,0 @@ -""" -GUI progressbar decorator for iterators. -Includes a default (x)range iterator printing to stderr. - -Usage: - >>> from tqdm.gui import trange[, tqdm] - >>> for i in trange(10): #same as: for i in tqdm(xrange(10)) - ... ... -""" -# future division is important to divide integers and get as -# a result precise floating numbers (instead of truncated int) -from __future__ import division, absolute_import -# import compatibility functions and utilities -from .utils import _range -# to inherit from the tqdm class -from .std import tqdm as std_tqdm -from .std import TqdmExperimentalWarning -from warnings import warn - - -__author__ = {"github.com/": ["casperdcl", "lrq3000"]} -__all__ = ['tqdm_gui', 'tgrange', 'tqdm', 'trange'] - - -class tqdm_gui(std_tqdm): # pragma: no cover - """ - Experimental GUI version of tqdm! - """ - - # TODO: @classmethod: write() on GUI? - - def __init__(self, *args, **kwargs): - import matplotlib as mpl - import matplotlib.pyplot as plt - from collections import deque - kwargs['gui'] = True - - super(tqdm_gui, self).__init__(*args, **kwargs) - - # Initialize the GUI display - if self.disable or not kwargs['gui']: - return - - warn('GUI is experimental/alpha', TqdmExperimentalWarning, stacklevel=2) - self.mpl = mpl - self.plt = plt - self.sp = None - - # Remember if external environment uses toolbars - self.toolbar = self.mpl.rcParams['toolbar'] - self.mpl.rcParams['toolbar'] = 'None' - - self.mininterval = max(self.mininterval, 0.5) - self.fig, ax = plt.subplots(figsize=(9, 2.2)) - # self.fig.subplots_adjust(bottom=0.2) - total = len(self) - if total is not None: - self.xdata = [] - self.ydata = [] - self.zdata = [] - else: - self.xdata = deque([]) - self.ydata = deque([]) - self.zdata = deque([]) - self.line1, = ax.plot(self.xdata, self.ydata, color='b') - self.line2, = ax.plot(self.xdata, self.zdata, color='k') - ax.set_ylim(0, 0.001) - if total is not None: - ax.set_xlim(0, 100) - ax.set_xlabel('percent') - self.fig.legend((self.line1, self.line2), ('cur', 'est'), - loc='center right') - # progressbar - self.hspan = plt.axhspan(0, 0.001, - xmin=0, xmax=0, color='g') - else: - # ax.set_xlim(-60, 0) - ax.set_xlim(0, 60) - ax.invert_xaxis() - ax.set_xlabel('seconds') - ax.legend(('cur', 'est'), loc='lower left') - ax.grid() - # ax.set_xlabel('seconds') - ax.set_ylabel((self.unit if self.unit else 'it') + '/s') - if self.unit_scale: - plt.ticklabel_format(style='sci', axis='y', - scilimits=(0, 0)) - ax.yaxis.get_offset_text().set_x(-0.15) - - # Remember if external environment is interactive - self.wasion = plt.isinteractive() - plt.ion() - self.ax = ax - - def __iter__(self): - # TODO: somehow allow the following: - # if not self.gui: - # return super(tqdm_gui, self).__iter__() - iterable = self.iterable - if self.disable: - for obj in iterable: - yield obj - return - - # ncols = self.ncols - mininterval = self.mininterval - maxinterval = self.maxinterval - miniters = self.miniters - dynamic_miniters = self.dynamic_miniters - last_print_t = self.last_print_t - last_print_n = self.last_print_n - n = self.n - # dynamic_ncols = self.dynamic_ncols - smoothing = self.smoothing - avg_time = self.avg_time - time = self._time - - for obj in iterable: - yield obj - # Update and possibly print the progressbar. - # Note: does not call self.update(1) for speed optimisation. - n += 1 - # check counter first to avoid calls to time() - if n - last_print_n >= self.miniters: - miniters = self.miniters # watch monitoring thread changes - delta_t = time() - last_print_t - if delta_t >= mininterval: - cur_t = time() - delta_it = n - last_print_n - # EMA (not just overall average) - if smoothing and delta_t and delta_it: - rate = delta_t / delta_it - avg_time = self.ema(rate, avg_time, smoothing) - self.avg_time = avg_time - - self.n = n - self.display() - - # If no `miniters` was specified, adjust automatically - # to the max iteration rate seen so far between 2 prints - if dynamic_miniters: - if maxinterval and delta_t >= maxinterval: - # Adjust miniters to time interval by rule of 3 - if mininterval: - # Set miniters to correspond to mininterval - miniters = delta_it * mininterval / delta_t - else: - # Set miniters to correspond to maxinterval - miniters = delta_it * maxinterval / delta_t - elif smoothing: - # EMA-weight miniters to converge - # towards the timeframe of mininterval - rate = delta_it - if mininterval and delta_t: - rate *= mininterval / delta_t - miniters = self.ema(rate, miniters, smoothing) - else: - # Maximum nb of iterations between 2 prints - miniters = max(miniters, delta_it) - - # Store old values for next call - self.n = self.last_print_n = last_print_n = n - self.last_print_t = last_print_t = cur_t - self.miniters = miniters - - # Closing the progress bar. - # Update some internal variables for close(). - self.last_print_n = last_print_n - self.n = n - self.miniters = miniters - self.close() - - def update(self, n=1): - # if not self.gui: - # return super(tqdm_gui, self).close() - if self.disable: - return - - if n < 0: - self.last_print_n += n # for auto-refresh logic to work - self.n += n - - # check counter first to reduce calls to time() - if self.n - self.last_print_n >= self.miniters: - delta_t = self._time() - self.last_print_t - if delta_t >= self.mininterval: - cur_t = self._time() - delta_it = self.n - self.last_print_n # >= n - # elapsed = cur_t - self.start_t - # EMA (not just overall average) - if self.smoothing and delta_t and delta_it: - rate = delta_t / delta_it - self.avg_time = self.ema( - rate, self.avg_time, self.smoothing) - - self.display() - - # If no `miniters` was specified, adjust automatically to the - # maximum iteration rate seen so far between two prints. - # e.g.: After running `tqdm.update(5)`, subsequent - # calls to `tqdm.update()` will only cause an update after - # at least 5 more iterations. - if self.dynamic_miniters: - if self.maxinterval and delta_t >= self.maxinterval: - if self.mininterval: - self.miniters = delta_it * self.mininterval \ - / delta_t - else: - self.miniters = delta_it * self.maxinterval \ - / delta_t - elif self.smoothing: - self.miniters = self.smoothing * delta_it * \ - (self.mininterval / delta_t - if self.mininterval and delta_t - else 1) + \ - (1 - self.smoothing) * self.miniters - else: - self.miniters = max(self.miniters, delta_it) - - # Store old values for next call - self.last_print_n = self.n - self.last_print_t = cur_t - - def close(self): - # if not self.gui: - # return super(tqdm_gui, self).close() - if self.disable: - return - - self.disable = True - - with self.get_lock(): - self._instances.remove(self) - - # Restore toolbars - self.mpl.rcParams['toolbar'] = self.toolbar - # Return to non-interactive mode - if not self.wasion: - self.plt.ioff() - if not self.leave: - self.plt.close(self.fig) - - def display(self): - n = self.n - cur_t = self._time() - elapsed = cur_t - self.start_t - delta_it = n - self.last_print_n - delta_t = cur_t - self.last_print_t - - # Inline due to multiple calls - total = self.total - xdata = self.xdata - ydata = self.ydata - zdata = self.zdata - ax = self.ax - line1 = self.line1 - line2 = self.line2 - # instantaneous rate - y = delta_it / delta_t - # overall rate - z = n / elapsed - # update line data - xdata.append(n * 100.0 / total if total else cur_t) - ydata.append(y) - zdata.append(z) - - # Discard old values - # xmin, xmax = ax.get_xlim() - # if (not total) and elapsed > xmin * 1.1: - if (not total) and elapsed > 66: - xdata.popleft() - ydata.popleft() - zdata.popleft() - - ymin, ymax = ax.get_ylim() - if y > ymax or z > ymax: - ymax = 1.1 * y - ax.set_ylim(ymin, ymax) - ax.figure.canvas.draw() - - if total: - line1.set_data(xdata, ydata) - line2.set_data(xdata, zdata) - try: - poly_lims = self.hspan.get_xy() - except AttributeError: - self.hspan = self.plt.axhspan( - 0, 0.001, xmin=0, xmax=0, color='g') - poly_lims = self.hspan.get_xy() - poly_lims[0, 1] = ymin - poly_lims[1, 1] = ymax - poly_lims[2] = [n / total, ymax] - poly_lims[3] = [poly_lims[2, 0], ymin] - if len(poly_lims) > 4: - poly_lims[4, 1] = ymin - self.hspan.set_xy(poly_lims) - else: - t_ago = [cur_t - i for i in xdata] - line1.set_data(t_ago, ydata) - line2.set_data(t_ago, zdata) - - ax.set_title(self.format_meter( - n, total, elapsed, 0, - self.desc, self.ascii, self.unit, self.unit_scale, - 1 / self.avg_time if self.avg_time else None, self.bar_format, - self.postfix, self.unit_divisor), - fontname="DejaVu Sans Mono", fontsize=11) - self.plt.pause(1e-9) - - -def tgrange(*args, **kwargs): - """ - A shortcut for `tqdm.gui.tqdm(xrange(*args), **kwargs)`. - On Python3+, `range` is used instead of `xrange`. - """ - return tqdm_gui(_range(*args), **kwargs) - - -# Aliases -tqdm = tqdm_gui -trange = tgrange diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/__init__.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/__init__.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/__init__.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,40 +0,0 @@ -from .std import tqdm, trange -from .gui import tqdm as tqdm_gui # TODO: remove in v5.0.0 -from .gui import trange as tgrange # TODO: remove in v5.0.0 -from ._tqdm_pandas import tqdm_pandas -from .cli import main # TODO: remove in v5.0.0 -from ._monitor import TMonitor, TqdmSynchronisationWarning -from ._version import __version__ # NOQA -from .std import TqdmTypeError, TqdmKeyError, TqdmWarning, \ - TqdmDeprecationWarning, TqdmExperimentalWarning, \ - TqdmMonitorWarning - -__all__ = ['tqdm', 'tqdm_gui', 'trange', 'tgrange', 'tqdm_pandas', - 'tqdm_notebook', 'tnrange', 'main', 'TMonitor', - 'TqdmTypeError', 'TqdmKeyError', - 'TqdmWarning', 'TqdmDeprecationWarning', - 'TqdmExperimentalWarning', - 'TqdmMonitorWarning', 'TqdmSynchronisationWarning', - '__version__'] - - -def tqdm_notebook(*args, **kwargs): # pragma: no cover - """See tqdm.notebook.tqdm for full documentation""" - from .notebook import tqdm as _tqdm_notebook - from warnings import warn - warn("This function will be removed in tqdm==5.0.0\n" - "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`", - TqdmDeprecationWarning, stacklevel=2) - return _tqdm_notebook(*args, **kwargs) - - -def tnrange(*args, **kwargs): # pragma: no cover - """ - A shortcut for `tqdm.notebook.tqdm(xrange(*args), **kwargs)`. - On Python3+, `range` is used instead of `xrange`. - """ - from .notebook import trange as _tnrange - from warnings import warn - warn("Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`", - TqdmDeprecationWarning, stacklevel=2) - return _tnrange(*args, **kwargs) diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/__main__.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/__main__.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/__main__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/__main__.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,2 +0,0 @@ -from .cli import main -main() diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/_main.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/_main.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/_main.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/_main.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,7 +0,0 @@ -from .cli import * # NOQA -from .cli import __all__ # NOQA -from .std import TqdmDeprecationWarning -from warnings import warn -warn("This function will be removed in tqdm==5.0.0\n" - "Please use `tqdm.cli.*` instead of `tqdm._main.*`", - TqdmDeprecationWarning, stacklevel=2) diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/_monitor.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/_monitor.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/_monitor.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/_monitor.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,99 +0,0 @@ -from threading import Event, Thread, current_thread -from time import time -from warnings import warn -import atexit -__all__ = ["TMonitor", "TqdmSynchronisationWarning"] - - -class TqdmSynchronisationWarning(RuntimeWarning): - """tqdm multi-thread/-process errors which may cause incorrect nesting - but otherwise no adverse effects""" - pass - - -class TMonitor(Thread): - """ - Monitoring thread for tqdm bars. - Monitors if tqdm bars are taking too much time to display - and readjusts miniters automatically if necessary. - - Parameters - ---------- - tqdm_cls : class - tqdm class to use (can be core tqdm or a submodule). - sleep_interval : fload - Time to sleep between monitoring checks. - """ - - # internal vars for unit testing - _time = None - _event = None - - def __init__(self, tqdm_cls, sleep_interval): - Thread.__init__(self) - self.daemon = True # kill thread when main killed (KeyboardInterrupt) - self.was_killed = Event() - self.woken = 0 # last time woken up, to sync with monitor - self.tqdm_cls = tqdm_cls - self.sleep_interval = sleep_interval - if TMonitor._time is not None: - self._time = TMonitor._time - else: - self._time = time - if TMonitor._event is not None: - self._event = TMonitor._event - else: - self._event = Event - atexit.register(self.exit) - self.start() - - def exit(self): - self.was_killed.set() - if self is not current_thread(): - self.join() - return self.report() - - def get_instances(self): - # returns a copy of started `tqdm_cls` instances - return [i for i in self.tqdm_cls._instances.copy() - # Avoid race by checking that the instance started - if hasattr(i, 'start_t')] - - def run(self): - cur_t = self._time() - while True: - # After processing and before sleeping, notify that we woke - # Need to be done just before sleeping - self.woken = cur_t - # Sleep some time... - self.was_killed.wait(self.sleep_interval) - # Quit if killed - if self.was_killed.is_set(): - return - # Then monitor! - # Acquire lock (to access _instances) - with self.tqdm_cls.get_lock(): - cur_t = self._time() - # Check tqdm instances are waiting too long to print - instances = self.get_instances() - for instance in instances: - # Check event in loop to reduce blocking time on exit - if self.was_killed.is_set(): - return - # Only if mininterval > 1 (else iterations are just slow) - # and last refresh exceeded maxinterval - if instance.miniters > 1 and \ - (cur_t - instance.last_print_t) >= \ - instance.maxinterval: - # force bypassing miniters on next iteration - # (dynamic_miniters adjusts mininterval automatically) - instance.miniters = 1 - # Refresh now! (works only for manual tqdm) - instance.refresh(nolock=True) - if instances != self.get_instances(): # pragma: nocover - warn("Set changed size during iteration" + - " (see https://github.com/tqdm/tqdm/issues/481)", - TqdmSynchronisationWarning, stacklevel=2) - - def report(self): - return not self.was_killed.is_set() diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/notebook.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/notebook.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/notebook.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/notebook.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,261 +0,0 @@ -""" -IPython/Jupyter Notebook progressbar decorator for iterators. -Includes a default (x)range iterator printing to stderr. - -Usage: - >>> from tqdm.notebook import trange[, tqdm] - >>> for i in trange(10): #same as: for i in tqdm(xrange(10)) - ... ... -""" -# future division is important to divide integers and get as -# a result precise floating numbers (instead of truncated int) -from __future__ import division, absolute_import -# import compatibility functions and utilities -import sys -from .utils import _range -# to inherit from the tqdm class -from .std import tqdm as std_tqdm - - -if True: # pragma: no cover - # import IPython/Jupyter base widget and display utilities - IPY = 0 - IPYW = 0 - try: # IPython 4.x - import ipywidgets - IPY = 4 - try: - IPYW = int(ipywidgets.__version__.split('.')[0]) - except AttributeError: # __version__ may not exist in old versions - pass - except ImportError: # IPython 3.x / 2.x - IPY = 32 - import warnings - with warnings.catch_warnings(): - warnings.filterwarnings( - 'ignore', - message=".*The `IPython.html` package has been deprecated.*") - try: - import IPython.html.widgets as ipywidgets - except ImportError: - pass - - try: # IPython 4.x / 3.x - if IPY == 32: - from IPython.html.widgets import FloatProgress as IProgress - from IPython.html.widgets import HBox, HTML - IPY = 3 - else: - from ipywidgets import FloatProgress as IProgress - from ipywidgets import HBox, HTML - except ImportError: - try: # IPython 2.x - from IPython.html.widgets import FloatProgressWidget as IProgress - from IPython.html.widgets import ContainerWidget as HBox - from IPython.html.widgets import HTML - IPY = 2 - except ImportError: - IPY = 0 - - try: - from IPython.display import display # , clear_output - except ImportError: - pass - - # HTML encoding - try: # Py3 - from html import escape - except ImportError: # Py2 - from cgi import escape - - -__author__ = {"github.com/": ["lrq3000", "casperdcl", "alexanderkuk"]} -__all__ = ['tqdm_notebook', 'tnrange', 'tqdm', 'trange'] - - -class tqdm_notebook(std_tqdm): - """ - Experimental IPython/Jupyter Notebook widget using tqdm! - """ - - @staticmethod - def status_printer(_, total=None, desc=None, ncols=None): - """ - Manage the printing of an IPython/Jupyter Notebook progress bar widget. - """ - # Fallback to text bar if there's no total - # DEPRECATED: replaced with an 'info' style bar - # if not total: - # return super(tqdm_notebook, tqdm_notebook).status_printer(file) - - # fp = file - - # Prepare IPython progress bar - try: - if total: - pbar = IProgress(min=0, max=total) - else: # No total? Show info style bar with no progress tqdm status - pbar = IProgress(min=0, max=1) - pbar.value = 1 - pbar.bar_style = 'info' - except NameError: - # #187 #451 #558 - raise ImportError( - "FloatProgress not found. Please update jupyter and ipywidgets." - " See https://ipywidgets.readthedocs.io/en/stable" - "/user_install.html") - - if desc: - pbar.description = desc - if IPYW >= 7: - pbar.style.description_width = 'initial' - # Prepare status text - ptext = HTML() - # Only way to place text to the right of the bar is to use a container - container = HBox(children=[pbar, ptext]) - # Prepare layout - if ncols is not None: # use default style of ipywidgets - # ncols could be 100, "100px", "100%" - ncols = str(ncols) # ipywidgets only accepts string - try: - if int(ncols) > 0: # isnumeric and positive - ncols += 'px' - except ValueError: - pass - pbar.layout.flex = '2' - container.layout.width = ncols - container.layout.display = 'inline-flex' - container.layout.flex_flow = 'row wrap' - display(container) - - return container - - def display(self, msg=None, pos=None, - # additional signals - close=False, bar_style=None): - # Note: contrary to native tqdm, msg='' does NOT clear bar - # goal is to keep all infos if error happens so user knows - # at which iteration the loop failed. - - # Clear previous output (really necessary?) - # clear_output(wait=1) - - if not msg and not close: - msg = self.__repr__() - - pbar, ptext = self.container.children - pbar.value = self.n - - if msg: - # html escape special characters (like '&') - if '' in msg: - left, right = map(escape, msg.split('', 1)) - else: - left, right = '', escape(msg) - - # remove inesthetical pipes - if left and left[-1] == '|': - left = left[:-1] - if right and right[0] == '|': - right = right[1:] - - # Update description - pbar.description = left - if IPYW >= 7: - pbar.style.description_width = 'initial' - - # never clear the bar (signal: msg='') - if right: - ptext.value = right - - # Change bar style - if bar_style: - # Hack-ish way to avoid the danger bar_style being overridden by - # success because the bar gets closed after the error... - if not (pbar.bar_style == 'danger' and bar_style == 'success'): - pbar.bar_style = bar_style - - # Special signal to close the bar - if close and pbar.bar_style != 'danger': # hide only if no error - try: - self.container.close() - except AttributeError: - self.container.visible = False - - def __init__(self, *args, **kwargs): - # Setup default output - file_kwarg = kwargs.get('file', sys.stderr) - if file_kwarg is sys.stderr or file_kwarg is None: - kwargs['file'] = sys.stdout # avoid the red block in IPython - - # Initialize parent class + avoid printing by using gui=True - kwargs['gui'] = True - kwargs.setdefault('bar_format', '{l_bar}{bar}{r_bar}') - kwargs['bar_format'] = kwargs['bar_format'].replace('{bar}', '') - super(tqdm_notebook, self).__init__(*args, **kwargs) - if self.disable or not kwargs['gui']: - return - - # Get bar width - self.ncols = '100%' if self.dynamic_ncols else kwargs.get("ncols", None) - - # Replace with IPython progress bar display (with correct total) - unit_scale = 1 if self.unit_scale is True else self.unit_scale or 1 - total = self.total * unit_scale if self.total else self.total - self.container = self.status_printer( - self.fp, total, self.desc, self.ncols) - self.sp = self.display - - # Print initial bar state - if not self.disable: - self.display() - - def __iter__(self, *args, **kwargs): - try: - for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs): - # return super(tqdm...) will not catch exception - yield obj - # NB: except ... [ as ...] breaks IPython async KeyboardInterrupt - except: # NOQA - self.sp(bar_style='danger') - raise - - def update(self, *args, **kwargs): - try: - super(tqdm_notebook, self).update(*args, **kwargs) - except Exception as exc: - # cannot catch KeyboardInterrupt when using manual tqdm - # as the interrupt will most likely happen on another statement - self.sp(bar_style='danger') - raise exc - - def close(self, *args, **kwargs): - super(tqdm_notebook, self).close(*args, **kwargs) - # If it was not run in a notebook, sp is not assigned, check for it - if hasattr(self, 'sp'): - # Try to detect if there was an error or KeyboardInterrupt - # in manual mode: if n < total, things probably got wrong - if self.total and self.n < self.total: - self.sp(bar_style='danger') - else: - if self.leave: - self.sp(bar_style='success') - else: - self.sp(close=True) - - def moveto(self, *args, **kwargs): - # void -> avoid extraneous `\n` in IPython output cell - return - - -def tnrange(*args, **kwargs): - """ - A shortcut for `tqdm.notebook.tqdm(xrange(*args), **kwargs)`. - On Python3+, `range` is used instead of `xrange`. - """ - return tqdm_notebook(_range(*args), **kwargs) - - -# Aliases -tqdm = tqdm_notebook -trange = tnrange diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/std.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/std.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/std.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/std.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,1464 +0,0 @@ -""" -Customisable progressbar decorator for iterators. -Includes a default (x)range iterator printing to stderr. - -Usage: - >>> from tqdm import trange[, tqdm] - >>> for i in trange(10): #same as: for i in tqdm(xrange(10)) - ... ... -""" -from __future__ import absolute_import, division -# compatibility functions and utilities -from .utils import _supports_unicode, _environ_cols_wrapper, _range, _unich, \ - _term_move_up, _unicode, WeakSet, _basestring, _OrderedDict, _text_width, \ - Comparable, RE_ANSI, _is_ascii, FormatReplace, \ - SimpleTextIOWrapper, CallbackIOWrapper -from ._monitor import TMonitor -# native libraries -from contextlib import contextmanager -import sys -from numbers import Number -from time import time -# For parallelism safety -import threading as th -from warnings import warn - -__author__ = {"github.com/": ["noamraph", "obiwanus", "kmike", "hadim", - "casperdcl", "lrq3000"]} -__all__ = ['tqdm', 'trange', - 'TqdmTypeError', 'TqdmKeyError', 'TqdmWarning', - 'TqdmExperimentalWarning', 'TqdmDeprecationWarning', - 'TqdmMonitorWarning'] - - -class TqdmTypeError(TypeError): - pass - - -class TqdmKeyError(KeyError): - pass - - -class TqdmWarning(Warning): - """base class for all tqdm warnings. - - Used for non-external-code-breaking errors, such as garbled printing. - """ - def __init__(self, msg, fp_write=None, *a, **k): - if fp_write is not None: - fp_write("\n" + self.__class__.__name__ + ": " + - str(msg).rstrip() + '\n') - else: - super(TqdmWarning, self).__init__(msg, *a, **k) - - -class TqdmExperimentalWarning(TqdmWarning, FutureWarning): - """beta feature, unstable API and behaviour""" - pass - - -class TqdmDeprecationWarning(TqdmWarning, DeprecationWarning): - # not suppressed if raised - pass - - -class TqdmMonitorWarning(TqdmWarning, RuntimeWarning): - """tqdm monitor errors which do not affect external functionality""" - pass - - -class TqdmDefaultWriteLock(object): - """ - Provide a default write lock for thread and multiprocessing safety. - Works only on platforms supporting `fork` (so Windows is excluded). - You must initialise a `tqdm` or `TqdmDefaultWriteLock` instance - before forking in order for the write lock to work. - On Windows, you need to supply the lock from the parent to the children as - an argument to joblib or the parallelism lib you use. - """ - def __init__(self): - # Create global parallelism locks to avoid racing issues with parallel - # bars works only if fork available (Linux/MacOSX, but not Windows) - self.create_mp_lock() - self.create_th_lock() - cls = type(self) - self.locks = [lk for lk in [cls.mp_lock, cls.th_lock] if lk is not None] - - def acquire(self, *a, **k): - for lock in self.locks: - lock.acquire(*a, **k) - - def release(self): - for lock in self.locks[::-1]: # Release in inverse order of acquisition - lock.release() - - def __enter__(self): - self.acquire() - - def __exit__(self, *exc): - self.release() - - @classmethod - def create_mp_lock(cls): - if not hasattr(cls, 'mp_lock'): - try: - from multiprocessing import RLock - cls.mp_lock = RLock() # multiprocessing lock - except ImportError: # pragma: no cover - cls.mp_lock = None - except OSError: # pragma: no cover - cls.mp_lock = None - - @classmethod - def create_th_lock(cls): - if not hasattr(cls, 'th_lock'): - try: - cls.th_lock = th.RLock() # thread lock - except OSError: # pragma: no cover - cls.th_lock = None - - -# Create a thread lock before instantiation so that no setup needs to be done -# before running in a multithreaded environment. -# Do not create the multiprocessing lock because it sets the multiprocessing -# context and does not allow the user to use 'spawn' or 'forkserver' methods. -TqdmDefaultWriteLock.create_th_lock() - - -class Bar(object): - """ - `str.format`-able bar with format specifiers: `[width][type]` - - - `width` - + unspecified (default): use `self.default_len` - + `int >= 0`: overrides `self.default_len` - + `int < 0`: subtract from `self.default_len` - - `type` - + `a`: ascii (`charset=self.ASCII` override) - + `u`: unicode (`charset=self.UTF` override) - + `b`: blank (`charset=" "` override) - """ - ASCII = " 123456789#" - UTF = u" " + u''.join(map(_unich, range(0x258F, 0x2587, -1))) - BLANK = " " - - def __init__(self, frac, default_len=10, charset=UTF): - if not (0 <= frac <= 1): - warn("clamping frac to range [0, 1]", TqdmWarning, stacklevel=2) - frac = max(0, min(1, frac)) - assert default_len > 0 - self.frac = frac - self.default_len = default_len - self.charset = charset - - def __format__(self, format_spec): - if format_spec: - _type = format_spec[-1].lower() - try: - charset = dict(a=self.ASCII, u=self.UTF, b=self.BLANK)[_type] - except KeyError: - charset = self.charset - else: - format_spec = format_spec[:-1] - if format_spec: - N_BARS = int(format_spec) - if N_BARS < 0: - N_BARS += self.default_len - else: - N_BARS = self.default_len - else: - charset = self.charset - N_BARS = self.default_len - - nsyms = len(charset) - 1 - bar_length, frac_bar_length = divmod( - int(self.frac * N_BARS * nsyms), nsyms) - - bar = charset[-1] * bar_length - frac_bar = charset[frac_bar_length] - - # whitespace padding - if bar_length < N_BARS: - return bar + frac_bar + \ - charset[0] * (N_BARS - bar_length - 1) - return bar - - -class tqdm(Comparable): - """ - Decorate an iterable object, returning an iterator which acts exactly - like the original iterable, but prints a dynamically updating - progressbar every time a value is requested. - """ - - monitor_interval = 10 # set to 0 to disable the thread - monitor = None - - @staticmethod - def format_sizeof(num, suffix='', divisor=1000): - """ - Formats a number (greater than unity) with SI Order of Magnitude - prefixes. - - Parameters - ---------- - num : float - Number ( >= 1) to format. - suffix : str, optional - Post-postfix [default: '']. - divisor : float, optional - Divisor between prefixes [default: 1000]. - - Returns - ------- - out : str - Number with Order of Magnitude SI unit postfix. - """ - for unit in ['', 'k', 'M', 'G', 'T', 'P', 'E', 'Z']: - if abs(num) < 999.5: - if abs(num) < 99.95: - if abs(num) < 9.995: - return '{0:1.2f}'.format(num) + unit + suffix - return '{0:2.1f}'.format(num) + unit + suffix - return '{0:3.0f}'.format(num) + unit + suffix - num /= divisor - return '{0:3.1f}Y'.format(num) + suffix - - @staticmethod - def format_interval(t): - """ - Formats a number of seconds as a clock time, [H:]MM:SS - - Parameters - ---------- - t : int - Number of seconds. - - Returns - ------- - out : str - [H:]MM:SS - """ - mins, s = divmod(int(t), 60) - h, m = divmod(mins, 60) - if h: - return '{0:d}:{1:02d}:{2:02d}'.format(h, m, s) - else: - return '{0:02d}:{1:02d}'.format(m, s) - - @staticmethod - def format_num(n): - """ - Intelligent scientific notation (.3g). - - Parameters - ---------- - n : int or float or Numeric - A Number. - - Returns - ------- - out : str - Formatted number. - """ - f = '{0:.3g}'.format(n).replace('+0', '+').replace('-0', '-') - n = str(n) - return f if len(f) < len(n) else n - - @staticmethod - def ema(x, mu=None, alpha=0.3): - """ - Exponential moving average: smoothing to give progressively lower - weights to older values. - - Parameters - ---------- - x : float - New value to include in EMA. - mu : float, optional - Previous EMA value. - alpha : float, optional - Smoothing factor in range [0, 1], [default: 0.3]. - Increase to give more weight to recent values. - Ranges from 0 (yields mu) to 1 (yields x). - """ - return x if mu is None else (alpha * x) + (1 - alpha) * mu - - @staticmethod - def status_printer(file): - """ - Manage the printing and in-place updating of a line of characters. - Note that if the string is longer than a line, then in-place - updating may not work (it will print a new line at each refresh). - """ - fp = file - fp_flush = getattr(fp, 'flush', lambda: None) # pragma: no cover - - def fp_write(s): - fp.write(_unicode(s)) - fp_flush() - - last_len = [0] - - def print_status(s): - len_s = len(s) - fp_write('\r' + s + (' ' * max(last_len[0] - len_s, 0))) - last_len[0] = len_s - - return print_status - - @staticmethod - def format_meter(n, total, elapsed, ncols=None, prefix='', ascii=False, - unit='it', unit_scale=False, rate=None, bar_format=None, - postfix=None, unit_divisor=1000, **extra_kwargs): - """ - Return a string-based progress bar given some parameters - - Parameters - ---------- - n : int or float - Number of finished iterations. - total : int or float - The expected total number of iterations. If meaningless (None), - only basic progress statistics are displayed (no ETA). - elapsed : float - Number of seconds passed since start. - ncols : int, optional - The width of the entire output message. If specified, - dynamically resizes `{bar}` to stay within this bound - [default: None]. If `0`, will not print any bar (only stats). - The fallback is `{bar:10}`. - prefix : str, optional - Prefix message (included in total width) [default: '']. - Use as {desc} in bar_format string. - ascii : bool, optional or str, optional - If not set, use unicode (smooth blocks) to fill the meter - [default: False]. The fallback is to use ASCII characters - " 123456789#". - unit : str, optional - The iteration unit [default: 'it']. - unit_scale : bool or int or float, optional - If 1 or True, the number of iterations will be printed with an - appropriate SI metric prefix (k = 10^3, M = 10^6, etc.) - [default: False]. If any other non-zero number, will scale - `total` and `n`. - rate : float, optional - Manual override for iteration rate. - If [default: None], uses n/elapsed. - bar_format : str, optional - Specify a custom bar string formatting. May impact performance. - [default: '{l_bar}{bar}{r_bar}'], where - l_bar='{desc}: {percentage:3.0f}%|' and - r_bar='| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, ' - '{rate_fmt}{postfix}]' - Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt, - percentage, elapsed, elapsed_s, ncols, desc, unit, - rate, rate_fmt, rate_noinv, rate_noinv_fmt, - rate_inv, rate_inv_fmt, postfix, unit_divisor, - remaining, remaining_s. - Note that a trailing ": " is automatically removed after {desc} - if the latter is empty. - postfix : *, optional - Similar to `prefix`, but placed at the end - (e.g. for additional stats). - Note: postfix is usually a string (not a dict) for this method, - and will if possible be set to postfix = ', ' + postfix. - However other types are supported (#382). - unit_divisor : float, optional - [default: 1000], ignored unless `unit_scale` is True. - - Returns - ------- - out : Formatted meter and stats, ready to display. - """ - - # sanity check: total - if total and n >= (total + 0.5): # allow float imprecision (#849) - total = None - - # apply custom scale if necessary - if unit_scale and unit_scale not in (True, 1): - if total: - total *= unit_scale - n *= unit_scale - if rate: - rate *= unit_scale # by default rate = 1 / self.avg_time - unit_scale = False - - elapsed_str = tqdm.format_interval(elapsed) - - # if unspecified, attempt to use rate = average speed - # (we allow manual override since predicting time is an arcane art) - if rate is None and elapsed: - rate = n / elapsed - inv_rate = 1 / rate if rate else None - format_sizeof = tqdm.format_sizeof - rate_noinv_fmt = ((format_sizeof(rate) if unit_scale else - '{0:5.2f}'.format(rate)) - if rate else '?') + unit + '/s' - rate_inv_fmt = ((format_sizeof(inv_rate) if unit_scale else - '{0:5.2f}'.format(inv_rate)) - if inv_rate else '?') + 's/' + unit - rate_fmt = rate_inv_fmt if inv_rate and inv_rate > 1 else rate_noinv_fmt - - if unit_scale: - n_fmt = format_sizeof(n, divisor=unit_divisor) - total_fmt = format_sizeof(total, divisor=unit_divisor) \ - if total is not None else '?' - else: - n_fmt = str(n) - total_fmt = str(total) if total is not None else '?' - - try: - postfix = ', ' + postfix if postfix else '' - except TypeError: - pass - - remaining = (total - n) / rate if rate and total else 0 - remaining_str = tqdm.format_interval(remaining) if rate else '?' - - # format the stats displayed to the left and right sides of the bar - if prefix: - # old prefix setup work around - bool_prefix_colon_already = (prefix[-2:] == ": ") - l_bar = prefix if bool_prefix_colon_already else prefix + ": " - else: - l_bar = '' - - r_bar = '| {0}/{1} [{2}<{3}, {4}{5}]'.format( - n_fmt, total_fmt, elapsed_str, remaining_str, rate_fmt, postfix) - - # Custom bar formatting - # Populate a dict with all available progress indicators - format_dict = dict( - # slight extension of self.format_dict - n=n, n_fmt=n_fmt, total=total, total_fmt=total_fmt, - elapsed=elapsed_str, elapsed_s=elapsed, - ncols=ncols, desc=prefix or '', unit=unit, - rate=inv_rate if inv_rate and inv_rate > 1 else rate, - rate_fmt=rate_fmt, rate_noinv=rate, - rate_noinv_fmt=rate_noinv_fmt, rate_inv=inv_rate, - rate_inv_fmt=rate_inv_fmt, - postfix=postfix, unit_divisor=unit_divisor, - # plus more useful definitions - remaining=remaining_str, remaining_s=remaining, - l_bar=l_bar, r_bar=r_bar, - **extra_kwargs) - - # total is known: we can predict some stats - if total: - # fractional and percentage progress - frac = n / total - percentage = frac * 100 - - l_bar += '{0:3.0f}%|'.format(percentage) - - if ncols == 0: - return l_bar[:-1] + r_bar[1:] - - format_dict.update(l_bar=l_bar) - if bar_format: - format_dict.update(percentage=percentage) - - # auto-remove colon for empty `desc` - if not prefix: - bar_format = bar_format.replace("{desc}: ", '') - else: - bar_format = "{l_bar}{bar}{r_bar}" - - full_bar = FormatReplace() - try: - nobar = bar_format.format(bar=full_bar, **format_dict) - except UnicodeEncodeError: - bar_format = _unicode(bar_format) - nobar = bar_format.format(bar=full_bar, **format_dict) - if not full_bar.format_called: - # no {bar}, we can just format and return - return nobar - - # Formatting progress bar space available for bar's display - full_bar = Bar( - frac, - max(1, ncols - _text_width(RE_ANSI.sub('', nobar))) - if ncols else 10, - charset=Bar.ASCII if ascii is True else ascii or Bar.UTF) - if not _is_ascii(full_bar.charset) and _is_ascii(bar_format): - bar_format = _unicode(bar_format) - return bar_format.format(bar=full_bar, **format_dict) - - elif bar_format: - # user-specified bar_format but no total - l_bar += '|' - format_dict.update(l_bar=l_bar, percentage=0) - full_bar = FormatReplace() - nobar = bar_format.format(bar=full_bar, **format_dict) - if not full_bar.format_called: - return nobar - full_bar = Bar( - 0, - max(1, ncols - _text_width(RE_ANSI.sub('', nobar))) - if ncols else 10, - charset=Bar.BLANK) - return bar_format.format(bar=full_bar, **format_dict) - else: - # no total: no progressbar, ETA, just progress stats - return ((prefix + ": ") if prefix else '') + \ - '{0}{1} [{2}, {3}{4}]'.format( - n_fmt, unit, elapsed_str, rate_fmt, postfix) - - def __new__(cls, *args, **kwargs): - # Create a new instance - instance = object.__new__(cls) - # Construct the lock if it does not exist - with cls.get_lock(): - # Add to the list of instances - if not hasattr(cls, '_instances'): - cls._instances = WeakSet() - cls._instances.add(instance) - # Create the monitoring thread - if cls.monitor_interval and (cls.monitor is None or not - cls.monitor.report()): - try: - cls.monitor = TMonitor(cls, cls.monitor_interval) - except Exception as e: # pragma: nocover - warn("tqdm:disabling monitor support" - " (monitor_interval = 0) due to:\n" + str(e), - TqdmMonitorWarning, stacklevel=2) - cls.monitor_interval = 0 - # Return the instance - return instance - - @classmethod - def _get_free_pos(cls, instance=None): - """Skips specified instance.""" - positions = set(abs(inst.pos) for inst in cls._instances - if inst is not instance and hasattr(inst, "pos")) - return min(set(range(len(positions) + 1)).difference(positions)) - - @classmethod - def _decr_instances(cls, instance): - """ - Remove from list and reposition other bars - so that newer bars won't overlap previous bars - """ - with cls._lock: - try: - cls._instances.remove(instance) - except KeyError: - # if not instance.gui: # pragma: no cover - # raise - pass # py2: maybe magically removed already - # else: - if not instance.gui: - for inst in cls._instances: - # negative `pos` means fixed - if hasattr(inst, "pos") and inst.pos > abs(instance.pos): - inst.clear(nolock=True) - inst.pos -= 1 - # TODO: check this doesn't overwrite another fixed bar - # Kill monitor if no instances are left - if not cls._instances and cls.monitor: - try: - cls.monitor.exit() - del cls.monitor - except AttributeError: # pragma: nocover - pass - else: - cls.monitor = None - - @classmethod - def write(cls, s, file=None, end="\n", nolock=False): - """Print a message via tqdm (without overlap with bars).""" - fp = file if file is not None else sys.stdout - with cls.external_write_mode(file=file, nolock=nolock): - # Write the message - fp.write(s) - fp.write(end) - - @classmethod - @contextmanager - def external_write_mode(cls, file=None, nolock=False): - """ - Disable tqdm within context and refresh tqdm when exits. - Useful when writing to standard output stream - """ - fp = file if file is not None else sys.stdout - - if not nolock: - cls.get_lock().acquire() - # Clear all bars - inst_cleared = [] - for inst in getattr(cls, '_instances', []): - # Clear instance if in the target output file - # or if write output + tqdm output are both either - # sys.stdout or sys.stderr (because both are mixed in terminal) - if hasattr(inst, "start_t") and (inst.fp == fp or all( - f in (sys.stdout, sys.stderr) for f in (fp, inst.fp))): - inst.clear(nolock=True) - inst_cleared.append(inst) - yield - # Force refresh display of bars we cleared - for inst in inst_cleared: - inst.refresh(nolock=True) - if not nolock: - cls._lock.release() - - @classmethod - def set_lock(cls, lock): - """Set the global lock.""" - cls._lock = lock - - @classmethod - def get_lock(cls): - """Get the global lock. Construct it if it does not exist.""" - if not hasattr(cls, '_lock'): - cls._lock = TqdmDefaultWriteLock() - return cls._lock - - @classmethod - def pandas(tclass, *targs, **tkwargs): - """ - Registers the given `tqdm` class with - pandas.core. - ( frame.DataFrame - | series.Series - | groupby.(generic.)DataFrameGroupBy - | groupby.(generic.)SeriesGroupBy - ).progress_apply - - A new instance will be create every time `progress_apply` is called, - and each instance will automatically close() upon completion. - - Parameters - ---------- - targs, tkwargs : arguments for the tqdm instance - - Examples - -------- - >>> import pandas as pd - >>> import numpy as np - >>> from tqdm import tqdm - >>> from tqdm.gui import tqdm as tqdm_gui - >>> - >>> df = pd.DataFrame(np.random.randint(0, 100, (100000, 6))) - >>> tqdm.pandas(ncols=50) # can use tqdm_gui, optional kwargs, etc - >>> # Now you can use `progress_apply` instead of `apply` - >>> df.groupby(0).progress_apply(lambda x: x**2) - - References - ---------- - https://stackoverflow.com/questions/18603270/ - progress-indicator-during-pandas-operations-python - """ - from pandas.core.frame import DataFrame - from pandas.core.series import Series - try: - from pandas import Panel - except ImportError: # TODO: pandas>0.25.2 - Panel = None - try: # pandas>=0.18.0 - from pandas.core.window import _Rolling_and_Expanding - except ImportError: # pragma: no cover - _Rolling_and_Expanding = None - try: # pandas>=0.25.0 - from pandas.core.groupby.generic import DataFrameGroupBy, \ - SeriesGroupBy # , NDFrameGroupBy - except ImportError: - try: # pandas>=0.23.0 - from pandas.core.groupby.groupby import DataFrameGroupBy, \ - SeriesGroupBy - except ImportError: - from pandas.core.groupby import DataFrameGroupBy, \ - SeriesGroupBy - try: # pandas>=0.23.0 - from pandas.core.groupby.groupby import GroupBy - except ImportError: - from pandas.core.groupby import GroupBy - - try: # pandas>=0.23.0 - from pandas.core.groupby.groupby import PanelGroupBy - except ImportError: - try: - from pandas.core.groupby import PanelGroupBy - except ImportError: # pandas>=0.25.0 - PanelGroupBy = None - - deprecated_t = [tkwargs.pop('deprecated_t', None)] - - def inner_generator(df_function='apply'): - def inner(df, func, *args, **kwargs): - """ - Parameters - ---------- - df : (DataFrame|Series)[GroupBy] - Data (may be grouped). - func : function - To be applied on the (grouped) data. - **kwargs : optional - Transmitted to `df.apply()`. - """ - - # Precompute total iterations - total = tkwargs.pop("total", getattr(df, 'ngroups', None)) - if total is None: # not grouped - if df_function == 'applymap': - total = df.size - elif isinstance(df, Series): - total = len(df) - elif _Rolling_and_Expanding is None or \ - not isinstance(df, _Rolling_and_Expanding): - # DataFrame or Panel - axis = kwargs.get('axis', 0) - if axis == 'index': - axis = 0 - elif axis == 'columns': - axis = 1 - # when axis=0, total is shape[axis1] - total = df.size // df.shape[axis] - - # Init bar - if deprecated_t[0] is not None: - t = deprecated_t[0] - deprecated_t[0] = None - else: - t = tclass(*targs, total=total, **tkwargs) - - if len(args) > 0: - # *args intentionally not supported (see #244, #299) - TqdmDeprecationWarning( - "Except func, normal arguments are intentionally" + - " not supported by" + - " `(DataFrame|Series|GroupBy).progress_apply`." + - " Use keyword arguments instead.", - fp_write=getattr(t.fp, 'write', sys.stderr.write)) - - try: - func = df._is_builtin_func(func) - except TypeError: - pass - - # Define bar updating wrapper - def wrapper(*args, **kwargs): - # update tbar correctly - # it seems `pandas apply` calls `func` twice - # on the first column/row to decide whether it can - # take a fast or slow code path; so stop when t.total==t.n - t.update(n=1 if not t.total or t.n < t.total else 0) - return func(*args, **kwargs) - - # Apply the provided function (in **kwargs) - # on the df using our wrapper (which provides bar updating) - result = getattr(df, df_function)(wrapper, **kwargs) - - # Close bar and return pandas calculation result - t.close() - return result - - return inner - - # Monkeypatch pandas to provide easy methods - # Enable custom tqdm progress in pandas! - Series.progress_apply = inner_generator() - SeriesGroupBy.progress_apply = inner_generator() - Series.progress_map = inner_generator('map') - SeriesGroupBy.progress_map = inner_generator('map') - - DataFrame.progress_apply = inner_generator() - DataFrameGroupBy.progress_apply = inner_generator() - DataFrame.progress_applymap = inner_generator('applymap') - - if Panel is not None: - Panel.progress_apply = inner_generator() - if PanelGroupBy is not None: - PanelGroupBy.progress_apply = inner_generator() - - GroupBy.progress_apply = inner_generator() - GroupBy.progress_aggregate = inner_generator('aggregate') - GroupBy.progress_transform = inner_generator('transform') - - if _Rolling_and_Expanding is not None: # pragma: no cover - _Rolling_and_Expanding.progress_apply = inner_generator() - - def __init__(self, iterable=None, desc=None, total=None, leave=True, - file=None, ncols=None, mininterval=0.1, maxinterval=10.0, - miniters=None, ascii=None, disable=False, unit='it', - unit_scale=False, dynamic_ncols=False, smoothing=0.3, - bar_format=None, initial=0, position=None, postfix=None, - unit_divisor=1000, write_bytes=None, lock_args=None, - gui=False, **kwargs): - """ - Parameters - ---------- - iterable : iterable, optional - Iterable to decorate with a progressbar. - Leave blank to manually manage the updates. - desc : str, optional - Prefix for the progressbar. - total : int or float, optional - The number of expected iterations. If unspecified, - len(iterable) is used if possible. If float("inf") or as a last - resort, only basic progress statistics are displayed - (no ETA, no progressbar). - If `gui` is True and this parameter needs subsequent updating, - specify an initial arbitrary large positive number, - e.g. 9e9. - leave : bool, optional - If [default: True], keeps all traces of the progressbar - upon termination of iteration. - If `None`, will leave only if `position` is `0`. - file : `io.TextIOWrapper` or `io.StringIO`, optional - Specifies where to output the progress messages - (default: sys.stderr). Uses `file.write(str)` and `file.flush()` - methods. For encoding, see `write_bytes`. - ncols : int, optional - The width of the entire output message. If specified, - dynamically resizes the progressbar to stay within this bound. - If unspecified, attempts to use environment width. The - fallback is a meter width of 10 and no limit for the counter and - statistics. If 0, will not print any meter (only stats). - mininterval : float, optional - Minimum progress display update interval [default: 0.1] seconds. - maxinterval : float, optional - Maximum progress display update interval [default: 10] seconds. - Automatically adjusts `miniters` to correspond to `mininterval` - after long display update lag. Only works if `dynamic_miniters` - or monitor thread is enabled. - miniters : int or float, optional - Minimum progress display update interval, in iterations. - If 0 and `dynamic_miniters`, will automatically adjust to equal - `mininterval` (more CPU efficient, good for tight loops). - If > 0, will skip display of specified number of iterations. - Tweak this and `mininterval` to get very efficient loops. - If your progress is erratic with both fast and slow iterations - (network, skipping items, etc) you should set miniters=1. - ascii : bool or str, optional - If unspecified or False, use unicode (smooth blocks) to fill - the meter. The fallback is to use ASCII characters " 123456789#". - disable : bool, optional - Whether to disable the entire progressbar wrapper - [default: False]. If set to None, disable on non-TTY. - unit : str, optional - String that will be used to define the unit of each iteration - [default: it]. - unit_scale : bool or int or float, optional - If 1 or True, the number of iterations will be reduced/scaled - automatically and a metric prefix following the - International System of Units standard will be added - (kilo, mega, etc.) [default: False]. If any other non-zero - number, will scale `total` and `n`. - dynamic_ncols : bool, optional - If set, constantly alters `ncols` to the environment (allowing - for window resizes) [default: False]. - smoothing : float, optional - Exponential moving average smoothing factor for speed estimates - (ignored in GUI mode). Ranges from 0 (average speed) to 1 - (current/instantaneous speed) [default: 0.3]. - bar_format : str, optional - Specify a custom bar string formatting. May impact performance. - [default: '{l_bar}{bar}{r_bar}'], where - l_bar='{desc}: {percentage:3.0f}%|' and - r_bar='| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, ' - '{rate_fmt}{postfix}]' - Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt, - percentage, elapsed, elapsed_s, ncols, desc, unit, - rate, rate_fmt, rate_noinv, rate_noinv_fmt, - rate_inv, rate_inv_fmt, postfix, unit_divisor, - remaining, remaining_s. - Note that a trailing ": " is automatically removed after {desc} - if the latter is empty. - initial : int or float, optional - The initial counter value. Useful when restarting a progress - bar [default: 0]. If using float, consider specifying `{n:.3f}` - or similar in `bar_format`, or specifying `unit_scale`. - position : int, optional - Specify the line offset to print this bar (starting from 0) - Automatic if unspecified. - Useful to manage multiple bars at once (eg, from threads). - postfix : dict or *, optional - Specify additional stats to display at the end of the bar. - Calls `set_postfix(**postfix)` if possible (dict). - unit_divisor : float, optional - [default: 1000], ignored unless `unit_scale` is True. - write_bytes : bool, optional - If (default: None) and `file` is unspecified, - bytes will be written in Python 2. If `True` will also write - bytes. In all other cases will default to unicode. - lock_args : tuple, optional - Passed to `refresh` for intermediate output - (initialisation, iterating, and updating). - gui : bool, optional - WARNING: internal parameter - do not use. - Use tqdm.gui.tqdm(...) instead. If set, will attempt to use - matplotlib animations for a graphical output [default: False]. - - Returns - ------- - out : decorated iterator. - """ - if write_bytes is None: - write_bytes = file is None and sys.version_info < (3,) - - if file is None: - file = sys.stderr - - if write_bytes: - # Despite coercing unicode into bytes, py2 sys.std* streams - # should have bytes written to them. - file = SimpleTextIOWrapper( - file, encoding=getattr(file, 'encoding', None) or 'utf-8') - - if disable is None and hasattr(file, "isatty") and not file.isatty(): - disable = True - - if total is None and iterable is not None: - try: - total = len(iterable) - except (TypeError, AttributeError): - total = None - if total == float("inf"): - # Infinite iterations, behave same as unknown - total = None - - if disable: - self.iterable = iterable - self.disable = disable - with self._lock: - self.pos = self._get_free_pos(self) - self._instances.remove(self) - self.n = initial - self.total = total - return - - if kwargs: - self.disable = True - with self._lock: - self.pos = self._get_free_pos(self) - self._instances.remove(self) - raise ( - TqdmDeprecationWarning( - "`nested` is deprecated and automated.\n" - "Use `position` instead for manual control.\n", - fp_write=getattr(file, 'write', sys.stderr.write)) - if "nested" in kwargs else - TqdmKeyError("Unknown argument(s): " + str(kwargs))) - - # Preprocess the arguments - if ((ncols is None) and (file in (sys.stderr, sys.stdout))) or \ - dynamic_ncols: # pragma: no cover - if dynamic_ncols: - dynamic_ncols = _environ_cols_wrapper() - if dynamic_ncols: - ncols = dynamic_ncols(file) - else: - _dynamic_ncols = _environ_cols_wrapper() - if _dynamic_ncols: - ncols = _dynamic_ncols(file) - - if miniters is None: - miniters = 0 - dynamic_miniters = True - else: - dynamic_miniters = False - - if mininterval is None: - mininterval = 0 - - if maxinterval is None: - maxinterval = 0 - - if ascii is None: - ascii = not _supports_unicode(file) - - if bar_format and not ((ascii is True) or _is_ascii(ascii)): - # Convert bar format into unicode since terminal uses unicode - bar_format = _unicode(bar_format) - - if smoothing is None: - smoothing = 0 - - # Store the arguments - self.iterable = iterable - self.desc = desc or '' - self.total = total - self.leave = leave - self.fp = file - self.ncols = ncols - self.mininterval = mininterval - self.maxinterval = maxinterval - self.miniters = miniters - self.dynamic_miniters = dynamic_miniters - self.ascii = ascii - self.disable = disable - self.unit = unit - self.unit_scale = unit_scale - self.unit_divisor = unit_divisor - self.lock_args = lock_args - self.gui = gui - self.dynamic_ncols = dynamic_ncols - self.smoothing = smoothing - self.avg_time = None - self._time = time - self.bar_format = bar_format - self.postfix = None - if postfix: - try: - self.set_postfix(refresh=False, **postfix) - except TypeError: - self.postfix = postfix - - # Init the iterations counters - self.last_print_n = initial - self.n = initial - - # if nested, at initial sp() call we replace '\r' by '\n' to - # not overwrite the outer progress bar - with self._lock: - if position is None: - self.pos = self._get_free_pos(self) - else: # mark fixed positions as negative - self.pos = -position - - if not gui: - # Initialize the screen printer - self.sp = self.status_printer(self.fp) - self.refresh(lock_args=self.lock_args) - - # Init the time counter - self.last_print_t = self._time() - # NB: Avoid race conditions by setting start_t at the very end of init - self.start_t = self.last_print_t - - def __bool__(self): - if self.total is not None: - return self.total > 0 - if self.iterable is None: - raise TypeError('bool() undefined when iterable == total == None') - return bool(self.iterable) - - def __nonzero__(self): - return self.__bool__() - - def __len__(self): - return self.total if self.iterable is None else \ - (self.iterable.shape[0] if hasattr(self.iterable, "shape") - else len(self.iterable) if hasattr(self.iterable, "__len__") - else getattr(self, "total", None)) - - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_value, traceback): - try: - self.close() - except AttributeError: - # maybe eager thread cleanup upon external error - if (exc_type, exc_value, traceback) == (None, None, None): - raise - warn("AttributeError ignored", TqdmWarning, stacklevel=2) - - def __del__(self): - self.close() - - def __repr__(self): - return self.format_meter(**self.format_dict) - - @property - def _comparable(self): - return abs(getattr(self, "pos", 1 << 31)) - - def __hash__(self): - return id(self) - - def __iter__(self): - """Backward-compatibility to use: for x in tqdm(iterable)""" - - # Inlining instance variables as locals (speed optimisation) - iterable = self.iterable - - # If the bar is disabled, then just walk the iterable - # (note: keep this check outside the loop for performance) - if self.disable: - for obj in iterable: - yield obj - return - - mininterval = self.mininterval - maxinterval = self.maxinterval - miniters = self.miniters - dynamic_miniters = self.dynamic_miniters - last_print_t = self.last_print_t - last_print_n = self.last_print_n - n = self.n - smoothing = self.smoothing - avg_time = self.avg_time - time = self._time - - if not hasattr(self, 'sp'): - raise TqdmDeprecationWarning( - "Please use `tqdm.gui.tqdm(...)` instead of" - " `tqdm(..., gui=True)`\n", - fp_write=getattr(self.fp, 'write', sys.stderr.write)) - - for obj in iterable: - yield obj - # Update and possibly print the progressbar. - # Note: does not call self.update(1) for speed optimisation. - n += 1 - # check counter first to avoid calls to time() - if n - last_print_n >= self.miniters: - miniters = self.miniters # watch monitoring thread changes - delta_t = time() - last_print_t - if delta_t >= mininterval: - cur_t = time() - delta_it = n - last_print_n - # EMA (not just overall average) - if smoothing and delta_t and delta_it: - rate = delta_t / delta_it - avg_time = self.ema(rate, avg_time, smoothing) - self.avg_time = avg_time - - self.n = n - self.refresh(lock_args=self.lock_args) - - # If no `miniters` was specified, adjust automatically - # to the max iteration rate seen so far between 2 prints - if dynamic_miniters: - if maxinterval and delta_t >= maxinterval: - # Adjust miniters to time interval by rule of 3 - if mininterval: - # Set miniters to correspond to mininterval - miniters = delta_it * mininterval / delta_t - else: - # Set miniters to correspond to maxinterval - miniters = delta_it * maxinterval / delta_t - elif smoothing: - # EMA-weight miniters to converge - # towards the timeframe of mininterval - rate = delta_it - if mininterval and delta_t: - rate *= mininterval / delta_t - miniters = self.ema(rate, miniters, smoothing) - else: - # Maximum nb of iterations between 2 prints - miniters = max(miniters, delta_it) - - # Store old values for next call - self.n = self.last_print_n = last_print_n = n - self.last_print_t = last_print_t = cur_t - self.miniters = miniters - - # Closing the progress bar. - # Update some internal variables for close(). - self.last_print_n = last_print_n - self.n = n - self.miniters = miniters - self.close() - - def update(self, n=1): - """ - Manually update the progress bar, useful for streams - such as reading files. - E.g.: - >>> t = tqdm(total=filesize) # Initialise - >>> for current_buffer in stream: - ... ... - ... t.update(len(current_buffer)) - >>> t.close() - The last line is highly recommended, but possibly not necessary if - `t.update()` will be called in such a way that `filesize` will be - exactly reached and printed. - - Parameters - ---------- - n : int or float, optional - Increment to add to the internal counter of iterations - [default: 1]. If using float, consider specifying `{n:.3f}` - or similar in `bar_format`, or specifying `unit_scale`. - """ - # N.B.: see __iter__() for more comments. - if self.disable: - return - - if n < 0: - self.last_print_n += n # for auto-refresh logic to work - self.n += n - - # check counter first to reduce calls to time() - if self.n - self.last_print_n >= self.miniters: - delta_t = self._time() - self.last_print_t - if delta_t >= self.mininterval: - cur_t = self._time() - delta_it = self.n - self.last_print_n # >= n - # elapsed = cur_t - self.start_t - # EMA (not just overall average) - if self.smoothing and delta_t and delta_it: - rate = delta_t / delta_it - self.avg_time = self.ema( - rate, self.avg_time, self.smoothing) - - if not hasattr(self, "sp"): - raise TqdmDeprecationWarning( - "Please use `tqdm.gui.tqdm(...)`" - " instead of `tqdm(..., gui=True)`\n", - fp_write=getattr(self.fp, 'write', sys.stderr.write)) - - self.refresh(lock_args=self.lock_args) - - # If no `miniters` was specified, adjust automatically to the - # maximum iteration rate seen so far between two prints. - # e.g.: After running `tqdm.update(5)`, subsequent - # calls to `tqdm.update()` will only cause an update after - # at least 5 more iterations. - if self.dynamic_miniters: - if self.maxinterval and delta_t >= self.maxinterval: - if self.mininterval: - self.miniters = delta_it * self.mininterval \ - / delta_t - else: - self.miniters = delta_it * self.maxinterval \ - / delta_t - elif self.smoothing: - self.miniters = self.smoothing * delta_it * \ - (self.mininterval / delta_t - if self.mininterval and delta_t - else 1) + \ - (1 - self.smoothing) * self.miniters - else: - self.miniters = max(self.miniters, delta_it) - - # Store old values for next call - self.last_print_n = self.n - self.last_print_t = cur_t - - def close(self): - """Cleanup and (if leave=False) close the progressbar.""" - if self.disable: - return - - # Prevent multiple closures - self.disable = True - - # decrement instance pos and remove from internal set - pos = abs(self.pos) - self._decr_instances(self) - - # GUI mode - if not hasattr(self, "sp"): - return - - # annoyingly, _supports_unicode isn't good enough - def fp_write(s): - self.fp.write(_unicode(s)) - - try: - fp_write('') - except ValueError as e: - if 'closed' in str(e): - return - raise # pragma: no cover - - leave = pos == 0 if self.leave is None else self.leave - - with self._lock: - if leave: - # stats for overall rate (no weighted average) - self.avg_time = None - self.display(pos=0) - fp_write('\n') - else: - self.display(msg='', pos=pos) - if not pos: - fp_write('\r') - - def clear(self, nolock=False): - """Clear current bar display.""" - if self.disable: - return - - if not nolock: - self._lock.acquire() - self.moveto(abs(self.pos)) - self.sp('') - self.fp.write('\r') # place cursor back at the beginning of line - self.moveto(-abs(self.pos)) - if not nolock: - self._lock.release() - - def refresh(self, nolock=False, lock_args=None): - """ - Force refresh the display of this bar. - - Parameters - ---------- - nolock : bool, optional - If `True`, does not lock. - If [default: `False`]: calls `acquire()` on internal lock. - lock_args : tuple, optional - Passed to internal lock's `acquire()`. - If specified, will only `display()` if `acquire()` returns `True`. - """ - if self.disable: - return - - if not nolock: - if lock_args: - if not self._lock.acquire(*lock_args): - return False - else: - self._lock.acquire() - self.display() - if not nolock: - self._lock.release() - return True - - def unpause(self): - """Restart tqdm timer from last print time.""" - cur_t = self._time() - self.start_t += cur_t - self.last_print_t - self.last_print_t = cur_t - - def reset(self, total=None): - """ - Resets to 0 iterations for repeated use. - - Consider combining with `leave=True`. - - Parameters - ---------- - total : int or float, optional. Total to use for the new bar. - """ - self.last_print_n = self.n = 0 - self.last_print_t = self.start_t = self._time() - if total is not None: - self.total = total - self.refresh() - - def set_description(self, desc=None, refresh=True): - """ - Set/modify description of the progress bar. - - Parameters - ---------- - desc : str, optional - refresh : bool, optional - Forces refresh [default: True]. - """ - self.desc = desc + ': ' if desc else '' - if refresh: - self.refresh() - - def set_description_str(self, desc=None, refresh=True): - """Set/modify description without ': ' appended.""" - self.desc = desc or '' - if refresh: - self.refresh() - - def set_postfix(self, ordered_dict=None, refresh=True, **kwargs): - """ - Set/modify postfix (additional stats) - with automatic formatting based on datatype. - - Parameters - ---------- - ordered_dict : dict or OrderedDict, optional - refresh : bool, optional - Forces refresh [default: True]. - kwargs : dict, optional - """ - # Sort in alphabetical order to be more deterministic - postfix = _OrderedDict([] if ordered_dict is None else ordered_dict) - for key in sorted(kwargs.keys()): - postfix[key] = kwargs[key] - # Preprocess stats according to datatype - for key in postfix.keys(): - # Number: limit the length of the string - if isinstance(postfix[key], Number): - postfix[key] = self.format_num(postfix[key]) - # Else for any other type, try to get the string conversion - elif not isinstance(postfix[key], _basestring): - postfix[key] = str(postfix[key]) - # Else if it's a string, don't need to preprocess anything - # Stitch together to get the final postfix - self.postfix = ', '.join(key + '=' + postfix[key].strip() - for key in postfix.keys()) - if refresh: - self.refresh() - - def set_postfix_str(self, s='', refresh=True): - """ - Postfix without dictionary expansion, similar to prefix handling. - """ - self.postfix = str(s) - if refresh: - self.refresh() - - def moveto(self, n): - # TODO: private method - self.fp.write(_unicode('\n' * n + _term_move_up() * -n)) - self.fp.flush() - - @property - def format_dict(self): - """Public API for read-only member access.""" - return dict( - n=self.n, total=self.total, - elapsed=self._time() - self.start_t - if hasattr(self, 'start_t') else 0, - ncols=self.dynamic_ncols(self.fp) - if self.dynamic_ncols else self.ncols, - prefix=self.desc, ascii=self.ascii, unit=self.unit, - unit_scale=self.unit_scale, - rate=1 / self.avg_time if self.avg_time else None, - bar_format=self.bar_format, postfix=self.postfix, - unit_divisor=self.unit_divisor) - - def display(self, msg=None, pos=None): - """ - Use `self.sp` to display `msg` in the specified `pos`. - - Consider overloading this function when inheriting to use e.g.: - `self.some_frontend(**self.format_dict)` instead of `self.sp`. - - Parameters - ---------- - msg : str, optional. What to display (default: `repr(self)`). - pos : int, optional. Position to `moveto` - (default: `abs(self.pos)`). - """ - if pos is None: - pos = abs(self.pos) - - if pos: - self.moveto(pos) - self.sp(self.__repr__() if msg is None else msg) - if pos: - self.moveto(-pos) - - @classmethod - @contextmanager - def wrapattr(tclass, stream, method, total=None, bytes=True, **tkwargs): - """ - stream : file-like object. - method : str, "read" or "write". The result of `read()` and - the first argument of `write()` should have a `len()`. - - >>> with tqdm.wrapattr(file_obj, "read", total=file_obj.size) as fobj: - ... while True: - ... chunk = fobj.read(chunk_size) - ... if not chunk: - ... break - """ - with tclass(total=total, **tkwargs) as t: - if bytes: - t.unit = "B" - t.unit_scale = True - t.unit_divisor = 1024 - yield CallbackIOWrapper(t.update, stream, method) - - -def trange(*args, **kwargs): - """ - A shortcut for tqdm(xrange(*args), **kwargs). - On Python3+ range is used instead of xrange. - """ - return tqdm(_range(*args), **kwargs) diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/tqdm.1 python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/tqdm.1 --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/tqdm.1 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/tqdm.1 1970-01-01 00:00:00.000000000 +0000 @@ -1,255 +0,0 @@ -.\" Automatically generated by Pandoc 1.19.2 -.\" -.TH "TQDM" "1" "2015\-2019" "tqdm User Manuals" "" -.hy -.SH NAME -.PP -tqdm \- fast, extensible progress bar for Python and CLI -.SH SYNOPSIS -.PP -tqdm [\f[I]options\f[]] -.SH DESCRIPTION -.PP -See . -Can be used as a pipe: -.IP -.nf -\f[C] -$\ #\ count\ lines\ of\ code -$\ cat\ *.py\ |\ tqdm\ |\ wc\ \-l -327it\ [00:00,\ 981773.38it/s] -327 - -$\ #\ find\ all\ files -$\ find\ .\ \-name\ "*.py"\ |\ tqdm\ |\ wc\ \-l -432it\ [00:00,\ 833842.30it/s] -432 - -#\ ...\ and\ more\ info -$\ find\ .\ \-name\ \[aq]*.py\[aq]\ \-exec\ wc\ \-l\ \\{}\ \\;\ \\ -\ \ |\ tqdm\ \-\-total\ 432\ \-\-unit\ files\ \-\-desc\ counting\ \\ -\ \ |\ awk\ \[aq]{\ sum\ +=\ $1\ };\ END\ {\ print\ sum\ }\[aq] -counting:\ 100%|█████████|\ 432/432\ [00:00<00:00,\ 794361.83files/s] -131998 -\f[] -.fi -.SH OPTIONS -.TP -.B \-h, \-\-help -Print this help and exit -.RS -.RE -.TP -.B \-v, \-\-version -Print version and exit -.RS -.RE -.TP -.B \-\-desc=\f[I]desc\f[] -str, optional. -Prefix for the progressbar. -.RS -.RE -.TP -.B \-\-total=\f[I]total\f[] -int, optional. -The number of expected iterations. -If unspecified, len(iterable) is used if possible. -If float("inf") or as a last resort, only basic progress statistics are -displayed (no ETA, no progressbar). -If \f[C]gui\f[] is True and this parameter needs subsequent updating, -specify an initial arbitrary large positive integer, e.g. -int(9e9). -.RS -.RE -.TP -.B \-\-leave=\f[I]leave\f[] -bool, optional. -If [default: True], keeps all traces of the progressbar upon termination -of iteration. -If \f[C]None\f[], will leave only if \f[C]position\f[] is \f[C]0\f[]. -.RS -.RE -.TP -.B \-\-ncols=\f[I]ncols\f[] -int, optional. -The width of the entire output message. -If specified, dynamically resizes the progressbar to stay within this -bound. -If unspecified, attempts to use environment width. -The fallback is a meter width of 10 and no limit for the counter and -statistics. -If 0, will not print any meter (only stats). -.RS -.RE -.TP -.B \-\-mininterval=\f[I]mininterval\f[] -float, optional. -Minimum progress display update interval [default: 0.1] seconds. -.RS -.RE -.TP -.B \-\-maxinterval=\f[I]maxinterval\f[] -float, optional. -Maximum progress display update interval [default: 10] seconds. -Automatically adjusts \f[C]miniters\f[] to correspond to -\f[C]mininterval\f[] after long display update lag. -Only works if \f[C]dynamic_miniters\f[] or monitor thread is enabled. -.RS -.RE -.TP -.B \-\-miniters=\f[I]miniters\f[] -int, optional. -Minimum progress display update interval, in iterations. -If 0 and \f[C]dynamic_miniters\f[], will automatically adjust to equal -\f[C]mininterval\f[] (more CPU efficient, good for tight loops). -If > 0, will skip display of specified number of iterations. -Tweak this and \f[C]mininterval\f[] to get very efficient loops. -If your progress is erratic with both fast and slow iterations (network, -skipping items, etc) you should set miniters=1. -.RS -.RE -.TP -.B \-\-ascii=\f[I]ascii\f[] -bool or str, optional. -If unspecified or False, use unicode (smooth blocks) to fill the meter. -The fallback is to use ASCII characters " 123456789#". -.RS -.RE -.TP -.B \-\-disable=\f[I]disable\f[] -bool, optional. -Whether to disable the entire progressbar wrapper [default: False]. -If set to None, disable on non\-TTY. -.RS -.RE -.TP -.B \-\-unit=\f[I]unit\f[] -str, optional. -String that will be used to define the unit of each iteration [default: -it]. -.RS -.RE -.TP -.B \-\-unit_scale=\f[I]unit_scale\f[] -bool or int or float, optional. -If 1 or True, the number of iterations will be reduced/scaled -automatically and a metric prefix following the International System of -Units standard will be added (kilo, mega, etc.) [default: False]. -If any other non\-zero number, will scale \f[C]total\f[] and \f[C]n\f[]. -.RS -.RE -.TP -.B \-\-dynamic_ncols=\f[I]dynamic_ncols\f[] -bool, optional. -If set, constantly alters \f[C]ncols\f[] to the environment (allowing -for window resizes) [default: False]. -.RS -.RE -.TP -.B \-\-smoothing=\f[I]smoothing\f[] -float, optional. -Exponential moving average smoothing factor for speed estimates (ignored -in GUI mode). -Ranges from 0 (average speed) to 1 (current/instantaneous speed) -[default: 0.3]. -.RS -.RE -.TP -.B \-\-bar_format=\f[I]bar_format\f[] -str, optional. -Specify a custom bar string formatting. -May impact performance. -[default: \[aq]{l_bar}{bar}{r_bar}\[aq]], where l_bar=\[aq]{desc}: -{percentage:3.0f}%|\[aq] and r_bar=\[aq]| {n_fmt}/{total_fmt} -[{elapsed}<{remaining}, \[aq] \[aq]{rate_fmt}{postfix}]\[aq] Possible -vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt, percentage, -elapsed, elapsed_s, ncols, desc, unit, rate, rate_fmt, rate_noinv, -rate_noinv_fmt, rate_inv, rate_inv_fmt, postfix, unit_divisor, -remaining, remaining_s. -Note that a trailing ": " is automatically removed after {desc} if the -latter is empty. -.RS -.RE -.TP -.B \-\-initial=\f[I]initial\f[] -int, optional. -The initial counter value. -Useful when restarting a progress bar [default: 0]. -.RS -.RE -.TP -.B \-\-position=\f[I]position\f[] -int, optional. -Specify the line offset to print this bar (starting from 0) Automatic if -unspecified. -Useful to manage multiple bars at once (eg, from threads). -.RS -.RE -.TP -.B \-\-postfix=\f[I]postfix\f[] -dict or *, optional. -Specify additional stats to display at the end of the bar. -Calls \f[C]set_postfix(**postfix)\f[] if possible (dict). -.RS -.RE -.TP -.B \-\-unit_divisor=\f[I]unit_divisor\f[] -float, optional. -[default: 1000], ignored unless \f[C]unit_scale\f[] is True. -.RS -.RE -.TP -.B \-\-write_bytes=\f[I]write_bytes\f[] -bool, optional. -If (default: None) and \f[C]file\f[] is unspecified, bytes will be -written in Python 2. -If \f[C]True\f[] will also write bytes. -In all other cases will default to unicode. -.RS -.RE -.TP -.B \-\-lock_args=\f[I]lock_args\f[] -tuple, optional. -Passed to \f[C]refresh\f[] for intermediate output (initialisation, -iterating, and updating). -.RS -.RE -.TP -.B \-\-delim=\f[I]delim\f[] -chr, optional. -Delimiting character [default: \[aq]\\n\[aq]]. -Use \[aq]\\0\[aq] for null. -N.B.: on Windows systems, Python converts \[aq]\\n\[aq] to -\[aq]\\r\\n\[aq]. -.RS -.RE -.TP -.B \-\-buf_size=\f[I]buf_size\f[] -int, optional. -String buffer size in bytes [default: 256] used when \f[C]delim\f[] is -specified. -.RS -.RE -.TP -.B \-\-bytes=\f[I]bytes\f[] -bool, optional. -If true, will count bytes, ignore \f[C]delim\f[], and default -\f[C]unit_scale\f[] to True, \f[C]unit_divisor\f[] to 1024, and -\f[C]unit\f[] to \[aq]B\[aq]. -.RS -.RE -.TP -.B \-\-manpath=\f[I]manpath\f[] -str, optional. -Directory in which to install tqdm man pages. -.RS -.RE -.TP -.B \-\-log=\f[I]log\f[] -str, optional. -CRITICAL|FATAL|ERROR|WARN(ING)|[default: \[aq]INFO\[aq]]|DEBUG|NOTSET. -.RS -.RE -.SH AUTHORS -tqdm developers . diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/_tqdm_gui.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/_tqdm_gui.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/_tqdm_gui.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/_tqdm_gui.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,7 +0,0 @@ -from .gui import * # NOQA -from .gui import __all__ # NOQA -from .std import TqdmDeprecationWarning -from warnings import warn -warn("This function will be removed in tqdm==5.0.0\n" - "Please use `tqdm.gui.*` instead of `tqdm._tqdm_gui.*`", - TqdmDeprecationWarning, stacklevel=2) diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/_tqdm_notebook.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/_tqdm_notebook.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/_tqdm_notebook.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/_tqdm_notebook.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,7 +0,0 @@ -from .notebook import * # NOQA -from .notebook import __all__ # NOQA -from .std import TqdmDeprecationWarning -from warnings import warn -warn("This function will be removed in tqdm==5.0.0\n" - "Please use `tqdm.notebook.*` instead of `tqdm._tqdm_notebook.*`", - TqdmDeprecationWarning, stacklevel=2) diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/_tqdm_pandas.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/_tqdm_pandas.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/_tqdm_pandas.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/_tqdm_pandas.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,46 +0,0 @@ -import sys - -__author__ = "github.com/casperdcl" -__all__ = ['tqdm_pandas'] - - -def tqdm_pandas(tclass, *targs, **tkwargs): - """ - Registers the given `tqdm` instance with - `pandas.core.groupby.DataFrameGroupBy.progress_apply`. - It will even close() the `tqdm` instance upon completion. - - Parameters - ---------- - tclass : tqdm class you want to use (eg, tqdm, tqdm_notebook, etc) - targs and tkwargs : arguments for the tqdm instance - - Examples - -------- - >>> import pandas as pd - >>> import numpy as np - >>> from tqdm import tqdm, tqdm_pandas - >>> - >>> df = pd.DataFrame(np.random.randint(0, 100, (100000, 6))) - >>> tqdm_pandas(tqdm, leave=True) # can use tqdm_gui, optional kwargs, etc - >>> # Now you can use `progress_apply` instead of `apply` - >>> df.groupby(0).progress_apply(lambda x: x**2) - - References - ---------- - https://stackoverflow.com/questions/18603270/ - progress-indicator-during-pandas-operations-python - """ - from tqdm import TqdmDeprecationWarning - - if isinstance(tclass, type) or (getattr(tclass, '__name__', '').startswith( - 'tqdm_')): # delayed adapter case - TqdmDeprecationWarning("""\ -Please use `tqdm.pandas(...)` instead of `tqdm_pandas(tqdm, ...)`. -""", fp_write=getattr(tkwargs.get('file', None), 'write', sys.stderr.write)) - tclass.pandas(*targs, **tkwargs) - else: - TqdmDeprecationWarning("""\ -Please use `tqdm.pandas(...)` instead of `tqdm_pandas(tqdm(...))`. -""", fp_write=getattr(tclass.fp, 'write', sys.stderr.write)) - type(tclass).pandas(deprecated_t=tclass) diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/_tqdm.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/_tqdm.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/_tqdm.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/_tqdm.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,7 +0,0 @@ -from .std import * # NOQA -from .std import __all__ # NOQA -from .std import TqdmDeprecationWarning -from warnings import warn -warn("This function will be removed in tqdm==5.0.0\n" - "Please use `tqdm.std.*` instead of `tqdm._tqdm.*`", - TqdmDeprecationWarning, stacklevel=2) diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/_utils.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/_utils.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/_utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/_utils.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,6 +0,0 @@ -from .utils import CUR_OS, IS_WIN, IS_NIX, RE_ANSI, _range, _unich, _unicode, colorama, WeakSet, _basestring, _OrderedDict, FormatReplace, Comparable, SimpleTextIOWrapper, _is_utf, _supports_unicode, _is_ascii, _environ_cols_wrapper, _environ_cols_windows, _environ_cols_tput, _environ_cols_linux, _term_move_up # NOQA -from .std import TqdmDeprecationWarning -from warnings import warn -warn("This function will be removed in tqdm==5.0.0\n" - "Please use `tqdm.utils.*` instead of `tqdm._utils.*`", - TqdmDeprecationWarning, stacklevel=2) diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/utils.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/utils.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/utils.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,345 +0,0 @@ -from functools import wraps -import os -from platform import system as _curos -import re -import subprocess -CUR_OS = _curos() -IS_WIN = CUR_OS in ['Windows', 'cli'] -IS_NIX = (not IS_WIN) and any( - CUR_OS.startswith(i) for i in - ['CYGWIN', 'MSYS', 'Linux', 'Darwin', 'SunOS', - 'FreeBSD', 'NetBSD', 'OpenBSD']) -RE_ANSI = re.compile(r"\x1b\[[;\d]*[A-Za-z]") - - -# Py2/3 compat. Empty conditional to avoid coverage -if True: # pragma: no cover - try: - _range = xrange - except NameError: - _range = range - - try: - _unich = unichr - except NameError: - _unich = chr - - try: - _unicode = unicode - except NameError: - _unicode = str - - try: - if IS_WIN: - import colorama - else: - raise ImportError - except ImportError: - colorama = None - else: - try: - colorama.init(strip=False) - except TypeError: - colorama.init() - - try: - from weakref import WeakSet - except ImportError: - WeakSet = set - - try: - _basestring = basestring - except NameError: - _basestring = str - - try: # py>=2.7,>=3.1 - from collections import OrderedDict as _OrderedDict - except ImportError: - try: # older Python versions with backported ordereddict lib - from ordereddict import OrderedDict as _OrderedDict - except ImportError: # older Python versions without ordereddict lib - # Py2.6,3.0 compat, from PEP 372 - from collections import MutableMapping - - class _OrderedDict(dict, MutableMapping): - # Methods with direct access to underlying attributes - def __init__(self, *args, **kwds): - if len(args) > 1: - raise TypeError('expected at 1 argument, got %d', - len(args)) - if not hasattr(self, '_keys'): - self._keys = [] - self.update(*args, **kwds) - - def clear(self): - del self._keys[:] - dict.clear(self) - - def __setitem__(self, key, value): - if key not in self: - self._keys.append(key) - dict.__setitem__(self, key, value) - - def __delitem__(self, key): - dict.__delitem__(self, key) - self._keys.remove(key) - - def __iter__(self): - return iter(self._keys) - - def __reversed__(self): - return reversed(self._keys) - - def popitem(self): - if not self: - raise KeyError - key = self._keys.pop() - value = dict.pop(self, key) - return key, value - - def __reduce__(self): - items = [[k, self[k]] for k in self] - inst_dict = vars(self).copy() - inst_dict.pop('_keys', None) - return self.__class__, (items,), inst_dict - - # Methods with indirect access via the above methods - setdefault = MutableMapping.setdefault - update = MutableMapping.update - pop = MutableMapping.pop - keys = MutableMapping.keys - values = MutableMapping.values - items = MutableMapping.items - - def __repr__(self): - pairs = ', '.join(map('%r: %r'.__mod__, self.items())) - return '%s({%s})' % (self.__class__.__name__, pairs) - - def copy(self): - return self.__class__(self) - - @classmethod - def fromkeys(cls, iterable, value=None): - d = cls() - for key in iterable: - d[key] = value - return d - - -class FormatReplace(object): - """ - >>> a = FormatReplace('something') - >>> "{:5d}".format(a) - 'something' - """ - def __init__(self, replace=''): - self.replace = replace - self.format_called = 0 - - def __format__(self, _): - self.format_called += 1 - return self.replace - - -class Comparable(object): - """Assumes child has self._comparable attr/@property""" - def __lt__(self, other): - return self._comparable < other._comparable - - def __le__(self, other): - return (self < other) or (self == other) - - def __eq__(self, other): - return self._comparable == other._comparable - - def __ne__(self, other): - return not self == other - - def __gt__(self, other): - return not self <= other - - def __ge__(self, other): - return not self < other - - -class ObjectWrapper(object): - def __getattr__(self, name): - return getattr(self._wrapped, name) - - def __setattr__(self, name, value): - return setattr(self._wrapped, name, value) - - def wrapper_getattr(self, name): - """Actual `self.getattr` rather than self._wrapped.getattr""" - try: - return object.__getattr__(self, name) - except AttributeError: # py2 - return getattr(self, name) - - def wrapper_setattr(self, name, value): - """Actual `self.setattr` rather than self._wrapped.setattr""" - return object.__setattr__(self, name, value) - - def __init__(self, wrapped): - """ - Thin wrapper around a given object - """ - self.wrapper_setattr('_wrapped', wrapped) - - -class SimpleTextIOWrapper(ObjectWrapper): - """ - Change only `.write()` of the wrapped object by encoding the passed - value and passing the result to the wrapped object's `.write()` method. - """ - # pylint: disable=too-few-public-methods - def __init__(self, wrapped, encoding): - super(SimpleTextIOWrapper, self).__init__(wrapped) - self.wrapper_setattr('encoding', encoding) - - def write(self, s): - """ - Encode `s` and pass to the wrapped object's `.write()` method. - """ - return self._wrapped.write(s.encode(self.wrapper_getattr('encoding'))) - - -class CallbackIOWrapper(ObjectWrapper): - def __init__(self, callback, stream, method="read"): - """ - Wrap a given `file`-like object's `read()` or `write()` to report - lengths to the given `callback` - """ - super(CallbackIOWrapper, self).__init__(stream) - func = getattr(stream, method) - if method == "write": - @wraps(func) - def write(data, *args, **kwargs): - res = func(data, *args, **kwargs) - callback(len(data)) - return res - self.wrapper_setattr('write', write) - elif method == "read": - @wraps(func) - def read(*args, **kwargs): - data = func(*args, **kwargs) - callback(len(data)) - return data - self.wrapper_setattr('read', read) - else: - raise KeyError("Can only wrap read/write methods") - - -def _is_utf(encoding): - try: - u'\u2588\u2589'.encode(encoding) - except UnicodeEncodeError: # pragma: no cover - return False - except Exception: # pragma: no cover - try: - return encoding.lower().startswith('utf-') or ('U8' == encoding) - except: - return False - else: - return True - - -def _supports_unicode(fp): - try: - return _is_utf(fp.encoding) - except AttributeError: - return False - - -def _is_ascii(s): - if isinstance(s, str): - for c in s: - if ord(c) > 255: - return False - return True - return _supports_unicode(s) - - -def _environ_cols_wrapper(): # pragma: no cover - """ - Return a function which gets width and height of console - (linux,osx,windows,cygwin). - """ - _environ_cols = None - if IS_WIN: - _environ_cols = _environ_cols_windows - if _environ_cols is None: - _environ_cols = _environ_cols_tput - if IS_NIX: - _environ_cols = _environ_cols_linux - return _environ_cols - - -def _environ_cols_windows(fp): # pragma: no cover - try: - from ctypes import windll, create_string_buffer - import struct - from sys import stdin, stdout - - io_handle = -12 # assume stderr - if fp == stdin: - io_handle = -10 - elif fp == stdout: - io_handle = -11 - - h = windll.kernel32.GetStdHandle(io_handle) - csbi = create_string_buffer(22) - res = windll.kernel32.GetConsoleScreenBufferInfo(h, csbi) - if res: - (_bufx, _bufy, _curx, _cury, _wattr, left, _top, right, _bottom, - _maxx, _maxy) = struct.unpack("hhhhHhhhhhh", csbi.raw) - # nlines = bottom - top + 1 - return right - left # +1 - except: - pass - return None - - -def _environ_cols_tput(*_): # pragma: no cover - """cygwin xterm (windows)""" - try: - import shlex - cols = int(subprocess.check_call(shlex.split('tput cols'))) - # rows = int(subprocess.check_call(shlex.split('tput lines'))) - return cols - except: - pass - return None - - -def _environ_cols_linux(fp): # pragma: no cover - - try: - from termios import TIOCGWINSZ - from fcntl import ioctl - from array import array - except ImportError: - return None - else: - try: - return array('h', ioctl(fp, TIOCGWINSZ, '\0' * 8))[1] - except: - try: - return int(os.environ["COLUMNS"]) - 1 - except KeyError: - return None - - -def _term_move_up(): # pragma: no cover - return '' if (os.name == 'nt') and (colorama is None) else '\x1b[A' - - -try: - # TODO consider using wcswidth third-party package for 0-width characters - from unicodedata import east_asian_width -except ImportError: - _text_width = len -else: - def _text_width(s): - return sum( - 2 if east_asian_width(ch) in 'FW' else 1 for ch in _unicode(s)) diff -Nru python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/_version.py python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/_version.py --- python-mne-0.23.4+dfsg/mne/externals/tqdm/_tqdm/_version.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/externals/tqdm/_tqdm/_version.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,59 +0,0 @@ -# Definition of the version number -import os -from io import open as io_open - -__all__ = ["__version__"] - -# major, minor, patch, -extra -version_info = 4, 40, 2 - -# Nice string for the version -__version__ = '.'.join(map(str, version_info)) - - -# auto -extra based on commit hash (if not tagged as release) -scriptdir = os.path.dirname(__file__) -gitdir = os.path.abspath(os.path.join(scriptdir, "..", ".git")) -if os.path.isdir(gitdir): # pragma: nocover - extra = None - # Open config file to check if we are in tqdm project - with io_open(os.path.join(gitdir, "config"), 'r') as fh_config: - if 'tqdm' in fh_config.read(): - # Open the HEAD file - with io_open(os.path.join(gitdir, "HEAD"), 'r') as fh_head: - extra = fh_head.readline().strip() - # in a branch => HEAD points to file containing last commit - if 'ref:' in extra: - # reference file path - ref_file = extra[5:] - branch_name = ref_file.rsplit('/', 1)[-1] - - ref_file_path = os.path.abspath(os.path.join(gitdir, ref_file)) - # check that we are in git folder - # (by stripping the git folder from the ref file path) - if os.path.relpath( - ref_file_path, gitdir).replace('\\', '/') != ref_file: - # out of git folder - extra = None - else: - # open the ref file - with io_open(ref_file_path, 'r') as fh_branch: - commit_hash = fh_branch.readline().strip() - extra = commit_hash[:8] - if branch_name != "master": - extra += '.' + branch_name - - # detached HEAD mode, already have commit hash - else: - extra = extra[:8] - - # Append commit hash (and branch) to version string if not tagged - if extra is not None: - try: - with io_open(os.path.join(gitdir, "refs", "tags", - 'v' + __version__)) as fdv: - if fdv.readline().strip()[:8] != extra[:8]: - __version__ += '-' + extra - except Exception as e: - if "No such file" not in str(e): - raise diff -Nru python-mne-0.23.4+dfsg/mne/filter.py python-mne-1.1.0+dfsg/mne/filter.py --- python-mne-0.23.4+dfsg/mne/filter.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/filter.py 2022-08-03 15:48:14.000000000 +0000 @@ -7,11 +7,10 @@ import numpy as np from .annotations import _annotations_starts_stops -from .fixes import _import_fft from .io.pick import _picks_to_idx from .cuda import (_setup_cuda_fft_multiply_repeated, _fft_multiply_repeated, _setup_cuda_fft_resample, _fft_resample, _smart_pad) -from .parallel import parallel_func, check_n_jobs +from .parallel import parallel_func from .time_frequency.multitaper import _mt_spectra, _compute_mt_params from .utils import (logger, verbose, sum_squared, warn, _pl, _check_preload, _validate_type, _check_option, _ensure_int) @@ -124,7 +123,7 @@ def _overlap_add_filter(x, h, n_fft=None, phase='zero', picks=None, - n_jobs=1, copy=True, pad='reflect_limited'): + n_jobs=None, copy=True, pad='reflect_limited'): """Filter the signal x using h with overlap-add FFTs. Parameters @@ -158,7 +157,6 @@ x : array, shape (n_signals, n_times) x filtered. """ - n_jobs = check_n_jobs(n_jobs, allow_cuda=True) # set up array for filtering, reshape to 2D, operate on last axis x, orig_shape, picks = _prep_for_filtering(x, copy, picks) # Extend the signal by mirroring the edges to reduce transient filter @@ -198,17 +196,16 @@ '2 * len(h) - 1 (%s), got %s' % (min_fft, n_fft)) # Figure out if we should use CUDA - n_jobs, cuda_dict = _setup_cuda_fft_multiply_repeated( - n_jobs, h, n_fft) + n_jobs, cuda_dict = _setup_cuda_fft_multiply_repeated(n_jobs, h, n_fft) # Process each row separately picks = _picks_to_idx(len(x), picks) + parallel, p_fun, _ = parallel_func(_1d_overlap_filter, n_jobs) if n_jobs == 1: for p in picks: x[p] = _1d_overlap_filter(x[p], len(h), n_edge, phase, cuda_dict, pad, n_fft) else: - parallel, p_fun, _ = parallel_func(_1d_overlap_filter, n_jobs) data_new = parallel(p_fun(x[p], len(h), n_edge, phase, cuda_dict, pad, n_fft) for p in picks) for pp, p in enumerate(picks): @@ -311,7 +308,7 @@ % (N, transition * sfreq / 2., this_N)) # Construct a lowpass this_h = firwin(this_N, (prev_freq + this_freq) / 2., - window=window, pass_zero=True, nyq=freq[-1]) + window=window, pass_zero=True, fs=freq[-1] * 2) assert this_h.shape == (this_N,) offset = (N - this_N) // 2 if this_gain == 0: @@ -429,7 +426,6 @@ # set up array for filtering, reshape to 2D, operate on last axis from scipy.signal import filtfilt, sosfiltfilt padlen = min(iir_params['padlen'], x.shape[-1] - 1) - n_jobs = check_n_jobs(n_jobs) x, orig_shape, picks = _prep_for_filtering(x, copy, picks) if 'sos' in iir_params: fun = partial(sosfiltfilt, sos=iir_params['sos'], padlen=padlen, @@ -439,11 +435,11 @@ fun = partial(filtfilt, b=iir_params['b'], a=iir_params['a'], padlen=padlen, axis=-1) _check_coefficients((iir_params['b'], iir_params['a'])) + parallel, p_fun, n_jobs = parallel_func(fun, n_jobs) if n_jobs == 1: for p in picks: x[p] = fun(x=x[p]) else: - parallel, p_fun, _ = parallel_func(fun, n_jobs) data_new = parallel(p_fun(x=x[p]) for p in picks) for pp, p in enumerate(picks): x[p] = data_new[pp] @@ -743,10 +739,10 @@ @verbose def filter_data(data, sfreq, l_freq, h_freq, picks=None, filter_length='auto', - l_trans_bandwidth='auto', h_trans_bandwidth='auto', n_jobs=1, - method='fir', iir_params=None, copy=True, phase='zero', - fir_window='hamming', fir_design='firwin', - pad='reflect_limited', verbose=None): + l_trans_bandwidth='auto', h_trans_bandwidth='auto', + n_jobs=None, method='fir', iir_params=None, copy=True, + phase='zero', fir_window='hamming', fir_design='firwin', + pad='reflect_limited', *, verbose=None): """Filter a subset of channels. Parameters @@ -763,8 +759,8 @@ %(filter_length)s %(l_trans_bandwidth)s %(h_trans_bandwidth)s - %(n_jobs-fir)s - %(method-fir)s + %(n_jobs_fir)s + %(method_fir)s %(iir_params)s copy : bool If True, a copy of x, filtered, is returned. Otherwise, it operates @@ -772,7 +768,7 @@ %(phase)s %(fir_window)s %(fir_design)s - %(pad-fir)s + %(pad_fir)s The default is ``'reflect_limited'``. .. versionadded:: 0.15 @@ -853,7 +849,7 @@ %(filter_length)s %(l_trans_bandwidth)s %(h_trans_bandwidth)s - %(method-fir)s + %(method_fir)s %(iir_params)s %(phase)s %(fir_window)s @@ -1072,9 +1068,9 @@ @verbose def notch_filter(x, Fs, freqs, filter_length='auto', notch_widths=None, trans_bandwidth=1, method='fir', iir_params=None, - mt_bandwidth=None, p_value=0.05, picks=None, n_jobs=1, + mt_bandwidth=None, p_value=0.05, picks=None, n_jobs=None, copy=True, phase='zero', fir_window='hamming', - fir_design='firwin', pad='reflect_limited', + fir_design='firwin', pad='reflect_limited', *, verbose=None): r"""Notch filter for the signal x. @@ -1098,7 +1094,7 @@ trans_bandwidth : float Width of the transition band in Hz. Only used for ``method='fir'``. - %(method-fir)s + %(method_fir)s 'spectrum_fit' will use multi-taper estimation of sinusoidal components. If freqs=None and method='spectrum_fit', significant sinusoidal components are detected using an F test, and noted by @@ -1115,14 +1111,15 @@ %(picks_nostr)s Only supported for 2D (n_channels, n_times) and 3D (n_epochs, n_channels, n_times) data. - %(n_jobs-fir)s + %(n_jobs_fir)s copy : bool If True, a copy of x, filtered, is returned. Otherwise, it operates on x in place. %(phase)s %(fir_window)s %(fir_design)s - %(pad-fir)s + %(pad_fir)s + The default is ``'reflect_limited'``. %(verbose)s Returns @@ -1158,6 +1155,7 @@ & Hemant Bokil, Oxford University Press, New York, 2008. Please cite this in publications if method 'spectrum_fit' is used. """ + x = _check_filterable(x, 'notch filtered', 'notch_filter') iir_params, method = _check_method(method, iir_params, ['spectrum_fit']) if freqs is not None: @@ -1221,7 +1219,6 @@ p_value, picks, n_jobs, copy, filter_length): """Call _mt_spectrum_remove.""" # set up array for filtering, reshape to 2D, operate on last axis - n_jobs = check_n_jobs(n_jobs) x, orig_shape, picks = _prep_for_filtering(x, copy, picks) if isinstance(filter_length, str) and filter_length == 'auto': filter_length = '10s' @@ -1232,6 +1229,7 @@ _get_window_thresh, sfreq=sfreq, mt_bandwidth=mt_bandwidth, p_value=p_value) window_fun, threshold = get_wt(filter_length) + parallel, p_fun, n_jobs = parallel_func(_mt_spectrum_remove_win, n_jobs) if n_jobs == 1: freq_list = list() for ii, x_ in enumerate(x): @@ -1241,7 +1239,6 @@ get_wt) freq_list.append(f) else: - parallel, p_fun, _ = parallel_func(_mt_spectrum_remove_win, n_jobs) data_new = parallel(p_fun(x_, sfreq, line_freqs, notch_widths, window_fun, threshold, get_wt) for xi, x_ in enumerate(x) @@ -1370,7 +1367,27 @@ return x - datafit, rm_freqs -def _check_filterable(x, kind='filtered'): +def _check_filterable(x, kind='filtered', alternative='filter'): + # Let's be fairly strict about this -- users can easily coerce to ndarray + # at their end, and we already should do it internally any time we are + # using these low-level functions. At the same time, let's + # help people who might accidentally use low-level functions that they + # shouldn't use by pushing them in the right direction + from .io.base import BaseRaw + from .epochs import BaseEpochs + from .evoked import Evoked + if isinstance(x, (BaseRaw, BaseEpochs, Evoked)): + try: + name = x.__class__.__name__ + except Exception: + pass + else: + raise TypeError( + 'This low-level function only operates on np.ndarray ' + f'instances. To get a {kind} {name} instance, use a method ' + f'like `inst_new = inst.copy().{alternative}(...)` ' + 'instead.') + _validate_type(x, (np.ndarray, list, tuple), f'Data to be {kind}') x = np.asanyarray(x) if x.dtype != np.float64: raise ValueError('Data to be %s must be real floating, got %s' @@ -1380,12 +1397,12 @@ def _resamp_ratio_len(up, down, n): ratio = float(up) / down - return ratio, int(round(ratio * n)) + return ratio, max(int(round(ratio * n)), 1) @verbose -def resample(x, up=1., down=1., npad=100, axis=-1, window='boxcar', n_jobs=1, - pad='reflect_limited', verbose=None): +def resample(x, up=1., down=1., npad=100, axis=-1, window='boxcar', + n_jobs=None, pad='reflect_limited', *, verbose=None): """Resample an array. Operates along the last dimension of the array. @@ -1401,9 +1418,9 @@ %(npad)s axis : int Axis along which to resample (default is the last axis). - %(window-resample)s - %(n_jobs-cuda)s - %(pad-fir)s + %(window_resample)s + %(n_jobs_cuda)s + %(pad)s The default is ``'reflect_limited'``. .. versionadded:: 0.15 @@ -1428,7 +1445,7 @@ up=up/down and down=1. """ from scipy.signal import get_window - ifftshift, fftfreq = _import_fft(('ifftshift', 'fftfreq')) + from scipy.fft import ifftshift, fftfreq # check explicitly for backwards compatibility if not isinstance(axis, int): err = ("The axis parameter needs to be an integer (got %s). " @@ -1438,7 +1455,7 @@ raise TypeError(err) # make sure our arithmetic will work - x = _check_filterable(x, 'resampled') + x = _check_filterable(x, 'resampled', 'resample') ratio, final_len = _resamp_ratio_len(up, down, x.shape[axis]) del up, down if axis < 0: @@ -1469,7 +1486,7 @@ # prep for resampling now x_flat = x.reshape((-1, x_len)) orig_len = x_len + npads.sum() # length after padding - new_len = int(round(ratio * orig_len)) # length after resampling + new_len = max(int(round(ratio * orig_len)), 1) # length after resampling to_removes = [int(round(ratio * npads[0]))] to_removes.append(new_len - final_len - to_removes[0]) to_removes = np.array(to_removes) @@ -1494,13 +1511,13 @@ # do the resampling using an adaptation of scipy's FFT-based resample() # use of the 'flat' window is recommended for minimal ringing + parallel, p_fun, n_jobs = parallel_func(_fft_resample, n_jobs) if n_jobs == 1: y = np.zeros((len(x_flat), new_len - to_removes.sum()), dtype=x.dtype) for xi, x_ in enumerate(x_flat): y[xi] = _fft_resample(x_, new_len, npads, to_removes, cuda_dict, pad) else: - parallel, p_fun, _ = parallel_func(_fft_resample, n_jobs) y = parallel(p_fun(x_, new_len, npads, to_removes, cuda_dict, pad) for x_ in x_flat) y = np.array(y) @@ -1856,7 +1873,7 @@ instead of FIR/IIR filtering. This parameter is thus used to determine the length of the window over which a 5th-order polynomial smoothing is used. - %(verbose_meth)s + %(verbose)s Returns ------- @@ -1904,10 +1921,10 @@ @verbose def filter(self, l_freq, h_freq, picks=None, filter_length='auto', - l_trans_bandwidth='auto', h_trans_bandwidth='auto', n_jobs=1, + l_trans_bandwidth='auto', h_trans_bandwidth='auto', n_jobs=None, method='fir', iir_params=None, phase='zero', fir_window='hamming', fir_design='firwin', - skip_by_annotation=('edge', 'bad_acq_skip'), pad='edge', + skip_by_annotation=('edge', 'bad_acq_skip'), pad='edge', *, verbose=None): """Filter a subset of channels. @@ -1919,8 +1936,8 @@ %(filter_length)s %(l_trans_bandwidth)s %(h_trans_bandwidth)s - %(n_jobs-fir)s - %(method-fir)s + %(n_jobs_fir)s + %(method_fir)s %(iir_params)s %(phase)s %(fir_window)s @@ -1936,8 +1953,8 @@ To disable, provide an empty list. Only used if ``inst`` is raw. .. versionadded:: 0.16. - %(pad-fir)s - %(verbose_meth)s + %(pad_fir)s + %(verbose)s Returns ------- @@ -2015,8 +2032,8 @@ return self @verbose - def resample(self, sfreq, npad='auto', window='boxcar', n_jobs=1, - pad='edge', verbose=None): # lgtm + def resample(self, sfreq, npad='auto', window='boxcar', n_jobs=None, + pad='edge', *, verbose=None): # lgtm """Resample data. If appropriate, an anti-aliasing filter is applied before resampling. @@ -2029,14 +2046,14 @@ sfreq : float New sample rate to use. %(npad)s - %(window-resample)s - %(n_jobs-cuda)s - %(pad-fir)s + %(window_resample)s + %(n_jobs_cuda)s + %(pad)s The default is ``'edge'``, which pads with the edge values of each vector. .. versionadded:: 0.15 - %(verbose_meth)s + %(verbose)s Returns ------- @@ -2064,24 +2081,22 @@ o_sfreq = self.info['sfreq'] self._data = resample(self._data, sfreq, o_sfreq, npad, window=window, n_jobs=n_jobs, pad=pad) - self.info['sfreq'] = float(sfreq) lowpass = self.info.get('lowpass') lowpass = np.inf if lowpass is None else lowpass - self.info['lowpass'] = min(lowpass, sfreq / 2.) + with self.info._unlock(): + self.info['lowpass'] = min(lowpass, sfreq / 2.) + self.info['sfreq'] = float(sfreq) new_times = (np.arange(self._data.shape[-1], dtype=np.float64) / sfreq + self.times[0]) # adjust indirectly affected variables - if isinstance(self, BaseEpochs): - self._set_times(new_times) - self._raw_times = self.times - else: # isinstance(self, Evoked) - self.times = new_times - self._update_first_last() + self._set_times(new_times) + self._raw_times = self.times + self._update_first_last() return self @verbose - def apply_hilbert(self, picks=None, envelope=False, n_jobs=1, n_fft='auto', - verbose=None): + def apply_hilbert(self, picks=None, envelope=False, n_jobs=None, + n_fft='auto', *, verbose=None): """Compute analytic signal or envelope for a subset of channels. Parameters @@ -2096,7 +2111,7 @@ will be padded with zeros before computing Hilbert, then cut back to original length. If None, n == self.n_times. If 'auto', the next highest fast FFT length will be use. - %(verbose_meth)s + %(verbose)s Returns ------- @@ -2165,6 +2180,7 @@ if dtype is not None and dtype != self._data.dtype: self._data = self._data.astype(dtype) + parallel, p_fun, n_jobs = parallel_func(_check_fun, n_jobs) if n_jobs == 1: # modify data inplace to save memory for idx in picks: @@ -2172,7 +2188,6 @@ _my_hilbert, data_in[..., idx, :], *args, **kwargs) else: # use parallel function - parallel, p_fun, _ = parallel_func(_check_fun, n_jobs) data_picks_new = parallel( p_fun(_my_hilbert, data_in[..., p, :], *args, **kwargs) for p in picks) @@ -2256,7 +2271,7 @@ 4197 frequencies are directly constructed, with zeroes in the stop-band and ones in the passband, with squared cosine ramps in between. """ - irfft = _import_fft('irfft') + from scipy.fft import irfft n_freqs = (4096 + 2 * 2048) // 2 + 1 freq_resp = np.ones(n_freqs) l_freq = 0 if l_freq is None else float(l_freq) @@ -2322,7 +2337,9 @@ if update_info: if h_freq is not None and (l_freq is None or l_freq < h_freq) and \ (info["lowpass"] is None or h_freq < info['lowpass']): - info['lowpass'] = float(h_freq) + with info._unlock(): + info['lowpass'] = float(h_freq) if l_freq is not None and (h_freq is None or l_freq < h_freq) and \ (info["highpass"] is None or l_freq > info['highpass']): - info['highpass'] = float(l_freq) + with info._unlock(): + info['highpass'] = float(l_freq) diff -Nru python-mne-0.23.4+dfsg/mne/fixes.py python-mne-1.1.0+dfsg/mne/fixes.py --- python-mne-0.23.4+dfsg/mne/fixes.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/fixes.py 2022-08-03 15:48:14.000000000 +0000 @@ -12,24 +12,53 @@ # Lars Buitinck # License: BSD -from distutils.version import LooseVersion -import functools import inspect from math import log +from pprint import pprint import os -from pathlib import Path import warnings import numpy as np ############################################################################### +# distutils + +# distutils has been deprecated since Python 3.10 and is scheduled for removal +# from the standard library with the release of Python 3.12. For version +# comparisons, we use setuptools's `parse_version` if available. + +def _compare_version(version_a, operator, version_b): + """Compare two version strings via a user-specified operator. + + Parameters + ---------- + version_a : str + First version string. + operator : '==' | '>' | '<' | '>=' | '<=' + Operator to compare ``version_a`` and ``version_b`` in the form of + ``version_a operator version_b``. + version_b : str + Second version string. + + Returns + ------- + bool + The result of the version comparison. + """ + from packaging.version import parse + with warnings.catch_warnings(record=True): + warnings.simplefilter('ignore') + return eval(f'parse("{version_a}") {operator} parse("{version_b}")') + + +############################################################################### # Misc def _median_complex(data, axis): """Compute marginal median on complex data safely. - XXX: Can be removed when numpy introduces a fix. + Can be removed when numpy introduces a fix. See: https://github.com/scipy/scipy/pull/12676/. """ # np.median must be passed real arrays for the desired result @@ -42,10 +71,15 @@ # helpers to get function arguments -def _get_args(function, varargs=False): +def _get_args(function, varargs=False, *, + exclude=('var_positional', 'var_keyword')): params = inspect.signature(function).parameters - args = [key for key, param in params.items() - if param.kind not in (param.VAR_POSITIONAL, param.VAR_KEYWORD)] + # As of Python 3.10: + # https://docs.python.org/3/library/inspect.html#inspect.Parameter.kind + # POSITIONAL_ONLY, POSITIONAL_OR_KEYWORD, VAR_POSITIONAL, KEYWORD_ONLY, + # VAR_KEYWORD + exclude = set(getattr(inspect.Parameter, ex.upper()) for ex in exclude) + args = [key for key, param in params.items() if param.kind not in exclude] if varargs: varargs = [param.name for param in params.values() if param.kind == param.VAR_POSITIONAL] @@ -69,12 +103,9 @@ return linalg.svd(A, **kwargs) except np.linalg.LinAlgError as exp: from .utils import warn - if 'lapack_driver' in _get_args(linalg.svd): - warn('SVD error (%s), attempting to use GESVD instead of GESDD' - % (exp,)) - return linalg.svd(A, lapack_driver='gesvd', **kwargs) - else: - raise + warn('SVD error (%s), attempting to use GESVD instead of GESDD' + % (exp,)) + return linalg.svd(A, lapack_driver='gesvd', **kwargs) def _csc_matrix_cast(x): @@ -147,7 +178,7 @@ else: raise ValueError("File does not appear to be a Freesurfer surface") - coords = coords.astype(np.float64) # XXX: due to mayavi bug on mac 32bits + coords = coords.astype(np.float64) ret = (coords, faces) if read_metadata: @@ -161,27 +192,6 @@ ############################################################################### -# Triaging FFT functions to get fast pocketfft (SciPy 1.4) - -@functools.lru_cache(None) -def _import_fft(name): - single = False - if not isinstance(name, tuple): - name = (name,) - single = True - try: - from scipy.fft import rfft # noqa analysis:ignore - except ImportError: - from numpy import fft # noqa - else: - from scipy import fft # noqa - out = [getattr(fft, n) for n in name] - if single: - out = out[0] - return out - - -############################################################################### # NumPy Generator (NumPy 1.17) @@ -450,10 +460,8 @@ return self def __repr__(self): - from sklearn.base import _pprint class_name = self.__class__.__name__ - return '%s(%s)' % (class_name, _pprint(self.get_params(deep=False), - offset=len(class_name),),) + return '%s(%s)' % (class_name, pprint(self.get_params(deep=False))) # __getstate__ and __setstate__ are omitted because they only contain # conditionals that are not satisfied by our objects (e.g., @@ -892,9 +900,9 @@ sgn = np.sign(s) s = np.abs(s) sidx = np.argsort(s)[..., ::-1] - sgn = take_along_axis(sgn, sidx, axis=-1) - s = take_along_axis(s, sidx, axis=-1) - u = take_along_axis(u, sidx[..., None, :], axis=-1) + sgn = np.take_along_axis(sgn, sidx, axis=-1) + s = np.take_along_axis(s, sidx, axis=-1) + u = np.take_along_axis(u, sidx[..., None, :], axis=-1) # singular values are unsigned, move the sign into v vt = (u * sgn[..., np.newaxis, :]).swapaxes(-2, -1).conj() np.abs(s, out=s) @@ -904,58 +912,6 @@ ############################################################################### -# NumPy einsum backward compat (allow "optimize" arg and fix 1.14.0 bug) -# XXX eventually we should hand-tune our `einsum` calls given our array sizes! - -def einsum(*args, **kwargs): - if 'optimize' not in kwargs: - kwargs['optimize'] = False - return np.einsum(*args, **kwargs) - - -try: - from numpy import take_along_axis -except ImportError: # NumPy < 1.15 - def take_along_axis(arr, indices, axis): - # normalize inputs - if axis is None: - arr = arr.flat - arr_shape = (len(arr),) # flatiter has no .shape - axis = 0 - else: - # there is a NumPy function for this, but rather than copy our - # internal uses should be correct, so just normalize quickly - if axis < 0: - axis += arr.ndim - assert 0 <= axis < arr.ndim - arr_shape = arr.shape - - # use the fancy index - return arr[_make_along_axis_idx(arr_shape, indices, axis)] - - def _make_along_axis_idx(arr_shape, indices, axis): - # compute dimensions to iterate over - if not np.issubdtype(indices.dtype, np.integer): - raise IndexError('`indices` must be an integer array') - if len(arr_shape) != indices.ndim: - raise ValueError( - "`indices` and `arr` must have the same number of dimensions") - shape_ones = (1,) * indices.ndim - dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim)) - - # build a fancy index, consisting of orthogonal aranges, with the - # requested index inserted at the right location - fancy_index = [] - for dim, n in zip(dest_dims, arr_shape): - if dim is None: - fancy_index.append(indices) - else: - ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:] - fancy_index.append(np.arange(n).reshape(ind_shape)) - - return tuple(fancy_index) - -############################################################################### # From nilearn @@ -980,7 +936,7 @@ # _outline was removed in # https://github.com/matplotlib/matplotlib/commit/03a542e875eba091a027046d5ec652daa8be6863 # so we use the code from there - if LooseVersion(matplotlib.__version__) >= LooseVersion("3.2.0"): + if _compare_version(matplotlib.__version__, '>=', '3.2.0'): cbar.ax.set_ylim(cbar_vmin, cbar_vmax) X = cbar._mesh()[0] X = np.array([X[0], X[-1]]) @@ -1006,23 +962,12 @@ ############################################################################### -# Matplotlib - -def _get_status(checks): - """Deal with old MPL to get check box statuses.""" - try: - return list(checks.get_status()) - except AttributeError: - return [x[0].get_visible() for x in checks.lines] - - -############################################################################### # Numba (optional requirement) # Here we choose different defaults to speed things up by default try: import numba - if LooseVersion(numba.__version__) < LooseVersion('0.40'): + if _compare_version(numba.__version__, '<', '0.48'): raise ImportError prange = numba.prange def jit(nopython=True, nogil=True, fastmath=True, cache=True, @@ -1073,16 +1018,20 @@ ############################################################################### -# Added in Python 3.7 (remove when we drop support for 3.6) - -try: - from contextlib import nullcontext -except ImportError: - from contextlib import contextmanager +# Matplotlib - @contextmanager - def nullcontext(): - yield +# workaround: plt.close() doesn't spawn close_event on Agg backend +# https://github.com/matplotlib/matplotlib/issues/18609 +# scheduled to be fixed by MPL 3.6 +def _close_event(fig): + """Force calling of the MPL figure close event.""" + from .utils import logger + try: + fig.canvas.close_event() + logger.debug(f'Called {fig!r}.canvas.close_event()') + except ValueError: # old mpl with Qt + logger.debug(f'Calling {fig!r}.canvas.close_event() failed') + pass # pragma: no cover def _is_last_row(ax): @@ -1098,7 +1047,6 @@ def pinvh(a, rtol=None): """Compute a pseudo-inverse of a Hermitian matrix.""" - from scipy.linalg.decomp import _asarray_validated s, u = np.linalg.eigh(a) del a if rtol is None: @@ -1121,3 +1069,23 @@ u = u[:, :rank] u /= s[:rank] return (u @ vh[:rank]).conj().T + + +############################################################################### +# PyVista + +# Deal with pyvista deprecation of point_data and cell_data +# (can be removed once we require 0.31+) + +def _point_data(obj): + try: + return obj.point_data + except AttributeError: + return obj.point_arrays + + +def _cell_data(obj): + try: + return obj.cell_data + except AttributeError: + return obj.cell_arrays diff -Nru python-mne-0.23.4+dfsg/mne/forward/_compute_forward.py python-mne-1.1.0+dfsg/mne/forward/_compute_forward.py --- python-mne-0.23.4+dfsg/mne/forward/_compute_forward.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/forward/_compute_forward.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,7 +5,7 @@ # Eric Larson # Mark Wronkiewicz # -# License: BSD (3-clause) +# License: BSD-3-Clause # The computations in this code were primarily derived from Matti Hämäläinen's # C code. @@ -89,7 +89,8 @@ Linear coefficients with lead fields for each BEM vertex on each sensor (?) """ - parallel, p_fun, _ = parallel_func(_do_lin_field_coeff, n_jobs) + parallel, p_fun, n_jobs = parallel_func( + _do_lin_field_coeff, n_jobs, max_jobs=len(surf['tris'])) nas = np.array_split coeffs = parallel(p_fun(surf['rr'], t, tn, ta, rmags, cosmags, ws, bins) for t, tn, ta in zip(nas(surf['tris'], n_jobs), @@ -432,7 +433,8 @@ # Both MEG and EEG have the inifinite-medium potentials # This could be just vectorized, but eats too much memory, so instead we # reduce memory by chunking within _do_inf_pots and parallelize, too: - parallel, p_fun, _ = parallel_func(_do_inf_pots, n_jobs) + parallel, p_fun, n_jobs = parallel_func( + _do_inf_pots, n_jobs, max_jobs=len(rr)) nas = np.array_split B = np.sum(parallel(p_fun(mri_rr, sr.copy(), np.ascontiguousarray(mri_Q), np.array(sol)) # copy and contig @@ -444,7 +446,7 @@ # Only MEG coils are sensitive to the primary current distribution. if coil_type == 'meg': # Primary current contribution (can be calc. in coil/dipole coords) - parallel, p_fun, _ = parallel_func(_do_prim_curr, n_jobs) + parallel, p_fun, n_jobs = parallel_func(_do_prim_curr, n_jobs) pcc = np.concatenate(parallel(p_fun(r, coils) for r in nas(rr, n_jobs)), axis=0) B += pcc @@ -531,7 +533,8 @@ n_jobs, coil_type): """Do potential or field for spherical model.""" fun = _eeg_spherepot_coil if coil_type == 'eeg' else _sphere_field - parallel, p_fun, _ = parallel_func(fun, n_jobs) + parallel, p_fun, n_jobs = parallel_func( + fun, n_jobs, max_jobs=len(rr)) B = np.concatenate(parallel(p_fun(r, coils, sphere) for r in np.array_split(rr, n_jobs))) return B @@ -839,7 +842,6 @@ Each element contains ndarray, shape (3 * n_dipoles, n_sensors) where n_sensors depends on which channel types are requested (MEG and/or EEG) """ - n_jobs = max(min(n_jobs, len(rr)), 1) Bs = list() # The dipole location and orientation must be transformed to mri coords mri_rr = None diff -Nru python-mne-0.23.4+dfsg/mne/forward/_field_interpolation.py python-mne-1.1.0+dfsg/mne/forward/_field_interpolation.py --- python-mne-0.23.4+dfsg/mne/forward/_field_interpolation.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/forward/_field_interpolation.py 2022-08-03 15:48:14.000000000 +0000 @@ -10,49 +10,28 @@ import numpy as np -from ..io.constants import FWD, FIFF from ..bem import _check_origin +from ..cov import make_ad_hoc_cov +from ..io.constants import FIFF from ..io.pick import pick_types, pick_info -from ..surface import get_head_surf, get_meg_helmet_surf - +from ..io.meas_info import _simplify_info from ..io.proj import _has_eeg_average_ref_proj, make_projector +from ..surface import get_head_surf, get_meg_helmet_surf from ..transforms import (transform_surface_to, read_trans, _find_trans, _ensure_trans) from ._make_forward import _create_meg_coils, _create_eeg_els, _read_coil_defs from ._lead_dots import (_do_self_dots, _do_surface_dots, _get_legen_table, _do_cross_dots) -from ..parallel import check_n_jobs -from ..utils import logger, verbose, _check_option, _reg_pinv, _pl +from ..utils import logger, verbose, _check_option, _reg_pinv, _pl, path_like from ..epochs import EpochsArray, BaseEpochs from ..evoked import Evoked, EvokedArray -def _is_axial_coil(coil): - """Determine if the coil is axial.""" - is_ax = coil['coil_class'] in ( - FWD.COILC_MAG, FWD.COILC_AXIAL_GRAD, FWD.COILC_AXIAL_GRAD2) - return is_ax - - -def _ad_hoc_noise(coils, ch_type='meg'): - """Create ad-hoc noise covariance.""" - # XXX should de-duplicate with make_ad_hoc_cov - v = np.empty(len(coils)) - if ch_type == 'meg': - axs = np.array([_is_axial_coil(coil) for coil in coils], dtype=bool) - v[axs] = 4e-28 # 20e-15 ** 2 - v[np.logical_not(axs)] = 2.5e-25 # 5e-13 ** 2 - else: - v.fill(1e-12) # 1e-6 ** 2 - cov = dict(diag=True, data=v, eig=None, eigvec=None) - return cov - - -def _setup_dots(mode, coils, ch_type): +def _setup_dots(mode, info, coils, ch_type): """Set up dot products.""" from scipy.interpolate import interp1d int_rad = 0.06 - noise = _ad_hoc_noise(coils, ch_type) + noise = make_ad_hoc_cov(info, dict(mag=20e-15, grad=5e-13, eeg=1e-6)) n_coeff, interp = (50, 'nearest') if mode == 'fast' else (100, 'linear') lut, n_fact = _get_legen_table(ch_type, False, n_coeff, verbose=False) lut_fun = interp1d(np.linspace(-1, 1, lut.shape[0]), lut, interp, axis=0) @@ -188,11 +167,12 @@ # # Step 2. Calculate the dot products # - int_rad, noise, lut_fun, n_fact = _setup_dots(mode, coils_from, kind) + int_rad, noise, lut_fun, n_fact = _setup_dots( + mode, info_from, coils_from, kind) logger.info(f' Computing dot products for {len(coils_from)} ' f'{kind.upper()} channel{_pl(coils_from)}...') self_dots = _do_self_dots(int_rad, False, coils_from, origin, kind, - lut_fun, n_fact, n_jobs=1) + lut_fun, n_fact, n_jobs=None) logger.info(f' Computing cross products for {len(coils_from)} → ' f'{len(coils_to)} {kind.upper()} channel{_pl(coils_to)}...') cross_dots = _do_cross_dots(int_rad, False, coils_from, coils_to, @@ -292,13 +272,12 @@ @verbose def _make_surface_mapping(info, surf, ch_type='meg', trans=None, mode='fast', - n_jobs=1, origin=(0., 0., 0.04), verbose=None): + n_jobs=None, origin=(0., 0., 0.04), verbose=None): """Re-map M/EEG data to a surface. Parameters ---------- - info : instance of Info - Measurement info. + %(info_not_none)s surf : dict The surface to map the data to. The required fields are `'rr'`, `'nn'`, and `'coord_frame'`. Must be in head coordinates. @@ -334,7 +313,6 @@ # deal with coordinate frames here -- always go to "head" (easiest) orig_surf = surf surf = transform_surface_to(deepcopy(surf), 'head', trans) - n_jobs = check_n_jobs(n_jobs) origin = _check_origin(origin, info) # @@ -353,23 +331,25 @@ # XXX this code does not do any checking for compensation channels, # but it seems like this must be intentional from the ref_meg=False # (presumably from the C code) - chs = [info['chs'][pick] for pick in picks] + dev_head_t = info['dev_head_t'] + info = pick_info(_simplify_info(info), picks) + info['dev_head_t'] = dev_head_t # create coil defs in head coordinates if ch_type == 'meg': # Put them in head coordinates - coils = _create_meg_coils(chs, 'normal', info['dev_head_t']) + coils = _create_meg_coils(info['chs'], 'normal', info['dev_head_t']) type_str = 'coils' miss = 1e-4 # Smoothing criterion for MEG else: # EEG - coils = _create_eeg_els(chs) + coils = _create_eeg_els(info['chs']) type_str = 'electrodes' miss = 1e-3 # Smoothing criterion for EEG # # Step 2. Calculate the dot products # - int_rad, noise, lut_fun, n_fact = _setup_dots(mode, coils, ch_type) + int_rad, noise, lut_fun, n_fact = _setup_dots(mode, info, coils, ch_type) logger.info('Computing dot products for %i %s...' % (len(coils), type_str)) self_dots = _do_self_dots(int_rad, False, coils, origin, ch_type, lut_fun, n_fact, n_jobs) @@ -383,8 +363,7 @@ # # Step 4. Return the result # - ch_names = [c['ch_name'] for c in chs] - fmd = dict(kind=ch_type, surf=surf, ch_names=ch_names, coils=coils, + fmd = dict(kind=ch_type, surf=surf, ch_names=info['ch_names'], coils=coils, origin=origin, noise=noise, self_dots=self_dots, surface_dots=surface_dots, int_rad=int_rad, miss=miss) logger.info('Field mapping data ready') @@ -404,7 +383,8 @@ @verbose def make_field_map(evoked, trans='auto', subject=None, subjects_dir=None, ch_type=None, mode='fast', meg_surf='helmet', - origin=(0., 0., 0.04), n_jobs=1, verbose=None): + origin=(0., 0., 0.04), n_jobs=None, *, + head_source=('bem', 'head'), verbose=None): """Compute surface maps used for field display in 3D. Parameters @@ -439,6 +419,9 @@ .. versionadded:: 0.11 %(n_jobs)s + %(head_source)s + + .. versionadded:: 1.1 %(verbose)s Returns @@ -467,7 +450,7 @@ raise RuntimeError('No data available for mapping.') if trans is not None: - if isinstance(trans, str): + if isinstance(trans, path_like): trans = read_trans(trans) trans = _ensure_trans(trans, 'head', 'mri') @@ -478,7 +461,8 @@ if this_type == 'meg' and meg_surf == 'helmet': surf = get_meg_helmet_surf(info, trans) else: - surf = get_head_surf(subject, subjects_dir=subjects_dir) + surf = get_head_surf( + subject, source=head_source, subjects_dir=subjects_dir) surfs.append(surf) surf_maps = list() diff -Nru python-mne-0.23.4+dfsg/mne/forward/forward.py python-mne-1.1.0+dfsg/mne/forward/forward.py --- python-mne-0.23.4+dfsg/mne/forward/forward.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/forward/forward.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Alexandre Gramfort # Martin Luessi # -# License: BSD (3-clause) +# License: BSD-3-Clause # The computations in this code were primarily derived from Matti Hämäläinen's # C code. @@ -32,7 +32,7 @@ pick_types) from ..io.write import (write_int, start_block, end_block, write_coord_trans, write_name_list, - write_string, start_file, end_file, write_id) + write_string, start_and_end_file, write_id) from ..io.base import BaseRaw from ..evoked import Evoked, EvokedArray from ..epochs import BaseEpochs @@ -49,43 +49,97 @@ _validate_type, _check_compensation_grade, _check_option, _check_stc_units, _stamp_to_dt, _on_missing) from ..label import Label -from ..fixes import einsum class Forward(dict): """Forward class to represent info from forward solution. + Like :class:`mne.Info`, this data structure behaves like a dictionary. + It contains all metadata necessary for a forward solution. + + .. warning:: + This class should not be modified or created by users. + Forward objects should be obtained using + :func:`mne.make_forward_solution` or :func:`mne.read_forward_solution`. + Attributes ---------- ch_names : list of str - List of channels' names. + A convenience wrapper accessible as ``fwd.ch_names`` which wraps + ``fwd['info']['ch_names']``. - .. versionadded:: 0.20.0 + See Also + -------- + mne.make_forward_solution + mne.read_forward_solution + + Notes + ----- + Forward data is accessible via string keys using standard + :class:`python:dict` access (e.g., ``fwd['nsource'] == 4096``): + + source_ori : int + The source orientation, either ``FIFF.FIFFV_MNE_FIXED_ORI`` or + ``FIFF.FIFFV_MNE_FREE_ORI``. + coord_frame : int + The coordinate frame of the forward solution, usually + ``FIFF.FIFFV_COORD_HEAD``. + nsource : int + The number of source locations. + nchan : int + The number of channels. + sol : dict + The forward solution, with entries: + + ``'data'`` : ndarray, shape (n_channels, nsource * n_ori) + The forward solution data. The shape will be + ``(n_channels, nsource)`` for a fixed-orientation forward and + ``(n_channels, nsource * 3)`` for a free-orientation forward. + ``'row_names'`` : list of str + The channel names. + mri_head_t : instance of Transform + The mri ↔ head transformation that was used. + info : instance of :class:`~mne.Info` + The measurement information (with contents reduced compared to that + of the original data). + src : instance of :class:`~mne.SourceSpaces` + The source space used during forward computation. This can differ + from the original source space as: + + 1. Source points are removed due to proximity to (or existing + outside) + the inner skull surface. + 2. The source space will be converted to the ``coord_frame`` of the + forward solution, which typically means it gets converted from + MRI to head coordinates. + source_rr : ndarray, shape (n_sources, 3) + The source locations. + source_nn : ndarray, shape (n_sources, 3) + The source normals. Will be all +Z (``(0, 0, 1.)``) for volume + source spaces. For surface source spaces, these are normal to the + cortical surface. + surf_ori : int + Whether ``sol`` is surface-oriented with the surface normal in the + Z component (``FIFF.FIFFV_MNE_FIXED_ORI``) or +Z in the given + ``coord_frame`` in the Z component (``FIFF.FIFFV_MNE_FREE_ORI``). + + Forward objects also have some attributes that are accessible via ``.`` + access, like ``fwd.ch_names``. """ def copy(self): """Copy the Forward instance.""" return Forward(deepcopy(self)) - def __repr__(self): - """Summarize forward info instead of printing all.""" - entr = ' head coordinate transformation + # Get the MRI <-> head coordinate transformation tag = find_tag(fid, parent_mri, FIFF.FIFF_COORD_TRANS) coord_head = FIFF.FIFFV_COORD_HEAD coord_mri = FIFF.FIFFV_COORD_MRI @@ -312,7 +394,7 @@ else: raise ValueError('MRI/head coordinate transformation not found') - # Get the MEG device <-> head coordinate transformation + # Get the MEG device <-> head coordinate transformation tag = find_tag(fid, parent_meg, FIFF.FIFF_COORD_TRANS) if tag is None: raise ValueError('MEG/head coordinate transformation not found') @@ -335,7 +417,7 @@ tag = find_tag(fid, parent_mri, 236) # Constant 236 used before v0.11 info['custom_ref_applied'] = int(tag.data) if tag is not None else False - info._check_consistency() + info._unlocked = False return info @@ -422,7 +504,7 @@ """ check_fname(fname, 'forward', ('-fwd.fif', '-fwd.fif.gz', '_fwd.fif', '_fwd.fif.gz')) - + fname = _check_fname(fname=fname, must_exist=True, overwrite='read') # Open the file, create directory logger.info('Reading forward solution from %s...' % fname) f, tree, _ = fiff_open(fname) @@ -506,10 +588,12 @@ parent_env = parent_env[0] tag = find_tag(fid, parent_env, FIFF.FIFF_MNE_ENV_WORKING_DIR) if tag is not None: - fwd['info']['working_dir'] = tag.data + with fwd['info']._unlock(): + fwd['info']['working_dir'] = tag.data tag = find_tag(fid, parent_env, FIFF.FIFF_MNE_ENV_COMMAND_LINE) if tag is not None: - fwd['info']['command_line'] = tag.data + with fwd['info']._unlock(): + fwd['info']['command_line'] = tag.data # Transform the source spaces to the correct coordinate frame # if necessary @@ -703,8 +787,8 @@ Parameters ---------- fname : str - File name to save the forward solution to. It should end with -fwd.fif - or -fwd.fif.gz. + File name to save the forward solution to. It should end with + ``-fwd.fif`` or ``-fwd.fif.gz``. fwd : Forward Forward solution. %(overwrite)s @@ -732,9 +816,13 @@ check_fname(fname, 'forward', ('-fwd.fif', '-fwd.fif.gz', '_fwd.fif', '_fwd.fif.gz')) - # check for file existence - _check_fname(fname, overwrite) - fid = start_file(fname) + # check for file existence and expand `~` if present + fname = _check_fname(fname, overwrite) + with start_and_end_file(fname) as fid: + _write_forward_solution(fid, fwd) + + +def _write_forward_solution(fid, fwd): start_block(fid, FIFF.FIFFB_MNE) # @@ -869,7 +957,6 @@ end_block(fid, FIFF.FIFFB_MNE_FORWARD_SOLUTION) end_block(fid, FIFF.FIFFB_MNE) - end_file(fid) def is_fixed_orient(forward, orig=False): @@ -895,6 +982,7 @@ return fixed_ori +@fill_doc def write_forward_meas_info(fid, info): """Write measurement info stored in forward solution. @@ -902,8 +990,7 @@ ---------- fid : file id The file id - info : instance of Info - The measurement info. + %(info_not_none)s """ info._check_consistency() # @@ -1003,10 +1090,10 @@ del fixed for key, this_loose in loose.items(): - if key != 'surface' and this_loose != 1: + if key not in ('surface', 'discrete') and this_loose != 1: raise ValueError( - 'loose parameter has to be 1 or "auto" for non-surface ' - f'source spaces, got loose["{key}"] = {this_loose}') + 'loose parameter has to be 1 or "auto" for non-surface/' + f'discrete source spaces, got loose["{key}"] = {this_loose}') if not 0 <= this_loose <= 1: raise ValueError( f'loose ({key}) must be between 0 and 1, got {this_loose}') @@ -1101,8 +1188,7 @@ ---------- forward : instance of Forward The forward solution. - info : instance of Info - The measurement info. + %(info_not_none)s exp : float Exponent for the depth weighting, must be between 0 and 1. limit : float | None @@ -1127,7 +1213,7 @@ ``limit_depth_chs='whiten'``. .. versionadded:: 0.18 - %(rank_None)s + %(rank_none)s .. versionadded:: 0.18 %(verbose)s @@ -1218,7 +1304,7 @@ # x = np.dot(Gk.T, Gk) # d[k] = linalg.svdvals(x)[0] G.shape = (G.shape[0], -1, 3) - d = np.linalg.norm(einsum('svj,svk->vjk', G, G), # vector dot products + d = np.linalg.norm(np.einsum('svj,svk->vjk', G, G), # vector dot prods ord=2, axis=(1, 2)) # ord=2 spectral (largest s.v.) G.shape = (G.shape[0], -1) @@ -1299,25 +1385,27 @@ return src_sel, stc_sel, out_vertices -def _fill_measurement_info(info, fwd, sfreq): +def _fill_measurement_info(info, fwd, sfreq, data): """Fill the measurement info of a Raw or Evoked object.""" sel = pick_channels(info['ch_names'], fwd['sol']['row_names']) info = pick_info(info, sel) info['bads'] = [] - # this is probably correct based on what's done in meas_info.py... - info['meas_id'] = fwd['info']['meas_id'] - info['file_id'] = info['meas_id'] - now = time() sec = np.floor(now) usec = 1e6 * (now - sec) - info.update(meas_date=_stamp_to_dt((int(sec), int(usec))), highpass=0., - lowpass=sfreq / 2., sfreq=sfreq, projs=[]) - info._check_consistency() + # this is probably correct based on what's done in meas_info.py... + with info._unlock(check_after=True): + info.update(meas_id=fwd['info']['meas_id'], file_id=info['meas_id'], + meas_date=_stamp_to_dt((int(sec), int(usec))), + highpass=0., lowpass=sfreq / 2., sfreq=sfreq, projs=[]) + + # reorder data (which is in fwd order) to match that of info + order = [fwd['sol']['row_names'].index(name) for name in info['ch_names']] + data = data[order] - return info + return info, data @verbose @@ -1379,8 +1467,7 @@ Forward operator to use. stc : SourceEstimate The source estimate from which the sensor space data is computed. - info : instance of Info - Measurement info to generate the evoked. + %(info_not_none)s start : int, optional Index of first time sample (index not time is seconds). stop : int, optional @@ -1419,11 +1506,11 @@ # fill the measurement info sfreq = float(1.0 / stc.tstep) - info_out = _fill_measurement_info(info, fwd, sfreq) + info, data = _fill_measurement_info(info, fwd, sfreq, data) - evoked = EvokedArray(data, info_out, times[0], nave=1) + evoked = EvokedArray(data, info, times[0], nave=1) - evoked.times = times + evoked._set_times(times) evoked._update_first_last() return evoked @@ -1449,8 +1536,7 @@ Forward operator to use. stc : SourceEstimate The source estimate from which the sensor space data is computed. - info : instance of Info - The measurement info. + %(info_not_none)s start : int, optional Index of first time sample (index not time is seconds). stop : int, optional @@ -1484,14 +1570,11 @@ use_cps=use_cps) sfreq = 1.0 / stc.tstep - info = _fill_measurement_info(info, fwd, sfreq) - info['projs'] = [] + info, data = _fill_measurement_info(info, fwd, sfreq, data) + with info._unlock(): + info['projs'] = [] # store sensor data in Raw object using the info - raw = RawArray(data, info) - raw.preload = True - - raw._first_samps = np.array([int(np.round(times[0] * sfreq))]) - raw._last_samps = np.array([raw.first_samp + raw._data.shape[1] - 1]) + raw = RawArray(data, info, first_samp=int(np.round(times[0] * sfreq))) raw._projector = None return raw diff -Nru python-mne-0.23.4+dfsg/mne/forward/_lead_dots.py python-mne-1.1.0+dfsg/mne/forward/_lead_dots.py --- python-mne-0.23.4+dfsg/mne/forward/_lead_dots.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/forward/_lead_dots.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Eric Larson # Mainak Jas # -# License: BSD (3-clause) +# License: BSD-3-Clause # The computations in this code were primarily derived from Matti Hämäläinen's # C code. @@ -13,7 +13,6 @@ import numpy as np from numpy.polynomial import legendre -from ..fixes import einsum from ..parallel import parallel_func from ..utils import logger, verbose, _get_extra_data_path, fill_doc @@ -158,7 +157,7 @@ # sums = np.sum(bbeta[:, :, np.newaxis].T * n_fact * coeffs, axis=1) # sums = np.rollaxis(sums, 2) # or - # sums = einsum('ji,jk,ijk->ki', bbeta, n_fact, lut_fun(ctheta))) + # sums = np.einsum('ji,jk,ijk->ki', bbeta, n_fact, lut_fun(ctheta))) sums = np.empty((n_fact.shape[1], len(beta))) # beta can be e.g. 3 million elements, which ends up using lots of memory # so we split up the computations into ~50 MB blocks @@ -168,8 +167,8 @@ bbeta = np.tile(beta[start:stop][np.newaxis], (n_fact.shape[0], 1)) bbeta[0] *= beta[start:stop] np.cumprod(bbeta, axis=0, out=bbeta) # run inplace - einsum('ji,jk,ijk->ki', bbeta, n_fact, lut_fun(ctheta[start:stop]), - out=sums[:, start:stop]) + np.einsum('ji,jk,ijk->ki', bbeta, n_fact, lut_fun(ctheta[start:stop]), + out=sums[:, start:stop]) return sums @@ -231,7 +230,7 @@ cosmags2 = np.concatenate(cosmags2s) # outer product, sum over coords - ct = einsum('ik,jk->ij', rr1_orig, rr2) + ct = np.einsum('ik,jk->ij', rr1_orig, rr2) np.clip(ct, -1, 1, ct) # expand axes @@ -253,11 +252,11 @@ # n2c1 = np.sum(cosmags2 * rr1, axis=2) # n2c2 = np.sum(cosmags2 * rr2, axis=2) # n1n2 = np.sum(cosmags1 * cosmags2, axis=2) - n1c1 = einsum('ik,ijk->ij', cosmags1, rr1) - n1c2 = einsum('ik,ijk->ij', cosmags1, rr2) - n2c1 = einsum('jk,ijk->ij', cosmags2, rr1) - n2c2 = einsum('jk,ijk->ij', cosmags2, rr2) - n1n2 = einsum('ik,jk->ij', cosmags1, cosmags2) + n1c1 = np.einsum('ik,ijk->ij', cosmags1, rr1) + n1c2 = np.einsum('ik,ijk->ij', cosmags1, rr2) + n2c1 = np.einsum('jk,ijk->ij', cosmags2, rr1) + n2c2 = np.einsum('jk,ijk->ij', cosmags2, rr2) + n1n2 = np.einsum('ik,jk->ij', cosmags1, cosmags2) part1 = ct * n1c1 * n2c2 part2 = n1c1 * n2c1 + n1c2 * n2c2 @@ -323,7 +322,7 @@ rmags = [r / rl[:, np.newaxis] for r, rl in zip(rmags, rlens)] cosmags = [coil['cosmag'] for coil in coils] ws = [coil['w'] for coil in coils] - parallel, p_fun, _ = parallel_func(_do_self_dots_subset, n_jobs) + parallel, p_fun, n_jobs = parallel_func(_do_self_dots_subset, n_jobs) prods = parallel(p_fun(intrad, rmags, rlens, cosmags, ws, volume, lut, n_fact, ch_type, idx) for idx in np.array_split(np.arange(len(rmags)), n_jobs)) @@ -462,7 +461,7 @@ this_nn = surf['nn'][sel] # loop over the coils - parallel, p_fun, _ = parallel_func(_do_surface_dots_subset, n_jobs) + parallel, p_fun, n_jobs = parallel_func(_do_surface_dots_subset, n_jobs) prods = parallel(p_fun(intrad, rsurf, rmags, rref, refl, lsurf, rlens, this_nn, cosmags, ws, volume, lut, n_fact, ch_type, idx) diff -Nru python-mne-0.23.4+dfsg/mne/forward/_make_forward.py python-mne-1.1.0+dfsg/mne/forward/_make_forward.py --- python-mne-0.23.4+dfsg/mne/forward/_make_forward.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/forward/_make_forward.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,13 +3,14 @@ # Martin Luessi # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause # The computations in this code were primarily derived from Matti Hämäläinen's # C code. from copy import deepcopy from contextlib import contextmanager +from pathlib import Path import os import os.path as op @@ -21,13 +22,12 @@ from ..io.constants import FIFF, FWD from ..transforms import (_ensure_trans, transform_surface_to, apply_trans, _get_trans, _print_coord_trans, _coord_frame_name, - Transform) -from ..utils import logger, verbose, warn, _pl -from ..parallel import check_n_jobs + Transform, invert_transform) +from ..utils import logger, verbose, warn, _pl, _validate_type, _check_fname from ..source_space import (_ensure_src, _filter_source_spaces, _make_discrete_source_space, _complete_vol_src) from ..source_estimate import VolSourceEstimate -from ..surface import _normalize_vectors +from ..surface import _normalize_vectors, _CheckInside from ..bem import read_bem_solution, _bem_find_surface, ConductorModel from .forward import Forward, _merge_meg_eeg_fwds, convert_forward_solution @@ -102,10 +102,13 @@ for p in range(npts): # get next non-comment line line = lines.pop() - while(line[0] == '#'): + while line[0] == '#': line = lines.pop() vals = np.fromstring(line, sep=' ') - assert len(vals) == 7 + if len(vals) != 7: + raise RuntimeError( + f'Could not interpret line {p + 1} as 7 points:\n' + f'{line}') # Read and verify data for each integration point w.append(vals[0]) rmag.append(vals[[1, 2, 3]]) @@ -230,12 +233,11 @@ if allow_none and bem is None: return None logger.info('') - if isinstance(bem, str): + _validate_type(bem, ('path-like', ConductorModel), bem) + if not isinstance(bem, ConductorModel): logger.info('Setting up the BEM model using %s...\n' % bem_extra) bem = read_bem_solution(bem) else: - if not isinstance(bem, ConductorModel): - raise TypeError('bem must be a string or ConductorModel') bem = bem.copy() if bem['is_sphere']: logger.info('Using the sphere model.\n') @@ -263,18 +265,16 @@ @verbose -def _prep_meg_channels(info, accurate=True, exclude=(), ignore_ref=False, +def _prep_meg_channels(info, accuracy='accurate', exclude=(), ignore_ref=False, head_frame=True, do_es=False, do_picking=True, verbose=None): """Prepare MEG coil definitions for forward calculation. Parameters ---------- - info : instance of Info - The measurement information dictionary - accurate : bool - If true (default) then use `accurate` coil definitions (more - integration points) + %(info_not_none)s + accuracy : str + Can be "normal" or "accurate" (default). exclude : list of str | str List of channels to exclude. If 'bads', exclude channels in info['bads'] @@ -299,7 +299,6 @@ meginfo : instance of Info Information subselected for just the set of MEG coils """ - accuracy = 'accurate' if accurate else 'normal' info_extra = 'info' megnames, megcoils, compcoils = [], [], [] @@ -378,8 +377,7 @@ Parameters ---------- - info : instance of Info - The measurement information dictionary + %(info_not_none)s exclude : list of str | str List of channels to exclude. If 'bads', exclude channels in info['bads'] @@ -446,8 +444,10 @@ cmd = 'make_forward_solution(%s)' % (', '.join([str(a) for a in arg_list])) mri_id = dict(machid=np.zeros(2, np.int32), version=0, secs=0, usecs=0) + info_trans = str(trans) if isinstance(trans, Path) else trans info = Info(chs=info['chs'], comps=info['comps'], - dev_head_t=info['dev_head_t'], mri_file=trans, mri_id=mri_id, + dev_head_t=info['dev_head_t'], mri_file=info_trans, + mri_id=mri_id, meas_file=info_extra, meas_id=None, working_dir=os.getcwd(), command_line=cmd, bads=info['bads'], mri_head_t=mri_head_t) info._update_redundant() @@ -483,11 +483,38 @@ bem = _setup_bem(bem, bem_extra, len(eegnames), mri_head_t, allow_none=allow_bem_none) - # Circumvent numerical problems by excluding points too close to the skull - if bem is not None and not bem['is_sphere']: - inner_skull = _bem_find_surface(bem, 'inner_skull') - _filter_source_spaces(inner_skull, mindist, mri_head_t, src, n_jobs) - logger.info('') + # Circumvent numerical problems by excluding points too close to the skull, + # and check that sensors are not inside any BEM surface + if bem is not None: + if not bem['is_sphere']: + check_surface = 'inner skull surface' + inner_skull = _bem_find_surface(bem, 'inner_skull') + check_inside = _filter_source_spaces( + inner_skull, mindist, mri_head_t, src, n_jobs) + logger.info('') + if len(bem['surfs']) == 3: + check_surface = 'scalp surface' + check_inside = _CheckInside( + _bem_find_surface(bem, 'head')) + else: + check_surface = 'outermost sphere shell' + if len(bem['layers']) == 0: + def check_inside(x): + return np.zeros(len(x), bool) + else: + def check_inside(x): + return (np.linalg.norm(x - bem['r0'], axis=1) < + bem['layers'][-1]['rad']) + if len(megcoils): + meg_loc = apply_trans( + invert_transform(mri_head_t), + np.array([coil['r0'] for coil in megcoils])) + n_inside = check_inside(meg_loc).sum() + if n_inside: + raise RuntimeError( + f'Found {n_inside} MEG sensor{_pl(n_inside)} inside the ' + f'{check_surface}, perhaps coordinate frames and/or ' + 'coregistration must be incorrect') rr = np.concatenate([s['rr'][s['vertno']] for s in src]) if len(rr) < 1: @@ -505,21 +532,18 @@ @verbose def make_forward_solution(info, trans, src, bem, meg=True, eeg=True, - mindist=0.0, ignore_ref=False, n_jobs=1, + mindist=0.0, ignore_ref=False, n_jobs=None, *, verbose=None): """Calculate a forward solution for a subject. Parameters ---------- - info : instance of mne.Info | str - If str, then it should be a filename to a Raw, Epochs, or Evoked - file with measurement information. If dict, should be an info - dict (such as one from Raw, Epochs, or Evoked). + %(info_str)s %(trans)s - src : str | instance of SourceSpaces + src : path-like | instance of SourceSpaces If string, should be a source space filename. Can also be an instance of loaded or generated SourceSpaces. - bem : dict | str + bem : path-like | dict Filename of the BEM (e.g., "sample-5120-5120-5120-bem-sol.fif") to use, or a loaded sphere model (dict). meg : bool @@ -564,14 +588,14 @@ bem_extra = 'instance of ConductorModel' else: bem_extra = bem - if not isinstance(info, (Info, str)): - raise TypeError('info should be an instance of Info or string') - if isinstance(info, str): + _validate_type(info, ('path-like', Info), 'info') + if not isinstance(info, Info): info_extra = op.split(info)[1] + info = _check_fname(info, must_exist=True, overwrite='read', + name='info') info = read_info(info, verbose=False) else: info_extra = 'instance of Info' - n_jobs = check_n_jobs(n_jobs) # Report the setup logger.info('Source space : %s' % src) @@ -618,7 +642,8 @@ @verbose -def make_forward_dipole(dipole, bem, info, trans=None, n_jobs=1, verbose=None): +def make_forward_dipole(dipole, bem, info, trans=None, n_jobs=None, *, + verbose=None): """Convert dipole object to source estimate and calculate forward operator. The instance of Dipole is converted to a discrete source space, @@ -634,10 +659,7 @@ Parameters ---------- - dipole : instance of Dipole - Dipole object containing position, orientation and amplitude of - one or more dipoles. Multiple simultaneous dipoles may be defined by - assigning them identical times. + %(dipole)s bem : str | dict The BEM filename (str) or a loaded sphere model (dict). info : instance of Info @@ -666,6 +688,10 @@ ----- .. versionadded:: 0.12.0 """ + if isinstance(dipole, list): + from ..dipole import _concatenate_dipoles # To avoid circular import + dipole = _concatenate_dipoles(dipole) + # Make copies to avoid mangling original dipole times = dipole.times.copy() pos = dipole.pos.copy() diff -Nru python-mne-0.23.4+dfsg/mne/forward/tests/test_field_interpolation.py python-mne-1.1.0+dfsg/mne/forward/tests/test_field_interpolation.py --- python-mne-0.23.4+dfsg/mne/forward/tests/test_field_interpolation.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/forward/tests/test_field_interpolation.py 2022-08-03 15:48:14.000000000 +0000 @@ -18,7 +18,6 @@ from mne.datasets import testing from mne import read_evokeds, pick_types, make_fixed_length_events, Epochs from mne.io import read_raw_fif -from mne.utils import run_tests_if_main base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data') @@ -193,7 +192,7 @@ evoked.pick_channels([evoked.ch_names[p] for p in picks]) evoked.info.normalize_proj() maps = make_field_map(evoked, trans_fname, subject='sample', - subjects_dir=subjects_dir, n_jobs=1, verbose='debug') + subjects_dir=subjects_dir, verbose='debug') assert_equal(maps[0]['data'].shape, (642, 6)) # EEG->Head assert_equal(maps[1]['data'].shape, (304, 31)) # MEG->Helmet # reasonable ranges @@ -213,7 +212,7 @@ def _setup_args(info): """Configure args for test_as_meg_type_evoked.""" coils = _create_meg_coils(info['chs'], 'normal', info['dev_head_t']) - int_rad, noise, lut_fun, n_fact = _setup_dots('fast', coils, 'meg') + int_rad, _, lut_fun, n_fact = _setup_dots('fast', info, coils, 'meg') my_origin = np.array([0., 0., 0.04]) args_dict = dict(intrad=int_rad, volume=False, coils1=coils, r0=my_origin, ch_type='meg', lut=lut_fun, n_fact=n_fact) @@ -274,6 +273,3 @@ virt_epochs = virt_epochs.as_type('mag') assert (all(ch.endswith('_v') for ch in virt_epochs.info['ch_names'])) assert_allclose(virt_epochs.get_data().mean(0), virt_evoked.data) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/forward/tests/test_forward.py python-mne-1.1.0+dfsg/mne/forward/tests/test_forward.py --- python-mne-0.23.4+dfsg/mne/forward/tests/test_forward.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/forward/tests/test_forward.py 2022-08-03 15:48:14.000000000 +0000 @@ -13,8 +13,7 @@ read_evokeds, VectorSourceEstimate) from mne.io import read_info from mne.label import read_label -from mne.utils import (requires_mne, run_subprocess, - run_tests_if_main) +from mne.utils import requires_mne, run_subprocess from mne.forward import (restrict_forward_to_stc, restrict_forward_to_label, Forward, is_fixed_orient, compute_orient_prior, compute_depth_prior) @@ -87,7 +86,7 @@ @pytest.mark.slowtest @testing.requires_testing_data -def test_io_forward(tmpdir): +def test_io_forward(tmp_path): """Test IO for forward solutions.""" # do extensive tests with MEEG + grad n_channels, n_src = 366, 108 @@ -98,7 +97,7 @@ leadfield = fwd['sol']['data'] assert_equal(leadfield.shape, (n_channels, n_src)) assert_equal(len(fwd['sol']['row_names']), n_channels) - fname_temp = tmpdir.join('test-fwd.fif') + fname_temp = tmp_path / 'test-fwd.fif' with pytest.warns(RuntimeWarning, match='stored on disk'): write_forward_solution(fname_temp, fwd, overwrite=True) @@ -168,7 +167,7 @@ # test warnings on bad filenames fwd = read_forward_solution(fname_meeg_grad) - fwd_badname = tmpdir.join('test-bad-name.fif.gz') + fwd_badname = tmp_path / 'test-bad-name.fif.gz' with pytest.warns(RuntimeWarning, match='end with'): write_forward_solution(fwd_badname, fwd) with pytest.warns(RuntimeWarning, match='end with'): @@ -240,7 +239,7 @@ @testing.requires_testing_data -def test_restrict_forward_to_stc(tmpdir): +def test_restrict_forward_to_stc(tmp_path): """Test restriction of source space to source SourceEstimate.""" start = 0 stop = 5 @@ -284,7 +283,7 @@ # Test saving the restricted forward object. This only works if all fields # are properly accounted for. - fname_copy = tmpdir.join('copy-fwd.fif') + fname_copy = tmp_path / 'copy-fwd.fif' with pytest.warns(RuntimeWarning, match='stored on disk'): write_forward_solution(fname_copy, fwd_out, overwrite=True) fwd_out_read = read_forward_solution(fname_copy) @@ -294,7 +293,7 @@ @testing.requires_testing_data -def test_restrict_forward_to_label(tmpdir): +def test_restrict_forward_to_label(tmp_path): """Test restriction of source space to label.""" fwd = read_forward_solution(fname_meeg) fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=True, @@ -353,7 +352,7 @@ # Test saving the restricted forward object. This only works if all fields # are properly accounted for. - fname_copy = tmpdir.join('copy-fwd.fif') + fname_copy = tmp_path / 'copy-fwd.fif' write_forward_solution(fname_copy, fwd_out, overwrite=True) fwd_out_read = read_forward_solution(fname_copy) assert_forward_allclose(fwd_out, fwd_out_read) @@ -361,7 +360,7 @@ @testing.requires_testing_data @requires_mne -def test_average_forward_solution(tmpdir): +def test_average_forward_solution(tmp_path): """Test averaging forward solutions.""" fwd = read_forward_solution(fname_meeg) # input not a list @@ -384,7 +383,7 @@ # modify a fwd solution, save it, use MNE to average with old one fwd_copy['sol']['data'] *= 0.5 - fname_copy = str(tmpdir.join('copy-fwd.fif')) + fname_copy = str(tmp_path / 'copy-fwd.fif') write_forward_solution(fname_copy, fwd_copy, overwrite=True) cmd = ('mne_average_forward_solutions', '--fwd', fname_meeg, '--fwd', fname_copy, '--out', fname_copy) @@ -446,6 +445,3 @@ fwd1, fwd2 = equalize_channels([fwd1, fwd2]) assert fwd1.ch_names == ['EEG 001', 'EEG 002'] assert fwd2.ch_names == ['EEG 001', 'EEG 002'] - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/forward/tests/test_make_forward.py python-mne-1.1.0+dfsg/mne/forward/tests/test_make_forward.py --- python-mne-0.23.4+dfsg/mne/forward/tests/test_make_forward.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/forward/tests/test_make_forward.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,7 @@ from itertools import product import os import os.path as op +from pathlib import Path import pytest import numpy as np @@ -14,17 +15,20 @@ make_forward_solution, convert_forward_solution, setup_volume_source_space, read_source_spaces, create_info, make_sphere_model, pick_types_forward, pick_info, pick_types, - read_evokeds, read_cov, read_dipole) -from mne.utils import (requires_mne, requires_nibabel, - run_tests_if_main, run_subprocess) + read_evokeds, read_cov, read_dipole, + get_volume_labels_from_aseg) +from mne.surface import _get_ico_surface +from mne.transforms import Transform +from mne.utils import (requires_mne, requires_nibabel, run_subprocess, + catch_logging) from mne.forward._make_forward import _create_meg_coils, make_forward_dipole from mne.forward._compute_forward import _magnetic_dipole_field_vec -from mne.forward import Forward, _do_forward_solution +from mne.forward import Forward, _do_forward_solution, use_coil_def from mne.dipole import Dipole, fit_dipole from mne.simulation import simulate_evoked from mne.source_estimate import VolSourceEstimate -from mne.source_space import (get_volume_labels_from_aseg, write_source_spaces, - _compare_source_spaces, setup_source_space) +from mne.source_space import (write_source_spaces, _compare_source_spaces, + setup_source_space) from mne.forward.tests.test_forward import assert_forward_allclose @@ -123,7 +127,7 @@ @pytest.mark.timeout(60) # can take longer than 30 sec on Travis @testing.requires_testing_data @requires_mne -def test_make_forward_solution_kit(tmpdir): +def test_make_forward_solution_kit(tmp_path): """Test making fwd using KIT, BTI, and CTF (compensated) files.""" kit_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'kit', 'tests', 'data') @@ -145,7 +149,7 @@ 'data', 'test_ctf_comp_raw.fif') # first set up a small testing source space - fname_src_small = tmpdir.join('sample-oct-2-src.fif') + fname_src_small = tmp_path / 'sample-oct-2-src.fif' src = setup_source_space('sample', 'oct2', subjects_dir=subjects_dir, add_dist=False) write_source_spaces(fname_src_small, src) # to enable working with MNE-C @@ -210,7 +214,7 @@ subjects_dir=subjects_dir) _compare_forwards(fwd, fwd_py, 274, n_src) - fname_temp = tmpdir.join('test-ctf-fwd.fif') + fname_temp = tmp_path / 'test-ctf-fwd.fif' write_forward_solution(fname_temp, fwd_py) fwd_py2 = read_forward_solution(fname_temp) _compare_forwards(fwd_py, fwd_py2, 274, n_src) @@ -221,8 +225,13 @@ @testing.requires_testing_data def test_make_forward_solution(): """Test making M-EEG forward solution from python.""" - fwd_py = make_forward_solution(fname_raw, fname_trans, fname_src, - fname_bem, mindist=5.) + with catch_logging() as log: + # make sure everything can be path-like (gh #10872) + fwd_py = make_forward_solution( + Path(fname_raw), Path(fname_trans), Path(fname_src), + Path(fname_bem), mindist=5., verbose=True) + log = log.getvalue() + assert 'Total 258/258 points inside the surface' in log assert (isinstance(fwd_py, Forward)) fwd = read_forward_solution(fname_meeg) assert (isinstance(fwd, Forward)) @@ -234,7 +243,7 @@ @testing.requires_testing_data -def test_make_forward_solution_discrete(tmpdir): +def test_make_forward_solution_discrete(tmp_path): """Test making and converting a forward solution with discrete src.""" # smoke test for depth weighting and discrete source spaces src = setup_source_space('sample', 'oct2', subjects_dir=subjects_dir, @@ -251,13 +260,13 @@ @testing.requires_testing_data @requires_mne @pytest.mark.timeout(90) # can take longer than 60 sec on Travis -def test_make_forward_solution_sphere(tmpdir): +def test_make_forward_solution_sphere(tmp_path): """Test making a forward solution with a sphere model.""" - fname_src_small = tmpdir.join('sample-oct-2-src.fif') + fname_src_small = tmp_path / 'sample-oct-2-src.fif' src = setup_source_space('sample', 'oct2', subjects_dir=subjects_dir, add_dist=False) write_source_spaces(fname_src_small, src) # to enable working with MNE-C - out_name = tmpdir.join('tmp-fwd.fif') + out_name = tmp_path / 'tmp-fwd.fif' run_subprocess(['mne_forward_solution', '--meg', '--eeg', '--meas', fname_raw, '--src', fname_src_small, '--mri', fname_trans, '--fwd', out_name]) @@ -295,7 +304,7 @@ @pytest.mark.slowtest @testing.requires_testing_data @requires_nibabel() -def test_forward_mixed_source_space(tmpdir): +def test_forward_mixed_source_space(tmp_path): """Test making the forward solution for a mixed source space.""" # get the surface source space rng = np.random.RandomState(0) @@ -304,9 +313,10 @@ # setup two volume source spaces label_names = get_volume_labels_from_aseg(fname_aseg) vol_labels = rng.choice(label_names, 2) - vol1 = setup_volume_source_space('sample', pos=20., mri=fname_aseg, - volume_label=vol_labels[0], - add_interpolator=False) + with pytest.warns(RuntimeWarning, match='Found no usable.*CC_Mid_Ant.*'): + vol1 = setup_volume_source_space('sample', pos=20., mri=fname_aseg, + volume_label=vol_labels[0], + add_interpolator=False) vol2 = setup_volume_source_space('sample', pos=20., mri=fname_aseg, volume_label=vol_labels[1], add_interpolator=False) @@ -328,7 +338,7 @@ assert ((coord_frames == FIFF.FIFFV_COORD_HEAD).all()) # run tests for SourceSpaces.export_volume - fname_img = tmpdir.join('temp-image.mgz') + fname_img = tmp_path / 'temp-image.mgz' # head coordinates and mri_resolution, but trans file with pytest.raises(ValueError, match='trans containing mri to head'): @@ -343,7 +353,7 @@ @pytest.mark.slowtest @testing.requires_testing_data -def test_make_forward_dipole(tmpdir): +def test_make_forward_dipole(tmp_path): """Test forward-projecting dipoles.""" rng = np.random.RandomState(0) @@ -440,7 +450,7 @@ dip_even_samp = Dipole(times, pos, amplitude, ori, gof) # I/O round-trip - fname = str(tmpdir.join('test-fwd.fif')) + fname = str(tmp_path / 'test-fwd.fif') with pytest.warns(RuntimeWarning, match='free orientation'): write_forward_solution(fname, fwd) fwd_read = convert_forward_solution( @@ -452,9 +462,15 @@ assert isinstance(stc, VolSourceEstimate) assert_allclose(stc.times, np.arange(0., 0.003, 0.001)) + # Test passing a list of Dipoles instead of a single Dipole object + fwd2, stc2 = make_forward_dipole([dip_even_samp[0], dip_even_samp[1:]], + sphere, info, trans=fname_trans) + assert_array_equal(fwd['sol']['data'], fwd2['sol']['data']) + assert_array_equal(stc.data, stc2.data) + @testing.requires_testing_data -def test_make_forward_no_meg(tmpdir): +def test_make_forward_no_meg(tmp_path): """Test that we can make and I/O forward solution with no MEG channels.""" pos = dict(rr=[[0.05, 0, 0]], nn=[[0, 0, 1.]]) src = setup_volume_source_space(pos=pos) @@ -463,10 +479,70 @@ montage = make_standard_montage('standard_1020') info = create_info(['Cz'], 1000., 'eeg').set_montage(montage) fwd = make_forward_solution(info, trans, src, bem) - fname = tmpdir.join('test-fwd.fif') + fname = tmp_path / 'test-fwd.fif' write_forward_solution(fname, fwd) fwd_read = read_forward_solution(fname) assert_allclose(fwd['sol']['data'], fwd_read['sol']['data']) -run_tests_if_main() +def test_use_coil_def(tmp_path): + """Test use_coil_def.""" + info = create_info(1, 1000., 'mag') + info['chs'][0]['coil_type'] = 9999 + info['chs'][0]['loc'][:] = [0, 0, 0.02, 1, 0, 0, 0, 1, 0, 0, 0, 1] + sphere = make_sphere_model((0., 0., 0.), 0.01) + src = setup_volume_source_space(pos=5, sphere=sphere) + trans = Transform('head', 'mri', None) + with pytest.raises(RuntimeError, match='coil definition not found'): + make_forward_solution(info, trans, src, sphere) + coil_fname = tmp_path / 'coil_def.dat' + with open(coil_fname, 'w') as fid: + fid.write("""# custom cube coil def +1 9999 2 8 3e-03 0.000e+00 "Test" + 0.1250 -0.750e-03 -0.750e-03 -0.750e-03 0.000 0.000""") + with pytest.raises(RuntimeError, match='Could not interpret'): + with use_coil_def(coil_fname): + make_forward_solution(info, trans, src, sphere) + with open(coil_fname, 'w') as fid: + fid.write("""# custom cube coil def +1 9999 2 8 3e-03 0.000e+00 "Test" + 0.1250 -0.750e-03 -0.750e-03 -0.750e-03 0.000 0.000 1.000 + 0.1250 -0.750e-03 0.750e-03 -0.750e-03 0.000 0.000 1.000 + 0.1250 0.750e-03 -0.750e-03 -0.750e-03 0.000 0.000 1.000 + 0.1250 0.750e-03 0.750e-03 -0.750e-03 0.000 0.000 1.000 + 0.1250 -0.750e-03 -0.750e-03 0.750e-03 0.000 0.000 1.000 + 0.1250 -0.750e-03 0.750e-03 0.750e-03 0.000 0.000 1.000 + 0.1250 0.750e-03 -0.750e-03 0.750e-03 0.000 0.000 1.000 + 0.1250 0.750e-03 0.750e-03 0.750e-03 0.000 0.000 1.000""") + with use_coil_def(coil_fname): + make_forward_solution(info, trans, src, sphere) + + +@pytest.mark.slowtest +@testing.requires_testing_data +def test_sensors_inside_bem(): + """Test that sensors inside the BEM are problematic.""" + rr = _get_ico_surface(1)['rr'] + rr /= np.linalg.norm(rr, axis=1, keepdims=True) + rr *= 0.1 + assert len(rr) == 42 + info = create_info(len(rr), 1000., 'mag') + info['dev_head_t'] = Transform('meg', 'head', np.eye(4)) + for ii, ch in enumerate(info['chs']): + ch['loc'][:] = np.concatenate((rr[ii], np.eye(3).ravel())) + trans = Transform('head', 'mri', np.eye(4)) + trans['trans'][2, 3] = 0.03 + sphere_noshell = make_sphere_model((0., 0., 0.), None) + sphere = make_sphere_model((0., 0., 0.), 1.01) + with pytest.raises(RuntimeError, match='.* 15 MEG.*inside the scalp.*'): + make_forward_solution(info, trans, fname_src, fname_bem) + make_forward_solution(info, trans, fname_src, fname_bem_meg) # okay + make_forward_solution(info, trans, fname_src, sphere_noshell) # okay + with pytest.raises(RuntimeError, match='.* 42 MEG.*outermost sphere sh.*'): + make_forward_solution(info, trans, fname_src, sphere) + sphere = make_sphere_model((0., 0., 2.0), 1.01) # weird, but okay + make_forward_solution(info, trans, fname_src, sphere) + for ch in info['chs']: + ch['loc'][:3] *= 0.1 + with pytest.raises(RuntimeError, match='.* 42 MEG.*the inner skull.*'): + make_forward_solution(info, trans, fname_src, fname_bem_meg) diff -Nru python-mne-0.23.4+dfsg/mne/_freesurfer.py python-mne-1.1.0+dfsg/mne/_freesurfer.py --- python-mne-0.23.4+dfsg/mne/_freesurfer.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/_freesurfer.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,753 @@ +# -*- coding: utf-8 -*- +"""Freesurfer handling functions.""" +# Authors: Alex Rockhill +# Eric Larson +# +# License: BSD-3-Clause + +import os.path as op +import numpy as np +from gzip import GzipFile + +from .bem import _bem_find_surface, read_bem_surfaces +from .io.constants import FIFF +from .io.meas_info import read_fiducials +from .transforms import (apply_trans, invert_transform, combine_transforms, + _ensure_trans, read_ras_mni_t, Transform) +from .surface import read_surface, _read_mri_surface +from .utils import (verbose, _validate_type, _check_fname, _check_option, + get_subjects_dir, _require_version, logger) + + +def _check_subject_dir(subject, subjects_dir): + """Check that the Freesurfer subject directory is as expected.""" + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + for img_name in ('T1', 'brain', 'aseg'): + if not op.isfile(op.join(subjects_dir, subject, 'mri', + f'{img_name}.mgz')): + raise ValueError('Freesurfer recon-all subject folder ' + 'is incorrect or improperly formatted, ' + f'got {op.join(subjects_dir, subject)}') + return op.join(subjects_dir, subject) + + +def _get_aseg(aseg, subject, subjects_dir): + """Check that the anatomical segmentation file exists and load it.""" + _require_version('nibabel', 'load aseg', '2.1.0') + import nibabel as nib + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + if not aseg.endswith('aseg'): + raise RuntimeError( + f'`aseg` file path must end with "aseg", got {aseg}') + aseg = _check_fname(op.join(subjects_dir, subject, 'mri', aseg + '.mgz'), + overwrite='read', must_exist=True) + aseg = nib.load(aseg) + aseg_data = np.array(aseg.dataobj) + return aseg, aseg_data + + +def _import_nibabel(why='use MRI files'): + try: + import nibabel as nib + except ImportError as exp: + msg = 'nibabel is required to %s, got:\n%s' % (why, exp) + else: + msg = '' + if msg: + raise ImportError(msg) + return nib + + +def _reorient_image(img, axcodes='RAS'): + """Reorient an image to a given orientation. + + Parameters + ---------- + img : instance of SpatialImage + The MRI image. + axcodes : tuple | str + The axis codes specifying the orientation, e.g. "RAS". + See :func:`nibabel.orientations.aff2axcodes`. + + Returns + ------- + img_data : ndarray + The reoriented image data. + vox_ras_t : ndarray + The new transform from the new voxels to surface RAS. + + Notes + ----- + .. versionadded:: 0.24 + """ + import nibabel as nib + orig_data = np.array(img.dataobj).astype(np.float32) + # reorient data to RAS + ornt = nib.orientations.axcodes2ornt( + nib.orientations.aff2axcodes(img.affine)).astype(int) + ras_ornt = nib.orientations.axcodes2ornt(axcodes) + ornt_trans = nib.orientations.ornt_transform(ornt, ras_ornt) + img_data = nib.orientations.apply_orientation(orig_data, ornt_trans) + orig_mgh = nib.MGHImage(orig_data, img.affine) + aff_trans = nib.orientations.inv_ornt_aff(ornt_trans, img.shape) + vox_ras_t = np.dot(orig_mgh.header.get_vox2ras_tkr(), aff_trans) + return img_data, vox_ras_t + + +def _mri_orientation(orientation): + """Get MRI orientation information from an image. + + Parameters + ---------- + orientation : str + Orientation that you want. Can be "axial", "sagittal", or "coronal". + + Returns + ------- + axis : int + The dimension of the axis to take slices over when plotting. + x : int + The dimension of the x axis. + y : int + The dimension of the y axis. + + Notes + ----- + .. versionadded:: 0.21 + .. versionchanged:: 0.24 + """ + _check_option('orientation', orientation, ('coronal', 'axial', 'sagittal')) + axis = dict(coronal=1, axial=2, sagittal=0)[orientation] + x, y = sorted(set([0, 1, 2]).difference(set([axis]))) + return axis, x, y + + +def _get_mri_info_data(mri, data): + # Read the segmentation data using nibabel + if data: + _import_nibabel('load MRI atlas data') + out = dict() + _, out['vox_mri_t'], out['mri_ras_t'], dims, _, mgz = _read_mri_info( + mri, return_img=True) + out.update( + mri_width=dims[0], mri_height=dims[1], + mri_depth=dims[1], mri_volume_name=mri) + if data: + assert mgz is not None + out['mri_vox_t'] = invert_transform(out['vox_mri_t']) + out['data'] = np.asarray(mgz.dataobj) + return out + + +def _get_mgz_header(fname): + """Adapted from nibabel to quickly extract header info.""" + fname = _check_fname(fname, overwrite='read', must_exist=True, + name='MRI image') + if not fname.endswith('.mgz'): + raise IOError('Filename must end with .mgz') + header_dtd = [('version', '>i4'), ('dims', '>i4', (4,)), + ('type', '>i4'), ('dof', '>i4'), ('goodRASFlag', '>i2'), + ('delta', '>f4', (3,)), ('Mdc', '>f4', (3, 3)), + ('Pxyz_c', '>f4', (3,))] + header_dtype = np.dtype(header_dtd) + with GzipFile(fname, 'rb') as fid: + hdr_str = fid.read(header_dtype.itemsize) + header = np.ndarray(shape=(), dtype=header_dtype, + buffer=hdr_str) + # dims + dims = header['dims'].astype(int) + dims = dims[:3] if len(dims) == 4 else dims + # vox2ras_tkr + delta = header['delta'] + ds = np.array(delta, float) + ns = np.array(dims * ds) / 2.0 + v2rtkr = np.array([[-ds[0], 0, 0, ns[0]], + [0, 0, ds[2], -ns[2]], + [0, -ds[1], 0, ns[1]], + [0, 0, 0, 1]], dtype=np.float32) + # ras2vox + d = np.diag(delta) + pcrs_c = dims / 2.0 + Mdc = header['Mdc'].T + pxyz_0 = header['Pxyz_c'] - np.dot(Mdc, np.dot(d, pcrs_c)) + M = np.eye(4, 4) + M[0:3, 0:3] = np.dot(Mdc, d) + M[0:3, 3] = pxyz_0.T + header = dict(dims=dims, vox2ras_tkr=v2rtkr, vox2ras=M, + zooms=header['delta']) + return header + + +def _get_atlas_values(vol_info, rr): + # Transform MRI coordinates (where our surfaces live) to voxels + rr_vox = apply_trans(vol_info['mri_vox_t'], rr) + good = ((rr_vox >= -.5) & + (rr_vox < np.array(vol_info['data'].shape, int) - 0.5)).all(-1) + idx = np.round(rr_vox[good].T).astype(np.int64) + values = np.full(rr.shape[0], np.nan) + values[good] = vol_info['data'][tuple(idx)] + return values + + +def get_volume_labels_from_aseg(mgz_fname, return_colors=False, + atlas_ids=None): + """Return a list of names and colors of segmented volumes. + + Parameters + ---------- + mgz_fname : str + Filename to read. Typically aseg.mgz or some variant in the freesurfer + pipeline. + return_colors : bool + If True returns also the labels colors. + atlas_ids : dict | None + A lookup table providing a mapping from region names (str) to ID values + (int). Can be None to use the standard Freesurfer LUT. + + .. versionadded:: 0.21.0 + + Returns + ------- + label_names : list of str + The names of segmented volumes included in this mgz file. + label_colors : list of str + The RGB colors of the labels included in this mgz file. + + See Also + -------- + read_freesurfer_lut + + Notes + ----- + .. versionchanged:: 0.21.0 + The label names are now sorted in the same order as their corresponding + values in the MRI file. + + .. versionadded:: 0.9.0 + """ + import nibabel as nib + if not op.isfile(mgz_fname): + raise IOError('aseg file "%s" not found' % mgz_fname) + atlas = nib.load(mgz_fname) + data = np.asarray(atlas.dataobj) # don't need float here + want = np.unique(data) + if atlas_ids is None: + atlas_ids, colors = read_freesurfer_lut() + elif return_colors: + raise ValueError('return_colors must be False if atlas_ids are ' + 'provided') + # restrict to the ones in the MRI, sorted by label name + keep = np.in1d(list(atlas_ids.values()), want) + keys = sorted((key for ki, key in enumerate(atlas_ids.keys()) if keep[ki]), + key=lambda x: atlas_ids[x]) + if return_colors: + colors = [colors[k] for k in keys] + out = keys, colors + else: + out = keys + return out + + +############################################################################## +# Head to MRI volume conversion + + +@verbose +def head_to_mri(pos, subject, mri_head_t, subjects_dir=None, + verbose=None): + """Convert pos from head coordinate system to MRI ones. + + This function converts to MRI RAS coordinates and not to surface + RAS. + + Parameters + ---------- + pos : array, shape (n_pos, 3) + The coordinates (in m) in head coordinate system. + %(subject)s + mri_head_t : instance of Transform + MRI<->Head coordinate transformation. + %(subjects_dir)s + %(verbose)s + + Returns + ------- + coordinates : array, shape (n_pos, 3) + The MRI RAS coordinates (in mm) of pos. + + Notes + ----- + This function requires nibabel. + """ + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + t1_fname = op.join(subjects_dir, subject, 'mri', 'T1.mgz') + head_mri_t = _ensure_trans(mri_head_t, 'head', 'mri') + _, _, mri_ras_t, _, _ = _read_mri_info(t1_fname) + head_ras_t = combine_transforms(head_mri_t, mri_ras_t, 'head', 'ras') + return 1e3 * apply_trans(head_ras_t, pos) # mm + + +############################################################################## +# Surface to MNI conversion + +@verbose +def vertex_to_mni(vertices, hemis, subject, subjects_dir=None, verbose=None): + """Convert the array of vertices for a hemisphere to MNI coordinates. + + Parameters + ---------- + vertices : int, or list of int + Vertex number(s) to convert. + hemis : int, or list of int + Hemisphere(s) the vertices belong to. + %(subject)s + subjects_dir : str, or None + Path to SUBJECTS_DIR if it is not set in the environment. + %(verbose)s + + Returns + ------- + coordinates : array, shape (n_vertices, 3) + The MNI coordinates (in mm) of the vertices. + """ + singleton = False + if not isinstance(vertices, list) and not isinstance(vertices, np.ndarray): + singleton = True + vertices = [vertices] + + if not isinstance(hemis, list) and not isinstance(hemis, np.ndarray): + hemis = [hemis] * len(vertices) + + if not len(hemis) == len(vertices): + raise ValueError('hemi and vertices must match in length') + + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + + surfs = [op.join(subjects_dir, subject, 'surf', '%s.white' % h) + for h in ['lh', 'rh']] + + # read surface locations in MRI space + rr = [read_surface(s)[0] for s in surfs] + + # take point locations in MRI space and convert to MNI coordinates + xfm = read_talxfm(subject, subjects_dir) + xfm['trans'][:3, 3] *= 1000. # m->mm + data = np.array([rr[h][v, :] for h, v in zip(hemis, vertices)]) + if singleton: + data = data[0] + return apply_trans(xfm['trans'], data) + + +############################################################################## +# Volume to MNI conversion + +@verbose +def head_to_mni(pos, subject, mri_head_t, subjects_dir=None, + verbose=None): + """Convert pos from head coordinate system to MNI ones. + + Parameters + ---------- + pos : array, shape (n_pos, 3) + The coordinates (in m) in head coordinate system. + %(subject)s + mri_head_t : instance of Transform + MRI<->Head coordinate transformation. + %(subjects_dir)s + %(verbose)s + + Returns + ------- + coordinates : array, shape (n_pos, 3) + The MNI coordinates (in mm) of pos. + + Notes + ----- + This function requires either nibabel. + """ + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + + # before we go from head to MRI (surface RAS) + head_mni_t = combine_transforms( + _ensure_trans(mri_head_t, 'head', 'mri'), + read_talxfm(subject, subjects_dir), 'head', 'mni_tal') + return apply_trans(head_mni_t, pos) * 1000. + + +@verbose +def get_mni_fiducials(subject, subjects_dir=None, verbose=None): + """Estimate fiducials for a subject. + + Parameters + ---------- + %(subject)s + %(subjects_dir)s + %(verbose)s + + Returns + ------- + fids_mri : list + List of estimated fiducials (each point in a dict), in the order + LPA, nasion, RPA. + + Notes + ----- + This takes the ``fsaverage-fiducials.fif`` file included with MNE—which + contain the LPA, nasion, and RPA for the ``fsaverage`` subject—and + transforms them to the given FreeSurfer subject's MRI space. + The MRI of ``fsaverage`` is already in MNI Talairach space, so applying + the inverse of the given subject's MNI Talairach affine transformation + (``$SUBJECTS_DIR/$SUBJECT/mri/transforms/talairach.xfm``) is used + to estimate the subject's fiducial locations. + + For more details about the coordinate systems and transformations involved, + see https://surfer.nmr.mgh.harvard.edu/fswiki/CoordinateSystems and + :ref:`tut-source-alignment`. + """ + # Eventually we might want to allow using the MNI Talairach with-skull + # transformation rather than the standard brain-based MNI Talaranch + # transformation, and/or project the points onto the head surface + # (if available). + fname_fids_fs = op.join(op.dirname(__file__), 'data', + 'fsaverage', 'fsaverage-fiducials.fif') + + # Read fsaverage fiducials file and subject Talairach. + fids, coord_frame = read_fiducials(fname_fids_fs) + assert coord_frame == FIFF.FIFFV_COORD_MRI + if subject == 'fsaverage': + return fids # special short-circuit for fsaverage + mni_mri_t = invert_transform(read_talxfm(subject, subjects_dir)) + for f in fids: + f['r'] = apply_trans(mni_mri_t, f['r']) + return fids + + +@verbose +def estimate_head_mri_t(subject, subjects_dir=None, verbose=None): + """Estimate the head->mri transform from fsaverage fiducials. + + A subject's fiducials can be estimated given a Freesurfer ``recon-all`` + by transforming ``fsaverage`` fiducials using the inverse Talairach + transform, see :func:`mne.coreg.get_mni_fiducials`. + + Parameters + ---------- + %(subject)s + %(subjects_dir)s + %(verbose)s + + Returns + ------- + %(trans_not_none)s + """ + from .channels.montage import make_dig_montage, compute_native_head_t + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + lpa, nasion, rpa = get_mni_fiducials(subject, subjects_dir) + montage = make_dig_montage(lpa=lpa['r'], nasion=nasion['r'], rpa=rpa['r'], + coord_frame='mri') + return invert_transform(compute_native_head_t(montage)) + + +def _ensure_image_in_surface_RAS(image, subject, subjects_dir): + """Check if the image is in Freesurfer surface RAS space.""" + import nibabel as nib + if not isinstance(image, nib.spatialimages.SpatialImage): + image = nib.load(image) + image = nib.MGHImage(image.dataobj.astype(np.float32), image.affine) + fs_img = nib.load(op.join(subjects_dir, subject, 'mri', 'brain.mgz')) + if not np.allclose(image.affine, fs_img.affine, atol=1e-6): + raise RuntimeError('The `image` is not aligned to Freesurfer ' + 'surface RAS space. This space is required as ' + 'it is the space where the anatomical ' + 'segmentation and reconstructed surfaces are') + return image # returns MGH image for header + + +@verbose +def read_lta(fname, verbose=None): + """Read a Freesurfer linear transform array file. + + Parameters + ---------- + fname : str | None + The transform filename. + %(verbose)s + + Returns + ------- + affine : ndarray + The affine transformation described by the lta file. + """ + _validate_type(fname, ('path-like', None), 'fname') + _check_fname(fname, 'read', must_exist=True) + with open(fname, 'r') as fid: + affine = np.loadtxt(fid.readlines()[5:9]) + return affine + + +@verbose +def read_talxfm(subject, subjects_dir=None, verbose=None): + """Compute MRI-to-MNI transform from FreeSurfer talairach.xfm file. + + Parameters + ---------- + %(subject)s + %(subjects_dir)s + %(verbose)s + + Returns + ------- + mri_mni_t : instance of Transform + The affine transformation from MRI to MNI space for the subject. + """ + # Adapted from freesurfer m-files. Altered to deal with Norig + # and Torig correctly + subjects_dir = get_subjects_dir(subjects_dir) + # Setup the RAS to MNI transform + ras_mni_t = read_ras_mni_t(subject, subjects_dir) + ras_mni_t['trans'][:3, 3] /= 1000. # mm->m + + # We want to get from Freesurfer surface RAS ('mri') to MNI ('mni_tal'). + # This file only gives us RAS (non-zero origin) ('ras') to MNI ('mni_tal'). + # Se we need to get the ras->mri transform from the MRI headers. + + # To do this, we get Norig and Torig + # (i.e. vox_ras_t and vox_mri_t, respectively) + path = op.join(subjects_dir, subject, 'mri', 'orig.mgz') + if not op.isfile(path): + path = op.join(subjects_dir, subject, 'mri', 'T1.mgz') + if not op.isfile(path): + raise IOError('mri not found: %s' % path) + _, _, mri_ras_t, _, _ = _read_mri_info(path) + mri_mni_t = combine_transforms(mri_ras_t, ras_mni_t, 'mri', 'mni_tal') + return mri_mni_t + + +def _check_mri(mri, subject, subjects_dir): + """Check whether an mri exists in the Freesurfer subject directory.""" + _validate_type(mri, 'path-like', 'mri') + if op.isfile(mri) and op.basename(mri) != mri: + return mri + if not op.isfile(mri): + if subject is None: + raise FileNotFoundError( + f'MRI file {mri!r} not found and no subject provided') + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + mri = op.join(subjects_dir, subject, 'mri', mri) + if not op.isfile(mri): + raise FileNotFoundError(f'MRI file {mri!r} not found') + if op.basename(mri) == mri: + err = (f'Ambiguous filename - found {mri!r} in current folder.\n' + 'If this is correct prefix name with relative or absolute path') + raise IOError(err) + return mri + + +def _read_mri_info(path, units='m', return_img=False, use_nibabel=False): + # This is equivalent but 100x slower, so only use nibabel if we need to + # (later): + if use_nibabel: + import nibabel + hdr = nibabel.load(path).header + n_orig = hdr.get_vox2ras() + t_orig = hdr.get_vox2ras_tkr() + dims = hdr.get_data_shape() + zooms = hdr.get_zooms()[:3] + else: + hdr = _get_mgz_header(path) + n_orig = hdr['vox2ras'] + t_orig = hdr['vox2ras_tkr'] + dims = hdr['dims'] + zooms = hdr['zooms'] + + # extract the MRI_VOXEL to RAS (non-zero origin) transform + vox_ras_t = Transform('mri_voxel', 'ras', n_orig) + + # extract the MRI_VOXEL to MRI transform + vox_mri_t = Transform('mri_voxel', 'mri', t_orig) + + # construct the MRI to RAS (non-zero origin) transform + mri_ras_t = combine_transforms( + invert_transform(vox_mri_t), vox_ras_t, 'mri', 'ras') + + assert units in ('m', 'mm') + if units == 'm': + conv = np.array([[1e-3, 1e-3, 1e-3, 1]]).T + # scaling and translation terms + vox_ras_t['trans'] *= conv + vox_mri_t['trans'] *= conv + # just the translation term + mri_ras_t['trans'][:, 3:4] *= conv + + out = (vox_ras_t, vox_mri_t, mri_ras_t, dims, zooms) + if return_img: + nibabel = _import_nibabel() + out += (nibabel.load(path),) + return out + + +def read_freesurfer_lut(fname=None): + """Read a Freesurfer-formatted LUT. + + Parameters + ---------- + fname : str | None + The filename. Can be None to read the standard Freesurfer LUT. + + Returns + ------- + atlas_ids : dict + Mapping from label names to IDs. + colors : dict + Mapping from label names to colors. + """ + lut = _get_lut(fname) + names, ids = lut['name'], lut['id'] + colors = np.array([lut['R'], lut['G'], lut['B'], lut['A']], float).T + atlas_ids = dict(zip(names, ids)) + colors = dict(zip(names, colors)) + return atlas_ids, colors + + +def _get_lut(fname=None): + """Get a FreeSurfer LUT.""" + _validate_type(fname, ('path-like', None), 'fname') + if fname is None: + fname = op.join(op.dirname(__file__), 'data', + 'FreeSurferColorLUT.txt') + _check_fname(fname, 'read', must_exist=True) + dtype = [('id', ' 0 + return lut + + +@verbose +def _get_head_surface(surf, subject, subjects_dir, bem=None, verbose=None): + """Get a head surface from the Freesurfer subject directory. + + Parameters + ---------- + surf : str + The name of the surface 'auto', 'head', 'outer_skin', 'head-dense' + or 'seghead'. + %(subject)s + %(subjects_dir)s + bem : mne.bem.ConductorModel | None + The conductor model that stores information about the head surface. + %(verbose)s + + Returns + ------- + head_surf : dict | None + A dictionary with keys 'rr', 'tris', 'ntri', 'use_tris', 'np' + and 'coord_frame' that store information for mesh plotting and other + useful information about the head surface. + + Notes + ----- + .. versionadded: 0.24 + """ + _check_option( + 'surf', surf, ('auto', 'head', 'outer_skin', 'head-dense', 'seghead')) + if surf in ('auto', 'head', 'outer_skin'): + if bem is not None: + try: + return _bem_find_surface(bem, 'head') + except RuntimeError: + logger.info('Could not find the surface for ' + 'head in the provided BEM model, ' + 'looking in the subject directory.') + if subject is None: + if surf == 'auto': + return + raise ValueError('To plot the head surface, the BEM/sphere' + ' model must contain a head surface ' + 'or "subject" must be provided (got ' + 'None)') + subject_dir = op.join( + get_subjects_dir(subjects_dir, raise_error=True), subject) + if surf in ('head-dense', 'seghead'): + try_fnames = [op.join(subject_dir, 'bem', f'{subject}-head-dense.fif'), + op.join(subject_dir, 'surf', 'lh.seghead')] + else: + try_fnames = [ + op.join(subject_dir, 'bem', 'outer_skin.surf'), + op.join(subject_dir, 'bem', 'flash', 'outer_skin.surf'), + op.join(subject_dir, 'bem', f'{subject}-head-sparse.fif'), + op.join(subject_dir, 'bem', f'{subject}-head.fif'), + ] + for fname in try_fnames: + if op.exists(fname): + logger.info(f'Using {op.basename(fname)} for head surface.') + if op.splitext(fname)[-1] == '.fif': + return read_bem_surfaces(fname, on_defects='warn')[0] + else: + return _read_mri_surface(fname) + raise IOError('No head surface found for subject ' + f'{subject} after trying:\n' + '\n'.join(try_fnames)) + + +@verbose +def _get_skull_surface(surf, subject, subjects_dir, bem=None, verbose=None): + """Get a skull surface from the Freesurfer subject directory. + + Parameters + ---------- + surf : str + The name of the surface 'outer' or 'inner'. + %(subject)s + %(subjects_dir)s + bem : mne.bem.ConductorModel | None + The conductor model that stores information about the skull surface. + %(verbose)s + + Returns + ------- + skull_surf : dict | None + A dictionary with keys 'rr', 'tris', 'ntri', 'use_tris', 'np' + and 'coord_frame' that store information for mesh plotting and other + useful information about the head surface. + + Notes + ----- + .. versionadded: 0.24 + """ + if bem is not None: + try: + return _bem_find_surface(bem, surf + '_skull') + except RuntimeError: + logger.info('Could not find the surface for ' + 'skull in the provided BEM model, ' + 'looking in the subject directory.') + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + fname = _check_fname(op.join(subjects_dir, subject, 'bem', + surf + '_skull.surf'), + overwrite='read', must_exist=True, + name=f'{surf} skull surface') + return _read_mri_surface(fname) + + +def _estimate_talxfm_rigid(subject, subjects_dir): + from .coreg import fit_matched_points, _trans_from_params + xfm = read_talxfm(subject, subjects_dir) + # XYZ+origin + halfway + pts_tal = np.concatenate([np.eye(4)[:, :3], np.eye(3) * 0.5]) + pts_subj = apply_trans(invert_transform(xfm), pts_tal) + # we fit with scaling enabled, but then discard it (we just need + # the rigid-body components) + params = fit_matched_points(pts_subj, pts_tal, scale=3, out='params') + rigid = _trans_from_params((True, True, False), params[:6]) + return rigid diff -Nru python-mne-0.23.4+dfsg/mne/gui/_backend.py python-mne-1.1.0+dfsg/mne/gui/_backend.py --- python-mne-0.23.4+dfsg/mne/gui/_backend.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/_backend.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,53 +0,0 @@ -"""Deal with pyface backend issues.""" -# Author: Christian Brodbeck -# -# License: BSD (3-clause) - -import os -import sys - -from ..utils import warn, _check_pyqt5_version - - -def _get_pyface_backend(): - """Check the currently selected Pyface backend. - - Returns - ------- - backend : str - Name of the backend. - result : 0 | 1 | 2 - 0: the backend has been tested and works. - 1: the backend has not been tested. - 2: the backend not been tested. - - Notes - ----- - See also http://docs.enthought.com/pyface/. - """ - from traitsui.toolkit import toolkit - from traits.etsconfig.api import ETSConfig - toolkit() - return ETSConfig.toolkit - - -def _check_backend(): - from pyface.api import warning - backend = _get_pyface_backend() - if backend == 'qt4': - _check_pyqt5_version() - # QT 5 macOS 11 compatibility: - if sys.platform == 'darwin' and 'QT_MAC_WANTS_LAYER' not in os.environ: - os.environ['QT_MAC_WANTS_LAYER'] = '1' - else: - msg = ("Using the currently selected Pyface backend %s is not " - "recommended, and it might not work properly. We recommend " - "using 'qt4' which can be enabled by installing the PyQt5" - "package." % backend) - warn(msg) - warning(None, msg, "Pyface Backend Warning") - - -def _testing_mode(): - """Determine if we're running tests.""" - return os.getenv('_MNE_GUI_TESTING_MODE', '') == 'true' diff -Nru python-mne-0.23.4+dfsg/mne/gui/_coreg_gui.py python-mne-1.1.0+dfsg/mne/gui/_coreg_gui.py --- python-mne-0.23.4+dfsg/mne/gui/_coreg_gui.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/_coreg_gui.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,2147 +0,0 @@ -# -*- coding: utf-8 -*- -u"""Traits-based GUI for head-MRI coregistration. - -Hierarchy ---------- -This is the hierarchy of classes for control. Brackets like [1] denote -properties that are set to be equivalent. - -:: - - CoregFrame: GUI for head-MRI coregistration. - |-- CoregModel (model): Traits object for estimating the head mri transform. - | |-- MRIHeadWithFiducialsModel (mri) [1]: Represent an MRI head shape (high and low res) with fiducials. - | | |-- SurfaceSource (bem_high_res): High-res MRI head - | | |-- SurfaceSource (bem_low_res): Low-res MRI head - | | +-- MRISubjectSource (subject_source) [2]: Find subjects in SUBJECTS_DIR and select one. - | |-- FiducialsSource (fid): Expose points of a given fiducials fif file. - | +-- DigSource (hsp): Expose measurement information from a inst file. - |-- MlabSceneModel (scene) [3]: mayavi.core.ui.mayavi_scene - |-- DataPanel (data_panel) - | |-- HeadViewController (headview) [4]: Set head views for the given coordinate system. - | | +-- MlabSceneModel (scene) [3*]: ``HeadViewController(scene=CoregFrame.scene)`` - | |-- SubjectSelectorPanel (subject_panel): Subject selector panel - | | +-- MRISubjectSource (model) [2*]: ``SubjectSelectorPanel(model=self.model.mri.subject_source)`` - | +-- FiducialsPanel (fid_panel): Set fiducials on an MRI surface. - | |-- MRIHeadWithFiducialsModel (model) [1*]: ``FiducialsPanel(model=CoregFrame.model.mri, headview=CoregFrame.headview)`` - | |-- HeadViewController (headview) [4*]: ``FiducialsPanel(model=CoregFrame.model.mri, headview=CoregFrame.headview)`` - | +-- SurfaceObject (hsp_obj) [5*]: ``CoregFrame.fid_panel.hsp_obj = CoregFrame.mri_obj`` - |-- CoregPanel (coreg_panel): Coregistration panel for Head<->MRI with scaling. - | +-- FittingOptionsPanel (fitting_options_panel): panel for fitting options. - |-- SurfaceObject (mri_obj) [5]: Represent a solid object in a mayavi scene. - +-- PointObject ({hsp, eeg, lpa, nasion, rpa, hsp_lpa, hsp_nasion, hsp_rpa} + _obj): Represent a group of individual points in a mayavi scene. - -In the MRI viewing frame, MRI points are transformed via scaling, then by -mri_head_t to the Neuromag head coordinate frame. Digitized points (in head -coordinate frame) are never transformed. - -Units ------ -User-facing GUI values are in readable units: - -- ``scale_*`` are in % -- ``trans_*`` are in mm -- ``rot_*`` are in ° - -Internal computation quantities ``parameters`` are in units of (for X/Y/Z): - -- ``parameters[:3]`` are in radians -- ``parameters[3:6]`` are in m -- ``paramteres[6:9]`` are in scale proportion - -Conversions are handled via `np.deg2rad`, `np.rad2deg`, and appropriate -multiplications / divisions. -""" # noqa: E501 - -# Authors: Christian Brodbeck -# -# License: BSD (3-clause) - -import os -import queue -import re -import time -from threading import Thread -import traceback -import warnings - -import numpy as np - -from mayavi.core.ui.mayavi_scene import MayaviScene -from mayavi.tools.mlab_scene_model import MlabSceneModel -from pyface.api import (error, confirm, OK, YES, NO, CANCEL, information, - FileDialog, GUI) -from traits.api import (Bool, Button, cached_property, DelegatesTo, Directory, - Enum, Float, HasTraits, HasPrivateTraits, Instance, - Int, on_trait_change, Property, Str, List) -from traitsui.api import (View, Item, Group, HGroup, VGroup, VGrid, EnumEditor, - Handler, Label, Spring, InstanceEditor, StatusItem, - UIInfo) -from traitsui.menu import Action, UndoButton, CancelButton, NoButtons -from tvtk.pyface.scene_editor import SceneEditor - -from ..bem import make_bem_solution, write_bem_solution -from ..coreg import bem_fname, trans_fname -from ..defaults import DEFAULTS -from ..surface import _DistanceQuery, _CheckInside -from ..transforms import (write_trans, read_trans, apply_trans, rotation, - rotation_angles, Transform, _ensure_trans, - rot_to_quat, _angle_between_quats) -from ..coreg import fit_matched_points, scale_mri, _find_fiducials_files -from ..viz.backends._pysurfer_mayavi import _toggle_mlab_render -from ..viz._3d import _get_3d_option -from ..utils import logger, set_config, _pl -from ._fiducials_gui import MRIHeadWithFiducialsModel, FiducialsPanel -from ._file_traits import trans_wildcard, DigSource, SubjectSelectorPanel -from ._viewer import (HeadViewController, PointObject, SurfaceObject, - _DEG_WIDTH, _MM_WIDTH, _BUTTON_WIDTH, - _SHOW_BORDER, _COREG_WIDTH, _SCALE_STEP_WIDTH, - _INC_BUTTON_WIDTH, _SCALE_WIDTH, _WEIGHT_WIDTH, - _MM_STEP_WIDTH, _DEG_STEP_WIDTH, _REDUCED_TEXT_WIDTH, - _RESET_LABEL, _RESET_WIDTH, - laggy_float_editor_scale, laggy_float_editor_deg, - laggy_float_editor_mm, laggy_float_editor_weight) - -try: - from traitsui.api import RGBColor -except ImportError: - from traits.api import RGBColor - -defaults = DEFAULTS['coreg'] - - -class busy(object): - """Set the GUI state to busy.""" - - def __enter__(self): # noqa: D105 - GUI.set_busy(True) - - def __exit__(self, type, value, traceback): # noqa: D105 - GUI.set_busy(False) - - -def _pass(x): - """Format text without changing it.""" - return x - - -class CoregModel(HasPrivateTraits): - """Traits object for estimating the head mri transform. - - Notes - ----- - Transform from head to mri space is modelled with the following steps: - - * move the head shape to its nasion position - * rotate the head shape with user defined rotation around its nasion - * move the head shape by user defined translation - * move the head shape origin to the mri nasion - - If MRI scaling is enabled, - - * the MRI is scaled relative to its origin center (prior to any - transformation of the digitizer head) - - Don't sync transforms to anything to prevent them from being recomputed - upon every parameter change. - """ - - # data sources - mri = Instance(MRIHeadWithFiducialsModel, ()) - hsp = Instance(DigSource, ()) - - # parameters - guess_mri_subject = Bool(True) # change MRI subject when dig file changes - grow_hair = Float(label=u"ΔHair", desc="Move the back of the MRI " - "head outwards to compensate for hair on the digitizer " - "head shape (mm)") - n_scale_params = Enum(0, 1, 3, desc="Scale the MRI to better fit the " - "subject's head shape (a new MRI subject will be " - "created with a name specified upon saving)") - scale_x = Float(100, label="X") - scale_y = Float(100, label="Y") - scale_z = Float(100, label="Z") - trans_x = Float(0, label=u"ΔX") - trans_y = Float(0, label=u"ΔY") - trans_z = Float(0, label=u"ΔZ") - rot_x = Float(0, label=u"∠X") - rot_y = Float(0, label=u"∠Y") - rot_z = Float(0, label=u"∠Z") - parameters = List() - last_parameters = List() - lpa_weight = Float(1.) - nasion_weight = Float(10.) - rpa_weight = Float(1.) - hsp_weight = Float(1.) - eeg_weight = Float(1.) - hpi_weight = Float(1.) - iteration = Int(-1) - icp_iterations = Int(20) - icp_start_time = Float(0.0) - icp_angle = Float(0.2) - icp_distance = Float(0.2) - icp_scale = Float(0.2) - icp_fid_match = Enum('nearest', 'matched') - fit_icp_running = Bool(False) - fits_icp_running = Bool(False) - coord_frame = Enum('mri', 'head', desc='Display coordinate frame') - status_text = Str() - - # options during scaling - scale_labels = Bool(True, desc="whether to scale *.label files") - copy_annot = Bool(True, desc="whether to copy *.annot files for scaled " - "subject") - prepare_bem_model = Bool(True, desc="whether to run make_bem_solution " - "after scaling the MRI") - - # secondary to parameters - has_nasion_data = Property( - Bool, depends_on=['mri:nasion', 'hsp:nasion']) - has_lpa_data = Property( - Bool, depends_on=['mri:lpa', 'hsp:lpa']) - has_rpa_data = Property( - Bool, depends_on=['mri:rpa', 'hsp:rpa']) - has_fid_data = Property( # conjunction - Bool, depends_on=['has_nasion_data', 'has_lpa_data', 'has_rpa_data']) - has_mri_data = Property( - Bool, depends_on=['transformed_high_res_mri_points']) - has_hsp_data = Property( - Bool, depends_on=['has_mri_data', 'hsp:points']) - has_eeg_data = Property( - Bool, depends_on=['has_mri_data', 'hsp:eeg_points']) - has_hpi_data = Property( - Bool, depends_on=['has_mri_data', 'hsp:hpi_points']) - n_icp_points = Property( - Int, depends_on=['has_nasion_data', 'nasion_weight', - 'has_lpa_data', 'lpa_weight', - 'has_rpa_data', 'rpa_weight', - 'hsp:points', 'hsp_weight', - 'hsp:eeg_points', 'eeg_weight', - 'hsp:hpi_points', 'hpi_weight']) - changes = Property(depends_on=['parameters', 'old_parameters']) - - # target transforms - mri_head_t = Property( - desc="Transformation of the scaled MRI to the head coordinate frame.", - depends_on=['parameters[]']) - head_mri_t = Property(depends_on=['mri_head_t']) - mri_trans_noscale = Property(depends_on=['mri_head_t', 'coord_frame']) - mri_trans = Property(depends_on=['mri_trans_noscale', 'parameters[]']) - hsp_trans = Property(depends_on=['head_mri_t', 'coord_frame']) - - # info - subject_has_bem = DelegatesTo('mri') - lock_fiducials = DelegatesTo('mri') - can_prepare_bem_model = Property( - Bool, - depends_on=['n_scale_params', 'subject_has_bem']) - can_save = Property(Bool, depends_on=['mri_head_t']) - raw_subject = Property( - desc="Subject guess based on the raw file name.", - depends_on=['hsp:inst_fname']) - - # Always computed in the MRI coordinate frame for speed - # (building the nearest-neighbor tree is slow!) - # though it will always need to be rebuilt in (non-uniform) scaling mode - nearest_calc = Instance(_DistanceQuery) - - # MRI geometry transformed to viewing coordinate system - processed_high_res_mri_points = Property( - depends_on=['mri:bem_high_res:surf', 'grow_hair']) - processed_low_res_mri_points = Property( - depends_on=['mri:bem_low_res:surf', 'grow_hair']) - transformed_high_res_mri_points = Property( - depends_on=['processed_high_res_mri_points', 'mri_trans']) - transformed_low_res_mri_points = Property( - depends_on=['processed_low_res_mri_points', 'mri_trans']) - nearest_transformed_high_res_mri_idx_lpa = Property( - depends_on=['nearest_calc', 'hsp:lpa', 'head_mri_t']) - nearest_transformed_high_res_mri_idx_nasion = Property( - depends_on=['nearest_calc', 'hsp:nasion', 'head_mri_t']) - nearest_transformed_high_res_mri_idx_rpa = Property( - depends_on=['nearest_calc', 'hsp:rpa', 'head_mri_t']) - nearest_transformed_high_res_mri_idx_hsp = Property( - depends_on=['nearest_calc', 'hsp:points', 'head_mri_t']) - nearest_transformed_high_res_mri_idx_orig_hsp = Property( - depends_on=['nearest_calc', 'hsp:points', 'head_mri_t']) - nearest_transformed_high_res_mri_idx_eeg = Property( - depends_on=['nearest_calc', 'hsp:eeg_points', 'head_mri_t']) - nearest_transformed_high_res_mri_idx_hpi = Property( - depends_on=['nearest_calc', 'hsp:hpi_points', 'head_mri_t']) - transformed_mri_lpa = Property( - depends_on=['mri:lpa', 'mri_trans']) - transformed_mri_nasion = Property( - depends_on=['mri:nasion', 'mri_trans']) - transformed_mri_rpa = Property( - depends_on=['mri:rpa', 'mri_trans']) - # HSP geometry transformed to viewing coordinate system - transformed_hsp_points = Property( - depends_on=['hsp:points', 'hsp_trans']) - transformed_orig_hsp_points = Property( - depends_on=['hsp:_hsp_points', 'hsp_trans']) - transformed_hsp_lpa = Property( - depends_on=['hsp:lpa', 'hsp_trans']) - transformed_hsp_nasion = Property( - depends_on=['hsp:nasion', 'hsp_trans']) - transformed_hsp_rpa = Property( - depends_on=['hsp:rpa', 'hsp_trans']) - transformed_hsp_eeg_points = Property( - depends_on=['hsp:eeg_points', 'hsp_trans']) - transformed_hsp_hpi = Property( - depends_on=['hsp:hpi_points', 'hsp_trans']) - - # fit properties - lpa_distance = Property( - depends_on=['transformed_mri_lpa', 'transformed_hsp_lpa']) - nasion_distance = Property( - depends_on=['transformed_mri_nasion', 'transformed_hsp_nasion']) - rpa_distance = Property( - depends_on=['transformed_mri_rpa', 'transformed_hsp_rpa']) - point_distance = Property( # use low res points - depends_on=['nearest_transformed_high_res_mri_idx_hsp', - 'nearest_transformed_high_res_mri_idx_eeg', - 'nearest_transformed_high_res_mri_idx_hpi', - 'hsp_weight', - 'eeg_weight', - 'hpi_weight']) - orig_hsp_point_distance = Property( # use low res points - depends_on=['nearest_transformed_high_res_mri_idx_orig_hsp', - 'hpi_weight']) - - # fit property info strings - fid_eval_str = Property( - depends_on=['lpa_distance', 'nasion_distance', 'rpa_distance']) - points_eval_str = Property( - depends_on=['point_distance']) - - def _parameters_default(self): - return list(_DEFAULT_PARAMETERS) - - def _last_parameters_default(self): - return list(_DEFAULT_PARAMETERS) - - @cached_property - def _get_can_prepare_bem_model(self): - return self.subject_has_bem and self.n_scale_params > 0 - - @cached_property - def _get_can_save(self): - return np.any(self.mri_head_t != np.eye(4)) - - @cached_property - def _get_has_lpa_data(self): - return (np.any(self.mri.lpa) and np.any(self.hsp.lpa)) - - @cached_property - def _get_has_nasion_data(self): - return (np.any(self.mri.nasion) and np.any(self.hsp.nasion)) - - @cached_property - def _get_has_rpa_data(self): - return (np.any(self.mri.rpa) and np.any(self.hsp.rpa)) - - @cached_property - def _get_has_fid_data(self): - return self.has_nasion_data and self.has_lpa_data and self.has_rpa_data - - @cached_property - def _get_has_mri_data(self): - return len(self.transformed_high_res_mri_points) > 0 - - @cached_property - def _get_has_hsp_data(self): - return (self.has_mri_data and - len(self.nearest_transformed_high_res_mri_idx_hsp) > 0) - - @cached_property - def _get_has_eeg_data(self): - return (self.has_mri_data and - len(self.nearest_transformed_high_res_mri_idx_eeg) > 0) - - @cached_property - def _get_has_hpi_data(self): - return (self.has_mri_data and - len(self.nearest_transformed_high_res_mri_idx_hpi) > 0) - - @cached_property - def _get_n_icp_points(self): - """Get parameters for an ICP iteration.""" - n = (self.hsp_weight > 0) * len(self.hsp.points) - for key in ('lpa', 'nasion', 'rpa'): - if getattr(self, 'has_%s_data' % key): - n += 1 - n += (self.eeg_weight > 0) * len(self.hsp.eeg_points) - n += (self.hpi_weight > 0) * len(self.hsp.hpi_points) - return n - - @cached_property - def _get_changes(self): - new = np.array(self.parameters, float) - old = np.array(self.last_parameters, float) - move = np.linalg.norm(old[3:6] - new[3:6]) * 1e3 - angle = np.rad2deg(_angle_between_quats( - rot_to_quat(rotation(*new[:3])[:3, :3]), - rot_to_quat(rotation(*old[:3])[:3, :3]))) - percs = 100 * (new[6:] - old[6:]) / old[6:] - return move, angle, percs - - @cached_property - def _get_mri_head_t(self): - # rotate and translate hsp - trans = rotation(*self.parameters[:3]) - trans[:3, 3] = np.array(self.parameters[3:6]) - return trans - - @cached_property - def _get_head_mri_t(self): - trans = rotation(*self.parameters[:3]).T - trans[:3, 3] = -np.dot(trans[:3, :3], self.parameters[3:6]) - # should be the same as np.linalg.inv(self.mri_head_t) - return trans - - @cached_property - def _get_processed_high_res_mri_points(self): - return self._get_processed_mri_points('high') - - @cached_property - def _get_processed_low_res_mri_points(self): - return self._get_processed_mri_points('low') - - def _get_processed_mri_points(self, res): - bem = self.mri.bem_low_res if res == 'low' else self.mri.bem_high_res - if self.grow_hair: - if len(bem.surf.nn): - scaled_hair_dist = (1e-3 * self.grow_hair / - np.array(self.parameters[6:9])) - points = bem.surf.rr.copy() - hair = points[:, 2] > points[:, 1] - points[hair] += bem.surf.nn[hair] * scaled_hair_dist - return points - else: - error(None, "Norms missing from bem, can't grow hair") - self.grow_hair = 0 - else: - return bem.surf.rr - - @cached_property - def _get_mri_trans(self): - t = self.mri_trans_noscale.copy() - t[:, :3] *= self.parameters[6:9] - return t - - @cached_property - def _get_mri_trans_noscale(self): - if self.coord_frame == 'head': - t = self.mri_head_t - else: - t = np.eye(4) - return t - - @cached_property - def _get_hsp_trans(self): - if self.coord_frame == 'head': - t = np.eye(4) - else: - t = self.head_mri_t - return t - - @cached_property - def _get_nearest_transformed_high_res_mri_idx_lpa(self): - return self.nearest_calc.query( - apply_trans(self.head_mri_t, self.hsp.lpa))[1] - - @cached_property - def _get_nearest_transformed_high_res_mri_idx_nasion(self): - return self.nearest_calc.query( - apply_trans(self.head_mri_t, self.hsp.nasion))[1] - - @cached_property - def _get_nearest_transformed_high_res_mri_idx_rpa(self): - return self.nearest_calc.query( - apply_trans(self.head_mri_t, self.hsp.rpa))[1] - - @cached_property - def _get_nearest_transformed_high_res_mri_idx_hsp(self): - return self.nearest_calc.query( - apply_trans(self.head_mri_t, self.hsp.points))[1] - - @cached_property - def _get_nearest_transformed_high_res_mri_idx_orig_hsp(self): - # This is redundant to some extent with the one above due to - # overlapping points, but it's fast and the refactoring to - # remove redundancy would be a pain. - return self.nearest_calc.query( - apply_trans(self.head_mri_t, self.hsp._hsp_points))[1] - - @cached_property - def _get_nearest_transformed_high_res_mri_idx_eeg(self): - return self.nearest_calc.query( - apply_trans(self.head_mri_t, self.hsp.eeg_points))[1] - - @cached_property - def _get_nearest_transformed_high_res_mri_idx_hpi(self): - return self.nearest_calc.query( - apply_trans(self.head_mri_t, self.hsp.hpi_points))[1] - - # MRI view-transformed data - @cached_property - def _get_transformed_low_res_mri_points(self): - points = apply_trans(self.mri_trans, - self.processed_low_res_mri_points) - return points - - def _nearest_calc_default(self): - return _DistanceQuery( - self.processed_high_res_mri_points * self.parameters[6:9]) - - @on_trait_change('processed_high_res_mri_points') - def _update_nearest_calc(self): - self.nearest_calc = self._nearest_calc_default() - - @cached_property - def _get_transformed_high_res_mri_points(self): - points = apply_trans(self.mri_trans, - self.processed_high_res_mri_points) - return points - - @cached_property - def _get_transformed_mri_lpa(self): - return apply_trans(self.mri_trans, self.mri.lpa) - - @cached_property - def _get_transformed_mri_nasion(self): - return apply_trans(self.mri_trans, self.mri.nasion) - - @cached_property - def _get_transformed_mri_rpa(self): - return apply_trans(self.mri_trans, self.mri.rpa) - - # HSP view-transformed data - @cached_property - def _get_transformed_hsp_points(self): - return apply_trans(self.hsp_trans, self.hsp.points) - - @cached_property - def _get_transformed_orig_hsp_points(self): - return apply_trans(self.hsp_trans, self.hsp._hsp_points) - - @cached_property - def _get_transformed_hsp_lpa(self): - return apply_trans(self.hsp_trans, self.hsp.lpa) - - @cached_property - def _get_transformed_hsp_nasion(self): - return apply_trans(self.hsp_trans, self.hsp.nasion) - - @cached_property - def _get_transformed_hsp_rpa(self): - return apply_trans(self.hsp_trans, self.hsp.rpa) - - @cached_property - def _get_transformed_hsp_eeg_points(self): - return apply_trans(self.hsp_trans, self.hsp.eeg_points) - - @cached_property - def _get_transformed_hsp_hpi(self): - return apply_trans(self.hsp_trans, self.hsp.hpi_points) - - # Distances, etc. - @cached_property - def _get_lpa_distance(self): - d = np.ravel(self.transformed_mri_lpa - self.transformed_hsp_lpa) - return np.linalg.norm(d) - - @cached_property - def _get_nasion_distance(self): - d = np.ravel(self.transformed_mri_nasion - self.transformed_hsp_nasion) - return np.linalg.norm(d) - - @cached_property - def _get_rpa_distance(self): - d = np.ravel(self.transformed_mri_rpa - self.transformed_hsp_rpa) - return np.linalg.norm(d) - - @cached_property - def _get_point_distance(self): - mri_points = list() - hsp_points = list() - if self.hsp_weight > 0 and self.has_hsp_data: - mri_points.append(self.transformed_high_res_mri_points[ - self.nearest_transformed_high_res_mri_idx_hsp]) - hsp_points.append(self.transformed_hsp_points) - assert len(mri_points[-1]) == len(hsp_points[-1]) - if self.eeg_weight > 0 and self.has_eeg_data: - mri_points.append(self.transformed_high_res_mri_points[ - self.nearest_transformed_high_res_mri_idx_eeg]) - hsp_points.append(self.transformed_hsp_eeg_points) - assert len(mri_points[-1]) == len(hsp_points[-1]) - if self.hpi_weight > 0 and self.has_hpi_data: - mri_points.append(self.transformed_high_res_mri_points[ - self.nearest_transformed_high_res_mri_idx_hpi]) - hsp_points.append(self.transformed_hsp_hpi) - assert len(mri_points[-1]) == len(hsp_points[-1]) - if all(len(h) == 0 for h in hsp_points): - return None - mri_points = np.concatenate(mri_points) - hsp_points = np.concatenate(hsp_points) - return np.linalg.norm(mri_points - hsp_points, axis=-1) - - @cached_property - def _get_orig_hsp_point_distance(self): - mri_points = self.transformed_high_res_mri_points[ - self.nearest_transformed_high_res_mri_idx_orig_hsp] - hsp_points = self.transformed_orig_hsp_points - return np.linalg.norm(mri_points - hsp_points, axis=-1) - - @cached_property - def _get_fid_eval_str(self): - d = (self.lpa_distance * 1000, self.nasion_distance * 1000, - self.rpa_distance * 1000) - return u'Fiducials: %.1f, %.1f, %.1f mm' % d - - @cached_property - def _get_points_eval_str(self): - if self.point_distance is None: - return "" - dists = 1000 * self.point_distance - av_dist = np.mean(dists) - std_dist = np.std(dists) - kinds = [kind for kind, check in - (('HSP', self.hsp_weight > 0 and self.has_hsp_data), - ('EEG', self.eeg_weight > 0 and self.has_eeg_data), - ('HPI', self.hpi_weight > 0 and self.has_hpi_data)) - if check] - return (u"%s %s: %.1f ± %.1f mm" - % (len(dists), '+'.join(kinds), av_dist, std_dist)) - - def _get_raw_subject(self): - # subject name guessed based on the inst file name - if '_' in self.hsp.inst_fname: - subject, _ = self.hsp.inst_fname.split('_', 1) - if subject: - return subject - - @on_trait_change('raw_subject') - def _on_raw_subject_change(self, subject): - if self.guess_mri_subject: - if subject in self.mri.subject_source.subjects: - self.mri.subject = subject - elif 'fsaverage' in self.mri.subject_source.subjects: - self.mri.subject = 'fsaverage' - - def omit_hsp_points(self, distance): - """Exclude head shape points that are far away from the MRI head. - - Parameters - ---------- - distance : float - Exclude all points that are further away from the MRI head than - this distance. Previously excluded points are still excluded unless - reset=True is specified. A value of distance <= 0 excludes nothing. - reset : bool - Reset the filter before calculating new omission (default is - False). - """ - distance = float(distance) - if distance <= 0: - return - - # find the new filter - mask = self.orig_hsp_point_distance <= distance - n_excluded = np.sum(~mask) - logger.info("Coregistration: Excluding %i head shape points with " - "distance >= %.3f m.", n_excluded, distance) - # set the filter - with warnings.catch_warnings(record=True): # comp to None in Traits - self.hsp.points_filter = mask - - def fit_fiducials(self, n_scale_params=None): - """Find rotation and translation to fit all 3 fiducials.""" - if n_scale_params is None: - n_scale_params = self.n_scale_params - head_pts = np.vstack((self.hsp.lpa, self.hsp.nasion, self.hsp.rpa)) - mri_pts = np.vstack((self.mri.lpa, self.mri.nasion, self.mri.rpa)) - weights = [self.lpa_weight, self.nasion_weight, self.rpa_weight] - assert n_scale_params in (0, 1) # guaranteed by GUI - if n_scale_params == 0: - mri_pts *= self.parameters[6:9] # not done in fit_matched_points - x0 = np.array(self.parameters[:6 + n_scale_params]) - est = fit_matched_points(mri_pts, head_pts, x0=x0, out='params', - scale=n_scale_params, weights=weights) - if n_scale_params == 0: - self.parameters[:6] = est - else: - self.parameters[:] = np.concatenate([est, [est[-1]] * 2]) - - def _setup_icp(self, n_scale_params): - """Get parameters for an ICP iteration.""" - head_pts = list() - mri_pts = list() - weights = list() - if self.has_hsp_data and self.hsp_weight > 0: # should be true - head_pts.append(self.hsp.points) - mri_pts.append(self.processed_high_res_mri_points[ - self.nearest_transformed_high_res_mri_idx_hsp]) - weights.append(np.full(len(head_pts[-1]), self.hsp_weight)) - for key in ('lpa', 'nasion', 'rpa'): - if getattr(self, 'has_%s_data' % key): - head_pts.append(getattr(self.hsp, key)) - if self.icp_fid_match == 'matched': - mri_pts.append(getattr(self.mri, key)) - else: - assert self.icp_fid_match == 'nearest' - mri_pts.append(self.processed_high_res_mri_points[ - getattr(self, 'nearest_transformed_high_res_mri_idx_%s' - % (key,))]) - weights.append(np.full(len(mri_pts[-1]), - getattr(self, '%s_weight' % key))) - if self.has_eeg_data and self.eeg_weight > 0: - head_pts.append(self.hsp.eeg_points) - mri_pts.append(self.processed_high_res_mri_points[ - self.nearest_transformed_high_res_mri_idx_eeg]) - weights.append(np.full(len(mri_pts[-1]), self.eeg_weight)) - if self.has_hpi_data and self.hpi_weight > 0: - head_pts.append(self.hsp.hpi_points) - mri_pts.append(self.processed_high_res_mri_points[ - self.nearest_transformed_high_res_mri_idx_hpi]) - weights.append(np.full(len(mri_pts[-1]), self.hpi_weight)) - head_pts = np.concatenate(head_pts) - mri_pts = np.concatenate(mri_pts) - weights = np.concatenate(weights) - if n_scale_params == 0: - mri_pts *= self.parameters[6:9] # not done in fit_matched_points - return head_pts, mri_pts, weights - - def fit_icp(self, n_scale_params=None): - """Find MRI scaling, translation, and rotation to match HSP.""" - if n_scale_params is None: - n_scale_params = self.n_scale_params - - # Initial guess (current state) - assert n_scale_params in (0, 1, 3) - est = self.parameters[:[6, 7, None, 9][n_scale_params]] - - # Do the fits, assigning and evaluating at each step - attr = 'fit_icp_running' if n_scale_params == 0 else 'fits_icp_running' - setattr(self, attr, True) - GUI.process_events() # update the cancel button - self.icp_start_time = time.time() - for self.iteration in range(self.icp_iterations): - head_pts, mri_pts, weights = self._setup_icp(n_scale_params) - est = fit_matched_points(mri_pts, head_pts, scale=n_scale_params, - x0=est, out='params', weights=weights) - if n_scale_params == 0: - self.parameters[:6] = est - elif n_scale_params == 1: - self.parameters[:] = list(est) + [est[-1]] * 2 - else: - self.parameters[:] = est - angle, move, scale = self.changes - if angle <= self.icp_angle and move <= self.icp_distance and \ - all(scale <= self.icp_scale): - self.status_text = self.status_text[:-1] + '; converged)' - break - if not getattr(self, attr): # canceled by user - self.status_text = self.status_text[:-1] + '; cancelled)' - break - GUI.process_events() # this will update the head view - else: - self.status_text = self.status_text[:-1] + '; did not converge)' - setattr(self, attr, False) - self.iteration = -1 - - def get_scaling_job(self, subject_to, skip_fiducials): - """Find all arguments needed for the scaling worker.""" - subjects_dir = self.mri.subjects_dir - subject_from = self.mri.subject - bem_names = [] - if self.can_prepare_bem_model and self.prepare_bem_model: - pattern = bem_fname.format(subjects_dir=subjects_dir, - subject=subject_from, name='(.+-bem)') - bem_dir, pattern = os.path.split(pattern) - for filename in os.listdir(bem_dir): - match = re.match(pattern, filename) - if match: - bem_names.append(match.group(1)) - - return (subjects_dir, subject_from, subject_to, self.parameters[6:9], - skip_fiducials, self.scale_labels, self.copy_annot, bem_names) - - def load_trans(self, fname): - """Load the head-mri transform from a fif file. - - Parameters - ---------- - fname : str - File path. - """ - self.set_trans(_ensure_trans(read_trans(fname, return_all=True), - 'mri', 'head')['trans']) - - def reset(self): - """Reset all the parameters affecting the coregistration.""" - with busy(): - self.reset_traits(('grow_hair', 'n_scaling_params')) - self.parameters[:] = _DEFAULT_PARAMETERS - self.omit_hsp_points(np.inf) - - def set_trans(self, mri_head_t): - """Set rotation and translation params from a transformation matrix. - - Parameters - ---------- - mri_head_t : array, shape (4, 4) - Transformation matrix from MRI to head space. - """ - with busy(): - rot_x, rot_y, rot_z = rotation_angles(mri_head_t) - x, y, z = mri_head_t[:3, 3] - self.parameters[:6] = [rot_x, rot_y, rot_z, x, y, z] - - def save_trans(self, fname): - """Save the head-mri transform as a fif file. - - Parameters - ---------- - fname : str - Target file path. - """ - if not self.can_save: - raise RuntimeError("Not enough information for saving transform") - write_trans(fname, Transform('head', 'mri', self.head_mri_t)) - - def _parameters_items_changed(self): - # Update GUI as necessary - n_scale = self.n_scale_params - for ii, key in enumerate(('rot_x', 'rot_y', 'rot_z')): - val = np.rad2deg(self.parameters[ii]) - if val != getattr(self, key): # prevent circular - setattr(self, key, val) - for ii, key in enumerate(('trans_x', 'trans_y', 'trans_z')): - val = self.parameters[ii + 3] * 1e3 - if val != getattr(self, key): # prevent circular - setattr(self, key, val) - for ii, key in enumerate(('scale_x', 'scale_y', 'scale_z')): - val = self.parameters[ii + 6] * 1e2 - if val != getattr(self, key): # prevent circular - setattr(self, key, val) - # Only update our nearest-neighbor if necessary - if self.parameters[6:9] != self.last_parameters[6:9]: - self._update_nearest_calc() - # Update the status text - move, angle, percs = self.changes - text = u'Change: Δ=%0.1f mm ∠=%0.2f°' % (move, angle) - if n_scale: - text += ' Scale ' if n_scale == 1 else ' Sx/y/z ' - text += '/'.join(['%+0.1f%%' % p for p in percs[:n_scale]]) - if self.iteration >= 0: - text += u' (iteration %d/%d, %0.1f sec)' % ( - self.iteration + 1, self.icp_iterations, - time.time() - self.icp_start_time) - self.last_parameters[:] = self.parameters[:] - self.status_text = text - - def _rot_x_changed(self): - self.parameters[0] = np.deg2rad(self.rot_x) - - def _rot_y_changed(self): - self.parameters[1] = np.deg2rad(self.rot_y) - - def _rot_z_changed(self): - self.parameters[2] = np.deg2rad(self.rot_z) - - def _trans_x_changed(self): - self.parameters[3] = self.trans_x * 1e-3 - - def _trans_y_changed(self): - self.parameters[4] = self.trans_y * 1e-3 - - def _trans_z_changed(self): - self.parameters[5] = self.trans_z * 1e-3 - - def _scale_x_changed(self): - if self.n_scale_params == 1: - self.parameters[6:9] = [self.scale_x * 1e-2] * 3 - else: - self.parameters[6] = self.scale_x * 1e-2 - - def _scale_y_changed(self): - self.parameters[7] = self.scale_y * 1e-2 - - def _scale_z_changed(self): - self.parameters[8] = self.scale_z * 1e-2 - - -class CoregFrameHandler(Handler): - """Check for unfinished processes before closing its window.""" - - def object_title_changed(self, info): - """Set the title when it gets changed.""" - info.ui.title = info.object.title - - def close(self, info, is_ok): - """Handle the close event.""" - if info.object.queue.unfinished_tasks: - information(None, "Can not close the window while saving is still " - "in progress. Please wait until all MRIs are " - "processed.", "Saving Still in Progress") - return False - else: - try: # works on Qt only for now - size = (info.ui.control.width(), info.ui.control.height()) - except AttributeError: - size = None - # store configuration, but don't prevent from closing on error - try: - info.object.save_config(size=size) - except Exception as exc: - warnings.warn("Error saving GUI configuration:\n%s" % (exc,)) - return True - - -class CoregPanelHandler(Handler): - """Open other windows with proper parenting.""" - - info = Instance(UIInfo) - - def object_fitting_options_panel_changed(self, info): # noqa: D102 - self.info = info - - def object_fitting_options_changed(self, info): # noqa: D102 - self.info.object.fitting_options_panel.edit_traits( - parent=self.info.ui.control) - - def object_load_trans_changed(self, info): # noqa: D102 - # find trans file destination - model = self.info.object.model - raw_dir = os.path.dirname(model.hsp.file) - subject = model.mri.subject - trans_file = trans_fname.format(raw_dir=raw_dir, subject=subject) - dlg = FileDialog(action="open", wildcard=trans_wildcard, - default_path=trans_file, parent=self.info.ui.control) - if dlg.open() != OK: - return - trans_file = dlg.path - try: - model.load_trans(trans_file) - except Exception as e: - error(None, "Error loading trans file %s: %s (See terminal " - "for details)" % (trans_file, e), "Error Loading Trans File") - raise - - def object_save_changed(self, info): # noqa: D102 - obj = self.info.object - subjects_dir = obj.model.mri.subjects_dir - subject_from = obj.model.mri.subject - - # check that fiducials are saved - skip_fiducials = False - if obj.n_scale_params and not _find_fiducials_files(subject_from, - subjects_dir): - msg = ("No fiducials file has been found for {src}. If fiducials " - "are not saved, they will not be available in the scaled " - "MRI. Should the current fiducials be saved now? " - "Select Yes to save the fiducials at " - "{src}/bem/{src}-fiducials.fif. " - "Select No to proceed scaling the MRI without fiducials.". - format(src=subject_from)) - title = "Save Fiducials for %s?" % subject_from - rc = confirm(self.info.ui.control, msg, title, cancel=True, - default=CANCEL) - if rc == CANCEL: - return - elif rc == YES: - obj.model.mri.save(obj.model.mri.default_fid_fname) - elif rc == NO: - skip_fiducials = True - else: - raise RuntimeError("rc=%s" % repr(rc)) - - # find target subject - if obj.n_scale_params: - subject_to = obj.model.raw_subject or subject_from - mridlg = NewMriDialog(subjects_dir=subjects_dir, - subject_from=subject_from, - subject_to=subject_to) - ui = mridlg.edit_traits(kind='modal', - parent=self.info.ui.control) - if not ui.result: # i.e., user pressed cancel - return - subject_to = mridlg.subject_to - else: - subject_to = subject_from - - # find trans file destination - raw_dir = os.path.dirname(obj.model.hsp.file) - trans_file = trans_fname.format(raw_dir=raw_dir, subject=subject_to) - dlg = FileDialog(action="save as", wildcard=trans_wildcard, - default_path=trans_file, - parent=self.info.ui.control) - dlg.open() - if dlg.return_code != OK: - return - trans_file = dlg.path - if not trans_file.endswith('.fif'): - trans_file += '.fif' - if os.path.exists(trans_file): - answer = confirm(None, "The file %r already exists. Should it " - "be replaced?", "Overwrite File?") - if answer != YES: - return - - # save the trans file - try: - obj.model.save_trans(trans_file) - except Exception as e: - error(None, "Error saving -trans.fif file: %s (See terminal for " - "details)" % (e,), "Error Saving Trans File") - raise - - # save the scaled MRI - if obj.n_scale_params: - job = obj.model.get_scaling_job(subject_to, skip_fiducials) - obj.queue.put(job) - obj.queue_len += 1 - - -def _make_view_data_panel(scrollable=False): - view = View(VGroup( - VGroup(Item('subject_panel', style='custom'), label="MRI Subject", - show_border=_SHOW_BORDER, show_labels=False), - VGroup(Item('lock_fiducials', style='custom', - editor=EnumEditor(cols=2, values={False: '2:Edit', - True: '1:Lock'}), - enabled_when='fid_ok'), - HGroup(Item('hsp_always_visible', - label='Show head shape points', show_label=True, - enabled_when='not lock_fiducials', width=-1), - show_left=False), - Item('fid_panel', style='custom'), label="MRI Fiducials", - show_border=_SHOW_BORDER, show_labels=False), - VGroup(Item('raw_src', style="custom"), - HGroup('guess_mri_subject', - Label('Guess subject from name'), show_labels=False), - VGrid(Item('grow_hair', editor=laggy_float_editor_mm, - width=_MM_WIDTH), - Label(u'ΔHair', show_label=True, width=-1), '0', - Item('distance', show_label=False, width=_MM_WIDTH, - editor=laggy_float_editor_mm), - Item('omit_points', width=_BUTTON_WIDTH), - Item('reset_omit_points', width=_RESET_WIDTH), - columns=3, show_labels=False), - Item('omitted_info', style='readonly', - width=_REDUCED_TEXT_WIDTH), label='Digitization source', - show_border=_SHOW_BORDER, show_labels=False), - VGroup(HGroup(Item('headview', style='custom'), Spring(), - show_labels=False), - Item('view_options', width=_REDUCED_TEXT_WIDTH), - label='View', show_border=_SHOW_BORDER, show_labels=False), - Spring(), - show_labels=False), kind='panel', buttons=[UndoButton], - scrollable=scrollable, handler=DataPanelHandler()) - return view - - -def _make_view_coreg_panel(scrollable=False): - """Generate View for CoregPanel.""" - view = View(VGroup( - # Scaling - HGroup(Item('n_scale_params', label='Scaling mode', - editor=EnumEditor(values={0: '1:None', - 1: '2:Uniform', - 3: '3:3-axis'})), Spring()), - VGrid(Item('scale_x', editor=laggy_float_editor_scale, - show_label=True, tooltip="Scale along right-left axis (%)", - enabled_when='n_scale_params > 0', width=_SCALE_WIDTH), - Item('scale_x_dec', enabled_when='n_scale_params > 0', - width=_INC_BUTTON_WIDTH), - Item('scale_x_inc', enabled_when='n_scale_params > 0', - width=_INC_BUTTON_WIDTH), - Item('scale_step', tooltip="Scaling step (%)", - enabled_when='n_scale_params > 0', width=_SCALE_STEP_WIDTH), - Spring(), - - Item('scale_y', editor=laggy_float_editor_scale, show_label=True, - enabled_when='n_scale_params > 1', - tooltip="Scale along anterior-posterior axis (%)", - width=_SCALE_WIDTH), - Item('scale_y_dec', enabled_when='n_scale_params > 1', - width=_INC_BUTTON_WIDTH), - Item('scale_y_inc', enabled_when='n_scale_params > 1', - width=_INC_BUTTON_WIDTH), - Label('(Step)', width=_SCALE_WIDTH), - Spring(), - - Item('scale_z', editor=laggy_float_editor_scale, show_label=True, - enabled_when='n_scale_params > 1', width=_SCALE_WIDTH, - tooltip="Scale along anterior-posterior axis (%)"), - Item('scale_z_dec', enabled_when='n_scale_params > 1', - width=_INC_BUTTON_WIDTH), - Item('scale_z_inc', enabled_when='n_scale_params > 1', - width=_INC_BUTTON_WIDTH), - '0', - Spring(), - - label='Scaling parameters', show_labels=False, columns=5, - show_border=_SHOW_BORDER), - VGrid(Item('fits_icp', enabled_when='n_scale_params > 0 and ' - 'n_icp_points >= 10', - tooltip="Rotate, translate, and scale the MRI to minimize " - "the distance from each digitizer point to the closest MRI " - "point (one ICP iteration)", width=_BUTTON_WIDTH), - Item('fits_fid', enabled_when='n_scale_params == 1 and ' - 'has_fid_data', - tooltip="Rotate, translate, and scale the MRI to minimize " - "the distance of the three fiducials.", - width=_BUTTON_WIDTH), - Item('cancels_icp', enabled_when="fits_icp_running", - tooltip='Stop ICP fitting', width=_RESET_WIDTH), - Item('reset_scale', enabled_when='n_scale_params', - tooltip="Reset scaling parameters", width=_RESET_WIDTH), - show_labels=False, columns=4), - # Translation and rotation - VGrid(Item('trans_x', editor=laggy_float_editor_mm, show_label=True, - tooltip="Move along right-left axis", width=_MM_WIDTH), - Item('trans_x_dec', width=_INC_BUTTON_WIDTH), - Item('trans_x_inc', width=_INC_BUTTON_WIDTH), - Item('trans_step', tooltip="Movement step (mm)", - width=_MM_STEP_WIDTH), - Spring(), - - Item('trans_y', editor=laggy_float_editor_mm, show_label=True, - tooltip="Move along anterior-posterior axis", - width=_MM_WIDTH), - Item('trans_y_dec', width=_INC_BUTTON_WIDTH), - Item('trans_y_inc', width=_INC_BUTTON_WIDTH), - Label('(Step)', width=_MM_WIDTH), - Spring(), - - Item('trans_z', editor=laggy_float_editor_mm, show_label=True, - tooltip="Move along anterior-posterior axis", - width=_MM_WIDTH), - Item('trans_z_dec', width=_INC_BUTTON_WIDTH), - Item('trans_z_inc', width=_INC_BUTTON_WIDTH), - '0', - Spring(), - - Item('rot_x', editor=laggy_float_editor_deg, show_label=True, - tooltip="Tilt the digitization backward (-) or forward (+)", - width=_DEG_WIDTH), - Item('rot_x_dec', width=_INC_BUTTON_WIDTH), - Item('rot_x_inc', width=_INC_BUTTON_WIDTH), - Item('rot_step', tooltip=u"Rotation step (°)", - width=_DEG_STEP_WIDTH), - Spring(), - - Item('rot_y', editor=laggy_float_editor_deg, show_label=True, - tooltip="Tilt the digitization rightward (-) or " - "leftward (+)", width=_DEG_WIDTH), - Item('rot_y_dec', width=_INC_BUTTON_WIDTH), - Item('rot_y_inc', width=_INC_BUTTON_WIDTH), - Label('(Step)', width=_DEG_WIDTH), - Spring(), - - Item('rot_z', editor=laggy_float_editor_deg, show_label=True, - tooltip="Turn the digitization leftward (-) or " - "rightward (+)", width=_DEG_WIDTH), - Item('rot_z_dec', width=_INC_BUTTON_WIDTH), - Item('rot_z_inc', width=_INC_BUTTON_WIDTH), - '0', - Spring(), - - columns=5, show_labels=False, show_border=_SHOW_BORDER, - label=u'Translation (Δ) and Rotation (∠)'), - VGroup(Item('fit_icp', enabled_when='n_icp_points >= 10', - tooltip="Rotate and translate the MRI to minimize the " - "distance from each digitizer point to the closest MRI " - "point (one ICP iteration)", width=_BUTTON_WIDTH), - Item('fit_fid', enabled_when="has_fid_data", - tooltip="Rotate and translate the MRI to minimize the " - "distance of the three fiducials.", width=_BUTTON_WIDTH), - Item('cancel_icp', enabled_when="fit_icp_running", - tooltip='Stop ICP iterations', width=_RESET_WIDTH), - Item('reset_tr', tooltip="Reset translation and rotation.", - width=_RESET_WIDTH), - show_labels=False, columns=4), - # Fitting weights - Item('fid_eval_str', style='readonly', tooltip='Fiducial differences', - width=_REDUCED_TEXT_WIDTH), - Item('points_eval_str', style='readonly', - tooltip='Point error (μ ± σ)', width=_REDUCED_TEXT_WIDTH), - Item('fitting_options', width=_REDUCED_TEXT_WIDTH, show_label=False), - VGrid(Item('scale_labels', label="Scale label files", - enabled_when='n_scale_params > 0'), - Item('copy_annot', label="Copy annotation files", - enabled_when='n_scale_params > 0'), - Item('prepare_bem_model', label="Prepare BEM", - enabled_when='can_prepare_bem_model'), - show_left=False, label='Subject-saving options', columns=1, - show_border=_SHOW_BORDER), - VGrid(Item('save', enabled_when='can_save', - tooltip="Save the trans file and (if scaling is enabled) " - "the scaled MRI", width=_BUTTON_WIDTH), - Item('load_trans', width=_BUTTON_WIDTH, - tooltip="Load Head<->MRI trans file"), - Item('reset_params', tooltip="Reset all coregistration " - "parameters", width=_RESET_WIDTH), - show_labels=False, columns=3), - Spring(), - show_labels=False), kind='panel', buttons=[UndoButton], - scrollable=scrollable, handler=CoregPanelHandler()) - return view - - -class FittingOptionsPanel(HasTraits): - """View options panel.""" - - model = Instance(CoregModel) - lpa_weight = DelegatesTo('model') - nasion_weight = DelegatesTo('model') - rpa_weight = DelegatesTo('model') - hsp_weight = DelegatesTo('model') - eeg_weight = DelegatesTo('model') - hpi_weight = DelegatesTo('model') - has_lpa_data = DelegatesTo('model') - has_nasion_data = DelegatesTo('model') - has_rpa_data = DelegatesTo('model') - has_hsp_data = DelegatesTo('model') - has_eeg_data = DelegatesTo('model') - has_hpi_data = DelegatesTo('model') - icp_iterations = DelegatesTo('model') - icp_start_time = DelegatesTo('model') - icp_angle = DelegatesTo('model') - icp_distance = DelegatesTo('model') - icp_scale = DelegatesTo('model') - icp_fid_match = DelegatesTo('model') - n_scale_params = DelegatesTo('model') - - view = View(VGroup( - VGrid(HGroup(Item('icp_iterations', label='Iterations', - width=_MM_WIDTH, tooltip='Maximum ICP iterations to ' - 'perform (per click)'), - Spring(), show_labels=True), label='ICP iterations (max)', - show_border=_SHOW_BORDER), - VGrid(Item('icp_angle', label=u'Angle (°)', width=_MM_WIDTH, - tooltip='Angle convergence threshold'), - Item('icp_distance', label='Distance (mm)', width=_MM_WIDTH, - tooltip='Distance convergence threshold'), - Item('icp_scale', label='Scale (%)', - tooltip='Scaling convergence threshold', width=_MM_WIDTH, - enabled_when='n_scale_params > 0'), - show_labels=True, label='ICP convergence limits', columns=3, - show_border=_SHOW_BORDER), - VGrid(Item('icp_fid_match', width=-1, show_label=False, - editor=EnumEditor(values=dict( - nearest='1:Closest to surface', - matched='2:MRI fiducials'), cols=2, - format_func=lambda x: x), - tooltip='Match digitization fiducials to MRI fiducials or ' - 'the closest surface point', style='custom'), - label='Fiducial point matching', show_border=_SHOW_BORDER), - VGrid( - VGrid(Item('lpa_weight', editor=laggy_float_editor_weight, - tooltip="Relative weight for LPA", width=_WEIGHT_WIDTH, - enabled_when='has_lpa_data', label='LPA'), - Item('nasion_weight', editor=laggy_float_editor_weight, - tooltip="Relative weight for nasion", label='Nasion', - width=_WEIGHT_WIDTH, enabled_when='has_nasion_data'), - Item('rpa_weight', editor=laggy_float_editor_weight, - tooltip="Relative weight for RPA", width=_WEIGHT_WIDTH, - enabled_when='has_rpa_data', label='RPA'), - columns=3, show_labels=True, show_border=_SHOW_BORDER, - label='Fiducials'), - VGrid(Item('hsp_weight', editor=laggy_float_editor_weight, - tooltip="Relative weight for head shape points", - enabled_when='has_hsp_data', - label='HSP', width=_WEIGHT_WIDTH,), - Item('eeg_weight', editor=laggy_float_editor_weight, - tooltip="Relative weight for EEG points", label='EEG', - enabled_when='has_eeg_data', width=_WEIGHT_WIDTH), - Item('hpi_weight', editor=laggy_float_editor_weight, - tooltip="Relative weight for HPI points", label='HPI', - enabled_when='has_hpi_data', width=_WEIGHT_WIDTH), - columns=3, show_labels=True, show_border=_SHOW_BORDER, - label='Other points (closest-point matched)'), - show_labels=False, label='Point weights', columns=2, - show_border=_SHOW_BORDER), - ), title="Fitting options") - - -_DEFAULT_PARAMETERS = (0., 0., 0., 0., 0., 0., 1., 1., 1.) - - -class CoregPanel(HasPrivateTraits): - """Coregistration panel for Head<->MRI with scaling.""" - - model = Instance(CoregModel) - - # parameters - reset_params = Button(label=_RESET_LABEL) - n_scale_params = DelegatesTo('model') - parameters = DelegatesTo('model') - scale_step = Float(1.) - scale_x = DelegatesTo('model') - scale_x_dec = Button('-') - scale_x_inc = Button('+') - scale_y = DelegatesTo('model') - scale_y_dec = Button('-') - scale_y_inc = Button('+') - scale_z = DelegatesTo('model') - scale_z_dec = Button('-') - scale_z_inc = Button('+') - rot_step = Float(1.) - rot_x = DelegatesTo('model') - rot_x_dec = Button('-') - rot_x_inc = Button('+') - rot_y = DelegatesTo('model') - rot_y_dec = Button('-') - rot_y_inc = Button('+') - rot_z = DelegatesTo('model') - rot_z_dec = Button('-') - rot_z_inc = Button('+') - trans_step = Float(1.) - trans_x = DelegatesTo('model') - trans_x_dec = Button('-') - trans_x_inc = Button('+') - trans_y = DelegatesTo('model') - trans_y_dec = Button('-') - trans_y_inc = Button('+') - trans_z = DelegatesTo('model') - trans_z_dec = Button('-') - trans_z_inc = Button('+') - - # fitting - has_lpa_data = DelegatesTo('model') - has_nasion_data = DelegatesTo('model') - has_rpa_data = DelegatesTo('model') - has_fid_data = DelegatesTo('model') - has_hsp_data = DelegatesTo('model') - has_eeg_data = DelegatesTo('model') - has_hpi_data = DelegatesTo('model') - n_icp_points = DelegatesTo('model') - # fitting with scaling - fits_icp = Button(label='Fit (ICP)') - fits_fid = Button(label='Fit Fid.') - cancels_icp = Button(u'■') - reset_scale = Button(label=_RESET_LABEL) - fits_icp_running = DelegatesTo('model') - # fitting without scaling - fit_icp = Button(label='Fit (ICP)') - fit_fid = Button(label='Fit Fid.') - cancel_icp = Button(label=u'■') - reset_tr = Button(label=_RESET_LABEL) - fit_icp_running = DelegatesTo('model') - - # fit info - fid_eval_str = DelegatesTo('model') - points_eval_str = DelegatesTo('model') - - # saving - can_prepare_bem_model = DelegatesTo('model') - can_save = DelegatesTo('model') - scale_labels = DelegatesTo('model') - copy_annot = DelegatesTo('model') - prepare_bem_model = DelegatesTo('model') - save = Button(label="Save...") - load_trans = Button(label='Load...') - queue = Instance(queue.Queue, ()) - queue_feedback = Str('') - queue_current = Str('') - queue_len = Int(0) - queue_status_text = Property( - Str, depends_on=['queue_feedback', 'queue_current', 'queue_len']) - - fitting_options_panel = Instance(FittingOptionsPanel) - fitting_options = Button('Fitting options...') - - def _fitting_options_panel_default(self): - return FittingOptionsPanel(model=self.model) - - view = _make_view_coreg_panel() - - def __init__(self, *args, **kwargs): # noqa: D102 - super(CoregPanel, self).__init__(*args, **kwargs) - - # Setup scaling worker - def worker(): - while True: - (subjects_dir, subject_from, subject_to, scale, skip_fiducials, - include_labels, include_annot, bem_names) = self.queue.get() - self.queue_len -= 1 - - # Scale MRI files - self.queue_current = 'Scaling %s...' % subject_to - try: - scale_mri(subject_from, subject_to, scale, True, - subjects_dir, skip_fiducials, include_labels, - include_annot) - except Exception: - logger.error('Error scaling %s:\n' % subject_to + - traceback.format_exc()) - self.queue_feedback = ('Error scaling %s (see Terminal)' % - subject_to) - bem_names = () # skip bem solutions - else: - self.queue_feedback = 'Done scaling %s' % subject_to - - # Precompute BEM solutions - for bem_name in bem_names: - self.queue_current = ('Computing %s solution...' % - bem_name) - try: - bem_file = bem_fname.format(subjects_dir=subjects_dir, - subject=subject_to, - name=bem_name) - bemsol = make_bem_solution(bem_file) - write_bem_solution(bem_file[:-4] + '-sol.fif', bemsol) - except Exception: - logger.error('Error computing %s solution:\n' % - bem_name + traceback.format_exc()) - self.queue_feedback = ('Error computing %s solution ' - '(see Terminal)' % bem_name) - else: - self.queue_feedback = ('Done computing %s solution' % - bem_name) - - # Finalize - self.queue_current = '' - self.queue.task_done() - - t = Thread(target=worker) - t.daemon = True - t.start() - - @cached_property - def _get_queue_status_text(self): - items = [] - if self.queue_current: - items.append(self.queue_current) - if self.queue_feedback: - items.append(self.queue_feedback) - if self.queue_len: - items.append("%i queued" % self.queue_len) - return ' | '.join(items) - - @cached_property - def _get_rotation(self): - rot = np.array([self.rot_x, self.rot_y, self.rot_z]) - return rot - - @cached_property - def _get_translation(self): - trans = np.array([self.trans_x, self.trans_y, self.trans_z]) - return trans - - def _n_scale_params_fired(self): - if self.n_scale_params == 0: - use = [1] * 3 - elif self.n_scale_params == 1: - use = [np.mean([self.scale_x, self.scale_y, self.scale_z]) / - 100.] * 3 - else: - use = self.parameters[6:9] - self.parameters[6:9] = use - - def _fit_fid_fired(self): - with busy(): - self.model.fit_fiducials(0) - - def _fit_icp_fired(self): - with busy(): - self.model.fit_icp(0) - - def _fits_fid_fired(self): - with busy(): - self.model.fit_fiducials() - - def _fits_icp_fired(self): - with busy(): - self.model.fit_icp() - - def _cancel_icp_fired(self): - self.fit_icp_running = False - - def _cancels_icp_fired(self): - self.fits_icp_running = False - - def _reset_scale_fired(self): - self.reset_traits(('scale_x', 'scale_y', 'scale_z')) - - def _reset_tr_fired(self): - self.reset_traits(('trans_x', 'trans_y', 'trans_z', - 'rot_x', 'rot_y', 'rot_z')) - - def _reset_params_fired(self): - self.model.reset() - - def _rot_x_dec_fired(self): - self.rot_x -= self.rot_step - - def _rot_x_inc_fired(self): - self.rot_x += self.rot_step - - def _rot_y_dec_fired(self): - self.rot_y -= self.rot_step - - def _rot_y_inc_fired(self): - self.rot_y += self.rot_step - - def _rot_z_dec_fired(self): - self.rot_z -= self.rot_step - - def _rot_z_inc_fired(self): - self.rot_z += self.rot_step - - def _scale_x_dec_fired(self): - self.scale_x -= self.scale_step - - def _scale_x_inc_fired(self): - self.scale_x += self.scale_step - - def _scale_y_dec_fired(self): - self.scale_y -= self.scale_step - - def _scale_y_inc_fired(self): - self.scale_y += self.scale_step - - def _scale_z_dec_fired(self): - self.scale_z -= self.scale_step - - def _scale_z_inc_fired(self): - self.scale_z += self.scale_step - - def _trans_x_dec_fired(self): - self.trans_x -= self.trans_step - - def _trans_x_inc_fired(self): - self.trans_x += self.trans_step - - def _trans_y_dec_fired(self): - self.trans_y -= self.trans_step - - def _trans_y_inc_fired(self): - self.trans_y += self.trans_step - - def _trans_z_dec_fired(self): - self.trans_z -= self.trans_step - - def _trans_z_inc_fired(self): - self.trans_z += self.trans_step - - -class NewMriDialog(HasPrivateTraits): - """New MRI dialog.""" - - # Dialog to determine target subject name for a scaled MRI - subjects_dir = Directory - subject_to = Str - subject_from = Str - subject_to_dir = Property(depends_on=['subjects_dir', 'subject_to']) - subject_to_exists = Property(Bool, depends_on='subject_to_dir') - - feedback = Str(' ' * 100) - can_overwrite = Bool - overwrite = Bool - can_save = Bool - - view = View(Item('subject_to', label='New MRI Subject Name', tooltip="A " - "new folder with this name will be created in the " - "current subjects_dir for the scaled MRI files"), - Item('feedback', show_label=False, style='readonly'), - Item('overwrite', enabled_when='can_overwrite', tooltip="If a " - "subject with the chosen name exists, delete the old " - "subject"), - buttons=[CancelButton, - Action(name='OK', enabled_when='can_save')]) - - def _can_overwrite_changed(self, new): - if not new: - self.overwrite = False - - @cached_property - def _get_subject_to_dir(self): - return os.path.join(self.subjects_dir, self.subject_to) - - @cached_property - def _get_subject_to_exists(self): - if not self.subject_to: - return False - elif os.path.exists(self.subject_to_dir): - return True - else: - return False - - @on_trait_change('subject_to_dir,overwrite') - def update_dialog(self): - if not self.subject_from: - # weird trait state that occurs even when subject_from is set - return - elif not self.subject_to: - self.feedback = "No subject specified..." - self.can_save = False - self.can_overwrite = False - elif self.subject_to == self.subject_from: - self.feedback = "Must be different from MRI source subject..." - self.can_save = False - self.can_overwrite = False - elif self.subject_to_exists: - if self.overwrite: - self.feedback = "%s will be overwritten." % self.subject_to - self.can_save = True - self.can_overwrite = True - else: - self.feedback = "Subject already exists..." - self.can_save = False - self.can_overwrite = True - else: - self.feedback = "Name ok." - self.can_save = True - self.can_overwrite = False - - -def _make_view(tabbed=False, split=False, width=800, height=600, - scrollable=True): - """Create a view for the CoregFrame.""" - # Set the width to 0.99 to "push out" as much as possible, use - # scene_width in the View below - scene = Item('scene', show_label=False, width=0.99, - editor=SceneEditor(scene_class=MayaviScene)) - - data_panel = VGroup( - Item('data_panel', style='custom', - width=_COREG_WIDTH if scrollable else 1, - editor=InstanceEditor(view=_make_view_data_panel(scrollable))), - label='Data', show_border=not scrollable, show_labels=False) - - # Setting `scrollable=True` for a Group does not seem to have any effect - # (macOS), in order to be effective the parameter has to be set for a View - # object; hence we use a special InstanceEditor to set the parameter - # programmatically: - coreg_panel = VGroup( - Item('coreg_panel', style='custom', - width=_COREG_WIDTH if scrollable else 1, - editor=InstanceEditor(view=_make_view_coreg_panel(scrollable))), - label="Coregistration", show_border=not scrollable, show_labels=False, - enabled_when="data_panel.fid_panel.locked") - - main_layout = 'split' if split else 'normal' - - if tabbed: - main = HGroup(scene, - Group(data_panel, coreg_panel, show_labels=False, - layout='tabbed'), - layout=main_layout) - else: - main = HGroup(data_panel, scene, coreg_panel, show_labels=False, - layout=main_layout) - - # Here we set the width and height to impossibly small numbers to force the - # window to be as tight as possible - view = View(main, resizable=True, handler=CoregFrameHandler(), - buttons=NoButtons, width=width, height=height, - statusbar=[StatusItem('status_text', width=0.55), - StatusItem('queue_status_text', width=0.45)]) - return view - - -class ViewOptionsPanel(HasTraits): - """View options panel.""" - - mri_obj = Instance(SurfaceObject) - hsp_obj = Instance(PointObject) - eeg_obj = Instance(PointObject) - hpi_obj = Instance(PointObject) - hsp_cf_obj = Instance(PointObject) - mri_cf_obj = Instance(PointObject) - bgcolor = RGBColor() - coord_frame = Enum('mri', 'head', label='Display coordinate frame') - head_high_res = Bool(True, label='Show high-resolution head') - head_inside = Bool(True, label='Add opaque inner head surface') - advanced_rendering = Bool(True, label='Use advanced OpenGL', - desc='Enable advanced OpenGL methods that do ' - 'not work with all renderers (e.g., depth ' - 'peeling)') - - view = View( - VGroup( - Item('mri_obj', style='custom', label="MRI"), - Item('hsp_obj', style='custom', label="Head shape"), - Item('eeg_obj', style='custom', label='EEG'), - Item('hpi_obj', style='custom', label='HPI'), - VGrid(Item('coord_frame', style='custom', - editor=EnumEditor(values={'mri': '1:MRI', - 'head': '2:Head'}, cols=2, - format_func=_pass)), - Item('head_high_res'), Spring(), - Item('advanced_rendering'), - Item('head_inside'), Spring(), Spring(), - columns=3, show_labels=True), - Item('hsp_cf_obj', style='custom', label='Head axes'), - Item('mri_cf_obj', style='custom', label='MRI axes'), - HGroup(Item('bgcolor', label='Background'), Spring()), - ), title="Display options") - - -class DataPanelHandler(Handler): - """Open other windows with proper parenting.""" - - info = Instance(UIInfo) - - def object_view_options_panel_changed(self, info): # noqa: D102 - self.info = info - - def object_view_options_changed(self, info): # noqa: D102 - self.info.object.view_options_panel.edit_traits( - parent=self.info.ui.control) - - -class DataPanel(HasTraits): - """Data loading panel.""" - - # Set by CoregPanel - model = Instance(CoregModel) - scene = Instance(MlabSceneModel, ()) - lock_fiducials = DelegatesTo('model') - guess_mri_subject = DelegatesTo('model') - raw_src = DelegatesTo('model', 'hsp') - # Set internally - subject_panel = Instance(SubjectSelectorPanel) - fid_panel = Instance(FiducialsPanel) - headview = Instance(HeadViewController) - view_options_panel = Instance(ViewOptionsPanel) - hsp_always_visible = Bool(False, label="Always Show Head Shape") - view_options = Button(label="Display options...") - - # Omit Points - distance = Float(10., desc="maximal distance for head shape points from " - "the surface (mm)") - omit_points = Button(label='Omit', desc="to omit head shape points " - "for the purpose of the automatic coregistration " - "procedure (mm).") - grow_hair = DelegatesTo('model') - reset_omit_points = Button(label=_RESET_LABEL, desc="to reset the " - "omission of head shape points to include all.") - omitted_info = Str('No points omitted') - - def _subject_panel_default(self): - return SubjectSelectorPanel(model=self.model.mri.subject_source) - - def _fid_panel_default(self): - return FiducialsPanel(model=self.model.mri, headview=self.headview) - - def _headview_default(self): - return HeadViewController(system='RAS', scene=self.scene) - - def _omit_points_fired(self): - distance = self.distance / 1000. - self.model.omit_hsp_points(distance) - n_omitted = self.model.hsp.n_omitted - self.omitted_info = ( - "%s pt%s omitted (%0.1f mm)" - % (n_omitted if n_omitted > 0 else 'No', _pl(n_omitted), - self.distance)) - - @on_trait_change('model:hsp:file') - def _file_change(self): - self._reset_omit_points_fired() - - def _reset_omit_points_fired(self): - self.model.omit_hsp_points(np.inf) - self.omitted_info = 'No points omitted (reset)' - - -class CoregFrame(HasTraits): - """GUI for head-MRI coregistration.""" - - model = Instance(CoregModel) - - scene = Instance(MlabSceneModel, ()) - head_high_res = Bool(True) - advanced_rendering = Bool(True) - head_inside = Bool(True) - - data_panel = Instance(DataPanel) - coreg_panel = Instance(CoregPanel) # right panel - - project_to_surface = DelegatesTo('eeg_obj') - orient_to_surface = DelegatesTo('hsp_obj') - scale_by_distance = DelegatesTo('hsp_obj') - mark_inside = DelegatesTo('hsp_obj') - status_text = DelegatesTo('model') - queue_status_text = DelegatesTo('coreg_panel') - - fid_ok = DelegatesTo('model', 'mri.fid_ok') - lock_fiducials = DelegatesTo('model') - title = Str('MNE Coreg') - - # visualization (MRI) - mri_obj = Instance(SurfaceObject) - mri_lpa_obj = Instance(PointObject) - mri_nasion_obj = Instance(PointObject) - mri_rpa_obj = Instance(PointObject) - bgcolor = RGBColor((0.5, 0.5, 0.5)) - # visualization (Digitization) - hsp_obj = Instance(PointObject) - eeg_obj = Instance(PointObject) - hpi_obj = Instance(PointObject) - hsp_lpa_obj = Instance(PointObject) - hsp_nasion_obj = Instance(PointObject) - hsp_rpa_obj = Instance(PointObject) - hsp_visible = Property(depends_on=['data_panel:hsp_always_visible', - 'lock_fiducials']) - # Coordinate frame axes - hsp_cf_obj = Instance(PointObject) - mri_cf_obj = Instance(PointObject) - - picker = Instance(object) - - # Processing - queue = DelegatesTo('coreg_panel') - - view = _make_view() - - def _model_default(self): - return CoregModel( - scale_labels=self._config.get( - 'MNE_COREG_SCALE_LABELS', 'true') == 'true', - copy_annot=self._config.get( - 'MNE_COREG_COPY_ANNOT', 'true') == 'true', - prepare_bem_model=self._config.get( - 'MNE_COREG_PREPARE_BEM', 'true') == 'true') - - def _data_panel_default(self): - return DataPanel(model=self.model, scene=self.scene) - - def _coreg_panel_default(self): - return CoregPanel(model=self.model) - - def __init__(self, raw=None, subject=None, subjects_dir=None, - guess_mri_subject=True, head_opacity=1., - head_high_res=True, trans=None, config=None, - project_eeg=False, orient_to_surface=False, - scale_by_distance=False, mark_inside=False, - interaction='trackball', scale=0.16, - advanced_rendering=True, head_inside=True): # noqa: D102 - self._config = config or {} - super(CoregFrame, self).__init__(guess_mri_subject=guess_mri_subject, - head_high_res=head_high_res, - advanced_rendering=advanced_rendering, - head_inside=head_inside) - self._initial_kwargs = dict(project_eeg=project_eeg, - orient_to_surface=orient_to_surface, - scale_by_distance=scale_by_distance, - mark_inside=mark_inside, - head_opacity=head_opacity, - interaction=interaction, - scale=scale, head_inside=head_inside) - self._locked_opacity = self._initial_kwargs['head_opacity'] - self._locked_head_inside = self._initial_kwargs['head_inside'] - if not 0 <= head_opacity <= 1: - raise ValueError( - "head_opacity needs to be a floating point number between 0 " - "and 1, got %r" % (head_opacity,)) - - if (subjects_dir is not None) and os.path.isdir(subjects_dir): - self.model.mri.subjects_dir = subjects_dir - - if raw is not None: - self.model.hsp.file = raw - - if subject is not None: - if subject not in self.model.mri.subject_source.subjects: - msg = "%s is not a valid subject. " % subject - # no subjects -> [''] - if any(self.model.mri.subject_source.subjects): - ss = ', '.join(self.model.mri.subject_source.subjects) - msg += ("The following subjects have been found: %s " - "(subjects_dir=%s). " % - (ss, self.model.mri.subjects_dir)) - else: - msg += ("No subjects were found in subjects_dir=%s. " % - self.model.mri.subjects_dir) - msg += ("Make sure all MRI subjects have head shape files " - "(run $ mne make_scalp_surfaces).") - raise ValueError(msg) - self.model.mri.subject = subject - if trans is not None: - try: - self.model.load_trans(trans) - except Exception as e: - error(None, "Error loading trans file %s: %s (See terminal " - "for details)" % (trans, e), "Error Loading Trans File") - - @on_trait_change('subject_panel:subject') - def _set_title(self): - self.title = '%s - MNE Coreg' % self.model.mri.subject - - @on_trait_change('scene:activated') - def _init_plot(self): - _toggle_mlab_render(self, False) - self._on_advanced_rendering_change() - - lpa_color = defaults['lpa_color'] - nasion_color = defaults['nasion_color'] - rpa_color = defaults['rpa_color'] - - # MRI scalp - # - # Due to MESA rendering / z-order bugs, this should be added and - # rendered first (see gh-5375). - color = defaults['head_color'] - self.mri_obj = SurfaceObject( - points=np.empty((0, 3)), color=color, tris=np.empty((0, 3)), - scene=self.scene, name="MRI Scalp", block_behind=True, - # opacity=self._initial_kwargs['head_opacity'], - # setting opacity here causes points to be - # [[0, 0, 0]] -- why?? - ) - self.mri_obj.opacity = self._initial_kwargs['head_opacity'] - self.mri_obj.rear_opacity = float(self.head_inside) - self.data_panel.fid_panel.hsp_obj = self.mri_obj - self._update_mri_obj() - self.mri_obj.plot() - # Do not do sync_trait here, instead use notifiers elsewhere - - # MRI Fiducials - point_scale = defaults['mri_fid_scale'] - self.mri_lpa_obj = PointObject(scene=self.scene, color=lpa_color, - has_norm=True, point_scale=point_scale, - name='LPA', view='oct') - self.model.sync_trait('transformed_mri_lpa', - self.mri_lpa_obj, 'points', mutual=False) - self.mri_nasion_obj = PointObject(scene=self.scene, color=nasion_color, - has_norm=True, - point_scale=point_scale, - name='Nasion', view='oct') - self.model.sync_trait('transformed_mri_nasion', - self.mri_nasion_obj, 'points', mutual=False) - self.mri_rpa_obj = PointObject(scene=self.scene, color=rpa_color, - has_norm=True, point_scale=point_scale, - name='RPA', view='oct') - self.model.sync_trait('transformed_mri_rpa', - self.mri_rpa_obj, 'points', mutual=False) - - # Digitizer Head Shape - kwargs = dict( - view='cloud', scene=self.scene, resolution=20, - orient_to_surface=self._initial_kwargs['orient_to_surface'], - scale_by_distance=self._initial_kwargs['scale_by_distance'], - mark_inside=self._initial_kwargs['mark_inside']) - self.hsp_obj = PointObject( - color=defaults['extra_color'], name='Extra', has_norm=True, - point_scale=defaults['extra_scale'], **kwargs) - self.model.sync_trait('transformed_hsp_points', - self.hsp_obj, 'points', mutual=False) - - # Digitizer EEG - self.eeg_obj = PointObject( - color=defaults['eeg_color'], point_scale=defaults['eeg_scale'], - name='EEG', projectable=True, has_norm=True, - project_to_surface=self._initial_kwargs['project_eeg'], **kwargs) - self.model.sync_trait('transformed_hsp_eeg_points', - self.eeg_obj, 'points', mutual=False) - - # Digitizer HPI - self.hpi_obj = PointObject( - color=defaults['hpi_color'], name='HPI', has_norm=True, - point_scale=defaults['hpi_scale'], **kwargs) - self.model.sync_trait('transformed_hsp_hpi', - self.hpi_obj, 'points', mutual=False) - for p in (self.hsp_obj, self.eeg_obj, self.hpi_obj): - p.inside_color = self.mri_obj.color - self.mri_obj.sync_trait('color', p, 'inside_color', - mutual=False) - - # Digitizer Fiducials - point_scale = defaults['dig_fid_scale'] - opacity = defaults['dig_fid_opacity'] - self.hsp_lpa_obj = PointObject( - scene=self.scene, color=lpa_color, opacity=opacity, - has_norm=True, point_scale=point_scale, name='HSP-LPA') - self.model.sync_trait('transformed_hsp_lpa', - self.hsp_lpa_obj, 'points', mutual=False) - self.hsp_nasion_obj = PointObject( - scene=self.scene, color=nasion_color, opacity=opacity, - has_norm=True, point_scale=point_scale, name='HSP-Nasion') - self.model.sync_trait('transformed_hsp_nasion', - self.hsp_nasion_obj, 'points', mutual=False) - self.hsp_rpa_obj = PointObject( - scene=self.scene, color=rpa_color, opacity=opacity, - has_norm=True, point_scale=point_scale, name='HSP-RPA') - self.model.sync_trait('transformed_hsp_rpa', - self.hsp_rpa_obj, 'points', mutual=False) - - # All points share these - for p in (self.hsp_obj, self.eeg_obj, self.hpi_obj, - self.hsp_lpa_obj, self.hsp_nasion_obj, self.hsp_rpa_obj): - self.sync_trait('hsp_visible', p, 'visible', mutual=False) - self.model.sync_trait('mri_trans_noscale', p, 'project_to_trans', - mutual=False) - - on_pick = self.scene.mayavi_scene.on_mouse_pick - self.picker = on_pick(self.data_panel.fid_panel._on_pick, type='cell') - - # Coordinate frame axes - self.mri_cf_obj = PointObject( - scene=self.scene, color=self.mri_obj.color, - opacity=self.mri_obj.opacity, label_scale=5e-3, - point_scale=0.02, name='MRI', view='arrow') - self.mri_obj.sync_trait('color', self.mri_cf_obj, mutual=False) - self._update_mri_axes() - self.hsp_cf_obj = PointObject( - scene=self.scene, color=self.hsp_obj.color, - opacity=self.mri_obj.opacity, label_scale=5e-3, - point_scale=0.02, name='Head', view='arrow') - self.hsp_cf_obj.sync_trait('color', self.hsp_cf_obj, mutual=False) - self._update_hsp_axes() - - self.sync_trait('bgcolor', self.scene, 'background') - - self._update_projection_surf() - - _toggle_mlab_render(self, True) - self.scene.render() - self.scene.camera.focal_point = (0., 0., 0.) - self.data_panel.view_options_panel = ViewOptionsPanel( - mri_obj=self.mri_obj, hsp_obj=self.hsp_obj, - eeg_obj=self.eeg_obj, hpi_obj=self.hpi_obj, - hsp_cf_obj=self.hsp_cf_obj, mri_cf_obj=self.mri_cf_obj, - head_high_res=self.head_high_res, head_inside=self.head_inside, - bgcolor=self.bgcolor, advanced_rendering=self.advanced_rendering) - self.data_panel.headview.scale = self._initial_kwargs['scale'] - self.data_panel.headview.interaction = \ - self._initial_kwargs['interaction'] - self.data_panel.headview.left = True - self.data_panel.view_options_panel.sync_trait( - 'coord_frame', self.model) - for key in ('head_high_res', 'advanced_rendering', 'bgcolor', - 'head_inside'): - self.data_panel.view_options_panel.sync_trait(key, self) - - @on_trait_change('advanced_rendering') - def _on_advanced_rendering_change(self): - renderer = getattr(self.scene, 'renderer', None) - if renderer is None: - return - if self.advanced_rendering: - renderer.use_depth_peeling = 1 - renderer.occlusion_ratio = 0.1 - renderer.maximum_number_of_peels = 100 - renderer.vtk_window.multi_samples = 0 - renderer.vtk_window.alpha_bit_planes = 1 - else: - renderer.use_depth_peeling = 0 - renderer.vtk_window.multi_samples = 8 - renderer.vtk_window.alpha_bit_planes = 0 - if hasattr(renderer, 'use_fxaa'): - self.scene.renderer.use_fxaa = _get_3d_option('antialias') - self.scene.render() - - @on_trait_change('lock_fiducials') - def _on_lock_change(self): - if not self.lock_fiducials: - if self.mri_obj is None: - self._initial_kwargs['head_opacity'] = 1. - else: - self._locked_opacity = self.mri_obj.opacity - self.mri_obj.opacity = 1. - self._locked_head_inside = self.head_inside - self.head_inside = False - else: - if self.mri_obj is not None: - self.mri_obj.opacity = self._locked_opacity - self.head_inside = self._locked_head_inside - - @on_trait_change('head_inside') - def _on_head_inside_change(self): - if self.mri_obj is not None: - self.mri_obj.rear_opacity = float(self.head_inside) # 0 or 1 - - @cached_property - def _get_hsp_visible(self): - return self.data_panel.hsp_always_visible or self.lock_fiducials - - @on_trait_change('model:mri_trans') - def _update_mri_axes(self): - if self.mri_cf_obj is None: - return - nn = apply_trans(self.model.mri_trans, np.eye(3), move=False) - pts = apply_trans(self.model.mri_trans, np.zeros((3, 3))) - self.mri_cf_obj.nn = nn - self.mri_cf_obj.points = pts - - @on_trait_change('model:hsp_trans') - def _update_hsp_axes(self): - if self.hsp_cf_obj is None: - return - nn = apply_trans(self.model.hsp_trans, np.eye(3), move=False) - pts = apply_trans(self.model.hsp_trans, np.zeros((3, 3))) - self.hsp_cf_obj.nn = nn - self.hsp_cf_obj.points = pts - - @on_trait_change('nearest_calc') - def _update_projection_surf(self): - if len(self.model.processed_low_res_mri_points) <= 1: - return - rr = (self.model.processed_low_res_mri_points * - self.model.parameters[6:9]) - surf = dict(rr=rr, tris=self.model.mri.bem_low_res.surf.tris, - nn=self.model.mri.bem_low_res.surf.nn) - check_inside = _CheckInside(surf) - nearest = _DistanceQuery(rr) - for p in (self.eeg_obj, self.hsp_obj, self.hpi_obj): - if p is not None: - p.check_inside = check_inside - p.nearest = nearest - - @on_trait_change('model:mri:bem_low_res:surf,head_high_res,' - 'model:transformed_high_res_mri_points') - def _update_mri_obj(self): - if self.mri_obj is None: - return - self.mri_obj.tris = getattr( - self.model.mri, 'bem_%s_res' - % ('high' if self.head_high_res else 'low',)).surf.tris - self.mri_obj.points = getattr( - self.model, 'transformed_%s_res_mri_points' - % ('high' if self.head_high_res else 'low',)) - - # automatically lock fiducials if a good fiducials file is loaded - @on_trait_change('model:mri:fid_file') - def _on_fid_file_loaded(self): - self.data_panel.fid_panel.locked = bool(self.model.mri.fid_file) - - def save_config(self, home_dir=None, size=None): - """Write configuration values.""" - def s_c(key, value, lower=True): - value = str(value) - if lower: - value = value.lower() - set_config(key, str(value).lower(), home_dir=home_dir, - set_env=False) - - s_c('MNE_COREG_GUESS_MRI_SUBJECT', self.model.guess_mri_subject) - s_c('MNE_COREG_ADVANCED_RENDERING', self.advanced_rendering) - s_c('MNE_COREG_HEAD_HIGH_RES', self.head_high_res) - if self.lock_fiducials: - opacity = self.mri_obj.opacity - head_inside = self.head_inside - else: - opacity = self._locked_opacity - head_inside = self._locked_head_inside - s_c('MNE_COREG_HEAD_INSIDE', head_inside) - s_c('MNE_COREG_HEAD_OPACITY', opacity) - if size is not None: - s_c('MNE_COREG_WINDOW_WIDTH', size[0]) - s_c('MNE_COREG_WINDOW_HEIGHT', size[1]) - s_c('MNE_COREG_SCENE_SCALE', self.data_panel.headview.scale) - s_c('MNE_COREG_SCALE_LABELS', self.model.scale_labels) - s_c('MNE_COREG_COPY_ANNOT', self.model.copy_annot) - s_c('MNE_COREG_PREPARE_BEM', self.model.prepare_bem_model) - if self.model.mri.subjects_dir: - s_c('MNE_COREG_SUBJECTS_DIR', self.model.mri.subjects_dir, False) - s_c('MNE_COREG_PROJECT_EEG', self.project_to_surface) - s_c('MNE_COREG_ORIENT_TO_SURFACE', self.orient_to_surface) - s_c('MNE_COREG_SCALE_BY_DISTANCE', self.scale_by_distance) - s_c('MNE_COREG_MARK_INSIDE', self.mark_inside) - s_c('MNE_COREG_INTERACTION', self.data_panel.headview.interaction) diff -Nru python-mne-0.23.4+dfsg/mne/gui/_coreg.py python-mne-1.1.0+dfsg/mne/gui/_coreg.py --- python-mne-0.23.4+dfsg/mne/gui/_coreg.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/_coreg.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,1895 @@ +from contextlib import contextmanager +from functools import partial +import inspect +import os +import os.path as op +import platform +from pathlib import Path +import time +import queue +import threading +import re + +import numpy as np +from traitlets import observe, HasTraits, Unicode, Bool, Float + +from ..io.constants import FIFF +from ..defaults import DEFAULTS +from ..io import read_info, read_fiducials, write_fiducials, read_raw +from ..io.pick import pick_types +from ..io.open import fiff_open, dir_tree_find +from ..io.meas_info import _empty_info +from ..io._read_raw import supported as raw_supported_types +from ..bem import make_bem_solution, write_bem_solution +from ..coreg import (Coregistration, _is_mri_subject, scale_mri, bem_fname, + _mri_subject_has_bem, fid_fname, _map_fid_name_to_idx, + _find_head_bem) +from ..viz._3d import (_plot_head_surface, _plot_head_fiducials, + _plot_head_shape_points, _plot_mri_fiducials, + _plot_hpi_coils, _plot_sensors, _plot_helmet) +from ..viz.utils import safe_event +from ..transforms import (read_trans, write_trans, _ensure_trans, _get_trans, + rotation_angles, _get_transforms_to_coord_frame) +from ..utils import (get_subjects_dir, check_fname, _check_fname, fill_doc, + verbose, logger, _validate_type) +from ..surface import _DistanceQuery, _CheckInside +from ..channels import read_dig_fif + + +class _WorkerData(): + def __init__(self, name, params=None): + self._name = name + self._params = params + + +def _get_subjects(sdir): + # XXX: would be nice to move this function to util + is_dir = sdir and op.isdir(sdir) + if is_dir: + dir_content = os.listdir(sdir) + subjects = [s for s in dir_content if _is_mri_subject(s, sdir)] + if len(subjects) == 0: + subjects.append('') + else: + subjects = [''] + return sorted(subjects) + + +@fill_doc +class CoregistrationUI(HasTraits): + """Class for coregistration assisted by graphical interface. + + Parameters + ---------- + info_file : None | str + The FIFF file with digitizer data for coregistration. + %(subject)s + %(subjects_dir)s + %(fiducials)s + head_resolution : bool + If True, use a high-resolution head surface. Defaults to False. + head_opacity : float + The opacity of the head surface. Defaults to 0.8. + hpi_coils : bool + If True, display the HPI coils. Defaults to True. + head_shape_points : bool + If True, display the head shape points. Defaults to True. + eeg_channels : bool + If True, display the EEG channels. Defaults to True. + orient_glyphs : bool + If True, orient the sensors towards the head surface. Default to False. + scale_by_distance : bool + If True, scale the sensors based on their distance to the head surface. + Defaults to True. + mark_inside : bool + If True, mark the head shape points that are inside the head surface + with a different color. Defaults to True. + sensor_opacity : float + The opacity of the sensors between 0 and 1. Defaults to 1.0. + trans : str + The path to the Head<->MRI transform FIF file ("-trans.fif"). + size : tuple + The dimensions (width, height) of the rendering view. The default is + (800, 600). + bgcolor : tuple | str + The background color as a tuple (red, green, blue) of float + values between 0 and 1 or a valid color name (i.e. 'white' + or 'w'). Defaults to 'grey'. + show : bool + Display the window as soon as it is ready. Defaults to True. + block : bool + Whether to halt program execution until the GUI has been closed + (``True``) or not (``False``, default). + %(fullscreen)s + The default is False. + + .. versionadded:: 1.1 + %(interaction_scene)s + Defaults to ``'terrain'``. + + .. versionadded:: 1.0 + %(verbose)s + + Attributes + ---------- + coreg : mne.coreg.Coregistration + The coregistration instance used by the graphical interface. + """ + + _subject = Unicode() + _subjects_dir = Unicode() + _lock_fids = Bool() + _current_fiducial = Unicode() + _info_file = Unicode() + _orient_glyphs = Bool() + _scale_by_distance = Bool() + _mark_inside = Bool() + _hpi_coils = Bool() + _head_shape_points = Bool() + _eeg_channels = Bool() + _head_resolution = Bool() + _head_opacity = Float() + _helmet = Bool() + _grow_hair = Float() + _subject_to = Unicode() + _scale_mode = Unicode() + _icp_fid_match = Unicode() + + @verbose + def __init__(self, info_file, *, subject=None, subjects_dir=None, + fiducials='auto', head_resolution=None, + head_opacity=None, hpi_coils=None, + head_shape_points=None, eeg_channels=None, orient_glyphs=None, + scale_by_distance=None, mark_inside=None, + sensor_opacity=None, trans=None, size=None, bgcolor=None, + show=True, block=False, fullscreen=False, + interaction='terrain', verbose=None): + from ..viz.backends.renderer import _get_renderer + from ..viz.backends._utils import _qt_app_exec + + def _get_default(var, val): + return var if var is not None else val + self._actors = dict() + self._surfaces = dict() + self._widgets = dict() + self._verbose = verbose + self._plot_locked = False + self._params_locked = False + self._refresh_rate_ms = max(int(round(1000. / 60.)), 1) + self._redraws_pending = set() + self._parameter_mutex = threading.Lock() + self._redraw_mutex = threading.Lock() + self._job_queue = queue.Queue() + self._parameter_queue = queue.Queue() + self._head_geo = None + self._check_inside = None + self._nearest = None + self._coord_frame = "mri" + self._mouse_no_mvt = -1 + self._to_cf_t = None + self._omit_hsp_distance = 0.0 + self._fiducials_file = None + self._trans_modified = False + self._mri_fids_modified = False + self._mri_scale_modified = False + self._accept_close_event = True + self._fid_colors = tuple( + DEFAULTS['coreg'][f'{key}_color'] for key in + ('lpa', 'nasion', 'rpa')) + self._defaults = dict( + size=_get_default(size, (800, 600)), + bgcolor=_get_default(bgcolor, "grey"), + orient_glyphs=_get_default(orient_glyphs, True), + scale_by_distance=_get_default(scale_by_distance, True), + mark_inside=_get_default(mark_inside, True), + hpi_coils=_get_default(hpi_coils, True), + head_shape_points=_get_default(head_shape_points, True), + eeg_channels=_get_default(eeg_channels, True), + head_resolution=_get_default(head_resolution, True), + head_opacity=_get_default(head_opacity, 0.8), + helmet=False, + sensor_opacity=_get_default(sensor_opacity, 1.0), + fiducials=("LPA", "Nasion", "RPA"), + fiducial="LPA", + lock_fids=True, + grow_hair=0.0, + subject_to="", + scale_modes=["None", "uniform", "3-axis"], + scale_mode="None", + icp_fid_matches=('nearest', 'matched'), + icp_fid_match='matched', + icp_n_iterations=20, + omit_hsp_distance=10.0, + lock_head_opacity=self._head_opacity < 1.0, + weights=dict( + lpa=1.0, + nasion=10.0, + rpa=1.0, + hsp=1.0, + eeg=1.0, + hpi=1.0, + ), + ) + + # process requirements + info = None + subjects_dir = get_subjects_dir( + subjects_dir=subjects_dir, raise_error=True) + subject = _get_default(subject, _get_subjects(subjects_dir)[0]) + + # setup the window + splash = 'Initializing coregistration GUI...' if show else False + self._renderer = _get_renderer( + size=self._defaults["size"], + bgcolor=self._defaults["bgcolor"], + splash=splash, + fullscreen=fullscreen, + ) + self._renderer._window_close_connect(self._clean) + self._renderer._window_close_connect(self._close_callback, after=False) + self._renderer.set_interaction(interaction) + + # coregistration model setup + self._immediate_redraw = (self._renderer._kind != 'qt') + self._info = info + self._fiducials = fiducials + self.coreg = Coregistration( + info=self._info, subject=subject, subjects_dir=subjects_dir, + fiducials=fiducials, + on_defects='ignore' # safe due to interactive visual inspection + ) + fid_accurate = self.coreg._fid_accurate + for fid in self._defaults["weights"].keys(): + setattr(self, f"_{fid}_weight", self._defaults["weights"][fid]) + + # set main traits + self._set_head_opacity(self._defaults["head_opacity"]) + self._old_head_opacity = self._head_opacity + self._set_subjects_dir(subjects_dir) + self._set_subject(subject) + self._set_info_file(info_file) + self._set_orient_glyphs(self._defaults["orient_glyphs"]) + self._set_scale_by_distance(self._defaults["scale_by_distance"]) + self._set_mark_inside(self._defaults["mark_inside"]) + self._set_hpi_coils(self._defaults["hpi_coils"]) + self._set_head_shape_points(self._defaults["head_shape_points"]) + self._set_eeg_channels(self._defaults["eeg_channels"]) + self._set_head_resolution(self._defaults["head_resolution"]) + self._set_helmet(self._defaults["helmet"]) + self._set_grow_hair(self._defaults["grow_hair"]) + self._set_omit_hsp_distance(self._defaults["omit_hsp_distance"]) + self._set_icp_n_iterations(self._defaults["icp_n_iterations"]) + self._set_icp_fid_match(self._defaults["icp_fid_match"]) + + # configure UI + self._reset_fitting_parameters() + self._configure_dialogs() + self._configure_status_bar() + self._configure_dock() + self._configure_picking() + self._configure_legend() + + # once the docks are initialized + self._set_current_fiducial(self._defaults["fiducial"]) + self._set_scale_mode(self._defaults["scale_mode"]) + self._set_subject_to(self._defaults["subject_to"]) + if trans is not None: + self._load_trans(trans) + self._redraw() # we need the elements to be present now + + if fid_accurate: + assert self.coreg._fid_filename is not None + # _set_fiducials_file() calls _update_fiducials_label() + # internally + self._set_fiducials_file(self.coreg._fid_filename) + else: + self._set_head_resolution('high') + self._forward_widget_command('high_res_head', "set_value", True) + self._set_lock_fids(True) # hack to make the dig disappear + self._update_fiducials_label() + self._update_fiducials() + + self._set_lock_fids(fid_accurate) + + # configure worker + self._configure_worker() + + # must be done last + if show: + self._renderer.show() + # update the view once shown + views = {True: dict(azimuth=90, elevation=90), # front + False: dict(azimuth=180, elevation=90)} # left + self._renderer.set_camera(distance=None, **views[self._lock_fids]) + self._redraw() + # XXX: internal plotter/renderer should not be exposed + if not self._immediate_redraw: + self._renderer.plotter.add_callback( + self._redraw, self._refresh_rate_ms) + self._renderer.plotter.show_axes() + # initialization does not count as modification by the user + self._trans_modified = False + self._mri_fids_modified = False + self._mri_scale_modified = False + if block and self._renderer._kind != 'notebook': + _qt_app_exec(self._renderer.figure.store["app"]) + + def _set_subjects_dir(self, subjects_dir): + if subjects_dir is None or not subjects_dir: + return + try: + subjects_dir = _check_fname( + subjects_dir, overwrite='read', must_exist=True, need_dir=True) + subjects = _get_subjects(subjects_dir) + low_res_path = _find_head_bem( + subjects[0], subjects_dir, high_res=False) + high_res_path = _find_head_bem( + subjects[0], subjects_dir, high_res=True) + valid = low_res_path is not None or high_res_path is not None + except Exception: + valid = False + if valid: + style = dict(border="initial") + self._subjects_dir = subjects_dir + else: + style = dict(border="2px solid #ff0000") + self._forward_widget_command("subjects_dir_field", "set_style", style) + + def _set_subject(self, subject): + self._subject = subject + + def _set_lock_fids(self, state): + self._lock_fids = bool(state) + + def _set_fiducials_file(self, fname): + if fname is None: + fids = 'auto' + else: + fname = _check_fname( + fname, overwrite='read', must_exist=True, need_dir=False + ) + fids, _ = read_fiducials(fname) + + self._fiducials_file = fname + self.coreg._setup_fiducials(fids) + self._update_distance_estimation() + self._update_fiducials_label() + self._update_fiducials() + self._reset(keep_trans=True) + + if fname is None: + self._set_lock_fids(False) + self._forward_widget_command( + 'reload_mri_fids', 'set_enabled', False + ) + else: + self._set_lock_fids(True) + self._forward_widget_command( + 'reload_mri_fids', 'set_enabled', True + ) + self._display_message( + f"Loading MRI fiducials from {fname}... Done!" + ) + + def _set_current_fiducial(self, fid): + self._current_fiducial = fid.lower() + + def _set_info_file(self, fname): + if fname is None: + return + + # info file can be anything supported by read_raw + try: + check_fname(fname, 'info', tuple(raw_supported_types.keys()), + endings_err=tuple(raw_supported_types.keys())) + fname = _check_fname(fname, overwrite='read') # convert to str + + # ctf ds `files` are actually directories + if fname.endswith(('.ds',)): + info_file = _check_fname( + fname, overwrite='read', must_exist=True, need_dir=True) + else: + info_file = _check_fname( + fname, overwrite='read', must_exist=True, need_dir=False) + valid = True + except IOError: + valid = False + if valid: + style = dict(border="initial") + self._info_file = info_file + else: + style = dict(border="2px solid #ff0000") + self._forward_widget_command("info_file_field", "set_style", style) + + def _set_omit_hsp_distance(self, distance): + self._omit_hsp_distance = distance + + def _set_orient_glyphs(self, state): + self._orient_glyphs = bool(state) + + def _set_scale_by_distance(self, state): + self._scale_by_distance = bool(state) + + def _set_mark_inside(self, state): + self._mark_inside = bool(state) + + def _set_hpi_coils(self, state): + self._hpi_coils = bool(state) + + def _set_head_shape_points(self, state): + self._head_shape_points = bool(state) + + def _set_eeg_channels(self, state): + self._eeg_channels = bool(state) + + def _set_head_resolution(self, state): + self._head_resolution = bool(state) + + def _set_head_opacity(self, value): + self._head_opacity = value + + def _set_helmet(self, state): + self._helmet = bool(state) + + def _set_grow_hair(self, value): + self._grow_hair = value + + def _set_subject_to(self, value): + self._subject_to = value + self._forward_widget_command( + "save_subject", "set_enabled", len(value) > 0) + if self._check_subject_exists(): + style = dict(border="2px solid #ff0000") + else: + style = dict(border="initial") + self._forward_widget_command( + "subject_to", "set_style", style) + + def _set_scale_mode(self, mode): + self._scale_mode = mode + + def _set_fiducial(self, value, coord): + self._mri_fids_modified = True + fid = self._current_fiducial + fid_idx = _map_fid_name_to_idx(name=fid) + + coords = ["X", "Y", "Z"] + coord_idx = coords.index(coord) + + self.coreg.fiducials.dig[fid_idx]['r'][coord_idx] = value / 1e3 + self._update_plot("mri_fids") + + def _set_parameter(self, value, mode_name, coord, plot_locked=False): + if mode_name == "scale": + self._mri_scale_modified = True + else: + self._trans_modified = True + if self._params_locked: + return + if mode_name == "scale" and self._scale_mode == "uniform": + with self._lock(params=True): + self._forward_widget_command( + ["sY", "sZ"], "set_value", value) + with self._parameter_mutex: + self. _set_parameter_safe(value, mode_name, coord) + if not plot_locked: + self._update_plot("sensors") + + def _set_parameter_safe(self, value, mode_name, coord): + params = dict( + rotation=self.coreg._rotation, + translation=self.coreg._translation, + scale=self.coreg._scale, + ) + idx = ["X", "Y", "Z"].index(coord) + if mode_name == "rotation": + params[mode_name][idx] = np.deg2rad(value) + elif mode_name == "translation": + params[mode_name][idx] = value / 1e3 + else: + assert mode_name == "scale" + if self._scale_mode == "uniform": + params[mode_name][:] = value / 1e2 + else: + params[mode_name][idx] = value / 1e2 + self._update_plot("head") + self.coreg._update_params( + rot=params["rotation"], + tra=params["translation"], + sca=params["scale"], + ) + + def _set_icp_n_iterations(self, n_iterations): + self._icp_n_iterations = n_iterations + + def _set_icp_fid_match(self, method): + self._icp_fid_match = method + + def _set_point_weight(self, weight, point): + funcs = { + 'hpi': '_set_hpi_coils', + 'hsp': '_set_head_shape_points', + 'eeg': '_set_eeg_channels', + } + if point in funcs.keys(): + getattr(self, funcs[point])(weight > 0) + setattr(self, f"_{point}_weight", weight) + setattr(self.coreg, f"_{point}_weight", weight) + self._update_distance_estimation() + + @observe("_subjects_dir") + def _subjects_dir_changed(self, change=None): + # XXX: add coreg.set_subjects_dir + self.coreg._subjects_dir = self._subjects_dir + subjects = _get_subjects(self._subjects_dir) + + if self._subject not in subjects: # Just pick the first available one + self._subject = subjects[0] + + self._reset() + + @observe("_subject") + def _subject_changed(self, change=None): + # XXX: add coreg.set_subject() + self.coreg._subject = self._subject + self.coreg._setup_bem() + self.coreg._setup_fiducials(self._fiducials) + self._reset() + + default_fid_fname = fid_fname.format( + subjects_dir=self._subjects_dir, subject=self._subject + ) + if Path(default_fid_fname).exists(): + fname = default_fid_fname + else: + fname = None + + self._set_fiducials_file(fname) + self._reset_fiducials() + + @observe("_lock_fids") + def _lock_fids_changed(self, change=None): + locked_widgets = [ + # MRI fiducials + "save_mri_fids", + # View options + "helmet", "head_opacity", "high_res_head", + # Digitization source + "info_file", "grow_hair", "omit_distance", "omit", "reset_omit", + # Scaling + "scaling_mode", "sX", "sY", "sZ", + # Transformation + "tX", "tY", "tZ", + "rX", "rY", "rZ", + # Fitting buttons + "fit_fiducials", "fit_icp", + # Transformation I/O + "save_trans", "load_trans", + "reset_trans", + # ICP + "icp_n_iterations", "icp_fid_match", "reset_fitting_options", + # Weights + "hsp_weight", "eeg_weight", "hpi_weight", + "lpa_weight", "nasion_weight", "rpa_weight", + ] + fits_widgets = ["fits_fiducials", "fits_icp"] + fid_widgets = ["fid_X", "fid_Y", "fid_Z", "fids_file", "fids"] + if self._lock_fids: + self._forward_widget_command(locked_widgets, "set_enabled", True) + self._forward_widget_command( + 'head_opacity', 'set_value', self._old_head_opacity + ) + self._scale_mode_changed() + self._display_message() + self._update_distance_estimation() + else: + self._old_head_opacity = self._head_opacity + self._forward_widget_command( + 'head_opacity', 'set_value', 1.0 + ) + self._forward_widget_command(locked_widgets, "set_enabled", False) + self._forward_widget_command(fits_widgets, "set_enabled", False) + self._display_message("Placing MRI fiducials - " + f"{self._current_fiducial.upper()}") + + self._set_sensors_visibility(self._lock_fids) + self._forward_widget_command("lock_fids", "set_value", self._lock_fids) + self._forward_widget_command(fid_widgets, "set_enabled", + not self._lock_fids) + + @observe("_current_fiducial") + def _current_fiducial_changed(self, change=None): + self._update_fiducials() + self._follow_fiducial_view() + if not self._lock_fids: + self._display_message("Placing MRI fiducials - " + f"{self._current_fiducial.upper()}") + + @observe("_info_file") + def _info_file_changed(self, change=None): + if not self._info_file: + return + elif self._info_file.endswith(('.fif', '.fif.gz')): + fid, tree, _ = fiff_open(self._info_file) + fid.close() + if len(dir_tree_find(tree, FIFF.FIFFB_MEAS_INFO)) > 0: + self._info = read_info(self._info_file, verbose=False) + elif len(dir_tree_find(tree, FIFF.FIFFB_ISOTRAK)) > 0: + self._info = _empty_info(1) + self._info['dig'] = read_dig_fif(fname=self._info_file).dig + self._info._unlocked = False + else: + self._info = read_raw(self._info_file).info + # XXX: add coreg.set_info() + self.coreg._info = self._info + self.coreg._setup_digs() + self._reset() + + @observe("_orient_glyphs") + def _orient_glyphs_changed(self, change=None): + self._update_plot(["hpi", "hsp", "eeg"]) + + @observe("_scale_by_distance") + def _scale_by_distance_changed(self, change=None): + self._update_plot(["hpi", "hsp", "eeg"]) + + @observe("_mark_inside") + def _mark_inside_changed(self, change=None): + self._update_plot("hsp") + + @observe("_hpi_coils") + def _hpi_coils_changed(self, change=None): + self._update_plot("hpi") + + @observe("_head_shape_points") + def _head_shape_point_changed(self, change=None): + self._update_plot("hsp") + + @observe("_eeg_channels") + def _eeg_channels_changed(self, change=None): + self._update_plot("eeg") + + @observe("_head_resolution") + def _head_resolution_changed(self, change=None): + self._update_plot(["head", "hsp"]) + + @observe("_head_opacity") + def _head_opacity_changed(self, change=None): + if "head" in self._actors: + self._actors["head"].GetProperty().SetOpacity(self._head_opacity) + self._renderer._update() + + @observe("_helmet") + def _helmet_changed(self, change=None): + self._update_plot("helmet") + + @observe("_grow_hair") + def _grow_hair_changed(self, change=None): + self.coreg.set_grow_hair(self._grow_hair) + self._update_plot("head") + self._update_plot("hsp") # inside/outside could change + + @observe("_scale_mode") + def _scale_mode_changed(self, change=None): + locked_widgets = ["sX", "sY", "sZ", "fits_icp", "subject_to"] + mode = None if self._scale_mode == "None" else self._scale_mode + self.coreg.set_scale_mode(mode) + if self._lock_fids: + self._forward_widget_command(locked_widgets, "set_enabled", + mode is not None) + self._forward_widget_command("fits_fiducials", "set_enabled", + mode not in (None, "3-axis")) + if self._scale_mode == "uniform": + self._forward_widget_command(["sY", "sZ"], "set_enabled", False) + + @observe("_icp_fid_match") + def _icp_fid_match_changed(self, change=None): + self.coreg.set_fid_match(self._icp_fid_match) + + def _run_worker(self, queue, jobs): + while True: + data = queue.get() + func = jobs[data._name] + if data._params is not None: + func(**data._params) + else: + func() + queue.task_done() + + def _configure_dialogs(self): + from ..viz.backends.renderer import MNE_3D_BACKEND_TESTING + for name, buttons in zip( + ["overwrite_subject", "overwrite_subject_exit"], + [["Yes", "No"], ["Yes", "Discard", "Cancel"]]): + self._widgets[name] = self._renderer._dialog_create( + title="CoregistrationUI", + text="The name of the output subject used to " + "save the scaled anatomy already exists.", + info_text="Do you want to overwrite?", + callback=self._overwrite_subject_callback, + buttons=buttons, + modal=not MNE_3D_BACKEND_TESTING, + ) + + def _configure_worker(self): + work_plan = { + "_job_queue": dict(save_subject=self._save_subject), + "_parameter_queue": dict(set_parameter=self._set_parameter), + } + for queue_name, jobs in work_plan.items(): + t = threading.Thread(target=partial( + self._run_worker, + queue=getattr(self, queue_name), + jobs=jobs, + )) + t.daemon = True + t.start() + + def _configure_picking(self): + self._renderer._update_picking_callback( + self._on_mouse_move, + self._on_button_press, + self._on_button_release, + self._on_pick + ) + + def _configure_legend(self): + colors = \ + [np.array(DEFAULTS['coreg'][f"{fid.lower()}_color"]).astype(float) + for fid in self._defaults['fiducials']] + labels = list(zip(self._defaults['fiducials'], colors)) + mri_fids_legend_actor = self._renderer.legend(labels=labels) + self._update_actor("mri_fids_legend", mri_fids_legend_actor) + + @verbose + def _redraw(self, verbose=None): + if not self._redraws_pending: + return + draw_map = dict( + head=self._add_head_surface, + mri_fids=self._add_mri_fiducials, + hsp=self._add_head_shape_points, + hpi=self._add_hpi_coils, + eeg=self._add_eeg_channels, + head_fids=self._add_head_fiducials, + helmet=self._add_helmet, + ) + with self._redraw_mutex: + # We need at least "head" before "hsp", because the grow_hair param + # for head sets the rr that are used for inside/outside hsp + redraws_ordered = sorted( + self._redraws_pending, + key=lambda key: list(draw_map).index(key)) + logger.debug(f'Redrawing {redraws_ordered}') + for ki, key in enumerate(redraws_ordered): + logger.debug(f'{ki}. Drawing {repr(key)}') + draw_map[key]() + self._redraws_pending.clear() + self._renderer._update() + # necessary for MacOS + if platform.system() == 'Darwin': + self._renderer._process_events() + + def _on_mouse_move(self, vtk_picker, event): + if self._mouse_no_mvt: + self._mouse_no_mvt -= 1 + + def _on_button_press(self, vtk_picker, event): + self._mouse_no_mvt = 2 + + def _on_button_release(self, vtk_picker, event): + if self._mouse_no_mvt > 0: + x, y = vtk_picker.GetEventPosition() + # XXX: internal plotter/renderer should not be exposed + plotter = self._renderer.figure.plotter + picked_renderer = self._renderer.figure.plotter.renderer + # trigger the pick + plotter.picker.Pick(x, y, 0, picked_renderer) + self._mouse_no_mvt = 0 + + def _on_pick(self, vtk_picker, event): + if self._lock_fids: + return + # XXX: taken from Brain, can be refactored + cell_id = vtk_picker.GetCellId() + mesh = vtk_picker.GetDataSet() + if mesh is None or cell_id == -1 or not self._mouse_no_mvt: + return + if not getattr(mesh, "_picking_target", False): + return + pos = np.array(vtk_picker.GetPickPosition()) + vtk_cell = mesh.GetCell(cell_id) + cell = [vtk_cell.GetPointId(point_id) for point_id + in range(vtk_cell.GetNumberOfPoints())] + vertices = mesh.points[cell] + idx = np.argmin(abs(vertices - pos), axis=0) + vertex_id = cell[idx[0]] + + fiducials = [s.lower() for s in self._defaults["fiducials"]] + idx = fiducials.index(self._current_fiducial.lower()) + # XXX: add coreg.set_fids + self.coreg._fid_points[idx] = self._surfaces["head"].points[vertex_id] + self.coreg._reset_fiducials() + self._update_fiducials() + self._update_plot("mri_fids") + + def _reset_fitting_parameters(self): + self._forward_widget_command("icp_n_iterations", "set_value", + self._defaults["icp_n_iterations"]) + self._forward_widget_command("icp_fid_match", "set_value", + self._defaults["icp_fid_match"]) + weights_widgets = [f"{w}_weight" + for w in self._defaults["weights"].keys()] + self._forward_widget_command(weights_widgets, "set_value", + list(self._defaults["weights"].values())) + + def _reset_fiducials(self): + self._set_current_fiducial(self._defaults["fiducial"]) + + def _omit_hsp(self): + self.coreg.omit_head_shape_points(self._omit_hsp_distance / 1e3) + n_omitted = np.sum(~self.coreg._extra_points_filter) + n_remaining = len(self.coreg._dig_dict['hsp']) - n_omitted + self._update_plot("hsp") + self._update_distance_estimation() + self._display_message( + f"{n_omitted} head shape points omitted, " + f"{n_remaining} remaining.") + + def _reset_omit_hsp_filter(self): + self.coreg._extra_points_filter = None + self.coreg._update_params(force_update=True) + self._update_plot("hsp") + self._update_distance_estimation() + n_total = len(self.coreg._dig_dict['hsp']) + self._display_message( + f"No head shape point is omitted, the total is {n_total}.") + + @verbose + def _update_plot(self, changes="all", verbose=None): + # Update list of things that need to be updated/plotted (and maybe + # draw them immediately) + try: + fun_name = inspect.currentframe().f_back.f_back.f_code.co_name + except Exception: # just in case one of these attrs is missing + fun_name = 'unknown' + logger.debug( + f'Updating plots based on {fun_name}: {repr(changes)}') + if self._plot_locked: + return + if self._info is None: + changes = ["head", "mri_fids"] + self._to_cf_t = dict(mri=dict(trans=np.eye(4)), head=None) + else: + self._to_cf_t = _get_transforms_to_coord_frame( + self._info, self.coreg.trans, coord_frame=self._coord_frame) + all_keys = ( + 'head', 'mri_fids', # MRI first + 'hsp', 'hpi', 'eeg', 'head_fids', # then dig + 'helmet', + ) + if changes == 'all': + changes = list(all_keys) + elif changes == 'sensors': + changes = all_keys[2:] # omit MRI ones + elif isinstance(changes, str): + changes = [changes] + changes = set(changes) + # ideally we would maybe have this in: + # with self._redraw_mutex: + # it would reduce "jerkiness" of the updates, but this should at least + # work okay + bad = changes.difference(set(all_keys)) + assert len(bad) == 0, f'Unknown changes: {bad}' + self._redraws_pending.update(changes) + if self._immediate_redraw: + self._redraw() + + @contextmanager + def _lock(self, plot=False, params=False, scale_mode=False, fitting=False): + """Which part of the UI to temporarily disable.""" + if plot: + old_plot_locked = self._plot_locked + self._plot_locked = True + if params: + old_params_locked = self._params_locked + self._params_locked = True + if scale_mode: + old_scale_mode = self.coreg._scale_mode + self.coreg._scale_mode = None + if fitting: + widgets = [ + "sX", "sY", "sZ", + "tX", "tY", "tZ", + "rX", "rY", "rZ", + "fit_icp", "fit_fiducials", "fits_icp", "fits_fiducials" + ] + states = [ + self._forward_widget_command( + w, "is_enabled", None, + input_value=False, output_value=True) + for w in widgets + ] + self._forward_widget_command(widgets, "set_enabled", False) + try: + yield + finally: + if plot: + self._plot_locked = old_plot_locked + if params: + self._params_locked = old_params_locked + if scale_mode: + self.coreg._scale_mode = old_scale_mode + if fitting: + for idx, w in enumerate(widgets): + self._forward_widget_command(w, "set_enabled", states[idx]) + + def _display_message(self, msg=""): + self._forward_widget_command('status_message', 'set_value', msg) + self._forward_widget_command( + 'status_message', 'show', None, input_value=False + ) + self._forward_widget_command( + 'status_message', 'update', None, input_value=False + ) + if msg: + logger.info(msg) + + def _follow_fiducial_view(self): + fid = self._current_fiducial.lower() + view = dict(lpa='left', rpa='right', nasion='front') + kwargs = dict(front=(90., 90.), left=(180, 90), right=(0., 90)) + kwargs = dict(zip(('azimuth', 'elevation'), kwargs[view[fid]])) + if not self._lock_fids: + self._renderer.set_camera(distance=None, **kwargs) + + def _update_fiducials(self): + fid = self._current_fiducial + if not fid: + return + + idx = _map_fid_name_to_idx(name=fid) + val = self.coreg.fiducials.dig[idx]['r'] * 1e3 + + with self._lock(plot=True): + self._forward_widget_command( + ["fid_X", "fid_Y", "fid_Z"], "set_value", val) + + def _update_distance_estimation(self): + value = self.coreg._get_fiducials_distance_str() + '\n' + \ + self.coreg._get_point_distance_str() + dists = self.coreg.compute_dig_mri_distances() * 1e3 + if self._hsp_weight > 0: + value += "\nHSP <-> MRI (mean/min/max): "\ + f"{np.mean(dists):.2f} "\ + f"/ {np.min(dists):.2f} / {np.max(dists):.2f} mm" + self._forward_widget_command("fit_label", "set_value", value) + + def _update_parameters(self): + with self._lock(plot=True, params=True): + # rotation + deg = np.rad2deg(self.coreg._rotation) + logger.debug(f' Rotation: {deg}') + self._forward_widget_command(["rX", "rY", "rZ"], "set_value", deg) + # translation + mm = self.coreg._translation * 1e3 + logger.debug(f' Translation: {mm}') + self._forward_widget_command(["tX", "tY", "tZ"], "set_value", mm) + # scale + sc = self.coreg._scale * 1e2 + logger.debug(f' Scale: {sc}') + self._forward_widget_command(["sX", "sY", "sZ"], "set_value", sc) + + def _reset(self, keep_trans=False): + """Refresh the scene, and optionally reset transformation & scaling. + + Parameters + ---------- + keep_trans : bool + Whether to retain translation, rotation, and scaling; or reset them + to their default values (no translation, no rotation, no scaling). + """ + if not keep_trans: + self.coreg.set_scale(self.coreg._default_parameters[6:9]) + self.coreg.set_rotation(self.coreg._default_parameters[:3]) + self.coreg.set_translation(self.coreg._default_parameters[3:6]) + self._update_plot() + self._update_parameters() + self._update_distance_estimation() + + def _forward_widget_command(self, names, command, value, + input_value=True, output_value=False): + """Invoke a method of one or more widgets if the widgets exist. + + Parameters + ---------- + names : str | array-like of str + The widget names to operate on. + command : str + The method to invoke. + value : object | array-like + The value(s) to pass to the method. + input_value : bool + Whether the ``command`` accepts a ``value``. If ``False``, no + ``value`` will be passed to ``command``. + output_value : bool + Whether to return the return value of ``command``. + + Returns + ------- + ret : object | None + ``None`` if ``output_value`` is ``False``, and the return value of + ``command`` otherwise. + """ + _validate_type( + item=names, + types=(str, list), + item_name='names' + ) + if isinstance(names, str): + names = [names] + + if not isinstance(value, (str, float, int, dict, type(None))): + value = list(value) + assert len(names) == len(value) + + for idx, name in enumerate(names): + val = value[idx] if isinstance(value, list) else value + if name in self._widgets and self._widgets[name] is not None: + if input_value: + ret = getattr(self._widgets[name], command)(val) + else: + ret = getattr(self._widgets[name], command)() + if output_value: + return ret + + def _set_sensors_visibility(self, state): + sensors = ["head_fiducials", "hpi_coils", "head_shape_points", + "eeg_channels"] + for sensor in sensors: + if sensor in self._actors and self._actors[sensor] is not None: + actors = self._actors[sensor] + actors = actors if isinstance(actors, list) else [actors] + for actor in actors: + actor.SetVisibility(state) + self._renderer._update() + + def _update_actor(self, actor_name, actor): + # XXX: internal plotter/renderer should not be exposed + self._renderer.plotter.remove_actor(self._actors.get(actor_name), + render=False) + self._actors[actor_name] = actor + + def _add_mri_fiducials(self): + mri_fids_actors = _plot_mri_fiducials( + self._renderer, self.coreg._fid_points, self._subjects_dir, + self._subject, self._to_cf_t, self._fid_colors) + # disable picking on the markers + for actor in mri_fids_actors: + actor.SetPickable(False) + self._update_actor("mri_fiducials", mri_fids_actors) + + def _add_head_fiducials(self): + head_fids_actors = _plot_head_fiducials( + self._renderer, self._info, self._to_cf_t, self._fid_colors) + self._update_actor("head_fiducials", head_fids_actors) + + def _add_hpi_coils(self): + if self._hpi_coils: + hpi_actors = _plot_hpi_coils( + self._renderer, self._info, self._to_cf_t, + opacity=self._defaults["sensor_opacity"], + scale=DEFAULTS["coreg"]["extra_scale"], + orient_glyphs=self._orient_glyphs, + scale_by_distance=self._scale_by_distance, + surf=self._head_geo, check_inside=self._check_inside, + nearest=self._nearest) + else: + hpi_actors = None + self._update_actor("hpi_coils", hpi_actors) + + def _add_head_shape_points(self): + if self._head_shape_points: + hsp_actors = _plot_head_shape_points( + self._renderer, self._info, self._to_cf_t, + opacity=self._defaults["sensor_opacity"], + orient_glyphs=self._orient_glyphs, + scale_by_distance=self._scale_by_distance, + mark_inside=self._mark_inside, surf=self._head_geo, + mask=self.coreg._extra_points_filter, + check_inside=self._check_inside, nearest=self._nearest) + else: + hsp_actors = None + self._update_actor("head_shape_points", hsp_actors) + + def _add_eeg_channels(self): + if self._eeg_channels: + eeg = ["original"] + picks = pick_types(self._info, eeg=(len(eeg) > 0), fnirs=True) + if len(picks) > 0: + actors = _plot_sensors( + self._renderer, self._info, self._to_cf_t, picks, + meg=False, eeg=eeg, fnirs=["sources", "detectors"], + warn_meg=False, head_surf=self._head_geo, units='m', + sensor_opacity=self._defaults["sensor_opacity"], + orient_glyphs=self._orient_glyphs, + scale_by_distance=self._scale_by_distance, + surf=self._head_geo, check_inside=self._check_inside, + nearest=self._nearest) + sens_actors = actors["eeg"] + sens_actors.extend(actors["fnirs"]) + else: + sens_actors = None + else: + sens_actors = None + self._update_actor("eeg_channels", sens_actors) + + def _add_head_surface(self): + bem = None + if self._head_resolution: + surface = 'head-dense' + key = 'high' + else: + surface = 'head' + key = 'low' + try: + head_actor, head_surf, _ = _plot_head_surface( + self._renderer, surface, self._subject, + self._subjects_dir, bem, self._coord_frame, self._to_cf_t, + alpha=self._head_opacity) + except IOError: + head_actor, head_surf, _ = _plot_head_surface( + self._renderer, "head", self._subject, self._subjects_dir, + bem, self._coord_frame, self._to_cf_t, + alpha=self._head_opacity) + key = 'low' + self._update_actor("head", head_actor) + # mark head surface mesh to restrict picking + head_surf._picking_target = True + # We need to use _get_processed_mri_points to incorporate grow_hair + rr = self.coreg._get_processed_mri_points(key) * self.coreg._scale.T + head_surf.points = rr + head_surf.compute_normals() + self._surfaces["head"] = head_surf + tris = self._surfaces["head"].faces.reshape(-1, 4)[:, 1:] + assert tris.ndim == 2 and tris.shape[1] == 3, tris.shape + nn = self._surfaces["head"].point_normals + assert nn.shape == (len(rr), 3), nn.shape + self._head_geo = dict(rr=rr, tris=tris, nn=nn) + self._check_inside = _CheckInside(head_surf, mode='pyvista') + self._nearest = _DistanceQuery(rr) + + def _add_helmet(self): + if self._helmet: + head_mri_t = _get_trans(self.coreg.trans, 'head', 'mri')[0] + helmet_actor, _, _ = _plot_helmet( + self._renderer, self._info, self._to_cf_t, head_mri_t, + self._coord_frame) + else: + helmet_actor = None + self._update_actor("helmet", helmet_actor) + + def _fit_fiducials(self): + with self._lock(scale_mode=True): + self._fits_fiducials() + + def _fits_fiducials(self): + with self._lock(params=True, fitting=True): + start = time.time() + self.coreg.fit_fiducials( + lpa_weight=self._lpa_weight, + nasion_weight=self._nasion_weight, + rpa_weight=self._rpa_weight, + verbose=self._verbose, + ) + end = time.time() + self._display_message( + f"Fitting fiducials finished in {end - start:.2f} seconds.") + self._update_plot("sensors") + self._update_parameters() + self._update_distance_estimation() + + def _fit_icp(self): + with self._lock(scale_mode=True): + self._fit_icp_real(update_head=False) + + def _fits_icp(self): + self._fit_icp_real(update_head=True) + + def _fit_icp_real(self, *, update_head): + with self._lock(params=True, fitting=True): + self._current_icp_iterations = 0 + updates = ['hsp', 'hpi', 'eeg', 'head_fids'] + if update_head: + updates.insert(0, 'head') + + def callback(iteration, n_iterations): + self._display_message( + f"Fitting ICP - iteration {iteration + 1}") + self._update_plot(updates) + self._current_icp_iterations += 1 + self._update_distance_estimation() + self._update_parameters() + self._renderer._process_events() # allow a draw or cancel + + start = time.time() + self.coreg.fit_icp( + n_iterations=self._icp_n_iterations, + lpa_weight=self._lpa_weight, + nasion_weight=self._nasion_weight, + rpa_weight=self._rpa_weight, + callback=callback, + verbose=self._verbose, + ) + end = time.time() + self._display_message() + self._display_message( + f"Fitting ICP finished in {end - start:.2f} seconds and " + f"{self._current_icp_iterations} iterations.") + del self._current_icp_iterations + + def _task_save_subject(self): + from ..viz.backends.renderer import MNE_3D_BACKEND_TESTING + if MNE_3D_BACKEND_TESTING: + self._save_subject() + else: + self._job_queue.put(_WorkerData("save_subject", None)) + + def _task_set_parameter(self, value, mode_name, coord): + from ..viz.backends.renderer import MNE_3D_BACKEND_TESTING + if MNE_3D_BACKEND_TESTING: + self._set_parameter(value, mode_name, coord, self._plot_locked) + else: + self._parameter_queue.put(_WorkerData("set_parameter", dict( + value=value, mode_name=mode_name, coord=coord, + plot_locked=self._plot_locked))) + + def _overwrite_subject_callback(self, button_name): + if button_name == "Yes": + self._save_subject_callback(overwrite=True) + elif button_name == "Cancel": + self._accept_close_event = False + else: + assert button_name == "No" or button_name == "Discard" + + def _check_subject_exists(self): + if not self._subject_to: + return False + subject_dirname = os.path.join('{subjects_dir}', '{subject}') + dest = subject_dirname.format(subject=self._subject_to, + subjects_dir=self._subjects_dir) + return os.path.exists(dest) + + def _save_subject(self, exit_mode=False): + dialog = "overwrite_subject_exit" if exit_mode else "overwrite_subject" + if self._check_subject_exists(): + self._forward_widget_command(dialog, "show", True) + else: + self._save_subject_callback() + + def _save_subject_callback(self, overwrite=False): + self._display_message(f"Saving {self._subject_to}...") + default_cursor = self._renderer._window_get_cursor() + self._renderer._window_set_cursor( + self._renderer._window_new_cursor("WaitCursor")) + + # prepare bem + bem_names = [] + if self._scale_mode != "None": + can_prepare_bem = _mri_subject_has_bem( + self._subject, self._subjects_dir) + else: + can_prepare_bem = False + if can_prepare_bem: + pattern = bem_fname.format(subjects_dir=self._subjects_dir, + subject=self._subject, + name='(.+-bem)') + bem_dir, pattern = os.path.split(pattern) + for filename in os.listdir(bem_dir): + match = re.match(pattern, filename) + if match: + bem_names.append(match.group(1)) + + # save the scaled MRI + try: + self._display_message(f"Scaling {self._subject_to}...") + scale_mri( + subject_from=self._subject, subject_to=self._subject_to, + scale=self.coreg._scale, overwrite=overwrite, + subjects_dir=self._subjects_dir, skip_fiducials=True, + labels=True, annot=True, on_defects='ignore' + ) + except Exception: + logger.error(f"Error scaling {self._subject_to}") + bem_names = [] + else: + self._display_message(f"Scaling {self._subject_to}... Done!") + + # Precompute BEM solutions + for bem_name in bem_names: + try: + self._display_message(f"Computing {bem_name} solution...") + bem_file = bem_fname.format(subjects_dir=self._subjects_dir, + subject=self._subject_to, + name=bem_name) + bemsol = make_bem_solution(bem_file) + write_bem_solution(bem_file[:-4] + '-sol.fif', bemsol) + except Exception: + logger.error(f"Error computing {bem_name} solution") + else: + self._display_message(f"Computing {bem_name} solution..." + " Done!") + self._display_message(f"Saving {self._subject_to}... Done!") + self._renderer._window_set_cursor(default_cursor) + self._mri_scale_modified = False + + def _save_mri_fiducials(self, fname): + self._display_message(f"Saving {fname}...") + dig_montage = self.coreg.fiducials + write_fiducials( + fname=fname, pts=dig_montage.dig, coord_frame='mri', overwrite=True + ) + self._set_fiducials_file(fname) + self._display_message(f"Saving {fname}... Done!") + self._mri_fids_modified = False + + def _save_trans(self, fname): + write_trans(fname, self.coreg.trans, overwrite=True) + self._display_message( + f"{fname} transform file is saved.") + self._trans_modified = False + + def _load_trans(self, fname): + mri_head_t = _ensure_trans(read_trans(fname, return_all=True), + 'mri', 'head')['trans'] + rot_x, rot_y, rot_z = rotation_angles(mri_head_t) + x, y, z = mri_head_t[:3, 3] + self.coreg._update_params( + rot=np.array([rot_x, rot_y, rot_z]), + tra=np.array([x, y, z]), + ) + self._update_parameters() + self._update_distance_estimation() + self._update_plot() + self._display_message( + f"{fname} transform file is loaded.") + + def _update_fiducials_label(self): + if self._fiducials_file is None: + text = ( + '

No custom MRI fiducials loaded!

' + '

MRI fiducials could not be found in the standard ' + 'location. The displayed initial MRI fiducial locations ' + '(diamonds) were derived from fsaverage. Place, lock, and ' + 'save fiducials to discard this message.

' + ) + else: + assert self._fiducials_file == fid_fname.format( + subjects_dir=self._subjects_dir, subject=self._subject + ) + assert self.coreg._fid_accurate is True + text = ( + f'

MRI fiducials (diamonds) loaded from ' + f'standard location:

' + f'

{self._fiducials_file}

' + ) + + self._forward_widget_command( + 'mri_fiducials_label', 'set_value', text + ) + + def _configure_dock(self): + if self._renderer._kind == 'notebook': + collapse = True # collapsible and collapsed + else: + collapse = None # not collapsible + self._renderer._dock_initialize( + name="Input", area="left", max_width="350px" + ) + mri_subject_layout = self._renderer._dock_add_group_box( + name="MRI Subject", + collapse=collapse, + ) + subjects_dir_layout = self._renderer._dock_add_layout( + vertical=False + ) + self._widgets["subjects_dir_field"] = self._renderer._dock_add_text( + name="subjects_dir_field", + value=self._subjects_dir, + placeholder="Subjects Directory", + callback=self._set_subjects_dir, + layout=subjects_dir_layout, + ) + self._widgets["subjects_dir"] = self._renderer._dock_add_file_button( + name="subjects_dir", + desc="Load", + func=self._set_subjects_dir, + is_directory=True, + icon=True, + tooltip="Load the path to the directory containing the " + "FreeSurfer subjects", + layout=subjects_dir_layout, + ) + self._renderer._layout_add_widget( + layout=mri_subject_layout, + widget=subjects_dir_layout, + ) + self._widgets["subject"] = self._renderer._dock_add_combo_box( + name="Subject", + value=self._subject, + rng=_get_subjects(self._subjects_dir), + callback=self._set_subject, + compact=True, + tooltip="Select the FreeSurfer subject name", + layout=mri_subject_layout, + ) + + mri_fiducials_layout = self._renderer._dock_add_group_box( + name="MRI Fiducials", + collapse=collapse, + ) + # Add MRI fiducials I/O widgets + self._widgets['mri_fiducials_label'] = self._renderer._dock_add_label( + value='', # Will be filled via _update_fiducials_label() + layout=mri_fiducials_layout, + selectable=True + ) + # Reload & Save buttons go into their own layout widget + mri_fiducials_button_layout = self._renderer._dock_add_layout( + vertical=False + ) + self._renderer._layout_add_widget( + layout=mri_fiducials_layout, + widget=mri_fiducials_button_layout + ) + self._widgets["reload_mri_fids"] = self._renderer._dock_add_button( + name='Reload MRI Fid.', + callback=lambda: self._set_fiducials_file(self._fiducials_file), + tooltip="Reload MRI fiducials from the standard location", + layout=mri_fiducials_button_layout, + ) + # Disable reload button until we've actually loaded a fiducial file + # (happens in _set_fiducials_file method) + self._forward_widget_command('reload_mri_fids', 'set_enabled', False) + + self._widgets["save_mri_fids"] = self._renderer._dock_add_button( + name="Save MRI Fid.", + callback=lambda: self._save_mri_fiducials( + fid_fname.format( + subjects_dir=self._subjects_dir, subject=self._subject + ) + ), + tooltip="Save MRI fiducials to the standard location. Fiducials " + "must be locked first!", + layout=mri_fiducials_button_layout, + ) + self._widgets["lock_fids"] = self._renderer._dock_add_check_box( + name="Lock fiducials", + value=self._lock_fids, + callback=self._set_lock_fids, + tooltip="Lock/Unlock interactive fiducial editing", + layout=mri_fiducials_layout, + ) + self._widgets["fids"] = self._renderer._dock_add_radio_buttons( + value=self._defaults["fiducial"], + rng=self._defaults["fiducials"], + callback=self._set_current_fiducial, + vertical=False, + layout=mri_fiducials_layout, + ) + fiducial_coords_layout = self._renderer._dock_add_layout() + for coord in ("X", "Y", "Z"): + name = f"fid_{coord}" + self._widgets[name] = self._renderer._dock_add_spin_box( + name=coord, + value=0., + rng=[-1e3, 1e3], + callback=partial( + self._set_fiducial, + coord=coord, + ), + compact=True, + double=True, + step=1, + tooltip=f"Set the {coord} fiducial coordinate", + layout=fiducial_coords_layout, + ) + self._renderer._layout_add_widget( + mri_fiducials_layout, fiducial_coords_layout) + + dig_source_layout = self._renderer._dock_add_group_box( + name="Info source with digitization", + collapse=collapse, + ) + info_file_layout = self._renderer._dock_add_layout( + vertical=False + ) + self._widgets["info_file_field"] = self._renderer._dock_add_text( + name="info_file_field", + value=self._info_file, + placeholder="Path to info", + callback=self._set_info_file, + layout=info_file_layout, + ) + self._widgets["info_file"] = self._renderer._dock_add_file_button( + name="info_file", + desc="Load", + func=self._set_info_file, + icon=True, + tooltip="Load the FIFF file with digitization data for " + "coregistration", + layout=info_file_layout, + ) + self._renderer._layout_add_widget( + layout=dig_source_layout, + widget=info_file_layout, + ) + self._widgets["grow_hair"] = self._renderer._dock_add_spin_box( + name="Grow Hair (mm)", + value=self._grow_hair, + rng=[0.0, 10.0], + callback=self._set_grow_hair, + tooltip="Compensate for hair on the digitizer head shape", + layout=dig_source_layout, + ) + omit_hsp_layout_1 = self._renderer._dock_add_layout(vertical=False) + omit_hsp_layout_2 = self._renderer._dock_add_layout(vertical=False) + self._widgets["omit_distance"] = self._renderer._dock_add_spin_box( + name="Omit Distance (mm)", + value=self._omit_hsp_distance, + rng=[0.0, 100.0], + callback=self._set_omit_hsp_distance, + tooltip="Set the head shape points exclusion distance", + layout=omit_hsp_layout_1, + ) + self._widgets["omit"] = self._renderer._dock_add_button( + name="Omit", + callback=self._omit_hsp, + tooltip="Exclude the head shape points that are far away from " + "the MRI head", + layout=omit_hsp_layout_2, + ) + self._widgets["reset_omit"] = self._renderer._dock_add_button( + name="Reset", + callback=self._reset_omit_hsp_filter, + tooltip="Reset all excluded head shape points", + layout=omit_hsp_layout_2, + ) + self._renderer._layout_add_widget(dig_source_layout, omit_hsp_layout_1) + self._renderer._layout_add_widget(dig_source_layout, omit_hsp_layout_2) + + view_options_layout = self._renderer._dock_add_group_box( + name="View Options", + collapse=collapse, + ) + self._widgets["helmet"] = self._renderer._dock_add_check_box( + name="Show MEG helmet", + value=self._helmet, + callback=self._set_helmet, + tooltip="Enable/Disable MEG helmet", + layout=view_options_layout, + ) + self._widgets["high_res_head"] = self._renderer._dock_add_check_box( + name="Show high-resolution head", + value=self._head_resolution, + callback=self._set_head_resolution, + tooltip="Enable/Disable high resolution head surface", + layout=view_options_layout, + ) + self._widgets["head_opacity"] = self._renderer._dock_add_slider( + name="Head opacity", + value=self._head_opacity, + rng=[0.25, 1.0], + callback=self._set_head_opacity, + compact=True, + double=True, + layout=view_options_layout, + ) + self._renderer._dock_add_stretch() + + self._renderer._dock_initialize( + name="Parameters", area="right", max_width="350px" + ) + mri_scaling_layout = self._renderer._dock_add_group_box( + name="MRI Scaling", + collapse=collapse, + ) + self._widgets["scaling_mode"] = self._renderer._dock_add_combo_box( + name="Scaling Mode", + value=self._defaults["scale_mode"], + rng=self._defaults["scale_modes"], + callback=self._set_scale_mode, + tooltip="Select the scaling mode", + compact=True, + layout=mri_scaling_layout, + ) + scale_params_layout = self._renderer._dock_add_group_box( + name="Scaling Parameters", + layout=mri_scaling_layout, + ) + coords = ["X", "Y", "Z"] + for coord in coords: + name = f"s{coord}" + attr = getattr(self.coreg, "_scale") + self._widgets[name] = self._renderer._dock_add_spin_box( + name=name, + value=attr[coords.index(coord)] * 1e2, + rng=[1., 10000.], # percent + callback=partial( + self._set_parameter, + mode_name="scale", + coord=coord, + ), + compact=True, + double=True, + step=1, + tooltip=f"Set the {coord} scaling parameter (in %)", + layout=scale_params_layout, + ) + + fit_scale_layout = self._renderer._dock_add_layout(vertical=False) + self._widgets["fits_fiducials"] = self._renderer._dock_add_button( + name="Fit fiducials with scaling", + callback=self._fits_fiducials, + tooltip="Find MRI scaling, rotation, and translation to fit all " + "3 fiducials", + layout=fit_scale_layout, + ) + self._widgets["fits_icp"] = self._renderer._dock_add_button( + name="Fit ICP with scaling", + callback=self._fits_icp, + tooltip="Find MRI scaling, rotation, and translation to match the " + "head shape points", + layout=fit_scale_layout, + ) + self._renderer._layout_add_widget( + scale_params_layout, fit_scale_layout) + subject_to_layout = self._renderer._dock_add_layout(vertical=False) + self._widgets["subject_to"] = self._renderer._dock_add_text( + name="subject-to", + value=self._subject_to, + placeholder="subject name", + callback=self._set_subject_to, + layout=subject_to_layout, + ) + self._widgets["save_subject"] = self._renderer._dock_add_button( + name="Save scaled anatomy", + callback=self._task_save_subject, + tooltip="Save scaled anatomy", + layout=subject_to_layout, + ) + self._renderer._layout_add_widget( + mri_scaling_layout, subject_to_layout) + param_layout = self._renderer._dock_add_group_box( + name="Translation (t) and Rotation (r)", + collapse=collapse, + ) + for coord in coords: + coord_layout = self._renderer._dock_add_layout(vertical=False) + for mode, mode_name in (("t", "Translation"), ("r", "Rotation")): + name = f"{mode}{coord}" + attr = getattr(self.coreg, f"_{mode_name.lower()}") + rng = [-360, 360] if mode_name == "Rotation" else [-100, 100] + unit = "°" if mode_name == "Rotation" else "mm" + self._widgets[name] = self._renderer._dock_add_spin_box( + name=name, + value=attr[coords.index(coord)] * 1e3, + rng=np.array(rng), + callback=partial( + self._task_set_parameter, + mode_name=mode_name.lower(), + coord=coord, + ), + compact=True, + double=True, + step=1, + tooltip=f"Set the {coord} {mode_name.lower()}" + f" parameter (in {unit})", + layout=coord_layout + ) + self._renderer._layout_add_widget(param_layout, coord_layout) + + fit_layout = self._renderer._dock_add_layout(vertical=False) + self._widgets["fit_fiducials"] = self._renderer._dock_add_button( + name="Fit fiducials", + callback=self._fit_fiducials, + tooltip="Find rotation and translation to fit all 3 fiducials", + layout=fit_layout, + ) + self._widgets["fit_icp"] = self._renderer._dock_add_button( + name="Fit ICP", + callback=self._fit_icp, + tooltip="Find rotation and translation to match the " + "head shape points", + layout=fit_layout, + ) + self._renderer._layout_add_widget(param_layout, fit_layout) + trans_layout = self._renderer._dock_add_group_box( + name="HEAD <> MRI Transform", + collapse=collapse, + ) + save_trans_layout = self._renderer._dock_add_layout(vertical=False) + self._widgets["save_trans"] = self._renderer._dock_add_file_button( + name="save_trans", + desc="Save...", + save=True, + func=self._save_trans, + tooltip="Save the transform file to disk", + layout=save_trans_layout, + filter='Head->MRI transformation (*-trans.fif *_trans.fif)', + initial_directory=str(Path(self._info_file).parent), + ) + self._widgets["load_trans"] = self._renderer._dock_add_file_button( + name="load_trans", + desc="Load...", + func=self._load_trans, + tooltip="Load the transform file from disk", + layout=save_trans_layout, + filter='Head->MRI transformation (*-trans.fif *_trans.fif)', + initial_directory=str(Path(self._info_file).parent), + ) + self._renderer._layout_add_widget(trans_layout, save_trans_layout) + self._widgets["reset_trans"] = self._renderer._dock_add_button( + name="Reset Parameters", + callback=self._reset, + tooltip="Reset all the parameters affecting the coregistration", + layout=trans_layout, + ) + + fitting_options_layout = self._renderer._dock_add_group_box( + name="Fitting Options", + collapse=collapse, + ) + self._widgets["fit_label"] = self._renderer._dock_add_label( + value="", + layout=fitting_options_layout, + ) + self._widgets["icp_n_iterations"] = self._renderer._dock_add_spin_box( + name="Number Of ICP Iterations", + value=self._defaults["icp_n_iterations"], + rng=[1, 100], + callback=self._set_icp_n_iterations, + compact=True, + double=False, + tooltip="Set the number of ICP iterations", + layout=fitting_options_layout, + ) + self._widgets["icp_fid_match"] = self._renderer._dock_add_combo_box( + name="Fiducial point matching", + value=self._defaults["icp_fid_match"], + rng=self._defaults["icp_fid_matches"], + callback=self._set_icp_fid_match, + compact=True, + tooltip="Select the fiducial point matching method", + layout=fitting_options_layout, + ) + weights_layout = self._renderer._dock_add_group_box( + name="Weights", + layout=fitting_options_layout, + ) + for point, fid in zip(("HSP", "EEG", "HPI"), + self._defaults["fiducials"]): + weight_layout = self._renderer._dock_add_layout(vertical=False) + point_lower = point.lower() + name = f"{point_lower}_weight" + self._widgets[name] = self._renderer._dock_add_spin_box( + name=point, + value=getattr(self, f"_{point_lower}_weight"), + rng=[0., 100.], + callback=partial(self._set_point_weight, point=point_lower), + compact=True, + double=True, + tooltip=f"Set the {point} weight", + layout=weight_layout, + ) + + fid_lower = fid.lower() + name = f"{fid_lower}_weight" + self._widgets[name] = self._renderer._dock_add_spin_box( + name=fid, + value=getattr(self, f"_{fid_lower}_weight"), + rng=[0., 100.], + callback=partial(self._set_point_weight, point=fid_lower), + compact=True, + double=True, + tooltip=f"Set the {fid} weight", + layout=weight_layout, + ) + self._renderer._layout_add_widget(weights_layout, weight_layout) + self._widgets['reset_fitting_options'] = ( + self._renderer._dock_add_button( + name="Reset Fitting Options", + callback=self._reset_fitting_parameters, + tooltip="Reset all the fitting parameters to default value", + layout=fitting_options_layout, + ) + ) + self._renderer._dock_add_stretch() + + def _configure_status_bar(self): + self._renderer._status_bar_initialize() + self._widgets['status_message'] = self._renderer._status_bar_add_label( + "", stretch=1 + ) + self._forward_widget_command( + 'status_message', 'hide', value=None, input_value=False + ) + + def _clean(self): + if not self._accept_close_event: + return + self._renderer = None + self._widgets.clear() + self._actors.clear() + self._surfaces.clear() + self._defaults.clear() + self._head_geo = None + self._check_inside = None + self._nearest = None + self._redraw = None + + @safe_event + def close(self): + """Close interface and cleanup data structure.""" + if self._renderer is not None: + self._renderer.close() + + def _close_dialog_callback(self, button_name): + from ..viz.backends.renderer import MNE_3D_BACKEND_TESTING + self._accept_close_event = True + if button_name == "Save": + if self._trans_modified: + self._forward_widget_command( + "save_trans", "set_value", None) + # cancel means _save_trans is not called + if self._trans_modified: + self._accept_close_event = False + if self._mri_fids_modified: + self._forward_widget_command( + "save_mri_fids", "set_value", None) + if self._mri_scale_modified: + if self._subject_to: + self._save_subject(exit_mode=True) + else: + dialog = self._renderer._dialog_create( + title="CoregistrationUI", + text="The name of the output subject used to " + "save the scaled anatomy is not set.", + info_text="Please set a subject name", + callback=lambda x: None, + buttons=["Ok"], + modal=not MNE_3D_BACKEND_TESTING, + ) + dialog.show() + self._accept_close_event = False + elif button_name == "Cancel": + self._accept_close_event = False + else: + assert button_name == "Discard" + + def _close_callback(self): + if self._trans_modified or self._mri_fids_modified or \ + self._mri_scale_modified: + from ..viz.backends.renderer import MNE_3D_BACKEND_TESTING + # prepare the dialog's text + text = "The following is/are not saved:" + text += "
    " + if self._trans_modified: + text += "
  • Head<>MRI transform
    • " + if self._mri_fids_modified: + text += "
  • MRI fiducials
    • " + if self._mri_scale_modified: + text += "
  • scaled subject MRI
    • " + text += "
" + self._widgets["close_dialog"] = self._renderer._dialog_create( + title="CoregistrationUI", + text=text, + info_text="Do you want to save?", + callback=self._close_dialog_callback, + buttons=["Save", "Discard", "Cancel"], + # modal=True means that the dialog blocks the application + # when show() is called, until one of the buttons is clicked + modal=not MNE_3D_BACKEND_TESTING, + ) + self._widgets["close_dialog"].show() + return self._accept_close_event diff -Nru python-mne-0.23.4+dfsg/mne/gui/_core.py python-mne-1.1.0+dfsg/mne/gui/_core.py --- python-mne-0.23.4+dfsg/mne/gui/_core.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/_core.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,483 @@ +# -*- coding: utf-8 -*- +"""Shared GUI classes and functions.""" + +# Authors: Alex Rockhill +# +# License: BSD (3-clause) + +import os +import os.path as op +import numpy as np +from functools import partial + +from qtpy import QtCore +from qtpy.QtCore import Slot +from qtpy.QtWidgets import (QMainWindow, QGridLayout, + QVBoxLayout, QHBoxLayout, QLabel, + QMessageBox, QWidget, QPlainTextEdit) + +from matplotlib import patheffects +from matplotlib.backends.backend_qt5agg import FigureCanvas +from matplotlib.figure import Figure +from matplotlib.patches import Rectangle + +from .._freesurfer import _import_nibabel +from ..viz.backends.renderer import _get_renderer +from ..viz.utils import safe_event +from ..surface import _read_mri_surface, _marching_cubes +from ..transforms import apply_trans, _frame_to_str +from ..utils import logger, _check_fname, verbose, warn, get_subjects_dir + +_IMG_LABELS = [['I', 'P'], ['I', 'L'], ['P', 'L']] +_ZOOM_STEP_SIZE = 5 + + +@verbose +def _load_image(img, verbose=None): + """Load data from a 3D image file (e.g. CT, MR).""" + nib = _import_nibabel('use GUI') + if not isinstance(img, nib.spatialimages.SpatialImage): + logger.info(f'Loading {img}') + _check_fname(img, overwrite='read', must_exist=True) + img = nib.load(img) + # get data + orig_data = np.array(img.dataobj).astype(np.float32) + # reorient data to RAS + ornt = nib.orientations.axcodes2ornt( + nib.orientations.aff2axcodes(img.affine)).astype(int) + ras_ornt = nib.orientations.axcodes2ornt('RAS') + ornt_trans = nib.orientations.ornt_transform(ornt, ras_ornt) + img_data = nib.orientations.apply_orientation(orig_data, ornt_trans) + orig_mgh = nib.MGHImage(orig_data, img.affine) + aff_trans = nib.orientations.inv_ornt_aff(ornt_trans, img.shape) + vox_ras_t = np.dot(orig_mgh.header.get_vox2ras_tkr(), aff_trans) + return img_data, vox_ras_t + + +def _make_slice_plot(width=4, height=4, dpi=300): + fig = Figure(figsize=(width, height), dpi=dpi) + canvas = FigureCanvas(fig) + ax = fig.subplots() + fig.subplots_adjust(bottom=0, left=0, right=1, top=1, wspace=0, hspace=0) + ax.set_facecolor('k') + # clean up excess plot text, invert + ax.invert_yaxis() + ax.set_xticks([]) + ax.set_yticks([]) + return canvas, fig + + +class SliceBrowser(QMainWindow): + """Navigate between slices of an MRI, CT, etc. image.""" + + _xy_idx = ( + (1, 2), + (0, 2), + (0, 1), + ) + + def __init__(self, base_image=None, subject=None, subjects_dir=None, + verbose=None): + """GUI for browsing slices of anatomical images.""" + # initialize QMainWindow class + super(SliceBrowser, self).__init__() + + self._verbose = verbose + # if bad/None subject, will raise an informative error when loading MRI + subject = os.environ.get('SUBJECT') if subject is None else subject + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + self._subject_dir = op.join(subjects_dir, subject) + self._load_image_data(base_image=base_image) + + # GUI design + + # Main plots: make one plot for each view; sagittal, coronal, axial + self._plt_grid = QGridLayout() + self._figs = list() + for i in range(3): + canvas, fig = _make_slice_plot() + self._plt_grid.addWidget(canvas, i // 2, i % 2) + self._figs.append(fig) + self._renderer = _get_renderer( + name='Slice Browser', size=(400, 400), bgcolor='w') + self._plt_grid.addWidget(self._renderer.plotter, 1, 1) + + self._set_ras([0., 0., 0.], update_plots=False) + + self._plot_images() + + self._configure_ui() + + def _configure_ui(self): + bottom_hbox = self._configure_status_bar() + + # Put everything together + plot_ch_hbox = QHBoxLayout() + plot_ch_hbox.addLayout(self._plt_grid) + + main_vbox = QVBoxLayout() + main_vbox.addLayout(plot_ch_hbox) + main_vbox.addLayout(bottom_hbox) + + central_widget = QWidget() + central_widget.setLayout(main_vbox) + self.setCentralWidget(central_widget) + + def _load_image_data(self, base_image=None): + """Get image data to display and transforms to/from vox/RAS.""" + # allows recon-all not to be finished (T1 made in a few minutes) + mri_img = 'brain' if op.isfile(op.join( + self._subject_dir, 'mri', 'brain.mgz')) else 'T1' + self._mri_data, self._vox_ras_t = _load_image( + op.join(self._subject_dir, 'mri', f'{mri_img}.mgz')) + self._ras_vox_t = np.linalg.inv(self._vox_ras_t) + + self._voxel_sizes = np.array(self._mri_data.shape) + + # We need our extents to land the centers of each pixel on the voxel + # number. This code assumes 1mm isotropic... + img_delta = 0.5 + self._img_extents = list( + [-img_delta, self._voxel_sizes[idx[0]] - img_delta, + -img_delta, self._voxel_sizes[idx[1]] - img_delta] + for idx in self._xy_idx) + + # ready alternate base image if provided, otherwise use brain/T1 + if base_image is None: + self._base_data = self._mri_data + else: + self._base_data, vox_ras_t = _load_image(base_image) + if self._mri_data.shape != self._base_data.shape or \ + not np.allclose(self._vox_ras_t, vox_ras_t, rtol=1e-6): + raise ValueError('Base image is not aligned to MRI, got ' + f'Base shape={self._base_data.shape}, ' + f'MRI shape={self._mri_data.shape}, ' + f'Base affine={vox_ras_t} and ' + f'MRI affine={self._vox_ras_t}') + + if op.exists(op.join(self._subject_dir, 'surf', 'lh.seghead')): + self._head = _read_mri_surface( + op.join(self._subject_dir, 'surf', 'lh.seghead')) + assert _frame_to_str[self._head['coord_frame']] == 'mri' + else: + warn('`seghead` not found, using marching cubes on CT for ' + 'head plot, use :ref:`mne.bem.make_scalp_surfaces` ' + 'to add the scalp surface instead of skull from the CT') + self._head = None + if op.exists(op.join(self._subject_dir, 'surf', 'lh.pial')): + self._lh = _read_mri_surface( + op.join(self._subject_dir, 'surf', 'lh.pial')) + assert _frame_to_str[self._lh['coord_frame']] == 'mri' + self._rh = _read_mri_surface( + op.join(self._subject_dir, 'surf', 'rh.pial')) + assert _frame_to_str[self._rh['coord_frame']] == 'mri' + else: + warn('`pial` surface not found, skipping adding to 3D ' + 'plot. This indicates the Freesurfer recon-all ' + 'has not finished or has been modified and ' + 'these files have been deleted.') + self._lh = self._rh = None + + def _plot_images(self): + """Use the MRI or CT to make plots.""" + # Plot sagittal (0), coronal (1) or axial (2) view + self._images = dict(base=list(), cursor_v=list(), cursor_h=list(), + bounds=list()) + img_min = np.nanmin(self._base_data) + img_max = np.nanmax(self._base_data) + text_kwargs = dict(fontsize='medium', weight='bold', color='#66CCEE', + family='monospace', ha='center', va='center', + path_effects=[patheffects.withStroke( + linewidth=4, foreground="k", alpha=0.75)]) + xyz = apply_trans(self._ras_vox_t, self._ras) + for axis in range(3): + plot_x_idx, plot_y_idx = self._xy_idx[axis] + fig = self._figs[axis] + ax = fig.axes[0] + img_data = np.take(self._base_data, self._current_slice[axis], + axis=axis).T + self._images['base'].append(ax.imshow( + img_data, cmap='gray', aspect='auto', zorder=1, + vmin=img_min, vmax=img_max)) + img_extent = self._img_extents[axis] # x0, x1, y0, y1 + w, h = np.diff(np.array(img_extent).reshape(2, 2), axis=1)[:, 0] + self._images['bounds'].append(Rectangle( + img_extent[::2], w, h, edgecolor='w', facecolor='none', + alpha=0.25, lw=0.5, zorder=1.5)) + ax.add_patch(self._images['bounds'][-1]) + v_x = (xyz[plot_x_idx],) * 2 + v_y = img_extent[2:4] + self._images['cursor_v'].append(ax.plot( + v_x, v_y, color='lime', linewidth=0.5, alpha=0.5, zorder=8)[0]) + h_y = (xyz[plot_y_idx],) * 2 + h_x = img_extent[0:2] + self._images['cursor_h'].append(ax.plot( + h_x, h_y, color='lime', linewidth=0.5, alpha=0.5, zorder=8)[0]) + # label axes + self._figs[axis].text(0.5, 0.05, _IMG_LABELS[axis][0], + **text_kwargs) + self._figs[axis].text(0.05, 0.5, _IMG_LABELS[axis][1], + **text_kwargs) + self._figs[axis].axes[0].axis(img_extent) + self._figs[axis].canvas.mpl_connect( + 'scroll_event', self._on_scroll) + self._figs[axis].canvas.mpl_connect( + 'button_release_event', partial(self._on_click, axis=axis)) + # add head and brain in mm (convert from m) + if self._head is None: + logger.info('Using marching cubes on CT for the ' + '3D visualization panel') + rr, tris = _marching_cubes(np.where( + self._base_data < np.quantile(self._base_data, 0.95), 0, 1), + [1])[0] + rr = apply_trans(self._vox_ras_t, rr) + self._renderer.mesh( + *rr.T, triangles=tris, color='gray', opacity=0.2, + reset_camera=False, render=False) + else: + self._renderer.mesh( + *self._head['rr'].T * 1000, triangles=self._head['tris'], + color='gray', opacity=0.2, reset_camera=False, render=False) + if self._lh is not None and self._rh is not None: + self._renderer.mesh( + *self._lh['rr'].T * 1000, triangles=self._lh['tris'], + color='white', opacity=0.2, reset_camera=False, render=False) + self._renderer.mesh( + *self._rh['rr'].T * 1000, triangles=self._rh['tris'], + color='white', opacity=0.2, reset_camera=False, render=False) + self._renderer.set_camera(azimuth=90, elevation=90, distance=300, + focalpoint=tuple(self._ras)) + # update plots + self._draw() + self._renderer._update() + + def _configure_status_bar(self, hbox=None): + """Make a bar at the bottom with information in it.""" + hbox = QHBoxLayout() if hbox is None else hbox + + self._intensity_label = QLabel('') # update later + hbox.addWidget(self._intensity_label) + + VOX_label = QLabel('VOX =') + self._VOX_textbox = QPlainTextEdit('') # update later + self._VOX_textbox.setMaximumHeight(25) + self._VOX_textbox.setMaximumWidth(125) + self._VOX_textbox.focusOutEvent = self._update_VOX + self._VOX_textbox.textChanged.connect(self._check_update_VOX) + hbox.addWidget(VOX_label) + hbox.addWidget(self._VOX_textbox) + + RAS_label = QLabel('RAS =') + self._RAS_textbox = QPlainTextEdit('') # update later + self._RAS_textbox.setMaximumHeight(25) + self._RAS_textbox.setMaximumWidth(200) + self._RAS_textbox.focusOutEvent = self._update_RAS + self._RAS_textbox.textChanged.connect(self._check_update_RAS) + hbox.addWidget(RAS_label) + hbox.addWidget(self._RAS_textbox) + self._update_moved() # update text now + return hbox + + def _update_camera(self, render=False): + """Update the camera position.""" + self._renderer.set_camera( + # needs fix, distance moves when focal point updates + distance=self._renderer.plotter.camera.distance * 0.9, + focalpoint=tuple(self._ras), + reset_camera=False) + + def _on_scroll(self, event): + """Process mouse scroll wheel event to zoom.""" + self._zoom(event.step, draw=True) + + def _zoom(self, sign=1, draw=False): + """Zoom in on the image.""" + delta = _ZOOM_STEP_SIZE * sign + for axis, fig in enumerate(self._figs): + xmid = self._images['cursor_v'][axis].get_xdata()[0] + ymid = self._images['cursor_h'][axis].get_ydata()[0] + xmin, xmax = fig.axes[0].get_xlim() + ymin, ymax = fig.axes[0].get_ylim() + xwidth = (xmax - xmin) / 2 - delta + ywidth = (ymax - ymin) / 2 - delta + if xwidth <= 0 or ywidth <= 0: + return + fig.axes[0].set_xlim(xmid - xwidth, xmid + xwidth) + fig.axes[0].set_ylim(ymid - ywidth, ymid + ywidth) + if draw: + self._figs[axis].canvas.draw() + + @Slot() + def _update_RAS(self, event): + """Interpret user input to the RAS textbox.""" + text = self._RAS_textbox.toPlainText() + ras = self._convert_text(text, 'ras') + if ras is not None: + self._set_ras(ras) + + @Slot() + def _update_VOX(self, event): + """Interpret user input to the RAS textbox.""" + text = self._VOX_textbox.toPlainText() + ras = self._convert_text(text, 'vox') + if ras is not None: + self._set_ras(ras) + + def _convert_text(self, text, text_kind): + text = text.replace('\n', '') + vals = text.split(',') + if len(vals) != 3: + vals = text.split(' ') # spaces also okay as in freesurfer + vals = [var.lstrip().rstrip() for var in vals] + try: + vals = np.array([float(var) for var in vals]).reshape(3) + except Exception: + self._update_moved() # resets RAS label + return + if text_kind == 'vox': + vox = vals + ras = apply_trans(self._vox_ras_t, vox) + else: + assert text_kind == 'ras' + ras = vals + vox = apply_trans(self._ras_vox_t, ras) + wrong_size = any(var < 0 or var > n - 1 for var, n in + zip(vox, self._voxel_sizes)) + if wrong_size: + self._update_moved() # resets RAS label + return + return ras + + @property + def _ras(self): + return self._ras_safe + + def _set_ras(self, ras, update_plots=True): + ras = np.asarray(ras, dtype=float) + assert ras.shape == (3,) + msg = ', '.join(f'{x:0.2f}' for x in ras) + logger.debug(f'Trying RAS: ({msg}) mm') + # clip to valid + vox = apply_trans(self._ras_vox_t, ras) + vox = np.array([ + np.clip(d, 0, self._voxel_sizes[ii] - 1) + for ii, d in enumerate(vox)]) + # transform back, make write-only + self._ras_safe = apply_trans(self._vox_ras_t, vox) + self._ras_safe.flags['WRITEABLE'] = False + msg = ', '.join(f'{x:0.2f}' for x in self._ras_safe) + logger.debug(f'Setting RAS: ({msg}) mm') + if update_plots: + self._move_cursors_to_pos() + + @property + def _vox(self): + return apply_trans(self._ras_vox_t, self._ras) + + @property + def _current_slice(self): + return self._vox.round().astype(int) + + @Slot() + def _check_update_RAS(self): + """Check whether the RAS textbox is done being edited.""" + if '\n' in self._RAS_textbox.toPlainText(): + self._update_RAS(event=None) + + @Slot() + def _check_update_VOX(self): + """Check whether the VOX textbox is done being edited.""" + if '\n' in self._VOX_textbox.toPlainText(): + self._update_VOX(event=None) + + def _draw(self, axis=None): + """Update the figures with a draw call.""" + for axis in (range(3) if axis is None else [axis]): + self._figs[axis].canvas.draw() + + def _update_base_images(self, axis=None, draw=False): + """Update the base images.""" + for axis in range(3) if axis is None else [axis]: + img_data = np.take(self._base_data, self._current_slice[axis], + axis=axis).T + self._images['base'][axis].set_data(img_data) + if draw: + self._draw(axis) + + def _update_images(self, axis=None, draw=True): + """Update CT and channel images when general changes happen.""" + self._update_base_images(axis=axis) + if draw: + self._draw(axis) + + def _move_cursors_to_pos(self): + """Move the cursors to a position.""" + for axis in range(3): + x, y = self._vox[list(self._xy_idx[axis])] + self._images['cursor_v'][axis].set_xdata([x, x]) + self._images['cursor_h'][axis].set_ydata([y, y]) + self._zoom(0) # doesn't actually zoom just resets view to center + self._update_images(draw=True) + self._update_moved() + + def _show_help(self): + """Show the help menu.""" + QMessageBox.information( + self, 'Help', + "Help:\n" + "'+'/'-': zoom\nleft/right arrow: left/right\n" + "up/down arrow: superior/inferior\n" + "left angle bracket/right angle bracket: anterior/posterior") + + def _key_press_event(self, event): + """Execute functions when the user presses a key.""" + if event.key() == 'escape': + self.close() + + if event.text() == 'h': + self._show_help() + + if event.text() in ('=', '+', '-'): + self._zoom(sign=-2 * (event.text() == '-') + 1, draw=True) + + # Changing slices + if event.key() in (QtCore.Qt.Key_Up, QtCore.Qt.Key_Down, + QtCore.Qt.Key_Left, QtCore.Qt.Key_Right, + QtCore.Qt.Key_Comma, QtCore.Qt.Key_Period, + QtCore.Qt.Key_PageUp, QtCore.Qt.Key_PageDown): + ras = np.array(self._ras) + if event.key() in (QtCore.Qt.Key_Up, QtCore.Qt.Key_Down): + ras[2] += 2 * (event.key() == QtCore.Qt.Key_Up) - 1 + elif event.key() in (QtCore.Qt.Key_Left, QtCore.Qt.Key_Right): + ras[0] += 2 * (event.key() == QtCore.Qt.Key_Right) - 1 + else: + ras[1] += 2 * (event.key() == QtCore.Qt.Key_PageUp or + event.key() == QtCore.Qt.Key_Period) - 1 + self._set_ras(ras) + + def _on_click(self, event, axis): + """Move to view on MRI and CT on click.""" + if event.inaxes is self._figs[axis].axes[0]: + # Data coordinates are voxel coordinates + pos = (event.xdata, event.ydata) + logger.info(f'Clicked {"XYZ"[axis]} ({axis}) axis at pos {pos}') + xyz = self._vox + xyz[list(self._xy_idx[axis])] = pos + logger.debug(f'Using voxel {list(xyz)}') + ras = apply_trans(self._vox_ras_t, xyz) + self._set_ras(ras) + + def _update_moved(self): + """Update when cursor position changes.""" + self._RAS_textbox.setPlainText('{:.2f}, {:.2f}, {:.2f}'.format( + *self._ras)) + self._VOX_textbox.setPlainText('{:3d}, {:3d}, {:3d}'.format( + *self._current_slice)) + self._intensity_label.setText('intensity = {:.2f}'.format( + self._base_data[tuple(self._current_slice)])) + + @safe_event + def closeEvent(self, event): + """Clean up upon closing the window.""" + self._renderer.plotter.close() + self.close() diff -Nru python-mne-0.23.4+dfsg/mne/gui/_fiducials_gui.py python-mne-1.1.0+dfsg/mne/gui/_fiducials_gui.py --- python-mne-0.23.4+dfsg/mne/gui/_fiducials_gui.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/_fiducials_gui.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,515 +0,0 @@ -# -*- coding: utf-8 -*- -"""Mayavi/traits GUI for setting MRI fiducials.""" - -# Authors: Christian Brodbeck -# -# License: BSD (3-clause) - -import os - -from mayavi.core.ui.mayavi_scene import MayaviScene -from mayavi.tools.mlab_scene_model import MlabSceneModel -import numpy as np -from pyface.api import confirm, error, FileDialog, OK, YES -from traits.api import (HasTraits, HasPrivateTraits, on_trait_change, - cached_property, DelegatesTo, Event, Instance, - Property, Array, Bool, Button, Enum) -from traitsui.api import HGroup, Item, VGroup, View, Handler, ArrayEditor -from traitsui.menu import NoButtons -from tvtk.pyface.scene_editor import SceneEditor - -from ..coreg import (fid_fname, _find_fiducials_files, _find_head_bem, - get_mni_fiducials) -from ..defaults import DEFAULTS -from ..io import write_fiducials -from ..io.constants import FIFF -from ..surface import complete_surface_info, decimate_surface -from ..utils import get_subjects_dir, logger, warn -from ..viz.backends._pysurfer_mayavi import _toggle_mlab_render -from ._file_traits import (SurfaceSource, fid_wildcard, FiducialsSource, - MRISubjectSource, SubjectSelectorPanel, - Surf) -from ._viewer import (HeadViewController, PointObject, SurfaceObject, - headview_borders, _BUTTON_WIDTH, - _MRI_FIDUCIALS_WIDTH, _MM_WIDTH, - _RESET_LABEL, _RESET_WIDTH, _mm_fmt) - -defaults = DEFAULTS['coreg'] - - -class MRIHeadWithFiducialsModel(HasPrivateTraits): - """Represent an MRI head shape (high and low res) with fiducials. - - Attributes - ---------- - points : array (n_points, 3) - MRI head surface points. - tris : array (n_tris, 3) - Triangles based on points. - lpa : array (1, 3) - Left peri-auricular point coordinates. - nasion : array (1, 3) - Nasion coordinates. - rpa : array (1, 3) - Right peri-auricular point coordinates. - """ - - subject_source = Instance(MRISubjectSource, ()) - bem_low_res = Instance(SurfaceSource, ()) - bem_high_res = Instance(SurfaceSource, ()) - fid = Instance(FiducialsSource, ()) - - fid_file = DelegatesTo('fid', 'file') - fid_fname = DelegatesTo('fid', 'fname') - fid_points = DelegatesTo('fid', 'points') - subjects_dir = DelegatesTo('subject_source') - subject = DelegatesTo('subject_source') - subject_has_bem = DelegatesTo('subject_source') - lpa = Array(float, (1, 3)) - nasion = Array(float, (1, 3)) - rpa = Array(float, (1, 3)) - - reset = Event(desc="Reset fiducials to the file.") - - # info - can_save = Property(depends_on=['file', 'can_save_as']) - can_save_as = Property(depends_on=['lpa', 'nasion', 'rpa']) - can_reset = Property(depends_on=['file', 'fid.points', 'lpa', 'nasion', - 'rpa']) - fid_ok = Property(depends_on=['lpa', 'nasion', 'rpa'], desc="All points " - "are set") - default_fid_fname = Property(depends_on=['subjects_dir', 'subject'], - desc="the default file name for the " - "fiducials fif file") - - # switch for the GUI (has no effect in the model) - lock_fiducials = Bool(False, desc="Used by GIU, has no effect in the " - "model.") - - @on_trait_change('fid_points') - def reset_fiducials(self): # noqa: D102 - if self.fid_points is not None: - self.lpa = self.fid_points[0:1] - self.nasion = self.fid_points[1:2] - self.rpa = self.fid_points[2:3] - - def save(self, fname=None): - """Save the current fiducials to a file. - - Parameters - ---------- - fname : str - Destination file path. If None, will use the current fid filename - if available, or else use the default pattern. - """ - if fname is None: - fname = self.fid_file - if not fname: - fname = self.default_fid_fname - - dig = [{'kind': FIFF.FIFFV_POINT_CARDINAL, - 'ident': FIFF.FIFFV_POINT_LPA, - 'r': np.array(self.lpa[0])}, - {'kind': FIFF.FIFFV_POINT_CARDINAL, - 'ident': FIFF.FIFFV_POINT_NASION, - 'r': np.array(self.nasion[0])}, - {'kind': FIFF.FIFFV_POINT_CARDINAL, - 'ident': FIFF.FIFFV_POINT_RPA, - 'r': np.array(self.rpa[0])}] - write_fiducials(fname, dig, FIFF.FIFFV_COORD_MRI) - self.fid_file = fname - - @cached_property - def _get_can_reset(self): - if not self.fid_file: - return False - elif np.any(self.lpa != self.fid.points[0:1]): - return True - elif np.any(self.nasion != self.fid.points[1:2]): - return True - elif np.any(self.rpa != self.fid.points[2:3]): - return True - return False - - @cached_property - def _get_can_save_as(self): - can = not (np.all(self.nasion == self.lpa) or - np.all(self.nasion == self.rpa) or - np.all(self.lpa == self.rpa)) - return can - - @cached_property - def _get_can_save(self): - if not self.can_save_as: - return False - elif self.fid_file: - return True - elif self.subjects_dir and self.subject: - return True - else: - return False - - @cached_property - def _get_default_fid_fname(self): - fname = fid_fname.format(subjects_dir=self.subjects_dir, - subject=self.subject) - return fname - - @cached_property - def _get_fid_ok(self): - return all(np.any(pt) for pt in (self.nasion, self.lpa, self.rpa)) - - def _reset_fired(self): - self.reset_fiducials() - - # if subject changed because of a change of subjects_dir this was not - # triggered - @on_trait_change('subjects_dir,subject') - def _subject_changed(self): - subject = self.subject - subjects_dir = self.subjects_dir - if not subjects_dir or not subject: - return - - # find high-res head model (if possible) - high_res_path = _find_head_bem(subject, subjects_dir, high_res=True) - low_res_path = _find_head_bem(subject, subjects_dir, high_res=False) - if high_res_path is None and low_res_path is None: - msg = 'No standard head model was found for subject %s' % subject - error(None, msg, "No head surfaces found") - raise RuntimeError(msg) - if high_res_path is not None: - self.bem_high_res.file = high_res_path - else: - self.bem_high_res.file = low_res_path - if low_res_path is None: - # This should be very rare! - warn('No low-resolution head found, decimating high resolution ' - 'mesh (%d vertices): %s' % (len(self.bem_high_res.surf.rr), - high_res_path,)) - # Create one from the high res one, which we know we have - rr, tris = decimate_surface(self.bem_high_res.surf.rr, - self.bem_high_res.surf.tris, - n_triangles=5120) - surf = complete_surface_info(dict(rr=rr, tris=tris), - copy=False, verbose=False) - # directly set the attributes of bem_low_res - self.bem_low_res.surf = Surf(tris=surf['tris'], rr=surf['rr'], - nn=surf['nn']) - else: - self.bem_low_res.file = low_res_path - - # Set MNI points - try: - fids = get_mni_fiducials(subject, subjects_dir) - except Exception: # some problem, leave at origin - self.fid.mni_points = None - else: - self.fid.mni_points = np.array([f['r'] for f in fids], float) - - # find fiducials file - fid_files = _find_fiducials_files(subject, subjects_dir) - if len(fid_files) == 0: - self.fid.reset_traits(['file']) - self.lock_fiducials = False - else: - self.fid_file = fid_files[0].format(subjects_dir=subjects_dir, - subject=subject) - self.lock_fiducials = True - - # does not seem to happen by itself ... so hard code it: - self.reset_fiducials() - - -class SetHandler(Handler): - """Handler to change style when setting MRI fiducials.""" - - def object_set_changed(self, info): # noqa: D102 - return self.object_locked_changed(info) - - def object_locked_changed(self, info): # noqa: D102 - if info.object.locked: - ss = '' - else: - ss = 'border-style: solid; border-color: red; border-width: 2px;' - # This will only work for Qt, but hopefully that's most users! - try: - _color_children(info.ui.info.ui.control, ss) - except AttributeError: # safeguard for wxpython - pass - - -def _color_children(obj, ss): - """Qt helper.""" - for child in obj.children(): - if 'QRadioButton' in repr(child): - child.setStyleSheet(ss if child.isChecked() else '') - elif 'QLineEdit' in repr(child): - child.setStyleSheet(ss) - elif 'QWidget' in repr(child): # on Linux it's nested - _color_children(child, ss) - - -_SET_TOOLTIP = ('Click on the MRI image to set the position, ' - 'or enter values below') - - -class FiducialsPanel(HasPrivateTraits): - """Set fiducials on an MRI surface.""" - - model = Instance(MRIHeadWithFiducialsModel) - - fid_file = DelegatesTo('model') - fid_fname = DelegatesTo('model') - lpa = DelegatesTo('model') - nasion = DelegatesTo('model') - rpa = DelegatesTo('model') - can_save = DelegatesTo('model') - can_save_as = DelegatesTo('model') - can_reset = DelegatesTo('model') - fid_ok = DelegatesTo('model') - locked = DelegatesTo('model', 'lock_fiducials') - - set = Enum('LPA', 'Nasion', 'RPA') - current_pos_mm = Array(float, (1, 3)) - - save_as = Button(label='Save as...') - save = Button(label='Save') - reset_fid = Button(label=_RESET_LABEL) - - headview = Instance(HeadViewController) - hsp_obj = Instance(SurfaceObject) - - picker = Instance(object) - - # the layout of the dialog created - view = View(VGroup( - HGroup(Item('fid_file', width=_MRI_FIDUCIALS_WIDTH, - tooltip='MRI fiducials file'), show_labels=False), - HGroup(Item('set', width=_MRI_FIDUCIALS_WIDTH, - format_func=lambda x: x, style='custom', - tooltip=_SET_TOOLTIP), show_labels=False), - HGroup(Item('current_pos_mm', - editor=ArrayEditor(width=_MM_WIDTH, format_func=_mm_fmt), - tooltip='MRI fiducial position (mm)'), show_labels=False), - HGroup(Item('save', enabled_when='can_save', - tooltip="If a filename is currently specified, save to " - "that file, otherwise save to the default file name", - width=_BUTTON_WIDTH), - Item('save_as', enabled_when='can_save_as', - width=_BUTTON_WIDTH), - Item('reset_fid', enabled_when='can_reset', width=_RESET_WIDTH, - tooltip='Reset to file values (if available)'), - show_labels=False), - enabled_when="locked==False", show_labels=False), handler=SetHandler()) - - def __init__(self, *args, **kwargs): # noqa: D102 - super(FiducialsPanel, self).__init__(*args, **kwargs) - - @on_trait_change('current_pos_mm') - def _update_pos(self): - attr = self.set.lower() - if not np.allclose(getattr(self, attr), self.current_pos_mm * 1e-3): - setattr(self, attr, self.current_pos_mm * 1e-3) - - @on_trait_change('model:lpa,model:nasion,model:rpa') - def _update_fiducial(self, value): - attr = self.set.lower() - self.current_pos_mm = getattr(self, attr) * 1000 - - def _reset_fid_fired(self): - self.model.reset = True - - def _save_fired(self): - self.model.save() - - def _save_as_fired(self): - if self.fid_file: - default_path = self.fid_file - else: - default_path = self.model.default_fid_fname - - dlg = FileDialog(action="save as", wildcard=fid_wildcard, - default_path=default_path) - dlg.open() - if dlg.return_code != OK: - return - - path = dlg.path - if not path.endswith('.fif'): - path = path + '.fif' - if os.path.exists(path): - answer = confirm(None, "The file %r already exists. Should it " - "be replaced?", "Overwrite File?") - if answer != YES: - return - - self.model.save(path) - - def _on_pick(self, picker): - if self.locked: - return - - self.picker = picker - n_pos = len(picker.picked_positions) - - if n_pos == 0: - logger.debug("GUI: picked empty location") - return - - if picker.actor is self.hsp_obj.surf.actor.actor: - idxs = [] - idx = None - pt = [picker.pick_position] - elif self.hsp_obj.surf.actor.actor in picker.actors: - idxs = [i for i in range(n_pos) if picker.actors[i] is - self.hsp_obj.surf.actor.actor] - idx = idxs[-1] - pt = [picker.picked_positions[idx]] - else: - logger.debug("GUI: picked object other than MRI") - return - - def round_(x): - return round(x, 3) - - poss = [map(round_, pos) for pos in picker.picked_positions] - pos = map(round_, picker.pick_position) - msg = ["Pick Event: %i picked_positions:" % n_pos] - - line = str(pos) - if idx is None: - line += " <-pick_position" - msg.append(line) - - for i, pos in enumerate(poss): - line = str(pos) - if i == idx: - line += " <- MRI mesh" - elif i in idxs: - line += " (<- also MRI mesh)" - msg.append(line) - logger.debug('\n'.join(msg)) - - set_ = self.set.lower() - assert set_ in _VIEW_DICT, set_ - setattr(self, set_, pt) - - @on_trait_change('set') - def _on_set_change(self, obj, name, old, new): - new = new.lower() - self._update_fiducial(None) - setattr(self.headview, _VIEW_DICT[new], True) - - -_VIEW_DICT = dict(lpa='left', nasion='front', rpa='right') - -# FiducialsPanel view that allows manipulating all coordinates numerically -view2 = View(VGroup(Item('fid_file', label='Fiducials File'), - Item('fid_fname', show_label=False, style='readonly'), - Item('set', style='custom'), 'lpa', 'nasion', 'rpa', - HGroup(Item('save', enabled_when='can_save'), - Item('save_as', enabled_when='can_save_as'), - Item('reset_fid', enabled_when='can_reset'), - show_labels=False), - enabled_when="locked==False")) - - -class FiducialsFrame(HasTraits): - """GUI for interpolating between two KIT marker files. - - Parameters - ---------- - subject : None | str - Set the subject which is initially selected. - subjects_dir : None | str - Override the SUBJECTS_DIR environment variable. - """ - - model = Instance(MRIHeadWithFiducialsModel, ()) - - scene = Instance(MlabSceneModel, ()) - headview = Instance(HeadViewController) - - spanel = Instance(SubjectSelectorPanel) - panel = Instance(FiducialsPanel) - - mri_obj = Instance(SurfaceObject) - point_scale = float(defaults['mri_fid_scale']) - lpa_obj = Instance(PointObject) - nasion_obj = Instance(PointObject) - rpa_obj = Instance(PointObject) - - def _headview_default(self): - return HeadViewController(scene=self.scene, system='RAS') - - def _panel_default(self): - panel = FiducialsPanel(model=self.model, headview=self.headview) - panel.trait_view('view', view2) - return panel - - def _spanel_default(self): - return SubjectSelectorPanel(model=self.model.subject_source) - - view = View(HGroup(Item('scene', - editor=SceneEditor(scene_class=MayaviScene), - dock='vertical'), - VGroup(headview_borders, - VGroup(Item('spanel', style='custom'), - label="Subject", show_border=True, - show_labels=False), - VGroup(Item('panel', style="custom"), - label="Fiducials", show_border=True, - show_labels=False), - show_labels=False), - show_labels=False), - resizable=True, - buttons=NoButtons) - - def __init__(self, subject=None, subjects_dir=None, - **kwargs): # noqa: D102 - super(FiducialsFrame, self).__init__(**kwargs) - - subjects_dir = get_subjects_dir(subjects_dir) - if subjects_dir is not None: - self.spanel.subjects_dir = subjects_dir - - if subject is not None: - if subject in self.spanel.subjects: - self.spanel.subject = subject - - @on_trait_change('scene.activated') - def _init_plot(self): - _toggle_mlab_render(self, False) - - # bem - color = defaults['mri_color'] - self.mri_obj = SurfaceObject(points=self.model.points, color=color, - tri=self.model.tris, scene=self.scene) - self.model.on_trait_change(self._on_mri_src_change, 'tris') - self.panel.hsp_obj = self.mri_obj - - # fiducials - for key in ('lpa', 'nasion', 'rpa'): - attr = f'{key}_obj' - setattr(self, attr, PointObject( - scene=self.scene, color=defaults[f'{key}_color'], - has_norm=True, point_scale=self.point_scale)) - obj = getattr(self, attr) - self.panel.sync_trait(key, obj, 'points', mutual=False) - self.sync_trait('point_scale', obj, mutual=False) - - self.headview.left = True - _toggle_mlab_render(self, True) - - # picker - self.scene.mayavi_scene.on_mouse_pick(self.panel._on_pick, type='cell') - - def _on_mri_src_change(self): - if (not np.any(self.model.points)) or (not np.any(self.model.tris)): - self.mri_obj.clear() - return - - self.mri_obj.points = self.model.points - self.mri_obj.tri = self.model.tris - self.mri_obj.plot() diff -Nru python-mne-0.23.4+dfsg/mne/gui/_file_traits.py python-mne-1.1.0+dfsg/mne/gui/_file_traits.py --- python-mne-0.23.4+dfsg/mne/gui/_file_traits.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/_file_traits.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,574 +0,0 @@ -# -*- coding: utf-8 -*- -"""File data sources for traits GUIs.""" - -# Authors: Christian Brodbeck -# -# License: BSD (3-clause) - -import os -import os.path as op - -import numpy as np - -from traits.api import (Any, HasTraits, HasPrivateTraits, cached_property, - on_trait_change, Array, Bool, Button, DelegatesTo, - Directory, Enum, Event, File, Instance, Int, List, - Property, Str, ArrayOrNone, BaseFile) -from traitsui.api import View, Item, VGroup -from pyface.api import DirectoryDialog, OK, ProgressDialog, error, information - -from ._viewer import _DIG_SOURCE_WIDTH - -from ..bem import read_bem_surfaces -from ..io.constants import FIFF -from ..io import read_info, read_fiducials, read_raw -from ..io._read_raw import supported -from ..io.meas_info import _empty_info -from ..io.open import fiff_open, dir_tree_find -from ..surface import read_surface, complete_surface_info -from ..coreg import (_is_mri_subject, _mri_subject_has_bem, - create_default_subject) -from ..utils import get_config, set_config -from ..viz._3d import _fiducial_coords -from ..channels import read_dig_fif - - -fid_wildcard = "*.fif" -trans_wildcard = "*.fif" -# for wx backend: -# fid_wildcard = "Fiducials FIFF file (*.fif)|*.fif" -# trans_wildcard = "Trans File (*.fif)|*.fif" - - -def _expand_path(p): - return op.abspath(op.expandvars(op.expanduser(p))) - - -def get_fs_home(): - """Get the FREESURFER_HOME directory. - - Returns - ------- - fs_home : None | str - The FREESURFER_HOME path or None if the user cancels. - - Notes - ----- - If FREESURFER_HOME can't be found, the user is prompted with a file dialog. - If specified successfully, the resulting path is stored with - mne.set_config(). - """ - return _get_root_home('FREESURFER_HOME', 'freesurfer', _fs_home_problem) - - -def _get_root_home(cfg, name, check_fun): - root = get_config(cfg) - problem = check_fun(root) - while problem: - info = ("Please select the %s directory. This is the root " - "directory of the %s installation." % (cfg, name)) - msg = '\n\n'.join((problem, info)) - information(None, msg, "Select the %s Directory" % cfg) - msg = "Please select the %s Directory" % cfg - dlg = DirectoryDialog(message=msg, new_directory=False) - if dlg.open() == OK: - root = dlg.path - problem = check_fun(root) - if problem is None: - set_config(cfg, root, set_env=False) - else: - return None - return root - - -def set_fs_home(): - """Set the FREESURFER_HOME environment variable. - - Returns - ------- - success : bool - True if the environment variable could be set, False if FREESURFER_HOME - could not be found. - - Notes - ----- - If FREESURFER_HOME can't be found, the user is prompted with a file dialog. - If specified successfully, the resulting path is stored with - mne.set_config(). - """ - fs_home = get_fs_home() - if fs_home is None: - return False - else: - os.environ['FREESURFER_HOME'] = fs_home - return True - - -def _fs_home_problem(fs_home): - """Check FREESURFER_HOME path. - - Return str describing problem or None if the path is okay. - """ - if fs_home is None: - return "FREESURFER_HOME is not set." - elif not op.exists(fs_home): - return "FREESURFER_HOME (%s) does not exist." % fs_home - else: - test_dir = op.join(fs_home, 'subjects', 'fsaverage') - if not op.exists(test_dir): - return ("FREESURFER_HOME (%s) does not contain the fsaverage " - "subject." % fs_home) - - -def _mne_root_problem(mne_root): - """Check MNE_ROOT path. - - Return str describing problem or None if the path is okay. - """ - if mne_root is None: - return "MNE_ROOT is not set." - elif not op.exists(mne_root): - return "MNE_ROOT (%s) does not exist." % mne_root - else: - test_dir = op.join(mne_root, 'share', 'mne', 'mne_analyze') - if not op.exists(test_dir): - return ("MNE_ROOT (%s) is missing files. If this is your MNE " - "installation, consider reinstalling." % mne_root) - - -class FileOrDir(File): - """Subclass File because *.mff files are actually directories.""" - - def validate(self, object, name, value): - """Validate that a specified value is valid for this trait.""" - value = os.fspath(value) - validated_value = super(BaseFile, self).validate(object, name, value) - if not self.exists: - return validated_value - elif op.exists(value): - return validated_value - - self.error(object, name, value) - - -class Surf(HasTraits): - """Expose a surface similar to the ones used elsewhere in MNE.""" - - rr = Array(shape=(None, 3), value=np.empty((0, 3))) - nn = Array(shape=(None, 3), value=np.empty((0, 3))) - tris = Array(shape=(None, 3), value=np.empty((0, 3))) - - -class SurfaceSource(HasTraits): - """Expose points and tris of a file storing a surface. - - Parameters - ---------- - file : File - Path to a *-bem.fif file or a surface containing a Freesurfer surface. - - Attributes - ---------- - pts : Array, shape = (n_pts, 3) - Point coordinates. - tris : Array, shape = (n_tri, 3) - Triangles. - - Notes - ----- - tri is always updated after pts, so in case downstream objects depend on - both, they should sync to a change in tris. - """ - - file = File(exists=True, filter=['*.fif', '*.*']) - surf = Instance(Surf) - - @on_trait_change('file') - def read_file(self): - """Read the file.""" - if op.exists(self.file): - if self.file.endswith('.fif'): - bem = read_bem_surfaces(self.file, verbose=False)[0] - else: - try: - bem = read_surface(self.file, return_dict=True)[2] - bem['rr'] *= 1e-3 - complete_surface_info(bem, copy=False) - except Exception: - error(parent=None, - message="Error loading surface from %s (see " - "Terminal for details)." % self.file, - title="Error Loading Surface") - self.reset_traits(['file']) - raise - self.surf = Surf(rr=bem['rr'], tris=bem['tris'], nn=bem['nn']) - else: - self.surf = self._default_surf() - - def _surf_default(self): - return Surf(rr=np.empty((0, 3)), - tris=np.empty((0, 3), int), nn=np.empty((0, 3))) - - -class FiducialsSource(HasTraits): - """Expose points of a given fiducials fif file. - - Parameters - ---------- - file : File - Path to a fif file with fiducials (*.fif). - - Attributes - ---------- - points : Array, shape = (n_points, 3) - Fiducials file points. - """ - - file = File(filter=[fid_wildcard]) - fname = Property(depends_on='file') - points = Property(ArrayOrNone, depends_on='file') - mni_points = ArrayOrNone(float, shape=(3, 3)) - - def _get_fname(self): - return op.basename(self.file) - - @cached_property - def _get_points(self): - if not op.exists(self.file): - return self.mni_points # can be None - try: - return _fiducial_coords(*read_fiducials(self.file)) - except Exception as err: - error(None, "Error reading fiducials from %s: %s (See terminal " - "for more information)" % (self.fname, str(err)), - "Error Reading Fiducials") - self.reset_traits(['file']) - raise - - -class DigSource(HasPrivateTraits): - """Expose digitization information from a file. - - Parameters - ---------- - file : File - Path to the BEM file (*.fif). - - Attributes - ---------- - fid : Array, shape = (3, 3) - Each row contains the coordinates for one fiducial point, in the order - Nasion, RAP, LAP. If no file is set all values are 0. - """ - - file = FileOrDir(exists=True, - filter=[' '.join([f'*{ext}' for ext in supported])]) - - inst_fname = Property(Str, depends_on='file') - inst_dir = Property(depends_on='file') - _info = Property(depends_on='file') - - points_filter = Any(desc="Index to select a subset of the head shape " - "points") - n_omitted = Property(Int, depends_on=['points_filter']) - - # head shape - _hsp_points = Property(depends_on='_info', - desc="Head shape points in the file (n x 3 array)") - points = Property(depends_on=['_hsp_points', 'points_filter'], - desc="Head shape points selected by the filter (n x 3 " - "array)") - - # fiducials - lpa = Property(depends_on='_info', - desc="LPA coordinates (1 x 3 array)") - nasion = Property(depends_on='_info', - desc="Nasion coordinates (1 x 3 array)") - rpa = Property(depends_on='_info', - desc="RPA coordinates (1 x 3 array)") - - # EEG - eeg_points = Property(depends_on='_info', - desc="EEG sensor coordinates (N x 3 array)") - hpi_points = Property(depends_on='_info', - desc='HPI coil coordinates (N x 3 array)') - - view = View(Item('file', width=_DIG_SOURCE_WIDTH, tooltip='FIF file ' - '(Raw, Epochs, Evoked, or DigMontage)', show_label=False)) - - @cached_property - def _get_n_omitted(self): - if self.points_filter is None: - return 0 - else: - return np.sum(self.points_filter == False) # noqa: E712 - - @cached_property - def _get__info(self): - if not self.file: - return - elif self.file.endswith(('.fif', '.fif.gz')): - info = None - fid, tree, _ = fiff_open(self.file) - fid.close() - if len(dir_tree_find(tree, FIFF.FIFFB_MEAS_INFO)) > 0: - info = read_info(self.file, verbose=False) - elif len(dir_tree_find(tree, FIFF.FIFFB_ISOTRAK)) > 0: - info = _empty_info(1) - info['dig'] = read_dig_fif(fname=self.file).dig - else: - info = read_raw(self.file).info - - # check that digitizer info is present - if info is None or info['dig'] is None: - error(None, "The selected file does not contain digitization " - "information. Please select a different file.", - "Error Reading Digitization File") - self.reset_traits(['file']) - return - - # check that all fiducial points are present - point_kinds = {d['kind'] for d in info['dig']} - missing = [key for key in ('LPA', 'Nasion', 'RPA') if - getattr(FIFF, f'FIFFV_POINT_{key.upper()}') not in - point_kinds] - if missing: - points = _fiducial_coords(info['dig']) - if len(points == 3): - _append_fiducials(info['dig'], *points.T) - else: - error(None, "The selected digitization file does not contain " - f"all cardinal points (missing: {', '.join(missing)}). " - "Please select a different file.", - "Error Reading Digitization File") - self.reset_traits(['file']) - return - return info - - @cached_property - def _get_inst_dir(self): - return op.dirname(self.file) - - @cached_property - def _get_inst_fname(self): - if self.file: - return op.basename(self.file) - else: - return '-' - - @cached_property - def _get__hsp_points(self): - if not self._info or not self._info['dig']: - return np.empty((0, 3)) - - points = np.array([d['r'] for d in self._info['dig'] - if d['kind'] == FIFF.FIFFV_POINT_EXTRA]) - points = np.empty((0, 3)) if len(points) == 0 else points - return points - - @cached_property - def _get_points(self): - if self.points_filter is None: - return self._hsp_points - else: - return self._hsp_points[self.points_filter] - - def _cardinal_point(self, ident): - """Coordinates for a cardinal point.""" - if not self._info or not self._info['dig']: - return np.zeros((1, 3)) - - for d in self._info['dig']: - if d['kind'] == FIFF.FIFFV_POINT_CARDINAL and d['ident'] == ident: - return d['r'][None, :] - return np.zeros((1, 3)) - - @cached_property - def _get_nasion(self): - return self._cardinal_point(FIFF.FIFFV_POINT_NASION) - - @cached_property - def _get_lpa(self): - return self._cardinal_point(FIFF.FIFFV_POINT_LPA) - - @cached_property - def _get_rpa(self): - return self._cardinal_point(FIFF.FIFFV_POINT_RPA) - - @cached_property - def _get_eeg_points(self): - if not self._info or not self._info['dig']: - return np.empty((0, 3)) - - out = [d['r'] for d in self._info['dig'] if - d['kind'] == FIFF.FIFFV_POINT_EEG and - d['coord_frame'] == FIFF.FIFFV_COORD_HEAD] - out = np.empty((0, 3)) if len(out) == 0 else np.array(out) - return out - - @cached_property - def _get_hpi_points(self): - if not self._info or not self._info['dig']: - return np.zeros((0, 3)) - - out = [d['r'] for d in self._info['dig'] if - d['kind'] == FIFF.FIFFV_POINT_HPI and - d['coord_frame'] == FIFF.FIFFV_COORD_HEAD] - out = np.empty((0, 3)) if len(out) == 0 else np.array(out) - return out - - def _file_changed(self): - self.reset_traits(('points_filter',)) - - -def _append_fiducials(dig, lpa, nasion, rpa): - dig.append({'coord_frame': FIFF.FIFFV_COORD_HEAD, - 'ident': FIFF.FIFFV_POINT_LPA, - 'kind': FIFF.FIFFV_POINT_CARDINAL, - 'r': lpa}) - dig.append({'coord_frame': FIFF.FIFFV_COORD_HEAD, - 'ident': FIFF.FIFFV_POINT_NASION, - 'kind': FIFF.FIFFV_POINT_CARDINAL, - 'r': nasion}) - dig.append({'coord_frame': FIFF.FIFFV_COORD_HEAD, - 'ident': FIFF.FIFFV_POINT_RPA, - 'kind': FIFF.FIFFV_POINT_CARDINAL, - 'r': rpa}) - - -class MRISubjectSource(HasPrivateTraits): - """Find subjects in SUBJECTS_DIR and select one. - - Parameters - ---------- - subjects_dir : directory - SUBJECTS_DIR. - subject : str - Subject, corresponding to a folder in SUBJECTS_DIR. - """ - - refresh = Event(desc="Refresh the subject list based on the directory " - "structure of subjects_dir.") - - # settings - subjects_dir = Directory(exists=True) - subjects = Property(List(Str), depends_on=['subjects_dir', 'refresh']) - subject = Enum(values='subjects') - - # info - can_create_fsaverage = Property(Bool, depends_on=['subjects_dir', - 'subjects']) - subject_has_bem = Property(Bool, depends_on=['subjects_dir', 'subject'], - desc="whether the subject has a file matching " - "the bem file name pattern") - bem_pattern = Property(depends_on='mri_dir') - - @cached_property - def _get_can_create_fsaverage(self): - if not op.exists(self.subjects_dir) or 'fsaverage' in self.subjects: - return False - return True - - @cached_property - def _get_mri_dir(self): - if not self.subject: - return - elif not self.subjects_dir: - return - else: - return op.join(self.subjects_dir, self.subject) - - @cached_property - def _get_subjects(self): - sdir = self.subjects_dir - is_dir = sdir and op.isdir(sdir) - if is_dir: - dir_content = os.listdir(sdir) - subjects = [s for s in dir_content if _is_mri_subject(s, sdir)] - if len(subjects) == 0: - subjects.append('') - else: - subjects = [''] - - return sorted(subjects) - - @cached_property - def _get_subject_has_bem(self): - if not self.subject: - return False - return _mri_subject_has_bem(self.subject, self.subjects_dir) - - def create_fsaverage(self): # noqa: D102 - if not self.subjects_dir: - raise RuntimeError( - "No subjects directory is selected. Please specify " - "subjects_dir first.") - - fs_home = get_fs_home() - if fs_home is None: - raise RuntimeError( - "FreeSurfer contains files that are needed for copying the " - "fsaverage brain. Please install FreeSurfer and try again.") - - create_default_subject(fs_home=fs_home, update=True, - subjects_dir=self.subjects_dir) - self.refresh = True - self.subject = 'fsaverage' - - @on_trait_change('subjects_dir') - def _emit_subject(self): - # This silliness is the only way I could figure out to get the - # on_trait_change('subject_panel.subject') in CoregFrame to work! - self.subject = self.subject - - -class SubjectSelectorPanel(HasPrivateTraits): - """Subject selector panel.""" - - model = Instance(MRISubjectSource) - - can_create_fsaverage = DelegatesTo('model') - subjects_dir = DelegatesTo('model') - subject = DelegatesTo('model') - subjects = DelegatesTo('model') - - create_fsaverage = Button( - u"fsaverage⇨SUBJECTS_DIR", - desc="whether to copy the files for the fsaverage subject to the " - "subjects directory. This button is disabled if " - "fsaverage already exists in the selected subjects directory.") - - view = View(VGroup(Item('subjects_dir', width=_DIG_SOURCE_WIDTH, - tooltip='Subject MRI structurals (SUBJECTS_DIR)'), - Item('subject', width=_DIG_SOURCE_WIDTH, - tooltip='Subject to use within SUBJECTS_DIR'), - Item('create_fsaverage', - enabled_when='can_create_fsaverage', - width=_DIG_SOURCE_WIDTH), - show_labels=False)) - - def _create_fsaverage_fired(self): - # progress dialog with indefinite progress bar - title = "Creating FsAverage ..." - message = "Copying fsaverage files ..." - prog = ProgressDialog(title=title, message=message) - prog.open() - prog.update(0) - - try: - self.model.create_fsaverage() - except Exception as err: - error(None, str(err), "Error Creating FsAverage") - raise - finally: - prog.close() - - def _subjects_dir_changed(self, old, new): - if new and self.subjects == ['']: - information(None, "The directory selected as subjects-directory " - "(%s) does not contain any valid MRI subjects. If " - "this is not expected make sure all MRI subjects have " - "head surface model files which " - "can be created by running:\n\n $ mne " - "make_scalp_surfaces" % self.subjects_dir, - "No Subjects Found") diff -Nru python-mne-0.23.4+dfsg/mne/gui/help/kit2fiff.json python-mne-1.1.0+dfsg/mne/gui/help/kit2fiff.json --- python-mne-0.23.4+dfsg/mne/gui/help/kit2fiff.json 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/help/kit2fiff.json 1970-01-01 00:00:00.000000000 +0000 @@ -1,10 +0,0 @@ -{ - "hsp_file": "*.hsp or *.txt file containing digitized head shape points", - "fid_file": "*.elp or *.txt file containing 8 digitized fiducial points", - "stim_chs": "Define the channels that are used to generate events. If the field is empty, the default channels are used (for NYU systems only). Channels can be defined as comma separated channel numbers (1, 2, 3, 4, 5, 6), ranges (1:7) and combinations of the two (1:4, 7, 10:13).", - "stim_coding": "Specifies how stim-channel events are translated into trigger values. Little- and big-endian assume binary coding. In little-endian order, the first channel is assigned the smallest value (1) and the last channel is assigned the highest value (with 8 channels this would be 128). Channel# implies a different method of coding in which an event in a given channel is assigned the channel number as value.", - "sqd_file": "*.sqd or *.con file containing recorded MEG data", - "stim_slope": "How events are marked in stim channels. Trough: normally the signal is high, events are marked by transitory signal decrease. Peak: normally signal is low, events are marked by an increase.", - "stim_threshold": "Threshold voltage to detect events in stim channels", - "use_mrk": "Determine which marker points are used for the MEG-head shape coregistration" -} diff -Nru python-mne-0.23.4+dfsg/mne/gui/_help.py python-mne-1.1.0+dfsg/mne/gui/_help.py --- python-mne-0.23.4+dfsg/mne/gui/_help.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/_help.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,16 +0,0 @@ -# Author: Christian Brodbeck -# -# License: BSD (3-clause) -import json -import os -from textwrap import TextWrapper - - -def read_tooltips(gui_name): - """Read and format tooltips, return a dict.""" - dirname = os.path.dirname(__file__) - help_path = os.path.join(dirname, 'help', gui_name + '.json') - with open(help_path) as fid: - raw_tooltips = json.load(fid) - format_ = TextWrapper(width=60, fix_sentence_endings=True).fill - return {key: format_(text) for key, text in raw_tooltips.items()} diff -Nru python-mne-0.23.4+dfsg/mne/gui/_ieeg_locate.py python-mne-1.1.0+dfsg/mne/gui/_ieeg_locate.py --- python-mne-0.23.4+dfsg/mne/gui/_ieeg_locate.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/_ieeg_locate.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,739 @@ +# -*- coding: utf-8 -*- +"""Intracranial elecrode localization GUI for finding contact locations.""" + +# Authors: Alex Rockhill +# +# License: BSD (3-clause) + +import numpy as np +import platform + +from scipy.ndimage import maximum_filter + +from qtpy import QtCore, QtGui +from qtpy.QtCore import Slot, Signal +from qtpy.QtWidgets import (QVBoxLayout, QHBoxLayout, QLabel, + QMessageBox, QWidget, QAbstractItemView, + QListView, QSlider, QPushButton, + QComboBox) + +from matplotlib.colors import LinearSegmentedColormap + +from ._core import SliceBrowser +from ..surface import _voxel_neighbors +from ..transforms import apply_trans, _get_trans, invert_transform +from ..utils import logger, _validate_type, verbose +from .. import pick_types + +_CH_PLOT_SIZE = 1024 +_RADIUS_SCALAR = 0.4 +_TUBE_SCALAR = 0.1 +_BOLT_SCALAR = 30 # mm +_CH_MENU_WIDTH = 30 if platform.system() == 'Windows' else 10 + +# 20 colors generated to be evenly spaced in a cube, worked better than +# matplotlib color cycle +_UNIQUE_COLORS = [(0.1, 0.42, 0.43), (0.9, 0.34, 0.62), (0.47, 0.51, 0.3), + (0.47, 0.55, 0.99), (0.79, 0.68, 0.06), (0.34, 0.74, 0.05), + (0.58, 0.87, 0.13), (0.86, 0.98, 0.4), (0.92, 0.91, 0.66), + (0.77, 0.38, 0.34), (0.9, 0.37, 0.1), (0.2, 0.62, 0.9), + (0.22, 0.65, 0.64), (0.14, 0.94, 0.8), (0.34, 0.31, 0.68), + (0.59, 0.28, 0.74), (0.46, 0.19, 0.94), (0.37, 0.93, 0.7), + (0.56, 0.86, 0.55), (0.67, 0.69, 0.44)] +_N_COLORS = len(_UNIQUE_COLORS) +_CMAP = LinearSegmentedColormap.from_list( + 'ch_colors', _UNIQUE_COLORS, N=_N_COLORS) + + +class ComboBox(QComboBox): + """Dropdown menu that emits a click when popped up.""" + + clicked = Signal() + + def showPopup(self): + """Override show popup method to emit click.""" + self.clicked.emit() + super(ComboBox, self).showPopup() + + +class IntracranialElectrodeLocator(SliceBrowser): + """Locate electrode contacts using a coregistered MRI and CT.""" + + def __init__(self, info, trans, aligned_ct, subject=None, + subjects_dir=None, groups=None, show=True, verbose=None): + """GUI for locating intracranial electrodes. + + .. note:: Images will be displayed using orientation information + obtained from the image header. Images will be resampled to + dimensions [256, 256, 256] for display. + """ + if not info.ch_names: + raise ValueError('No channels found in `info` to locate') + + # store info for modification + self._info = info + self._seeg_idx = pick_types(self._info, meg=False, seeg=True) + self._verbose = verbose + + # channel plotting default parameters + self._ch_alpha = 0.5 + self._radius = int(_CH_PLOT_SIZE // 100) # starting 1/100 of image + + # initialize channel data + self._ch_index = 0 + # load data, apply trans + self._head_mri_t = _get_trans(trans, 'head', 'mri')[0] + self._mri_head_t = invert_transform(self._head_mri_t) + # load channels, convert from m to mm + self._chs = {name: apply_trans(self._head_mri_t, ch['loc'][:3]) * 1000 + for name, ch in zip(info.ch_names, info['chs'])} + self._ch_names = list(self._chs.keys()) + self._group_channels(groups) + + # Initialize GUI + super(IntracranialElectrodeLocator, self).__init__( + base_image=aligned_ct, subject=subject, subjects_dir=subjects_dir) + + # set current position as current contact location if exists + if not np.isnan(self._chs[self._ch_names[self._ch_index]]).any(): + self._set_ras(self._chs[self._ch_names[self._ch_index]], + update_plots=False) + + # add plots of contacts on top + self._plot_ch_images() + + # Add lines + self._lines = dict() + self._lines_2D = dict() + for group in set(self._groups.values()): + self._update_lines(group) + + # ready for user + self._move_cursors_to_pos() + self._ch_list.setFocus() # always focus on list + + if show: + self.show() + + def _configure_ui(self): + # data is loaded for an abstract base image, associate with ct + self._ct_data = self._base_data + self._images['ct'] = self._images['base'] + self._ct_maxima = None # don't compute until turned on + + toolbar = self._configure_toolbar() + slider_bar = self._configure_sliders() + status_bar = self._configure_status_bar() + self._ch_list = self._configure_channel_sidebar() # need for updating + + plot_layout = QHBoxLayout() + plot_layout.addLayout(self._plt_grid) + plot_layout.addWidget(self._ch_list) + + main_vbox = QVBoxLayout() + main_vbox.addLayout(toolbar) + main_vbox.addLayout(slider_bar) + main_vbox.addLayout(plot_layout) + main_vbox.addLayout(status_bar) + + central_widget = QWidget() + central_widget.setLayout(main_vbox) + self.setCentralWidget(central_widget) + + def _configure_channel_sidebar(self): + """Configure the sidebar to select channels/contacts.""" + ch_list = QListView() + ch_list.setSelectionMode(QAbstractItemView.SingleSelection) + max_ch_name_len = max([len(name) for name in self._chs]) + ch_list.setMinimumWidth(max_ch_name_len * _CH_MENU_WIDTH) + ch_list.setMaximumWidth(max_ch_name_len * _CH_MENU_WIDTH) + self._ch_list_model = QtGui.QStandardItemModel(ch_list) + for name in self._ch_names: + self._ch_list_model.appendRow(QtGui.QStandardItem(name)) + self._color_list_item(name=name) + ch_list.setModel(self._ch_list_model) + ch_list.clicked.connect(self._go_to_ch) + ch_list.setCurrentIndex( + self._ch_list_model.index(self._ch_index, 0)) + ch_list.keyPressEvent = self._key_press_event + return ch_list + + def _make_ch_image(self, axis, proj=False): + """Make a plot to display the channel locations.""" + # Make channel data higher resolution so it looks better. + ch_image = np.zeros((_CH_PLOT_SIZE, _CH_PLOT_SIZE)) * np.nan + vxyz = self._voxel_sizes + + def color_ch_radius(ch_image, xf, yf, group, radius): + # Take the fraction across each dimension of the RAS + # coordinates converted to xyz and put a circle in that + # position in this larger resolution image + ex, ey = np.round(np.array([xf, yf]) * _CH_PLOT_SIZE).astype(int) + ii = np.arange(-radius, radius + 1) + ii_sq = ii * ii + idx = np.where(ii_sq + ii_sq[:, np.newaxis] < radius * radius) + # negative y because y axis is inverted + ch_image[-(ey + ii[idx[1]]), ex + ii[idx[0]]] = group + return ch_image + + for name, ras in self._chs.items(): + # move from middle-centered (half coords positive, half negative) + # to bottom-left corner centered (all coords positive). + if np.isnan(ras).any(): + continue + xyz = apply_trans(self._ras_vox_t, ras) + # check if closest to that voxel + dist = np.linalg.norm(xyz - self._current_slice) + if proj or dist < self._radius: + group = self._groups[name] + r = self._radius if proj else \ + self._radius - np.round(abs(dist)).astype(int) + xf, yf = (xyz / vxyz)[list(self._xy_idx[axis])] + ch_image = color_ch_radius(ch_image, xf, yf, group, r) + return ch_image + + @verbose + def _save_ch_coords(self, info=None, verbose=None): + """Save the location of the electrode contacts.""" + logger.info('Saving channel positions to `info`') + if info is None: + info = self._info + with info._unlock(): + for name, ch in zip(info.ch_names, info['chs']): + loc = ch['loc'].copy() + loc[:3] = apply_trans( + self._mri_head_t, self._chs[name] / 1000) # mm->m + ch['loc'] = loc + + def _plot_ch_images(self): + img_delta = 0.5 + ch_deltas = list(img_delta * (self._voxel_sizes[ii] / _CH_PLOT_SIZE) + for ii in range(3)) + self._ch_extents = list( + [-ch_delta, self._voxel_sizes[idx[0]] - ch_delta, + -ch_delta, self._voxel_sizes[idx[1]] - ch_delta] + for idx, ch_delta in zip(self._xy_idx, ch_deltas)) + self._images['chs'] = list() + for axis in range(3): + fig = self._figs[axis] + ax = fig.axes[0] + self._images['chs'].append(ax.imshow( + self._make_ch_image(axis), aspect='auto', + extent=self._ch_extents[axis], zorder=3, + cmap=_CMAP, alpha=self._ch_alpha, vmin=0, vmax=_N_COLORS)) + self._3d_chs = dict() + for name in self._chs: + self._plot_3d_ch(name) + + def _plot_3d_ch(self, name, render=False): + """Plot a single 3D channel.""" + if name in self._3d_chs: + self._renderer.plotter.remove_actor( + self._3d_chs.pop(name), render=False) + if not any(np.isnan(self._chs[name])): + self._3d_chs[name] = self._renderer.sphere( + tuple(self._chs[name]), scale=1, + color=_CMAP(self._groups[name])[:3], opacity=self._ch_alpha)[0] + # The actor scale is managed differently than the glyph scale + # in order not to recreate objects, we use the actor scale + self._3d_chs[name].SetOrigin(self._chs[name]) + self._3d_chs[name].SetScale(self._radius * _RADIUS_SCALAR) + if render: + self._renderer._update() + + def _configure_toolbar(self): + """Make a bar with buttons for user interactions.""" + hbox = QHBoxLayout() + + help_button = QPushButton('Help') + help_button.released.connect(self._show_help) + hbox.addWidget(help_button) + + hbox.addStretch(8) + + hbox.addWidget(QLabel('Snap to Center')) + self._snap_button = QPushButton('Off') + self._snap_button.setMaximumWidth(25) # not too big + hbox.addWidget(self._snap_button) + self._snap_button.released.connect(self._toggle_snap) + self._toggle_snap() # turn on to start + + hbox.addStretch(1) + + self._toggle_brain_button = QPushButton('Show Brain') + self._toggle_brain_button.released.connect(self._toggle_show_brain) + hbox.addWidget(self._toggle_brain_button) + + hbox.addStretch(1) + + mark_button = QPushButton('Mark') + hbox.addWidget(mark_button) + mark_button.released.connect(self._mark_ch) + + remove_button = QPushButton('Remove') + hbox.addWidget(remove_button) + remove_button.released.connect(self._remove_ch) + + self._group_selector = ComboBox() + group_model = self._group_selector.model() + + for i in range(_N_COLORS): + self._group_selector.addItem(' ') + color = QtGui.QColor() + color.setRgb(*(255 * np.array(_CMAP(i))).round().astype(int)) + brush = QtGui.QBrush(color) + brush.setStyle(QtCore.Qt.SolidPattern) + group_model.setData(group_model.index(i, 0), + brush, QtCore.Qt.BackgroundRole) + self._group_selector.clicked.connect(self._select_group) + self._group_selector.currentIndexChanged.connect( + self._select_group) + hbox.addWidget(self._group_selector) + + # update background color for current selection + self._update_group() + + return hbox + + def _configure_sliders(self): + """Make a bar with sliders on it.""" + + def make_label(name): + label = QLabel(name) + label.setAlignment(QtCore.Qt.AlignCenter) + return label + + def make_slider(smin, smax, sval, sfun=None): + slider = QSlider(QtCore.Qt.Horizontal) + slider.setMinimum(int(round(smin))) + slider.setMaximum(int(round(smax))) + slider.setValue(int(round(sval))) + slider.setTracking(False) # only update on release + if sfun is not None: + slider.valueChanged.connect(sfun) + slider.keyPressEvent = self._key_press_event + return slider + + slider_hbox = QHBoxLayout() + + ch_vbox = QVBoxLayout() + ch_vbox.addWidget(make_label('ch alpha')) + ch_vbox.addWidget(make_label('ch radius')) + slider_hbox.addLayout(ch_vbox) + + ch_slider_vbox = QVBoxLayout() + self._alpha_slider = make_slider(0, 100, self._ch_alpha * 100, + self._update_ch_alpha) + ch_plot_max = _CH_PLOT_SIZE // 50 # max 1 / 50 of plot size + ch_slider_vbox.addWidget(self._alpha_slider) + self._radius_slider = make_slider(0, ch_plot_max, self._radius, + self._update_radius) + ch_slider_vbox.addWidget(self._radius_slider) + slider_hbox.addLayout(ch_slider_vbox) + + ct_vbox = QVBoxLayout() + ct_vbox.addWidget(make_label('CT min')) + ct_vbox.addWidget(make_label('CT max')) + slider_hbox.addLayout(ct_vbox) + + ct_slider_vbox = QVBoxLayout() + ct_min = int(round(np.nanmin(self._ct_data))) + ct_max = int(round(np.nanmax(self._ct_data))) + self._ct_min_slider = make_slider( + ct_min, ct_max, ct_min, self._update_ct_scale) + ct_slider_vbox.addWidget(self._ct_min_slider) + self._ct_max_slider = make_slider( + ct_min, ct_max, ct_max, self._update_ct_scale) + ct_slider_vbox.addWidget(self._ct_max_slider) + slider_hbox.addLayout(ct_slider_vbox) + return slider_hbox + + def _configure_status_bar(self, hbox=None): + hbox = QHBoxLayout() if hbox is None else hbox + + hbox.addStretch(3) + + self._toggle_show_mip_button = QPushButton('Show Max Intensity Proj') + self._toggle_show_mip_button.released.connect( + self._toggle_show_mip) + hbox.addWidget(self._toggle_show_mip_button) + + self._toggle_show_max_button = QPushButton('Show Maxima') + self._toggle_show_max_button.released.connect( + self._toggle_show_max) + hbox.addWidget(self._toggle_show_max_button) + + self._intensity_label = QLabel('') # update later + hbox.addWidget(self._intensity_label) + + # add SliceBrowser navigation items + super(IntracranialElectrodeLocator, self)._configure_status_bar( + hbox=hbox) + return hbox + + def _move_cursors_to_pos(self): + super(IntracranialElectrodeLocator, self)._move_cursors_to_pos() + self._ch_list.setFocus() # remove focus from text edit + + def _group_channels(self, groups): + """Automatically find a group based on the name of the channel.""" + if groups is not None: + for name in self._ch_names: + if name not in groups: + raise ValueError(f'{name} not found in ``groups``') + _validate_type(groups[name], (float, int), f'groups[{name}]') + self.groups = groups + else: + i = 0 + self._groups = dict() + base_names = dict() + for name in self._ch_names: + # strip all numbers from the name + base_name = ''.join([letter for letter in name if + not letter.isdigit() and letter != ' ']) + if base_name in base_names: + # look up group number by base name + self._groups[name] = base_names[base_name] + else: + self._groups[name] = i + base_names[base_name] = i + i += 1 + + def _update_lines(self, group, only_2D=False): + """Draw lines that connect the points in a group.""" + if group in self._lines_2D: # remove existing 2D lines first + for line in self._lines_2D[group]: + line.remove() + self._lines_2D.pop(group) + if only_2D: # if not in projection, don't add 2D lines + if self._toggle_show_mip_button.text() == \ + 'Show Max Intensity Proj': + return + elif group in self._lines: # if updating 3D, remove first + self._renderer.plotter.remove_actor( + self._lines[group], render=False) + pos = np.array([ + self._chs[ch] for i, ch in enumerate(self._ch_names) + if self._groups[ch] == group and i in self._seeg_idx and + not np.isnan(self._chs[ch]).any()]) + if len(pos) < 2: # not enough points for line + return + # first, the insertion will be the point farthest from the origin + # brains are a longer posterior-anterior, scale for this (80%) + insert_idx = np.argmax(np.linalg.norm(pos * np.array([1, 0.8, 1]), + axis=1)) + # second, find the farthest point from the insertion + target_idx = np.argmax(np.linalg.norm(pos[insert_idx] - pos, axis=1)) + # third, make a unit vector and to add to the insertion for the bolt + elec_v = pos[insert_idx] - pos[target_idx] + elec_v /= np.linalg.norm(elec_v) + if not only_2D: + self._lines[group] = self._renderer.tube( + [pos[target_idx]], [pos[insert_idx] + elec_v * _BOLT_SCALAR], + radius=self._radius * _TUBE_SCALAR, color=_CMAP(group)[:3])[0] + if self._toggle_show_mip_button.text() == 'Hide Max Intensity Proj': + # add 2D lines on each slice plot if in max intensity projection + target_vox = apply_trans(self._ras_vox_t, pos[target_idx]) + insert_vox = apply_trans(self._ras_vox_t, + pos[insert_idx] + elec_v * _BOLT_SCALAR) + lines_2D = list() + for axis in range(3): + x, y = self._xy_idx[axis] + lines_2D.append(self._figs[axis].axes[0].plot( + [target_vox[x], insert_vox[x]], + [target_vox[y], insert_vox[y]], + color=_CMAP(group), linewidth=0.25, zorder=7)[0]) + self._lines_2D[group] = lines_2D + + def _select_group(self): + """Change the group label to the selection.""" + group = self._group_selector.currentIndex() + self._groups[self._ch_names[self._ch_index]] = group + # color differently if found already + self._color_list_item(self._ch_names[self._ch_index]) + self._update_group() + + def _update_group(self): + """Set background for closed group menu.""" + group = self._group_selector.currentIndex() + rgb = (255 * np.array(_CMAP(group))).round().astype(int) + self._group_selector.setStyleSheet( + 'background-color: rgb({:d},{:d},{:d})'.format(*rgb)) + self._group_selector.update() + + def _update_ch_selection(self): + """Update which channel is selected.""" + name = self._ch_names[self._ch_index] + self._ch_list.setCurrentIndex( + self._ch_list_model.index(self._ch_index, 0)) + self._group_selector.setCurrentIndex(self._groups[name]) + self._update_group() + if not np.isnan(self._chs[name]).any(): + self._set_ras(self._chs[name]) + self._update_camera(render=True) + self._draw() + + def _go_to_ch(self, index): + """Change current channel to the item selected.""" + self._ch_index = index.row() + self._update_ch_selection() + + @Slot() + def _next_ch(self): + """Increment the current channel selection index.""" + self._ch_index = (self._ch_index + 1) % len(self._ch_names) + self._update_ch_selection() + + def _color_list_item(self, name=None): + """Color the item in the view list for easy id of marked channels.""" + name = self._ch_names[self._ch_index] if name is None else name + color = QtGui.QColor('white') + if not np.isnan(self._chs[name]).any(): + group = self._groups[name] + color.setRgb(*[int(c * 255) for c in _CMAP(group)]) + brush = QtGui.QBrush(color) + brush.setStyle(QtCore.Qt.SolidPattern) + self._ch_list_model.setData( + self._ch_list_model.index(self._ch_names.index(name), 0), + brush, QtCore.Qt.BackgroundRole) + # color text black + color = QtGui.QColor('black') + brush = QtGui.QBrush(color) + brush.setStyle(QtCore.Qt.SolidPattern) + self._ch_list_model.setData( + self._ch_list_model.index(self._ch_names.index(name), 0), + brush, QtCore.Qt.ForegroundRole) + + @Slot() + def _toggle_snap(self): + """Toggle snapping the contact location to the center of mass.""" + if self._snap_button.text() == 'Off': + self._snap_button.setText('On') + self._snap_button.setStyleSheet("background-color: green") + else: # text == 'On', turn off + self._snap_button.setText('Off') + self._snap_button.setStyleSheet("background-color: red") + + @Slot() + def _mark_ch(self): + """Mark the current channel as being located at the crosshair.""" + name = self._ch_names[self._ch_index] + if self._snap_button.text() == 'Off': + self._chs[name][:] = self._ras + else: + shape = np.mean(self._mri_data.shape) # Freesurfer shape (256) + voxels_max = int( + 4 / 3 * np.pi * (shape * self._radius / _CH_PLOT_SIZE)**3) + neighbors = _voxel_neighbors( + self._vox, self._ct_data, thresh=0.5, + voxels_max=voxels_max, use_relative=True) + self._chs[name][:] = apply_trans( # to surface RAS + self._vox_ras_t, np.array(list(neighbors)).mean(axis=0)) + self._color_list_item() + self._update_lines(self._groups[name]) + self._update_ch_images(draw=True) + self._plot_3d_ch(name, render=True) + self._save_ch_coords() + self._next_ch() + self._ch_list.setFocus() + + @Slot() + def _remove_ch(self): + """Remove the location data for the current channel.""" + name = self._ch_names[self._ch_index] + self._chs[name] *= np.nan + self._color_list_item() + self._save_ch_coords() + self._update_lines(self._groups[name]) + self._update_ch_images(draw=True) + self._plot_3d_ch(name, render=True) + self._next_ch() + self._ch_list.setFocus() + + def _update_ch_images(self, axis=None, draw=False): + """Update the channel image(s).""" + for axis in range(3) if axis is None else [axis]: + self._images['chs'][axis].set_data( + self._make_ch_image(axis)) + if self._toggle_show_mip_button.text() == \ + 'Hide Max Intensity Proj': + self._images['mip_chs'][axis].set_data( + self._make_ch_image(axis, proj=True)) + if draw: + self._draw(axis) + + def _update_ct_images(self, axis=None, draw=False): + """Update the CT image(s).""" + for axis in range(3) if axis is None else [axis]: + ct_data = np.take(self._ct_data, self._current_slice[axis], + axis=axis).T + # Threshold the CT so only bright objects (electrodes) are visible + ct_data[ct_data < self._ct_min_slider.value()] = np.nan + ct_data[ct_data > self._ct_max_slider.value()] = np.nan + self._images['ct'][axis].set_data(ct_data) + if 'local_max' in self._images: + ct_max_data = np.take( + self._ct_maxima, self._current_slice[axis], axis=axis).T + self._images['local_max'][axis].set_data(ct_max_data) + if draw: + self._draw(axis) + + def _update_mri_images(self, axis=None, draw=False): + """Update the CT image(s).""" + if 'mri' in self._images: + for axis in range(3) if axis is None else [axis]: + self._images['mri'][axis].set_data( + np.take(self._mri_data, self._current_slice[axis], + axis=axis).T) + if draw: + self._draw(axis) + + def _update_images(self, axis=None, draw=True): + """Update CT and channel images when general changes happen.""" + self._update_ch_images(axis=axis) + self._update_mri_images(axis=axis) + super()._update_images() + + def _update_ct_scale(self): + """Update CT min slider value.""" + new_min = self._ct_min_slider.value() + new_max = self._ct_max_slider.value() + # handle inversions + self._ct_min_slider.setValue(min([new_min, new_max])) + self._ct_max_slider.setValue(max([new_min, new_max])) + self._update_ct_images(draw=True) + + def _update_radius(self): + """Update channel plot radius.""" + self._radius = np.round(self._radius_slider.value()).astype(int) + if self._toggle_show_max_button.text() == 'Hide Maxima': + self._update_ct_maxima() + self._update_ct_images() + else: + self._ct_maxima = None # signals ct max is out-of-date + self._update_ch_images(draw=True) + for name, actor in self._3d_chs.items(): + if not np.isnan(self._chs[name]).any(): + actor.SetOrigin(self._chs[name]) + actor.SetScale(self._radius * _RADIUS_SCALAR) + self._renderer._update() + self._ch_list.setFocus() # remove focus from 3d plotter + + def _update_ch_alpha(self): + """Update channel plot alpha.""" + self._ch_alpha = self._alpha_slider.value() / 100 + for axis in range(3): + self._images['chs'][axis].set_alpha(self._ch_alpha) + self._draw() + for actor in self._3d_chs.values(): + actor.GetProperty().SetOpacity(self._ch_alpha) + self._renderer._update() + self._ch_list.setFocus() # remove focus from 3d plotter + + def _show_help(self): + """Show the help menu.""" + QMessageBox.information( + self, 'Help', + "Help:\n'm': mark channel location\n" + "'r': remove channel location\n" + "'b': toggle viewing of brain in T1\n" + "'+'/'-': zoom\nleft/right arrow: left/right\n" + "up/down arrow: superior/inferior\n" + "left angle bracket/right angle bracket: anterior/posterior") + + def _update_ct_maxima(self): + """Compute the maximum voxels based on the current radius.""" + self._ct_maxima = maximum_filter( + self._ct_data, (self._radius,) * 3) == self._ct_data + self._ct_maxima[self._ct_data <= np.median(self._ct_data)] = \ + False + self._ct_maxima = np.where(self._ct_maxima, 1, np.nan) # transparent + + def _toggle_show_mip(self): + """Toggle whether the maximum-intensity projection is shown.""" + if self._toggle_show_mip_button.text() == 'Show Max Intensity Proj': + self._toggle_show_mip_button.setText('Hide Max Intensity Proj') + self._images['mip'] = list() + self._images['mip_chs'] = list() + ct_min, ct_max = np.nanmin(self._ct_data), np.nanmax(self._ct_data) + for axis in range(3): + ct_mip_data = np.max(self._ct_data, axis=axis).T + self._images['mip'].append( + self._figs[axis].axes[0].imshow( + ct_mip_data, cmap='gray', aspect='auto', + vmin=ct_min, vmax=ct_max, zorder=5)) + # add circles for each channel + xs, ys, colors = list(), list(), list() + for name, ras in self._chs.items(): + xyz = self._vox + xs.append(xyz[self._xy_idx[axis][0]]) + ys.append(xyz[self._xy_idx[axis][1]]) + colors.append(_CMAP(self._groups[name])) + self._images['mip_chs'].append( + self._figs[axis].axes[0].imshow( + self._make_ch_image(axis, proj=True), aspect='auto', + extent=self._ch_extents[axis], zorder=6, + cmap=_CMAP, alpha=1, vmin=0, vmax=_N_COLORS)) + for group in set(self._groups.values()): + self._update_lines(group, only_2D=True) + else: + for img in self._images['mip'] + self._images['mip_chs']: + img.remove() + self._images.pop('mip') + self._images.pop('mip_chs') + self._toggle_show_mip_button.setText('Show Max Intensity Proj') + for group in set(self._groups.values()): # remove lines + self._update_lines(group, only_2D=True) + self._draw() + + def _toggle_show_max(self): + """Toggle whether to color local maxima differently.""" + if self._toggle_show_max_button.text() == 'Show Maxima': + self._toggle_show_max_button.setText('Hide Maxima') + # happens on initiation or if the radius is changed with it off + if self._ct_maxima is None: # otherwise don't recompute + self._update_ct_maxima() + self._images['local_max'] = list() + for axis in range(3): + ct_max_data = np.take(self._ct_maxima, + self._current_slice[axis], axis=axis).T + self._images['local_max'].append( + self._figs[axis].axes[0].imshow( + ct_max_data, cmap='autumn', aspect='auto', + vmin=0, vmax=1, zorder=4)) + else: + for img in self._images['local_max']: + img.remove() + self._images.pop('local_max') + self._toggle_show_max_button.setText('Show Maxima') + self._draw() + + def _toggle_show_brain(self): + """Toggle whether the brain/MRI is being shown.""" + if 'mri' in self._images: + for img in self._images['mri']: + img.remove() + self._images.pop('mri') + self._toggle_brain_button.setText('Show Brain') + else: + self._images['mri'] = list() + for axis in range(3): + mri_data = np.take(self._mri_data, + self._current_slice[axis], axis=axis).T + self._images['mri'].append(self._figs[axis].axes[0].imshow( + mri_data, cmap='hot', aspect='auto', alpha=0.25, zorder=2)) + self._toggle_brain_button.setText('Hide Brain') + self._draw() + + def _key_press_event(self, event): + """Execute functions when the user presses a key.""" + super(IntracranialElectrodeLocator, self)._key_press_event(event) + + if event.text() == 'm': + self._mark_ch() + + if event.text() == 'r': + self._remove_ch() + + if event.text() == 'b': + self._toggle_show_brain() diff -Nru python-mne-0.23.4+dfsg/mne/gui/__init__.py python-mne-1.1.0+dfsg/mne/gui/__init__.py --- python-mne-0.23.4+dfsg/mne/gui/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,39 +2,30 @@ # Authors: Christian Brodbeck # -# License: BSD (3-clause) +# License: BSD-3-Clause -import os - -from ..utils import _check_mayavi_version, verbose, get_config -from ._backend import _testing_mode - - -def _initialize_gui(frame, view=None): - """Initialize GUI depending on testing mode.""" - if _testing_mode(): # open without entering mainloop - return frame.edit_traits(view=view), frame - else: - frame.configure_traits(view=view) - return frame +from ..utils import verbose, get_config, warn @verbose def coregistration(tabbed=False, split=True, width=None, inst=None, subject=None, subjects_dir=None, guess_mri_subject=None, height=None, head_opacity=None, head_high_res=None, - trans=None, scrollable=True, project_eeg=None, - orient_to_surface=None, scale_by_distance=None, - mark_inside=None, interaction=None, scale=None, - advanced_rendering=None, head_inside=True, verbose=None): + trans=None, scrollable=True, *, + orient_to_surface=True, scale_by_distance=True, + mark_inside=True, interaction=None, scale=None, + advanced_rendering=None, head_inside=True, + fullscreen=None, show=True, block=False, verbose=None): """Coregister an MRI with a subject's head shape. - The recommended way to use the GUI is through bash with: + The GUI can be launched through the command line interface: .. code-block:: bash $ mne coreg + or using a python interpreter as shown in :ref:`tut-source-alignment`. + Parameters ---------- tabbed : bool @@ -72,32 +63,28 @@ The transform file to use. scrollable : bool Make the coregistration panel vertically scrollable (default True). - project_eeg : bool | None - If True (default None), project EEG electrodes to the head surface. - This is only for visualization purposes and does not affect fitting. - - .. versionadded:: 0.16 orient_to_surface : bool | None - If True (default None), orient EEG electrode and head shape points + If True (default), orient EEG electrode and head shape points to the head surface. .. versionadded:: 0.16 scale_by_distance : bool | None - If True (default None), scale the digitization points by their + If True (default), scale the digitization points by their distance from the scalp surface. .. versionadded:: 0.16 mark_inside : bool | None - If True (default None), mark points inside the head surface in a + If True (default), mark points inside the head surface in a different color. .. versionadded:: 0.16 - interaction : str | None - Can be 'terrain' (default None), use terrain-style interaction (where - "up" is the Z/superior direction), or 'trackball' to use - orientationless interactions. + %(interaction_scene_none)s + Defaults to ``'terrain'``. .. versionadded:: 0.16 + .. versionchanged:: 1.0 + Default interaction mode if ``None`` and no config setting found + changed from ``'trackball'`` to ``'terrain'``. scale : float | None The scaling for the scene. @@ -113,26 +100,50 @@ points behind the head. .. versionadded:: 0.23 + %(fullscreen)s + Default is None, which uses ``MNE_COREG_FULLSCREEN`` config value + (which defaults to False). + + .. versionadded:: 1.1 + show : bool + Show the GUI if True. + block : bool + Whether to halt program execution until the figure is closed. %(verbose)s Returns ------- - frame : instance of CoregFrame + frame : instance of CoregistrationUI The coregistration frame. Notes ----- - Many parameters (e.g., ``project_eeg``) take None as a parameter, + Many parameters (e.g., ``head_opacity``) take None as a parameter, which means that the default will be read from the MNE-Python configuration file (which gets saved when exiting). - Step by step instructions for the coregistrations can be accessed as - slides, `for subjects with structural MRI - `_ and `for - subjects for which no MRI is available - `_. + Step by step instructions for the coregistrations are shown below: + + .. youtube:: uK4n5g6DBcg """ - config = get_config(home_dir=os.environ.get('_MNE_FAKE_HOME_DIR')) + unsupported_params = { + 'tabbed': (tabbed, False), + 'split': (split, True), + 'scrollable': (scrollable, True), + 'head_inside': (head_inside, True), + 'guess_mri_subject': guess_mri_subject, + 'scale': scale, + 'advanced_rendering': advanced_rendering, + } + for key, val in unsupported_params.items(): + if isinstance(val, tuple): + to_raise = val[0] != val[1] + else: + to_raise = val is not None + if to_raise: + warn(f"The parameter {key} is not supported with" + " the pyvistaqt 3d backend. It will be ignored.") + config = get_config() if guess_mri_subject is None: guess_mri_subject = config.get( 'MNE_COREG_GUESS_MRI_SUBJECT', 'true') == 'true' @@ -142,7 +153,7 @@ advanced_rendering = \ config.get('MNE_COREG_ADVANCED_RENDERING', 'true') == 'true' if head_opacity is None: - head_opacity = config.get('MNE_COREG_HEAD_OPACITY', 1.) + head_opacity = config.get('MNE_COREG_HEAD_OPACITY', 0.8) if head_inside is None: head_inside = \ config.get('MNE_COREG_HEAD_INSIDE', 'true').lower() == 'true' @@ -155,8 +166,6 @@ subjects_dir = config['SUBJECTS_DIR'] elif 'MNE_COREG_SUBJECTS_DIR' in config: subjects_dir = config['MNE_COREG_SUBJECTS_DIR'] - if project_eeg is None: - project_eeg = config.get('MNE_COREG_PROJECT_EEG', '') == 'true' if orient_to_surface is None: orient_to_surface = (config.get('MNE_COREG_ORIENT_TO_SURFACE', '') == 'true') @@ -164,78 +173,124 @@ scale_by_distance = (config.get('MNE_COREG_SCALE_BY_DISTANCE', '') == 'true') if interaction is None: - interaction = config.get('MNE_COREG_INTERACTION', 'trackball') + interaction = config.get('MNE_COREG_INTERACTION', 'terrain') if mark_inside is None: mark_inside = config.get('MNE_COREG_MARK_INSIDE', '') == 'true' if scale is None: scale = config.get('MNE_COREG_SCENE_SCALE', 0.16) + if fullscreen is None: + fullscreen = config.get('MNE_COREG_FULLSCREEN', '') == 'true' head_opacity = float(head_opacity) head_inside = bool(head_inside) width = int(width) height = int(height) scale = float(scale) - _check_mayavi_version() - from ._backend import _check_backend - _check_backend() - from ._coreg_gui import CoregFrame, _make_view - view = _make_view(tabbed, split, width, height, scrollable) - frame = CoregFrame(inst, subject, subjects_dir, guess_mri_subject, - head_opacity, head_high_res, trans, config, - project_eeg=project_eeg, - orient_to_surface=orient_to_surface, - scale_by_distance=scale_by_distance, - mark_inside=mark_inside, interaction=interaction, - scale=scale, advanced_rendering=advanced_rendering, - head_inside=head_inside) - return _initialize_gui(frame, view) + + from ..viz.backends.renderer import MNE_3D_BACKEND_TESTING + from ._coreg import CoregistrationUI + if MNE_3D_BACKEND_TESTING: + show = block = False + return CoregistrationUI( + info_file=inst, subject=subject, subjects_dir=subjects_dir, + head_resolution=head_high_res, head_opacity=head_opacity, + orient_glyphs=orient_to_surface, scale_by_distance=scale_by_distance, + mark_inside=mark_inside, trans=trans, size=(width, height), show=show, + block=block, interaction=interaction, fullscreen=fullscreen, + verbose=verbose + ) -def fiducials(subject=None, fid_file=None, subjects_dir=None): - """Set the fiducials for an MRI subject. +@verbose +def locate_ieeg(info, trans, aligned_ct, subject=None, subjects_dir=None, + groups=None, show=True, block=False, verbose=None): + """Locate intracranial electrode contacts. Parameters ---------- - subject : str - Name of the mri subject. - fid_file : None | str - Load a fiducials file different form the subject's default - ("{subjects_dir}/{subject}/bem/{subject}-fiducials.fif"). - subjects_dir : None | str - Overrule the subjects_dir environment variable. - - Returns - ------- - frame : instance of FiducialsFrame - The GUI frame. - - Notes - ----- - All parameters are optional, since they can be set through the GUI. - The functionality in this GUI is also part of :func:`coregistration`. - """ - _check_mayavi_version() - from ._backend import _check_backend - _check_backend() - from ._fiducials_gui import FiducialsFrame - frame = FiducialsFrame(subject, subjects_dir, fid_file=fid_file) - return _initialize_gui(frame) - - -def kit2fiff(): - """Convert KIT files to the fiff format. - - The recommended way to use the GUI is through bash with:: - - $ mne kit2fiff + %(info_not_none)s + %(trans_not_none)s + aligned_ct : path-like | nibabel.spatialimages.SpatialImage + The CT image that has been aligned to the Freesurfer T1. Path-like + inputs and nibabel image objects are supported. + %(subject)s + %(subjects_dir)s + groups : dict | None + A dictionary with channels as keys and their group index as values. + If None, the groups will be inferred by the channel names. Channel + names must have a format like ``LAMY 7`` where a string prefix + like ``LAMY`` precedes a numeric index like ``7``. If the channels + are formatted improperly, group plotting will work incorrectly. + Group assignments can be adjusted in the GUI. + show : bool + Show the GUI if True. + block : bool + Whether to halt program execution until the figure is closed. + %(verbose)s Returns ------- - frame : instance of Kit2FiffFrame - The GUI frame. + gui : instance of IntracranialElectrodeLocator + The graphical user interface (GUI) window. """ - _check_mayavi_version() - from ._backend import _check_backend - _check_backend() - from ._kit2fiff_gui import Kit2FiffFrame - frame = Kit2FiffFrame() - return _initialize_gui(frame) + from ..viz.backends._utils import _qt_app_exec + from ._ieeg_locate import IntracranialElectrodeLocator + from qtpy.QtWidgets import QApplication + # get application + app = QApplication.instance() + if app is None: + app = QApplication(["Intracranial Electrode Locator"]) + gui = IntracranialElectrodeLocator( + info, trans, aligned_ct, subject=subject, subjects_dir=subjects_dir, + groups=groups, show=show, verbose=verbose) + if block: + _qt_app_exec(app) + return gui + + +class _GUIScraper(object): + """Scrape GUI outputs.""" + + def __repr__(self): + return '' + + def __call__(self, block, block_vars, gallery_conf): + from ._ieeg_locate import IntracranialElectrodeLocator + from ._coreg import CoregistrationUI + from sphinx_gallery.scrapers import figure_rst + from qtpy import QtGui + for gui in block_vars['example_globals'].values(): + if (isinstance(gui, (IntracranialElectrodeLocator, + CoregistrationUI)) and + not getattr(gui, '_scraped', False) and + gallery_conf['builder_name'] == 'html'): + gui._scraped = True # monkey-patch but it's easy enough + img_fname = next(block_vars['image_path_iterator']) + # TODO fix in window refactor + window = gui if hasattr(gui, 'grab') else gui._renderer._window + # window is QWindow + # https://doc.qt.io/qt-5/qwidget.html#grab + pixmap = window.grab() + if hasattr(gui, '_renderer'): # if no renderer, no need + # Now the tricky part: we need to get the 3D renderer, + # extract the image from it, and put it in the correct + # place in the pixmap. The easiest way to do this is + # actually to save the 3D image first, then load it + # using QPixmap and Qt geometry. + plotter = gui._renderer.plotter + plotter.screenshot(img_fname) + sub_pixmap = QtGui.QPixmap(img_fname) + # https://doc.qt.io/qt-5/qwidget.html#mapTo + # https://doc.qt.io/qt-5/qpainter.html#drawPixmap-1 + QtGui.QPainter(pixmap).drawPixmap( + plotter.mapTo(window, plotter.rect().topLeft()), + sub_pixmap) + # https://doc.qt.io/qt-5/qpixmap.html#save + pixmap.save(img_fname) + try: # for compatibility with both GUIs, will be refactored + gui._renderer.close() # TODO should be triggered by close + except Exception: + pass + gui.close() + return figure_rst( + [img_fname], gallery_conf['src_dir'], 'GUI') + return '' diff -Nru python-mne-0.23.4+dfsg/mne/gui/_kit2fiff_gui.py python-mne-1.1.0+dfsg/mne/gui/_kit2fiff_gui.py --- python-mne-0.23.4+dfsg/mne/gui/_kit2fiff_gui.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/_kit2fiff_gui.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,757 +0,0 @@ -"""Mayavi/traits GUI for converting data from KIT systems.""" - -# Authors: Christian Brodbeck -# -# License: BSD (3-clause) - -from collections import Counter -import os -import queue -import sys -from threading import Thread - -import numpy as np - -from mayavi.core.ui.mayavi_scene import MayaviScene -from mayavi.tools.mlab_scene_model import MlabSceneModel -from pyface.api import (confirm, error, FileDialog, OK, YES, information, - ProgressDialog, warning) -from traits.api import (HasTraits, HasPrivateTraits, cached_property, Instance, - Property, Bool, Button, Enum, File, Float, Int, List, - Str, Array, DelegatesTo, on_trait_change) -from traits.trait_base import ETSConfig -from traitsui.api import (View, Item, HGroup, VGroup, spring, TextEditor, - CheckListEditor, EnumEditor, Handler) -from traitsui.menu import NoButtons -from tvtk.pyface.scene_editor import SceneEditor - -from ..io.constants import FIFF -from ..io._digitization import _make_dig_points -from ..io.kit.coreg import _read_dig_kit -from ..io.kit.kit import (RawKIT, KIT, _make_stim_channel, _default_stim_chs, - UnsupportedKITFormat) -from ..transforms import (apply_trans, als_ras_trans, - get_ras_to_neuromag_trans, Transform) -from ..coreg import _decimate_points, fit_matched_points -from ..utils import get_config, set_config, logger, warn -from ._backend import _get_pyface_backend -from ..event import _find_events -from ._marker_gui import CombineMarkersPanel, CombineMarkersModel -from ._help import read_tooltips -from ._viewer import HeadViewController, PointObject - -use_editor = CheckListEditor(cols=5, values=[(i, str(i)) for i in range(5)]) - -if _get_pyface_backend() == 'wx': - # wx backend allows labels for wildcards - hsp_wildcard = ['Head Shape Points (*.hsp;*.txt)|*.hsp;*.txt'] - elp_wildcard = ['Head Shape Fiducials (*.elp;*.txt)|*.elp;*.txt'] - kit_con_wildcard = ['Continuous KIT Files (*.sqd;*.con)|*.sqd;*.con'] -if sys.platform in ('win32', 'linux2'): - # on Windows and Ubuntu, multiple wildcards does not seem to work - hsp_wildcard = ['*.hsp', '*.txt'] - elp_wildcard = ['*.elp', '*.txt'] - kit_con_wildcard = ['*.sqd', '*.con'] -else: - hsp_wildcard = ['*.hsp;*.txt'] - elp_wildcard = ['*.elp;*.txt'] - kit_con_wildcard = ['*.sqd;*.con'] - - -tooltips = read_tooltips('kit2fiff') - - -class Kit2FiffModel(HasPrivateTraits): - """Data Model for Kit2Fiff conversion. - - - Markers are transformed into RAS coordinate system (as are the sensor - coordinates). - - Head shape digitizer data is transformed into neuromag-like space. - """ - - # Input Traits - markers = Instance(CombineMarkersModel, ()) - sqd_file = File(exists=True, filter=kit_con_wildcard) - allow_unknown_format = Bool(False) - hsp_file = File(exists=True, filter=hsp_wildcard) - fid_file = File(exists=True, filter=elp_wildcard) - stim_coding = Enum(">", "<", "channel") - stim_chs = Str("") - stim_chs_array = Property(depends_on=['raw', 'stim_chs', 'stim_coding']) - stim_chs_ok = Property(depends_on='stim_chs_array') - stim_chs_comment = Property(depends_on='stim_chs_array') - stim_slope = Enum("-", "+") - stim_threshold = Float(1.) - - # Marker Points - use_mrk = List(list(range(5)), desc="Which marker points to use for the " - "device head coregistration.") - - # Derived Traits - mrk = Property(depends_on='markers.mrk3.points') - - # Polhemus Fiducials - elp_raw = Property(depends_on=['fid_file']) - hsp_raw = Property(depends_on=['hsp_file']) - polhemus_neuromag_trans = Property(depends_on=['elp_raw']) - - # Polhemus data (in neuromag space) - elp = Property(depends_on=['elp_raw', 'polhemus_neuromag_trans']) - fid = Property(depends_on=['elp_raw', 'polhemus_neuromag_trans']) - hsp = Property(depends_on=['hsp_raw', 'polhemus_neuromag_trans']) - - # trans - dev_head_trans = Property(depends_on=['elp', 'mrk', 'use_mrk']) - head_dev_trans = Property(depends_on=['dev_head_trans']) - - # event preview - raw = Property(depends_on='sqd_file') - misc_chs = Property(List, depends_on='raw') - misc_chs_desc = Property(Str, depends_on='misc_chs') - misc_data = Property(Array, depends_on='raw') - can_test_stim = Property(Bool, depends_on='raw') - - # info - sqd_fname = Property(Str, depends_on='sqd_file') - hsp_fname = Property(Str, depends_on='hsp_file') - fid_fname = Property(Str, depends_on='fid_file') - can_save = Property(Bool, depends_on=['stim_chs_ok', 'fid', - 'elp', 'hsp', 'dev_head_trans']) - - # Show GUI feedback (like error messages and progress bar) - show_gui = Bool(False) - - @cached_property - def _get_can_save(self): - """Only allow saving when all or no head shape elements are set.""" - if not self.stim_chs_ok: - return False - - has_all_hsp = (np.any(self.dev_head_trans) and np.any(self.hsp) and - np.any(self.elp) and np.any(self.fid)) - if has_all_hsp: - return True - - has_any_hsp = self.hsp_file or self.fid_file or np.any(self.mrk) - return not has_any_hsp - - @cached_property - def _get_can_test_stim(self): - return self.raw is not None - - @cached_property - def _get_dev_head_trans(self): - if (self.mrk is None) or not np.any(self.fid): - return np.eye(4) - - src_pts = self.mrk - dst_pts = self.elp - - n_use = len(self.use_mrk) - if n_use < 3: - if self.show_gui: - error(None, "Estimating the device head transform requires at " - "least 3 marker points. Please adjust the markers used.", - "Not Enough Marker Points") - return - elif n_use < 5: - src_pts = src_pts[self.use_mrk] - dst_pts = dst_pts[self.use_mrk] - - trans = fit_matched_points(src_pts, dst_pts, out='trans') - return trans - - @cached_property - def _get_elp(self): - if self.elp_raw is None: - return np.empty((0, 3)) - pts = self.elp_raw[3:8] - pts = apply_trans(self.polhemus_neuromag_trans, pts) - return pts - - @cached_property - def _get_elp_raw(self): - if not self.fid_file: - return - - try: - pts = _read_dig_kit(self.fid_file) - if len(pts) < 8: - raise ValueError("File contains %i points, need 8" % len(pts)) - except Exception as err: - if self.show_gui: - error(None, str(err), "Error Reading Fiducials") - self.reset_traits(['fid_file']) - raise - else: - return pts - - @cached_property - def _get_fid(self): - if self.elp_raw is None: - return np.empty((0, 3)) - pts = self.elp_raw[:3] - pts = apply_trans(self.polhemus_neuromag_trans, pts) - return pts - - @cached_property - def _get_fid_fname(self): - if self.fid_file: - return os.path.basename(self.fid_file) - else: - return '-' - - @cached_property - def _get_head_dev_trans(self): - return np.linalg.inv(self.dev_head_trans) - - @cached_property - def _get_hsp(self): - if (self.hsp_raw is None) or not np.any(self.polhemus_neuromag_trans): - return np.empty((0, 3)) - else: - pts = apply_trans(self.polhemus_neuromag_trans, self.hsp_raw) - return pts - - @cached_property - def _get_hsp_fname(self): - if self.hsp_file: - return os.path.basename(self.hsp_file) - else: - return '-' - - @cached_property - def _get_hsp_raw(self): - fname = self.hsp_file - if not fname: - return - - try: - pts = _read_dig_kit(fname) - n_pts = len(pts) - if n_pts > KIT.DIG_POINTS: - msg = ("The selected head shape contains {n_in} points, " - "which is more than the recommended maximum ({n_rec}). " - "The file will be automatically downsampled, which " - "might take a while. A better way to downsample is " - "using FastScan.". - format(n_in=n_pts, n_rec=KIT.DIG_POINTS)) - if self.show_gui: - information(None, msg, "Too Many Head Shape Points") - pts = _decimate_points(pts, 5) - - except Exception as err: - if self.show_gui: - error(None, str(err), "Error Reading Head Shape") - self.reset_traits(['hsp_file']) - raise - else: - return pts - - @cached_property - def _get_misc_chs(self): - if not self.raw: - return - return [i for i, ch in enumerate(self.raw.info['chs']) if - ch['kind'] == FIFF.FIFFV_MISC_CH] - - @cached_property - def _get_misc_chs_desc(self): - if self.misc_chs is None: - return "No SQD file selected..." - elif np.all(np.diff(self.misc_chs) == 1): - return "%i:%i" % (self.misc_chs[0], self.misc_chs[-1] + 1) - else: - return "%i... (discontinuous)" % self.misc_chs[0] - - @cached_property - def _get_misc_data(self): - if not self.raw: - return - if self.show_gui: - # progress dialog with indefinite progress bar - prog = ProgressDialog(title="Loading SQD data...", - message="Loading stim channel data from SQD " - "file ...") - prog.open() - prog.update(0) - else: - prog = None - - try: - data, times = self.raw[self.misc_chs] - except Exception as err: - if self.show_gui: - error(None, "Error reading SQD data file: %s (Check the " - "terminal output for details)" % str(err), - "Error Reading SQD File") - raise - finally: - if self.show_gui: - prog.close() - return data - - @cached_property - def _get_mrk(self): - return apply_trans(als_ras_trans, self.markers.mrk3.points) - - @cached_property - def _get_polhemus_neuromag_trans(self): - if self.elp_raw is None: - return - nasion, lpa, rpa = apply_trans(als_ras_trans, self.elp_raw[:3]) - trans = get_ras_to_neuromag_trans(nasion, lpa, rpa) - return np.dot(trans, als_ras_trans) - - @cached_property - def _get_raw(self): - if not self.sqd_file: - return - try: - return RawKIT(self.sqd_file, stim=None, - allow_unknown_format=self.allow_unknown_format) - except UnsupportedKITFormat as exception: - warning( - None, - "The selected SQD file is written in an old file format (%s) " - "that is not officially supported. Confirm that the results " - "are as expected. This warning is displayed only once per " - "session." % (exception.sqd_version,), - "Unsupported SQD File Format") - self.allow_unknown_format = True - return self._get_raw() - except Exception as err: - self.reset_traits(['sqd_file']) - if self.show_gui: - error(None, "Error reading SQD data file: %s (Check the " - "terminal output for details)" % str(err), - "Error Reading SQD File") - raise - - @cached_property - def _get_sqd_fname(self): - if self.sqd_file: - return os.path.basename(self.sqd_file) - else: - return '-' - - @cached_property - def _get_stim_chs_array(self): - if self.raw is None: - return - elif not self.stim_chs.strip(): - picks = _default_stim_chs(self.raw.info) - else: - try: - picks = eval("r_[%s]" % self.stim_chs, vars(np)) - if picks.dtype.kind != 'i': - raise TypeError("Need array of int") - except Exception: - return None - - if self.stim_coding == '<': # Big-endian - return picks[::-1] - else: - return picks - - @cached_property - def _get_stim_chs_comment(self): - if self.raw is None: - return "" - elif not self.stim_chs_ok: - return "Invalid!" - elif not self.stim_chs.strip(): - return "Default: The first 8 MISC channels" - else: - return "Ok: %i channels" % len(self.stim_chs_array) - - @cached_property - def _get_stim_chs_ok(self): - return self.stim_chs_array is not None - - def clear_all(self): - """Clear all specified input parameters.""" - self.markers.clear = True - self.reset_traits(['sqd_file', 'hsp_file', 'fid_file', 'use_mrk']) - - def get_event_info(self): - """Count events with current stim channel settings. - - Returns - ------- - event_count : Counter - Counter mapping event ID to number of occurrences. - """ - if self.misc_data is None: - return - idx = [self.misc_chs.index(ch) for ch in self.stim_chs_array] - data = self.misc_data[idx] - if self.stim_coding == 'channel': - coding = 'channel' - else: - coding = 'binary' - stim_ch = _make_stim_channel(data, self.stim_slope, - self.stim_threshold, coding, - self.stim_chs_array) - events = _find_events(stim_ch, self.raw.first_samp, consecutive=True, - min_samples=3) - return Counter(events[:, 2]) - - def get_raw(self, preload=False): - """Create a raw object based on the current model settings.""" - if not self.can_save: - raise ValueError("Not all necessary parameters are set") - - # stim channels and coding - if self.stim_coding == 'channel': - stim_code = 'channel' - elif self.stim_coding in '<>': - stim_code = 'binary' - else: - raise RuntimeError("stim_coding=%r" % self.stim_coding) - - logger.info("Creating raw with stim=%r, slope=%r, stim_code=%r, " - "stimthresh=%r", self.stim_chs_array, self.stim_slope, - stim_code, self.stim_threshold) - raw = RawKIT(self.sqd_file, preload=preload, stim=self.stim_chs_array, - slope=self.stim_slope, stim_code=stim_code, - stimthresh=self.stim_threshold, - allow_unknown_format=self.allow_unknown_format) - - if np.any(self.fid): - raw.info['dig'] = _make_dig_points(self.fid[0], self.fid[1], - self.fid[2], self.elp, - self.hsp) - raw.info['dev_head_t'] = Transform('meg', 'head', - self.dev_head_trans) - return raw - - -class Kit2FiffFrameHandler(Handler): - """Check for unfinished processes before closing its window.""" - - def close(self, info, is_ok): # noqa: D102 - if info.object.kit2fiff_panel.queue.unfinished_tasks: - msg = ("Can not close the window while saving is still in " - "progress. Please wait until all files are processed.") - title = "Saving Still in Progress" - information(None, msg, title) - return False - else: - # store configuration, but don't prevent from closing on error - try: - info.object.save_config() - except Exception as exc: - warn("Error saving GUI configuration:\n%s" % (exc,)) - return True - - -class Kit2FiffPanel(HasPrivateTraits): - """Control panel for kit2fiff conversion.""" - - model = Instance(Kit2FiffModel) - - # model copies for view - use_mrk = DelegatesTo('model') - sqd_file = DelegatesTo('model') - hsp_file = DelegatesTo('model') - fid_file = DelegatesTo('model') - stim_coding = DelegatesTo('model') - stim_chs = DelegatesTo('model') - stim_chs_ok = DelegatesTo('model') - stim_chs_comment = DelegatesTo('model') - stim_slope = DelegatesTo('model') - stim_threshold = DelegatesTo('model') - - # info - can_save = DelegatesTo('model') - sqd_fname = DelegatesTo('model') - hsp_fname = DelegatesTo('model') - fid_fname = DelegatesTo('model') - misc_chs_desc = DelegatesTo('model') - can_test_stim = DelegatesTo('model') - test_stim = Button(label="Find Events") - plot_raw = Button(label="Plot Raw") - - # Source Files - reset_dig = Button - - # Visualization - scene = Instance(MlabSceneModel) - fid_obj = Instance(PointObject) - elp_obj = Instance(PointObject) - hsp_obj = Instance(PointObject) - - # Output - save_as = Button(label='Save FIFF...') - clear_all = Button(label='Clear All') - queue = Instance(queue.Queue, ()) - queue_feedback = Str('') - queue_current = Str('') - queue_len = Int(0) - queue_len_str = Property(Str, depends_on=['queue_len']) - error = Str('') - - view = View( - VGroup(VGroup(Item('sqd_file', label="Data", - tooltip=tooltips['sqd_file']), - Item('sqd_fname', show_label=False, style='readonly'), - Item('hsp_file', label='Digitizer\nHead Shape', - tooltip=tooltips['hsp_file']), - Item('hsp_fname', show_label=False, style='readonly'), - Item('fid_file', label='Digitizer\nFiducials', - tooltip=tooltips['fid_file']), - Item('fid_fname', show_label=False, style='readonly'), - Item('reset_dig', label='Clear Digitizer Files', - show_label=False), - Item('use_mrk', editor=use_editor, style='custom', - tooltip=tooltips['use_mrk']), - label="Sources", show_border=True), - VGroup(Item('misc_chs_desc', label='MISC Channels', - style='readonly'), - Item('stim_slope', label="Event Onset", style='custom', - tooltip=tooltips['stim_slope'], - editor=EnumEditor( - values={'+': '2:Peak (0 to 5 V)', - '-': '1:Trough (5 to 0 V)'}, - cols=2)), - Item('stim_coding', label="Value Coding", style='custom', - editor=EnumEditor(values={'>': '1:little-endian', - '<': '2:big-endian', - 'channel': '3:Channel#'}, - cols=3), - tooltip=tooltips["stim_coding"]), - Item('stim_chs', label='Channels', style='custom', - tooltip=tooltips["stim_chs"], - editor=TextEditor(evaluate_name='stim_chs_ok', - auto_set=True)), - Item('stim_chs_comment', label='Evaluation', - style='readonly', show_label=False), - Item('stim_threshold', label='Threshold', - tooltip=tooltips['stim_threshold']), - HGroup(Item('test_stim', enabled_when='can_test_stim', - show_label=False), - Item('plot_raw', enabled_when='can_test_stim', - show_label=False), - show_labels=False), - label='Events', show_border=True), - HGroup(Item('save_as', enabled_when='can_save'), spring, - 'clear_all', show_labels=False), - Item('queue_feedback', show_label=False, style='readonly'), - Item('queue_current', show_label=False, style='readonly'), - Item('queue_len_str', show_label=False, style='readonly') - ) - ) - - def __init__(self, *args, **kwargs): # noqa: D102 - super(Kit2FiffPanel, self).__init__(*args, **kwargs) - - # setup save worker - def worker(): # noqa: D102 - while True: - raw, fname = self.queue.get() - basename = os.path.basename(fname) - self.queue_len -= 1 - self.queue_current = 'Processing: %s' % basename - - # task - try: - raw.save(fname, overwrite=True) - except Exception as err: - self.error = str(err) - res = "Error saving: %s" - else: - res = "Saved: %s" - - # finalize - self.queue_current = '' - self.queue_feedback = res % basename - self.queue.task_done() - - t = Thread(target=worker) - t.daemon = True - t.start() - - # setup mayavi visualization - self.fid_obj = PointObject(scene=self.scene, color=(0.1, 1., 0.1), - point_scale=5e-3, name='Fiducials') - self._update_fid() - self.elp_obj = PointObject(scene=self.scene, - color=(0.196, 0.196, 0.863), - point_scale=1e-2, opacity=.2, name='ELP') - self._update_elp() - self.hsp_obj = PointObject(scene=self.scene, color=(0.784,) * 3, - point_scale=2e-3, name='HSP') - self._update_hsp() - self.scene.camera.parallel_scale = 0.15 - self.scene.mlab.view(0, 0, .15) - - @on_trait_change('model:fid,model:head_dev_trans') - def _update_fid(self): - if self.fid_obj is not None: - self.fid_obj.points = apply_trans(self.model.head_dev_trans, - self.model.fid) - - @on_trait_change('model:hsp,model:head_dev_trans') - def _update_hsp(self): - if self.hsp_obj is not None: - self.hsp_obj.points = apply_trans(self.model.head_dev_trans, - self.model.hsp) - - @on_trait_change('model:elp,model:head_dev_trans') - def _update_elp(self): - if self.elp_obj is not None: - self.elp_obj.points = apply_trans(self.model.head_dev_trans, - self.model.elp) - - def _clear_all_fired(self): - self.model.clear_all() - - @cached_property - def _get_queue_len_str(self): - if self.queue_len: - return "Queue length: %i" % self.queue_len - else: - return '' - - def _plot_raw_fired(self): - self.model.raw.plot() - - def _reset_dig_fired(self): - self.reset_traits(['hsp_file', 'fid_file']) - - def _save_as_fired(self): - # create raw - try: - raw = self.model.get_raw() - except Exception as err: - error(None, str(err), "Error Creating KIT Raw") - raise - - # find default path - stem, _ = os.path.splitext(self.sqd_file) - if not stem.endswith('raw'): - stem += '-raw' - default_path = stem + '.fif' - - # save as dialog - dlg = FileDialog(action="save as", - wildcard="fiff raw file (*.fif)|*.fif", - default_path=default_path) - dlg.open() - if dlg.return_code != OK: - return - - fname = dlg.path - if not fname.endswith('.fif'): - fname += '.fif' - if os.path.exists(fname): - answer = confirm(None, "The file %r already exists. Should it " - "be replaced?", "Overwrite File?") - if answer != YES: - return - - self.queue.put((raw, fname)) - self.queue_len += 1 - - def _test_stim_fired(self): - try: - events = self.model.get_event_info() - except Exception as err: - error(None, "Error reading events from SQD data file: %s (Check " - "the terminal output for details)" % str(err), - "Error Reading events from SQD file") - raise - - if len(events) == 0: - information(None, "No events were found with the current " - "settings.", "No Events Found") - else: - lines = ["Events found (ID: n events):"] - for id_ in sorted(events): - lines.append("%3i: \t%i" % (id_, events[id_])) - information(None, '\n'.join(lines), "Events in SQD File") - - -class Kit2FiffFrame(HasTraits): - """GUI for interpolating between two KIT marker files.""" - - model = Instance(Kit2FiffModel) - scene = Instance(MlabSceneModel, ()) - headview = Instance(HeadViewController) - marker_panel = Instance(CombineMarkersPanel) - kit2fiff_panel = Instance(Kit2FiffPanel) - - view = View(HGroup(VGroup(Item('marker_panel', style='custom'), - show_labels=False), - VGroup(Item('scene', - editor=SceneEditor(scene_class=MayaviScene), - dock='vertical', show_label=False), - VGroup(Item('headview', style='custom'), - show_labels=False), - ), - VGroup(Item('kit2fiff_panel', style='custom'), - show_labels=False), - show_labels=False, - ), - handler=Kit2FiffFrameHandler(), - height=700, resizable=True, buttons=NoButtons) - - def __init__(self, *args, **kwargs): # noqa: D102 - logger.debug( - "Initializing Kit2fiff-GUI with %s backend", ETSConfig.toolkit) - HasTraits.__init__(self, *args, **kwargs) - - # can't be static method due to Traits - def _model_default(self): - # load configuration values and make sure they're valid - config = get_config(home_dir=os.environ.get('_MNE_FAKE_HOME_DIR')) - stim_threshold = 1. - if 'MNE_KIT2FIFF_STIM_CHANNEL_THRESHOLD' in config: - try: - stim_threshold = float( - config['MNE_KIT2FIFF_STIM_CHANNEL_THRESHOLD']) - except ValueError: - warn("Ignoring invalid configuration value for " - "MNE_KIT2FIFF_STIM_CHANNEL_THRESHOLD: %r (expected " - "float)" % - (config['MNE_KIT2FIFF_STIM_CHANNEL_THRESHOLD'],)) - stim_slope = config.get('MNE_KIT2FIFF_STIM_CHANNEL_SLOPE', '-') - if stim_slope not in '+-': - warn("Ignoring invalid configuration value for " - "MNE_KIT2FIFF_STIM_CHANNEL_THRESHOLD: %s (expected + or -)" % - stim_slope) - stim_slope = '-' - stim_coding = config.get('MNE_KIT2FIFF_STIM_CHANNEL_CODING', '>') - if stim_coding not in ('<', '>', 'channel'): - warn("Ignoring invalid configuration value for " - "MNE_KIT2FIFF_STIM_CHANNEL_CODING: %s (expected <, > or " - "channel)" % stim_coding) - stim_coding = '>' - return Kit2FiffModel( - stim_chs=config.get('MNE_KIT2FIFF_STIM_CHANNELS', ''), - stim_coding=stim_coding, - stim_slope=stim_slope, - stim_threshold=stim_threshold, - show_gui=True) - - def _headview_default(self): - return HeadViewController(scene=self.scene, scale=160, system='RAS') - - def _kit2fiff_panel_default(self): - return Kit2FiffPanel(scene=self.scene, model=self.model) - - def _marker_panel_default(self): - return CombineMarkersPanel(scene=self.scene, model=self.model.markers, - trans=als_ras_trans) - - def save_config(self, home_dir=None): - """Write configuration values.""" - set_config('MNE_KIT2FIFF_STIM_CHANNELS', self.model.stim_chs, home_dir, - set_env=False) - set_config('MNE_KIT2FIFF_STIM_CHANNEL_CODING', self.model.stim_coding, - home_dir, set_env=False) - set_config('MNE_KIT2FIFF_STIM_CHANNEL_SLOPE', self.model.stim_slope, - home_dir, set_env=False) - set_config('MNE_KIT2FIFF_STIM_CHANNEL_THRESHOLD', - str(self.model.stim_threshold), home_dir, set_env=False) diff -Nru python-mne-0.23.4+dfsg/mne/gui/_marker_gui.py python-mne-1.1.0+dfsg/mne/gui/_marker_gui.py --- python-mne-0.23.4+dfsg/mne/gui/_marker_gui.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/_marker_gui.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,455 +0,0 @@ -"""Mayavi/traits GUI for averaging two sets of KIT marker points.""" - -# Authors: Christian Brodbeck -# -# License: BSD (3-clause) - -import os -import sys - -import numpy as np - -from mayavi.tools.mlab_scene_model import MlabSceneModel -from pyface.api import confirm, error, FileDialog, OK, YES -from traits.api import (HasTraits, HasPrivateTraits, on_trait_change, - cached_property, Instance, Property, Array, Bool, - Button, Enum, File, Float, List, Str, ArrayOrNone) -from traitsui.api import View, Item, HGroup, VGroup, CheckListEditor -from traitsui.menu import Action, CancelButton - -from ..transforms import apply_trans, rotation, translation -from ..coreg import fit_matched_points -from ..io.kit import read_mrk -from ..io._digitization import _write_dig_points -from ._viewer import PointObject -from ._backend import _get_pyface_backend - - -if _get_pyface_backend() == 'wx': - mrk_wildcard = [ - 'Supported Files (*.sqd, *.mrk, *.txt, *.pickled)|*.sqd;*.mrk;*.txt;*.pickled', # noqa:E501 - 'Sqd marker file (*.sqd;*.mrk)|*.sqd;*.mrk', - 'Text marker file (*.txt)|*.txt', - 'Pickled markers (*.pickled)|*.pickled'] - mrk_out_wildcard = ["Tab separated values file (*.txt)|*.txt"] -else: - if sys.platform in ('win32', 'linux2'): - # on Windows and Ubuntu, multiple wildcards does not seem to work - mrk_wildcard = ["*.sqd", "*.mrk", "*.txt", "*.pickled"] - else: - mrk_wildcard = ["*.sqd;*.mrk;*.txt;*.pickled"] - mrk_out_wildcard = "*.txt" -out_ext = '.txt' - - -use_editor_v = CheckListEditor(cols=1, values=[(i, str(i)) for i in range(5)]) -use_editor_h = CheckListEditor(cols=5, values=[(i, str(i)) for i in range(5)]) - -mrk_view_editable = View( - VGroup('file', - Item('name', show_label=False, style='readonly'), - HGroup( - Item('use', editor=use_editor_v, enabled_when="enabled", - style='custom'), - 'points', - ), - HGroup(Item('clear', enabled_when="can_save", show_label=False), - Item('save_as', enabled_when="can_save", - show_label=False)), - )) - -mrk_view_basic = View( - VGroup('file', - Item('name', show_label=False, style='readonly'), - Item('use', editor=use_editor_h, enabled_when="enabled", - style='custom'), - HGroup(Item('clear', enabled_when="can_save", show_label=False), - Item('edit', show_label=False), - Item('switch_left_right', label="Switch Left/Right", - show_label=False), - Item('reorder', show_label=False), - Item('save_as', enabled_when="can_save", - show_label=False)), - )) - -mrk_view_edit = View(VGroup('points')) - - -class ReorderDialog(HasPrivateTraits): - """Dialog for reordering marker points.""" - - order = Str("0 1 2 3 4") - index = Property(List, depends_on='order') - is_ok = Property(Bool, depends_on='order') - - view = View( - Item('order', label='New order (five space delimited numbers)'), - buttons=[CancelButton, Action(name='OK', enabled_when='is_ok')]) - - def _get_index(self): - try: - return [int(i) for i in self.order.split()] - except ValueError: - return [] - - def _get_is_ok(self): - return sorted(self.index) == [0, 1, 2, 3, 4] - - -class MarkerPoints(HasPrivateTraits): - """Represent 5 marker points.""" - - points = Array(float, (5, 3)) - - can_save = Property(depends_on='points') - save_as = Button() - - view = View(VGroup('points', - Item('save_as', enabled_when='can_save'))) - - @cached_property - def _get_can_save(self): - return np.any(self.points) - - def _save_as_fired(self): - dlg = FileDialog(action="save as", wildcard=mrk_out_wildcard, - default_filename=self.name, - default_directory=self.dir) - dlg.open() - if dlg.return_code != OK: - return - - path, ext = os.path.splitext(dlg.path) - if not path.endswith(out_ext) and len(ext) != 0: - ValueError("The extension '%s' is not supported." % ext) - path = path + out_ext - - if os.path.exists(path): - answer = confirm(None, "The file %r already exists. Should it " - "be replaced?", "Overwrite File?") - if answer != YES: - return - self.save(path) - - def save(self, path): - """Save the marker points. - - Parameters - ---------- - path : str - Path to the file to write. The kind of file to write is determined - based on the extension: '.txt' for tab separated text file, - '.pickled' for pickled file. - """ - _write_dig_points(path, self.points) - - -class MarkerPointSource(MarkerPoints): # noqa: D401 - """MarkerPoints subclass for source files.""" - - file = File(filter=mrk_wildcard, exists=True) - name = Property(Str, depends_on='file') - dir = Property(Str, depends_on='file') - - use = List(list(range(5)), desc="Which points to use for the interpolated " - "marker.") - enabled = Property(Bool, depends_on=['points', 'use']) - clear = Button(desc="Clear the current marker data") - edit = Button(desc="Edit the marker coordinates manually") - switch_left_right = Button( - desc="Switch left and right marker points; this is intended to " - "correct for markers that were attached in the wrong order") - reorder = Button(desc="Change the order of the marker points") - - view = mrk_view_basic - - @cached_property - def _get_enabled(self): - return np.any(self.points) - - @cached_property - def _get_dir(self): - if self.file: - return os.path.dirname(self.file) - - @cached_property - def _get_name(self): - if self.file: - return os.path.basename(self.file) - - @on_trait_change('file') - def load(self, fname): - if not fname: - self.reset_traits(['points']) - return - - try: - pts = read_mrk(fname) - except Exception as err: - error(None, str(err), "Error Reading mrk") - self.reset_traits(['points']) - else: - self.points = pts - - def _clear_fired(self): - self.reset_traits(['file', 'points', 'use']) - - def _edit_fired(self): - self.edit_traits(view=mrk_view_edit) - - def _reorder_fired(self): - dlg = ReorderDialog() - ui = dlg.edit_traits(kind='modal') - if not ui.result: # user pressed cancel - return - self.points = self.points[dlg.index] - - def _switch_left_right_fired(self): - self.points = self.points[[1, 0, 2, 4, 3]] - - -class MarkerPointDest(MarkerPoints): # noqa: D401 - """MarkerPoints subclass that serves for derived points.""" - - src1 = Instance(MarkerPointSource) - src2 = Instance(MarkerPointSource) - - name = Property(Str, depends_on='src1.name,src2.name') - dir = Property(Str, depends_on='src1.dir,src2.dir') - - points = Property(ArrayOrNone(float, (5, 3)), - depends_on=['method', 'src1.points', 'src1.use', - 'src2.points', 'src2.use']) - enabled = Property(Bool, depends_on=['points']) - - method = Enum('Transform', 'Average', desc="Transform: estimate a rotation" - "/translation from mrk1 to mrk2; Average: use the average " - "of the mrk1 and mrk2 coordinates for each point.") - - view = View(VGroup(Item('method', style='custom'), - Item('save_as', enabled_when='can_save', - show_label=False))) - - @cached_property - def _get_dir(self): - return self.src1.dir - - @cached_property - def _get_name(self): - n1 = self.src1.name - n2 = self.src2.name - - if not n1: - if n2: - return n2 - else: - return '' - elif not n2: - return n1 - - if n1 == n2: - return n1 - - i = 0 - l1 = len(n1) - 1 - l2 = len(n1) - 2 - while n1[i] == n2[i]: - if i == l1: - return n1 - elif i == l2: - return n2 - - i += 1 - - return n1[:i] - - @cached_property - def _get_enabled(self): - return np.any(self.points) - - @cached_property - def _get_points(self): - # in case only one or no source is enabled - if not (self.src1 and self.src1.enabled): - if (self.src2 and self.src2.enabled): - return self.src2.points - else: - return np.zeros((5, 3)) - elif not (self.src2 and self.src2.enabled): - return self.src1.points - - # Average method - if self.method == 'Average': - if len(np.union1d(self.src1.use, self.src2.use)) < 5: - error(None, "Need at least one source for each point.", - "Marker Average Error") - return np.zeros((5, 3)) - - pts = (self.src1.points + self.src2.points) / 2. - for i in np.setdiff1d(self.src1.use, self.src2.use): - pts[i] = self.src1.points[i] - for i in np.setdiff1d(self.src2.use, self.src1.use): - pts[i] = self.src2.points[i] - - return pts - - # Transform method - idx = np.intersect1d(np.array(self.src1.use), - np.array(self.src2.use), assume_unique=True) - if len(idx) < 3: - error(None, "Need at least three shared points for trans" - "formation.", "Marker Interpolation Error") - return np.zeros((5, 3)) - - src_pts = self.src1.points[idx] - tgt_pts = self.src2.points[idx] - est = fit_matched_points(src_pts, tgt_pts, out='params') - rot = np.array(est[:3]) / 2. - tra = np.array(est[3:]) / 2. - - if len(self.src1.use) == 5: - trans = np.dot(translation(*tra), rotation(*rot)) - pts = apply_trans(trans, self.src1.points) - elif len(self.src2.use) == 5: - trans = np.dot(translation(* -tra), rotation(* -rot)) - pts = apply_trans(trans, self.src2.points) - else: - trans1 = np.dot(translation(*tra), rotation(*rot)) - pts = apply_trans(trans1, self.src1.points) - trans2 = np.dot(translation(* -tra), rotation(* -rot)) - for i in np.setdiff1d(self.src2.use, self.src1.use): - pts[i] = apply_trans(trans2, self.src2.points[i]) - - return pts - - -class CombineMarkersModel(HasPrivateTraits): - """Combine markers model.""" - - mrk1_file = Instance(File) - mrk2_file = Instance(File) - mrk1 = Instance(MarkerPointSource) - mrk2 = Instance(MarkerPointSource) - mrk3 = Instance(MarkerPointDest) - - clear = Button(desc="Clear the current marker data") - - # stats - distance = Property(Str, depends_on=['mrk1.points', 'mrk2.points']) - - def _clear_fired(self): - self.mrk1.clear = True - self.mrk2.clear = True - self.mrk3.reset_traits(['method']) - - def _mrk1_default(self): - return MarkerPointSource() - - def _mrk1_file_default(self): - return self.mrk1.trait('file') - - def _mrk2_default(self): - return MarkerPointSource() - - def _mrk2_file_default(self): - return self.mrk2.trait('file') - - def _mrk3_default(self): - return MarkerPointDest(src1=self.mrk1, src2=self.mrk2) - - @cached_property - def _get_distance(self): - if (self.mrk1 is None or self.mrk2 is None or - (not np.any(self.mrk1.points)) or - (not np.any(self.mrk2.points))): - return "" - - ds = np.sqrt(np.sum((self.mrk1.points - self.mrk2.points) ** 2, 1)) - desc = '\t'.join('%.1f mm' % (d * 1000) for d in ds) - return desc - - -class CombineMarkersPanel(HasTraits): # noqa: D401 - """Has two marker points sources and interpolates to a third one.""" - - model = Instance(CombineMarkersModel, ()) - - # model references for UI - mrk1 = Instance(MarkerPointSource) - mrk2 = Instance(MarkerPointSource) - mrk3 = Instance(MarkerPointDest) - distance = Str - - # Visualization - scene = Instance(MlabSceneModel) - scale = Float(5e-3) - mrk1_obj = Instance(PointObject) - mrk2_obj = Instance(PointObject) - mrk3_obj = Instance(PointObject) - trans = Array() - - view = View(VGroup(VGroup(Item('mrk1', style='custom'), - Item('mrk1_obj', style='custom'), - show_labels=False, - label="Source Marker 1", show_border=True), - VGroup(Item('mrk2', style='custom'), - Item('mrk2_obj', style='custom'), - show_labels=False, - label="Source Marker 2", show_border=True), - VGroup(Item('distance', style='readonly'), - label='Stats', show_border=True), - VGroup(Item('mrk3', style='custom'), - Item('mrk3_obj', style='custom'), - show_labels=False, - label="New Marker", show_border=True), - )) - - def _mrk1_default(self): - return self.model.mrk1 - - def _mrk2_default(self): - return self.model.mrk2 - - def _mrk3_default(self): - return self.model.mrk3 - - def __init__(self, *args, **kwargs): # noqa: D102 - super(CombineMarkersPanel, self).__init__(*args, **kwargs) - - self.model.sync_trait('distance', self, 'distance', mutual=False) - - self.mrk1_obj = PointObject(scene=self.scene, - color=(0.608, 0.216, 0.216), - point_scale=self.scale) - self.model.mrk1.sync_trait( - 'enabled', self.mrk1_obj, 'visible', mutual=False) - - self.mrk2_obj = PointObject(scene=self.scene, - color=(0.216, 0.608, 0.216), - point_scale=self.scale) - self.model.mrk2.sync_trait( - 'enabled', self.mrk2_obj, 'visible', mutual=False) - - self.mrk3_obj = PointObject(scene=self.scene, - color=(0.588, 0.784, 1.), - point_scale=self.scale) - self.model.mrk3.sync_trait( - 'enabled', self.mrk3_obj, 'visible', mutual=False) - - @on_trait_change('model:mrk1:points,trans') - def _update_mrk1(self): - if self.mrk1_obj is not None: - self.mrk1_obj.points = apply_trans(self.trans, - self.model.mrk1.points) - - @on_trait_change('model:mrk2:points,trans') - def _update_mrk2(self): - if self.mrk2_obj is not None: - self.mrk2_obj.points = apply_trans(self.trans, - self.model.mrk2.points) - - @on_trait_change('model:mrk3:points,trans') - def _update_mrk3(self): - if self.mrk3_obj is not None: - self.mrk3_obj.points = apply_trans(self.trans, - self.model.mrk3.points) diff -Nru python-mne-0.23.4+dfsg/mne/gui/tests/test_coreg_gui.py python-mne-1.1.0+dfsg/mne/gui/tests/test_coreg_gui.py --- python-mne-0.23.4+dfsg/mne/gui/tests/test_coreg_gui.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/tests/test_coreg_gui.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,342 +0,0 @@ -# Author: Christian Brodbeck -# -# License: BSD (3-clause) - -import os -import os.path as op -import re -import shutil - -import numpy as np -from numpy.testing import assert_allclose, assert_array_almost_equal -import pytest - -import mne -from mne.datasets import testing -from mne.io.kit.tests import data_dir as kit_data_dir -from mne.surface import dig_mri_distances -from mne.transforms import invert_transform -from mne.utils import (run_tests_if_main, requires_mayavi, traits_test, - modified_env) - -data_path = testing.data_path(download=False) -raw_path = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif') -fname_trans = op.join(data_path, 'MEG', 'sample', - 'sample_audvis_trunc-trans.fif') -kit_raw_path = op.join(kit_data_dir, 'test_bin_raw.fif') -subjects_dir = op.join(data_path, 'subjects') - - -@testing.requires_testing_data -@requires_mayavi -@traits_test -def test_coreg_model_decimation(subjects_dir_tmp): - """Test CoregModel decimation of high-res to low-res head.""" - from mne.gui._coreg_gui import CoregModel - # This makes the test much faster - subject_dir = op.join(subjects_dir_tmp, 'sample') - shutil.move(op.join(subject_dir, 'bem', 'outer_skin.surf'), - op.join(subject_dir, 'surf', 'lh.seghead')) - for fname in ('sample-head.fif', 'sample-head-dense.fif'): - os.remove(op.join(subject_dir, 'bem', fname)) - - model = CoregModel(guess_mri_subject=False) - with pytest.warns(RuntimeWarning, match='No low-resolution'): - model.mri.subjects_dir = op.dirname(subject_dir) - assert model.mri.subject == 'sample' # already set by setting subjects_dir - assert model.mri.bem_low_res.file == '' - assert len(model.mri.bem_low_res.surf.rr) == 2562 - assert len(model.mri.bem_high_res.surf.rr) == 2562 # because we moved it - - -@requires_mayavi -@traits_test -def test_coreg_model(subjects_dir_tmp): - """Test CoregModel.""" - from mne.gui._coreg_gui import CoregModel - trans_dst = op.join(subjects_dir_tmp, 'test-trans.fif') - # make it use MNI fiducials - os.remove(op.join(subjects_dir_tmp, 'sample', 'bem', - 'sample-fiducials.fif')) - - model = CoregModel() - with pytest.raises(RuntimeError, match='Not enough information for savin'): - model.save_trans('blah.fif') - - model.mri.subjects_dir = subjects_dir_tmp - model.mri.subject = 'sample' - - assert model.mri.fid_ok # automated using MNI fiducials - - model.hsp.file = raw_path - assert_allclose(model.hsp.lpa, [[-7.137e-2, 0, 5.122e-9]], 1e-4) - assert_allclose(model.hsp.rpa, [[+7.527e-2, 0, 5.588e-9]], 1e-4) - assert_allclose(model.hsp.nasion, [[+3.725e-9, 1.026e-1, 4.191e-9]], 1e-4) - assert model.has_lpa_data - assert model.has_nasion_data - assert model.has_rpa_data - assert len(model.hsp.eeg_points) > 1 - - assert len(model.mri.bem_low_res.surf.rr) == 2562 - assert len(model.mri.bem_high_res.surf.rr) == 267122 - - lpa_distance = model.lpa_distance - nasion_distance = model.nasion_distance - rpa_distance = model.rpa_distance - avg_point_distance = np.mean(model.point_distance) - - model.nasion_weight = 1. - model.fit_fiducials(0) - old_x = lpa_distance ** 2 + rpa_distance ** 2 + nasion_distance ** 2 - new_x = (model.lpa_distance ** 2 + model.rpa_distance ** 2 + - model.nasion_distance ** 2) - assert new_x < old_x - - model.fit_icp(0) - new_dist = np.mean(model.point_distance) - assert new_dist < avg_point_distance - - model.save_trans(trans_dst) - trans = mne.read_trans(trans_dst) - assert_allclose(trans['trans'], model.head_mri_t) - - # test restoring trans - x, y, z = 100, 200, 50 - rot_x, rot_y, rot_z = np.rad2deg([1.5, 0.1, -1.2]) - model.trans_x = x - model.trans_y = y - model.trans_z = z - model.rot_x = rot_x - model.rot_y = rot_y - model.rot_z = rot_z - trans = model.mri_head_t - model.reset_traits(["trans_x", "trans_y", "trans_z", "rot_x", "rot_y", - "rot_z"]) - assert model.trans_x == 0 - model.set_trans(trans) - assert_array_almost_equal(model.trans_x, x) - assert_array_almost_equal(model.trans_y, y) - assert_array_almost_equal(model.trans_z, z) - assert_array_almost_equal(model.rot_x, rot_x) - assert_array_almost_equal(model.rot_y, rot_y) - assert_array_almost_equal(model.rot_z, rot_z) - - # info - assert isinstance(model.fid_eval_str, str) - assert isinstance(model.points_eval_str, str) - - # scaling job - assert not model.can_prepare_bem_model - model.n_scale_params = 1 - assert model.can_prepare_bem_model - model.prepare_bem_model = True - sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \ - model.get_scaling_job('sample2', False) - assert sdir == subjects_dir_tmp - assert sfrom == 'sample' - assert sto == 'sample2' - assert_allclose(scale, model.parameters[6:9]) - assert skip_fiducials is False - # find BEM files - bems = set() - for fname in os.listdir(op.join(subjects_dir, 'sample', 'bem')): - match = re.match(r'sample-(.+-bem)\.fif', fname) - if match: - bems.add(match.group(1)) - assert set(bemsol) == bems - model.prepare_bem_model = False - sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \ - model.get_scaling_job('sample2', True) - assert bemsol == [] - assert (skip_fiducials) - - model.load_trans(fname_trans) - model.save_trans(trans_dst) - trans = mne.read_trans(trans_dst) - assert_allclose(trans['trans'], model.head_mri_t) - assert_allclose(invert_transform(trans)['trans'][:3, 3] * 1000., - [model.trans_x, model.trans_y, model.trans_z]) - - -@requires_mayavi -@traits_test -def test_coreg_gui_display(subjects_dir_tmp, check_gui_ci): - """Test CoregFrame.""" - from mayavi import mlab - from tvtk.api import tvtk - home_dir = subjects_dir_tmp - # Remove the two files that will make the fiducials okay via MNI estimation - os.remove(op.join(subjects_dir_tmp, 'sample', 'bem', - 'sample-fiducials.fif')) - os.remove(op.join(subjects_dir_tmp, 'sample', 'mri', 'transforms', - 'talairach.xfm')) - with modified_env(_MNE_GUI_TESTING_MODE='true', - _MNE_FAKE_HOME_DIR=home_dir): - with pytest.raises(ValueError, match='not a valid subject'): - mne.gui.coregistration( - subject='Elvis', subjects_dir=subjects_dir_tmp) - - # avoid modal dialog if SUBJECTS_DIR is set to a directory that - # does not contain valid subjects - ui, frame = mne.gui.coregistration(subjects_dir='') - mlab.process_ui_events() - ui.dispose() - mlab.process_ui_events() - - ui, frame = mne.gui.coregistration(subjects_dir=subjects_dir_tmp, - subject='sample') - mlab.process_ui_events() - - assert not frame.model.mri.fid_ok - frame.model.mri.lpa = [[-0.06, 0, 0]] - frame.model.mri.nasion = [[0, 0.05, 0]] - frame.model.mri.rpa = [[0.08, 0, 0]] - assert frame.model.mri.fid_ok - frame.data_panel.raw_src.file = raw_path - assert isinstance(frame.eeg_obj.glyph.glyph.glyph_source.glyph_source, - tvtk.SphereSource) - frame.data_panel.view_options_panel.eeg_obj.project_to_surface = True - assert isinstance(frame.eeg_obj.glyph.glyph.glyph_source.glyph_source, - tvtk.CylinderSource) - mlab.process_ui_events() - - # grow hair (faster for low-res) - assert frame.data_panel.view_options_panel.head_high_res - frame.data_panel.view_options_panel.head_high_res = False - frame.model.grow_hair = 40. - - # scale - frame.coreg_panel.n_scale_params = 3 - frame.coreg_panel.scale_x_inc = True - assert frame.model.scale_x == 101. - frame.coreg_panel.scale_y_dec = True - assert frame.model.scale_y == 99. - - # reset parameters - frame.coreg_panel.reset_params = True - assert frame.model.grow_hair == 0 - assert not frame.data_panel.view_options_panel.head_high_res - - # configuration persistence - assert (frame.model.prepare_bem_model) - frame.model.prepare_bem_model = False - frame.save_config(home_dir) - ui.dispose() - mlab.process_ui_events() - - ui, frame = mne.gui.coregistration(subjects_dir=subjects_dir_tmp) - assert not frame.model.prepare_bem_model - assert not frame.data_panel.view_options_panel.head_high_res - ui.dispose() - mlab.process_ui_events() - - -@testing.requires_testing_data -@requires_mayavi -@traits_test -def test_coreg_model_with_fsaverage(tmpdir): - """Test CoregModel with the fsaverage brain data.""" - tempdir = str(tmpdir) - from mne.gui._coreg_gui import CoregModel - - mne.create_default_subject(subjects_dir=tempdir, - fs_home=op.join(subjects_dir, '..')) - - model = CoregModel() - model.mri.subjects_dir = tempdir - model.mri.subject = 'fsaverage' - assert model.mri.fid_ok - - model.hsp.file = raw_path - lpa_distance = model.lpa_distance - nasion_distance = model.nasion_distance - rpa_distance = model.rpa_distance - avg_point_distance = np.mean(model.point_distance) - - # test hsp point omission - model.nasion_weight = 1. - model.trans_y = -0.008 - model.fit_fiducials(0) - model.omit_hsp_points(0.02) - assert model.hsp.n_omitted == 1 - model.omit_hsp_points(np.inf) - assert model.hsp.n_omitted == 0 - model.omit_hsp_points(0.02) - assert model.hsp.n_omitted == 1 - model.omit_hsp_points(0.01) - assert model.hsp.n_omitted == 4 - model.omit_hsp_points(0.005) - assert model.hsp.n_omitted == 40 - model.omit_hsp_points(0.01) - assert model.hsp.n_omitted == 4 - model.omit_hsp_points(0.02) - assert model.hsp.n_omitted == 1 - - # scale with 1 parameter - model.n_scale_params = 1 - model.fit_fiducials(1) - old_x = lpa_distance ** 2 + rpa_distance ** 2 + nasion_distance ** 2 - new_x = (model.lpa_distance ** 2 + model.rpa_distance ** 2 + - model.nasion_distance ** 2) - assert (new_x < old_x) - - model.fit_icp(1) - avg_point_distance_1param = np.mean(model.point_distance) - assert (avg_point_distance_1param < avg_point_distance) - - # scaling job - sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \ - model.get_scaling_job('scaled', False) - assert sdir == tempdir - assert sfrom == 'fsaverage' - assert sto == 'scaled' - assert_allclose(scale, model.parameters[6:9]) - assert set(bemsol) == {'inner_skull-bem'} - model.prepare_bem_model = False - sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \ - model.get_scaling_job('scaled', False) - assert bemsol == [] - - # scale with 3 parameters - model.n_scale_params = 3 - model.fit_icp(3) - assert (np.mean(model.point_distance) < avg_point_distance_1param) - - # test switching raw disables point omission - assert model.hsp.n_omitted == 1 - model.hsp.file = kit_raw_path - assert model.hsp.n_omitted == 0 - - -@testing.requires_testing_data -@requires_mayavi -@traits_test -def test_coreg_gui_automation(): - """Test that properties get properly updated.""" - from mne.gui._file_traits import DigSource - from mne.gui._fiducials_gui import MRIHeadWithFiducialsModel - from mne.gui._coreg_gui import CoregModel - subject = 'sample' - hsp = DigSource() - hsp.file = raw_path - mri = MRIHeadWithFiducialsModel(subjects_dir=subjects_dir, subject=subject) - model = CoregModel(mri=mri, hsp=hsp) - # gh-7254 - assert not (model.nearest_transformed_high_res_mri_idx_hsp == 0).all() - model.fit_fiducials() - model.icp_iterations = 2 - model.nasion_weight = 2. - model.fit_icp() - model.omit_hsp_points(distance=5e-3) - model.icp_iterations = 2 - model.fit_icp() - errs_icp = np.median( - model._get_point_distance()) - assert 2e-3 < errs_icp < 3e-3 - info = mne.io.read_info(raw_path) - errs_nearest = np.median( - dig_mri_distances(info, fname_trans, subject, subjects_dir)) - assert 1e-3 < errs_nearest < 2e-3 - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/gui/tests/test_coreg.py python-mne-1.1.0+dfsg/mne/gui/tests/test_coreg.py --- python-mne-0.23.4+dfsg/mne/gui/tests/test_coreg.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/tests/test_coreg.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,310 @@ +# Author: Christian Brodbeck +# +# License: BSD-3-Clause + +from contextlib import nullcontext +import os.path as op +import os + +import pytest +from numpy.testing import assert_allclose +import numpy as np + +import mne +from mne.datasets import testing +from mne.io import read_info +from mne.io.kit.tests import data_dir as kit_data_dir +from mne.io.constants import FIFF +from mne.utils import get_config, catch_logging +from mne.channels import DigMontage +from mne.coreg import Coregistration +from mne.viz import _3d + + +data_path = testing.data_path(download=False) +raw_path = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif') +fname_trans = op.join(data_path, 'MEG', 'sample', + 'sample_audvis_trunc-trans.fif') +kit_raw_path = op.join(kit_data_dir, 'test_bin_raw.fif') +subjects_dir = op.join(data_path, 'subjects') +fid_fname = op.join(subjects_dir, 'sample', 'bem', 'sample-fiducials.fif') +ctf_raw_path = op.join(data_path, 'CTF', 'catch-alp-good-f.ds') +nirx_15_0_raw_path = op.join(data_path, 'NIRx', 'nirscout', + 'nirx_15_0_recording', 'NIRS-2019-10-27_003.hdr') +nirsport2_raw_path = op.join(data_path, 'NIRx', 'nirsport_v2', 'aurora_2021_9', + '2021-10-01_002_config.hdr') +snirf_nirsport2_raw_path = op.join(data_path, 'SNIRF', 'NIRx', 'NIRSport2', + '1.0.3', '2021-05-05_001.snirf') + + +class TstVTKPicker(object): + """Class to test cell picking.""" + + def __init__(self, mesh, cell_id, event_pos): + self.mesh = mesh + self.cell_id = cell_id + self.point_id = None + self.event_pos = event_pos + + def GetCellId(self): + """Return the picked cell.""" + return self.cell_id + + def GetDataSet(self): + """Return the picked mesh.""" + return self.mesh + + def GetPickPosition(self): + """Return the picked position.""" + vtk_cell = self.mesh.GetCell(self.cell_id) + cell = [vtk_cell.GetPointId(point_id) for point_id + in range(vtk_cell.GetNumberOfPoints())] + self.point_id = cell[0] + return self.mesh.points[self.point_id] + + def GetEventPosition(self): + """Return event position.""" + return self.event_pos + + +@pytest.mark.slowtest +@testing.requires_testing_data +@pytest.mark.parametrize( + 'inst_path', (raw_path, 'gen_montage', ctf_raw_path, nirx_15_0_raw_path, + nirsport2_raw_path, snirf_nirsport2_raw_path)) +def test_coreg_gui_pyvista_file_support(inst_path, tmp_path, + renderer_interactive_pyvistaqt): + """Test reading supported files.""" + from mne.gui import coregistration + + if inst_path == 'gen_montage': + # generate a montage fig to use as inst. + tmp_info = read_info(raw_path) + eeg_chans = [] + for pt in tmp_info['dig']: + if pt['kind'] == FIFF.FIFFV_POINT_EEG: + eeg_chans.append(f"EEG {pt['ident']:03d}") + + dig = DigMontage(dig=tmp_info['dig'], + ch_names=eeg_chans) + inst_path = tmp_path / 'tmp-dig.fif' + dig.save(inst_path) + + if inst_path == ctf_raw_path: + ctx = pytest.warns(RuntimeWarning, match='MEG ref channel RMSP') + elif inst_path == snirf_nirsport2_raw_path: # TODO: This is maybe a bug? + ctx = pytest.warns(RuntimeWarning, match='assuming "head"') + else: + ctx = nullcontext() + with ctx: + coregistration(inst=inst_path, subject='sample', + subjects_dir=subjects_dir) + + +@pytest.mark.slowtest +@testing.requires_testing_data +def test_coreg_gui_pyvista_basic(tmp_path, renderer_interactive_pyvistaqt, + monkeypatch): + """Test that using CoregistrationUI matches mne coreg.""" + from mne.gui import coregistration + config = get_config() + # the sample subject in testing has MRI fids + assert op.isfile(op.join( + subjects_dir, 'sample', 'bem', 'sample-fiducials.fif')) + + coreg = coregistration(subject='sample', subjects_dir=subjects_dir, + trans=fname_trans) + assert coreg._lock_fids + coreg._reset_fiducials() + coreg.close() + + # make it always log the distances + monkeypatch.setattr(_3d.logger, 'info', _3d.logger.warning) + with catch_logging() as log: + coreg = coregistration(inst=raw_path, subject='sample', + head_high_res=False, # for speed + subjects_dir=subjects_dir, verbose='debug') + log = log.getvalue() + assert 'Total 16/78 points inside the surface' in log + coreg._set_fiducials_file(fid_fname) + assert coreg._fiducials_file == fid_fname + + # fitting (with scaling) + assert not coreg._mri_scale_modified + coreg._reset() + coreg._reset_fitting_parameters() + coreg._set_scale_mode("uniform") + coreg._fits_fiducials() + assert_allclose(coreg.coreg._scale, + np.array([97.46, 97.46, 97.46]) * 1e-2, + atol=1e-3) + shown_scale = [coreg._widgets[f's{x}'].get_value() for x in 'XYZ'] + assert_allclose(shown_scale, coreg.coreg._scale * 100, atol=1e-2) + coreg._set_icp_fid_match("nearest") + coreg._set_scale_mode("3-axis") + coreg._fits_icp() + assert_allclose(coreg.coreg._scale, + np.array([104.43, 101.47, 125.78]) * 1e-2, + atol=1e-3) + shown_scale = [coreg._widgets[f's{x}'].get_value() for x in 'XYZ'] + assert_allclose(shown_scale, coreg.coreg._scale * 100, atol=1e-2) + coreg._set_scale_mode("None") + coreg._set_icp_fid_match("matched") + assert coreg._mri_scale_modified + + # unlock fiducials + assert coreg._lock_fids + coreg._set_lock_fids(False) + assert not coreg._lock_fids + + # picking + assert not coreg._mri_fids_modified + vtk_picker = TstVTKPicker(coreg._surfaces['head'], 0, (0, 0)) + coreg._on_mouse_move(vtk_picker, None) + coreg._on_button_press(vtk_picker, None) + coreg._on_pick(vtk_picker, None) + coreg._on_button_release(vtk_picker, None) + coreg._on_pick(vtk_picker, None) # also pick when locked + assert coreg._mri_fids_modified + + # lock fiducials + coreg._set_lock_fids(True) + assert coreg._lock_fids + + # fitting (no scaling) + assert coreg._nasion_weight == 10. + coreg._set_point_weight(11., 'nasion') + assert coreg._nasion_weight == 11. + coreg._fit_fiducials() + with catch_logging() as log: + coreg._redraw() # actually emit the log + log = log.getvalue() + assert 'Total 6/78 points inside the surface' in log + with catch_logging() as log: + coreg._fit_icp() + coreg._redraw() + log = log.getvalue() + assert 'Total 38/78 points inside the surface' in log + assert coreg.coreg._extra_points_filter is None + coreg._omit_hsp() + with catch_logging() as log: + coreg._redraw() + log = log.getvalue() + assert 'Total 29/53 points inside the surface' in log + assert coreg.coreg._extra_points_filter is not None + coreg._reset_omit_hsp_filter() + with catch_logging() as log: + coreg._redraw() + log = log.getvalue() + assert 'Total 38/78 points inside the surface' in log + assert coreg.coreg._extra_points_filter is None + + assert coreg._grow_hair == 0 + coreg._fit_fiducials() # go back to few inside to start + with catch_logging() as log: + coreg._redraw() + log = log.getvalue() + assert 'Total 6/78 points inside the surface' in log + norm = np.linalg.norm(coreg._head_geo['rr']) # what's used for inside + assert_allclose(norm, 5.949288, atol=1e-3) + coreg._set_grow_hair(20.0) + with catch_logging() as log: + coreg._redraw() + assert coreg._grow_hair == 20.0 + norm = np.linalg.norm(coreg._head_geo['rr']) + assert_allclose(norm, 6.555220, atol=1e-3) # outward + log = log.getvalue() + assert 'Total 8/78 points inside the surface' in log # more outside now + + # visualization + assert not coreg._helmet + coreg._set_helmet(True) + assert coreg._helmet + assert coreg._orient_glyphs + assert coreg._scale_by_distance + assert coreg._mark_inside + assert_allclose( + coreg._head_opacity, + float(config.get('MNE_COREG_HEAD_OPACITY', '0.8'))) + assert coreg._hpi_coils + assert coreg._eeg_channels + assert coreg._head_shape_points + assert coreg._scale_mode == 'None' + assert coreg._icp_fid_match == 'matched' + assert coreg._head_resolution is False + + assert coreg._trans_modified + tmp_trans = tmp_path / 'tmp-trans.fif' + coreg._save_trans(tmp_trans) + assert not coreg._trans_modified + assert op.isfile(tmp_trans) + + # first, disable auto cleanup + coreg._renderer._window_close_disconnect(after=True) + # test _close_callback() + coreg.close() + coreg._widgets['close_dialog'].trigger('Discard') # do not save + coreg._clean() # finally, cleanup internal structures + + # Coregistration instance should survive + assert isinstance(coreg.coreg, Coregistration) + + # Fullscreen mode + coreg = coregistration( + subject='sample', subjects_dir=subjects_dir, fullscreen=True + ) + + +@pytest.mark.slowtest +@testing.requires_testing_data +def test_coreg_gui_scraper(tmp_path, renderer_interactive_pyvistaqt): + """Test the scrapper for the coregistration GUI.""" + from mne.gui import coregistration + coreg = coregistration(subject='sample', subjects_dir=subjects_dir, + trans=fname_trans) + (tmp_path / '_images').mkdir() + image_path = str(tmp_path / '_images' / 'temp.png') + gallery_conf = dict(builder_name='html', src_dir=str(tmp_path)) + block_vars = dict( + example_globals=dict(gui=coreg), + image_path_iterator=iter([image_path])) + assert not op.isfile(image_path) + assert not getattr(coreg, '_scraped', False) + mne.gui._GUIScraper()(None, block_vars, gallery_conf) + assert op.isfile(image_path) + assert coreg._scraped + + +@pytest.mark.slowtest +@testing.requires_testing_data +def test_coreg_gui_notebook(renderer_notebook, nbexec): + """Test the coregistration UI in a notebook.""" + import os + import mne + from mne.datasets import testing + from mne.gui import coregistration + mne.viz.set_3d_backend('notebook') # set the 3d backend + with mne.utils.modified_env(_MNE_FAKE_HOME_DIR=None): + data_path = testing.data_path(download=False) + subjects_dir = os.path.join(data_path, 'subjects') + coregistration(subject='sample', subjects_dir=subjects_dir) + + +@pytest.mark.slowtest +def test_no_sparse_head(subjects_dir_tmp, renderer_interactive_pyvistaqt, + monkeypatch): + """Test mne.gui.coregistration with no sparse head.""" + from mne.gui import coregistration + subject = 'sample' + out_rr, out_tris = mne.read_surface( + op.join(subjects_dir_tmp, subject, 'bem', 'outer_skin.surf')) + for head in ('sample-head.fif', 'outer_skin.surf'): + os.remove(op.join(subjects_dir_tmp, subject, 'bem', head)) + # Avoid actually doing the decimation (it's slow) + monkeypatch.setattr( + mne.coreg, 'decimate_surface', + lambda rr, tris, n_triangles: (out_rr, out_tris)) + with pytest.warns(RuntimeWarning, match='No low-resolution head found'): + coreg = coregistration( + inst=raw_path, subject=subject, subjects_dir=subjects_dir_tmp) + coreg.close() diff -Nru python-mne-0.23.4+dfsg/mne/gui/tests/test_core.py python-mne-1.1.0+dfsg/mne/gui/tests/test_core.py --- python-mne-0.23.4+dfsg/mne/gui/tests/test_core.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/tests/test_core.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,82 @@ +# -*- coding: utf-8 -*- +# Authors: Alex Rockhill +# +# License: BSD-3-clause + +import os.path as op +import numpy as np +from numpy.testing import assert_allclose + +import pytest + +from mne.datasets import testing +from mne.utils import requires_nibabel, catch_logging, use_log_level +from mne.viz.utils import _fake_click + +data_path = testing.data_path(download=False) +subject = 'sample' +subjects_dir = op.join(data_path, 'subjects') + + +@pytest.fixture +def _slice_browser(renderer_interactive_pyvistaqt): + from qtpy.QtWidgets import QApplication + from mne.gui._core import SliceBrowser + app = QApplication.instance() + if app is None: + app = QApplication(["Slice Browser"]) + # Use a fixture to create these classes so we can ensure that they + # are closed at the end of the test + guis = list() + + def fun(*args, **kwargs): + guis.append(SliceBrowser(*args, **kwargs)) + return guis[-1] + + yield fun + + for gui in guis: + try: + gui.close() + except Exception: + pass + + +@requires_nibabel() +def test_slice_browser_io(_slice_browser): + """Test the input/output of the slice browser GUI.""" + import nibabel as nib + with pytest.raises(ValueError, match='Base image is not aligned to MRI'): + _slice_browser(nib.MGHImage( + np.ones((96, 96, 96), dtype=np.float32), np.eye(4)), + subject=subject, subjects_dir=subjects_dir) + + +@testing.requires_testing_data +def test_slice_browser_display(_slice_browser): + """Test that the slice browser GUI displays properly.""" + # test no seghead, fsaverage doesn't have seghead + with pytest.warns(RuntimeWarning, match='`seghead` not found'): + with catch_logging() as log: + _slice_browser(subject='fsaverage', subjects_dir=subjects_dir, + verbose=True) + log = log.getvalue() + assert 'using marching cubes' in log + + # test functions + with pytest.warns(RuntimeWarning, match='`pial` surface not found'): + gui = _slice_browser(subject=subject, subjects_dir=subjects_dir) + + # test RAS + gui._RAS_textbox.setPlainText('10 10 10\n') + assert_allclose(gui._ras, [10, 10, 10]) + + # test vox + gui._VOX_textbox.setPlainText('150, 150, 150\n') + assert_allclose(gui._ras, [23, 22, 23]) + + # test click + with use_log_level('debug'): + _fake_click(gui._figs[2], gui._figs[2].axes[0], + [137, 140], xform='data', kind='release') + assert_allclose(gui._ras, [10, 12, 23]) diff -Nru python-mne-0.23.4+dfsg/mne/gui/tests/test_fiducials_gui.py python-mne-1.1.0+dfsg/mne/gui/tests/test_fiducials_gui.py --- python-mne-0.23.4+dfsg/mne/gui/tests/test_fiducials_gui.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/tests/test_fiducials_gui.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,71 +0,0 @@ -# Authors: Christian Brodbeck -# -# License: BSD (3-clause) - -import os -import os.path as op - -from numpy.testing import assert_array_equal - -from mne.utils import requires_mayavi, run_tests_if_main, traits_test - - -@requires_mayavi -@traits_test -def test_mri_model(subjects_dir_tmp): - """Test MRIHeadWithFiducialsModel Traits Model.""" - from mne.gui._fiducials_gui import MRIHeadWithFiducialsModel - tgt_fname = op.join(subjects_dir_tmp, 'test-fiducials.fif') - - # Remove the two files that will make the fiducials okay via MNI estimation - os.remove(op.join(subjects_dir_tmp, 'sample', 'bem', - 'sample-fiducials.fif')) - os.remove(op.join(subjects_dir_tmp, 'sample', 'mri', 'transforms', - 'talairach.xfm')) - - model = MRIHeadWithFiducialsModel(subjects_dir=subjects_dir_tmp) - model.subject = 'sample' - assert model.default_fid_fname[-20:] == "sample-fiducials.fif" - assert not model.can_reset - assert not model.can_save - model.lpa = [[-1, 0, 0]] - model.nasion = [[0, 1, 0]] - model.rpa = [[1, 0, 0]] - assert not model.can_reset - assert model.can_save - - bem_fname = op.basename(model.bem_high_res.file) - assert not model.can_reset - assert bem_fname == 'sample-head-dense.fif' - - model.save(tgt_fname) - assert model.fid_file == tgt_fname - - # resetting the file should not affect the model's fiducials - model.fid_file = '' - assert_array_equal(model.lpa, [[-1, 0, 0]]) - assert_array_equal(model.nasion, [[0, 1, 0]]) - assert_array_equal(model.rpa, [[1, 0, 0]]) - - # reset model - model.lpa = [[0, 0, 0]] - model.nasion = [[0, 0, 0]] - model.rpa = [[0, 0, 0]] - assert_array_equal(model.lpa, [[0, 0, 0]]) - assert_array_equal(model.nasion, [[0, 0, 0]]) - assert_array_equal(model.rpa, [[0, 0, 0]]) - - # loading the file should assign the model's fiducials - model.fid_file = tgt_fname - assert_array_equal(model.lpa, [[-1, 0, 0]]) - assert_array_equal(model.nasion, [[0, 1, 0]]) - assert_array_equal(model.rpa, [[1, 0, 0]]) - - # after changing from file model should be able to reset - model.nasion = [[1, 1, 1]] - assert model.can_reset - model.reset = True - assert_array_equal(model.nasion, [[0, 1, 0]]) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/gui/tests/test_file_traits.py python-mne-1.1.0+dfsg/mne/gui/tests/test_file_traits.py --- python-mne-0.23.4+dfsg/mne/gui/tests/test_file_traits.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/tests/test_file_traits.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,141 +0,0 @@ -# Author: Christian Brodbeck -# -# License: BSD (3-clause) - -import os.path as op - -from numpy import array -from numpy.testing import assert_allclose -import pytest - -from mne.datasets import testing -from mne.utils import (requires_mayavi, run_tests_if_main, traits_test, - modified_env) -from mne.channels import read_dig_fif - -data_path = testing.data_path(download=False) -subjects_dir = op.join(data_path, 'subjects') -bem_path = op.join(subjects_dir, 'sample', 'bem', 'sample-1280-bem.fif') -inst_path = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif') -fid_path = op.join(op.dirname(__file__), '..', '..', 'data', 'fsaverage', - 'fsaverage-fiducials.fif') - - -@testing.requires_testing_data -@requires_mayavi -@traits_test -def test_bem_source(): - """Test SurfaceSource.""" - from mne.gui._file_traits import SurfaceSource - - bem = SurfaceSource() - assert bem.surf.rr.shape == (0, 3) - assert bem.surf.tris.shape == (0, 3) - - bem.file = bem_path - assert bem.surf.rr.shape == (642, 3) - assert bem.surf.tris.shape == (1280, 3) - - -@testing.requires_testing_data -@requires_mayavi -@traits_test -def test_fiducials_source(): - """Test FiducialsSource.""" - from mne.gui._file_traits import FiducialsSource - - fid = FiducialsSource() - fid.file = fid_path - - points = array([[-0.08061612, -0.02908875, -0.04131077], - [0.00146763, 0.08506715, -0.03483611], - [0.08436285, -0.02850276, -0.04127743]]) - assert_allclose(fid.points, points, 1e-6) - - fid.file = '' - assert fid.points is None - - -@testing.requires_testing_data -@requires_mayavi -@traits_test -def test_digitization_source(tmpdir): - """Test DigSource.""" - from mne.gui._file_traits import DigSource - tempdir = str(tmpdir) - - inst = DigSource() - assert inst.inst_fname == '-' - - inst.file = inst_path - assert inst.inst_dir == op.dirname(inst_path) - - # FIFF - lpa = array([[-7.13766068e-02, 0.00000000e+00, 5.12227416e-09]]) - nasion = array([[3.72529030e-09, 1.02605611e-01, 4.19095159e-09]]) - rpa = array([[7.52676800e-02, 0.00000000e+00, 5.58793545e-09]]) - assert_allclose(inst.lpa, lpa) - assert_allclose(inst.nasion, nasion) - assert_allclose(inst.rpa, rpa) - - # DigMontage - montage = read_dig_fif(inst_path) - montage_path = op.join(tempdir, 'temp_montage.fif') - montage.save(montage_path) - inst.file = montage_path - assert_allclose(inst.lpa, lpa) - assert_allclose(inst.nasion, nasion) - assert_allclose(inst.rpa, rpa) - - # EGI MFF - inst.file = op.join(data_path, 'EGI', 'test_egi.mff') - assert len(inst.points) == 0 - assert len(inst.eeg_points) == 130 - assert_allclose(inst.lpa * 1000, [[-67.1, 0, 0]], atol=0.1) - assert_allclose(inst.nasion * 1000, [[0.0, 103.6, 0]], atol=0.1) - assert_allclose(inst.rpa * 1000, [[67.1, 0, 0]], atol=0.1) - - # CTF - inst.file = op.join(data_path, 'CTF', 'testdata_ctf.ds') - assert len(inst.points) == 0 - assert len(inst.eeg_points) == 8 - assert_allclose(inst.lpa * 1000, [[-74.3, 0.0, 0.0]], atol=0.1) - assert_allclose(inst.nasion * 1000, [[0.0, 117.7, 0.0]], atol=0.1) - assert_allclose(inst.rpa * 1000, [[84.9, -0.0, 0.0]], atol=0.1) - - -@testing.requires_testing_data -@requires_mayavi -@traits_test -def test_subject_source(): - """Test SubjectSelector.""" - from mne.gui._file_traits import MRISubjectSource - - mri = MRISubjectSource() - mri.subjects_dir = subjects_dir - assert 'sample' in mri.subjects - mri.subject = 'sample' - - -@testing.requires_testing_data -@requires_mayavi -@traits_test -def test_subject_source_with_fsaverage(tmpdir): - """Test SubjectSelector.""" - from mne.gui._file_traits import MRISubjectSource - tempdir = str(tmpdir) - - mri = MRISubjectSource() - assert not mri.can_create_fsaverage - pytest.raises(RuntimeError, mri.create_fsaverage) - - mri.subjects_dir = tempdir - assert mri.can_create_fsaverage - assert not op.isdir(op.join(tempdir, 'fsaverage')) - # fake FREESURFER_HOME - with modified_env(FREESURFER_HOME=data_path): - mri.create_fsaverage() - assert op.isdir(op.join(tempdir, 'fsaverage')) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/gui/tests/test_gui_api.py python-mne-1.1.0+dfsg/mne/gui/tests/test_gui_api.py --- python-mne-0.23.4+dfsg/mne/gui/tests/test_gui_api.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/tests/test_gui_api.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,392 @@ +# Authors: Guillaume Favelier +# +# License: Simplified BSD + +import sys +import pytest + +from mne.utils import _check_qt_version + +# This will skip all tests in this scope +pytestmark = pytest.mark.skipif( + sys.platform.startswith('win'), reason='nbexec does not work on Windows') + + +def test_gui_api(renderer_notebook, nbexec, n_warn=0): + """Test GUI API.""" + import contextlib + import mne + import warnings + import sys + try: + # Function + n_warn # noqa + except Exception: + # Notebook standalone mode + n_warn = 0 + # nbexec does not expose renderer_notebook so I use a + # temporary variable to synchronize the tests + try: + assert mne.MNE_PYVISTAQT_BACKEND_TEST + except AttributeError: + mne.viz.set_3d_backend('notebook') + backend = 'notebook' + else: + backend = 'qt' + renderer = mne.viz.backends.renderer._get_renderer(size=(300, 300)) + + # theme + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + renderer._window_set_theme('/does/not/exist') + if backend == 'qt': + assert len(w) == 1 + assert 'not found' in str(w[0].message), str(w[0].message) + else: + assert len(w) == 0 + with mne.utils._record_warnings() as w: + renderer._window_set_theme('dark') + if sys.platform != 'darwin': # sometimes this is fine + assert len(w) == n_warn + + # window without 3d plotter + if backend == 'qt': + window = renderer._window_create() + widget = renderer._window_create() + central_layout = renderer._layout_create(orientation='grid') + renderer._layout_add_widget(central_layout, widget, row=0, col=0) + renderer._window_initialize(window=window, + central_layout=central_layout) + + from unittest.mock import Mock + mock = Mock() + + @contextlib.contextmanager + def _check_widget_trigger(widget, mock, before, after, call_count=True, + get_value=True): + if get_value: + assert widget.get_value() == before + old_call_count = mock.call_count + try: + yield + finally: + if get_value: + assert widget.get_value() == after + if call_count: + assert mock.call_count == old_call_count + 1 + + # --- BEGIN: dock --- + renderer._dock_initialize(name='', area='left') + + # label (not interactive) + widget = renderer._dock_add_label( + value='', + align=False, + selectable=True, + ) + widget = renderer._dock_add_label( + value='', + align=True, + ) + widget.update() + # labels are disabled by default with the notebook backend + widget.set_enabled(False) + assert not widget.is_enabled() + widget.set_enabled(True) + assert widget.is_enabled() + + # ToolButton + widget = renderer._dock_add_button( + name='', + callback=mock, + style='toolbutton', + tooltip='button', + ) + with _check_widget_trigger(widget, mock, None, None, get_value=False): + widget.set_value(True) + + # PushButton + widget = renderer._dock_add_button( + name='', + callback=mock, + ) + with _check_widget_trigger(widget, mock, None, None, get_value=False): + widget.set_value(True) + + # slider + widget = renderer._dock_add_slider( + name='', + value=0, + rng=[0, 10], + callback=mock, + tooltip='slider', + ) + with _check_widget_trigger(widget, mock, 0, 5): + widget.set_value(5) + + # check box + widget = renderer._dock_add_check_box( + name='', + value=False, + callback=mock, + tooltip='check box', + ) + with _check_widget_trigger(widget, mock, False, True): + widget.set_value(True) + + # spin box + renderer._dock_add_spin_box( + name='', + value=0, + rng=[0, 1], + callback=mock, + step=0.1, + tooltip='spin box', + ) + widget = renderer._dock_add_spin_box( + name='', + value=0, + rng=[0, 1], + callback=mock, + step=None, + ) + with _check_widget_trigger(widget, mock, 0, 0.5): + widget.set_value(0.5) + + # combo box + widget = renderer._dock_add_combo_box( + name='', + value='foo', + rng=['foo', 'bar'], + callback=mock, + tooltip='combo box', + ) + with _check_widget_trigger(widget, mock, 'foo', 'bar'): + widget.set_value('bar') + + # radio buttons + widget = renderer._dock_add_radio_buttons( + value='foo', + rng=['foo', 'bar'], + callback=mock, + ) + with _check_widget_trigger(widget, mock, None, None, get_value=False): + widget.set_value(1, 'bar') + assert widget.get_value(0) == 'foo' + assert widget.get_value(1) == 'bar' + widget.set_enabled(False) + + # text field + widget = renderer._dock_add_text( + name='', + value='foo', + placeholder='', + callback=mock, + ) + with _check_widget_trigger(widget, mock, 'foo', 'bar'): + widget.set_value('bar') + widget.set_style(dict(border="2px solid #ff0000")) + + # file button + renderer._dock_add_file_button( + name='', + desc='', + func=mock, + is_directory=True, + tooltip='file button', + ) + renderer._dock_add_file_button( + name='', + desc='', + func=mock, + initial_directory='', + ) + renderer._dock_add_file_button( + name='', + desc='', + func=mock, + ) + widget = renderer._dock_add_file_button( + name='', + desc='', + func=mock, + save=True + ) + # XXX: the internal file dialogs may hang without signals + widget.set_enabled(False) + + renderer._dock_initialize(name='', area='right') + renderer._dock_named_layout(name='') + for collapse in (None, True, False): + renderer._dock_add_group_box(name='', collapse=collapse) + renderer._dock_add_stretch() + renderer._dock_add_layout() + renderer._dock_finalize() + renderer._dock_hide() + renderer._dock_show() + # --- END: dock --- + + # --- BEGIN: tool bar --- + renderer._tool_bar_initialize( + name="default", + window=None, + ) + + # button + assert 'reset' not in renderer.actions + renderer._tool_bar_add_button( + name='reset', + desc='', + func=mock, + icon_name='help', + ) + assert 'reset' in renderer.actions + + # icon + renderer._tool_bar_update_button_icon( + name='reset', + icon_name='reset', + ) + + # text + renderer._tool_bar_add_text( + name='', + value='', + placeholder='', + ) + + # spacer + renderer._tool_bar_add_spacer() + + # file button + assert 'help' not in renderer.actions + renderer._tool_bar_add_file_button( + name='help', + desc='', + func=mock, + shortcut=None, + ) + renderer.actions['help'].trigger() + + # play button + assert 'play' not in renderer.actions + renderer._tool_bar_add_play_button( + name='play', + desc='', + func=mock, + shortcut=None, + ) + assert 'play' in renderer.actions + # --- END: tool bar --- + + # --- BEGIN: menu bar --- + renderer._menu_initialize() + + # submenu + renderer._menu_add_submenu(name='foo', desc='foo') + assert 'foo' in renderer._menus + assert 'foo' in renderer._menu_actions + + # button + renderer._menu_add_button( + menu_name='foo', + name='bar', + desc='bar', + func=mock, + ) + assert 'bar' in renderer._menu_actions['foo'] + with _check_widget_trigger(None, mock, '', '', get_value=False): + renderer._menu_actions['foo']['bar'].trigger() + + # --- END: menu bar --- + + # --- BEGIN: status bar --- + renderer._status_bar_initialize() + renderer._status_bar_update() + + # label + widget = renderer._status_bar_add_label(value='foo', stretch=0) + assert widget.get_value() == 'foo' + + # progress bar + widget = renderer._status_bar_add_progress_bar(stretch=0) + # by default, get_value() is -1 for Qt and 0 for Ipywidgets + widget.set_value(0) + assert widget.get_value() == 0 + # --- END: status bar --- + + # --- BEGIN: tooltips --- + widget = renderer._dock_add_button( + name='', + callback=mock, + tooltip='foo' + ) + assert widget.get_tooltip() == 'foo' + # Change it … + widget.set_tooltip('bar') + assert widget.get_tooltip() == 'bar' + # --- END: tooltips --- + + # --- BEGIN: dialog --- + # dialogs are not supported yet on notebook + if renderer._kind == 'qt': + # warning + buttons = ["Save", "Cancel"] + widget = renderer._dialog_create( + title='', + text='', + info_text='', + callback=mock, + buttons=buttons, + modal=False, + ) + widget.show() + for button in buttons: + with _check_widget_trigger(None, mock, '', '', get_value=False): + widget.trigger(button=button) + assert mock.call_args.args == (button,) + + # buttons list empty means OK button (default) + button = 'Ok' + widget = renderer._dialog_create( + title='', + text='', + info_text='', + callback=mock, + icon='NoIcon', + modal=False, + ) + widget.show() + with _check_widget_trigger(None, mock, '', '', get_value=False): + widget.trigger(button=button) + assert mock.call_args.args == (button,) + # --- END: dialog --- + + # --- BEGIN: keypress --- + renderer._keypress_initialize() + renderer._keypress_add('a', mock) + # keypress is not supported yet on notebook + if renderer._kind == 'qt': + with _check_widget_trigger(None, mock, '', '', get_value=False): + renderer._keypress_trigger('a') + # --- END: keypress --- + + renderer.show() + + renderer._window_close_connect(lambda: mock('first'), after=False) + renderer._window_close_connect(lambda: mock('last')) + old_call_count = mock.call_count + renderer.close() + if renderer._kind == 'qt': + assert mock.call_count == old_call_count + 2 + assert mock.call_args_list[-1].args == ('last',) + assert mock.call_args_list[-2].args == ('first',) + + +def test_gui_api_qt(renderer_interactive_pyvistaqt): + """Test GUI API with the Qt backend.""" + import mne + mne.MNE_PYVISTAQT_BACKEND_TEST = True + _, api = _check_qt_version(return_api=True) + n_warn = int(api in ('PySide6', 'PyQt6')) + test_gui_api(None, None, n_warn=n_warn) diff -Nru python-mne-0.23.4+dfsg/mne/gui/tests/test_ieeg_locate.py python-mne-1.1.0+dfsg/mne/gui/tests/test_ieeg_locate.py --- python-mne-0.23.4+dfsg/mne/gui/tests/test_ieeg_locate.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/tests/test_ieeg_locate.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,207 @@ +# -*- coding: utf-8 -*- +# Authors: Alex Rockhill +# +# License: BSD-3-clause + +import os.path as op +import numpy as np +from numpy.testing import assert_allclose + +import pytest + +import mne +from mne.datasets import testing +from mne.transforms import apply_trans +from mne.utils import requires_nibabel, requires_version, use_log_level +from mne.viz.utils import _fake_click + +data_path = testing.data_path(download=False) +subject = 'sample' +subjects_dir = op.join(data_path, 'subjects') +sample_dir = op.join(data_path, 'MEG', subject) +raw_path = op.join(sample_dir, 'sample_audvis_trunc_raw.fif') +fname_trans = op.join(sample_dir, 'sample_audvis_trunc-trans.fif') + + +@requires_nibabel() +@pytest.fixture +def _fake_CT_coords(skull_size=5, contact_size=2): + """Make somewhat realistic CT data with contacts.""" + import nibabel as nib + brain = nib.load( + op.join(subjects_dir, subject, 'mri', 'brain.mgz')) + verts = mne.read_surface( + op.join(subjects_dir, subject, 'bem', 'outer_skull.surf'))[0] + verts = apply_trans(np.linalg.inv(brain.header.get_vox2ras_tkr()), verts) + x, y, z = np.array(brain.shape).astype(int) // 2 + coords = [(x, y - 14, z), (x - 10, y - 15, z), + (x - 20, y - 16, z + 1), (x - 30, y - 16, z + 1)] + center = np.array(brain.shape) / 2 + # make image + np.random.seed(99) + ct_data = np.random.random(brain.shape).astype(np.float32) * 100 + # make skull + for vert in verts: + x, y, z = np.round(vert).astype(int) + ct_data[slice(x - skull_size, x + skull_size + 1), + slice(y - skull_size, y + skull_size + 1), + slice(z - skull_size, z + skull_size + 1)] = 1000 + # add electrode with contacts + for (x, y, z) in coords: + # make sure not in skull + assert np.linalg.norm(center - np.array((x, y, z))) < 50 + ct_data[slice(x - contact_size, x + contact_size + 1), + slice(y - contact_size, y + contact_size + 1), + slice(z - contact_size, z + contact_size + 1)] = \ + 1000 - np.linalg.norm(np.array(np.meshgrid( + *[range(-contact_size, contact_size + 1)] * 3)), axis=0) + ct = nib.MGHImage(ct_data, brain.affine) + coords = apply_trans(ct.header.get_vox2ras_tkr(), np.array(coords)) + return ct, coords + + +@pytest.fixture +def _locate_ieeg(renderer_interactive_pyvistaqt): + # Use a fixture to create these classes so we can ensure that they + # are closed at the end of the test + guis = list() + + def fun(*args, **kwargs): + guis.append(mne.gui.locate_ieeg(*args, **kwargs)) + return guis[-1] + + yield fun + + for gui in guis: + try: + gui.close() + except Exception: + pass + + +def test_ieeg_elec_locate_io(_locate_ieeg): + """Test the input/output of the intracranial location GUI.""" + import nibabel as nib + info = mne.create_info([], 1000) + aligned_ct = nib.MGHImage(np.zeros((256, 256, 256), dtype=np.float32), + np.eye(4)) + trans = mne.transforms.Transform('head', 'mri') + with pytest.raises(ValueError, + match='No channels found in `info` to locate'): + _locate_ieeg(info, trans, aligned_ct, subject, subjects_dir) + + +@requires_version('sphinx_gallery') +@testing.requires_testing_data +def test_locate_scraper(_locate_ieeg, _fake_CT_coords, tmp_path): + """Test sphinx-gallery scraping of the GUI.""" + raw = mne.io.read_raw_fif(raw_path) + raw.pick_types(eeg=True) + ch_dict = {'EEG 001': 'LAMY 1', 'EEG 002': 'LAMY 2', + 'EEG 003': 'LSTN 1', 'EEG 004': 'LSTN 2'} + raw.pick_channels(list(ch_dict.keys())) + raw.rename_channels(ch_dict) + raw.set_montage(None) + aligned_ct, _ = _fake_CT_coords + trans = mne.read_trans(fname_trans) + with pytest.warns(RuntimeWarning, match='`pial` surface not found'): + gui = _locate_ieeg(raw.info, trans, aligned_ct, + subject=subject, subjects_dir=subjects_dir) + (tmp_path / '_images').mkdir() + image_path = str(tmp_path / '_images' / 'temp.png') + gallery_conf = dict(builder_name='html', src_dir=str(tmp_path)) + block_vars = dict( + example_globals=dict(gui=gui), + image_path_iterator=iter([image_path])) + assert not op.isfile(image_path) + assert not getattr(gui, '_scraped', False) + mne.gui._GUIScraper()(None, block_vars, gallery_conf) + assert op.isfile(image_path) + assert gui._scraped + + +@testing.requires_testing_data +def test_ieeg_elec_locate_display(_locate_ieeg, _fake_CT_coords): + """Test that the intracranial location GUI displays properly.""" + raw = mne.io.read_raw_fif(raw_path, preload=True) + raw.pick_types(eeg=True) + ch_dict = {'EEG 001': 'LAMY 1', 'EEG 002': 'LAMY 2', + 'EEG 003': 'LSTN 1', 'EEG 004': 'LSTN 2'} + raw.pick_channels(list(ch_dict.keys())) + raw.rename_channels(ch_dict) + raw.set_eeg_reference('average') + raw.set_channel_types({name: 'seeg' for name in raw.ch_names}) + raw.set_montage(None) + aligned_ct, coords = _fake_CT_coords + trans = mne.read_trans(fname_trans) + + with pytest.warns(RuntimeWarning, match='`pial` surface not found'): + gui = _locate_ieeg(raw.info, trans, aligned_ct, + subject=subject, subjects_dir=subjects_dir, + verbose=True) + + with pytest.raises(ValueError, match='read-only'): + gui._ras[:] = coords[0] # start in the right position + gui._set_ras(coords[0]) + gui._mark_ch() + assert not gui._lines and not gui._lines_2D # no lines for one contact + for ci, coord in enumerate(coords[1:], 1): + coord_vox = apply_trans(gui._ras_vox_t, coord) + with use_log_level('debug'): + _fake_click(gui._figs[2], gui._figs[2].axes[0], + coord_vox[:-1], xform='data', kind='release') + assert_allclose(coord[:2], gui._ras[:2], atol=0.1, + err_msg=f'coords[{ci}][:2]') + assert_allclose(coord[2], gui._ras[2], atol=2, + err_msg=f'coords[{ci}][2]') + gui._mark_ch() + + # ensure a 3D line was made for each group + assert len(gui._lines) == 2 + + # test snap to center + gui._ch_index = 0 + gui._set_ras(coords[0]) # move to first position + gui._mark_ch() + assert_allclose(coords[0], gui._chs['LAMY 1'], atol=0.2) + gui._snap_button.click() + assert gui._snap_button.text() == 'Off' + # now make sure no snap happens + gui._ch_index = 0 + gui._set_ras(coords[1] + 1) + gui._mark_ch() + assert_allclose(coords[1] + 1, gui._chs['LAMY 1'], atol=0.01) + # check that it turns back on + gui._snap_button.click() + assert gui._snap_button.text() == 'On' + + # test remove + gui._ch_index = 1 + gui._update_ch_selection() + gui._remove_ch() + assert np.isnan(gui._chs['LAMY 2']).all() + + # check that raw object saved + assert not np.isnan(raw.info['chs'][0]['loc'][:3]).any() # LAMY 1 + assert np.isnan(raw.info['chs'][1]['loc'][:3]).all() # LAMY 2 (removed) + + # move sliders + gui._alpha_slider.setValue(75) + assert gui._ch_alpha == 0.75 + gui._radius_slider.setValue(5) + assert gui._radius == 5 + ct_sum_before = np.nansum(gui._images['ct'][0].get_array().data) + gui._ct_min_slider.setValue(500) + assert np.nansum(gui._images['ct'][0].get_array().data) < ct_sum_before + + # test buttons + gui._toggle_show_brain() + assert 'mri' in gui._images + assert 'local_max' not in gui._images + gui._toggle_show_max() + assert 'local_max' in gui._images + assert 'mip' not in gui._images + gui._toggle_show_mip() + assert 'mip' in gui._images + assert 'mip_chs' in gui._images + assert len(gui._lines_2D) == 1 # LAMY only has one contact diff -Nru python-mne-0.23.4+dfsg/mne/gui/tests/test_kit2fiff_gui.py python-mne-1.1.0+dfsg/mne/gui/tests/test_kit2fiff_gui.py --- python-mne-0.23.4+dfsg/mne/gui/tests/test_kit2fiff_gui.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/tests/test_kit2fiff_gui.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,163 +0,0 @@ -# Authors: Christian Brodbeck -# -# License: BSD (3-clause) - -import os - -import numpy as np -from numpy.testing import assert_allclose, assert_array_equal - -import mne -from mne.io.kit.tests import data_dir as kit_data_dir -from mne.io import read_raw_fif -from mne.utils import (requires_mayavi, run_tests_if_main, traits_test, - modified_env) - -mrk_pre_path = os.path.join(kit_data_dir, 'test_mrk_pre.sqd') -mrk_post_path = os.path.join(kit_data_dir, 'test_mrk_post.sqd') -sqd_path = os.path.join(kit_data_dir, 'test.sqd') -hsp_path = os.path.join(kit_data_dir, 'test_hsp.txt') -fid_path = os.path.join(kit_data_dir, 'test_elp.txt') -fif_path = os.path.join(kit_data_dir, 'test_bin_raw.fif') - - -@requires_mayavi -@traits_test -def test_kit2fiff_model(tmpdir): - """Test Kit2Fiff model.""" - from mne.gui._kit2fiff_gui import Kit2FiffModel - tempdir = str(tmpdir) - tgt_fname = os.path.join(tempdir, 'test-raw.fif') - - model = Kit2FiffModel() - assert not model.can_save - assert model.misc_chs_desc == "No SQD file selected..." - assert model.stim_chs_comment == "" - model.markers.mrk1.file = mrk_pre_path - model.markers.mrk2.file = mrk_post_path - model.sqd_file = sqd_path - assert model.misc_chs_desc == "160:192" - model.hsp_file = hsp_path - assert not model.can_save - model.fid_file = fid_path - assert model.can_save - - # events - model.stim_slope = '+' - assert model.get_event_info() == {1: 2} - model.stim_slope = '-' - assert model.get_event_info() == {254: 2, 255: 2} - - # stim channels - model.stim_chs = "181:184, 186" - assert_array_equal(model.stim_chs_array, [181, 182, 183, 186]) - assert model.stim_chs_ok - assert model.get_event_info() == {} - model.stim_chs = "181:184, bad" - assert not model.stim_chs_ok - assert not model.can_save - model.stim_chs = "" - assert model.can_save - - # export raw - raw_out = model.get_raw() - raw_out.save(tgt_fname) - raw = read_raw_fif(tgt_fname) - - # Compare exported raw with the original binary conversion - raw_bin = read_raw_fif(fif_path) - trans_bin = raw.info['dev_head_t']['trans'] - want_keys = list(raw_bin.info.keys()) - assert sorted(want_keys) == sorted(list(raw.info.keys())) - trans_transform = raw_bin.info['dev_head_t']['trans'] - assert_allclose(trans_transform, trans_bin, 0.1) - - # Averaging markers - model.markers.mrk3.method = "Average" - trans_avg = model.dev_head_trans - assert not np.all(trans_avg == trans_transform) - assert_allclose(trans_avg, trans_bin, 0.1) - - # Test exclusion of one marker - model.markers.mrk3.method = "Transform" - model.use_mrk = [1, 2, 3, 4] - assert not np.all(model.dev_head_trans == trans_transform) - assert not np.all(model.dev_head_trans == trans_avg) - assert not np.all(model.dev_head_trans == np.eye(4)) - - # test setting stim channels - model.stim_slope = '+' - events_bin = mne.find_events(raw_bin, stim_channel='STI 014') - - model.stim_coding = '<' - raw = model.get_raw() - events = mne.find_events(raw, stim_channel='STI 014') - assert_array_equal(events, events_bin) - - events_rev = events_bin.copy() - events_rev[:, 2] = 1 - model.stim_coding = '>' - raw = model.get_raw() - events = mne.find_events(raw, stim_channel='STI 014') - assert_array_equal(events, events_rev) - - model.stim_coding = 'channel' - model.stim_chs = "160:161" - raw = model.get_raw() - events = mne.find_events(raw, stim_channel='STI 014') - assert_array_equal(events, events_bin + [0, 0, 32]) - - # test reset - model.clear_all() - assert model.use_mrk == [0, 1, 2, 3, 4] - assert model.sqd_file == "" - - -@requires_mayavi -@traits_test -def test_kit2fiff_gui(check_gui_ci, tmpdir): - """Test Kit2Fiff GUI.""" - home_dir = str(tmpdir) - with modified_env(_MNE_GUI_TESTING_MODE='true', - _MNE_FAKE_HOME_DIR=home_dir): - from pyface.api import GUI - gui = GUI() - gui.process_events() - - ui, frame = mne.gui.kit2fiff() - assert not frame.model.can_save - assert frame.model.stim_threshold == 1. - frame.model.stim_threshold = 10. - frame.model.stim_chs = 'save this!' - frame.save_config(home_dir) - ui.dispose() - - gui.process_events() - - # test setting persistence - ui, frame = mne.gui.kit2fiff() - assert frame.model.stim_threshold == 10. - assert frame.model.stim_chs == 'save this!' - - # set and reset marker file - points = [[-0.084612, 0.021582, -0.056144], - [0.080425, 0.021995, -0.061171], - [-0.000787, 0.105530, 0.014168], - [-0.047943, 0.091835, 0.010240], - [0.042976, 0.094380, 0.010807]] - assert_array_equal(frame.marker_panel.mrk1_obj.points, 0) - assert_array_equal(frame.marker_panel.mrk3_obj.points, 0) - frame.model.markers.mrk1.file = mrk_pre_path - assert_allclose(frame.marker_panel.mrk1_obj.points, points, atol=1e-6) - assert_allclose(frame.marker_panel.mrk3_obj.points, points, atol=1e-6) - frame.marker_panel.mrk1_obj.label = True - frame.marker_panel.mrk1_obj.label = False - frame.kit2fiff_panel.clear_all = True - assert_array_equal(frame.marker_panel.mrk1_obj.points, 0) - assert_array_equal(frame.marker_panel.mrk3_obj.points, 0) - ui.dispose() - - gui.process_events() - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/gui/tests/test_marker_gui.py python-mne-1.1.0+dfsg/mne/gui/tests/test_marker_gui.py --- python-mne-0.23.4+dfsg/mne/gui/tests/test_marker_gui.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/tests/test_marker_gui.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,82 +0,0 @@ -# Authors: Christian Brodbeck -# -# License: BSD (3-clause) - -import os - -import numpy as np -from numpy.testing import assert_array_equal - -from mne.io.kit.tests import data_dir as kit_data_dir -from mne.io.kit import read_mrk -from mne.utils import (requires_mayavi, run_tests_if_main, traits_test, - modified_env) - -mrk_pre_path = os.path.join(kit_data_dir, 'test_mrk_pre.sqd') -mrk_post_path = os.path.join(kit_data_dir, 'test_mrk_post.sqd') -mrk_avg_path = os.path.join(kit_data_dir, 'test_mrk.sqd') - - -@requires_mayavi -@traits_test -def test_combine_markers_model(tmpdir): - """Test CombineMarkersModel Traits Model.""" - from mne.gui._marker_gui import CombineMarkersModel - tempdir = str(tmpdir) - tgt_fname = os.path.join(tempdir, 'test.txt') - - model = CombineMarkersModel() - - # set one marker file - assert not model.mrk3.can_save - model.mrk1.file = mrk_pre_path - assert model.mrk3.can_save - assert_array_equal(model.mrk3.points, model.mrk1.points) - - # setting second marker file - model.mrk2.file = mrk_pre_path - assert_array_equal(model.mrk3.points, model.mrk1.points) - - # set second marker - model.mrk2.clear = True - model.mrk2.file = mrk_post_path - assert np.any(model.mrk3.points) - points_interpolate_mrk1_mrk2 = model.mrk3.points - - # change interpolation method - model.mrk3.method = 'Average' - mrk_avg = read_mrk(mrk_avg_path) - assert_array_equal(model.mrk3.points, mrk_avg) - - # clear second marker - model.mrk2.clear = True - assert_array_equal(model.mrk1.points, model.mrk3.points) - - # I/O - model.mrk2.file = mrk_post_path - model.mrk3.save(tgt_fname) - mrk_io = read_mrk(tgt_fname) - assert_array_equal(mrk_io, model.mrk3.points) - - # exclude an individual marker - model.mrk1.use = [1, 2, 3, 4] - assert_array_equal(model.mrk3.points[0], model.mrk2.points[0]) - assert_array_equal(model.mrk3.points[1:], mrk_avg[1:]) - - # reset model - model.clear = True - model.mrk1.file = mrk_pre_path - model.mrk2.file = mrk_post_path - assert_array_equal(model.mrk3.points, points_interpolate_mrk1_mrk2) - - -@requires_mayavi -@traits_test -def test_combine_markers_panel(check_gui_ci): - """Test CombineMarkersPanel.""" - from mne.gui._marker_gui import CombineMarkersPanel - with modified_env(_MNE_GUI_TESTING_MODE='true'): - CombineMarkersPanel() - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/gui/_viewer.py python-mne-1.1.0+dfsg/mne/gui/_viewer.py --- python-mne-0.23.4+dfsg/mne/gui/_viewer.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/gui/_viewer.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,564 +0,0 @@ -# -*- coding: utf-8 -*- -"""Mayavi/traits GUI visualization elements.""" - -# Authors: Christian Brodbeck -# -# License: BSD (3-clause) - -import numpy as np - -from mayavi.mlab import pipeline, text3d -from mayavi.modules.glyph import Glyph -from mayavi.modules.surface import Surface -from mayavi.sources.vtk_data_source import VTKDataSource -from mayavi.tools.mlab_scene_model import MlabSceneModel -from traits.api import (HasTraits, HasPrivateTraits, on_trait_change, - Instance, Array, Bool, Button, Enum, Float, Int, List, - Range, Str, Property, cached_property, ArrayOrNone) -from traitsui.api import (View, Item, HGroup, VGrid, VGroup, Spring, - TextEditor) -from tvtk.api import tvtk - -from ..defaults import DEFAULTS -from ..surface import _CheckInside, _DistanceQuery -from ..transforms import apply_trans, rotation -from ..utils import SilenceStdout -from ..viz.backends._pysurfer_mayavi import (_create_mesh_surf, _oct_glyph, - _toggle_mlab_render) - -try: - from traitsui.api import RGBColor -except ImportError: - from traits.api import RGBColor - -headview_borders = VGroup(Item('headview', style='custom', show_label=False), - show_border=True, label='View') - - -def _mm_fmt(x): - """Format data in units of mm.""" - return '%0.1f' % x - - -laggy_float_editor_mm = TextEditor(auto_set=False, enter_set=True, - evaluate=float, - format_func=lambda x: '%0.1f' % x) - -laggy_float_editor_scale = TextEditor(auto_set=False, enter_set=True, - evaluate=float, - format_func=lambda x: '%0.1f' % x) - -laggy_float_editor_headscale = TextEditor(auto_set=False, enter_set=True, - evaluate=float, - format_func=lambda x: '%0.3f' % x) - -laggy_float_editor_weight = TextEditor(auto_set=False, enter_set=True, - evaluate=float, - format_func=lambda x: '%0.2f' % x) - -laggy_float_editor_deg = TextEditor(auto_set=False, enter_set=True, - evaluate=float, - format_func=lambda x: '%0.1f' % x) - -_BUTTON_WIDTH = -80 -_DEG_WIDTH = -50 # radian floats -_MM_WIDTH = _DEG_WIDTH # mm floats -_SCALE_WIDTH = _DEG_WIDTH # scale floats -_INC_BUTTON_WIDTH = -25 # inc/dec buttons -_DEG_STEP_WIDTH = -50 -_MM_STEP_WIDTH = _DEG_STEP_WIDTH -_SCALE_STEP_WIDTH = _DEG_STEP_WIDTH -_WEIGHT_WIDTH = -60 # weight floats -_VIEW_BUTTON_WIDTH = -60 -# width is optimized for macOS and Linux avoid a horizontal scroll-bar -# even when a vertical one is present -_COREG_WIDTH = -290 -_TEXT_WIDTH = -260 -_REDUCED_TEXT_WIDTH = _TEXT_WIDTH - 40 * np.sign(_TEXT_WIDTH) -_DIG_SOURCE_WIDTH = _TEXT_WIDTH - 50 * np.sign(_TEXT_WIDTH) -_MRI_FIDUCIALS_WIDTH = _TEXT_WIDTH - 60 * np.sign(_TEXT_WIDTH) -_SHOW_BORDER = True -_RESET_LABEL = u"↻" -_RESET_WIDTH = _INC_BUTTON_WIDTH - - -class HeadViewController(HasTraits): - """Set head views for the given coordinate system. - - Parameters - ---------- - system : 'RAS' | 'ALS' | 'ARI' - Coordinate system described as initials for directions associated with - the x, y, and z axes. Relevant terms are: Anterior, Right, Left, - Superior, Inferior. - """ - - system = Enum("RAS", "ALS", "ARI", desc="Coordinate system: directions of " - "the x, y, and z axis.") - - right = Button() - front = Button() - left = Button() - top = Button() - interaction = Enum('trackball', 'terrain') - - scale = Float(0.16) - - scene = Instance(MlabSceneModel) - - view = View(VGroup( - VGrid('0', Item('top', width=_VIEW_BUTTON_WIDTH), '0', - Item('right', width=_VIEW_BUTTON_WIDTH), - Item('front', width=_VIEW_BUTTON_WIDTH), - Item('left', width=_VIEW_BUTTON_WIDTH), - columns=3, show_labels=False), - '_', - HGroup(Item('scale', label='Scale', - editor=laggy_float_editor_headscale, - width=_SCALE_WIDTH, show_label=True), - Item('interaction', tooltip='Mouse interaction mode', - show_label=False), Spring()), - show_labels=False)) - - @on_trait_change('scene.activated') - def _init_view(self): - self.scene.parallel_projection = True - - # apparently scene,activated happens several times - if self.scene.renderer: - self.sync_trait('scale', self.scene.camera, 'parallel_scale') - # and apparently this does not happen by default: - self.on_trait_change(self.scene.render, 'scale') - self.interaction = self.interaction # could be delayed - - @on_trait_change('interaction') - def on_set_interaction(self, _, interaction): - if self.scene is None or self.scene.interactor is None: - return - # Ensure we're in the correct orientation for the - # InteractorStyleTerrain to have the correct "up" - self.on_set_view('front', '') - self.scene.mlab.draw() - self.scene.interactor.interactor_style = \ - tvtk.InteractorStyleTerrain() if interaction == 'terrain' else \ - tvtk.InteractorStyleTrackballCamera() - # self.scene.interactor.interactor_style. - self.on_set_view('front', '') - self.scene.mlab.draw() - - @on_trait_change('top,left,right,front') - def on_set_view(self, view, _): - if self.scene is None: - return - - system = self.system - kwargs = dict(ALS=dict(front=(0, 90, -90), - left=(90, 90, 180), - right=(-90, 90, 0), - top=(0, 0, -90)), - RAS=dict(front=(90., 90., 180), - left=(180, 90, 90), - right=(0., 90, 270), - top=(90, 0, 180)), - ARI=dict(front=(0, 90, 90), - left=(-90, 90, 180), - right=(90, 90, 0), - top=(0, 180, 90))) - if system not in kwargs: - raise ValueError("Invalid system: %r" % system) - if view not in kwargs[system]: - raise ValueError("Invalid view: %r" % view) - kwargs = dict(zip(('azimuth', 'elevation', 'roll'), - kwargs[system][view])) - kwargs['focalpoint'] = (0., 0., 0.) - with SilenceStdout(): - self.scene.mlab.view(distance=None, reset_roll=True, - figure=self.scene.mayavi_scene, **kwargs) - - -class Object(HasPrivateTraits): - """Represent a 3d object in a mayavi scene.""" - - points = Array(float, shape=(None, 3)) - nn = Array(float, shape=(None, 3)) - name = Str - - scene = Instance(MlabSceneModel, ()) - src = Instance(VTKDataSource) - - # This should be Tuple, but it is broken on Anaconda as of 2016/12/16 - color = RGBColor((1., 1., 1.)) - # Due to a MESA bug, we use 0.99 opacity to force alpha blending - opacity = Range(low=0., high=1., value=0.99) - visible = Bool(True) - - def _update_points(self): - """Update the location of the plotted points.""" - if hasattr(self.src, 'data'): - self.src.data.points = self.points - return True - - -class PointObject(Object): - """Represent a group of individual points in a mayavi scene.""" - - label = Bool(False) - label_scale = Float(0.01) - projectable = Bool(False) # set based on type of points - orientable = Property(depends_on=['nearest']) - text3d = List - point_scale = Float(10, label='Point Scale') - - # projection onto a surface - nearest = Instance(_DistanceQuery) - check_inside = Instance(_CheckInside) - project_to_trans = ArrayOrNone(float, shape=(4, 4)) - project_to_surface = Bool(False, label='Project', desc='project points ' - 'onto the surface') - orient_to_surface = Bool(False, label='Orient', desc='orient points ' - 'toward the surface') - scale_by_distance = Bool(False, label='Dist.', desc='scale points by ' - 'distance from the surface') - mark_inside = Bool(False, label='Mark', desc='mark points inside the ' - 'surface in a different color') - inside_color = RGBColor((0., 0., 0.)) - - glyph = Instance(Glyph) - resolution = Int(8) - - view = View(HGroup(Item('visible', show_label=False), - Item('color', show_label=False), - Item('opacity'))) - - def __init__(self, view='points', has_norm=False, *args, **kwargs): - """Init. - - Parameters - ---------- - view : 'points' | 'cloud' | 'arrow' | 'oct' - Whether the view options should be tailored to individual points - or a point cloud. - has_norm : bool - Whether a norm can be defined; adds view options based on point - norms (default False). - """ - assert view in ('points', 'cloud', 'arrow', 'oct') - self._view = view - self._has_norm = bool(has_norm) - super(PointObject, self).__init__(*args, **kwargs) - - def default_traits_view(self): # noqa: D102 - color = Item('color', show_label=False) - scale = Item('point_scale', label='Size', width=_SCALE_WIDTH, - editor=laggy_float_editor_headscale) - orient = Item('orient_to_surface', - enabled_when='orientable and not project_to_surface', - tooltip='Orient points toward the surface') - dist = Item('scale_by_distance', - enabled_when='orientable and not project_to_surface', - tooltip='Scale points by distance from the surface') - mark = Item('mark_inside', - enabled_when='orientable and not project_to_surface', - tooltip='Mark points inside the surface using a different ' - 'color') - if self._view == 'arrow': - visible = Item('visible', label='Show', show_label=False) - return View(HGroup(visible, scale, 'opacity', 'label', Spring())) - elif self._view in ('points', 'oct'): - visible = Item('visible', label='Show', show_label=True) - views = (visible, color, scale, 'label') - else: - assert self._view == 'cloud' - visible = Item('visible', show_label=False) - views = (visible, color, scale) - - if not self._has_norm: - return View(HGroup(*views)) - - group2 = HGroup(dist, Item('project_to_surface', show_label=True, - enabled_when='projectable', - tooltip='Project points onto the surface ' - '(for visualization, does not affect ' - 'fitting)'), - orient, mark, Spring(), show_left=False) - return View(HGroup(HGroup(*views), group2)) - - @on_trait_change('label') - def _show_labels(self, show): - _toggle_mlab_render(self, False) - while self.text3d: - text = self.text3d.pop() - text.remove() - - if show and len(self.src.data.points) > 0: - fig = self.scene.mayavi_scene - if self._view == 'arrow': # for axes - x, y, z = self.src.data.points[0] - self.text3d.append(text3d( - x, y, z, self.name, scale=self.label_scale, - color=self.color, figure=fig)) - else: - for i, (x, y, z) in enumerate(np.array(self.src.data.points)): - self.text3d.append(text3d( - x, y, z, ' %i' % i, scale=self.label_scale, - color=self.color, figure=fig)) - _toggle_mlab_render(self, True) - - @on_trait_change('visible') - def _on_hide(self): - if not self.visible: - self.label = False - - @on_trait_change('scene.activated') - def _plot_points(self): - """Add the points to the mayavi pipeline""" - if self.scene is None: - return - if hasattr(self.glyph, 'remove'): - self.glyph.remove() - if hasattr(self.src, 'remove'): - self.src.remove() - - _toggle_mlab_render(self, False) - x, y, z = self.points.T - fig = self.scene.mayavi_scene - scatter = pipeline.scalar_scatter(x, y, z, fig=fig) - if not scatter.running: - # this can occur sometimes during testing w/ui.dispose() - return - # fig.scene.engine.current_object is scatter - mode = {'cloud': 'sphere', 'points': 'sphere', 'oct': 'sphere'}.get( - self._view, self._view) - assert mode in ('sphere', 'arrow') - glyph = pipeline.glyph(scatter, color=self.color, - figure=fig, scale_factor=self.point_scale, - opacity=1., resolution=self.resolution, - mode=mode) - if self._view == 'oct': - _oct_glyph(glyph.glyph.glyph_source, rotation(0, 0, np.pi / 4)) - glyph.actor.property.backface_culling = True - glyph.glyph.glyph.vector_mode = 'use_normal' - glyph.glyph.glyph.clamping = False - if mode == 'arrow': - glyph.glyph.glyph_source.glyph_position = 'tail' - - glyph.actor.mapper.color_mode = 'map_scalars' - glyph.actor.mapper.scalar_mode = 'use_point_data' - glyph.actor.mapper.use_lookup_table_scalar_range = False - - self.src = scatter - self.glyph = glyph - - self.sync_trait('point_scale', self.glyph.glyph.glyph, 'scale_factor') - self.sync_trait('color', self.glyph.actor.property, mutual=False) - self.sync_trait('visible', self.glyph) - self.sync_trait('opacity', self.glyph.actor.property) - self.sync_trait('mark_inside', self.glyph.actor.mapper, - 'scalar_visibility') - self.on_trait_change(self._update_points, 'points') - self._update_marker_scaling() - self._update_marker_type() - self._update_colors() - _toggle_mlab_render(self, True) - # self.scene.camera.parallel_scale = _scale - - def _nearest_default(self): - return _DistanceQuery(np.zeros((1, 3))) - - def _get_nearest(self, proj_rr): - idx = self.nearest.query(proj_rr)[1] - proj_pts = apply_trans( - self.project_to_trans, self.nearest.data[idx]) - proj_nn = apply_trans( - self.project_to_trans, self.check_inside.surf['nn'][idx], - move=False) - return proj_pts, proj_nn - - @on_trait_change('points,project_to_trans,project_to_surface,mark_inside,' - 'nearest') - def _update_projections(self): - """Update the styles of the plotted points.""" - if not hasattr(self.src, 'data'): - return - if self._view == 'arrow': - self.src.data.point_data.normals = self.nn - self.src.data.point_data.update() - return - # projections - if len(self.nearest.data) <= 1 or len(self.points) == 0: - return - - # Do the projections - pts = self.points - inv_trans = np.linalg.inv(self.project_to_trans) - proj_rr = apply_trans(inv_trans, self.points) - proj_pts, proj_nn = self._get_nearest(proj_rr) - vec = pts - proj_pts # point to the surface - if self.project_to_surface: - pts = proj_pts - nn = proj_nn - if self.mark_inside and not self.project_to_surface: - scalars = (~self.check_inside(proj_rr, verbose=False)).astype(int) - else: - scalars = np.ones(len(pts)) - # With this, a point exactly on the surface is of size point_scale - dist = np.linalg.norm(vec, axis=-1, keepdims=True) - self.src.data.point_data.normals = (250 * dist + 1) * nn - self.src.data.point_data.scalars = scalars - self.glyph.actor.mapper.scalar_range = [0., 1.] - self.src.data.points = pts # projection can change this - self.src.data.point_data.update() - - @on_trait_change('color,inside_color') - def _update_colors(self): - if self.glyph is None: - return - # inside_color is the surface color, let's try to get far - # from that - inside = np.array(self.inside_color) - # if it's too close to gray, just use black: - if np.mean(np.abs(inside - 0.5)) < 0.2: - inside.fill(0.) - else: - inside = 1 - inside - colors = np.array([tuple(inside) + (1,), - tuple(self.color) + (1,)]) * 255. - self.glyph.module_manager.scalar_lut_manager.lut.table = colors - - @on_trait_change('project_to_surface,orient_to_surface') - def _update_marker_type(self): - # not implemented for arrow - if self.glyph is None or self._view == 'arrow': - return - defaults = DEFAULTS['coreg'] - gs = self.glyph.glyph.glyph_source - res = getattr(gs.glyph_source, 'theta_resolution', - getattr(gs.glyph_source, 'resolution', None)) - if res is None: - return - if self.project_to_surface or self.orient_to_surface: - gs.glyph_source = tvtk.CylinderSource() - gs.glyph_source.height = defaults['eegp_height'] - gs.glyph_source.center = (0., -defaults['eegp_height'], 0) - gs.glyph_source.resolution = res - else: - gs.glyph_source = tvtk.SphereSource() - gs.glyph_source.phi_resolution = res - gs.glyph_source.theta_resolution = res - - @on_trait_change('scale_by_distance,project_to_surface') - def _update_marker_scaling(self): - if self.glyph is None: - return - if self.scale_by_distance and not self.project_to_surface: - self.glyph.glyph.scale_mode = 'scale_by_vector' - else: - self.glyph.glyph.scale_mode = 'data_scaling_off' - - def _resolution_changed(self, new): - if not self.glyph: - return - gs = self.glyph.glyph.glyph_source.glyph_source - if isinstance(gs, tvtk.SphereSource): - gs.phi_resolution = new - gs.theta_resolution = new - elif isinstance(gs, tvtk.CylinderSource): - gs.resolution = new - else: # ArrowSource - gs.tip_resolution = new - gs.shaft_resolution = new - - @cached_property - def _get_orientable(self): - return len(self.nearest.data) > 1 - - -class SurfaceObject(Object): - """Represent a solid object in a mayavi scene. - - Notes - ----- - Doesn't automatically update plot because update requires both - :attr:`points` and :attr:`tris`. Call :meth:`plot` after updating both - attributes. - """ - - rep = Enum("Surface", "Wireframe") - tris = Array(int, shape=(None, 3)) - - surf = Instance(Surface) - surf_rear = Instance(Surface) - rear_opacity = Float(1.) - - view = View(HGroup(Item('visible', show_label=False), - Item('color', show_label=False), - Item('opacity'))) - - def __init__(self, block_behind=False, **kwargs): # noqa: D102 - self._block_behind = block_behind - self._deferred_tri_update = False - super(SurfaceObject, self).__init__(**kwargs) - - def clear(self): # noqa: D102 - if hasattr(self.src, 'remove'): - self.src.remove() - if hasattr(self.surf, 'remove'): - self.surf.remove() - if hasattr(self.surf_rear, 'remove'): - self.surf_rear.remove() - self.reset_traits(['src', 'surf']) - - @on_trait_change('scene.activated') - def plot(self): - """Add the points to the mayavi pipeline""" - _scale = self.scene.camera.parallel_scale - self.clear() - - if not np.any(self.tris): - return - - fig = self.scene.mayavi_scene - surf = dict(rr=self.points, tris=self.tris) - normals = _create_mesh_surf(surf, fig=fig) - self.src = normals.parent - rep = 'wireframe' if self.rep == 'Wireframe' else 'surface' - # Add the opaque "inside" first to avoid the translucent "outside" - # from being occluded (gh-5152) - if self._block_behind: - surf_rear = pipeline.surface( - normals, figure=fig, color=self.color, representation=rep, - line_width=1) - surf_rear.actor.property.frontface_culling = True - self.surf_rear = surf_rear - self.sync_trait('color', self.surf_rear.actor.property, - mutual=False) - self.sync_trait('visible', self.surf_rear, 'visible') - self.surf_rear.actor.property.opacity = self.rear_opacity - self.sync_trait( - 'rear_opacity', self.surf_rear.actor.property, 'opacity') - surf = pipeline.surface( - normals, figure=fig, color=self.color, representation=rep, - line_width=1) - surf.actor.property.backface_culling = True - self.surf = surf - self.sync_trait('visible', self.surf, 'visible') - self.sync_trait('color', self.surf.actor.property, mutual=False) - self.sync_trait('opacity', self.surf.actor.property) - - self.scene.camera.parallel_scale = _scale - - @on_trait_change('tris') - def _update_tris(self): - self._deferred_tris_update = True - - @on_trait_change('points') - def _update_points(self): - # Nuke the tris before setting the points otherwise we can get - # a nasty segfault (gh-5728) - if self._deferred_tris_update and self.src is not None: - self.src.data.polys = None - if Object._update_points(self): - if self._deferred_tris_update: - self.src.data.polys = self.tris - self._deffered_tris_update = False - self.src.update() # necessary for SurfaceObject since Mayavi 4.5.0 diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/__init__.py python-mne-1.1.0+dfsg/mne/html_templates/__init__.py --- python-mne-0.23.4+dfsg/mne/html_templates/__init__.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,3 @@ +"""Jinja2 HTML templates.""" + +from ._templates import repr_templates_env, report_templates_env diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/report/bem.html.jinja python-mne-1.1.0+dfsg/mne/html_templates/report/bem.html.jinja --- python-mne-0.23.4+dfsg/mne/html_templates/report/bem.html.jinja 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/report/bem.html.jinja 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,8 @@ +{% extends "section.html.jinja" %} +{% block html_content %} +
+ {{ html_slider_axial | safe }} + {{ html_slider_sagittal | safe }} + {{ html_slider_coronal | safe }} +
+{% endblock html_content %} diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/report/code.html.jinja python-mne-1.1.0+dfsg/mne/html_templates/report/code.html.jinja --- python-mne-0.23.4+dfsg/mne/html_templates/report/code.html.jinja 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/report/code.html.jinja 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,6 @@ +{% extends "section.html.jinja" %} +{% block html_content %} +
+  {{ code }}
+
+{% endblock html_content %} diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/report/footer.html.jinja python-mne-1.1.0+dfsg/mne/html_templates/report/footer.html.jinja --- python-mne-0.23.4+dfsg/mne/html_templates/report/footer.html.jinja 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/report/footer.html.jinja 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,10 @@ +
+
+
+ +
+ + + diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/report/forward.html.jinja python-mne-1.1.0+dfsg/mne/html_templates/report/forward.html.jinja --- python-mne-0.23.4+dfsg/mne/html_templates/report/forward.html.jinja 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/report/forward.html.jinja 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,5 @@ +{% extends "section.html.jinja" %} +{% block html_content %} +{{repr | safe}} +{{sensitivity_maps | safe}} +{% endblock html_content %} diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/report/header.html.jinja python-mne-1.1.0+dfsg/mne/html_templates/report/header.html.jinja --- python-mne-0.23.4+dfsg/mne/html_templates/report/header.html.jinja 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/report/header.html.jinja 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,56 @@ + + + + + + {{include | safe }} + + + + + {{ title }} + + + + diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/report/html.html.jinja python-mne-1.1.0+dfsg/mne/html_templates/report/html.html.jinja --- python-mne-0.23.4+dfsg/mne/html_templates/report/html.html.jinja 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/report/html.html.jinja 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,23 @@ +
+
+ +
+ + +
+
+ {{ html | safe }} +
+
+
diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/report/image.html.jinja python-mne-1.1.0+dfsg/mne/html_templates/report/image.html.jinja --- python-mne-0.23.4+dfsg/mne/html_templates/report/image.html.jinja 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/report/image.html.jinja 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,17 @@ +{% extends "section.html.jinja" %} +{% block html_content %} +
+ {% if image_format == 'svg' %} +
+ {{ img }} +
+ {% else %} + {{ title }} + {% endif %} + + {% if caption is not none %} +
{{ caption }}
+ {% endif %} +
+{% endblock html_content %} diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/report/inverse.html.jinja python-mne-1.1.0+dfsg/mne/html_templates/report/inverse.html.jinja --- python-mne-0.23.4+dfsg/mne/html_templates/report/inverse.html.jinja 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/report/inverse.html.jinja 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,5 @@ +{% extends "section.html.jinja" %} +{% block html_content %} +{{repr | safe}} +{{source_space | safe}} +{% endblock html_content %} diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/report/section.html.jinja python-mne-1.1.0+dfsg/mne/html_templates/report/section.html.jinja --- python-mne-0.23.4+dfsg/mne/html_templates/report/section.html.jinja 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/report/section.html.jinja 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,27 @@ +
+
+ +
+ + +
+
+ {% block html_content %} + {% for html in htmls %} + {{ html | safe }} + {% endfor %} + {% endblock %} +
+
+
diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/report/slider.html.jinja python-mne-1.1.0+dfsg/mne/html_templates/report/slider.html.jinja --- python-mne-0.23.4+dfsg/mne/html_templates/report/slider.html.jinja 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/report/slider.html.jinja 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,60 @@ +
+
+ +
+ +
+
+
+ + +
+ + +
+
+
diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/report/toc.html.jinja python-mne-1.1.0+dfsg/mne/html_templates/report/toc.html.jinja --- python-mne-0.23.4+dfsg/mne/html_templates/report/toc.html.jinja 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/report/toc.html.jinja 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,12 @@ +
+
+
+
Table of contents
+ +
+
diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/repr/epochs.html.jinja python-mne-1.1.0+dfsg/mne/html_templates/repr/epochs.html.jinja --- python-mne-0.23.4+dfsg/mne/html_templates/repr/epochs.html.jinja 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/repr/epochs.html.jinja 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,22 @@ + + + + + + + + {% if events is not none %} + + {% else %} + + {% endif %} + + + + + + + + + +
Number of events{{ epochs.events|length }}
Events{{ events|join('
') | safe }}		Not available
Time range{{ '%.3f'|format(epochs.tmin) }} – {{ '%.3f'|format(epochs.tmax) }} sec
Baseline{{ baseline }}
diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/repr/forward.html.jinja python-mne-1.1.0+dfsg/mne/html_templates/repr/forward.html.jinja --- python-mne-0.23.4+dfsg/mne/html_templates/repr/forward.html.jinja 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/repr/forward.html.jinja 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,18 @@ + + + + + + + + + + + + + + + + + +
Good channels{{ good_channels }}
Bad channels{{ bad_channels }}
Source space{{ source_space_descr }}
Source orientation{{ source_orientation }}
diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/repr/ica.html.jinja python-mne-1.1.0+dfsg/mne/html_templates/repr/ica.html.jinja --- python-mne-0.23.4+dfsg/mne/html_templates/repr/ica.html.jinja 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/repr/ica.html.jinja 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,32 @@ + + + + + + + + + + {% if fit_on %} + + + + + + + + + + + + + + + + + + + + + {% endif %} +
Method{{ method }}
Fit{% if fit_on %}{{ n_iter }} iterations on {{ fit_on }} ({{ n_samples }} samples){% else %}no{% endif %}
ICA components{{ n_components }}
Explained variance{{ (explained_variance * 100) | round(1) }} %
Available PCA components{{ n_pca_components }}
Channel types{{ ch_types|join(', ') }}
ICA components marked for exclusion{% if excludes %}{{ excludes|join('
' | safe) }}{% else %}—{% endif %}
diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/repr/info.html.jinja python-mne-1.1.0+dfsg/mne/html_templates/repr/info.html.jinja --- python-mne-0.23.4+dfsg/mne/html_templates/repr/info.html.jinja 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/repr/info.html.jinja 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,74 @@ + + + + {% if meas_date is not none %} + + {% else %} + + {% endif %} + + + + {% if experimenter is not none %} + + {% else %} + + {% endif %} + + + {% if subject_info is not none %} + {% if 'his_id' in subject_info.keys() %} + + {% endif %} + {% else %} + + {% endif %} + + + + {% if dig is not none %} + + {% else %} + + {% endif %} + + + + + + + + + + + + + + + + + {% if sfreq is not none %} + + + + + {% endif %} + {% if highpass is not none %} + + + + + {% endif %} + {% if lowpass is not none %} + + + + + {% endif %} + {% if projs is not none %} + + + + + {% endif %} +
Measurement date{{ meas_date }}Unknown
Experimenter{{ experimenter }}Unknown
Participant{{ subject_info['his_id'] }}Unknown
Digitized points{{ dig|length }} pointsNot available
Good channels{{ good_channels }}
Bad channels{{ bad_channels }}
EOG channels{{ eog }}
ECG channels{{ ecg }}
Sampling frequency{{ '%0.2f'|format(sfreq) }} Hz
Highpass{{ '%0.2f'|format(highpass) }} Hz
Lowpass{{ '%0.2f'|format(lowpass) }} Hz
Projections{{ projs|join('
') | safe }}
diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/repr/inverse_operator.html.jinja python-mne-1.1.0+dfsg/mne/html_templates/repr/inverse_operator.html.jinja --- python-mne-0.23.4+dfsg/mne/html_templates/repr/inverse_operator.html.jinja 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/repr/inverse_operator.html.jinja 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,14 @@ + + + + + + + + + + + + + +
Channels{{ channels }}
Source space{{ source_space_descr }}
Source orientation{{ source_orientation }}
diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/repr/raw.html.jinja python-mne-1.1.0+dfsg/mne/html_templates/repr/raw.html.jinja --- python-mne-0.23.4+dfsg/mne/html_templates/repr/raw.html.jinja 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/repr/raw.html.jinja 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,12 @@ +{{ info_repr[:-9] | safe }} + {% if filenames %} + + Filenames + {{ filenames|join('
') }} + + {% endif %} + + Duration + {{ duration }} (HH:MM:SS) + + diff -Nru python-mne-0.23.4+dfsg/mne/html_templates/_templates.py python-mne-1.1.0+dfsg/mne/html_templates/_templates.py --- python-mne-0.23.4+dfsg/mne/html_templates/_templates.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/html_templates/_templates.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,25 @@ +import jinja2 + +autoescape = jinja2.select_autoescape( + default=True, + default_for_string=True +) + +# For _html_repr_() +repr_templates_env = jinja2.Environment( + loader=jinja2.PackageLoader( + package_name='mne.html_templates', + package_path='repr' + ), + autoescape=autoescape +) + +# For mne.Report +report_templates_env = jinja2.Environment( + loader=jinja2.PackageLoader( + package_name='mne.html_templates', + package_path='report' + ), + autoescape=autoescape +) +report_templates_env.filters['zip'] = zip diff -Nru python-mne-0.23.4+dfsg/mne/icons/clear-black-18dp.svg python-mne-1.1.0+dfsg/mne/icons/clear-black-18dp.svg --- python-mne-0.23.4+dfsg/mne/icons/clear-black-18dp.svg 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/clear-black-18dp.svg 1970-01-01 00:00:00.000000000 +0000 @@ -1 +0,0 @@ - \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/dark/actions/clear.svg python-mne-1.1.0+dfsg/mne/icons/dark/actions/clear.svg --- python-mne-0.23.4+dfsg/mne/icons/dark/actions/clear.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/dark/actions/clear.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/dark/actions/folder.svg python-mne-1.1.0+dfsg/mne/icons/dark/actions/folder.svg --- python-mne-0.23.4+dfsg/mne/icons/dark/actions/folder.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/dark/actions/folder.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/dark/actions/help.svg python-mne-1.1.0+dfsg/mne/icons/dark/actions/help.svg --- python-mne-0.23.4+dfsg/mne/icons/dark/actions/help.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/dark/actions/help.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/dark/actions/movie.svg python-mne-1.1.0+dfsg/mne/icons/dark/actions/movie.svg --- python-mne-0.23.4+dfsg/mne/icons/dark/actions/movie.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/dark/actions/movie.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/dark/actions/pause.svg python-mne-1.1.0+dfsg/mne/icons/dark/actions/pause.svg --- python-mne-0.23.4+dfsg/mne/icons/dark/actions/pause.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/dark/actions/pause.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/dark/actions/play.svg python-mne-1.1.0+dfsg/mne/icons/dark/actions/play.svg --- python-mne-0.23.4+dfsg/mne/icons/dark/actions/play.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/dark/actions/play.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/dark/actions/reset.svg python-mne-1.1.0+dfsg/mne/icons/dark/actions/reset.svg --- python-mne-0.23.4+dfsg/mne/icons/dark/actions/reset.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/dark/actions/reset.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/dark/actions/restore.svg python-mne-1.1.0+dfsg/mne/icons/dark/actions/restore.svg --- python-mne-0.23.4+dfsg/mne/icons/dark/actions/restore.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/dark/actions/restore.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/dark/actions/scale.svg python-mne-1.1.0+dfsg/mne/icons/dark/actions/scale.svg --- python-mne-0.23.4+dfsg/mne/icons/dark/actions/scale.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/dark/actions/scale.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/dark/actions/screenshot.svg python-mne-1.1.0+dfsg/mne/icons/dark/actions/screenshot.svg --- python-mne-0.23.4+dfsg/mne/icons/dark/actions/screenshot.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/dark/actions/screenshot.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/dark/actions/visibility_off.svg python-mne-1.1.0+dfsg/mne/icons/dark/actions/visibility_off.svg --- python-mne-0.23.4+dfsg/mne/icons/dark/actions/visibility_off.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/dark/actions/visibility_off.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/dark/actions/visibility_on.svg python-mne-1.1.0+dfsg/mne/icons/dark/actions/visibility_on.svg --- python-mne-0.23.4+dfsg/mne/icons/dark/actions/visibility_on.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/dark/actions/visibility_on.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/dark/index.theme python-mne-1.1.0+dfsg/mne/icons/dark/index.theme --- python-mne-0.23.4+dfsg/mne/icons/dark/index.theme 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/dark/index.theme 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,11 @@ +[Icon Theme] +Name=dark + +Directories=actions + +[actions] +Size=32 +Context=Actions +MinSize=16 +MaxSize=128 +Type=Scalable diff -Nru python-mne-0.23.4+dfsg/mne/icons/help-black-18dp.svg python-mne-1.1.0+dfsg/mne/icons/help-black-18dp.svg --- python-mne-0.23.4+dfsg/mne/icons/help-black-18dp.svg 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/help-black-18dp.svg 1970-01-01 00:00:00.000000000 +0000 @@ -1 +0,0 @@ - \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/light/actions/clear.svg python-mne-1.1.0+dfsg/mne/icons/light/actions/clear.svg --- python-mne-0.23.4+dfsg/mne/icons/light/actions/clear.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/light/actions/clear.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/light/actions/folder.svg python-mne-1.1.0+dfsg/mne/icons/light/actions/folder.svg --- python-mne-0.23.4+dfsg/mne/icons/light/actions/folder.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/light/actions/folder.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/light/actions/help.svg python-mne-1.1.0+dfsg/mne/icons/light/actions/help.svg --- python-mne-0.23.4+dfsg/mne/icons/light/actions/help.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/light/actions/help.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/light/actions/movie.svg python-mne-1.1.0+dfsg/mne/icons/light/actions/movie.svg --- python-mne-0.23.4+dfsg/mne/icons/light/actions/movie.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/light/actions/movie.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/light/actions/pause.svg python-mne-1.1.0+dfsg/mne/icons/light/actions/pause.svg --- python-mne-0.23.4+dfsg/mne/icons/light/actions/pause.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/light/actions/pause.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/light/actions/play.svg python-mne-1.1.0+dfsg/mne/icons/light/actions/play.svg --- python-mne-0.23.4+dfsg/mne/icons/light/actions/play.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/light/actions/play.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/light/actions/reset.svg python-mne-1.1.0+dfsg/mne/icons/light/actions/reset.svg --- python-mne-0.23.4+dfsg/mne/icons/light/actions/reset.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/light/actions/reset.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/light/actions/restore.svg python-mne-1.1.0+dfsg/mne/icons/light/actions/restore.svg --- python-mne-0.23.4+dfsg/mne/icons/light/actions/restore.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/light/actions/restore.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/light/actions/scale.svg python-mne-1.1.0+dfsg/mne/icons/light/actions/scale.svg --- python-mne-0.23.4+dfsg/mne/icons/light/actions/scale.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/light/actions/scale.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/light/actions/screenshot.svg python-mne-1.1.0+dfsg/mne/icons/light/actions/screenshot.svg --- python-mne-0.23.4+dfsg/mne/icons/light/actions/screenshot.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/light/actions/screenshot.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/light/actions/visibility_off.svg python-mne-1.1.0+dfsg/mne/icons/light/actions/visibility_off.svg --- python-mne-0.23.4+dfsg/mne/icons/light/actions/visibility_off.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/light/actions/visibility_off.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/light/actions/visibility_on.svg python-mne-1.1.0+dfsg/mne/icons/light/actions/visibility_on.svg --- python-mne-0.23.4+dfsg/mne/icons/light/actions/visibility_on.svg 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/light/actions/visibility_on.svg 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ + \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/light/index.theme python-mne-1.1.0+dfsg/mne/icons/light/index.theme --- python-mne-0.23.4+dfsg/mne/icons/light/index.theme 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/light/index.theme 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,11 @@ +[Icon Theme] +Name=light + +Directories=actions + +[actions] +Size=32 +Context=Actions +MinSize=16 +MaxSize=128 +Type=Scalable Binary files /tmp/tmprw82qwlm/pMK45BCM_h/python-mne-0.23.4+dfsg/mne/icons/mne_bigsur_icon.png and /tmp/tmprw82qwlm/AKapBaGA2j/python-mne-1.1.0+dfsg/mne/icons/mne_bigsur_icon.png differ Binary files /tmp/tmprw82qwlm/pMK45BCM_h/python-mne-0.23.4+dfsg/mne/icons/mne-circle-black.png and /tmp/tmprw82qwlm/AKapBaGA2j/python-mne-1.1.0+dfsg/mne/icons/mne-circle-black.png differ Binary files /tmp/tmprw82qwlm/pMK45BCM_h/python-mne-0.23.4+dfsg/mne/icons/mne_default_icon.png and /tmp/tmprw82qwlm/AKapBaGA2j/python-mne-1.1.0+dfsg/mne/icons/mne_default_icon.png differ Binary files /tmp/tmprw82qwlm/pMK45BCM_h/python-mne-0.23.4+dfsg/mne/icons/mne_icon-cropped.png and /tmp/tmprw82qwlm/AKapBaGA2j/python-mne-1.1.0+dfsg/mne/icons/mne_icon-cropped.png differ Binary files /tmp/tmprw82qwlm/pMK45BCM_h/python-mne-0.23.4+dfsg/mne/icons/mne_icon.png and /tmp/tmprw82qwlm/AKapBaGA2j/python-mne-1.1.0+dfsg/mne/icons/mne_icon.png differ diff -Nru python-mne-0.23.4+dfsg/mne/icons/mne.qrc python-mne-1.1.0+dfsg/mne/icons/mne.qrc --- python-mne-0.23.4+dfsg/mne/icons/mne.qrc 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/mne.qrc 1970-01-01 00:00:00.000000000 +0000 @@ -1,16 +0,0 @@ - - - visibility_on-black-18dp.svg - visibility_off-black-18dp.svg - help-black-18dp.svg - play-black-18dp.svg - reset-black-18dp.svg - pause-black-18dp.svg - scale-black-18dp.svg - restore-black-18dp.svg - clear-black-18dp.svg - screenshot-black-18dp.svg - movie-black-18dp.svg - mne-circle-black.png - - Binary files /tmp/tmprw82qwlm/pMK45BCM_h/python-mne-0.23.4+dfsg/mne/icons/mne_splash.png and /tmp/tmprw82qwlm/AKapBaGA2j/python-mne-1.1.0+dfsg/mne/icons/mne_splash.png differ diff -Nru python-mne-0.23.4+dfsg/mne/icons/movie-black-18dp.svg python-mne-1.1.0+dfsg/mne/icons/movie-black-18dp.svg --- python-mne-0.23.4+dfsg/mne/icons/movie-black-18dp.svg 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/movie-black-18dp.svg 1970-01-01 00:00:00.000000000 +0000 @@ -1,72 +0,0 @@ - - - - - - image/svg+xml - - - - - - - - - - - - diff -Nru python-mne-0.23.4+dfsg/mne/icons/pause-black-18dp.svg python-mne-1.1.0+dfsg/mne/icons/pause-black-18dp.svg --- python-mne-0.23.4+dfsg/mne/icons/pause-black-18dp.svg 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/pause-black-18dp.svg 1970-01-01 00:00:00.000000000 +0000 @@ -1 +0,0 @@ - \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/play-black-18dp.svg python-mne-1.1.0+dfsg/mne/icons/play-black-18dp.svg --- python-mne-0.23.4+dfsg/mne/icons/play-black-18dp.svg 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/play-black-18dp.svg 1970-01-01 00:00:00.000000000 +0000 @@ -1 +0,0 @@ - \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/README.rst python-mne-1.1.0+dfsg/mne/icons/README.rst --- python-mne-0.23.4+dfsg/mne/icons/README.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/README.rst 2022-08-03 15:48:14.000000000 +0000 @@ -4,15 +4,8 @@ Documentation ============= -The icons are used in ``mne/viz/_brain/_Brain.py`` for the toolbar. -It is necessary to compile those icons into a resource file for proper use by -the application. +The icons are used in ``mne/viz/_brain/_brain.py`` for the toolbar. +These Material design icons are provided by Google under the `Apache 2.0`_ license. -The resource configuration file ``mne/icons/mne.qrc`` describes the location of -the resources in the filesystem and also defines aliases for their use in the code. -To automatically generate the resource file in ``mne/icons``: - -.. code-block:: bash - - pyrcc5 -o mne/icons/resources.py mne/icons/mne.qrc +.. _Apache 2.0: https://github.com/google/material-design-icons/blob/master/LICENSE diff -Nru python-mne-0.23.4+dfsg/mne/icons/reset-black-18dp.svg python-mne-1.1.0+dfsg/mne/icons/reset-black-18dp.svg --- python-mne-0.23.4+dfsg/mne/icons/reset-black-18dp.svg 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/reset-black-18dp.svg 1970-01-01 00:00:00.000000000 +0000 @@ -1 +0,0 @@ - \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/resources.py python-mne-1.1.0+dfsg/mne/icons/resources.py --- python-mne-0.23.4+dfsg/mne/icons/resources.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/resources.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,1268 +0,0 @@ -# -*- coding: utf-8 -*- - -# Resource object code -# -# Created by: The Resource Compiler for PyQt5 (Qt v5.15.0) -# -# WARNING! All changes made in this file will be lost! - -from PyQt5 import QtCore - -qt_resource_data = b"\ -\x00\x00\x01\x90\ -\x3c\ -\x73\x76\x67\x20\x78\x6d\x6c\x6e\x73\x3d\x22\x68\x74\x74\x70\x3a\ -\x2f\x2f\x77\x77\x77\x2e\x77\x33\x2e\x6f\x72\x67\x2f\x32\x30\x30\ -\x30\x2f\x73\x76\x67\x22\x20\x76\x69\x65\x77\x42\x6f\x78\x3d\x22\ -\x30\x20\x30\x20\x32\x34\x20\x32\x34\x22\x20\x66\x69\x6c\x6c\x3d\ -\x22\x62\x6c\x61\x63\x6b\x22\x20\x77\x69\x64\x74\x68\x3d\x22\x31\ -\x38\x70\x78\x22\x20\x68\x65\x69\x67\x68\x74\x3d\x22\x31\x38\x70\ -\x78\x22\x3e\x3c\x70\x61\x74\x68\x20\x64\x3d\x22\x4d\x30\x20\x30\ -\x68\x32\x34\x76\x32\x34\x48\x30\x56\x30\x7a\x22\x20\x66\x69\x6c\ -\x6c\x3d\x22\x6e\x6f\x6e\x65\x22\x2f\x3e\x3c\x70\x61\x74\x68\x20\ -\x64\x3d\x22\x4d\x31\x31\x20\x31\x38\x68\x32\x76\x2d\x32\x68\x2d\ -\x32\x76\x32\x7a\x6d\x31\x2d\x31\x36\x43\x36\x2e\x34\x38\x20\x32\ -\x20\x32\x20\x36\x2e\x34\x38\x20\x32\x20\x31\x32\x73\x34\x2e\x34\ -\x38\x20\x31\x30\x20\x31\x30\x20\x31\x30\x20\x31\x30\x2d\x34\x2e\ 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python-mne-0.23.4+dfsg/mne/icons/scale-black-18dp.svg python-mne-1.1.0+dfsg/mne/icons/scale-black-18dp.svg --- python-mne-0.23.4+dfsg/mne/icons/scale-black-18dp.svg 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/scale-black-18dp.svg 1970-01-01 00:00:00.000000000 +0000 @@ -1 +0,0 @@ - \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/screenshot-black-18dp.svg python-mne-1.1.0+dfsg/mne/icons/screenshot-black-18dp.svg --- python-mne-0.23.4+dfsg/mne/icons/screenshot-black-18dp.svg 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/screenshot-black-18dp.svg 1970-01-01 00:00:00.000000000 +0000 @@ -1 +0,0 @@ - \ No newline at end of file Binary files /tmp/tmprw82qwlm/pMK45BCM_h/python-mne-0.23.4+dfsg/mne/icons/toolbar_move_horizontal@2x.png and /tmp/tmprw82qwlm/AKapBaGA2j/python-mne-1.1.0+dfsg/mne/icons/toolbar_move_horizontal@2x.png differ Binary files /tmp/tmprw82qwlm/pMK45BCM_h/python-mne-0.23.4+dfsg/mne/icons/toolbar_move_vertical@2x.png and /tmp/tmprw82qwlm/AKapBaGA2j/python-mne-1.1.0+dfsg/mne/icons/toolbar_move_vertical@2x.png differ Binary files /tmp/tmprw82qwlm/pMK45BCM_h/python-mne-0.23.4+dfsg/mne/icons/toolbar_separator_horizontal@2x.png and /tmp/tmprw82qwlm/AKapBaGA2j/python-mne-1.1.0+dfsg/mne/icons/toolbar_separator_horizontal@2x.png differ Binary files /tmp/tmprw82qwlm/pMK45BCM_h/python-mne-0.23.4+dfsg/mne/icons/toolbar_separator_horizontal.png and /tmp/tmprw82qwlm/AKapBaGA2j/python-mne-1.1.0+dfsg/mne/icons/toolbar_separator_horizontal.png differ Binary files /tmp/tmprw82qwlm/pMK45BCM_h/python-mne-0.23.4+dfsg/mne/icons/toolbar_separator_vertical@2x.png and /tmp/tmprw82qwlm/AKapBaGA2j/python-mne-1.1.0+dfsg/mne/icons/toolbar_separator_vertical@2x.png differ diff -Nru python-mne-0.23.4+dfsg/mne/icons/visibility_off-black-18dp.svg python-mne-1.1.0+dfsg/mne/icons/visibility_off-black-18dp.svg --- python-mne-0.23.4+dfsg/mne/icons/visibility_off-black-18dp.svg 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/visibility_off-black-18dp.svg 1970-01-01 00:00:00.000000000 +0000 @@ -1 +0,0 @@ - \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/icons/visibility_on-black-18dp.svg python-mne-1.1.0+dfsg/mne/icons/visibility_on-black-18dp.svg --- python-mne-0.23.4+dfsg/mne/icons/visibility_on-black-18dp.svg 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/icons/visibility_on-black-18dp.svg 1970-01-01 00:00:00.000000000 +0000 @@ -1 +0,0 @@ - \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/__init__.py python-mne-1.1.0+dfsg/mne/__init__.py --- python-mne-0.23.4+dfsg/mne/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -21,7 +21,8 @@ # have to import verbose first since it's needed by many things from .utils import (set_log_level, set_log_file, verbose, set_config, get_config, get_config_path, set_cache_dir, - set_memmap_min_size, grand_average, sys_info, open_docs) + set_memmap_min_size, grand_average, sys_info, open_docs, + use_log_level) from .io.pick import (pick_types, pick_channels, pick_channels_regexp, pick_channels_forward, pick_types_forward, pick_channels_cov, @@ -43,6 +44,9 @@ from .event import (read_events, write_events, find_events, merge_events, pick_events, make_fixed_length_events, concatenate_events, find_stim_steps, AcqParserFIF) +from ._freesurfer import (head_to_mni, head_to_mri, read_talxfm, + get_volume_labels_from_aseg, read_freesurfer_lut, + vertex_to_mni, read_lta) from .forward import (read_forward_solution, apply_forward, apply_forward_raw, average_forward_solutions, Forward, write_forward_solution, make_forward_solution, @@ -62,22 +66,22 @@ spatio_temporal_dist_adjacency, extract_label_time_course, stc_near_sensors) from .surface import (read_surface, write_surface, decimate_surface, read_tri, - read_morph_map, get_head_surf, get_meg_helmet_surf, - dig_mri_distances) + get_head_surf, get_meg_helmet_surf, dig_mri_distances, + warp_montage_volume, get_montage_volume_labels) +from .morph_map import read_morph_map from .morph import (SourceMorph, read_source_morph, grade_to_vertices, compute_source_morph) -from .source_space import (read_source_spaces, vertex_to_mni, - head_to_mni, head_to_mri, read_talxfm, +from .source_space import (read_source_spaces, write_source_spaces, setup_source_space, setup_volume_source_space, SourceSpaces, add_source_space_distances, morph_source_spaces, - get_volume_labels_from_aseg, - get_volume_labels_from_src, read_freesurfer_lut) + get_volume_labels_from_src) from .annotations import (Annotations, read_annotations, annotations_from_events, events_from_annotations) from .epochs import (BaseEpochs, Epochs, EpochsArray, read_epochs, concatenate_epochs, make_fixed_length_epochs) -from .evoked import Evoked, EvokedArray, read_evokeds, write_evokeds, combine_evoked +from .evoked import (Evoked, EvokedArray, read_evokeds, write_evokeds, + combine_evoked) from .label import (read_label, label_sign_flip, write_label, stc_to_label, grow_labels, Label, split_label, BiHemiLabel, read_labels_from_annot, write_labels_to_annot, @@ -94,21 +98,20 @@ read_vectorview_selection) from .report import Report, open_report -from .io import read_epochs_fieldtrip, read_evoked_fieldtrip, read_evokeds_mff +from .io import (read_epochs_fieldtrip, read_evoked_besa, + read_evoked_fieldtrip, read_evokeds_mff) from .rank import compute_rank from . import beamformer from . import channels from . import chpi from . import commands -from . import connectivity from . import coreg from . import cuda from . import datasets from . import dipole from . import epochs from . import event -from . import externals from . import io from . import filter from . import gui @@ -121,10 +124,7 @@ from . import time_frequency from . import viz from . import decoding - -# deprecations -from .utils import deprecated_alias -deprecated_alias('read_selection', read_vectorview_selection) +from . import export # initialize logging set_log_level(None, False) diff -Nru python-mne-0.23.4+dfsg/mne/inverse_sparse/_gamma_map.py python-mne-1.1.0+dfsg/mne/inverse_sparse/_gamma_map.py --- python-mne-0.23.4+dfsg/mne/inverse_sparse/_gamma_map.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/inverse_sparse/_gamma_map.py 2022-08-03 15:48:14.000000000 +0000 @@ -207,7 +207,7 @@ If True, the residual is returned as an Evoked instance. return_as_dipoles : bool If True, the sources are returned as a list of Dipole instances. - %(rank_None)s + %(rank_none)s .. versionadded:: 0.18 %(pick_ori)s diff -Nru python-mne-0.23.4+dfsg/mne/inverse_sparse/mxne_debiasing.py python-mne-1.1.0+dfsg/mne/inverse_sparse/mxne_debiasing.py --- python-mne-0.23.4+dfsg/mne/inverse_sparse/mxne_debiasing.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/inverse_sparse/mxne_debiasing.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors: Daniel Strohmeier # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause from math import sqrt import numpy as np diff -Nru python-mne-0.23.4+dfsg/mne/inverse_sparse/mxne_inverse.py python-mne-1.1.0+dfsg/mne/inverse_sparse/mxne_inverse.py --- python-mne-0.23.4+dfsg/mne/inverse_sparse/mxne_inverse.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/inverse_sparse/mxne_inverse.py 2022-08-03 15:48:14.000000000 +0000 @@ -11,12 +11,13 @@ from ..forward import is_fixed_orient from ..io.pick import pick_channels_evoked from ..io.proj import deactivate_proj -from ..utils import logger, verbose, _check_depth, _check_option, sum_squared +from ..utils import (logger, verbose, _check_depth, _check_option, sum_squared, + _validate_type, check_random_state, warn) from ..dipole import Dipole from .mxne_optim import (mixed_norm_solver, iterative_mixed_norm_solver, _Phi, tf_mixed_norm_solver, iterative_tf_mixed_norm_solver, - norm_l2inf, norm_epsilon_inf) + norm_l2inf, norm_epsilon_inf, groups_norm2) def _check_ori(pick_ori, forward): @@ -97,8 +98,9 @@ non_active_projs.append(p) if len(active_projs) > 0: - r_tmp.info['projs'] = deactivate_proj(active_projs, copy=True, - verbose=False) + with r_tmp.info._unlock(): + r_tmp.info['projs'] = deactivate_proj(active_projs, copy=True, + verbose=False) r_tmp.apply_proj(verbose=False) r_tmp.add_proj(non_active_projs, remove_existing=False, verbose=False) @@ -161,7 +163,8 @@ assert gain.shape[0] == M.shape[0], (gain.shape, M.shape) # find an orthonormal basis for our matrices that spans the actual data U, s, _ = np.linalg.svd(gain, full_matrices=False) - U = U[:, s >= s[0] * 1e-6] + if U.shape[1] > 0: + U = U[:, s >= s[0] * 1e-6] # the part that gets explained fit_orth = U.T @ M # the part that got over-explained (landed in residual) @@ -284,12 +287,12 @@ @verbose -def mixed_norm(evoked, forward, noise_cov, alpha, loose='auto', depth=0.8, - maxit=3000, tol=1e-4, active_set_size=10, +def mixed_norm(evoked, forward, noise_cov, alpha='sure', loose='auto', + depth=0.8, maxit=3000, tol=1e-4, active_set_size=10, debias=True, time_pca=True, weights=None, weights_min=0., solver='auto', n_mxne_iter=1, return_residual=False, return_as_dipoles=False, dgap_freq=10, rank=None, pick_ori=None, - verbose=None): + sure_alpha_grid="auto", random_state=None, verbose=None): """Mixed-norm estimate (MxNE) and iterative reweighted MxNE (irMxNE). Compute L1/L2 mixed-norm solution :footcite:`GramfortEtAl2012` or L0.5/L2 @@ -303,9 +306,14 @@ Forward operator. noise_cov : instance of Covariance Noise covariance to compute whitener. - alpha : float in range [0, 100) - Regularization parameter. 0 means no regularization, 100 would give 0 - active dipole. + alpha : float | str + Regularization parameter. If float it should be in the range [0, 100): + 0 means no regularization, 100 would give 0 active dipole. + If ``'sure'`` (default), the SURE method from + :footcite:`DeledalleEtAl2014` will be used. + + .. versionchanged:: 0.24 + The default was changed to ``'sure'``. %(loose)s %(depth)s maxit : int @@ -326,9 +334,8 @@ weights_min : float Do not consider in the estimation sources for which weights is less than weights_min. - solver : 'prox' | 'cd' | 'bcd' | 'auto' - The algorithm to use for the optimization. 'prox' stands for - proximal iterations using the FISTA algorithm, 'cd' uses + solver : 'cd' | 'bcd' | 'auto' + The algorithm to use for the optimization. 'cd' uses coordinate descent, and 'bcd' applies block coordinate descent. 'cd' is only available for fixed orientation. n_mxne_iter : int @@ -341,10 +348,21 @@ dgap_freq : int or np.inf The duality gap is evaluated every dgap_freq iterations. Ignored if solver is 'cd'. - %(rank_None)s + %(rank_none)s .. versionadded:: 0.18 %(pick_ori)s + sure_alpha_grid : array | str + If ``'auto'`` (default), the SURE is evaluated along 15 uniformly + distributed alphas between alpha_max and 0.1 * alpha_max. If array, the + grid is directly specified. Ignored if alpha is not "sure". + + .. versionadded:: 0.24 + random_state : int | None + The random state used in a random number generator for delta and + epsilon used for the SURE computation. Defaults to None. + + .. versionadded:: 0.24 %(verbose)s Returns @@ -364,8 +382,11 @@ .. footbibliography:: """ from scipy import linalg - if not (0. <= alpha < 100.): - raise ValueError('alpha must be in [0, 100). ' + _validate_type(alpha, ('numeric', str), 'alpha') + if isinstance(alpha, str): + _check_option('alpha', alpha, ('sure',)) + elif not 0. <= alpha < 100: + raise ValueError('If not equal to "sure" alpha must be in [0, 100). ' 'Got alpha = %s' % alpha) if n_mxne_iter < 1: raise ValueError('MxNE has to be computed at least 1 time. ' @@ -373,7 +394,14 @@ if dgap_freq <= 0.: raise ValueError('dgap_freq must be a positive integer.' ' Got dgap_freq = %s' % dgap_freq) - + if not (isinstance(sure_alpha_grid, (np.ndarray, list)) or + sure_alpha_grid == "auto"): + raise ValueError('If not equal to "auto" sure_alpha_grid must be an ' + 'array. Got %s' % type(sure_alpha_grid)) + if ((isinstance(sure_alpha_grid, str) and sure_alpha_grid != "auto") + and (isinstance(alpha, str) and alpha != "sure")): + raise Exception('If sure_alpha_grid is manually specified, alpha must ' + 'be "sure". Got %s' % alpha) pca = True if not isinstance(evoked, list): evoked = [evoked] @@ -413,16 +441,30 @@ gain /= alpha_max source_weighting /= alpha_max - if n_mxne_iter == 1: - X, active_set, E = mixed_norm_solver( - M, gain, alpha, maxit=maxit, tol=tol, - active_set_size=active_set_size, n_orient=n_dip_per_pos, - debias=debias, solver=solver, dgap_freq=dgap_freq, verbose=verbose) - else: - X, active_set, E = iterative_mixed_norm_solver( - M, gain, alpha, n_mxne_iter, maxit=maxit, tol=tol, + # Alpha selected automatically by SURE minimization + if alpha == "sure": + alpha_grid = sure_alpha_grid + if isinstance(sure_alpha_grid, str) and sure_alpha_grid == "auto": + alpha_grid = np.geomspace(100, 10, num=15) + X, active_set, best_alpha_ = _compute_mxne_sure( + M, gain, alpha_grid, sigma=1, random_state=random_state, + n_mxne_iter=n_mxne_iter, maxit=maxit, tol=tol, n_orient=n_dip_per_pos, active_set_size=active_set_size, debias=debias, solver=solver, dgap_freq=dgap_freq, verbose=verbose) + logger.info('Selected alpha: %s' % best_alpha_) + else: + if n_mxne_iter == 1: + X, active_set, E = mixed_norm_solver( + M, gain, alpha, maxit=maxit, tol=tol, + active_set_size=active_set_size, n_orient=n_dip_per_pos, + debias=debias, solver=solver, dgap_freq=dgap_freq, + verbose=verbose) + else: + X, active_set, E = iterative_mixed_norm_solver( + M, gain, alpha, n_mxne_iter, maxit=maxit, tol=tol, + n_orient=n_dip_per_pos, active_set_size=active_set_size, + debias=debias, solver=solver, dgap_freq=dgap_freq, + verbose=verbose) if time_pca: X = np.dot(X, Vh) @@ -436,14 +478,15 @@ del active_set_tmp if active_set.sum() == 0: - raise Exception("No active dipoles found. alpha is too big.") - - # Reapply weights to have correct unit - X = _reapply_source_weighting(X, source_weighting, active_set) - source_weighting[source_weighting == 0] = 1 # zeros - gain_active /= source_weighting[active_set] - del source_weighting - M_estimate = np.dot(gain_active, X) + warn("No active dipoles found. alpha is too big.") + M_estimate = np.zeros_like(M) + else: + # Reapply weights to have correct unit + X = _reapply_source_weighting(X, source_weighting, active_set) + source_weighting[source_weighting == 0] = 1 # zeros + gain_active /= source_weighting[active_set] + del source_weighting + M_estimate = np.dot(gain_active, X) outs = list() residual = list() @@ -571,7 +614,7 @@ * l1_ratio. 0 means no time regularization a.k.a. MxNE. dgap_freq : int or np.inf The duality gap is evaluated every dgap_freq iterations. - %(rank_None)s + %(rank_none)s .. versionadded:: 0.18 %(pick_ori)s @@ -700,3 +743,154 @@ out = out, residual return out + + +@verbose +def _compute_mxne_sure(M, gain, alpha_grid, sigma, n_mxne_iter, maxit, tol, + n_orient, active_set_size, debias, solver, dgap_freq, + random_state, verbose): + """Stein Unbiased Risk Estimator (SURE). + + Implements the finite-difference Monte-Carlo approximation + of the SURE for Multi-Task LASSO. + + See reference :footcite:`DeledalleEtAl2014`. + + Parameters + ---------- + M : array, shape (n_sensors, n_times) + The data. + gain : array, shape (n_sensors, n_dipoles) + The gain matrix a.k.a. lead field. + alpha_grid : array, shape (n_alphas,) + The grid of alphas used to evaluate the SURE. + sigma : float + The true or estimated noise level in the data. Usually 1 if the data + has been previously whitened using MNE whitener. + n_mxne_iter : int + The number of MxNE iterations. If > 1, iterative reweighting is + applied. + maxit : int + Maximum number of iterations. + tol : float + Tolerance parameter. + n_orient : int + The number of orientation (1 : fixed or 3 : free or loose). + active_set_size : int + Size of active set increase at each iteration. + debias : bool + Debias source estimates. + solver : 'cd' | 'bcd' | 'auto' + The algorithm to use for the optimization. + dgap_freq : int or np.inf + The duality gap is evaluated every dgap_freq iterations. + random_state : int | None + The random state used in a random number generator for delta and + epsilon used for the SURE computation. + + Returns + ------- + X : array, shape (n_active, n_times) + Coefficient matrix. + active_set : array, shape (n_dipoles,) + Array of indices of non-zero coefficients. + best_alpha_ : float + Alpha that minimizes the SURE. + + References + ---------- + .. footbibliography:: + """ + def g(w): + return np.sqrt(np.sqrt(groups_norm2(w.copy(), n_orient))) + + def gprime(w): + return 2. * np.repeat(g(w), n_orient).ravel() + + def _run_solver(alpha, M, n_mxne_iter, as_init=None, X_init=None, + w_init=None): + if n_mxne_iter == 1: + X, active_set, _ = mixed_norm_solver( + M, gain, alpha, maxit=maxit, tol=tol, + active_set_size=active_set_size, n_orient=n_orient, + debias=debias, solver=solver, dgap_freq=dgap_freq, + active_set_init=as_init, X_init=X_init, verbose=False) + else: + X, active_set, _ = iterative_mixed_norm_solver( + M, gain, alpha, n_mxne_iter, maxit=maxit, tol=tol, + n_orient=n_orient, active_set_size=active_set_size, + debias=debias, solver=solver, dgap_freq=dgap_freq, + weight_init=w_init, verbose=False) + return X, active_set + + def _fit_on_grid(gain, M, eps, delta): + coefs_grid_1_0 = np.zeros((len(alpha_grid), gain.shape[1], M.shape[1])) + coefs_grid_2_0 = np.zeros((len(alpha_grid), gain.shape[1], M.shape[1])) + active_sets, active_sets_eps = [], [] + M_eps = M + eps * delta + # warm start - first iteration (leverages convexity) + logger.info('Warm starting...') + for j, alpha in enumerate(alpha_grid): + logger.info('alpha: %s' % alpha) + X, a_set = _run_solver(alpha, M, 1) + X_eps, a_set_eps = _run_solver(alpha, M_eps, 1) + coefs_grid_1_0[j][a_set, :] = X + coefs_grid_2_0[j][a_set_eps, :] = X_eps + active_sets.append(a_set) + active_sets_eps.append(a_set_eps) + # next iterations + if n_mxne_iter == 1: + return coefs_grid_1_0, coefs_grid_2_0, active_sets + else: + coefs_grid_1 = coefs_grid_1_0.copy() + coefs_grid_2 = coefs_grid_2_0.copy() + logger.info('Fitting SURE on grid.') + for j, alpha in enumerate(alpha_grid): + logger.info('alpha: %s' % alpha) + if active_sets[j].sum() > 0: + w = gprime(coefs_grid_1[j]) + X, a_set = _run_solver(alpha, M, n_mxne_iter - 1, + w_init=w) + coefs_grid_1[j][a_set, :] = X + active_sets[j] = a_set + if active_sets_eps[j].sum() > 0: + w_eps = gprime(coefs_grid_2[j]) + X_eps, a_set_eps = _run_solver(alpha, M_eps, + n_mxne_iter - 1, + w_init=w_eps) + coefs_grid_2[j][a_set_eps, :] = X_eps + active_sets_eps[j] = a_set_eps + + return coefs_grid_1, coefs_grid_2, active_sets + + def _compute_sure_val(coef1, coef2, gain, M, sigma, delta, eps): + n_sensors, n_times = gain.shape[0], M.shape[1] + dof = (gain @ (coef2 - coef1) * delta).sum() / eps + df_term = np.linalg.norm(M - gain @ coef1) ** 2 + sure = df_term - n_sensors * n_times * sigma ** 2 + sure += 2 * dof * sigma ** 2 + return sure + + sure_path = np.empty(len(alpha_grid)) + + rng = check_random_state(random_state) + # See Deledalle et al. 20214 Sec. 5.1 + eps = 2 * sigma / (M.shape[0] ** 0.3) + delta = rng.randn(*M.shape) + + coefs_grid_1, coefs_grid_2, active_sets = _fit_on_grid(gain, M, eps, delta) + + logger.info("Computing SURE values on grid.") + for i, (coef1, coef2) in enumerate(zip(coefs_grid_1, coefs_grid_2)): + sure_path[i] = _compute_sure_val( + coef1, coef2, gain, M, sigma, delta, eps) + if verbose: + logger.info("alpha %s :: sure %s" % (alpha_grid[i], sure_path[i])) + best_alpha_ = alpha_grid[np.argmin(sure_path)] + + X = coefs_grid_1[np.argmin(sure_path)] + active_set = active_sets[np.argmin(sure_path)] + + X = X[active_set, :] + + return X, active_set, best_alpha_ diff -Nru python-mne-0.23.4+dfsg/mne/inverse_sparse/mxne_optim.py python-mne-1.1.0+dfsg/mne/inverse_sparse/mxne_optim.py --- python-mne-0.23.4+dfsg/mne/inverse_sparse/mxne_optim.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/inverse_sparse/mxne_optim.py 2022-08-03 15:48:14.000000000 +0000 @@ -9,7 +9,8 @@ import numpy as np from .mxne_debiasing import compute_bias -from ..utils import logger, verbose, sum_squared, warn, _get_blas_funcs +from ..utils import (logger, verbose, sum_squared, warn, _get_blas_funcs, + _validate_type, _check_option) from ..time_frequency._stft import stft_norm1, stft_norm2, stft, istft @@ -42,89 +43,21 @@ return np.sum(np.sqrt(groups_norm2(A, n_orient))) -def prox_l21(Y, alpha, n_orient, shape=None, is_stft=False): - """Proximity operator for l21 norm. +def _primal_l21(M, G, X, active_set, alpha, n_orient): + """Primal objective for the mixed-norm inverse problem. - L2 over columns and L1 over rows => groups contain n_orient rows. - - It can eventually take into account the negative frequencies - when a complex value is passed and is_stft=True. - - Parameters - ---------- - Y : array, shape (n_sources, n_coefs) - The input data. - alpha : float - The regularization parameter. - n_orient : int - Number of dipoles per locations (typically 1 or 3). - shape : None | tuple - Shape of TF coefficients matrix. - is_stft : bool - If True, Y contains TF coefficients. - - Returns - ------- - Y : array, shape (n_sources, n_coefs) - The output data. - active_set : array of bool, shape (n_sources, ) - Mask of active sources - - Examples - -------- - >>> Y = np.tile(np.array([0, 4, 3, 0, 0], dtype=np.float64), (2, 1)) - >>> Y = np.r_[Y, np.zeros_like(Y)] - >>> print(Y) # doctest:+SKIP - [[ 0. 4. 3. 0. 0.] - [ 0. 4. 3. 0. 0.] - [ 0. 0. 0. 0. 0.] - [ 0. 0. 0. 0. 0.]] - >>> Yp, active_set = prox_l21(Y, 2, 2) - >>> print(Yp) # doctest:+SKIP - [[0. 2.86862915 2.15147186 0. 0. ] - [0. 2.86862915 2.15147186 0. 0. ]] - >>> print(active_set) - [ True True False False] - """ - if len(Y) == 0: - return np.zeros_like(Y), np.zeros((0,), dtype=bool) - if shape is not None: - shape_init = Y.shape - Y = Y.reshape(*shape) - n_positions = Y.shape[0] // n_orient - - if is_stft: - rows_norm = np.sqrt(stft_norm2(Y).reshape(n_positions, -1).sum(axis=1)) - else: - rows_norm = np.sqrt((Y * Y.conj()).real.reshape(n_positions, - -1).sum(axis=1)) - # Ensure shrink is >= 0 while avoiding any division by zero - shrink = np.maximum(1.0 - alpha / np.maximum(rows_norm, alpha), 0.0) - active_set = shrink > 0.0 - if n_orient > 1: - active_set = np.tile(active_set[:, None], [1, n_orient]).ravel() - shrink = np.tile(shrink[:, None], [1, n_orient]).ravel() - Y = Y[active_set] - if shape is None: - Y *= shrink[active_set][:, np.newaxis] - else: - Y *= shrink[active_set][:, np.newaxis, np.newaxis] - Y = Y.reshape(-1, *shape_init[1:]) - return Y, active_set - - -def prox_l1(Y, alpha, n_orient): - """Proximity operator for l1 norm with multiple orientation support. - - Please note that this function computes a soft-thresholding if - n_orient == 1 and a block soft-thresholding (L2 over orientation and - L1 over position (space + time)) if n_orient == 3. See also - :footcite:`GramfortEtAl2013b`. + See :footcite:`GramfortEtAl2012`. Parameters ---------- - Y : array, shape (n_sources, n_coefs) - The input data. + M : array, shape (n_sensors, n_times) + The data. + G : array, shape (n_sensors, n_active) + The gain matrix a.k.a. lead field. + X : array, shape (n_active, n_times) + Sources. + active_set : array of bool, shape (n_sources,) + Mask of active sources. alpha : float The regularization parameter. n_orient : int @@ -132,45 +65,21 @@ Returns ------- - Y : array, shape (n_sources, n_coefs) - The output data. - active_set : array of bool, shape (n_sources, ) - Mask of active sources. - - References - ---------- - .. footbibliography:: - - Examples - -------- - >>> Y = np.tile(np.array([1, 2, 3, 2, 0], dtype=np.float64), (2, 1)) - >>> Y = np.r_[Y, np.zeros_like(Y)] - >>> print(Y) # doctest:+SKIP - [[ 1. 2. 3. 2. 0.] - [ 1. 2. 3. 2. 0.] - [ 0. 0. 0. 0. 0.] - [ 0. 0. 0. 0. 0.]] - >>> Yp, active_set = prox_l1(Y, 2, 2) - >>> print(Yp) # doctest:+SKIP - [[0. 0.58578644 1.58578644 0.58578644 0. ] - [0. 0.58578644 1.58578644 0.58578644 0. ]] - >>> print(active_set) - [ True True False False] + p_obj : float + Primal objective. + R : array, shape (n_sensors, n_times) + Current residual (M - G * X). + nR2 : float + Data-fitting term. + GX : array, shape (n_sensors, n_times) + Forward prediction. """ - n_positions = Y.shape[0] // n_orient - norms = np.sqrt((Y * Y.conj()).real.T.reshape(-1, n_orient).sum(axis=1)) - # Ensure shrink is >= 0 while avoiding any division by zero - shrink = np.maximum(1.0 - alpha / np.maximum(norms, alpha), 0.0) - shrink = shrink.reshape(-1, n_positions).T - active_set = np.any(shrink > 0.0, axis=1) - shrink = shrink[active_set] - if n_orient > 1: - active_set = np.tile(active_set[:, None], [1, n_orient]).ravel() - Y = Y[active_set] - if len(Y) > 0: - for o in range(n_orient): - Y[o::n_orient] *= shrink - return Y, active_set + GX = np.dot(G[:, active_set], X) + R = M - GX + penalty = norm_l21(X, n_orient, copy=True) + nR2 = sum_squared(R) + p_obj = 0.5 * nR2 + alpha * penalty + return p_obj, R, nR2, GX def dgap_l21(M, G, X, active_set, alpha, n_orient): @@ -208,12 +117,7 @@ ---------- .. footbibilography:: """ - GX = np.dot(G[:, active_set], X) - R = M - GX - penalty = norm_l21(X, n_orient, copy=True) - nR2 = sum_squared(R) - p_obj = 0.5 * nR2 + alpha * penalty - + p_obj, R, nR2, GX = _primal_l21(M, G, X, active_set, alpha, n_orient) dual_norm = norm_l2inf(np.dot(G.T, R), n_orient, copy=False) scaling = alpha / dual_norm scaling = min(scaling, 1.0) @@ -224,73 +128,6 @@ @verbose -def _mixed_norm_solver_prox(M, G, alpha, lipschitz_constant, maxit=200, - tol=1e-8, verbose=None, init=None, n_orient=1, - dgap_freq=10): - """Solve L21 inverse problem with proximal iterations and FISTA.""" - n_sensors, n_times = M.shape - _, n_sources = G.shape - - if n_sources < n_sensors: - gram = np.dot(G.T, G) - GTM = np.dot(G.T, M) - else: - gram = None - - if init is None: - X = 0.0 - R = M.copy() - if gram is not None: - R = np.dot(G.T, R) - else: - X = init - if gram is None: - R = M - np.dot(G, X) - else: - R = GTM - np.dot(gram, X) - - t = 1.0 - Y = np.zeros((n_sources, n_times)) # FISTA aux variable - E = [] # track primal objective function - highest_d_obj = - np.inf - active_set = np.ones(n_sources, dtype=bool) # start with full AS - - for i in range(maxit): - X0, active_set_0 = X, active_set # store previous values - if gram is None: - Y += np.dot(G.T, R) / lipschitz_constant # ISTA step - else: - Y += R / lipschitz_constant # ISTA step - X, active_set = prox_l21(Y, alpha / lipschitz_constant, n_orient) - - t0 = t - t = 0.5 * (1.0 + sqrt(1.0 + 4.0 * t ** 2)) - Y.fill(0.0) - dt = ((t0 - 1.0) / t) - Y[active_set] = (1.0 + dt) * X - Y[active_set_0] -= dt * X0 - Y_as = active_set_0 | active_set - - if gram is None: - R = M - np.dot(G[:, Y_as], Y[Y_as]) - else: - R = GTM - np.dot(gram[:, Y_as], Y[Y_as]) - - if (i + 1) % dgap_freq == 0: - _, p_obj, d_obj, _ = dgap_l21(M, G, X, active_set, alpha, - n_orient) - highest_d_obj = max(d_obj, highest_d_obj) - gap = p_obj - highest_d_obj - E.append(p_obj) - logger.debug("p_obj : %s -- gap : %s" % (p_obj, gap)) - if gap < tol: - logger.debug('Convergence reached ! (gap: %s < %s)' - % (gap, tol)) - break - return X, active_set, E - - -@verbose def _mixed_norm_solver_cd(M, G, alpha, lipschitz_constant, maxit=10000, tol=1e-8, verbose=None, init=None, n_orient=1, dgap_freq=10): @@ -300,8 +137,7 @@ assert M.ndim == G.ndim and M.shape[0] == G.shape[0] clf = MultiTaskLasso(alpha=alpha / len(M), tol=tol / sum_squared(M), - normalize=False, fit_intercept=False, max_iter=maxit, - warm_start=True) + fit_intercept=False, max_iter=maxit, warm_start=True) if init is not None: clf.coef_ = init.T else: @@ -318,10 +154,10 @@ @verbose def _mixed_norm_solver_bcd(M, G, alpha, lipschitz_constant, maxit=200, tol=1e-8, verbose=None, init=None, n_orient=1, - dgap_freq=10): + dgap_freq=10, use_accel=True, K=5): """Solve L21 inverse problem with block coordinate descent.""" - n_sensors, n_times = M.shape - n_sensors, n_sources = G.shape + _, n_times = M.shape + _, n_sources = G.shape n_positions = n_sources // n_orient if init is None: @@ -337,6 +173,10 @@ alpha_lc = alpha / lipschitz_constant + if use_accel: + last_K_X = np.empty((K + 1, n_sources, n_times)) + U = np.zeros((K, n_sources * n_times)) + # First make G fortran for faster access to blocks of columns G = np.asfortranarray(G) # Ensure these are correct for dgemm @@ -355,8 +195,7 @@ list_G_j_c.append(np.ascontiguousarray(G[:, idx])) for i in range(maxit): - _bcd(G, X, R, active_set, one_ovr_lc, n_orient, n_positions, - alpha_lc, list_G_j_c) + _bcd(G, X, R, active_set, one_ovr_lc, n_orient, alpha_lc, list_G_j_c) if (i + 1) % dgap_freq == 0: _, p_obj, d_obj, _ = dgap_l21(M, G, X[active_set], active_set, @@ -372,13 +211,49 @@ % (gap, tol)) break + # using Anderson acceleration of the primal variable for faster + # convergence + if use_accel: + last_K_X[i % (K + 1)] = X + + if i % (K + 1) == K: + for k in range(K): + U[k] = last_K_X[k + 1].ravel() - last_K_X[k].ravel() + C = U @ U.T + # at least on ARM64 we can't rely on np.linalg.solve to + # reliably raise LinAlgError here, so use SVD instead + # equivalent to: + # z = np.linalg.solve(C, np.ones(K)) + u, s, _ = np.linalg.svd(C, hermitian=True) + if s[-1] <= 1e-6 * s[0]: + logger.debug("Iteration %d: LinAlg Error" % (i + 1)) + continue + z = ((u * 1 / s) @ u.T).sum(0) + c = z / z.sum() + X_acc = np.sum( + last_K_X[:-1] * c[:, None, None], axis=0 + ) + _grp_norm2_acc = groups_norm2(X_acc, n_orient) + active_set_acc = _grp_norm2_acc != 0 + if n_orient > 1: + active_set_acc = np.kron( + active_set_acc, np.ones(n_orient, dtype=bool) + ) + p_obj = _primal_l21(M, G, X[active_set], active_set, alpha, + n_orient)[0] + p_obj_acc = _primal_l21(M, G, X_acc[active_set_acc], + active_set_acc, alpha, n_orient)[0] + if p_obj_acc < p_obj: + X = X_acc + active_set = active_set_acc + R = M - G[:, active_set] @ X[active_set] + X = X[active_set] return X, active_set, E -def _bcd(G, X, R, active_set, one_ovr_lc, n_orient, n_positions, - alpha_lc, list_G_j_c): +def _bcd(G, X, R, active_set, one_ovr_lc, n_orient, alpha_lc, list_G_j_c): """Implement one full pass of BCD. BCD stands for Block Coordinate Descent. @@ -403,7 +278,7 @@ alpha_lc: array, shape (n_positions, ) alpha * (Lipschitz constants). """ - X_j_new = np.zeros_like(X[0:n_orient, :], order='C') + X_j_new = np.zeros_like(X[:n_orient, :], order='C') dgemm = _get_dgemm() for j, G_j_c in enumerate(list_G_j_c): @@ -438,10 +313,12 @@ @verbose def mixed_norm_solver(M, G, alpha, maxit=3000, tol=1e-8, verbose=None, active_set_size=50, debias=True, n_orient=1, - solver='auto', return_gap=False, dgap_freq=10): + solver='auto', return_gap=False, dgap_freq=10, + active_set_init=None, X_init=None): """Solve L1/L2 mixed-norm inverse problem with active set strategy. - See references :footcite:`GramfortEtAl2012,StrohmeierEtAl2016`. + See references :footcite:`GramfortEtAl2012,StrohmeierEtAl2016, + BertrandEtAl2020`. Parameters ---------- @@ -463,20 +340,28 @@ Debias source estimates. n_orient : int The number of orientation (1 : fixed or 3 : free or loose). - solver : 'prox' | 'cd' | 'bcd' | 'auto' - The algorithm to use for the optimization. + solver : 'cd' | 'bcd' | 'auto' + The algorithm to use for the optimization. Block Coordinate Descent + (BCD) uses Anderson acceleration for faster convergence. return_gap : bool Return final duality gap. dgap_freq : int The duality gap is computed every dgap_freq iterations of the solver on the active set. + active_set_init : array, shape (n_dipoles,) or None + The initial active set (boolean array) used at the first iteration. + If None, the usual active set strategy is applied. + X_init : array, shape (n_dipoles, n_times) or None + The initial weight matrix used for warm starting the solver. If None, + the weights are initialized at zero. Returns ------- X : array, shape (n_active, n_times) The source estimates. - active_set : array - The mask of active sources. + active_set : array, shape (new_active_set_size,) + The mask of active sources. Note that new_active_set_size is the size + of the active set after convergence of the solver. E : list The value of the objective function over the iterations. gap : float @@ -488,10 +373,11 @@ """ n_dipoles = G.shape[1] n_positions = n_dipoles // n_orient - n_sensors, n_times = M.shape + _, n_times = M.shape alpha_max = norm_l2inf(np.dot(G.T, M), n_orient, copy=False) logger.info("-- ALPHA MAX : %s" % alpha_max) alpha = float(alpha) + X = np.zeros((n_dipoles, n_times), dtype=G.dtype) has_sklearn = True try: @@ -499,6 +385,8 @@ except ImportError: has_sklearn = False + _validate_type(solver, str, 'solver') + _check_option('solver', solver, ('cd', 'bcd', 'auto')) if solver == 'auto': if has_sklearn and (n_orient == 1): solver = 'cd' @@ -520,7 +408,8 @@ logger.info("Using coordinate descent") l21_solver = _mixed_norm_solver_cd lc = None - elif solver == 'bcd': + else: + assert solver == 'bcd' logger.info("Using block coordinate descent") l21_solver = _mixed_norm_solver_bcd G = np.asfortranarray(G) @@ -531,16 +420,14 @@ for j in range(n_positions): G_tmp = G[:, (j * n_orient):((j + 1) * n_orient)] lc[j] = np.linalg.norm(np.dot(G_tmp.T, G_tmp), ord=2) - else: - logger.info("Using proximal iterations") - l21_solver = _mixed_norm_solver_prox - lc = 1.01 * np.linalg.norm(G, ord=2) ** 2 if active_set_size is not None: E = list() highest_d_obj = - np.inf - X_init = None - active_set = np.zeros(n_dipoles, dtype=bool) + if X_init is not None and X_init.shape != (n_dipoles, n_times): + raise ValueError('Wrong dim for initialized coefficients.') + active_set = (active_set_init if active_set_init is not None else + np.zeros(n_dipoles, dtype=bool)) idx_large_corr = np.argsort(groups_norm2(np.dot(G.T, M), n_orient)) new_active_idx = idx_large_corr[-active_set_size:] if n_orient > 1: @@ -548,6 +435,7 @@ np.arange(n_orient)[None, :]).ravel() active_set[new_active_idx] = True as_size = np.sum(active_set) + gap = np.inf for k in range(maxit): if solver == 'bcd': lc_tmp = lc[active_set[::n_orient]] @@ -566,7 +454,7 @@ highest_d_obj = max(d_obj, highest_d_obj) gap = p_obj - highest_d_obj E.append(p_obj) - logger.info("Iteration %d :: p_obj %f :: dgap %f ::" + logger.info("Iteration %d :: p_obj %f :: dgap %f :: " "n_active_start %d :: n_active_end %d" % ( k + 1, p_obj, gap, as_size // n_orient, np.sum(active_set) // n_orient)) @@ -614,7 +502,7 @@ def iterative_mixed_norm_solver(M, G, alpha, n_mxne_iter, maxit=3000, tol=1e-8, verbose=None, active_set_size=50, debias=True, n_orient=1, dgap_freq=10, - solver='auto'): + solver='auto', weight_init=None): """Solve L0.5/L2 mixed-norm inverse problem with active set strategy. See reference :footcite:`StrohmeierEtAl2016`. @@ -644,8 +532,11 @@ The number of orientation (1 : fixed or 3 : free or loose). dgap_freq : int or np.inf The duality gap is evaluated every dgap_freq iterations. - solver : 'prox' | 'cd' | 'bcd' | 'auto' + solver : 'cd' | 'bcd' | 'auto' The algorithm to use for the optimization. + weight_init : array, shape (n_dipoles,) or None + The initial weight used for reweighting the gain matrix. If None, the + weights are initialized with ones. Returns ------- @@ -668,8 +559,13 @@ E = list() - active_set = np.ones(G.shape[1], dtype=bool) - weights = np.ones(G.shape[1]) + if weight_init is not None and weight_init.shape != (G.shape[1],): + raise ValueError('Wrong dimension for weight initialization. Got %s. ' + 'Expected %s.' % (weight_init.shape, (G.shape[1],))) + + weights = weight_init if weight_init is not None else np.ones(G.shape[1]) + active_set = (weights != 0) + weights = weights[active_set] X = np.zeros((G.shape[1], M.shape[1])) for k in range(n_mxne_iter): @@ -698,7 +594,6 @@ if _active_set.sum() > 0: active_set[active_set] = _active_set - # Reapply weights to have correct unit X *= weights[_active_set][:, np.newaxis] weights = gprime(X) @@ -1472,9 +1367,7 @@ The number of orientation (1 : fixed or 3 : free or loose). dgap_freq : int or np.inf The duality gap is evaluated every dgap_freq iterations. - verbose : bool, str, int, or None - If not None, override default verbose level (see :func:`mne.verbose` - and :ref:`Logging documentation ` for more). + %(verbose)s Returns ------- diff -Nru python-mne-0.23.4+dfsg/mne/inverse_sparse/tests/test_gamma_map.py python-mne-1.1.0+dfsg/mne/inverse_sparse/tests/test_gamma_map.py --- python-mne-0.23.4+dfsg/mne/inverse_sparse/tests/test_gamma_map.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/inverse_sparse/tests/test_gamma_map.py 2022-08-03 15:48:14.000000000 +0000 @@ -18,7 +18,7 @@ from mne.minimum_norm.tests.test_inverse import (assert_stc_res, assert_var_exp_log) from mne import pick_types_forward -from mne.utils import assert_stcs_equal, run_tests_if_main, catch_logging +from mne.utils import assert_stcs_equal, catch_logging from mne.dipole import Dipole data_path = testing.data_path(download=False) @@ -130,16 +130,14 @@ eeg=False, meg=True) alpha = 0.5 - pytest.raises(ValueError, gamma_map, evoked, fwd, cov, alpha, - loose=0, return_residual=False) - - pytest.raises(ValueError, gamma_map, evoked, fwd, cov, alpha, - loose=0.2, return_residual=False) - stc = gamma_map(evoked, fwd, cov, alpha, tol=1e-4, xyz_same_gamma=False, update_mode=2, return_residual=False) + assert_array_almost_equal(stc.times, evoked.times, 5) + # Computing inverse with restricted orientations should also work, since + # we have a discrete source space. + stc = gamma_map(evoked, fwd, cov, alpha, loose=0.2, return_residual=False) assert_array_almost_equal(stc.times, evoked.times, 5) # Compare orientation obtained using fit_dipole and gamma_map @@ -159,6 +157,3 @@ dip_fit = mne.fit_dipole(evoked_dip, cov, sphere)[0] assert (np.abs(np.dot(dip_fit.ori[0], dip_gmap.ori[0])) > 0.99) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/inverse_sparse/tests/test_mxne_debiasing.py python-mne-1.1.0+dfsg/mne/inverse_sparse/tests/test_mxne_debiasing.py --- python-mne-0.23.4+dfsg/mne/inverse_sparse/tests/test_mxne_debiasing.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/inverse_sparse/tests/test_mxne_debiasing.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors: Daniel Strohmeier # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np from numpy.testing import assert_almost_equal diff -Nru python-mne-0.23.4+dfsg/mne/inverse_sparse/tests/test_mxne_inverse.py python-mne-1.1.0+dfsg/mne/inverse_sparse/tests/test_mxne_inverse.py --- python-mne-0.23.4+dfsg/mne/inverse_sparse/tests/test_mxne_inverse.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/inverse_sparse/tests/test_mxne_inverse.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,9 +4,10 @@ # License: Simplified BSD import os.path as op + import numpy as np from numpy.testing import (assert_array_almost_equal, assert_allclose, - assert_array_less) + assert_array_less, assert_array_equal) import pytest import mne @@ -16,18 +17,22 @@ convert_forward_solution) from mne.inverse_sparse import mixed_norm, tf_mixed_norm from mne.inverse_sparse.mxne_inverse import make_stc_from_dipoles, _split_gof +from mne.inverse_sparse.mxne_inverse import _compute_mxne_sure +from mne.inverse_sparse.mxne_optim import norm_l2inf from mne.minimum_norm import apply_inverse, make_inverse_operator from mne.minimum_norm.tests.test_inverse import \ assert_var_exp_log, assert_stc_res -from mne.utils import assert_stcs_equal, run_tests_if_main, catch_logging +from mne.utils import assert_stcs_equal, catch_logging, _record_warnings from mne.dipole import Dipole from mne.source_estimate import VolSourceEstimate +from mne.simulation import simulate_sparse_stc, simulate_evoked data_path = testing.data_path(download=False) # NOTE: These use the ave and cov from sample dataset (no _trunc) fname_data = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif') fname_cov = op.join(data_path, 'MEG', 'sample', 'sample_audvis-cov.fif') +fname_raw = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif') fname_fwd = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc-meg-eeg-oct-6-fwd.fif') label = 'Aud-rh' @@ -76,11 +81,7 @@ # MxNE tests alpha = 70 # spatial regularization parameter - stc_prox = mixed_norm(evoked_l21, forward, cov, alpha, loose=loose, - depth=depth, maxit=300, tol=1e-8, - active_set_size=10, weights=stc_dspm, - weights_min=weights_min, solver='prox') - with pytest.warns(None): # CD + with _record_warnings(): # CD stc_cd = mixed_norm(evoked_l21, forward, cov, alpha, loose=loose, depth=depth, maxit=300, tol=1e-8, active_set_size=10, weights=stc_dspm, @@ -89,28 +90,25 @@ depth=depth, maxit=300, tol=1e-8, active_set_size=10, weights=stc_dspm, weights_min=weights_min, solver='bcd') - assert_array_almost_equal(stc_prox.times, evoked_l21.times, 5) assert_array_almost_equal(stc_cd.times, evoked_l21.times, 5) assert_array_almost_equal(stc_bcd.times, evoked_l21.times, 5) - assert_allclose(stc_prox.data, stc_cd.data, rtol=1e-3, atol=0.0) - assert_allclose(stc_prox.data, stc_bcd.data, rtol=1e-3, atol=0.0) assert_allclose(stc_cd.data, stc_bcd.data, rtol=1e-3, atol=0.0) - assert stc_prox.vertices[1][0] in label.vertices assert stc_cd.vertices[1][0] in label.vertices assert stc_bcd.vertices[1][0] in label.vertices # vector - with pytest.warns(None): # no convergence + with _record_warnings(): # no convergence stc = mixed_norm(evoked_l21, forward, cov, alpha, loose=1, maxit=2) - with pytest.warns(None): # no convergence + with _record_warnings(): # no convergence stc_vec = mixed_norm(evoked_l21, forward, cov, alpha, loose=1, maxit=2, pick_ori='vector') assert_stcs_equal(stc_vec.magnitude(), stc) - with pytest.warns(None), pytest.raises(ValueError, match='pick_ori='): + with _record_warnings(), \ + pytest.raises(ValueError, match='pick_ori='): mixed_norm(evoked_l21, forward, cov, alpha, loose=0, maxit=2, pick_ori='vector') - with pytest.warns(None), catch_logging() as log: # CD + with _record_warnings(), catch_logging() as log: # CD dips = mixed_norm(evoked_l21, forward, cov, alpha, loose=loose, depth=depth, maxit=300, tol=1e-8, active_set_size=10, weights=stc_dspm, weights_min=weights_min, @@ -122,7 +120,7 @@ assert_var_exp_log(log.getvalue(), 51, 53) # 51.8 # Single time point things should match - with pytest.warns(None), catch_logging() as log: + with _record_warnings(), catch_logging() as log: dips = mixed_norm(evoked_l21.copy().crop(0.081, 0.081), forward, cov, alpha, loose=loose, depth=depth, maxit=300, tol=1e-8, active_set_size=10, @@ -132,7 +130,7 @@ gof = sum(dip.gof[0] for dip in dips) # these are now partial exp vars assert_allclose(gof, 37.9, atol=0.1) - with pytest.warns(None), catch_logging() as log: + with _record_warnings(), catch_logging() as log: stc, res = mixed_norm(evoked_l21, forward, cov, alpha, loose=loose, depth=depth, maxit=300, tol=1e-8, weights=stc_dspm, # gh-6382 @@ -145,7 +143,7 @@ assert_stc_res(evoked_l21, stc, forward, res) # irMxNE tests - with pytest.warns(None), catch_logging() as log: # CD + with _record_warnings(), catch_logging() as log: # CD stc, residual = mixed_norm( evoked_l21, forward, cov, alpha, n_mxne_iter=5, loose=0.0001, depth=depth, maxit=300, tol=1e-8, active_set_size=10, @@ -207,13 +205,13 @@ bem=sphere, eeg=False, meg=True) alpha = 80. - pytest.raises(ValueError, mixed_norm, evoked, fwd, cov, alpha, - loose=0.0, return_residual=False, - maxit=3, tol=1e-8, active_set_size=10) - - pytest.raises(ValueError, mixed_norm, evoked, fwd, cov, alpha, - loose=0.2, return_residual=False, - maxit=3, tol=1e-8, active_set_size=10) + + # Computing inverse with restricted orientations should also work, since + # we have a discrete source space. + stc = mixed_norm(evoked_l21, fwd, cov, alpha, loose=0.2, + return_residual=False, maxit=3, tol=1e-8, + active_set_size=10) + assert_array_almost_equal(stc.times, evoked_l21.times, 5) # irMxNE tests with catch_logging() as log: @@ -336,4 +334,102 @@ assert_allclose(gof_split.sum(), 100, rtol=1e-5) -run_tests_if_main() +@pytest.mark.parametrize('n_sensors, n_dipoles, n_times', [ + (10, 15, 7), + (20, 60, 20), +]) +@pytest.mark.parametrize('nnz', [2, 4]) +@pytest.mark.parametrize('corr', [0.75]) +@pytest.mark.parametrize('n_orient', [1, 3]) +def test_mxne_inverse_sure_synthetic(n_sensors, n_dipoles, n_times, nnz, corr, + n_orient, snr=4): + """Tests SURE criterion for automatic alpha selection on synthetic data.""" + rng = np.random.RandomState(0) + sigma = np.sqrt(1 - corr ** 2) + U = rng.randn(n_sensors) + # generate gain matrix + G = np.empty([n_sensors, n_dipoles], order='F') + G[:, :n_orient] = np.expand_dims(U, axis=-1) + n_dip_per_pos = n_dipoles // n_orient + for j in range(1, n_dip_per_pos): + U *= corr + U += sigma * rng.randn(n_sensors) + G[:, j * n_orient:(j + 1) * n_orient] = np.expand_dims(U, axis=-1) + # generate coefficient matrix + support = rng.choice(n_dip_per_pos, nnz, replace=False) + X = np.zeros((n_dipoles, n_times)) + for k in support: + X[k * n_orient:(k + 1) * n_orient, :] = rng.normal( + size=(n_orient, n_times)) + # generate measurement matrix + M = G @ X + noise = rng.randn(n_sensors, n_times) + sigma = 1 / np.linalg.norm(noise) * np.linalg.norm(M) / snr + M += sigma * noise + # inverse modeling with sure + alpha_max = norm_l2inf(np.dot(G.T, M), n_orient, copy=False) + alpha_grid = np.geomspace(alpha_max, alpha_max / 10, num=15) + _, active_set, _ = _compute_mxne_sure(M, G, alpha_grid, sigma=sigma, + n_mxne_iter=5, maxit=3000, tol=1e-4, + n_orient=n_orient, + active_set_size=10, debias=True, + solver="auto", dgap_freq=10, + random_state=0, verbose=False) + assert np.count_nonzero(active_set, axis=-1) == n_orient * nnz + + +@pytest.mark.slowtest # slow on Azure +@testing.requires_testing_data +def test_mxne_inverse_sure(): + """Tests SURE criterion for automatic alpha selection on MEG data.""" + def data_fun(times): + data = np.zeros(times.shape) + data[times >= 0] = 50e-9 + return data + n_dipoles = 2 + raw = mne.io.read_raw_fif(fname_raw) + info = mne.io.read_info(fname_data) + with info._unlock(): + info['projs'] = [] + noise_cov = mne.make_ad_hoc_cov(info) + label_names = ['Aud-lh', 'Aud-rh'] + labels = [ + mne.read_label(data_path / 'MEG' / 'sample' / 'labels' / f'{ln}.label') + for ln in label_names] + fname_fwd = op.join(data_path, 'MEG', 'sample', + 'sample_audvis_trunc-meg-eeg-oct-4-fwd.fif') + forward = mne.read_forward_solution(fname_fwd) + forward = mne.pick_types_forward(forward, meg="grad", eeg=False, + exclude=raw.info['bads']) + times = np.arange(100, dtype=np.float64) / raw.info['sfreq'] - 0.1 + stc = simulate_sparse_stc(forward['src'], n_dipoles=n_dipoles, times=times, + random_state=1, labels=labels, data_fun=data_fun) + nave = 30 + evoked = simulate_evoked(forward, stc, info, noise_cov, nave=nave, + use_cps=False, iir_filter=None) + evoked = evoked.crop(tmin=0, tmax=10e-3) + stc_ = mixed_norm(evoked, forward, noise_cov, loose=0.9, n_mxne_iter=5, + depth=0.9) + assert_array_equal(stc_.vertices, stc.vertices) + + +@pytest.mark.slowtest # slow on Azure +@testing.requires_testing_data +def test_mxne_inverse_empty(): + """Tests solver with too high alpha.""" + evoked = read_evokeds(fname_data, condition=0, baseline=(None, 0)) + evoked.pick("grad", exclude="bads") + fname_fwd = op.join(data_path, 'MEG', 'sample', + 'sample_audvis_trunc-meg-eeg-oct-4-fwd.fif') + forward = mne.read_forward_solution(fname_fwd) + forward = mne.pick_types_forward(forward, meg="grad", eeg=False, + exclude=evoked.info['bads']) + cov = read_cov(fname_cov) + with pytest.warns(RuntimeWarning, match='too big'): + stc, residual = mixed_norm( + evoked, forward, cov, n_mxne_iter=3, alpha=99, + return_residual=True) + assert stc.data.size == 0 + assert stc.vertices[0].size == 0 + assert stc.vertices[1].size == 0 + assert_allclose(evoked.data, residual.data) diff -Nru python-mne-0.23.4+dfsg/mne/inverse_sparse/tests/test_mxne_optim.py python-mne-1.1.0+dfsg/mne/inverse_sparse/tests/test_mxne_optim.py --- python-mne-0.23.4+dfsg/mne/inverse_sparse/tests/test_mxne_optim.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/inverse_sparse/tests/test_mxne_optim.py 2022-08-03 15:48:14.000000000 +0000 @@ -15,6 +15,7 @@ norm_epsilon_inf, norm_epsilon, _Phi, _PhiT, dgap_l21l1) from mne.time_frequency._stft import stft_norm2 +from mne.utils import catch_logging, _record_warnings def _generate_tf_data(): @@ -46,47 +47,31 @@ M = np.dot(G, X) args = (M, G, alpha, 1000, 1e-8) - with pytest.warns(None): # CD - X_hat_prox, active_set, _ = mixed_norm_solver( - *args, active_set_size=None, - debias=True, solver='prox') - assert_array_equal(np.where(active_set)[0], [0, 4]) - with pytest.warns(None): # CD + with _record_warnings(): # CD X_hat_cd, active_set, _, gap_cd = mixed_norm_solver( *args, active_set_size=None, debias=True, solver='cd', return_gap=True) assert_array_less(gap_cd, 1e-8) assert_array_equal(np.where(active_set)[0], [0, 4]) - with pytest.warns(None): # CD + with _record_warnings(): # CD X_hat_bcd, active_set, E, gap_bcd = mixed_norm_solver( M, G, alpha, maxit=1000, tol=1e-8, active_set_size=None, debias=True, solver='bcd', return_gap=True) assert_array_less(gap_bcd, 9.6e-9) assert_array_equal(np.where(active_set)[0], [0, 4]) - assert_allclose(X_hat_prox, X_hat_cd, rtol=1e-2) - assert_allclose(X_hat_prox, X_hat_bcd, rtol=1e-2) assert_allclose(X_hat_bcd, X_hat_cd, rtol=1e-2) - with pytest.warns(None): # CD - X_hat_prox, active_set, _ = mixed_norm_solver( - *args, active_set_size=2, debias=True, solver='prox') - assert_array_equal(np.where(active_set)[0], [0, 4]) - with pytest.warns(None): # CD + with _record_warnings(): # CD X_hat_cd, active_set, _ = mixed_norm_solver( *args, active_set_size=2, debias=True, solver='cd') assert_array_equal(np.where(active_set)[0], [0, 4]) - with pytest.warns(None): # CD + with _record_warnings(): # CD X_hat_bcd, active_set, _ = mixed_norm_solver( *args, active_set_size=2, debias=True, solver='bcd') assert_array_equal(np.where(active_set)[0], [0, 4]) assert_allclose(X_hat_bcd, X_hat_cd, rtol=1e-2) - assert_allclose(X_hat_bcd, X_hat_prox, rtol=1e-2) - with pytest.warns(None): # CD - X_hat_prox, active_set, _ = mixed_norm_solver( - *args, active_set_size=2, debias=True, n_orient=2, solver='prox') - assert_array_equal(np.where(active_set)[0], [0, 1, 4, 5]) - with pytest.warns(None): # CD + with _record_warnings(): # CD X_hat_bcd, active_set, _ = mixed_norm_solver( *args, active_set_size=2, debias=True, n_orient=2, solver='bcd') assert_array_equal(np.where(active_set)[0], [0, 1, 4, 5]) @@ -96,24 +81,40 @@ X_hat_cd, active_set, _ = mixed_norm_solver( *args, active_set_size=2, debias=True, n_orient=2, solver='cd') assert_array_equal(np.where(active_set)[0], [0, 1, 4, 5]) - assert_allclose(X_hat_bcd, X_hat_prox, rtol=1e-2) assert_allclose(X_hat_bcd, X_hat_cd, rtol=1e-2) - with pytest.warns(None): # CD + with _record_warnings(): # CD X_hat_bcd, active_set, _ = mixed_norm_solver( *args, active_set_size=2, debias=True, n_orient=5, solver='bcd') assert_array_equal(np.where(active_set)[0], [0, 1, 2, 3, 4]) - with pytest.warns(None): # CD - X_hat_prox, active_set, _ = mixed_norm_solver( - *args, active_set_size=2, debias=True, n_orient=5, solver='prox') - assert_array_equal(np.where(active_set)[0], [0, 1, 2, 3, 4]) with pytest.warns(RuntimeWarning, match='descent'): X_hat_cd, active_set, _ = mixed_norm_solver( *args, active_set_size=2, debias=True, n_orient=5, solver='cd') assert_array_equal(np.where(active_set)[0], [0, 1, 2, 3, 4]) - assert_array_equal(X_hat_bcd, X_hat_cd) - assert_allclose(X_hat_bcd, X_hat_prox, rtol=1e-2) + assert_allclose(X_hat_bcd, X_hat_cd) + + +@pytest.mark.slowtest +def test_non_convergence(): + """Test non-convergence of MxNE solver to catch unexpected bugs.""" + n, p, t, alpha = 30, 40, 20, 1. + rng = np.random.RandomState(0) + G = rng.randn(n, p) + G /= np.std(G, axis=0)[None, :] + X = np.zeros((p, t)) + X[0] = 3 + X[4] = -2 + M = np.dot(G, X) + + # Impossible to converge with only 1 iteration and tol 1e-12 + # In case of non-convegence, we test that no error is returned. + args = (M, G, alpha, 1, 1e-12) + with catch_logging() as log: + mixed_norm_solver(*args, active_set_size=None, debias=True, + solver='bcd', verbose=True) + log = log.getvalue() + assert 'Convergence reached' not in log def test_tf_mxne(): @@ -123,7 +124,7 @@ M, G, active_set = _generate_tf_data() - with pytest.warns(None): # CD + with _record_warnings(): # CD X_hat_tf, active_set_hat_tf, E, gap_tfmxne = tf_mixed_norm_solver( M, G, alpha_space, alpha_time, maxit=200, tol=1e-8, verbose=True, n_orient=1, tstep=4, wsize=32, return_gap=True) @@ -251,40 +252,29 @@ X[4] = -2 M = np.dot(G, X) - with pytest.warns(None): # CD + with _record_warnings(): # CD X_hat_l21, _, _ = mixed_norm_solver( M, G, alpha, maxit=1000, tol=1e-8, verbose=False, n_orient=1, active_set_size=None, debias=False, solver='bcd') - with pytest.warns(None): # CD + with _record_warnings(): # CD X_hat_bcd, active_set, _ = iterative_mixed_norm_solver( M, G, alpha, 1, maxit=1000, tol=1e-8, active_set_size=None, debias=False, solver='bcd') - with pytest.warns(None): # CD - X_hat_prox, active_set, _ = iterative_mixed_norm_solver( - M, G, alpha, 1, maxit=1000, tol=1e-8, active_set_size=None, - debias=False, solver='prox') assert_allclose(X_hat_bcd, X_hat_l21, rtol=1e-3) - assert_allclose(X_hat_prox, X_hat_l21, rtol=1e-3) - with pytest.warns(None): # CD - X_hat_prox, active_set, _ = iterative_mixed_norm_solver( - M, G, alpha, 5, maxit=1000, tol=1e-8, active_set_size=None, - debias=True, solver='prox') - assert_array_equal(np.where(active_set)[0], [0, 4]) - with pytest.warns(None): # CD + with _record_warnings(): # CD X_hat_bcd, active_set, _ = iterative_mixed_norm_solver( M, G, alpha, 5, maxit=1000, tol=1e-8, active_set_size=2, debias=True, solver='bcd') assert_array_equal(np.where(active_set)[0], [0, 4]) - with pytest.warns(None): # CD + with _record_warnings(): # CD X_hat_cd, active_set, _ = iterative_mixed_norm_solver( M, G, alpha, 5, maxit=1000, tol=1e-8, active_set_size=None, debias=True, solver='cd') assert_array_equal(np.where(active_set)[0], [0, 4]) - assert_array_almost_equal(X_hat_prox, X_hat_cd, 5) assert_array_almost_equal(X_hat_bcd, X_hat_cd, 5) - with pytest.warns(None): # CD + with _record_warnings(): # CD X_hat_bcd, active_set, _ = iterative_mixed_norm_solver( M, G, alpha, 5, maxit=1000, tol=1e-8, active_set_size=2, debias=True, n_orient=2, solver='bcd') @@ -295,7 +285,7 @@ M, G, alpha, 5, maxit=1000, tol=1e-8, active_set_size=2, debias=True, n_orient=2, solver='cd') assert_array_equal(np.where(active_set)[0], [0, 1, 4, 5]) - assert_array_equal(X_hat_bcd, X_hat_cd, 5) + assert_allclose(X_hat_bcd, X_hat_cd) X_hat_bcd, active_set, _ = iterative_mixed_norm_solver( M, G, alpha, 5, maxit=1000, tol=1e-8, active_set_size=2, debias=True, @@ -306,7 +296,7 @@ M, G, alpha, 5, maxit=1000, tol=1e-8, active_set_size=2, debias=True, n_orient=5, solver='cd') assert_array_equal(np.where(active_set)[0], [0, 1, 2, 3, 4]) - assert_array_equal(X_hat_bcd, X_hat_cd, 5) + assert_allclose(X_hat_bcd, X_hat_cd) @pytest.mark.slowtest diff -Nru python-mne-0.23.4+dfsg/mne/io/array/array.py python-mne-1.1.0+dfsg/mne/io/array/array.py --- python-mne-0.23.4+dfsg/mne/io/array/array.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/array/array.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Authors: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np @@ -18,8 +18,7 @@ ---------- data : array, shape (n_channels, n_times) The channels' time series. See notes for proper units of measure. - info : instance of Info - Info dictionary. Consider using :func:`mne.create_info` to populate + %(info_not_none)s Consider using :func:`mne.create_info` to populate this structure. This may be modified in place by the class. first_samp : int First sample offset used during recording (default 0). diff -Nru python-mne-0.23.4+dfsg/mne/io/array/tests/test_array.py python-mne-1.1.0+dfsg/mne/io/array/tests/test_array.py --- python-mne-0.23.4+dfsg/mne/io/array/tests/test_array.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/array/tests/test_array.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op @@ -16,7 +16,6 @@ from mne.io.tests.test_raw import _test_raw_reader from mne.io.meas_info import create_info from mne.io.pick import get_channel_type_constants -from mne.utils import run_tests_if_main from mne.channels import make_dig_montage base_dir = op.join(op.dirname(__file__), '..', '..', 'tests', 'data') @@ -26,7 +25,7 @@ def test_long_names(): """Test long name support.""" info = create_info(['a' * 15 + 'b', 'a' * 16], 1000., verbose='error') - data = np.empty((2, 1000)) + data = np.zeros((2, 1000)) raw = RawArray(data, info) assert raw.ch_names == ['a' * 15 + 'b', 'a' * 16] # and a way to get the old behavior @@ -34,7 +33,7 @@ allow_duplicates=True, verbose='error') assert raw.ch_names == ['a' * 13 + '-0', 'a' * 13 + '-1'] info = create_info(['a' * 16] * 11, 1000., verbose='error') - data = np.empty((11, 1000)) + data = np.zeros((11, 1000)) raw = RawArray(data, info) assert raw.ch_names == ['a' * 16 + '-%s' % ii for ii in range(11)] @@ -42,7 +41,7 @@ def test_array_copy(): """Test copying during construction.""" info = create_info(1, 1000.) - data = np.empty((1, 1000)) + data = np.zeros((1, 1000)) # 'auto' (default) raw = RawArray(data, info) assert raw._data is data @@ -180,6 +179,3 @@ raw.set_montage(montage) raw.plot_psd(average=False) # looking for nonexistent layout raw.plot_psd_topo() - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/io/artemis123/artemis123.py python-mne-1.1.0+dfsg/mne/io/artemis123/artemis123.py --- python-mne-0.23.4+dfsg/mne/io/artemis123/artemis123.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/artemis123/artemis123.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Author: Luke Bloy # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np import os.path as op @@ -123,9 +123,9 @@ for k in ['Temporal Filter Active?', 'Decimation Active?', 'Spatial Filter Active?']: - if(header_info[k] != 'FALSE'): + if header_info[k] != 'FALSE': warn('%s - set to but is not supported' % k) - if(header_info['filter_hist']): + if header_info['filter_hist']: warn('Non-Empty Filter history found, BUT is not supported' % k) # build mne info struct @@ -194,7 +194,7 @@ t['unit'] = FIFF.FIFF_UNIT_T t['unit_mul'] = FIFF.FIFF_UNITM_F - # 3 axis referance magnetometers + # 3 axis reference magnetometers elif (chan['name'] in ref_mag_names): t['coil_type'] = FIFF.FIFFV_COIL_ARTEMIS123_REF_MAG t['kind'] = FIFF.FIFFV_REF_MEG_CH @@ -276,13 +276,13 @@ info['hpi_subsystem'] = hpi_sub info['hpi_meas'] = [{'hpi_coils': hpi_coils}] - # read in digitized points if supplied if pos_fname is not None: info['dig'] = _read_pos(pos_fname) else: info['dig'] = [] + info._unlocked = False info._update_redundant() return info, header_info @@ -326,7 +326,7 @@ super(RawArtemis123, self).__init__( info, preload, filenames=[input_fname], raw_extras=[header_info], - last_samps=last_samps, orig_format=np.float32, + last_samps=last_samps, orig_format="single", verbose=verbose) if add_head_trans: @@ -339,17 +339,19 @@ 'head localization\n *NO* head localization performed') else: # Localized HPIs using the 1st 250 milliseconds of data. - info['hpi_results'] = [ - dict( + with info._unlock(): + info['hpi_results'] = [dict( dig_points=[dict( - r=np.zeros(3), coord_frame=FIFF.FIFFV_COORD_DEVICE, + r=np.zeros(3), + coord_frame=FIFF.FIFFV_COORD_DEVICE, ident=ii + 1) for ii in range(n_hpis)], coord_trans=Transform('meg', 'head'))] coil_amplitudes = compute_chpi_amplitudes( self, tmin=0, tmax=0.25, t_window=0.25, t_step_min=0.25) assert len(coil_amplitudes['times']) == 1 coil_locs = compute_chpi_locs(self.info, coil_amplitudes) - info['hpi_results'] = None + with info._unlock(): + info['hpi_results'] = None hpi_g = coil_locs['gofs'][0] hpi_dev = coil_locs['rrs'][0] @@ -410,9 +412,10 @@ lpa = hpi_dev[2] rpa = hpi_dev[1] t = get_ras_to_neuromag_trans(nas, lpa, rpa) - self.info['dev_head_t'] = \ - Transform(FIFF.FIFFV_COORD_DEVICE, - FIFF.FIFFV_COORD_HEAD, t) + with self.info._unlock(): + self.info['dev_head_t'] = \ + Transform(FIFF.FIFFV_COORD_DEVICE, + FIFF.FIFFV_COORD_HEAD, t) # transform fiducial points nas = apply_trans(t, nas) @@ -420,8 +423,11 @@ rpa = apply_trans(t, rpa) hpi = apply_trans(self.info['dev_head_t'], hpi_dev) - self.info['dig'] = _make_dig_points(nasion=nas, lpa=lpa, - rpa=rpa, hpi=hpi) + with self.info._unlock(): + self.info['dig'] = _make_dig_points(nasion=nas, + lpa=lpa, + rpa=rpa, + hpi=hpi) order = np.array([0, 1, 2]) dist_limit = 0.005 @@ -454,7 +460,8 @@ 'beware of *POOR* head localization') # store it - self.info['hpi_results'] = [hpi_result] + with self.info._unlock(): + self.info['hpi_results'] = [hpi_result] def _read_segment_file(self, data, idx, fi, start, stop, cals, mult): """Read a chunk of raw data.""" diff -Nru python-mne-0.23.4+dfsg/mne/io/artemis123/__init__.py python-mne-1.1.0+dfsg/mne/io/artemis123/__init__.py --- python-mne-0.23.4+dfsg/mne/io/artemis123/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/artemis123/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,6 +2,6 @@ # Author: Luke Bloy # -# License: BSD (3-clause) +# License: BSD-3-Clause from .artemis123 import read_raw_artemis123 diff -Nru python-mne-0.23.4+dfsg/mne/io/artemis123/tests/test_artemis123.py python-mne-1.1.0+dfsg/mne/io/artemis123/tests/test_artemis123.py --- python-mne-0.23.4+dfsg/mne/io/artemis123/tests/test_artemis123.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/artemis123/tests/test_artemis123.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Author: Luke Bloy # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op @@ -9,7 +9,6 @@ from numpy.testing import assert_allclose, assert_equal import pytest -from mne.utils import run_tests_if_main, _TempDir from mne.io import read_raw_artemis123 from mne.io.tests.test_raw import _test_raw_reader from mne.datasets import testing @@ -49,11 +48,16 @@ @pytest.mark.timeout(60) # ~25 sec on Travis Linux OpenBLAS @testing.requires_testing_data -def test_data(): +def test_artemis_reader(): """Test reading raw Artemis123 files.""" _test_raw_reader(read_raw_artemis123, input_fname=short_hpi_1kz_fname, pos_fname=dig_fname, verbose='error') + +@pytest.mark.timeout(60) +@testing.requires_testing_data +def test_dev_head_t(): + """Test dev_head_t computation for Artemis123.""" # test a random selected point raw = read_raw_artemis123(short_hpi_1kz_fname, preload=True, add_head_trans=False) @@ -98,10 +102,10 @@ assert_equal(raw.info['sfreq'], 1000.0) -def test_utils(): +def test_utils(tmp_path): """Test artemis123 utils.""" # make a tempfile - tmp_dir = _TempDir() + tmp_dir = str(tmp_path) tmp_fname = op.join(tmp_dir, 'test_gen_mne_locs.csv') _generate_mne_locs_file(tmp_fname) installed_locs = _load_mne_locs() @@ -109,6 +113,3 @@ assert_equal(set(installed_locs.keys()), set(generated_locs.keys())) for key in installed_locs.keys(): assert_allclose(installed_locs[key], generated_locs[key], atol=1e-7) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/io/base.py python-mne-1.1.0+dfsg/mne/io/base.py --- python-mne-0.23.4+dfsg/mne/io/base.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/base.py 2022-08-03 15:48:14.000000000 +0000 @@ -8,8 +8,9 @@ # Stefan Appelhoff # Clemens Brunner # -# License: BSD (3-clause) +# License: BSD-3-Clause +from contextlib import nullcontext from copy import deepcopy from datetime import timedelta import os @@ -20,18 +21,15 @@ import numpy as np from .constants import FIFF -from .utils import _construct_bids_filename, _check_orig_units, \ - _get_als_coords_from_chs -from ..utils.check import _infer_check_export_fmt +from .utils import _construct_bids_filename, _check_orig_units from .pick import (pick_types, pick_channels, pick_info, _picks_to_idx, channel_type) -from .meas_info import write_meas_info +from .meas_info import write_meas_info, _ensure_infos_match, ContainsMixin from .proj import setup_proj, activate_proj, _proj_equal, ProjMixin -from ..channels.channels import (ContainsMixin, UpdateChannelsMixin, - SetChannelsMixin, InterpolationMixin, - _unit2human) +from ..channels.channels import (UpdateChannelsMixin, SetChannelsMixin, + InterpolationMixin, _unit2human) from .compensator import set_current_comp, make_compensator -from .write import (start_file, end_file, start_block, end_block, +from .write import (start_and_end_file, start_block, end_block, write_dau_pack16, write_float, write_double, write_complex64, write_complex128, write_int, write_id, write_string, _get_split_size, _NEXT_FILE_BUFFER) @@ -40,7 +38,6 @@ _handle_meas_date) from ..filter import (FilterMixin, notch_filter, resample, _resamp_ratio_len, _resample_stim_channels, _check_fun) -from ..fixes import nullcontext from ..parallel import parallel_func from ..utils import (_check_fname, _check_pandas_installed, sizeof_fmt, _check_pandas_index_arguments, fill_doc, copy_doc, @@ -49,44 +46,11 @@ copy_function_doc_to_method_doc, _validate_type, _check_preload, _get_argvalues, _check_option, _build_data_frame, _convert_times, _scale_dataframe_data, - _check_time_format, _arange_div, - _check_eeglabio_installed) + _check_time_format, _arange_div, TimeMixin) from ..defaults import _handle_default from ..viz import plot_raw, plot_raw_psd, plot_raw_psd_topo, _RAW_CLIP_DEF from ..event import find_events, concatenate_events from ..annotations import Annotations, _combine_annotations, _sync_onset -from ..data.html_templates import raw_template - - -class TimeMixin(object): - """Class to add sfreq and time_as_index capabilities to certain classes.""" - - # Overridden method signature does not match call... - def time_as_index(self, times, use_rounding=False): # lgtm - """Convert time to indices. - - Parameters - ---------- - times : list-like | float | int - List of numbers or a number representing points in time. - use_rounding : bool - If True, use rounding (instead of truncation) when converting - times to indices. This can help avoid non-unique indices. - - Returns - ------- - index : ndarray - Indices corresponding to the times supplied. - """ - from ..source_estimate import _BaseSourceEstimate - if isinstance(self, _BaseSourceEstimate): - sfreq = 1. / self.tstep - else: - sfreq = self.info['sfreq'] - index = (np.atleast_1d(times) - self.times[0]) * sfreq - if use_rounding: - index = np.round(index) - return index.astype(int) @fill_doc @@ -96,8 +60,7 @@ Parameters ---------- - info : dict - A dict passed from the subclass. + %(info_not_none)s preload : bool | str | ndarray Preload data into memory for data manipulation and faster indexing. If True, the data will be preloaded into memory (fast, requires @@ -158,7 +121,7 @@ filenames=(None,), raw_extras=(None,), orig_format='double', dtype=np.float64, buffer_size_sec=1., orig_units=None, - verbose=None): # noqa: D102 + *, verbose=None): # noqa: D102 # wait until the end to preload data, but triage here if isinstance(preload, np.ndarray): # some functions (e.g., filtering) only work w/64-bit data @@ -184,9 +147,10 @@ self._last_samps = np.array(last_samps) self._first_samps = np.array(first_samps) orig_ch_names = info['ch_names'] - if isinstance(info['meas_date'], tuple): # be permissive of old code - info['meas_date'] = _stamp_to_dt(info['meas_date']) - info._check_consistency() # make sure subclass did a good job + with info._unlock(check_after=True): + # be permissive of old code + if isinstance(info['meas_date'], tuple): + info['meas_date'] = _stamp_to_dt(info['meas_date']) self.info = info self.buffer_size_sec = float(buffer_size_sec) cals = np.empty(info['nchan']) @@ -196,7 +160,6 @@ if len(bad) > 0: raise ValueError('Bad cals for channels %s' % {ii: self.ch_names[ii] for ii in bad}) - self.verbose = verbose self._cals = cals self._raw_extras = list(dict() if r is None else r for r in raw_extras) for r in self._raw_extras: @@ -211,6 +174,10 @@ % self._read_comp_grade) self._comp = None self._filenames = list(filenames) + _validate_type(orig_format, str, "orig_format") + _check_option( + "orig_format", orig_format, ("double", "single", "int", "short") + ) self.orig_format = orig_format # Sanity check and set original units, if provided by the reader: @@ -252,6 +219,7 @@ self._projector = None self._dtype_ = dtype self.set_annotations(None) + self._cropped_samp = first_samps[0] # If we have True or a string, actually do the preloading if load_from_disk: self._preload_data(preload) @@ -273,7 +241,7 @@ ---------- grade : int CTF gradient compensation level. - %(verbose_meth)s + %(verbose)s Returns ------- @@ -331,7 +299,7 @@ to store the data. projector : array SSP operator to apply to the data. - %(verbose_meth)s + %(verbose)s Returns ------- @@ -509,7 +477,7 @@ Parameters ---------- - %(verbose_meth)s + %(verbose)s Returns ------- @@ -547,8 +515,11 @@ @property def first_samp(self): - """The first data sample.""" - return self._first_samps[0] + """The first data sample. + + See :term:`first_samp`. + """ + return self._cropped_samp @property def first_time(self): @@ -577,14 +548,14 @@ times to indices. This can help avoid non-unique indices. origin : datetime | float | int | None Time reference for times. If None, ``times`` are assumed to be - relative to ``first_samp``. + relative to :term:`first_samp`. .. versionadded:: 0.17.0 Returns ------- index : ndarray - Indices relative to ``first_samp`` corresponding to the times + Indices relative to :term:`first_samp` corresponding to the times supplied. """ origin = _handle_meas_date(origin) @@ -627,10 +598,10 @@ annotations : instance of mne.Annotations | None Annotations to set. If None, the annotations is defined but empty. - emit_warning : bool - Whether to emit warnings when cropping or omitting annotations. + %(emit_warning)s + The default is True. %(on_missing_ch_names)s - %(verbose_meth)s + %(verbose)s Returns ------- @@ -803,7 +774,7 @@ @verbose def get_data(self, picks=None, start=0, stop=None, reject_by_annotation=None, return_times=False, units=None, - verbose=None): + *, tmin=None, tmax=None, verbose=None): """Get data in the given range. Parameters @@ -820,23 +791,18 @@ 'bad' are omitted. If 'NaN', the bad samples are filled with NaNs. return_times : bool Whether to return times as well. Defaults to False. - units : str | dict | None - Specify the unit(s) that the data should be returned in. If - ``None`` (default), the data is returned in the - channel-type-specific default units, which are SI units (see - :ref:`units` and :term:`data channels`). If a string, must be a - sub-multiple of SI units that will be used to scale the data from - all channels of the type associated with that unit. This only works - if the data contains one channel type that has a unit (unitless - channel types are left unchanged). For example if there are only - EEG and STIM channels, ``units='uV'`` will scale EEG channels to - micro-Volts while STIM channels will be unchanged. Finally, if a - dictionary is provided, keys must be channel types, and values must - be units to scale the data of that channel type to. For example - ``dict(grad='fT/cm', mag='fT')`` will scale the corresponding types - accordingly, but all other channel types will remain in their - channel-type-specific default unit. - %(verbose_meth)s + %(units)s + tmin : int | float | None + Start time of data to get in seconds. The ``tmin`` parameter is + ignored if the ``start`` parameter is bigger than 0. + + .. versionadded:: 0.24.0 + tmax : int | float | None + End time of data to get in seconds. The ``tmax`` parameter is + ignored if the ``stop`` parameter is defined. + + .. versionadded:: 0.24.0 + %(verbose)s Returns ------- @@ -850,48 +816,43 @@ ----- .. versionadded:: 0.14.0 """ + # validate types + _validate_type(start, types=('int-like'), item_name='start', + type_name='int') + _validate_type(stop, types=('int-like', None), item_name='stop', + type_name='int, None') + picks = _picks_to_idx(self.info, picks, 'all', exclude=()) - # Convert into the specified unit - _validate_type(units, types=(None, str, dict), item_name="units") - needs_conversion = False - ch_factors = np.ones(len(picks)) - si_units = _handle_default('si_units') - # Convert to dict if str units - if isinstance(units, str): - # Check that there is only one channel type - ch_types = self.get_channel_types(picks=picks, unique=True) - unit_ch_type = list(set(ch_types) & set(si_units.keys())) - if len(unit_ch_type) > 1: - raise ValueError('"units" cannot be str if there is more than ' - 'one channel type with a unit ' - f'{unit_ch_type}.') - units = {unit_ch_type[0]: units} # make the str argument a dict - # Loop over the dict to get channel factors - if isinstance(units, dict): - for ch_type, ch_unit in units.items(): - # Get the scaling factors - scaling = _get_scaling(ch_type, ch_unit) - if scaling != 1: - needs_conversion = True - ch_types = self.get_channel_types(picks=picks) - indices = [i_ch for i_ch, ch in enumerate(ch_types) - if ch == ch_type] - ch_factors[indices] *= scaling + # Get channel factors for conversion into specified unit + # (vector of ones if no conversion needed) + if units is not None: + ch_factors = _get_ch_factors(self, units, picks) # convert to ints picks = np.atleast_1d(np.arange(self.info['nchan'])[picks]) - start = 0 if start is None else start - stop = min(self.n_times if stop is None else stop, self.n_times) + + # handle start/tmin stop/tmax + tmin_start, tmax_stop = self._handle_tmin_tmax(tmin, tmax) + + # tmin/tmax are ignored if start/stop are defined to + # something other than their defaults + start = tmin_start if start == 0 else start + stop = tmax_stop if stop is None else stop + + # truncate start/stop to the open interval [0, n_times] + start = min(max(0, start), self.n_times) + stop = min(max(0, stop), self.n_times) + if len(self.annotations) == 0 or reject_by_annotation is None: getitem = self._getitem( (picks, slice(start, stop)), return_times=return_times) if return_times: data, times = getitem - if needs_conversion: + if units is not None: data *= ch_factors[:, np.newaxis] return data, times - if needs_conversion: + if units is not None: getitem *= ch_factors[:, np.newaxis] return getitem _check_option('reject_by_annotation', reject_by_annotation.lower(), @@ -902,7 +863,7 @@ ends = np.minimum(ends[keep], stop) if len(onsets) == 0: data, times = self[picks, start:stop] - if needs_conversion: + if units is not None: data *= ch_factors[:, np.newaxis] if return_times: return data, times @@ -945,14 +906,14 @@ else: data, times = self[picks, start:stop] - if needs_conversion: + if units is not None: data *= ch_factors[:, np.newaxis] if return_times: return data, times return data @verbose - def apply_function(self, fun, picks=None, dtype=None, n_jobs=1, + def apply_function(self, fun, picks=None, dtype=None, n_jobs=None, channel_wise=True, verbose=None, **kwargs): """Apply a function to a subset of channels. @@ -960,15 +921,15 @@ Parameters ---------- - %(applyfun_fun)s + %(fun_applyfun)s %(picks_all_data_noref)s - %(applyfun_dtype)s + %(dtype_applyfun)s %(n_jobs)s - %(applyfun_chwise)s + %(channel_wise_applyfun)s .. versionadded:: 0.18 - %(verbose_meth)s - %(kwarg_fun)s + %(verbose)s + %(kwargs_fun)s Returns ------- @@ -986,6 +947,7 @@ self._data = self._data.astype(dtype) if channel_wise: + parallel, p_fun, n_jobs = parallel_func(_check_fun, n_jobs) if n_jobs == 1: # modify data inplace to save memory for idx in picks: @@ -993,7 +955,6 @@ **kwargs) else: # use parallel function - parallel, p_fun, _ = parallel_func(_check_fun, n_jobs) data_picks_new = parallel( p_fun(fun, data_in[p], **kwargs) for p in picks) for pp, p in enumerate(picks): @@ -1007,19 +968,21 @@ # Need a separate method because the default pad is different for raw @copy_doc(FilterMixin.filter) def filter(self, l_freq, h_freq, picks=None, filter_length='auto', - l_trans_bandwidth='auto', h_trans_bandwidth='auto', n_jobs=1, + l_trans_bandwidth='auto', h_trans_bandwidth='auto', n_jobs=None, method='fir', iir_params=None, phase='zero', fir_window='hamming', fir_design='firwin', skip_by_annotation=('edge', 'bad_acq_skip'), pad='reflect_limited', verbose=None): # noqa: D102 return super().filter( l_freq, h_freq, picks, filter_length, l_trans_bandwidth, - h_trans_bandwidth, n_jobs, method, iir_params, phase, - fir_window, fir_design, skip_by_annotation, pad, verbose) + h_trans_bandwidth, n_jobs=n_jobs, method=method, + iir_params=iir_params, phase=phase, fir_window=fir_window, + fir_design=fir_design, skip_by_annotation=skip_by_annotation, + pad=pad, verbose=verbose) @verbose def notch_filter(self, freqs, picks=None, filter_length='auto', - notch_widths=None, trans_bandwidth=1.0, n_jobs=1, + notch_widths=None, trans_bandwidth=1.0, n_jobs=None, method='fir', iir_params=None, mt_bandwidth=None, p_value=0.05, phase='zero', fir_window='hamming', fir_design='firwin', pad='reflect_limited', verbose=None): @@ -1029,36 +992,37 @@ ---------- freqs : float | array of float | None Specific frequencies to filter out from data, e.g., - np.arange(60, 241, 60) in the US or np.arange(50, 251, 50) in - Europe. None can only be used with the mode 'spectrum_fit', - where an F test is used to find sinusoidal components. + ``np.arange(60, 241, 60)`` in the US or ``np.arange(50, 251, 50)`` + in Europe. ``None`` can only be used with the mode + ``'spectrum_fit'``, where an F test is used to find sinusoidal + components. %(picks_all_data)s %(filter_length_notch)s notch_widths : float | array of float | None Width of each stop band (centred at each freq in freqs) in Hz. - If None, freqs / 200 is used. + If None, ``freqs / 200`` is used. trans_bandwidth : float Width of the transition band in Hz. Only used for ``method='fir'``. - %(n_jobs-fir)s - %(method-fir)s + %(n_jobs_fir)s + %(method_fir)s %(iir_params)s mt_bandwidth : float | None The bandwidth of the multitaper windowing function in Hz. Only used in 'spectrum_fit' mode. p_value : float P-value to use in F-test thresholding to determine significant - sinusoidal components to remove when method='spectrum_fit' and - freqs=None. Note that this will be Bonferroni corrected for the + sinusoidal components to remove when ``method='spectrum_fit'`` and + ``freqs=None``. Note that this will be Bonferroni corrected for the number of frequencies, so large p-values may be justified. %(phase)s %(fir_window)s %(fir_design)s - %(pad-fir)s + %(pad_fir)s The default is ``'reflect_limited'``. .. versionadded:: 0.15 - %(verbose_meth)s + %(verbose)s Returns ------- @@ -1098,7 +1062,7 @@ @verbose def resample(self, sfreq, npad='auto', window='boxcar', stim_picks=None, - n_jobs=1, events=None, pad='reflect_limited', + n_jobs=None, events=None, pad='reflect_limited', verbose=None): # lgtm """Resample all channels. @@ -1127,23 +1091,23 @@ sfreq : float New sample rate to use. %(npad)s - %(window-resample)s + %(window_resample)s stim_picks : list of int | None Stim channels. These channels are simply subsampled or supersampled (without applying any filtering). This reduces resampling artifacts in stim channels, but may lead to missing triggers. If None, stim channels are automatically chosen using :func:`mne.pick_types`. - %(n_jobs-cuda)s + %(n_jobs_cuda)s events : 2D array, shape (n_events, 3) | None An optional event matrix. When specified, the onsets of the events are resampled jointly with the data. NB: The input events are not modified, but a new array is returned with the raw instead. - %(pad-fir)s + %(pad)s The default is ``'reflect_limited'``. .. versionadded:: 0.15 - %(verbose_meth)s + %(verbose)s Returns ------- @@ -1227,16 +1191,18 @@ resamp = resample(data_chunk, **kwargs) new_data[ci, this_sl] = resamp - self._first_samps = (self._first_samps * ratio).astype(int) + self._cropped_samp = int(np.round(self._cropped_samp * ratio)) + self._first_samps = np.round(self._first_samps * ratio).astype(int) self._last_samps = (np.array(self._first_samps) + n_news - 1) self._raw_lengths[ri] = list(n_news) assert np.array_equal(n_news, self._last_samps - self._first_samps + 1) self._data = new_data self.preload = True - self.info['sfreq'] = sfreq lowpass = self.info.get('lowpass') lowpass = np.inf if lowpass is None else lowpass - self.info['lowpass'] = min(lowpass, sfreq / 2.) + with self.info._unlock(): + self.info['lowpass'] = min(lowpass, sfreq / 2.) + self.info['sfreq'] = sfreq # See the comment above why we ignore all errors here. if events is None: @@ -1262,23 +1228,25 @@ ) return self, events - @fill_doc - def crop(self, tmin=0.0, tmax=None, include_tmax=True): + @verbose + def crop(self, tmin=0.0, tmax=None, include_tmax=True, *, verbose=None): """Crop raw data file. Limit the data from the raw file to go between specific times. Note - that the new tmin is assumed to be t=0 for all subsequently called - functions (e.g., time_as_index, or Epochs). New first_samp and - last_samp are set accordingly. + that the new ``tmin`` is assumed to be ``t=0`` for all subsequently + called functions (e.g., :meth:`~mne.io.Raw.time_as_index`, or + :class:`~mne.Epochs`). New :term:`first_samp` and :term:`last_samp` + are set accordingly. Thus function operates in-place on the instance. Use :meth:`mne.io.Raw.copy` if operation on a copy is desired. Parameters ---------- - %(raw_tmin)s - %(raw_tmax)s + %(tmin_raw)s + %(tmax_raw)s %(include_tmax)s + %(verbose)s Returns ------- @@ -1307,6 +1275,9 @@ keepers = np.logical_and(np.less(smin, cumul_lens[1:]), np.greater_equal(smax, cumul_lens[:-1])) keepers = np.where(keepers)[0] + # if we drop file(s) from the beginning, we need to keep track of + # how many samples we dropped relative to that one + self._cropped_samp += smin self._first_samps = np.atleast_1d(self._first_samps[keepers]) # Adjust first_samp of first used file! self._first_samps[0] += smin - cumul_lens[keepers[0]] @@ -1321,14 +1292,52 @@ # slice and copy to avoid the reference to large array self._data = self._data[:, smin:smax + 1].copy() - if self.annotations.orig_time is None: - self.annotations.onset -= tmin + annotations = self.annotations # now call setter to filter out annotations outside of interval - self.set_annotations(self.annotations, False) + if annotations.orig_time is None: + assert self.info['meas_date'] is None + # When self.info['meas_date'] is None (which is guaranteed if + # self.annotations.orig_time is None), when we do the + # self.set_annotations, it's assumed that the annotations onset + # are relative to first_time, so we have to subtract it, then + # set_annotations will put it back. + annotations.onset -= self.first_time + self.set_annotations(annotations, False) return self @verbose + def crop_by_annotations(self, annotations=None, *, verbose=None): + """Get crops of raw data file for selected annotations. + + Parameters + ---------- + annotations : instance of Annotations | None + The annotations to use for cropping the raw file. If None, + the annotations from the instance are used. + %(verbose)s + + Returns + ------- + raws : list + The cropped raw objects. + """ + if annotations is None: + annotations = self.annotations + + raws = [] + for annot in annotations: + onset = annot["onset"] - self.first_time + # be careful about near-zero errors (crop is very picky about this, + # e.g., -1e-8 is an error) + if -self.info['sfreq'] / 2 < onset < 0: + onset = 0 + raw_crop = self.copy().crop(onset, onset + annot["duration"]) + raws.append(raw_crop) + + return raws + + @verbose def save(self, fname, picks=None, tmin=0, tmax=None, buffer_size_sec=None, drop_small_buffer=False, proj=False, fmt='single', overwrite=False, split_size='2GB', split_naming='neuromag', @@ -1347,8 +1356,8 @@ or ``_ieeg.fif`` (common intracranial EEG data). You may also append an additional ``.gz`` suffix to enable gzip compression. %(picks_all)s - %(raw_tmin)s - %(raw_tmax)s + %(tmin_raw)s + %(tmax_raw)s buffer_size_sec : float | None Size of data chunks in seconds. If None (default), the buffer size of the original file is used. @@ -1381,11 +1390,10 @@ .. note:: Due to FIFF file limitations, the maximum split size is 2GB. - split_naming : {'neuromag' | 'bids'} - Add the filename partition with the appropriate naming schema. + %(split_naming)s .. versionadded:: 0.17 - %(verbose_meth)s + %(verbose)s Notes ----- @@ -1395,12 +1403,17 @@ work properly on a saved concatenated file (e.g., probably some or all forms of SSS). It is recommended not to concatenate and then save raw files for this reason. + + Samples annotated ``BAD_ACQ_SKIP`` are not stored in order to optimize + memory. Whatever values, they will be loaded as 0s when reading file. """ - fname = op.abspath(fname) endings = ('raw.fif', 'raw_sss.fif', 'raw_tsss.fif', '_meg.fif', '_eeg.fif', '_ieeg.fif') endings += tuple([f'{e}.gz' for e in endings]) endings_err = ('.fif', '.fif.gz') + + # convert to str, check for overwrite a few lines later + fname = _check_fname(fname, overwrite=True, verbose="error") check_fname(fname, 'raw', endings, endings_err=endings_err) split_size = _get_split_size(split_size) @@ -1427,8 +1440,9 @@ raise ValueError('Complex data must be saved as "single" or ' '"double", not "short"') - # check for file existence - _check_fname(fname, overwrite) + # check for file existence and expand `~` if present + fname = _check_fname(fname=fname, overwrite=overwrite, + verbose="error") if proj: info = deepcopy(self.info) @@ -1454,56 +1468,37 @@ split_size, split_naming, 0, None, overwrite) @verbose - def export(self, fname, fmt='auto', verbose=None): + def export(self, fname, fmt='auto', physical_range='auto', + add_ch_type=False, *, overwrite=False, verbose=None): """Export Raw to external formats. - Supported formats: EEGLAB (set, uses :mod:`eeglabio`) + %(export_fmt_support_raw)s + %(export_warning)s Parameters ---------- - %(export_params_fname)s - %(export_params_fmt)s + %(fname_export_params)s + %(export_fmt_params_raw)s + %(physical_range_export_params)s + %(add_ch_type_export_params)s + %(overwrite)s + + .. versionadded:: 0.24.1 %(verbose)s Notes ----- + .. versionadded:: 0.24 + + %(export_warning_note_raw)s %(export_eeglab_note)s + %(export_edf_note)s """ - supported_export_formats = { # format : extensions - 'eeglab': ('set',), - 'edf': ('edf',), - 'brainvision': ('eeg', 'vmrk', 'vhdr',) - } - fmt = _infer_check_export_fmt(fmt, fname, supported_export_formats) - - if fmt == 'eeglab': - _check_eeglabio_installed() - import eeglabio.raw - # load data first - self.load_data() - - # remove extra epoc and STI channels - drop_chs = ['epoc'] - if not (self.filenames[0].endswith('.fif')): - drop_chs.append('STI 014') - - ch_names = [ch for ch in self.ch_names if ch not in drop_chs] - cart_coords = _get_als_coords_from_chs(self.info['chs'], - drop_chs) - - annotations = [self.annotations.description, - self.annotations.onset, - self.annotations.duration] - eeglabio.raw.export_set(fname, data=self.get_data(picks=ch_names), - sfreq=self.info['sfreq'], - ch_names=ch_names, - ch_locs=cart_coords, - annotations=annotations) - elif fmt == 'edf': - raise NotImplementedError('Export to EDF format not implemented.') - elif fmt == 'brainvision': - raise NotImplementedError('Export to BrainVision not implemented.') + from ..export import export_raw + export_raw(fname, self, fmt, physical_range=physical_range, + add_ch_type=add_ch_type, overwrite=overwrite, + verbose=verbose) def _tmin_tmax_to_start_stop(self, tmin, tmax): start = int(np.floor(tmin * self.info['sfreq'])) @@ -1521,20 +1516,25 @@ @copy_function_doc_to_method_doc(plot_raw) def plot(self, events=None, duration=10.0, start=0.0, n_channels=20, - bgcolor='w', color=None, bad_color=(0.8, 0.8, 0.8), + bgcolor='w', color=None, bad_color='lightgray', event_color='cyan', scalings=None, remove_dc=True, order=None, show_options=False, title=None, show=True, block=False, highpass=None, lowpass=None, filtorder=4, clipping=_RAW_CLIP_DEF, show_first_samp=False, proj=True, group_by='type', butterfly=False, decim='auto', noise_cov=None, event_id=None, - show_scrollbars=True, show_scalebars=True, verbose=None): + show_scrollbars=True, show_scalebars=True, time_format='float', + precompute=None, use_opengl=None, *, theme=None, + overview_mode=None, verbose=None): return plot_raw(self, events, duration, start, n_channels, bgcolor, color, bad_color, event_color, scalings, remove_dc, order, show_options, title, show, block, highpass, - lowpass, filtorder, clipping, show_first_samp, proj, - group_by, butterfly, decim, noise_cov=noise_cov, + lowpass, filtorder, clipping, show_first_samp, + proj, group_by, butterfly, decim, noise_cov=noise_cov, event_id=event_id, show_scrollbars=show_scrollbars, - show_scalebars=show_scalebars, verbose=verbose) + show_scalebars=show_scalebars, time_format=time_format, + precompute=precompute, use_opengl=use_opengl, + theme=theme, overview_mode=overview_mode, + verbose=verbose) @verbose @copy_function_doc_to_method_doc(plot_raw_psd) @@ -1542,9 +1542,9 @@ n_fft=None, n_overlap=0, reject_by_annotation=True, picks=None, ax=None, color='black', xscale='linear', area_mode='std', area_alpha=0.33, dB=True, estimate='auto', - show=True, n_jobs=1, average=False, line_alpha=None, + show=True, n_jobs=None, average=False, line_alpha=None, spatial_colors=True, sphere=None, window='hamming', - verbose=None): + exclude='bads', verbose=None): return plot_raw_psd(self, fmin=fmin, fmax=fmax, tmin=tmin, tmax=tmax, proj=proj, n_fft=n_fft, n_overlap=n_overlap, reject_by_annotation=reject_by_annotation, @@ -1553,13 +1553,13 @@ dB=dB, estimate=estimate, show=show, n_jobs=n_jobs, average=average, line_alpha=line_alpha, spatial_colors=spatial_colors, sphere=sphere, - window=window, verbose=verbose) + window=window, exclude=exclude, verbose=verbose) @copy_function_doc_to_method_doc(plot_raw_psd_topo) def plot_psd_topo(self, tmin=0., tmax=None, fmin=0, fmax=100, proj=False, n_fft=2048, n_overlap=0, layout=None, color='w', fig_facecolor='k', axis_facecolor='k', dB=True, - show=True, block=False, n_jobs=1, axes=None, + show=True, block=False, n_jobs=None, axes=None, verbose=None): return plot_raw_psd_topo(self, tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax, proj=proj, n_fft=n_fft, @@ -1603,7 +1603,8 @@ """ return self.n_times - def load_bad_channels(self, bad_file=None, force=False): + @verbose + def load_bad_channels(self, bad_file=None, force=False, verbose=None): """Mark channels as bad from a text file. This function operates mostly in the style of the C function @@ -1612,34 +1613,39 @@ Parameters ---------- - bad_file : str - File name of the text file containing bad channels - If bad_file = None, bad channels are cleared, but this - is more easily done directly as raw.info['bads'] = []. + bad_file : path-like | None + File name of the text file containing bad channels. + If ``None`` (default), bad channels are cleared, but this + is more easily done directly with ``raw.info['bads'] = []``. force : bool Whether or not to force bad channel marking (of those that exist) if channels are not found, instead of - raising an error. + raising an error. Defaults to ``False``. + %(verbose)s """ + prev_bads = self.info['bads'] + new_bads = [] if bad_file is not None: # Check to make sure bad channels are there names = frozenset(self.info['ch_names']) with open(bad_file) as fid: bad_names = [line for line in fid.read().splitlines() if line] - names_there = [ci for ci in bad_names if ci in names] - count_diff = len(bad_names) - len(names_there) + new_bads = [ci for ci in bad_names if ci in names] + count_diff = len(bad_names) - len(new_bads) if count_diff > 0: + msg = (f'{count_diff} bad channel(s) from:' + f'\n{bad_file}\nnot found in:\n{self.filenames[0]}') if not force: - raise ValueError('Bad channels from:\n%s\n not found ' - 'in:\n%s' % (bad_file, - self.filenames[0])) + raise ValueError(msg) else: - warn('%d bad channels from:\n%s\nnot found in:\n%s' - % (count_diff, bad_file, self.filenames[0])) - self.info['bads'] = names_there + warn(msg) + + if prev_bads != new_bads: + logger.info(f'Updating bad channels: {prev_bads} -> {new_bads}') + self.info['bads'] = new_bads else: - self.info['bads'] = [] + logger.info(f'No channels updated. Bads are: {prev_bads}') @fill_doc def append(self, raws, preload=None): @@ -1712,7 +1718,7 @@ annotations = _combine_annotations( annotations, r.annotations, n_samples, self.first_samp, r.first_samp, - self.info['sfreq'], self.info['meas_date']) + self.info['sfreq']) edge_samps.append(sum(self._last_samps) - sum(self._first_samps) + (ri + 1)) self._first_samps = np.r_[self._first_samps, r._first_samps] @@ -1721,6 +1727,12 @@ self._raw_extras += r._raw_extras self._filenames += r._filenames assert annotations.orig_time == self.info['meas_date'] + # The above _combine_annotations gets everything synchronized to + # first_samp. set_annotations (with no absolute time reference) assumes + # that the annotations being set are relative to first_samp, and will + # add it back on. So here we have to remove it: + if annotations.orig_time is None: + annotations.onset -= self.first_samp / self.info['sfreq'] self.set_annotations(annotations) for edge_samp in edge_samps: onset = _sync_onset(self, (edge_samp) / self.info['sfreq'], True) @@ -1760,13 +1772,18 @@ return "<%s | %s>" % (self.__class__.__name__, s) def _repr_html_(self, caption=None): - basenames = [os.path.basename(f) for f in self._filenames] + from ..html_templates import repr_templates_env + basenames = [ + os.path.basename(f) for f in self._filenames if f is not None + ] m, s = divmod(self._last_time - self.first_time, 60) h, m = divmod(m, 60) duration = f'{int(h):02d}:{int(m):02d}:{int(s):02d}' - return raw_template.substitute( + raw_template = repr_templates_env.get_template('raw.html.jinja') + return raw_template.render( info_repr=self.info._repr_html_(caption=caption), - filenames=basenames, duration=duration) + filenames=basenames, duration=duration + ) def add_events(self, events, stim_channel=None, replace=False): """Add events to stim channel. @@ -1817,10 +1834,11 @@ buffer_size_sec = float(buffer_size_sec) return int(np.ceil(buffer_size_sec * self.info['sfreq'])) - @fill_doc + @verbose def to_data_frame(self, picks=None, index=None, scalings=None, copy=True, start=None, stop=None, - long_format=False, time_format='ms'): + long_format=False, time_format=None, *, + verbose=None): """Export data in tabular structure as a pandas DataFrame. Channels are converted to columns in the DataFrame. By default, an @@ -1830,10 +1848,10 @@ Parameters ---------- %(picks_all)s - %(df_index_raw)s + %(index_df_raw)s Defaults to ``None``. - %(df_scalings)s - %(df_copy)s + %(scalings_df)s + %(copy_df)s start : int | None Starting sample index for creating the DataFrame from a temporal span of the Raw object. ``None`` (the default) uses the first @@ -1841,10 +1859,11 @@ stop : int | None Ending sample index for creating the DataFrame from a temporal span of the Raw object. ``None`` (the default) uses the last sample. - %(df_longform_raw)s - %(df_time_format_raw)s + %(long_format_df_raw)s + %(time_format_df_raw)s .. versionadded:: 0.20 + %(verbose)s Returns ------- @@ -1938,11 +1957,11 @@ f"{'name':<{lens['name']}} " f"{'type':<{lens['type']}} " f"{'unit':<{lens['unit']}} " - f"{'min':>8} " - f"{'Q1':>8} " - f"{'median':>8} " - f"{'Q3':>8} " - f"{'max':>8}") + f"{'min':>9} " + f"{'Q1':>9} " + f"{'median':>9} " + f"{'Q3':>9} " + f"{'max':>9}") # print description for each channel for i in range(nchan): msg = (f"{i:>{lens['ch']}} " @@ -1950,8 +1969,8 @@ f"{cols['type'][i].upper():<{lens['type']}} " f"{cols['unit'][i]:<{lens['unit']}} ") for col in ["min", "Q1", "median", "Q3"]: - msg += f"{cols[col][i]:>8.2f} " - msg += f"{cols['max'][i]:>8.2f}" + msg += f"{cols[col][i]:>9.2f} " + msg += f"{cols['max'][i]:>9.2f}" print(msg) @@ -1992,6 +2011,50 @@ return sel +def _get_ch_factors(inst, units, picks_idxs): + """Get scaling factors for data, given units. + + Parameters + ---------- + inst : instance of Raw | Epochs | Evoked + The instance. + %(units)s + picks_idxs : ndarray + The picks as provided through _picks_to_idx. + + Returns + ------- + ch_factors : ndarray of floats, shape(len(picks),) + The sacling factors for each channel, ordered according + to picks. + + """ + _validate_type(units, types=(None, str, dict), item_name="units") + ch_factors = np.ones(len(picks_idxs)) + si_units = _handle_default('si_units') + ch_types = inst.get_channel_types(picks=picks_idxs) + # Convert to dict if str units + if isinstance(units, str): + # Check that there is only one channel type + unit_ch_type = list(set(ch_types) & set(si_units.keys())) + if len(unit_ch_type) > 1: + raise ValueError('"units" cannot be str if there is more than ' + 'one channel type with a unit ' + f'{unit_ch_type}.') + units = {unit_ch_type[0]: units} # make the str argument a dict + # Loop over the dict to get channel factors + if isinstance(units, dict): + for ch_type, ch_unit in units.items(): + # Get the scaling factors + scaling = _get_scaling(ch_type, ch_unit) + if scaling != 1: + indices = [i_ch for i_ch, ch in enumerate(ch_types) + if ch == ch_type] + ch_factors[indices] *= scaling + + return ch_factors + + def _get_scaling(ch_type, target_unit): """Return the scaling factor based on the channel type and a target unit. @@ -2107,6 +2170,9 @@ raise RuntimeError('Cannot write raw file with no data: %s -> %s ' '(max: %s) requested' % (start, stop, n_times_max)) + # Expand `~` if present + fname = _check_fname(fname=fname, overwrite=overwrite) + base, ext = op.splitext(fname) if part_idx > 0: if split_naming == 'neuromag': @@ -2133,14 +2199,17 @@ logger.info('Writing %s' % use_fname) picks = _picks_to_idx(info, picks, 'all', ()) - fid, cals = _start_writing_raw(use_fname, info, picks, data_type, - reset_range, raw.annotations) - with ctx, fid: - final_fname = _write_raw_fid( - raw, info, picks, fid, cals, part_idx, start, stop, - buffer_size, prev_fname, split_size, use_fname, - projector, drop_small_buffer, fmt, fname, reserved_fname, - data_type, reset_range, split_naming, overwrite) + with start_and_end_file(use_fname) as fid: + cals = _start_writing_raw(fid, info, picks, data_type, + reset_range, raw.annotations) + with ctx: + final_fname = _write_raw_fid( + raw, info, picks, fid, cals, part_idx, start, stop, + buffer_size, prev_fname, split_size, use_fname, + projector, drop_small_buffer, fmt, fname, reserved_fname, + data_type, reset_range, split_naming, + overwrite=True # we've started writing already above + ) if final_fname != use_fname: assert split_naming == 'bids' logger.info(f'Renaming BIDS split file {op.basename(final_fname)}') @@ -2236,7 +2305,7 @@ logger.info('Skipping data chunk due to small buffer ... ' '[done]') break - logger.debug('Writing ...') + logger.debug(f'Writing FIF {first:6d} ... {last:6d} ...') _write_raw_buffer(fid, data, cals, fmt) pos = fid.tell() @@ -2280,20 +2349,19 @@ else: end_block(fid, FIFF.FIFFB_RAW_DATA) end_block(fid, FIFF.FIFFB_MEAS) - end_file(fid) return final_fname -def _start_writing_raw(name, info, sel, data_type, +@fill_doc +def _start_writing_raw(fid, info, sel, data_type, reset_range, annotations): """Start write raw data in file. Parameters ---------- - name : string - Name of the file to create. - info : dict - Measurement info. + fid : file + The created file. + %(info_not_none)s sel : array of int | None Indices of channels to include. If None, all channels are included. @@ -2320,10 +2388,8 @@ # # Create the file and save the essentials # - fid = start_file(name) start_block(fid, FIFF.FIFFB_MEAS) write_id(fid, FIFF.FIFF_BLOCK_ID) - # XXX do we need this? if info['meas_id'] is not None: write_id(fid, FIFF.FIFF_PARENT_BLOCK_ID, info['meas_id']) @@ -2353,7 +2419,7 @@ else: start_block(fid, FIFF.FIFFB_RAW_DATA) - return fid, cals + return cals def _write_raw_buffer(fid, buf, cals, fmt): @@ -2431,8 +2497,9 @@ @verbose -def concatenate_raws(raws, preload=None, events_list=None, verbose=None): - """Concatenate raw instances as if they were continuous. +def concatenate_raws(raws, preload=None, events_list=None, *, + on_mismatch='raise', verbose=None): + """Concatenate `~mne.io.Raw` instances as if they were continuous. .. note:: ``raws[0]`` is modified in-place to achieve the concatenation. Boundaries of the raw files are annotated bad. If you wish to use @@ -2443,10 +2510,11 @@ Parameters ---------- raws : list - List of Raw instances to concatenate (in order). + List of `~mne.io.Raw` instances to concatenate (in order). %(preload_concatenate)s events_list : None | list - The events to concatenate. Defaults to None. + The events to concatenate. Defaults to ``None``. + %(on_mismatch_info)s %(verbose)s Returns @@ -2456,6 +2524,10 @@ events : ndarray of int, shape (n_events, 3) The events. Only returned if ``event_list`` is not None. """ + for idx, raw in enumerate(raws[1:], start=1): + _ensure_infos_match(info1=raws[0].info, info2=raw.info, + name=f'raws[{idx}]', on_mismatch=on_mismatch) + if events_list is not None: if len(events_list) != len(raws): raise ValueError('`raws` and `event_list` are required ' diff -Nru python-mne-0.23.4+dfsg/mne/io/besa/besa.py python-mne-1.1.0+dfsg/mne/io/besa/besa.py --- python-mne-0.23.4+dfsg/mne/io/besa/besa.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/besa/besa.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,243 @@ +from collections import OrderedDict +from pathlib import Path +import numpy as np + +from ...utils import logger, fill_doc, verbose +from ..meas_info import create_info +from ...evoked import EvokedArray + + +@fill_doc +@verbose +def read_evoked_besa(fname, verbose=None): + """Reader function for BESA .avr or .mul files. + + When a .elp sidecar file is present, it will be used to determine electrode + information. + + Parameters + ---------- + fname : str | Path + Path to the .avr or .mul file. + %(verbose)s + + Returns + ------- + ev : Evoked + The evoked data in the .avr or .mul file. + """ + fname = Path(fname) + if fname.suffix == '.avr': + return _read_evoked_besa_avr(fname, verbose) + elif fname.suffix == '.mul': + return _read_evoked_besa_mul(fname, verbose) + else: + raise ValueError('Filename must end in either .avr or .mul') + + +@verbose +def _read_evoked_besa_avr(fname, verbose): + """Create EvokedArray from a BESA .avr file.""" + with open(fname) as f: + header = f.readline().strip() + + # There are two versions of .avr files. The old style, generated by + # BESA 1, 2 and 3 does not define Nchan and does not have channel names + # in the file. + new_style = 'Nchan=' in header + if new_style: + ch_names = f.readline().strip().split() + else: + ch_names = None + + fields = _parse_header(header) + data = np.loadtxt(fname, skiprows=2 if new_style else 1, ndmin=2) + ch_types = _read_elp_sidecar(fname) + + # Consolidate channel names + if new_style: + if len(ch_names) != len(data): + raise RuntimeError( + 'Mismatch between the number of channel names defined in ' + f'the .avr file ({len(ch_names)}) and the number of rows ' + f'in the data matrix ({len(data)}).') + else: + # Determine channel names from the .elp sidecar file + if ch_types is not None: + ch_names = list(ch_types.keys()) + if len(ch_names) != len(data): + raise RuntimeError('Mismatch between the number of channels ' + f'defined in the .avr file ({len(data)}) ' + f'and .elp file ({len(ch_names)}).') + else: + logger.info('No .elp file found and no channel names present in ' + 'the .avr file. Falling back to generic names. ') + ch_names = [f'CH{i + 1:02d}' for i in range(len(data))] + + # Consolidate channel types + if ch_types is None: + logger.info('Marking all channels as EEG.') + ch_types = ['eeg'] * len(ch_names) + else: + ch_types = [ch_types[ch] for ch in ch_names] + + # Go over all the header fields and make sure they are all defined to + # something sensible. + if 'Npts' in fields: + fields['Npts'] = int(fields['Npts']) + if fields['Npts'] != data.shape[1]: + logger.warn(f'The size of the data matrix ({data.shape}) does not ' + f'match the "Npts" field ({fields["Npts"]}).') + if 'Nchan' in fields: + fields['Nchan'] = int(fields['Nchan']) + if fields['Nchan'] != data.shape[0]: + logger.warn(f'The size of the data matrix ({data.shape}) does not ' + f'match the "Nchan" field ({fields["Nchan"]}).') + if 'DI' in fields: + fields['DI'] = float(fields['DI']) + else: + raise RuntimeError('No "DI" field present. Could not determine ' + 'sampling frequency.') + if 'TSB' in fields: + fields['TSB'] = float(fields['TSB']) + else: + fields['TSB'] = 0 + if 'SB' in fields: + fields['SB'] = float(fields['SB']) + else: + fields['SB'] = 1.0 + if 'SegmentName' not in fields: + fields['SegmentName'] = '' + + # Build the Evoked object based on the header fields. + info = create_info(ch_names, sfreq=1000 / fields['DI'], ch_types='eeg') + return EvokedArray(data / fields['SB'] / 1E6, info, + tmin=fields['TSB'] / 1000, + comment=fields['SegmentName'], verbose=verbose) + + +@verbose +def _read_evoked_besa_mul(fname, verbose): + """Create EvokedArray from a BESA .mul file.""" + with open(fname) as f: + header = f.readline().strip() + ch_names = f.readline().strip().split() + + fields = _parse_header(header) + data = np.loadtxt(fname, skiprows=2, ndmin=2) + + if len(ch_names) != data.shape[1]: + raise RuntimeError('Mismatch between the number of channel names ' + f'defined in the .mul file ({len(ch_names)}) ' + 'and the number of columns in the data matrix ' + f'({data.shape[1]}).') + + # Consolidate channel types + ch_types = _read_elp_sidecar(fname) + if ch_types is None: + logger.info('Marking all channels as EEG.') + ch_types = ['eeg'] * len(ch_names) + else: + ch_types = [ch_types[ch] for ch in ch_names] + + # Go over all the header fields and make sure they are all defined to + # something sensible. + if 'TimePoints' in fields: + fields['TimePoints'] = int(fields['TimePoints']) + if fields['TimePoints'] != data.shape[0]: + logger.warn( + f'The size of the data matrix ({data.shape}) does not ' + f'match the "TimePoints" field ({fields["TimePoints"]}).') + if 'Channels' in fields: + fields['Channels'] = int(fields['Channels']) + if fields['Channels'] != data.shape[1]: + logger.warn(f'The size of the data matrix ({data.shape}) does not ' + f'match the "Channels" field ({fields["Channels"]}).') + if 'SamplingInterval[ms]' in fields: + fields['SamplingInterval[ms]'] = float(fields['SamplingInterval[ms]']) + else: + raise RuntimeError('No "SamplingInterval[ms]" field present. Could ' + 'not determine sampling frequency.') + if 'BeginSweep[ms]' in fields: + fields['BeginSweep[ms]'] = float(fields['BeginSweep[ms]']) + else: + fields['BeginSweep[ms]'] = 0.0 + if 'Bins/uV' in fields: + fields['Bins/uV'] = float(fields['Bins/uV']) + else: + fields['Bins/uV'] = 1 + if 'SegmentName' not in fields: + fields['SegmentName'] = '' + + # Build the Evoked object based on the header fields. + info = create_info(ch_names, sfreq=1000 / fields['SamplingInterval[ms]'], + ch_types=ch_types) + return EvokedArray(data.T / fields['Bins/uV'] / 1E6, info, + tmin=fields['BeginSweep[ms]'] / 1000, + comment=fields['SegmentName'], verbose=verbose) + + +def _parse_header(header): + """Parse an .avr or .mul header string into name/val pairs. + + The header line looks like: + Npts= 256 TSB= 0.000 DI= 4.000000 SB= 1.000 SC= 200.0 Nchan= 27 + No consistent use of separation chars, so parsing this is a bit iffy. + + Parameters + ---------- + header : str + The first line of the file. + + Returns + ------- + fields : dict + The parsed header fields + """ + parts = header.split() # Splits on one or more spaces + name_val_pairs = zip(parts[::2], parts[1::2]) + return dict((name.replace('=', ''), val) for name, val in name_val_pairs) + + +def _read_elp_sidecar(fname): + """Read a possible .elp sidecar file with electrode information. + + The reason we don't use the read_custom_montage for this is that we are + interested in the channel types, which a DigMontage object does not provide + us. + + Parameters + ---------- + fname : Path + The path of the .avr or .mul file. The corresponding .elp file will be + derived from this path. + + Returns + ------- + ch_type : OrderedDict | None + If the sidecar file exists, return a dictionary mapping channel names + to channel types. Otherwise returns ``None``. + """ + fname_elp = fname.parent / (fname.stem + '.elp') + if not fname_elp.exists(): + logger.info(f'No {fname_elp} file present containing electrode ' + 'information.') + return None + + logger.info(f'Reading electrode names and types from {fname_elp}') + ch_types = OrderedDict() + with open(fname_elp) as f: + lines = f.readlines() + if len(lines[0].split()) > 3: + # Channel types present + for line in lines: + ch_type, ch_name = line.split()[:2] + ch_types[ch_name] = ch_type.lower() + else: + # No channel types present + logger.info('No channel types present in .elp file. Marking all ' + 'channels as EEG.') + for line in lines: + ch_name = line.split()[:1] + ch_types[ch_name] = 'eeg' + return ch_types diff -Nru python-mne-0.23.4+dfsg/mne/io/besa/__init__.py python-mne-1.1.0+dfsg/mne/io/besa/__init__.py --- python-mne-0.23.4+dfsg/mne/io/besa/__init__.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/besa/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,7 @@ +"""Support for various BESA file formats.""" + +# Author: Marijn van Vliet +# +# License: BSD-3-Clause + +from .besa import read_evoked_besa diff -Nru python-mne-0.23.4+dfsg/mne/io/besa/tests/data/simulation.avr python-mne-1.1.0+dfsg/mne/io/besa/tests/data/simulation.avr --- python-mne-0.23.4+dfsg/mne/io/besa/tests/data/simulation.avr 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/besa/tests/data/simulation.avr 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,35 @@ +Npts= 200 TSB= -100 DI= 5 SB= 1.00 SC= 500.0 Nchan= 33 SegmentName= simulation +Fp1 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-0.269 -0.249 -0.228 -0.207 -0.187 -0.167 -0.148 -0.129 -0.111 -0.0938 -0.0777 -0.0628 -0.0491 -0.0368 -0.0261 -0.0171 -0.00979 -0.00443 -0.00113 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 diff -Nru python-mne-0.23.4+dfsg/mne/io/besa/tests/data/simulation_oldstyle.elp python-mne-1.1.0+dfsg/mne/io/besa/tests/data/simulation_oldstyle.elp --- python-mne-0.23.4+dfsg/mne/io/besa/tests/data/simulation_oldstyle.elp 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/besa/tests/data/simulation_oldstyle.elp 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,33 @@ +EEG Fp1 -92 -72 1 +EEG Fp2 92 72 1 +EEG F9 -115 -35 1 +EEG F7 -92 -36 1 +EEG F3 -60 -51 1 +EEG Fz 46 90 1 +EEG F4 60 51 1 +EEG F8 92 36 1 +EEG F10 115 35 1 +EEG FC5 -71 -21 1 +EEG FC1 -32 -45 1 +EEG FC2 32 45 1 +EEG FC6 71 21 1 +EEG T9 -115 0 1 +EEG T7 -92 0 1 +EEG C3 -46 0 1 +EEG Cz 0 0 1 +EEG C4 46 0 1 +EEG T8 92 0 1 +EEG T10 115 0 1 +EEG CP5 -71 21 1 +EEG CP1 -32 45 1 +EEG CP2 32 -45 1 +EEG CP6 71 -21 1 +EEG P9 -115 36 1 +EEG P7 -92 36 1 +EEG P3 -60 51 1 +EEG Pz 46 -90 1 +EEG P4 60 -51 1 +EEG P8 92 -36 1 +EEG P10 115 -36 1 +EEG O1 -92 72 1 +EEG O2 92 -72 1 diff -Nru python-mne-0.23.4+dfsg/mne/io/besa/tests/test_besa.py python-mne-1.1.0+dfsg/mne/io/besa/tests/test_besa.py --- python-mne-0.23.4+dfsg/mne/io/besa/tests/test_besa.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/besa/tests/test_besa.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,74 @@ +"""Test reading BESA fileformats.""" +import inspect +import pytest +from pathlib import Path + +from mne.io import read_evoked_besa +from mne.channels import read_custom_montage + + +FILE = Path(inspect.getfile(inspect.currentframe())) +data_dir = FILE.parent / 'data' +avr_file = data_dir / 'simulation.avr' +avr_file_oldstyle = data_dir / 'simulation_oldstyle.avr' +mul_file = data_dir / 'simulation.mul' +montage = read_custom_montage(data_dir / 'simulation.elp') + + +@pytest.mark.filterwarnings("ignore:Fiducial point nasion not found") +@pytest.mark.parametrize('fname', (avr_file, avr_file_oldstyle, mul_file)) +def test_read_evoked_besa(fname): + """Test reading MESA .avr and .mul files.""" + ev = read_evoked_besa(fname) + assert len(ev.ch_names) == len(ev.data) == 33 + assert ev.info['sfreq'] == 200 + assert ev.tmin == -0.1 + assert len(ev.times) == 200 + assert ev.ch_names == montage.ch_names + assert ev.comment == 'simulation' + + +def test_read_evoked_besa_avr_incomplete(tmp_path): + """Test reading incomplete BESA .avr files.""" + # Check old style .avr file without an .elp sidecar + with open(f'{tmp_path}/missing.avr', 'w') as f: + f.write('Npts= 1 TSB= 0 SB= 1.00 SC= 500.0 DI= 5\n0\n1\n2\n') + ev = read_evoked_besa(f'{tmp_path}/missing.avr') + assert ev.ch_names == ['CH01', 'CH02', 'CH03'] + + # Create BESA file with missing header fields and verify things don't break + with open(f'{tmp_path}/missing.avr', 'w') as f: + f.write('DI= 5\n0\n') + ev = read_evoked_besa(f'{tmp_path}/missing.avr') + assert len(ev.ch_names) == len(ev.data) == 1 + assert ev.info['sfreq'] == 200 + assert ev.tmin == 0 + assert len(ev.times) == 1 + assert ev.ch_names == ['CH01'] + assert ev.comment == '' + + # The DI field (sample frequency) must exist + with open(f'{tmp_path}/missing.avr', 'w') as f: + f.write('Npts= 1 TSB= 0 SB= 1.00 SC= 500.0\n0\n') + with pytest.raises(RuntimeError, match='No "DI" field present'): + ev = read_evoked_besa(f'{tmp_path}/missing.avr') + + +def test_read_evoked_besa_mul_incomplete(tmp_path): + """Test reading incomplete BESA .mul files.""" + # Create BESA file with missing header fields and verify things don't break + with open(f'{tmp_path}/missing.mul', 'w') as f: + f.write('SamplingInterval[ms]= 5\nCH1\n0\n') + ev = read_evoked_besa(f'{tmp_path}/missing.mul') + assert len(ev.ch_names) == len(ev.data) == 1 + assert ev.info['sfreq'] == 200 + assert ev.tmin == 0 + assert len(ev.times) == 1 + assert ev.ch_names == ['CH1'] + assert ev.comment == '' + + # The SamplingInterval[ms] field (sample frequency) must exist + with open(f'{tmp_path}/missing.mul', 'w') as f: + f.write('TimePoints= 1 Channels= 1\nCH1\n0\n') + with pytest.raises(RuntimeError, match=r'No "SamplingInterval\[ms\]"'): + ev = read_evoked_besa(f'{tmp_path}/missing.mul') diff -Nru python-mne-0.23.4+dfsg/mne/io/boxy/boxy.py python-mne-1.1.0+dfsg/mne/io/boxy/boxy.py --- python-mne-0.23.4+dfsg/mne/io/boxy/boxy.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/boxy/boxy.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Kyle Mathewson, Jonathan Kuziek # -# License: BSD (3-clause) +# License: BSD-3-Clause import re as re diff -Nru python-mne-0.23.4+dfsg/mne/io/boxy/__init__.py python-mne-1.1.0+dfsg/mne/io/boxy/__init__.py --- python-mne-0.23.4+dfsg/mne/io/boxy/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/boxy/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,6 +2,6 @@ # Authors: Kyle Mathewson, Jonathan Kuziek # -# License: BSD (3-clause) +# License: BSD-3-Clause from .boxy import read_raw_boxy diff -Nru python-mne-0.23.4+dfsg/mne/io/boxy/tests/test_boxy.py python-mne-1.1.0+dfsg/mne/io/boxy/tests/test_boxy.py --- python-mne-0.23.4+dfsg/mne/io/boxy/tests/test_boxy.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/boxy/tests/test_boxy.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Kyle Mathewson, Jonathan Kuziek # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op diff -Nru python-mne-0.23.4+dfsg/mne/io/brainvision/brainvision.py python-mne-1.1.0+dfsg/mne/io/brainvision/brainvision.py --- python-mne-0.23.4+dfsg/mne/io/brainvision/brainvision.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/brainvision/brainvision.py 2022-08-03 15:48:14.000000000 +0000 @@ -8,7 +8,7 @@ # Okba Bekhelifi # Stefan Appelhoff # -# License: BSD (3-clause) +# License: BSD-3-Clause import configparser import os @@ -26,6 +26,7 @@ from ..utils import _read_segments_file, _mult_cal_one from ...annotations import Annotations, read_annotations from ...channels import make_dig_montage +from ...defaults import HEAD_SIZE_DEFAULT @fill_doc @@ -38,13 +39,13 @@ Path to the EEG header file. eog : list or tuple Names of channels or list of indices that should be designated - EOG channels. Values should correspond to the vhdr file. + EOG channels. Values should correspond to the header file. Default is ``('HEOGL', 'HEOGR', 'VEOGb')``. misc : list or tuple of str | 'auto' Names of channels or list of indices that should be designated MISC channels. Values should correspond to the electrodes - in the vhdr file. If 'auto', units in vhdr file are used for inferring - misc channels. Default is ``'auto'``. + in the header file. If 'auto', units in header file are used for + inferring misc channels. Default is ``'auto'``. scale : float The scaling factor for EEG data. Unless specified otherwise by header file, units are in microvolts. Default scale factor is 1. @@ -67,9 +68,11 @@ scale=1., preload=False, verbose=None): # noqa: D107 # Channel info and events logger.info('Extracting parameters from %s...' % vhdr_fname) - vhdr_fname = op.abspath(vhdr_fname) + hdr_fname = op.abspath(vhdr_fname) + ext = op.splitext(hdr_fname)[-1] + ahdr_format = True if ext == '.ahdr' else False (info, data_fname, fmt, order, n_samples, mrk_fname, montage, - orig_units) = _get_vhdr_info(vhdr_fname, eog, misc, scale) + orig_units) = _get_hdr_info(hdr_fname, eog, misc, scale) with open(data_fname, 'rb') as f: if isinstance(fmt, dict): # ASCII, this will be slow :( @@ -89,24 +92,29 @@ offsets = None n_samples = n_samples // (dtype_bytes * n_data_ch) + orig_format = "single" if isinstance(fmt, dict) else fmt raw_extras = dict( offsets=offsets, fmt=fmt, order=order, n_samples=n_samples) super(RawBrainVision, self).__init__( info, last_samps=[n_samples - 1], filenames=[data_fname], - orig_format=fmt, preload=preload, verbose=verbose, + orig_format=orig_format, preload=preload, verbose=verbose, raw_extras=[raw_extras], orig_units=orig_units) self.set_montage(montage) - settings, cfg, cinfo, _ = _aux_vhdr_info(vhdr_fname) + settings, cfg, cinfo, _ = _aux_hdr_info(hdr_fname) split_settings = settings.splitlines() self.impedances = _parse_impedance(split_settings, self.info['meas_date']) - # Get annotations from vmrk file + # Get annotations from marker file annots = read_annotations(mrk_fname, info['sfreq']) self.set_annotations(annots) + # Drop the fake ahdr channel if needed + if ahdr_format: + self.drop_channels(_AHDR_CHANNEL_NAME) + def _read_segment_file(self, data, idx, fi, start, stop, cals, mult): """Read a chunk of raw data.""" # read data @@ -125,8 +133,28 @@ block = np.empty((n_data_ch, stop - start)) for ii in range(stop - start): line = fid.readline().decode('ASCII') - line = line.strip().replace(',', '.').split() - block[:n_data_ch, ii] = [float(part) for part in line] + line = line.strip() + + # Not sure why we special-handle the "," character here, + # but let's just keep this for historical and backward- + # compat reasons + if (isinstance(fmt, dict) and + 'decimalsymbol' in fmt and + fmt['decimalsymbol'] != '.'): + line = line.replace(',', '.') + + if ' ' in line: + line_data = line.split() + elif ',' in line: + # likely exported from BrainVision Analyzer? + line_data = line.split(',') + else: + raise RuntimeError( + 'Unknown BrainVision data format encountered. ' + 'Please contact the MNE-Python developers.' + ) + + block[:n_data_ch, ii] = [float(part) for part in line_data] _mult_cal_one(data, block, idx, cals, mult) @@ -146,13 +174,13 @@ _mult_cal_one(data, block, idx, cals, mult) -def _read_vmrk(fname): - """Read annotations from a vmrk file. +def _read_mrk(fname): + """Read annotations from a vmrk/amrk file. Parameters ---------- fname : str - vmrk file to be read. + vmrk/amrk file to be read. Returns ------- @@ -166,7 +194,7 @@ The recording time as a string. Defaults to empty string if no recording time is found. """ - # read vmrk file + # read marker file with open(fname, 'rb') as fid: txt = fid.read() @@ -239,35 +267,39 @@ def _read_annotations_brainvision(fname, sfreq='auto'): - """Create Annotations from BrainVision vrmk. + """Create Annotations from BrainVision vmrk/amrk. - This function reads a .vrmk file and makes an + This function reads a .vmrk or .amrk file and makes an :class:`mne.Annotations` object. Parameters ---------- fname : str | object - The path to the .vmrk file. + The path to the .vmrk/.amrk file. sfreq : float | 'auto' The sampling frequency in the file. It's necessary - as Annotations are expressed in seconds and vmrk + as Annotations are expressed in seconds and vmrk/amrk files are in samples. If set to 'auto' then - the sfreq is taken from the .vhdr file that + the sfreq is taken from the .vhdr/.ahdr file that has the same name (without file extension). So - data.vrmk looks for sfreq in data.vhdr. + data.vmrk/amrk looks for sfreq in data.vhdr or, + if it does not exist, in data.ahdr. Returns ------- annotations : instance of Annotations The annotations present in the file. """ - onset, duration, description, date_str = _read_vmrk(fname) + onset, duration, description, date_str = _read_mrk(fname) orig_time = _str_to_meas_date(date_str) if sfreq == 'auto': - vhdr_fname = op.splitext(fname)[0] + '.vhdr' - logger.info("Finding 'sfreq' from header file: %s" % vhdr_fname) - _, _, _, info = _aux_vhdr_info(vhdr_fname) + hdr_fname = op.splitext(fname)[0] + '.vhdr' + # if vhdr file does not exist assume that the format is ahdr + if not op.exists(hdr_fname): + hdr_fname = op.splitext(fname)[0] + '.ahdr' + logger.info("Finding 'sfreq' from header file: %s" % hdr_fname) + _, _, _, info = _aux_hdr_info(hdr_fname) sfreq = info['sfreq'] onset = np.array(onset, dtype=float) / sfreq @@ -278,22 +310,26 @@ return annotations -_data_err = """\ -MNE-Python currently only supports %s versions 1.0 and 2.0, got unparsable \ -%r. Contact MNE-Python developers for support.""" -# optional space, optional Core, Version/Header, optional comma, 1/2 -_data_re = r'Brain ?Vision( Core)? Data Exchange %s File,? Version %s\.0' - - def _check_bv_version(header, kind): """Check the header version.""" + _data_err = """\ + MNE-Python currently only supports %s versions 1.0 and 2.0, got unparsable\ + %r. Contact MNE-Python developers for support.""" + # optional space, optional Core or V-Amp, optional Exchange, + # Version/Header, optional comma, 1/2 + _data_re = (r"Brain ?Vision( Core| V-Amp)? Data( Exchange)? " + r"%s File,? Version %s\.0") + assert kind in ('header', 'marker') + + if header == '': + warn(f'Missing header in {kind} file.') for version in range(1, 3): this_re = _data_re % (kind.capitalize(), version) if re.search(this_re, header) is not None: return version else: - raise ValueError(_data_err % (kind, header)) + warn(_data_err % (kind, header)) _orientation_dict = dict(MULTIPLEXED='F', VECTORIZED='C') @@ -305,7 +341,9 @@ 'uV': 1e-6, 'mV': 1e-3, 'nV': 1e-9, - 'C': 1, # C stands for celsius + 'C': 1, # C stands for Celsius + '°C': 1, # degrees Celsius + 'n/a': 1, # unit "not available" (or applicable) 'µS': 1e-6, # S stands for Siemens 'uS': 1e-6, 'ARU': 1, # ARU is the unity for the breathing data @@ -333,9 +371,9 @@ return meas_date -def _aux_vhdr_info(vhdr_fname): - """Aux function for _get_vhdr_info.""" - with open(vhdr_fname, 'rb') as f: +def _aux_hdr_info(hdr_fname): + """Aux function for _get_hdr_info.""" + with open(hdr_fname, 'rb') as f: # extract the first section to resemble a cfg header = f.readline() codepage = 'utf-8' @@ -384,23 +422,25 @@ # Sampling interval is given in microsec sfreq = 1e6 / cfg.getfloat(cinfostr, 'SamplingInterval') info = _empty_info(sfreq) + info._unlocked = False return settings, cfg, cinfostr, info -def _get_vhdr_info(vhdr_fname, eog, misc, scale): +@fill_doc +def _get_hdr_info(hdr_fname, eog, misc, scale): """Extract all the information from the header file. Parameters ---------- - vhdr_fname : str + hdr_fname : str Raw EEG header to be read. eog : list of str Names of channels that should be designated EOG channels. Names should - correspond to the vhdr file. + correspond to the header file. misc : list or tuple of str | 'auto' Names of channels or list of indices that should be designated - MISC channels. Values should correspond to the electrodes - in the vhdr file. If 'auto', units in vhdr file are used for inferring + MISC channels. Values should correspond to the electrodes in the + header file. If 'auto', units in header file are used for inferring misc channels. Default is ``'auto'``. scale : float The scaling factor for EEG data. Unless specified otherwise by @@ -408,8 +448,7 @@ Returns ------- - info : Info - The measurement info. + %(info_not_none)s data_fname : str Path to the binary data file. fmt : str @@ -427,12 +466,14 @@ the header file. Example: {'FC1': 'nV'} """ scale = float(scale) - ext = op.splitext(vhdr_fname)[-1] - if ext != '.vhdr': + ext = op.splitext(hdr_fname)[-1] + ahdr_format = (ext == '.ahdr') + if ext not in ('.vhdr', '.ahdr'): raise IOError("The header file must be given to read the data, " "not a file with extension '%s'." % ext) - settings, cfg, cinfostr, info = _aux_vhdr_info(vhdr_fname) + settings, cfg, cinfostr, info = _aux_hdr_info(hdr_fname) + info._unlocked = True order = cfg.get(cinfostr, 'DataOrientation') if order not in _orientation_dict: @@ -455,7 +496,7 @@ for key in cfg.options('ASCII Infos')} # locate EEG binary file and marker file for the stim channel - path = op.dirname(vhdr_fname) + path = op.dirname(hdr_fname) data_fname = op.join(path, cfg.get(cinfostr, 'DataFile')) mrk_fname = op.join(path, cfg.get(cinfostr, 'MarkerFile')) @@ -467,25 +508,26 @@ for line in lines: match = re.findall(regexp, line.strip()) - # Always take first measurement date we find if match: date_str = match[0] info['meas_date'] = _str_to_meas_date(date_str) break - else: info['meas_date'] = None # load channel labels nchan = cfg.getint(cinfostr, 'NumberOfChannels') + if ahdr_format: + # add one fake channel for ahdr format + nchan += 1 n_samples = None if order == 'C': try: n_samples = cfg.getint(cinfostr, 'DataPoints') except configparser.NoOptionError: - logger.warning('No info on DataPoints found. Inferring number of ' - 'samples from the data file size.') + warn('No info on DataPoints found. Inferring number of ' + 'samples from the data file size.') with open(data_fname, 'rb') as fid: fid.seek(0, 2) n_bytes = fid.tell() @@ -518,7 +560,7 @@ ch_dict[chan] = name ch_names[n] = name if resolution == "": - if not(unit): # For truncated vhdrs (e.g. EEGLAB export) + if not unit: # For truncated vhdrs (e.g. EEGLAB export) resolution = 0.000001 else: resolution = 1. # for files with units specified, but not res @@ -529,13 +571,21 @@ if unit not in ('V', 'mV', 'µV', 'uV', 'nV'): misc_chs[name] = (FIFF.FIFF_UNIT_CEL if unit == 'C' else FIFF.FIFF_UNIT_NONE) + if ahdr_format: + ch_dict[_AHDR_CHANNEL_NAME] = _AHDR_CHANNEL_NAME + ch_names[-1] = _AHDR_CHANNEL_NAME + orig_units[_AHDR_CHANNEL_NAME] = 'V' + cals[-1] = 1. + ranges[-1] = 1 + misc = list(misc_chs.keys()) if misc == 'auto' else misc - # create montage: 'Coordinates' section in VHDR file corresponds to "BVEF" - # BrainVision Electrode File. The data are based on BrainVision Analyzer - # coordinate system: Defined between standard electrode positions: X-axis - # from T7 to T8, Y-axis from Oz to Fpz, Z-axis orthogonal from XY-plane - # through Cz, fit to a sphere if idealized (when radius=1), specified in mm + # create montage: 'Coordinates' section in VHDR/AHDR file corresponds to + # "BVEF" BrainVision Electrode File. The data are based on BrainVision + # Analyzer coordinate system: Defined between standard electrode positions: + # X-axis from T7 to T8, Y-axis from Oz to Fpz, Z-axis orthogonal from + # XY-plane through Cz, fit to a sphere if idealized (when radius=1), + # specified in mm montage = None if cfg.has_section('Coordinates'): from ...transforms import _sph_to_cart @@ -552,8 +602,8 @@ az = np.deg2rad(phi) # Coordinates could be "idealized" (spherical head model) if rad == 1: - # scale up to realistic head radius (8.5cm == 85mm) - rad *= 85. + # scale up to realistic head radius: *1000 to convert m to mm + rad *= HEAD_SIZE_DEFAULT * 1000 pos = _sph_to_cart(np.array([[rad, az, pol]]))[0] if (pos == 0).all() and ch_name not in list(eog) + misc: to_misc.append(ch_name) @@ -634,6 +684,8 @@ lp_s = '[s]' in header[lp_col] for i, ch in enumerate(ch_names, 1): + if ahdr_format and i == len(ch_names) and ch == _AHDR_CHANNEL_NAME: + break # double check alignment with channel by using the hw settings if idx == idx_amp: line_amp = settings[idx + i] @@ -652,6 +704,7 @@ line = re.split(divider, settings[idx + i]) highpass.append(line[hp_col + real_shift]) lowpass.append(line[lp_col + real_shift]) + if len(highpass) == 0: pass elif len(set(highpass)) == 1: @@ -701,7 +754,7 @@ if len(lowpass) == 0: pass elif len(set(lowpass)) == 1: - if lowpass[0] in ('NaN', 'Off'): + if lowpass[0] in ('NaN', 'Off', '0'): pass # Placeholder for future use. Lowpass set in _empty_info else: info['lowpass'] = float(lowpass[0]) @@ -714,7 +767,7 @@ if lp_s: # We convert channels with disabled filters to having # infinitely relaxed / no filters - lowpass = [float(filt) if filt not in ('NaN', 'Off') + lowpass = [float(filt) if filt not in ('NaN', 'Off', '0') else 0.0 for filt in lowpass] info['lowpass'] = np.min(np.array(lowpass, dtype=np.float64)) try: @@ -736,7 +789,7 @@ else: # We convert channels with disabled filters to having # infinitely relaxed / no filters - lowpass = [float(filt) if filt not in ('NaN', 'Off') + lowpass = [float(filt) if filt not in ('NaN', 'Off', '0') else np.Inf for filt in lowpass] info['lowpass'] = np.max(np.array(lowpass, dtype=np.float64)) @@ -792,6 +845,7 @@ unit=unit, unit_mul=FIFF.FIFF_UNITM_NONE, coord_frame=FIFF.FIFFV_COORD_HEAD)) + info._unlocked = False info._update_redundant() return (info, data_fname, fmt, order, n_samples, mrk_fname, montage, orig_units) @@ -809,12 +863,12 @@ Path to the EEG header file. eog : list or tuple of str Names of channels or list of indices that should be designated - EOG channels. Values should correspond to the vhdr file + EOG channels. Values should correspond to the header file Default is ``('HEOGL', 'HEOGR', 'VEOGb')``. misc : list or tuple of str | 'auto' Names of channels or list of indices that should be designated - MISC channels. Values should correspond to the electrodes - in the vhdr file. If 'auto', units in vhdr file are used for inferring + MISC channels. Values should correspond to the electrodes in the + header file. If 'auto', units in header file are used for inferring misc channels. Default is ``'auto'``. scale : float The scaling factor for EEG data. Unless specified otherwise by @@ -842,6 +896,7 @@ 'New Segment/': 99999, 'SyncStatus/Sync On': 99998 } _OTHER_OFFSET = 10001 # where to start "unknown" event_ids +_AHDR_CHANNEL_NAME = "AHDR_CHANNEL" class _BVEventParser(_DefaultEventParser): @@ -876,9 +931,9 @@ Parameters ---------- settings : list - The header settings lines fom the VHDR file. + The header settings lines fom the VHDR/AHDR file. recording_date : datetime.datetime | None - The date of the recording as extracted from the VMRK file. + The date of the recording as extracted from the VMRK/AMRK file. Returns ------- @@ -938,7 +993,7 @@ Parameters ---------- settings : list - The header settings lines fom the VHDR file. + The header settings lines fom the VHDR/AHDR file. Returns ------- diff -Nru python-mne-0.23.4+dfsg/mne/io/brainvision/__init__.py python-mne-1.1.0+dfsg/mne/io/brainvision/__init__.py --- python-mne-0.23.4+dfsg/mne/io/brainvision/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/brainvision/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,6 +3,6 @@ # Author: Teon Brooks # Stefan Appelhoff # -# License: BSD (3-clause) +# License: BSD-3-Clause from .brainvision import read_raw_brainvision diff -Nru python-mne-0.23.4+dfsg/mne/io/brainvision/tests/test_brainvision.py python-mne-1.1.0+dfsg/mne/io/brainvision/tests/test_brainvision.py --- python-mne-0.23.4+dfsg/mne/io/brainvision/tests/test_brainvision.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/brainvision/tests/test_brainvision.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Author: Teon Brooks # Stefan Appelhoff # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op import re import shutil @@ -14,7 +14,7 @@ import pytest import datetime -from mne.utils import run_tests_if_main, _stamp_to_dt, object_diff +from mne.utils import _stamp_to_dt, object_diff from mne import pick_types, read_annotations, concatenate_raws from mne.io.constants import FIFF from mne.io import read_raw_fif, read_raw_brainvision @@ -53,6 +53,10 @@ data_path = testing.data_path(download=False) neuroone_vhdr = op.join(data_path, 'Brainvision', 'test_NO.vhdr') +# AHDR exported with VAmp +data_path = testing.data_path(download=False) +vamp_ahdr = op.join(data_path, 'Brainvision', 'test_VAmp.ahdr') + # Test for nanovolts as unit vhdr_units_path = op.join(data_dir, 'test_units.vhdr') @@ -117,12 +121,12 @@ @pytest.fixture(scope='session') -def _mocked_meas_date_data(tmpdir_factory): +def _mocked_meas_date_data(tmp_path_factory): """Prepare files for mocked_meas_date_file fixture.""" # Prepare the files - tmpdir = str(tmpdir_factory.mktemp('brainvision_mocked_meas_date')) + tmp_path = str(tmp_path_factory.mktemp('brainvision_mocked_meas_date')) vhdr_fname, vmrk_fname, eeg_fname = [ - op.join(tmpdir, op.basename(ff)) + op.join(tmp_path, op.basename(ff)) for ff in [vhdr_path, vmrk_path, eeg_path] ] for orig, dest in zip([vhdr_path, eeg_path], [vhdr_fname, eeg_fname]): @@ -162,11 +166,11 @@ assert raw.info['meas_date'] == expected_meas -def test_vhdr_codepage_ansi(tmpdir): +def test_vhdr_codepage_ansi(tmp_path): """Test BV reading with ANSI codepage.""" raw_init = read_raw_brainvision(vhdr_path) data_expected, times_expected = raw_init[:] - tempdir = str(tmpdir) + tempdir = str(tmp_path) ansi_vhdr_path = op.join(tempdir, op.split(vhdr_path)[-1]) ansi_vmrk_path = op.join(tempdir, op.split(vmrk_path)[-1]) ansi_eeg_path = op.join(tempdir, op.split(eeg_path)[-1]) @@ -201,45 +205,59 @@ b'BrainVision Data Exchange %s File Version 1.0\n', # 2.0, space, core, comma b'Brain Vision Core Data Exchange %s File, Version 2.0\n', - # bad + # unsupported version b'Brain Vision Core Data Exchange %s File, Version 3.0\n', + # missing header + b'\n', ]) -def test_vhdr_versions(tmpdir, header): +def test_vhdr_versions(tmp_path, header): """Test BV reading with different header variants.""" raw_init = read_raw_brainvision(vhdr_path) data_expected, times_expected = raw_init[:] - use_vhdr_path = op.join(tmpdir, op.split(vhdr_path)[-1]) - use_vmrk_path = op.join(tmpdir, op.split(vmrk_path)[-1]) - use_eeg_path = op.join(tmpdir, op.split(eeg_path)[-1]) + use_vhdr_path = op.join(tmp_path, op.split(vhdr_path)[-1]) + use_vmrk_path = op.join(tmp_path, op.split(vmrk_path)[-1]) + use_eeg_path = op.join(tmp_path, op.split(eeg_path)[-1]) shutil.copy(eeg_path, use_eeg_path) with open(use_vhdr_path, 'wb') as fout: with open(vhdr_path, 'rb') as fin: for line in fin: # Common Infos section if line.startswith(b'Brain'): - line = header % b'Header' + if header != b'\n': + line = header % b'Header' + else: + line = header fout.write(line) with open(use_vmrk_path, 'wb') as fout: with open(vmrk_path, 'rb') as fin: for line in fin: # Common Infos section if line.startswith(b'Brain'): - line = header % b'Marker' + if header != b'\n': + line = header % b'Marker' + else: + line = header fout.write(line) - if b'3.0' in header: # bad case - with pytest.raises(ValueError, match=r'3\.0.*Contact MNE-Python'): + if (b'3.0' in header): # unsupported version + with pytest.warns(RuntimeWarning, match=r'3\.0.*Contact MNE-Python'): read_raw_brainvision(use_vhdr_path) return - raw = read_raw_brainvision(use_vhdr_path) - data_new, _ = raw[:] - assert_allclose(data_new, data_expected, atol=1e-15) + elif header == b'\n': # no version header + with pytest.warns(RuntimeWarning, match='Missing header'): + read_raw_brainvision(use_vhdr_path) + return + else: + raw = read_raw_brainvision(use_vhdr_path) + data_new, _ = raw[:] + assert_allclose(data_new, data_expected, atol=1e-15) -def test_ascii(tmpdir): +@pytest.mark.parametrize('data_sep', (b' ', b',', b'+')) +def test_ascii(tmp_path, data_sep): """Test ASCII BV reading.""" raw = read_raw_brainvision(vhdr_path) - ascii_vhdr_path = op.join(tmpdir, op.split(vhdr_path)[-1]) + ascii_vhdr_path = op.join(tmp_path, op.split(vhdr_path)[-1]) # copy marker file shutil.copy(vhdr_path.replace('.vhdr', '.vmrk'), ascii_vhdr_path.replace('.vhdr', '.vmrk')) @@ -265,17 +283,24 @@ # create the .dat file data, times = raw[:] with open(ascii_vhdr_path.replace('.vhdr', '.dat'), 'wb') as fid: - fid.write(b' '.join(ch_name.encode('ASCII') - for ch_name in raw.ch_names) + b'\n') + fid.write(data_sep.join(ch_name.encode('ASCII') + for ch_name in raw.ch_names) + b'\n') fid.write(b'\n'.join(b' '.join(b'%.3f' % dd for dd in d) for d in data.T / raw._cals)) + + if data_sep == b';': + with pytest.raises(RuntimeError, match='Unknown.*data format'): + read_raw_brainvision(ascii_vhdr_path) + return + raw = read_raw_brainvision(ascii_vhdr_path) + assert isinstance(raw.orig_format, str) data_new, times_new = raw[:] assert_allclose(data_new, data, atol=1e-15) assert_allclose(times_new, times) -def test_ch_names_comma(tmpdir): +def test_ch_names_comma(tmp_path): """Test that channel names containing commas are properly read.""" # commas in BV are encoded as \1 replace_dict = { @@ -283,13 +308,13 @@ r"^4\s\s\s\s\sF4": "4 F4,foo ", } - # Copy existing vhdr file to tmpdir and manipulate to contain + # Copy existing vhdr file to tmp_path and manipulate to contain # a channel with comma for src, dest in zip((vhdr_path, vmrk_path, eeg_path), ('test.vhdr', 'test.vmrk', 'test.eeg')): - shutil.copyfile(src, tmpdir / dest) + shutil.copyfile(src, tmp_path / dest) - comma_vhdr = tmpdir / 'test.vhdr' + comma_vhdr = tmp_path / 'test.vhdr' with open(comma_vhdr, 'r') as fin: lines = fin.readlines() @@ -613,7 +638,7 @@ @testing.requires_testing_data -def test_read_vmrk_annotations(tmpdir): +def test_read_vmrk_annotations(tmp_path): """Test load brainvision annotations.""" sfreq = 1000.0 @@ -621,7 +646,7 @@ # delete=False is for Windows compatibility with open(vmrk_path) as myfile: head = [next(myfile) for x in range(6)] - fname = tmpdir.join('temp.vmrk') + fname = tmp_path / 'temp.vmrk' with open(str(fname), 'w') as temp: for item in head: temp.write(item) @@ -629,15 +654,15 @@ @testing.requires_testing_data -def test_read_vhdr_annotations_and_events(tmpdir): +def test_read_vhdr_annotations_and_events(tmp_path): """Test load brainvision annotations and parse them to events.""" # First we add a custom event that contains a comma in its description for src, dest in zip((vhdr_path, vmrk_path, eeg_path), ('test.vhdr', 'test.vmrk', 'test.eeg')): - shutil.copyfile(src, tmpdir / dest) + shutil.copyfile(src, tmp_path / dest) # Commas are encoded as "\1" - with open(tmpdir / 'test.vmrk', 'a') as fout: + with open(tmp_path / 'test.vmrk', 'a') as fout: fout.write(r"Mk15=Comma\1Type,CommaValue\11,7800,1,0\n") sfreq = 1000.0 @@ -663,7 +688,7 @@ 'Response/R255': 1255, 'SyncStatus/Sync On': 99998, 'Optic/O 1': 2001, 'Comma,Type/CommaValue,1': 10001} - raw = read_raw_brainvision(tmpdir / 'test.vhdr', eog=eog) + raw = read_raw_brainvision(tmp_path / 'test.vhdr', eog=eog) # validate annotations assert raw.annotations.orig_time == expected_orig_time @@ -707,9 +732,9 @@ @testing.requires_testing_data -def test_event_id_stability_when_save_and_fif_reload(tmpdir): +def test_event_id_stability_when_save_and_fif_reload(tmp_path): """Test load events from brainvision annotations when read_raw_fif.""" - fname = tmpdir / 'bv-raw.fif' + fname = tmp_path / 'bv-raw.fif' raw = read_raw_brainvision(vhdr_path, eog=eog) original_events, original_event_id = events_from_annotations(raw) @@ -774,4 +799,14 @@ assert object_diff(expected_impedances, raw.impedances) == '' -run_tests_if_main() +@testing.requires_testing_data +def test_ahdr_format(): + """Test case for parsing data in ahdr format.""" + expected_num_channels = 6 + expected_hp = 0.0 + expected_lp = 250.0 + + raw = read_raw_brainvision(vamp_ahdr) + assert raw.info['nchan'] == expected_num_channels + assert raw.info['highpass'] == expected_hp + assert raw.info['lowpass'] == expected_lp diff -Nru python-mne-0.23.4+dfsg/mne/io/bti/bti.py python-mne-1.1.0+dfsg/mne/io/bti/bti.py --- python-mne-0.23.4+dfsg/mne/io/bti/bti.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/bti/bti.py 2022-08-03 15:48:14.000000000 +0000 @@ -14,7 +14,7 @@ import numpy as np -from ...utils import logger, verbose, _stamp_to_dt +from ...utils import logger, verbose, _stamp_to_dt, path_like from ...transforms import (combine_transforms, invert_transform, Transform) from .._digitization import _make_bti_dig_points @@ -27,18 +27,6 @@ read_uint32, read_double_matrix, read_float_matrix, read_int16_matrix, read_dev_header) -FIFF_INFO_CHS_FIELDS = ('loc', - 'ch_name', 'unit_mul', 'coord_frame', - 'coil_type', - 'range', 'unit', 'cal', - 'scanno', 'kind', 'logno') - -FIFF_INFO_CHS_DEFAULTS = (np.array([0, 0, 0, 1] * 3, dtype='f4'), - None, FIFF.FIFF_UNITM_NONE, FIFF.FIFFV_COORD_UNKNOWN, - FIFF.FIFFV_COIL_NONE, - 1.0, FIFF.FIFF_UNIT_V, 1.0, - None, FIFF.FIFFV_ECG_CH, None) - FIFF_INFO_DIG_FIELDS = ('kind', 'ident', 'r', 'coord_frame') FIFF_INFO_DIG_DEFAULTS = (None, None, None, FIFF.FIFFV_COORD_HEAD) @@ -49,6 +37,21 @@ DTYPES = {i: np.dtype(t) for i, t in dtypes} +def _instantiate_default_info_chs(): + """Populate entries in info['chs'] with default values.""" + return dict(loc=np.array([0, 0, 0, 1] * 3, dtype='f4'), + ch_name=None, + unit_mul=FIFF.FIFF_UNITM_NONE, + coord_frame=FIFF.FIFFV_COORD_UNKNOWN, + coil_type=FIFF.FIFFV_COIL_NONE, + range=1.0, + unit=FIFF.FIFF_UNIT_V, + cal=1.0, + scanno=None, + kind=FIFF.FIFFV_ECG_CH, + logno=None) + + class _bytes_io_mock_context(): """Make a context for BytesIO.""" @@ -64,7 +67,7 @@ def _bti_open(fname, *args, **kwargs): """Handle BytesIO.""" - if isinstance(fname, str): + if isinstance(fname, path_like): return open(fname, *args, **kwargs) elif isinstance(fname, BytesIO): return _bytes_io_mock_context(fname) @@ -144,7 +147,7 @@ return new -# XXX: This is the guy reading the points +# read the points def _read_head_shape(fname): """Read the head shape.""" with _bti_open(fname, 'rb') as fid: @@ -153,7 +156,7 @@ idx_points = read_double_matrix(fid, BTI.DATA_N_IDX_POINTS, 3) dig_points = read_double_matrix(fid, _n_dig_points, 3) - # XXX : reorder to lpa, rpa, nasion so = is direct. + # reorder to lpa, rpa, nasion so = is direct. nasion, lpa, rpa = [idx_points[_, :] for _ in [2, 0, 1]] hpi = idx_points[3:len(idx_points), :] @@ -852,7 +855,7 @@ if ch_cfg.get('dev', dict()).get('transform', None) is not None: ch['loc'] = _coil_trans_to_loc(ch_cfg['dev']['transform']) else: - ch['loc'] = None + ch['loc'] = np.full(12, np.nan) if pdf_fname is not None: if info['data_format'] <= 2: # see DTYPES, implies integer ch['cal'] = ch['scale'] * ch['upb'] / float(ch['gain']) @@ -996,19 +999,21 @@ def _make_bti_digitization( info, head_shape_fname, convert, use_hpi, bti_dev_t, dev_ctf_t): - if head_shape_fname: - logger.info('... Reading digitization points from %s' % - head_shape_fname) - - nasion, lpa, rpa, hpi, dig_points = _read_head_shape(head_shape_fname) - info['dig'], dev_head_t, ctf_head_t = _make_bti_dig_points( - nasion, lpa, rpa, hpi, dig_points, - convert, use_hpi, bti_dev_t, dev_ctf_t) - else: - logger.info('... no headshape file supplied, doing nothing.') - info['dig'] = None - dev_head_t = Transform('meg', 'head', trans=None) - ctf_head_t = Transform('ctf_head', 'head', trans=None) + with info._unlock(): + if head_shape_fname: + logger.info('... Reading digitization points from %s' % + head_shape_fname) + + nasion, lpa, rpa, hpi, dig_points = _read_head_shape( + head_shape_fname) + info['dig'], dev_head_t, ctf_head_t = _make_bti_dig_points( + nasion, lpa, rpa, hpi, dig_points, + convert, use_hpi, bti_dev_t, dev_ctf_t) + else: + logger.info('... no headshape file supplied, doing nothing.') + info['dig'] = None + dev_head_t = Transform('meg', 'head', trans=None) + ctf_head_t = Transform('ctf_head', 'head', trans=None) info.update(dev_head_t=dev_head_t, dev_ctf_t=dev_ctf_t, ctf_head_t=ctf_head_t) @@ -1134,7 +1139,7 @@ logger.info('... Setting channel info structure.') for idx, (chan_4d, chan_neuromag) in enumerate(ch_mapping): - chan_info = dict(zip(FIFF_INFO_CHS_FIELDS, FIFF_INFO_CHS_DEFAULTS)) + chan_info = _instantiate_default_info_chs() chan_info['ch_name'] = chan_neuromag if rename_channels else chan_4d chan_info['logno'] = idx + BTI.FIFF_LOGNO chan_info['scanno'] = idx + 1 @@ -1189,7 +1194,7 @@ chan_info['kind'] = FIFF.FIFFV_STIM_CH elif chan_4d == 'TRIGGER': chan_info['kind'] = FIFF.FIFFV_STIM_CH - elif chan_4d.startswith('EOG'): + elif chan_4d.startswith('EOG') or chan_4d in eog_ch: chan_info['kind'] = FIFF.FIFFV_EOG_CH elif chan_4d == ecg_ch: chan_info['kind'] = FIFF.FIFFV_ECG_CH @@ -1213,6 +1218,7 @@ 'the 4D "print_table" routine.') # check that the info is complete + info._unlocked = False info._update_redundant() info._check_consistency() return info, bti_info diff -Nru python-mne-0.23.4+dfsg/mne/io/bti/constants.py python-mne-1.1.0+dfsg/mne/io/bti/constants.py --- python-mne-0.23.4+dfsg/mne/io/bti/constants.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/bti/constants.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Denis Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause from ...utils import BunchConst diff -Nru python-mne-0.23.4+dfsg/mne/io/bti/tests/test_bti.py python-mne-1.1.0+dfsg/mne/io/bti/tests/test_bti.py --- python-mne-0.23.4+dfsg/mne/io/bti/tests/test_bti.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/bti/tests/test_bti.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Denis Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause from io import BytesIO import os @@ -12,20 +12,20 @@ assert_allclose, assert_equal) import pytest +import mne from mne.datasets import testing from mne.io import read_raw_fif, read_raw_bti from mne.io._digitization import _make_bti_dig_points -from mne.io.bti.bti import (_read_config, +from mne.io.bti.bti import (_read_config, _read_head_shape, _read_bti_header, _get_bti_dev_t, _correct_trans, _get_bti_info, _loc_to_coil_trans, _convert_coil_trans, _check_nan_dev_head_t, _rename_channels) -from mne.io.bti.bti import _read_head_shape from mne.io.tests.test_raw import _test_raw_reader from mne.io.pick import pick_info from mne.io.constants import FIFF from mne import pick_types -from mne.utils import assert_dig_allclose, run_tests_if_main +from mne.utils import assert_dig_allclose from mne.transforms import Transform, combine_transforms, invert_transform base_dir = op.join(op.abspath(op.dirname(__file__)), 'data') @@ -55,6 +55,25 @@ _test_raw_reader(read_raw_bti, pdf_fname=fname_2500, head_shape_fname=None) +def test_no_loc_none(monkeypatch): + """Test that we don't set loc to None when no trans is found.""" + ch_name = 'MLzA' + + def _read_config_bad(*args, **kwargs): + cfg = _read_config(*args, **kwargs) + idx = [ch['name'] for ch in cfg['chs']].index(ch_name) + del cfg['chs'][idx]['dev']['transform'] + return cfg + + monkeypatch.setattr(mne.io.bti.bti, '_read_config', _read_config_bad) + kwargs = dict(pdf_fname=pdf_fnames[0], config_fname=config_fnames[0], + head_shape_fname=hs_fnames[0], rename_channels=False, + sort_by_ch_name=False) + raw = read_raw_bti(**kwargs) + idx = raw.ch_names.index(ch_name) + assert_allclose(raw.info['chs'][idx]['loc'], np.full(12, np.nan)) + + def test_read_config(): """Test read bti config file.""" # for config in config_fname, config_solaris_fname: @@ -357,4 +376,10 @@ read_raw_bti(fname, preload=preload) # used to fail with ascii decode err -run_tests_if_main() +@testing.requires_testing_data +def test_bti_set_eog(): + """Check that EOG channels can be set (gh-10092).""" + raw = read_raw_bti(fname_sim, + preload=False, + eog_ch=('X65', 'X67', 'X69', 'X66', 'X68')) + assert_equal(len(pick_types(raw.info, eog=True)), 5) diff -Nru python-mne-0.23.4+dfsg/mne/io/cnt/cnt.py python-mne-1.1.0+dfsg/mne/io/cnt/cnt.py --- python-mne-0.23.4+dfsg/mne/io/cnt/cnt.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/cnt/cnt.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Author: Jaakko Leppakangas # Joan Massich # -# License: BSD (3-clause) +# License: BSD-3-Clause from os import path import numpy as np @@ -167,8 +167,6 @@ def _get_cnt_info(input_fname, eog, ecg, emg, misc, data_format, date_format): """Read the cnt header.""" - # XXX stim_channel_toggle is used because stim_channel was in use already - data_offset = 900 # Size of the 'SETUP' header. cnt_info = dict() # Reading only the fields of interest. Structure of the whole header at @@ -300,7 +298,6 @@ FIFF.FIFFV_EEG_CH, eog, ecg, emg, misc) eegs = [idx for idx, ch in enumerate(chs) if ch['coil_type'] == FIFF.FIFFV_COIL_EEG] - # XXX this should probably use mne.transforms._topo_to_sph and _sph_to_cart coords = _topo_to_sphere(pos, eegs) locs = np.full((len(chs), 12), np.nan) locs[:, :3] = coords @@ -314,6 +311,7 @@ info.update(meas_date=meas_date, description=session_label, bads=bads, subject_info=subject_info, chs=chs) + info._unlocked = False info._update_redundant() return info, cnt_info diff -Nru python-mne-0.23.4+dfsg/mne/io/cnt/tests/test_cnt.py python-mne-1.1.0+dfsg/mne/io/cnt/tests/test_cnt.py --- python-mne-0.23.4+dfsg/mne/io/cnt/tests/test_cnt.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/cnt/tests/test_cnt.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Author: Jaakko Leppakangas # Joan Massich # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op @@ -11,7 +11,6 @@ import pytest from mne import pick_types -from mne.utils import run_tests_if_main from mne.datasets import testing from mne.io.tests.test_raw import _test_raw_reader from mne.io.cnt import read_raw_cnt @@ -35,7 +34,7 @@ assert len(eog_chs) == 2 # test eog='auto' assert raw.info['bads'] == ['LEFT_EAR', 'VEOGR'] # test bads - # XXX: the data has "05/10/200 17:35:31" so it is set to None + # the data has "05/10/200 17:35:31" so it is set to None assert raw.info['meas_date'] is None @@ -54,6 +53,3 @@ assert len(annot) == 6 assert_array_equal(annot.onset[:-1], events[:, 0] / raw.info['sfreq']) assert 'STI 014' not in raw.info['ch_names'] - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/io/cnt/_utils.py python-mne-1.1.0+dfsg/mne/io/cnt/_utils.py --- python-mne-0.23.4+dfsg/mne/io/cnt/_utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/cnt/_utils.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Author: Joan Massich # -# License: BSD (3-clause) +# License: BSD-3-Clause from struct import Struct from collections import namedtuple diff -Nru python-mne-0.23.4+dfsg/mne/io/compensator.py python-mne-1.1.0+dfsg/mne/io/compensator.py --- python-mne-0.23.4+dfsg/mne/io/compensator.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/compensator.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,7 @@ import numpy as np from .constants import FIFF +from ..utils import fill_doc def get_current_comp(info): @@ -62,6 +63,7 @@ ' found' % grade) +@fill_doc def make_compensator(info, from_, to, exclude_comp_chs=False): """Return compensation matrix eg. for CTF system. @@ -70,8 +72,7 @@ Parameters ---------- - info : dict - The measurement info. + %(info_not_none)s from_ : int Compensation in the input data. to : int diff -Nru python-mne-0.23.4+dfsg/mne/io/constants.py python-mne-1.1.0+dfsg/mne/io/constants.py --- python-mne-0.23.4+dfsg/mne/io/constants.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/constants.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors: Alexandre Gramfort # Matti Hämäläinen # -# License: BSD (3-clause) +# License: BSD-3-Clause from ..utils._bunch import BunchConstNamed @@ -312,7 +312,7 @@ FIFF.FIFF_SQUID_OFFSET = 702 FIFF.FIFF_SQUID_GATE = 703 # -# Aspect values used to save charactersitic curves of SQUIDs. (mjk) +# Aspect values used to save characteristic curves of SQUIDs. (mjk) # FIFF.FIFFV_ASPECT_IFII_LOW = 1100 FIFF.FIFFV_ASPECT_IFII_HIGH = 1101 @@ -979,6 +979,14 @@ FIFF.FIFFV_COIL_QUSPIN_ZFOPM_MAG = 8001 FIFF.FIFFV_COIL_QUSPIN_ZFOPM_MAG2 = 8002 # +# FieldLine sensors +# +FIFF.FIFFV_COIL_FIELDLINE_OPM_MAG_GEN1 = 8101 +# +# Kernel sensors +# +FIFF.FIFFV_COIL_KERNEL_OPM_MAG_GEN1 = 8201 +# # KRISS sensors # FIFF.FIFFV_COIL_KRISS_GRAD = 9001 @@ -1011,6 +1019,8 @@ FIFF.FIFFV_COIL_BABY_REF_MAG2, FIFF.FIFFV_COIL_ARTEMIS123_GRAD, FIFF.FIFFV_COIL_ARTEMIS123_REF_MAG, FIFF.FIFFV_COIL_ARTEMIS123_REF_GRAD, FIFF.FIFFV_COIL_QUSPIN_ZFOPM_MAG, FIFF.FIFFV_COIL_QUSPIN_ZFOPM_MAG2, + FIFF.FIFFV_COIL_FIELDLINE_OPM_MAG_GEN1, + FIFF.FIFFV_COIL_KERNEL_OPM_MAG_GEN1, FIFF.FIFFV_COIL_KRISS_GRAD, FIFF.FIFFV_COIL_COMPUMEDICS_ADULT_GRAD, FIFF.FIFFV_COIL_COMPUMEDICS_PEDIATRIC_GRAD, )} @@ -1023,6 +1033,7 @@ FIFF.FIFF_MNE_EPOCHS_SELECTION = 3800 # the epochs selection FIFF.FIFF_MNE_EPOCHS_DROP_LOG = 3801 # the drop log FIFF.FIFF_MNE_EPOCHS_REJECT_FLAT = 3802 # rejection and flat params +FIFF.FIFF_MNE_EPOCHS_RAW_SFREQ = 3803 # original raw sfreq # MNE annotations FIFF.FIFFB_MNE_ANNOTATIONS = 3810 # annotations block @@ -1045,7 +1056,11 @@ 'T5': 'T9', 'T6': 'T10', 'M1': 'TP9', - 'M2': 'TP10' + 'M2': 'TP10', + # EGI ref chan is named VREF/Vertex Ref. + # In the standard montages for EGI, the ref is named Cz + 'VREF': 'Cz', + 'Vertex Reference': 'Cz' # add a comment here (with doi of a published source) above any new # aliases, as they are added } diff -Nru python-mne-0.23.4+dfsg/mne/io/ctf/constants.py python-mne-1.1.0+dfsg/mne/io/ctf/constants.py --- python-mne-0.23.4+dfsg/mne/io/ctf/constants.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/ctf/constants.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Authors: Matti Hämäläinen # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause from ...utils import BunchConst diff -Nru python-mne-0.23.4+dfsg/mne/io/ctf/ctf.py python-mne-1.1.0+dfsg/mne/io/ctf/ctf.py --- python-mne-0.23.4+dfsg/mne/io/ctf/ctf.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/ctf/ctf.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Authors: Matti Hämäläinen # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import os @@ -57,6 +57,11 @@ Notes ----- .. versionadded:: 0.11 + + To read in the Polhemus digitization data (for example, from + a .pos file), include the file in the CTF directory. The + points will then automatically be read into the `mne.io.Raw` + instance via `mne.io.read_raw_ctf`. """ return RawCTF(directory, system_clock, preload=preload, clean_names=clean_names, verbose=verbose) @@ -108,22 +113,28 @@ # Compose a structure which makes fiff writing a piece of cake info = _compose_meas_info(res4, coils, coord_trans, eeg) - info['dig'] += digs - info['dig'] = _format_dig_points(info['dig']) + with info._unlock(): + info['dig'] += digs + info['dig'] = _format_dig_points(info['dig']) info['bads'] += _read_bad_chans(directory, info) # Determine how our data is distributed across files fnames = list() last_samps = list() raw_extras = list() - while(True): + missing_names = list() + no_samps = list() + while True: suffix = 'meg4' if len(fnames) == 0 else ('%d_meg4' % len(fnames)) - meg4_name = _make_ctf_name(directory, suffix, raise_error=False) - if meg4_name is None: + meg4_name, found = _make_ctf_name( + directory, suffix, raise_error=False) + if not found: + missing_names.append(os.path.relpath(meg4_name, directory)) break # check how much data is in the file sample_info = _get_sample_info(meg4_name, res4, system_clock) if sample_info['n_samp'] == 0: + no_samps.append(os.path.relpath(meg4_name, directory)) break if len(fnames) == 0: buffer_size_sec = sample_info['block_size'] / info['sfreq'] @@ -133,6 +144,11 @@ last_samps.append(sample_info['n_samp'] - 1) raw_extras.append(sample_info) first_samps = [0] * len(last_samps) + if len(fnames) == 0: + raise IOError( + f'Could not find any data, could not find the following ' + f'file(s): {missing_names}, and the following file(s) had no ' + f'valid samples: {no_samps}') super(RawCTF, self).__init__( info, preload, first_samps=first_samps, last_samps=last_samps, filenames=fnames, @@ -166,8 +182,11 @@ samp_offset = (bi + trial_start_idx) * si['res4_nsamp'] n_read = min(si['n_samp_tot'] - samp_offset, si['block_size']) # read the chunk of data - pos = CTF.HEADER_SIZE - pos += samp_offset * si['n_chan'] * 4 + # have to be careful on Windows and make sure we are using + # 64-bit integers here + with np.errstate(over='raise'): + pos = np.int64(CTF.HEADER_SIZE) + pos += np.int64(samp_offset) * si['n_chan'] * 4 fid.seek(pos, 0) this_data = np.fromfile(fid, '>i4', count=si['n_chan'] * n_read) diff -Nru python-mne-0.23.4+dfsg/mne/io/ctf/eeg.py python-mne-1.1.0+dfsg/mne/io/ctf/eeg.py --- python-mne-0.23.4+dfsg/mne/io/ctf/eeg.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/ctf/eeg.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np from os.path import join @@ -22,8 +22,8 @@ def _read_eeg(directory): """Read the .eeg file.""" # Missing file is ok - fname = _make_ctf_name(directory, 'eeg', raise_error=False) - if fname is None: + fname, found = _make_ctf_name(directory, 'eeg', raise_error=False) + if not found: logger.info(' Separate EEG position data file not present.') return eeg = dict(labels=list(), kinds=list(), ids=list(), rr=list(), np=0, diff -Nru python-mne-0.23.4+dfsg/mne/io/ctf/hc.py python-mne-1.1.0+dfsg/mne/io/ctf/hc.py --- python-mne-0.23.4+dfsg/mne/io/ctf/hc.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/ctf/hc.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np @@ -64,13 +64,13 @@ def _read_hc(directory): """Read the hc file to get the HPI info and to prepare for coord trans.""" - fname = _make_ctf_name(directory, 'hc', raise_error=False) - if fname is None: + fname, found = _make_ctf_name(directory, 'hc', raise_error=False) + if not found: logger.info(' hc data not present') return None s = list() with open(fname, 'rb') as fid: - while(True): + while True: p = _read_one_coil_point(fid) if p is None: # First point bad indicates that the file is empty diff -Nru python-mne-0.23.4+dfsg/mne/io/ctf/info.py python-mne-1.1.0+dfsg/mne/io/ctf/info.py --- python-mne-0.23.4+dfsg/mne/io/ctf/info.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/ctf/info.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause from time import strptime from calendar import timegm @@ -73,7 +73,13 @@ def _convert_time(date_str, time_str): """Convert date and time strings to float time.""" - for fmt in ("%d/%m/%Y", "%d-%b-%Y", "%a, %b %d, %Y"): + if date_str == time_str == '': + date_str = '01/01/1970' + time_str = '00:00:00' + logger.info('No date or time found, setting to the start of the ' + 'POSIX epoch (1970/01/01 midnight)') + + for fmt in ("%d/%m/%Y", "%d-%b-%Y", "%a, %b %d, %Y", "%Y/%m/%d"): try: date = strptime(date_str.strip(), fmt) except ValueError: @@ -98,7 +104,7 @@ raise RuntimeError('Illegal time: %s' % time_str) # MNE-C uses mktime which uses local time, but here we instead decouple # conversion location from the process, and instead assume that the - # acquisiton was in GMT. This will be wrong for most sites, but at least + # acquisition was in GMT. This will be wrong for most sites, but at least # the value we obtain here won't depend on the geographical location # that the file was converted. res = timegm((date.tm_year, date.tm_mon, date.tm_mday, @@ -443,6 +449,7 @@ eeg = _pick_eeg_pos(info) _add_eeg_pos(eeg, trans, info) logger.info(' Measurement info composed.') + info._unlocked = False info._update_redundant() return info diff -Nru python-mne-0.23.4+dfsg/mne/io/ctf/__init__.py python-mne-1.1.0+dfsg/mne/io/ctf/__init__.py --- python-mne-0.23.4+dfsg/mne/io/ctf/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/ctf/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,6 +2,6 @@ # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause from .ctf import read_raw_ctf, RawCTF diff -Nru python-mne-0.23.4+dfsg/mne/io/ctf/markers.py python-mne-1.1.0+dfsg/mne/io/ctf/markers.py --- python-mne-0.23.4+dfsg/mne/io/ctf/markers.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/ctf/markers.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Author: Joan Massich # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np import os.path as op @@ -13,7 +13,7 @@ def _get_markers(fname): def consume(fid, predicate): # just a consumer to move around conveniently - while(predicate(fid.readline())): + while predicate(fid.readline()): pass def parse_marker(string): # XXX: there should be a nicer way to do that diff -Nru python-mne-0.23.4+dfsg/mne/io/ctf/res4.py python-mne-1.1.0+dfsg/mne/io/ctf/res4.py --- python-mne-0.23.4+dfsg/mne/io/ctf/res4.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/ctf/res4.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Authors: Matti Hämäläinen # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op @@ -16,12 +16,12 @@ def _make_ctf_name(directory, extra, raise_error=True): """Make a CTF name.""" fname = op.join(directory, op.basename(directory)[:-3] + '.' + extra) + found = True if not op.isfile(fname): if raise_error: raise IOError('Standard file %s not found' % fname) - else: - return None - return fname + found = False + return fname, found def _read_double(fid, n=1): @@ -99,7 +99,7 @@ def _read_res4(dsdir): """Read the magical res4 file.""" # adapted from read_res4.c - name = _make_ctf_name(dsdir, 'res4') + name, _ = _make_ctf_name(dsdir, 'res4') res = dict() with open(name, 'rb') as fid: # Read the fields diff -Nru python-mne-0.23.4+dfsg/mne/io/ctf/tests/test_ctf.py python-mne-1.1.0+dfsg/mne/io/ctf/tests/test_ctf.py --- python-mne-0.23.4+dfsg/mne/io/ctf/tests/test_ctf.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/ctf/tests/test_ctf.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,8 +1,9 @@ # Authors: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import copy +from datetime import datetime, timezone import os from os import path as op import shutil @@ -13,20 +14,22 @@ import pytest import mne +import mne.io.ctf.info from mne import (pick_types, read_annotations, create_info, events_from_annotations, make_forward_solution) from mne.transforms import apply_trans from mne.io import read_raw_fif, read_raw_ctf, RawArray from mne.io.compensator import get_current_comp from mne.io.ctf.constants import CTF +from mne.io.ctf.info import _convert_time from mne.io.tests.test_raw import _test_raw_reader from mne.tests.test_annotations import _assert_annotations_equal -from mne.utils import (run_tests_if_main, _clean_names, catch_logging, - _stamp_to_dt) +from mne.utils import (_clean_names, catch_logging, _stamp_to_dt, + _record_warnings) from mne.datasets import testing, spm_face, brainstorm from mne.io.constants import FIFF -ctf_dir = op.join(testing.data_path(download=False), 'CTF') +ctf_dir = testing.data_path(download=False) / 'CTF' ctf_fname_continuous = 'testdata_ctf.ds' ctf_fname_1_trial = 'testdata_ctf_short.ds' ctf_fname_2_trials = 'testdata_ctf_pseudocontinuous.ds' @@ -37,6 +40,7 @@ brainstorm.bst_raw.data_path(download=False), 'MEG', 'bst_raw', 'subj001_somatosensory_20111109_01_AUX-f.ds' ) +spm_path = spm_face.data_path(download=False) block_sizes = { ctf_fname_continuous: 12000, @@ -56,9 +60,9 @@ @pytest.mark.slowtest @testing.requires_testing_data -def test_read_ctf(tmpdir): +def test_read_ctf(tmp_path): """Test CTF reader.""" - temp_dir = str(tmpdir) + temp_dir = str(tmp_path) out_fname = op.join(temp_dir, 'test_py_raw.fif') # Create a dummy .eeg file so we can test our reading/application of it @@ -106,7 +110,8 @@ use_fnames = [op.join(ctf_dir, c) for c in ctf_fnames] for fname in use_fnames: raw_c = read_raw_fif(fname + '_raw.fif', preload=True) - with pytest.warns(None): # sometimes matches "MISC channel" + # sometimes matches "MISC channel" + with _record_warnings(): raw = read_raw_ctf(fname) # check info match @@ -185,7 +190,8 @@ 'dig points must be in FIFF.FIFFV_COORD_HEAD' if fname.endswith('catch-alp-good-f.ds'): # omit points from .pos file - raw.info['dig'] = raw.info['dig'][:-10] + with raw.info._unlock(): + raw.info['dig'] = raw.info['dig'][:-10] # XXX: Next test would fail because c-tools assign the fiducials from # CTF data as HPI. Should eventually clarify/unify with Matti. @@ -267,8 +273,7 @@ @spm_face.requires_spm_data def test_read_spm_ctf(): """Test CTF reader with omitted samples.""" - data_path = spm_face.data_path() - raw_fname = op.join(data_path, 'MEG', 'spm', + raw_fname = op.join(spm_path, 'MEG', 'spm', 'SPM_CTF_MEG_example_faces1_3D.ds') raw = read_raw_ctf(raw_fname) extras = raw._raw_extras[0] @@ -288,9 +293,9 @@ @testing.requires_testing_data @pytest.mark.parametrize('comp_grade', [0, 1]) -def test_saving_picked(tmpdir, comp_grade): +def test_saving_picked(tmp_path, comp_grade): """Test saving picked CTF instances.""" - temp_dir = str(tmpdir) + temp_dir = str(tmp_path) out_fname = op.join(temp_dir, 'test_py_raw.fif') raw = read_raw_ctf(op.join(ctf_dir, ctf_fname_1_trial)) assert raw.info['meas_date'] == _stamp_to_dt((1367228160, 0)) @@ -404,7 +409,19 @@ @testing.requires_testing_data -def test_read_ctf_mag_bad_comp(tmpdir, monkeypatch): +def test_missing_res4(tmp_path): + """Test that res4 missing is handled gracefully.""" + use_ds = tmp_path / ctf_fname_continuous + shutil.copytree(ctf_dir / ctf_fname_continuous, + tmp_path / ctf_fname_continuous) + read_raw_ctf(use_ds) + os.remove(use_ds / (ctf_fname_continuous[:-2] + 'meg4')) + with pytest.raises(IOError, match='could not find the following'): + read_raw_ctf(use_ds) + + +@testing.requires_testing_data +def test_read_ctf_mag_bad_comp(tmp_path, monkeypatch): """Test CTF reader with mag comps and bad comps.""" path = op.join(ctf_dir, ctf_fname_continuous) raw_orig = read_raw_ctf(path) @@ -427,4 +444,15 @@ read_raw_ctf(path) -run_tests_if_main() +@testing.requires_testing_data +def test_invalid_meas_date(monkeypatch): + """Test handling of invalid meas_date.""" + def _convert_time_bad(date_str, time_str): + return _convert_time('', '') + monkeypatch.setattr(mne.io.ctf.info, '_convert_time', _convert_time_bad) + + with catch_logging() as log: + raw = read_raw_ctf(ctf_dir / ctf_fname_continuous, verbose=True) + log = log.getvalue() + assert 'No date or time found' in log + assert raw.info['meas_date'] == datetime.fromtimestamp(0, tz=timezone.utc) diff -Nru python-mne-0.23.4+dfsg/mne/io/ctf/trans.py python-mne-1.1.0+dfsg/mne/io/ctf/trans.py --- python-mne-0.23.4+dfsg/mne/io/ctf/trans.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/ctf/trans.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np diff -Nru python-mne-0.23.4+dfsg/mne/io/ctf_comp.py python-mne-1.1.0+dfsg/mne/io/ctf_comp.py --- python-mne-0.23.4+dfsg/mne/io/ctf_comp.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/ctf_comp.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Matti Hämäläinen # Denis Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause from copy import deepcopy diff -Nru python-mne-0.23.4+dfsg/mne/io/curry/curry.py python-mne-1.1.0+dfsg/mne/io/curry/curry.py --- python-mne-0.23.4+dfsg/mne/io/curry/curry.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/curry/curry.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Authors: Dirk Gütlin # # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op from collections import namedtuple @@ -30,14 +30,16 @@ "info": ".dap", "data": ".dat", "labels": ".rs3", - "events": ".cef", + "events_cef": ".cef", + "events_ceo": ".ceo", "hpi": ".hpi", }, "Curry 8": { "info": ".cdt.dpa", "data": ".cdt", "labels": ".cdt.dpa", - "events": ".cdt.cef", + "events_cef": ".cdt.cef", + "events_ceo": ".cdt.ceo", "hpi": ".cdt.hpi", } } @@ -69,10 +71,11 @@ fname_base, ext = fname.split(".", maxsplit=1) version = _get_curry_version(ext) my_curry = dict() - for key in ('info', 'data', 'labels', 'events', 'hpi'): + for key in ('info', 'data', 'labels', 'events_cef', 'events_ceo', 'hpi'): fname = fname_base + FILE_EXTENSIONS[version][key] if op.isfile(fname): - my_curry[key] = fname + _key = 'events' if key.startswith('events') else key + my_curry[_key] = fname missing = [field for field in required if field not in my_curry] if missing: @@ -286,7 +289,8 @@ ch_names = [chan["ch_name"] for chan in all_chans] info = create_info(ch_names, curry_params.sfreq) - info['meas_date'] = curry_params.dt_start # for Git issue #8398 + with info._unlock(): + info['meas_date'] = curry_params.dt_start # for Git issue #8398 _make_trans_dig(curry_paths, info, curry_dev_dev_t) for ind, ch_dict in enumerate(info["chs"]): @@ -321,7 +325,8 @@ key = 'LM_REMARKS' + CHANTYPES['meg'] remarks = _read_curry_lines(label_fname, [key])[key] assert len(remarks) == len(lm) - info['dig'] = list() + with info._unlock(): + info['dig'] = list() cards = dict() for remark, r in zip(remarks, lm): kind = ident = None @@ -336,7 +341,8 @@ info['dig'].append(dict( kind=kind, ident=ident, r=r, coord_frame=FIFF.FIFFV_COORD_UNKNOWN)) - info['dig'].sort(key=lambda x: (x['kind'], x['ident'])) + with info._unlock(): + info['dig'].sort(key=lambda x: (x['kind'], x['ident'])) has_cards = len(cards) == 3 has_hpi = 'hpi' in curry_paths if has_cards and has_hpi: # have all three @@ -360,11 +366,12 @@ *(cards[key] for key in (FIFF.FIFFV_POINT_NASION, FIFF.FIFFV_POINT_LPA, FIFF.FIFFV_POINT_RPA)))) - info['dev_head_t'] = combine_transforms( - invert_transform(unknown_dev_t), unknown_head_t, 'meg', 'head') - for d in info['dig']: - d.update(coord_frame=FIFF.FIFFV_COORD_HEAD, - r=apply_trans(unknown_head_t, d['r'])) + with info._unlock(): + info['dev_head_t'] = combine_transforms( + invert_transform(unknown_dev_t), unknown_head_t, 'meg', 'head') + for d in info['dig']: + d.update(coord_frame=FIFF.FIFFV_COORD_HEAD, + r=apply_trans(unknown_head_t, d['r'])) else: if has_cards: no_msg += ' (no .hpi file found)' @@ -408,8 +415,8 @@ events : ndarray, shape (n_events, 3) The array of events. """ - check_fname(fname, 'curry event', ('.cef', '.cdt.cef'), - endings_err=('.cef', '.cdt.cef')) + check_fname(fname, 'curry event', ('.cef', '.ceo', '.cdt.cef', '.cdt.ceo'), + endings_err=('.cef', '.ceo', '.cdt.cef', '.cdt.ceo')) events_dict = _read_curry_lines(fname, ["NUMBER_LIST"]) # The first 3 column seem to contain the event information diff -Nru python-mne-0.23.4+dfsg/mne/io/curry/__init__.py python-mne-1.1.0+dfsg/mne/io/curry/__init__.py --- python-mne-0.23.4+dfsg/mne/io/curry/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/curry/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,6 +2,6 @@ # Author: Dirk Gütlin # -# License: BSD (3-clause) +# License: BSD-3-Clause from .curry import read_raw_curry diff -Nru python-mne-0.23.4+dfsg/mne/io/curry/tests/test_curry.py python-mne-1.1.0+dfsg/mne/io/curry/tests/test_curry.py --- python-mne-0.23.4+dfsg/mne/io/curry/tests/test_curry.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/curry/tests/test_curry.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,14 +3,14 @@ # Authors: Dirk Gütlin # # -# License: BSD (3-clause) +# License: BSD-3-Clause +from datetime import datetime, timezone import os.path as op -import numpy as np from shutil import copyfile -from datetime import datetime, timezone import pytest +import numpy as np from numpy.testing import assert_allclose, assert_array_equal from mne.annotations import events_from_annotations @@ -22,7 +22,7 @@ from mne.io.edf import read_raw_bdf from mne.io.bti import read_raw_bti from mne.io.curry import read_raw_curry -from mne.utils import check_version, run_tests_if_main, catch_logging +from mne.utils import check_version, catch_logging, _record_warnings from mne.annotations import read_annotations from mne.io.curry.curry import (_get_curry_version, _get_curry_file_structure, _read_events_curry, FILE_EXTENSIONS) @@ -70,7 +70,7 @@ @pytest.mark.parametrize('preload', [True, False]) def test_read_raw_curry(fname, tol, preload, bdf_curry_ref): """Test reading CURRY files.""" - with pytest.warns(None) as wrn: + with _record_warnings() as wrn: raw = read_raw_curry(fname, preload=preload) if not check_version('numpy', '1.16') and preload and fname.endswith( @@ -87,12 +87,12 @@ assert_array_equal([ch[field] for ch in raw.info['chs']], [ch[field] for ch in bdf_curry_ref.info['chs']]) - raw.verbose = 'error' # don't emit warnings about slow reading - assert_allclose(raw.get_data(), bdf_curry_ref.get_data(), atol=tol) + assert_allclose(raw.get_data(verbose='error'), + bdf_curry_ref.get_data(), atol=tol) picks, start, stop = ["C3", "C4"], 200, 800 assert_allclose( - raw.get_data(picks=picks, start=start, stop=stop), + raw.get_data(picks=picks, start=start, stop=stop, verbose='error'), bdf_curry_ref.get_data(picks=picks, start=start, stop=stop), rtol=tol) assert raw.info['dev_head_t'] is None @@ -205,21 +205,21 @@ pytest.param(curry8_rfDC_file, 1e-3, id='curry 8'), ]) @pytest.mark.parametrize('mock_dev_head_t', [True, False]) -def test_read_raw_curry_rfDC(fname, tol, mock_dev_head_t, tmpdir): +def test_read_raw_curry_rfDC(fname, tol, mock_dev_head_t, tmp_path): """Test reading CURRY files.""" if mock_dev_head_t: if 'Curry 7' in fname: # not supported yet return - # copy files to tmpdir + # copy files to tmp_path base = op.splitext(fname)[0] for ext in ('.cdt', '.cdt.dpa'): src = base + ext - dst = op.join(tmpdir, op.basename(base) + ext) + dst = op.join(tmp_path, op.basename(base) + ext) copyfile(src, dst) if ext == '.cdt.dpa': with open(dst, 'a') as fid: fid.write(LM_CONTENT) - fname = op.join(tmpdir, op.basename(fname)) + fname = op.join(tmp_path, op.basename(fname)) with open(fname + '.hpi', 'w') as fid: fid.write(HPI_CONTENT) @@ -331,12 +331,12 @@ pytest.param(dict(sfreq=500, time_step=42), id='mismatch', marks=pytest.mark.xfail(raises=ValueError)), ]) -def sfreq_testing_data(tmpdir, request): +def sfreq_testing_data(tmp_path, request): """Generate different sfreq, time_step scenarios to be tested.""" sfreq, time_step = request.param['sfreq'], request.param['time_step'] in_base_name = curry7_bdf_file.strip('dat') - out_base_name = str(tmpdir.join('curry.')) + out_base_name = str(tmp_path / 'curry.') # create dummy empty files for 'dat' and 'rs3' for fname in [out_base_name + ext for ext in ['dat', 'rs3']]: @@ -391,8 +391,9 @@ version = _get_curry_version(ext) original['info'] = original['base'] + FILE_EXTENSIONS[version]["info"] - modified['base'] = str(mock_dir.join('curry')) - modified['event'] = modified['base'] + FILE_EXTENSIONS[version]["events"] + modified['base'] = str(mock_dir / 'curry') + modified['event'] = (modified['base'] + + FILE_EXTENSIONS[version]["events_cef"]) modified['info'] = modified['base'] + FILE_EXTENSIONS[version]["info"] return original, modified @@ -405,7 +406,7 @@ pytest.param('7 ASCII.cef', id='7 (ascii)'), pytest.param('8 ASCII.cdt.cef', id='8 (ascii)'), ]) -def test_read_curry_annotations_using_mocked_info(tmpdir, name_part): +def test_read_curry_annotations_using_mocked_info(tmp_path, name_part): """Test reading for Curry events file.""" EXPECTED_ONSET = [0.484, 0.486, 0.62, 0.622, 1.904, 1.906, 3.212, 3.214, 4.498, 4.5, 5.8, 5.802, 7.074, 7.076, 8.324, 8.326, 9.58, @@ -416,7 +417,7 @@ '1', '50000', '1', '50000'] original, fname = _get_read_annotations_mock_info("Curry " + name_part, - tmpdir) + tmp_path) copyfile(src=original['event'], dst=fname['event']) _msg = 'required files cannot be found' @@ -477,6 +478,3 @@ # If the information is not valid, raw.info['meas_date'] should be None raw = read_raw_curry(fname, preload=False) assert raw.info['meas_date'] == expected_meas_date - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/io/_digitization.py python-mne-1.1.0+dfsg/mne/io/_digitization.py --- python-mne-0.23.4+dfsg/mne/io/_digitization.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/_digitization.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,7 +5,7 @@ # Stefan Appelhoff # Joan Massich # -# License: BSD (3-clause) +# License: BSD-3-Clause import heapq from collections import Counter @@ -15,12 +15,12 @@ import numpy as np -from ..utils import logger, warn, Bunch, _validate_type +from ..utils import logger, warn, Bunch, _validate_type, _check_fname, verbose from .constants import FIFF, _coord_frame_named from .tree import dir_tree_find from .tag import read_tag -from .write import (start_file, end_file, write_dig_points) +from .write import (start_and_end_file, write_dig_points) from ..transforms import (apply_trans, Transform, get_ras_to_neuromag_trans, combine_transforms, @@ -138,7 +138,11 @@ id_ = ('%s #%s' % (id_, self['ident'])) id_ = id_.rjust(10) cf = _coord_frame_name(self['coord_frame']) - pos = ('(%0.1f, %0.1f, %0.1f) mm' % tuple(1000 * self['r'])).ljust(25) + if 'voxel' in cf: + pos = ('(%0.1f, %0.1f, %0.1f)' % tuple(self['r'])).ljust(25) + else: + pos = ('(%0.1f, %0.1f, %0.1f) mm' % + tuple(1000 * self['r'])).ljust(25) return ('' % (id_, pos, cf)) # speed up info copy by only deep copying the mutable item @@ -189,12 +193,13 @@ return _format_dig_points(dig) -def write_dig(fname, pts, coord_frame=None): +@verbose +def write_dig(fname, pts, coord_frame=None, *, overwrite=False, verbose=None): """Write digitization data to a FIF file. Parameters ---------- - fname : str + fname : path-like Destination file name. pts : iterator of dict Iterator through digitizer points. Each point is a dictionary with @@ -203,7 +208,14 @@ If all the points have the same coordinate frame, specify the type here. Can be None (default) if the points could have varying coordinate frames. + %(overwrite)s + + .. versionadded:: 1.0 + %(verbose)s + + .. versionadded:: 1.0 """ + fname = _check_fname(fname, overwrite=overwrite) if coord_frame is not None: coord_frame = _to_const(coord_frame) pts_frames = {pt.get('coord_frame', coord_frame) for pt in pts} @@ -213,9 +225,8 @@ 'Points have coord_frame entries that are incompatible with ' 'coord_frame=%i: %s.' % (coord_frame, str(tuple(bad_frames)))) - with start_file(fname) as fid: + with start_and_end_file(fname) as fid: write_dig_points(fid, pts, block=True, coord_frame=coord_frame) - end_file(fid) _cardinal_ident_mapping = { @@ -229,7 +240,7 @@ # This does something really similar to _read_dig_montage_fif but: # - does not check coord_frame # - does not do any operation that implies assumptions with the names -def _get_data_as_dict_from_dig(dig): +def _get_data_as_dict_from_dig(dig, exclude_ref_channel=True): """Obtain coordinate data from a Dig. Parameters @@ -252,18 +263,18 @@ elif d['kind'] == FIFF.FIFFV_POINT_HPI: hpi.append(d['r']) elp.append(d['r']) - # XXX: point_names.append('HPI%03d' % d['ident']) elif d['kind'] == FIFF.FIFFV_POINT_EXTRA: hsp.append(d['r']) elif d['kind'] == FIFF.FIFFV_POINT_EEG: - # XXX: dig_ch_pos['EEG%03d' % d['ident']] = d['r'] - if d['ident'] != 0: # ref channel + if d['ident'] != 0 or not exclude_ref_channel: dig_ch_pos_location.append(d['r']) dig_coord_frames = set([d['coord_frame'] for d in dig]) - assert len(dig_coord_frames) == 1, \ - 'Only single coordinate frame in dig is supported' # XXX - + if len(dig_coord_frames) != 1: + raise RuntimeError('Only single coordinate frame in dig is supported, ' + f'got {dig_coord_frames}') + dig_ch_pos_location = np.array(dig_ch_pos_location) + dig_ch_pos_location.shape = (-1, 3) # empty will be (0, 3) return Bunch( nasion=fids.get('nasion', None), lpa=fids.get('lpa', None), @@ -414,12 +425,22 @@ 'kind': FIFF.FIFFV_POINT_EXTRA, 'coord_frame': coord_frame}) if dig_ch_pos is not None: - try: # use the last 3 as int if possible (e.g., EEG001->1) - idents = [] - for key in dig_ch_pos: - _validate_type(key, str, 'dig_ch_pos') + idents = [] + use_arange = False + for key, value in dig_ch_pos.items(): + _validate_type(key, str, 'dig_ch_pos') + try: idents.append(int(key[-3:])) - except ValueError: # and if any conversion fails, simply use arange + except ValueError: + use_arange = True + _validate_type(value, (np.ndarray, list, tuple), 'dig_ch_pos') + value = np.array(value, dtype=float) + dig_ch_pos[key] = value + if value.shape != (3, ): + raise RuntimeError( + "The position should be a 1D array of 3 floats. " + f"Provided shape {value.shape}.") + if use_arange: idents = np.arange(1, len(dig_ch_pos) + 1) for key, ident in zip(dig_ch_pos, idents): dig.append({'r': dig_ch_pos[key], 'ident': int(ident), diff -Nru python-mne-0.23.4+dfsg/mne/io/edf/edf.py python-mne-1.1.0+dfsg/mne/io/edf/edf.py --- python-mne-0.23.4+dfsg/mne/io/edf/edf.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/edf/edf.py 2022-08-03 15:48:14.000000000 +0000 @@ -9,7 +9,7 @@ # Clemens Brunner # Jeroen Van Der Donckt (IDlab - imec) # -# License: BSD (3-clause) +# License: BSD-3-Clause from datetime import datetime, timezone, timedelta import os @@ -27,6 +27,22 @@ from ...annotations import Annotations +# common channel type names mapped to internal ch types +CH_TYPE_MAPPING = { + 'EEG': FIFF.FIFFV_EEG_CH, + 'SEEG': FIFF.FIFFV_SEEG_CH, + 'ECOG': FIFF.FIFFV_ECOG_CH, + 'DBS': FIFF.FIFFV_DBS_CH, + 'EOG': FIFF.FIFFV_EOG_CH, + 'ECG': FIFF.FIFFV_ECG_CH, + 'EMG': FIFF.FIFFV_EMG_CH, + 'BIO': FIFF.FIFFV_BIO_CH, + 'RESP': FIFF.FIFFV_RESP_CH, + 'MISC': FIFF.FIFFV_MISC_CH, + 'SAO2': FIFF.FIFFV_BIO_CH, +} + + @fill_doc class RawEDF(BaseRaw): """Raw object from EDF, EDF+ or BDF file. @@ -48,17 +64,24 @@ 'trigger' (case insensitive) are set to STIM. If str (or list of str), all channels matching the name(s) are set to STIM. If int (or list of ints), the channels corresponding to the indices are set to STIM. - - .. warning:: 0.18 does not allow for stim channel synthesis from TAL - channels called 'EDF Annotations' or 'BDF Annotations' - anymore. Instead, TAL channels are parsed and extracted - annotations are stored in raw.annotations. Use - :func:`mne.events_from_annotations` to obtain events from - these annotations. - exclude : list of str Channel names to exclude. This can help when reading data with different sampling rates to avoid unnecessary resampling. + infer_types : bool + If True, try to infer channel types from channel labels. If a channel + label starts with a known type (such as 'EEG') followed by a space and + a name (such as 'Fp1'), the channel type will be set accordingly, and + the channel will be renamed to the original label without the prefix. + For unknown prefixes, the type will be 'EEG' and the name will not be + modified. If False, do not infer types and assume all channels are of + type 'EEG'. + + .. versionadded:: 0.24.1 + include : list of str | str + Channel names to be included. A str is interpreted as a regular + expression. 'exclude' must be empty if include is assigned. + + .. versionadded:: 1.1 %(preload)s %(verbose)s @@ -107,13 +130,14 @@ """ @verbose - def __init__(self, input_fname, eog=None, misc=None, - stim_channel='auto', exclude=(), preload=False, verbose=None): + def __init__(self, input_fname, eog=None, misc=None, stim_channel='auto', + exclude=(), infer_types=False, preload=False, include=None, + verbose=None): logger.info('Extracting EDF parameters from {}...'.format(input_fname)) input_fname = os.path.abspath(input_fname) - info, edf_info, orig_units = _get_info(input_fname, - stim_channel, eog, misc, - exclude, preload) + info, edf_info, orig_units = _get_info(input_fname, stim_channel, eog, + misc, exclude, infer_types, + preload, include) logger.info('Creating raw.info structure...') # Raw attributes @@ -167,6 +191,13 @@ exclude : list of str Channel names to exclude. This can help when reading data with different sampling rates to avoid unnecessary resampling. + + .. versionadded:: 0.24.1 + include : list of str | str + Channel names to be included. A str is interpreted as a regular + expression. 'exclude' must be empty if include is assigned. + + .. versionadded:: 1.1 %(preload)s %(verbose)s @@ -184,12 +215,13 @@ @verbose def __init__(self, input_fname, eog=None, misc=None, - stim_channel='auto', exclude=(), preload=False, verbose=None): + stim_channel='auto', exclude=(), preload=False, include=None, + verbose=None): logger.info('Extracting EDF parameters from {}...'.format(input_fname)) input_fname = os.path.abspath(input_fname) - info, edf_info, orig_units = _get_info(input_fname, - stim_channel, eog, misc, - exclude, preload) + info, edf_info, orig_units = _get_info(input_fname, stim_channel, eog, + misc, exclude, True, preload, + include) logger.info('Creating raw.info structure...') # Raw attributes @@ -283,7 +315,8 @@ one = np.zeros((len(orig_sel), d_eidx - d_sidx), dtype=data.dtype) for ii, ci in enumerate(read_sel): # This now has size (n_chunks_read, n_samp[ci]) - ch_data = many_chunk[:, ch_offsets[ci]:ch_offsets[ci + 1]] + ch_data = many_chunk[:, + ch_offsets[ci]:ch_offsets[ci + 1]].copy() if ci in tal_idx: tal_data.append(ch_data) @@ -324,16 +357,28 @@ return tal_data -def _read_header(fname, exclude): - """Unify edf, bdf and gdf _read_header call. +def _read_header(fname, exclude, infer_types, include=None): + """Unify EDF, BDF and GDF _read_header call. Parameters ---------- fname : str Path to the EDF+, BDF, or GDF file. - exclude : list of str + exclude : list of str | str Channel names to exclude. This can help when reading data with - different sampling rates to avoid unnecessary resampling. + different sampling rates to avoid unnecessary resampling. A str is + interpreted as a regular expression. + infer_types : bool + If True, try to infer channel types from channel labels. If a channel + label starts with a known type (such as 'EEG') followed by a space and + a name (such as 'Fp1'), the channel type will be set accordingly, and + the channel will be renamed to the original label without the prefix. + For unknown prefixes, the type will be 'EEG' and the name will not be + modified. If False, do not infer types and assume all channels are of + type 'EEG'. + include : list of str | str + Channel names to be included. A str is interpreted as a regular + expression. 'exclude' must be empty if include is assigned. Returns ------- @@ -342,20 +387,21 @@ ext = os.path.splitext(fname)[1][1:].lower() logger.info('%s file detected' % ext.upper()) if ext in ('bdf', 'edf'): - return _read_edf_header(fname, exclude) + return _read_edf_header(fname, exclude, infer_types, include) elif ext == 'gdf': - return _read_gdf_header(fname, exclude), None + return _read_gdf_header(fname, exclude, include), None else: raise NotImplementedError( f'Only GDF, EDF, and BDF files are supported, got {ext}.') -def _get_info(fname, stim_channel, eog, misc, exclude, preload): - """Extract all the information from the EDF+, BDF or GDF file.""" +def _get_info(fname, stim_channel, eog, misc, exclude, infer_types, preload, + include=None): + """Extract information from EDF+, BDF or GDF file.""" eog = eog if eog is not None else [] misc = misc if misc is not None else [] - edf_info, orig_units = _read_header(fname, exclude) + edf_info, orig_units = _read_header(fname, exclude, infer_types, include) # XXX: `tal_ch_names` to pass to `_check_stim_channel` should be computed # from `edf_info['ch_names']` and `edf_info['tal_idx']` but 'tal_idx' @@ -365,7 +411,14 @@ sel = edf_info['sel'] # selection of channels not excluded ch_names = edf_info['ch_names'] # of length len(sel) - n_samps = edf_info['n_samps'][sel] + if 'ch_types' in edf_info: + ch_types = edf_info['ch_types'] # of length len(sel) + else: + ch_types = [None] * len(sel) + if len(sel) == 0: # only want stim channels + n_samps = edf_info['n_samps'][[0]] + else: + n_samps = edf_info['n_samps'][sel] nchan = edf_info['nchan'] physical_ranges = edf_info['physical_max'] - edf_info['physical_min'] cals = edf_info['digital_max'] - edf_info['digital_min'] @@ -385,6 +438,8 @@ chs = list() pick_mask = np.ones(len(ch_names)) + chs_without_types = list() + for idx, ch_name in enumerate(ch_names): chan_info = {} chan_info['cal'] = 1. @@ -397,7 +452,19 @@ chan_info['coord_frame'] = FIFF.FIFFV_COORD_HEAD chan_info['coil_type'] = FIFF.FIFFV_COIL_EEG chan_info['kind'] = FIFF.FIFFV_EEG_CH - chan_info['loc'] = np.zeros(12) + # montage can't be stored in EDF so channel locs are unknown: + chan_info['loc'] = np.full(12, np.nan) + + # if the edf info contained channel type information + # set it now + ch_type = ch_types[idx] + if ch_type is not None and ch_type in CH_TYPE_MAPPING: + chan_info['kind'] = CH_TYPE_MAPPING.get(ch_type) + if ch_type not in ['EEG', 'ECOG', 'SEEG', 'DBS']: + chan_info['coil_type'] = FIFF.FIFFV_COIL_NONE + pick_mask[idx] = False + # if user passes in explicit mapping for eog, misc and stim + # channels set them here if ch_name in eog or idx in eog or idx - nchan in eog: chan_info['coil_type'] = FIFF.FIFFV_COIL_NONE chan_info['kind'] = FIFF.FIFFV_EOG_CH @@ -414,10 +481,17 @@ chan_info['ch_name'] = ch_name ch_names[idx] = chan_info['ch_name'] edf_info['units'][idx] = 1 + elif ch_type not in CH_TYPE_MAPPING: + chs_without_types.append(ch_name) chs.append(chan_info) - edf_info['stim_channel_idxs'] = stim_channel_idxs + # warn if channel type was not inferable + if len(chs_without_types): + msg = ('Could not determine channel type of the following channels, ' + f'they will be set as EEG:\n{", ".join(chs_without_types)}') + logger.info(msg) + edf_info['stim_channel_idxs'] = stim_channel_idxs if any(pick_mask): picks = [item for item, mask in zip(range(nchan), pick_mask) if mask] edf_info['max_samp'] = max_samp = n_samps[picks].max() @@ -429,8 +503,11 @@ not_stim_ch = [x for x in range(n_samps.shape[0]) if x not in stim_channel_idxs] + if len(not_stim_ch) == 0: # only loading stim channels + not_stim_ch = list(range(len(n_samps))) sfreq = np.take(n_samps, not_stim_ch).max() * \ edf_info['record_length'][1] / edf_info['record_length'][0] + del n_samps info = _empty_info(sfreq) info['meas_date'] = edf_info['meas_date'] info['chs'] = chs @@ -443,7 +520,8 @@ pass elif all(highpass): if highpass[0] == 'NaN': - pass # Placeholder for future use. Highpass set in _empty_info. + # Placeholder for future use. Highpass set in _empty_info. + pass elif highpass[0] == 'DC': info['highpass'] = 0. else: @@ -460,10 +538,12 @@ if np.isnan(info['highpass']): info['highpass'] = 0. if lowpass.size == 0: - pass # Placeholder for future use. Lowpass set in _empty_info. + # Placeholder for future use. Lowpass set in _empty_info. + pass elif all(lowpass): if lowpass[0] in ('NaN', '0', '0.0'): - pass # Placeholder for future use. Lowpass set in _empty_info. + # Placeholder for future use. Lowpass set in _empty_info. + pass else: info['lowpass'] = float(lowpass[0]) else: @@ -474,8 +554,8 @@ info['lowpass'] = info['sfreq'] / 2. if info['highpass'] > info['lowpass']: - warn(f'Highpass cutoff frequency {info["highpass"]} is greater than ' - f'lowpass cutoff frequency {info["lowpass"]}, ' + warn(f'Highpass cutoff frequency {info["highpass"]} is greater ' + f'than lowpass cutoff frequency {info["lowpass"]}, ' 'setting values to 0 and Nyquist.') info['highpass'] = 0. info['lowpass'] = info['sfreq'] / 2. @@ -484,6 +564,7 @@ info['description'] = None edf_info['nsamples'] = int(edf_info['n_records'] * max_samp) + info._unlocked = False info._update_redundant() # Later used for reading @@ -520,7 +601,11 @@ return x.decode('latin-1').split('\x00')[0] -def _read_edf_header(fname, exclude): +def _edf_str_num(x): + return _edf_str(x).replace(",", ".") + + +def _read_edf_header(fname, exclude, infer_types, include=None): """Read header information from EDF+ or BDF file.""" edf_info = {'events': []} @@ -595,7 +680,7 @@ record_length = float(_edf_str(fid.read(8))) record_length = np.array([record_length, 1.]) # in seconds if record_length[0] == 0: - record_length = record_length[0] = 1. + record_length[0] = 1. warn('Header information is incorrect for record length. Default ' 'record length set to 1.\nIt is possible that this file only' ' contains annotations and no signals. In that case, please ' @@ -603,8 +688,31 @@ nchan = int(_edf_str(fid.read(4))) channels = list(range(nchan)) - ch_names = [fid.read(16).strip().decode('latin-1') for ch in channels] - exclude = _find_exclude_idx(ch_names, exclude) + + # read in 16 byte labels and strip any extra spaces at the end + ch_labels = [fid.read(16).strip().decode('latin-1') for _ in channels] + + # get channel names and optionally channel type + # EDF specification contains 16 bytes that encode channel names, + # optionally prefixed by a string representing channel type separated + # by a space + if infer_types: + ch_types, ch_names = [], [] + for ch_label in ch_labels: + ch_type, ch_name = 'EEG', ch_label # default to EEG + parts = ch_label.split(' ') + if len(parts) > 1: + if parts[0].upper() in CH_TYPE_MAPPING: + ch_type = parts[0].upper() + ch_name = ' '.join(parts[1:]) + logger.info(f"Channel '{ch_label}' recognized as type " + f"{ch_type} (renamed to '{ch_name}').") + ch_types.append(ch_type) + ch_names.append(ch_name) + else: + ch_types, ch_names = ['EEG'] * nchan, ch_labels + + exclude = _find_exclude_idx(ch_names, exclude, include) tal_idx = _find_tal_idx(ch_names) exclude = np.concatenate([exclude, tal_idx]) sel = np.setdiff1d(np.arange(len(ch_names)), exclude) @@ -615,8 +723,8 @@ for i, unit in enumerate(units): if i in exclude: continue - # allow both μ (greek mu) and µ (micro symbol) codepoints - if unit in ('\u03BCV', '\u00B5V', 'uV'): + # allow μ (greek mu), µ (micro symbol) and μ (sjis mu) codepoints + if unit in ('\u03BCV', '\u00B5V', '\x83\xCAV', 'uV'): edf_info['units'].append(1e-6) elif unit == 'mV': edf_info['units'].append(1e-3) @@ -632,13 +740,13 @@ orig_units = dict(zip(ch_names, units)) physical_min = np.array( - [float(_edf_str(fid.read(8))) for ch in channels])[sel] + [float(_edf_str_num(fid.read(8))) for ch in channels])[sel] physical_max = np.array( - [float(_edf_str(fid.read(8))) for ch in channels])[sel] + [float(_edf_str_num(fid.read(8))) for ch in channels])[sel] digital_min = np.array( - [float(_edf_str(fid.read(8))) for ch in channels])[sel] + [float(_edf_str_num(fid.read(8))) for ch in channels])[sel] digital_max = np.array( - [float(_edf_str(fid.read(8))) for ch in channels])[sel] + [float(_edf_str_num(fid.read(8))) for ch in channels])[sel] prefiltering = [_edf_str(fid.read(80)).strip() for ch in channels][:-1] highpass, lowpass = _parse_prefilter_string(prefiltering) @@ -647,7 +755,7 @@ # Populate edf_info edf_info.update( - ch_names=ch_names, data_offset=header_nbytes, + ch_names=ch_names, ch_types=ch_types, data_offset=header_nbytes, digital_max=digital_max, digital_min=digital_min, highpass=highpass, sel=sel, lowpass=lowpass, meas_date=meas_date, n_records=n_records, n_samps=n_samps, nchan=nchan, @@ -708,7 +816,7 @@ return dtype_np[0], dtype_byte[0] -def _read_gdf_header(fname, exclude): +def _read_gdf_header(fname, exclude, include=None): """Read GDF 1.x and GDF 2.x header info.""" edf_info = dict() events = None @@ -765,7 +873,7 @@ nchan = np.fromfile(fid, UINT32, 1)[0] channels = list(range(nchan)) ch_names = [_edf_str(fid.read(16)).strip() for ch in channels] - exclude = _find_exclude_idx(ch_names, exclude) + exclude = _find_exclude_idx(ch_names, exclude, include) sel = np.setdiff1d(np.arange(len(ch_names)), exclude) fid.seek(80 * len(channels), 1) # transducer units = [_edf_str(fid.read(8)).strip() for ch in channels] @@ -945,7 +1053,7 @@ # Channels (variable header) channels = list(range(nchan)) ch_names = [_edf_str(fid.read(16)).strip() for ch in channels] - exclude = _find_exclude_idx(ch_names, exclude) + exclude = _find_exclude_idx(ch_names, exclude, include) sel = np.setdiff1d(np.arange(len(ch_names)), exclude) fid.seek(80 * len(channels), 1) # reserved space @@ -1133,12 +1241,34 @@ return stim_channel_idxs, names -def _find_exclude_idx(ch_names, exclude): - """Find the index of all channels to exclude. +def _find_exclude_idx(ch_names, exclude, include=None): + """Find indices of all channels to exclude. - If there are several channels called "A" and we want to exclude "A", - then add (the index of) all "A" channels to the exclusion list. + If there are several channels called "A" and we want to exclude "A", then + add (the index of) all "A" channels to the exclusion list. """ + if include: # find other than include channels + if exclude: + raise ValueError( + "'exclude' must be empty if 'include' is assigned. " + f"Got {exclude}.") + if isinstance(include, str): # regex for channel names + indices_include = [] + for idx, ch in enumerate(ch_names): + if re.match(include, ch): + indices_include.append(idx) + indices = np.setdiff1d(np.arange(len(ch_names)), indices_include) + return indices + # list of channel names + return [idx for idx, ch in enumerate(ch_names) if ch not in include] + + if isinstance(exclude, str): # regex for channel names + indices = [] + for idx, ch in enumerate(ch_names): + if re.match(exclude, ch): + indices.append(idx) + return indices + # list of channel names return [idx for idx, ch in enumerate(ch_names) if ch in exclude] @@ -1150,8 +1280,9 @@ @fill_doc -def read_raw_edf(input_fname, eog=None, misc=None, - stim_channel='auto', exclude=(), preload=False, verbose=None): +def read_raw_edf(input_fname, eog=None, misc=None, stim_channel='auto', + exclude=(), infer_types=False, include=None, preload=False, + verbose=None): """Reader function for EDF or EDF+ files. Parameters @@ -1171,17 +1302,25 @@ 'trigger' (case insensitive) are set to STIM. If str (or list of str), all channels matching the name(s) are set to STIM. If int (or list of ints), channels corresponding to the indices are set to STIM. - - .. warning:: 0.18 does not allow for stim channel synthesis from TAL - channels called 'EDF Annotations' anymore. Instead, TAL - channels are parsed and extracted annotations are stored - in raw.annotations. Use - :func:`mne.events_from_annotations` to obtain events from - these annotations. - - exclude : list of str + exclude : list of str | str Channel names to exclude. This can help when reading data with - different sampling rates to avoid unnecessary resampling. + different sampling rates to avoid unnecessary resampling. A str is + interpreted as a regular expression. + infer_types : bool + If True, try to infer channel types from channel labels. If a channel + label starts with a known type (such as 'EEG') followed by a space and + a name (such as 'Fp1'), the channel type will be set accordingly, and + the channel will be renamed to the original label without the prefix. + For unknown prefixes, the type will be 'EEG' and the name will not be + modified. If False, do not infer types and assume all channels are of + type 'EEG'. + + .. versionadded:: 0.24.1 + include : list of str | str + Channel names to be included. A str is interpreted as a regular + expression. 'exclude' must be empty if include is assigned. + + .. versionadded:: 1.1 %(preload)s %(verbose)s @@ -1194,6 +1333,7 @@ -------- mne.io.read_raw_bdf : Reader function for BDF files. mne.io.read_raw_gdf : Reader function for GDF files. + mne.export.export_raw : Export function for EDF files. Notes ----- @@ -1211,20 +1351,46 @@ If channels named 'status' or 'trigger' are present, they are considered as STIM channels by default. Use func:`mne.find_events` to parse events encoded in such analog stim channels. + + The EDF specification allows optional storage of channel types in the + prefix of the signal label for each channel. For example, ``EEG Fz`` + implies that ``Fz`` is an EEG channel and ``MISC E`` would imply ``E`` is + a MISC channel. However, there is no standard way of specifying all + channel types. MNE-Python will try to infer the channel type, when such a + string exists, defaulting to EEG, when there is no prefix or the prefix is + not recognized. + + The following prefix strings are mapped to MNE internal types: + + - 'EEG': 'eeg' + - 'SEEG': 'seeg' + - 'ECOG': 'ecog' + - 'DBS': 'dbs' + - 'EOG': 'eog' + - 'ECG': 'ecg' + - 'EMG': 'emg' + - 'BIO': 'bio' + - 'RESP': 'resp' + - 'MISC': 'misc' + - 'SAO2': 'bio' + + The EDF specification allows storage of subseconds in measurement date. + However, this reader currently sets subseconds to 0 by default. """ input_fname = os.path.abspath(input_fname) ext = os.path.splitext(input_fname)[1][1:].lower() if ext != 'edf': - raise NotImplementedError( - 'Only EDF files are supported by read_raw_edf, got %s' % (ext,)) + raise NotImplementedError(f'Only EDF files are supported, got {ext}.') return RawEDF(input_fname=input_fname, eog=eog, misc=misc, - stim_channel=stim_channel, exclude=exclude, preload=preload, + stim_channel=stim_channel, exclude=exclude, + infer_types=infer_types, preload=preload, include=include, verbose=verbose) @fill_doc -def read_raw_bdf(input_fname, eog=None, misc=None, - stim_channel='auto', exclude=(), preload=False, verbose=None): +def read_raw_bdf(input_fname, eog=None, misc=None, stim_channel='auto', + exclude=(), infer_types=False, include=None, preload=False, + verbose=None): """Reader function for BDF files. Parameters @@ -1244,17 +1410,25 @@ 'trigger' (case insensitive) are set to STIM. If str (or list of str), all channels matching the name(s) are set to STIM. If int (or list of ints), channels corresponding to the indices are set to STIM. - - .. warning:: 0.18 does not allow for stim channel synthesis from TAL - channels called 'BDF Annotations' anymore. Instead, TAL - channels are parsed and extracted annotations are stored - in raw.annotations. Use - :func:`mne.events_from_annotations` to obtain events from - these annotations. - - exclude : list of str + exclude : list of str | str Channel names to exclude. This can help when reading data with - different sampling rates to avoid unnecessary resampling. + different sampling rates to avoid unnecessary resampling. A str is + interpreted as a regular expression. + infer_types : bool + If True, try to infer channel types from channel labels. If a channel + label starts with a known type (such as 'EEG') followed by a space and + a name (such as 'Fp1'), the channel type will be set accordingly, and + the channel will be renamed to the original label without the prefix. + For unknown prefixes, the type will be 'EEG' and the name will not be + modified. If False, do not infer types and assume all channels are of + type 'EEG'. + + .. versionadded:: 0.24.1 + include : list of str | str + Channel names to be included. A str is interpreted as a regular + expression. 'exclude' must be empty if include is assigned. + + .. versionadded:: 1.1 %(preload)s %(verbose)s @@ -1307,16 +1481,16 @@ input_fname = os.path.abspath(input_fname) ext = os.path.splitext(input_fname)[1][1:].lower() if ext != 'bdf': - raise NotImplementedError('Only BDF files are supported, got ' - '{}.'.format(ext)) + raise NotImplementedError(f'Only BDF files are supported, got {ext}.') return RawEDF(input_fname=input_fname, eog=eog, misc=misc, - stim_channel=stim_channel, exclude=exclude, preload=preload, + stim_channel=stim_channel, exclude=exclude, + infer_types=infer_types, preload=preload, include=include, verbose=verbose) @fill_doc -def read_raw_gdf(input_fname, eog=None, misc=None, - stim_channel='auto', exclude=(), preload=False, verbose=None): +def read_raw_gdf(input_fname, eog=None, misc=None, stim_channel='auto', + exclude=(), include=None, preload=False, verbose=None): """Reader function for GDF files. Parameters @@ -1336,9 +1510,13 @@ 'trigger' (case insensitive) are set to STIM. If str (or list of str), all channels matching the name(s) are set to STIM. If int (or list of ints), channels corresponding to the indices are set to STIM. - exclude : list of str + exclude : list of str | str Channel names to exclude. This can help when reading data with - different sampling rates to avoid unnecessary resampling. + different sampling rates to avoid unnecessary resampling. A str is + interpreted as a regular expression. + include : list of str | str + Channel names to be included. A str is interpreted as a regular + expression. 'exclude' must be empty if include is assigned. %(preload)s %(verbose)s @@ -1361,11 +1539,10 @@ input_fname = os.path.abspath(input_fname) ext = os.path.splitext(input_fname)[1][1:].lower() if ext != 'gdf': - raise NotImplementedError('Only GDF files are supported, got ' - '{}.'.format(ext)) + raise NotImplementedError(f'Only BDF files are supported, got {ext}.') return RawGDF(input_fname=input_fname, eog=eog, misc=misc, stim_channel=stim_channel, exclude=exclude, preload=preload, - verbose=verbose) + include=include, verbose=verbose) def _read_annotations_edf(annotations): @@ -1410,10 +1587,7 @@ tals.extend(np.uint8([this_chan % 256, this_chan // 256]) .flatten('F')) - # use of latin-1 because characters are only encoded for the first 256 - # code points and utf-8 can triggers an "invalid continuation byte" - # error - triggers = re.findall(pat, tals.decode('latin-1')) + triggers = re.findall(pat, tals.decode('utf8')) events = [] offset = 0. diff -Nru python-mne-0.23.4+dfsg/mne/io/edf/__init__.py python-mne-1.1.0+dfsg/mne/io/edf/__init__.py --- python-mne-0.23.4+dfsg/mne/io/edf/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/edf/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,6 +2,6 @@ # Author: Teon Brooks # -# License: BSD (3-clause) +# License: BSD-3-Clause from .edf import read_raw_edf, read_raw_bdf, read_raw_gdf diff -Nru python-mne-0.23.4+dfsg/mne/io/edf/tests/test_edf.py python-mne-1.1.0+dfsg/mne/io/edf/tests/test_edf.py --- python-mne-0.23.4+dfsg/mne/io/edf/tests/test_edf.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/edf/tests/test_edf.py 2022-08-03 15:48:14.000000000 +0000 @@ -6,8 +6,9 @@ # Stefan Appelhoff # Joan Massich # -# License: BSD (3-clause) +# License: BSD-3-Clause +from contextlib import nullcontext from functools import partial import os.path as op import inspect @@ -20,16 +21,16 @@ import pytest from mne import pick_types, Annotations +from mne.annotations import events_from_annotations, read_annotations from mne.datasets import testing -from mne.fixes import nullcontext -from mne.utils import requires_pandas +from mne.utils import requires_pandas, _record_warnings from mne.io import read_raw_edf, read_raw_bdf, read_raw_fif, edf, read_raw_gdf from mne.io.tests.test_raw import _test_raw_reader from mne.io.edf.edf import (_get_edf_default_event_id, _read_annotations_edf, _read_ch, _parse_prefilter_string, _edf_str, _read_edf_header, _read_header) from mne.io.pick import channel_indices_by_type, get_channel_type_constants -from mne.annotations import events_from_annotations, read_annotations +from mne.tests.test_annotations import _assert_annotations_equal td_mark = testing._pytest_mark() @@ -52,12 +53,15 @@ edf_overlap_annot_path = op.join(data_path, 'EDF', 'test_edf_overlapping_annotations.edf') edf_reduced = op.join(data_path, 'EDF', 'test_reduced.edf') +edf_annot_only = op.join(data_path, 'EDF', 'SC4001EC-Hypnogram.edf') bdf_stim_channel_path = op.join(data_path, 'BDF', 'test_bdf_stim_channel.bdf') bdf_multiple_annotations_path = op.join(data_path, 'BDF', 'multiple_annotation_chans.bdf') test_generator_bdf = op.join(data_path, 'BDF', 'test_generator_2.bdf') test_generator_edf = op.join(data_path, 'EDF', 'test_generator_2.edf') edf_annot_sub_s_path = op.join(data_path, 'EDF', 'subsecond_starttime.edf') +edf_chtypes_path = op.join(data_path, 'EDF', 'chtypes_edf.edf') +edf_utf8_annotations = op.join(data_path, 'EDF', 'test_utf8_annotations.edf') eog = ['REOG', 'LEOG', 'IEOG'] misc = ['EXG1', 'EXG5', 'EXG8', 'M1', 'M2'] @@ -73,7 +77,7 @@ assert orig_units['A1'] == 'µV' # formerly 'uV' edit by _check_orig_units -def test_subject_info(tmpdir): +def test_subject_info(tmp_path): """Test exposure of original channel units.""" raw = read_raw_edf(edf_path) assert raw.info['subject_info'] is None # XXX this is arguably a bug @@ -82,7 +86,7 @@ want = {'id': 'X', 'sex': 'X', 'birthday': 'X', 'name': 'X'} for key, val in want.items(): assert edf_info['subject_info'][key] == val, key - fname = tmpdir.join('test_raw.fif') + fname = tmp_path / 'test_raw.fif' raw.save(fname) raw = read_raw_fif(fname) assert raw.info['subject_info'] is None # XXX should eventually round-trip @@ -122,10 +126,10 @@ @testing.requires_testing_data -def test_bdf_crop_save_stim_channel(tmpdir): +def test_bdf_crop_save_stim_channel(tmp_path): """Test EDF with various sampling rates.""" raw = read_raw_bdf(bdf_stim_channel_path) - raw.save(tmpdir.join('test-raw.fif'), tmin=1.2, tmax=4.0, overwrite=True) + raw.save(tmp_path / 'test-raw.fif', tmin=1.2, tmax=4.0, overwrite=True) @testing.requires_testing_data @@ -141,7 +145,7 @@ verbose='error') -def test_edf_data_broken(tmpdir): +def test_edf_data_broken(tmp_path): """Test edf files.""" raw = _test_raw_reader(read_raw_edf, input_fname=edf_path, exclude=['Ergo-Left', 'H10'], verbose='error') @@ -150,7 +154,7 @@ assert_equal(len(raw.ch_names) + 2, len(raw_py.ch_names)) # Test with number of records not in header (-1). - broken_fname = op.join(tmpdir, 'broken.edf') + broken_fname = op.join(tmp_path, 'broken.edf') with open(edf_path, 'rb') as fid_in: fid_in.seek(0, 2) n_bytes = fid_in.tell() @@ -185,7 +189,7 @@ assert raw.ch_names == EXPECTED_CHANNEL_NAMES -def test_parse_annotation(tmpdir): +def test_parse_annotation(tmp_path): """Test parsing the tal channel.""" # test the parser annot = (b'+180\x14Lights off\x14Close door\x14\x00\x00\x00\x00\x00' @@ -194,15 +198,16 @@ b'+3.14\x1504.20\x14nothing\x14\x00\x00\x00\x00' b'+1800.2\x1525.5\x14Apnea\x14\x00\x00\x00\x00\x00\x00\x00' b'+123\x14\x14\x00\x00\x00\x00\x00\x00\x00') - annot_file = tmpdir.join('annotations.txt') - annot_file.write(annot) + annot_file = tmp_path / 'annotations.txt' + with open(annot_file, "wb") as f: + f.write(annot) annot = [a for a in bytes(annot)] annot[1::2] = [a * 256 for a in annot[1::2]] tal_channel_A = np.array(list(map(sum, zip(annot[0::2], annot[1::2]))), dtype=np.int64) - with open(str(annot_file), 'rb') as fid: + with open(annot_file, 'rb') as fid: # ch_data = np.fromfile(fid, dtype=' 15 assert_allclose(raw.info["lowpass"], raw.info["sfreq"] / 2) @@ -420,7 +451,7 @@ assert_allclose(raw.annotations.onset, [1.951172, 3.492188]) -def test_invalid_date(tmpdir): +def test_invalid_date(tmp_path): """Test handling of invalid date in EDF header.""" with open(edf_path, 'rb') as f: # read valid test file edf = bytearray(f.read()) @@ -433,7 +464,7 @@ # one wrong: no warning edf[101:104] = b'FEB' assert edf[172] == ord('4') - fname = op.join(str(tmpdir), "temp.edf") + fname = op.join(str(tmp_path), "temp.edf") with open(fname, "wb") as f: f.write(edf) read_raw_edf(fname) @@ -509,6 +540,72 @@ def test_degenerate(): """Test checking of some bad inputs.""" for func in (read_raw_edf, read_raw_bdf, read_raw_gdf, - partial(_read_header, exclude=())): + partial(_read_header, exclude=(), infer_types=False)): with pytest.raises(NotImplementedError, match='Only.*txt.*'): func(edf_txt_stim_channel_path) + + +def test_exclude(): + """Test exclude parameter.""" + exclude = ["I1", "I2", "I3", "I4"] # list of excluded channels + + raw = read_raw_edf(edf_path, exclude=["I1", "I2", "I3", "I4"]) + for ch in exclude: + assert ch not in raw.ch_names + + raw = read_raw_edf(edf_path, exclude="I[1-4]") + for ch in exclude: + assert ch not in raw.ch_names + + +def test_include(): + """Test include parameter.""" + raw = read_raw_edf(edf_path, include=["I1", "I2"]) + assert sorted(raw.ch_names) == ["I1", "I2"] + + raw = read_raw_edf(edf_path, include="I[1-4]") + assert sorted(raw.ch_names) == ["I1", "I2", "I3", "I4"] + + with pytest.raises(ValueError) as e: + raw = read_raw_edf(edf_path, include=["I1", "I2"], exclude="I[1-4]") + assert str(e.value) == "'exclude' must be empty" \ + "if 'include' is assigned." + + +@testing.requires_testing_data +def test_ch_types(): + """Test reading of channel types from EDF channel label.""" + raw = read_raw_edf(edf_chtypes_path) # infer_types=False + + labels = ['EEG Fp1-Ref', 'EEG Fp2-Ref', 'EEG F3-Ref', 'EEG F4-Ref', + 'EEG C3-Ref', 'EEG C4-Ref', 'EEG P3-Ref', 'EEG P4-Ref', + 'EEG O1-Ref', 'EEG O2-Ref', 'EEG F7-Ref', 'EEG F8-Ref', + 'EEG T7-Ref', 'EEG T8-Ref', 'EEG P7-Ref', 'EEG P8-Ref', + 'EEG Fz-Ref', 'EEG Cz-Ref', 'EEG Pz-Ref', 'POL E', 'POL PG1', + 'POL PG2', 'EEG A1-Ref', 'EEG A2-Ref', 'POL T1', 'POL T2', + 'ECG ECG1', 'ECG ECG2', 'EEG F9-Ref', 'EEG T9-Ref', 'EEG P9-Ref', + 'EEG F10-Ref', 'EEG T10-Ref', 'EEG P10-Ref', 'SaO2 X9', + 'SaO2 X10', 'POL DC01', 'POL DC02', 'POL DC03', 'POL DC04', + 'POL $A1', 'POL $A2'] + + # by default all types are 'eeg' + assert all(t == 'eeg' for t in raw.get_channel_types()) + assert raw.ch_names == labels + + raw = read_raw_edf(edf_chtypes_path, infer_types=True) + + labels = ['Fp1-Ref', 'Fp2-Ref', 'F3-Ref', 'F4-Ref', 'C3-Ref', 'C4-Ref', + 'P3-Ref', 'P4-Ref', 'O1-Ref', 'O2-Ref', 'F7-Ref', 'F8-Ref', + 'T7-Ref', 'T8-Ref', 'P7-Ref', 'P8-Ref', 'Fz-Ref', 'Cz-Ref', + 'Pz-Ref', 'POL E', 'POL PG1', 'POL PG2', 'A1-Ref', 'A2-Ref', + 'POL T1', 'POL T2', 'ECG1', 'ECG2', 'F9-Ref', 'T9-Ref', 'P9-Ref', + 'F10-Ref', 'T10-Ref', 'P10-Ref', 'X9', 'X10', 'POL DC01', + 'POL DC02', 'POL DC03', 'POL DC04', 'POL $A1', 'POL $A2'] + types = ['eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', + 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', + 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'ecg', + 'ecg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'bio', 'bio', + 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg'] + + assert raw.get_channel_types() == types + assert raw.ch_names == labels diff -Nru python-mne-0.23.4+dfsg/mne/io/edf/tests/test_gdf.py python-mne-1.1.0+dfsg/mne/io/edf/tests/test_gdf.py --- python-mne-0.23.4+dfsg/mne/io/edf/tests/test_gdf.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/edf/tests/test_gdf.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors: Alexandre Barachant # Nicolas Barascud # -# License: BSD (3-clause) +# License: BSD-3-Clause from datetime import datetime, timezone, timedelta import os.path as op @@ -16,12 +16,11 @@ from mne.datasets import testing from mne.io import read_raw_gdf from mne.io.tests.test_raw import _test_raw_reader -from mne.utils import run_tests_if_main from mne import pick_types, find_events, events_from_annotations data_path = testing.data_path(download=False) -gdf1_path = op.join(data_path, 'GDF', 'test_gdf_1.25') -gdf2_path = op.join(data_path, 'GDF', 'test_gdf_2.20') +gdf1_path = str(op.join(data_path, 'GDF', 'test_gdf_1.25')) +gdf2_path = str(op.join(data_path, 'GDF', 'test_gdf_2.20')) gdf_1ch_path = op.join(data_path, 'GDF', 'test_1ch.gdf') @@ -59,9 +58,9 @@ @testing.requires_testing_data -def test_gdf2_birthday(tmpdir): +def test_gdf2_birthday(tmp_path): """Test reading raw GDF 2.x files.""" - new_fname = str(tmpdir.join('temp.gdf')) + new_fname = tmp_path / 'temp.gdf' shutil.copyfile(gdf2_path + '.gdf', new_fname) # go back 44.5 years so the subject should show up as 44 offset_edf = ( # to their ref @@ -133,6 +132,15 @@ raw = read_raw_gdf(gdf2_path + '.gdf', exclude=('Fp1', 'O1')) assert 'Fp1' not in raw.ch_names assert 'O1' not in raw.ch_names + raw = read_raw_gdf(gdf2_path + '.gdf', exclude=".+z$") + assert 'AFz' not in raw.ch_names + assert 'Cz' not in raw.ch_names + assert 'Pz' not in raw.ch_names + assert 'Oz' not in raw.ch_names -run_tests_if_main() +@testing.requires_testing_data +def test_gdf_include(): + """Test reading GDF data with include.""" + raw = read_raw_gdf(gdf1_path + '.gdf', include=('FP1', 'O1')) + assert sorted(raw.ch_names) == ['FP1', 'O1'] diff -Nru python-mne-0.23.4+dfsg/mne/io/eeglab/eeglab.py python-mne-1.1.0+dfsg/mne/io/eeglab/eeglab.py --- python-mne-0.23.4+dfsg/mne/io/eeglab/eeglab.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/eeglab/eeglab.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,17 +2,19 @@ # Jona Sassenhagen # Stefan Appelhoff # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op import numpy as np +from ..pick import _PICK_TYPES_KEYS from ..utils import _read_segments_file, _find_channels from ..constants import FIFF from ..meas_info import create_info from ..base import BaseRaw -from ...utils import logger, verbose, warn, fill_doc, Bunch, _check_fname +from ...utils import (logger, verbose, warn, fill_doc, Bunch, _check_fname, + _import_pymatreader_funcs) from ...channels import make_dig_montage from ...epochs import BaseEpochs from ...event import read_events @@ -55,7 +57,7 @@ def _check_load_mat(fname, uint16_codec): """Check if the mat struct contains 'EEG'.""" - from ...externals.pymatreader import read_mat + read_mat = _import_pymatreader_funcs('EEGLAB I/O') eeg = read_mat(fname, uint16_codec=uint16_codec) if 'ALLEEG' in eeg: raise NotImplementedError( @@ -80,13 +82,15 @@ def _eeg_has_montage_information(eeg): - from scipy import io - + try: + from scipy.io.matlab import mat_struct + except ImportError: # SciPy < 1.8 + from scipy.io.matlab.mio5_params import mat_struct if not len(eeg.chanlocs): has_pos = False else: pos_fields = ['X', 'Y', 'Z'] - if isinstance(eeg.chanlocs[0], io.matlab.mio5_params.mat_struct): + if isinstance(eeg.chanlocs[0], mat_struct): has_pos = all(hasattr(eeg.chanlocs[0], fld) for fld in pos_fields) elif isinstance(eeg.chanlocs[0], np.ndarray): @@ -102,29 +106,66 @@ return has_pos -def _get_eeg_montage_information(eeg, get_pos): - - pos_ch_names, ch_names, pos = list(), list(), list() +def _get_montage_information(eeg, get_pos): + """Get channel name, type and montage information from ['chanlocs'].""" + ch_names, ch_types, pos_ch_names, pos = list(), list(), list(), list() + unknown_types = dict() for chanloc in eeg.chanlocs: + # channel name ch_names.append(chanloc['labels']) + + # channel type + ch_type = 'eeg' + try_type = chanloc.get('type', None) + if isinstance(try_type, str): + try_type = try_type.strip().lower() + if try_type in _PICK_TYPES_KEYS: + ch_type = try_type + else: + if try_type in unknown_types: + unknown_types[try_type].append(chanloc['labels']) + else: + unknown_types[try_type] = [chanloc['labels']] + ch_types.append(ch_type) + + # channel loc if get_pos: loc_x = _to_loc(chanloc['X']) loc_y = _to_loc(chanloc['Y']) loc_z = _to_loc(chanloc['Z']) locs = np.r_[-loc_y, loc_x, loc_z] - if not np.any(np.isnan(locs)): - pos_ch_names.append(chanloc['labels']) - pos.append(locs) + pos_ch_names.append(chanloc['labels']) + pos.append(locs) + + # warn if unknown types were provided + if len(unknown_types): + warn('Unknown types found, setting as type EEG:\n' + + '\n'.join([f'{key}: {sorted(unknown_types[key])}' + for key in sorted(unknown_types)])) + + lpa, rpa, nasion = None, None, None + if (hasattr(eeg, "chaninfo") and + "nodatchans" in eeg.chaninfo and + len(eeg.chaninfo['nodatchans'])): + for item in list(zip(*eeg.chaninfo['nodatchans'].values())): + d = dict(zip(eeg.chaninfo['nodatchans'].keys(), item)) + if d["type"] != 'FID': + continue + elif d['description'] == 'Nasion': + nasion = np.array([d["X"], d["Y"], d["Z"]]) + elif d['description'] == 'Right periauricular point': + rpa = np.array([d["X"], d["Y"], d["Z"]]) + elif d['description'] == 'Left periauricular point': + lpa = np.array([d["X"], d["Y"], d["Z"]]) if pos_ch_names: montage = make_dig_montage( ch_pos=dict(zip(ch_names, np.array(pos))), - coord_frame='head', - ) + coord_frame='head', lpa=lpa, rpa=rpa, nasion=nasion) else: montage = None - return ch_names, montage + return ch_names, ch_types, montage def _get_info(eeg, eog=()): @@ -140,14 +181,16 @@ if eeg_has_ch_names_info: has_pos = _eeg_has_montage_information(eeg) - ch_names, eeg_montage = _get_eeg_montage_information(eeg, has_pos) + ch_names, ch_types, eeg_montage = \ + _get_montage_information(eeg, has_pos) update_ch_names = False else: # if eeg.chanlocs is empty, we still need default chan names ch_names = ["EEG %03d" % ii for ii in range(eeg.nbchan)] + ch_types = 'eeg' eeg_montage = None update_ch_names = True - info = create_info(ch_names, sfreq=eeg.srate, ch_types='eeg') + info = create_info(ch_names, sfreq=eeg.srate, ch_types=ch_types) eog = _find_channels(ch_names, ch_type='EOG') if eog == 'auto' else eog for idx, ch in enumerate(info['chs']): @@ -446,6 +489,8 @@ baseline=None, reject=None, flat=None, reject_tmin=None, reject_tmax=None, eog=(), verbose=None, uint16_codec=None): # noqa: D102 + input_fname = _check_fname(fname=input_fname, must_exist=True, + overwrite='read') eeg = _check_load_mat(input_fname, uint16_codec) if not ((events is None and event_id is None) or @@ -507,7 +552,6 @@ events = read_events(events) logger.info('Extracting parameters from %s...' % input_fname) - input_fname = op.abspath(input_fname) info, eeg_montage, _ = _get_info(eeg, eog=eog) for key, val in event_id.items(): diff -Nru python-mne-0.23.4+dfsg/mne/io/eeglab/tests/test_eeglab.py python-mne-1.1.0+dfsg/mne/io/eeglab/tests/test_eeglab.py --- python-mne-0.23.4+dfsg/mne/io/eeglab/tests/test_eeglab.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/eeglab/tests/test_eeglab.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,13 +2,11 @@ # Mikolaj Magnuski # Stefan Appelhoff # -# License: BSD (3-clause) +# License: BSD-3-Clause from copy import deepcopy -from distutils.version import LooseVersion import os.path as op import shutil -from unittest import SkipTest import numpy as np from numpy.testing import (assert_array_equal, assert_array_almost_equal, @@ -17,12 +15,13 @@ from scipy import io from mne import write_events, read_epochs_eeglab +from mne.channels import read_custom_montage from mne.io import read_raw_eeglab +from mne.io.eeglab.eeglab import _get_montage_information, _dol_to_lod from mne.io.tests.test_raw import _test_raw_reader from mne.datasets import testing -from mne.utils import check_version +from mne.utils import Bunch from mne.annotations import events_from_annotations, read_annotations -from mne.io.eeglab.tests._utils import _read_eeglab_montage base_dir = op.join(testing.data_path(download=False), 'EEGLAB') @@ -34,6 +33,7 @@ raw_mat_fnames = [raw_fname_mat, raw_fname_onefile_mat] epochs_mat_fnames = [epochs_fname_mat, epochs_fname_onefile_mat] raw_fname_chanloc = op.join(base_dir, 'test_raw_chanloc.set') +raw_fname_chanloc_fids = op.join(base_dir, 'test_raw_chanloc_fids.set') raw_fname_2021 = op.join(base_dir, 'test_raw_2021.set') raw_fname_h5 = op.join(base_dir, 'test_raw_h5.set') raw_fname_onefile_h5 = op.join(base_dir, 'test_raw_onefile_h5.set') @@ -45,18 +45,18 @@ montage_path = op.join(base_dir, 'test_chans.locs') -needs_h5 = pytest.mark.skipif(not check_version('h5py'), reason='Needs h5py') +pymatreader = pytest.importorskip('pymatreader') # module-level @testing.requires_testing_data @pytest.mark.parametrize('fname', [ raw_fname_mat, - pytest.param(raw_fname_h5, marks=needs_h5), + raw_fname_h5, raw_fname_chanloc, ], ids=op.basename) def test_io_set_raw(fname): """Test importing EEGLAB .set files.""" - montage = _read_eeglab_montage(montage_path) + montage = read_custom_montage(montage_path) montage.ch_names = [ 'EEG {0:03d}'.format(ii) for ii in range(len(montage.ch_names)) ] @@ -101,14 +101,14 @@ @testing.requires_testing_data -def test_io_set_raw_more(tmpdir): +def test_io_set_raw_more(tmp_path): """Test importing EEGLAB .set files.""" - tmpdir = str(tmpdir) + tmp_path = str(tmp_path) eeg = io.loadmat(raw_fname_mat, struct_as_record=False, squeeze_me=True)['EEG'] # test reading file with one event (read old version) - negative_latency_fname = op.join(tmpdir, 'test_negative_latency.set') + negative_latency_fname = op.join(tmp_path, 'test_negative_latency.set') evnts = deepcopy(eeg.event[0]) evnts.latency = 0 io.savemat(negative_latency_fname, @@ -137,7 +137,7 @@ read_raw_eeglab(input_fname=negative_latency_fname, preload=True) # test overlapping events - overlap_fname = op.join(tmpdir, 'test_overlap_event.set') + overlap_fname = op.join(tmp_path, 'test_overlap_event.set') io.savemat(overlap_fname, {'EEG': {'trials': eeg.trials, 'srate': eeg.srate, 'nbchan': eeg.nbchan, 'data': 'test_overlap_event.fdt', @@ -150,7 +150,7 @@ read_raw_eeglab(input_fname=overlap_fname, preload=True) # test reading file with empty event durations - empty_dur_fname = op.join(tmpdir, 'test_empty_durations.set') + empty_dur_fname = op.join(tmp_path, 'test_empty_durations.set') evnts = deepcopy(eeg.event) for ev in evnts: ev.duration = np.array([], dtype='float') @@ -179,7 +179,7 @@ read_raw_eeglab(input_fname=overlap_fname, preload=True) # raise error when both EEG.data and fdt name from set are wrong - overlap_fname = op.join(tmpdir, 'test_ovrlap_event.set') + overlap_fname = op.join(tmp_path, 'test_ovrlap_event.set') io.savemat(overlap_fname, {'EEG': {'trials': eeg.trials, 'srate': eeg.srate, 'nbchan': eeg.nbchan, 'data': 'test_overla_event.fdt', @@ -191,7 +191,7 @@ read_raw_eeglab(input_fname=overlap_fname, preload=True) # test reading file with one channel - one_chan_fname = op.join(tmpdir, 'test_one_channel.set') + one_chan_fname = op.join(tmp_path, 'test_one_channel.set') io.savemat(one_chan_fname, {'EEG': {'trials': eeg.trials, 'srate': eeg.srate, 'nbchan': 1, 'data': np.random.random((1, 3)), @@ -219,14 +219,9 @@ # SciPy writing bug that pops up sometimes: # nopos_chanlocs = np.array(chanlocs[['labels', 'Z']]) - if LooseVersion(np.__version__) == '1.14.0': - # There is a bug in 1.14.0 (or maybe with SciPy 1.0.0?) that causes - # this write to fail! - raise SkipTest('Need to fix bug in NumPy 1.14.0!') - # test reading channel names but not positions when there is no X (only Z) # field in the EEG.chanlocs structure - nopos_fname = op.join(tmpdir, 'test_no_chanpos.set') + nopos_fname = op.join(tmp_path, 'test_no_chanpos.set') io.savemat(nopos_fname, {'EEG': {'trials': eeg.trials, 'srate': eeg.srate, 'nbchan': 3, 'data': np.random.random((3, 2)), 'epoch': eeg.epoch, @@ -247,7 +242,7 @@ @testing.requires_testing_data @pytest.mark.parametrize('fnames', [ epochs_mat_fnames, - pytest.param(epochs_h5_fnames, marks=[needs_h5, pytest.mark.slowtest]), + pytest.param(epochs_h5_fnames, marks=[pytest.mark.slowtest]), ]) def test_io_set_epochs(fnames): """Test importing EEGLAB .set epochs files.""" @@ -262,14 +257,14 @@ @testing.requires_testing_data -def test_io_set_epochs_events(tmpdir): +def test_io_set_epochs_events(tmp_path): """Test different combinations of events and event_ids.""" - tmpdir = str(tmpdir) - out_fname = op.join(tmpdir, 'test-eve.fif') + tmp_path = str(tmp_path) + out_fname = op.join(tmp_path, 'test-eve.fif') events = np.array([[4, 0, 1], [12, 0, 2], [20, 0, 3], [26, 0, 3]]) write_events(out_fname, events) event_id = {'S255/S8': 1, 'S8': 2, 'S255/S9': 3} - out_fname = op.join(tmpdir, 'test-eve.fif') + out_fname = op.join(tmp_path, 'test-eve.fif') epochs = read_epochs_eeglab(epochs_fname_mat, events, event_id) assert_equal(len(epochs.events), 4) assert epochs.preload @@ -282,14 +277,14 @@ @testing.requires_testing_data -def test_degenerate(tmpdir): +def test_degenerate(tmp_path): """Test some degenerate conditions.""" # test if .dat file raises an error - tmpdir = str(tmpdir) + tmp_path = str(tmp_path) eeg = io.loadmat(epochs_fname_mat, struct_as_record=False, squeeze_me=True)['EEG'] eeg.data = 'epochs_fname.dat' - bad_epochs_fname = op.join(tmpdir, 'test_epochs.set') + bad_epochs_fname = op.join(tmp_path, 'test_epochs.set') io.savemat(bad_epochs_fname, {'EEG': {'trials': eeg.trials, 'srate': eeg.srate, 'nbchan': eeg.nbchan, 'data': eeg.data, @@ -297,7 +292,7 @@ 'chanlocs': eeg.chanlocs, 'pnts': eeg.pnts}}, appendmat=False, oned_as='row') shutil.copyfile(op.join(base_dir, 'test_epochs.fdt'), - op.join(tmpdir, 'test_epochs.dat')) + op.join(tmp_path, 'test_epochs.dat')) with pytest.warns(RuntimeWarning, match='multiple events'): pytest.raises(NotImplementedError, read_epochs_eeglab, bad_epochs_fname) @@ -339,7 +334,6 @@ @testing.requires_testing_data def test_eeglab_event_from_annot(): """Test all forms of obtaining annotations.""" - base_dir = op.join(testing.data_path(download=False), 'EEGLAB') raw_fname_mat = op.join(base_dir, 'test_raw.set') raw_fname = raw_fname_mat event_id = {'rt': 1, 'square': 2} @@ -358,7 +352,7 @@ @pytest.fixture(scope='session') -def one_chanpos_fname(tmpdir_factory): +def one_chanpos_fname(tmp_path_factory): """Test file with 3 channels to exercise EEGLAB reader. File characteristics @@ -369,15 +363,15 @@ Notes from when this code was factorized: # test reading file with one event (read old version) """ - fname = str(tmpdir_factory.mktemp('data').join('test_chanpos.set')) + fname = str(tmp_path_factory.mktemp('data') / 'test_chanpos.set') file_conent = dict(EEG={ 'trials': 1, 'nbchan': 3, 'pnts': 3, 'epoch': [], 'event': [], 'srate': 128, 'times': np.array([0., 0.1, 0.2]), 'data': np.empty([3, 3]), 'chanlocs': np.array( [(b'F3', 1., 4., 7.), - (b'unknown', 2., 5., 8.), - (b'FPz', np.nan, np.nan, np.nan)], + (b'unknown', np.nan, np.nan, np.nan), + (b'FPz', 2., 5., 8.)], dtype=[('labels', 'S10'), ('X', 'f8'), ('Y', 'f8'), ('Z', 'f8')] ) }) @@ -394,8 +388,8 @@ nan = np.nan EXPECTED_LOCATIONS_FROM_FILE = np.array([ [-4., 1., 7., 0., 0., 0., nan, nan, nan, nan, nan, nan], - [-5., 2., 8., 0., 0., 0., nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], + [-5., 2., 8., 0., 0., 0., nan, nan, nan, nan, nan, nan], ]) EXPECTED_LOCATIONS_FROM_MONTAGE = np.array([ @@ -420,9 +414,6 @@ _assert_array_allclose_nan(np.array([ch['loc'] for ch in raw.info['chs']]), EXPECTED_LOCATIONS_FROM_MONTAGE) - _assert_array_allclose_nan(np.array([ch['loc'] for ch in raw.info['chs']]), - EXPECTED_LOCATIONS_FROM_MONTAGE) - @testing.requires_testing_data def test_io_set_raw_2021(): @@ -437,3 +428,41 @@ """Test reading raw set file as an Epochs instance.""" with pytest.raises(ValueError, match='trials less than 2'): read_epochs_eeglab(raw_fname_mat) + + +@testing.requires_testing_data +def test_get_montage_info_with_ch_type(): + """Test that the channel types are properly returned.""" + mat = pymatreader.read_mat(raw_fname_onefile_mat, uint16_codec=None) + n = len(mat['EEG']['chanlocs']['labels']) + mat['EEG']['chanlocs']['type'] = ['eeg'] * (n - 2) + ['eog'] + ['stim'] + mat['EEG']['chanlocs'] = _dol_to_lod(mat['EEG']['chanlocs']) + mat['EEG'] = Bunch(**mat['EEG']) + ch_names, ch_types, montage = _get_montage_information(mat['EEG'], False) + assert len(ch_names) == len(ch_types) == n + assert ch_types == ['eeg'] * (n - 2) + ['eog'] + ['stim'] + assert montage is None + + # test unknown type warning + mat = pymatreader.read_mat(raw_fname_onefile_mat, uint16_codec=None) + n = len(mat['EEG']['chanlocs']['labels']) + mat['EEG']['chanlocs']['type'] = ['eeg'] * (n - 2) + ['eog'] + ['unknown'] + mat['EEG']['chanlocs'] = _dol_to_lod(mat['EEG']['chanlocs']) + mat['EEG'] = Bunch(**mat['EEG']) + with pytest.warns(RuntimeWarning, match='Unknown types found'): + ch_names, ch_types, montage = \ + _get_montage_information(mat['EEG'], False) + + +@testing.requires_testing_data +def test_fidsposition_information(): + """Test reading file with 3 fiducial locations.""" + raw = read_raw_eeglab(raw_fname_chanloc_fids) + montage = raw.get_montage() + pos = montage.get_positions() + assert pos['nasion'] is not None + assert pos['lpa'] is not None + assert pos['rpa'] is not None + assert len(pos['nasion']) == 3 + assert len(pos['lpa']) == 3 + assert len(pos['rpa']) == 3 diff -Nru python-mne-0.23.4+dfsg/mne/io/eeglab/tests/_utils.py python-mne-1.1.0+dfsg/mne/io/eeglab/tests/_utils.py --- python-mne-0.23.4+dfsg/mne/io/eeglab/tests/_utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/eeglab/tests/_utils.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,38 +0,0 @@ -# Author: Joan Massich -# -# License: BSD (3-clause) - -import numpy as np -from mne.channels import make_dig_montage -from mne.transforms import _sph_to_cart, _topo_to_sph - - -# XXX: This is a workaround to get the previous behavior. -def _read_eeglab_montage(fname): - """Read an EEGLAB digitization file. - - Parameters - ---------- - fname : str - The filepath of Polhemus ISOTrak formatted file. - File extension is expected to be '.loc', '.locs' or '.eloc'. - - Returns - ------- - montage : instance of DigMontage - The montage. - - See Also - -------- - make_dig_montage - """ - ch_names = np.genfromtxt(fname, dtype=str, usecols=3).tolist() - topo = np.loadtxt(fname, dtype=float, usecols=[1, 2]) - sph = _topo_to_sph(topo) - pos = _sph_to_cart(sph) - pos[:, [0, 1]] = pos[:, [1, 0]] * [-1, 1] - - return make_dig_montage( - ch_pos=dict(zip(ch_names, pos)), - coord_frame='head', - ) diff -Nru python-mne-0.23.4+dfsg/mne/io/egi/egimff.py python-mne-1.1.0+dfsg/mne/io/egi/egimff.py --- python-mne-0.23.4+dfsg/mne/io/egi/egimff.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/egi/egimff.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,7 +5,6 @@ import math import os.path as op import re -import time from xml.dom.minidom import parse import numpy as np @@ -22,6 +21,8 @@ from ...utils import verbose, logger, warn, _check_option, _check_fname from ...evoked import EvokedArray +REFERENCE_NAMES = ('VREF', 'Vertex Reference') + def _read_mff_header(filepath): """Read mff header.""" @@ -104,6 +105,8 @@ # Add the sensor info. sensor_layout_file = op.join(filepath, 'sensorLayout.xml') sensor_layout_obj = parse(sensor_layout_file) + summaryinfo['device'] = (sensor_layout_obj.getElementsByTagName('name') + [0].firstChild.data) sensors = sensor_layout_obj.getElementsByTagName('sensor') chan_type = list() chan_unit = list() @@ -209,24 +212,27 @@ mff_hdr = _read_mff_header(input_fname) with open(input_fname + '/signal1.bin', 'rb') as fid: version = np.fromfile(fid, np.int32, 1)[0] - # This should be equivalent to the following, but no need for external dep: - # import dateutil.parser - # time_n = dateutil.parser.parse(mff_hdr['date']) - dt = mff_hdr['date'][:26] - assert mff_hdr['date'][-6] in ('+', '-') - sn = -1 if mff_hdr['date'][-6] == '-' else 1 # + - tz = [sn * int(t) for t in (mff_hdr['date'][-5:-3], mff_hdr['date'][-2:])] - time_n = datetime.datetime.strptime(dt, '%Y-%m-%dT%H:%M:%S.%f') - time_n = time_n.replace(tzinfo=_FixedOffset(60 * tz[0] + tz[1])) + ''' + the datetime.strptime .f directive (milleseconds) + will only accept up to 6 digits. if there are more than + six millesecond digits in the provided timestamp string + (i.e. because of trailing zeros, as in test_egi_pns.mff) + then slice both the first 26 elements and the last 6 + elements of the timestamp string to truncate the + milleseconds to 6 digits and extract the timezone, + and then piece these together and assign back to mff_hdr['date'] + ''' + if len(mff_hdr['date']) > 32: + dt, tz = [mff_hdr['date'][:26], mff_hdr['date'][-6:]] + mff_hdr['date'] = dt + tz + + time_n = (datetime.datetime.strptime( + mff_hdr['date'], '%Y-%m-%dT%H:%M:%S.%f%z')) + info = dict( version=version, - year=int(time_n.strftime('%Y')), - month=int(time_n.strftime('%m')), - day=int(time_n.strftime('%d')), - hour=int(time_n.strftime('%H')), - minute=int(time_n.strftime('%M')), - second=int(time_n.strftime('%S')), - millisecond=int(time_n.strftime('%f')), + meas_dt_local=time_n, + utc_offset=time_n.strftime('%z'), gain=0, bits=0, value_range=0) @@ -250,13 +256,16 @@ return cals -def _read_locs(filepath, chs, egi_info): +def _read_locs(filepath, egi_info, channel_naming): """Read channel locations.""" from ...channels.montage import make_dig_montage fname = op.join(filepath, 'coordinates.xml') if not op.exists(fname): - return chs, None - reference_names = ('VREF', 'Vertex Reference') + logger.warn( + 'File coordinates.xml not found, not setting channel locations') + ch_names = [channel_naming % (i + 1) for i in + range(egi_info['n_channels'])] + return ch_names, None dig_ident_map = { 'Left periauricular point': 'lpa', 'Right periauricular point': 'rpa', @@ -268,10 +277,14 @@ ch_pos = OrderedDict() hsp = list() nlr = dict() + ch_names = list() + for sensor in sensors: name_element = sensor.getElementsByTagName('name')[0].firstChild - name = '' if name_element is None else name_element.data - nr = sensor.getElementsByTagName('number')[0].firstChild.data.encode() + num_element = sensor.getElementsByTagName('number')[0].firstChild + name = (channel_naming % int(num_element.data) if name_element is None + else name_element.data) + nr = num_element.data.encode() coords = [float(sensor.getElementsByTagName(coord)[0].firstChild.data) for coord in 'xyz'] loc = np.array(coords) / 100 # cm -> m @@ -279,16 +292,17 @@ if name in dig_ident_map: nlr[dig_ident_map[name]] = loc else: - if name in reference_names: - ch_pos['EEG000'] = loc - # add location to channel entry + # id_ is the index of the channel in egi_info['numbers'] id_ = np.flatnonzero(numbers == nr) + # if it's not in egi_info['numbers'], it's a headshape point if len(id_) == 0: hsp.append(loc) + # not HSP, must be a data or reference channel else: - ch_pos[chs[id_[0]]['ch_name']] = loc + ch_names.append(name) + ch_pos[name] = loc mon = make_dig_montage(ch_pos=ch_pos, hsp=hsp, **nlr) - return chs, mon + return ch_names, mon def _add_pns_channel_info(chs, egi_info, ch_names): @@ -450,17 +464,16 @@ self.event_id = None egi_info['new_trigger'] = None event_codes = [] - info = _empty_info(egi_info['sfreq']) - my_time = datetime.datetime( - egi_info['year'], egi_info['month'], egi_info['day'], - egi_info['hour'], egi_info['minute'], egi_info['second']) - my_timestamp = time.mktime(my_time.timetuple()) - info['meas_date'] = _ensure_meas_date_none_or_dt((my_timestamp, 0)) - # First: EEG - ch_names = [channel_naming % (i + 1) for i in - range(egi_info['n_channels'])] + meas_dt_utc = (egi_info['meas_dt_local'] + .astimezone(datetime.timezone.utc)) + info = _empty_info(egi_info['sfreq']) + info['meas_date'] = _ensure_meas_date_none_or_dt(meas_dt_utc) + info['utc_offset'] = egi_info['utc_offset'] + info['device_info'] = dict(type=egi_info['device']) + # read in the montage, if it exists + ch_names, mon = _read_locs(input_fname, egi_info, channel_naming) # Second: Stim ch_names.extend(list(egi_info['event_codes'])) if egi_info['new_trigger'] is not None: @@ -477,7 +490,7 @@ ch_coil = FIFF.FIFFV_COIL_EEG ch_kind = FIFF.FIFFV_EEG_CH chs = _create_chs(ch_names, cals, ch_coil, ch_kind, eog, (), (), misc) - chs, mon = _read_locs(input_fname, chs, egi_info) + sti_ch_idx = [i for i, name in enumerate(ch_names) if name.startswith('STI') or name in event_codes] for idx in sti_ch_idx: @@ -488,9 +501,24 @@ 'unit': FIFF.FIFF_UNIT_NONE}) chs = _add_pns_channel_info(chs, egi_info, ch_names) info['chs'] = chs + info._unlocked = False info._update_redundant() + if mon is not None: info.set_montage(mon, on_missing='ignore') + + ref_idx = np.flatnonzero(np.in1d(mon.ch_names, REFERENCE_NAMES)) + if len(ref_idx): + ref_coords = info['chs'][int(ref_idx)]['loc'][:3] + for chan in info['chs']: + is_eeg = chan['kind'] == FIFF.FIFFV_EEG_CH + is_not_ref = chan['ch_name'] not in REFERENCE_NAMES + if is_eeg and is_not_ref: + chan['loc'][3:6] = ref_coords + + # Cz ref was applied during acquisition, so mark as already set. + with info._unlock(): + info['custom_ref_applied'] = FIFF.FIFFV_MNE_CUSTOM_REF_ON file_bin = op.join(input_fname, egi_info['eeg_fname']) egi_info['egi_events'] = egi_events @@ -542,7 +570,7 @@ self._raw_extras = [egi_info] super(RawMff, self).__init__( - info, preload=preload, orig_format='float', filenames=[file_bin], + info, preload=preload, orig_format="single", filenames=[file_bin], first_samps=first_samps, last_samps=last_samps, raw_extras=[egi_info], verbose=verbose) @@ -561,6 +589,7 @@ def _read_segment_file(self, data, idx, fi, start, stop, cals, mult): """Read a chunk of data.""" + logger.debug(f'Reading MFF {start:6d} ... {stop:6d} ...') dtype = ' -1)[0] - if len(disk_use_idx): - start = disk_samps[disk_use_idx[0]] - stop = disk_samps[disk_use_idx[-1]] + 1 - assert len(disk_use_idx) == stop - start + # short circuit in case we don't need any samples + if not len(disk_use_idx): + _mult_cal_one(data, one, idx, cals, mult) + return + + start = disk_samps[disk_use_idx[0]] + stop = disk_samps[disk_use_idx[-1]] + 1 + assert len(disk_use_idx) == stop - start # Get starting/stopping block/samples block_samples_offset = np.cumsum(samples_block) - offset_blocks = np.sum(block_samples_offset < start) + offset_blocks = np.sum(block_samples_offset <= start) offset_samples = start - (block_samples_offset[offset_blocks - 1] if offset_blocks > 0 else 0) + # TODO: Refactor this reading with the PNS reading in a single function + # (DRY) samples_to_read = stop - start with open(self._filenames[fi], 'rb', buffering=0) as fid: # Go to starting block @@ -615,11 +650,13 @@ if this_block_info is not None: current_block_info = this_block_info fid.seek(current_block_info['block_size'], 1) - current_block = current_block + 1 + current_block += 1 # Start reading samples while samples_to_read > 0: + logger.debug(f' Reading from block {current_block}') this_block_info = _block_r(fid) + current_block += 1 if this_block_info is not None: current_block_info = this_block_info @@ -630,6 +667,8 @@ # Compute indexes samples_read = block_data.shape[1] + logger.debug(f' Read {samples_read} samples') + logger.debug(f' Offset {offset_samples} samples') if offset_samples > 0: # First block read, skip to the offset: block_data = block_data[:, offset_samples:] @@ -639,6 +678,7 @@ # Last block to read, skip the last samples block_data = block_data[:, :samples_to_read] samples_read = samples_to_read + logger.debug(f' Keep {samples_read} samples') s_start = current_data_sample s_end = s_start + samples_read @@ -675,7 +715,7 @@ if this_block_info is not None: current_block_info = this_block_info fid.seek(current_block_info['block_size'], 1) - current_block = current_block + 1 + current_block += 1 # Start reading samples while samples_to_read > 0: @@ -777,8 +817,12 @@ raise ValueError('fname must be an MFF file with extension ".mff".') # Confirm the input MFF is averaged mff = mffpy.Reader(fname) - if mff.flavor != 'averaged': - raise ValueError(f'{fname} is a {mff.flavor} MFF file. ' + try: + flavor = mff.mff_flavor + except AttributeError: # < 6.3 + flavor = mff.flavor + if flavor not in ('averaged', 'segmented'): # old, new names + raise ValueError(f'{fname} is a {flavor} MFF file. ' 'fname must be the path to an averaged MFF file.') # Check for categories.xml file if 'categories.xml' not in mff.directory.listdir(): @@ -837,11 +881,12 @@ # Load metadata into info object # Exclude info['meas_date'] because record time info in # averaged MFF is the time of the averaging, not true record time. - ch_names = [channel_naming % (i + 1) for i in - range(mff.num_channels['EEG'])] + ch_names, mon = _read_locs(fname, egi_info, channel_naming) ch_names.extend(egi_info['pns_names']) info = create_info(ch_names, mff.sampling_rates['EEG'], ch_types) - info['nchan'] = sum(mff.num_channels.values()) + with info._unlock(): + info['device_info'] = dict(type=egi_info['device']) + info['nchan'] = sum(mff.num_channels.values()) # Add individual channel info # Get calibration info for EEG channels @@ -851,10 +896,10 @@ ch_coil = FIFF.FIFFV_COIL_EEG ch_kind = FIFF.FIFFV_EEG_CH chs = _create_chs(ch_names, cals, ch_coil, ch_kind, (), (), (), ()) - chs, mon = _read_locs(fname, chs, egi_info) # Update PNS channel info chs = _add_pns_channel_info(chs, egi_info, ch_names) - info['chs'] = chs + with info._unlock(): + info['chs'] = chs if mon is not None: info.set_montage(mon, on_missing='ignore') @@ -873,7 +918,7 @@ if entry['signalBin'] == 1: # Add bad EEG channels for ch in entry['channels']: - bads.append(channel_naming % ch) + bads.append(ch_names[ch - 1]) elif entry['signalBin'] == 2: # Add bad PNS channels for ch in entry['channels']: @@ -882,17 +927,23 @@ # Add EEG reference to info # Initialize 'custom_ref_applied' to False - info['custom_ref_applied'] = False - with mff.directory.filepointer('history') as fp: - history = mffpy.XML.from_file(fp) - for entry in history.entries: - if entry['method'] == 'Montage Operations Tool': - if 'Average Reference' in entry['settings']: - # Average reference has been applied - projector, info = setup_proj(info) - else: - # Custom reference has been applied that is not an average - info['custom_ref_applied'] = True + with info._unlock(): + info['custom_ref_applied'] = False + try: + fp = mff.directory.filepointer('history') + except (ValueError, FileNotFoundError): # old (<=0.6.3) vs new mffpy + pass + else: + with fp: + history = mffpy.XML.from_file(fp) + for entry in history.entries: + if entry['method'] == 'Montage Operations Tool': + if 'Average Reference' in entry['settings']: + # Average reference has been applied + projector, info = setup_proj(info) + else: + # Custom reference has been applied that is not an average + info['custom_ref_applied'] = True # Get nave from categories.xml try: diff -Nru python-mne-0.23.4+dfsg/mne/io/egi/egi.py python-mne-1.1.0+dfsg/mne/io/egi/egi.py --- python-mne-0.23.4+dfsg/mne/io/egi/egi.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/egi/egi.py 2022-08-03 15:48:14.000000000 +0000 @@ -152,7 +152,7 @@ """ _validate_type(input_fname, 'path-like', 'input_fname') input_fname = str(input_fname) - if input_fname.endswith('.mff'): + if input_fname.rstrip('/\\').endswith('.mff'): # allows .mff or .mff/ return _read_raw_egi_mff(input_fname, eog, misc, include, exclude, preload, channel_naming, verbose) return RawEGI(input_fname, eog, misc, include, exclude, preload, @@ -256,9 +256,12 @@ 'coil_type': FIFF.FIFFV_COIL_NONE, 'unit': FIFF.FIFF_UNIT_NONE}) info['chs'] = chs + info._unlocked = False info._update_redundant() + orig_format = egi_info["orig_format"] \ + if egi_info["orig_format"] != "float" else "single" super(RawEGI, self).__init__( - info, preload, orig_format=egi_info['orig_format'], + info, preload, orig_format=orig_format, filenames=[input_fname], last_samps=[egi_info['n_samples'] - 1], raw_extras=[egi_info], verbose=verbose) diff -Nru python-mne-0.23.4+dfsg/mne/io/egi/events.py python-mne-1.1.0+dfsg/mne/io/egi/events.py --- python-mne-0.23.4+dfsg/mne/io/egi/events.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/egi/events.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- # -# License: BSD (3-clause) +# License: BSD-3-Clause from datetime import datetime from glob import glob diff -Nru python-mne-0.23.4+dfsg/mne/io/egi/general.py python-mne-1.1.0+dfsg/mne/io/egi/general.py --- python-mne-0.23.4+dfsg/mne/io/egi/general.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/egi/general.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- # -# License: BSD (3-clause) +# License: BSD-3-Clause import os from xml.dom.minidom import parse diff -Nru python-mne-0.23.4+dfsg/mne/io/egi/tests/test_egi.py python-mne-1.1.0+dfsg/mne/io/egi/tests/test_egi.py --- python-mne-0.23.4+dfsg/mne/io/egi/tests/test_egi.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/egi/tests/test_egi.py 2022-08-03 15:48:14.000000000 +0000 @@ -7,14 +7,15 @@ import os.path as op import os import shutil +from datetime import datetime, timezone import numpy as np from numpy.testing import assert_array_equal, assert_allclose import pytest from scipy import io as sio -from mne import find_events, pick_types -from mne.io import read_raw_egi, read_evokeds_mff +from mne import find_events, pick_types, pick_channels +from mne.io import read_raw_egi, read_evokeds_mff, read_raw_fif from mne.io.constants import FIFF from mne.io.egi.egi import _combine_triggers from mne.io.tests.test_raw import _test_raw_reader @@ -24,7 +25,8 @@ base_dir = op.join(op.dirname(op.abspath(__file__)), 'data') egi_fname = op.join(base_dir, 'test_egi.raw') egi_txt_fname = op.join(base_dir, 'test_egi.txt') -egi_path = op.join(data_path(download=False), 'EGI') +testing_path = data_path(download=False) +egi_path = op.join(testing_path, 'EGI') egi_mff_fname = op.join(egi_path, 'test_egi.mff') egi_mff_pns_fname = op.join(egi_path, 'test_egi_pns.mff') egi_pause_fname = op.join(egi_path, 'test_egi_multiepoch_paused.mff') @@ -105,33 +107,63 @@ @requires_testing_data +@pytest.mark.parametrize('fname', [ + egi_pause_fname, + egi_eprime_pause_fname, + egi_pause_w1337_fname, +]) +def test_egi_mff_pause_chunks(fname, tmp_path): + """Test that on-demand of all short segments works (via I/O).""" + fname_temp = tmp_path / 'test_raw.fif' + raw_data = read_raw_egi(fname, preload=True).get_data() + raw = read_raw_egi(fname) + with pytest.warns(RuntimeWarning, match='Acquisition skips detected'): + raw.save(fname_temp) + del raw + raw_data_2 = read_raw_fif(fname_temp).get_data() + assert_allclose(raw_data, raw_data_2) + + +@requires_testing_data def test_io_egi_mff(): """Test importing EGI MFF simple binary files.""" + # want vars for n chans + n_ref = 1 + n_eeg = 128 + n_card = 3 + raw = read_raw_egi(egi_mff_fname, include=None) assert ('RawMff' in repr(raw)) + assert raw.orig_format == "single" include = ['DIN1', 'DIN2', 'DIN3', 'DIN4', 'DIN5', 'DIN7'] raw = _test_raw_reader(read_raw_egi, input_fname=egi_mff_fname, include=include, channel_naming='EEG %03d', test_scaling=False, # XXX probably some bug ) assert raw.info['sfreq'] == 1000. - # The ref here is redundant, but we don't currently have a way in - # DigMontage to mark that a given channel is actually the ref so... - assert len(raw.info['dig']) == 133 # 129 eeg + 1 ref + 3 cardinal points - assert raw.info['dig'][0]['ident'] == 1 # EEG channel E1 - assert raw.info['dig'][3]['ident'] == 0 # Reference channel - assert raw.info['dig'][-1]['ident'] == 129 # Reference channel - ref_loc = raw.info['dig'][3]['r'] + assert len(raw.info['dig']) == n_card + n_eeg + n_ref + assert raw.info['dig'][0]['ident'] == FIFF.FIFFV_POINT_LPA + assert raw.info['dig'][0]['kind'] == FIFF.FIFFV_POINT_CARDINAL + assert raw.info['dig'][3]['kind'] == FIFF.FIFFV_POINT_EEG + assert raw.info['dig'][-1]['ident'] == 129 + assert raw.info['custom_ref_applied'] == FIFF.FIFFV_MNE_CUSTOM_REF_ON + ref_loc = raw.info['dig'][-1]['r'] eeg_picks = pick_types(raw.info, eeg=True) - assert len(eeg_picks) == 129 + assert len(eeg_picks) == n_eeg + n_ref # 129 + # ref channel doesn't store its own loc as ref location + # so don't test it + ref_pick = pick_channels(raw.info['ch_names'], ['VREF']) + eeg_picks = np.setdiff1d(eeg_picks, ref_pick) for i in eeg_picks: loc = raw.info['chs'][i]['loc'] assert loc[:3].any(), loc[:3] assert_array_equal(loc[3:6], ref_loc, err_msg=f'{i}') + assert raw.info['device_info']['type'] == 'HydroCel GSN 128 1.0' assert 'eeg' in raw + # test our custom channel naming logic functionality eeg_chan = [c for c in raw.ch_names if 'EEG' in c] - assert len(eeg_chan) == 129 + assert len(eeg_chan) == n_eeg # 128: VREF will not match in comprehension assert 'STI 014' in raw.ch_names events = find_events(raw, stim_channel='STI 014') @@ -177,7 +209,7 @@ ) assert 'eeg' in raw - + assert raw.orig_format == "single" eeg_chan = [c for c in raw.ch_names if c.startswith('E')] assert len(eeg_chan) == 256 picks = pick_types(raw.info, eeg=True) @@ -207,7 +239,7 @@ @requires_testing_data -def test_io_egi_pns_mff(tmpdir): +def test_io_egi_pns_mff(tmp_path): """Test importing EGI MFF with PNS data.""" raw = read_raw_egi(egi_mff_pns_fname, include=None, preload=True, verbose='error') @@ -237,7 +269,7 @@ 'Resp_Effort_Abdomen', 'EMGLeg' ] - egi_fname_mat = op.join(data_path(), 'EGI', 'test_egi_pns.mat') + egi_fname_mat = op.join(testing_path, 'EGI', 'test_egi_pns.mat') mc = sio.loadmat(egi_fname_mat) for ch_name, ch_idx, mat_name in zip(pns_names, pns_chans, mat_names): print('Testing {}'.format(ch_name)) @@ -248,7 +280,7 @@ assert_array_equal(mat_data, raw_data) # EEG missing - new_mff = str(tmpdir.join('temp.mff')) + new_mff = tmp_path / 'temp.mff' shutil.copytree(egi_mff_pns_fname, new_mff) read_raw_egi(new_mff, verbose='error') os.remove(op.join(new_mff, 'info1.xml')) @@ -261,14 +293,14 @@ @pytest.mark.parametrize('preload', (True, False)) def test_io_egi_pns_mff_bug(preload): """Test importing EGI MFF with PNS data (BUG).""" - egi_fname_mff = op.join(data_path(), 'EGI', 'test_egi_pns_bug.mff') + egi_fname_mff = op.join(testing_path, 'EGI', 'test_egi_pns_bug.mff') with pytest.warns(RuntimeWarning, match='EGI PSG sample bug'): raw = read_raw_egi(egi_fname_mff, include=None, preload=preload, verbose='warning') assert len(raw.annotations) == 1 assert_allclose(raw.annotations.duration, [0.004]) assert_allclose(raw.annotations.onset, [13.948]) - egi_fname_mat = op.join(data_path(), 'EGI', 'test_egi_pns.mat') + egi_fname_mat = op.join(testing_path, 'EGI', 'test_egi_pns.mat') mc = sio.loadmat(egi_fname_mat) pns_chans = pick_types(raw.info, ecg=True, bio=True, emg=True) pns_names = ['Resp. Temperature'[:15], @@ -301,26 +333,32 @@ @requires_testing_data def test_io_egi_crop_no_preload(): """Test crop non-preloaded EGI MFF data (BUG).""" - egi_fname_mff = op.join(data_path(), 'EGI', 'test_egi.mff') - raw = read_raw_egi(egi_fname_mff, preload=False) + raw = read_raw_egi(egi_mff_fname, preload=False) raw.crop(17.5, 20.5) raw.load_data() - raw_preload = read_raw_egi(egi_fname_mff, preload=True) + raw_preload = read_raw_egi(egi_mff_fname, preload=True) raw_preload.crop(17.5, 20.5) raw_preload.load_data() assert_allclose(raw._data, raw_preload._data) +@pytest.mark.filterwarnings('ignore::FutureWarning') @requires_version('mffpy', '0.5.7') @requires_testing_data @pytest.mark.parametrize('idx, cond, tmax, signals, bads', [ (0, 'Category 1', 0.016, egi_txt_evoked_cat1_fname, ['E8', 'E11', 'E17', 'E28', 'ECG']), (1, 'Category 2', 0.0, egi_txt_evoked_cat2_fname, - ['E257', 'EMG']) + ['VREF', 'EMG']) ]) def test_io_egi_evokeds_mff(idx, cond, tmax, signals, bads): """Test reading evoked MFF file.""" + # expected n channels + n_eeg = 256 + n_ref = 1 + n_card = 3 + n_pns = 2 # 1 ECG + 1 EMG + # Test reading all conditions from evokeds evokeds = read_evokeds_mff(egi_mff_evoked_fname) assert len(evokeds) == 2 @@ -331,7 +369,7 @@ with pytest.raises(ValueError) as exc_info: read_evokeds_mff(egi_mff_evoked_fname, condition='Invalid Condition') message = "Invalid value for the 'condition' parameter provided as " \ - "category name. Allowed values are 'Category 1', and " \ + "category name. Allowed values are 'Category 1' and " \ "'Category 2', but got 'Invalid Condition' instead." assert str(exc_info.value) == message with pytest.raises(ValueError) as exc_info: @@ -359,19 +397,21 @@ assert object_diff(evoked_cond.info, evoked_idx.info) == '' assert evoked_cond.info['description'] == cond assert evoked_cond.info['bads'] == bads - assert len(evoked_cond.info['ch_names']) == 259 + assert len(evoked_cond.info['ch_names']) == n_eeg + n_ref + n_pns # 259 assert 'ECG' in evoked_cond.info['ch_names'] assert 'EMG' in evoked_cond.info['ch_names'] assert 'ecg' in evoked_cond assert 'emg' in evoked_cond pick_eeg = pick_types(evoked_cond.info, eeg=True, exclude=[]) - assert len(pick_eeg) == 257 - assert evoked_cond.info['nchan'] == 259 + assert len(pick_eeg) == n_eeg + n_ref # 257 + assert evoked_cond.info['nchan'] == n_eeg + n_ref + n_pns # 259 assert evoked_cond.info['sfreq'] == 250.0 assert not evoked_cond.info['custom_ref_applied'] - assert evoked_cond.info['dig'] is None + assert len(evoked_cond.info['dig']) == n_card + n_eeg + n_ref + assert evoked_cond.info['device_info']['type'] == 'HydroCel GSN 256 1.0' +@pytest.mark.filterwarnings('ignore::FutureWarning') @requires_version('mffpy', '0.5.7') @requires_testing_data def test_read_evokeds_mff_bad_input(): @@ -415,3 +455,40 @@ assert_allclose(loc[1:], 0, atol=1e-7, err_msg='RPA') for d in info['dig'][3:]: assert d['kind'] == FIFF.FIFFV_POINT_EEG + + +@requires_testing_data +@pytest.mark.parametrize('fname, timestamp, utc_offset', [ + (egi_mff_fname, '2017-02-23T11:35:13.220824+01:00', '+0100'), + (egi_mff_pns_fname, '2017-09-20T09:55:44.072000+01:00', '+0100'), + (egi_eprime_pause_fname, '2018-07-30T10:46:09.621673-04:00', '-0400'), + (egi_pause_w1337_fname, '2019-10-14T10:54:27.395210-07:00', '-0700'), +]) +def test_meas_date(fname, timestamp, utc_offset): + """Test meas date conversion.""" + raw = read_raw_egi(fname, verbose='warning') + dt = datetime.strptime(timestamp, '%Y-%m-%dT%H:%M:%S.%f%z') + measdate = dt.astimezone(timezone.utc) + hour_local = int(dt.strftime('%H')) + hour_utc = int(raw.info['meas_date'].strftime('%H')) + local_utc_diff = hour_local - hour_utc + assert raw.info['meas_date'] == measdate + assert raw.info['utc_offset'] == utc_offset + assert local_utc_diff == int(utc_offset[:-2]) + + +@requires_testing_data +@pytest.mark.parametrize('fname, standard_montage', [ + (egi_mff_fname, 'GSN-HydroCel-129'), # 129 chan EGI file + (egi_mff_pns_fname, 'GSN-HydroCel-257') # 257 chan EGI file +]) +def test_set_standard_montage(fname, standard_montage): + """Test setting a standard montage.""" + raw = read_raw_egi(fname, verbose='warning') + dig_before_mon = raw.info['dig'] + + raw.set_montage(standard_montage, match_alias=True, on_missing='ignore') + dig_after_mon = raw.info['dig'] + + # No dig entries should have been dropped while setting montage + assert len(dig_before_mon) == len(dig_after_mon) diff -Nru python-mne-0.23.4+dfsg/mne/io/eximia/eximia.py python-mne-1.1.0+dfsg/mne/io/eximia/eximia.py --- python-mne-0.23.4+dfsg/mne/io/eximia/eximia.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/eximia/eximia.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors: Eric Larson # Federico Raimondo # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op diff -Nru python-mne-0.23.4+dfsg/mne/io/eximia/__init__.py python-mne-1.1.0+dfsg/mne/io/eximia/__init__.py --- python-mne-0.23.4+dfsg/mne/io/eximia/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/eximia/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,6 +2,6 @@ # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause from .eximia import read_raw_eximia diff -Nru python-mne-0.23.4+dfsg/mne/io/eximia/tests/test_eximia.py python-mne-1.1.0+dfsg/mne/io/eximia/tests/test_eximia.py --- python-mne-0.23.4+dfsg/mne/io/eximia/tests/test_eximia.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/eximia/tests/test_eximia.py 2022-08-03 15:48:14.000000000 +0000 @@ -8,20 +8,21 @@ from mne.io import read_raw_eximia from mne.io.tests.test_raw import _test_raw_reader -from mne.utils import run_tests_if_main from mne.datasets.testing import data_path, requires_testing_data +testing_path = data_path(download=False) + @requires_testing_data def test_eximia_nxe(): """Test reading Eximia NXE files.""" - fname = op.join(data_path(), 'eximia', 'test_eximia.nxe') + fname = op.join(testing_path, 'eximia', 'test_eximia.nxe') raw = read_raw_eximia(fname, preload=True) assert 'RawEximia' in repr(raw) _test_raw_reader(read_raw_eximia, fname=fname, test_scaling=False, # XXX probably a scaling problem ) - fname_mat = op.join(data_path(), 'eximia', 'test_eximia.mat') + fname_mat = op.join(testing_path, 'eximia', 'test_eximia.mat') mc = sio.loadmat(fname_mat) m_data = mc['data'] m_header = mc['header'] @@ -42,6 +43,3 @@ assert ch_types == m_ch_types assert_array_equal(m_data, raw._data) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/io/fieldtrip/fieldtrip.py python-mne-1.1.0+dfsg/mne/io/fieldtrip/fieldtrip.py --- python-mne-0.23.4+dfsg/mne/io/fieldtrip/fieldtrip.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/fieldtrip/fieldtrip.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,13 +2,13 @@ # Authors: Thomas Hartmann # Dirk Gütlin # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np from .utils import _create_info, _set_tmin, _create_events, \ _create_event_metadata, _validate_ft_struct -from ...utils import _check_fname +from ...utils import _check_fname, _import_pymatreader_funcs from ..array.array import RawArray from ...epochs import EpochsArray from ...evoked import EvokedArray @@ -44,7 +44,7 @@ raw : instance of RawArray A Raw Object containing the loaded data. """ - from ...externals.pymatreader import read_mat + read_mat = _import_pymatreader_funcs('FieldTrip I/O') fname = _check_fname(fname, overwrite='read', must_exist=True) ft_struct = read_mat(fname, @@ -109,7 +109,7 @@ epochs : instance of EpochsArray An EpochsArray containing the loaded data. """ - from ...externals.pymatreader import read_mat + read_mat = _import_pymatreader_funcs('FieldTrip I/O') ft_struct = read_mat(fname, ignore_fields=['previous'], variable_names=[data_name]) @@ -166,7 +166,7 @@ evoked : instance of EvokedArray An EvokedArray containing the loaded data. """ - from ...externals.pymatreader import read_mat + read_mat = _import_pymatreader_funcs('FieldTrip I/O') ft_struct = read_mat(fname, ignore_fields=['previous'], variable_names=[data_name]) diff -Nru python-mne-0.23.4+dfsg/mne/io/fieldtrip/__init__.py python-mne-1.1.0+dfsg/mne/io/fieldtrip/__init__.py --- python-mne-0.23.4+dfsg/mne/io/fieldtrip/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/fieldtrip/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Authors: Thomas Hartmann # Dirk Gütlin # -# License: BSD (3-clause) +# License: BSD-3-Clause from .fieldtrip import (read_evoked_fieldtrip, read_epochs_fieldtrip, read_raw_fieldtrip) diff -Nru python-mne-0.23.4+dfsg/mne/io/fieldtrip/tests/helpers.py python-mne-1.1.0+dfsg/mne/io/fieldtrip/tests/helpers.py --- python-mne-0.23.4+dfsg/mne/io/fieldtrip/tests/helpers.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/fieldtrip/tests/helpers.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,27 +2,27 @@ # Authors: Thomas Hartmann # Dirk Gütlin # -# License: BSD (3-clause) +# License: BSD-3-Clause from functools import partial import os -import types import numpy as np import mne +from mne.io.constants import FIFF +from mne.utils import object_diff info_ignored_fields = ('file_id', 'hpi_results', 'hpi_meas', 'meas_id', 'meas_date', 'highpass', 'lowpass', 'subject_info', 'hpi_subsystem', 'experimenter', 'description', 'proj_id', 'proj_name', 'line_freq', 'gantry_angle', - 'dev_head_t', 'dig', 'bads', 'projs', 'ctf_head_t', - 'dev_ctf_t') + 'dev_head_t', 'bads', 'ctf_head_t', 'dev_ctf_t') ch_ignore_fields = ('logno', 'cal', 'range', 'scanno', 'coil_type', 'kind', 'loc', 'coord_frame', 'unit') -info_long_fields = ('hpi_meas', ) +info_long_fields = ('hpi_meas', 'projs') system_to_reader_fn_dict = {'neuromag306': mne.io.read_raw_fif, 'CNT': partial(mne.io.read_raw_cnt), @@ -47,13 +47,7 @@ 'returning the original trialinfo matrix as ' \ 'metadata.' - -def _has_h5py(): - try: - import h5py # noqa - return True - except ImportError: - return False +testing_path = mne.datasets.testing.data_path(download=False) def _remove_ignored_ch_fields(info): @@ -76,19 +70,28 @@ del info[cur_field] _remove_ignored_ch_fields(info) + _remove_bad_dig_fields(info) + + +def _remove_bad_dig_fields(info): + # The reference location appears to be lost, so we cannot add it. + # Similarly, fiducial locations do not appear to be stored, so we + # cannot add those, either. Same with HPI coils. + if info['dig'] is not None: + with info._unlock(): + info['dig'] = [d for d in info['dig'] + if d['kind'] == FIFF.FIFFV_POINT_EEG and + d['ident'] != 0] # ref def get_data_paths(system): """Return common paths for all tests.""" - test_data_folder_ft = os.path.join(mne.datasets.testing.data_path(), - 'fieldtrip/ft_test_data', system) - - return test_data_folder_ft + return testing_path / 'fieldtrip' / 'ft_test_data' / system def get_cfg_local(system): """Return cfg_local field for the system.""" - from mne.externals.pymatreader import read_mat + from pymatreader import read_mat cfg_local = read_mat(os.path.join(get_data_paths(system), 'raw_v7.mat'), ['cfg_local'])['cfg_local'] @@ -99,12 +102,12 @@ """Return the info dict of the raw data.""" cfg_local = get_cfg_local(system) - raw_data_file = os.path.join(mne.datasets.testing.data_path(), - cfg_local['file_name']) + raw_data_file = os.path.join(testing_path, cfg_local['file_name']) reader_function = system_to_reader_fn_dict[system] info = reader_function(raw_data_file, preload=False).info - info['comps'] = [] + with info._unlock(): + info['comps'] = [] return info @@ -112,8 +115,7 @@ """Find, load and process the raw data.""" cfg_local = get_cfg_local(system) - raw_data_file = os.path.join(mne.datasets.testing.data_path(), - cfg_local['file_name']) + raw_data_file = os.path.join(testing_path, cfg_local['file_name']) reader_function = system_to_reader_fn_dict[system] raw_data = reader_function(raw_data_file, preload=True) @@ -122,7 +124,8 @@ crop -= 0.5 * (1.0 / raw_data.info['sfreq']) raw_data.crop(0, crop) raw_data.del_proj('all') - raw_data.info['comps'] = [] + with raw_data.info._unlock(): + raw_data.info['comps'] = [] raw_data.drop_channels(cfg_local['removed_chan_names']) if system in ['EGI']: @@ -175,7 +178,6 @@ def get_evoked(system): """Find, load and process the avg data.""" epochs = get_epochs(system) - return epochs.average(picks=np.arange(len(epochs.ch_names))) @@ -192,11 +194,18 @@ _remove_ignored_info_fields(expected) _remove_ignored_info_fields(actual) - if info_long_fields: - _remove_long_info_fields(expected) - _remove_long_info_fields(actual) + _remove_long_info_fields(expected) + _remove_long_info_fields(actual) + + # we annoyingly have two ways of representing this, so just always use + # an empty list here + for obj in (expected, actual): + if obj['dig'] is None: + with obj._unlock(): + obj['dig'] = [] - assert_deep_almost_equal(expected, actual) + d = object_diff(actual, expected, allclose=True) + assert d == '', d def check_data(expected, actual, system): @@ -208,63 +217,6 @@ np.testing.assert_almost_equal(expected, actual, decimal=decimal) -def assert_deep_almost_equal(expected, actual, *args, **kwargs): - """ - Assert that two complex structures have almost equal contents. - - Compares lists, dicts and tuples recursively. Checks numeric values - using test_case's :py:meth:`unittest.TestCase.assertAlmostEqual` and - checks all other values with :py:meth:`unittest.TestCase.assertEqual`. - Accepts additional positional and keyword arguments and pass those - intact to assertAlmostEqual() (that's how you specify comparison - precision). - - This code has been adapted from - https://github.com/larsbutler/oq-engine/blob/master/tests/utils/helpers.py - """ - is_root = '__trace' not in kwargs - trace = kwargs.pop('__trace', 'ROOT') - - if isinstance(expected, np.ndarray) and expected.size == 0: - expected = None - - if isinstance(actual, np.ndarray) and actual.size == 0: - actual = None - - try: - if isinstance(expected, (int, float, complex)): - np.testing.assert_almost_equal(expected, actual, *args, **kwargs) - elif isinstance(expected, (list, tuple, np.ndarray, - types.GeneratorType)): - if isinstance(expected, types.GeneratorType): - expected = list(expected) - actual = list(actual) - - np.testing.assert_equal(len(expected), len(actual)) - for index in range(len(expected)): - v1, v2 = expected[index], actual[index] - assert_deep_almost_equal(v1, v2, - __trace=repr(index), *args, **kwargs) - elif isinstance(expected, dict): - np.testing.assert_equal(set(expected), set(actual)) - for key in expected: - assert_deep_almost_equal(expected[key], actual[key], - __trace=repr(key), *args, **kwargs) - else: - np.testing.assert_equal(expected, actual) - except AssertionError as exc: - exc.__dict__.setdefault('traces', []).append(trace) - if is_root: - trace = ' -> '.join(reversed(exc.traces)) - message = '' - try: - message = exc.message - except AttributeError: - pass - exc = AssertionError("%s\nTRACE: %s" % (message, trace)) - raise exc - - def assert_warning_in_record(warning_message, warn_record): """Assert that a warning message is in the records.""" all_messages = [str(w.message) for w in warn_record] diff -Nru python-mne-0.23.4+dfsg/mne/io/fieldtrip/tests/__init__.py python-mne-1.1.0+dfsg/mne/io/fieldtrip/tests/__init__.py --- python-mne-0.23.4+dfsg/mne/io/fieldtrip/tests/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/fieldtrip/tests/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,4 +2,4 @@ # Authors: Thomas Hartmann # Dirk Gütlin # -# License: BSD (3-clause) +# License: BSD-3-Clause diff -Nru python-mne-0.23.4+dfsg/mne/io/fieldtrip/tests/test_fieldtrip.py python-mne-1.1.0+dfsg/mne/io/fieldtrip/tests/test_fieldtrip.py --- python-mne-0.23.4+dfsg/mne/io/fieldtrip/tests/test_fieldtrip.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/fieldtrip/tests/test_fieldtrip.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,23 +2,24 @@ # Authors: Thomas Hartmann # Dirk Gütlin # -# License: BSD (3-clause) +# License: BSD-3-Clause -import mne -import os.path -import pytest +from contextlib import nullcontext import copy import itertools +import os.path + +import pytest import numpy as np + +import mne from mne.datasets import testing from mne.io.fieldtrip.utils import NOINFO_WARNING, _create_events -from mne.utils import _check_pandas_installed, requires_h5py -from mne.io.fieldtrip.tests.helpers import (check_info_fields, get_data_paths, - get_raw_data, get_epochs, - get_evoked, _has_h5py, - pandas_not_found_warning_msg, - get_raw_info, check_data, - assert_warning_in_record) +from mne.utils import _check_pandas_installed, _record_warnings +from mne.io.fieldtrip.tests.helpers import ( + check_info_fields, get_data_paths, get_raw_data, get_epochs, get_evoked, + pandas_not_found_warning_msg, get_raw_info, check_data, + assert_warning_in_record) # missing: KIT: biggest problem here is that the channels do not have the same # names. @@ -45,6 +46,9 @@ no_info_warning = {'expected_warning': RuntimeWarning, 'match': NOINFO_WARNING} +pymatreader = pytest.importorskip('pymatreader') # module-level +testing_path = mne.datasets.testing.data_path(download=False) + @pytest.mark.slowtest @testing.requires_testing_data @@ -60,19 +64,14 @@ mne_avg = get_evoked(cur_system) if use_info: info = get_raw_info(cur_system) - pytestwarning = {'expected_warning': None} + ctx = nullcontext() else: info = None - pytestwarning = no_info_warning + ctx = pytest.warns(**no_info_warning) cur_fname = os.path.join(test_data_folder_ft, 'averaged_%s.mat' % (version,)) - if version == 'v73' and not _has_h5py(): - with pytest.raises(ImportError): - mne.io.read_evoked_fieldtrip(cur_fname, info) - return - - with pytest.warns(**pytestwarning): + with ctx: avg_ft = mne.io.read_evoked_fieldtrip(cur_fname, info) mne_data = mne_avg.data[:, :-1] @@ -102,49 +101,40 @@ mne_epoched = get_epochs(cur_system) if use_info: info = get_raw_info(cur_system) - pytestwarning = {'expected_warning': None} + ctx = nullcontext() else: info = None - pytestwarning = no_info_warning + ctx = pytest.warns(**no_info_warning) cur_fname = os.path.join(test_data_folder_ft, 'epoched_%s.mat' % (version,)) if has_pandas: - if version == 'v73' and not _has_h5py(): - with pytest.raises(ImportError): - mne.io.read_epochs_fieldtrip(cur_fname, info) - return - with pytest.warns(**pytestwarning): + with ctx: epoched_ft = mne.io.read_epochs_fieldtrip(cur_fname, info) assert isinstance(epoched_ft.metadata, pandas.DataFrame) else: - with pytest.warns(None) as warn_record: - if version == 'v73' and not _has_h5py(): - with pytest.raises(ImportError): - mne.io.read_epochs_fieldtrip(cur_fname, info) - return + with _record_warnings() as warn_record: epoched_ft = mne.io.read_epochs_fieldtrip(cur_fname, info) assert epoched_ft.metadata is None assert_warning_in_record(pandas_not_found_warning_msg, warn_record) - if pytestwarning['expected_warning'] is not None: - assert_warning_in_record(pytestwarning['match'], warn_record) + if info is None: + assert_warning_in_record(NOINFO_WARNING, warn_record) mne_data = mne_epoched.get_data()[:, :, :-1] ft_data = epoched_ft.get_data() check_data(mne_data, ft_data, cur_system) check_info_fields(mne_epoched, epoched_ft, use_info) + read_mat = pymatreader.read_mat # weird sfreq - from mne.externals.pymatreader import read_mat - def modify_mat(fname, variable_names=None, ignore_fields=None): out = read_mat(fname, variable_names, ignore_fields) if 'fsample' in out['data']: out['data']['fsample'] = np.repeat(out['data']['fsample'], 2) return out - monkeypatch.setattr(mne.externals.pymatreader, 'read_mat', modify_mat) + monkeypatch.setattr(pymatreader, 'read_mat', modify_mat) with pytest.warns(RuntimeWarning, match='multiple'): mne.io.read_epochs_fieldtrip(cur_fname, info) @@ -162,19 +152,18 @@ raw_fiff_mne = get_raw_data(cur_system, drop_extra_chs=True) if use_info: info = get_raw_info(cur_system) - pytestwarning = {'expected_warning': None} + if cur_system in ('BTI', 'eximia'): + ctx = pytest.warns(RuntimeWarning, match='cannot be found in') + else: + ctx = nullcontext() else: info = None - pytestwarning = no_info_warning + ctx = pytest.warns(**no_info_warning) cur_fname = os.path.join(test_data_folder_ft, 'raw_%s.mat' % (version,)) - if version == 'v73' and not _has_h5py(): - with pytest.raises(ImportError): - mne.io.read_raw_fieldtrip(cur_fname, info) - return - with pytest.warns(**pytestwarning): + with ctx: raw_fiff_ft = mne.io.read_raw_fieldtrip(cur_fname, info) if cur_system == 'BTI' and not use_info: @@ -221,11 +210,9 @@ @testing.requires_testing_data def test_create_events(): """Test 2dim trialinfo fields.""" - from mne.externals.pymatreader import read_mat - test_data_folder_ft = get_data_paths('neuromag306') cur_fname = os.path.join(test_data_folder_ft, 'epoched_v7.mat') - original_data = read_mat(cur_fname, ['data', ]) + original_data = pymatreader.read_mat(cur_fname, ['data', ]) new_data = copy.deepcopy(original_data) new_data['trialinfo'] = np.array([[1, 2, 3, 4], @@ -245,10 +232,9 @@ @testing.requires_testing_data @pytest.mark.parametrize('version', all_versions) -@requires_h5py def test_one_channel_elec_bug(version): """Test if loading data having only one elec in the elec field works.""" - fname = os.path.join(mne.datasets.testing.data_path(), 'fieldtrip', + fname = os.path.join(testing_path, 'fieldtrip', 'one_channel_elec_bug_data_%s.mat' % (version, )) with pytest.warns(**no_info_warning): @@ -262,7 +248,6 @@ @pytest.mark.filterwarnings('ignore:.*number of bytes.*:RuntimeWarning') @pytest.mark.parametrize('version', all_versions) @pytest.mark.parametrize('type', ['averaged', 'epoched', 'raw']) -@requires_h5py def test_throw_exception_on_cellarray(version, type): """Test for a meaningful exception when the data is a cell array.""" fname = os.path.join(get_data_paths('cellarray'), @@ -303,9 +288,7 @@ @pytest.mark.filterwarnings('ignore: Cannot guess the correct type') def test_throw_error_on_non_uniform_time_field(): """Test if an error is thrown when time fields are not uniform.""" - fname = os.path.join(mne.datasets.testing.data_path(), - 'fieldtrip', - 'not_uniform_time.mat') + fname = os.path.join(testing_path, 'fieldtrip', 'not_uniform_time.mat') with pytest.raises(RuntimeError, match='Loading data with non-uniform ' 'times per epoch is not supported'): @@ -316,9 +299,7 @@ @pytest.mark.filterwarnings('ignore: Importing FieldTrip data without an info') def test_throw_error_when_importing_old_ft_version_data(): """Test if an error is thrown if the data was saved with an old version.""" - fname = os.path.join(mne.datasets.testing.data_path(), - 'fieldtrip', - 'old_version.mat') + fname = os.path.join(testing_path, 'fieldtrip', 'old_version.mat') with pytest.raises(RuntimeError, match='This file was created with ' 'an old version of FieldTrip. You ' diff -Nru python-mne-0.23.4+dfsg/mne/io/fieldtrip/utils.py python-mne-1.1.0+dfsg/mne/io/fieldtrip/utils.py --- python-mne-0.23.4+dfsg/mne/io/fieldtrip/utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/fieldtrip/utils.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,15 +2,15 @@ # Authors: Thomas Hartmann # Dirk Gütlin # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np +from .._digitization import DigPoint +from ..constants import FIFF from ..meas_info import create_info +from ..pick import pick_info from ...transforms import rotation3d_align_z_axis -from ...channels import make_dig_montage -from ..constants import FIFF from ...utils import warn, _check_pandas_installed -from ..pick import pick_info _supported_megs = ['neuromag306'] @@ -69,17 +69,15 @@ missing_chan_idx, axis=0) - info['sfreq'] = sfreq + with info._unlock(): + info['sfreq'] = sfreq ch_idx = [info['ch_names'].index(ch) for ch in ch_names] pick_info(info, ch_idx, copy=False) else: - montage = _create_montage(ft_struct) - info = create_info(ch_names, sfreq) - info.set_montage(montage) - chs = _create_info_chs(ft_struct) - info['chs'] = chs - info._update_redundant() + chs, dig = _create_info_chs_dig(ft_struct) + with info._unlock(update_redundant=True): + info.update(chs=chs, dig=dig) return info @@ -102,7 +100,7 @@ return trial -def _create_info_chs(ft_struct): +def _create_info_chs_dig(ft_struct): """Create the chs info field from the FieldTrip structure.""" all_channels = ft_struct['label'] ch_defaults = dict(coord_frame=FIFF.FIFFV_COORD_UNKNOWN, @@ -125,8 +123,16 @@ warn('The supplied FieldTrip structure does not have an elec or grad ' 'field. No channel locations will extracted and the kind of ' 'channel might be inaccurate.') + if 'chanpos' not in (elec or grad or {'chanpos': None}): + raise RuntimeError( + 'This file was created with an old version of FieldTrip. You can ' + 'convert the data to the new version by loading it into FieldTrip ' + 'and applying ft_selectdata with an empty cfg structure on it. ' + 'Otherwise you can supply the Info field.') chs = list() + dig = list() + counter = 0 for idx_chan, cur_channel_label in enumerate(all_channels): cur_ch = ch_defaults.copy() cur_ch['ch_name'] = cur_channel_label @@ -134,7 +140,12 @@ cur_ch['scanno'] = idx_chan + 1 if elec and cur_channel_label in elec['label']: cur_ch = _process_channel_eeg(cur_ch, elec) - + # Ref gets ident=0 and we don't have it, so start at 1 + counter += 1 + d = DigPoint( + r=cur_ch['loc'][:3], coord_frame=FIFF.FIFFV_COORD_HEAD, + kind=FIFF.FIFFV_POINT_EEG, ident=counter) + dig.append(d) elif grad and cur_channel_label in grad['label']: cur_ch = _process_channel_meg(cur_ch, grad) else: @@ -155,46 +166,7 @@ chs.append(cur_ch) - return chs - - -def _create_montage(ft_struct): - """Create a montage from the FieldTrip data.""" - # try to create a montage - montage_pos, montage_ch_names = list(), list() - - for cur_ch_type in ('grad', 'elec'): - if cur_ch_type in ft_struct: - cur_ch_struct = ft_struct[cur_ch_type] - available_channels = np.where(np.in1d(cur_ch_struct['label'], - ft_struct['label']))[0] - tmp_labels = cur_ch_struct['label'] - if not isinstance(tmp_labels, list): - tmp_labels = [tmp_labels] - cur_labels = np.asanyarray(tmp_labels) - montage_ch_names.extend( - cur_labels[available_channels]) - try: - montage_pos.extend( - cur_ch_struct['chanpos'][available_channels]) - except KeyError: - raise RuntimeError('This file was created with an old version ' - 'of FieldTrip. You can convert the data to ' - 'the new version by loading it into ' - 'FieldTrip and applying ft_selectdata with ' - 'an empty cfg structure on it. ' - 'Otherwise you can supply the Info field.') - - montage = None - - if (len(montage_ch_names) > 0 and len(montage_pos) > 0 and - len(montage_ch_names) == len(montage_pos)): - montage = make_dig_montage( - ch_pos=dict(zip(montage_ch_names, montage_pos)), - # XXX: who grants 'head'?? this is BACKCOMPAT but seems a BUG - coord_frame='head', - ) - return montage + return chs, dig def _set_sfreq(ft_struct): @@ -293,14 +265,12 @@ all_labels = np.asanyarray(elec['label']) chan_idx_in_elec = np.where(all_labels == cur_ch['ch_name'])[0][0] position = np.squeeze(elec['chanpos'][chan_idx_in_elec, :]) - chanunit = elec['chanunit'][chan_idx_in_elec] + # chanunit = elec['chanunit'][chan_idx_in_elec] # not used/needed yet position_unit = elec['unit'] position = position * _unit_dict[position_unit] cur_ch['loc'] = np.hstack((position, np.zeros((9,)))) - cur_ch['loc'][-1] = 1 cur_ch['unit'] = FIFF.FIFF_UNIT_V - cur_ch['unit_mul'] = np.log10(_unit_dict[chanunit[0]]) cur_ch['kind'] = FIFF.FIFFV_EEG_CH cur_ch['coil_type'] = FIFF.FIFFV_COIL_EEG cur_ch['coord_frame'] = FIFF.FIFFV_COORD_HEAD @@ -350,7 +320,7 @@ orientation = np.eye(3) assert orientation.shape == (3, 3) orientation = orientation.flatten() - chanunit = grad['chanunit'][chan_idx_in_grad] + # chanunit = grad['chanunit'][chan_idx_in_grad] # not used/needed yet cur_ch['loc'] = np.hstack((position, orientation)) cur_ch['kind'] = FIFF.FIFFV_MEG_CH @@ -373,7 +343,6 @@ raise RuntimeError('Unexpected coil type: %s.' % ( chantype,)) - cur_ch['unit_mul'] = np.log10(_unit_dict[chanunit[0]]) cur_ch['coord_frame'] = FIFF.FIFFV_COORD_HEAD return cur_ch diff -Nru python-mne-0.23.4+dfsg/mne/io/fiff/raw.py python-mne-1.1.0+dfsg/mne/io/fiff/raw.py --- python-mne-0.23.4+dfsg/mne/io/fiff/raw.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/fiff/raw.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,7 +4,7 @@ # Denis Engemann # Teon Brooks # -# License: BSD (3-clause) +# License: BSD-3-Clause import copy import os @@ -56,8 +56,7 @@ Attributes ---------- - info : dict - :class:`Measurement info `. + %(info_not_none)s ch_names : list of string List of channels' names. n_times : int @@ -170,7 +169,8 @@ if (len(raw_node) == 0): raise ValueError('No raw data in %s' % fname_rep) _check_maxshield(allow_maxshield) - info['maxshield'] = True + with info._unlock(): + info['maxshield'] = True del meas if len(raw_node) == 1: @@ -202,6 +202,11 @@ raw = _RawShell() raw.filename = fname raw.first_samp = first_samp + if info['meas_date'] is None and annotations is not None: + # we need to adjust annotations.onset as when there is no meas + # date set_annotations considers that the origin of time is the + # first available sample (ignores first_samp) + annotations.onset -= first_samp / info['sfreq'] raw.set_annotations(annotations) # Go through the remaining tags in the directory @@ -309,7 +314,6 @@ float(raw.last_samp) / info['sfreq'])) raw.info = info - raw.verbose = verbose logger.info('Ready.') @@ -469,6 +473,11 @@ Notes ----- .. versionadded:: 0.9.0 + + When reading a FIF file, note that the first N seconds annotated + ``BAD_ACQ_SKIP`` are **skipped**. They are removed from ``raw.times`` and + ``raw.n_times`` parameters but ``raw.first_samp`` and ``raw.first_time`` + are updated accordingly. """ return Raw(fname=fname, allow_maxshield=allow_maxshield, preload=preload, verbose=verbose, diff -Nru python-mne-0.23.4+dfsg/mne/io/fiff/tests/test_raw_fiff.py python-mne-1.1.0+dfsg/mne/io/fiff/tests/test_raw_fiff.py --- python-mne-0.23.4+dfsg/mne/io/fiff/tests/test_raw_fiff.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/fiff/tests/test_raw_fiff.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,9 +2,10 @@ # Author: Alexandre Gramfort # Denis Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause from copy import deepcopy +from pathlib import Path from functools import partial from io import BytesIO import os @@ -30,7 +31,7 @@ compute_proj_raw, pick_types, pick_channels, create_info, pick_info) from mne.utils import (requires_pandas, assert_object_equal, _dt_to_stamp, - requires_mne, run_subprocess, + requires_mne, run_subprocess, _record_warnings, assert_and_remove_boundary_annot) from mne.annotations import Annotations @@ -53,7 +54,7 @@ @testing.requires_testing_data -def test_acq_skip(tmpdir): +def test_acq_skip(tmp_path): """Test treatment of acquisition skips.""" raw = read_raw_fif(skip_fname, preload=True) picks = [1, 2, 10] @@ -82,7 +83,7 @@ assert_allclose(data, np.concatenate(expected_data, axis=-1), atol=1e-22) # Check that acquisition skips are handled properly during I/O - fname = tmpdir.join('test_raw.fif') + fname = tmp_path / 'test_raw.fif' raw.save(fname, fmt=raw.orig_format) # first: file size should not increase much (orig data is missing # 7 of 17 buffers, so if we write them out it should increase the file @@ -97,7 +98,7 @@ assert_allclose(raw_read[:][0], raw[:][0], atol=1e-17) # Saving with a bad buffer length emits warning raw.pick_channels(raw.ch_names[:2]) - with pytest.warns(None) as w: + with _record_warnings() as w: raw.save(fname, buffer_size_sec=0.5, overwrite=True) assert len(w) == 0 with pytest.warns(RuntimeWarning, match='did not fit evenly'): @@ -106,10 +107,14 @@ def test_fix_types(): """Test fixing of channel types.""" - for fname, change in ((hp_fif_fname, True), (test_fif_fname, False), - (ctf_fname, False)): + for fname, change, bads in ( + (hp_fif_fname, True, ["MEG0111"]), + (test_fif_fname, False, []), + (ctf_fname, False, []) + ): raw = read_raw_fif(fname) - mag_picks = pick_types(raw.info, meg='mag') + raw.info["bads"] = bads + mag_picks = pick_types(raw.info, meg='mag', exclude=[]) other_picks = np.setdiff1d(np.arange(len(raw.ch_names)), mag_picks) # we don't actually have any files suffering from this problem, so # fake it @@ -128,12 +133,12 @@ FIFF.FIFFV_COIL_VV_MAG_T3).all()) -def test_concat(tmpdir): +def test_concat(tmp_path): """Test RawFIF concatenation.""" # we trim the file to save lots of memory and some time raw = read_raw_fif(test_fif_fname) raw.crop(0, 2.) - test_name = tmpdir.join('test_raw.fif') + test_name = tmp_path / 'test_raw.fif' raw.save(test_name) # now run the standard test _test_concat(partial(read_raw_fif), test_name) @@ -168,7 +173,7 @@ @testing.requires_testing_data -def test_subject_info(tmpdir): +def test_subject_info(tmp_path): """Test reading subject information.""" raw = read_raw_fif(fif_fname).crop(0, 1) assert (raw.info['subject_info'] is None) @@ -180,7 +185,7 @@ for key, val in zip(keys, vals): subject_info[key] = val raw.info['subject_info'] = subject_info - out_fname = tmpdir.join('test_subj_info_raw.fif') + out_fname = tmp_path / 'test_subj_info_raw.fif' raw.save(out_fname, overwrite=True) raw_read = read_raw_fif(out_fname) for key in keys: @@ -204,7 +209,7 @@ @testing.requires_testing_data -def test_output_formats(tmpdir): +def test_output_formats(tmp_path): """Test saving and loading raw data using multiple formats.""" formats = ['short', 'int', 'single', 'double'] tols = [1e-4, 1e-7, 1e-7, 1e-15] @@ -212,7 +217,7 @@ # let's fake a raw file with different formats raw = read_raw_fif(test_fif_fname).crop(0, 1) - temp_file = tmpdir.join('raw.fif') + temp_file = tmp_path / 'raw.fif' for ii, (fmt, tol) in enumerate(zip(formats, tols)): # Let's test the overwriting error throwing while we're at it if ii > 0: @@ -234,7 +239,7 @@ @pytest.mark.slowtest @testing.requires_testing_data -def test_multiple_files(tmpdir): +def test_multiple_files(tmp_path): """Test loading multiple files simultaneously.""" # split file raw = read_raw_fif(fif_fname).crop(0, 10) @@ -252,7 +257,7 @@ # going in reverse order so the last fname is the first file (need later) raws = [None] * len(tmins) for ri in range(len(tmins) - 1, -1, -1): - fname = tmpdir.join('test_raw_split-%d_raw.fif' % ri) + fname = tmp_path / ('test_raw_split-%d_raw.fif' % ri) raw.save(fname, tmin=tmins[ri], tmax=tmaxs[ri]) raws[ri] = read_raw_fif(fname) assert (len(raws[ri].times) == @@ -360,23 +365,43 @@ @testing.requires_testing_data +@pytest.mark.parametrize('on_mismatch', ('ignore', 'warn', 'raise')) +def test_concatenate_raws(on_mismatch): + """Test error handling during raw concatenation.""" + raw = read_raw_fif(fif_fname).crop(0, 10) + raws = [raw, raw.copy()] + raws[1].info['dev_head_t']['trans'] += 0.1 + kws = dict(raws=raws, on_mismatch=on_mismatch) + + if on_mismatch == 'ignore': + concatenate_raws(**kws) + elif on_mismatch == 'warn': + with pytest.warns(RuntimeWarning, match='different head positions'): + concatenate_raws(**kws) + elif on_mismatch == 'raise': + with pytest.raises(ValueError, match='different head positions'): + concatenate_raws(**kws) + + +@testing.requires_testing_data @pytest.mark.parametrize('mod', ( 'meg', - pytest.param('raw', marks=[pytest.mark.filterwarnings( - 'ignore:.*naming conventions.*:RuntimeWarning')]), + pytest.param('raw', marks=[ + pytest.mark.filterwarnings( + 'ignore:.*naming conventions.*:RuntimeWarning'), + pytest.mark.slowtest]), )) -def test_split_files(tmpdir, mod, monkeypatch): +def test_split_files(tmp_path, mod, monkeypatch): """Test writing and reading of split raw files.""" raw_1 = read_raw_fif(fif_fname, preload=True) # Test a very close corner case - raw_crop = raw_1.copy().crop(0, 1.) assert_allclose(raw_1.buffer_size_sec, 10., atol=1e-2) # samp rate - split_fname = tmpdir.join(f'split_raw_{mod}.fif') + split_fname = tmp_path / f'split_raw_{mod}.fif' # intended filenames - split_fname_elekta_part2 = tmpdir.join(f'split_raw_{mod}-1.fif') - split_fname_bids_part1 = tmpdir.join(f'split_raw_split-01_{mod}.fif') - split_fname_bids_part2 = tmpdir.join(f'split_raw_split-02_{mod}.fif') + split_fname_elekta_part2 = tmp_path / f'split_raw_{mod}-1.fif' + split_fname_bids_part1 = tmp_path / f'split_raw_split-01_{mod}.fif' + split_fname_bids_part2 = tmp_path / f'split_raw_split-02_{mod}.fif' raw_1.set_annotations(Annotations([2.], [5.5], 'test')) # Check that if BIDS is used and no split is needed it defaults to @@ -411,7 +436,7 @@ annot = Annotations(np.arange(20), np.ones((20,)), 'test') raw_1.set_annotations(annot) - split_fname = op.join(tmpdir, 'split_raw.fif') + split_fname = op.join(tmp_path, 'split_raw.fif') raw_1.save(split_fname, buffer_size_sec=1.0, split_size='10MB') raw_2 = read_raw_fif(split_fname) assert_allclose(raw_2.buffer_size_sec, 1., atol=1e-2) # samp rate @@ -491,18 +516,18 @@ assert_allclose(raw_crop[:][0], raw_read[:][0]) # proper ending - assert op.isdir(tmpdir) + assert op.isdir(tmp_path) with pytest.raises(ValueError, match='must end with an underscore'): raw_crop.save( - tmpdir.join('test.fif'), split_naming='bids', verbose='error') + tmp_path / 'test.fif', split_naming='bids', verbose='error') # reserved file is deleted - fname = tmpdir.join('test_raw.fif') + fname = tmp_path / 'test_raw.fif' monkeypatch.setattr(base, '_write_raw_fid', _err) with pytest.raises(RuntimeError, match='Killed mid-write'): raw_1.save(fname, split_size='10MB', split_naming='bids') assert op.isfile(fname) - assert not op.isfile(tmpdir.join('test_split-01_raw.fif')) + assert not op.isfile(tmp_path / 'test_split-01_raw.fif') def _err(*args, **kwargs): @@ -514,12 +539,12 @@ return -def test_split_numbers(tmpdir, monkeypatch): +def test_split_numbers(tmp_path, monkeypatch): """Test handling of split files using numbers instead of names.""" monkeypatch.setattr(base, 'write_string', _no_write_file_name) raw = read_raw_fif(test_fif_fname).pick('eeg') # gh-8339 - dashes_fname = tmpdir.join('sub-1_ses-2_task-3_raw.fif') + dashes_fname = tmp_path / 'sub-1_ses-2_task-3_raw.fif' raw.save(dashes_fname, split_size='5MB', buffer_size_sec=1.) assert op.isfile(dashes_fname) @@ -530,7 +555,7 @@ assert_allclose(raw.get_data(), raw_read.get_data(), atol=1e-16) -def test_load_bad_channels(tmpdir): +def test_load_bad_channels(tmp_path): """Test reading/writing of bad channels.""" # Load correctly marked file (manually done in mne_process_raw) raw_marked = read_raw_fif(fif_bad_marked_fname) @@ -542,8 +567,8 @@ # Test normal case raw.load_bad_channels(bad_file_works) # Write it out, read it in, and check - raw.save(tmpdir.join('foo_raw.fif')) - raw_new = read_raw_fif(tmpdir.join('foo_raw.fif')) + raw.save(tmp_path / 'foo_raw.fif') + raw_new = read_raw_fif(tmp_path / 'foo_raw.fif') assert correct_bads == raw_new.info['bads'] # Reset it raw.info['bads'] = [] @@ -554,21 +579,22 @@ # Test forcing the bad case with pytest.warns(RuntimeWarning, match='1 bad channel'): raw.load_bad_channels(bad_file_wrong, force=True) - # write it out, read it in, and check - raw.save(tmpdir.join('foo_raw.fif'), overwrite=True) - raw_new = read_raw_fif(tmpdir.join('foo_raw.fif')) + + # write it out, read it in, and check + raw.save(tmp_path / 'foo_raw.fif', overwrite=True) + raw_new = read_raw_fif(tmp_path / 'foo_raw.fif') assert correct_bads == raw_new.info['bads'] # Check that bad channels are cleared raw.load_bad_channels(None) - raw.save(tmpdir.join('foo_raw.fif'), overwrite=True) - raw_new = read_raw_fif(tmpdir.join('foo_raw.fif')) + raw.save(tmp_path / 'foo_raw.fif', overwrite=True) + raw_new = read_raw_fif(tmp_path / 'foo_raw.fif') assert raw_new.info['bads'] == [] @pytest.mark.slowtest @testing.requires_testing_data -def test_io_raw(tmpdir): +def test_io_raw(tmp_path): """Test IO for raw data (Neuromag).""" rng = np.random.RandomState(0) # test unicode io @@ -577,7 +603,7 @@ assert ('Raw' in repr(r)) assert (op.basename(fif_fname) in repr(r)) r.info['description'] = chars - temp_file = tmpdir.join('raw.fif') + temp_file = tmp_path / 'raw.fif' r.save(temp_file, overwrite=True) with read_raw_fif(temp_file) as r2: desc2 = r2.info['description'] @@ -590,7 +616,7 @@ data = rng.randn(raw._data.shape[0], raw._data.shape[1]) raw._data[:, :] = data # save it somewhere - fname = tmpdir.join('test_copy_raw.fif') + fname = tmp_path / 'test_copy_raw.fif' raw.save(fname, buffer_size_sec=1.0) # read it in, make sure the whole thing matches raw = read_raw_fif(fname) @@ -603,11 +629,11 @@ @pytest.mark.parametrize('fname_in, fname_out', [ (test_fif_fname, 'raw.fif'), - (test_fif_gz_fname, 'raw.fif.gz'), + pytest.param(test_fif_gz_fname, 'raw.fif.gz', marks=pytest.mark.slowtest), (ctf_fname, 'raw.fif')]) -def test_io_raw_additional(fname_in, fname_out, tmpdir): +def test_io_raw_additional(fname_in, fname_out, tmp_path): """Test IO for raw data (Neuromag + CTF + gz).""" - fname_out = tmpdir.join(fname_out) + fname_out = tmp_path / fname_out raw = read_raw_fif(fname_in).crop(0, 2) nchan = raw.info['nchan'] @@ -676,7 +702,7 @@ assert_allclose(raw.info['dig'][0]['r'], raw2.info['dig'][0]['r']) # test warnings on bad filenames - raw_badname = tmpdir.join('test-bad-name.fif.gz') + raw_badname = tmp_path / 'test-bad-name.fif.gz' with pytest.warns(RuntimeWarning, match='raw.fif'): raw.save(raw_badname) with pytest.warns(RuntimeWarning, match='raw.fif'): @@ -684,38 +710,28 @@ @testing.requires_testing_data -def test_io_complex(tmpdir): +@pytest.mark.parametrize('dtype', ('complex128', 'complex64')) +def test_io_complex(tmp_path, dtype): """Test IO with complex data types.""" rng = np.random.RandomState(0) - dtypes = [np.complex64, np.complex128] - - raw = _test_raw_reader(partial(read_raw_fif), - fname=fif_fname) - picks = np.arange(5) - start, stop = raw.time_as_index([0, 5]) - - data_orig, _ = raw[picks, start:stop] - - for di, dtype in enumerate(dtypes): - imag_rand = np.array(1j * rng.randn(data_orig.shape[0], - data_orig.shape[1]), dtype) - - raw_cp = raw.copy() - raw_cp._data = np.array(raw_cp._data, dtype) - raw_cp._data[picks, start:stop] += imag_rand - with pytest.warns(RuntimeWarning, match='Saving .* complex data.'): - raw_cp.save(tmpdir.join('raw.fif'), picks, tmin=0, tmax=5, - overwrite=True) - - raw2 = read_raw_fif(tmpdir.join('raw.fif')) - raw2_data, _ = raw2[picks, :] - n_samp = raw2_data.shape[1] - assert_allclose(raw2_data[:, :n_samp], raw_cp._data[picks, :n_samp]) - # with preloading - raw2 = read_raw_fif(tmpdir.join('raw.fif'), preload=True) - raw2_data, _ = raw2[picks, :] - n_samp = raw2_data.shape[1] - assert_allclose(raw2_data[:, :n_samp], raw_cp._data[picks, :n_samp]) + n_ch = 5 + raw = read_raw_fif(fif_fname).crop(0, 1).pick(np.arange(n_ch)).load_data() + data_orig = raw.get_data() + imag_rand = np.array(1j * rng.randn(n_ch, len(raw.times)), dtype=dtype) + raw_cp = raw.copy() + raw_cp._data = np.array(raw_cp._data, dtype) + raw_cp._data += imag_rand + with pytest.warns(RuntimeWarning, match='Saving .* complex data.'): + raw_cp.save(tmp_path / 'raw.fif', overwrite=True) + + raw2 = read_raw_fif(tmp_path / 'raw.fif') + raw2_data, _ = raw2[:] + assert_allclose(raw2_data, raw_cp._data) + # with preloading + raw2 = read_raw_fif(tmp_path / 'raw.fif', preload=True) + raw2_data, _ = raw2[:] + assert_allclose(raw2_data, raw_cp._data) + assert_allclose(data_orig, raw_cp._data.real) @testing.requires_testing_data @@ -745,7 +761,7 @@ @testing.requires_testing_data -def test_proj(tmpdir): +def test_proj(tmp_path): """Test SSP proj operations.""" for proj in [True, False]: raw = read_raw_fif(fif_fname, preload=False) @@ -785,8 +801,8 @@ raw = read_raw_fif(fif_fname, preload=preload) # write the file with proj. activated, make sure proj has been applied - raw.save(tmpdir.join('raw.fif'), proj=True, overwrite=True) - raw2 = read_raw_fif(tmpdir.join('raw.fif')) + raw.save(tmp_path / 'raw.fif', proj=True, overwrite=True) + raw2 = read_raw_fif(tmp_path / 'raw.fif') data_proj_2, _ = raw2[:, 0:2] assert_allclose(data_proj_1, data_proj_2) assert (all(p['active'] for p in raw2.info['projs'])) @@ -818,11 +834,11 @@ # I/O roundtrip of an MEG projector with a Raw that only contains EEG # data. - out_fname = tmpdir.join('test_raw.fif') + out_fname = tmp_path / 'test_raw.fif' raw = read_raw_fif(test_fif_fname, preload=True).crop(0, 0.002) proj = raw.info['projs'][-1] raw.pick_types(meg=False, eeg=True) - raw.info['projs'] = [proj] # Restore, because picking removed it! + raw.add_proj(proj) # Restore, because picking removed it! raw._data.fill(0) raw._data[-1] = 1. raw.save(out_fname) @@ -833,7 +849,7 @@ @testing.requires_testing_data @pytest.mark.parametrize('preload', [False, True, 'memmap.dat']) -def test_preload_modify(preload, tmpdir): +def test_preload_modify(preload, tmp_path): """Test preloading and modifying data.""" rng = np.random.RandomState(0) raw = read_raw_fif(fif_fname, preload=preload) @@ -851,7 +867,7 @@ else: raise - tmp_fname = tmpdir.join('raw.fif') + tmp_fname = tmp_path / 'raw.fif' raw.save(tmp_fname, overwrite=True) raw_new = read_raw_fif(tmp_fname) @@ -943,7 +959,8 @@ # filter should set the "lowpass" and "highpass" parameters raw = RawArray(np.random.randn(3, 1000), create_info(3, 1000., ['eeg'] * 2 + ['stim'])) - raw.info['lowpass'] = raw.info['highpass'] = None + with raw.info._unlock(): + raw.info['lowpass'] = raw.info['highpass'] = None for kind in ('none', 'lowpass', 'highpass', 'bandpass', 'bandstop'): print(kind) h_freq = l_freq = None @@ -1068,12 +1085,13 @@ assert_allclose(raw._data, raw_preload._data) +@pytest.mark.slowtest @testing.requires_testing_data @pytest.mark.parametrize('preload, n, npad', [ (True, 512, 'auto'), (False, 512, 0), ]) -def test_resample(tmpdir, preload, n, npad): +def test_resample(tmp_path, preload, n, npad): """Test resample (with I/O and multiple files).""" raw = read_raw_fif(fif_fname) raw.crop(0, raw.times[n - 1]) @@ -1085,15 +1103,14 @@ # test parallel on upsample raw_resamp.resample(sfreq * 2, n_jobs=2, npad=npad) assert raw_resamp.n_times == len(raw_resamp.times) - raw_resamp.save(tmpdir.join('raw_resamp-raw.fif')) - raw_resamp = read_raw_fif(tmpdir.join('raw_resamp-raw.fif'), - preload=True) + raw_resamp.save(tmp_path / 'raw_resamp-raw.fif') + raw_resamp = read_raw_fif(tmp_path / 'raw_resamp-raw.fif', preload=True) assert sfreq == raw_resamp.info['sfreq'] / 2 assert raw.n_times == raw_resamp.n_times // 2 assert raw_resamp.get_data().shape[1] == raw_resamp.n_times assert raw.get_data().shape[0] == raw_resamp._data.shape[0] # test non-parallel on downsample - raw_resamp.resample(sfreq, n_jobs=1, npad=npad) + raw_resamp.resample(sfreq, n_jobs=None, npad=npad) assert raw_resamp.info['sfreq'] == sfreq assert raw.get_data().shape == raw_resamp._data.shape assert raw.first_samp == raw_resamp.first_samp @@ -1197,7 +1214,8 @@ # resample should still work even when no stim channel is present raw = RawArray(np.random.randn(1, 100), create_info(1, 100, ['eeg'])) - raw.info['lowpass'] = 50. + with raw.info._unlock(): + raw.info['lowpass'] = 50. raw.resample(10, npad=npad) assert raw.info['lowpass'] == 5. assert len(raw) == 10 @@ -1265,18 +1283,16 @@ def test_to_data_frame(): """Test raw Pandas exporter.""" from pandas import Timedelta - raw = read_raw_fif(test_fif_fname, preload=True) - _, times = raw[0, :10] + raw = read_raw_fif(test_fif_fname).crop(0, 1).load_data() df = raw.to_data_frame(index='time') assert ((df.columns == raw.ch_names).all()) - assert_array_equal(np.round(times * 1e3), df.index.values[:10]) df = raw.to_data_frame(index=None) assert ('time' in df.columns) assert_array_equal(df.values[:, 1], raw._data[0] * 1e13) assert_array_equal(df.values[:, 3], raw._data[2] * 1e15) # test long format df_long = raw.to_data_frame(long_format=True) - assert(len(df_long) == raw.get_data().size) + assert len(df_long) == raw.get_data().size expected = ('time', 'channel', 'ch_type', 'value') assert set(expected) == set(df_long.columns) # test bad time format @@ -1293,13 +1309,27 @@ @pytest.mark.parametrize('time_format', (None, 'ms', 'timedelta', 'datetime')) def test_to_data_frame_time_format(time_format): """Test time conversion in epochs Pandas exporter.""" - from pandas import Timedelta, Timestamp + from pandas import Timedelta, Timestamp, to_timedelta raw = read_raw_fif(test_fif_fname, preload=True) # test time_format df = raw.to_data_frame(time_format=time_format) dtypes = {None: np.float64, 'ms': np.int64, 'timedelta': Timedelta, 'datetime': Timestamp} assert isinstance(df['time'].iloc[0], dtypes[time_format]) + # test values + _, times = raw[0, :10] + offset = 0. + if time_format == 'datetime': + times += raw.first_time + offset = raw.info['meas_date'] + elif time_format == 'timedelta': + offset = Timedelta(0.) + funcs = {None: lambda x: x, + 'ms': lambda x: np.rint(x * 1e3).astype(int), # s → ms + 'timedelta': partial(to_timedelta, unit='s'), + 'datetime': partial(to_timedelta, unit='s') + } + assert_array_equal(funcs[time_format](times) + offset, df['time'][:10]) def test_add_channels(): @@ -1350,7 +1380,8 @@ # Now test errors raw_badsf = raw_eeg.copy() - raw_badsf.info['sfreq'] = 3.1415927 + with raw_badsf.info._unlock(): + raw_badsf.info['sfreq'] = 3.1415927 raw_eeg.crop(.5) pytest.raises(RuntimeError, raw_meg.add_channels, [raw_nopre]) @@ -1361,9 +1392,9 @@ @testing.requires_testing_data -def test_save(tmpdir): +def test_save(tmp_path): """Test saving raw.""" - temp_fname = tmpdir.join('test_raw.fif') + temp_fname = tmp_path / 'test_raw.fif' shutil.copyfile(fif_fname, temp_fname) raw = read_raw_fif(temp_fname, preload=False) # can't write over file being read @@ -1379,7 +1410,7 @@ annot = Annotations([10], [5], ['test'], orig_time=orig_time) raw.set_annotations(annot) annot = raw.annotations - new_fname = tmpdir.join('break_raw.fif') + new_fname = tmp_path / 'break_raw.fif' raw.save(new_fname, overwrite=True) new_raw = read_raw_fif(new_fname, preload=False) pytest.raises(ValueError, new_raw.save, new_fname) @@ -1396,7 +1427,7 @@ @testing.requires_testing_data -def test_annotation_crop(tmpdir): +def test_annotation_crop(tmp_path): """Test annotation sync after cropping and concatenating.""" annot = Annotations([5., 11., 15.], [2., 1., 3.], ['test', 'test', 'test']) raw = read_raw_fif(fif_fname, preload=False) @@ -1422,7 +1453,7 @@ [1., 1. + 1. / raw.info['sfreq']], atol=1e-3) # make sure we can overwrite the file we loaded when preload=True - new_fname = tmpdir.join('break_raw.fif') + new_fname = tmp_path / 'break_raw.fif' raw.save(new_fname) new_raw = read_raw_fif(new_fname, preload=True) new_raw.save(new_fname, overwrite=True) @@ -1436,7 +1467,7 @@ print(raw_) -def test_compensation_raw(tmpdir): +def test_compensation_raw(tmp_path): """Test Raw compensation.""" raw_3 = read_raw_fif(ctf_comp_fname) assert raw_3.compensation_grade == 3 @@ -1499,7 +1530,7 @@ assert_allclose(data_3, data_3_new, **tols) # Try IO with compensation - temp_file = tmpdir.join('raw.fif') + temp_file = tmp_path / 'raw.fif' raw_3.save(temp_file, overwrite=True) for preload in (True, False): raw_read = read_raw_fif(temp_file, preload=preload) @@ -1535,10 +1566,10 @@ @requires_mne -def test_compensation_raw_mne(tmpdir): +def test_compensation_raw_mne(tmp_path): """Test Raw compensation by comparing with MNE-C.""" def compensate_mne(fname, grad): - tmp_fname = tmpdir.join('mne_ctf_test_raw.fif') + tmp_fname = tmp_path / 'mne_ctf_test_raw.fif' cmd = ['mne_process_raw', '--raw', fname, '--save', tmp_fname, '--grad', str(grad), '--projoff', '--filteroff'] run_subprocess(cmd) @@ -1626,10 +1657,10 @@ assert ch_names == e.ch_names -def test_memmap(tmpdir): +def test_memmap(tmp_path): """Test some interesting memmapping cases.""" # concatenate_raw - memmaps = [tmpdir.join(str(ii)) for ii in range(3)] + memmaps = [str(tmp_path / str(ii)) for ii in range(3)] raw_0 = read_raw_fif(test_fif_fname, preload=memmaps[0]) assert raw_0._data.filename == memmaps[0] raw_1 = read_raw_fif(test_fif_fname, preload=memmaps[1]) @@ -1668,20 +1699,30 @@ # require them. -@pytest.mark.parametrize('split', (False, True)) -@pytest.mark.parametrize('kind', ('file', 'bytes')) -@pytest.mark.parametrize('preload', (True, str)) -def test_file_like(kind, preload, split, tmpdir): +# These are slow on Azure Windows so let's do a subset +@pytest.mark.parametrize('kind', [ + 'file', + pytest.param('bytes', marks=pytest.mark.slowtest), +]) +@pytest.mark.parametrize('preload', [ + True, + pytest.param(str, marks=pytest.mark.slowtest), +]) +@pytest.mark.parametrize('split', [ + False, + pytest.param(True, marks=pytest.mark.slowtest), +]) +def test_file_like(kind, preload, split, tmp_path): """Test handling with file-like objects.""" if split: - fname = tmpdir.join('test_raw.fif') + fname = tmp_path / 'test_raw.fif' read_raw_fif(test_fif_fname).save(fname, split_size='5MB') assert op.isfile(fname) assert op.isfile(str(fname)[:-4] + '-1.fif') else: fname = test_fif_fname if preload is str: - preload = str(tmpdir.join('memmap')) + preload = str(tmp_path / 'memmap') with open(str(fname), 'rb') as file_fid: fid = BytesIO(file_fid.read()) if kind == 'bytes' else file_fid assert not fid.closed @@ -1729,17 +1770,20 @@ @testing.requires_testing_data @pytest.mark.skipif(sys.platform not in ('darwin', 'linux'), reason='Needs proper symlinking') -def test_split_symlink(tmpdir): +def test_split_symlink(tmp_path): """Test split files with symlinks.""" # regression test for gh-9221 - first = str(tmpdir.mkdir('first').join('test_raw.fif')) + (tmp_path / 'first').mkdir() + first = tmp_path / 'first' / 'test_raw.fif' raw = read_raw_fif(fif_fname).pick('meg').load_data() raw.save(first, buffer_size_sec=1, split_size='10MB', verbose=True) - second = first[:-4] + '-1.fif' + second = str(first)[:-4] + '-1.fif' assert op.isfile(second) - assert not op.isfile(first[:-4] + '-2.fif') - new_first = tmpdir.mkdir('a').join('test_raw.fif') - new_second = tmpdir.mkdir('b').join('test_raw-1.fif') + assert not op.isfile(str(first)[:-4] + '-2.fif') + (tmp_path / 'a').mkdir() + (tmp_path / 'b').mkdir() + new_first = tmp_path / 'a' / 'test_raw.fif' + new_second = tmp_path / 'b' / 'test_raw-1.fif' shutil.move(first, new_first) shutil.move(second, new_second) os.symlink(new_first, first) @@ -1749,7 +1793,7 @@ @testing.requires_testing_data -def test_corrupted(tmpdir): +def test_corrupted(tmp_path): """Test that a corrupted file can still be read.""" # Must be a file written by Neuromag, not us, since we don't write the dir # at the end, so use the skip one (straight from acq). @@ -1761,9 +1805,28 @@ assert dirpos == 12641532 fid.seek(0) data = fid.read(dirpos) - bad_fname = tmpdir.join('test_raw.fif') + bad_fname = tmp_path / 'test_raw.fif' with open(bad_fname, 'wb') as fid: fid.write(data) with pytest.warns(RuntimeWarning, match='.*tag directory.*corrupt.*'): raw_bad = read_raw_fif(bad_fname) assert_allclose(raw.get_data(), raw_bad.get_data()) + + +@testing.requires_testing_data +def test_expand_user(tmp_path, monkeypatch): + """Test that we're expanding `~` before reading and writing.""" + monkeypatch.setenv('HOME', str(tmp_path)) + monkeypatch.setenv('USERPROFILE', str(tmp_path)) # Windows + + path_in = Path(fif_fname) + path_out = tmp_path / path_in.name + path_home = Path('~') / path_in.name + + shutil.copyfile( + src=path_in, + dst=path_out + ) + + raw = read_raw_fif(fname=path_home, preload=True) + raw.save(fname=path_home, overwrite=True) diff -Nru python-mne-0.23.4+dfsg/mne/io/hitachi/hitachi.py python-mne-1.1.0+dfsg/mne/io/hitachi/hitachi.py --- python-mne-0.23.4+dfsg/mne/io/hitachi/hitachi.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/hitachi/hitachi.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,286 @@ +# Authors: Eric Larson +# +# License: BSD-3-Clause + +import datetime as dt +import re + +import numpy as np + +from ..base import BaseRaw +from ..constants import FIFF +from ..meas_info import create_info +from ..nirx.nirx import _read_csv_rows_cols +from ..utils import _mult_cal_one +from ...utils import (logger, verbose, fill_doc, warn, _check_fname, + _check_option) + + +@fill_doc +def read_raw_hitachi(fname, preload=False, verbose=None): + """Reader for a Hitachi fNIRS recording. + + Parameters + ---------- + fname : str + Path to the Hitachi CSV file. + %(preload)s + %(verbose)s + + Returns + ------- + raw : instance of RawHitachi + A Raw object containing Hitachi data. + + See Also + -------- + mne.io.Raw : Documentation of attribute and methods. + + Notes + ----- + %(hitachi_notes)s + """ + return RawHitachi(fname, preload, verbose=verbose) + + +def _check_bad(cond, msg): + if cond: + raise RuntimeError(f'Could not parse file: {msg}') + + +@fill_doc +class RawHitachi(BaseRaw): + """Raw object from a Hitachi fNIRS file. + + Parameters + ---------- + fname : str + Path to the Hitachi CSV file. + %(preload)s + %(verbose)s + + See Also + -------- + mne.io.Raw : Documentation of attribute and methods. + + Notes + ----- + %(hitachi_notes)s + """ + + @verbose + def __init__(self, fname, preload=False, *, verbose=None): + fname = _check_fname(fname, 'read', True, 'fname') + logger.info('Loading %s' % fname) + + raw_extra = dict(fname=fname) + info_extra = dict() + subject_info = dict() + ch_wavelengths = dict() + fnirs_wavelengths = [None, None] + meas_date = age = ch_names = sfreq = None + with open(fname, 'rb') as fid: + lines = fid.read() + lines = lines.decode('latin-1').rstrip('\r\n') + oldlen = len(lines) + assert len(lines) == oldlen + bounds = [0] + end = '\n' if '\n' in lines else '\r' + bounds.extend(a.end() for a in re.finditer(end, lines)) + bounds.append(len(lines)) + lines = lines.split(end) + assert len(bounds) == len(lines) + 1 + line = lines[0].rstrip(',\r\n') + _check_bad(line != 'Header', 'no header found') + li = 0 + mode = None + for li, line in enumerate(lines[1:], 1): + # Newer format has some blank lines + if len(line) == 0: + continue + parts = line.rstrip(',\r\n').split(',') + if len(parts) == 0: # some header lines are blank + continue + kind, parts = parts[0], parts[1:] + if len(parts) == 0: + parts = [''] # some fields (e.g., Comment) meaningfully blank + if kind == 'File Version': + logger.info(f'Reading Hitachi fNIRS file version {parts[0]}') + elif kind == 'AnalyzeMode': + _check_bad( + parts != ['Continuous'], f'not continuous data ({parts})') + elif kind == 'Sampling Period[s]': + sfreq = 1 / float(parts[0]) + elif kind == 'Exception': + raise NotImplementedError(kind) + elif kind == 'Comment': + info_extra['description'] = parts[0] + elif kind == 'ID': + subject_info['his_id'] = parts[0] + elif kind == 'Name': + if len(parts): + name = parts[0].split(' ') + if len(name): + subject_info['first_name'] = name[0] + subject_info['last_name'] = ' '.join(name[1:]) + elif kind == 'Age': + age = int(parts[0].rstrip('y')) + elif kind == 'Mode': + mode = parts[0] + elif kind in ('HPF[Hz]', 'LPF[Hz]'): + try: + freq = float(parts[0]) + except ValueError: + pass + else: + info_extra[{'HPF[Hz]': 'highpass', + 'LPF[Hz]': 'lowpass'}[kind]] = freq + elif kind == 'Date': + # 5/17/04 5:14 + try: + mdy, HM = parts[0].split(' ') + H, M = HM.split(':') + if len(H) == 1: + H = f'0{H}' + mdyHM = ' '.join([mdy, ':'.join([H, M])]) + for fmt in ('%m/%d/%y %H:%M', '%Y/%m/%d %H:%M'): + try: + meas_date = dt.datetime.strptime(mdyHM, fmt) + except Exception: + pass + else: + break + else: + raise RuntimeError # unknown format + except Exception: + warn('Extraction of measurement date failed. ' + 'Please report this as a github issue. ' + 'The date is being set to January 1st, 2000, ' + f'instead of {repr(parts[0])}') + elif kind == 'Sex': + try: + subject_info['sex'] = dict( + female=FIFF.FIFFV_SUBJ_SEX_FEMALE, + male=FIFF.FIFFV_SUBJ_SEX_MALE)[parts[0].lower()] + except KeyError: + pass + elif kind == 'Wave[nm]': + fnirs_wavelengths[:] = [int(part) for part in parts] + elif kind == 'Wave Length': + ch_regex = re.compile(r'^(.*)\(([0-9\.]+)\)$') + for ent in parts: + _, v = ch_regex.match(ent).groups() + ch_wavelengths[ent] = float(v) + elif kind == 'Data': + break + fnirs_wavelengths = np.array(fnirs_wavelengths, int) + assert len(fnirs_wavelengths) == 2 + ch_names = lines[li + 1].rstrip(',\r\n').split(',') + # cull to correct ones + raw_extra['keep_mask'] = ~np.in1d(ch_names, ['Probe1', 'Time']) + # set types + ch_names = [ch_name for ci, ch_name in enumerate(ch_names) + if raw_extra['keep_mask'][ci]] + ch_types = ['fnirs_cw_amplitude' if ch_name.startswith('CH') + else 'stim' + for ch_name in ch_names] + # get locations + nirs_names = [ch_name for ch_name, ch_type in zip(ch_names, ch_types) + if ch_type == 'fnirs_cw_amplitude'] + n_nirs = len(nirs_names) + assert n_nirs % 2 == 0 + names = { + '3x3': 'ETG-100', + '3x5': 'ETG-7000', + '4x4': 'ETG-7000', + '3x11': 'ETG-4000', + } + _check_option('Hitachi mode', mode, sorted(names)) + n_row, n_col = [int(x) for x in mode.split('x')] + logger.info(f'Constructing pairing matrix for {names[mode]} ({mode})') + pairs = _compute_pairs(n_row, n_col, n=1 + (mode == '3x3')) + assert n_nirs == len(pairs) * 2 + locs = np.zeros((len(ch_names), 12)) + idxs = np.where(np.array(ch_types, 'U') == 'fnirs_cw_amplitude')[0] + for ii, idx in enumerate(idxs): + ch_name = ch_names[idx] + # Use the actual/accurate wavelength in loc + acc_freq = ch_wavelengths[ch_name] + locs[idx][9] = acc_freq + # Rename channel based on standard naming scheme, using the + # nominal wavelength + sidx, didx = pairs[ii // 2] + nom_freq = fnirs_wavelengths[np.argmin(np.abs( + acc_freq - fnirs_wavelengths))] + ch_names[idx] = f'S{sidx + 1}_D{didx + 1} {nom_freq}' + + # figure out bounds + bounds = raw_extra['bounds'] = bounds[li + 2:] + last_samp = len(bounds) - 2 + + if age is not None and meas_date is not None: + subject_info['birthday'] = (meas_date.year - age, + meas_date.month, + meas_date.day) + if meas_date is None: + meas_date = dt.datetime(2000, 1, 1, 0, 0, 0) + meas_date = meas_date.replace(tzinfo=dt.timezone.utc) + if subject_info: + info_extra['subject_info'] = subject_info + + # Create mne structure + info = create_info(ch_names, sfreq, ch_types=ch_types) + with info._unlock(): + info.update(info_extra) + info['meas_date'] = meas_date + for li, loc in enumerate(locs): + info['chs'][li]['loc'][:] = loc + + super().__init__( + info, preload, filenames=[fname], last_samps=[last_samp], + raw_extras=[raw_extra], verbose=verbose) + + def _read_segment_file(self, data, idx, fi, start, stop, cals, mult): + """Read a segment of data from a file.""" + this_data = _read_csv_rows_cols( + self._raw_extras[fi]['fname'], + start, stop, self._raw_extras[fi]['keep_mask'], + self._raw_extras[fi]['bounds'], sep=',', + replace=lambda x: + x.replace('\r', '\n') + .replace('\n\n', '\n') + .replace('\n', ',') + .replace(':', '')).T + _mult_cal_one(data, this_data, idx, cals, mult) + return data + + +def _compute_pairs(n_rows, n_cols, n=1): + n_tot = n_rows * n_cols + sd_idx = (np.arange(n_tot) // 2).reshape(n_rows, n_cols) + d_bool = np.empty((n_rows, n_cols), bool) + for ri in range(n_rows): + d_bool[ri] = np.arange(ri, ri + n_cols) % 2 + pairs = list() + for ri in range(n_rows): + # First iterate over connections within the row + for ci in range(n_cols - 1): + pair = (sd_idx[ri, ci], sd_idx[ri, ci + 1]) + if d_bool[ri, ci]: # reverse + pair = pair[::-1] + pairs.append(pair) + # Next iterate over row-row connections, if applicable + if ri >= n_rows - 1: + continue + for ci in range(n_cols): + pair = (sd_idx[ri, ci], sd_idx[ri + 1, ci]) + if d_bool[ri, ci]: + pair = pair[::-1] + pairs.append(pair) + if n > 1: + assert n == 2 # only one supported for now + pairs = np.array(pairs, int) + second = pairs + pairs.max(axis=0) + 1 + pairs = np.r_[pairs, second] + pairs = tuple(tuple(row) for row in pairs) + return tuple(pairs) diff -Nru python-mne-0.23.4+dfsg/mne/io/hitachi/__init__.py python-mne-1.1.0+dfsg/mne/io/hitachi/__init__.py --- python-mne-0.23.4+dfsg/mne/io/hitachi/__init__.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/hitachi/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,7 @@ +"""fNIRS module for conversion to FIF.""" + +# Author: Eric Larson +# +# License: BSD-3-Clause + +from .hitachi import read_raw_hitachi diff -Nru python-mne-0.23.4+dfsg/mne/io/hitachi/tests/test_hitachi.py python-mne-1.1.0+dfsg/mne/io/hitachi/tests/test_hitachi.py --- python-mne-0.23.4+dfsg/mne/io/hitachi/tests/test_hitachi.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/hitachi/tests/test_hitachi.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,317 @@ +# -*- coding: utf-8 -*- +# Authors: Eric Larson +# +# License: BSD-3-Clause + +import datetime as dt + +import pytest +import numpy as np +from numpy.testing import assert_allclose, assert_array_less + +from mne.channels import make_standard_montage +from mne.io import read_raw_hitachi +from mne.io.hitachi.hitachi import _compute_pairs +from mne.io.tests.test_raw import _test_raw_reader +from mne.preprocessing.nirs import (source_detector_distances, + optical_density, tddr, beer_lambert_law, + scalp_coupling_index) + + +CONTENTS = dict() +CONTENTS['1.18'] = b"""\ +Header,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +File Version,1.18,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Patient Information,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +ID,TestID,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Name,Test,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Comment,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Age, 45y,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Sex,Female,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Analyze Information,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +AnalyzeMode,Continuous,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Pre Time[s],9,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Post Time[s],7,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Recovery Time[s],12,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Base Time[s],10,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Fitting Degree,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +HPF[Hz],No Filter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +LPF[Hz],0.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Moving Average[s],0.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Measure Information,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Date,5/17/04 5:14,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Mode,3x5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Wave[nm],695,830,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Wave Length,CH1(700.1),CH1(829.1),CH2(699.3),CH2(827.9),CH3(699.3),CH3(827.9),CH4(698.6),CH4(828.1),CH5(700.1),CH5(829.1),CH6(698.5),CH6(828.2),CH7(699.3),CH7(827.9),CH8(699.8),CH8(828.5),CH9(698.6),CH9(828.1),CH10(698.5),CH10(828.2),CH11(698.5),CH11(828.2),CH12(699.8),CH12(828.5),CH13(699.8),CH13(828.5),CH14(699.0),CH14(828.2),CH15(698.5),CH15(828.2),CH16(699.5),CH16(828.1),CH17(699.8),CH17(828.5),CH18(699.5),CH18(828.5),CH19(699.0),CH19(828.2),CH20(699.5),CH20(828.1),CH21(699.5),CH21(828.1),CH22(699.5),CH22(828.5),,,,, +Analog Gain,30.117647,30.117647,30.117647,30.117647,94.117647,94.117647,94.117647,94.117647,10.27451,10.27451,30.117647,30.117647,59.607843,59.607843,94.117647,94.117647,110.588235,110.588235,10.27451,10.27451,59.607843,59.607843,59.607843,59.607843,110.588235,110.588235,10.27451,10.27451,41.176471,41.176471,59.607843,59.607843,9.333333,9.333333,110.588235,110.588235,41.176471,41.176471,41.176471,41.176471,9.333333,9.333333,9.333333,9.333333,,,,, +Digital Gain,13.38,2.82,44.57,7.52,29.46,4.12,36.56,4.84,6.67,1.37,22.61,3.28,79.19,10.88,16.37,2.78,39.97,4.09,100,38.02,36.58,3.95,61.77,7.67,100,15.23,31.52,7.15,53.46,5.05,13.61,2.07,63.74,11.16,14.35,2.05,58.64,8.93,14.53,1.99,8.16,1.82,27.84,5.97,,,,, +Sampling Period[s],0.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +StimType,BLOCK,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Stim Time[s],20,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +F1,15,F2,15,F3,15,F4,15,F5,15,F6,15,F7,15,F8,15,F9,15,M,15,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Repeat Count,3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Exception Ch,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Data,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Probe1,CH1(700.1),CH1(829.1),CH2(699.3),CH2(827.9),CH3(699.3),CH3(827.9),CH4(698.6),CH4(828.1),CH5(700.1),CH5(829.1),CH6(698.5),CH6(828.2),CH7(699.3),CH7(827.9),CH8(699.8),CH8(828.5),CH9(698.6),CH9(828.1),CH10(698.5),CH10(828.2),CH11(698.5),CH11(828.2),CH12(699.8),CH12(828.5),CH13(699.8),CH13(828.5),CH14(699.0),CH14(828.2),CH15(698.5),CH15(828.2),CH16(699.5),CH16(828.1),CH17(699.8),CH17(828.5),CH18(699.5),CH18(828.5),CH19(699.0),CH19(828.2),CH20(699.5),CH20(828.1),CH21(699.5),CH21(828.1),CH22(699.5),CH22(828.5),Mark,Time,BodyMovement,RemovalMark,PreScan 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+60,1.99523926,1.97143555,2.33352661,2.19512939,2.22671509,2.16888428,2.11273193,2.10479736,1.7414856,1.65023804,2.02011108,1.90368652,2.15698242,2.06314087,2.06939697,1.96411133,1.98226929,1.96411133,0.68206787,1.85348511,1.97357178,1.89147949,1.96670532,1.89239502,1.45980835,1.9569397,1.90994263,1.75567627,2.19894409,2.01599121,1.91940308,1.88644409,1.87759399,1.80664063,1.8296814,1.83166504,2.4055481,2.14035034,2.10906982,2.04696655,1.93634033,1.91757202,2.12265015,2.05444336,0,14:14.2,0,0,1 +""" # noqa: E501 + + +CONTENTS['1.25'] = b"""\ +Header +File Version,1.25 +Patient Information +ID,REDACTED +Name, +Comment,sentence,, +Birth Date,1999/09/09 +Age, 99y +Sex,Male +Analyze Information +AnalyzeMode,Continuous +Pre Time[s],9.0 +Post Time[s],7.0 +Recovery Time[s],12.0 +Base Time[s],5 +Fitting Degree,1 +HPF[Hz],No Filter +LPF[Hz],No Filter +Moving Average[s],0.1 +Measure Information +Date,2020/02/02 11:20 +Probe Type,adult +Mode,3x11 +Wave[nm],695,830 +Wave Length,CH1(703.6),CH1(829.0),CH2(703.9),CH2(829.3),CH3(703.9),CH3(829.3),CH4(703.9),CH4(828.8),CH5(703.9),CH5(828.8),CH6(703.1),CH6(828.8),CH7(703.1),CH7(828.8),CH8(702.9),CH8(829.0),CH9(702.9),CH9(829.0),CH10(703.6),CH10(829.0),CH11(703.6),CH11(829.0),CH12(703.4),CH12(829.0),CH13(703.9),CH13(829.3),CH14(703.6),CH14(828.5),CH15(703.9),CH15(828.8),CH16(704.1),CH16(829.8),CH17(703.1),CH17(828.8),CH18(704.4),CH18(829.0),CH19(702.9),CH19(829.0),CH20(703.9),CH20(828.8),CH21(703.6),CH21(829.0),CH22(703.4),CH22(829.0),CH23(703.4),CH23(829.0),CH24(703.6),CH24(828.5),CH25(703.6),CH25(828.5),CH26(704.1),CH26(829.8),CH27(704.1),CH27(829.8),CH28(704.4),CH28(829.0),CH29(704.4),CH29(829.0),CH30(703.9),CH30(828.8),CH31(703.9),CH31(828.8),CH32(704.1),CH32(828.8),CH33(703.4),CH33(829.0),CH34(703.1),CH34(828.8),CH35(703.6),CH35(828.5),CH36(704.4),CH36(828.8),CH37(704.1),CH37(829.8),CH38(703.6),CH38(828.5),CH39(704.4),CH39(829.0),CH40(703.9),CH40(829.0),CH41(703.9),CH41(828.8),CH42(704.4),CH42(829.0),CH43(704.1),CH43(828.8),CH44(703.1),CH44(828.8),CH45(703.1),CH45(828.8),CH46(704.4),CH46(828.8),CH47(704.4),CH47(828.8),CH48(703.6),CH48(828.5),CH49(703.6),CH49(828.5),CH50(703.9),CH50(829.0),CH51(703.9),CH51(829.0),CH52(704.4),CH52(829.0) +Analog Gain,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,6.58823500,6.58823500,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,6.58823500,6.58823500,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,6.58823500,6.58823500,2.74509800,2.74509800,1.25490200,1.25490200,4.07843100,4.07843100,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,2.74509800,2.74509800,2.74509800,2.74509800,4.07843100,4.07843100,4.07843100,4.07843100,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200,1.25490200 +Digital Gain,5.99000000,3.61000000,1.93000000,1.12000000,5.43000000,3.20000000,7.11000000,4.02000000,11.82000000,6.06000000,16.42000000,7.48000000,15.82000000,7.56000000,12.13000000,6.80000000,14.62000000,7.78000000,5.35000000,2.57000000,10.27000000,5.01000000,52.84000000,26.95000000,9.75000000,6.12000000,6.97000000,3.99000000,11.31000000,5.91000000,2.26000000,1.15000000,9.49000000,4.58000000,3.21000000,2.25000000,2.97000000,1.65000000,12.34000000,6.77000000,33.81000000,17.23000000,7.10000000,2.92000000,12.78000000,7.02000000,17.47000000,9.94000000,16.93000000,9.55000000,6.87000000,3.47000000,5.96000000,3.07000000,20.56000000,13.42000000,3.67000000,2.53000000,17.39000000,8.72000000,2.96000000,1.61000000,14.25000000,5.67000000,8.87000000,4.21000000,21.68000000,11.89000000,12.24000000,6.47000000,28.07000000,16.60000000,13.95000000,7.05000000,9.37000000,5.53000000,18.15000000,11.62000000,39.72000000,19.87000000,20.78000000,9.56000000,91.12000000,43.86000000,17.86000000,7.84000000,5.47000000,3.40000000,4.83000000,3.38000000,7.61000000,5.15000000,4.65000000,2.86000000,1.33000000,0.88000000,16.06000000,11.46000000,11.94000000,6.47000000,25.92000000,12.83000000,4.35000000,2.44000000 +Sampling Period[s],0.1 +StimType,STIM +Stim Time[s] +A,15,B,15,C,15,D,15,E,15,F,15,G,15,H,15,I,15,J,15 +Repeat Count,20 +Exception Ch,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 + + + + + + +Data +Probe1,CH1(703.6),CH1(829.0),CH2(703.9),CH2(829.3),CH3(703.9),CH3(829.3),CH4(703.9),CH4(828.8),CH5(703.9),CH5(828.8),CH6(703.1),CH6(828.8),CH7(703.1),CH7(828.8),CH8(702.9),CH8(829.0),CH9(702.9),CH9(829.0),CH10(703.6),CH10(829.0),CH11(703.6),CH11(829.0),CH12(703.4),CH12(829.0),CH13(703.9),CH13(829.3),CH14(703.6),CH14(828.5),CH15(703.9),CH15(828.8),CH16(704.1),CH16(829.8),CH17(703.1),CH17(828.8),CH18(704.4),CH18(829.0),CH19(702.9),CH19(829.0),CH20(703.9),CH20(828.8),CH21(703.6),CH21(829.0),CH22(703.4),CH22(829.0),CH23(703.4),CH23(829.0),CH24(703.6),CH24(828.5),CH25(703.6),CH25(828.5),CH26(704.1),CH26(829.8),CH27(704.1),CH27(829.8),CH28(704.4),CH28(829.0),CH29(704.4),CH29(829.0),CH30(703.9),CH30(828.8),CH31(703.9),CH31(828.8),CH32(704.1),CH32(828.8),CH33(703.4),CH33(829.0),CH34(703.1),CH34(828.8),CH35(703.6),CH35(828.5),CH36(704.4),CH36(828.8),CH37(704.1),CH37(829.8),CH38(703.6),CH38(828.5),CH39(704.4),CH39(829.0),CH40(703.9),CH40(829.0),CH41(703.9),CH41(828.8),CH42(704.4),CH42(829.0),CH43(704.1),CH43(828.8),CH44(703.1),CH44(828.8),CH45(703.1),CH45(828.8),CH46(704.4),CH46(828.8),CH47(704.4),CH47(828.8),CH48(703.6),CH48(828.5),CH49(703.6),CH49(828.5),CH50(703.9),CH50(829.0),CH51(703.9),CH51(829.0),CH52(704.4),CH52(829.0),Mark,Time,BodyMovement,RemovalMark,PreScan +1,2.01614380,2.01263428,2.01354980,2.01248169,2.02392578,2.01644897,2.02453613,2.01934814,2.01873779,2.01919556,2.02087402,2.01889038,2.02621460,2.03781128,2.02423096,2.02011108,2.00881958,2.00637817,2.02087402,2.01644897,2.02209473,2.00988770,2.03155518,2.01919556,2.02148438,2.01370239,2.01324463,2.00973511,2.00973511,2.01477051,2.02117920,2.02636719,2.00805664,2.01583862,2.02529907,2.03033447,2.00424194,1.99935913,2.01202393,2.01568604,2.01583862,2.01095581,2.02545166,2.01721191,2.02758789,2.02957153,2.00622559,2.00073242,2.00759888,2.00485229,2.02041626,2.03094482,2.01782227,2.01675415,2.00820923,2.01110840,2.01354980,2.00637817,2.01538086,2.00271606,2.01156616,2.01217651,2.01736450,1.99386597,2.03353882,2.02362061,2.03598022,2.02728271,2.00866699,1.99676514,2.00714111,2.00744629,2.02087402,2.01492310,2.01202393,2.01644897,2.00057983,1.99584961,2.01370239,1.99050903,1.99783325,1.99371338,2.01431274,1.99920654,1.99630737,1.97814941,2.02545166,2.01126099,2.02102661,2.00790405,2.00912476,1.99813843,2.01156616,1.99981689,1.99829102,1.99523926,2.01141357,2.00424194,2.00164795,1.98730469,2.03948975,2.00637817,2.00958252,1.99615479,0,15:40:49.15,0,0,1 +2,2.02087402,2.02194214,2.01782227,2.02026367,2.02743530,2.02453613,2.02789307,2.02758789,2.02255249,2.02880859,2.02819824,2.02880859,2.03445435,2.04879761,2.02850342,2.02865601,2.01583862,2.01477051,2.02529907,2.02499390,2.03033447,2.02178955,2.03308105,2.02636719,2.02545166,2.02224731,2.01690674,2.01751709,2.01522827,2.02423096,2.02590942,2.03536987,2.01507568,2.02850342,2.03033447,2.04040527,2.00729370,2.00546265,2.01431274,2.02407837,2.02224731,2.02346802,2.02972412,2.02682495,2.03186035,2.03720093,2.01171875,2.00851440,2.00897217,2.01370239,2.02468872,2.04177856,2.02224731,2.02606201,2.01339722,2.02499390,2.01766968,2.01583862,2.01690674,2.00851440,2.01568604,2.02117920,2.02087402,2.00164795,2.03689575,2.03247070,2.04132080,2.03613281,2.01110840,2.00332642,2.01293945,2.01583862,2.02560425,2.02224731,2.01828003,2.02621460,2.00637817,2.00607300,2.01431274,1.99890137,2.00714111,1.99813843,2.01721191,2.00942993,1.99874878,1.98593140,2.02941895,2.02087402,2.02743530,2.01690674,2.01263428,2.00668335,2.01690674,2.00592041,2.00210571,2.00210571,2.01568604,2.01110840,2.00378418,1.99447632,2.04177856,2.01278687,2.01217651,2.00210571,0,15:40:49.26,0,0,1 +3,2.01614380,2.01263428,2.01354980,2.01248169,2.02392578,2.01644897,2.02453613,2.01934814,2.01873779,2.01919556,2.02087402,2.01889038,2.02621460,2.03781128,2.02423096,2.02011108,2.00881958,2.00637817,2.02087402,2.01644897,2.02209473,2.00988770,2.03155518,2.01919556,2.02148438,2.01370239,2.01324463,2.00973511,2.00973511,2.01477051,2.02117920,2.02636719,2.00805664,2.01583862,2.02529907,2.03033447,2.00424194,1.99935913,2.01202393,2.01568604,2.01583862,2.01095581,2.02545166,2.01721191,2.02758789,2.02957153,2.00622559,2.00073242,2.00759888,2.00485229,2.02041626,2.03094482,2.01782227,2.01675415,2.00820923,2.01110840,2.01354980,2.00637817,2.01538086,2.00271606,2.01156616,2.01217651,2.01736450,1.99386597,2.03353882,2.02362061,2.03598022,2.02728271,2.00866699,1.99676514,2.00714111,2.00744629,2.02087402,2.01492310,2.01202393,2.01644897,2.00057983,1.99584961,2.01370239,1.99050903,1.99783325,1.99371338,2.01431274,1.99920654,1.99630737,1.97814941,2.02545166,2.01126099,2.02102661,2.00790405,2.00912476,1.99813843,2.01156616,1.99981689,1.99829102,1.99523926,2.01141357,2.00424194,2.00164795,1.98730469,2.03948975,2.00637817,2.00958252,1.99615479,0,15:40:49.15,0,0,1 +4,2.02087402,2.02194214,2.01782227,2.02026367,2.02743530,2.02453613,2.02789307,2.02758789,2.02255249,2.02880859,2.02819824,2.02880859,2.03445435,2.04879761,2.02850342,2.02865601,2.01583862,2.01477051,2.02529907,2.02499390,2.03033447,2.02178955,2.03308105,2.02636719,2.02545166,2.02224731,2.01690674,2.01751709,2.01522827,2.02423096,2.02590942,2.03536987,2.01507568,2.02850342,2.03033447,2.04040527,2.00729370,2.00546265,2.01431274,2.02407837,2.02224731,2.02346802,2.02972412,2.02682495,2.03186035,2.03720093,2.01171875,2.00851440,2.00897217,2.01370239,2.02468872,2.04177856,2.02224731,2.02606201,2.01339722,2.02499390,2.01766968,2.01583862,2.01690674,2.00851440,2.01568604,2.02117920,2.02087402,2.00164795,2.03689575,2.03247070,2.04132080,2.03613281,2.01110840,2.00332642,2.01293945,2.01583862,2.02560425,2.02224731,2.01828003,2.02621460,2.00637817,2.00607300,2.01431274,1.99890137,2.00714111,1.99813843,2.01721191,2.00942993,1.99874878,1.98593140,2.02941895,2.02087402,2.02743530,2.01690674,2.01263428,2.00668335,2.01690674,2.00592041,2.00210571,2.00210571,2.01568604,2.01110840,2.00378418,1.99447632,2.04177856,2.01278687,2.01217651,2.00210571,0,15:40:49.26,0,0,1 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+""" # noqa: E501 + + +@pytest.mark.parametrize('preload', (True, False)) +@pytest.mark.parametrize('version, n_ch, n_times, lowpass, sex, date, end', [ + ('1.18', 48, 60, 0.1, 2, (2004, 5, 17, 5, 14, 0, 0), None), + ('1.25', 108, 10, 5., 1, (2020, 2, 2, 11, 20, 0, 0), b'\r'), + ('1.25', 108, 10, 5., 1, (2020, 2, 2, 11, 20, 0, 0), b'\n'), + ('1.25', 108, 10, 5., 1, (2020, 2, 2, 11, 20, 0, 0), b'\r\n'), +]) +def test_hitachi_basic(preload, version, n_ch, n_times, lowpass, sex, date, + end, tmp_path): + """Test NIRSport1 file with no saturation.""" + fname = tmp_path / 'test.csv' + contents = CONTENTS[version] + if end is not None: + contents = contents.replace(b'\r', b'\n').replace(b'\n\n', b'\n') + contents = contents.replace(b'\n', end) + with open(fname, 'wb') as fid: + fid.write(CONTENTS[version]) + raw = read_raw_hitachi(fname, preload=preload, verbose=True) + data = raw.get_data() + assert data.shape == (n_ch, n_times) + assert raw.info['sfreq'] == 10 + assert raw.info['lowpass'] == lowpass + assert raw.info['subject_info']['sex'] == sex + assert np.isfinite(raw.get_data()).all() + assert raw.info['meas_date'] == dt.datetime(*date, tzinfo=dt.timezone.utc) + # bad distances (zero) + distances = source_detector_distances(raw.info) + want = [0.] * (n_ch - 4) + assert_allclose(distances, want, atol=0.) + raw_od_bad = optical_density(raw) + with pytest.warns(RuntimeWarning, match='will be zero'): + beer_lambert_law(raw_od_bad, ppf=6) + # bad distances (too big) + if version == '1.18': + need = sum(([f'S{ii}', f'D{ii}'] for ii in range(1, 9)), [])[:-1] + have = 'P7 FC3 C3 CP3 P3 F5 FC5 C5 CP5 P5 F7 FT7 T7 TP7 F3'.split() + assert len(need) == len(have) + mon = make_standard_montage('standard_1020') + mon.rename_channels(dict(zip(have, need))) + raw.set_montage(mon) + raw_od_bad = optical_density(raw) + with pytest.warns(RuntimeWarning, match='greater than 10 cm'): + beer_lambert_law(raw_od_bad, ppf=6) + # good distances + mon = make_standard_montage('standard_1020') + if version == '1.18': + need = sum(([f'S{ii}', f'D{ii}'] for ii in range(1, 9)), [])[:-1] + have = 'F3 FC3 C3 CP3 P3 F5 FC5 C5 CP5 P5 F7 FT7 T7 TP7 P7'.split() + else: + need = sum(([f'S{ii}', f'D{ii}'] for ii in range(1, 18)), [])[:-1] + have = ('FT9 FT7 FC5 FC3 FC1 FCz FC2 FC4 FC6 FT8 FT10 ' + 'T9 T7 C5 C3 C1 Cz C2 C4 C6 T8 T10 ' + 'TP9 TP7 CP5 CP3 CP1 CPz CP2 CP4 CP6 TP8 TP10').split() + assert len(need) == len(have) + mon.rename_channels(dict(zip(have, need))) + raw.set_montage(mon) + distances = source_detector_distances(raw.info) + want = [0.03] * (n_ch - 4) + assert_allclose(distances, want, atol=0.01) + test_rank = 'less' if n_times < n_ch else True + _test_raw_reader(read_raw_hitachi, fname=fname, + boundary_decimal=1, test_rank=test_rank) # low fs + + # TODO: eventually we should refactor these to be in + # mne/io/tests/test_raw.py and run them for all fNIRS readers + + # OD + raw_od = optical_density(raw) + assert np.isfinite(raw_od.get_data()).all() + sci = scalp_coupling_index(raw_od, verbose='error') + lo, mi, hi = np.percentile(sci, [5, 50, 95]) + if version == '1.18': + assert -0.1 < lo < 0.1 # not great + assert 0.4 < mi < 0.5 + assert 0.8 < hi < 0.9 + else: + assert 0.99 <= lo <= hi <= 1 + # TDDR + raw_tddr = tddr(raw_od) + data = raw_tddr.get_data('fnirs') + assert np.isfinite(data.all()) + peaks = np.ptp(data, axis=-1) + assert_array_less(1e-4, peaks, err_msg='TDDR too small') + assert_array_less(peaks, 1, err_msg='TDDR too big') + # HbO/HbR + raw_tddr.set_montage(mon) + raw_h = beer_lambert_law(raw_tddr, ppf=6) + data = raw_h.get_data('fnirs') + assert np.isfinite(data).all() + assert data.shape == (n_ch - 4, n_times) + peaks = np.ptp(data, axis=-1) + assert_array_less(1e-10, peaks, err_msg='Beer-Lambert too small') + assert_array_less(peaks, 1e-5, err_msg='Beer-Lambert too big') + + +# From Hitachi 2 Homer +KNOWN_PAIRS = { + (3, 3, 2): ( + (0, 0), (1, 0), (0, 1), (2, 0), (1, 2), + (2, 1), (2, 2), (3, 1), (2, 3), (4, 2), + (3, 3), (4, 3), (5, 4), (6, 4), (5, 5), + (7, 4), (6, 6), (7, 5), (7, 6), (8, 5), + (7, 7), (9, 6), (8, 7), (9, 7)), + (3, 5, 1): ( + (0, 0), (1, 0), (1, 1), (2, 1), (0, 2), + (3, 0), (1, 3), (4, 1), (2, 4), (3, 2), + (3, 3), (4, 3), (4, 4), (5, 2), (3, 5), + (6, 3), (4, 6), (7, 4), (5, 5), (6, 5), + (6, 6), (7, 6)), + (4, 4, 1): ( + (0, 0), (1, 0), (1, 1), (0, 2), (2, 0), + (1, 3), (3, 1), (2, 2), (2, 3), (3, 3), + (4, 2), (2, 4), (5, 3), (3, 5), (4, 4), + (5, 4), (5, 5), (4, 6), (6, 4), (5, 7), + (7, 5), (6, 6), (6, 7), (7, 7)), + (3, 11, 1): ( + (0, 0), (1, 0), (1, 1), (2, 1), (2, 2), + (3, 2), (3, 3), (4, 3), (4, 4), (5, 4), + (0, 5), (6, 0), (1, 6), (7, 1), (2, 7), + (8, 2), (3, 8), (9, 3), (4, 9), (10, 4), + (5, 10), (6, 5), (6, 6), (7, 6), (7, 7), + (8, 7), (8, 8), (9, 8), (9, 9), (10, 9), + (10, 10), (11, 5), (6, 11), (12, 6), (7, 12), + (13, 7), (8, 13), (14, 8), (9, 14), (15, 9), + (10, 15), (16, 10), (11, 11), (12, 11), (12, 12), + (13, 12), (13, 13), (14, 13), (14, 14), (15, 14), + (15, 15), (16, 15)), +} + + +@pytest.mark.parametrize('n_rows, n_cols, n', list(KNOWN_PAIRS)) +def test_compute_pairs(n_rows, n_cols, n): + """Test computation of S-D pairings.""" + want = KNOWN_PAIRS[(n_rows, n_cols, n)] + got = _compute_pairs(n_rows, n_cols, n) + assert got == want diff -Nru python-mne-0.23.4+dfsg/mne/io/__init__.py python-mne-1.1.0+dfsg/mne/io/__init__.py --- python-mne-0.23.4+dfsg/mne/io/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Authors: Alexandre Gramfort # Matti Hämäläinen # -# License: BSD (3-clause) +# License: BSD-3-Clause from .open import fiff_open, show_fiff, _fiff_get_fid from .meas_info import (read_fiducials, write_fiducials, read_info, write_info, @@ -34,6 +34,7 @@ from . import nihon from .array import RawArray +from .besa import read_evoked_besa from .brainvision import read_raw_brainvision from .bti import read_raw_bti from .cnt import read_raw_cnt @@ -48,6 +49,7 @@ from .artemis123 import read_raw_artemis123 from .eeglab import read_raw_eeglab, read_epochs_eeglab from .eximia import read_raw_eximia +from .hitachi import read_raw_hitachi from .nirx import read_raw_nirx from .boxy import read_raw_boxy from .snirf import read_raw_snirf diff -Nru python-mne-0.23.4+dfsg/mne/io/kit/constants.py python-mne-1.1.0+dfsg/mne/io/kit/constants.py --- python-mne-0.23.4+dfsg/mne/io/kit/constants.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/kit/constants.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Authors: Teon Brooks # Christian Brodbeck # -# License: BSD (3-clause) +# License: BSD-3-Clause from ..constants import FIFF from ...utils import BunchConst diff -Nru python-mne-0.23.4+dfsg/mne/io/kit/coreg.py python-mne-1.1.0+dfsg/mne/io/kit/coreg.py --- python-mne-0.23.4+dfsg/mne/io/kit/coreg.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/kit/coreg.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Author: Teon Brooks # -# License: BSD (3-clause) +# License: BSD-3-Clause from collections import OrderedDict from os import SEEK_CUR, path as op diff -Nru python-mne-0.23.4+dfsg/mne/io/kit/__init__.py python-mne-1.1.0+dfsg/mne/io/kit/__init__.py --- python-mne-0.23.4+dfsg/mne/io/kit/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/kit/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Author: Teon Brooks # -# License: BSD (3-clause) +# License: BSD-3-Clause from .kit import read_raw_kit, read_epochs_kit from .coreg import read_mrk diff -Nru python-mne-0.23.4+dfsg/mne/io/kit/kit.py python-mne-1.1.0+dfsg/mne/io/kit/kit.py --- python-mne-0.23.4+dfsg/mne/io/kit/kit.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/kit/kit.py 2022-08-03 15:48:14.000000000 +0000 @@ -7,7 +7,7 @@ # Joan Massich # Christian Brodbeck # -# License: BSD (3-clause) +# License: BSD-3-Clause from collections import defaultdict, OrderedDict from math import sin, cos @@ -17,7 +17,7 @@ from ..pick import pick_types from ...utils import (verbose, logger, warn, fill_doc, _check_option, - _stamp_to_dt) + _stamp_to_dt, _check_fname) from ...transforms import apply_trans, als_ras_trans from ..base import BaseRaw from ..utils import _mult_cal_one @@ -49,8 +49,9 @@ # setup digitization if mrk is not None and elp is not None and hsp is not None: - info['dig'], info['dev_head_t'], info['hpi_results'] = _set_dig_kit( - mrk, elp, hsp, kit_info['eeg_dig']) + with info._unlock(): + info['dig'], info['dev_head_t'], info['hpi_results'] = \ + _set_dig_kit(mrk, elp, hsp, kit_info['eeg_dig']) elif mrk is not None or elp is not None or hsp is not None: raise ValueError("mrk, elp and hsp need to be provided as a group " "(all or none)") @@ -176,6 +177,7 @@ return stim_ch + @fill_doc def _set_stimchannels(self, info, stim, stim_code): """Specify how the trigger channel is synthesized from analog channels. @@ -185,8 +187,7 @@ Parameters ---------- - info : instance of MeasInfo - The measurement info. + %(info_not_none)s stim : list of int | '<' | '>' Can be submitted as list of trigger channels. If a list is not specified, the default triggers extracted from @@ -386,11 +387,12 @@ if isinstance(events, str): events = read_events(events) + input_fname = _check_fname(fname=input_fname, must_exist=True, + overwrite='read') logger.info('Extracting KIT Parameters from %s...' % input_fname) - input_fname = op.abspath(input_fname) self.info, kit_info = get_kit_info( input_fname, allow_unknown_format, standardize_names) - kit_info.update(filename=input_fname) + kit_info.update(input_fname=input_fname) self._raw_extras = [kit_info] self._filenames = [] if len(events) != self._raw_extras[0]['n_epochs']: @@ -437,11 +439,11 @@ epoch_length = info['frame_length'] n_epochs = info['n_epochs'] n_samples = info['n_samples'] - filename = info['filename'] + input_fname = info['input_fname'] dtype = info['dtype'] nchan = info['nchan'] - with open(filename, 'rb', buffering=0) as fid: + with open(input_fname, 'rb', buffering=0) as fid: fid.seek(info['dirs'][KIT.DIR_INDEX_RAW_DATA]['offset']) count = n_samples * nchan data = np.fromfile(fid, dtype=dtype, count=count) @@ -488,8 +490,7 @@ Returns ------- - info : instance of Info - An Info for the instance. + %(info_not_none)s sqd : dict A dict containing all the sqd parameter settings. """ @@ -512,7 +513,7 @@ version_string = "V%iR%03i" % (version, revision) if allow_unknown_format: unsupported_format = True - logger.warning("Force loading KIT format %s", version_string) + warn("Force loading KIT format %s" % version_string) else: raise UnsupportedKITFormat( version_string, @@ -734,6 +735,7 @@ # coregistration fid.seek(cor_dir['offset']) mrk = np.zeros((elp.shape[0] - 3, 3)) + meg_done = [True] * 5 for _ in range(cor_dir['count']): done = np.fromfile(fid, INT32, 1)[0] fid.seek(16 * KIT.DOUBLE + # meg_to_mri @@ -744,12 +746,17 @@ continue assert marker_count >= len(mrk) for mi in range(len(mrk)): - mri_type, meg_type, mri_done, meg_done = \ + mri_type, meg_type, mri_done, this_meg_done = \ np.fromfile(fid, INT32, 4) - assert meg_done + meg_done[mi] = bool(this_meg_done) fid.seek(3 * KIT.DOUBLE, SEEK_CUR) # mri_pos mrk[mi] = np.fromfile(fid, FLOAT64, 3) fid.seek(256, SEEK_CUR) # marker_file (char) + if not all(meg_done): + logger.info(f'Keeping {sum(meg_done)}/{len(meg_done)} HPI ' + 'coils that were digitized') + elp = elp[[True] * 3 + meg_done] + mrk = mrk[meg_done] sqd.update(hsp=hsp, elp=elp, mrk=mrk) # precompute conversion factor for reading data @@ -763,7 +770,7 @@ ad_to_tesla = ad_to_volt / amp_gain * channel_gain conv_factor = np.where(is_meg, ad_to_tesla, ad_to_volt) # XXX this is a bit of a hack. Should probably do this more cleanly at - # some point... the 2 ** (adc_stored - 14) was emperically determined using + # some point... the 2 ** (adc_stored - 14) was empirically determined using # the test files with known amplitudes. The conv_factors need to be # replaced by these values otherwise we're off by a factor off 5000.0 # for the EEG data. @@ -836,6 +843,7 @@ coord_frame=FIFF.FIFFV_COORD_DEVICE, coil_type=KIT.CH_TO_FIFF_COIL[ch['type']], kind=KIT.CH_TO_FIFF_KIND[ch['type']], loc=loc)) + info._unlocked = False info._update_redundant() return info, sqd @@ -922,18 +930,8 @@ ---------- input_fname : str Path to the sqd file. - events : array, shape (n_events, 3) - The events typically returned by the read_events function. - If some events don't match the events of interest as specified - by event_id, they will be marked as 'IGNORED' in the drop log. - event_id : int | list of int | dict | None - The id of the event to consider. If dict, - the keys can later be used to access associated events. Example: - dict(auditory=1, visual=3). If int, a dict will be created with - the id as string. If a list, all events with the IDs specified - in the list are used. If None, all events will be used with - and a dict is created with string integer names corresponding - to the event id integers. + %(events_epochs)s + %(event_id)s mrk : None | str | array_like, shape (5, 3) | list of str or array_like Marker points representing the location of the marker coils with respect to the MEG Sensors, or path to a marker file. diff -Nru python-mne-0.23.4+dfsg/mne/io/kit/tests/test_coreg.py python-mne-1.1.0+dfsg/mne/io/kit/tests/test_coreg.py --- python-mne-0.23.4+dfsg/mne/io/kit/tests/test_coreg.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/kit/tests/test_coreg.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Christian Brodbeck # -# License: BSD (3-clause) +# License: BSD-3-Clause import inspect import os @@ -11,7 +11,6 @@ from mne.io.kit import read_mrk from mne.io._digitization import _write_dig_points -from mne.utils import _TempDir FILE = inspect.getfile(inspect.currentframe()) @@ -20,9 +19,9 @@ mrk_fname = os.path.join(data_dir, 'test_mrk.sqd') -def test_io_mrk(): +def test_io_mrk(tmp_path): """Test IO for mrk files.""" - tempdir = _TempDir() + tempdir = str(tmp_path) pts = read_mrk(mrk_fname) # txt diff -Nru python-mne-0.23.4+dfsg/mne/io/kit/tests/test_kit.py python-mne-1.1.0+dfsg/mne/io/kit/tests/test_kit.py --- python-mne-0.23.4+dfsg/mne/io/kit/tests/test_kit.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/kit/tests/test_kit.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Author: Teon Brooks # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op @@ -18,6 +18,7 @@ from mne.utils import assert_dig_allclose from mne.io import read_raw_fif, read_raw_kit, read_epochs_kit from mne.io.constants import FIFF +from mne.io.kit.kit import get_kit_info from mne.io.kit.coreg import read_sns from mne.io.kit.constants import KIT from mne.io.tests.test_raw import _test_raw_reader @@ -49,11 +50,13 @@ data_path, 'KIT', 'Example_RICOH160-1_10020-export_anonymyze.con')] ricoh_systems_paths += [op.join( data_path, 'KIT', 'Example_RICOH160-1_10021-export_anonymyze.con')] +ricoh_systems_paths += [op.join( + data_path, 'KIT', '010409_Motor_task_coregist-export_tiny_1s.con')] berlin_path = op.join(data_path, 'KIT', 'data_berlin.con') @requires_testing_data -def test_data(tmpdir): +def test_data(tmp_path): """Test reading raw kit files.""" pytest.raises(TypeError, read_raw_kit, epochs_path) pytest.raises(TypeError, read_epochs_kit, sqd_path) @@ -147,6 +150,29 @@ assert_array_equal(find_events(raw), [[91, 0, 2]]) +@requires_testing_data +def test_unknown_format(tmp_path): + """Test our warning about an unknown format.""" + fname = tmp_path / op.basename(ricoh_path) + _, kit_info = get_kit_info(ricoh_path, allow_unknown_format=False) + n_before = kit_info['dirs'][KIT.DIR_INDEX_SYSTEM]['offset'] + with open(fname, 'wb') as fout: + with open(ricoh_path, 'rb') as fin: + fout.write(fin.read(n_before)) + version, revision = np.fromfile(fin, ' 2 # good + version = 1 # bad + np.array([version, revision], ' # Matti Hämäläinen # -# License: BSD (3-clause) +# License: BSD-3-Clause from .constants import FIFF from .tag import find_tag, has_tag diff -Nru python-mne-0.23.4+dfsg/mne/io/meas_info.py python-mne-1.1.0+dfsg/mne/io/meas_info.py --- python-mne-0.23.4+dfsg/mne/io/meas_info.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/meas_info.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,42 +4,47 @@ # Teon Brooks # Stefan Appelhoff # -# License: BSD (3-clause) +# License: BSD-3-Clause from collections import Counter, OrderedDict +from collections.abc import Mapping import contextlib from copy import deepcopy import datetime from io import BytesIO import operator from textwrap import shorten +import string import numpy as np -from .pick import (channel_type, pick_channels, pick_info, - get_channel_type_constants, pick_types) +from .pick import (channel_type, _get_channel_types, + get_channel_type_constants, pick_types, _contains_ch_type) from .constants import FIFF, _coord_frame_named from .open import fiff_open from .tree import dir_tree_find from .tag import (read_tag, find_tag, _ch_coord_dict, _update_ch_info_named, _rename_list) from .proj import (_read_proj, _write_proj, _uniquify_projs, _normalize_proj, - Projection) + _proj_equal, Projection) from .ctf_comp import _read_ctf_comp, write_ctf_comp -from .write import (start_file, end_file, start_block, end_block, +from .write import (start_and_end_file, start_block, end_block, write_string, write_dig_points, write_float, write_int, write_coord_trans, write_ch_info, write_name_list, write_julian, write_float_matrix, write_id, DATE_NONE) from .proc_history import _read_proc_history, _write_proc_history -from ..transforms import invert_transform, Transform, _coord_frame_name +from ..transforms import (invert_transform, Transform, _coord_frame_name, + _ensure_trans, _frame_to_str) from ..utils import (logger, verbose, warn, object_diff, _validate_type, _stamp_to_dt, _dt_to_stamp, _pl, _is_numeric, - _check_option) + _check_option, _on_missing, _check_on_missing, fill_doc, + _check_fname) from ._digitization import (_format_dig_points, _dig_kind_proper, DigPoint, _dig_kind_rev, _dig_kind_ints, _read_dig_fif) -from ._digitization import write_dig as _dig_write_dig +from ._digitization import write_dig, _get_data_as_dict_from_dig from .compensator import get_current_comp -from ..data.html_templates import info_template +from ..defaults import _handle_default + b = bytes # alias @@ -106,6 +111,7 @@ @verbose def _unique_channel_names(ch_names, max_length=None, verbose=None): """Ensure unique channel names.""" + suffixes = tuple(string.ascii_lowercase) if max_length is not None: ch_names[:] = [name[:max_length] for name in ch_names] unique_ids = np.unique(ch_names, return_index=True)[1] @@ -126,24 +132,76 @@ n_keep = min(len(ch_stem), n_keep) ch_stem = ch_stem[:n_keep] for idx, ch_idx in enumerate(overlaps): - ch_name = ch_stem + '-%s' % idx + # try idx first, then loop through lower case chars + for suffix in (idx,) + suffixes: + ch_name = ch_stem + '-%s' % suffix + if ch_name not in ch_names: + break if ch_name not in ch_names: ch_names[ch_idx] = ch_name else: - raise ValueError('Adding a running number for a ' + raise ValueError('Adding a single alphanumeric for a ' 'duplicate resulted in another ' 'duplicate name %s' % ch_name) - return ch_names class MontageMixin(object): - """Mixin for Montage setting.""" + """Mixin for Montage getting and setting.""" + + @fill_doc + def get_montage(self): + """Get a DigMontage from instance. + + Returns + ------- + %(montage)s + """ + from ..channels.montage import make_dig_montage + info = self if isinstance(self, Info) else self.info + if info['dig'] is None: + return None + # obtain coord_frame, and landmark coords + # (nasion, lpa, rpa, hsp, hpi) from DigPoints + montage_bunch = _get_data_as_dict_from_dig(info['dig']) + coord_frame = _frame_to_str.get(montage_bunch.coord_frame) + + # get the channel names and chs data structure + ch_names, chs = info['ch_names'], info['chs'] + picks = pick_types(info, meg=False, eeg=True, seeg=True, + ecog=True, dbs=True, fnirs=True, exclude=[]) + + # channel positions from dig do not match ch_names one to one, + # so use loc[:3] instead + ch_pos = {ch_names[ii]: chs[ii]['loc'][:3] for ii in picks} + + # fNIRS uses multiple channels for the same sensors, we use + # a private function to format these for dig montage. + fnirs_picks = pick_types(info, fnirs=True, exclude=[]) + if len(ch_pos) == len(fnirs_picks): + ch_pos = _get_fnirs_ch_pos(info) + elif len(fnirs_picks) > 0: + raise ValueError("MNE does not support getting the montage " + "for a mix of fNIRS and other data types. " + "Please raise a GitHub issue if you " + "require this feature.") + + # create montage + montage = make_dig_montage( + ch_pos=ch_pos, + coord_frame=coord_frame, + nasion=montage_bunch.nasion, + lpa=montage_bunch.lpa, + rpa=montage_bunch.rpa, + hsp=montage_bunch.hsp, + hpi=montage_bunch.hpi, + ) + return montage @verbose def set_montage(self, montage, match_case=True, match_alias=False, on_missing='raise', verbose=None): - """Set EEG sensor configuration and head digitization. + """Set %(montage_types)s channel positions and digitization points. Parameters ---------- @@ -151,16 +209,26 @@ %(match_case)s %(match_alias)s %(on_missing_montage)s - %(verbose_meth)s + %(verbose)s Returns ------- inst : instance of Raw | Epochs | Evoked - The instance. + The instance, modified in-place. + + See Also + -------- + mne.channels.make_standard_montage + mne.channels.make_dig_montage + mne.channels.read_custom_montage Notes ----- - Operates in place. + .. warning:: + Only %(montage_types)s channels can have their positions set using + a montage. Other channel types (e.g., MEG channels) should have + their positions defined properly using their data reading + functions. """ # How to set up a montage to old named fif file (walk through example) # https://gist.github.com/massich/f6a9f4799f1fbeb8f5e8f8bc7b07d3df @@ -171,6 +239,68 @@ return self +class ContainsMixin(object): + """Mixin class for Raw, Evoked, Epochs and Info.""" + + def __contains__(self, ch_type): + """Check channel type membership. + + Parameters + ---------- + ch_type : str + Channel type to check for. Can be e.g. 'meg', 'eeg', 'stim', etc. + + Returns + ------- + in : bool + Whether or not the instance contains the given channel type. + + Examples + -------- + Channel type membership can be tested as:: + + >>> 'meg' in inst # doctest: +SKIP + True + >>> 'seeg' in inst # doctest: +SKIP + False + + """ + info = self if isinstance(self, Info) else self.info + if ch_type == 'meg': + has_ch_type = (_contains_ch_type(info, 'mag') or + _contains_ch_type(info, 'grad')) + else: + has_ch_type = _contains_ch_type(info, ch_type) + return has_ch_type + + @property + def compensation_grade(self): + """The current gradient compensation grade.""" + info = self if isinstance(self, Info) else self.info + return get_current_comp(info) + + @fill_doc + def get_channel_types(self, picks=None, unique=False, only_data_chs=False): + """Get a list of channel type for each channel. + + Parameters + ---------- + %(picks_all)s + unique : bool + Whether to return only unique channel types. Default is ``False``. + only_data_chs : bool + Whether to ignore non-data channels. Default is ``False``. + + Returns + ------- + channel_types : list + The channel types. + """ + info = self if isinstance(self, Info) else self.info + return _get_channel_types(info, picks=picks, unique=unique, + only_data_chs=only_data_chs) + + def _format_trans(obj, key): try: t = obj[key] @@ -194,8 +324,51 @@ f'key{_pl(bad)} missing for {name}[{ci}]: {bad}',) -# XXX Eventually this should be de-duplicated with the MNE-MATLAB stuff... -class Info(dict, MontageMixin): +# As options are added here, test_meas_info.py:test_info_bad should be updated +def _check_bads(bads): + _validate_type(bads, list, 'bads') + return bads + + +def _check_description(description): + _validate_type(description, (None, str), "info['description']") + return description + + +def _check_dev_head_t(dev_head_t): + _validate_type(dev_head_t, (Transform, None), "info['dev_head_t']") + if dev_head_t is not None: + dev_head_t = _ensure_trans(dev_head_t, 'meg', 'head') + return dev_head_t + + +def _check_experimenter(experimenter): + _validate_type(experimenter, (None, str), 'experimenter') + return experimenter + + +def _check_line_freq(line_freq): + _validate_type(line_freq, (None, 'numeric'), 'line_freq') + line_freq = float(line_freq) if line_freq is not None else line_freq + return line_freq + + +def _check_subject_info(subject_info): + _validate_type(subject_info, (None, dict), 'subject_info') + return subject_info + + +def _check_device_info(device_info): + _validate_type(device_info, (None, dict, ), 'device_info') + return device_info + + +def _check_helium_info(helium_info): + _validate_type(helium_info, (None, dict, ), 'helium_info') + return helium_info + + +class Info(dict, MontageMixin, ContainsMixin): """Measurement information. This data structure behaves like a dictionary. It contains all metadata @@ -205,10 +378,14 @@ so new entries should not be manually added. .. warning:: The only entries that should be manually changed by the user - are ``info['bads']`` and ``info['description']``. All other - entries should be considered read-only, though they can be - modified by various MNE-Python functions or methods (which - have safeguards to ensure all fields remain in sync). + are ``info['bads']``, ``info['description']``, + ``info['device_info']``, ``info['dev_head_t']``, + ``info['experimenter']``, info['helium_info'], + ``info['line_freq']``, ``info['temp']`` and + ``info['subject_info']``. All other entries should be + considered read-only, though they can be modified by various + MNE-Python functions or methods (which have safeguards to + ensure all fields remain in sync). .. warning:: This class should not be instantiated directly. To create a measurement information structure, use @@ -224,7 +401,7 @@ Attributes ---------- acq_pars : str | None - MEG system acquition parameters. + MEG system acquisition parameters. See :class:`mne.AcqParserFIF` for details. acq_stim : str | None MEG system stimulus parameters. @@ -236,6 +413,9 @@ chs : list of dict A list of channel information dictionaries, one per channel. See Notes for more information. + command_line : str + Contains the command and arguments used to create the source space + (used for source estimation). comps : list of dict CTF software gradient compensation data. See Notes for more information. @@ -253,6 +433,10 @@ This is only present in 4D/CTF data. dev_head_t : dict | None The device to head transformation. + device_info : dict | None + Information about the acquisition device. See Notes for details. + + .. versionadded:: 0.19 dig : list of dict | None The Polhemus digitization data in head coordinates. See Notes for more information. @@ -265,6 +449,12 @@ Name of the person that ran the experiment. file_id : dict | None The FIF globally unique ID. See Notes for more information. + gantry_angle : float | None + Tilt angle of the gantry in degrees. + helium_info : dict | None + Information about the device helium. See Notes for details. + + .. versionadded:: 0.19 highpass : float Highpass corner frequency in Hertz. Zero indicates a DC recording. hpi_meas : list of dict @@ -279,27 +469,35 @@ Information about the HPI subsystem that was used (e.g., event channel used for cHPI measurements). See Notes for details. + kit_system_id : int + Identifies the KIT system. line_freq : float | None Frequency of the power line in Hertz. - gantry_angle : float | None - Tilt angle of the gantry in degrees. lowpass : float Lowpass corner frequency in Hertz. It is automatically set to half the sampling rate if there is otherwise no low-pass applied to the data. + maxshield : bool + True if active shielding (IAS) was active during recording. meas_date : datetime The time (UTC) of the recording. .. versionchanged:: 0.20 This is stored as a :class:`~python:datetime.datetime` object instead of a tuple of seconds/microseconds. - utc_offset : str - "UTC offset of related meas_date (sHH:MM). - - .. versionadded:: 0.19 + meas_file : str | None + Raw measurement file (used for source estimation). meas_id : dict | None The ID assigned to this measurement by the acquisition system or during file conversion. Follows the same format as ``file_id``. + mri_file : str | None + File containing the MRI to head transformation (used for source + estimation). + mri_head_t : dict | None + Transformation from MRI to head coordinates (used for source + estimation). + mri_id : dict | None + MRI unique ID (used for source estimation). nchan : int Number of channels. proc_history : list of dict @@ -317,14 +515,20 @@ subject_info : dict | None Information about the subject. See Notes for details. - device_info : dict | None - Information about the acquisition device. See Notes for details. + temp : object | None + Can be used to store temporary objects in an Info instance. It will not + survive an I/O roundtrip. - .. versionadded:: 0.19 - helium_info : dict | None - Information about the device helium. See Notes for details. + .. versionadded:: 0.24 + utc_offset : str + "UTC offset of related meas_date (sHH:MM). .. versionadded:: 0.19 + working_dir : str + Working directory used when the source space was created (used for + source estimation). + xplotter_layout : str + Layout of the Xplotter (Neuromag system only). See Also -------- @@ -382,6 +586,17 @@ save_calibrated : bool Were the compensation data saved in calibrated form. + * ``device_info`` dict: + + type : str + Device type. + model : str + Device model. + serial : str + Device serial. + site : str + Device site. + * ``dig`` list of dict: kind : int @@ -414,6 +629,17 @@ usecs : int Time in microseconds. + * ``helium_info`` dict: + + he_level_raw : float + Helium level (%) before position correction. + helium_level : float + Helium level (%) after position correction. + orig_file_guid : str + Original file GUID. + meas_date : tuple of int + The helium level meas date. + * ``hpi_meas`` list of dict: creator : str @@ -477,6 +703,17 @@ (using the first element of these arrays is typically sufficient). + * ``mri_id`` dict: + + version : int + FIF format version, i.e. ``FIFFC_VERSION``. + machid : ndarray, shape (2,) + Unique machine ID, usually derived from the MAC address. + secs : int + Time in seconds. + usecs : int + Time in microseconds. + * ``proc_history`` list of dict: block_id : dict @@ -501,7 +738,7 @@ smartshield : dict MaxShield information. This dictionary is (always?) empty, but its presence implies that MaxShield was used during - acquisiton. + acquisition. * ``subject_info`` dict: @@ -521,32 +758,80 @@ Subject sex (0=unknown, 1=male, 2=female). hand : int Handedness (1=right, 2=left, 3=ambidextrous). - - * ``device_info`` dict: - - type : str - Device type. - model : str - Device model. - serial : str - Device serial. - site : str - Device site. - - * ``helium_info`` dict: - - he_level_raw : float - Helium level (%) before position correction. - helium_level : float - Helium level (%) after position correction. - orig_file_guid : str - Original file GUID. - meas_date : tuple of int - The helium level meas date. """ + _attributes = { + 'acq_pars': 'acq_pars cannot be set directly. ' + 'See mne.AcqParserFIF() for details.', + 'acq_stim': 'acq_stim cannot be set directly.', + 'bads': _check_bads, + 'ch_names': 'ch_names cannot be set directly. ' + 'Please use methods inst.add_channels(), ' + 'inst.drop_channels(), inst.pick_channels(), ' + 'inst.rename_channels(), inst.reorder_channels() ' + 'and inst.set_channel_types() instead.', + 'chs': 'chs cannot be set directly. ' + 'Please use methods inst.add_channels(), ' + 'inst.drop_channels(), inst.pick_channels(), ' + 'inst.rename_channels(), inst.reorder_channels() ' + 'and inst.set_channel_types() instead.', + 'command_line': 'command_line cannot be set directly.', + 'comps': 'comps cannot be set directly. ' + 'Please use method Raw.apply_gradient_compensation() ' + 'instead.', + 'ctf_head_t': 'ctf_head_t cannot be set directly.', + 'custom_ref_applied': 'custom_ref_applied cannot be set directly. ' + 'Please use method inst.set_eeg_reference() ' + 'instead.', + 'description': _check_description, + 'dev_ctf_t': 'dev_ctf_t cannot be set directly.', + 'dev_head_t': _check_dev_head_t, + 'device_info': _check_device_info, + 'dig': 'dig cannot be set directly. ' + 'Please use method inst.set_montage() instead.', + 'events': 'events cannot be set directly.', + 'experimenter': _check_experimenter, + 'file_id': 'file_id cannot be set directly.', + 'gantry_angle': 'gantry_angle cannot be set directly.', + 'helium_info': _check_helium_info, + 'highpass': 'highpass cannot be set directly. ' + 'Please use method inst.filter() instead.', + 'hpi_meas': 'hpi_meas can not be set directly.', + 'hpi_results': 'hpi_results cannot be set directly.', + 'hpi_subsystem': 'hpi_subsystem cannot be set directly.', + 'kit_system_id': 'kit_system_id cannot be set directly.', + 'line_freq': _check_line_freq, + 'lowpass': 'lowpass cannot be set directly. ' + 'Please use method inst.filter() instead.', + 'maxshield': 'maxshield cannot be set directly.', + 'meas_date': 'meas_date cannot be set directly. ' + 'Please use method inst.set_meas_date() instead.', + 'meas_file': 'meas_file cannot be set directly.', + 'meas_id': 'meas_id cannot be set directly.', + 'mri_file': 'mri_file cannot be set directly.', + 'mri_head_t': 'mri_head_t cannot be set directly.', + 'mri_id': 'mri_id cannot be set directly.', + 'nchan': 'nchan cannot be set directly. ' + 'Please use methods inst.add_channels(), ' + 'inst.drop_channels(), and inst.pick_channels() instead.', + 'proc_history': 'proc_history cannot be set directly.', + 'proj_id': 'proj_id cannot be set directly.', + 'proj_name': 'proj_name cannot be set directly.', + 'projs': 'projs cannot be set directly. ' + 'Please use methods inst.add_proj() and inst.del_proj() ' + 'instead.', + 'sfreq': 'sfreq cannot be set directly. ' + 'Please use method inst.resample() instead.', + 'subject_info': _check_subject_info, + 'temp': lambda x: x, + 'utc_offset': 'utc_offset cannot be set directly.', + 'working_dir': 'working_dir cannot be set directly.', + 'xplotter_layout': 'xplotter_layout cannot be set directly.' + } + def __init__(self, *args, **kwargs): - super(Info, self).__init__(*args, **kwargs) + self._unlocked = True + super().__init__(*args, **kwargs) # Deal with h5io writing things as dict for key in ('dev_head_t', 'ctf_head_t', 'dev_ctf_t'): _format_trans(self, key) @@ -563,7 +848,7 @@ self['chs'] = _dict_unpack(self['chs'], _CH_CAST) for pi, proj in enumerate(self.get('projs', [])): if not isinstance(proj, Projection): - self['projs'][pi] = Projection(proj) + self['projs'][pi] = Projection(**proj) # Old files could have meas_date as tuple instead of datetime try: meas_date = self['meas_date'] @@ -571,6 +856,61 @@ pass else: self['meas_date'] = _ensure_meas_date_none_or_dt(meas_date) + self._unlocked = False + + def __getstate__(self): + """Get state (for pickling).""" + return {'_unlocked': self._unlocked} + + def __setstate__(self, state): + """Set state (for pickling).""" + self._unlocked = state['_unlocked'] + + def __setitem__(self, key, val): + """Attribute setter.""" + # During unpickling, the _unlocked attribute has not been set, so + # let __setstate__ do it later and act unlocked now + unlocked = getattr(self, '_unlocked', True) + if key in self._attributes: + if isinstance(self._attributes[key], str): + if not unlocked: + raise RuntimeError(self._attributes[key]) + else: + val = self._attributes[key](val) # attribute checker function + else: + raise RuntimeError( + f"Info does not support directly setting the key {repr(key)}. " + "You can set info['temp'] to store temporary objects in an " + "Info instance, but these will not survive an I/O round-trip.") + super().__setitem__(key, val) + + def update(self, other=None, **kwargs): + """Update method using __setitem__().""" + iterable = other.items() if isinstance(other, Mapping) else other + if other is not None: + for key, val in iterable: + self[key] = val + for key, val in kwargs.items(): + self[key] = val + + @contextlib.contextmanager + def _unlock(self, *, update_redundant=False, check_after=False): + """Context manager unlocking access to attributes.""" + # needed for nested _unlock() + state = self._unlocked if hasattr(self, '_unlocked') else False + + self._unlocked = True + try: + yield + except Exception: + raise + else: + if update_redundant: + self._update_redundant() + if check_after: + self._check_consistency() + finally: + self._unlocked = state def copy(self): """Copy the instance. @@ -603,6 +943,7 @@ MAX_WIDTH = 68 strs = [' 0: + bad_channels = ', '.join(self['bads']) + else: + bad_channels = 'None' + + return good_channels, bad_channels, ecg, eog def _repr_html_(self, caption=None): + """Summarize info for HTML representation.""" + from ..html_templates import repr_templates_env if isinstance(caption, str): html = f'

{caption}

' else: html = '' - n_eeg = len(pick_types(self, meg=False, eeg=True)) - n_grad = len(pick_types(self, meg='grad')) - n_mag = len(pick_types(self, meg='mag')) - pick_eog = pick_types(self, meg=False, eog=True) - if len(pick_eog) > 0: - eog = ', '.join(np.array(self['ch_names'])[pick_eog]) - else: - eog = 'Not available' - pick_ecg = pick_types(self, meg=False, ecg=True) - if len(pick_ecg) > 0: - ecg = ', '.join(np.array(self['ch_names'])[pick_ecg]) - else: - ecg = 'Not available' - meas_date = self['meas_date'] + + good_channels, bad_channels, ecg, eog = self._get_chs_for_repr() + + # TODO + # Most of the following checks are to ensure that we get a proper repr + # for Forward['info'] (and probably others like + # InverseOperator['info']??), which doesn't seem to follow our standard + # Info structure used elsewhere. + # Proposed solution for a future refactoring: + # Forward['info'] should get its own Info subclass (with respective + # repr). + + # meas date + meas_date = self.get('meas_date') if meas_date is not None: meas_date = meas_date.strftime("%B %d, %Y %H:%M:%S") + ' GMT' - html += info_template.substitute( - caption=caption, info=self, meas_date=meas_date, n_eeg=n_eeg, - n_grad=n_grad, n_mag=n_mag, eog=eog, ecg=ecg) - return html + projs = self.get('projs') + if projs: + projs = [ + f'{p["desc"]} : {"on" if p["active"] else "off"}' + for p in self['projs'] + ] + else: + projs = None + + info_template = repr_templates_env.get_template('info.html.jinja') + return html + info_template.render( + caption=caption, + meas_date=meas_date, + projs=projs, + ecg=ecg, + eog=eog, + good_channels=good_channels, + bad_channels=bad_channels, + dig=self.get('dig'), + subject_info=self.get('subject_info'), + lowpass=self.get('lowpass'), + highpass=self.get('highpass'), + sfreq=self.get('sfreq'), + experimenter=self.get('experimenter'), + ) def _simplify_info(info): @@ -855,18 +1239,23 @@ Parameters ---------- - fname : str + fname : path-like The filename to read. %(verbose)s Returns ------- - pts : list of dicts + pts : list of dict List of digitizer points (each point in a dict). coord_frame : int The coordinate frame of the points (one of - mne.io.constants.FIFF.FIFFV_COORD_...). + ``mne.io.constants.FIFF.FIFFV_COORD_...``). """ + fname = _check_fname( + fname=fname, + overwrite='read', + must_exist=True + ) fid, tree, _ = fiff_open(fname) with fid: isotrak = dir_tree_find(tree, FIFF.FIFFB_ISOTRAK) @@ -892,41 +1281,30 @@ @verbose -def write_fiducials(fname, pts, coord_frame=FIFF.FIFFV_COORD_UNKNOWN, +def write_fiducials(fname, pts, coord_frame='unknown', *, overwrite=False, verbose=None): """Write fiducials to a fiff file. Parameters ---------- - fname : str + fname : path-like Destination file name. pts : iterator of dict Iterator through digitizer points. Each point is a dictionary with the keys 'kind', 'ident' and 'r'. - coord_frame : int - The coordinate frame of the points (one of - mne.io.constants.FIFF.FIFFV_COORD_...). - %(verbose)s - """ - _dig_write_dig(fname, pts, coord_frame) + coord_frame : str | int + The coordinate frame of the points. If a string, must be one of + ``'meg'``, ``'mri'``, ``'mri_voxel'``, ``'head'``, + ``'mri_tal'``, ``'ras'``, ``'fs_tal'``, ``'ctf_head'``, + ``'ctf_meg'``, and ``'unknown'`` + If an integer, must be one of the constants defined as + ``mne.io.constants.FIFF.FIFFV_COORD_...``. + %(overwrite)s - -def write_dig(fname, pts, coord_frame=None): - """Write digitization data to a FIF file. - - Parameters - ---------- - fname : str - Destination file name. - pts : iterator of dict - Iterator through digitizer points. Each point is a dictionary with - the keys 'kind', 'ident' and 'r'. - coord_frame : int | str | None - If all the points have the same coordinate frame, specify the type - here. Can be None (default) if the points could have varying - coordinate frames. + .. versionadded:: 1.0 + %(verbose)s """ - return _dig_write_dig(fname, pts, coord_frame=coord_frame) + write_dig(fname, pts, coord_frame, overwrite=overwrite) @verbose @@ -941,8 +1319,7 @@ Returns ------- - info : instance of Info - Measurement information for the dataset. + %(info_not_none)s """ f, tree, _ = fiff_open(fname) with f as fid: @@ -1000,8 +1377,7 @@ Returns ------- - info : instance of Info - Info on dataset. + %(info_not_none)s meas : dict Node in tree that contains the info. """ @@ -1180,10 +1556,8 @@ # # Put the data together # - if tree['id'] is not None: - info = Info(file_id=tree['id']) - else: - info = Info(file_id=None) + info = Info(file_id=tree['id']) + info._unlocked = True # Locate events list events = dir_tree_find(meas_info, FIFF.FIFFB_EVENTS) @@ -1437,7 +1811,7 @@ info['chs'] = chs # - # Add the coordinate transformations + # Add the coordinate transformations # info['dev_head_t'] = dev_head_t info['ctf_head_t'] = ctf_head_t @@ -1462,6 +1836,7 @@ info['xplotter_layout'] = xplotter_layout info['kit_system_id'] = kit_system_id info._check_consistency() + info._unlocked = False return info, meas @@ -1549,6 +1924,7 @@ (np.iinfo('>i4').max, 0), meas_date_stamp[0],)) +@fill_doc def write_meas_info(fid, info, data_type=None, reset_range=True): """Write measurement info into a file id (from a fif file). @@ -1556,8 +1932,7 @@ ---------- fid : file Open file descriptor. - info : instance of Info - The measurement info structure. + %(info_not_none)s data_type : int The data_type in case it is necessary. Should be 4 (FIFFT_FLOAT), 5 (FIFFT_DOUBLE), or 16 (FIFFT_DAU_PACK16) for @@ -1801,6 +2176,7 @@ _write_proc_history(fid, info) +@fill_doc def write_info(fname, info, data_type=None, reset_range=True): """Write measurement info in fif file. @@ -1808,8 +2184,7 @@ ---------- fname : str The name of the file. Should end by -info.fif. - info : instance of Info - The measurement info structure. + %(info_not_none)s data_type : int The data_type in case it is necessary. Should be 4 (FIFFT_FLOAT), 5 (FIFFT_DOUBLE), or 16 (FIFFT_DAU_PACK16) for @@ -1817,11 +2192,10 @@ reset_range : bool If True, info['chs'][k]['range'] will be set to unity. """ - with start_file(fname) as fid: + with start_and_end_file(fname) as fid: start_block(fid, FIFF.FIFFB_MEAS) write_meas_info(fid, info, data_type, reset_range) end_block(fid, FIFF.FIFFB_MEAS) - end_file(fid) @verbose @@ -1946,6 +2320,7 @@ infos = deepcopy(infos) _force_update_info(infos[0], infos[1:]) info = Info() + info._unlocked = True info['chs'] = [] for this_info in infos: info['chs'].extend(this_info['chs']) @@ -2005,11 +2380,13 @@ 'line_freq', 'lowpass', 'meas_id', 'proj_id', 'proj_name', 'projs', 'sfreq', 'gantry_angle', 'subject_info', 'sfreq', 'xplotter_layout', 'proc_history'] + for k in other_fields: info[k] = _merge_info_values(infos, k) info['meas_date'] = infos[0]['meas_date'] - info._check_consistency() + info._unlocked = False + return info @@ -2034,8 +2411,7 @@ Returns ------- - info : instance of Info - The measurement info. + %(info_not_none)s Notes ----- @@ -2099,6 +2475,7 @@ info._update_redundant() info._check_consistency() + info._unlocked = False return info @@ -2125,6 +2502,7 @@ _list_keys = ('bads', 'chs', 'comps', 'events', 'hpi_meas', 'hpi_results', 'projs', 'proc_history') info = Info() + info._unlocked = True for k in _none_keys: info[k] = None for k in _list_keys: @@ -2165,7 +2543,8 @@ if key in exclude_keys: continue for i_targ in info_target: - i_targ[key] = val + with i_targ._unlock(): + i_targ[key] = val def _add_timedelta_to_stamp(meas_date_stamp, delta_t): @@ -2188,9 +2567,9 @@ Parameters ---------- - info : dict, instance of Info - Measurement information for the dataset. - %(anonymize_info_parameters)s + %(info_not_none)s + %(daysback_anonymize_info)s + %(keep_his_anonymize_info)s %(verbose)s Returns @@ -2215,18 +2594,18 @@ none_meas_date = info['meas_date'] is None if none_meas_date: - warn('Input info has \'meas_date\' set to None.' - ' Removing all information from time/date structures.' - ' *NOT* performing any time shifts') - info['meas_date'] = None + if daysback is not None: + warn('Input info has "meas_date" set to None. ' + 'Removing all information from time/date structures, ' + '*NOT* performing any time shifts!') else: # compute timeshift delta if daysback is None: delta_t = info['meas_date'] - default_anon_dos else: delta_t = datetime.timedelta(days=daysback) - # adjust meas_date - info['meas_date'] = info['meas_date'] - delta_t + with info._unlock(): + info['meas_date'] = info['meas_date'] - delta_t # file_id and meas_id for key in ('file_id', 'meas_id'): @@ -2285,13 +2664,13 @@ info['experimenter'] = default_str info['description'] = default_desc - - if info['proj_id'] is not None: - info['proj_id'] = np.zeros_like(info['proj_id']) - if info['proj_name'] is not None: - info['proj_name'] = default_str - if info['utc_offset'] is not None: - info['utc_offset'] = None + with info._unlock(): + if info['proj_id'] is not None: + info['proj_id'] = np.zeros_like(info['proj_id']) + if info['proj_name'] is not None: + info['proj_name'] = default_str + if info['utc_offset'] is not None: + info['utc_offset'] = None proc_hist = info.get('proc_history') if proc_hist is not None: @@ -2339,13 +2718,13 @@ return info +@fill_doc def _bad_chans_comp(info, ch_names): """Check if channel names are consistent with current compensation status. Parameters ---------- - info : dict, instance of Info - Measurement information for the dataset. + %(info_not_none)s ch_names : list of str The channel names to check. @@ -2413,17 +2792,18 @@ # Make info writing faster by packing chs and dig into numpy arrays orig_dig = info.get('dig', None) orig_chs = info['chs'] - try: - if orig_dig is not None and len(orig_dig) > 0: - info['dig'] = _dict_pack(info['dig'], _DIG_CAST) - info['chs'] = _dict_pack(info['chs'], _CH_CAST) - info['chs']['ch_name'] = np.char.encode( - info['chs']['ch_name'], encoding='utf8') - yield - finally: - if orig_dig is not None: - info['dig'] = orig_dig - info['chs'] = orig_chs + with info._unlock(): + try: + if orig_dig is not None and len(orig_dig) > 0: + info['dig'] = _dict_pack(info['dig'], _DIG_CAST) + info['chs'] = _dict_pack(info['chs'], _CH_CAST) + info['chs']['ch_name'] = np.char.encode( + info['chs']['ch_name'], encoding='utf8') + yield + finally: + if orig_dig is not None: + info['dig'] = orig_dig + info['chs'] = orig_chs def _dict_pack(obj, casts): @@ -2471,3 +2851,67 @@ for (key, (const, _, write)) in _CH_INFO_MAP.items(): write(fid, const, ch[key]) end_block(fid, FIFF.FIFFB_CH_INFO) + + +def _ensure_infos_match(info1, info2, name, *, on_mismatch='raise'): + """Check if infos match. + + Parameters + ---------- + info1, info2 : instance of Info + The infos to compare. + name : str + The name of the object appearing in the error message of the comparison + fails. + on_mismatch : 'raise' | 'warn' | 'ignore' + What to do in case of a mismatch of ``dev_head_t`` between ``info1`` + and ``info2``. + """ + _check_on_missing(on_missing=on_mismatch, name='on_mismatch') + + info1._check_consistency() + info2._check_consistency() + + if info1['nchan'] != info2['nchan']: + raise ValueError(f'{name}.info[\'nchan\'] must match') + if set(info1['bads']) != set(info2['bads']): + raise ValueError(f'{name}.info[\'bads\'] must match') + if info1['sfreq'] != info2['sfreq']: + raise ValueError(f'{name}.info[\'sfreq\'] must match') + if set(info1['ch_names']) != set(info2['ch_names']): + raise ValueError(f'{name}.info[\'ch_names\'] must match') + if len(info2['projs']) != len(info1['projs']): + raise ValueError(f'SSP projectors in {name} must be the same') + if any(not _proj_equal(p1, p2) for p1, p2 in + zip(info2['projs'], info1['projs'])): + raise ValueError(f'SSP projectors in {name} must be the same') + if (info1['dev_head_t'] is None) != (info2['dev_head_t'] is None) or \ + (info1['dev_head_t'] is not None and not + np.allclose(info1['dev_head_t']['trans'], + info2['dev_head_t']['trans'], rtol=1e-6)): + msg = (f"{name}.info['dev_head_t'] differs. The " + f"instances probably come from different runs, and " + f"are therefore associated with different head " + f"positions. Manually change info['dev_head_t'] to " + f"avoid this message but beware that this means the " + f"MEG sensors will not be properly spatially aligned. " + f"See mne.preprocessing.maxwell_filter to realign the " + f"runs to a common head position.") + _on_missing(on_missing=on_mismatch, msg=msg, + name='on_mismatch') + + +def _get_fnirs_ch_pos(info): + """Return positions of each fNIRS optode. + + fNIRS uses two types of optodes, sources and detectors. + There can be multiple connections between each source + and detector at different wavelengths. This function + returns the location of each source and detector. + """ + from ..preprocessing.nirs import _fnirs_optode_names, _optode_position + srcs, dets = _fnirs_optode_names(info) + ch_pos = {} + for optode in [*srcs, *dets]: + ch_pos[optode] = _optode_position(info, optode) + return ch_pos diff -Nru python-mne-0.23.4+dfsg/mne/io/nedf/__init__.py python-mne-1.1.0+dfsg/mne/io/nedf/__init__.py --- python-mne-0.23.4+dfsg/mne/io/nedf/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/nedf/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,6 +2,6 @@ # Author: Tristan Stenner # -# License: BSD (3-clause) +# License: BSD-3-Clause from .nedf import read_raw_nedf, _parse_nedf_header diff -Nru python-mne-0.23.4+dfsg/mne/io/nedf/nedf.py python-mne-1.1.0+dfsg/mne/io/nedf/nedf.py --- python-mne-0.23.4+dfsg/mne/io/nedf/nedf.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/nedf/nedf.py 2022-08-03 15:48:14.000000000 +0000 @@ -144,7 +144,8 @@ # (https://www.neuroelectrics.com/resources/software/): for ch in info['chs'][:-1]: ch['cal'] = 2.4 / (6.0 * 8388607) - info['meas_date'] = header['meas_date'] + with info._unlock(): + info['meas_date'] = header['meas_date'] raw_extra = dict(dt=dt, dt_last=dt_last, n_full=n_full) super().__init__( info, preload=preload, filenames=[filename], verbose=verbose, diff -Nru python-mne-0.23.4+dfsg/mne/io/nedf/tests/test_nedf.py python-mne-1.1.0+dfsg/mne/io/nedf/tests/test_nedf.py --- python-mne-0.23.4+dfsg/mne/io/nedf/tests/test_nedf.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/nedf/tests/test_nedf.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ """Test reading of NEDF format.""" # Author: Tristan Stenner # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op diff -Nru python-mne-0.23.4+dfsg/mne/io/nicolet/__init__.py python-mne-1.1.0+dfsg/mne/io/nicolet/__init__.py --- python-mne-0.23.4+dfsg/mne/io/nicolet/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/nicolet/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,6 +2,6 @@ # Author: Jaakko Leppakangas # -# License: BSD (3-clause) +# License: BSD-3-Clause from .nicolet import read_raw_nicolet diff -Nru python-mne-0.23.4+dfsg/mne/io/nicolet/nicolet.py python-mne-1.1.0+dfsg/mne/io/nicolet/nicolet.py --- python-mne-0.23.4+dfsg/mne/io/nicolet/nicolet.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/nicolet/nicolet.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Author: Jaakko Leppakangas # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np from os import path @@ -120,6 +120,7 @@ misc) info['highpass'] = 0. info['lowpass'] = info['sfreq'] / 2.0 + info._unlocked = False info._update_redundant() return info, header_info diff -Nru python-mne-0.23.4+dfsg/mne/io/nicolet/tests/test_nicolet.py python-mne-1.1.0+dfsg/mne/io/nicolet/tests/test_nicolet.py --- python-mne-0.23.4+dfsg/mne/io/nicolet/tests/test_nicolet.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/nicolet/tests/test_nicolet.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Author: Jaakko Leppakangas # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op import inspect diff -Nru python-mne-0.23.4+dfsg/mne/io/nihon/__init__.py python-mne-1.1.0+dfsg/mne/io/nihon/__init__.py --- python-mne-0.23.4+dfsg/mne/io/nihon/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/nihon/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,6 +2,6 @@ # Author: Fede Raimondo # -# License: BSD (3-clause) +# License: BSD-3-Clause from .nihon import read_raw_nihon diff -Nru python-mne-0.23.4+dfsg/mne/io/nihon/nihon.py python-mne-1.1.0+dfsg/mne/io/nihon/nihon.py --- python-mne-0.23.4+dfsg/mne/io/nihon/nihon.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/nihon/nihon.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Federico Raimondo # -# License: BSD (3-clause) +# License: BSD-3-Clause from collections import OrderedDict from datetime import datetime, timezone @@ -98,6 +98,8 @@ _default_chan_labels += [f'X{i}' for i in range(16, 166)] _default_chan_labels += ['NA28', 'Z'] +_encodings = ('utf-8', 'latin1') + def _read_21e_file(fname): fname = _ensure_path(fname) @@ -106,21 +108,31 @@ if e_fname.exists(): # Read the 21E file and update the labels accordingly. logger.info('Found 21E file, reading channel names.') - with open(e_fname, 'r') as fid: - keep_parsing = False - for line in fid: - if line.startswith('['): - if 'ELECTRODE' in line or 'REFERENCE' in line: - keep_parsing = True - else: - keep_parsing = False - elif keep_parsing is True: - idx, name = line.split('=') - idx = int(idx) - if idx >= len(_chan_labels): - n = idx - len(_chan_labels) + 1 - _chan_labels.extend(['UNK'] * n) - _chan_labels[idx] = name.strip() + for enc in _encodings: + try: + with open(e_fname, 'r', encoding=enc) as fid: + keep_parsing = False + for line in fid: + if line.startswith('['): + if 'ELECTRODE' in line or 'REFERENCE' in line: + keep_parsing = True + else: + keep_parsing = False + elif keep_parsing is True: + idx, name = line.split('=') + idx = int(idx) + if idx >= len(_chan_labels): + n = idx - len(_chan_labels) + 1 + _chan_labels.extend(['UNK'] * n) + _chan_labels[idx] = name.strip() + except UnicodeDecodeError: + pass + else: + break + else: + warn(f'Could not decode 21E file as one of {_encodings}; ' + f'Default channel names are chosen.') + return _chan_labels @@ -215,20 +227,33 @@ if header['n_ctlblocks'] != 1: raise NotImplementedError('I dont know how to read more than one ' 'control block for this type of file :(') - if header['controlblocks'][0]['n_datablocks'] != 1: - raise NotImplementedError('I dont know how to read more than one ' - 'data block for this type of file :(') + if header['controlblocks'][0]['n_datablocks'] > 1: + # Multiple blocks, check that they all have the same kind of data + datablocks = header['controlblocks'][0]['datablocks'] + block_0 = datablocks[0] + for t_block in datablocks[1:]: + if block_0['n_channels'] != t_block['n_channels']: + raise ValueError( + 'Cannot read NK file with different number of channels ' + 'in each datablock') + if block_0['channels'] != t_block['channels']: + raise ValueError( + 'Cannot read NK file with different channels in each ' + 'datablock') + if block_0['sfreq'] != t_block['sfreq']: + raise ValueError( + 'Cannot read NK file with different sfreq in each ' + 'datablock') return header -def _read_nihon_annotations(fname, orig_time): +def _read_nihon_annotations(fname): fname = _ensure_path(fname) - annotations = None log_fname = fname.with_suffix('.LOG') if not log_fname.exists(): warn('No LOG file exists. Annotations will not be read') - return annotations + return dict(onset=[], duration=[], description=[]) logger.info('Found LOG file, reading events.') with open(log_fname, 'r') as fid: version = np.fromfile(fid, '|S16', 1).astype('U16')[0] @@ -246,9 +271,18 @@ fid.seek(t_blk_address + 0x12) n_logs = np.fromfile(fid, np.uint8, 1)[0] fid.seek(t_blk_address + 0x14) - t_logs = np.fromfile(fid, '|S45', n_logs).astype('U45') - + t_logs = np.fromfile(fid, '|S45', n_logs) for t_log in t_logs: + for enc in _encodings: + try: + t_log = t_log.decode(enc) + except UnicodeDecodeError: + pass + else: + break + else: + warn(f'Could not decode log as one of {_encodings}') + continue t_desc = t_log[:20].strip('\x00') t_onset = datetime.strptime(t_log[20:26], '%H%M%S') t_onset = (t_onset.hour * 3600 + t_onset.minute * 60 + @@ -256,13 +290,17 @@ all_onsets.append(t_onset) all_descriptions.append(t_desc) - annotations = Annotations(all_onsets, 0.0, all_descriptions, orig_time) - return annotations + annots = dict( + onset=all_onsets, + duration=[0] * len(all_onsets), + description=all_descriptions) + return annots def _map_ch_to_type(ch_name): ch_type_pattern = OrderedDict([ - ('stim', ('Mark',)), ('misc', ('DC', 'NA', 'Z')), ('bio', ('X',))]) + ('stim', ('Mark',)), ('misc', ('DC', 'NA', 'Z', '$')), + ('bio', ('X',))]) for key, kinds in ch_type_pattern.items(): if any(kind in ch_name for kind in kinds): return key @@ -326,16 +364,16 @@ n_samples = header['n_samples'] if 'meas_date' in metadata: - info['meas_date'] = metadata['meas_date'] - chs = {x: _map_ch_to_specs(x) for x in ch_names} + with info._unlock(): + info['meas_date'] = metadata['meas_date'] + chs = {x: _map_ch_to_specs(x) for x in info['ch_names']} - orig_ch_names = header['ch_names'] cal = np.array( - [chs[x]['cal'] for x in orig_ch_names], float)[:, np.newaxis] + [chs[x]['cal'] for x in info['ch_names']], float)[:, np.newaxis] offsets = np.array( - [chs[x]['offset'] for x in orig_ch_names], float)[:, np.newaxis] + [chs[x]['offset'] for x in info['ch_names']], float)[:, np.newaxis] gains = np.array( - [chs[x]['unit'] for x in orig_ch_names], float)[:, np.newaxis] + [chs[x]['unit'] for x in info['ch_names']], float)[:, np.newaxis] raw_extras = dict( cal=cal, offsets=offsets, gains=gains, header=header) @@ -352,12 +390,30 @@ raw_extras=[raw_extras]) # Get annotations from LOG file - annots = _read_nihon_annotations(fname, orig_time=info['meas_date']) - self.set_annotations(annots) + annots = _read_nihon_annotations(fname) + + # Annotate acqusition skips + controlblock = self._header['controlblocks'][0] + cur_sample = 0 + if controlblock['n_datablocks'] > 1: + for i_block in range(controlblock['n_datablocks'] - 1): + t_block = controlblock['datablocks'][i_block] + cur_sample = cur_sample + t_block['n_samples'] + cur_tpoint = (cur_sample - 0.5) / t_block['sfreq'] + # Add annotations as in append raw + annots['onset'].append(cur_tpoint) + annots['duration'].append(0.0) + annots['description'].append('BAD boundary') + annots['onset'].append(cur_tpoint) + annots['duration'].append(0.0) + annots['description'].append('EDGE boundary') + + annotations = Annotations(**annots, orig_time=info['meas_date']) + self.set_annotations(annotations) def _read_segment_file(self, data, idx, fi, start, stop, cals, mult): """Read a chunk of raw data.""" - # For now we assume one control block and one data block. + # For now we assume one control block header = self._raw_extras[fi]['header'] # Get the original cal, offsets and gains @@ -365,18 +421,56 @@ offsets = self._raw_extras[fi]['offsets'] gains = self._raw_extras[fi]['gains'] - datablock = header['controlblocks'][0]['datablocks'][0] - n_channels = datablock['n_channels'] + 1 - datastart = (datablock['address'] + 0x27 + - (datablock['n_channels'] * 10)) - with open(self._filenames[fi], 'rb') as fid: - start_offset = datastart + start * n_channels * 2 - to_read = (stop - start) * n_channels - fid.seek(start_offset) - block_data = np.fromfile(fid, ' # simplified BSD-3 license -from pathlib import Path import pytest from numpy.testing import assert_array_almost_equal from mne.io import read_raw_nihon, read_raw_edf from mne.io.tests.test_raw import _test_raw_reader -from mne.utils import run_tests_if_main -from mne.datasets.testing import data_path, requires_testing_data +from mne.datasets import testing from mne.io.nihon.nihon import (_read_nihon_header, _read_nihon_metadata, _read_nihon_annotations) +from mne.io.nihon import nihon +data_path = testing.data_path(download=False) -@requires_testing_data + +@testing.requires_testing_data def test_nihon_eeg(): """Test reading Nihon Kohden EEG files.""" - fname = Path(data_path()) / 'NihonKohden' / 'MB0400FU.EEG' + fname = data_path / 'NihonKohden' / 'MB0400FU.EEG' raw = read_raw_nihon(fname.as_posix(), preload=True) assert 'RawNihon' in repr(raw) _test_raw_reader(read_raw_nihon, fname=fname, test_scaling=False) - fname_edf = Path(data_path()) / 'NihonKohden' / 'MB0400FU.EDF' + fname_edf = data_path / 'NihonKohden' / 'MB0400FU.EDF' raw_edf = read_raw_edf(fname_edf, preload=True) assert raw._data.shape == raw_edf._data.shape @@ -49,7 +50,7 @@ with pytest.raises(ValueError, match='Not a valid Nihon Kohden EEG file'): raw = _read_nihon_header(fname_edf) - bad_fname = Path(data_path()) / 'eximia' / 'text_eximia.nxe' + bad_fname = data_path / 'eximia' / 'text_eximia.nxe' msg = 'No PNT file exists. Metadata will be blank' with pytest.warns(RuntimeWarning, match=msg): @@ -58,8 +59,33 @@ msg = 'No LOG file exists. Annotations will not be read' with pytest.warns(RuntimeWarning, match=msg): - annot = _read_nihon_annotations(bad_fname, orig_time=None) - assert annot is None + annot = _read_nihon_annotations(bad_fname) + assert all(len(x) == 0 for x in annot.values()) + # the nihon test file has $A1 and $A2 in it, which are not EEG + assert '$A1' in raw.ch_names -run_tests_if_main() + # assert that channels with $ are 'misc' + picks = [ch for ch in raw.ch_names if ch.startswith('$')] + ch_types = raw.get_channel_types(picks=picks) + assert all(ch == 'misc' for ch in ch_types) + + +@testing.requires_testing_data +def test_nihon_duplicate_channels(monkeypatch): + """Test deduplication of channel names.""" + fname = data_path / 'NihonKohden' / 'MB0400FU.EEG' + + def return_channel_duplicates(fname): + ch_names = nihon._default_chan_labels + ch_names[1] = ch_names[0] + return ch_names + + monkeypatch.setattr(nihon, '_read_21e_file', return_channel_duplicates) + + assert len(nihon._read_21e_file(fname)) > \ + len(set(nihon._read_21e_file(fname))) + msg = 'Channel names are not unique, found duplicates for: ' \ + '{\'FP1\'}. Applying running numbers for duplicates.' + with pytest.warns(RuntimeWarning, match=msg): + read_raw_nihon(fname) diff -Nru python-mne-0.23.4+dfsg/mne/io/nirx/__init__.py python-mne-1.1.0+dfsg/mne/io/nirx/__init__.py --- python-mne-0.23.4+dfsg/mne/io/nirx/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/nirx/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,6 +2,6 @@ # Author: Robert Luke # -# License: BSD (3-clause) +# License: BSD-3-Clause from .nirx import read_raw_nirx diff -Nru python-mne-0.23.4+dfsg/mne/io/nirx/_localized_abbr.py python-mne-1.1.0+dfsg/mne/io/nirx/_localized_abbr.py --- python-mne-0.23.4+dfsg/mne/io/nirx/_localized_abbr.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/nirx/_localized_abbr.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,60 @@ +"""Localizations for meas_date extraction.""" +# Authors: Eric Larson +# +# License: BSD-3-Clause + +# This file was generated on 2021/01/31 on an Ubuntu system. +# When getting "unsupported locale setting" on Ubuntu (e.g., with localepurge), +# use "sudo locale-gen de_DE" etc. then "sudo update-locale". + +""" +import datetime +import locale +print('_localized_abbr = {') +for loc in ('en_US.utf8', 'de_DE', 'fr_FR', 'it_IT'): + print(f' {repr(loc)}: {{') + print(' "month": {', end='') + month_abbr = set() + for month in range(1, 13): # Month as locale’s abbreviated name + locale.setlocale(locale.LC_TIME, "en_US.utf8") + dt = datetime.datetime(year=2000, month=month, day=1) + val = dt.strftime("%b").lower() + locale.setlocale(locale.LC_TIME, loc) + key = dt.strftime("%b").lower() + month_abbr.add(key) + print(f'{repr(key)}: {repr(val)}, ', end='') + print('}, # noqa') + print(' "weekday": {', end='') + weekday_abbr = set() + for day in range(1, 8): # Weekday as locale’s abbreviated name. + locale.setlocale(locale.LC_TIME, "en_US.utf8") + dt = datetime.datetime(year=2000, month=1, day=day) + val = dt.strftime("%a").lower() + locale.setlocale(locale.LC_TIME, loc) + key = dt.strftime("%a").lower() + assert key not in weekday_abbr, key + weekday_abbr.add(key) + print(f'{repr(key)}: {repr(val)}, ', end='') + print('}, # noqa') + print(' },') +print('}\n') +""" + +_localized_abbr = { + 'en_US.utf8': { + "month": {'jan': 'jan', 'feb': 'feb', 'mar': 'mar', 'apr': 'apr', 'may': 'may', 'jun': 'jun', 'jul': 'jul', 'aug': 'aug', 'sep': 'sep', 'oct': 'oct', 'nov': 'nov', 'dec': 'dec', }, # noqa + "weekday": {'sat': 'sat', 'sun': 'sun', 'mon': 'mon', 'tue': 'tue', 'wed': 'wed', 'thu': 'thu', 'fri': 'fri', }, # noqa + }, + 'de_DE': { + "month": {'jan': 'jan', 'feb': 'feb', 'mär': 'mar', 'apr': 'apr', 'mai': 'may', 'jun': 'jun', 'jul': 'jul', 'aug': 'aug', 'sep': 'sep', 'okt': 'oct', 'nov': 'nov', 'dez': 'dec', }, # noqa + "weekday": {'sa': 'sat', 'so': 'sun', 'mo': 'mon', 'di': 'tue', 'mi': 'wed', 'do': 'thu', 'fr': 'fri', }, # noqa + }, + 'fr_FR': { + "month": {'janv.': 'jan', 'févr.': 'feb', 'mars': 'mar', 'avril': 'apr', 'mai': 'may', 'juin': 'jun', 'juil.': 'jul', 'août': 'aug', 'sept.': 'sep', 'oct.': 'oct', 'nov.': 'nov', 'déc.': 'dec', }, # noqa + "weekday": {'sam.': 'sat', 'dim.': 'sun', 'lun.': 'mon', 'mar.': 'tue', 'mer.': 'wed', 'jeu.': 'thu', 'ven.': 'fri', }, # noqa + }, + 'it_IT': { + "month": {'gen': 'jan', 'feb': 'feb', 'mar': 'mar', 'apr': 'apr', 'mag': 'may', 'giu': 'jun', 'lug': 'jul', 'ago': 'aug', 'set': 'sep', 'ott': 'oct', 'nov': 'nov', 'dic': 'dec', }, # noqa + "weekday": {'sab': 'sat', 'dom': 'sun', 'lun': 'mon', 'mar': 'tue', 'mer': 'wed', 'gio': 'thu', 'ven': 'fri', }, # noqa + }, +} diff -Nru python-mne-0.23.4+dfsg/mne/io/nirx/nirx.py python-mne-1.1.0+dfsg/mne/io/nirx/nirx.py --- python-mne-0.23.4+dfsg/mne/io/nirx/nirx.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/nirx/nirx.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,35 +1,37 @@ # Authors: Robert Luke # -# License: BSD (3-clause) +# License: BSD-3-Clause from configparser import ConfigParser, RawConfigParser import glob as glob import re as re import os.path as op import datetime as dt +import json import numpy as np +from ._localized_abbr import _localized_abbr from ..base import BaseRaw from ..utils import _mult_cal_one from ..constants import FIFF from ..meas_info import create_info, _format_dig_points from ...annotations import Annotations +from ..._freesurfer import get_mni_fiducials from ...transforms import apply_trans, _get_trans from ...utils import (logger, verbose, fill_doc, warn, _check_fname, - _validate_type) + _validate_type, _check_option, _mask_to_onsets_offsets) @fill_doc -def read_raw_nirx(fname, preload=False, verbose=None): +def read_raw_nirx(fname, saturated='annotate', preload=False, verbose=None): """Reader for a NIRX fNIRS recording. - This function has only been tested with NIRScout devices. - Parameters ---------- fname : str Path to the NIRX data folder or header file. + %(saturated)s %(preload)s %(verbose)s @@ -41,8 +43,12 @@ See Also -------- mne.io.Raw : Documentation of attribute and methods. + + Notes + ----- + %(nirx_notes)s """ - return RawNIRX(fname, preload, verbose) + return RawNIRX(fname, saturated, preload, verbose) def _open(fname): @@ -57,128 +63,232 @@ ---------- fname : str Path to the NIRX data folder or header file. + %(saturated)s %(preload)s %(verbose)s See Also -------- mne.io.Raw : Documentation of attribute and methods. + + Notes + ----- + %(nirx_notes)s """ @verbose - def __init__(self, fname, preload=False, verbose=None): - from ...externals.pymatreader import read_mat - from ...coreg import get_mni_fiducials # avoid circular import prob + def __init__(self, fname, saturated, preload=False, verbose=None): + from scipy.io import loadmat logger.info('Loading %s' % fname) _validate_type(fname, 'path-like', 'fname') + _validate_type(saturated, str, 'saturated') + _check_option('saturated', saturated, ('annotate', 'nan', 'ignore')) fname = str(fname) if fname.endswith('.hdr'): fname = op.dirname(op.abspath(fname)) fname = _check_fname(fname, 'read', True, 'fname', need_dir=True) + json_config = glob.glob('%s/*%s' % (fname, "config.json")) + if len(json_config): + is_aurora = True + else: + is_aurora = False + + if is_aurora: + # NIRSport2 devices using Aurora software + keys = ('hdr', 'config.json', 'description.json', + 'wl1', 'wl2', 'probeInfo.mat', 'tri') + else: + # NIRScout devices and NIRSport1 devices + keys = ('hdr', 'inf', 'set', 'tpl', 'wl1', 'wl2', + 'config.txt', 'probeInfo.mat') + n_dat = len(glob.glob('%s/*%s' % (fname, 'dat'))) + if n_dat != 1: + warn("A single dat file was expected in the specified path, " + f"but got {n_dat}. This may indicate that the file " + "structure has been modified since the measurement " + "was saved.") + # Check if required files exist and store names for later use files = dict() - keys = ('hdr', 'inf', 'set', 'tpl', 'wl1', 'wl2', - 'config.txt', 'probeInfo.mat') + nan_mask = dict() for key in keys: files[key] = glob.glob('%s/*%s' % (fname, key)) + fidx = 0 if len(files[key]) != 1: - raise RuntimeError('Expect one %s file, got %d' % - (key, len(files[key]),)) - files[key] = files[key][0] - if len(glob.glob('%s/*%s' % (fname, 'dat'))) != 1: - warn("A single dat file was expected in the specified path, but " - "got %d. This may indicate that the file structure has been " - "modified since the measurement was saved." % - (len(glob.glob('%s/*%s' % (fname, 'dat'))))) + if key not in ('wl1', 'wl2'): + raise RuntimeError( + f'Need one {key} file, got {len(files[key])}') + noidx = np.where(['nosatflags_' in op.basename(x) + for x in files[key]])[0] + if len(noidx) != 1 or len(files[key]) != 2: + raise RuntimeError( + f'Need one nosatflags and one standard {key} file, ' + f'got {len(files[key])}') + # Here two files have been found, one that is called + # no sat flags. The nosatflag file has no NaNs in it. + noidx = noidx[0] + if saturated == 'ignore': + # Ignore NaN and return values + fidx = noidx + elif saturated == 'nan': + # Return NaN + fidx = 0 if noidx == 1 else 1 + else: + assert saturated == 'annotate' # guaranteed above + fidx = noidx + nan_mask[key] = files[key][0 if noidx == 1 else 1] + files[key] = files[key][fidx] # Read number of rows/samples of wavelength data - last_sample = -1 with _open(files['wl1']) as fid: - for line in fid: - last_sample += 1 + last_sample = fid.read().count('\n') - 1 # Read header file # The header file isn't compliant with the configparser. So all the # text between comments must be removed before passing to parser with _open(files['hdr']) as f: - hdr_str = f.read() - hdr_str = re.sub('#.*?#', '', hdr_str, flags=re.DOTALL) + hdr_str_all = f.read() + hdr_str = re.sub('#.*?#', '', hdr_str_all, flags=re.DOTALL) + if is_aurora: + hdr_str = re.sub('(\\[DataStructure].*)', '', + hdr_str, flags=re.DOTALL) hdr = RawConfigParser() hdr.read_string(hdr_str) # Check that the file format version is supported - if hdr['GeneralInfo']['NIRStar'] not in ['"15.0"', '"15.2"', '"15.3"']: - raise RuntimeError('MNE does not support this NIRStar version' - ' (%s)' % (hdr['GeneralInfo']['NIRStar'],)) - if "NIRScout" not in hdr['GeneralInfo']['Device']: - warn("Only import of data from NIRScout devices have been " - "thoroughly tested. You are using a %s device. " % - hdr['GeneralInfo']['Device']) + if is_aurora: + # We may need to ease this requirement back + if hdr['GeneralInfo']['Version'] not in ['2021.4.0-34-ge9fdbbc8', + '2021.9.0-5-g3eb32851', + '2021.9.0-6-g14ef4a71']: + warn("MNE has not been tested with Aurora version " + f"{hdr['GeneralInfo']['Version']}") + else: + if hdr['GeneralInfo']['NIRStar'] not in ['"15.0"', '"15.2"', + '"15.3"']: + raise RuntimeError('MNE does not support this NIRStar version' + ' (%s)' % (hdr['GeneralInfo']['NIRStar'],)) + if "NIRScout" not in hdr['GeneralInfo']['Device'] \ + and "NIRSport" not in hdr['GeneralInfo']['Device']: + warn("Only import of data from NIRScout devices have been " + "thoroughly tested. You are using a %s device. " % + hdr['GeneralInfo']['Device']) # Parse required header fields # Extract measurement date and time - datetime_str = hdr['GeneralInfo']['Date'] + hdr['GeneralInfo']['Time'] + if is_aurora: + datetime_str = hdr['GeneralInfo']['Date'] + else: + datetime_str = hdr['GeneralInfo']['Date'] + \ + hdr['GeneralInfo']['Time'] + meas_date = None # Several formats have been observed so we try each in turn - for dt_code in ['"%a, %b %d, %Y""%H:%M:%S.%f"', - '"%a, %d %b %Y""%H:%M:%S.%f"']: - try: - meas_date = dt.datetime.strptime(datetime_str, dt_code) - meas_date = meas_date.replace(tzinfo=dt.timezone.utc) + for loc, translations in _localized_abbr.items(): + do_break = False + # So far we are lucky in that all the formats below, if they + # include %a (weekday abbr), always come first. Thus we can use + # a .split(), replace, and rejoin. + loc_datetime_str = datetime_str.split(' ') + for key, val in translations['weekday'].items(): + loc_datetime_str[0] = loc_datetime_str[0].replace(key, val) + for ii in range(1, len(loc_datetime_str)): + for key, val in translations['month'].items(): + loc_datetime_str[ii] = \ + loc_datetime_str[ii].replace(key, val) + loc_datetime_str = ' '.join(loc_datetime_str) + logger.debug(f'Trying {loc} datetime: {loc_datetime_str}') + for dt_code in ['"%a, %b %d, %Y""%H:%M:%S.%f"', + '"%a %d %b %Y""%H:%M:%S.%f"', + '"%a, %d %b %Y""%H:%M:%S.%f"', + '%Y-%m-%d %H:%M:%S.%f']: + try: + meas_date = dt.datetime.strptime(loc_datetime_str, dt_code) + except ValueError: + pass + else: + meas_date = meas_date.replace(tzinfo=dt.timezone.utc) + do_break = True + logger.debug( + f'Measurement date language {loc} detected: {dt_code}') + break + if do_break: break - except ValueError: - pass if meas_date is None: warn("Extraction of measurement date from NIRX file failed. " - "This can be caused by files saved in certain locales. " + "This can be caused by files saved in certain locales " + f"(currently only {list(_localized_abbr)} supported). " "Please report this as a github issue. " "The date is being set to January 1st, 2000, " - "instead of {}".format(datetime_str)) + f"instead of {repr(datetime_str)}.") meas_date = dt.datetime(2000, 1, 1, 0, 0, 0, tzinfo=dt.timezone.utc) # Extract frequencies of light used by machine - fnirs_wavelengths = [int(s) for s in - re.findall(r'(\d+)', - hdr['ImagingParameters'][ - 'Wavelengths'])] + if is_aurora: + fnirs_wavelengths = [760, 850] + else: + fnirs_wavelengths = [int(s) for s in + re.findall(r'(\d+)', + hdr['ImagingParameters'][ + 'Wavelengths'])] # Extract source-detectors - sources = np.asarray([int(s) for s in re.findall(r'(\d+)-\d+:\d+', - hdr['DataStructure'][ - 'S-D-Key'])], int) - detectors = np.asarray([int(s) for s in re.findall(r'\d+-(\d+):\d+', - hdr['DataStructure'] - ['S-D-Key'])], - int) + if is_aurora: + sources = re.findall(r'(\d+)-\d+', hdr_str_all.split("\n")[-2]) + detectors = re.findall(r'\d+-(\d+)', hdr_str_all.split("\n")[-2]) + sources = [int(s) + 1 for s in sources] + detectors = [int(d) + 1 for d in detectors] + + else: + sources = np.asarray([int(s) for s in + re.findall(r'(\d+)-\d+:\d+', + hdr['DataStructure'] + ['S-D-Key'])], int) + detectors = np.asarray([int(s) for s in + re.findall(r'\d+-(\d+):\d+', + hdr['DataStructure'] + ['S-D-Key'])], int) # Extract sampling rate - samplingrate = float(hdr['ImagingParameters']['SamplingRate']) + if is_aurora: + samplingrate = float(hdr['GeneralInfo']['Sampling rate']) + else: + samplingrate = float(hdr['ImagingParameters']['SamplingRate']) # Read participant information file - inf = ConfigParser(allow_no_value=True) - inf.read(files['inf']) - inf = inf._sections['Subject Demographics'] + if is_aurora: + with open(files['description.json']) as f: + inf = json.load(f) + else: + inf = ConfigParser(allow_no_value=True) + inf.read(files['inf']) + inf = inf._sections['Subject Demographics'] # Store subject information from inf file in mne format # Note: NIRX also records "Study Type", "Experiment History", # "Additional Notes", "Contact Information" and this information # is currently discarded # NIRStar does not record an id, or handedness by default + # The name field is used to populate the his_id variable. subject_info = {} - names = inf['name'].split() + if is_aurora: + names = inf["subject"].split() + else: + names = inf['name'].replace('"', "").split() + subject_info['his_id'] = "_".join(names) if len(names) > 0: subject_info['first_name'] = \ - inf['name'].split()[0].replace("\"", "") + names[0].replace("\"", "") if len(names) > 1: subject_info['last_name'] = \ - inf['name'].split()[-1].replace("\"", "") + names[-1].replace("\"", "") if len(names) > 2: subject_info['middle_name'] = \ - inf['name'].split()[-2].replace("\"", "") + names[-2].replace("\"", "") subject_info['sex'] = inf['gender'].replace("\"", "") # Recode values if subject_info['sex'] in {'M', 'Male', '1'}: @@ -187,9 +297,10 @@ subject_info['sex'] = FIFF.FIFFV_SUBJ_SEX_FEMALE else: subject_info['sex'] = FIFF.FIFFV_SUBJ_SEX_UNKNOWN - subject_info['birthday'] = (meas_date.year - int(inf['age']), - meas_date.month, - meas_date.day) + if inf['age'] != '': + subject_info['birthday'] = (meas_date.year - int(inf['age']), + meas_date.month, + meas_date.day) # Read information about probe/montage/optodes # A word on terminology used here: @@ -198,18 +309,17 @@ # Sources and detectors are both called optodes # Each source - detector pair produces a channel # Channels are defined as the midpoint between source and detector - mat_data = read_mat(files['probeInfo.mat'], uint16_codec=None) - requested_channels = mat_data['probeInfo']['probes']['index_c'] - src_locs = mat_data['probeInfo']['probes']['coords_s3'] / 100. - det_locs = mat_data['probeInfo']['probes']['coords_d3'] / 100. - ch_locs = mat_data['probeInfo']['probes']['coords_c3'] / 100. + mat_data = loadmat(files['probeInfo.mat']) + probes = mat_data['probeInfo']['probes'][0, 0] + requested_channels = probes['index_c'][0, 0] + src_locs = probes['coords_s3'][0, 0] / 100. + det_locs = probes['coords_d3'][0, 0] / 100. + ch_locs = probes['coords_c3'][0, 0] / 100. # These are all in MNI coordinates, so let's transform them to # the Neuromag head coordinate frame - mri_head_t, _ = _get_trans('fsaverage', 'mri', 'head') - src_locs = apply_trans(mri_head_t, src_locs) - det_locs = apply_trans(mri_head_t, det_locs) - ch_locs = apply_trans(mri_head_t, ch_locs) + src_locs, det_locs, ch_locs, mri_head_t = _convert_fnirs_to_head( + 'fsaverage', 'mri', 'head', src_locs, det_locs, ch_locs) # Set up digitization dig = get_mni_fiducials('fsaverage', verbose=False) @@ -237,14 +347,8 @@ req_ind = np.concatenate((req_ind, sd_idx[0])) req_ind = req_ind.astype(int) - # Generate meaningful channel names - def prepend(li, str): - str += '{0}' - li = [str.format(i) for i in li] - return li - - snames = prepend(sources[req_ind], 'S') - dnames = prepend(detectors[req_ind], '_D') + snames = [f"S{sources[idx]}" for idx in req_ind] + dnames = [f"_D{detectors[idx]}" for idx in req_ind] sdnames = [m + str(n) for m, n in zip(snames, dnames)] sd1 = [s + ' ' + str(fnirs_wavelengths[0]) for s in sdnames] sd2 = [s + ' ' + str(fnirs_wavelengths[1]) for s in sdnames] @@ -254,8 +358,9 @@ info = create_info(chnames, samplingrate, ch_types='fnirs_cw_amplitude') - info.update(subject_info=subject_info, dig=dig) - info['meas_date'] = meas_date + with info._unlock(): + info.update(subject_info=subject_info, dig=dig) + info['meas_date'] = meas_date # Store channel, source, and detector locations # The channel location is stored in the first 3 entries of loc. @@ -266,18 +371,17 @@ for ch_idx2 in range(requested_channels.shape[0]): # Find source and store location src = int(requested_channels[ch_idx2, 0]) - 1 - info['chs'][ch_idx2 * 2]['loc'][3:6] = src_locs[src, :] - info['chs'][ch_idx2 * 2 + 1]['loc'][3:6] = src_locs[src, :] # Find detector and store location det = int(requested_channels[ch_idx2, 1]) - 1 - info['chs'][ch_idx2 * 2]['loc'][6:9] = det_locs[det, :] - info['chs'][ch_idx2 * 2 + 1]['loc'][6:9] = det_locs[det, :] # Store channel location as midpoint between source and detector. midpoint = (src_locs[src, :] + det_locs[det, :]) / 2 - info['chs'][ch_idx2 * 2]['loc'][:3] = midpoint - info['chs'][ch_idx2 * 2 + 1]['loc'][:3] = midpoint - info['chs'][ch_idx2 * 2]['loc'][9] = fnirs_wavelengths[0] - info['chs'][ch_idx2 * 2 + 1]['loc'][9] = fnirs_wavelengths[1] + for ii in range(2): + ch_idx3 = ch_idx2 * 2 + ii + info['chs'][ch_idx3]['loc'][3:6] = src_locs[src, :] + info['chs'][ch_idx3]['loc'][6:9] = det_locs[det, :] + info['chs'][ch_idx3]['loc'][:3] = midpoint + info['chs'][ch_idx3]['loc'][9] = fnirs_wavelengths[ii] + info['chs'][ch_idx3]['coord_frame'] = FIFF.FIFFV_COORD_HEAD # Extract the start/stop numbers for samples in the CSV. In theory the # sample bounds should just be 10 * the number of channels, but some @@ -299,27 +403,64 @@ 'sd_index': req_ind, 'files': files, 'bounds': bounds, + 'nan_mask': nan_mask, } + # Get our saturated mask + annot_mask = None + for ki, key in enumerate(('wl1', 'wl2')): + if nan_mask.get(key, None) is None: + continue + mask = np.isnan(_read_csv_rows_cols( + nan_mask[key], 0, last_sample + 1, req_ind, {0: 0, 1: None}).T) + if saturated == 'nan': + nan_mask[key] = mask + else: + assert saturated == 'annotate' + if annot_mask is None: + annot_mask = np.zeros( + (len(info['ch_names']) // 2, last_sample + 1), bool) + annot_mask |= mask + nan_mask[key] = None # shouldn't need again super(RawNIRX, self).__init__( info, preload, filenames=[fname], last_samps=[last_sample], raw_extras=[raw_extras], verbose=verbose) + # make onset/duration/description + onset, duration, description, ch_names = list(), list(), list(), list() + if annot_mask is not None: + for ci, mask in enumerate(annot_mask): + on, dur = _mask_to_onsets_offsets(mask) + on = on / info['sfreq'] + dur = dur / info['sfreq'] + dur -= on + onset.extend(on) + duration.extend(dur) + description.extend(['BAD_SATURATED'] * len(on)) + ch_names.extend([self.ch_names[2 * ci:2 * ci + 2]] * len(on)) + # Read triggers from event file - if op.isfile(files['hdr'][:-3] + 'evt'): - with _open(files['hdr'][:-3] + 'evt') as fid: + if not is_aurora: + files['tri'] = files['hdr'][:-3] + 'evt' + if op.isfile(files['tri']): + with _open(files['tri']) as fid: t = [re.findall(r'(\d+)', line) for line in fid] - onset = np.zeros(len(t), float) - duration = np.zeros(len(t), float) - description = [''] * len(t) - for t_idx in range(len(t)): - binary_value = ''.join(t[t_idx][1:])[::-1] - trigger_frame = float(t[t_idx][0]) - onset[t_idx] = (trigger_frame) * (1.0 / samplingrate) - duration[t_idx] = 1.0 # No duration info stored in files - description[t_idx] = int(binary_value, 2) * 1. - annot = Annotations(onset, duration, description) - self.set_annotations(annot) + if is_aurora: + tf_idx, desc_idx = _determine_tri_idxs(t[0]) + for t_ in t: + if is_aurora: + trigger_frame = float(t_[tf_idx]) + desc = float(t_[desc_idx]) + else: + binary_value = ''.join(t_[1:])[::-1] + desc = float(int(binary_value, 2)) + trigger_frame = float(t_[0]) + onset.append(trigger_frame / samplingrate) + duration.append(1.) # No duration info stored in files + description.append(desc) + ch_names.append(list()) + annot = Annotations(onset, duration, description, ch_names=ch_names) + self.set_annotations(annot) def _read_segment_file(self, data, idx, fi, start, stop, cals, mult): """Read a segment of data from a file. @@ -327,15 +468,18 @@ The NIRX machine records raw data as two different wavelengths. The returned data interleaves the wavelengths. """ - sdindex = self._raw_extras[fi]['sd_index'] + sd_index = self._raw_extras[fi]['sd_index'] - wls = [ - _read_csv_rows_cols( + wls = list() + for key in ('wl1', 'wl2'): + d = _read_csv_rows_cols( self._raw_extras[fi]['files'][key], - start, stop, sdindex, + start, stop, sd_index, self._raw_extras[fi]['bounds'][key]).T - for key in ('wl1', 'wl2') - ] + nan_mask = self._raw_extras[fi]['nan_mask'].get(key, None) + if nan_mask is not None: + d[nan_mask[:, start:stop]] = np.nan + wls.append(d) # TODO: Make this more efficient by only indexing above what we need. # For now let's just construct the full data matrix and index. @@ -347,11 +491,41 @@ return data -def _read_csv_rows_cols(fname, start, stop, cols, bounds): +def _read_csv_rows_cols(fname, start, stop, cols, bounds, + sep=' ', replace=None): with open(fname, 'rb') as fid: fid.seek(bounds[start]) - data = fid.read(bounds[stop] - bounds[start]).decode('latin-1') - x = np.fromstring(data, float, sep=' ') + args = list() + if bounds[1] is not None: + args.append(bounds[stop] - bounds[start]) + data = fid.read(*args).decode('latin-1') + if replace is not None: + data = replace(data) + x = np.fromstring(data, float, sep=sep) x.shape = (stop - start, -1) x = x[:, cols] return x + + +def _convert_fnirs_to_head(trans, fro, to, src_locs, det_locs, ch_locs): + mri_head_t, _ = _get_trans(trans, fro, to) + src_locs = apply_trans(mri_head_t, src_locs) + det_locs = apply_trans(mri_head_t, det_locs) + ch_locs = apply_trans(mri_head_t, ch_locs) + return src_locs, det_locs, ch_locs, mri_head_t + + +def _determine_tri_idxs(trigger): + """Determine tri file indexes for frame and description.""" + if len(trigger) == 12: + # Aurora version 2021.9.6 or greater + trigger_frame_idx = 7 + desc_idx = 10 + elif len(trigger) == 9: + # Aurora version 2021.9.5 or earlier + trigger_frame_idx = 7 + desc_idx = 8 + else: + raise RuntimeError("Unable to read trigger file.") + + return trigger_frame_idx, desc_idx diff -Nru python-mne-0.23.4+dfsg/mne/io/nirx/tests/test_nirx.py python-mne-1.1.0+dfsg/mne/io/nirx/tests/test_nirx.py --- python-mne-0.23.4+dfsg/mne/io/nirx/tests/test_nirx.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/nirx/tests/test_nirx.py 2022-08-03 15:48:14.000000000 +0000 @@ -7,6 +7,7 @@ import shutil import os import datetime as dt +import numpy as np import pytest from numpy.testing import assert_allclose, assert_array_equal @@ -15,21 +16,213 @@ from mne.datasets.testing import data_path, requires_testing_data from mne.io import read_raw_nirx from mne.io.tests.test_raw import _test_raw_reader +from mne.preprocessing import annotate_nan from mne.transforms import apply_trans, _get_trans -from mne.utils import run_tests_if_main from mne.preprocessing.nirs import source_detector_distances,\ - short_channels + short_channels, _reorder_nirx from mne.io.constants import FIFF -fname_nirx_15_0 = op.join(data_path(download=False), - 'NIRx', 'nirscout', 'nirx_15_0_recording') -fname_nirx_15_2 = op.join(data_path(download=False), - 'NIRx', 'nirscout', 'nirx_15_2_recording') -fname_nirx_15_2_short = op.join(data_path(download=False), - 'NIRx', 'nirscout', - 'nirx_15_2_recording_w_short') -fname_nirx_15_3_short = op.join(data_path(download=False), - 'NIRx', 'nirscout', 'nirx_15_3_recording') +testing_path = data_path(download=False) +fname_nirx_15_0 = op.join( + testing_path, 'NIRx', 'nirscout', 'nirx_15_0_recording') +fname_nirx_15_2 = op.join( + testing_path, 'NIRx', 'nirscout', 'nirx_15_2_recording') +fname_nirx_15_2_short = op.join( + testing_path, 'NIRx', 'nirscout', 'nirx_15_2_recording_w_short') +fname_nirx_15_3_short = op.join( + testing_path, 'NIRx', 'nirscout', 'nirx_15_3_recording') + + +# This file has no saturated sections +nirsport1_wo_sat = op.join(testing_path, 'NIRx', 'nirsport_v1', + 'nirx_15_3_recording_wo_saturation') +# This file has saturation, but not on the optode pairing in montage +nirsport1_w_sat = op.join(testing_path, 'NIRx', 'nirsport_v1', + 'nirx_15_3_recording_w_saturation_' + 'not_on_montage_channels') +# This file has saturation in channels of interest +nirsport1_w_fullsat = op.join( + testing_path, 'NIRx', 'nirsport_v1', 'nirx_15_3_recording_w_' + 'saturation_on_montage_channels') + +# NIRSport2 device using Aurora software +nirsport2 = op.join( + testing_path, 'NIRx', 'nirsport_v2', 'aurora_recording _w_short_and_acc') +nirsport2_2021_9 = op.join( + testing_path, 'NIRx', 'nirsport_v2', 'aurora_2021_9') +nirsport2_2021_9_6 = op.join( + testing_path, 'NIRx', 'nirsport_v2', 'aurora_2021_9_6') + + +def test_nirsport_v2_matches_snirf(nirx_snirf): + """Test NIRSport2 raw files return same data as snirf.""" + raw, raw_snirf = nirx_snirf + _reorder_nirx(raw_snirf) + assert raw.ch_names == raw_snirf.ch_names + + assert_allclose(raw._data, raw_snirf._data) + + # Check the timing of annotations match (naming is different) + assert_allclose(raw.annotations.onset, raw_snirf.annotations.onset) + + assert_array_equal(raw.ch_names, raw_snirf.ch_names) + + # This test fails as snirf encodes name incorrectly. + # assert raw.info["subject_info"]["first_name"] == + # raw_snirf.info["subject_info"]["first_name"] + + +@requires_testing_data +@pytest.mark.filterwarnings('ignore:.*Extraction of measurement.*:') +def test_nirsport_v2(): + """Test NIRSport2 file.""" + raw = read_raw_nirx(nirsport2, preload=True) + assert raw._data.shape == (40, 128) + + # Test distance between optodes matches values from + # nirsite https://github.com/mne-tools/mne-testing-data/pull/86 + # figure 3 + allowed_distance_error = 0.005 + assert_allclose(source_detector_distances(raw.copy(). + pick("S1_D1 760").info), + [0.0304], atol=allowed_distance_error) + assert_allclose(source_detector_distances(raw.copy(). + pick("S2_D2 760").info), + [0.0400], atol=allowed_distance_error) + + # Test location of detectors + # The locations of detectors can be seen in the first + # figure on this page... + # https://github.com/mne-tools/mne-testing-data/pull/86 + allowed_dist_error = 0.0002 + locs = [ch['loc'][6:9] for ch in raw.info['chs']] + head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri') + mni_locs = apply_trans(head_mri_t, locs) + + assert raw.info['ch_names'][0][3:5] == 'D1' + assert_allclose( + mni_locs[0], [-0.0841, -0.0464, -0.0129], atol=allowed_dist_error) + + assert raw.info['ch_names'][2][3:5] == 'D6' + assert_allclose( + mni_locs[2], [-0.0841, -0.0138, 0.0248], atol=allowed_dist_error) + + assert raw.info['ch_names'][34][3:5] == 'D5' + assert_allclose( + mni_locs[34], [0.0845, -0.0451, -0.0123], atol=allowed_dist_error) + + # Test location of sensors + # The locations of sensors can be seen in the second + # figure on this page... + # https://github.com/mne-tools/mne-testing-data/pull/86 + locs = [ch['loc'][3:6] for ch in raw.info['chs']] + head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri') + mni_locs = apply_trans(head_mri_t, locs) + + assert raw.info['ch_names'][0][:2] == 'S1' + assert_allclose( + mni_locs[0], [-0.0848, -0.0162, -0.0163], atol=allowed_dist_error) + + assert raw.info['ch_names'][9][:2] == 'S2' + assert_allclose( + mni_locs[9], [-0.0, -0.1195, 0.0142], atol=allowed_dist_error) + + assert raw.info['ch_names'][39][:2] == 'S8' + assert_allclose( + mni_locs[34], [0.0828, -0.046, 0.0285], atol=allowed_dist_error) + + assert len(raw.annotations) == 3 + assert raw.annotations.description[0] == '1.0' + assert raw.annotations.description[2] == '6.0' + # Lose tolerance as I am eyeballing the time differences on screen + assert_allclose( + np.diff(raw.annotations.onset), [2.3, 3.1], atol=0.1) + + mon = raw.get_montage() + assert len(mon.dig) == 27 + + +@requires_testing_data +@pytest.mark.filterwarnings('ignore:.*Extraction of measurement.*:') +def test_nirsport_v1_wo_sat(): + """Test NIRSport1 file with no saturation.""" + raw = read_raw_nirx(nirsport1_wo_sat, preload=True) + + # Test data import + assert raw._data.shape == (26, 164) + assert raw.info['sfreq'] == 10.416667 + + # By default real data is returned + assert np.sum(np.isnan(raw.get_data())) == 0 + + raw = read_raw_nirx(nirsport1_wo_sat, preload=True, saturated='nan') + data = raw.get_data() + assert data.shape == (26, 164) + assert np.sum(np.isnan(data)) == 0 + + raw = read_raw_nirx(nirsport1_wo_sat, saturated='annotate') + data = raw.get_data() + assert data.shape == (26, 164) + assert np.sum(np.isnan(data)) == 0 + + +@pytest.mark.filterwarnings('ignore:.*Extraction of measurement.*:') +@requires_testing_data +def test_nirsport_v1_w_sat(): + """Test NIRSport1 file with NaNs but not in channel of interest.""" + raw = read_raw_nirx(nirsport1_w_sat) + + # Test data import + data = raw.get_data() + assert data.shape == (26, 176) + assert raw.info['sfreq'] == 10.416667 + assert np.sum(np.isnan(data)) == 0 + + raw = read_raw_nirx(nirsport1_w_sat, saturated='nan') + data = raw.get_data() + assert data.shape == (26, 176) + assert np.sum(np.isnan(data)) == 0 + + raw = read_raw_nirx(nirsport1_w_sat, saturated='annotate') + data = raw.get_data() + assert data.shape == (26, 176) + assert np.sum(np.isnan(data)) == 0 + + +@pytest.mark.filterwarnings('ignore:.*Extraction of measurement.*:') +@requires_testing_data +@pytest.mark.parametrize('preload', (True, False)) +@pytest.mark.parametrize('meas_date', (None, "orig")) +def test_nirsport_v1_w_bad_sat(preload, meas_date): + """Test NIRSport1 file with NaNs.""" + fname = nirsport1_w_fullsat + raw = read_raw_nirx(fname, preload=preload) + data = raw.get_data() + assert not np.isnan(data).any() + assert len(raw.annotations) == 5 + # annotated version and ignore should have same data but different annot + raw_ignore = read_raw_nirx(fname, saturated='ignore', preload=preload) + assert_allclose(raw_ignore.get_data(), data) + assert len(raw_ignore.annotations) == 2 + assert not any('NAN' in d for d in raw_ignore.annotations.description) + # nan version should not have same data, but we can give it the same annot + raw_nan = read_raw_nirx(fname, saturated='nan', preload=preload) + data_nan = raw_nan.get_data() + assert np.isnan(data_nan).any() + assert not np.allclose(raw_nan.get_data(), data) + raw_nan_annot = raw_ignore.copy() + if meas_date is None: + raw.set_meas_date(None) + raw_nan.set_meas_date(None) + raw_nan_annot.set_meas_date(None) + nan_annots = annotate_nan(raw_nan) + assert nan_annots.orig_time == raw_nan.info["meas_date"] + raw_nan_annot.set_annotations(nan_annots) + use_mask = np.where(raw.annotations.description == 'BAD_SATURATED') + for key in ('onset', 'duration'): + a = getattr(raw_nan_annot.annotations, key)[::2] # one ch in each + b = getattr(raw.annotations, key)[use_mask] # two chs in each + assert_allclose(a, b) @requires_testing_data @@ -51,24 +244,24 @@ @requires_testing_data -def test_nirx_missing_evt(tmpdir): +def test_nirx_missing_evt(tmp_path): """Test reading NIRX files when missing data.""" - shutil.copytree(fname_nirx_15_2_short, str(tmpdir) + "/data/") - os.rename(str(tmpdir) + "/data" + "/NIRS-2019-08-23_001.evt", - str(tmpdir) + "/data" + "/NIRS-2019-08-23_001.xxx") - fname = str(tmpdir) + "/data" + "/NIRS-2019-08-23_001.hdr" + shutil.copytree(fname_nirx_15_2_short, str(tmp_path) + "/data/") + os.rename(tmp_path / "data" / "NIRS-2019-08-23_001.evt", + tmp_path / "data" / "NIRS-2019-08-23_001.xxx") + fname = tmp_path / "data" / "NIRS-2019-08-23_001.hdr" raw = read_raw_nirx(fname, preload=True) assert raw.annotations.onset.shape == (0, ) @requires_testing_data -def test_nirx_dat_warn(tmpdir): +def test_nirx_dat_warn(tmp_path): """Test reading NIRX files when missing data.""" - shutil.copytree(fname_nirx_15_2_short, str(tmpdir) + "/data/") - os.rename(str(tmpdir) + "/data" + "/NIRS-2019-08-23_001.dat", - str(tmpdir) + "/data" + "/NIRS-2019-08-23_001.tmp") - fname = str(tmpdir) + "/data" + "/NIRS-2019-08-23_001.hdr" - with pytest.raises(RuntimeWarning, match='A single dat'): + shutil.copytree(fname_nirx_15_2_short, str(tmp_path) + "/data/") + os.rename(tmp_path / "data" / "NIRS-2019-08-23_001.dat", + tmp_path / "data" / "NIRS-2019-08-23_001.tmp") + fname = tmp_path / "data" / "NIRS-2019-08-23_001.hdr" + with pytest.warns(RuntimeWarning, match='A single dat'): read_raw_nirx(fname, preload=True) @@ -96,17 +289,19 @@ assert raw.info['subject_info'] == dict(sex=1, first_name="MNE", middle_name="Test", last_name="Recording", - birthday=(2014, 8, 23)) + birthday=(2014, 8, 23), + his_id="MNE_Test_Recording") # Test distance between optodes matches values from # nirsite https://github.com/mne-tools/mne-testing-data/pull/51 # step 4 figure 2 allowed_distance_error = 0.0002 - distances = source_detector_distances(raw.info) - assert_allclose(distances[::2], [ - 0.0304, 0.0078, 0.0310, 0.0086, 0.0416, - 0.0072, 0.0389, 0.0075, 0.0558, 0.0562, - 0.0561, 0.0565, 0.0077], atol=allowed_distance_error) + assert_allclose(source_detector_distances(raw.copy(). + pick("S1_D1 760").info), + [0.0304], atol=allowed_distance_error) + assert_allclose(source_detector_distances(raw.copy(). + pick("S2_D10 760").info), + [0.0086], atol=allowed_distance_error) # Test which channels are short # These are the ones marked as red at @@ -186,16 +381,19 @@ assert raw.info['subject_info'] == dict(birthday=(2020, 8, 18), sex=0, first_name="testMontage\\0A" - "TestMontage") + "TestMontage", + his_id="testMontage\\0A" + "TestMontage") # Test distance between optodes matches values from # https://github.com/mne-tools/mne-testing-data/pull/72 allowed_distance_error = 0.001 - distances = source_detector_distances(raw.info) - assert_allclose(distances[::2], [ - 0.0304, 0.0078, 0.0310, 0.0086, 0.0416, - 0.0072, 0.0389, 0.0075, 0.0558, 0.0562, - 0.0561, 0.0565, 0.0077], atol=allowed_distance_error) + assert_allclose(source_detector_distances(raw.copy(). + pick("S1_D2 760").info), + [0.0304], atol=allowed_distance_error) + assert_allclose(source_detector_distances(raw.copy(). + pick("S5_D13 760").info), + [0.0076], atol=allowed_distance_error) # Test which channels are short # These are the ones marked as red at @@ -250,21 +448,36 @@ @requires_testing_data -def test_encoding(tmpdir): +def test_locale_encoding(tmp_path): """Test NIRx encoding.""" - fname = str(tmpdir.join('latin')) + fname = tmp_path / 'latin' shutil.copytree(fname_nirx_15_2, fname) hdr_fname = op.join(fname, 'NIRS-2019-10-02_003.hdr') hdr = list() with open(hdr_fname, 'rb') as fid: hdr.extend(line for line in fid) + # French hdr[2] = b'Date="jeu. 13 f\xe9vr. 2020"\r\n' with open(hdr_fname, 'wb') as fid: for line in hdr: fid.write(line) - # smoke test - with pytest.raises(RuntimeWarning, match='Extraction of measurement date'): - read_raw_nirx(fname) + read_raw_nirx(fname, verbose='debug') + # German + hdr[2] = b'Date="mi 13 dez 2020"\r\n' + with open(hdr_fname, 'wb') as fid: + for line in hdr: + fid.write(line) + read_raw_nirx(fname, verbose='debug') + # Italian + hdr[2] = b'Date="ven 24 gen 2020"\r\n' + hdr[3] = b'Time="10:57:41.454"\r\n' + with open(hdr_fname, 'wb') as fid: + for line in hdr: + fid.write(line) + raw = read_raw_nirx(fname, verbose='debug') + want_dt = dt.datetime( + 2020, 1, 24, 10, 57, 41, 454000, tzinfo=dt.timezone.utc) + assert raw.info['meas_date'] == want_dt @requires_testing_data @@ -284,7 +497,8 @@ # Test info import assert raw.info['subject_info'] == dict(sex=1, first_name="TestRecording", - birthday=(1989, 10, 2)) + birthday=(1989, 10, 2), + his_id="TestRecording") # Test trigger events assert_array_equal(raw.annotations.description, ['4.0', '6.0', '2.0']) @@ -314,6 +528,15 @@ @requires_testing_data +def test_nirx_aurora_2021_9_6(): + """Test reading NIRX files.""" + raw = read_raw_nirx(nirsport2_2021_9_6, preload=True) + assert len(raw.annotations) == 3 + assert raw.annotations.description[0] == "1.0" + assert raw.annotations.description[2] == "3.0" + + +@requires_testing_data def test_nirx_15_0(): """Test reading NIRX files.""" raw = read_raw_nirx(fname_nirx_15_0, preload=True) @@ -337,7 +560,8 @@ assert raw.info['subject_info'] == {'birthday': (2004, 10, 27), 'first_name': 'NIRX', 'last_name': 'Test', - 'sex': FIFF.FIFFV_SUBJ_SEX_UNKNOWN} + 'sex': FIFF.FIFFV_SUBJ_SEX_UNKNOWN, + 'his_id': "NIRX_Test"} # Test trigger events assert_array_equal(raw.annotations.description, ['1.0', '2.0', '2.0']) @@ -358,17 +582,21 @@ # Test distance between optodes matches values from allowed_distance_error = 0.0002 - distances = source_detector_distances(raw.info) - assert_allclose(distances[::2], [ - 0.0301, 0.0315, 0.0343, 0.0368, 0.0408, - 0.0399, 0.0393, 0.0367, 0.0336, 0.0447], atol=allowed_distance_error) + + assert_allclose(source_detector_distances(raw.copy(). + pick("S1_D1 760").info), + [0.0300], atol=allowed_distance_error) + assert_allclose(source_detector_distances(raw.copy(). + pick("S7_D7 760").info), + [0.0392], atol=allowed_distance_error) @requires_testing_data @pytest.mark.parametrize('fname, boundary_decimal', ( [fname_nirx_15_2_short, 1], [fname_nirx_15_2, 0], - [fname_nirx_15_0, 0] + [fname_nirx_15_2, 0], + [nirsport2_2021_9, 0], )) def test_nirx_standard(fname, boundary_decimal): """Test standard operations.""" @@ -376,4 +604,22 @@ boundary_decimal=boundary_decimal) # low fs -run_tests_if_main() +# Below are the native (on-disk) orders, which should be preserved +@requires_testing_data +@pytest.mark.parametrize('fname, want_order', [ + (fname_nirx_15_0, ['S1_D1', 'S2_D2', 'S3_D3', 'S4_D4', 'S5_D5', 'S6_D6', 'S7_D7', 'S8_D8', 'S9_D9', 'S10_D10']), # noqa: E501 + (fname_nirx_15_2, ['S1_D1', 'S1_D10', 'S2_D1', 'S2_D2', 'S3_D2', 'S3_D3', 'S4_D3', 'S4_D4', 'S5_D4', 'S5_D5', 'S6_D5', 'S6_D6', 'S7_D6', 'S7_D7', 'S8_D7', 'S8_D8', 'S9_D8', 'S9_D9', 'S10_D9', 'S10_D10', 'S11_D11', 'S11_D12', 'S12_D12', 'S12_D13', 'S13_D13', 'S13_D14', 'S14_D14', 'S14_D15', 'S15_D15', 'S15_D16', 'S16_D11', 'S16_D16']), # noqa: E501 + (fname_nirx_15_2_short, ['S1_D1', 'S1_D9', 'S2_D3', 'S2_D10', 'S3_D2', 'S3_D11', 'S4_D4', 'S4_D12', 'S5_D5', 'S5_D6', 'S5_D7', 'S5_D8', 'S5_D13']), # noqa: E501 + (fname_nirx_15_3_short, ['S1_D2', 'S1_D9', 'S2_D1', 'S2_D10', 'S3_D3', 'S3_D11', 'S4_D4', 'S4_D12', 'S5_D5', 'S5_D6', 'S5_D7', 'S5_D8', 'S5_D13']), # noqa: E501 + (nirsport1_wo_sat, ['S1_D4', 'S1_D5', 'S1_D6', 'S2_D5', 'S2_D6', 'S3_D5', 'S4_D1', 'S4_D3', 'S4_D4', 'S5_D1', 'S5_D2', 'S6_D1', 'S6_D3']), # noqa: E501 + (nirsport2, ['S1_D1', 'S1_D6', 'S1_D9', 'S2_D2', 'S2_D10', 'S3_D5', 'S3_D7', 'S3_D11', 'S4_D8', 'S4_D12', 'S5_D3', 'S5_D13', 'S6_D4', 'S6_D14', 'S7_D1', 'S7_D6', 'S7_D15', 'S8_D5', 'S8_D7', 'S8_D16']), # noqa: E501 + (nirsport2_2021_9, ['S1_D1', 'S1_D3', 'S2_D1', 'S2_D2', 'S2_D4', 'S3_D2', 'S3_D5', 'S4_D1', 'S4_D3', 'S4_D4', 'S4_D6', 'S5_D2', 'S5_D4', 'S5_D5', 'S5_D7', 'S6_D3', 'S6_D6', 'S7_D4', 'S7_D6', 'S7_D7', 'S8_D5', 'S8_D7']), # noqa: E501 +]) +def test_channel_order(fname, want_order): + """Test that logical channel order is preserved.""" + raw = read_raw_nirx(fname) + ch_names = raw.ch_names + prefixes = [ch_name.split()[0] for ch_name in ch_names] + assert prefixes[::2] == prefixes[1::2] + prefixes = prefixes[::2] + assert prefixes == want_order diff -Nru python-mne-0.23.4+dfsg/mne/io/open.py python-mne-1.1.0+dfsg/mne/io/open.py --- python-mne-0.23.4+dfsg/mne/io/open.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/open.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Authors: Alexandre Gramfort # Matti Hämäläinen # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op from io import BytesIO, SEEK_SET @@ -140,19 +140,20 @@ tag = read_tag_info(fid) # Check that this looks like a fif file + prefix = f'file {repr(fname)} does not' if tag.kind != FIFF.FIFF_FILE_ID: - raise ValueError('file does not start with a file id tag') + raise ValueError(f'{prefix} start with a file id tag') if tag.type != FIFF.FIFFT_ID_STRUCT: - raise ValueError('file does not start with a file id tag') + raise ValueError(f'{prefix} start with a file id tag') if tag.size != 20: - raise ValueError('file does not start with a file id tag') + raise ValueError(f'{prefix} start with a file id tag') tag = read_tag(fid) if tag.kind != FIFF.FIFF_DIR_POINTER: - raise ValueError('file does not have a directory pointer') + raise ValueError(f'{prefix} have a directory pointer') # Read or create the directory tree logger.debug(' Creating tag directory for %s...' % fname) diff -Nru python-mne-0.23.4+dfsg/mne/io/persyst/__init__.py python-mne-1.1.0+dfsg/mne/io/persyst/__init__.py --- python-mne-0.23.4+dfsg/mne/io/persyst/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/persyst/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,6 +2,6 @@ # Author: Adam Li # -# License: BSD (3-clause) +# License: BSD-3-Clause from .persyst import read_raw_persyst diff -Nru python-mne-0.23.4+dfsg/mne/io/persyst/persyst.py python-mne-1.1.0+dfsg/mne/io/persyst/persyst.py --- python-mne-0.23.4+dfsg/mne/io/persyst/persyst.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/persyst/persyst.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Adam Li # -# License: BSD (3-clause) +# License: BSD-3-Clause import os import os.path as op from collections import OrderedDict @@ -95,8 +95,11 @@ if key == '': continue - # make sure key are lowercase for everything, but electrodes - if key is not None and section != 'channelmap': + # Make sure key are lowercase for everything, but electrodes. + # We also do not want to lower-case comments because those + # are free-form text where casing may matter. + if key is not None and section not in ['channelmap', + 'comments']: key = key.lower() # FileInfo @@ -182,10 +185,11 @@ ch_types = 'eeg' info = create_info(ch_names, sfreq, ch_types=ch_types) info.update(subject_info=subject_info) - for idx in range(n_chs): - # calibration brings to uV then 1e-6 brings to V - info['chs'][idx]['cal'] = cal * 1.0e-6 - info['meas_date'] = meas_date + with info._unlock(): + for idx in range(n_chs): + # calibration brings to uV then 1e-6 brings to V + info['chs'][idx]['cal'] = cal * 1.0e-6 + info['meas_date'] = meas_date # determine number of samples in file # Note: We do not use the lay file to do this diff -Nru python-mne-0.23.4+dfsg/mne/io/persyst/tests/test_persyst.py python-mne-1.1.0+dfsg/mne/io/persyst/tests/test_persyst.py --- python-mne-0.23.4+dfsg/mne/io/persyst/tests/test_persyst.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/persyst/tests/test_persyst.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,7 @@ # -*- coding: utf-8 -*- # Authors: Adam Li -# simplified BSD-3 license +# +# License: BSD-3-Clause import os import os.path as op @@ -10,17 +11,16 @@ from numpy.testing import assert_array_equal import numpy as np -import mne from mne.datasets.testing import data_path, requires_testing_data from mne.io import read_raw_persyst from mne.io.tests.test_raw import _test_raw_reader -from mne.utils import run_tests_if_main +testing_path = data_path(download=False) fname_lay = op.join( - data_path(download=False), 'Persyst', + testing_path, 'Persyst', 'sub-pt1_ses-02_task-monitor_acq-ecog_run-01_clip2.lay') fname_dat = op.join( - data_path(download=False), 'Persyst', + testing_path, 'Persyst', 'sub-pt1_ses-02_task-monitor_acq-ecog_run-01_clip2.dat') @@ -78,10 +78,10 @@ @requires_testing_data -def test_persyst_dates(): +def test_persyst_dates(tmp_path): """Test different Persyst date formats for meas date.""" # now test what if you change contents of the lay file - out_dir = mne.utils._TempDir() + out_dir = str(tmp_path) new_fname_lay = op.join(out_dir, op.basename(fname_lay)) new_fname_dat = op.join(out_dir, op.basename(fname_dat)) shutil.copy(fname_dat, new_fname_dat) @@ -119,12 +119,12 @@ @requires_testing_data -def test_persyst_wrong_file(tmpdir): +def test_persyst_wrong_file(tmp_path): """Test reading Persyst files when passed in wrong file path.""" with pytest.raises(FileNotFoundError, match='The path you'): read_raw_persyst(fname_dat, preload=True) - out_dir = str(tmpdir) + out_dir = str(tmp_path) new_fname_lay = op.join(out_dir, op.basename(fname_lay)) new_fname_dat = op.join(out_dir, op.basename(fname_dat)) shutil.copy(fname_lay, new_fname_lay) @@ -143,9 +143,9 @@ @requires_testing_data -def test_persyst_moved_file(tmpdir): +def test_persyst_moved_file(tmp_path): """Test reader - Persyst files need to be in same directory.""" - out_dir = str(tmpdir) + out_dir = str(tmp_path) new_fname_lay = op.join(out_dir, op.basename(fname_lay)) new_fname_dat = op.join(out_dir, op.basename(fname_dat)) shutil.copy(fname_lay, new_fname_lay) @@ -192,15 +192,15 @@ @requires_testing_data -def test_persyst_annotations(tmpdir): +def test_persyst_annotations(tmp_path): """Test annotations reading in Persyst.""" - out_dir = str(tmpdir) - new_fname_lay = op.join(out_dir, op.basename(fname_lay)) - new_fname_dat = op.join(out_dir, op.basename(fname_dat)) + new_fname_lay = tmp_path / op.basename(fname_lay) + new_fname_dat = tmp_path / op.basename(fname_dat) shutil.copy(fname_dat, new_fname_dat) shutil.copy(fname_lay, new_fname_lay) raw = read_raw_persyst(new_fname_lay) + raw.crop(tmin=0, tmax=4) # get the annotations and make sure that repeated annotations # are in the dataset @@ -209,12 +209,13 @@ # make sure annotation with a "," character is in there assert 'seizure1,2' in annotations.description + assert 'CLip2' in annotations.description @requires_testing_data -def test_persyst_errors(): +def test_persyst_errors(tmp_path): """Test reading Persyst files when passed in wrong file path.""" - out_dir = mne.utils._TempDir() + out_dir = str(tmp_path) new_fname_lay = op.join(out_dir, op.basename(fname_lay)) new_fname_dat = op.join(out_dir, op.basename(fname_dat)) shutil.copy(fname_dat, new_fname_dat) @@ -260,6 +261,3 @@ match='Cannot read in the measurement date'): raw = read_raw_persyst(new_fname_lay) assert raw.info['meas_date'] is None - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/io/pick.py python-mne-1.1.0+dfsg/mne/io/pick.py --- python-mne-0.23.4+dfsg/mne/io/pick.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/pick.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Matti Hämäläinen # Martin Luessi # -# License: BSD (3-clause) +# License: BSD-3-Clause from copy import deepcopy import re @@ -12,7 +12,7 @@ from .constants import FIFF from ..utils import (logger, verbose, _validate_type, fill_doc, _ensure_int, - _check_option) + _check_option, warn) def get_channel_type_constants(include_defaults=False): @@ -169,13 +169,13 @@ } +@fill_doc def channel_type(info, idx): """Get channel type. Parameters ---------- - info : instance of Info - A measurement info object. + %(info_not_none)s idx : int Index of channel. @@ -366,6 +366,7 @@ return exclude +@fill_doc def pick_types(info, meg=False, eeg=False, stim=False, eog=False, ecg=False, emg=False, ref_meg='auto', misc=False, resp=False, chpi=False, exci=False, ias=False, syst=False, seeg=False, dipole=False, @@ -375,8 +376,7 @@ Parameters ---------- - info : dict - The measurement info. + %(info_not_none)s meg : bool | str If True include MEG channels. If string it can be 'mag', 'grad', 'planar1' or 'planar2' to select only magnetometers, all gradiometers, @@ -514,8 +514,7 @@ Parameters ---------- - info : dict - Info structure from evoked or raw data. + %(info_not_none)s sel : list of int | None Indices of channels to include. If None, all channels are included. @@ -550,11 +549,12 @@ logger.info('Removing %d compensators from info because ' 'not all compensation channels were picked.' % (len(info['comps']),)) - info['comps'] = [] - info['chs'] = [info['chs'][k] for k in sel] + with info._unlock(): + info['comps'] = [] + with info._unlock(): + info['chs'] = [info['chs'][k] for k in sel] info._update_redundant() info['bads'] = [ch for ch in info['bads'] if ch in info['ch_names']] - if 'comps' in info: comps = deepcopy(info['comps']) for c in comps: @@ -566,8 +566,10 @@ c['data']['nrow'] = len(row_names) c['data']['row_names'] = row_names c['data']['data'] = c['data']['data'][row_idx] - info['comps'] = comps + with info._unlock(): + info['comps'] = comps info._check_consistency() + return info @@ -696,7 +698,8 @@ fwd['sol']['row_names'] = ch_names # Pick the appropriate channel names from the info-dict using sel_info - fwd['info']['chs'] = [fwd['info']['chs'][k] for k in sel_info] + with fwd['info']._unlock(): + fwd['info']['chs'] = [fwd['info']['chs'][k] for k in sel_info] fwd['info']._update_redundant() fwd['info']['bads'] = [b for b in fwd['info']['bads'] if b in ch_names] @@ -759,8 +762,7 @@ Parameters ---------- - info : instance of Info - A measurement info object. + %(info_not_none)s %(picks_all)s Returns @@ -844,13 +846,13 @@ for ph in info.get('proc_history', [])) +@fill_doc def _contains_ch_type(info, ch_type): """Check whether a certain channel type is in an info object. Parameters ---------- - info : instance of Info - The measurement information. + %(info_not_none)s ch_type : str the channel type to be checked for @@ -873,13 +875,13 @@ for ii in range(info['nchan'])) +@fill_doc def _picks_by_type(info, meg_combined=False, ref_meg=False, exclude='bads'): """Get data channel indices as separate list of tuples. Parameters ---------- - info : instance of mne.measuerment_info.Info - The info. + %(info_not_none)s meg_combined : bool | 'auto' Whether to return combined picks for grad and mag. Can be 'auto' to choose based on Maxwell filtering status. @@ -1106,14 +1108,13 @@ # second: match all to channel names # - bad_name = None + bad_names = [] picks_name = list() for pick in picks: try: picks_name.append(info['ch_names'].index(pick)) except ValueError: - bad_name = pick - break + bad_names.append(pick) # # third: match all to types @@ -1160,7 +1161,7 @@ 'picks (%s) could not be interpreted as ' 'channel names (no channel "%s"), channel types (no ' 'type "%s"), or a generic type (just "all" or "data")' - % (repr(orig_picks) + extra_repr, bad_name, bad_type)) + % (repr(orig_picks) + extra_repr, str(bad_names), bad_type)) picks = np.array([], int) elif sum(any_found) > 1: raise RuntimeError('Some channel names are ambiguously equivalent to ' @@ -1168,8 +1169,13 @@ 'picks for these') else: picks = np.array(all_picks[np.where(any_found)[0][0]]) + + picked_ch_type_or_generic = not len(picks_name) + if len(bad_names) > 0 and not picked_ch_type_or_generic: + warn(f'Channel(s) {bad_names} could not be picked, because ' + 'they are not present in the info instance.') + if return_kind: - picked_ch_type_or_generic = not len(picks_name) return picks, picked_ch_type_or_generic return picks @@ -1198,4 +1204,7 @@ if only_data_chs: ch_types = [ch_type for ch_type in ch_types if ch_type in _DATA_CH_TYPES_SPLIT] - return set(ch_types) if unique is True else ch_types + if unique: + # set does not preserve order but dict does, so let's just use it + ch_types = list({k: k for k in ch_types}.keys()) + return ch_types diff -Nru python-mne-0.23.4+dfsg/mne/io/proc_history.py python-mne-1.1.0+dfsg/mne/io/proc_history.py --- python-mne-0.23.4+dfsg/mne/io/proc_history.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/proc_history.py 2022-08-03 15:48:14.000000000 +0000 @@ -226,8 +226,7 @@ if kind == FIFF.FIFF_PROJ_ITEM_CH_NAME_LIST: tag = read_tag(fid, pos) chs = tag.data.split(':') - # XXX for some reason this list can have a bunch of junk - # in the last entry, e.g.: + # This list can null chars in the last entry, e.g.: # [..., u'MEG2642', u'MEG2643', u'MEG2641\x00 ... \x00'] chs[-1] = chs[-1].split('\x00')[0] sss_ctc['proj_items_chs'] = chs diff -Nru python-mne-0.23.4+dfsg/mne/io/proj.py python-mne-1.1.0+dfsg/mne/io/proj.py --- python-mne-0.23.4+dfsg/mne/io/proj.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/proj.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,21 +3,21 @@ # Denis Engemann # Teon Brooks # -# License: BSD (3-clause) +# License: BSD-3-Clause from copy import deepcopy from itertools import count -from math import sqrt import numpy as np -from .tree import dir_tree_find -from .tag import find_tag, _rename_list from .constants import FIFF from .pick import pick_types, pick_info +from .tag import find_tag, _rename_list +from .tree import dir_tree_find from .write import (write_int, write_float, write_string, write_name_list, write_float_matrix, end_block, start_block) -from ..defaults import _BORDER_DEFAULT, _EXTRAPOLATE_DEFAULT +from ..defaults import (_INTERPOLATION_DEFAULT, _BORDER_DEFAULT, + _EXTRAPOLATE_DEFAULT) from ..utils import logger, verbose, warn, fill_doc, _validate_type @@ -25,12 +25,35 @@ """Projection vector. A basic class to proj a meaningful print for projection vectors. + + .. warning:: This class is generally not meant to be instantiated + directly, use ``compute_proj_*`` functions instead. + + Parameters + ---------- + data : dict + The data dictionary. + desc : str + The projector description. + kind : int + The projector kind. + active : bool + Whether or not the projector has been applied. + explained_var : float | None + The explained variance (proportion). """ + def __init__(self, *, data, desc='', kind=FIFF.FIFFV_PROJ_ITEM_FIELD, + active=False, explained_var=None): + super().__init__(desc=desc, kind=kind, active=active, data=data, + explained_var=explained_var) + def __repr__(self): # noqa: D105 s = "%s" % self['desc'] s += ", active : %s" % self['active'] s += f", n_channels : {len(self['data']['col_names'])}" + if self['explained_var'] is not None: + s += f', exp. var : {self["explained_var"] * 100:0.2f}%' return "" % s # speed up info copy by taking advantage of mutability @@ -50,18 +73,18 @@ @fill_doc def plot_topomap(self, info, cmap=None, sensors=True, colorbar=False, res=64, size=1, show=True, - outlines='head', contours=6, image_interp='bilinear', + outlines='head', contours=6, + image_interp=_INTERPOLATION_DEFAULT, axes=None, vlim=(None, None), sphere=None, border=_BORDER_DEFAULT): """Plot topographic maps of SSP projections. Parameters ---------- - info : instance of Info | None - The measurement information to use to determine the layout. + %(info_not_none)s Used to determine the layout. %(proj_topomap_kwargs)s - %(topomap_sphere_auto)s - %(topomap_border)s + %(sphere_topomap_auto)s + %(border_topomap)s Returns ------- @@ -73,10 +96,11 @@ .. versionadded:: 0.15.0 """ # noqa: E501 from ..viz.topomap import plot_projs_topomap - return plot_projs_topomap(self, info, cmap, sensors, colorbar, - res, size, show, outlines, - contours, image_interp, axes, vlim, - sphere=sphere, border=border) + return plot_projs_topomap( + self, info, cmap, sensors, colorbar=colorbar, res=res, size=size, + show=show, outlines=outlines, contours=contours, + image_interp=image_interp, axes=axes, vlim=vlim, + sphere=sphere, border=border) class ProjMixin(object): @@ -121,7 +145,7 @@ List with projection vectors. remove_existing : bool Remove the projection vectors currently in the file. - %(verbose_meth)s + %(verbose)s Returns ------- @@ -137,18 +161,21 @@ 'something else.') # mark proj as inactive, as they have not been applied - projs = deactivate_proj(projs, copy=True, verbose=self.verbose) + projs = deactivate_proj(projs, copy=True) if remove_existing: # we cannot remove the proj if they are active if any(p['active'] for p in self.info['projs']): raise ValueError('Cannot remove projectors that have ' 'already been applied') - self.info['projs'] = projs + with self.info._unlock(): + self.info['projs'] = projs else: self.info['projs'].extend(projs) # We don't want to add projectors that are activated again. - self.info['projs'] = _uniquify_projs(self.info['projs'], - check_active=False, sort=False) + with self.info._unlock(): + self.info['projs'] = _uniquify_projs(self.info['projs'], + check_active=False, + sort=False) return self @verbose @@ -157,7 +184,7 @@ Parameters ---------- - %(verbose_meth)s + %(verbose)s Returns ------- @@ -203,7 +230,7 @@ return self _projector, info = setup_proj(deepcopy(self.info), add_eeg_ref=False, - activate=True, verbose=self.verbose) + activate=True) # let's not raise a RuntimeError here, otherwise interactive plotting if _projector is None: # won't be fun. logger.info('The projections don\'t apply to these data.' @@ -255,14 +282,16 @@ keep = np.ones(len(self.info['projs'])) keep[idx] = False # works with negative indexing and does checks - self.info['projs'] = [p for p, k in zip(self.info['projs'], keep) if k] + with self.info._unlock(): + self.info['projs'] = [p for p, k in zip(self.info['projs'], keep) + if k] return self @fill_doc def plot_projs_topomap(self, ch_type=None, cmap=None, sensors=True, colorbar=False, res=64, size=1, show=True, outlines='head', contours=6, - image_interp='bilinear', axes=None, + image_interp=_INTERPOLATION_DEFAULT, axes=None, vlim=(None, None), sphere=None, extrapolate=_EXTRAPOLATE_DEFAULT, border=_BORDER_DEFAULT): @@ -276,11 +305,12 @@ (default), it will return all channel types present. If a list of ch_types is provided, it will return multiple figures. %(proj_topomap_kwargs)s - %(topomap_sphere_auto)s - %(topomap_extrapolate)s + %(sphere_topomap_auto)s + %(image_interp_topomap)s + %(extrapolate_topomap)s .. versionadded:: 0.20 - %(topomap_border)s + %(border_topomap)s Returns ------- @@ -313,10 +343,12 @@ continue info_from = pick_info(self.info, picks) info_to = info_from.copy() - info_to['projs'] = [] - if kind == 'eeg' and _has_eeg_average_ref_proj(info_from['projs']): - info_to['projs'] = [ - make_eeg_average_ref_proj(info_to, verbose=False)] + with info_to._unlock(): + info_to['projs'] = [] + if kind == 'eeg' and _has_eeg_average_ref_proj( + info_from['projs']): + info_to['projs'] = [ + make_eeg_average_ref_proj(info_to, verbose=False)] mapping = _map_meg_or_eeg_channels( info_from, info_to, mode=mode, origin=origin) self.data[..., picks, :] = np.matmul( @@ -407,7 +439,7 @@ tag = find_tag(fid, item, FIFF.FIFF_MNE_ICA_PCA_EXPLAINED_VAR) if tag is not None: - explained_var = tag.data + explained_var = float(tag.data) else: explained_var = None @@ -592,21 +624,31 @@ # Rescale for better detection of small singular values for v in range(p['data']['nrow']): - psize = sqrt(np.sum(this_vecs[:, v] * this_vecs[:, v])) + psize = np.linalg.norm(this_vecs[:, v]) if psize > 0: orig_n = p['data']['data'].any(axis=0).sum() # Average ref still works if channels are removed - if len(vecsel) < 0.9 * orig_n and not inplace and \ + # Use relative power to determine if we're in trouble. + # 10% loss is hopefully a reasonable threshold. + if psize < 0.9 and not inplace and \ (p['kind'] != FIFF.FIFFV_PROJ_ITEM_EEG_AVREF or len(vecsel) == 1): - warn('Projection vector "%s" has magnitude %0.2f ' - '(should be unity), applying projector with ' - '%s/%s of the original channels available may ' - 'be dangerous, consider recomputing and adding ' - 'projection vectors for channels that are ' - 'eventually used. If this is intentional, ' - 'consider using info.normalize_proj()' - % (p['desc'], psize, len(vecsel), orig_n)) + warn( + f'Projection vector {repr(p["desc"])} has been ' + f'reduced to {100 * psize:0.2f}% of its ' + 'original magnitude by subselecting ' + f'{len(vecsel)}/{orig_n} of the original ' + 'channels. If the ignored channels were bad ' + 'during SSP computation, we recommend ' + 'recomputing proj (via compute_proj_raw ' + 'or related functions) with the bad channels ' + 'properly marked, because computing SSP with bad ' + 'channels present in the data but unmarked is ' + 'dangerous (it can bias the PCA used by SSP). ' + 'On the other hand, if you know that all channels ' + 'were good during SSP computation, you can safely ' + 'use info.normalize_proj() to suppress this ' + 'warning during projection.') this_vecs[:, v] /= psize nonzero += 1 # If doing "inplace" mode, "fix" the projectors to only operate @@ -650,6 +692,7 @@ info['bads'], include_active=True, inplace=True) +@fill_doc def make_projector_info(info, include_active=True): """Make an SSP operator using the measurement info. @@ -657,8 +700,7 @@ Parameters ---------- - info : dict - Measurement info. + %(info_not_none)s include_active : bool Also include projectors that are already active. @@ -742,8 +784,7 @@ Parameters ---------- - info : dict - Measurement info. + %(info_not_none)s activate : bool If True projections are activated. %(verbose)s @@ -769,7 +810,7 @@ raise ValueError('Cannot create EEG average reference projector ' '(no EEG data found)') vec = np.ones((1, n_eeg)) - vec /= n_eeg + vec /= np.sqrt(n_eeg) explained_var = None eeg_proj_data = dict(col_names=eeg_names, row_names=None, data=vec, nrow=1, ncol=n_eeg) @@ -813,8 +854,7 @@ Parameters ---------- - info : dict - The measurement info. + %(info_not_none)s Warning: will be modified in-place. add_eeg_ref : bool If True, an EEG average reference will be added (unless one already exists). @@ -826,8 +866,8 @@ ------- projector : array of shape [n_channels, n_channels] The projection operator to apply to the data. - info : dict - The modified measurement info (Warning: info is modified inplace). + info : mne.Info + The modified measurement info. """ # Add EEG ref reference proj if necessary if add_eeg_ref and _needs_eeg_average_ref_proj(info): @@ -847,7 +887,8 @@ # The projection items have been activated if activate: - info['projs'] = activate_proj(info['projs'], copy=False) + with info._unlock(): + info['projs'] = activate_proj(info['projs'], copy=False) return projector, info diff -Nru python-mne-0.23.4+dfsg/mne/io/_read_raw.py python-mne-1.1.0+dfsg/mne/io/_read_raw.py --- python-mne-0.23.4+dfsg/mne/io/_read_raw.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/_read_raw.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Authors: Clemens Brunner # -# License: BSD (3-clause) +# License: BSD-3-Clause from pathlib import Path @@ -12,7 +12,7 @@ read_raw_fif, read_raw_eeglab, read_raw_cnt, read_raw_egi, read_raw_eximia, read_raw_nirx, read_raw_fieldtrip, read_raw_artemis123, read_raw_nicolet, read_raw_kit, - read_raw_ctf, read_raw_boxy) + read_raw_ctf, read_raw_boxy, read_raw_snirf) from ..utils import fill_doc @@ -32,6 +32,7 @@ ".bdf": read_raw_bdf, ".gdf": read_raw_gdf, ".vhdr": read_raw_brainvision, + ".ahdr": read_raw_brainvision, ".fif": read_raw_fif, ".fif.gz": read_raw_fif, ".set": read_raw_eeglab, @@ -39,6 +40,7 @@ ".mff": read_raw_egi, ".nxe": read_raw_eximia, ".hdr": read_raw_nirx, + ".snirf": read_raw_snirf, ".mat": read_raw_fieldtrip, ".bin": read_raw_artemis123, ".data": read_raw_nicolet, @@ -50,6 +52,7 @@ # known but unsupported file formats suggested = {".vmrk": partial(_read_unsupported, suggest=".vhdr"), + ".amrk": partial(_read_unsupported, suggest=".ahdr"), ".eeg": partial(_read_unsupported, suggest=".vhdr")} # all known file formats diff -Nru python-mne-0.23.4+dfsg/mne/io/reference.py python-mne-1.1.0+dfsg/mne/io/reference.py --- python-mne-0.23.4+dfsg/mne/io/reference.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/reference.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Alexandre Gramfort # Teon Brooks # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np @@ -53,7 +53,8 @@ _check_preload(inst, "Applying a reference") ch_type = _get_ch_type(inst, ch_type) - ch_dict = {ch_type: True, 'meg': False, 'ref_meg': False} + ch_dict = {**{type_: True for type_ in ch_type}, + 'meg': False, 'ref_meg': False} eeg_idx = pick_types(inst.info, **ch_dict) if ref_to is None: @@ -100,7 +101,8 @@ # info that a non-CAR has been applied. ref_to_channels = pick_channels(inst.ch_names, ref_to, ordered=True) if len(np.intersect1d(ref_to_channels, eeg_idx)) > 0: - inst.info['custom_ref_applied'] = FIFF.FIFFV_MNE_CUSTOM_REF_ON + with inst.info._unlock(): + inst.info['custom_ref_applied'] = FIFF.FIFFV_MNE_CUSTOM_REF_ON return ref_to @@ -209,9 +211,8 @@ dl['kind'] == FIFF.FIFFV_POINT_EEG and dl['ident'] == 0)] if len(ref_channels) > 1 or len(ref_dig_loc) != len(ref_channels): - ref_dig_array = np.zeros(12) - warn('The locations of multiple reference channels are ignored ' - '(set to zero).') + ref_dig_array = np.full(12, np.nan) + warn('The locations of multiple reference channels are ignored.') else: # n_ref_channels == 1 and a single ref digitization exists ref_dig_array = np.concatenate((ref_dig_loc[0]['r'], ref_dig_loc[0]['r'], np.zeros(6))) @@ -219,12 +220,12 @@ for idx in pick_types(inst.info, meg=False, eeg=True, exclude=[]): inst.info['chs'][idx]['loc'][3:6] = ref_dig_loc[0]['r'] else: - # we should actually be able to do this from the montage, but - # it looks like the montage isn't stored, so we can't extract - # this information. The user will just have to call set_montage() - # by setting this to zero, we fall back to the old behavior - # when missing digitisation - ref_dig_array = np.zeros(12) + # Ideally we'd fall back on getting the location from a montage, but + # locations for non-present channels aren't stored, so location is + # unknown. Users can call set_montage() again if needed. + ref_dig_array = np.full(12, np.nan) + logger.info('Location for this channel is unknown; consider calling ' + 'set_montage() again if needed.') for ch in ref_channels: chan_info = {'ch_name': ch, @@ -240,12 +241,16 @@ 'loc': ref_dig_array} inst.info['chs'].append(chan_info) inst.info._update_redundant() + range_ = np.arange(1, len(ref_channels) + 1) if isinstance(inst, BaseRaw): inst._cals = np.hstack((inst._cals, [1] * len(ref_channels))) - range_ = np.arange(1, len(ref_channels) + 1) for pi, picks in enumerate(inst._read_picks): inst._read_picks[pi] = np.concatenate( [picks, np.max(picks) + range_]) + elif isinstance(inst, BaseEpochs): + picks = inst.picks + inst.picks = np.concatenate( + [picks, np.max(picks) + range_]) inst.info._check_consistency() set_eeg_reference(inst, ref_channels=ref_channels, copy=False, verbose=False) @@ -279,17 +284,21 @@ This function will re-reference the data according to the desired reference. + Note that it is also possible to re-reference the signal using a + Laplacian (LAP) "reference-free" transformation using the + :func:`.compute_current_source_density` function. + Parameters ---------- inst : instance of Raw | Epochs | Evoked Instance of Raw or Epochs with EEG channels and reference channel(s). - %(set_eeg_reference_ref_channels)s + %(ref_channels_set_eeg_reference)s copy : bool Specifies whether the data will be copied (True) or modified in-place (False). Defaults to True. - %(set_eeg_reference_projection)s - %(set_eeg_reference_ch_type)s - %(set_eeg_reference_forward)s + %(projection_set_eeg_reference)s + %(ch_type_set_eeg_reference)s + %(forward_set_eeg_reference)s %(verbose)s Returns @@ -319,11 +328,14 @@ # sure that the custom_ref_applied flag is left untouched. custom_ref_applied = inst.info['custom_ref_applied'] try: - inst.info['custom_ref_applied'] = FIFF.FIFFV_MNE_CUSTOM_REF_OFF + with inst.info._unlock(): + inst.info['custom_ref_applied'] = \ + FIFF.FIFFV_MNE_CUSTOM_REF_OFF inst.add_proj(make_eeg_average_ref_proj(inst.info, - activate=False)) + activate=False)) except Exception: - inst.info['custom_ref_applied'] = custom_ref_applied + with inst.info._unlock(): + inst.info['custom_ref_applied'] = custom_ref_applied raise # If the data has been preloaded, projections will no # longer be automatically applied. @@ -336,9 +348,9 @@ inst = inst.copy() if copy else inst ch_type = _get_ch_type(inst, ch_type) - ch_dict = {ch_type: True, 'meg': False, 'ref_meg': False} - eeg_idx = pick_types(inst.info, **ch_dict) - ch_sel = [inst.ch_names[i] for i in eeg_idx] + ch_dict = {**{type_: True for type_ in ch_type}, + 'meg': False, 'ref_meg': False} + ch_sel = [inst.ch_names[i] for i in pick_types(inst.info, **ch_dict)] if ref_channels == 'REST': _validate_type(forward, Forward, 'forward when ref_channels="REST"') @@ -352,22 +364,34 @@ if ref_channels == []: logger.info('EEG data marked as already having the desired reference.') else: - logger.info('Applying a custom %s ' - 'reference.' % DEFAULTS['titles'][ch_type]) + logger.info( + 'Applying a custom ' + f"{tuple(DEFAULTS['titles'][type_] for type_ in ch_type)} " + 'reference.') return _apply_reference(inst, ref_channels, ch_sel, forward, ch_type=ch_type) def _get_ch_type(inst, ch_type): - _validate_type(ch_type, str, 'ch_type') - _check_option('ch_type', ch_type, ('auto', 'eeg', 'ecog', 'seeg', 'dbs')) + _validate_type(ch_type, (str, list, tuple), 'ch_type') + valid_ch_types = ('auto', 'eeg', 'ecog', 'seeg', 'dbs') + if isinstance(ch_type, str): + _check_option('ch_type', ch_type, valid_ch_types) + if ch_type != 'auto': + ch_type = [ch_type] + elif isinstance(ch_type, (list, tuple)): + for type_ in ch_type: + _validate_type(type_, str, 'ch_type') + _check_option('ch_type', type_, valid_ch_types[1:]) + ch_type = list(ch_type) + # if ch_type is 'auto', search through list to find first reasonable # reference-able channel type. if ch_type == 'auto': for type_ in ['eeg', 'ecog', 'seeg', 'dbs']: if type_ in inst: - ch_type = type_ + ch_type = [type_] logger.info('%s channel type selected for ' 're-referencing' % DEFAULTS['titles'][type_]) break @@ -512,7 +536,8 @@ # Set other info-keys from original instance. pick_info = {k: v for k, v in inst.info.items() if k not in ['chs', 'ch_names', 'bads', 'nchan', 'sfreq']} - ref_info.update(pick_info) + with ref_info._unlock(): + ref_info.update(pick_info) # Rereferencing of data. ref_data = multiplier @ inst._data diff -Nru python-mne-0.23.4+dfsg/mne/io/snirf/__init__.py python-mne-1.1.0+dfsg/mne/io/snirf/__init__.py --- python-mne-0.23.4+dfsg/mne/io/snirf/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/snirf/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,6 +2,6 @@ # Author: Robert Luke # -# License: BSD (3-clause) +# License: BSD-3-Clause from ._snirf import read_raw_snirf diff -Nru python-mne-0.23.4+dfsg/mne/io/snirf/_snirf.py python-mne-1.1.0+dfsg/mne/io/snirf/_snirf.py --- python-mne-0.23.4+dfsg/mne/io/snirf/_snirf.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/snirf/_snirf.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,24 +1,26 @@ # Authors: Robert Luke # -# License: BSD (3-clause) +# License: BSD-3-Clause import re import numpy as np import datetime from ..base import BaseRaw -from ..meas_info import create_info +from ..meas_info import create_info, _format_dig_points from ..utils import _mult_cal_one from ...annotations import Annotations -from ...utils import logger, verbose, fill_doc, warn, _check_fname -from ...utils.check import _require_version +from ...utils import (logger, verbose, fill_doc, warn, _check_fname, + _import_h5py) from ..constants import FIFF from .._digitization import _make_dig_points -from ...transforms import _frame_to_str +from ...transforms import _frame_to_str, apply_trans +from ..nirx.nirx import _convert_fnirs_to_head +from ..._freesurfer import get_mni_fiducials @fill_doc -def read_raw_snirf(fname, preload=False, verbose=None): +def read_raw_snirf(fname, optode_frame="unknown", preload=False, verbose=None): """Reader for a continuous wave SNIRF data. .. note:: This reader supports the .snirf file type only, @@ -32,6 +34,11 @@ ---------- fname : str Path to the SNIRF data file. + optode_frame : str + Coordinate frame used for the optode positions. The default is unknown, + in which case the positions are not modified. If a known coordinate + frame is provided (head, meg, mri), then the positions are transformed + in to the Neuromag head coordinate frame (head). %(preload)s %(verbose)s @@ -44,7 +51,7 @@ -------- mne.io.Raw : Documentation of attribute and methods. """ - return RawSNIRF(fname, preload, verbose) + return RawSNIRF(fname, optode_frame, preload, verbose) def _open(fname): @@ -59,6 +66,11 @@ ---------- fname : str Path to the SNIRF data file. + optode_frame : str + Coordinate frame used for the optode positions. The default is unknown, + in which case the positions are not modified. If a known coordinate + frame is provided (head, meg, mri), then the positions are transformed + in to the Neuromag head coordinate frame (head). %(preload)s %(verbose)s @@ -68,9 +80,8 @@ """ @verbose - def __init__(self, fname, preload=False, verbose=None): - _require_version('h5py', 'read raw SNIRF data') - from ...externals.pymatreader.utils import _import_h5py + def __init__(self, fname, optode_frame="unknown", + preload=False, verbose=None): # Must be here due to circular import error from ...preprocessing.nirs import _validate_nirs_info h5py = _import_h5py() @@ -85,37 +96,38 @@ "MNE does not support this feature. " "Only the first dataset will be processed.") - if np.array(dat.get('nirs/data1/measurementList1/dataType')) != 1: - raise RuntimeError('File does not contain continuous wave ' - 'data. MNE only supports reading continuous' - ' wave amplitude SNIRF files. Expected type' - ' code 1 but received type code %d' % - (np.array(dat.get( - 'nirs/data1/measurementList1/dataType' - )))) + manafacturer = _get_metadata_str(dat, "ManufacturerName") + if (optode_frame == "unknown") & (manafacturer == "Gowerlabs"): + optode_frame = "head" + + snirf_data_type = np.array(dat.get('nirs/data1/measurementList1' + '/dataType')).item() + if snirf_data_type not in [1, 99999]: + # 1 = Continuous Wave + # 99999 = Processed + raise RuntimeError('MNE only supports reading continuous' + ' wave amplitude and processed haemoglobin' + ' SNIRF files. Expected type' + ' code 1 or 99999 but received type ' + f'code {snirf_data_type}') last_samps = dat.get('/nirs/data1/dataTimeSeries').shape[0] - 1 - samplingrate_raw = np.array(dat.get('nirs/data1/time')) - sampling_rate = 0 - if samplingrate_raw.shape == (2, 1): - # specified as onset/samplerate - warn("Onset/sample rate SNIRF not yet supported.") - else: - # specified as time points - fs_diff = np.around(np.diff(samplingrate_raw), decimals=4) - if len(np.unique(fs_diff)) == 1: - # Uniformly sampled data - sampling_rate = 1. / np.unique(fs_diff) - else: - # print(np.unique(fs_diff)) - warn("Non uniform sampled data not supported.") + sampling_rate = _extract_sampling_rate(dat) + if sampling_rate == 0: warn("Unable to extract sample rate from SNIRF file.") # Extract wavelengths fnirs_wavelengths = np.array(dat.get('nirs/probe/wavelengths')) fnirs_wavelengths = [int(w) for w in fnirs_wavelengths] + if len(fnirs_wavelengths) != 2: + raise RuntimeError(f'The data contains ' + f'{len(fnirs_wavelengths)}' + f' wavelengths: {fnirs_wavelengths}. ' + f'MNE only supports reading continuous' + ' wave amplitude SNIRF files ' + 'with two wavelengths.') # Extract channels def atoi(text): @@ -131,24 +143,29 @@ # Source and detector labels are optional fields. # Use S1, S2, S3, etc if not specified. - if 'sourceLabels' in dat['nirs/probe']: + if 'sourceLabels_disabled' in dat['nirs/probe']: + # This is disabled as + # MNE-Python does not currently support custom source names. + # Instead, sources must be integer values. sources = np.array(dat.get('nirs/probe/sourceLabels')) sources = [s.decode('UTF-8') for s in sources] else: - sources = np.unique([dat.get('nirs/data1/' + c + - '/sourceIndex')[0] - for c in channels]) - sources = [f"S{s}" for s in sources] - - if 'detectorLabels' in dat['nirs/probe']: + sources = np.unique([_correct_shape(np.array(dat.get( + 'nirs/data1/' + c + '/sourceIndex')))[0] + for c in channels]) + sources = [f"S{int(s)}" for s in sources] + + if 'detectorLabels_disabled' in dat['nirs/probe']: + # This is disabled as + # MNE-Python does not currently support custom detector names. + # Instead, detector must be integer values. detectors = np.array(dat.get('nirs/probe/detectorLabels')) detectors = [d.decode('UTF-8') for d in detectors] - else: - detectors = np.unique([dat.get('nirs/data1/' + c + - '/detectorIndex')[0] - for c in channels]) - detectors = [f"D{d}" for d in detectors] + detectors = np.unique([_correct_shape(np.array(dat.get( + 'nirs/data1/' + c + '/detectorIndex')))[0] + for c in channels]) + detectors = [f"D{int(d)}" for d in detectors] # Extract source and detector locations # 3D positions are optional in SNIRF, @@ -187,26 +204,55 @@ assert len(detectors) == detPos3D.shape[0] chnames = [] + ch_types = [] for chan in channels: - src_idx = int(np.array(dat.get('nirs/data1/' + - chan + '/sourceIndex'))[0]) - det_idx = int(np.array(dat.get('nirs/data1/' + - chan + '/detectorIndex'))[0]) - wve_idx = int(np.array(dat.get('nirs/data1/' + - chan + '/wavelengthIndex'))[0]) - ch_name = sources[src_idx - 1] + '_' +\ - detectors[det_idx - 1] + ' ' +\ - str(fnirs_wavelengths[wve_idx - 1]) - chnames.append(ch_name) + src_idx = int(_correct_shape(np.array(dat.get('nirs/data1/' + + chan + '/sourceIndex')))[0]) + det_idx = int(_correct_shape(np.array(dat.get('nirs/data1/' + + chan + '/detectorIndex')))[0]) + + if snirf_data_type == 1: + wve_idx = int(_correct_shape(np.array( + dat.get('nirs/data1/' + chan + + '/wavelengthIndex')))[0]) + ch_name = sources[src_idx - 1] + '_' +\ + detectors[det_idx - 1] + ' ' +\ + str(fnirs_wavelengths[wve_idx - 1]) + chnames.append(ch_name) + ch_types.append('fnirs_cw_amplitude') + + elif snirf_data_type == 99999: + dt_id = _correct_shape( + np.array(dat.get('nirs/data1/' + chan + + '/dataTypeLabel')))[0].decode('UTF-8') + + # Convert between SNIRF processed names and MNE type names + dt_id = dt_id.lower().replace("dod", "fnirs_od") + + ch_name = sources[src_idx - 1] + '_' + \ + detectors[det_idx - 1] + + if dt_id == "fnirs_od": + wve_idx = int(_correct_shape(np.array( + dat.get('nirs/data1/' + chan + + '/wavelengthIndex')))[0]) + suffix = ' ' + str(fnirs_wavelengths[wve_idx - 1]) + else: + suffix = ' ' + dt_id.lower() + ch_name = ch_name + suffix + + chnames.append(ch_name) + ch_types.append(dt_id) # Create mne structure info = create_info(chnames, sampling_rate, - ch_types='fnirs_cw_amplitude') + ch_types=ch_types) subject_info = {} names = np.array(dat.get('nirs/metaDataTags/SubjectID')) - subject_info['first_name'] = names[0].decode('UTF-8') + subject_info['first_name'] = \ + _correct_shape(names)[0].decode('UTF-8') # Read non standard (but allowed) custom metadata tags if 'lastName' in dat.get('nirs/metaDataTags/'): ln = dat.get('/nirs/metaDataTags/lastName')[0].decode('UTF-8') @@ -226,58 +272,95 @@ # Update info info.update(subject_info=subject_info) - LengthUnit = np.array(dat.get('/nirs/metaDataTags/LengthUnit')) - LengthUnit = LengthUnit[0].decode('UTF-8') - scal = 1 - if "cm" in LengthUnit: - scal = 100 - elif "mm" in LengthUnit: - scal = 1000 + length_unit = _get_metadata_str(dat, "LengthUnit") + length_scaling = _get_lengthunit_scaling(length_unit) + + srcPos3D /= length_scaling + detPos3D /= length_scaling + + if optode_frame in ["mri", "meg"]: + # These are all in MNI or MEG coordinates, so let's transform + # them to the Neuromag head coordinate frame + srcPos3D, detPos3D, _, head_t = _convert_fnirs_to_head( + 'fsaverage', optode_frame, 'head', srcPos3D, detPos3D, []) + else: + head_t = np.eye(4) + + if optode_frame in ["head", "mri", "meg"]: + # Then the transformation to head was performed above + coord_frame = FIFF.FIFFV_COORD_HEAD + elif 'MNE_coordFrame' in dat.get('nirs/metaDataTags/'): + coord_frame = int(dat.get('/nirs/metaDataTags/MNE_coordFrame') + [0]) + else: + coord_frame = FIFF.FIFFV_COORD_UNKNOWN for idx, chan in enumerate(channels): - src_idx = int(np.array(dat.get('nirs/data1/' + - chan + '/sourceIndex'))[0]) - det_idx = int(np.array(dat.get('nirs/data1/' + - chan + '/detectorIndex'))[0]) - wve_idx = int(np.array(dat.get('nirs/data1/' + - chan + '/wavelengthIndex'))[0]) - info['chs'][idx]['loc'][3:6] = srcPos3D[src_idx - 1, :] / scal - info['chs'][idx]['loc'][6:9] = detPos3D[det_idx - 1, :] / scal + src_idx = int(_correct_shape(np.array(dat.get('nirs/data1/' + + chan + '/sourceIndex')))[0]) + det_idx = int(_correct_shape(np.array(dat.get('nirs/data1/' + + chan + '/detectorIndex')))[0]) + + info['chs'][idx]['loc'][3:6] = srcPos3D[src_idx - 1, :] + info['chs'][idx]['loc'][6:9] = detPos3D[det_idx - 1, :] # Store channel as mid point midpoint = (info['chs'][idx]['loc'][3:6] + info['chs'][idx]['loc'][6:9]) / 2 info['chs'][idx]['loc'][0:3] = midpoint - info['chs'][idx]['loc'][9] = fnirs_wavelengths[wve_idx - 1] + info['chs'][idx]['coord_frame'] = coord_frame - if 'MNE_coordFrame' in dat.get('nirs/metaDataTags/'): - coord_frame = int(dat.get('/nirs/metaDataTags/MNE_coordFrame') - [0]) - else: - coord_frame = FIFF.FIFFV_COORD_UNKNOWN + if (snirf_data_type in [1]) or \ + ((snirf_data_type == 99999) and + (ch_types[idx] == "fnirs_od")): + wve_idx = int(_correct_shape(np.array(dat.get( + 'nirs/data1/' + chan + '/wavelengthIndex')))[0]) + info['chs'][idx]['loc'][9] = fnirs_wavelengths[wve_idx - 1] if 'landmarkPos3D' in dat.get('nirs/probe/'): diglocs = np.array(dat.get('/nirs/probe/landmarkPos3D')) + diglocs /= length_scaling digname = np.array(dat.get('/nirs/probe/landmarkLabels')) nasion, lpa, rpa, hpi = None, None, None, None extra_ps = dict() for idx, dign in enumerate(digname): - if dign == b'LPA': - lpa = diglocs[idx, :] - elif dign == b'NASION': - nasion = diglocs[idx, :] - elif dign == b'RPA': - rpa = diglocs[idx, :] + dign = dign.lower() + if dign in [b'lpa', b'al']: + lpa = diglocs[idx, :3] + elif dign in [b'nasion']: + nasion = diglocs[idx, :3] + elif dign in [b'rpa', b'ar']: + rpa = diglocs[idx, :3] else: - extra_ps[f'EEG{len(extra_ps) + 1:03d}'] = diglocs[idx] - info['dig'] = _make_dig_points(nasion=nasion, lpa=lpa, rpa=rpa, - hpi=hpi, dig_ch_pos=extra_ps, - coord_frame=_frame_to_str[ - coord_frame]) - - str_date = np.array((dat.get( - '/nirs/metaDataTags/MeasurementDate')))[0].decode('UTF-8') - str_time = np.array((dat.get( - '/nirs/metaDataTags/MeasurementTime')))[0].decode('UTF-8') + extra_ps[f'EEG{len(extra_ps) + 1:03d}'] = \ + diglocs[idx, :3] + dig = _make_dig_points( + nasion=nasion, lpa=lpa, rpa=rpa, hpi=hpi, + dig_ch_pos=extra_ps, + coord_frame=_frame_to_str[coord_frame]) + else: + ch_locs = [info['chs'][idx]['loc'][0:3] + for idx in range(len(channels))] + # Set up digitization + dig = get_mni_fiducials('fsaverage', verbose=False) + for fid in dig: + fid['r'] = apply_trans(head_t, fid['r']) + fid['coord_frame'] = FIFF.FIFFV_COORD_HEAD + for ii, ch_loc in enumerate(ch_locs, 1): + dig.append(dict( + kind=FIFF.FIFFV_POINT_EEG, # misnomer prob okay + r=ch_loc, + ident=ii, + coord_frame=FIFF.FIFFV_COORD_HEAD, + )) + dig = _format_dig_points(dig) + del head_t + with info._unlock(): + info['dig'] = dig + + str_date = _correct_shape(np.array((dat.get( + '/nirs/metaDataTags/MeasurementDate'))))[0].decode('UTF-8') + str_time = _correct_shape(np.array((dat.get( + '/nirs/metaDataTags/MeasurementTime'))))[0].decode('UTF-8') str_datetime = str_date + str_time # Several formats have been observed so we try each in turn @@ -296,17 +379,19 @@ f"instead of {str_datetime}") meas_date = datetime.datetime(2000, 1, 1, 0, 0, 0) meas_date = meas_date.replace(tzinfo=datetime.timezone.utc) - info['meas_date'] = meas_date + with info._unlock(): + info['meas_date'] = meas_date if 'DateOfBirth' in dat.get('nirs/metaDataTags/'): str_birth = np.array((dat.get('/nirs/metaDataTags/' 'DateOfBirth')))[0].decode() birth_matched = re.fullmatch(r'(\d+)-(\d+)-(\d+)', str_birth) if birth_matched is not None: - info["subject_info"]['birthday'] = ( - int(birth_matched.groups()[0]), - int(birth_matched.groups()[1]), - int(birth_matched.groups()[2])) + birthday = (int(birth_matched.groups()[0]), + int(birth_matched.groups()[1]), + int(birth_matched.groups()[2])) + with info._unlock(): + info["subject_info"]['birthday'] = birthday super(RawSNIRF, self).__init__(info, preload, filenames=[fname], last_samps=[last_samps], @@ -319,16 +404,10 @@ data = np.atleast_2d(np.array( dat.get('/nirs/' + key + '/data'))) if data.size > 0: - annot.append(data[:, 0], 1.0, key[4:]) - self.set_annotations(annot) - - # Reorder channels to match expected ordering in MNE - num_chans = len(self.ch_names) - chans = [] - for idx in range(num_chans // 2): - chans.append(idx) - chans.append(idx + num_chans // 2) - self.pick(picks=chans) + desc = _correct_shape(np.array(dat.get( + '/nirs/' + key + '/name')))[0] + annot.append(data[:, 0], 1.0, desc.decode('UTF-8')) + self.set_annotations(annot, emit_warning=False) # Validate that the fNIRS info is correctly formatted _validate_nirs_info(self.info) @@ -341,3 +420,65 @@ one = dat['/nirs/data1/dataTimeSeries'][start:stop].T _mult_cal_one(data, one, idx, cals, mult) + + +# Helper function for when the numpy array has shape (), i.e. just one element. +def _correct_shape(arr): + if arr.shape == (): + arr = arr[np.newaxis] + return arr + + +def _get_timeunit_scaling(time_unit): + """MNE expects time in seconds, return required scaling.""" + scalings = {'ms': 1000, 's': 1, 'unknown': 1} + if time_unit in scalings: + return scalings[time_unit] + else: + raise RuntimeError(f'The time unit {time_unit} is not supported by ' + 'MNE. Please report this error as a GitHub ' + 'issue to inform the developers.') + + +def _get_lengthunit_scaling(length_unit): + """MNE expects distance in m, return required scaling.""" + scalings = {'m': 1, 'cm': 100, 'mm': 1000} + if length_unit in scalings: + return scalings[length_unit] + else: + raise RuntimeError(f'The length unit {length_unit} is not supported ' + 'by MNE. Please report this error as a GitHub ' + 'issue to inform the developers.') + + +def _extract_sampling_rate(dat): + """Extract the sample rate from the time field.""" + time_data = np.array(dat.get('nirs/data1/time')) + sampling_rate = 0 + if len(time_data) == 2: + # specified as onset, samplerate + sampling_rate = 1. / (time_data[1] - time_data[0]) + else: + # specified as time points + fs_diff = np.around(np.diff(time_data), decimals=4) + if len(np.unique(fs_diff)) == 1: + # Uniformly sampled data + sampling_rate = 1. / np.unique(fs_diff) + else: + warn("MNE does not currently support reading " + "SNIRF files with non-uniform sampled data.") + + time_unit = _get_metadata_str(dat, "TimeUnit") + time_unit_scaling = _get_timeunit_scaling(time_unit) + sampling_rate *= time_unit_scaling + + return sampling_rate + + +def _get_metadata_str(dat, field): + if field not in np.array(dat.get('nirs/metaDataTags')): + return None + data = dat.get(f'/nirs/metaDataTags/{field}') + data = _correct_shape(np.array(data)) + data = str(data[0], 'utf-8') + return data diff -Nru python-mne-0.23.4+dfsg/mne/io/snirf/tests/test_snirf.py python-mne-1.1.0+dfsg/mne/io/snirf/tests/test_snirf.py --- python-mne-0.23.4+dfsg/mne/io/snirf/tests/test_snirf.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/snirf/tests/test_snirf.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,42 +3,74 @@ # simplified BSD-3 license import os.path as op -from numpy.testing import assert_allclose, assert_almost_equal +import numpy as np +from numpy.testing import assert_allclose, assert_almost_equal, assert_equal import shutil import pytest from mne.datasets.testing import data_path, requires_testing_data -from mne.utils import run_tests_if_main, requires_h5py from mne.io import read_raw_snirf, read_raw_nirx from mne.io.tests.test_raw import _test_raw_reader from mne.preprocessing.nirs import (optical_density, beer_lambert_law, - short_channels) + short_channels, source_detector_distances, + _reorder_nirx) +from mne.transforms import apply_trans, _get_trans +from mne.io.constants import FIFF + + +testing_path = data_path(download=False) # SfNIRS files -sfnirs_homer_103_wShort = op.join(data_path(download=False), - 'SNIRF', 'SfNIRS', 'snirf_homer3', '1.0.3', +sfnirs_homer_103_wShort = op.join(testing_path, 'SNIRF', 'SfNIRS', + 'snirf_homer3', '1.0.3', 'snirf_1_3_nirx_15_2_' 'recording_w_short.snirf') -sfnirs_homer_103_wShort_original = op.join(data_path(download=False), - 'NIRx', 'nirscout', +sfnirs_homer_103_wShort_original = op.join(testing_path, 'NIRx', 'nirscout', 'nirx_15_2_recording_w_short') -sfnirs_homer_103_153 = op.join(data_path(download=False), - 'SNIRF', 'SfNIRS', 'snirf_homer3', '1.0.3', - 'nirx_15_3_recording.snirf') +sfnirs_homer_103_153 = op.join(testing_path, 'SNIRF', 'SfNIRS', 'snirf_homer3', + '1.0.3', 'nirx_15_3_recording.snirf') + +# NIRSport2 files +nirx_nirsport2_103 = op.join(testing_path, 'SNIRF', 'NIRx', 'NIRSport2', + '1.0.3', '2021-04-23_005.snirf') +nirx_nirsport2_103_2 = op.join(testing_path, 'SNIRF', 'NIRx', 'NIRSport2', + '1.0.3', '2021-05-05_001.snirf') +snirf_nirsport2_20219 = op.join(testing_path, 'SNIRF', 'NIRx', 'NIRSport2', + '2021.9', '2021-10-01_002.snirf') +nirx_nirsport2_20219 = op.join(testing_path, 'NIRx', 'nirsport_v2', + 'aurora_2021_9') + +# Kernel +kernel_hb = op.join(testing_path, 'SNIRF', 'Kernel', 'Flow50', + 'Portal_2021_11', 'hb.snirf') + +h5py = pytest.importorskip('h5py') # module-level + +# Fieldtrip +ft_od = op.join(testing_path, 'SNIRF', 'FieldTrip', + '220307_opticaldensity.snirf') -# NIRx files -nirx_nirsport2_103 = op.join(data_path(download=False), - 'SNIRF', 'NIRx', 'NIRSport2', '1.0.3', - '2021-04-23_005.snirf') +# GowerLabs +lumo110 = op.join(testing_path, 'SNIRF', 'GowerLabs', 'lumomat-1-1-0.snirf') + + +def _get_loc(raw, ch_name): + return raw.copy().pick(ch_name).info['chs'][0]['loc'] -@requires_h5py @requires_testing_data @pytest.mark.filterwarnings('ignore:.*contains 2D location.*:') +@pytest.mark.filterwarnings('ignore:.*measurement date.*:') @pytest.mark.parametrize('fname', ([sfnirs_homer_103_wShort, nirx_nirsport2_103, - sfnirs_homer_103_153])) + sfnirs_homer_103_153, + nirx_nirsport2_103, + nirx_nirsport2_103_2, + nirx_nirsport2_103_2, + kernel_hb, + lumo110 + ])) def test_basic_reading_and_min_process(fname): """Test reading SNIRF files and minimum typical processing.""" raw = read_raw_snirf(fname, preload=True) @@ -46,12 +78,37 @@ if 'fnirs_cw_amplitude' in raw: raw = optical_density(raw) if 'fnirs_od' in raw: - raw = beer_lambert_law(raw) + raw = beer_lambert_law(raw, ppf=6) assert 'hbo' in raw + assert 'hbr' in raw + + +@requires_testing_data +@pytest.mark.filterwarnings('ignore:.*measurement date.*:') +def test_snirf_gowerlabs(): + """Test reading SNIRF files.""" + raw = read_raw_snirf(lumo110, preload=True) + + assert raw._data.shape == (216, 274) + assert raw.info['dig'][0]['coord_frame'] == FIFF.FIFFV_COORD_HEAD + assert len(raw.ch_names) == 216 + assert_allclose(raw.info['sfreq'], 10.0) + # we don't force them to be sorted according to a naive split + assert raw.ch_names != sorted(raw.ch_names) + # ... but this file does have a nice logical ordering already + print(raw.ch_names) + assert raw.ch_names == sorted( + raw.ch_names, + # use a key which is (src triplet, freq, src, freq, det) + key=lambda name: ( + (int(name.split()[0].split('_')[0][1:]) - 1) // 3, + int(name.split()[1]), + int(name.split()[0].split('_')[0][1:]), + int(name.split()[0].split('_')[1][1:]) + )) @requires_testing_data -@requires_h5py def test_snirf_basic(): """Test reading SNIRF files.""" raw = read_raw_snirf(sfnirs_homer_103_wShort, preload=True) @@ -61,44 +118,44 @@ assert raw.info['sfreq'] == 12.5 # Test channel naming - assert raw.info['ch_names'][:4] == ["S1_D1 760", "S1_D1 850", - "S1_D9 760", "S1_D9 850"] - assert raw.info['ch_names'][24:26] == ["S5_D13 760", "S5_D13 850"] + assert raw.info['ch_names'][:4] == ["S1_D1 760", "S1_D9 760", + "S2_D3 760", "S2_D10 760"] + assert raw.info['ch_names'][24:26] == ['S5_D8 850', 'S5_D13 850'] # Test frequency encoding assert raw.info['chs'][0]['loc'][9] == 760 - assert raw.info['chs'][1]['loc'][9] == 850 + assert raw.info['chs'][24]['loc'][9] == 850 # Test source locations assert_allclose([-8.6765 * 1e-2, 0.0049 * 1e-2, -2.6167 * 1e-2], - raw.info['chs'][0]['loc'][3:6], rtol=0.02) + _get_loc(raw, 'S1_D1 760')[3:6], rtol=0.02) assert_allclose([7.9579 * 1e-2, -2.7571 * 1e-2, -2.2631 * 1e-2], - raw.info['chs'][4]['loc'][3:6], rtol=0.02) + _get_loc(raw, 'S2_D3 760')[3:6], rtol=0.02) assert_allclose([-2.1387 * 1e-2, -8.8874 * 1e-2, 3.8393 * 1e-2], - raw.info['chs'][8]['loc'][3:6], rtol=0.02) + _get_loc(raw, 'S3_D2 760')[3:6], rtol=0.02) assert_allclose([1.8602 * 1e-2, 9.7164 * 1e-2, 1.7539 * 1e-2], - raw.info['chs'][12]['loc'][3:6], rtol=0.02) + _get_loc(raw, 'S4_D4 760')[3:6], rtol=0.02) assert_allclose([-0.1108 * 1e-2, 0.7066 * 1e-2, 8.9883 * 1e-2], - raw.info['chs'][16]['loc'][3:6], rtol=0.02) + _get_loc(raw, 'S5_D5 760')[3:6], rtol=0.02) # Test detector locations assert_allclose([-8.0409 * 1e-2, -2.9677 * 1e-2, -2.5415 * 1e-2], - raw.info['chs'][0]['loc'][6:9], rtol=0.02) + _get_loc(raw, 'S1_D1 760')[6:9], rtol=0.02) assert_allclose([-8.7329 * 1e-2, 0.7577 * 1e-2, -2.7980 * 1e-2], - raw.info['chs'][3]['loc'][6:9], rtol=0.02) + _get_loc(raw, 'S1_D9 850')[6:9], rtol=0.02) assert_allclose([9.2027 * 1e-2, 0.0161 * 1e-2, -2.8909 * 1e-2], - raw.info['chs'][5]['loc'][6:9], rtol=0.02) + _get_loc(raw, 'S2_D3 850')[6:9], rtol=0.02) assert_allclose([7.7548 * 1e-2, -3.5901 * 1e-2, -2.3179 * 1e-2], - raw.info['chs'][7]['loc'][6:9], rtol=0.02) + _get_loc(raw, 'S2_D10 850')[6:9], rtol=0.02) assert 'fnirs_cw_amplitude' in raw @requires_testing_data -@requires_h5py def test_snirf_against_nirx(): """Test against file snirf was created from.""" raw = read_raw_snirf(sfnirs_homer_103_wShort, preload=True) + _reorder_nirx(raw) raw_orig = read_raw_nirx(sfnirs_homer_103_wShort_original, preload=True) # Check annotations are the same @@ -121,14 +178,11 @@ assert_allclose(raw.get_data(), raw_orig.get_data()) -@requires_h5py @requires_testing_data -def test_snirf_nonstandard(tmpdir): +def test_snirf_nonstandard(tmp_path): """Test custom tags.""" - from mne.externals.pymatreader.utils import _import_h5py - h5py = _import_h5py() - shutil.copy(sfnirs_homer_103_wShort, str(tmpdir) + "/mod.snirf") - fname = str(tmpdir) + "/mod.snirf" + shutil.copy(sfnirs_homer_103_wShort, str(tmp_path) + "/mod.snirf") + fname = str(tmp_path) + "/mod.snirf" # Manually mark up the file to match MNE-NIRS custom tags with h5py.File(fname, "r+") as f: f.create_dataset("nirs/metaDataTags/middleName", @@ -159,7 +213,6 @@ @requires_testing_data -@requires_h5py def test_snirf_nirsport2(): """Test reading SNIRF files.""" raw = read_raw_snirf(nirx_nirsport2_103, preload=True) @@ -169,8 +222,47 @@ assert_almost_equal(raw.info['sfreq'], 7.6, decimal=1) # Test channel naming + assert raw.info['ch_names'][:4] == ['S1_D1 760', 'S1_D3 760', + 'S1_D9 760', 'S1_D16 760'] + assert raw.info['ch_names'][24:26] == ['S8_D15 760', 'S8_D20 760'] + + # Test frequency encoding + assert raw.info['chs'][0]['loc'][9] == 760 + assert raw.info['chs'][-1]['loc'][9] == 850 + + assert sum(short_channels(raw.info)) == 16 + + +@requires_testing_data +def test_snirf_coordframe(): + """Test reading SNIRF files.""" + raw = read_raw_snirf(nirx_nirsport2_103, optode_frame="head").\ + info['chs'][3]['coord_frame'] + assert raw == FIFF.FIFFV_COORD_HEAD + + raw = read_raw_snirf(nirx_nirsport2_103, optode_frame="mri").\ + info['chs'][3]['coord_frame'] + assert raw == FIFF.FIFFV_COORD_HEAD + + raw = read_raw_snirf(nirx_nirsport2_103, optode_frame="unknown").\ + info['chs'][3]['coord_frame'] + assert raw == FIFF.FIFFV_COORD_UNKNOWN + + +@requires_testing_data +def test_snirf_nirsport2_w_positions(): + """Test reading SNIRF files with known positions.""" + raw = read_raw_snirf(nirx_nirsport2_103_2, preload=True, + optode_frame="mri") + _reorder_nirx(raw) + + # Test data import + assert raw._data.shape == (40, 128) + assert_almost_equal(raw.info['sfreq'], 10.2, decimal=1) + + # Test channel naming assert raw.info['ch_names'][:4] == ['S1_D1 760', 'S1_D1 850', - 'S1_D3 760', 'S1_D3 850'] + 'S1_D6 760', 'S1_D6 850'] assert raw.info['ch_names'][24:26] == ['S6_D4 760', 'S6_D4 850'] # Test frequency encoding @@ -179,13 +271,120 @@ assert sum(short_channels(raw.info)) == 16 + # Test distance between optodes matches values from + # nirsite https://github.com/mne-tools/mne-testing-data/pull/86 + # figure 3 + allowed_distance_error = 0.005 + assert_allclose(source_detector_distances(raw.copy(). + pick("S1_D1 760").info), + [0.0304], atol=allowed_distance_error) + assert_allclose(source_detector_distances(raw.copy(). + pick("S2_D2 760").info), + [0.0400], atol=allowed_distance_error) + + # Test location of detectors + # The locations of detectors can be seen in the first + # figure on this page... + # https://github.com/mne-tools/mne-testing-data/pull/86 + allowed_dist_error = 0.0002 + locs = [ch['loc'][6:9] for ch in raw.info['chs']] + head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri') + mni_locs = apply_trans(head_mri_t, locs) + + assert raw.info['ch_names'][0][3:5] == 'D1' + assert_allclose( + mni_locs[0], [-0.0841, -0.0464, -0.0129], atol=allowed_dist_error) + + assert raw.info['ch_names'][2][3:5] == 'D6' + assert_allclose( + mni_locs[2], [-0.0841, -0.0138, 0.0248], atol=allowed_dist_error) + + assert raw.info['ch_names'][34][3:5] == 'D5' + assert_allclose( + mni_locs[34], [0.0845, -0.0451, -0.0123], atol=allowed_dist_error) + + # Test location of sensors + # The locations of sensors can be seen in the second + # figure on this page... + # https://github.com/mne-tools/mne-testing-data/pull/86 + allowed_dist_error = 0.0002 + locs = [ch['loc'][3:6] for ch in raw.info['chs']] + head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri') + mni_locs = apply_trans(head_mri_t, locs) + + assert raw.info['ch_names'][0][:2] == 'S1' + assert_allclose( + mni_locs[0], [-0.0848, -0.0162, -0.0163], atol=allowed_dist_error) + + assert raw.info['ch_names'][9][:2] == 'S2' + assert_allclose( + mni_locs[9], [-0.0, -0.1195, 0.0142], atol=allowed_dist_error) + + assert raw.info['ch_names'][34][:2] == 'S8' + assert_allclose( + mni_locs[34], [0.0828, -0.046, 0.0285], atol=allowed_dist_error) + + mon = raw.get_montage() + assert len(mon.dig) == 27 + @requires_testing_data -@requires_h5py -def test_snirf_standard(): +def test_snirf_fieldtrip_od(): + """Test reading FieldTrip SNIRF files with optical density data.""" + raw = read_raw_snirf(ft_od, preload=True) + + # Test data import + assert raw._data.shape == (72, 500) + assert raw.copy().pick('fnirs')._data.shape == (72, 500) + assert raw.copy().pick('fnirs_od')._data.shape == (72, 500) + with pytest.raises(ValueError, match='not be interpreted as channel'): + raw.copy().pick('hbo') + with pytest.raises(ValueError, match='not be interpreted as channel'): + raw.copy().pick('hbr') + + assert_allclose(raw.info['sfreq'], 50) + + +@requires_testing_data +def test_snirf_kernel_hb(): + """Test reading Kernel SNIRF files with haemoglobin data.""" + raw = read_raw_snirf(kernel_hb, preload=True) + + # Test data import + assert raw._data.shape == (180 * 2, 14) + assert raw.copy().pick('hbo')._data.shape == (180, 14) + assert raw.copy().pick('hbr')._data.shape == (180, 14) + + assert_allclose(raw.info['sfreq'], 8.257638) + + bad_nans = np.isnan(raw.get_data()).any(axis=1) + assert np.sum(bad_nans) == 20 + + assert len(raw.annotations.description) == 2 + assert raw.annotations.onset[0] == 0.036939 + assert raw.annotations.onset[1] == 0.874633 + assert raw.annotations.description[0] == "StartTrial" + assert raw.annotations.description[1] == "StartIti" + + +@requires_testing_data +@pytest.mark.parametrize('fname, boundary_decimal, test_scaling, test_rank', ( + [sfnirs_homer_103_wShort, 0, True, True], + [nirx_nirsport2_103, 0, True, False], # strange rank behavior + [nirx_nirsport2_103_2, 0, False, True], # weirdly small values + [snirf_nirsport2_20219, 0, True, True], +)) +def test_snirf_standard(fname, boundary_decimal, test_scaling, test_rank): """Test standard operations.""" - _test_raw_reader(read_raw_snirf, fname=sfnirs_homer_103_wShort, - boundary_decimal=0) # low fs + _test_raw_reader(read_raw_snirf, fname=fname, + boundary_decimal=boundary_decimal, + test_scaling=test_scaling, + test_rank=test_rank) # low fs -run_tests_if_main() +@requires_testing_data +def test_annotation_description_from_stim_groups(): + """Test annotation descriptions parsed from stim group names.""" + raw = read_raw_snirf(nirx_nirsport2_103_2, preload=True) + expected_descriptions = ['1', '2', '6'] + assert_equal(expected_descriptions, raw.annotations.description) diff -Nru python-mne-0.23.4+dfsg/mne/io/tag.py python-mne-1.1.0+dfsg/mne/io/tag.py --- python-mne-0.23.4+dfsg/mne/io/tag.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/tag.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors: Alexandre Gramfort # Matti Hämäläinen # -# License: BSD (3-clause) +# License: BSD-3-Clause from functools import partial import struct @@ -333,6 +333,10 @@ FIFF.FIFFV_MEG_CH: FIFF.FIFFV_COORD_DEVICE, FIFF.FIFFV_REF_MEG_CH: FIFF.FIFFV_COORD_DEVICE, FIFF.FIFFV_EEG_CH: FIFF.FIFFV_COORD_HEAD, + FIFF.FIFFV_ECOG_CH: FIFF.FIFFV_COORD_HEAD, + FIFF.FIFFV_SEEG_CH: FIFF.FIFFV_COORD_HEAD, + FIFF.FIFFV_DBS_CH: FIFF.FIFFV_COORD_HEAD, + FIFF.FIFFV_FNIRS_CH: FIFF.FIFFV_COORD_HEAD } diff -Nru python-mne-0.23.4+dfsg/mne/io/tests/test_apply_function.py python-mne-1.1.0+dfsg/mne/io/tests/test_apply_function.py --- python-mne-0.23.4+dfsg/mne/io/tests/test_apply_function.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/tests/test_apply_function.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np import pytest diff -Nru python-mne-0.23.4+dfsg/mne/io/tests/test_compensator.py python-mne-1.1.0+dfsg/mne/io/tests/test_compensator.py --- python-mne-0.23.4+dfsg/mne/io/tests/test_compensator.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/tests/test_compensator.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Author: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op import numpy as np @@ -10,7 +10,7 @@ from mne import Epochs, read_evokeds, pick_types from mne.io.compensator import make_compensator, get_current_comp from mne.io import read_raw_fif -from mne.utils import requires_mne, run_subprocess, run_tests_if_main +from mne.utils import requires_mne, run_subprocess base_dir = op.join(op.dirname(__file__), 'data') ctf_comp_fname = op.join(base_dir, 'test_ctf_comp_raw.fif') @@ -41,7 +41,7 @@ @pytest.mark.parametrize('preload', (True, False)) @pytest.mark.parametrize('pick', (False, True)) -def test_compensation_apply(tmpdir, preload, pick): +def test_compensation_apply(tmp_path, preload, pick): """Test applying compensation.""" # make sure that changing the comp doesn't modify the original data raw = read_raw_fif(ctf_comp_fname, preload=preload) @@ -56,7 +56,7 @@ assert raw2._comp is None else: assert raw2._comp.shape == (len(raw2.ch_names),) * 2 - fname = op.join(tmpdir, 'ctf-raw.fif') + fname = op.join(tmp_path, 'ctf-raw.fif') raw2.save(fname) raw2 = read_raw_fif(fname) assert raw2.compensation_grade == 2 @@ -71,7 +71,7 @@ @requires_mne -def test_compensation_mne(tmpdir): +def test_compensation_mne(tmp_path): """Test comensation by comparing with MNE.""" def make_evoked(fname, comp): """Make evoked data.""" @@ -93,7 +93,7 @@ return read_evokeds(tmp_fname)[0] # save evoked response with default compensation - fname_default = op.join(tmpdir, 'ctf_default-ave.fif') + fname_default = op.join(tmp_path, 'ctf_default-ave.fif') make_evoked(ctf_comp_fname, None).save(fname_default) for comp in [0, 1, 2, 3]: @@ -107,6 +107,3 @@ chs_c = [evoked_c.info['chs'][ii] for ii in picks_c] for ch_py, ch_c in zip(chs_py, chs_c): assert ch_py['coil_type'] == ch_c['coil_type'] - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/io/tests/test_constants.py python-mne-1.1.0+dfsg/mne/io/tests/test_constants.py --- python-mne-0.23.4+dfsg/mne/io/tests/test_constants.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/tests/test_constants.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op import re @@ -8,6 +8,7 @@ import zipfile import numpy as np +import pooch import pytest from mne.io.constants import (FIFF, FWD, _coord_frame_named, _ch_kind_named, @@ -15,12 +16,12 @@ _ch_coil_type_named, _dig_kind_named, _dig_cardinal_named) from mne.forward._make_forward import _read_coil_defs -from mne.utils import _fetch_file, requires_good_network +from mne.utils import requires_good_network # https://github.com/mne-tools/fiff-constants/commits/master REPO = 'mne-tools' -COMMIT = '5bd84d224de502bee66f70b7867b8f45b45264c1' +COMMIT = 'aa49e20cff5791fbaf01d77ad4ec2e0ecb69840d' # These are oddities that we won't address: iod_dups = (355, 359) # these are in both MEGIN and MNE files @@ -78,19 +79,26 @@ @requires_good_network -def test_constants(tmpdir): +def test_constants(tmp_path): """Test compensation.""" - tmpdir = str(tmpdir) # old pytest... - dest = op.join(tmpdir, 'fiff.zip') - _fetch_file('https://codeload.github.com/' - f'{REPO}/fiff-constants/zip/{COMMIT}', dest) + tmp_path = str(tmp_path) # old pytest... + fname = 'fiff.zip' + dest = op.join(tmp_path, fname) + pooch.retrieve( + url='https://codeload.github.com/' + f'{REPO}/fiff-constants/zip/{COMMIT}', + path=tmp_path, + fname=fname, + known_hash=None + ) names = list() with zipfile.ZipFile(dest, 'r') as ff: for name in ff.namelist(): if 'Dictionary' in name: - ff.extract(name, tmpdir) + ff.extract(name, tmp_path) names.append(op.basename(name)) - shutil.move(op.join(tmpdir, name), op.join(tmpdir, names[-1])) + shutil.move(op.join(tmp_path, name), + op.join(tmp_path, names[-1])) names = sorted(names) assert names == ['DictionaryIOD.txt', 'DictionaryIOD_MNE.txt', 'DictionaryStructures.txt', @@ -101,7 +109,7 @@ con = dict(iod=dict(), tags=dict(), types=dict(), defines=dict()) fiff_version = None for name in ['DictionaryIOD.txt', 'DictionaryIOD_MNE.txt']: - with open(op.join(tmpdir, name), 'rb') as fid: + with open(op.join(tmp_path, name), 'rb') as fid: for line in fid: line = line.decode('latin1').strip() if line.startswith('# Packing revision'): @@ -125,7 +133,7 @@ assert id_ not in fif['iod'] fif['iod'][id_] = [kind, desc] # Tags (MEGIN) - with open(op.join(tmpdir, 'DictionaryTags.txt'), 'rb') as fid: + with open(op.join(tmp_path, 'DictionaryTags.txt'), 'rb') as fid: for line in fid: line = line.decode('ISO-8859-1').strip() if (line.startswith('#') or line.startswith('alias') or @@ -142,7 +150,7 @@ assert id_ not in fif['tags'], (fif['tags'].get(id_), val) fif['tags'][id_] = val # Tags (MNE) - with open(op.join(tmpdir, 'DictionaryTags_MNE.txt'), 'rb') as fid: + with open(op.join(tmp_path, 'DictionaryTags_MNE.txt'), 'rb') as fid: for li, line in enumerate(fid): line = line.decode('ISO-8859-1').strip() # ignore continuation lines (*) @@ -177,7 +185,7 @@ re_defi = re.compile(r'#define\s*(\S*)\s*(\S*)\s*"(.*)"$') used_enums = list() for extra in ('', '_MNE'): - with open(op.join(tmpdir, 'DictionaryTypes%s.txt' + with open(op.join(tmp_path, 'DictionaryTypes%s.txt' % (extra,)), 'rb') as fid: for li, line in enumerate(fid): line = line.decode('ISO-8859-1').strip() @@ -337,4 +345,4 @@ for e in extras: got.add(e) want = set(dict_) - assert got == want + assert got == want, match diff -Nru python-mne-0.23.4+dfsg/mne/io/tests/test_meas_info.py python-mne-1.1.0+dfsg/mne/io/tests/test_meas_info.py --- python-mne-0.23.4+dfsg/mne/io/tests/test_meas_info.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/tests/test_meas_info.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,28 +2,31 @@ # # Authors: MNE Developers # Stefan Appelhoff # -# License: BSD (3-clause) +# License: BSD-3-Clause +from datetime import datetime, timedelta, timezone, date import hashlib import os.path as op -from datetime import datetime, timedelta, timezone, date +import pickle import pytest import numpy as np from numpy.testing import assert_array_equal, assert_allclose from scipy import sparse +import string from mne import (Epochs, read_events, pick_info, pick_types, Annotations, read_evokeds, make_forward_solution, make_sphere_model, setup_volume_source_space, write_forward_solution, read_forward_solution, write_cov, read_cov, read_epochs, compute_covariance) -from mne.channels import read_polhemus_fastscan +from mne.channels import (read_polhemus_fastscan, make_standard_montage, + equalize_channels) from mne.event import make_fixed_length_events from mne.datasets import testing from mne.io import (read_fiducials, write_fiducials, _coil_trans_to_loc, _loc_to_coil_trans, read_raw_fif, read_info, write_info, - meas_info, Projection, BaseRaw) + meas_info, Projection, BaseRaw, read_raw_ctf, RawArray) from mne.io.constants import FIFF from mne.io.write import _generate_meas_id, DATE_NONE from mne.io.meas_info import (Info, create_info, _merge_info, @@ -33,11 +36,9 @@ _add_timedelta_to_stamp, _read_extended_ch_info) from mne.minimum_norm import (make_inverse_operator, write_inverse_operator, read_inverse_operator, apply_inverse) -from mne.io._digitization import _write_dig_points, _make_dig_points -from mne.io import read_raw_ctf +from mne.io._digitization import _write_dig_points, _make_dig_points, DigPoint from mne.transforms import Transform -from mne.utils import catch_logging, assert_object_equal -from mne.channels import make_standard_montage, equalize_channels +from mne.utils import catch_logging, assert_object_equal, _record_warnings fiducials_fname = op.join(op.dirname(__file__), '..', '..', 'data', 'fsaverage', 'fsaverage-fiducials.fif') @@ -139,18 +140,25 @@ info = create_info(['A', 'A', 'A'], 1000., verbose='error') assert info['ch_names'] == ['A-0', 'A-1', 'A-2'] - # When running number is not possible - with pytest.raises(ValueError, match='Adding a running number'): - create_info(['A', 'A', 'A-0'], 1000., verbose='error') + # When running number is not possible but alpha numeric is + info = create_info(['A', 'A', 'A-0'], 1000., verbose='error') + assert info['ch_names'] == ['A-a', 'A-1', 'A-0'] + + # When a single addition is not sufficient + with pytest.raises(ValueError, match='Adding a single alphanumeric'): + ch_n = ['A', 'A'] + # add all options for first duplicate channel (0) + ch_n.extend([f'{ch_n[0]}-{c}' for c in string.ascii_lowercase + '0']) + create_info(ch_n, 1000., verbose='error') -def test_fiducials_io(tmpdir): +def test_fiducials_io(tmp_path): """Test fiducials i/o.""" pts, coord_frame = read_fiducials(fiducials_fname) assert pts[0]['coord_frame'] == FIFF.FIFFV_COORD_MRI assert pts[0]['ident'] == FIFF.FIFFV_POINT_CARDINAL - temp_fname = tmpdir.join('test.fif') + temp_fname = tmp_path / 'test.fif' write_fiducials(temp_fname, pts, coord_frame) pts_1, coord_frame_1 = read_fiducials(temp_fname) assert coord_frame == coord_frame_1 @@ -159,10 +167,13 @@ assert pt['ident'] == pt_1['ident'] assert pt['coord_frame'] == pt_1['coord_frame'] assert_array_equal(pt['r'], pt_1['r']) + assert isinstance(pt, DigPoint) + assert isinstance(pt_1, DigPoint) # test safeguards pts[0]['coord_frame'] += 1 - pytest.raises(ValueError, write_fiducials, temp_fname, pts, coord_frame) + with pytest.raises(ValueError, match='coord_frame entries that are incom'): + write_fiducials(temp_fname, pts, coord_frame, overwrite=True) def test_info(): @@ -179,8 +190,6 @@ info = Info(a=7, b='aaaaa') assert ('a' in info) assert ('b' in info) - info[42] = 'foo' - assert (info[42] == 'foo') # Test info attribute in API objects for obj in [raw, epochs, evoked]: @@ -205,8 +214,8 @@ assert list(info['ch_names']) == ch_names # Deleting of regular fields should work - info['foo'] = 'bar' - del info['foo'] + info['experimenter'] = 'bar' + del info['experimenter'] # Test updating of fields del info['chs'][-1] @@ -224,10 +233,10 @@ assert info == info2 -def test_read_write_info(tmpdir): +def test_read_write_info(tmp_path): """Test IO of info.""" info = read_info(raw_fname) - temp_file = str(tmpdir.join('info.fif')) + temp_file = tmp_path / 'info.fif' # check for bug `#1198` info['dev_head_t']['trans'] = np.eye(4) t1 = info['dev_head_t']['trans'] @@ -245,8 +254,9 @@ info['subject_info']['weight'] = 11.1 info['subject_info']['height'] = 2.3 - if info['gantry_angle'] is None: # future testing data may include it - info['gantry_angle'] = 0. # Elekta supine position + with info._unlock(): + if info['gantry_angle'] is None: # future testing data may include it + info['gantry_angle'] = 0. # Elekta supine position gantry_angle = info['gantry_angle'] meas_id = info['meas_id'] @@ -267,7 +277,7 @@ with open(temp_file, 'rb') as fid: m1.update(fid.read()) m1 = m1.hexdigest() - temp_file_2 = tmpdir.join('info2.fif') + temp_file_2 = tmp_path / 'info2.fif' assert temp_file_2 != temp_file write_info(temp_file_2, info) m2 = hashlib.md5() @@ -277,29 +287,30 @@ assert m1 == m2 info = read_info(raw_fname) - info['meas_date'] = None + with info._unlock(): + info['meas_date'] = None anonymize_info(info, verbose='error') assert info['meas_date'] is None - tmp_fname_3 = tmpdir.join('info3.fif') + tmp_fname_3 = tmp_path / 'info3.fif' write_info(tmp_fname_3, info) assert info['meas_date'] is None info2 = read_info(tmp_fname_3) assert info2['meas_date'] is None # Check that having a very old date in fine until you try to save it to fif - info['meas_date'] = datetime(1800, 1, 1, 0, 0, 0, tzinfo=timezone.utc) - info._check_consistency() - fname = tmpdir.join('test.fif') + with info._unlock(check_after=True): + info['meas_date'] = datetime(1800, 1, 1, 0, 0, 0, tzinfo=timezone.utc) + fname = tmp_path / 'test.fif' with pytest.raises(RuntimeError, match='must be between '): write_info(fname, info) -def test_io_dig_points(tmpdir): +def test_io_dig_points(tmp_path): """Test Writing for dig files.""" points = read_polhemus_fastscan(hsp_fname, on_header_missing='ignore') - dest = str(tmpdir.join('test.txt')) - dest_bad = str(tmpdir.join('test.mne')) + dest = tmp_path / 'test.txt' + dest_bad = tmp_path / 'test.mne' with pytest.raises(ValueError, match='must be of shape'): _write_dig_points(dest, points[:, :2]) with pytest.raises(ValueError, match='extension'): @@ -317,6 +328,18 @@ read_polhemus_fastscan(dest, on_header_missing='warn') +def test_io_coord_frame(tmp_path): + """Test round trip for coordinate frame.""" + fname = tmp_path / 'test.fif' + for ch_type in ('eeg', 'seeg', 'ecog', 'dbs', 'hbo', 'hbr'): + info = create_info( + ch_names=['Test Ch'], sfreq=1000., ch_types=[ch_type]) + info['chs'][0]['loc'][:3] = [0.05, 0.01, -0.03] + write_info(fname, info) + info2 = read_info(fname) + assert info2['chs'][0]['coord_frame'] == FIFF.FIFFV_COORD_HEAD + + def test_make_dig_points(): """Test application of Polhemus HSP to info.""" extra_points = read_polhemus_fastscan( @@ -324,7 +347,8 @@ info = create_info(ch_names=['Test Ch'], sfreq=1000.) assert info['dig'] is None - info['dig'] = _make_dig_points(extra_points=extra_points) + with info._unlock(): + info['dig'] = _make_dig_points(extra_points=extra_points) assert (info['dig']) assert_allclose(info['dig'][0]['r'], [-.10693, .09980, .06881]) @@ -333,7 +357,8 @@ info = create_info(ch_names=['Test Ch'], sfreq=1000.) assert info['dig'] is None - info['dig'] = _make_dig_points(nasion, lpa, rpa, elp_points[3:], None) + with info._unlock(): + info['dig'] = _make_dig_points(nasion, lpa, rpa, elp_points[3:], None) assert (info['dig']) idx = [d['ident'] for d in info['dig']].index(FIFF.FIFFV_POINT_NASION) assert_allclose(info['dig'][idx]['r'], [.0013930, .0131613, -.0046967]) @@ -388,6 +413,7 @@ pytest.raises(ValueError, _force_update_info, info_a, dict([('sfreq', 1000.)])) # KIT System-ID + info_a._unlocked = info_b._unlocked = True info_a['kit_system_id'] = 50 assert _merge_info((info_a, info_b))['kit_system_id'] == 50 info_b['kit_system_id'] = 50 @@ -402,6 +428,7 @@ assert not info_merged['hpi_results'] info_a['hpi_meas'] = [{'f1': 3, 'f2': 4}] assert _merge_info([info_a, info_d])['hpi_meas'] == info_a['hpi_meas'] + info_d._unlocked = True info_d['hpi_meas'] = [{'f1': 3, 'f2': 4}] assert _merge_info([info_a, info_d])['hpi_meas'] == info_d['hpi_meas'] # This will break because of inconsistency @@ -412,7 +439,7 @@ info_0['bads'] = ['MEG 2443', 'EEG 053'] assert len(info_0['chs']) == 376 assert len(info_0['dig']) == 146 - info_1 = create_info(["STI XXX"], info_0['sfreq'], ['stim']) + info_1 = create_info(["STI YYY"], info_0['sfreq'], ['stim']) assert info_1['bads'] == [] info_out = _merge_info([info_0, info_1], force_update_to_first=True) assert len(info_out['chs']) == 377 @@ -441,35 +468,34 @@ # Bad data types info2 = info.copy() - info2['sfreq'] = 'foo' + with info2._unlock(): + info2['sfreq'] = 'foo' pytest.raises(ValueError, info2._check_consistency) info2 = info.copy() - info2['highpass'] = 'foo' + with info2._unlock(): + info2['highpass'] = 'foo' pytest.raises(ValueError, info2._check_consistency) info2 = info.copy() - info2['lowpass'] = 'foo' + with info2._unlock(): + info2['lowpass'] = 'foo' pytest.raises(ValueError, info2._check_consistency) - info2 = info.copy() - info2['filename'] = 'foo' - with pytest.warns(RuntimeWarning, match='filename'): - info2._check_consistency() - # Silent type conversion to float info2 = info.copy() - info2['sfreq'] = 1 - info2['highpass'] = 2 - info2['lowpass'] = 2 - info2._check_consistency() + with info2._unlock(check_after=True): + info2['sfreq'] = 1 + info2['highpass'] = 2 + info2['lowpass'] = 2 assert (isinstance(info2['sfreq'], float)) assert (isinstance(info2['highpass'], float)) assert (isinstance(info2['lowpass'], float)) # Duplicate channel names info2 = info.copy() - info2['chs'][2]['ch_name'] = 'b' + with info2._unlock(): + info2['chs'][2]['ch_name'] = 'b' pytest.raises(RuntimeError, info2._check_consistency) # Duplicates appended with running numbers @@ -522,16 +548,20 @@ # Fake some subject data meas_date = datetime(2010, 1, 1, 0, 0, 0, tzinfo=timezone.utc) - base_info['meas_date'] = meas_date - - base_info['subject_info'] = dict(id=1, his_id='foobar', last_name='bar', - first_name='bar', birthday=(1987, 4, 8), - sex=0, hand=1) + with base_info._unlock(): + base_info['meas_date'] = meas_date + base_info['subject_info'] = dict(id=1, + his_id='foobar', + last_name='bar', + first_name='bar', + birthday=(1987, 4, 8), + sex=0, hand=1) # generate expected info... # first expected result with no options. # will move DOS from 2010/1/1 to 2000/1/1 which is 3653 days. exp_info = base_info.copy() + exp_info._unlocked = True exp_info['description'] = default_desc exp_info['experimenter'] = default_str exp_info['proj_name'] = default_str @@ -548,6 +578,7 @@ # 2010 and 2000. exp_info['subject_info']['birthday'] = (1977, 4, 7) exp_info['meas_date'] = default_anon_dos + exp_info._unlocked = False # make copies exp_info_3 = exp_info.copy() @@ -566,14 +597,16 @@ # exp 2 tests the keep_his option exp_info_2 = exp_info.copy() - exp_info_2['subject_info']['his_id'] = 'foobar' - exp_info_2['subject_info']['sex'] = 0 - exp_info_2['subject_info']['hand'] = 1 + with exp_info_2._unlock(): + exp_info_2['subject_info']['his_id'] = 'foobar' + exp_info_2['subject_info']['sex'] = 0 + exp_info_2['subject_info']['hand'] = 1 # exp 3 tests is a supplied daysback delta_t_2 = timedelta(days=43) - exp_info_3['subject_info']['birthday'] = (1987, 2, 24) - exp_info_3['meas_date'] = meas_date - delta_t_2 + with exp_info_3._unlock(): + exp_info_3['subject_info']['birthday'] = (1987, 2, 24) + exp_info_3['meas_date'] = meas_date - delta_t_2 for key in ('file_id', 'meas_id'): value = exp_info_3.get(key) if value is not None: @@ -601,13 +634,16 @@ # assert_object_equal(new_info, exp_info_4) # test with meas_date = None - base_info['meas_date'] = None + with base_info._unlock(): + base_info['meas_date'] = None + exp_info_3._unlocked = True exp_info_3['meas_date'] = None exp_info_3['file_id']['secs'] = DATE_NONE[0] exp_info_3['file_id']['usecs'] = DATE_NONE[1] exp_info_3['meas_id']['secs'] = DATE_NONE[0] exp_info_3['meas_id']['usecs'] = DATE_NONE[1] exp_info_3['subject_info'].pop('birthday', None) + exp_info_3._unlocked = False if base_info['meas_date'] is None: with pytest.warns(RuntimeWarning, match='all information'): @@ -617,7 +653,7 @@ new_info = anonymize_info(base_info.copy(), daysback=delta_t_2.days) assert_object_equal(new_info, exp_info_3) - with pytest.warns(None): # meas_date is None + with _record_warnings(): # meas_date is None new_info = anonymize_info(base_info.copy()) assert_object_equal(new_info, exp_info_3) @@ -637,11 +673,12 @@ assert meas_datetime == datetime(*dt, tzinfo=timezone.utc) # smoke test for info __repr__ info = create_info(1, 1000., 'eeg') - info['meas_date'] = meas_datetime + with info._unlock(): + info['meas_date'] = meas_datetime assert str(dt[0]) in repr(info) -def test_anonymize(tmpdir): +def test_anonymize(tmp_path): """Test that sensitive information can be anonymized.""" pytest.raises(TypeError, anonymize_info, 'foo') @@ -681,7 +718,7 @@ # write to disk & read back inst_type = 'raw' if isinstance(inst, BaseRaw) else 'epo' fname = 'tmp_raw.fif' if inst_type == 'raw' else 'tmp_epo.fif' - out_path = tmpdir.join(fname) + out_path = tmp_path / fname inst.save(out_path, overwrite=True) if inst_type == 'raw': read_raw_fif(out_path) @@ -696,19 +733,21 @@ stamp = _dt_to_stamp(raw.info['meas_date']) assert raw.annotations.orig_time == _stamp_to_dt(stamp) - raw.info['meas_date'] = None + with raw.info._unlock(): + raw.info['meas_date'] = None + raw.anonymize(daysback=None) with pytest.warns(RuntimeWarning, match='None'): - raw.anonymize() + raw.anonymize(daysback=123) assert raw.annotations.orig_time is None assert raw.first_samp == first_samp assert_allclose(raw.annotations.onset, expected_onset) -def test_anonymize_with_io(tmpdir): +def test_anonymize_with_io(tmp_path): """Test that IO does not break anonymization.""" raw = read_raw_fif(raw_fname) - temp_path = tmpdir.join('tmp_raw.fif') + temp_path = tmp_path / 'tmp_raw.fif' raw.save(temp_path) raw2 = read_raw_fif(temp_path) @@ -718,7 +757,7 @@ @testing.requires_testing_data -def test_csr_csc(tmpdir): +def test_csr_csc(tmp_path): """Test CSR and CSC.""" info = read_info(sss_ctc_fname) info = pick_info(info, pick_types(info, meg=True, exclude=[])) @@ -726,7 +765,7 @@ ct = sss_ctc['decoupler'].copy() # CSC assert isinstance(ct, sparse.csc_matrix) - fname = tmpdir.join('test.fif') + fname = tmp_path / 'test.fif' write_info(fname, info) info_read = read_info(fname) ct_read = info_read['proc_history'][0]['max_info']['sss_ctc']['decoupler'] @@ -737,7 +776,7 @@ assert isinstance(csr, sparse.csr_matrix) assert_array_equal(csr.toarray(), ct.toarray()) info['proc_history'][0]['max_info']['sss_ctc']['decoupler'] = csr - fname = tmpdir.join('test1.fif') + fname = tmp_path / 'test1.fif' write_info(fname, info) info_read = read_info(fname) ct_read = info_read['proc_history'][0]['max_info']['sss_ctc']['decoupler'] @@ -770,22 +809,23 @@ with catch_logging() as log: Epochs(raw, events, None, -0.2, 0.2, preload=False, picks=picks, verbose=True) - assert'Removing 5 compensators' in log.getvalue() + assert 'Removing 5 compensators' in log.getvalue() -def test_field_round_trip(tmpdir): +def test_field_round_trip(tmp_path): """Test round-trip for new fields.""" info = create_info(1, 1000., 'eeg') - for key in ('file_id', 'meas_id'): - info[key] = _generate_meas_id() - info['device_info'] = dict( - type='a', model='b', serial='c', site='d') - info['helium_info'] = dict( - he_level_raw=1., helium_level=2., orig_file_guid='e', meas_date=(1, 2)) - fname = tmpdir.join('temp-info.fif') + with info._unlock(): + for key in ('file_id', 'meas_id'): + info[key] = _generate_meas_id() + info['device_info'] = dict( + type='a', model='b', serial='c', site='d') + info['helium_info'] = dict( + he_level_raw=1., helium_level=2., + orig_file_guid='e', meas_date=(1, 2)) + fname = tmp_path / 'temp-info.fif' write_info(fname, info) info_read = read_info(fname) - info_read['dig'] = None # XXX eventually this should go away assert_object_equal(info, info_read) @@ -804,9 +844,28 @@ info = create_info(1, 1000, 'eeg') assert '7 non-empty values' in repr(info) - t = Transform(1, 2, np.ones((4, 4))) + t = Transform('meg', 'head', np.ones((4, 4))) info['dev_head_t'] = t - assert 'dev_head_t: MEG device -> isotrak transform' in repr(info) + assert 'dev_head_t: MEG device -> head transform' in repr(info) + + +def test_repr_html(): + """Test Info HTML repr.""" + info = read_info(raw_fname) + assert 'Projections' in info._repr_html_() + with info._unlock(): + info['projs'] = [] + assert 'Projections' not in info._repr_html_() + info['bads'] = [] + assert 'None' in info._repr_html_() + info['bads'] = ['MEG 2443', 'EEG 053'] + assert 'MEG 2443' in info._repr_html_() + assert 'EEG 053' in info._repr_html_() + + html = info._repr_html_() + for ch in ['204 Gradiometers', '102 Magnetometers', '9 Stimulus', + '60 EEG', '1 EOG']: + assert ch in html @testing.requires_testing_data @@ -821,7 +880,7 @@ pytest.param(ctf_fname, marks=testing._pytest_mark()), raw_fname, ]) -def test_channel_name_limit(tmpdir, monkeypatch, fname): +def test_channel_name_limit(tmp_path, monkeypatch, fname): """Test that our remapping works properly.""" # # raw @@ -845,7 +904,7 @@ raw.info.normalize_proj() raw.pick_channels(data_names + ref_names).crop(0, 2) long_names = ['123456789abcdefg' + name for name in raw.ch_names] - fname = tmpdir.join('test-raw.fif') + fname = tmp_path / 'test-raw.fif' with catch_logging() as log: raw.save(fname) log = log.getvalue() @@ -903,7 +962,7 @@ # epochs # epochs = Epochs(raw, make_fixed_length_events(raw)) - fname = tmpdir.join('test-epo.fif') + fname = tmp_path / 'test-epo.fif' epochs.save(fname) epochs_read = read_epochs(fname) for ep in (epochs, epochs_read): @@ -913,7 +972,7 @@ # cov epochs.info['bads'] = [] cov = compute_covariance(epochs, verbose='error') - fname = tmpdir.join('test-cov.fif') + fname = tmp_path / 'test-cov.fif' write_cov(fname, cov) cov_read = read_cov(fname) for co in (cov, cov_read): @@ -927,7 +986,7 @@ evoked = epochs.average() evoked.info['bads'] = bads assert evoked.nave == 1 - fname = tmpdir.join('test-ave.fif') + fname = tmp_path / 'test-ave.fif' evoked.save(fname) evoked_read = read_evokeds(fname)[0] for ev in (evoked, evoked_read): @@ -938,12 +997,12 @@ # # forward # - with pytest.warns(None): # not enough points for CTF + with _record_warnings(): # not enough points for CTF sphere = make_sphere_model('auto', 'auto', evoked.info) src = setup_volume_source_space( pos=dict(rr=[[0, 0, 0.04]], nn=[[0, 1., 0.]])) fwd = make_forward_solution(evoked.info, None, src, sphere) - fname = tmpdir.join('temp-fwd.fif') + fname = tmp_path / 'temp-fwd.fif' write_forward_solution(fname, fwd) fwd_read = read_forward_solution(fname) for fw in (fwd, fwd_read): @@ -956,9 +1015,68 @@ # inv # inv = make_inverse_operator(evoked.info, fwd, cov) - fname = tmpdir.join('test-inv.fif') + fname = tmp_path / 'test-inv.fif' write_inverse_operator(fname, inv) inv_read = read_inverse_operator(fname) for iv in (inv, inv_read): assert iv['info']['ch_names'] == good_long_data_names apply_inverse(evoked, inv) # smoke test + + +@pytest.mark.parametrize('fname_info', (raw_fname, 'create_info')) +@pytest.mark.parametrize('unlocked', (True, False)) +def test_pickle(fname_info, unlocked): + """Test that Info can be (un)pickled.""" + if fname_info == 'create_info': + info = create_info(3, 1000., 'eeg') + else: + info = read_info(fname_info) + assert not info._unlocked + info._unlocked = unlocked + data = pickle.dumps(info) + info_un = pickle.loads(data) + assert isinstance(info_un, Info) + assert_object_equal(info, info_un) + assert info_un._unlocked == unlocked + + +def test_info_bad(): + """Test our info sanity checkers.""" + info = create_info(2, 1000., 'eeg') + info['description'] = 'foo' + info['experimenter'] = 'bar' + info['line_freq'] = 50. + info['bads'] = info['ch_names'][:1] + info['temp'] = ('whatever', 1.) + # After 0.24 these should be pytest.raises calls + check, klass = pytest.raises, RuntimeError + with check(klass, match=r"info\['temp'\]"): + info['bad_key'] = 1. + for (key, match) in ([ + ('sfreq', r'inst\.resample'), + ('chs', r'inst\.add_channels')]): + with check(klass, match=match): + info[key] = info[key] + with pytest.raises(ValueError, match='between meg<->head'): + info['dev_head_t'] = Transform('mri', 'head', np.eye(4)) + + +def test_get_montage(): + """Test ContainsMixin.get_montage().""" + ch_names = make_standard_montage('standard_1020').ch_names + sfreq = 512 + data = np.zeros((len(ch_names), sfreq * 2)) + raw = RawArray(data, create_info(ch_names, sfreq, 'eeg')) + raw.set_montage('standard_1020') + + assert len(raw.get_montage().ch_names) == len(ch_names) + raw.info['bads'] = [ch_names[0]] + assert len(raw.get_montage().ch_names) == len(ch_names) + + # test info + raw = RawArray(data, create_info(ch_names, sfreq, 'eeg')) + raw.set_montage('standard_1020') + + assert len(raw.info.get_montage().ch_names) == len(ch_names) + raw.info['bads'] = [ch_names[0]] + assert len(raw.info.get_montage().ch_names) == len(ch_names) diff -Nru python-mne-0.23.4+dfsg/mne/io/tests/test_pick.py python-mne-1.1.0+dfsg/mne/io/tests/test_pick.py --- python-mne-0.23.4+dfsg/mne/io/tests/test_pick.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/tests/test_pick.py 2022-08-03 15:48:14.000000000 +0000 @@ -11,6 +11,8 @@ from mne import __file__ as _root_init_fname from mne.io import (read_raw_fif, RawArray, read_raw_bti, read_raw_kit, read_info) +from mne.channels import make_standard_montage +from mne.preprocessing import compute_current_source_density from mne.io.pick import (channel_indices_by_type, channel_type, pick_types_forward, _picks_by_type, _picks_to_idx, _contains_ch_type, pick_channels_cov, @@ -18,7 +20,7 @@ _DATA_CH_TYPES_SPLIT) from mne.io.constants import FIFF from mne.datasets import testing -from mne.utils import run_tests_if_main, catch_logging, assert_object_equal +from mne.utils import catch_logging, assert_object_equal data_path = testing.data_path(download=False) fname_meeg = op.join(data_path, 'MEG', 'sample', @@ -599,6 +601,23 @@ assert list(pick_types(info2, eeg=True)) == [0, 1] +def test_pick_types_csd(): + """Test pick_types(csd=True).""" + # info with laplacian/CSD channels at indices 1, 2 + names = ['F1', 'F2', 'C1', 'C2', 'A1', 'A2', 'misc1', 'CSD1'] + info1 = create_info(names, 256, ["eeg", "eeg", "eeg", "eeg", "mag", + "mag", 'misc', 'csd']) + raw = RawArray(np.zeros((8, 512)), info1) + raw.set_montage(make_standard_montage('standard_1020'), verbose='error') + raw_csd = compute_current_source_density(raw, verbose='error') + + assert_array_equal(pick_types(info1, csd=True), [7]) + + # pick from the raw object + assert raw_csd.copy().pick_types(csd=True).ch_names == [ + 'F1', 'F2', 'C1', 'C2', 'CSD1'] + + @pytest.mark.parametrize('meg', [True, False, 'grad', 'mag']) @pytest.mark.parametrize('eeg', [True, False]) @pytest.mark.parametrize('ordered', [True, False]) @@ -616,6 +635,3 @@ types = np.array(raw.get_channel_types(picks=picks)) types_iter = np.array([channel_type(raw.info, idx) for idx in picks]) assert_array_equal(types, types_iter) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/io/tests/test_raw.py python-mne-1.1.0+dfsg/mne/io/tests/test_raw.py --- python-mne-0.23.4+dfsg/mne/io/tests/test_raw.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/tests/test_raw.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Authors: MNE Developers # Stefan Appelhoff # -# License: BSD (3-clause) +# License: BSD-3-Clause from contextlib import redirect_stdout from io import StringIO @@ -18,19 +18,22 @@ from numpy.testing import (assert_allclose, assert_array_almost_equal, assert_array_equal, assert_array_less) +import mne from mne import concatenate_raws, create_info, Annotations, pick_types from mne.datasets import testing -from mne.externals.h5io import read_hdf5, write_hdf5 from mne.io import read_raw_fif, RawArray, BaseRaw, Info, _writing_info_hdf5 from mne.io.base import _get_scaling from mne.utils import (_TempDir, catch_logging, _raw_annot, _stamp_to_dt, object_diff, check_version, requires_pandas, - _check_eeglabio_installed) + _import_h5io_funcs) from mne.io.meas_info import _get_valid_units from mne.io._digitization import DigPoint from mne.io.proj import Projection from mne.io.utils import _mult_cal_one -from mne.io import read_raw_eeglab +from mne.io.constants import FIFF + +raw_fname = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', + 'data', 'test_raw.fif') def assert_named_constants(info): @@ -129,6 +132,10 @@ meg, atol = 'grad', 1e-24 elif 'mag' in raw: meg, atol = 'mag', 1e-24 + elif 'hbo' in raw: + fnirs, atol = 'hbo', 1e-10 + elif 'hbr' in raw: + fnirs, atol = 'hbr', 1e-10 else: assert 'fnirs_cw_amplitude' in raw, 'New channel type necessary?' fnirs, atol = 'fnirs_cw_amplitude', 1e-10 @@ -136,7 +143,7 @@ other_raw.info, meg=meg, eeg=eeg, fnirs=fnirs) col_names = [other_raw.ch_names[pick] for pick in picks] proj = np.ones((1, len(picks))) - proj /= proj.shape[1] + proj /= np.sqrt(proj.shape[1]) proj = Projection( data=dict(data=proj, nrow=1, row_names=None, col_names=col_names, ncol=len(picks)), @@ -201,15 +208,18 @@ # ranks should all be reduced by 1 if test_rank == 'less': cmp = np.less - else: + elif test_rank is False: + cmp = None + else: # anything else is like True or 'equal' + assert test_rank is True or test_rank == 'equal', test_rank cmp = np.equal rank_load_apply_get = np.linalg.matrix_rank(data_load_apply_get) rank_apply_get = np.linalg.matrix_rank(data_apply_get) rank_apply_load_get = np.linalg.matrix_rank(data_apply_load_get) - rank_apply_load_get = np.linalg.matrix_rank(data_apply_load_get) - assert cmp(rank_load_apply_get, len(col_names) - 1) - assert cmp(rank_apply_get, len(col_names) - 1) - assert cmp(rank_apply_load_get, len(col_names) - 1) + if cmp is not None: + assert cmp(rank_load_apply_get, len(col_names) - 1) + assert cmp(rank_apply_get, len(col_names) - 1) + assert cmp(rank_apply_load_get, len(col_names) - 1) # and they should all match t_kw = dict( atol=atol, err_msg='before != after, likely _mult_cal_one prob') @@ -228,13 +238,21 @@ this_proj = other_raw.info['projs'][0]['data'] assert this_proj['col_names'] == col_names assert this_proj['data'].shape == proj['data']['data'].shape + assert_allclose( + np.linalg.norm(proj['data']['data']), 1., atol=1e-6) + assert_allclose( + np.linalg.norm(this_proj['data']), 1., atol=1e-6) assert_allclose(this_proj['data'], proj['data']['data']) proj = other_raw.apply_proj().get_data() assert_allclose(proj[picks], data_load_apply_get, atol=1e-10) assert_allclose(proj, direct, atol=1e-10, err_msg=t_kw['err_msg']) else: raw = reader(**kwargs) + n_samp = len(raw.times) assert_named_constants(raw.info) + # smoke test for gh #9743 + ids = [id(ch['loc']) for ch in raw.info['chs']] + assert len(set(ids)) == len(ids) full_data = raw._data assert raw.__class__.__name__ in repr(raw) # to test repr @@ -254,6 +272,9 @@ meas_date = raw.info['meas_date'] assert meas_date is None or meas_date >= _stamp_to_dt((0, 0)) + # test repr_html + assert 'Good channels' in raw.info._repr_html_() + # test resetting raw if test_kwargs: raw2 = reader(**raw._init_kwargs) @@ -275,7 +296,7 @@ assert set(raw.info.keys()) == set(raw3.info.keys()) assert_allclose(raw3[0:20][0], full_data[0:20], rtol=1e-6, atol=1e-20) # atol is very small but > 0 - assert_array_almost_equal(raw.times, raw3.times) + assert_allclose(raw.times, raw3.times, atol=1e-6, rtol=1e-6) assert not math.isnan(raw3.info['highpass']) assert not math.isnan(raw3.info['lowpass']) @@ -347,7 +368,8 @@ # Make sure that writing info to h5 format # (all fields should be compatible) - if check_version('h5py'): + if check_version('h5io'): + read_hdf5, write_hdf5 = _import_h5io_funcs() fname_h5 = op.join(tempdir, 'info.h5') with _writing_info_hdf5(raw.info): write_hdf5(fname_h5, raw.info) @@ -385,9 +407,61 @@ os.chdir(orig_dir) raw_chdir.load_data() + # make sure that cropping works (with first_samp shift) + if n_samp >= 50: # we crop to this number of samples below + for t_prop in (0., 0.5): + _test_raw_crop(reader, t_prop, kwargs) + if test_preloading: + use_kwargs = kwargs.copy() + use_kwargs['preload'] = True + _test_raw_crop(reader, t_prop, use_kwargs) + return raw +def _test_raw_crop(reader, t_prop, kwargs): + raw_1 = reader(**kwargs) + n_samp = 50 # crop to this number of samples (per instance) + crop_t = n_samp / raw_1.info['sfreq'] + t_start = t_prop * crop_t # also crop to some fraction into the first inst + extra = f' t_start={t_start}, preload={kwargs.get("preload", False)}' + stop = (n_samp - 1) / raw_1.info['sfreq'] + raw_1.crop(0, stop) + assert len(raw_1.times) == 50 + first_time = raw_1.first_time + atol = 0.5 / raw_1.info['sfreq'] + assert_allclose(raw_1.times[-1], stop, atol=atol) + raw_2, raw_3 = raw_1.copy(), raw_1.copy() + t_tot = raw_1.times[-1] * 3 + 2. / raw_1.info['sfreq'] + raw_concat = concatenate_raws([raw_1, raw_2, raw_3]) + assert len(raw_concat._filenames) == 3 + assert_allclose(raw_concat.times[-1], t_tot) + assert_allclose(raw_concat.first_time, first_time) + # keep all instances, but crop to t_start at the beginning + raw_concat.crop(t_start, None) + assert len(raw_concat._filenames) == 3 + assert_allclose(raw_concat.times[-1], t_tot - t_start, atol=atol) + assert_allclose( + raw_concat.first_time, first_time + t_start, atol=atol, + err_msg=f'Base concat, {extra}') + # drop the first instance + raw_concat.crop(crop_t, None) + assert len(raw_concat._filenames) == 2 + assert_allclose( + raw_concat.times[-1], t_tot - t_start - crop_t, atol=atol) + assert_allclose( + raw_concat.first_time, first_time + t_start + crop_t, + atol=atol, err_msg=f'Dropping one, {extra}') + # drop the second instance, leaving just one + raw_concat.crop(crop_t, None) + assert len(raw_concat._filenames) == 1 + assert_allclose( + raw_concat.times[-1], t_tot - t_start - 2 * crop_t, atol=atol) + assert_allclose( + raw_concat.first_time, first_time + t_start + 2 * crop_t, + atol=atol, err_msg=f'Dropping two, {extra}') + + def _test_concat(reader, *args): """Test concatenation of raw classes that allow not preloading.""" data = None @@ -423,19 +497,48 @@ @testing.requires_testing_data def test_time_as_index(): """Test indexing of raw times.""" - raw_fname = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', - 'data', 'test_raw.fif') raw = read_raw_fif(raw_fname) # Test original (non-rounding) indexing behavior orig_inds = raw.time_as_index(raw.times) - assert(len(set(orig_inds)) != len(orig_inds)) + assert len(set(orig_inds)) != len(orig_inds) # Test new (rounding) indexing behavior new_inds = raw.time_as_index(raw.times, use_rounding=True) assert_array_equal(new_inds, np.arange(len(raw.times))) +@pytest.mark.parametrize('meas_date', [None, "orig"]) +@pytest.mark.parametrize('first_samp', [0, 10000]) +def test_crop_by_annotations(meas_date, first_samp): + """Test crop by annotations of raw.""" + raw = read_raw_fif(raw_fname) + + if meas_date is None: + raw.set_meas_date(None) + + raw = mne.io.RawArray(raw.get_data(), raw.info, first_samp=first_samp) + + onset = np.array([0, 1.5], float) + if meas_date is not None: + onset += raw.first_time + annot = mne.Annotations( + onset=onset, + duration=[1, 0.5], + description=["a", "b"], + orig_time=raw.info['meas_date']) + + raw.set_annotations(annot) + raws = raw.crop_by_annotations() + assert len(raws) == 2 + assert len(raws[0].annotations) == 1 + assert raws[0].times[-1] == pytest.approx(annot[:1].duration[0], rel=1e-3) + assert raws[0].annotations.description[0] == annot.description[0] + assert len(raws[1].annotations) == 1 + assert raws[1].times[-1] == pytest.approx(annot[1:2].duration[0], rel=5e-3) + assert raws[1].annotations.description[0] == annot.description[1] + + @pytest.mark.parametrize('offset, origin', [ pytest.param(0, None, id='times in s. relative to first_samp (default)'), pytest.param(0, 2.0, id='times in s. relative to first_samp'), @@ -556,9 +659,11 @@ """Test repr of Raw.""" sfreq = 256 info = create_info(3, sfreq) - r = repr(RawArray(np.zeros((3, 10 * sfreq)), info)) + raw = RawArray(np.zeros((3, 10 * sfreq)), info) + r = repr(raw) assert re.search('', r) is not None, r + assert raw._repr_html_() # A class that sets channel data to np.arange, for testing _test_raw_reader @@ -600,9 +705,9 @@ assert re.match( r'', s[0]) is not None, s[0] - assert s[1] == " ch name type unit min Q1 median Q3 max" # noqa - assert s[2] == " 0 MEG 0113 GRAD fT/cm -221.80 -38.57 -9.64 19.29 414.67" # noqa - assert s[-1] == "375 EOG 061 EOG µV -231.41 271.28 277.16 285.66 334.69" # noqa + assert s[1] == " ch name type unit min Q1 median Q3 max" # noqa + assert s[2] == " 0 MEG 0113 GRAD fT/cm -221.80 -38.57 -9.64 19.29 414.67" # noqa + assert s[-1] == "375 EOG 061 EOG µV -231.41 271.28 277.16 285.66 334.69" # noqa @requires_pandas @@ -697,22 +802,52 @@ raw.get_data(units=['fT/cm', 'fT', 'uV']) -@pytest.mark.skipif(not _check_eeglabio_installed(strict=False), - reason='eeglabio not installed') -def test_export_eeglab(): - """Test saving a Raw instance to EEGLAB's set format.""" +def test_repr_dig_point(): + """Test printing of DigPoint.""" + dp = DigPoint(r=np.arange(3), coord_frame=FIFF.FIFFV_COORD_HEAD, + kind=FIFF.FIFFV_POINT_EEG, ident=0) + assert 'mm' in repr(dp) + + dp = DigPoint(r=np.arange(3), coord_frame=FIFF.FIFFV_MNE_COORD_MRI_VOXEL, + kind=FIFF.FIFFV_POINT_CARDINAL, ident=0) + assert 'mm' not in repr(dp) + assert 'voxel' in repr(dp) + + +def test_get_data_tmin_tmax(): + """Test tmin and tmax parameters of get_data method.""" fname = Path(__file__).parent / "data" / "test_raw.fif" raw = read_raw_fif(fname) - raw.load_data() - tmpdir = _TempDir() - temp_fname = op.join(str(tmpdir), 'test.set') - raw.export(temp_fname) - raw.drop_channels([ch for ch in ['epoc'] - if ch in raw.ch_names]) - raw_read = read_raw_eeglab(temp_fname, preload=True) - assert raw.ch_names == raw_read.ch_names - cart_coords = np.array([d['loc'][:3] for d in raw.info['chs']]) # just xyz - cart_coords_read = np.array([d['loc'][:3] for d in raw_read.info['chs']]) - assert_allclose(cart_coords, cart_coords_read) - assert_allclose(raw.times, raw_read.times) - assert_allclose(raw.get_data(), raw_read.get_data()) + + # tmin and tmax just use time_as_index under the hood + tmin, tmax = (1, 9) + d1 = raw.get_data() + d2 = raw.get_data(tmin=tmin, tmax=tmax) + + idxs = raw.time_as_index([tmin, tmax]) + assert_allclose(d1[:, idxs[0]:idxs[1]], d2) + + # specifying a too low tmin truncates to idx 0 + d3 = raw.get_data(tmin=-5) + assert_allclose(d3, d1) + + # specifying a too high tmax truncates to idx n_times + d4 = raw.get_data(tmax=1e6) + assert_allclose(d4, d1) + + # when start/stop are passed, tmin/tmax are ignored + d5 = raw.get_data(start=1, stop=2, tmin=tmin, tmax=tmax) + assert d5.shape[1] == 1 + + # validate inputs are properly raised + with pytest.raises(TypeError, match='start must be .* int'): + raw.get_data(start=None) + + with pytest.raises(TypeError, match='stop must be .* int'): + raw.get_data(stop=2.3) + + with pytest.raises(TypeError, match='tmin must be .* float'): + raw.get_data(tmin=[1, 2]) + + with pytest.raises(TypeError, match='tmax must be .* float'): + raw.get_data(tmax=[1, 2]) diff -Nru python-mne-0.23.4+dfsg/mne/io/tests/test_read_raw.py python-mne-1.1.0+dfsg/mne/io/tests/test_read_raw.py --- python-mne-0.23.4+dfsg/mne/io/tests/test_read_raw.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/tests/test_read_raw.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Authors: Clemens Brunner # -# License: BSD (3-clause) +# License: BSD-3-Clause from pathlib import Path diff -Nru python-mne-0.23.4+dfsg/mne/io/tests/test_reference.py python-mne-1.1.0+dfsg/mne/io/tests/test_reference.py --- python-mne-0.23.4+dfsg/mne/io/tests/test_reference.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/tests/test_reference.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,12 +2,13 @@ # Alexandre Gramfort # Teon Brooks # -# License: BSD (3-clause) +# License: BSD-3-Clause +from contextlib import nullcontext import itertools import os.path as op -import numpy as np +import numpy as np from numpy.testing import assert_array_equal, assert_allclose, assert_equal import pytest @@ -17,14 +18,13 @@ make_forward_solution, setup_volume_source_space, pick_channels_forward, read_evokeds, find_events) -from mne.epochs import BaseEpochs -from mne.fixes import nullcontext +from mne.epochs import BaseEpochs, make_fixed_length_epochs from mne.io import RawArray, read_raw_fif from mne.io.constants import FIFF from mne.io.proj import _has_eeg_average_ref_proj, Projection from mne.io.reference import _apply_reference from mne.datasets import testing -from mne.utils import run_tests_if_main, catch_logging +from mne.utils import catch_logging, _record_warnings base_dir = op.join(op.dirname(__file__), 'data') raw_fname = op.join(base_dir, 'test_raw.fif') @@ -142,7 +142,8 @@ _apply_reference(raw, ['EEG 003'], ['EEG 004']) # CSD cannot be rereferenced - raw.info['custom_ref_applied'] = FIFF.FIFFV_MNE_CUSTOM_REF_CSD + with raw.info._unlock(): + raw.info['custom_ref_applied'] = FIFF.FIFFV_MNE_CUSTOM_REF_CSD with pytest.raises(RuntimeError, match="Cannot set.* type 'CSD'"): raw.set_eeg_reference() @@ -151,7 +152,8 @@ def test_set_eeg_reference(): """Test rereference eeg data.""" raw = read_raw_fif(fif_fname, preload=True) - raw.info['projs'] = [] + with raw.info._unlock(): + raw.info['projs'] = [] # Test setting an average reference projection assert (not _has_eeg_average_ref_proj(raw.info['projs'])) @@ -172,7 +174,8 @@ # Test setting an average reference on non-preloaded data raw_nopreload = read_raw_fif(fif_fname, preload=False) - raw_nopreload.info['projs'] = [] + with raw_nopreload.info._unlock(): + raw_nopreload.info['projs'] = [] reref, ref_data = set_eeg_reference(raw_nopreload, projection=True) assert _has_eeg_average_ref_proj(reref.info['projs']) assert not reref.info['projs'][0]['active'] @@ -196,7 +199,8 @@ # When creating an average reference fails, make sure the # custom_ref_applied flag remains untouched. reref = raw.copy() - reref.info['custom_ref_applied'] = FIFF.FIFFV_MNE_CUSTOM_REF_ON + with reref.info._unlock(): + reref.info['custom_ref_applied'] = FIFF.FIFFV_MNE_CUSTOM_REF_ON reref.pick_types(meg=True, eeg=False) # Cause making average ref fail pytest.raises(ValueError, set_eeg_reference, reref, projection=True) assert reref.info['custom_ref_applied'] == FIFF.FIFFV_MNE_CUSTOM_REF_ON @@ -221,7 +225,8 @@ # Test that average reference gives identical results when calculated # via SSP projection (projection=True) or directly (projection=False) - raw.info['projs'] = [] + with raw.info._unlock(): + raw.info['projs'] = [] reref_1, _ = set_eeg_reference(raw.copy(), projection=True) reref_1.apply_proj() reref_2, _ = set_eeg_reference(raw.copy(), projection=False) @@ -238,8 +243,12 @@ set_eeg_reference(raw, ['EEG 001'], True, True) -@pytest.mark.parametrize('ch_type', ('auto', 'ecog', 'dbs')) -def test_set_eeg_reference_ch_type(ch_type): +@pytest.mark.parametrize('ch_type, msg', + [('auto', ('ECoG',)), + ('ecog', ('ECoG',)), + ('dbs', ('DBS',)), + (['ecog', 'dbs'], ('ECoG', 'DBS'))]) +def test_set_eeg_reference_ch_type(ch_type, msg): """Test setting EEG reference for ECoG or DBS.""" # gh-6454 # gh-8739 added DBS @@ -249,17 +258,13 @@ raw = RawArray(data, create_info(ch_names, 1000., ['ecog'] * 2 + ['dbs'] * 2 + ['misc'])) if ch_type == 'auto': - ref_ch = ch_names[:2] else: ref_ch = raw.copy().pick(picks=ch_type).ch_names with catch_logging() as log: reref, ref_data = set_eeg_reference(raw.copy(), ch_type=ch_type, verbose=True) - if ch_type in ['auto', 'ecog']: - assert 'Applying a custom ECoG' in log.getvalue() - else: - assert 'Applying a custom DBS' in log.getvalue() + assert f"Applying a custom {msg}" in log.getvalue() assert reref.info['custom_ref_applied'] # gh-7350 _test_reference(raw, reref, ref_data, ref_ch) with pytest.raises(ValueError, match='No channels supplied'): @@ -537,7 +542,7 @@ # create epochs in delayed mode, allowing removal of CAR when re-reffing epochs = Epochs(raw, events=events, event_id=1, tmin=-0.2, tmax=0.5, picks=picks_eeg, preload=True, proj='delayed') - with pytest.warns(RuntimeWarning, match='ignored .set to zero.'): + with pytest.warns(RuntimeWarning, match='reference channels are ignored'): epochs_ref = add_reference_channels(epochs, ['M1', 'M2'], copy=True) assert_equal(epochs_ref._data.shape[1], epochs._data.shape[1] + 2) _check_channel_names(epochs_ref, ['M1', 'M2']) @@ -577,7 +582,7 @@ epochs = Epochs(raw, events=events, event_id=1, tmin=-0.2, tmax=0.5, picks=picks_eeg, preload=True, proj='delayed') evoked = epochs.average() - with pytest.warns(RuntimeWarning, match='ignored .set to zero.'): + with pytest.warns(RuntimeWarning, match='reference channels are ignored'): evoked_ref = add_reference_channels(evoked, ['M1', 'M2'], copy=True) assert_equal(evoked_ref.data.shape[0], evoked.data.shape[0] + 2) _check_channel_names(evoked_ref, ['M1', 'M2']) @@ -599,6 +604,19 @@ with pytest.raises(TypeError, match='instance of'): add_reference_channels(raw, 1) + # gh-10878 + raw = read_raw_fif(raw_fname).crop(0, 1, include_tmax=False).load_data() + data = raw.copy().add_reference_channels(['REF']).pick_types(eeg=True) + data = data.get_data() + epochs = make_fixed_length_epochs(raw).load_data() + data_2 = epochs.copy().add_reference_channels(['REF']).pick_types(eeg=True) + data_2 = data_2.get_data()[0] + assert_allclose(data, data_2) + evoked = epochs.average() + data_3 = evoked.copy().add_reference_channels(['REF']).pick_types(eeg=True) + data_3 = data_3.get_data() + assert_allclose(data, data_3) + @pytest.mark.parametrize('n_ref', (1, 2)) def test_add_reorder(n_ref): @@ -608,7 +626,7 @@ assert len(raw.ch_names) == 60 chs = ['EEG %03d' % (60 + ii) for ii in range(1, n_ref)] + ['EEG 000'] with pytest.raises(RuntimeError, match='preload'): - with pytest.warns(None): # ignore multiple warning + with _record_warnings(): # ignore multiple warning add_reference_channels(raw, chs, copy=False) raw.load_data() if n_ref == 1: @@ -661,15 +679,11 @@ # check if reference channels have been kept correctly. assert (len(raw_test.ch_names) == len(a_channels) + len(ch_names)) for idx, ch_label in enumerate(ch_names): - manual_ch = raw_data[idx, :] - assert_array_equal( - raw_test._data[raw_test.ch_names.index(ch_label), :], manual_ch) + manual_ch = raw_data[np.newaxis, idx] + assert_array_equal(raw_test.get_data(ch_label), manual_ch) # test bipolars with a channel in both list (anode & cathode). raw_test = set_bipolar_reference( raw, ['CH2', 'CH1'], ['CH1', 'CH2'], copy=True) _check_bipolar(raw_test, 'CH2', 'CH1') _check_bipolar(raw_test, 'CH1', 'CH2') - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/io/tests/test_show_fiff.py python-mne-1.1.0+dfsg/mne/io/tests/test_show_fiff.py --- python-mne-0.23.4+dfsg/mne/io/tests/test_show_fiff.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/tests/test_show_fiff.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op diff -Nru python-mne-0.23.4+dfsg/mne/io/tests/test_utils.py python-mne-1.1.0+dfsg/mne/io/tests/test_utils.py --- python-mne-0.23.4+dfsg/mne/io/tests/test_utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/tests/test_utils.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ """Run tests for the utilities.""" # Author: Stefan Appelhoff # -# License: BSD (3-clause) +# License: BSD-3-Clause from mne.io.utils import _check_orig_units diff -Nru python-mne-0.23.4+dfsg/mne/io/tests/test_what.py python-mne-1.1.0+dfsg/mne/io/tests/test_what.py --- python-mne-0.23.4+dfsg/mne/io/tests/test_what.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/tests/test_what.py 2022-08-03 15:48:14.000000000 +0000 @@ -11,7 +11,7 @@ from mne.datasets import testing from mne.io import RawArray from mne.preprocessing import ICA -from mne.utils import run_tests_if_main, requires_sklearn +from mne.utils import requires_sklearn, _record_warnings data_path = testing.data_path(download=False) @@ -19,15 +19,15 @@ @pytest.mark.slowtest @requires_sklearn @testing.requires_testing_data -def test_what(tmpdir, verbose_debug): +def test_what(tmp_path, verbose_debug): """Test mne.what.""" # ICA ica = ICA(max_iter=1) raw = RawArray(np.random.RandomState(0).randn(3, 10), create_info(3, 1000., 'eeg')) - with pytest.warns(None): # convergence sometimes + with _record_warnings(): # convergence sometimes ica.fit(raw) - fname = op.join(str(tmpdir), 'x-ica.fif') + fname = op.join(str(tmp_path), 'x-ica.fif') ica.save(fname) assert what(fname) == 'ica' # test files @@ -50,6 +50,3 @@ assert this == want_dict[kind] fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave_xfit.dip') assert what(fname) == 'unknown' - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/io/tests/test_write.py python-mne-1.1.0+dfsg/mne/io/tests/test_write.py --- python-mne-0.23.4+dfsg/mne/io/tests/test_write.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/tests/test_write.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ """Run tests for writing.""" # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import pytest @@ -10,9 +10,9 @@ from mne.io.write import start_file, write_int -def test_write_int(tmpdir): +def test_write_int(tmp_path): """Test that write_int raises an error on bad values.""" - with start_file(tmpdir.join('temp.fif')) as fid: + with start_file(tmp_path / 'temp.fif') as fid: write_int(fid, FIFF.FIFF_MNE_EVENT_LIST, [2147483647]) # 2 ** 31 - 1 write_int(fid, FIFF.FIFF_MNE_EVENT_LIST, []) # 2 ** 31 - 1 with pytest.raises(TypeError, match=r'.*exceeds max.*EVENT_LIST\)'): diff -Nru python-mne-0.23.4+dfsg/mne/io/tree.py python-mne-1.1.0+dfsg/mne/io/tree.py --- python-mne-0.23.4+dfsg/mne/io/tree.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/tree.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors: Alexandre Gramfort # Matti Hämäläinen # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np diff -Nru python-mne-0.23.4+dfsg/mne/io/utils.py python-mne-1.1.0+dfsg/mne/io/utils.py --- python-mne-0.23.4+dfsg/mne/io/utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/utils.py 2022-08-03 15:48:14.000000000 +0000 @@ -8,7 +8,7 @@ # Mainak Jas # Stefan Appelhoff # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np import os @@ -53,6 +53,7 @@ remap_dict = dict() remap_dict['uv'] = 'µV' remap_dict['μv'] = 'µV' # greek letter mu vs micro sign. use micro + remap_dict['\x83\xeav'] = 'µV' # for shift-jis mu, use micro if unit.lower() in remap_dict: orig_units_remapped[ch_name] = remap_dict[unit.lower()] continue @@ -297,39 +298,18 @@ return stim_channel -def _construct_bids_filename(base, ext, part_idx): +def _construct_bids_filename(base, ext, part_idx, validate=True): """Construct a BIDS compatible filename for split files.""" # insert index in filename dirname = op.dirname(base) base = op.basename(base) deconstructed_base = base.split('_') - if len(deconstructed_base) < 2: + if len(deconstructed_base) < 2 and validate: raise ValueError('Filename base must end with an underscore followed ' f'by the modality (e.g., _eeg or _meg), got {base}') - modality = deconstructed_base[-1] + suffix = deconstructed_base[-1] base = '_'.join(deconstructed_base[:-1]) - use_fname = '{}_split-{:02}_{}{}'.format(base, part_idx, modality, ext) + use_fname = '{}_split-{:02}_{}{}'.format(base, part_idx, suffix, ext) if dirname: use_fname = op.join(dirname, use_fname) return use_fname - - -def _get_als_coords_from_chs(chs, drop_chs=None): - """Extract channel locations in ALS format (x, y, z) from a chs instance. - - Returns - ------- - None if no valid coordinates are found (all zeros) - """ - if drop_chs is None: - drop_chs = [] - cart_coords = np.array([d['loc'][:3] for d in chs - if d['ch_name'] not in drop_chs]) - if cart_coords.any(): # has coordinates - # (-y x z) to (x y z) - cart_coords[:, 0] = -cart_coords[:, 0] # -y to y - # swap x (1) and y (0) - cart_coords[:, [0, 1]] = cart_coords[:, [1, 0]] - else: - cart_coords = None - return cart_coords diff -Nru python-mne-0.23.4+dfsg/mne/io/what.py python-mne-1.1.0+dfsg/mne/io/what.py --- python-mne-0.23.4+dfsg/mne/io/what.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/what.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # Authors: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause from collections import OrderedDict diff -Nru python-mne-0.23.4+dfsg/mne/io/write.py python-mne-1.1.0+dfsg/mne/io/write.py --- python-mne-0.23.4+dfsg/mne/io/write.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/io/write.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,8 +1,9 @@ # Authors: Alexandre Gramfort # Matti Hämäläinen # -# License: BSD (3-clause) +# License: BSD-3-Clause +from contextlib import contextmanager from gzip import GzipFile import os.path as op import re @@ -333,6 +334,14 @@ return fid +@contextmanager +def start_and_end_file(fname, id_=None): + """Start and (if successfully written) close the file.""" + with start_file(fname, id_=id_) as fid: + yield fid + end_file(fid) # we only hit this line if the yield does not err + + def check_fiff_length(fid, close=True): """Ensure our file hasn't grown too large to work properly.""" if fid.tell() > 2147483648: # 2 ** 31, FIFF uses signed 32-bit locations diff -Nru python-mne-0.23.4+dfsg/mne/label.py python-mne-1.1.0+dfsg/mne/label.py --- python-mne-0.23.4+dfsg/mne/label.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/label.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,28 +2,30 @@ # Martin Luessi # Denis Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause from collections import defaultdict from colorsys import hsv_to_rgb, rgb_to_hsv +import copy as cp import os import os.path as op -import copy as cp import re import numpy as np -from .parallel import parallel_func, check_n_jobs +from .morph_map import read_morph_map +from .parallel import parallel_func from .source_estimate import (SourceEstimate, VolSourceEstimate, _center_of_mass, extract_label_time_course, spatial_src_adjacency) -from .source_space import add_source_space_distances, SourceSpaces +from .source_space import (add_source_space_distances, SourceSpaces, + _ensure_src) from .stats.cluster_level import _find_clusters, _get_components -from .surface import (read_surface, fast_cross_3d, mesh_edges, mesh_dist, - read_morph_map) +from .surface import (complete_surface_info, read_surface, fast_cross_3d, + _mesh_borders, mesh_edges, mesh_dist) from .utils import (get_subjects_dir, _check_subject, logger, verbose, warn, check_random_state, _validate_type, fill_doc, - _check_option, check_version) + _check_option, check_version, _check_fname) def _blend_colors(color_1, color_2): @@ -138,7 +140,7 @@ @fill_doc -class Label(object): +class Label: """A FreeSurfer/MNE label with vertices restricted to one hemisphere. Labels can be combined with the ``+`` operator: @@ -164,8 +166,7 @@ Kept as information but not used by the object itself. filename : str Kept as information but not used by the object itself. - subject : str | None - Name of the subject the label is from. + %(subject_label)s color : None | matplotlib color Default label color and alpha (e.g., ``(1., 0., 0., 1.)`` for red). %(verbose)s @@ -184,11 +185,11 @@ pos : array, shape (N, 3) Locations in meters. subject : str | None - Subject name. It is best practice to set this to the proper + The label subject. + It is best practice to set this to the proper value on initialization, but it can also be set manually. values : array, shape (N,) Values at the vertices. - %(verbose)s vertices : array, shape (N,) Vertex indices (0 based) """ @@ -196,7 +197,7 @@ @verbose def __init__(self, vertices=(), pos=None, values=None, hemi=None, comment="", name=None, filename=None, subject=None, - color=None, verbose=None): # noqa: D102 + color=None, *, verbose=None): # noqa: D102 # check parameters if not isinstance(hemi, str): raise ValueError('hemi must be a string, not %s' % type(hemi)) @@ -231,7 +232,6 @@ self.values = values self.hemi = hemi self.comment = comment - self.verbose = verbose self.subject = _check_subject(None, subject, raise_error=False) self.color = color self.name = name @@ -243,7 +243,6 @@ self.values = state['values'] self.hemi = state['hemi'] self.comment = state['comment'] - self.verbose = state['verbose'] self.subject = state.get('subject', None) self.color = state.get('color', None) self.name = state['name'] @@ -255,7 +254,6 @@ values=self.values, hemi=self.hemi, comment=self.comment, - verbose=self.verbose, subject=self.subject, color=self.color, name=self.name, @@ -341,10 +339,9 @@ name = "%s + %s" % (name0, name1) color = _blend_colors(self.color, other.color) - verbose = self.verbose or other.verbose label = Label(vertices, pos, values, self.hemi, comment, name, None, - self.subject, color, verbose) + self.subject, color) return label def __sub__(self, other): @@ -372,7 +369,7 @@ name = "%s - %s" % (self.name or 'unnamed', other.name or 'unnamed') return Label(self.vertices[keep], self.pos[keep], self.values[keep], self.hemi, self.comment, name, None, self.subject, - self.color, self.verbose) + self.color) def save(self, filename): r"""Write to disk as FreeSurfer \*.label file. @@ -498,7 +495,7 @@ @verbose def smooth(self, subject=None, smooth=2, grade=None, - subjects_dir=None, n_jobs=1, verbose=None): + subjects_dir=None, n_jobs=None, verbose=None): """Smooth the label. Useful for filling in labels made in a @@ -506,9 +503,7 @@ Parameters ---------- - subject : str | None - The name of the subject used. If None, the value will be - taken from self.subject. + %(subject_label)s smooth : int Number of iterations for the smoothing of the surface data. Cannot be None here since not all vertices are used. For a @@ -527,7 +522,7 @@ a label filling the surface, use None. %(subjects_dir)s %(n_jobs)s - %(verbose_meth)s + %(verbose)s Returns ------- @@ -542,11 +537,11 @@ """ subject = _check_subject(self.subject, subject) return self.morph(subject, subject, smooth, grade, subjects_dir, - n_jobs, verbose) + n_jobs, verbose=verbose) @verbose def morph(self, subject_from=None, subject_to=None, smooth=5, grade=None, - subjects_dir=None, n_jobs=1, verbose=None): + subjects_dir=None, n_jobs=None, verbose=None): """Morph the label. Useful for transforming a label from one subject to another. @@ -575,7 +570,7 @@ a label filling the surface, use None. %(subjects_dir)s %(n_jobs)s - %(verbose_meth)s + %(verbose)s Returns ------- @@ -640,9 +635,7 @@ or 'contiguous' to split the label into connected components. If a number or 'contiguous' is specified, names of the new labels will be the input label's name with div1, div2 etc. appended. - subject : None | str - Subject which this label belongs to (needed to locate surface file; - should only be specified if it is not specified in the label). + %(subject_label)s %(subjects_dir)s freesurfer : bool By default (``False``) ``split_label`` uses an algorithm that is @@ -742,8 +735,7 @@ Parameters ---------- - subject : str | None - The subject the label is defined for. + %(subject_label)s restrict_vertices : bool | array of int | instance of SourceSpaces If True, returned vertex will be one from the label. Otherwise, it could be any vertex from surf. If an array of int, the @@ -790,9 +782,93 @@ subject, subjects_dir, restrict_vertices) return vertex + @verbose + def distances_to_outside(self, subject=None, subjects_dir=None, + surface='white', *, verbose=None): + """Compute the distance from each vertex to outside the label. + + Parameters + ---------- + %(subject_label)s + %(subjects_dir)s + %(surface)s + %(verbose)s + + Returns + ------- + dist : ndarray, shape (n_vertices,) + The distance from each vertex in ``self.vertices`` to exit the + label. + outside_vertices : ndarray, shape (n_vertices,) + For each vertex in the label, the nearest vertex outside the + label. + + Notes + ----- + Distances are computed along the cortical surface. + + .. versionadded:: 0.24 + """ + from scipy.sparse.csgraph import dijkstra + if not check_version('scipy', '1.3'): + raise RuntimeError( + 'scipy >= 1.3 is required to calculate distances to the edge') + rr, tris = self._load_surface(subject, subjects_dir, surface) + adjacency = mesh_dist(tris, rr) + mask = np.zeros(len(rr)) + mask[self.vertices] = 1 + border_vert = _mesh_borders(tris, mask) + # vertices on the edge + outside_vert = np.setdiff1d(border_vert, self.vertices) + dist, _, outside = dijkstra( + adjacency, indices=outside_vert, min_only=True, + return_predecessors=True) + dist = dist[self.vertices] * 1e-3 # mm to m + outside = outside[self.vertices] + return dist, outside + + @verbose + def compute_area(self, subject=None, subjects_dir=None, surface='white', + *, verbose=None): + """Compute the surface area of a label. + + Parameters + ---------- + %(subject_label)s + %(subjects_dir)s + %(surface)s + %(verbose)s + + Returns + ------- + area : float + The area (in m²) of the label. + + Notes + ----- + ..versionadded:: 0.24 + """ + _, _, surf = self._load_surface(subject, subjects_dir, surface, + return_dict=True) + complete_surface_info( + surf, do_neighbor_vert=False, do_neighbor_tri=False, copy=False) + in_ = np.in1d(surf['tris'], self.vertices).reshape(surf['tris'].shape) + tidx = np.where(in_.all(-1))[0] + if len(tidx) == 0: + warn('No complete triangles found, perhaps label is not filled?') + return surf['tri_area'][tidx].sum() * 1e-6 # mm² -> m² + + def _load_surface(self, subject, subjects_dir, surface, **kwargs): + subject = _check_subject(self.subject, subject) + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + fname = op.join( + subjects_dir, subject, 'surf', f'{self.hemi}.{surface}') + _check_fname(fname, overwrite='read', must_exist=True, name='Surface') + return read_surface(fname, **kwargs) + def _get_label_src(label, src): - _validate_type(src, SourceSpaces, 'src') + src = _ensure_src(src) if src.kind != 'surface': raise RuntimeError('Cannot operate on SourceSpaces that are not ' 'surface type, got %s' % (src.kind,)) @@ -828,7 +904,7 @@ name : None | str A name for the label. It is OK to change that attribute manually. subject : str | None - Subject the label is from. + The name of the subject. """ def __init__(self, lh, rh, name=None, color=None): # noqa: D102 @@ -900,15 +976,15 @@ return BiHemiLabel(lh, rh, name, self.color) -def read_label(filename, subject=None, color=None): +@verbose +def read_label(filename, subject=None, color=None, *, verbose=None): """Read FreeSurfer Label file. Parameters ---------- filename : str Path to label file. - subject : str | None - Name of the subject the data are defined for. + %(subject_label)s It is good practice to set this attribute to avoid combining incompatible labels and SourceEstimates (e.g., ones from other subjects). Note that due to file specification limitations, the @@ -917,6 +993,7 @@ Default label color and alpha (e.g., ``(1., 0., 0., 1.)`` for red). Note that due to file specification limitations, the color isn't saved to or loaded from files written to disk. + %(verbose)s Returns ------- @@ -973,7 +1050,7 @@ values = values[order] label = Label(vertices, pos, values, hemi, comment, name, filename, - subject, color) + subject, color, verbose=verbose) return label @@ -1052,9 +1129,7 @@ ---------- label_to_split : Label | str Label which is to be split (Label object or path to a label file). - subject : None | str - Subject which this label belongs to (needed to locate surface file; - should only be specified if it is not specified in the label). + %(subject_label)s %(subjects_dir)s Returns @@ -1138,9 +1213,7 @@ posterior to anterior), or the number of new labels to create (default is 2). If a number is specified, names of the new labels will be the input label's name with div1, div2 etc. appended. - subject : None | str - Subject which this label belongs to (needed to locate surface file; - should only be specified if it is not specified in the label). + %(subject_label)s %(subjects_dir)s freesurfer : bool By default (``False``) ``split_label`` uses an algorithm that is @@ -1514,7 +1587,7 @@ @fill_doc -def grow_labels(subject, seeds, extents, hemis, subjects_dir=None, n_jobs=1, +def grow_labels(subject, seeds, extents, hemis, subjects_dir=None, n_jobs=None, overlap=True, names=None, surface='white', colors=None): """Generate circular labels in source space with region growing. @@ -1525,8 +1598,7 @@ Parameters ---------- - subject : str - Name of the subject as in SUBJECTS_DIR. + %(subject)s seeds : int | list Seed, or list of seeds. Each seed can be either a vertex number or a list of vertex numbers. @@ -1545,8 +1617,7 @@ names : None | list of str Assign names to the new labels (list needs to have the same length as seeds). - surface : str - The surface used to grow the labels, defaults to the white surface. + %(surface)s colors : array, shape (n, 4) or (, 4) | None How to assign colors to each label. If None then unique colors will be chosen automatically (default), otherwise colors will be broadcast @@ -1568,7 +1639,6 @@ used for each label. """ subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) - n_jobs = check_n_jobs(n_jobs) # make sure the inputs are arrays if np.isscalar(seeds): @@ -1634,7 +1704,7 @@ if overlap: # create the patches - parallel, my_grow_labels, _ = parallel_func(_grow_labels, n_jobs) + parallel, my_grow_labels, n_jobs = parallel_func(_grow_labels, n_jobs) seeds = np.array_split(np.array(seeds, dtype='O'), n_jobs) extents = np.array_split(extents, n_jobs) hemis = np.array_split(hemis, n_jobs) @@ -1742,8 +1812,7 @@ Parameters ---------- - subject : str - Name of the subject as in SUBJECTS_DIR. + %(subject)s n_parcel : int Total number of cortical parcels. hemi : str @@ -1751,8 +1820,7 @@ of 'both', both hemispheres are processed with (n_parcel // 2) parcels per hemisphere. %(subjects_dir)s - surface : str - The surface used to grow the labels, defaults to the white surface. + %(surface)s %(random_state)s Returns @@ -2031,8 +2099,7 @@ Parameters ---------- - subject : str - The subject for which to read the parcellation. + %(subject)s parc : str The parcellation to use, e.g., 'aparc' or 'aparc.a2009s'. hemi : str @@ -2080,6 +2147,7 @@ # now we are ready to create the labels n_read = 0 labels = list() + orig_names = set() for fname, hemi in zip(annot_fname, hemis): # read annotation annot, ctab, label_names = _read_annot(fname) @@ -2096,7 +2164,9 @@ if len(vertices) == 0: # label is not part of cortical surface continue - name = label_name.decode() + '-' + hemi + label_name = label_name.decode() + orig_names.add(label_name) + name = f'{label_name}-{hemi}' if (regexp is not None) and not r_.match(name): continue pos = vert_pos[vertices, :] @@ -2114,7 +2184,9 @@ if len(labels) == 0: msg = 'No labels found.' if regexp is not None: - msg += ' Maybe the regular expression %r did not match?' % regexp + orig_names = '\n'.join(sorted(orig_names)) + msg += (f' Maybe the regular expression {repr(regexp)} did not ' + f'match any of:\n{orig_names}') raise RuntimeError(msg) return labels @@ -2192,7 +2264,7 @@ pos = vert_poss[li][vertices] out_labels.append( Label(vertices, pos, values, label.hemi, label.comment, label.name, - filename, subject_to, label.color, label.verbose)) + filename, subject_to, label.color)) return out_labels @@ -2206,16 +2278,15 @@ Parameters ---------- - %(eltc_labels)s + %(labels_eltc)s values : ndarray, shape (n_labels, ...) The values in each label. Can be 1D or 2D. tmin : float The tmin to use for the STC. tstep : float The tstep to use for the STC. - subject : str | None - The subject for which to create the STC. - %(eltc_src)s + %(subject)s + %(src_eltc)s Can be omitted if using a surface source space, in which case the label vertices will determine the output STC vertices. Required if using a volumetric source space. @@ -2269,7 +2340,7 @@ rev_op = np.zeros(label_op.shape[::-1]) rev_op[np.arange(label_op.shape[1]), np.argmax(label_op, axis=0)] = 1. data = rev_op @ values - return klass(data, vertices, tmin, tstep, subject, verbose) + return klass(data, vertices, tmin, tstep, subject, verbose=verbose) def _check_values_labels(values, n_labels): @@ -2358,8 +2429,7 @@ ---------- labels : list with instances of mne.Label The labels to create a parcellation from. - subject : str | None - The subject for which to write the parcellation. + %(subject)s parc : str | None The parcellation name to use. overwrite : bool diff -Nru python-mne-0.23.4+dfsg/mne/minimum_norm/_eloreta.py python-mne-1.1.0+dfsg/mne/minimum_norm/_eloreta.py --- python-mne-0.23.4+dfsg/mne/minimum_norm/_eloreta.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/minimum_norm/_eloreta.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause from functools import partial import numpy as np diff -Nru python-mne-0.23.4+dfsg/mne/minimum_norm/inverse.py python-mne-1.1.0+dfsg/mne/minimum_norm/inverse.py --- python-mne-0.23.4+dfsg/mne/minimum_norm/inverse.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/minimum_norm/inverse.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Matti Hämäläinen # Teon Brooks # -# License: BSD (3-clause) +# License: BSD-3-Clause from copy import deepcopy from math import sqrt @@ -20,8 +20,8 @@ from ..io.proj import (_read_proj, make_projector, _write_proj, _needs_eeg_average_ref_proj) from ..io.tree import dir_tree_find -from ..io.write import (write_int, write_float_matrix, start_file, - start_block, end_block, end_file, write_float, +from ..io.write import (write_int, write_float_matrix, start_and_end_file, + start_block, end_block, write_float, write_coord_trans, write_string) from ..io.pick import channel_type, pick_info, pick_types, pick_channels @@ -41,7 +41,7 @@ from ..source_estimate import _make_stc, _get_src_type from ..utils import (check_fname, logger, verbose, warn, _validate_type, _check_compensation_grade, _check_option, - _check_depth, _check_src_normal) + _check_depth, _check_src_normal, _check_fname) INVERSE_METHODS = ('MNE', 'dSPM', 'sLORETA', 'eLORETA') @@ -54,25 +54,46 @@ """Return a copy of the InverseOperator.""" return InverseOperator(deepcopy(self)) + def _get_chs_and_src_info_for_repr(self): + n_chs_meg = len(pick_types(self['info'], meg=True, eeg=False)) + n_chs_eeg = len(pick_types(self['info'], meg=False, eeg=True)) + + src_space_descr = f"{self['src'].kind} with {self['nsource']} sources" + + src_ori_fiff_to_name_map = { + FIFF.FIFFV_MNE_UNKNOWN_ORI: 'Unknown', + FIFF.FIFFV_MNE_FIXED_ORI: 'Fixed', + FIFF.FIFFV_MNE_FREE_ORI: 'Free' + } + src_ori = src_ori_fiff_to_name_map[self['source_ori']] + return n_chs_meg, n_chs_eeg, src_space_descr, src_ori + def __repr__(self): # noqa: D105 """Summarize inverse info instead of printing all.""" - entr = ' 1: # Mixed source space + raise ValueError('Computing inverse solutions for mixed source ' + 'spaces with fixed orientations is not ' + 'supported.') + if 'volume' in loose: + raise ValueError('Computing inverse solutions for volume source ' + 'spaces with fixed orientations is not ' + 'supported.') + if loose.get('volume', 1) < 1: + raise ValueError('Computing inverse solutions with restricted ' + 'orientations (loose < 1) is not supported for ' + 'volume source spaces.') + # Deal with "depth" if exp is not None: exp = float(exp) @@ -1377,14 +1423,14 @@ f'equal to 0, got {exp}') exp = exp or None # alias 0. -> None - # put the forward solution in correct orientation + # Put the forward solution in correct orientation. # (delaying for the case of fixed ori with depth weighting if # allow_fixed_depth is True) - if loose.get('surface', 1.) == 0. and len(loose) == 1: + if fixed_inverse: if not is_fixed_orient(forward): if allow_fixed_depth: # can convert now - logger.info('Converting forward solution to fixed orietnation') + logger.info('Converting forward solution to fixed orientation') convert_forward_solution( forward, force_fixed=True, use_cps=True, copy=False) elif exp is not None and not allow_fixed_depth: @@ -1398,7 +1444,7 @@ 'Forward operator has fixed orientation and can only ' 'be used to make a fixed-orientation inverse ' 'operator.') - if loose.get('surface', 1.) < 1. and not forward['surf_ori']: + if constrained_inverse and not forward['surf_ori']: logger.info('Converting forward solution to surface orientation') convert_forward_solution( forward, surf_ori=True, use_cps=use_cps, copy=False) @@ -1416,7 +1462,7 @@ rank=rank) # Deal with fixed orientation forward / inverse - if loose.get('surface', 1.) == 0. and len(loose) == 1: + if fixed_inverse: orient_prior = None if not is_fixed_orient(forward): if depth_prior is not None: @@ -1428,8 +1474,8 @@ convert_forward_solution( forward, surf_ori=True, force_fixed=True, use_cps=use_cps, copy=False) - else: - if loose.get('surface', 1.) < 1: + else: # Free or loose orientation + if constrained_inverse: assert forward['surf_ori'] # In theory we could have orient_prior=None for loose=1., but # the MNE-C code does not do this @@ -1471,9 +1517,9 @@ Parameters ---------- - info : dict - The measurement info to specify the channels to include. - Bad channels in info['bads'] are not used. + %(info_not_none)s + Specifies the channels to include. Bad channels (in ``info['bads']``) + are not used. forward : dict Forward operator. noise_cov : instance of Covariance @@ -1484,7 +1530,7 @@ Use fixed source orientations normal to the cortical mantle. If True, the loose parameter must be "auto" or 0. If 'auto', the loose value is used. - %(rank_None)s + %(rank_none)s %(use_cps)s %(verbose)s @@ -1680,7 +1726,7 @@ \tilde{M} = R^\frac{1}{2}V\Gamma U^T The values in the diagonal matrix :math:`\Gamma` are expressed in terms - of the chosen regularization :math:`\lambda\approx\frac{1}{\rm{SNR}^2}` + of the chosen regularization :math:`\lambda^2 \sim 1/\rm{SNR}^2` and singular values :math:`\lambda_k` as: .. math:: diff -Nru python-mne-0.23.4+dfsg/mne/minimum_norm/resolution_matrix.py python-mne-1.1.0+dfsg/mne/minimum_norm/resolution_matrix.py --- python-mne-0.23.4+dfsg/mne/minimum_norm/resolution_matrix.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/minimum_norm/resolution_matrix.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ """Compute resolution matrix for linear estimators.""" # Authors: olaf.hauk@mrc-cbu.cam.ac.uk # -# License: BSD (3-clause) +# License: BSD-3-Clause from copy import deepcopy import numpy as np @@ -74,19 +74,19 @@ Forward Operator. src : Source Space Source space used to compute resolution matrix. - %(pctf_idx)s + %(idx_pctf)s func : str ('psf' | 'ctf') Whether to produce PSFs or CTFs. Defaults to psf. - %(pctf_mode)s - %(pctf_n_comp)s - %(pctf_norm)s - %(pctf_return_pca_vars)s + %(mode_pctf)s + %(n_comp_pctf_n)s + %(norm_pctf)s + %(return_pca_vars_pctf)s %(verbose)s Returns ------- - %(pctf_stcs)s - %(pctf_pca_vars)s + %(stcs_pctf)s + %(pca_vars_pctf)s """ # check for consistencies in input parameters _check_get_psf_ctf_params(mode, n_comp, return_pca_vars) @@ -247,17 +247,17 @@ Forward Operator. src : instance of SourceSpaces Source space used to compute resolution matrix. - %(pctf_idx)s - %(pctf_mode)s - %(pctf_n_comp)s - %(pctf_norm)s - %(pctf_return_pca_vars)s + %(idx_pctf)s + %(mode_pctf)s + %(n_comp_pctf_n)s + %(norm_pctf)s + %(return_pca_vars_pctf)s %(verbose)s Returns ------- - %(pctf_stcs)s - %(pctf_pca_vars)s + %(stcs_pctf)s + %(pca_vars_pctf)s """ return _get_psf_ctf(resmat, src, idx, func='psf', mode=mode, n_comp=n_comp, norm=norm, return_pca_vars=return_pca_vars) @@ -274,17 +274,17 @@ Forward Operator. src : instance of SourceSpaces Source space used to compute resolution matrix. - %(pctf_idx)s - %(pctf_mode)s - %(pctf_n_comp)s - %(pctf_norm)s - %(pctf_return_pca_vars)s + %(idx_pctf)s + %(mode_pctf)s + %(n_comp_pctf_n)s + %(norm_pctf)s + %(return_pca_vars_pctf)s %(verbose)s Returns ------- - %(pctf_stcs)s - %(pctf_pca_vars)s + %(stcs_pctf)s + %(pca_vars_pctf)s """ return _get_psf_ctf(resmat, src, idx, func='ctf', mode=mode, n_comp=n_comp, norm=norm, return_pca_vars=return_pca_vars) @@ -322,8 +322,9 @@ # it's an epoch, see in loop below), uses 'info' from inverse solution # because this has all the correct projector information info = deepcopy(inverse_operator['info']) - info['sfreq'] = 1000. # necessary - info['projs'] = inverse_operator['projs'] + with info._unlock(): + info['sfreq'] = 1000. # necessary + info['projs'] = inverse_operator['projs'] return info diff -Nru python-mne-0.23.4+dfsg/mne/minimum_norm/spatial_resolution.py python-mne-1.1.0+dfsg/mne/minimum_norm/spatial_resolution.py --- python-mne-0.23.4+dfsg/mne/minimum_norm/spatial_resolution.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/minimum_norm/spatial_resolution.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # Authors: Olaf Hauk # -# License: BSD (3-clause) +# License: BSD-3-Clause """Compute resolution metrics from resolution matrix. Resolution metrics: localisation error, spatial extent, relative amplitude. diff -Nru python-mne-0.23.4+dfsg/mne/minimum_norm/tests/test_inverse.py python-mne-1.1.0+dfsg/mne/minimum_norm/tests/test_inverse.py --- python-mne-0.23.4+dfsg/mne/minimum_norm/tests/test_inverse.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/minimum_norm/tests/test_inverse.py 2022-08-03 15:48:14.000000000 +0000 @@ -31,7 +31,7 @@ make_inverse_operator, apply_inverse_cov, write_inverse_operator, prepare_inverse_operator, compute_rank_inverse, INVERSE_METHODS) -from mne.utils import _TempDir, catch_logging +from mne.utils import catch_logging, _record_warnings test_path = testing.data_path(download=False) s_path = op.join(test_path, 'MEG', 'sample') @@ -113,7 +113,7 @@ assert (len(a) == len(b)) for i, j in zip(a, b): _compare(i, j) - elif isinstance(a, sparse.csr.csr_matrix): + elif isinstance(a, sparse.csr_matrix): assert_array_almost_equal(a.data, b.data) assert_equal(a.indices, b.indices) assert_equal(a.indptr, b.indptr) @@ -184,9 +184,8 @@ err_msg='%s: %s' % (method, corr)) -def _compare_io(inv_op, out_file_ext='.fif'): +def _compare_io(inv_op, *, out_file_ext='.fif', tempdir): """Compare inverse IO.""" - tempdir = _TempDir() if out_file_ext == '.fif': out_file = op.join(tempdir, 'test-inv.fif') elif out_file_ext == '.gz': @@ -196,7 +195,7 @@ out_file = Path(out_file) # Test io operations inv_init = copy.deepcopy(inv_op) - write_inverse_operator(out_file, inv_op) + write_inverse_operator(out_file, inv_op, overwrite=True) read_inv_op = read_inverse_operator(out_file) _compare(inv_init, read_inv_op) _compare(inv_init, inv_op) @@ -205,7 +204,8 @@ def test_warn_inverse_operator(evoked, noise_cov): """Test MNE inverse warning without average EEG projection.""" bad_info = evoked.info - bad_info['projs'] = list() + with bad_info._unlock(): + bad_info['projs'] = list() fwd_op = convert_forward_solution(read_forward_solution(fname_fwd), surf_ori=True, copy=False) with pytest.raises(ValueError, match='greater than or'): @@ -216,7 +216,7 @@ @pytest.mark.slowtest -def test_make_inverse_operator_loose(evoked): +def test_make_inverse_operator_loose(evoked, tmp_path): """Test MNE inverse computation (precomputed and non-precomputed).""" # Test old version of inverse computation starting from forward operator noise_cov = read_cov(fname_cov) @@ -230,7 +230,7 @@ log = log.getvalue() assert 'MEG: rank 302 computed' in log assert 'limit = 1/%d' % fwd_op['nsource'] in log - _compare_io(my_inv_op) + _compare_io(my_inv_op, tempdir=str(tmp_path)) assert_equal(inverse_operator['units'], 'Am') _compare_inverses_approx(my_inv_op, inverse_operator, evoked, rtol=1e-2, atol=1e-5, depth_atol=1e-3) @@ -241,7 +241,7 @@ fixed=False, verbose=True) log = log.getvalue() assert 'MEG: rank 302 computed from 305' in log - _compare_io(my_inv_op) + _compare_io(my_inv_op, tempdir=str(tmp_path)) _compare_inverses_approx(my_inv_op, inverse_operator, evoked, rtol=1e-3, atol=1e-5) assert ('dev_head_t' in my_inv_op['info']) @@ -271,9 +271,8 @@ randomiser = np.random.RandomState(42) randomiser.shuffle(new_order) evoked.data = evoked.data[new_order] - evoked.info['chs'] = [evoked.info['chs'][n] for n in new_order] - evoked.info._update_redundant() - evoked.info._check_consistency() + with evoked.info._unlock(update_redundant=True, check_after=True): + evoked.info['chs'] = [evoked.info['chs'][n] for n in new_order] cov_ch_reorder = [c for c in evoked.info['ch_names'] if (c in noise_cov.ch_names)] @@ -310,8 +309,8 @@ ('MNE', 89, 92, dict(limit_depth_chs='whiten')), # sparse default ('dSPM', 96, 98, 0.8), ('sLORETA', 100, 100, 0.8), - ('eLORETA', 100, 100, None), - ('eLORETA', 100, 100, 0.8), + pytest.param('eLORETA', 100, 100, None, marks=pytest.mark.slowtest), + pytest.param('eLORETA', 100, 100, 0.8, marks=pytest.mark.slowtest), ]) def test_localization_bias_fixed(bias_params_fixed, method, lower, upper, depth): @@ -333,9 +332,9 @@ ('MNE', 89, 92, dict(limit_depth_chs='whiten'), 0.2), # sparse default ('dSPM', 85, 87, 0.8, 0.2), ('sLORETA', 100, 100, 0.8, 0.2), - ('eLORETA', 99, 100, None, 0.2), - ('eLORETA', 99, 100, 0.8, 0.2), - ('eLORETA', 99, 100, 0.8, 0.001), + pytest.param('eLORETA', 99, 100, None, 0.2, marks=pytest.mark.slowtest), + pytest.param('eLORETA', 99, 100, 0.8, 0.2, marks=pytest.mark.slowtest), + pytest.param('eLORETA', 99, 100, 0.8, 0.001, marks=pytest.mark.slowtest), ]) @pytest.mark.parametrize('pick_ori', (None, 'vector')) def test_localization_bias_loose(bias_params_fixed, method, lower, upper, @@ -377,11 +376,15 @@ dict(limit_depth_chs='whiten'), 1), # sparse default ('dSPM', 40, 45, 0.96, 0.97, {}, 0.8, 1), ('sLORETA', 93, 95, 0.95, 0.96, {}, 0.8, 1), - ('eLORETA', 93, 100, 0.95, 0.96, - dict(method_params=dict(force_equal=True)), None, 1), - ('eLORETA', 100, 100, 0.98, 0.99, {}, None, 1.0), - ('eLORETA', 100, 100, 0.98, 0.99, {}, 0.8, 1.0), - ('eLORETA', 100, 100, 0.98, 0.99, {}, 0.8, 0.999), + pytest.param('eLORETA', 93, 100, 0.95, 0.96, + dict(method_params=dict(force_equal=True)), None, 1, + marks=pytest.mark.slowtest), + pytest.param('eLORETA', 100, 100, 0.98, 0.99, {}, None, 1.0, + marks=pytest.mark.slowtest), + pytest.param('eLORETA', 100, 100, 0.98, 0.99, {}, 0.8, 1.0, + marks=pytest.mark.slowtest), + pytest.param('eLORETA', 100, 100, 0.98, 0.99, {}, 0.8, 0.999, + marks=pytest.mark.slowtest), ] ) def test_localization_bias_free(bias_params_free, method, lower, upper, @@ -402,10 +405,11 @@ @pytest.mark.slowtest -def test_apply_inverse_sphere(evoked): +def test_apply_inverse_sphere(evoked, tmp_path): """Test applying an inverse with a sphere model (rank-deficient).""" evoked.pick_channels(evoked.ch_names[:306:8]) - evoked.info['projs'] = [] + with evoked.info._unlock(): + evoked.info['projs'] = [] cov = make_ad_hoc_cov(evoked.info) sphere = make_sphere_model('auto', 'auto', evoked.info) fwd = read_forward_solution(fname_fwd) @@ -420,7 +424,7 @@ assert fwd['sol']['nrow'] == 39 assert fwd['nsource'] == 101 assert fwd['sol']['ncol'] == 303 - tempdir = _TempDir() + tempdir = str(tmp_path) temp_fname = op.join(tempdir, 'temp-inv.fif') inv = make_inverse_operator(evoked.info, fwd, cov, loose=1.) # This forces everything to be float32 @@ -530,11 +534,13 @@ apply_inverse(mne.io.RawArray(evoked.data, evoked.info), inv_op) # Test we get errors when using custom ref or no average proj is present - evoked.info['custom_ref_applied'] = True + with evoked.info._unlock(): + evoked.info['custom_ref_applied'] = True with pytest.raises(ValueError, match='Custom EEG reference'): apply_inverse(evoked, inv_op, lambda2, "MNE") - evoked.info['custom_ref_applied'] = False - evoked.info['projs'] = [] # remove EEG proj + with evoked.info._unlock(): + evoked.info['custom_ref_applied'] = False + evoked.info['projs'] = [] # remove EEG proj with pytest.raises(ValueError, match='EEG average reference.*mandatory'): apply_inverse(evoked, inv_op, lambda2, "MNE") @@ -542,6 +548,7 @@ apply_inverse(evoked, inv_op_meg, 1. / 9.) +@pytest.mark.slowtest # lots of params here, adds up @pytest.mark.parametrize('method', INVERSE_METHODS) @pytest.mark.parametrize('looses, vmin, vmax, nmin, nmax', [ ((1., 0.8), 0.87, 0.94, 0.9, 1.1), # almost the same as free @@ -592,7 +599,7 @@ def assert_stc_res(evoked, stc, forward, res, atol=1e-20): """Assert that orig == residual + estimate.""" __tracebackhide__ = True - with pytest.warns(None): # could be all positive or large values + with _record_warnings(): # all positive or large values estimated = apply_forward(forward, stc, evoked.info) meg, eeg = 'meg' in estimated, 'eeg' in estimated evoked = evoked.copy().pick_types(meg=meg, eeg=eeg, exclude=()) @@ -621,6 +628,8 @@ @pytest.mark.parametrize('pick_ori', (None, 'vector')) def test_inverse_residual(evoked, method, pick_ori): """Test MNE inverse application.""" + if method == 'eLORETA' and pick_ori == 'vector': # works but slow + return # use fname_inv as it will be faster than fname_full (fewer verts and chs) evoked = evoked.pick_types(meg=True) if pick_ori is None: # use fixed @@ -654,6 +663,7 @@ assert_array_less(np.abs(residual.data), 1e-15) +@pytest.mark.slowtest def test_make_inverse_operator_fixed(evoked, noise_cov): """Test MNE inverse computation (fixed orientation).""" fwd = read_forward_solution_meg(fname_fwd) @@ -723,6 +733,7 @@ assert_allclose(stc_surf.data, stc.data, atol=1e-2) +@pytest.mark.slowtest def test_make_inverse_operator_vector(evoked, noise_cov): """Test MNE inverse computation (vector result).""" fwd_surf = read_forward_solution_meg(fname_fwd, surf_ori=True) @@ -763,18 +774,20 @@ atol=1e-20) -def test_make_inverse_operator_diag(evoked, noise_cov): +def test_make_inverse_operator_diag(evoked, noise_cov, tmp_path, + azure_windows): """Test MNE inverse computation with diagonal noise cov.""" noise_cov = noise_cov.as_diag() fwd_op = convert_forward_solution(read_forward_solution(fname_fwd), surf_ori=True) inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov, loose=0.2, depth=0.8) - _compare_io(inv_op) + _compare_io(inv_op, tempdir=str(tmp_path)) inverse_operator_diag = read_inverse_operator(fname_inv_meeg_diag) - # This one is pretty bad + # This one is pretty bad, and for some reason it's worse on Azure Windows + ctol = 0.75 if azure_windows else 0.99 _compare_inverses_approx(inverse_operator_diag, inv_op, evoked, - rtol=1e-1, atol=1e-1, ctol=0.99, check_K=False) + rtol=1e-1, atol=1e-1, ctol=ctol, check_K=False) # Inverse has 366 channels - 6 proj = 360 assert (compute_rank_inverse(inverse_operator_diag) == 360) @@ -795,9 +808,9 @@ assert (compute_rank_inverse(inv) == 20) -def test_inverse_operator_volume(evoked): +def test_inverse_operator_volume(evoked, tmp_path): """Test MNE inverse computation on volume source space.""" - tempdir = _TempDir() + tempdir = str(tmp_path) inv_vol = read_inverse_operator(fname_vol_inv) assert (repr(inv_vol)) stc = apply_inverse(evoked, inv_vol, lambda2, 'dSPM') @@ -819,16 +832,36 @@ @pytest.mark.slowtest +def test_inverse_operator_discrete(evoked, tmp_path): + """Test MNE inverse computation on discrete source space.""" + # Make discrete source space + src = mne.setup_volume_source_space( + pos=dict(rr=[[0, 0, 0.1], [0, -0.01, 0.05]], + nn=[[0, 1, 0], [1, 0, 0]]), + bem=fname_bem) + + # Perform inverse + fwd = mne.make_forward_solution( + evoked.info, mne.Transform('head', 'mri'), src, fname_bem) + inv = make_inverse_operator( + evoked.info, fwd, make_ad_hoc_cov(evoked.info), loose=0, fixed=True, + depth=0) + stc = apply_inverse(evoked, inv) + assert (isinstance(stc, VolSourceEstimate)) + assert stc.data.shape == (2, len(evoked.times)) + + +@pytest.mark.slowtest @testing.requires_testing_data -def test_io_inverse_operator(): +def test_io_inverse_operator(tmp_path): """Test IO of inverse_operator.""" - tempdir = _TempDir() + tempdir = str(tmp_path) inverse_operator = read_inverse_operator(fname_inv) x = repr(inverse_operator) assert (x) assert (isinstance(inverse_operator['noise_cov'], Covariance)) # just do one example for .gz, as it should generalize - _compare_io(inverse_operator, '.gz') + _compare_io(inverse_operator, out_file_ext='.gz', tempdir=tempdir) # test warnings on bad filenames inv_badname = op.join(tempdir, 'test-bad-name.fif.gz') @@ -982,6 +1015,7 @@ assert_array_almost_equal(stc.data, stc3.data) +@pytest.mark.slowtest @testing.requires_testing_data def test_apply_mne_inverse_epochs(): """Test MNE with precomputed inverse operator on Epochs.""" @@ -1076,6 +1110,7 @@ assert len(set(inv_['info']['bads']) - union_bads) == 0 +@pytest.mark.slowtest @testing.requires_testing_data def test_inverse_ctf_comp(): """Test interpolation with compensated CTF data.""" @@ -1097,6 +1132,7 @@ apply_inverse_raw(raw, inv, 1. / 9.) +@pytest.mark.slowtest def test_inverse_mixed(all_src_types_inv_evoked): """Test creating and applying an inverse to mixed source spaces.""" stcs = dict() @@ -1105,10 +1141,10 @@ ('volume', mne.VolVectorSourceEstimate), ('mixed', mne.MixedVectorSourceEstimate)]: assert invs[kind]['src'].kind == kind - with pytest.warns(RuntimeWarning, match='has magnitude'): + with pytest.warns(RuntimeWarning, match='has been reduced'): stc = apply_inverse(evoked, invs[kind]) assert isinstance(stc, klass._scalar_class) - with pytest.warns(RuntimeWarning, match='has magnitude'): + with pytest.warns(RuntimeWarning, match='has been reduced'): stc_vec = apply_inverse(evoked, invs[kind], pick_ori='vector') stcs[kind] = stc_vec assert isinstance(stc_vec, klass) @@ -1131,6 +1167,7 @@ stcs['surface'].project('normal', surf_src)[0].data) +@pytest.mark.slowtest # slow on Azure def test_inverse_mixed_loose(mixed_fwd_cov_evoked): """Test loose mixed source spaces.""" fwd, cov, evoked = mixed_fwd_cov_evoked diff -Nru python-mne-0.23.4+dfsg/mne/minimum_norm/tests/test_resolution_matrix.py python-mne-1.1.0+dfsg/mne/minimum_norm/tests/test_resolution_matrix.py --- python-mne-0.23.4+dfsg/mne/minimum_norm/tests/test_resolution_matrix.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/minimum_norm/tests/test_resolution_matrix.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Alexandre Gramfort # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op import numpy as np diff -Nru python-mne-0.23.4+dfsg/mne/minimum_norm/tests/test_resolution_metrics.py python-mne-1.1.0+dfsg/mne/minimum_norm/tests/test_resolution_metrics.py --- python-mne-0.23.4+dfsg/mne/minimum_norm/tests/test_resolution_metrics.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/minimum_norm/tests/test_resolution_metrics.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Authors: Olaf Hauk # Daniel McCloy # -# License: BSD (3-clause) +# License: BSD-3-Clause """ Test the following properties for resolution metrics. @@ -22,7 +22,6 @@ from mne.minimum_norm.resolution_matrix import make_inverse_resolution_matrix from mne.minimum_norm.spatial_resolution import (resolution_metrics, _rectify_resolution_matrix) -from mne.utils import run_tests_if_main data_path = testing.data_path(download=False) subjects_dir = op.join(data_path, 'subjects') @@ -144,6 +143,3 @@ r2 = _rectify_resolution_matrix(r1) assert_array_equal(r2, np.sqrt(2) * np.ones((4, 4))) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/minimum_norm/tests/test_snr.py python-mne-1.1.0+dfsg/mne/minimum_norm/tests/test_snr.py --- python-mne-0.23.4+dfsg/mne/minimum_norm/tests/test_snr.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/minimum_norm/tests/test_snr.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Authors: Eric Larson # Matti Hämäläinen # -# License: BSD (3-clause) +# License: BSD-3-Clause import os from os import path as op @@ -13,7 +13,7 @@ from mne.datasets import testing from mne.minimum_norm import read_inverse_operator, estimate_snr -from mne.utils import _TempDir, requires_mne, run_subprocess +from mne.utils import requires_mne, run_subprocess s_path = op.join(testing.data_path(download=False), 'MEG', 'sample') fname_inv = op.join(s_path, 'sample_audvis_trunc-meg-eeg-oct-6-meg-inv.fif') @@ -22,9 +22,9 @@ @testing.requires_testing_data @requires_mne -def test_snr(): +def test_snr(tmp_path): """Test SNR calculation.""" - tempdir = _TempDir() + tempdir = str(tmp_path) inv = read_inverse_operator(fname_inv) evoked = read_evokeds(fname_evoked, baseline=(None, 0))[0] snr = estimate_snr(evoked, inv)[0] diff -Nru python-mne-0.23.4+dfsg/mne/minimum_norm/tests/test_time_frequency.py python-mne-1.1.0+dfsg/mne/minimum_norm/tests/test_time_frequency.py --- python-mne-0.23.4+dfsg/mne/minimum_norm/tests/test_time_frequency.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/minimum_norm/tests/test_time_frequency.py 2022-08-03 15:48:14.000000000 +0000 @@ -8,7 +8,6 @@ from mne import find_events, Epochs, pick_types from mne.io import read_raw_fif from mne.io.constants import FIFF -from mne.utils import run_tests_if_main from mne.label import read_label from mne.minimum_norm import (read_inverse_operator, apply_inverse_epochs, prepare_inverse_operator, INVERSE_METHODS) @@ -86,7 +85,7 @@ freqs = np.arange(7, 30, 2) # define frequencies of interest power, phase_lock = source_induced_power( epochs, inv, freqs, label, baseline=(-0.1, 0), baseline_mode='percent', - n_cycles=2, n_jobs=1, method=method, prepared=True) + n_cycles=2, n_jobs=None, method=method, prepared=True) assert np.all(phase_lock > 0) assert np.all(phase_lock <= 1) assert 5 < np.max(power) < 7 @@ -206,6 +205,3 @@ one_epochs, inv, lambda2=lambda2, method=method, pick_ori="normal", label=label, bandwidth=0.01, low_bias=True, fmin=fmin, fmax=fmax, return_generator=False, prepared=True) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/minimum_norm/time_frequency.py python-mne-1.1.0+dfsg/mne/minimum_norm/time_frequency.py --- python-mne-0.23.4+dfsg/mne/minimum_norm/time_frequency.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/minimum_norm/time_frequency.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors: Alexandre Gramfort # Martin Luessi # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np @@ -65,7 +65,7 @@ lambda2=1.0 / 9.0, method="dSPM", nave=1, n_cycles=5, df=1, use_fft=False, decim=1, baseline=None, baseline_mode='logratio', - pca=True, n_jobs=1, prepared=False, + pca=True, n_jobs=None, prepared=False, method_params=None, use_cps=True, verbose=None): """Compute source space induced power in given frequency bands. @@ -265,7 +265,7 @@ def _source_induced_power(epochs, inverse_operator, freqs, label=None, lambda2=1.0 / 9.0, method="dSPM", nave=1, n_cycles=5, decim=1, use_fft=False, pca=True, pick_ori="normal", - n_jobs=1, with_plv=True, zero_mean=False, + n_jobs=None, with_plv=True, zero_mean=False, prepared=False, method_params=None, use_cps=True, verbose=None): """Aux function for source induced power.""" @@ -278,14 +278,13 @@ inv = inverse_operator parallel, my_compute_source_tfrs, n_jobs = parallel_func( - _compute_pow_plv, n_jobs) + _compute_pow_plv, n_jobs, max_jobs=len(epochs_data)) Fs = epochs.info['sfreq'] # sampling in Hz logger.info('Computing source power ...') Ws = morlet(Fs, freqs, n_cycles=n_cycles, zero_mean=zero_mean) - n_jobs = min(n_jobs, len(epochs_data)) out = parallel(my_compute_source_tfrs(data=data, K=K, sel=sel, Ws=Ws, source_ori=inv['source_ori'], use_fft=use_fft, Vh=Vh, @@ -313,7 +312,7 @@ lambda2=1.0 / 9.0, method="dSPM", nave=1, n_cycles=5, decim=1, use_fft=False, pick_ori=None, baseline=None, baseline_mode='logratio', pca=True, - n_jobs=1, zero_mean=False, prepared=False, + n_jobs=None, zero_mean=False, prepared=False, method_params=None, use_cps=True, verbose=None): """Compute induced power and phase lock. @@ -412,8 +411,8 @@ n_fft=2048, overlap=0.5, pick_ori=None, label=None, nave=1, pca=True, prepared=False, method_params=None, inv_split=None, bandwidth='hann', adaptive=False, - low_bias=False, n_jobs=1, return_sensor=False, dB=False, - verbose=None): + low_bias=False, n_jobs=None, return_sensor=False, + dB=False, *, verbose=None): """Compute source power spectral density (PSD). Parameters @@ -560,7 +559,7 @@ method="dSPM", fmin=0., fmax=200., pick_ori=None, label=None, nave=1, pca=True, inv_split=None, bandwidth=4., - adaptive=False, low_bias=True, n_jobs=1, + adaptive=False, low_bias=True, n_jobs=None, prepared=False, method_params=None, return_sensor=False, use_cps=True): """Generate compute_source_psd_epochs.""" @@ -620,7 +619,8 @@ if k == 0: freq_mask = (freqs >= fmin) & (freqs <= fmax) fstep = np.mean(np.diff(freqs)) - evoked_info['sfreq'] = 1. / fstep + with evoked_info._unlock(): + evoked_info['sfreq'] = 1. / fstep freqs = freqs[freq_mask] # sensor space PSD @@ -695,7 +695,7 @@ pick_ori=None, label=None, nave=1, pca=True, inv_split=None, bandwidth=4., adaptive=False, low_bias=True, - return_generator=False, n_jobs=1, + return_generator=False, n_jobs=None, prepared=False, method_params=None, return_sensor=False, use_cps=True, verbose=None): """Compute source power spectral density (PSD) from Epochs. diff -Nru python-mne-0.23.4+dfsg/mne/misc.py python-mne-1.1.0+dfsg/mne/misc.py --- python-mne-0.23.4+dfsg/mne/misc.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/misc.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors: Alexandre Gramfort # Scott Burns # -# License: BSD (3-clause) +# License: BSD-3-Clause def parse_config(fname): diff -Nru python-mne-0.23.4+dfsg/mne/morph_map.py python-mne-1.1.0+dfsg/mne/morph_map.py --- python-mne-0.23.4+dfsg/mne/morph_map.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/morph_map.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,233 @@ +# Authors: Matti Hämäläinen +# Alexandre Gramfort +# Matti Hämäläinen +# Denis A. Engemann +# +# License: BSD-3-Clause + +# Many of the computations in this code were derived from Matti Hämäläinen's +# C code. + +import os +import os.path as op + +import numpy as np + +from .io.constants import FIFF +from .io.open import fiff_open +from .io.tag import find_tag +from .io.tree import dir_tree_find +from .io.write import (start_block, end_block, write_string, + start_and_end_file, write_float_sparse_rcs, write_int) +from .surface import (read_surface, _triangle_neighbors, _compute_nearest, + _normalize_vectors, _get_tri_supp_geom, + _find_nearest_tri_pts) +from .utils import get_subjects_dir, warn, logger, verbose + + +@verbose +def read_morph_map(subject_from, subject_to, subjects_dir=None, xhemi=False, + verbose=None): + """Read morph map. + + Morph maps can be generated with mne_make_morph_maps. If one isn't + available, it will be generated automatically and saved to the + ``subjects_dir/morph_maps`` directory. + + Parameters + ---------- + subject_from : str + Name of the original subject as named in the SUBJECTS_DIR. + subject_to : str + Name of the subject on which to morph as named in the SUBJECTS_DIR. + subjects_dir : str + Path to SUBJECTS_DIR is not set in the environment. + xhemi : bool + Morph across hemisphere. Currently only implemented for + ``subject_to == subject_from``. See notes of + :func:`mne.compute_source_morph`. + %(verbose)s + + Returns + ------- + left_map, right_map : ~scipy.sparse.csr_matrix + The morph maps for the 2 hemispheres. + """ + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + + # First check for morph-map dir existence + mmap_dir = op.join(subjects_dir, 'morph-maps') + if not op.isdir(mmap_dir): + try: + os.mkdir(mmap_dir) + except Exception: + warn('Could not find or make morph map directory "%s"' % mmap_dir) + + # filename components + if xhemi: + if subject_to != subject_from: + raise NotImplementedError( + "Morph-maps between hemispheres are currently only " + "implemented for subject_to == subject_from") + map_name_temp = '%s-%s-xhemi' + log_msg = 'Creating morph map %s -> %s xhemi' + else: + map_name_temp = '%s-%s' + log_msg = 'Creating morph map %s -> %s' + + map_names = [map_name_temp % (subject_from, subject_to), + map_name_temp % (subject_to, subject_from)] + + # find existing file + fname = None + for map_name in map_names: + fname = op.join(mmap_dir, '%s-morph.fif' % map_name) + if op.exists(fname): + return _read_morph_map(fname, subject_from, subject_to) + # if file does not exist, make it + logger.info('Morph map "%s" does not exist, creating it and saving it to ' + 'disk' % fname) + logger.info(log_msg % (subject_from, subject_to)) + mmap_1 = _make_morph_map(subject_from, subject_to, subjects_dir, xhemi) + if subject_to == subject_from: + mmap_2 = None + else: + logger.info(log_msg % (subject_to, subject_from)) + mmap_2 = _make_morph_map(subject_to, subject_from, subjects_dir, + xhemi) + _write_morph_map(fname, subject_from, subject_to, mmap_1, mmap_2) + return mmap_1 + + +def _read_morph_map(fname, subject_from, subject_to): + """Read a morph map from disk.""" + f, tree, _ = fiff_open(fname) + with f as fid: + # Locate all maps + maps = dir_tree_find(tree, FIFF.FIFFB_MNE_MORPH_MAP) + if len(maps) == 0: + raise ValueError('Morphing map data not found') + + # Find the correct ones + left_map = None + right_map = None + for m in maps: + tag = find_tag(fid, m, FIFF.FIFF_MNE_MORPH_MAP_FROM) + if tag.data == subject_from: + tag = find_tag(fid, m, FIFF.FIFF_MNE_MORPH_MAP_TO) + if tag.data == subject_to: + # Names match: which hemishere is this? + tag = find_tag(fid, m, FIFF.FIFF_MNE_HEMI) + if tag.data == FIFF.FIFFV_MNE_SURF_LEFT_HEMI: + tag = find_tag(fid, m, FIFF.FIFF_MNE_MORPH_MAP) + left_map = tag.data + logger.info(' Left-hemisphere map read.') + elif tag.data == FIFF.FIFFV_MNE_SURF_RIGHT_HEMI: + tag = find_tag(fid, m, FIFF.FIFF_MNE_MORPH_MAP) + right_map = tag.data + logger.info(' Right-hemisphere map read.') + + if left_map is None or right_map is None: + raise ValueError('Could not find both hemispheres in %s' % fname) + + return left_map, right_map + + +def _write_morph_map(fname, subject_from, subject_to, mmap_1, mmap_2): + """Write a morph map to disk.""" + try: + with start_and_end_file(fname) as fid: + _write_morph_map_(fid, subject_from, subject_to, mmap_1, mmap_2) + except Exception as exp: + warn('Could not write morph-map file "%s" (error: %s)' + % (fname, exp)) + + +def _write_morph_map_(fid, subject_from, subject_to, mmap_1, mmap_2): + assert len(mmap_1) == 2 + hemis = [FIFF.FIFFV_MNE_SURF_LEFT_HEMI, FIFF.FIFFV_MNE_SURF_RIGHT_HEMI] + for m, hemi in zip(mmap_1, hemis): + start_block(fid, FIFF.FIFFB_MNE_MORPH_MAP) + write_string(fid, FIFF.FIFF_MNE_MORPH_MAP_FROM, subject_from) + write_string(fid, FIFF.FIFF_MNE_MORPH_MAP_TO, subject_to) + write_int(fid, FIFF.FIFF_MNE_HEMI, hemi) + write_float_sparse_rcs(fid, FIFF.FIFF_MNE_MORPH_MAP, m) + end_block(fid, FIFF.FIFFB_MNE_MORPH_MAP) + # don't write mmap_2 if it is identical (subject_to == subject_from) + if mmap_2 is not None: + assert len(mmap_2) == 2 + for m, hemi in zip(mmap_2, hemis): + start_block(fid, FIFF.FIFFB_MNE_MORPH_MAP) + write_string(fid, FIFF.FIFF_MNE_MORPH_MAP_FROM, subject_to) + write_string(fid, FIFF.FIFF_MNE_MORPH_MAP_TO, subject_from) + write_int(fid, FIFF.FIFF_MNE_HEMI, hemi) + write_float_sparse_rcs(fid, FIFF.FIFF_MNE_MORPH_MAP, m) + end_block(fid, FIFF.FIFFB_MNE_MORPH_MAP) + + +def _make_morph_map(subject_from, subject_to, subjects_dir, xhemi): + """Construct morph map from one subject to another. + + Note that this is close, but not exactly like the C version. + For example, parts are more accurate due to double precision, + so expect some small morph-map differences! + + Note: This seems easily parallelizable, but the overhead + of pickling all the data structures makes it less efficient + than just running on a single core :( + """ + subjects_dir = get_subjects_dir(subjects_dir) + if xhemi: + reg = '%s.sphere.left_right' + hemis = (('lh', 'rh'), ('rh', 'lh')) + else: + reg = '%s.sphere.reg' + hemis = (('lh', 'lh'), ('rh', 'rh')) + + return [_make_morph_map_hemi(subject_from, subject_to, subjects_dir, + reg % hemi_from, reg % hemi_to) + for hemi_from, hemi_to in hemis] + + +def _make_morph_map_hemi(subject_from, subject_to, subjects_dir, reg_from, + reg_to): + """Construct morph map for one hemisphere.""" + from scipy.sparse import csr_matrix, eye as speye + # add speedy short-circuit for self-maps + if subject_from == subject_to and reg_from == reg_to: + fname = op.join(subjects_dir, subject_from, 'surf', reg_from) + n_pts = len(read_surface(fname, verbose=False)[0]) + return speye(n_pts, n_pts, format='csr') + + # load surfaces and normalize points to be on unit sphere + fname = op.join(subjects_dir, subject_from, 'surf', reg_from) + from_rr, from_tri = read_surface(fname, verbose=False) + fname = op.join(subjects_dir, subject_to, 'surf', reg_to) + to_rr = read_surface(fname, verbose=False)[0] + _normalize_vectors(from_rr) + _normalize_vectors(to_rr) + + # from surface: get nearest neighbors, find triangles for each vertex + nn_pts_idx = _compute_nearest(from_rr, to_rr, method='cKDTree') + from_pt_tris = _triangle_neighbors(from_tri, len(from_rr)) + from_pt_tris = [from_pt_tris[pt_idx].astype(int) for pt_idx in nn_pts_idx] + from_pt_lens = np.cumsum([0] + [len(x) for x in from_pt_tris]) + from_pt_tris = np.concatenate(from_pt_tris) + assert from_pt_tris.ndim == 1 + assert from_pt_lens[-1] == len(from_pt_tris) + + # find triangle in which point lies and assoc. weights + tri_inds = [] + weights = [] + tri_geom = _get_tri_supp_geom(dict(rr=from_rr, tris=from_tri)) + weights, tri_inds = _find_nearest_tri_pts( + to_rr, from_pt_tris, from_pt_lens, run_all=False, reproject=False, + **tri_geom) + + nn_idx = from_tri[tri_inds] + weights = np.array(weights) + + row_ind = np.repeat(np.arange(len(to_rr)), 3) + this_map = csr_matrix((weights.ravel(), (row_ind, nn_idx.ravel())), + shape=(len(to_rr), len(from_rr))) + return this_map diff -Nru python-mne-0.23.4+dfsg/mne/morph.py python-mne-1.1.0+dfsg/mne/morph.py --- python-mne-0.23.4+dfsg/mne/morph.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/morph.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,26 +2,27 @@ # Alexandre Gramfort # Eric Larson -# License: BSD (3-clause) +# License: BSD-3-Clause +import copy import os.path as op import warnings -import copy + import numpy as np from .fixes import _get_img_fdata +from .morph_map import read_morph_map from .parallel import parallel_func from .source_estimate import ( _BaseSurfaceSourceEstimate, _BaseVolSourceEstimate, _BaseSourceEstimate, _get_ico_tris) from .source_space import SourceSpaces, _ensure_src, _grid_interp -from .surface import read_morph_map, mesh_edges, read_surface, _compute_nearest -from .transforms import _angle_between_quats, rot_to_quat +from .surface import mesh_edges, read_surface, _compute_nearest from .utils import (logger, verbose, check_version, get_subjects_dir, warn as warn_, fill_doc, _check_option, _validate_type, - BunchConst, wrapped_stdout, _check_fname, warn, - _ensure_int, ProgressBar, use_log_level) -from .externals.h5io import read_hdf5, write_hdf5 + BunchConst, _check_fname, warn, _custom_lru_cache, + _ensure_int, ProgressBar, use_log_level, + _import_h5io_funcs) @verbose @@ -292,11 +293,11 @@ 'subject_from', 'subject_to', 'kind', 'zooms', 'niter_affine', 'niter_sdr', 'spacing', 'smooth', 'xhemi', 'morph_mat', 'vertices_to', 'shape', 'affine', 'pre_affine', 'sdr_morph', 'src_data', - 'vol_morph_mat', 'verbose'] + 'vol_morph_mat'] @fill_doc -class SourceMorph(object): +class SourceMorph: """Morph source space data from one subject to another. .. note:: This class should not be instantiated directly. @@ -360,11 +361,12 @@ .. footbibliography:: """ + @verbose def __init__(self, subject_from, subject_to, kind, zooms, niter_affine, niter_sdr, spacing, smooth, xhemi, morph_mat, vertices_to, shape, affine, pre_affine, sdr_morph, src_data, - vol_morph_mat, verbose=None): + vol_morph_mat, *, verbose=None): # universal self.subject_from = subject_from self.subject_to = subject_to @@ -387,7 +389,6 @@ # used by both self.src_data = src_data self.vol_morph_mat = vol_morph_mat - self.verbose = verbose # compute vertices_to here (partly for backward compat and no src # provided) if vertices_to is None or len(vertices_to) == 0 and kind == 'volume': @@ -432,7 +433,7 @@ mri_space : bool | None Whether the image to world registration should be in mri space. The default (None) is mri_space=mri_resolution. - %(verbose_meth)s + %(verbose)s Returns ------- @@ -473,7 +474,7 @@ Parameters ---------- - %(verbose_meth)s + %(verbose)s Returns ------- @@ -646,8 +647,9 @@ The stem of the file name. '-morph.h5' will be added if fname does not end with '.h5'. %(overwrite)s - %(verbose_meth)s + %(verbose)s """ + _, write_hdf5 = _import_h5io_funcs() fname = _check_fname(fname, overwrite=overwrite, must_exist=False) if not fname.endswith('.h5'): fname = '%s-morph.h5' % fname @@ -663,8 +665,10 @@ def _debug_img(data, affine, title, shape=None): - # XXX uncomment these lines for debugging help with volume morph + # Uncomment these lines for debugging help with volume morph: + # # import nibabel as nib + # from scipy import sparse # if sparse.issparse(data): # data = data.toarray() # data = np.asarray(data) @@ -741,6 +745,7 @@ source_morph : instance of SourceMorph The loaded morph. """ + read_hdf5, _ = _import_h5io_funcs() vals = read_hdf5(fname) if vals['pre_affine'] is not None: # reconstruct from dipy.align.imaffine import AffineMap @@ -1003,94 +1008,20 @@ ############################################################################### # Morph for VolSourceEstimate -def _compute_r2(a, b): - return 100 * (a.ravel() @ b.ravel()) / \ - (np.linalg.norm(a) * np.linalg.norm(b)) - - def _compute_morph_sdr(mri_from, mri_to, niter_affine, niter_sdr, zooms): """Get a matrix that morphs data from one subject to another.""" - with np.testing.suppress_warnings(): - from dipy.align import imaffine, imwarp, metrics, transforms - from dipy.align.reslice import reslice - - logger.info('Computing nonlinear Symmetric Diffeomorphic Registration...') - - # reslice mri_from to zooms - mri_from_orig = mri_from - mri_from, mri_from_affine = reslice( - _get_img_fdata(mri_from_orig), mri_from_orig.affine, - mri_from_orig.header.get_zooms()[:3], zooms) - - # reslice mri_to to zooms - mri_to, affine = reslice( - _get_img_fdata(mri_to), mri_to.affine, - mri_to.header.get_zooms()[:3], zooms) - - mri_to /= mri_to.max() - mri_from /= mri_from.max() # normalize - - # compute center of mass - c_of_mass = imaffine.transform_centers_of_mass( - mri_to, affine, mri_from, mri_from_affine) - - # set up Affine Registration - affreg = imaffine.AffineRegistration( - metric=imaffine.MutualInformationMetric(nbins=32), - level_iters=list(niter_affine), - sigmas=[3.0, 1.0, 0.0], - factors=[4, 2, 1]) - - # translation - logger.info('Optimizing translation:') - with wrapped_stdout(indent=' ', cull_newlines=True): - translation = affreg.optimize( - mri_to, mri_from, transforms.TranslationTransform3D(), None, - affine, mri_from_affine, starting_affine=c_of_mass.affine) - - # rigid body transform (translation + rotation) - logger.info('Optimizing rigid-body:') - with wrapped_stdout(indent=' ', cull_newlines=True): - rigid = affreg.optimize( - mri_to, mri_from, transforms.RigidTransform3D(), None, - affine, mri_from_affine, starting_affine=translation.affine) - mri_from_to = rigid.transform(mri_from) - dist = np.linalg.norm(rigid.affine[:3, 3]) - angle = np.rad2deg(_angle_between_quats( - np.zeros(3), rot_to_quat(rigid.affine[:3, :3]))) - - logger.info(f' Translation: {dist:6.1f} mm') - logger.info(f' Rotation: {angle:6.1f}°') - logger.info(f' R²: {_compute_r2(mri_to, mri_from_to):6.1f}%') - - # affine transform (translation + rotation + scaling) - logger.info('Optimizing full affine:') - with wrapped_stdout(indent=' ', cull_newlines=True): - pre_affine = affreg.optimize( - mri_to, mri_from, transforms.AffineTransform3D(), None, - affine, mri_from_affine, starting_affine=rigid.affine) - mri_from_to = pre_affine.transform(mri_from) - logger.info(f' R²: {_compute_r2(mri_to, mri_from_to):6.1f}%') - - # SDR - shape = tuple(pre_affine.domain_shape) - if len(niter_sdr): - sdr = imwarp.SymmetricDiffeomorphicRegistration( - metrics.CCMetric(3), list(niter_sdr)) - logger.info('Optimizing SDR:') - with wrapped_stdout(indent=' ', cull_newlines=True): - sdr_morph = sdr.optimize(mri_to, pre_affine.transform(mri_from)) - assert shape == tuple(sdr_morph.domain_shape) # should be tuple of int - mri_from_to = sdr_morph.transform(mri_from_to) - else: - sdr_morph = None - - logger.info(f' R²: {_compute_r2(mri_to, mri_from_to):6.1f}%') - _debug_img(mri_from_orig.dataobj, mri_from_orig.affine, 'From') - _debug_img(mri_from, affine, 'From-reslice') - _debug_img(mri_from_to, affine, 'From-reslice') - _debug_img(mri_to, affine, 'To-reslice') - return shape, zooms, affine, pre_affine, sdr_morph + from .transforms import _compute_volume_registration + from dipy.align.imaffine import AffineMap + pipeline = 'all' if niter_sdr else 'affines' + niter = dict(translation=niter_affine, rigid=niter_affine, + affine=niter_affine, + sdr=niter_sdr if niter_sdr else (1,)) + pre_affine, sdr_morph, to_shape, to_affine, from_shape, from_affine = \ + _compute_volume_registration( + mri_from, mri_to, zooms=zooms, niter=niter, pipeline=pipeline) + pre_affine = AffineMap( + pre_affine, to_shape, to_affine, from_shape, from_affine) + return to_shape, zooms, to_affine, pre_affine, sdr_morph def _compute_morph_matrix(subject_from, subject_to, vertices_from, vertices_to, @@ -1135,18 +1066,29 @@ def _hemi_morph(tris, vertices_to, vertices_from, smooth, maps, warn): from scipy import sparse + _validate_type(smooth, (str, None, 'int-like'), 'smoothing steps') if len(vertices_from) == 0: return sparse.csr_matrix((len(vertices_to), 0)) e = mesh_edges(tris) e.data[e.data == 2] = 1 n_vertices = e.shape[0] - e = e + sparse.eye(n_vertices) + e += sparse.eye(n_vertices, format='csr') if isinstance(smooth, str): _check_option('smooth', smooth, ('nearest',), extra=' when used as a string.') mm = _surf_nearest(vertices_from, e).tocsr() + elif smooth == 0: + mm = sparse.csc_matrix( + (np.ones(len(vertices_from)), # data, indices, indptr + vertices_from, + np.arange(len(vertices_from) + 1)), + shape=(e.shape[0], len(vertices_from))).tocsr() else: - mm = _surf_upsampling_mat(vertices_from, e, smooth, warn=warn) + mm, n_missing, n_iter = _surf_upsampling_mat(vertices_from, e, smooth) + if n_missing and warn: + warn_(f'{n_missing}/{e.shape[0]} vertices not included in ' + 'smoothing, consider increasing the number of steps') + logger.info(f' {n_iter} smooth iterations done.') assert mm.shape == (n_vertices, len(vertices_from)) if maps is not None: mm = maps[vertices_to] * mm @@ -1157,7 +1099,7 @@ @verbose -def grade_to_vertices(subject, grade, subjects_dir=None, n_jobs=1, +def grade_to_vertices(subject, grade, subjects_dir=None, n_jobs=None, verbose=None): """Convert a grade to source space vertices for a given subject. @@ -1230,6 +1172,8 @@ return vertices +# Takes ~20 ms to hash, ~100 ms to compute (5x speedup) +@_custom_lru_cache(20) def _surf_nearest(vertices, adj_mat): from scipy import sparse from scipy.sparse.csgraph import dijkstra @@ -1257,7 +1201,12 @@ data.data /= np.where(row_norm, row_norm, 1).repeat(np.diff(data.indptr)) -def _surf_upsampling_mat(idx_from, e, smooth, warn=True): +# upsamplers are generally not very big (< 1 MB), and users might have a lot +# For 5 smoothing steps for example: +# smoothing_steps=5 takes ~20 ms to hash, ~100 ms to compute (5x speedup) +# smoothing_steps=None takes ~20 ms to hash, ~400 ms to compute (20x speedup) +@_custom_lru_cache(20) +def _surf_upsampling_mat(idx_from, e, smooth): """Upsample data on a subject's surface given mesh edges.""" # we're in CSR format and it's to==from from scipy import sparse @@ -1270,9 +1219,9 @@ _validate_type(smooth, ('int-like', str, None), 'smoothing steps') if smooth is not None: # number of steps smooth = _ensure_int(smooth, 'smoothing steps') - if smooth < 1: + if smooth <= 0: # == 0 is handled in a shortcut above raise ValueError( - 'The number of smoothing operations has to be at least 1, got ' + 'The number of smoothing operations has to be at least 0, got ' f'{smooth}') smooth = smooth - 1 # idx will gradually expand from idx_from -> np.arange(n_tot) @@ -1302,11 +1251,9 @@ if k == smooth or (smooth is None and len(idx) == n_tot): break # last iteration / done assert data.shape == (n_tot, len(idx_from)) - if len(idx) != n_tot and warn: - warn_(f'{n_tot-len(idx)}/{n_tot} vertices not included in smoothing, ' - 'consider increasing the number of steps') - logger.info(f' {k + 1} smooth iterations done.') - return data + n_missing = n_tot - len(idx) + n_iter = k + 1 + return data, n_missing, n_iter def _sparse_argmax_nnz_row(csr_mat): diff -Nru python-mne-0.23.4+dfsg/mne/_ola.py python-mne-1.1.0+dfsg/mne/_ola.py --- python-mne-0.23.4+dfsg/mne/_ola.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/_ola.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # Authors: Eric Larson -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np @@ -218,7 +218,7 @@ return store -class _COLA(object): +class _COLA: r"""Constant overlap-add processing helper. Parameters @@ -263,7 +263,7 @@ @verbose def __init__(self, process, store, n_total, n_samples, n_overlap, - sfreq, window='hann', tol=1e-10, verbose=None): + sfreq, window='hann', tol=1e-10, *, verbose=None): from scipy.signal import get_window n_samples = _ensure_int(n_samples, 'n_samples') n_overlap = _ensure_int(n_overlap, 'n_overlap') @@ -310,7 +310,6 @@ if delta > 0: logger.info(' The final %0.3f sec will be lumped into the ' 'final window' % (delta / sfreq,)) - self.verbose = verbose @property def _in_offset(self): @@ -318,7 +317,7 @@ return self.starts[self._idx] + self._in_buffers[0].shape[-1] @verbose - def feed(self, *datas, **kwargs): + def feed(self, *datas, verbose=None, **kwargs): """Pass in a chunk of data.""" # Append to our input buffer if self._in_buffers is None: diff -Nru python-mne-0.23.4+dfsg/mne/parallel.py python-mne-1.1.0+dfsg/mne/parallel.py --- python-mne-0.23.4+dfsg/mne/parallel.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/parallel.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,22 +5,17 @@ # License: Simplified BSD import logging +import multiprocessing import os from . import get_config -from .utils import logger, verbose, warn, ProgressBar -from .utils.check import int_like -from .fixes import _get_args - -if 'MNE_FORCE_SERIAL' in os.environ: - _force_serial = True -else: - _force_serial = None +from .utils import (logger, verbose, warn, ProgressBar, _validate_type, + _check_option, _ensure_int, deprecated) @verbose def parallel_func(func, n_jobs, max_nbytes='auto', pre_dispatch='n_jobs', - total=None, prefer=None, verbose=None): + total=None, prefer=None, *, max_jobs=None, verbose=None): """Return parallel instance with delayed function. Util function to use joblib only if available @@ -29,8 +24,7 @@ ---------- func : callable A function. - n_jobs : int - Number of jobs to run in parallel. + %(n_jobs)s max_nbytes : int, str, or None Threshold on the minimum size of arrays passed to the workers that triggers automated memory mapping. Can be an int in Bytes, @@ -46,9 +40,13 @@ If None (default), do not add a progress bar. prefer : str | None If str, can be "processes" or "threads". See :class:`joblib.Parallel`. - Ignored if the joblib version is too old to support this. .. versionadded:: 0.18 + max_jobs : int | None + The upper limit of jobs to use. This is useful when you know ahead + of a the maximum number of calls into :class:`joblib.Parallel` that + you will possibly want or need, and the returned ``n_jobs`` should not + exceed this value regardless of how many jobs the user requests. %(verbose)s INFO or DEBUG will print parallel status, others will not. @@ -59,58 +57,48 @@ my_func: callable ``func`` if not parallel or delayed(func). n_jobs: int - Number of jobs >= 0. + Number of jobs >= 1. """ should_print = (logger.level <= logging.INFO) # for a single job, we don't need joblib + _validate_type(n_jobs, ('int-like', None)) if n_jobs != 1: try: from joblib import Parallel, delayed except ImportError: - try: - from sklearn.externals.joblib import Parallel, delayed - except ImportError: + if n_jobs is not None: warn('joblib not installed. Cannot run in parallel.') - n_jobs = 1 + n_jobs = 1 if n_jobs == 1: n_jobs = 1 my_func = func parallel = list else: # check if joblib is recent enough to support memmaping - p_args = _get_args(Parallel.__init__) - joblib_mmap = ('temp_folder' in p_args and 'max_nbytes' in p_args) - cache_dir = get_config('MNE_CACHE_DIR', None) if isinstance(max_nbytes, str) and max_nbytes == 'auto': max_nbytes = get_config('MNE_MEMMAP_MIN_SIZE', None) - if max_nbytes is not None: - if not joblib_mmap and cache_dir is not None: - warn('"MNE_CACHE_DIR" is set but a newer version of joblib is ' - 'needed to use the memmapping pool.') - if joblib_mmap and cache_dir is None: - logger.info( - 'joblib supports memapping pool but "MNE_CACHE_DIR" ' - 'is not set in MNE-Python config. To enable it, use, ' - 'e.g., mne.set_cache_dir(\'/tmp/shm\'). This will ' - 'store temporary files under /dev/shm and can result ' - 'in large memory savings.') + if max_nbytes is not None and cache_dir is None: + logger.info( + 'joblib supports memapping pool but "MNE_CACHE_DIR" ' + 'is not set in MNE-Python config. To enable it, use, ' + 'e.g., mne.set_cache_dir(\'/tmp/shm\'). This will ' + 'store temporary files under /dev/shm and can result ' + 'in large memory savings.') # create keyword arguments for Parallel kwargs = {'verbose': 5 if should_print and total is None else 0} kwargs['pre_dispatch'] = pre_dispatch - if 'prefer' in p_args: - kwargs['prefer'] = prefer - - if joblib_mmap: - if cache_dir is None: - max_nbytes = None # disable memmaping - kwargs['temp_folder'] = cache_dir - kwargs['max_nbytes'] = max_nbytes - - n_jobs = check_n_jobs(n_jobs) - parallel = _check_wrapper(Parallel(n_jobs, **kwargs)) + kwargs['prefer'] = prefer + if cache_dir is None: + max_nbytes = None # disable memmaping + kwargs['temp_folder'] = cache_dir + kwargs['max_nbytes'] = max_nbytes + parallel = Parallel(n_jobs, **kwargs) + n_jobs = _check_n_jobs(parallel.n_jobs) + if max_jobs is not None: + n_jobs = min(n_jobs, max(_ensure_int(max_jobs, 'max_jobs'), 1)) my_func = delayed(func) if total is not None: @@ -122,22 +110,9 @@ return parallel_out, my_func, n_jobs -def _check_wrapper(fun): - def run(*args, **kwargs): - try: - return fun(*args, **kwargs) - except RuntimeError as err: - msg = str(err.args[0]) if err.args else '' - if msg.startswith('The task could not be sent to the workers'): - raise RuntimeError( - msg + ' Consider using joblib memmap caching to get ' - 'around this problem. See mne.set_mmap_min_size, ' - 'mne.set_cache_dir, and buffer_size parallel function ' - 'arguments (if applicable).') - raise - return run - - +# this isn't really meant to be public but it's easy enough to deprecate +@deprecated('check_n_jobs is deprecated and will be removed in 1.1, use ' + 'parallel_func directly') def check_n_jobs(n_jobs, allow_cuda=False): """Check n_jobs in particular for negative values. @@ -154,29 +129,30 @@ The checked number of jobs. Always positive (or 'cuda' if applicable). """ - if not isinstance(n_jobs, int_like): - if not allow_cuda: - raise ValueError('n_jobs must be an integer') - elif not isinstance(n_jobs, str) or n_jobs != 'cuda': - raise ValueError('n_jobs must be an integer, or "cuda"') - # else, we have n_jobs='cuda' and this is okay, so do nothing - elif _force_serial: + types = ('int-like', None) + if allow_cuda: + types = types + ('str',) + _validate_type(n_jobs, types, 'n_jobs') + if isinstance(n_jobs, str): + # We can only be in this path if allow_cuda + _check_option('n_jobs', n_jobs, ('cuda',), extra='when str') + return 'cuda' # return 'cuda' + return _check_n_jobs(n_jobs) + + +def _check_n_jobs(n_jobs): + n_jobs = _ensure_int(n_jobs, 'n_jobs', must_be='an int or None') + if os.getenv('MNE_FORCE_SERIAL', '').lower() in ('true', '1') and \ + n_jobs != 1: n_jobs = 1 logger.info('... MNE_FORCE_SERIAL set. Processing in forced ' 'serial mode.') elif n_jobs <= 0: - try: - import multiprocessing - n_cores = multiprocessing.cpu_count() - n_jobs = min(n_cores + n_jobs + 1, n_cores) - if n_jobs <= 0: - raise ValueError('If n_jobs has a negative value it must not ' - 'be less than the number of CPUs present. ' - 'You\'ve got %s CPUs' % n_cores) - except ImportError: - # only warn if they tried to use something other than 1 job - if n_jobs != 1: - warn('multiprocessing not installed. Cannot run in parallel.') - n_jobs = 1 - + n_cores = multiprocessing.cpu_count() + n_jobs_orig = n_jobs + n_jobs = min(n_cores + n_jobs + 1, n_cores) + if n_jobs <= 0: + raise ValueError( + f'If n_jobs has a non-positive value ({n_jobs_orig}) it must ' + f'not be less than the number of CPUs present ({n_cores})') return n_jobs diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/annotate_amplitude.py python-mne-1.1.0+dfsg/mne/preprocessing/annotate_amplitude.py --- python-mne-0.23.4+dfsg/mne/preprocessing/annotate_amplitude.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/annotate_amplitude.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,254 @@ +# Author: Mathieu Scheltienne +# +# License: BSD-3-Clause + +import numpy as np + +from ..fixes import jit +from ..io import BaseRaw +from ..annotations import (Annotations, _adjust_onset_meas_date, + _annotations_starts_stops) +from ..io.pick import _picks_to_idx, _picks_by_type, _get_channel_types +from ..utils import _validate_type, verbose, logger, _mask_to_onsets_offsets + + +@verbose +def annotate_amplitude(raw, peak=None, flat=None, bad_percent=5, + min_duration=0.005, picks=None, *, verbose=None): + """Annotate raw data based on peak-to-peak amplitude. + + Creates annotations ``BAD_peak`` or ``BAD_flat`` for spans of data where + consecutive samples exceed the threshold in ``peak`` or fall below the + threshold in ``flat`` for more than ``min_duration``. + Channels where more than ``bad_percent`` of the total recording length + should be annotated with either ``BAD_peak`` or ``BAD_flat`` are returned + in ``bads`` instead. + Note that the annotations and the bads are not automatically added to the + :class:`~mne.io.Raw` object; use :meth:`~mne.io.Raw.set_annotations` and + :class:`info['bads'] ` to do so. + + Parameters + ---------- + raw : instance of Raw + The raw data. + peak : float | dict | None + Annotate segments based on **maximum** peak-to-peak signal amplitude + (PTP). Valid **keys** can be any channel type present in the object. + The **values** are floats that set the maximum acceptable PTP. If the + PTP is larger than this threshold, the segment will be annotated. + If float, the minimum acceptable PTP is applied to all channels. + flat : float | dict | None + Annotate segments based on **minimum** peak-to-peak signal amplitude + (PTP). Valid **keys** can be any channel type present in the object. + The **values** are floats that set the minimum acceptable PTP. If the + PTP is smaller than this threshold, the segment will be annotated. + If float, the minimum acceptable PTP is applied to all channels. + bad_percent : float + The percentage of the time a channel can be above or below thresholds. + Below this percentage, :class:`~mne.Annotations` are created. + Above this percentage, the channel involved is return in ``bads``. Note + the returned ``bads`` are not automatically added to + :class:`info['bads'] `. + Defaults to ``5`` (5%%). + min_duration : float + The minimum duration (sec) required by consecutives samples to be above + ``peak`` or below ``flat`` thresholds to be considered. + to consider as above or below threshold. + For some systems, adjacent time samples with exactly the same value are + not totally uncommon. Defaults to ``0.005`` (5 ms). + %(picks_good_data)s + %(verbose)s + + Returns + ------- + annotations : instance of Annotations + The annotated bad segments. + bads : list + The channels detected as bad. + + Notes + ----- + This function does not use a window to detect small peak-to-peak or large + peak-to-peak amplitude changes as the ``reject`` and ``flat`` argument from + :class:`~mne.Epochs` does. Instead, it looks at the difference between + consecutive samples. + + - When used to detect segments below ``flat``, at least ``min_duration`` + seconds of consecutive samples must respect + ``abs(a[i+1] - a[i]) ≤ flat``. + - When used to detect segments above ``peak``, at least ``min_duration`` + seconds of consecutive samples must respect + ``abs(a[i+1] - a[i]) ≥ peak``. + + Thus, this function does not detect every temporal event with large + peak-to-peak amplitude, but only the ones where the peak-to-peak amplitude + is supra-threshold between consecutive samples. For instance, segments + experiencing a DC shift will not be picked up. Only the edges from the DC + shift will be annotated (and those only if the edge transitions are longer + than ``min_duration``). + + This function may perform faster if data is loaded in memory, as it + loads data one channel type at a time (across all time points), which is + typically not an efficient way to read raw data from disk. + + .. versionadded:: 1.0 + """ + _validate_type(raw, BaseRaw, 'raw') + picks_ = _picks_to_idx(raw.info, picks, 'data_or_ica', exclude='bads') + peak = _check_ptp(peak, 'peak', raw.info, picks_) + flat = _check_ptp(flat, 'flat', raw.info, picks_) + if peak is None and flat is None: + raise ValueError( + "At least one of the arguments 'peak' or 'flat' must not be None.") + bad_percent = _check_bad_percent(bad_percent) + min_duration = _check_min_duration(min_duration, + raw.times.size * 1 / raw.info['sfreq']) + min_duration_samples = int(np.round(min_duration * raw.info['sfreq'])) + bads = list() + + # grouping picks by channel types to avoid operating on each channel + # individually + picks = { + ch_type: np.intersect1d(picks_of_type, picks_, assume_unique=True) + for ch_type, picks_of_type in _picks_by_type(raw.info, exclude='bads') + if np.intersect1d(picks_of_type, picks_, assume_unique=True).size != 0 + } + del picks_ # re-using this variable name in for loop + + # skip BAD_acq_skip sections + onsets, ends = _annotations_starts_stops(raw, 'bad_acq_skip', invert=True) + index = np.concatenate([np.arange(raw.times.size)[onset:end] + for onset, end in zip(onsets, ends)]) + + # size matching the diff a[i+1] - a[i] + any_flat = np.zeros(len(raw.times) - 1, bool) + any_peak = np.zeros(len(raw.times) - 1, bool) + + # look for discrete difference above or below thresholds + logger.info('Finding segments below or above PTP threshold.') + for ch_type, picks_ in picks.items(): + data = np.concatenate([raw[picks_, onset:end][0] + for onset, end in zip(onsets, ends)], axis=1) + diff = np.abs(np.diff(data, axis=1)) + + if flat is not None: + flat_ = diff <= flat[ch_type] + # reject too short segments + flat_ = _reject_short_segments(flat_, min_duration_samples) + # reject channels above maximum bad_percentage + flat_count = flat_.sum(axis=1) + flat_count[np.nonzero(flat_count)] += 1 # offset by 1 due to diff + flat_mean = flat_count / raw.times.size * 100 + flat_ch_to_set_bad = picks_[np.where(flat_mean >= bad_percent)[0]] + bads.extend(flat_ch_to_set_bad) + # add onset/offset for annotations + flat_ch_to_annotate = \ + np.where((0 < flat_mean) & (flat_mean < bad_percent))[0] + # convert from raw.times[onset:end] - 1 to raw.times[:] - 1 + idx = index[np.where(flat_[flat_ch_to_annotate, :])[1]] + any_flat[idx] = True + + if peak is not None: + peak_ = diff >= peak[ch_type] + # reject too short segments + peak_ = _reject_short_segments(peak_, min_duration_samples) + # reject channels above maximum bad_percentage + peak_count = peak_.sum(axis=1) + peak_count[np.nonzero(peak_count)] += 1 # offset by 1 due to diff + peak_mean = peak_count / raw.times.size * 100 + peak_ch_to_set_bad = picks_[np.where(peak_mean >= bad_percent)[0]] + bads.extend(peak_ch_to_set_bad) + # add onset/offset for annotations + peak_ch_to_annotate = \ + np.where((0 < peak_mean) & (peak_mean < bad_percent))[0] + # convert from raw.times[onset:end] - 1 to raw.times[:] - 1 + idx = index[np.where(peak_[peak_ch_to_annotate, :])[1]] + any_peak[idx] = True + + # annotation for flat + annotation_flat = _create_annotations(any_flat, 'flat', raw) + # annotation for peak + annotation_peak = _create_annotations(any_peak, 'peak', raw) + # group + annotations = annotation_flat + annotation_peak + # bads + bads = [raw.ch_names[bad] for bad in bads if bad not in raw.info['bads']] + + return annotations, bads + + +def _check_ptp(ptp, name, info, picks): + """Check the PTP threhsold argument, and converts it to dict if needed.""" + _validate_type(ptp, ('numeric', dict, None)) + + if ptp is not None and not isinstance(ptp, dict): + if ptp < 0: + raise ValueError( + f"Argument '{name}' should define a positive threshold. " + f"Provided: '{ptp}'.") + ch_types = set(_get_channel_types(info, picks)) + ptp = {ch_type: ptp for ch_type in ch_types} + elif isinstance(ptp, dict): + for key, value in ptp.items(): + if value < 0: + raise ValueError( + f"Argument '{name}' should define positive thresholds. " + f"Provided for channel type '{key}': '{value}'.") + return ptp + + +def _check_bad_percent(bad_percent): + """Check that bad_percent is a valid percentage and converts to float.""" + _validate_type(bad_percent, 'numeric', 'bad_percent') + bad_percent = float(bad_percent) + if not 0 <= bad_percent <= 100: + raise ValueError( + "Argument 'bad_percent' should define a percentage between 0% " + f"and 100%. Provided: {bad_percent}%.") + return bad_percent + + +def _check_min_duration(min_duration, raw_duration): + """Check that min_duration is a valid duration and converts to float.""" + _validate_type(min_duration, 'numeric', 'min_duration') + min_duration = float(min_duration) + if min_duration < 0: + raise ValueError( + "Argument 'min_duration' should define a positive duration in " + f"seconds. Provided: '{min_duration}' seconds.") + if min_duration >= raw_duration: + raise ValueError( + "Argument 'min_duration' should define a positive duration in " + f"seconds shorter than the raw duration ({raw_duration} seconds). " + f"Provided: '{min_duration}' seconds.") + return min_duration + + +def _reject_short_segments(arr, min_duration_samples): + """Check if flat or peak segments are longer than the minimum duration.""" + assert arr.dtype == bool and arr.ndim == 2 + for k, ch in enumerate(arr): + onsets, offsets = _mask_to_onsets_offsets(ch) + _mark_inner(arr[k], onsets, offsets, min_duration_samples) + return arr + + +@jit() +def _mark_inner(arr_k, onsets, offsets, min_duration_samples): + """Inner loop of _reject_short_segments().""" + for start, stop in zip(onsets, offsets): + if stop - start < min_duration_samples: + arr_k[start:stop] = False + + +def _create_annotations(any_arr, kind, raw): + """Create the peak of flat annotations from the any_arr.""" + assert kind in ('peak', 'flat') + starts, stops = _mask_to_onsets_offsets(any_arr) + starts, stops = np.array(starts), np.array(stops) + onsets = starts / raw.info['sfreq'] + durations = (stops - starts) / raw.info['sfreq'] + annot = Annotations(onsets, durations, [f'BAD_{kind}'] * len(onsets), + orig_time=raw.info['meas_date']) + _adjust_onset_meas_date(annot, raw) + return annot diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/annotate_nan.py python-mne-1.1.0+dfsg/mne/preprocessing/annotate_nan.py --- python-mne-0.23.4+dfsg/mne/preprocessing/annotate_nan.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/annotate_nan.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,37 @@ +# Author: David Julien +# +# License: BSD-3-Clause + +import numpy as np + +from ..annotations import Annotations, _adjust_onset_meas_date +from ..utils import verbose +from .artifact_detection import _annotations_from_mask + + +@verbose +def annotate_nan(raw, *, verbose=None): + """Detect segments with NaN and return a new Annotations instance. + + Parameters + ---------- + raw : instance of Raw + Data to find segments with NaN values. + %(verbose)s + + Returns + ------- + annot : instance of Annotations + New channel-specific annotations for the data. + """ + data, times = raw.get_data(return_times=True) + onsets, durations, ch_names = list(), list(), list() + for row, ch_name in zip(data, raw.ch_names): + annot = _annotations_from_mask(times, np.isnan(row), 'BAD_NAN') + onsets.extend(annot.onset) + durations.extend(annot.duration) + ch_names.extend([[ch_name]] * len(annot)) + annot = Annotations(onsets, durations, 'BAD_NAN', ch_names=ch_names, + orig_time=raw.info['meas_date']) + _adjust_onset_meas_date(annot, raw) + return annot diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/artifact_detection.py python-mne-1.1.0+dfsg/mne/preprocessing/artifact_detection.py --- python-mne-0.23.4+dfsg/mne/preprocessing/artifact_detection.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/artifact_detection.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,20 +1,24 @@ # Authors: Adonay Nunes # Luke Bloy -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np -from ..annotations import (Annotations, _annotations_starts_stops) + +from ..io.base import BaseRaw +from ..annotations import (Annotations, _annotations_starts_stops, + annotations_from_events, _adjust_onset_meas_date) from ..transforms import (quat_to_rot, _average_quats, _angle_between_quats, apply_trans, _quat_to_affine) from ..filter import filter_data from .. import Transform -from ..utils import (_mask_to_onsets_offsets, logger, verbose) +from ..utils import (_mask_to_onsets_offsets, logger, verbose, _validate_type, + _pl) @verbose def annotate_muscle_zscore(raw, threshold=4, ch_type=None, min_length_good=0.1, - filter_freq=(110, 140), n_jobs=1, verbose=None): + filter_freq=(110, 140), n_jobs=None, verbose=None): """Create annotations for segments that likely contain muscle artifacts. Detects data segments containing activity in the frequency range given by @@ -112,8 +116,10 @@ if len(l_idx) < min_samps: art_mask[l_idx] = True - annot = _annotations_from_mask(raw_copy.times, art_mask, 'BAD_muscle') - + annot = _annotations_from_mask(raw_copy.times, + art_mask, 'BAD_muscle', + orig_time=raw.info['meas_date']) + _adjust_onset_meas_date(annot, raw) return annot, scores_muscle @@ -158,12 +164,11 @@ compute_average_dev_head_t """ sfreq = raw.info['sfreq'] - hp_ts = pos[:, 0].copy() - hp_ts -= raw.first_samp / sfreq + hp_ts = pos[:, 0].copy() - raw.first_time dt = np.diff(hp_ts) hp_ts = np.concatenate([hp_ts, [hp_ts[-1] + 1. / sfreq]]) - - annot = Annotations([], [], [], orig_time=None) # rel to data start + orig_time = raw.info['meas_date'] + annot = Annotations([], [], [], orig_time=orig_time) # Annotate based on rotational velocity t_tot = raw.times[-1] @@ -180,7 +185,8 @@ u'ω >= %5.1f°/s (max: %0.1f°/s)' % (bad_pct, len(onsets), rotation_velocity_limit, np.rad2deg(r.max()))) - annot += _annotations_from_mask(hp_ts, bad_mask, 'BAD_mov_rotat_vel') + annot += _annotations_from_mask( + hp_ts, bad_mask, 'BAD_mov_rotat_vel', orig_time=orig_time) # Annotate based on translational velocity limit if translation_velocity_limit is not None: @@ -195,7 +201,8 @@ u'v >= %5.4fm/s (max: %5.4fm/s)' % (bad_pct, len(onsets), translation_velocity_limit, v.max())) - annot += _annotations_from_mask(hp_ts, bad_mask, 'BAD_mov_trans_vel') + annot += _annotations_from_mask( + hp_ts, bad_mask, 'BAD_mov_trans_vel', orig_time=orig_time) # Annotate based on displacement from mean head position disp = [] @@ -238,7 +245,9 @@ logger.info(u'Omitting %5.1f%% (%3d segments): ' u'disp >= %5.4fm (max: %5.4fm)' % (bad_pct, len(onsets), mean_distance_limit, disp.max())) - annot += _annotations_from_mask(hp_ts, bad_mask, 'BAD_mov_dist') + annot += _annotations_from_mask( + hp_ts, bad_mask, 'BAD_mov_dist', orig_time=orig_time) + _adjust_onset_meas_date(annot, raw) return annot, disp @@ -316,20 +325,247 @@ return dev_head_t -def _annotations_from_mask(times, art_mask, art_name): +def _annotations_from_mask(times, mask, annot_name, orig_time=None): """Construct annotations from boolean mask of the data.""" - from scipy.ndimage import label - comps, num_comps = label(art_mask) - onsets, durations, desc = [], [], [] - n_times = len(times) - for lbl in range(1, num_comps + 1): - l_idx = np.nonzero(comps == lbl)[0] - onsets.append(times[l_idx[0]]) - # duration is to the time after the last labeled time - # or to the end of the times. - if 1 + l_idx[-1] < n_times: - durations.append(times[1 + l_idx[-1]] - times[l_idx[0]]) + from scipy.ndimage import distance_transform_edt + from scipy.signal import find_peaks + mask_tf = distance_transform_edt(mask) + # Overcome the shortcoming of find_peaks + # in finding a marginal peak, by + # inserting 0s at the front and the + # rear, then subtracting in index + ins_mask_tf = np.concatenate((np.zeros(1), mask_tf, np.zeros(1))) + left_midpt_index = find_peaks(ins_mask_tf)[0] - 1 + right_midpt_index = np.flip(len(ins_mask_tf) - 1 - find_peaks( + ins_mask_tf[::-1])[0]) - 1 + onsets_index = left_midpt_index - mask_tf[left_midpt_index].astype(int) + 1 + ends_index = right_midpt_index + mask_tf[right_midpt_index].astype(int) + # Ensure onsets_index >= 0, + # otherwise the duration starts from the beginning + onsets_index[onsets_index < 0] = 0 + # Ensure ends_index < len(times), + # otherwise the duration is to the end of times + if len(times) == len(mask): + ends_index[ends_index >= len(times)] = len(times) - 1 + # To be consistent with the original code, + # possibly a bug in tests code + else: + ends_index[ends_index >= len(mask)] = len(mask) + onsets = times[onsets_index] + ends = times[ends_index] + durations = ends - onsets + desc = [annot_name] * len(durations) + return Annotations(onsets, durations, desc, orig_time=orig_time) + + +@verbose +def annotate_break(raw, events=None, + min_break_duration=15., + t_start_after_previous=5., + t_stop_before_next=5., + ignore=('bad', 'edge'), + *, + verbose=None): + """Create `~mne.Annotations` for breaks in an ongoing recording. + + This function first searches for segments in the data that are not + annotated or do not contain any events and are at least + ``min_break_duration`` seconds long, and then proceeds to creating + annotations for those break periods. + + Parameters + ---------- + raw : instance of Raw + The continuous data to analyze. + events : None | array, shape (n_events, 3) + If ``None`` (default), operate based solely on the annotations present + in ``raw``. If an events array, ignore any annotations in the raw data, + and operate based on these events only. + min_break_duration : float + The minimum time span in seconds between the offset of one and the + onset of the subsequent annotation (if ``events`` is ``None``) or + between two consecutive events (if ``events`` is an array) to consider + this period a "break". Defaults to 15 seconds. + + .. note:: This value defines the minimum duration of a break period in + the data, **not** the minimum duration of the generated + annotations! See also ``t_start_after_previous`` and + ``t_stop_before_next`` for details. + + t_start_after_previous, t_stop_before_next : float + Specifies how far the to-be-created "break" annotation extends towards + the two annotations or events spanning the break. This can be used to + ensure e.g. that the break annotation doesn't start and end immediately + with a stimulation event. If, for example, your data contains a break + of 30 seconds between two stimuli, and ``t_start_after_previous`` is + set to ``5`` and ``t_stop_before_next`` is set to ``3``, the break + annotation will start 5 seconds after the first stimulus, and end 3 + seconds before the second stimulus, yielding an annotated break of + ``30 - 5 - 3 = 22`` seconds. Both default to 5 seconds. + + .. note:: The beginning and the end of the recording will be annotated + as breaks, too, if the period from recording start until the + first annotation or event (or from last annotation or event + until recording end) is at least ``min_break_duration`` + seconds long. + + ignore : iterable of str + Annotation descriptions starting with these strings will be ignored by + the break-finding algorithm. The string comparison is case-insensitive, + i.e., ``('bad',)`` and ``('BAD',)`` are equivalent. By default, all + annotation descriptions starting with "bad" and annotations + indicating "edges" (produced by data concatenation) will be + ignored. Pass an empty list or tuple to take all existing annotations + into account. If ``events`` is passed, this parameter has no effect. + %(verbose)s + + Returns + ------- + break_annotations : instance of Annotations + The break annotations, each with the description ``'BAD_break'``. If + no breaks could be found given the provided function parameters, an + empty `~mne.Annotations` object will be returned. + + Notes + ----- + .. versionadded:: 0.24 + """ + _validate_type(item=raw, item_name='raw', types=BaseRaw, type_name='Raw') + _validate_type(item=events, item_name='events', types=(None, np.ndarray)) + + if min_break_duration - t_start_after_previous - t_stop_before_next <= 0: + annot_dur = (min_break_duration - t_start_after_previous - + t_stop_before_next) + raise ValueError( + f'The result of ' + f'min_break_duration - t_start_after_previous - ' + f't_stop_before_next must be greater than 0, but it is: ' + f'{annot_dur}' + ) + + if events is not None and events.size == 0: + raise ValueError('The events array must not be empty.') + + if events is not None or not ignore: + ignore = tuple() + else: + ignore = tuple(ignore) + + for item in ignore: + _validate_type(item=item, types='str', + item_name='All elements of "ignore"') + + if events is None: + annotations = raw.annotations.copy() + if ignore: + logger.info(f'Ignoring annotations with descriptions starting ' + f'with: {", ".join(ignore)}') + else: + annotations = annotations_from_events( + events=events, + sfreq=raw.info['sfreq'], + orig_time=raw.info['meas_date'] + ) + + if not annotations: + raise ValueError('Could not find (or generate) any annotations in ' + 'your data.') + + # Only keep annotations of interest and extract annotated time periods + # Ignore case + ignore = tuple(i.lower() for i in ignore) + keep_mask = [True] * len(annotations) + for idx, description in enumerate(annotations.description): + description = description.lower() + if any(description.startswith(i) for i in ignore): + keep_mask[idx] = False + + annotated_intervals = [ + [onset, onset + duration] for onset, duration in + zip(annotations.onset[keep_mask], annotations.duration[keep_mask]) + ] + + # Merge overlapping annotation intervals + # Pre-load `merged_intervals` with the first interval to simplify + # processing + merged_intervals = [annotated_intervals[0]] + for interval in annotated_intervals: + merged_interval_stop = merged_intervals[-1][1] + interval_start, interval_stop = interval + + if interval_stop < merged_interval_stop: + # Current interval ends sooner than the merged one; skip it + continue + elif (interval_start <= merged_interval_stop and + interval_stop >= merged_interval_stop): + # Expand duration of the merged interval + merged_intervals[-1][1] = interval_stop else: - durations.append(times[l_idx[-1]] - times[l_idx[0]]) - desc.append(art_name) - return Annotations(onsets, durations, desc) + # No overlap between the current interval and the existing merged + # time period; proceed to the next interval + merged_intervals.append(interval) + + merged_intervals = np.array(merged_intervals) + merged_intervals -= raw.first_time # work in zero-based time + + # Now extract the actual break periods + break_onsets = [] + break_durations = [] + + # Handle the time period up until the first annotation + if (0 < merged_intervals[0][0] and + merged_intervals[0][0] >= min_break_duration): + onset = 0 # don't add t_start_after_previous here + offset = merged_intervals[0][0] - t_stop_before_next + duration = offset - onset + break_onsets.append(onset) + break_durations.append(duration) + + # Handle the time period between first and last annotation + for idx, _ in enumerate(merged_intervals[1:, :], start=1): + this_start = merged_intervals[idx, 0] + previous_stop = merged_intervals[idx - 1, 1] + if this_start - previous_stop < min_break_duration: + continue + + onset = previous_stop + t_start_after_previous + offset = this_start - t_stop_before_next + duration = offset - onset + break_onsets.append(onset) + break_durations.append(duration) + + # Handle the time period after the last annotation + if (raw.times[-1] > merged_intervals[-1][1] and + raw.times[-1] - merged_intervals[-1][1] >= min_break_duration): + onset = merged_intervals[-1][1] + t_start_after_previous + offset = raw.times[-1] # don't subtract t_stop_before_next here + duration = offset - onset + break_onsets.append(onset) + break_durations.append(duration) + + # Finally, create the break annotations + break_annotations = Annotations( + onset=break_onsets, + duration=break_durations, + description=['BAD_break'], + orig_time=raw.info['meas_date'], + ) + + # Log some info + n_breaks = len(break_annotations) + break_times = [ + f'{o:.1f} – {o+d:.1f} sec [{d:.1f} sec]' + for o, d in zip(break_annotations.onset, + break_annotations.duration) + ] + break_times = '\n '.join(break_times) + total_break_dur = sum(break_annotations.duration) + fraction_breaks = total_break_dur / raw.times[-1] + logger.info(f'\nDetected {n_breaks} break period{_pl(n_breaks)} of >= ' + f'{min_break_duration} sec duration:\n {break_times}\n' + f'In total, {round(100 * fraction_breaks, 1):.1f}% of the ' + f'data ({round(total_break_dur, 1):.1f} sec) have been marked ' + f'as a break.\n') + _adjust_onset_meas_date(break_annotations, raw) + + return break_annotations diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/bads.py python-mne-1.1.0+dfsg/mne/preprocessing/bads.py --- python-mne-0.23.4+dfsg/mne/preprocessing/bads.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/bads.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,5 +1,5 @@ # Authors: Denis Engemann -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/_csd.py python-mne-1.1.0+dfsg/mne/preprocessing/_csd.py --- python-mne-0.23.4+dfsg/mne/preprocessing/_csd.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/_csd.py 2022-08-03 15:48:14.000000000 +0000 @@ -9,16 +9,17 @@ # Authors: Denis A. Engeman # Alex Rockhill # -# License: Relicensed under BSD (3-clause) and adapted with +# License: Relicensed under BSD-3-Clause and adapted with # permission from authors of original GPL code import numpy as np from .. import pick_types -from ..utils import _validate_type, _ensure_int, _check_preload +from ..utils import (_validate_type, _ensure_int, _check_preload, verbose, + logger) from ..io import BaseRaw from ..io.constants import FIFF -from ..epochs import BaseEpochs +from ..epochs import BaseEpochs, make_fixed_length_epochs from ..evoked import Evoked from ..bem import fit_sphere_to_headshape from ..channels.interpolation import _calc_g, _calc_h @@ -51,14 +52,18 @@ return X +@verbose def compute_current_source_density(inst, sphere='auto', lambda2=1e-5, stiffness=4, n_legendre_terms=50, - copy=True): + copy=True, *, verbose=None): """Get the current source density (CSD) transformation. Transformation based on spherical spline surface Laplacian :footcite:`PerrinEtAl1987,PerrinEtAl1989,Cohen2014,KayserTenke2015`. + This function can be used to re-reference the signal using a Laplacian + (LAP) "reference-free" transformation. + Parameters ---------- inst : instance of Raw, Epochs or Evoked @@ -75,6 +80,7 @@ Number of Legendre terms to evaluate. copy : bool Whether to overwrite instance data or create a copy. + %(verbose)s Returns ------- @@ -167,8 +173,134 @@ epochs = [inst._data] if not isinstance(inst, BaseEpochs) else inst._data for epo in epochs: epo[picks] = np.dot(trans_csd, epo[picks]) - inst.info['custom_ref_applied'] = FIFF.FIFFV_MNE_CUSTOM_REF_CSD + with inst.info._unlock(): + inst.info['custom_ref_applied'] = FIFF.FIFFV_MNE_CUSTOM_REF_CSD for pick in picks: inst.info['chs'][pick].update(coil_type=FIFF.FIFFV_COIL_EEG_CSD, unit=FIFF.FIFF_UNIT_V_M2) + + # Remove rejection thresholds for EEG + if isinstance(inst, BaseEpochs): + if inst.reject and 'eeg' in inst.reject: + del inst.reject['eeg'] + if inst.flat and 'eeg' in inst.flat: + del inst.flat['eeg'] + return inst + + +@verbose +def compute_bridged_electrodes(inst, lm_cutoff=16, epoch_threshold=0.5, + l_freq=0.5, h_freq=30, epoch_duration=2, + bw_method=None, verbose=None): + r"""Compute bridged EEG electrodes using the intrinsic Hjorth algorithm. + + First an electrical distance matrix is computed by taking the pairwise + variance. Then, a local maximum near 0 :math:`{\mu}V^2` and a + a local minimum below 5 :math:`{\mu}V^2` are found, the presence + of which is indicative of bridging. Finally, electrode distances below + the local minimum are marked as bridged as long as they happen on more + than the ``epoch_threshold`` proportion of epochs. + + Based on :footcite:`TenkeKayser2001,GreischarEtAl2004,DelormeMakeig2004` + and the `EEGLAB implementation + `_. + + Parameters + ---------- + inst : instance of Raw, Epochs or Evoked + The data to compute electrode bridging on. + lm_cutoff : float + The distance in :math:`{\mu}V^2` cutoff below which to + search for a local minimum (lm) indicative of bridging. + Defaults to 16 :math:`{\mu}V^2` to be conservative based + on the distributions in :footcite:`GreischarEtAl2004`. + epoch_threshold : float + The proportion of epochs with electrical distance less than + ``ed_threshold`` in order to consider the channel bridged. + The default is 0.5. + l_freq : float + The low cutoff frequency to use. Default is 0.5 Hz. + h_freq : float + The high cutoff frequency to use. Default is 30 Hz. + epoch_duration : float + The time in seconds to divide the raw into fixed-length epochs + to check for consistent bridging. Only used if ``inst`` is + :class:`mne.io.BaseRaw`. The default is 2 seconds. + bw_method : None + ``bw_method`` to pass to :class:`scipy.stats.gaussian_kde`. + %(verbose)s + + Returns + ------- + bridged_idx : list of tuple + The indices of channels marked as bridged with each bridged + pair stored as a tuple. + ed_matrix : ndarray of float, shape (n_epochs, n_channels, n_channels) + The electrical distance matrix for each pair of EEG electrodes. + + Notes + ----- + .. versionadded:: 1.1 + + References + ---------- + .. footbibliography:: + """ + from scipy.stats import gaussian_kde + from scipy.optimize import minimize_scalar + _check_preload(inst, 'Computing bridged electrodes') + inst = inst.copy() # don't modify original + picks = pick_types(inst.info, eeg=True) + if len(picks) == 0: + raise RuntimeError('No EEG channels found, cannot compute ' + 'electrode bridging') + # first, filter + inst.filter(l_freq=l_freq, h_freq=h_freq, picks=picks, verbose=False) + + if isinstance(inst, BaseRaw): + inst = make_fixed_length_epochs(inst, duration=epoch_duration, + preload=True, verbose=False) + + # standardize shape + data = inst.get_data(picks=picks) + if isinstance(inst, Evoked): + data = data[np.newaxis, ...] # expand evoked + + # next, compute electrical distance matrix, upper triangular + n_epochs = data.shape[0] + ed_matrix = np.zeros((n_epochs, picks.size, picks.size)) * np.nan + for i in range(picks.size): + for j in range(i + 1, picks.size): + ed_matrix[:, i, j] = np.var(data[:, i] - data[:, j], axis=1) + + # scale, fill in other half, diagonal + ed_matrix *= 1e12 # scale to muV**2 + + # initialize bridged indices + bridged_idx = list() + + # if not enough values below local minimum cutoff, return no bridges + ed_flat = ed_matrix[~np.isnan(ed_matrix)] + if ed_flat[ed_flat < lm_cutoff].size / n_epochs < epoch_threshold: + return bridged_idx, ed_matrix + + # kernel density estimation + kde = gaussian_kde(ed_flat[ed_flat < lm_cutoff]) + with np.errstate(invalid='ignore'): + local_minimum = float(minimize_scalar( + lambda x: kde(x) if x < lm_cutoff and x > 0 else np.inf).x) + logger.info(f'Local minimum {local_minimum} found') + + # find electrodes that are below the cutoff local minimum on + # `epochs_threshold` proportion of epochs + for i in range(picks.size): + for j in range(i + 1, picks.size): + bridged_count = np.sum(ed_matrix[:, i, j] < local_minimum) + if bridged_count / n_epochs > epoch_threshold: + logger.info('Bridge detected between ' + f'{inst.ch_names[picks[i]]} and ' + f'{inst.ch_names[picks[j]]}') + bridged_idx.append((picks[i], picks[j])) + + return bridged_idx, ed_matrix diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/_css.py python-mne-1.1.0+dfsg/mne/preprocessing/_css.py --- python-mne-0.23.4+dfsg/mne/preprocessing/_css.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/_css.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,91 @@ +# Author: John Samuelsson + +import numpy as np + +from ..evoked import Evoked +from ..io.pick import _picks_to_idx +from ..utils import verbose, _validate_type, _ensure_int + + +def _temp_proj(ref_2, ref_1, raw_data, n_proj=6): + # Orthonormalize gradiometer and magnetometer data by a QR decomposition + ref_1_orth = np.linalg.qr(ref_1.T)[0] + ref_2_orth = np.linalg.qr(ref_2.T)[0] + + # Calculate cross-correlation + cross_corr = np.dot(ref_1_orth.T, ref_2_orth) + + # Channel weights for common temporal subspace by SVD of cross-correlation + ref_1_ch_weights, _, _ = np.linalg.svd(cross_corr) + + # Get temporal signals from channel weights + proj_mat = ref_1_orth @ ref_1_ch_weights + + # Project out common subspace + filtered_data = raw_data + proj_vec = proj_mat[:, :n_proj] + weights = filtered_data @ proj_vec + filtered_data -= weights @ proj_vec.T + + +@verbose +def cortical_signal_suppression(evoked, picks=None, mag_picks=None, + grad_picks=None, n_proj=6, *, verbose=None): + """Apply cortical signal suppression (CSS) to evoked data. + + Parameters + ---------- + evoked : instance of Evoked + The evoked object to use for CSS. Must contain magnetometer, + gradiometer, and EEG channels. + %(picks_good_data)s + mag_picks : array-like of int + Array of the magnetometer channel indices that will be used to find + the reference data. If None (default), all magnetometers will + be used. + grad_picks : array-like of int + Array of the gradiometer channel indices that will be used to find + the reference data. If None (default), all gradiometers will + be used. + n_proj : int + The number of projection vectors. + %(verbose)s + + Returns + ------- + evoked_subcortical : instance of Evoked + The evoked object with cortical contributions to the EEG data + suppressed. + + Notes + ----- + This method removes the common signal subspace between the magnetometer + data and the gradiometer data from the EEG data. This is done by a temporal + projection using ``n_proj`` number of projection vectors. For reference, + see :footcite:`Samuelsson2019`. + + References + ---------- + .. footbibliography:: + """ + _validate_type(evoked, Evoked, 'evoked') + n_proj = _ensure_int(n_proj, 'n_proj') + picks = _picks_to_idx( + evoked.info, picks, none='data', exclude='bads') + mag_picks = _picks_to_idx( + evoked.info, mag_picks, none='mag', exclude='bads') + grad_picks = _picks_to_idx( + evoked.info, grad_picks, none='grad', exclude='bads') + evoked_subcortical = evoked.copy() + + # Get data + all_data = evoked.data + mag_data = all_data[mag_picks] + grad_data = all_data[grad_picks] + + # Process data with temporal projection algorithm + data = all_data[picks] + _temp_proj(mag_data, grad_data, data, n_proj=n_proj) + evoked_subcortical.data[picks, :] = data + + return evoked_subcortical diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/ecg.py python-mne-1.1.0+dfsg/mne/preprocessing/ecg.py --- python-mne-0.23.4+dfsg/mne/preprocessing/ecg.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/ecg.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,12 +2,12 @@ # Denis Engemann # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np from ..annotations import _annotations_starts_stops -from ..utils import logger, verbose, sum_squared, warn +from ..utils import logger, verbose, sum_squared, warn, int_like from ..filter import filter_data from ..epochs import Epochs, BaseEpochs from ..io.base import BaseRaw @@ -42,8 +42,8 @@ Low pass frequency h_freq : float High pass frequency - %(ecg_tstart)s - %(ecg_filter_length)s + %(tstart_ecg)s + %(filter_length_ecg)s %(verbose)s Returns @@ -147,15 +147,15 @@ ---------- raw : instance of Raw The raw data. - %(ecg_event_id)s - %(ecg_ch_name)s - %(ecg_tstart)s - %(ecg_filter_freqs)s + %(event_id_ecg)s + %(ch_name_ecg)s + %(tstart_ecg)s + %(l_freq_ecg_filter)s qrs_threshold : float | str Between 0 and 1. qrs detection threshold. Can also be "auto" to automatically choose the threshold that generates a reasonable number of heartbeats (40-160 beats / min). - %(ecg_filter_length)s + %(filter_length_ecg)s return_ecg : bool Return the ECG data. This is especially useful if no ECG channel is present in the input data, so one will be synthesized. Defaults to @@ -191,7 +191,7 @@ % raw.ch_names[idx_ecg]) ecg = raw.get_data(picks=idx_ecg) else: - ecg = _make_ecg(raw, None, None)[0] + ecg, _ = _make_ecg(raw, start=None, stop=None) assert ecg.ndim == 2 and ecg.shape[0] == 1 ecg = ecg[0] # Deal with filtering the same way we do in raw, i.e. filter each good @@ -291,14 +291,14 @@ ---------- raw : instance of Raw The raw data. - %(ecg_ch_name)s - %(ecg_event_id)s + %(ch_name_ecg)s + %(event_id_ecg)s %(picks_all)s tmin : float Start time before event. tmax : float End time after event. - %(ecg_filter_freqs)s + %(l_freq_ecg_filter)s %(reject_epochs)s %(flat)s %(baseline_epochs)s @@ -326,6 +326,17 @@ -------- find_ecg_events compute_proj_ecg + + Notes + ----- + If you already have a list of R-peak times, or want to compute R-peaks + outside MNE-Python using a different algorithm, the recommended approach is + to call the :class:`~mne.Epochs` constructor directly, with your R-peaks + formatted as an :term:`events` array (here we also demonstrate the relevant + default values):: + + mne.Epochs(raw, r_peak_events_array, tmin=-0.5, tmax=0.5, + baseline=None, preload=True, proj=False) # doctest: +SKIP """ has_ecg = 'ecg' in raw or ch_name is not None if keep_ecg and (has_ecg or not preload): @@ -351,10 +362,11 @@ ecg, create_info(ch_names=['ECG-SYN'], sfreq=raw.info['sfreq'], ch_types=['ecg']), first_samp=raw.first_samp) - ignore = ['ch_names', 'chs', 'nchan', 'bads'] - for k, v in raw.info.items(): - if k not in ignore: - ecg_raw.info[k] = v + with ecg_raw.info._unlock(): + ignore = ['ch_names', 'chs', 'nchan', 'bads'] + for k, v in raw.info.items(): + if k not in ignore: + ecg_raw.info[k] = v syn_epochs = Epochs(ecg_raw, events=ecg_epochs.events, event_id=event_id, tmin=tmin, tmax=tmax, proj=False, picks=[0], baseline=baseline, @@ -376,14 +388,38 @@ .format({'mag': 'Magnetometers', 'grad': 'Gradiometers'}[ch])) picks = pick_types(inst.info, meg=ch, eeg=False, ref_meg=False) + + # Handle start/stop + msg = ('integer arguments for the start and stop parameters are ' + 'not supported for Epochs and Evoked objects. Please ' + 'consider using float arguments specifying start and stop ' + 'time in seconds.') + begin_param_name = 'tmin' + if isinstance(start, int_like): + if isinstance(inst, BaseRaw): + # Raw has start param, can just use int + begin_param_name = 'start' + else: + raise ValueError(msg) + + end_param_name = 'tmax' + if isinstance(start, int_like): + if isinstance(inst, BaseRaw): + # Raw has stop param, can just use int + end_param_name = 'stop' + else: + raise ValueError(msg) + + kwargs = {begin_param_name: start, end_param_name: stop} + if isinstance(inst, BaseRaw): reject_by_annotation = 'omit' if reject_by_annotation else None - ecg, times = inst.get_data(picks, start, stop, reject_by_annotation, - True) + ecg, times = inst.get_data(picks, return_times=True, **kwargs, + reject_by_annotation=reject_by_annotation) elif isinstance(inst, BaseEpochs): - ecg = np.hstack(inst.copy().crop(start, stop).get_data()) + ecg = np.hstack(inst.copy().get_data(picks, **kwargs)) times = inst.times elif isinstance(inst, Evoked): - ecg = inst.data + ecg = inst.get_data(picks, **kwargs) times = inst.times return ecg.mean(0, keepdims=True), times diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/eog.py python-mne-1.1.0+dfsg/mne/preprocessing/eog.py --- python-mne-0.23.4+dfsg/mne/preprocessing/eog.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/eog.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,13 +2,13 @@ # Denis Engemann # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np from ._peak_finder import peak_finder from .. import pick_types, pick_channels -from ..utils import logger, verbose, _pl, warn, _validate_type +from ..utils import logger, verbose, _pl, _validate_type from ..filter import filter_data from ..epochs import Epochs @@ -34,7 +34,7 @@ High cut-off frequency to apply to the EOG channel in Hz. filter_length : str | int | None Number of taps to use for filtering. - %(eog_ch_name)s + %(ch_name_eog)s tstart : float Start detection after tstart seconds. reject_by_annotation : bool @@ -139,24 +139,10 @@ eog=True, ecg=False, emg=False, ref_meg=False, exclude='bads') if eog_inds.size == 0: - warn('No EOG channel found. Trying with EEG 061 and EEG 062. ' - 'This functionality will be removed in version 0.24', - DeprecationWarning) - eog_inds = pick_channels(inst.ch_names, - include=['EEG 061', 'EEG 062']) - if eog_inds.size == 0: - raise ValueError('Could not find any EOG channels.') - + raise RuntimeError('No EOG channel(s) found') ch_names = [inst.ch_names[i] for i in eog_inds] elif isinstance(ch_name, str): - if ',' in ch_name: - warn(f'You supplied multiple EOG channel names, separated by a ' - f'comma: {repr(ch_name)}. We will stop supporting this in ' - f'version 0.24. Please pass a list of channels instead: ' - f'{ch_name.split(",")}', DeprecationWarning) - ch_names = ch_name.split(',') - else: - ch_names = [ch_name] + ch_names = [ch_name] else: # it's a list ch_names = ch_name.copy() @@ -187,7 +173,7 @@ ---------- raw : instance of Raw The raw data. - %(eog_ch_name)s + %(ch_name_eog)s event_id : int The index to assign to found events. %(picks_all)s diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/_fine_cal.py python-mne-1.1.0+dfsg/mne/preprocessing/_fine_cal.py --- python-mne-0.23.4+dfsg/mne/preprocessing/_fine_cal.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/_fine_cal.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # Authors: Eric Larson -# License: BSD (3-clause) +# License: BSD-3-Clause from collections import defaultdict from functools import partial @@ -37,12 +37,12 @@ t_window : float Time window to use for surface normal rotation in seconds. Default is 10. - %(maxwell_ext)s + %(ext_order_maxwell)s Default is 2, which is lower than the default (3) for :func:`mne.preprocessing.maxwell_filter` because it tends to yield more stable parameter estimates. - %(maxwell_origin)s - %(maxwell_cross)s + %(origin_maxwell)s + %(cross_talk_maxwell)s calibration : dict | None Dictionary with existing calibration. If provided, the magnetometer imbalances and adjusted normals will be used and only the gradiometer diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/flat.py python-mne-1.1.0+dfsg/mne/preprocessing/flat.py --- python-mne-0.23.4+dfsg/mne/preprocessing/flat.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/flat.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,105 +0,0 @@ -# Authors: Eric Larson -# -# License: BSD (3-clause) - -import numpy as np - -from ..annotations import _annotations_starts_stops, Annotations -from ..io import BaseRaw -from ..io.pick import _picks_to_idx -from ..utils import (_validate_type, verbose, logger, _pl, - _mask_to_onsets_offsets, ProgressBar) - - -@verbose -def annotate_flat(raw, bad_percent=5., min_duration=0.005, picks=None, - verbose=None): - """Annotate flat segments of raw data (or add to a bad channel list). - - Parameters - ---------- - raw : instance of Raw - The raw data. - bad_percent : float - The percentage of the time a channel can be bad. - Below this percentage, temporal bad marking (:class:`~mne.Annotations`) - will be used. Above this percentage, spatial bad marking - (:class:`info['bads'] `) will be used. - Defaults to 5 (5%%). - min_duration : float - The minimum duration (sec) to consider as actually flat. - For some systems with low bit data representations, adjacent - time samples with exactly the same value are not totally uncommon. - Defaults to 0.005 (5 ms). - %(picks_good_data)s - %(verbose)s - - Returns - ------- - annot : instance of Annotations - The annotated bad segments. - bads : list - The channels detected as bad. - - Notes - ----- - This function is useful both for removing short segments of data where - the acquisition system clipped (i.e., hit the ADC limit of the hardware) - and for automatically identifying channels that were flat for a large - proportion of a given recording. - - This function may perform much faster if data are loaded - in memory, as it loads data one channel at a time (across all - time points), which is typically not an efficient way to read - raw data from disk. - - .. versionadded:: 0.18 - """ - _validate_type(raw, BaseRaw, 'raw') - bad_percent = float(bad_percent) - min_duration = float(min_duration) - picks = _picks_to_idx(raw.info, picks, 'data_or_ica', exclude='bads') - # This will not be so efficient for most readers, but we can optimize - # it later - any_flat = np.zeros(len(raw.times), bool) - bads = list() - time_thresh = int(np.round(min_duration * raw.info['sfreq'])) - onsets, ends = _annotations_starts_stops(raw, 'bad_acq_skip', invert=True) - idx = np.concatenate([np.arange(onset, end) - for onset, end in zip(onsets, ends)]) - logger.info('Finding flat segments') - for pick in ProgressBar(picks, mesg='Channels'): - data = np.concatenate([raw[pick, onset:end][0][0] - for onset, end in zip(onsets, ends)]) - flat = np.diff(data) == 0 - flat = np.concatenate( - [flat[[0]], flat[1:] | flat[:-1], flat[[-1]]]) - starts, stops = _mask_to_onsets_offsets(flat) - for start, stop in zip(starts, stops): - if stop - start < time_thresh: - flat[start:stop] = False - flat_mean = flat.mean() - if flat_mean: # only do something if there are actually flat parts - flat_mean *= 100 - if flat_mean > bad_percent: - kind, comp = 'bads', '>' - bads.append(raw.ch_names[pick]) - else: - kind, comp = 'BAD_', '≤' - any_flat[idx] |= flat - logger.debug('%s: %s (%s %s %s)' - % (kind, raw.ch_names[pick], - flat_mean, comp, bad_percent)) - starts, stops = _mask_to_onsets_offsets(any_flat) - logger.info('Marking %0.2f%% of time points (%d segment%s) and ' - '%d/%d channel%s bad%s' - % (100 * any_flat[idx].mean(), len(starts), _pl(starts), - len(bads), len(picks), _pl(bads), - (': %s' % (bads,)) if bads else '')) - bads = [bad for bad in bads if bad not in raw.info['bads']] - starts, stops = np.array(starts), np.array(stops) - onsets = (starts + raw.first_samp) / raw.info['sfreq'] - durations = (stops - starts) / raw.info['sfreq'] - annot = Annotations(onsets, durations, ['BAD_flat'] * len(onsets), - orig_time=raw.annotations.orig_time) - return annot, bads diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/ica.py python-mne-1.1.0+dfsg/mne/preprocessing/ica.py --- python-mne-0.23.4+dfsg/mne/preprocessing/ica.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/ica.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,16 +4,17 @@ # Alexandre Gramfort # Juergen Dammers # -# License: BSD (3-clause) +# License: BSD-3-Clause from inspect import isfunction from collections import namedtuple from copy import deepcopy from numbers import Integral from time import time +from dataclasses import dataclass +from typing import Optional, List import math -import os import json import numpy as np @@ -25,6 +26,7 @@ from ..cov import compute_whitener from .. import Covariance, Evoked +from ..defaults import _INTERPOLATION_DEFAULT from ..io.pick import (pick_types, pick_channels, pick_info, _picks_to_idx, _get_channel_types, _DATA_CH_TYPES_SPLIT) from ..io.proj import make_projector @@ -34,7 +36,7 @@ from ..io.tree import dir_tree_find from ..io.open import fiff_open from ..io.tag import read_tag -from ..io.meas_info import write_meas_info, read_meas_info +from ..io.meas_info import write_meas_info, read_meas_info, ContainsMixin from ..io.constants import FIFF from ..io.base import BaseRaw from ..io.eeglab.eeglab import _get_info, _check_load_mat @@ -45,21 +47,24 @@ from ..viz.ica import plot_ica_properties from ..viz.topomap import _plot_corrmap -from ..channels.channels import _contains_ch_type, ContainsMixin -from ..io.write import start_file, end_file, write_id -from ..utils import (check_version, logger, check_fname, verbose, +from ..channels.channels import _contains_ch_type +from ..channels.layout import _find_topomap_coords +from ..time_frequency import psd_welch, psd_multitaper +from ..io.write import start_and_end_file, write_id +from ..utils import (logger, check_fname, _check_fname, verbose, _reject_data_segments, check_random_state, _validate_type, compute_corr, _get_inst_data, _ensure_int, copy_function_doc_to_method_doc, _pl, warn, Bunch, _check_preload, _check_compensation_grade, fill_doc, - _check_option, _PCA, int_like, + _check_option, _PCA, int_like, _require_version, _check_all_same_channel_names) from ..fixes import _get_args, _safe_svd from ..filter import filter_data from .bads import _find_outliers from .ctps_ import ctps -from ..io.pick import pick_channels_regexp +from ..io.pick import pick_channels_regexp, _picks_by_type + __all__ = ('ICA', 'ica_find_ecg_events', 'ica_find_eog_events', 'get_score_funcs', 'read_ica', 'read_ica_eeglab') @@ -105,9 +110,13 @@ score_funcs.update({n: _make_xy_sfunc(f) for n, f in xy_arg_dist_funcs if _get_args(f) == ['u', 'v']}) + # In SciPy 1.9+, pearsonr has (u, v, *, alternative='two-sided'), so we + # should just look at the positional_only and positional_or_keyword entries + exclude = ('var_positional', 'var_keyword', 'keyword_only') score_funcs.update({n: _make_xy_sfunc(f, ndim_output=True) for n, f in xy_arg_stats_funcs - if _get_args(f) == ['x', 'y']}) + if _get_args(f, exclude=exclude) == ['x', 'y']}) + assert 'pearsonr' in score_funcs return score_funcs @@ -183,9 +192,7 @@ are scaled to unit variance ("z-standardized") as a group by channel type prior to the whitening by PCA. %(random_state)s - As estimation can be non-deterministic it can be useful to fix the - random state to have reproducible results. - method : {'fastica', 'infomax', 'picard'} + method : 'fastica' | 'infomax' | 'picard' The ICA method to use in the fit method. Use the ``fit_params`` argument to set additional parameters. Specifically, if you want Extended Infomax, set ``method='infomax'`` and ``fit_params=dict(extended=True)`` @@ -193,7 +200,9 @@ For reference, see :footcite:`Hyvarinen1999,BellSejnowski1995,LeeEtAl1999,AblinEtAl2018`. fit_params : dict | None Additional parameters passed to the ICA estimator as specified by - ``method``. + ``method``. Allowed entries are determined by the various algorithm + implementations: see :class:`~sklearn.decomposition.FastICA`, + :func:`~picard.picard`, :func:`~mne.preprocessing.infomax`. max_iter : int | 'auto' Maximum number of iterations during fit. If ``'auto'``, it will set maximum iterations to ``1000`` for ``'fastica'`` @@ -245,8 +254,7 @@ (There is also an ``exclude`` parameter in the :meth:`ICA.apply` method.) To scrap all marked components, set this attribute to an empty list. - info : None | instance of Info - The measurement info copied from the object fitted. + %(info)s n_samples_ : int The number of samples used on fit. labels_ : dict @@ -266,10 +274,9 @@ .. versionchanged:: 0.23 Warn if `~mne.Epochs` were baseline-corrected. - .. note:: If you intend to clean fit ICA on `~mne.Epochs`, it is - recommended to high-pass filter, but **not** baseline correct the - data for good ICA performance. A warning will be emitted - otherwise. + .. note:: If you intend to fit ICA on `~mne.Epochs`, it is recommended to + high-pass filter, but **not** baseline correct the data for good + ICA performance. A warning will be emitted otherwise. A trailing ``_`` in an attribute name signifies that the attribute was added to the object during fitting, consistent with standard scikit-learn @@ -375,19 +382,13 @@ @verbose def __init__(self, n_components=None, *, noise_cov=None, random_state=None, method='fastica', fit_params=None, - max_iter=None, allow_ref_meg=False, + max_iter='auto', allow_ref_meg=False, verbose=None): # noqa: D102 _validate_type(method, str, 'method') _validate_type(n_components, (float, 'int-like', None)) if method != 'imported_eeglab': # internal use only _check_option('method', method, _KNOWN_ICA_METHODS) - if method == 'fastica' and not check_version('sklearn'): - raise ImportError( - 'The scikit-learn package is required for method="fastica".') - if method == 'picard' and not check_version('picard'): - raise ImportError( - 'The python-picard package is required for method="picard".') self.noise_cov = noise_cov @@ -402,7 +403,6 @@ 'supported') self.current_fit = 'unfitted' - self.verbose = verbose self.n_components = n_components # In newer ICAs this should always be None, but keep it for # backward compat with older versions of MNE that used it @@ -424,15 +424,9 @@ # extended=True is default in underlying function, but we want # default False here unless user specified True: fit_params.setdefault('extended', False) - if max_iter is None: - depr_message = ('Version 0.23 introduced max_iter="auto", ' - 'setting max_iter=1000 for `fastica` and ' - 'max_iter=500 for `infomax` and `picard`. The ' - 'current default of max_iter=200 will be changed ' - 'to "auto" in version 0.24.') - warn(depr_message, DeprecationWarning) - max_iter = 200 - elif max_iter == 'auto': + _validate_type(max_iter, (str, 'int-like'), 'max_iter') + if isinstance(max_iter, str): + _check_option('max_iter', max_iter, ('auto',), 'when str') if method == 'fastica': max_iter = 1000 elif method in ['infomax', 'picard']: @@ -447,27 +441,103 @@ self.labels_ = dict() self.allow_ref_meg = allow_ref_meg - def __repr__(self): - """ICA fit information.""" + def _get_infos_for_repr(self): + @dataclass + class _InfosForRepr: + # XXX replace with Optional[Literal['raw data', 'epochs'] once we + # drop support for Py 3.7 + fit_on: Optional[str] + # XXX replace with fit_method: Literal['fastica', 'infomax', + # 'extended-infomax', 'picard'] once we drop support for Py 3.7 + fit_method: str + fit_n_iter: Optional[int] + fit_n_samples: Optional[int] + fit_n_components: Optional[int] + fit_n_pca_components: Optional[int] + fit_explained_variance: Optional[float] + ch_types: List[str] + excludes: List[str] + if self.current_fit == 'unfitted': - s = 'no' + fit_on = None elif self.current_fit == 'raw': - s = 'raw data' + fit_on = 'raw data' else: - s = 'epochs' - s += ' decomposition, ' - s += 'fit (%s): %s samples, ' % (self.method, - str(getattr(self, 'n_samples_', ''))) - s += ('%s components' % str(self.n_components_) if - hasattr(self, 'n_components_') else - 'no dimension reduction') + fit_on = 'epochs' + + fit_method = self.method + fit_n_iter = getattr(self, 'n_iter_', None) + fit_n_samples = getattr(self, 'n_samples_', None) + fit_n_components = getattr(self, 'n_components_', None) + fit_n_pca_components = getattr(self, 'pca_components_', None) + if fit_n_pca_components is not None: + fit_n_pca_components = len(self.pca_components_) + fit_explained_variance = getattr(self, 'pca_explained_variance_', None) + if fit_explained_variance is not None: + abs_vars = self.pca_explained_variance_ + rel_vars = abs_vars / abs_vars.sum() + fit_explained_variance = rel_vars[:fit_n_components].sum() + if self.info is not None: - ch_fit = ['"%s"' % c for c in _DATA_CH_TYPES_SPLIT if c in self] - s += ', channels used: {}'.format('; '.join(ch_fit)) + ch_types = [c for c in _DATA_CH_TYPES_SPLIT if c in self] + else: + ch_types = [] + if self.exclude: - s += ', %i sources marked for exclusion' % len(self.exclude) + excludes = [self._ica_names[i] for i in self.exclude] + else: + excludes = [] + + infos_for_repr = _InfosForRepr( + fit_on=fit_on, + fit_method=fit_method, + fit_n_iter=fit_n_iter, + fit_n_samples=fit_n_samples, + fit_n_components=fit_n_components, + fit_n_pca_components=fit_n_pca_components, + fit_explained_variance=fit_explained_variance, + ch_types=ch_types, + excludes=excludes + ) + return infos_for_repr - return '' % s + def __repr__(self): + """ICA fit information.""" + infos = self._get_infos_for_repr() + + s = (f'{infos.fit_on or "no"} decomposition, ' + f'method: {infos.fit_method}') + + if infos.fit_on is not None: + s += ( + f' (fit in {infos.fit_n_iter} iterations on ' + f'{infos.fit_n_samples} samples), ' + f'{infos.fit_n_components} ICA components ' + f'explaining {round(infos.fit_explained_variance * 100, 1)} % ' + f'of variance ' + f'({infos.fit_n_pca_components} PCA components available), ' + f'channel types: {", ".join(infos.ch_types)}, ' + f'{len(infos.excludes) or "no"} sources marked for exclusion' + ) + + return f'' + + def _repr_html_(self): + from ..html_templates import repr_templates_env + infos = self._get_infos_for_repr() + t = repr_templates_env.get_template('ica.html.jinja') + html = t.render( + fit_on=infos.fit_on, + method=infos.fit_method, + n_iter=infos.fit_n_iter, + n_samples=infos.fit_n_samples, + n_components=infos.fit_n_components, + n_pca_components=infos.fit_n_pca_components, + explained_variance=infos.fit_explained_variance, + ch_types=infos.ch_types, + excludes=infos.excludes + ) + return html @verbose def fit(self, inst, picks=None, start=None, stop=None, decim=None, @@ -486,48 +556,58 @@ The data to be decomposed. %(picks_good_data_noref)s This selection remains throughout the initialized ICA solution. - start : int | float | None - First sample to include. If float, data will be interpreted as - time in seconds. If None, data will be used from the first sample. - stop : int | float | None - Last sample to not include. If float, data will be interpreted as - time in seconds. If None, data will be used to the last sample. + start, stop : int | float | None + First and last sample to include. If float, data will be + interpreted as time in seconds. If ``None``, data will be used from + the first sample and to the last sample, respectively. + + .. note:: These parameters only have an effect if ``inst`` is + `~mne.io.Raw` data. decim : int | None - Increment for selecting each nth time slice. If None, all samples - within ``start`` and ``stop`` are used. - reject : dict | None - Rejection parameters based on peak-to-peak amplitude. - Valid keys are 'grad', 'mag', 'eeg', 'seeg', 'dbs', 'ecog', 'eog', - 'ecg', 'hbo', 'hbr'. - If reject is None then no rejection is done. Example:: - - reject = dict(grad=4000e-13, # T / m (gradiometers) - mag=4e-12, # T (magnetometers) - eeg=40e-6, # V (EEG channels) - eog=250e-6 # V (EOG channels) - ) - - It only applies if ``inst`` is of type Raw. - flat : dict | None - Rejection parameters based on flatness of signal. - Valid keys are 'grad', 'mag', 'eeg', 'seeg', 'dbs', 'ecog', 'eog', - 'ecg', 'hbo', 'hbr'. - Values are floats that set the minimum acceptable peak-to-peak - amplitude. If flat is None then no rejection is done. - It only applies if ``inst`` is of type Raw. + Increment for selecting only each n-th sampling point. If ``None``, + all samples between ``start`` and ``stop`` (inclusive) are used. + reject, flat : dict | None + Rejection parameters based on peak-to-peak amplitude (PTP) + in the continuous data. Signal periods exceeding the thresholds + in ``reject`` or less than the thresholds in ``flat`` will be + removed before fitting the ICA. + + .. note:: These parameters only have an effect if ``inst`` is + `~mne.io.Raw` data. For `~mne.Epochs`, perform PTP + rejection via :meth:`~mne.Epochs.drop_bad`. + + Valid keys are all channel types present in the data. Values must + be integers or floats. + + If ``None``, no PTP-based rejection will be performed. Example:: + + reject = dict( + grad=4000e-13, # T / m (gradiometers) + mag=4e-12, # T (magnetometers) + eeg=40e-6, # V (EEG channels) + eog=250e-6 # V (EOG channels) + ) + flat = None # no rejection based on flatness tstep : float Length of data chunks for artifact rejection in seconds. - It only applies if ``inst`` is of type Raw. + + .. note:: This parameter only has an effect if ``inst`` is + `~mne.io.Raw` data. %(reject_by_annotation_raw)s .. versionadded:: 0.14.0 - %(verbose_meth)s + %(verbose)s Returns ------- self : instance of ICA Returns the modified instance. """ + req_map = dict(fastica='sklearn', picard='picard') + for method, mod in req_map.items(): + if self.method == method: + _require_version(mod, f'use method={repr(method)}') + _validate_type(inst, (BaseRaw, BaseEpochs), 'inst', 'Raw or Epochs') if np.isclose(inst.info['highpass'], 0.): @@ -540,6 +620,19 @@ 'However, we suggest to fit ICA only on data that has been ' 'high-pass filtered, but NOT baseline-corrected.') + if not isinstance(inst, BaseRaw): + ignored_params = [ + param_name for param_name, param_val in zip( + ('start', 'stop', 'reject', 'flat'), + (start, stop, reject, flat) + ) + if param_val is not None + ] + if ignored_params: + warn(f'The following parameters passed to ICA.fit() will be ' + f'ignored, as they only affect raw data (and it appears ' + f'you passed epochs): {", ".join(ignored_params)}') + picks = _picks_to_idx(inst.info, picks, allow_empty=False, with_ref_meg=self.allow_ref_meg) _check_for_unsupported_ica_channels( @@ -563,7 +656,8 @@ self.info = pick_info(inst.info, picks) if self.info['comps']: - self.info['comps'] = [] + with self.info._unlock(): + self.info['comps'] = [] self.ch_names = self.info['ch_names'] if isinstance(inst, BaseRaw): @@ -646,31 +740,17 @@ # Scale (z-score) the data by channel type info = self.info pre_whitener = np.empty([len(data), 1]) - for ch_type in _DATA_CH_TYPES_SPLIT + ('eog', "ref_meg"): - if _contains_ch_type(info, ch_type): - if ch_type == 'seeg': - this_picks = pick_types(info, meg=False, seeg=True) - elif ch_type == 'dbs': - this_picks = pick_types(info, meg=False, dbs=True) - elif ch_type == 'ecog': - this_picks = pick_types(info, meg=False, ecog=True) - elif ch_type == 'eeg': - this_picks = pick_types(info, meg=False, eeg=True) - elif ch_type in ('mag', 'grad'): - this_picks = pick_types(info, meg=ch_type) - elif ch_type == 'eog': - this_picks = pick_types(info, meg=False, eog=True) - elif ch_type in ('hbo', 'hbr'): - this_picks = pick_types(info, meg=False, fnirs=ch_type) - elif ch_type == 'ref_meg': - this_picks = pick_types(info, meg=False, ref_meg=True) - else: - raise RuntimeError('Should not be reached.' - 'Unsupported channel {}' - .format(ch_type)) - pre_whitener[this_picks] = np.std(data[this_picks]) + for _, picks_ in _picks_by_type(info, ref_meg=False, exclude=[]): + pre_whitener[picks_] = np.std(data[picks_]) + if _contains_ch_type(info, "ref_meg"): + picks_ = pick_types(info, ref_meg=True, exclude=[]) + pre_whitener[picks_] = np.std(data[picks_]) + if _contains_ch_type(info, "eog"): + picks_ = pick_types(info, eog=True, exclude=[]) + pre_whitener[picks_] = np.std(data[picks_]) else: - pre_whitener, _ = compute_whitener(self.noise_cov, self.info) + pre_whitener, _ = compute_whitener( + self.noise_cov, self.info, picks=self.info.ch_names) assert data.shape[0] == pre_whitener.shape[1] self.pre_whitener_ = pre_whitener @@ -827,16 +907,7 @@ if not hasattr(self, 'mixing_matrix_'): raise RuntimeError('No fit available. Please fit ICA.') start, stop = _check_start_stop(raw, start, stop) - - picks = pick_types(raw.info, include=self.ch_names, exclude='bads', - meg=False, ref_meg=False) - if len(picks) != len(self.ch_names): - raise RuntimeError('Raw doesn\'t match fitted data: %i channels ' - 'fitted but %i channels supplied. \nPlease ' - 'provide Raw compatible with ' - 'ica.ch_names' % (len(self.ch_names), - len(picks))) - + picks = self._get_picks(raw) reject = 'omit' if reject_by_annotation else None data = raw.get_data(picks, start, stop, reject) return self._transform(data) @@ -845,44 +916,41 @@ """Aux method.""" if not hasattr(self, 'mixing_matrix_'): raise RuntimeError('No fit available. Please fit ICA.') - - picks = pick_types(epochs.info, include=self.ch_names, exclude='bads', - meg=False, ref_meg=False) - # special case where epochs come picked but fit was 'unpicked'. - if len(picks) != len(self.ch_names): - raise RuntimeError('Epochs don\'t match fitted data: %i channels ' - 'fitted but %i channels supplied. \nPlease ' - 'provide Epochs compatible with ' - 'ica.ch_names' % (len(self.ch_names), - len(picks))) - + picks = self._get_picks(epochs) data = np.hstack(epochs.get_data()[:, picks]) sources = self._transform(data) - if not concatenate: # Put the data back in 3D sources = np.array(np.split(sources, len(epochs.events), 1)) - return sources def _transform_evoked(self, evoked): """Aux method.""" if not hasattr(self, 'mixing_matrix_'): raise RuntimeError('No fit available. Please fit ICA.') + picks = self._get_picks(evoked) + return self._transform(evoked.data[picks]) - picks = pick_types(evoked.info, include=self.ch_names, exclude='bads', - meg=False, ref_meg=False) - + def _get_picks(self, inst): + """Pick logic for _transform method.""" + picks = _picks_to_idx( + inst.info, self.ch_names, exclude=[], allow_empty=True) if len(picks) != len(self.ch_names): - raise RuntimeError('Evoked doesn\'t match fitted data: %i channels' - ' fitted but %i channels supplied. \nPlease ' - 'provide Evoked compatible with ' - 'ica.ch_names' % (len(self.ch_names), - len(picks))) - - sources = self._transform(evoked.data[picks]) - - return sources + if isinstance(inst, BaseRaw): + kind, do = 'Raw', "doesn't" + elif isinstance(inst, BaseEpochs): + kind, do = 'Epochs', "don't" + elif isinstance(inst, Evoked): + kind, do = 'Evoked', "doesn't" + else: + raise ValueError('Data input must be of Raw, Epochs or Evoked ' + 'type') + raise RuntimeError("%s %s match fitted data: %i channels " + "fitted but %i channels supplied. \nPlease " + "provide %s compatible with ica.ch_names" + % (kind, do, len(self.ch_names), len(picks), + kind)) + return picks def get_components(self): """Get ICA topomap for components as numpy arrays. @@ -947,13 +1015,23 @@ start, stop = _check_start_stop(raw, start, stop) data_ = self._transform_raw(raw, start=start, stop=stop) assert data_.shape[1] == stop - start - if raw.preload: # get data and temporarily delete - data = raw._data - del raw._data - out = raw.copy() # copy and reappend + preloaded = raw.preload if raw.preload: - raw._data = data + # get data and temporarily delete + data = raw._data + raw.preload = False + del raw._data + # copy and crop here so that things like annotations are adjusted + try: + out = raw.copy().crop( + start / raw.info['sfreq'], + (stop - 1) / raw.info['sfreq']) + finally: + # put the data back (always) + if preloaded: + raw.preload = True + raw._data = data # populate copied raw. if add_channels is not None and len(add_channels): @@ -961,10 +1039,10 @@ data_ = np.concatenate([ data_, raw.get_data(picks, start=start, stop=stop)]) out._data = data_ + out._first_samps = [out.first_samp] + out._last_samps = [out.last_samp] out._filenames = [None] out.preload = True - out._first_samps[:] = [out.first_samp + start] - out._last_samps[:] = [out.first_samp + data_.shape[1] - 1] out._projector = None self._export_info(out.info, raw, add_channels) @@ -1010,13 +1088,15 @@ """Aux method.""" # set channel names and info ch_names = [] - ch_info = info['chs'] = [] + ch_info = [] for ii, name in enumerate(self._ica_names): ch_names.append(name) ch_info.append(dict( ch_name=name, cal=1, logno=ii + 1, - coil_type=FIFF.FIFFV_COIL_NONE, kind=FIFF.FIFFV_MISC_CH, - coord_frame=FIFF.FIFFV_COORD_UNKNOWN, unit=FIFF.FIFF_UNIT_NONE, + coil_type=FIFF.FIFFV_COIL_NONE, + kind=FIFF.FIFFV_MISC_CH, + coord_frame=FIFF.FIFFV_COORD_UNKNOWN, + unit=FIFF.FIFF_UNIT_NONE, loc=np.zeros(12, dtype='f4'), range=1.0, scanno=ii + 1, unit_mul=0)) @@ -1026,10 +1106,10 @@ # re-append additionally picked ch_info ch_info += [k for k in container.info['chs'] if k['ch_name'] in add_channels] - info['bads'] = [ch_names[k] for k in self.exclude] - info['projs'] = [] # make sure projections are removed. - info._update_redundant() - info._check_consistency() + with info._unlock(update_redundant=True, check_after=True): + info['chs'] = ch_info + info['bads'] = [ch_names[k] for k in self.exclude] + info['projs'] = [] # make sure projections are removed. @verbose def score_sources(self, inst, target=None, score_func='pearsonr', @@ -1069,7 +1149,7 @@ %(reject_by_annotation_all)s .. versionadded:: 0.14.0 - %(verbose_meth)s + %(verbose)s Returns ------- @@ -1202,18 +1282,14 @@ This function finds the threshold of Kuiper index based on the threshold of pk. Kuiper statistic that minimizes the difference between - pk and the pk threshold (defaults to 20 [1]) is returned. It is assumed - that the data are appropriately filtered and bad data are rejected at - least based on peak-to-peak amplitude when/before running the ICA - decomposition on data. + pk and the pk threshold (defaults to 20 :footcite:`DammersEtAl2008`) + is returned. It is assumed that the data are appropriately filtered and + bad data are rejected at least based on peak-to-peak amplitude + when/before running the ICA decomposition on data. References ---------- - [1] Dammers, J., Schiek, M., Boers, F., Silex, C., Zvyagintsev, - M., Pietrzyk, U., Mathiak, K., 2008. Integration of amplitude - and phase statistics for complete artifact removal in independent - components of neuromagnetic recordings. Biomedical - Engineering, IEEE Transactions on 55 (10), pp.2356. + .. footbibliography:: """ N = self.info['sfreq'] Vs = np.arange(1, 100) / 100 @@ -1232,8 +1308,8 @@ verbose=None): """Detect ECG related components. - Cross-trial phase statistics (default) or Pearson correlation can be - used for detection. + Cross-trial phase statistics :footcite:`DammersEtAl2008` or Pearson + correlation can be used for detection. .. note:: If no ECG channel is available, routine attempts to create an artificial ECG based on cross-channel averaging. @@ -1246,45 +1322,31 @@ The name of the channel to use for ECG peak detection. The argument is mandatory if the dataset contains no ECG channels. - threshold : float | str - The value above which a feature is classified as outlier. If 'auto' - and method is 'ctps', automatically compute the threshold. If - 'auto' and method is 'correlation', defaults to 3.0. The default - translates to 0.25 for 'ctps' and 3.0 for 'correlation' in version - 0.21 but will change to 'auto' in version 0.22. + threshold : float | 'auto' + Value above which a feature is classified as outlier. See Notes. .. versionchanged:: 0.21 start : int | float | None First sample to include. If float, data will be interpreted as time in seconds. If None, data will be used from the first sample. + When working with Epochs or Evoked objects, must be float or None. stop : int | float | None Last sample to not include. If float, data will be interpreted as time in seconds. If None, data will be used to the last sample. + When working with Epochs or Evoked objects, must be float or None. l_freq : float Low pass frequency. h_freq : float High pass frequency. - method : {'ctps', 'correlation'} - The method used for detection. If 'ctps', cross-trial phase - statistics [1] are used to detect ECG related components. - Thresholding is then based on the significance value of a Kuiper - statistic. - If 'correlation', detection is based on Pearson correlation - between the filtered data and the filtered ECG channel. - Thresholding is based on iterative z-scoring. The above - threshold components will be masked and the z-score will - be recomputed until no supra-threshold component remains. - Defaults to 'ctps'. + method : 'ctps' | 'correlation' + The method used for detection. If ``'ctps'``, cross-trial phase + statistics :footcite:`DammersEtAl2008` are used to detect + ECG-related components. See Notes. %(reject_by_annotation_all)s .. versionadded:: 0.14.0 - measure : 'zscore' | 'correlation' - Which method to use for finding outliers. ``'zscore'`` (default) is - the iterated Z-scoring method, and ``'correlation'`` is an absolute - raw correlation threshold with a range of 0 to 1. - - .. versionadded:: 0.21 - %(verbose_meth)s + %(measure)s + %(verbose)s Returns ------- @@ -1296,16 +1358,36 @@ See Also -------- - find_bads_eog, find_bads_ref + find_bads_eog, find_bads_ref, find_bads_muscle + + Notes + ----- + The ``threshold``, ``method``, and ``measure`` parameters interact in + the following ways: + + - If ``method='ctps'``, ``threshold`` refers to the significance value + of a Kuiper statistic, and ``threshold='auto'`` will compute the + threshold automatically based on the sampling frequency. + - If ``method='correlation'`` and ``measure='correlation'``, + ``threshold`` refers to the Pearson correlation value, and + ``threshold='auto'`` sets the threshold to 0.9. + - If ``method='correlation'`` and ``measure='zscore'``, ``threshold`` + refers to the z-score value (i.e., standard deviations) used in the + iterative z-scoring method, and ``threshold='auto'`` sets the + threshold to 3.0. References ---------- - [1] Dammers, J., Schiek, M., Boers, F., Silex, C., Zvyagintsev, - M., Pietrzyk, U., Mathiak, K., 2008. Integration of amplitude - and phase statistics for complete artifact removal in independent - components of neuromagnetic recordings. Biomedical - Engineering, IEEE Transactions on 55 (10), 2353-2362. + .. footbibliography:: """ + _validate_type(threshold, (str, 'numeric'), 'threshold') + if isinstance(threshold, str): + _check_option('threshold', threshold, ('auto',), extra='when str') + _validate_type(method, str, 'method') + _check_option('method', method, ('ctps', 'correlation')) + _validate_type(measure, str, 'measure') + _check_option('measure', measure, ('zscore', 'correlation')) + idx_ecg = _get_ecg_channel_index(ch_name, inst) if idx_ecg is None: @@ -1314,9 +1396,6 @@ else: ecg = inst.ch_names[idx_ecg] - _validate_type(threshold, (str, 'numeric'), 'threshold') - if isinstance(threshold, str): - _check_option('threshold', threshold, ('auto',), extra='when str') if method == 'ctps': if threshold == 'auto': threshold = self._get_ctps_threshold() @@ -1347,14 +1426,15 @@ ch_name = 'ECG-MAG' self.labels_['ecg/%s' % ch_name] = list(ecg_idx) elif method == 'correlation': - if threshold == 'auto': + if threshold == 'auto' and measure == 'zscore': threshold = 3.0 + elif threshold == 'auto' and measure == 'correlation': + threshold = 0.9 self.labels_['ecg'], scores = self._find_bads_ch( inst, [ecg], threshold=threshold, start=start, stop=stop, l_freq=l_freq, h_freq=h_freq, prefix="ecg", reject_by_annotation=reject_by_annotation, measure=measure) - else: - raise ValueError('Method "%s" not supported.' % method) + return self.labels_['ecg'], scores @verbose @@ -1372,8 +1452,19 @@ ch_name : list of str Which MEG reference components to use. If None, then all channels that begin with REF_ICA. - threshold : int | float - The value above which a feature is classified as outlier. + threshold : float | str + Value above which a feature is classified as outlier. + + - If ``measure`` is ``'zscore'``, defines the threshold on the + z-score used in the iterative z-scoring method. + - If ``measure`` is ``'correlation'``, defines the absolute + threshold on the correlation between 0 and 1. + - If ``'auto'``, defaults to 3.0 if ``measure`` is ``'zscore'`` and + 0.9 if ``measure`` is ``'correlation'``. + + .. warning:: + If ``method`` is ``'together'``, the iterative z-score method + is always used. start : int | float | None First sample to include. If float, data will be interpreted as time in seconds. If None, data will be used from the first sample. @@ -1390,13 +1481,8 @@ Defaults to ``'together'``. See notes. .. versionadded:: 0.21 - measure : 'zscore' | 'correlation' - Which method to use for finding outliers. ``'zscore'`` (default) is - the iterated Z-scoring method, and ``'correlation'`` is an absolute - raw correlation threshold with a range of 0 to 1. - - .. versionadded:: 0.21 - %(verbose_meth)s + %(measure)s + %(verbose)s Returns ------- @@ -1407,31 +1493,36 @@ See Also -------- - find_bads_ecg, find_bads_eog + find_bads_ecg, find_bads_eog, find_bads_muscle Notes ----- ICA decomposition on MEG reference channels is used to assess external magnetic noise and remove it from the MEG. Two methods are supported: - With the "together" method, only one ICA fit is used, which + With the ``'together'`` method, only one ICA fit is used, which encompasses both MEG and reference channels together. Components which have particularly strong weights on the reference channels may be thresholded and marked for removal. - With "separate," selected components from a separate ICA decomposition - on the reference channels are used as a ground truth for identifying - bad components in an ICA fit done on MEG channels only. The logic here - is similar to an EOG/ECG, with reference components replacing the - EOG/ECG channels. Recommended procedure is to perform ICA separately - on reference channels, extract them using .get_sources(), and then - append them to the inst using :meth:`~mne.io.Raw.add_channels`, - preferably with the prefix ``REF_ICA`` so that they can be - automatically detected. - - Thresholding in both cases is based on adaptive z-scoring: - The above-threshold components will be masked and the z-score will be - recomputed until no supra-threshold component remains. + With ``'separate'`` selected components from a separate ICA + decomposition on the reference channels are used as a ground truth for + identifying bad components in an ICA fit done on MEG channels only. The + logic here is similar to an EOG/ECG, with reference components + replacing the EOG/ECG channels. Recommended procedure is to perform ICA + separately on reference channels, extract them using + :meth:`~mne.preprocessing.ICA.get_sources`, and then append them to the + inst using :meth:`~mne.io.Raw.add_channels`, preferably with the prefix + ``REF_ICA`` so that they can be automatically detected. + + With ``'together'``, thresholding is based on adaptative z-scoring. + + With ``'separate'``: + + - If ``measure`` is ``'zscore'``, thresholding is based on adaptative + z-scoring. + - If ``measure`` is ``'correlation'``, threshold defines the absolute + threshold on the correlation between 0 and 1. Validation and further documentation for this technique can be found in :footcite:`HannaEtAl2020`. @@ -1442,7 +1533,20 @@ ---------- .. footbibliography:: """ + _validate_type(threshold, (str, 'numeric'), 'threshold') + if isinstance(threshold, str): + _check_option('threshold', threshold, ('auto',), extra='when str') + _validate_type(method, str, 'method') + _check_option('method', method, ('together', 'separate')) + _validate_type(measure, str, 'measure') + _check_option('measure', measure, ('zscore', 'correlation')) + if method == "separate": + if threshold == 'auto' and measure == 'zscore': + threshold = 3.0 + elif threshold == 'auto' and measure == 'correlation': + threshold = 0.9 + if not ch_name: inds = pick_channels_regexp(inst.ch_names, 'REF_ICA*') else: @@ -1459,28 +1563,129 @@ reject_by_annotation=reject_by_annotation, measure=measure) elif method == 'together': + if threshold == 'auto': + threshold = 3.0 + if measure != 'zscore': + logger.info( + "With method 'together', only 'zscore' measure is" + f"supported. Using 'zscore' instead of '{measure}'.") + meg_picks = pick_types(self.info, meg=True, ref_meg=False) ref_picks = pick_types(self.info, meg=False, ref_meg=True) if not any(meg_picks) or not any(ref_picks): - raise ValueError('ICA solution must contain both reference and\ - MEG channels.') + raise ValueError('ICA solution must contain both reference and' + ' MEG channels.') weights = self.get_components() # take norm of component weights on reference channels for each # component, divide them by the norm on the standard channels, # log transform to approximate normal distribution - normrats = np.linalg.norm(weights[ref_picks], - axis=0) / np.linalg.norm(weights[meg_picks], # noqa - axis=0) + normrats = np.linalg.norm(weights[ref_picks], axis=0) \ + / np.linalg.norm(weights[meg_picks], axis=0) scores = np.log(normrats) - self.labels_['ref_meg'] = list(_find_outliers(scores, - threshold=threshold, - tail=1)) - else: - raise ValueError('Method "%s" not supported.' % method) + self.labels_['ref_meg'] = list(_find_outliers( + scores, threshold=threshold, tail=1)) return self.labels_['ref_meg'], scores @verbose + def find_bads_muscle(self, inst, threshold=0.5, start=None, + stop=None, l_freq=7, h_freq=45, sphere=None, + verbose=None): + """Detect muscle related components. + + Detection is based on :footcite:`DharmapraniEtAl2016` which uses + data from a subject who has been temporarily paralyzed + :footcite:`WhithamEtAl2007`. The criteria are threefold: + 1) Positive log-log spectral slope from 7 to 45 Hz + 2) Peripheral component power (farthest away from the vertex) + 3) A single focal point measured by low spatial smoothness + + The threshold is relative to the slope, focal point and smoothness + of a typical muscle-related ICA component. Note the high frequency + of the power spectral density slope was 75 Hz in the reference but + has been modified to 45 Hz as a default based on the criteria being + more accurate in practice. + + Parameters + ---------- + inst : instance of Raw, Epochs or Evoked + Object to compute sources from. + threshold : float | str + Value above which a component should be marked as muscle-related, + relative to a typical muscle component. + start : int | float | None + First sample to include. If float, data will be interpreted as + time in seconds. If None, data will be used from the first sample. + stop : int | float | None + Last sample to not include. If float, data will be interpreted as + time in seconds. If None, data will be used to the last sample. + l_freq : float + Low frequency for muscle-related power. + h_freq : float + High frequency for msucle related power. + %(sphere_topomap_auto)s + %(verbose)s + + Returns + ------- + muscle_idx : list of int + The indices of EOG related components, sorted by score. + scores : np.ndarray of float, shape (``n_components_``) | list of array + The correlation scores. + + See Also + -------- + find_bads_ecg, find_bads_eog, find_bads_ref + + Notes + ----- + .. versionadded:: 1.1 + """ + from scipy.spatial.distance import pdist, squareform + _validate_type(threshold, 'numeric', 'threshold') + + sources = self.get_sources(inst, start=start, stop=stop) + components = self.get_components() + + # compute metric #1: slope of the log-log psd + psd_func = psd_welch if isinstance(inst, BaseRaw) else psd_multitaper + psds, freqs = psd_func(sources, fmin=l_freq, fmax=h_freq, picks='misc') + slopes = np.polyfit(np.log10(freqs), np.log10(psds).T, 1)[0] + + # compute metric #2: distance from the vertex of focus + components_norm = abs(components) / np.max(abs(components), axis=0) + pos = _find_topomap_coords(inst.info, picks='data', sphere=sphere) + assert pos.shape[0] == components.shape[0] # pos for each sensor + pos -= pos.mean(axis=0) # center + dists = np.linalg.norm(pos, axis=1) + dists /= dists.max() + focus_dists = np.dot(dists, components_norm) + + # compute metric #3: smoothness + smoothnesses = np.zeros((components.shape[1],)) + dists = squareform(pdist(pos)) + dists = 1 - (dists / dists.max()) # invert + for idx, comp in enumerate(components.T): + comp_dists = squareform(pdist(comp[:, np.newaxis])) + comp_dists /= comp_dists.max() + smoothnesses[idx] = np.multiply(dists, comp_dists).sum() + + # typical muscle slope is ~0.15, non-muscle components negative + # so logistic with shift -0.5 and slope 0.25 so -0.5 -> 0.5 and 0->1 + # focus distance is ~65% of max electrode distance with 10% slope + # (assumes typical head size) + # smoothnessness is around 150 for muscle and 450 otherwise + # so use reversed logistic centered at 300 with 100 slope + # multiply so that all three components must be present + scores = (1 / (1 + np.exp(-(slopes + 0.5) / 0.25))) * \ + (1 / (1 + np.exp(-(focus_dists - 0.65) / 0.1))) * \ + (1 - (1 / (1 + np.exp(-(smoothnesses - 300) / 100)))) + # scale the threshold by the use of three metrics + self.labels_['muscle'] = [idx for idx, score in enumerate(scores) + if score > threshold**3] + return self.labels_['muscle'], scores + + @verbose def find_bads_eog(self, inst, ch_name=None, threshold=3.0, start=None, stop=None, l_freq=1, h_freq=10, reject_by_annotation=True, measure='zscore', @@ -1501,8 +1706,15 @@ The name of the channel to use for EOG peak detection. The argument is mandatory if the dataset contains no EOG channels. - threshold : int | float - The value above which a feature is classified as outlier. + threshold : float | str + Value above which a feature is classified as outlier. + + - If ``measure`` is ``'zscore'``, defines the threshold on the + z-score used in the iterative z-scoring method. + - If ``measure`` is ``'correlation'``, defines the absolute + threshold on the correlation between 0 and 1. + - If ``'auto'``, defaults to 3.0 if ``measure`` is ``'zscore'`` and + 0.9 if ``measure`` is ``'correlation'``. start : int | float | None First sample to include. If float, data will be interpreted as time in seconds. If None, data will be used from the first sample. @@ -1516,13 +1728,8 @@ %(reject_by_annotation_all)s .. versionadded:: 0.14.0 - measure : 'zscore' | 'correlation' - Which method to use for finding outliers. ``'zscore'`` (default) is - the iterated Z-scoring method, and ``'correlation'`` is an absolute - raw correlation threshold with a range of 0 to 1. - - .. versionadded:: 0.21 - %(verbose_meth)s + %(measure)s + %(verbose)s Returns ------- @@ -1535,9 +1742,20 @@ -------- find_bads_ecg, find_bads_ref """ + _validate_type(threshold, (str, 'numeric'), 'threshold') + if isinstance(threshold, str): + _check_option('threshold', threshold, ('auto',), extra='when str') + _validate_type(measure, str, 'measure') + _check_option('measure', measure, ('zscore', 'correlation')) + eog_inds = _get_eog_channel_index(ch_name, inst) eog_chs = [inst.ch_names[k] for k in eog_inds] + if threshold == 'auto' and measure == 'zscore': + threshold = 3.0 + elif threshold == 'auto' and measure == 'correlation': + threshold = 0.9 + self.labels_['eog'], scores = self._find_bads_ch( inst, eog_chs, threshold=threshold, start=start, stop=stop, l_freq=l_freq, h_freq=h_freq, prefix="eog", @@ -1561,10 +1779,15 @@ in-place. include : array_like of int The indices referring to columns in the ummixing matrix. The - components to be kept. + components to be kept. If ``None`` (default), all components + will be included (minus those defined in ``ica.exclude`` + and the ``exclude`` parameter, see below). exclude : array_like of int The indices referring to columns in the ummixing matrix. The - components to be zeroed out. + components to be zeroed out. If ``None`` (default) or an + empty list, only components from ``ica.exclude`` will be + excluded. Else, the union of ``exclude`` and ``ica.exclude`` + will be excluded. %(n_pca_components_apply)s start : int | float | None First sample to include. If float, data will be interpreted as @@ -1572,7 +1795,7 @@ stop : int | float | None Last sample to not include. If float, data will be interpreted as time in seconds. If None, data will be used to the last sample. - %(verbose_meth)s + %(verbose)s Returns ------- @@ -1761,7 +1984,7 @@ return data @verbose - def save(self, fname, verbose=None): + def save(self, fname, *, overwrite=False, verbose=None): """Store ICA solution into a fiff file. Parameters @@ -1769,7 +1992,10 @@ fname : str The absolute path of the file name to save the ICA solution into. The file name should end with -ica.fif or -ica.fif.gz. - %(verbose_meth)s + %(overwrite)s + + .. versionadded:: 1.0 + %(verbose)s Returns ------- @@ -1785,18 +2011,11 @@ check_fname(fname, 'ICA', ('-ica.fif', '-ica.fif.gz', '_ica.fif', '_ica.fif.gz')) + fname = _check_fname(fname, overwrite=overwrite) logger.info('Writing ICA solution to %s...' % fname) - fid = start_file(fname) - - try: + with start_and_end_file(fname) as fid: _write_ica(fid, self) - end_file(fid) - except Exception: - end_file(fid) - os.remove(fname) - raise - return self def copy(self): @@ -1813,10 +2032,10 @@ def plot_components(self, picks=None, ch_type=None, res=64, vmin=None, vmax=None, cmap='RdBu_r', sensors=True, colorbar=False, title=None, show=True, outlines='head', - contours=6, image_interp='bilinear', - inst=None, plot_std=True, topomap_args=None, - image_args=None, psd_args=None, reject='auto', - sphere=None, verbose=None): + contours=6, image_interp=_INTERPOLATION_DEFAULT, + inst=None, plot_std=True, + topomap_args=None, image_args=None, psd_args=None, + reject='auto', sphere=None, verbose=None): return plot_ica_components(self, picks=picks, ch_type=ch_type, res=res, vmin=vmin, vmax=vmax, cmap=cmap, sensors=sensors, @@ -1831,11 +2050,13 @@ @copy_function_doc_to_method_doc(plot_ica_properties) def plot_properties(self, inst, picks=None, axes=None, dB=True, - plot_std=True, topomap_args=None, image_args=None, - psd_args=None, figsize=None, show=True, reject='auto', - reject_by_annotation=True, *, verbose=None): + plot_std=True, log_scale=False, topomap_args=None, + image_args=None, psd_args=None, figsize=None, + show=True, reject='auto', reject_by_annotation=True, + *, verbose=None): return plot_ica_properties(self, inst, picks=picks, axes=axes, dB=dB, plot_std=plot_std, + log_scale=log_scale, topomap_args=topomap_args, image_args=image_args, psd_args=psd_args, figsize=figsize, show=show, reject=reject, @@ -1845,11 +2066,16 @@ @copy_function_doc_to_method_doc(plot_ica_sources) def plot_sources(self, inst, picks=None, start=None, stop=None, title=None, show=True, block=False, - show_first_samp=False, show_scrollbars=True): + show_first_samp=False, show_scrollbars=True, + time_format='float', precompute=None, + use_opengl=None, *, theme=None, overview_mode=None): return plot_ica_sources(self, inst=inst, picks=picks, start=start, stop=stop, title=title, show=show, block=block, show_first_samp=show_first_samp, - show_scrollbars=show_scrollbars) + show_scrollbars=show_scrollbars, + time_format=time_format, + precompute=precompute, use_opengl=use_opengl, + theme=theme, overview_mode=overview_mode) @copy_function_doc_to_method_doc(plot_ica_scores) def plot_scores(self, scores, exclude=None, labels=None, axhline=None, @@ -1867,124 +2093,6 @@ start=start, stop=stop, title=title, show=show, n_pca_components=n_pca_components) - def detect_artifacts(self, raw, start_find=None, stop_find=None, - ecg_ch=None, ecg_score_func='pearsonr', - ecg_criterion=0.1, eog_ch=None, - eog_score_func='pearsonr', - eog_criterion=0.1, skew_criterion=0, - kurt_criterion=0, var_criterion=-1, - add_nodes=None): - """Run ICA artifacts detection workflow. - - Note. This is still experimental and will most likely change over - the next releases. For maximum control use the workflow exposed in - the examples. - - Hints and caveats: - - It is highly recommended to bandpass filter ECG and EOG - data and pass them instead of the channel names as ecg_ch and eog_ch - arguments. - - please check your results. Detection by kurtosis and variance - may be powerful but misclassification of brain signals as - noise cannot be precluded. - - Consider using shorter times for start_find and stop_find than - for start and stop. It can save you much time. - - Example invocation (taking advantage of the defaults):: - - ica.detect_artifacts(ecg_channel='MEG 1531', eog_channel='EOG 061') - - Parameters - ---------- - raw : instance of Raw - Raw object to draw sources from. No components are actually removed - here, i.e. ica is not applied to raw in this function. Use - `ica.apply() ` for this after inspection of the - identified components. - start_find : int | float | None - First sample to include for artifact search. If float, data will be - interpreted as time in seconds. If None, data will be used from the - first sample. - stop_find : int | float | None - Last sample to not include for artifact search. If float, data will - be interpreted as time in seconds. If None, data will be used to - the last sample. - ecg_ch : str | ndarray | None - The ``target`` argument passed to ica.find_sources_raw. Either the - name of the ECG channel or the ECG time series. If None, this step - will be skipped. - ecg_score_func : str | callable - The ``score_func`` argument passed to ica.find_sources_raw. Either - the name of function supported by ICA or a custom function. - ecg_criterion : float | int | list-like | slice - The indices of the sorted ecg scores. If float, sources with - absolute scores greater than the criterion will be dropped. Else, - the absolute scores sorted in descending order will be indexed - accordingly. E.g. range(2) would return the two sources with the - highest absolute score. If None, this step will be skipped. - eog_ch : list | str | ndarray | None - The ``target`` argument or the list of target arguments - subsequently passed to ica.find_sources_raw. Either the name of the - vertical EOG channel or the corresponding EOG time series. If None, - this step will be skipped. - eog_score_func : str | callable - The ``score_func`` argument passed to ica.find_sources_raw. Either - the name of function supported by ICA or a custom function. - eog_criterion : float | int | list-like | slice - The indices of the sorted eog scores. If float, sources with - absolute scores greater than the criterion will be dropped. Else, - the absolute scores sorted in descending order will be indexed - accordingly. E.g. range(2) would return the two sources with the - highest absolute score. If None, this step will be skipped. - skew_criterion : float | int | list-like | slice - The indices of the sorted skewness scores. If float, sources with - absolute scores greater than the criterion will be dropped. Else, - the absolute scores sorted in descending order will be indexed - accordingly. E.g. range(2) would return the two sources with the - highest absolute score. If None, this step will be skipped. - kurt_criterion : float | int | list-like | slice - The indices of the sorted kurtosis scores. If float, sources with - absolute scores greater than the criterion will be dropped. Else, - the absolute scores sorted in descending order will be indexed - accordingly. E.g. range(2) would return the two sources with the - highest absolute score. If None, this step will be skipped. - var_criterion : float | int | list-like | slice - The indices of the sorted variance scores. If float, sources with - absolute scores greater than the criterion will be dropped. Else, - the absolute scores sorted in descending order will be indexed - accordingly. E.g. range(2) would return the two sources with the - highest absolute score. If None, this step will be skipped. - add_nodes : list of tuple - Additional list if tuples carrying the following parameters - of ica nodes: - (name : str, target : str | array, score_func : callable, - criterion : float | int | list-like | slice). This parameter is a - generalization of the artifact specific parameters above and has - the same structure. Example:: - - add_nodes=('ECG phase lock', ECG 01', - my_phase_lock_function, 0.5) - - Returns - ------- - self : instance of ICA - The ICA object with the detected artifact indices marked for - exclusion. - """ - logger.info(' Searching for artifacts...') - _detect_artifacts(self, raw=raw, start_find=start_find, - stop_find=stop_find, ecg_ch=ecg_ch, - ecg_score_func=ecg_score_func, - ecg_criterion=ecg_criterion, - eog_ch=eog_ch, eog_score_func=eog_score_func, - eog_criterion=eog_criterion, - skew_criterion=skew_criterion, - kurt_criterion=kurt_criterion, - var_criterion=var_criterion, - add_nodes=add_nodes) - - return self - @verbose def _check_n_pca_components(self, _n_pca_comp, verbose=None): """Aux function.""" @@ -2434,61 +2542,6 @@ _ica_node = namedtuple('Node', 'name target score_func criterion') -def _detect_artifacts(ica, raw, start_find, stop_find, ecg_ch, ecg_score_func, - ecg_criterion, eog_ch, eog_score_func, eog_criterion, - skew_criterion, kurt_criterion, var_criterion, - add_nodes): - """Aux Function.""" - from scipy import stats - - nodes = [] - if ecg_ch is not None: - nodes += [_ica_node('ECG', ecg_ch, ecg_score_func, ecg_criterion)] - - if eog_ch not in [None, []]: - if not isinstance(eog_ch, list): - eog_ch = [eog_ch] - for idx, ch in enumerate(eog_ch): - nodes += [_ica_node('EOG %02d' % idx, ch, eog_score_func, - eog_criterion)] - - if skew_criterion is not None: - nodes += [_ica_node('skewness', None, stats.skew, skew_criterion)] - - if kurt_criterion is not None: - nodes += [_ica_node('kurtosis', None, stats.kurtosis, kurt_criterion)] - - if var_criterion is not None: - nodes += [_ica_node('variance', None, np.var, var_criterion)] - - if add_nodes is not None: - nodes.extend(add_nodes) - - for node in nodes: - scores = ica.score_sources(raw, start=start_find, stop=stop_find, - target=node.target, - score_func=node.score_func) - if isinstance(node.criterion, float): - found = list(np.where(np.abs(scores) > node.criterion)[0]) - else: - # Sort in descending order; use (-abs()), rather than [::-1] to - # keep any NaN values in the end (and also keep the order of same - # values): - found = list(np.atleast_1d((-np.abs(scores)).argsort() - [node.criterion])) - - case = (len(found), _pl(found), node.name) - logger.info(' found %s artifact%s by %s' % case) - ica.exclude = list(ica.exclude) + found - - logger.info('Artifact indices found:\n ' + str(ica.exclude).strip('[]')) - if len(set(ica.exclude)) != len(ica.exclude): - logger.info(' Removing duplicate indices...') - ica.exclude = list(set(ica.exclude)) - - logger.info('Ready.') - - @verbose def _band_pass_filter(inst, sources, target, l_freq, h_freq, verbose=None): """Optionally band-pass filter the data.""" @@ -2609,7 +2662,7 @@ to True. show : bool Show figures if True. - %(topomap_outlines)s + %(outlines_topomap)s sensors : bool | str Add markers for sensor locations to the plot. Accepts matplotlib plot format string (e.g., 'r+' for red plusses). If True, a circle will be @@ -2623,7 +2676,7 @@ cmap : None | matplotlib colormap Colormap for the plot. If ``None``, defaults to 'Reds_r' for norm data, otherwise to 'RdBu_r'. - %(topomap_sphere_auto)s + %(sphere_topomap_auto)s %(verbose)s Returns diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/ieeg/__init__.py python-mne-1.1.0+dfsg/mne/preprocessing/ieeg/__init__.py --- python-mne-0.23.4+dfsg/mne/preprocessing/ieeg/__init__.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/ieeg/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,7 @@ +"""Intracranial EEG specific preprocessing functions.""" + +# Authors: Alex Rockhill +# +# License: BSD-3-Clause + +from ._projection import project_sensors_onto_brain diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/ieeg/_projection.py python-mne-1.1.0+dfsg/mne/preprocessing/ieeg/_projection.py --- python-mne-0.23.4+dfsg/mne/preprocessing/ieeg/_projection.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/ieeg/_projection.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,102 @@ +# Authors: Alex Rockhill +# +# License: BSD-3-Clause + +from os import path as op + +from itertools import combinations +import numpy as np + +from ...io.pick import _picks_to_idx +from ...surface import _read_mri_surface, fast_cross_3d +from ...transforms import (apply_trans, invert_transform, _cart_to_sph, + _ensure_trans) +from ...utils import verbose, get_subjects_dir, _validate_type, _ensure_int + + +@verbose +def project_sensors_onto_brain(info, trans, subject, subjects_dir=None, + picks=None, n_neighbors=10, copy=True, + verbose=None): + """Project sensors onto the brain surface. + + Parameters + ---------- + %(info_not_none)s + %(trans_not_none)s + %(subject)s + %(subjects_dir)s + %(picks_base)s only ``ecog`` channels. + n_neighbors : int + The number of neighbors to use to compute the normal vectors + for the projection. Must be 2 or greater. More neighbors makes + a normal vector with greater averaging which preserves the grid + structure. Fewer neighbors has less averaging which better + preserves contours in the grid. + copy : bool + If ``True``, return a new instance of ``info``, if ``False`` + ``info`` is modified in place. + %(verbose)s + + Returns + ------- + %(info_not_none)s + + Notes + ----- + This is useful in ECoG analysis for compensating for "brain shift" + or shrinking of the brain away from the skull due to changes + in pressure during the craniotomy. + + To use the brain surface, a BEM model must be created e.g. using + :ref:`mne watershed_bem` using the T1 or :ref:`mne flash_bem` + using a FLASH scan. + """ + from scipy.spatial.distance import pdist, squareform + n_neighbors = _ensure_int(n_neighbors, 'n_neighbors') + _validate_type(copy, bool, 'copy') + if copy: + info = info.copy() + if n_neighbors < 2: + raise ValueError( + f'n_neighbors must be 2 or greater, got {n_neighbors}') + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + try: + surf = _read_mri_surface(op.join( + subjects_dir, subject, 'bem', 'brain.surf')) + except FileNotFoundError as err: + raise RuntimeError(f'{err}\n\nThe brain surface requires generating ' + 'a BEM using `mne flash_bem` (if you have ' + 'the FLASH scan) or `mne watershed_bem` (to ' + 'use the T1)') from None + # get channel locations + picks_idx = _picks_to_idx(info, 'ecog' if picks is None else picks) + locs = np.array([info['chs'][idx]['loc'][:3] for idx in picks_idx]) + trans = _ensure_trans(trans, 'head', 'mri') + locs = apply_trans(trans, locs) + # compute distances for nearest neighbors + dists = squareform(pdist(locs)) + # find angles for brain surface and points + angles = _cart_to_sph(locs) + surf_angles = _cart_to_sph(surf['rr']) + # initialize projected locs + proj_locs = np.zeros(locs.shape) * np.nan + for i, loc in enumerate(locs): + neighbor_pts = locs[np.argsort(dists[i])[:n_neighbors + 1]] + pt1, pt2, pt3 = map(np.array, zip(*combinations(neighbor_pts, 3))) + normals = fast_cross_3d(pt1 - pt2, pt1 - pt3) + normals[normals @ loc < 0] *= -1 + normal = np.mean(normals, axis=0) + normal /= np.linalg.norm(normal) + # find the correct orientation brain surface point nearest the line + # https://mathworld.wolfram.com/Point-LineDistance3-Dimensional.html + use_rr = surf['rr'][abs( + surf_angles[:, 1:] - angles[i, 1:]).sum(axis=1) < np.pi / 4] + surf_dists = np.linalg.norm( + fast_cross_3d(use_rr - loc, use_rr - loc + normal), axis=1) + proj_locs[i] = use_rr[np.argmin(surf_dists)] + # back to the "head" coordinate frame for storing in ``raw`` + proj_locs = apply_trans(invert_transform(trans), proj_locs) + for idx, loc in zip(picks_idx, proj_locs): + info['chs'][idx]['loc'][:3] = loc + return info diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/ieeg/tests/test_projection.py python-mne-1.1.0+dfsg/mne/preprocessing/ieeg/tests/test_projection.py --- python-mne-0.23.4+dfsg/mne/preprocessing/ieeg/tests/test_projection.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/ieeg/tests/test_projection.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,68 @@ +# -*- coding: utf-8 -*- +"""Test the compute_current_source_density function. + +For each supported file format, implement a test. +""" +# Authors: Alex Rockhill +# +# License: BSD-3-Clause + +import os +import os.path as op +from shutil import copyfile +import numpy as np +from numpy.testing import assert_allclose +import pytest + +import mne +from mne.preprocessing.ieeg import project_sensors_onto_brain +from mne.datasets import testing +from mne.transforms import _get_trans + +data_path = testing.data_path(download=False) +subjects_dir = op.join(data_path, 'subjects') +fname_trans = op.join(data_path, 'MEG', 'sample', + 'sample_audvis_trunc-trans.fif') +fname_raw = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif') + + +@testing.requires_testing_data +def test_project_sensors_onto_brain(tmp_path): + """Test projecting sensors onto the brain surface.""" + tempdir = str(tmp_path) + raw = mne.io.read_raw_fif(fname_raw) + trans = _get_trans(fname_trans)[0] + # test informative error for no surface first + with pytest.raises(RuntimeError, match='requires generating a BEM'): + project_sensors_onto_brain(raw.info, trans, 'sample', + subjects_dir=tempdir) + brain_surf_fname = op.join(tempdir, 'sample', 'bem', 'brain.surf') + if not op.isdir(op.dirname(brain_surf_fname)): + os.makedirs(op.dirname(brain_surf_fname)) + if not op.isfile(brain_surf_fname): + copyfile(op.join(subjects_dir, 'sample', 'bem', 'inner_skull.surf'), + brain_surf_fname) + # now make realistic ECoG grid + raw.pick_types(meg=False, eeg=True) + raw.load_data() + raw.set_eeg_reference([]) + raw.set_channel_types({ch: 'ecog' for ch in raw.ch_names}) + pos = np.zeros((49, 3)) + pos[:, :2] = np.array( + np.meshgrid(np.linspace(0, 0.02, 7), + np.linspace(0, 0.02, 7))).reshape(2, -1).T + pos[:, 2] = 0.12 + raw.drop_channels(raw.ch_names[49:]) + raw.set_montage(mne.channels.make_dig_montage( + ch_pos=dict(zip(raw.ch_names[:49], pos)), coord_frame='head')) + raw.info = project_sensors_onto_brain( + raw.info, trans, 'sample', subjects_dir=tempdir) + # plot to check, should be projected down onto inner skull + # brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, alpha=0.5, + # surf='white') + # brain.add_sensors(raw.info, trans=trans) + test_locs = [[0.00149, -0.001588, 0.133029], + [0.004302, 0.001959, 0.133922], + [0.008602, 0.00116, 0.133723]] + for ch, test_loc in zip(raw.info['chs'][:3], test_locs): + assert_allclose(ch['loc'][:3], test_loc, rtol=0.01) diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/infomax_.py python-mne-1.1.0+dfsg/mne/preprocessing/infomax_.py --- python-mne-0.23.4+dfsg/mne/preprocessing/infomax_.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/infomax_.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Juergen Dammers # Denis A. Engeman # -# License: BSD (3-clause) +# License: BSD-3-Clause import math @@ -101,6 +101,8 @@ and supergaussian sources. Neural Computation, 11(2), 417-441, 1999. """ from scipy.stats import kurtosis + from scipy.special import expit + rng = check_random_state(random_state) # define some default parameters @@ -189,7 +191,7 @@ else: # logistic ICA weights update - y = 1.0 / (1.0 + np.exp(-u)) + y = expit(u) weights += l_rate * np.dot(weights, BI + np.dot(u.T, (1.0 - 2.0 * y))) diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/__init__.py python-mne-1.1.0+dfsg/mne/preprocessing/__init__.py --- python-mne-0.23.4+dfsg/mne/preprocessing/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,9 +5,9 @@ # Martin Luessi # Denis Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause -from .flat import annotate_flat +from .annotate_amplitude import annotate_amplitude from .maxfilter import apply_maxfilter from .ssp import compute_proj_ecg, compute_proj_eog from .eog import find_eog_events, create_eog_epochs @@ -19,14 +19,17 @@ from .infomax_ import infomax from .stim import fix_stim_artifact from .maxwell import (maxwell_filter, find_bad_channels_maxwell, - compute_maxwell_basis) + compute_maxwell_basis, maxwell_filter_prepare_emptyroom) from .realign import realign_raw from .xdawn import Xdawn -from ._csd import compute_current_source_density +from ._csd import compute_current_source_density, compute_bridged_electrodes from . import nirs from .artifact_detection import (annotate_movement, compute_average_dev_head_t, - annotate_muscle_zscore) + annotate_muscle_zscore, annotate_break) from ._regress import regress_artifact -from ._fine_cal import (compute_fine_calibration, read_fine_calibration, +from ._fine_cal import (compute_fine_calibration, read_fine_calibration, write_fine_calibration) -from .interpolate import equalize_bads +from .annotate_nan import annotate_nan +from .interpolate import equalize_bads, interpolate_bridged_electrodes +from . import ieeg +from ._css import cortical_signal_suppression diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/interpolate.py python-mne-1.1.0+dfsg/mne/preprocessing/interpolate.py --- python-mne-0.23.4+dfsg/mne/preprocessing/interpolate.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/interpolate.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,7 +5,12 @@ from itertools import chain import numpy as np -from ..epochs import BaseEpochs +from ..utils import _validate_type +from ..io import BaseRaw, RawArray +from ..io.meas_info import create_info +from ..epochs import BaseEpochs, EpochsArray +from ..evoked import Evoked, EvokedArray +from ..transforms import _sph_to_cart, _cart_to_sph def equalize_bads(insts, interp_thresh=1., copy=True): @@ -65,3 +70,86 @@ inst.info['bads'] = bads_keep return insts + + +def interpolate_bridged_electrodes(inst, bridged_idx): + """Interpolate bridged electrode pairs. + + Because bridged electrodes contain brain signal, it's just that the + signal is spatially smeared between the two electrodes, we can + make a virtual channel midway between the bridged pairs and use + that to aid in interpolation rather than completely discarding the + data from the two channels. + + Parameters + ---------- + inst : instance of Epochs, Evoked, or Raw + The data object with channels that are to be interpolated. + bridged_idx : list of tuple + The indices of channels marked as bridged with each bridged + pair stored as a tuple. + + Returns + ------- + inst : instance of Epochs, Evoked, or Raw + The modified data object. + + See Also + -------- + mne.preprocessing.compute_bridged_electrodes + """ + _validate_type(inst, (BaseRaw, BaseEpochs, Evoked)) + montage = inst.get_montage() + if montage is None: + raise RuntimeError('No channel positions found in ``inst``') + pos = montage.get_positions() + if pos['coord_frame'] != 'head': + raise RuntimeError('Montage channel positions must be in ``head``' + 'got {}'.format(pos['coord_frame'])) + ch_pos = pos['ch_pos'] + # store bads orig to put back at the end + bads_orig = inst.info['bads'] + inst.info['bads'] = list() + + # make virtual channels + virtual_chs = dict() + bads = set() + data = inst.get_data() + for idx0, idx1 in bridged_idx: + ch0 = inst.ch_names[idx0] + ch1 = inst.ch_names[idx1] + bads = bads.union([ch0, ch1]) + # compute midway position in spherical coordinates in "head" + # (more accurate than cutting though the scalp by using cartesian) + pos0 = _cart_to_sph(ch_pos[ch0]) + pos1 = _cart_to_sph(ch_pos[ch1]) + pos_virtual = _sph_to_cart((pos0 + pos1) / 2) + virtual_info = create_info( + [f'{ch0}-{ch1} virtual'], inst.info['sfreq'], 'eeg') + virtual_info['chs'][0]['loc'][:3] = pos_virtual + if isinstance(inst, BaseRaw): + virtual_ch = RawArray( + (data[idx0:idx0 + 1] + data[idx1:idx1 + 1]) / 2, + virtual_info, first_samp=inst.first_samp) + elif isinstance(inst, BaseEpochs): + virtual_ch = EpochsArray( + (data[:, idx0:idx0 + 1] + data[:, idx1:idx1 + 1]) / 2, + virtual_info, tmin=inst.tmin) + else: # evoked + virtual_ch = EvokedArray( + (data[idx0:idx0 + 1] + data[idx1:idx1 + 1]) / 2, + virtual_info, tmin=inst.tmin, nave=inst.nave, kind=inst.kind) + virtual_chs[f'{ch0}-{ch1} virtual'] = virtual_ch + + # add the virtual channels + inst.add_channels(list(virtual_chs.values()), force_update_info=True) + + # use the virtual channels to interpolate + inst.info['bads'] = list(bads) + inst.interpolate_bads() + + # drop virtual channels + inst.drop_channels(list(virtual_chs.keys())) + + inst.info['bads'] = bads_orig + return inst diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/maxfilter.py python-mne-1.1.0+dfsg/mne/preprocessing/maxfilter.py --- python-mne-0.23.4+dfsg/mne/preprocessing/maxfilter.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/maxfilter.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Matti Hämäläinen # Martin Luessi # -# License: BSD (3-clause) +# License: BSD-3-Clause import os diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/maxwell.py python-mne-1.1.0+dfsg/mne/preprocessing/maxwell.py --- python-mne-0.23.4+dfsg/mne/preprocessing/maxwell.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/maxwell.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,7 +4,7 @@ # Jussi Nurminen -# License: BSD (3-clause) +# License: BSD-3-Clause from collections import Counter, OrderedDict from functools import partial @@ -32,7 +32,7 @@ from ..io.pick import pick_types, pick_info from ..utils import (verbose, logger, _clean_names, warn, _time_mask, _pl, _check_option, _ensure_int, _validate_type, use_log_level) -from ..fixes import _safe_svd, einsum, bincount +from ..fixes import _safe_svd, bincount from ..channels.channels import _get_T1T2_mag_inds, fix_mag_coil_types @@ -41,6 +41,144 @@ # differences between algorithms +@verbose +def maxwell_filter_prepare_emptyroom( + raw_er, *, raw, bads='from_raw', annotations='from_raw', meas_date='keep', + emit_warning=False, verbose=None +): + """Prepare an empty-room recording for Maxwell filtering. + + Empty-room data by default lacks certain properties that are required to + ensure running :func:`~mne.preprocessing.maxwell_filter` will process the + empty-room recording the same way as the experimental data. This function + preconditions an empty-room raw data instance accordingly so it can be used + for Maxwell filtering. Please see the ``Notes`` section for details. + + Parameters + ---------- + raw_er : instance of Raw + The empty-room recording. It will not be modified. + raw : instance of Raw + The experimental recording, typically this will be the reference run + used for Maxwell filtering. + bads : 'from_raw' | 'union' | 'keep' + How to populate the list of bad channel names to be injected into + the empty-room recording. If ``'from_raw'`` (default) the list of bad + channels will be overwritten with that of ``raw``. If ``'union'``, will + use the union of bad channels in ``raw`` and ``raw_er``. Note that + this may lead to additional bad channels in the empty-room in + comparison to the experimental recording. If ``'keep'``, don't alter + the existing list of bad channels. + + .. note:: + Non-MEG channels are silently dropped from the list of bads. + annotations : 'from_raw' | 'union' | 'keep' + Whether to copy the annotations over from ``raw`` (default), + use the union of the annotations, or to keep them unchanged. + meas_date : 'keep' | 'from_raw' + Whether to transfer the measurement date from ``raw`` or to keep + it as is (default). If you intend to manually transfer annotations + from ``raw`` **after** running this function, you should set this to + ``'from_raw'``. + %(emit_warning)s + Unlike :meth:`raw.set_annotations `, the + default here is ``False``, as empty-room recordings are often shorter + than raw. + %(verbose)s + + Returns + ------- + raw_er_prepared : instance of Raw + A copy of the passed empty-room recording, ready for Maxwell filtering. + + Notes + ----- + This function will: + + * Compile the list of bad channels according to the ``bads`` parameter. + * Inject the device-to-head transformation matrix from the experimental + recording into the empty-room recording. + * Set the following properties of the empty-room recording to match the + experimental recording: + + * Montage + * ``raw.first_time`` and ``raw.first_samp`` + + * Adjust annotations according to the ``annotations`` parameter. + * Adjust the measurement date according to the ``meas_date`` parameter. + + .. versionadded:: 1.1 + """ # noqa: E501 + _validate_type(item=raw_er, types=BaseRaw, item_name='raw_er') + _validate_type(item=raw, types=BaseRaw, item_name='raw') + _validate_type(item=bads, types=str, item_name='bads') + _check_option( + parameter='bads', value=bads, + allowed_values=['from_raw', 'union', 'keep'] + ) + _validate_type(item=annotations, types=str, item_name='annotations') + _check_option( + parameter='annotations', value=annotations, + allowed_values=['from_raw', 'union', 'keep'] + ) + _validate_type(item=meas_date, types=str, item_name='meas_date') + _check_option( + parameter='meas_date', value=annotations, + allowed_values=['from_raw', 'keep'] + ) + + raw_er_prepared = raw_er.copy() + del raw_er # just to be sure + + # handle bads; only keep MEG channels + if bads == 'from_raw': + bads = raw.info['bads'] + elif bads == 'union': + bads = sorted( + set(raw.info['bads'] + raw_er_prepared.info['bads']) + ) + elif bads == 'keep': + bads = raw_er_prepared.info['bads'] + + bads = [ch_name for ch_name in bads + if ch_name.startswith('MEG')] + raw_er_prepared.info['bads'] = bads + + # handle dev_head_t + raw_er_prepared.info['dev_head_t'] = raw.info['dev_head_t'] + + # handle montage + montage = raw.get_montage() + raw_er_prepared.set_montage(montage) + + # handle first_samp + raw_er_prepared.annotations.onset += ( + raw.first_time - raw_er_prepared.first_time + ) + raw_er_prepared._cropped_samp = raw._cropped_samp + + # handle annotations + if annotations != 'keep': + er_annot = raw_er_prepared.annotations + if annotations == 'from_raw': + er_annot.delete(np.arange(len(er_annot))) + er_annot.append( + raw.annotations.onset, + raw.annotations.duration, + raw.annotations.description, + raw.annotations.ch_names + ) + if raw_er_prepared.info['meas_date'] is None: + er_annot.onset -= raw_er_prepared.first_time + raw_er_prepared.set_annotations(er_annot, emit_warning) + + # handle measurement date + if meas_date == 'from_raw': + raw_er_prepared.set_meas_date(raw.info['meas_date']) + + return raw_er_prepared + + # Changes to arguments here should also be made in find_bad_channels_maxwell @verbose def maxwell_filter(raw, origin='auto', int_order=8, ext_order=3, @@ -61,11 +199,11 @@ ``raw.info['bads']`` prior to processing in order to prevent artifact spreading. Manual inspection and use of :func:`~find_bad_channels_maxwell` is recommended. - %(maxwell_origin)s - %(maxwell_int)s - %(maxwell_ext)s - %(maxwell_cal)s - %(maxwell_cross)s + %(origin_maxwell)s + %(int_order_maxwell)s + %(ext_order_maxwell)s + %(calibration_maxwell_cal)s + %(cross_talk_maxwell)s st_duration : float | None If not None, apply spatiotemporal SSS with specified buffer duration (in seconds). MaxFilter™'s default is 10.0 seconds in v2.2. @@ -79,22 +217,22 @@ st_correlation : float Correlation limit between inner and outer subspaces used to reject ovwrlapping intersecting inner/outer signals during spatiotemporal SSS. - %(maxwell_coord)s - %(maxwell_dest)s - %(maxwell_reg)s - %(maxwell_ref)s - %(maxwell_cond)s - %(maxwell_pos)s + %(coord_frame_maxwell)s + %(destination_maxwell_dest)s + %(regularize_maxwell_reg)s + %(ignore_ref_maxwell)s + %(bad_condition_maxwell_cond)s + %(head_pos_maxwell)s .. versionadded:: 0.12 - %(maxwell_st_fixed_only)s - %(maxwell_mag)s + %(st_fixed_maxwell_only)s + %(mag_scale_maxwell)s .. versionadded:: 0.13 - %(maxwell_skip)s + %(skip_by_annotation_maxwell)s .. versionadded:: 0.17 - %(maxwell_extended)s + %(extended_proj_maxwell)s %(verbose)s Returns @@ -104,7 +242,7 @@ See Also -------- - mne.preprocessing.annotate_flat + mne.preprocessing.annotate_amplitude mne.preprocessing.find_bad_channels_maxwell mne.chpi.filter_chpi mne.chpi.read_head_pos @@ -648,7 +786,7 @@ def _prep_mf_coils(info, ignore_ref=True, verbose=None): """Get all coil integration information loaded and sorted.""" coils, comp_coils = _prep_meg_channels( - info, accurate=True, head_frame=False, + info, head_frame=False, ignore_ref=ignore_ref, do_picking=False, verbose=False)[:2] mag_mask = _get_mag_mask(coils) if len(comp_coils) > 0: @@ -752,7 +890,8 @@ """Load data for processing and (maybe) add cHPI pos channels.""" if copy: raw = raw.copy() - raw.info['chs'] = info['chs'] # updated coil types + with raw.info._unlock(): + raw.info['chs'] = info['chs'] # updated coil types if add_channels: kinds = [FIFF.FIFFV_QUAT_1, FIFF.FIFFV_QUAT_2, FIFF.FIFFV_QUAT_3, FIFF.FIFFV_QUAT_4, FIFF.FIFFV_QUAT_5, FIFF.FIFFV_QUAT_6, @@ -1162,11 +1301,11 @@ orders_pos_neg.append(-order) for gr, oo in zip(grads_pos_neg, orders_pos_neg): # Gradients dotted w/integration point weighted normals - gr = einsum('ij,ij->i', gr, cosmags) + gr = np.einsum('ij,ij->i', gr, cosmags) vals = np.bincount(bins, gr, len(coils)) spc[:, _deg_ord_idx(degree, oo)] = -vals else: - grads = einsum('ij,ij->i', grads, ezs) + grads = np.einsum('ij,ij->i', grads, ezs) v = (np.bincount(bins, grads.real, len(coils)) + 1j * np.bincount(bins, grads.imag, len(coils))) spc[:, _deg_ord_idx(degree, order)] = -v @@ -1509,7 +1648,8 @@ Extended external bases. """ n_in, n_out = _get_n_moments([int_order, ext_order]) - raw.info['maxshield'] = False + with raw.info._unlock(): + raw.info['maxshield'] = False components = np.zeros(n_in + n_out + len(extended_proj)).astype('int32') components[reg_moments] = 1 sss_info_dict = dict(in_order=int_order, out_order=ext_order, @@ -1527,11 +1667,13 @@ # Reset 'bads' for any MEG channels since they've been reconstructed _reset_meg_bads(raw.info) # set the reconstruction transform - raw.info['dev_head_t'] = recon_trans + with raw.info._unlock(): + raw.info['dev_head_t'] = recon_trans block_id = _generate_meas_id() - raw.info['proc_history'].insert(0, dict( - max_info=max_info_dict, block_id=block_id, date=DATE_NONE, - creator='mne-python v%s' % __version__, experimenter='')) + with raw.info._unlock(): + raw.info['proc_history'].insert(0, dict( + max_info=max_info_dict, block_id=block_id, date=DATE_NONE, + creator='mne-python v%s' % __version__, experimenter='')) def _reset_meg_bads(info): @@ -1964,24 +2106,24 @@ developers. .. versionadded:: 0.21 - %(maxwell_origin)s - %(maxwell_int)s - %(maxwell_ext)s - %(maxwell_cal)s - %(maxwell_cross)s - %(maxwell_coord)s - %(maxwell_reg)s - %(maxwell_ref)s - %(maxwell_cond)s - %(maxwell_pos)s - %(maxwell_mag)s - %(maxwell_skip)s + %(origin_maxwell)s + %(int_order_maxwell)s + %(ext_order_maxwell)s + %(calibration_maxwell_cal)s + %(cross_talk_maxwell)s + %(coord_frame_maxwell)s + %(regularize_maxwell_reg)s + %(ignore_ref_maxwell)s + %(bad_condition_maxwell_cond)s + %(head_pos_maxwell)s + %(mag_scale_maxwell)s + %(skip_by_annotation_maxwell)s h_freq : float | None The cutoff frequency (in Hz) of the low-pass filter that will be applied before processing the data. This defaults to ``40.``, which should provide similar results to MaxFilter. If you do not wish to apply a filter, set this to ``None``. - %(maxwell_extended)s + %(extended_proj_maxwell)s %(verbose)s Returns @@ -2026,7 +2168,7 @@ See Also -------- - annotate_flat + annotate_amplitude maxwell_filter Notes @@ -2073,11 +2215,12 @@ .. versionadded:: 0.20 """ if h_freq is not None: - if raw.info.get('lowpass') and raw.info['lowpass'] < h_freq: + if raw.info.get('lowpass') and raw.info['lowpass'] <= h_freq: + freq_loc = 'below' if raw.info['lowpass'] < h_freq else 'equal to' msg = (f'The input data has already been low-pass filtered with a ' f'{raw.info["lowpass"]} Hz cutoff frequency, which is ' - f'below the requested cutoff of {h_freq} Hz. Not applying ' - f'low-pass filter.') + f'{freq_loc} the requested cutoff of {h_freq} Hz. Not ' + f'applying low-pass filter.') logger.info(msg) else: logger.info(f'Applying low-pass filter with {h_freq} Hz cutoff ' @@ -2173,6 +2316,14 @@ chunk_flats = sorted(all_flats) these_picks = [pick for pick in good_meg_picks if raw.ch_names[pick] not in chunk_flats] + if len(these_picks) == 0: + logger.info(f' Flat ({len(chunk_flats):2d}): ') + warn('All-flat segment detected, all channels will be marked as ' + f'flat and processing will stop (t={t[0]:0.3f}). ' + 'Consider using annotate_amplitude before calling this ' + 'function with skip_by_annotation="bad_flat" (or similar) to ' + 'properly process all segments.') + break # no reason to continue # Bad pass chunk_noisy = list() params['st_duration'] = int(round( @@ -2281,18 +2432,17 @@ Parameters ---------- - info : instance of Info - The measurement info. - %(maxwell_origin)s - %(maxwell_int)s - %(maxwell_ext)s - %(maxwell_cal)s - %(maxwell_coord)s - %(maxwell_reg)s - %(maxwell_ref)s - %(maxwell_cond)s - %(maxwell_mag)s - %(maxwell_extended)s + %(info_not_none)s + %(origin_maxwell)s + %(int_order_maxwell)s + %(ext_order_maxwell)s + %(calibration_maxwell_cal)s + %(coord_frame_maxwell)s + %(regularize_maxwell_reg)s + %(ignore_ref_maxwell)s + %(bad_condition_maxwell_cond)s + %(mag_scale_maxwell)s + %(extended_proj_maxwell)s %(verbose)s Returns diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/nirs/_beer_lambert_law.py python-mne-1.1.0+dfsg/mne/preprocessing/nirs/_beer_lambert_law.py --- python-mne-0.23.4+dfsg/mne/preprocessing/nirs/_beer_lambert_law.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/nirs/_beer_lambert_law.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Eric Larson # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op @@ -10,12 +10,11 @@ from ...io import BaseRaw from ...io.constants import FIFF -from ...utils import _validate_type -from ..nirs import source_detector_distances, _channel_frequencies,\ - _check_channels_ordered, _channel_chromophore +from ...utils import _validate_type, warn +from ..nirs import source_detector_distances, _validate_nirs_info -def beer_lambert_law(raw, ppf=0.1): +def beer_lambert_law(raw, ppf=6.): r"""Convert NIRS optical density data to haemoglobin concentration. Parameters @@ -33,31 +32,42 @@ from scipy import linalg raw = raw.copy().load_data() _validate_type(raw, BaseRaw, 'raw') - - freqs = np.unique(_channel_frequencies(raw.info)) - picks = _check_channels_ordered(raw.info, freqs) + _validate_type(ppf, 'numeric', 'ppf') + ppf = float(ppf) + picks = _validate_nirs_info(raw.info, fnirs='od', which='Beer-lambert') + # This is the one place we *really* need the actual/accurate frequencies + freqs = np.array( + [raw.info['chs'][pick]['loc'][9] for pick in picks], float) abs_coef = _load_absorption(freqs) distances = source_detector_distances(raw.info) - - for ii in picks[::2]: - + if (distances == 0).any(): + warn('Source-detector distances are zero, some resulting ' + 'concentrations will be zero. Consider setting a montage ' + 'with raw.set_montage.') + if (distances > 0.1).any(): + warn('Source-detector distances are greater than 10 cm. ' + 'Large distances will result in invalid data, and are ' + 'likely due to optode locations being stored in a ' + ' unit other than meters.') + rename = dict() + for ii, jj in zip(picks[::2], picks[1::2]): EL = abs_coef * distances[ii] * ppf iEL = linalg.pinv(EL) - raw._data[[ii, ii + 1]] = iEL @ raw._data[[ii, ii + 1]] * 1e-3 + raw._data[[ii, jj]] = iEL @ raw._data[[ii, jj]] * 1e-3 # Update channel information coil_dict = dict(hbo=FIFF.FIFFV_COIL_FNIRS_HBO, hbr=FIFF.FIFFV_COIL_FNIRS_HBR) - for ki, kind in enumerate(('hbo', 'hbr')): - ch = raw.info['chs'][ii + ki] + for ki, kind in zip((ii, jj), ('hbo', 'hbr')): + ch = raw.info['chs'][ki] ch.update(coil_type=coil_dict[kind], unit=FIFF.FIFF_UNIT_MOL) - raw.rename_channels({ - ch['ch_name']: '%s %s' % (ch['ch_name'][:-4], kind)}) + new_name = f'{ch["ch_name"].split(" ")[0]} {kind}' + rename[ch['ch_name']] = new_name + raw.rename_channels(rename) # Validate the format of data after transformation is valid - chroma = np.unique(_channel_chromophore(raw.info)) - _check_channels_ordered(raw.info, chroma) + _validate_nirs_info(raw.info, fnirs='hb') return raw diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/nirs/__init__.py python-mne-1.1.0+dfsg/mne/preprocessing/nirs/__init__.py --- python-mne-0.23.4+dfsg/mne/preprocessing/nirs/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/nirs/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,12 +4,13 @@ # Eric Larson # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause from .nirs import (short_channels, source_detector_distances, _check_channels_ordered, _channel_frequencies, - _fnirs_check_bads, _fnirs_spread_bads, _channel_chromophore, - _validate_nirs_info) + _fnirs_spread_bads, _channel_chromophore, + _validate_nirs_info, _fnirs_optode_names, _optode_position, + _reorder_nirx) from ._optical_density import optical_density from ._beer_lambert_law import beer_lambert_law from ._scalp_coupling_index import scalp_coupling_index diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/nirs/nirs.py python-mne-1.1.0+dfsg/mne/preprocessing/nirs/nirs.py --- python-mne-0.23.4+dfsg/mne/preprocessing/nirs/nirs.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/nirs/nirs.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,13 +2,18 @@ # Eric Larson # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import re import numpy as np -from ...io.pick import _picks_to_idx -from ...utils import fill_doc +from ...io.pick import _picks_to_idx, pick_types +from ...utils import fill_doc, _check_option, _validate_type + + +# Standardized fNIRS channel name regexs +_S_D_F_RE = re.compile(r'S(\d+)_D(\d+) (\d+\.?\d*)') +_S_D_H_RE = re.compile(r'S(\d+)_D(\d+) (\w+)') @fill_doc @@ -17,8 +22,7 @@ Parameters ---------- - info : Info - The measurement info. + %(info_not_none)s %(picks_all)s Returns @@ -27,12 +31,13 @@ Array containing distances in meters. Of shape equal to number of channels, or shape of picks if supplied. """ - dist = [np.linalg.norm(ch['loc'][3:6] - ch['loc'][6:9]) - for ch in info['chs']] - picks = _picks_to_idx(info, picks, exclude=[]) - return np.array(dist, float)[picks] + return np.array([ + np.linalg.norm(np.diff( + info['chs'][pick]['loc'][3:9].reshape(2, 3), axis=0)[0]) + for pick in _picks_to_idx(info, picks, exclude=[])], float) +@fill_doc def short_channels(info, threshold=0.01): r"""Determine which NIRS channels are short. @@ -41,8 +46,7 @@ Parameters ---------- - info : Info - The measurement info. + %(info_not_none)s threshold : float The threshold distance for what is considered short in meters. @@ -60,10 +64,11 @@ # Only valid for fNIRS data before conversion to haemoglobin picks = _picks_to_idx(info, ['fnirs_cw_amplitude', 'fnirs_od'], exclude=[], allow_empty=True) - freqs = np.empty(picks.size, int) - for ii in picks: - freqs[ii] = info['chs'][ii]['loc'][9] - return freqs + freqs = list() + for pick in picks: + freqs.append(round(float( + _S_D_F_RE.match(info['ch_names'][pick]).groups()[2]))) + return np.array(freqs, int) def _channel_chromophore(info): @@ -76,8 +81,17 @@ return chroma -def _check_channels_ordered(info, pair_vals): - """Check channels follow expected fNIRS format.""" +def _check_channels_ordered(info, pair_vals, *, throw_errors=True, + check_bads=True): + """Check channels follow expected fNIRS format. + + If the channels are correctly ordered then an array of valid picks + will be returned. + + If throw_errors is True then any errors in fNIRS formatting will be + thrown to inform the user. If throw_errors is False then an empty array + will be returned if the channels are not sufficiently formatted. + """ # Every second channel should be same SD pair # and have the specified light frequencies. @@ -87,125 +101,212 @@ # All chromophore fNIRS data picks_chroma = _picks_to_idx(info, ['hbo', 'hbr'], exclude=[], allow_empty=True) - # All continuous wave fNIRS data - picks_cw = np.hstack([picks_chroma, picks_wave]) if (len(picks_wave) > 0) & (len(picks_chroma) > 0): - raise ValueError( + picks = _throw_or_return_empty( 'MNE does not support a combination of amplitude, optical ' - 'density, and haemoglobin data in the same raw structure.') + 'density, and haemoglobin data in the same raw structure.', + throw_errors) - if len(picks_cw) % 2 != 0: + # All continuous wave fNIRS data + if len(picks_wave): + error_word = "frequencies" + use_RE = _S_D_F_RE + picks = picks_wave + else: + error_word = "chromophore" + use_RE = _S_D_H_RE + picks = picks_chroma + + pair_vals = np.array(pair_vals) + if pair_vals.shape != (2,): + raise ValueError( + f'Exactly two {error_word} must exist in info, got ' + f'{list(pair_vals)}') + # In principle we do not need to require that these be sorted -- + # all we need to do is change our sorted() below to make use of a + # pair_vals.index(...) in a sort key -- but in practice we always want + # (hbo, hbr) or (lower_freq, upper_freq) pairings, both of which will + # work with a naive string sort, so let's just enforce sorted-ness here + is_str = pair_vals.dtype.kind == 'U' + pair_vals = list(pair_vals) + if is_str: + if pair_vals != ['hbo', 'hbr']: + raise ValueError( + f'The {error_word} in info must be ["hbo", "hbr"], but got ' + f'{pair_vals} instead') + elif not np.array_equal(np.unique(pair_vals), pair_vals): raise ValueError( + f'The {error_word} in info must be unique and sorted, but got ' + f'got {pair_vals} instead') + + if len(picks) % 2 != 0: + picks = _throw_or_return_empty( 'NIRS channels not ordered correctly. An even number of NIRS ' f'channels is required. {len(info.ch_names)} channels were' - f'provided: {info.ch_names}') + f'provided', throw_errors) # Ensure wavelength info exists for waveform data all_freqs = [info["chs"][ii]["loc"][9] for ii in picks_wave] if np.any(np.isnan(all_freqs)): - raise ValueError( - 'NIRS channels is missing wavelength information in the' - f'info["chs"] structure. The encoded wavelengths are {all_freqs}.') + picks = _throw_or_return_empty( + f'NIRS channels is missing wavelength information in the ' + f'info["chs"] structure. The encoded wavelengths are {all_freqs}.', + throw_errors) + + # Validate the channel naming scheme + for pick in picks: + ch_name_info = use_RE.match(info['chs'][pick]['ch_name']) + if not bool(ch_name_info): + picks = _throw_or_return_empty( + 'NIRS channels have specified naming conventions. ' + 'The provided channel name can not be parsed: ' + f'{repr(info.ch_names[pick])}', throw_errors) + break + value = ch_name_info.groups()[2] + if len(picks_wave): + value = value + else: # picks_chroma + if value not in ["hbo", "hbr"]: + picks = _throw_or_return_empty( + "NIRS channels have specified naming conventions." + "Chromophore data must be labeled either hbo or hbr. " + f"The failing channel is {info['chs'][pick]['ch_name']}", + throw_errors) + break + + # Reorder to be paired (naive sort okay here given validation above) + picks = picks[np.argsort([info['ch_names'][pick] for pick in picks])] + + # Validate our paired ordering + for ii, jj in zip(picks[::2], picks[1::2]): + ch1_name = info['chs'][ii]['ch_name'] + ch2_name = info['chs'][jj]['ch_name'] + ch1_re = use_RE.match(ch1_name) + ch2_re = use_RE.match(ch2_name) + ch1_S, ch1_D, ch1_value = ch1_re.groups()[:3] + ch2_S, ch2_D, ch2_value = ch2_re.groups()[:3] + if len(picks_wave): + ch1_value, ch2_value = float(ch1_value), float(ch2_value) + if (ch1_S != ch2_S) or (ch1_D != ch2_D) or \ + (ch1_value != pair_vals[0]) or (ch2_value != pair_vals[1]): + picks = _throw_or_return_empty( + 'NIRS channels not ordered correctly. Channels must be ' + 'ordered as source detector pairs with alternating' + f' {error_word} {pair_vals[0]} & {pair_vals[1]}, but got ' + f'S{ch1_S}_D{ch1_D} pair ' + f'{repr(ch1_name)} and {repr(ch2_name)}', + throw_errors) + break + + if check_bads: + for ii, jj in zip(picks[::2], picks[1::2]): + want = [info.ch_names[ii], info.ch_names[jj]] + got = list(set(info['bads']).intersection(want)) + if len(got) == 1: + raise RuntimeError( + f'NIRS bad labelling is not consistent, found {got} but ' + f'needed {want}') + return picks - for ii in picks_cw[::2]: - ch1_name_info = re.match(r'S(\d+)_D(\d+) (\d+)', - info['chs'][ii]['ch_name']) - ch2_name_info = re.match(r'S(\d+)_D(\d+) (\d+)', - info['chs'][ii + 1]['ch_name']) - - if bool(ch2_name_info) & bool(ch1_name_info): - - if info['chs'][ii]['loc'][9] != \ - float(ch1_name_info.groups()[2]) or \ - info['chs'][ii + 1]['loc'][9] != \ - float(ch2_name_info.groups()[2]): - raise ValueError( - 'NIRS channels not ordered correctly. ' - 'Channel name and NIRS' - ' frequency do not match: %s -> %s & %s -> %s' - % (info['chs'][ii]['ch_name'], - info['chs'][ii]['loc'][9], - info['chs'][ii + 1]['ch_name'], - info['chs'][ii + 1]['loc'][9])) - - first_value = int(ch1_name_info.groups()[2]) - second_value = int(ch2_name_info.groups()[2]) - error_word = "frequencies" - - else: - ch1_name_info = re.match(r'S(\d+)_D(\d+) (\w+)', - info['chs'][ii]['ch_name']) - ch2_name_info = re.match(r'S(\d+)_D(\d+) (\w+)', - info['chs'][ii + 1]['ch_name']) - - if bool(ch2_name_info) & bool(ch1_name_info): - - first_value = ch1_name_info.groups()[2] - second_value = ch2_name_info.groups()[2] - error_word = "chromophore" - - if (first_value not in ["hbo", "hbr"] or - second_value not in ["hbo", "hbr"]): - raise ValueError( - "NIRS channels have specified naming conventions." - "Chromophore data must be labeled either hbo or hbr." - "Failing channels are " - f"{info['chs'][ii]['ch_name']}, " - f"{info['chs'][ii + 1]['ch_name']}") - - else: - raise ValueError( - 'NIRS channels have specified naming conventions.' - 'The provided channel names can not be parsed.' - f'Channels are {info.ch_names}') - - if (ch1_name_info.groups()[0] != ch2_name_info.groups()[0]) or \ - (ch1_name_info.groups()[1] != ch2_name_info.groups()[1]) or \ - (first_value != pair_vals[0]) or \ - (second_value != pair_vals[1]): - raise ValueError( - 'NIRS channels not ordered correctly. Channels must be ordered' - ' as source detector pairs with alternating' - f' {error_word}: {pair_vals[0]} & {pair_vals[1]}') - return picks_cw +def _throw_or_return_empty(msg, throw_errors): + if throw_errors: + raise ValueError(msg) + else: + return [] -def _validate_nirs_info(info): +def _validate_nirs_info(info, *, throw_errors=True, fnirs=None, which=None, + check_bads=True, allow_empty=True): """Apply all checks to fNIRS info. Works on all continuous wave types.""" + _validate_type(fnirs, (None, str), 'fnirs') + kinds = dict( + od='optical density', + cw_amplitude='continuous wave', + hb='chromophore', + ) + _check_option('fnirs', fnirs, (None,) + tuple(kinds)) + if fnirs is not None: + kind = kinds[fnirs] + fnirs = ['hbo', 'hbr'] if fnirs == 'hb' else f'fnirs_{fnirs}' + if not len(pick_types(info, fnirs=fnirs)): + raise RuntimeError( + f'{which} must operate on {kind} data, but none was found.') freqs = np.unique(_channel_frequencies(info)) if freqs.size > 0: - picks = _check_channels_ordered(info, freqs) + pair_vals = freqs else: - picks = _check_channels_ordered(info, - np.unique(_channel_chromophore(info))) - return picks - + pair_vals = np.unique(_channel_chromophore(info)) + out = _check_channels_ordered( + info, pair_vals, throw_errors=throw_errors, + check_bads=check_bads) + return out -def _fnirs_check_bads(raw): - """Check consistent labeling of bads across fnirs optodes.""" - # For an optode pair, if one component (light frequency or chroma) is - # marked as bad then they all should be. This function checks that all - # optodes are marked bad consistently. - picks = _picks_to_idx(raw.info, 'fnirs', exclude=[]) - for ii in picks[::2]: - bad_opto = set(raw.info['bads']).intersection(raw.ch_names[ii:ii + 2]) - if len(bad_opto) == 1: - raise RuntimeError('NIRS bad labelling is not consistent') - -def _fnirs_spread_bads(raw): +def _fnirs_spread_bads(info): """Spread bad labeling across fnirs channels.""" - # For an optode if any component (light frequency or chroma) is marked + # For an optode pair if any component (light frequency or chroma) is marked # as bad, then they all should be. This function will find any pairs marked # as bad and spread the bad marking to all components of the optode pair. - picks = _picks_to_idx(raw.info, 'fnirs', exclude=[]) - new_bads = list() - for ii in picks[::2]: - bad_opto = set(raw.info['bads']).intersection(raw.ch_names[ii:ii + 2]) - if len(bad_opto) > 0: - new_bads.extend(raw.ch_names[ii:ii + 2]) - raw.info['bads'] = new_bads + picks = _validate_nirs_info(info, check_bads=False) + new_bads = set(info['bads']) + for ii, jj in zip(picks[::2], picks[1::2]): + ch1_name, ch2_name = info.ch_names[ii], info.ch_names[jj] + if ch1_name in new_bads: + new_bads.add(ch2_name) + elif ch2_name in new_bads: + new_bads.add(ch1_name) + info['bads'] = sorted(new_bads) + + return info + + +def _fnirs_optode_names(info): + """Return list of unique optode names.""" + picks_wave = _picks_to_idx(info, ['fnirs_cw_amplitude', 'fnirs_od'], + exclude=[], allow_empty=True) + picks_chroma = _picks_to_idx(info, ['hbo', 'hbr'], + exclude=[], allow_empty=True) + + if len(picks_wave) > 0: + regex = _S_D_F_RE + elif len(picks_chroma) > 0: + regex = _S_D_H_RE + else: + return [], [] - return raw + sources = np.unique([int(regex.match(ch).groups()[0]) + for ch in info.ch_names]) + detectors = np.unique([int(regex.match(ch).groups()[1]) + for ch in info.ch_names]) + + src_names = [f"S{s}" for s in sources] + det_names = [f"D{d}" for d in detectors] + + return src_names, det_names + + +def _optode_position(info, optode): + """Find the position of an optode.""" + idx = [optode in a for a in info.ch_names].index(True) + + if "S" in optode: + loc_idx = range(3, 6) + elif "D" in optode: + loc_idx = range(6, 9) + + return info["chs"][idx]["loc"][loc_idx] + + +def _reorder_nirx(raw): + # Maybe someday we should make this public like + # mne.preprocessing.nirs.reorder_standard(raw, order='nirx') + info = raw.info + picks = pick_types(info, fnirs=True, exclude=[]) + prefixes = [info['ch_names'][pick].split()[0] for pick in picks] + nirs_names = [info['ch_names'][pick] for pick in picks] + nirs_sorted = sorted(nirs_names, + key=lambda name: (prefixes.index(name.split()[0]), + name.split(maxsplit=1)[1])) + raw.reorder_channels(nirs_sorted) diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/nirs/_optical_density.py python-mne-1.1.0+dfsg/mne/preprocessing/nirs/_optical_density.py --- python-mne-0.23.4+dfsg/mne/preprocessing/nirs/_optical_density.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/nirs/_optical_density.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,24 +2,25 @@ # Eric Larson # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np from ...io import BaseRaw from ...io.constants import FIFF -from ...utils import _validate_type, warn -from ...io.pick import _picks_to_idx -from ..nirs import _channel_frequencies, _check_channels_ordered +from ...utils import _validate_type, warn, verbose +from ..nirs import _validate_nirs_info -def optical_density(raw): +@verbose +def optical_density(raw, *, verbose=None): r"""Convert NIRS raw data to optical density. Parameters ---------- raw : instance of Raw The raw data. + %(verbose)s Returns ------- @@ -28,11 +29,7 @@ """ raw = raw.copy().load_data() _validate_type(raw, BaseRaw, 'raw') - _check_channels_ordered(raw.info, - np.unique(_channel_frequencies(raw.info))) - - picks = _picks_to_idx(raw.info, 'fnirs_cw_amplitude') - data_means = np.mean(raw.get_data(), axis=1) + picks = _validate_nirs_info(raw.info, fnirs='cw_amplitude') # The devices measure light intensity. Negative light intensities should # not occur. If they do it is likely due to hardware or movement issues. @@ -40,12 +37,19 @@ # that the means are all greater than zero for the division below. if np.any(raw._data[picks] <= 0): warn("Negative intensities encountered. Setting to abs(x)") - raw._data[picks] = np.abs(raw._data[picks]) - - for ii in picks: - raw._data[ii] /= data_means[ii] - np.log(raw._data[ii], out=raw._data[ii]) - raw._data[ii] *= -1 - raw.info['chs'][ii]['coil_type'] = FIFF.FIFFV_COIL_FNIRS_OD + min_ = np.inf + for pi in picks: + np.abs(raw._data[pi], out=raw._data[pi]) + min_ = min(min_, raw._data[pi].min() or min_) + # avoid == 0 + for pi in picks: + np.maximum(raw._data[pi], min_, out=raw._data[pi]) + + for pi in picks: + data_mean = np.mean(raw._data[pi]) + raw._data[pi] /= data_mean + np.log(raw._data[pi], out=raw._data[pi]) + raw._data[pi] *= -1 + raw.info['chs'][pi]['coil_type'] = FIFF.FIFFV_COIL_FNIRS_OD return raw diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/nirs/_scalp_coupling_index.py python-mne-1.1.0+dfsg/mne/preprocessing/nirs/_scalp_coupling_index.py --- python-mne-0.23.4+dfsg/mne/preprocessing/nirs/_scalp_coupling_index.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/nirs/_scalp_coupling_index.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,15 +2,13 @@ # Eric Larson # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np -from ... import pick_types from ...io import BaseRaw from ...utils import _validate_type, verbose -from ..nirs import _channel_frequencies, _check_channels_ordered -from ...filter import filter_data +from ..nirs import _validate_nirs_info @verbose @@ -42,25 +40,24 @@ ---------- .. footbibliography:: """ - raw = raw.copy().load_data() _validate_type(raw, BaseRaw, 'raw') + picks = _validate_nirs_info( + raw.info, fnirs='od', which='Scalp coupling index') - if not len(pick_types(raw.info, fnirs='fnirs_od')): - raise RuntimeError('Scalp coupling index ' - 'should be run on optical density data.') - - freqs = np.unique(_channel_frequencies(raw.info)) - picks = _check_channels_ordered(raw.info, freqs) - - filtered_data = filter_data(raw._data, raw.info['sfreq'], l_freq, h_freq, - picks=picks, verbose=verbose, - l_trans_bandwidth=l_trans_bandwidth, - h_trans_bandwidth=h_trans_bandwidth) + raw = raw.copy().pick(picks).load_data() + zero_mask = np.std(raw._data, axis=-1) == 0 + filtered_data = raw.filter( + l_freq, h_freq, l_trans_bandwidth=l_trans_bandwidth, + h_trans_bandwidth=h_trans_bandwidth, verbose=verbose).get_data() sci = np.zeros(picks.shape) - for ii in picks[::2]: - c = np.corrcoef(filtered_data[ii], filtered_data[ii + 1])[0][1] + for ii in range(0, len(picks), 2): + with np.errstate(invalid='ignore'): + c = np.corrcoef(filtered_data[ii], filtered_data[ii + 1])[0][1] + if not np.isfinite(c): # someone had std=0 + c = 0 sci[ii] = c sci[ii + 1] = c - + sci[zero_mask] = 0 + sci = sci[np.argsort(picks)] # restore original order return sci diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/nirs/_tddr.py python-mne-1.1.0+dfsg/mne/preprocessing/nirs/_tddr.py --- python-mne-0.23.4+dfsg/mne/preprocessing/nirs/_tddr.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/nirs/_tddr.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,16 +1,14 @@ # Authors: Robert Luke # Frank Fishburn # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np -from ... import pick_types from ...io import BaseRaw from ...utils import _validate_type, verbose -from ...io.pick import _picks_to_idx -from ..nirs import _channel_frequencies, _check_channels_ordered +from ..nirs import _validate_nirs_info @verbose @@ -34,6 +32,11 @@ Notes ----- + TDDR was initially designed to be used on optical density fNIRS data but + has been enabled to be applied on hemoglobin concentration fNIRS data as + well in MNE. We recommend applying the algorithm to optical density fNIRS + data as intended by the original author wherever possible. + There is a shorter alias ``mne.preprocessing.nirs.tddr`` that can be used instead of this function (e.g. if line length is an issue). @@ -43,13 +46,11 @@ """ raw = raw.copy().load_data() _validate_type(raw, BaseRaw, 'raw') - _check_channels_ordered(raw.info, - np.unique(_channel_frequencies(raw.info))) - - if not len(pick_types(raw.info, fnirs='fnirs_od')): - raise RuntimeError('TDDR should be run on optical density data.') + picks = _validate_nirs_info(raw.info) - picks = _picks_to_idx(raw.info, 'fnirs_od', exclude=[]) + if not len(picks): + raise RuntimeError('TDDR should be run on optical density or ' + 'hemoglobin data.') for pick in picks: raw._data[pick] = _TDDR(raw._data[pick], raw.info['sfreq']) @@ -76,8 +77,8 @@ # signals_corrected = TDDR( signals , sample_rate ); # # Inputs: - # signals: A [sample x channel] matrix of uncorrected optical density - # data + # signals: A [sample x channel] matrix of uncorrected optical density or + # hemoglobin data # sample_rate: A scalar reflecting the rate of acquisition in Hz # # Outputs: @@ -132,6 +133,8 @@ sigma = 1.4826 * np.median(dev) # Step 3d. Scale deviations by standard deviation and tuning parameter + if sigma == 0: + break r = dev / (sigma * tune) # Step 3e. Calculate new weights according to Tukey's biweight function diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/nirs/tests/test_beer_lambert_law.py python-mne-1.1.0+dfsg/mne/preprocessing/nirs/tests/test_beer_lambert_law.py --- python-mne-0.23.4+dfsg/mne/preprocessing/nirs/tests/test_beer_lambert_law.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/nirs/tests/test_beer_lambert_law.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Eric Larson # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op @@ -12,16 +12,15 @@ from mne.datasets.testing import data_path from mne.io import read_raw_nirx, BaseRaw, read_raw_fif from mne.preprocessing.nirs import optical_density, beer_lambert_law -from mne.utils import _validate_type +from mne.utils import _validate_type, requires_version from mne.datasets import testing -from mne.externals.pymatreader import read_mat - -fname_nirx_15_0 = op.join(data_path(download=False), +testing_path = data_path(download=False) +fname_nirx_15_0 = op.join(testing_path, 'NIRx', 'nirscout', 'nirx_15_0_recording') -fname_nirx_15_2 = op.join(data_path(download=False), +fname_nirx_15_2 = op.join(testing_path, 'NIRx', 'nirscout', 'nirx_15_2_recording') -fname_nirx_15_2_short = op.join(data_path(download=False), +fname_nirx_15_2_short = op.join(testing_path, 'NIRx', 'nirscout', 'nirx_15_2_recording_w_short') @@ -30,13 +29,13 @@ @pytest.mark.parametrize('fname', ([fname_nirx_15_2_short, fname_nirx_15_2, fname_nirx_15_0])) @pytest.mark.parametrize('fmt', ('nirx', 'fif')) -def test_beer_lambert(fname, fmt, tmpdir): +def test_beer_lambert(fname, fmt, tmp_path): """Test converting NIRX files.""" assert fmt in ('nirx', 'fif') raw = read_raw_nirx(fname) if fmt == 'fif': - raw.save(tmpdir.join('test_raw.fif')) - raw = read_raw_fif(tmpdir.join('test_raw.fif')) + raw.save(tmp_path / 'test_raw.fif') + raw = read_raw_fif(tmp_path / 'test_raw.fif') assert 'fnirs_cw_amplitude' in raw assert 'fnirs_od' not in raw raw = optical_density(raw) @@ -64,25 +63,28 @@ # Test that an error is thrown if channel naming frequency doesn't match # what is stored in loc[9], which should hold the light frequency too. raw_od = optical_density(raw) - raw_od.rename_channels({'S2_D2 760': 'S2_D2 770'}) - with pytest.raises(ValueError, match='frequency do not match'): - beer_lambert_law(raw_od) - - # Test that an error is thrown if inconsistent frequencies used in data - raw_od.info['chs'][2]['loc'][9] = 770.0 - with pytest.raises(ValueError, match='with alternating frequencies'): + ch_name = raw.ch_names[0] + assert ch_name == 'S1_D1 760' + idx = raw_od.ch_names.index(ch_name) + assert idx == 0 + raw_od.info['chs'][idx]['loc'][9] = 770 + raw_od.rename_channels({ch_name: ch_name.replace('760', '770')}) + assert raw_od.ch_names[0] == 'S1_D1 770' + with pytest.raises(ValueError, match='Exactly two frequencies'): beer_lambert_law(raw_od) +@requires_version('pymatreader') @testing.requires_testing_data def test_beer_lambert_v_matlab(): """Compare MNE results to MATLAB toolbox.""" + from pymatreader import read_mat raw = read_raw_nirx(fname_nirx_15_0) raw = optical_density(raw) raw = beer_lambert_law(raw, ppf=0.121) raw._data *= 1e6 # Scale to uM for comparison to MATLAB - matlab_fname = op.join(data_path(download=False), + matlab_fname = op.join(testing_path, 'NIRx', 'nirscout', 'validation', 'nirx_15_0_recording_bl.mat') matlab_data = read_mat(matlab_fname) diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/nirs/tests/test_nirs.py python-mne-1.1.0+dfsg/mne/preprocessing/nirs/tests/test_nirs.py --- python-mne-0.23.4+dfsg/mne/preprocessing/nirs/tests/test_nirs.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/nirs/tests/test_nirs.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,21 +2,24 @@ # Eric Larson # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op import pytest import numpy as np -from numpy.testing import assert_array_equal +from numpy.testing import (assert_array_equal, assert_array_almost_equal, + assert_allclose) from mne import create_info from mne.datasets.testing import data_path from mne.io import read_raw_nirx, RawArray from mne.preprocessing.nirs import (optical_density, beer_lambert_law, - _fnirs_check_bads, _fnirs_spread_bads, - _check_channels_ordered, - _channel_frequencies, _channel_chromophore) + _fnirs_spread_bads, _validate_nirs_info, + _check_channels_ordered, tddr, + _channel_frequencies, _channel_chromophore, + _fnirs_optode_names, _optode_position, + scalp_coupling_index) from mne.io.pick import _picks_to_idx from mne.datasets import testing @@ -93,6 +96,11 @@ pytest.raises(ValueError, _picks_to_idx, raw.info, 'fnirs_fd_phase') +# Backward compat wrapper for simplicity below +def _fnirs_check_bads(info): + _validate_nirs_info(info) + + @testing.requires_testing_data @pytest.mark.parametrize('fname', ([fname_nirx_15_2_short, fname_nirx_15_2, fname_nirx_15_0])) @@ -100,29 +108,34 @@ """Test checking of bad markings.""" # No bad channels, so these should all pass raw = read_raw_nirx(fname) - _fnirs_check_bads(raw) + _fnirs_check_bads(raw.info) raw = optical_density(raw) - _fnirs_check_bads(raw) + _fnirs_check_bads(raw.info) raw = beer_lambert_law(raw) - _fnirs_check_bads(raw) + _fnirs_check_bads(raw.info) # Mark pairs of bad channels, so these should all pass raw = read_raw_nirx(fname) raw.info['bads'] = raw.ch_names[0:2] - _fnirs_check_bads(raw) + _fnirs_check_bads(raw.info) raw = optical_density(raw) - _fnirs_check_bads(raw) + _fnirs_check_bads(raw.info) raw = beer_lambert_law(raw) - _fnirs_check_bads(raw) + _fnirs_check_bads(raw.info) # Mark single channel as bad, so these should all fail raw = read_raw_nirx(fname) raw.info['bads'] = raw.ch_names[0:1] - pytest.raises(RuntimeError, _fnirs_check_bads, raw) + pytest.raises(RuntimeError, _fnirs_check_bads, raw.info) + with pytest.raises(RuntimeError, match='bad labelling'): + raw = optical_density(raw) + raw.info['bads'] = [] raw = optical_density(raw) - pytest.raises(RuntimeError, _fnirs_check_bads, raw) - raw = beer_lambert_law(raw) - pytest.raises(RuntimeError, _fnirs_check_bads, raw) + raw.info['bads'] = raw.ch_names[0:1] + pytest.raises(RuntimeError, _fnirs_check_bads, raw.info) + with pytest.raises(RuntimeError, match='bad labelling'): + raw = beer_lambert_law(raw) + pytest.raises(RuntimeError, _fnirs_check_bads, raw.info) @testing.requires_testing_data @@ -133,20 +146,20 @@ # Test spreading upwards in frequency and on raw data raw = read_raw_nirx(fname) raw.info['bads'] = ['S1_D1 760'] - raw = _fnirs_spread_bads(raw) - assert raw.info['bads'] == ['S1_D1 760', 'S1_D1 850'] + info = _fnirs_spread_bads(raw.info) + assert info['bads'] == ['S1_D1 760', 'S1_D1 850'] # Test spreading downwards in frequency and on od data raw = optical_density(raw) raw.info['bads'] = raw.ch_names[5:6] - raw = _fnirs_spread_bads(raw) - assert raw.info['bads'] == raw.ch_names[4:6] + info = _fnirs_spread_bads(raw.info) + assert info['bads'] == raw.ch_names[4:6] # Test spreading multiple bads and on chroma data raw = beer_lambert_law(raw) raw.info['bads'] = [raw.ch_names[x] for x in [1, 8]] - raw = _fnirs_spread_bads(raw) - assert raw.info['bads'] == [raw.ch_names[x] for x in [0, 1, 8, 9]] + info = _fnirs_spread_bads(raw.info) + assert info['bads'] == [info.ch_names[x] for x in [0, 1, 8, 9]] @testing.requires_testing_data @@ -173,14 +186,20 @@ with pytest.raises(ValueError, match='not ordered correctly'): _check_channels_ordered(raw_dropped.info, freqs) - # The ordering must match the passed in argument + # The ordering must be increasing for the pairs, if provided raw_names_reversed = raw.copy().ch_names raw_names_reversed.reverse() raw_reversed = raw.copy().pick_channels(raw_names_reversed, ordered=True) - with pytest.raises(ValueError, match='not ordered .* frequencies'): - _check_channels_ordered(raw_reversed.info, freqs) + with pytest.raises(ValueError, match='The frequencies.*sorted.*'): + _check_channels_ordered(raw_reversed.info, [850, 760]) # So if we flip the second argument it should pass again - _check_channels_ordered(raw_reversed.info, [850, 760]) + picks = _check_channels_ordered(raw_reversed.info, freqs) + got_first = set( + raw_reversed.ch_names[pick].split()[1] for pick in picks[::2]) + assert got_first == {'760'} + got_second = set( + raw_reversed.ch_names[pick].split()[1] for pick in picks[1::2]) + assert got_second == {'850'} # Check on OD data raw = optical_density(raw) @@ -200,8 +219,8 @@ assert_array_equal(chroma, ["hbo", "hbr"]) picks = _check_channels_ordered(raw.info, chroma) assert len(picks) == len(raw.ch_names) - with pytest.raises(ValueError, match='not ordered .* chromophore'): - _check_channels_ordered(raw.info, ["hbx", "hbr"]) + with pytest.raises(ValueError, match='chromophore in info'): + _check_channels_ordered(raw.info, ["hbr", "hbo"]) def test_fnirs_channel_naming_and_order_custom_raw(): @@ -234,7 +253,7 @@ for idx, f in enumerate(freqs): raw.info["chs"][idx]["loc"][9] = f - picks = _check_channels_ordered(raw.info, [920, 850]) + picks = _check_channels_ordered(raw.info, [850, 920]) assert len(picks) == len(raw.ch_names) assert len(picks) == 6 @@ -249,8 +268,8 @@ freqs = np.tile([920, 850], 3) for idx, f in enumerate(freqs): raw.info["chs"][idx]["loc"][9] = f - with pytest.raises(ValueError, match='name and NIRS frequency do not'): - _check_channels_ordered(raw.info, [920, 850]) + with pytest.raises(ValueError, match='not ordered'): + _check_channels_ordered(raw.info, [850, 920]) # Catch if someone doesn't set the info field ch_names = ['S1_D1 760', 'S1_D1 850', 'S2_D1 760', 'S2_D1 850', @@ -259,7 +278,7 @@ info = create_info(ch_names=ch_names, ch_types=ch_types, sfreq=1.0) raw = RawArray(data, info, verbose=True) with pytest.raises(ValueError, match='missing wavelength information'): - _check_channels_ordered(raw.info, [920, 850]) + _check_channels_ordered(raw.info, [850, 920]) # I have seen data encoded not in alternating frequency, but blocked. ch_names = ['S1_D1 760', 'S2_D1 760', 'S3_D1 760', @@ -270,10 +289,6 @@ freqs = np.repeat([760, 850], 3) for idx, f in enumerate(freqs): raw.info["chs"][idx]["loc"][9] = f - with pytest.raises(ValueError, match='channels not ordered correctly'): - _check_channels_ordered(raw.info, [760, 850]) - # and this is how you would fix the ordering, then it should pass - raw.pick(picks=[0, 3, 1, 4, 2, 5]) _check_channels_ordered(raw.info, [760, 850]) @@ -306,9 +321,9 @@ freqs = np.repeat([760, 850], 3) for idx, f in enumerate(freqs): raw.info["chs"][idx]["loc"][9] = f - with pytest.raises(ValueError, match='channels not ordered correctly'): - _check_channels_ordered(raw.info, [760, 850]) - # and this is how you would fix the ordering, then it should pass + # no problems here + _check_channels_ordered(raw.info, [760, 850]) + # or with this (nirx) reordering raw.pick(picks=[0, 3, 1, 4, 2, 5]) _check_channels_ordered(raw.info, [760, 850]) @@ -346,13 +361,13 @@ ch_types = np.repeat(["hbo", "hbr"], 3) info = create_info(ch_names=ch_names, ch_types=ch_types, sfreq=1.0) raw = RawArray(data, info, verbose=True) - with pytest.raises(ValueError, match='not ordered .* chromophore'): - _check_channels_ordered(raw.info, ["hbo", "hbr"]) - # Reordering should fix + # no issue here + _check_channels_ordered(raw.info, ["hbo", "hbr"]) + # reordering okay, too raw.pick(picks=[0, 3, 1, 4, 2, 5]) _check_channels_ordered(raw.info, ["hbo", "hbr"]) # Wrong names should fail - with pytest.raises(ValueError, match='not ordered .* chromophore'): + with pytest.raises(ValueError, match='chromophore in info'): _check_channels_ordered(raw.info, ["hbb", "hbr"]) # Test weird naming @@ -362,7 +377,7 @@ info = create_info(ch_names=ch_names, ch_types=ch_types, sfreq=1.0) raw = RawArray(data, info, verbose=True) with pytest.raises(ValueError, match='naming conventions'): - _check_channels_ordered(raw.info, ["hbb", "hbr"]) + _check_channels_ordered(raw.info, ["hbo", "hbr"]) # Check more weird naming ch_names = ['S1_DX hbo', 'S1_DX hbr', 'S2_D1 hbo', 'S2_D1 hbr', @@ -372,3 +387,84 @@ raw = RawArray(data, info, verbose=True) with pytest.raises(ValueError, match='can not be parsed'): _check_channels_ordered(raw.info, ["hbo", "hbr"]) + + +def test_optode_names(): + """Ensure optode name extraction is correct.""" + ch_names = ['S11_D2 760', 'S11_D2 850', 'S3_D1 760', + 'S3_D1 850', 'S2_D13 760', 'S2_D13 850'] + ch_types = np.repeat("fnirs_od", 6) + info = create_info(ch_names=ch_names, ch_types=ch_types, sfreq=1.0) + src_names, det_names = _fnirs_optode_names(info) + assert_array_equal(src_names, [f"S{n}" for n in ["2", "3", "11"]]) + assert_array_equal(det_names, [f"D{n}" for n in ["1", "2", "13"]]) + + ch_names = ['S1_D11 hbo', 'S1_D11 hbr', 'S2_D17 hbo', 'S2_D17 hbr', + 'S3_D1 hbo', 'S3_D1 hbr'] + ch_types = np.tile(["hbo", "hbr"], 3) + info = create_info(ch_names=ch_names, ch_types=ch_types, sfreq=1.0) + src_names, det_names = _fnirs_optode_names(info) + assert_array_equal(src_names, [f"S{n}" for n in range(1, 4)]) + assert_array_equal(det_names, [f"D{n}" for n in ["1", "11", "17"]]) + + +@testing.requires_testing_data +def test_optode_loc(): + """Ensure optode location extraction is correct.""" + raw = read_raw_nirx(fname_nirx_15_2_short) + loc = _optode_position(raw.info, "D3") + assert_array_almost_equal(loc, [0.082804, 0.01573, 0.024852]) + + +def test_order_agnostic(nirx_snirf): + """Test that order does not matter to (pre)processing results.""" + raw_nirx, raw_snirf = nirx_snirf + raw_random = raw_nirx.copy().pick( + np.random.RandomState(0).permutation(len(raw_nirx.ch_names))) + raws = dict(nirx=raw_nirx, snirf=raw_snirf, random=raw_random) + del raw_nirx, raw_snirf, raw_random + orders = dict() + # continuous wave + for key, r in raws.items(): + assert set(r.get_channel_types()) == {'fnirs_cw_amplitude'} + orders[key] = [ + r.ch_names.index(name) for name in raws['nirx'].ch_names] + assert_array_equal( + raws['nirx'].ch_names, np.array(r.ch_names)[orders[key]]) + assert_allclose( + raws['nirx'].get_data(), r.get_data(orders[key]), err_msg=key) + assert_array_equal(orders['nirx'], np.arange(len(raws['nirx'].ch_names))) + # optical density + for key, r in raws.items(): + raws[key] = r = optical_density(r) + assert_allclose( + raws['nirx'].get_data(), r.get_data(orders[key]), err_msg=key) + assert set(r.get_channel_types()) == {'fnirs_od'} + # scalp-coupling index + sci = dict() + for key, r in raws.items(): + sci[key] = r = scalp_coupling_index(r) + assert_allclose(sci['nirx'], r[orders[key]], err_msg=key, rtol=0.01) + # TDDR (on optical) + tddrs = dict() + for key, r in raws.items(): + tddrs[key] = r = tddr(r) + assert_allclose( + tddrs['nirx'].get_data(), r.get_data(orders[key]), err_msg=key, + atol=1e-4) + assert set(r.get_channel_types()) == {'fnirs_od'} + # beer-lambert + for key, r in raws.items(): + raws[key] = r = beer_lambert_law(r) + assert_allclose( + raws['nirx'].get_data(), r.get_data(orders[key]), err_msg=key, + rtol=2e-7) + assert set(r.get_channel_types()) == {'hbo', 'hbr'} + # TDDR (on haemo) + tddrs = dict() + for key, r in raws.items(): + tddrs[key] = r = tddr(r) + assert_allclose( + tddrs['nirx'].get_data(), r.get_data(orders[key]), err_msg=key, + atol=1e-9) + assert set(r.get_channel_types()) == {'hbo', 'hbr'} diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/nirs/tests/test_optical_density.py python-mne-1.1.0+dfsg/mne/preprocessing/nirs/tests/test_optical_density.py --- python-mne-0.23.4+dfsg/mne/preprocessing/nirs/tests/test_optical_density.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/nirs/tests/test_optical_density.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Eric Larson # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op import pytest as pytest @@ -30,6 +30,8 @@ _validate_type(raw, BaseRaw, 'raw') assert 'fnirs_cw_amplitude' not in raw assert 'fnirs_od' in raw + with pytest.raises(RuntimeError, match='on continuous wave'): + optical_density(raw) @testing.requires_testing_data @@ -37,8 +39,10 @@ """Test that optical density can process zero mean data.""" raw = read_raw_nirx(fname_nirx, preload=True) raw._data[4] -= np.mean(raw._data[4]) - with pytest.warns(RuntimeWarning, match='Negative'): - raw = optical_density(raw) + raw._data[4, -1] = 0 + with np.errstate(invalid='raise', divide='raise'): + with pytest.warns(RuntimeWarning, match='Negative'): + raw = optical_density(raw) assert 'fnirs_od' in raw diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/nirs/tests/test_scalp_coupling_index.py python-mne-1.1.0+dfsg/mne/preprocessing/nirs/tests/test_scalp_coupling_index.py --- python-mne-0.23.4+dfsg/mne/preprocessing/nirs/tests/test_scalp_coupling_index.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/nirs/tests/test_scalp_coupling_index.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Eric Larson # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op @@ -29,10 +29,10 @@ @pytest.mark.parametrize('fname', ([fname_nirx_15_2_short, fname_nirx_15_2, fname_nirx_15_0])) @pytest.mark.parametrize('fmt', ('nirx', 'fif')) -def test_scalp_coupling_index(fname, fmt, tmpdir): +def test_scalp_coupling_index(fname, fmt, tmp_path): """Test converting NIRX files.""" assert fmt in ('nirx', 'fif') - raw = read_raw_nirx(fname).load_data() + raw = read_raw_nirx(fname) with pytest.raises(RuntimeError, match='Scalp'): scalp_coupling_index(raw) @@ -58,13 +58,19 @@ # Set next two channels to be uncorrelated raw._data[6] = new_data raw._data[7] = rng.rand(raw._data[0].shape[0]) + # Set next channel to have zero std + raw._data[8] = 0. + raw._data[9] = 1. + raw._data[10] = 2. + raw._data[11] = 3. # Check values sci = scalp_coupling_index(raw) assert_allclose(sci[0:6], [1, 1, 1, 1, -1, -1], atol=0.01) assert np.abs(sci[6]) < 0.5 assert np.abs(sci[7]) < 0.5 + assert_allclose(sci[8:12], 0, atol=1e-10) # Ensure function errors if wrong type is passed in - raw = beer_lambert_law(raw) + raw = beer_lambert_law(raw, ppf=6) with pytest.raises(RuntimeError, match='Scalp'): scalp_coupling_index(raw) diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/nirs/tests/test_temporal_derivative_distribution_repair.py python-mne-1.1.0+dfsg/mne/preprocessing/nirs/tests/test_temporal_derivative_distribution_repair.py --- python-mne-0.23.4+dfsg/mne/preprocessing/nirs/tests/test_temporal_derivative_distribution_repair.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/nirs/tests/test_temporal_derivative_distribution_repair.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,15 +1,16 @@ # Authors: Robert Luke # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op import pytest import numpy as np +from numpy.testing import assert_allclose from mne.datasets.testing import data_path from mne.io import read_raw_nirx -from mne.preprocessing.nirs import optical_density, tddr +from mne.preprocessing.nirs import beer_lambert_law, optical_density, tddr from mne.datasets import testing @@ -19,16 +20,38 @@ @testing.requires_testing_data @pytest.mark.parametrize('fname', ([fname_nirx_15_2])) -def test_temporal_derivative_distribution_repair(fname, tmpdir): +def test_temporal_derivative_distribution_repair(fname, tmp_path): """Test running artifact rejection.""" raw = read_raw_nirx(fname) - raw = optical_density(raw) + raw_od = optical_density(raw) + raw_hb = beer_lambert_law(raw_od) + # With optical densities # Add a baseline shift artifact about half way through data - max_shift = np.max(np.diff(raw._data[0])) + max_shift = np.max(np.diff(raw_od._data[0])) shift_amp = 5 * max_shift - raw._data[0, 0:30] = raw._data[0, 0:30] - (shift_amp) - assert np.max(np.diff(raw._data[0])) > shift_amp + raw_od._data[0, 0:30] = raw_od._data[0, 0:30] - shift_amp + # make one channel zero std + raw_od._data[1] = 0. + raw_od._data[2] = 1. + assert np.max(np.diff(raw_od._data[0])) > shift_amp # Ensure that applying the algorithm reduces the step change - raw = tddr(raw) - assert np.max(np.diff(raw._data[0])) < shift_amp + raw_od = tddr(raw_od) + assert np.max(np.diff(raw_od._data[0])) < shift_amp + assert_allclose(raw_od._data[1], 0.) # unchanged + assert_allclose(raw_od._data[2], 1.) # unchanged + + # With Hb + # Add a baseline shift artifact about half way through data + max_shift = np.max(np.diff(raw_hb._data[0])) + shift_amp = 5 * max_shift + raw_hb._data[0, 0:30] = raw_hb._data[0, 0:30] - (1.1 * shift_amp) + # make one channel zero std + raw_hb._data[1] = 0. + raw_hb._data[2] = 1. + assert np.max(np.diff(raw_hb._data[0])) > shift_amp + # Ensure that applying the algorithm reduces the step change + raw_hb = tddr(raw_hb) + assert np.max(np.diff(raw_hb._data[0])) < shift_amp + assert_allclose(raw_hb._data[1], 0.) # unchanged + assert_allclose(raw_hb._data[2], 1.) # unchanged diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/otp.py python-mne-1.1.0+dfsg/mne/preprocessing/otp.py --- python-mne-0.23.4+dfsg/mne/preprocessing/otp.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/otp.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Authors: Samu Taulu # Eric Larson -# License: BSD (3-clause) +# License: BSD-3-Clause from functools import partial diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/realign.py python-mne-1.1.0+dfsg/mne/preprocessing/realign.py --- python-mne-0.23.4+dfsg/mne/preprocessing/realign.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/realign.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,9 +1,10 @@ # -*- coding: utf-8 -*- # Authors: Eric Larson -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np +from numpy.polynomial.polynomial import Polynomial from ..io import BaseRaw from ..utils import _validate_type, warn, logger, verbose @@ -62,7 +63,11 @@ warn('Fewer than 20 times passed, results may be unreliable') # 1. Compute correction factors - coef = np.polyfit(t_other, t_raw, deg=1) + poly = Polynomial.fit(x=t_other, y=t_raw, deg=1) + converted = poly.convert(domain=(-1, 1)) + [zero_ord, first_ord] = converted.coef + logger.info(f'Zero order coefficient: {zero_ord} \n' + f'First order coefficient: {first_ord}') r, p = stats.pearsonr(t_other, t_raw) msg = f'Linear correlation computed as R={r:0.3f} and p={p:0.2e}' if p > 0.05 or r <= 0: @@ -71,29 +76,29 @@ warn(msg + ', results may be unreliable') else: logger.info(msg) - dr_ms_s = 1000 * abs(1 - coef[0]) + dr_ms_s = 1000 * abs(1 - first_ord) logger.info( f'Drift rate: {1000 * dr_ms_s:0.1f} μs/sec ' f'(total drift over {raw.times[-1]:0.1f} sec recording: ' f'{raw.times[-1] * dr_ms_s:0.1f} ms)') # 2. Crop start of recordings to match using the zero-order term - msg = f'Cropping {coef[1]:0.3f} sec from the start of ' - if coef[1] > 0: # need to crop start of raw to match other + msg = f'Cropping {zero_ord:0.3f} sec from the start of ' + if zero_ord > 0: # need to crop start of raw to match other logger.info(msg + 'raw') - raw.crop(coef[1], None) - t_raw -= coef[1] + raw.crop(zero_ord, None) + t_raw -= zero_ord else: # need to crop start of other to match raw logger.info(msg + 'other') - other.crop(-coef[1], None) - t_other += coef[1] + other.crop(-zero_ord, None) + t_other += zero_ord # 3. Resample data using the first-order term logger.info('Resampling other') - coef = coef[0] - sfreq_new = raw.info['sfreq'] * coef + sfreq_new = raw.info['sfreq'] * first_ord other.load_data().resample(sfreq_new, verbose=True) - other.info['sfreq'] = raw.info['sfreq'] + with other.info._unlock(): + other.info['sfreq'] = raw.info['sfreq'] # 4. Crop the end of one of the recordings if necessary delta = raw.times[-1] - other.times[-1] diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/_regress.py python-mne-1.1.0+dfsg/mne/preprocessing/_regress.py --- python-mne-0.23.4+dfsg/mne/preprocessing/_regress.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/_regress.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/ssp.py python-mne-1.1.0+dfsg/mne/preprocessing/ssp.py --- python-mne-0.23.4+dfsg/mne/preprocessing/ssp.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/ssp.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Matti Hämäläinen # Martin Luessi # -# License: BSD (3-clause) +# License: BSD-3-Clause import copy as cp @@ -142,7 +142,7 @@ @verbose def compute_proj_ecg(raw, raw_event=None, tmin=-0.2, tmax=0.4, n_grad=2, n_mag=2, n_eeg=2, l_freq=1.0, h_freq=35.0, - average=True, filter_length='10s', n_jobs=1, + average=True, filter_length='10s', n_jobs=None, ch_name=None, reject=dict(grad=2000e-13, mag=3000e-15, eeg=50e-6, eog=250e-6), flat=None, bads=[], avg_ref=False, @@ -258,7 +258,7 @@ @verbose def compute_proj_eog(raw, raw_event=None, tmin=-0.2, tmax=0.2, n_grad=2, n_mag=2, n_eeg=2, l_freq=1.0, h_freq=35.0, - average=True, filter_length='10s', n_jobs=1, + average=True, filter_length='10s', n_jobs=None, reject=dict(grad=2000e-13, mag=3000e-15, eeg=500e-6, eog=np.inf), flat=None, bads=[], avg_ref=False, no_proj=False, event_id=998, eog_l_freq=1, diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/stim.py python-mne-1.1.0+dfsg/mne/preprocessing/stim.py --- python-mne-0.23.4+dfsg/mne/preprocessing/stim.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/stim.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Daniel Strohmeier # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np from ..evoked import Evoked diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_annotate_amplitude.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_annotate_amplitude.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_annotate_amplitude.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_annotate_amplitude.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,394 @@ +# Author: Mathieu Scheltienne +# +# License: BSD-3-Clause + +import datetime +import itertools +from pathlib import Path +import re + +import numpy as np +import pytest + +from mne import create_info +from mne.annotations import Annotations +from mne.datasets import testing +from mne.io import RawArray, read_raw_fif +from mne.preprocessing import annotate_amplitude + + +date = datetime.datetime(2021, 12, 10, 7, 52, 24, 405305, + tzinfo=datetime.timezone.utc) +data_path = Path(testing.data_path(download=False)) +skip_fname = data_path / 'misc' / 'intervalrecording_raw.fif' + + +@pytest.mark.parametrize('meas_date', (None, date)) +@pytest.mark.parametrize('first_samp', (0, 10000)) +def test_annotate_amplitude(meas_date, first_samp): + """Test automatic annotation for segments based on peak-to-peak value.""" + n_ch, n_times = 11, 1000 + data = np.random.RandomState(0).randn(n_ch, n_times) + assert not (np.diff(data, axis=-1) == 0).any() # nothing flat at first + info = create_info(n_ch, 1000., 'eeg') + # from annotate_flat: test first_samp != for gh-6295 + raw = RawArray(data, info, first_samp=first_samp) + raw.info['bads'] = [raw.ch_names[-1]] + raw.set_meas_date(meas_date) + + # -- test bad channels spatial marking -- + for perc, dur in itertools.product((5, 99.9, 100.), (0.005, 0.95, 0.99)): + kwargs = dict(bad_percent=perc, min_duration=dur) + + # test entire channel flat + raw_ = raw.copy() + raw_._data[0] = 0. + annots, bads = annotate_amplitude(raw_, peak=None, flat=0., **kwargs) + assert len(annots) == 0 + assert bads == ['0'] + + # test multiple channels flat + raw_ = raw.copy() + raw_._data[0] = 0. + raw_._data[2] = 0. + annots, bads = annotate_amplitude(raw_, peak=None, flat=0., **kwargs) + assert len(annots) == 0 + assert bads == ['0', '2'] + + # test entire channel drifting + raw_ = raw.copy() + raw_._data[0] = np.arange(0, raw.times.size * 10, 10) + annots, bads = annotate_amplitude(raw_, peak=5, flat=None, **kwargs) + assert len(annots) == 0 + assert bads == ['0'] + + # test multiple channels drifting + raw_ = raw.copy() + raw_._data[0] = np.arange(0, raw.times.size * 10, 10) + raw_._data[2] = np.arange(0, raw.times.size * 10, 10) + annots, bads = annotate_amplitude(raw_, peak=5, flat=None, **kwargs) + assert len(annots) == 0 + assert bads == ['0', '2'] + + # -- test bad channels temporal marking -- + # flat channel for the 20% last points + n_good_times = int(round(0.8 * n_times)) + raw_ = raw.copy() + raw_._data[0, n_good_times:] = 0. + for perc in (5, 20): + annots, bads = annotate_amplitude(raw_, peak=None, flat=0., + bad_percent=perc) + assert len(annots) == 0 + assert bads == ['0'] + annots, bads = annotate_amplitude(raw_, peak=None, flat=0., + bad_percent=20.1) + assert len(annots) == 1 + assert len(bads) == 0 + # check annotation instance + assert annots[0]['description'] == 'BAD_flat' + _check_annotation(raw_, annots[0], meas_date, first_samp, n_good_times, -1) + + # test multiple channels flat and multiple channels drift + raw_ = raw.copy() + raw_._data[0, 800:] = 0. + raw_._data[1, 850:950] = 0. + raw_._data[2, :200] = np.arange(0, 200 * 10, 10) + raw_._data[2, 200:] += raw_._data[2, 199] # add offset for next samples + raw_._data[3, 50:150] = np.arange(0, 100 * 10, 10) + raw_._data[3, 150:] += raw_._data[3, 149] # add offset for next samples + for perc in (5, 10): + annots, bads = annotate_amplitude(raw_, peak=5, flat=0., + bad_percent=perc) + assert len(annots) == 0 + assert bads == ['0', '1', '2', '3'] + for perc in (10.1, 20): + annots, bads = annotate_amplitude(raw_, peak=5, flat=0., + bad_percent=perc) + assert len(annots) == 2 + assert bads == ['0', '2'] + # check annotation instance + assert all(annot['description'] in ('BAD_flat', 'BAD_peak') + for annot in annots) + for annot in annots: + start_idx = 50 if annot['description'] == 'BAD_peak' else 850 + stop_idx = 149 if annot['description'] == 'BAD_peak' else 949 + _check_annotation(raw_, annot, meas_date, first_samp, start_idx, + stop_idx) + annots, bads = annotate_amplitude(raw_, peak=5, flat=0., bad_percent=20.1) + assert len(annots) == 2 + assert len(bads) == 0 + # check annotation instance + assert all(annot['description'] in ('BAD_flat', 'BAD_peak') + for annot in annots) + for annot in annots: + start_idx = 0 if annot['description'] == 'BAD_peak' else 800 + stop_idx = 199 if annot['description'] == 'BAD_peak' else -1 + _check_annotation(raw_, annot, meas_date, first_samp, start_idx, + stop_idx) + + # test flat on already marked bad channel + raw_ = raw.copy() + raw_._data[-1, :] = 0. # this channel is already in info['bads'] + annots, bads = annotate_amplitude(raw_, peak=None, flat=0., bad_percent=5) + assert len(annots) == 0 + assert len(bads) == 0 + + # test drift on already marked bad channel + raw_ = raw.copy() + raw_._data[-1, :] = np.arange(0, raw.times.size * 10, 10) + annots, bads = annotate_amplitude(raw_, peak=5, flat=None, bad_percent=5) + assert len(annots) == 0 + assert len(bads) == 0 + + +@pytest.mark.parametrize('meas_date', (None, date)) +@pytest.mark.parametrize('first_samp', (0, 10000)) +def test_annotate_amplitude_with_overlap(meas_date, first_samp): + """Test cases with overlap between annotations.""" + n_ch, n_times = 11, 1000 + data = np.random.RandomState(0).randn(n_ch, n_times) + assert not (np.diff(data, axis=-1) == 0).any() # nothing flat at first + info = create_info(n_ch, 1000., 'eeg') + # from annotate_flat: test first_samp != for gh-6295 + raw = RawArray(data, info, first_samp=first_samp) + raw.info['bads'] = [raw.ch_names[-1]] + raw.set_meas_date(meas_date) + + # -- overlap between peak and flat -- + raw_ = raw.copy() + raw_._data[0, 800:] = 0. + raw_._data[1, 700:900] = np.arange(0, 200 * 10, 10) + raw_._data[1, 900:] += raw_._data[1, 899] # add offset for next samples + annots, bads = annotate_amplitude(raw_, peak=5, flat=0, bad_percent=25) + assert len(annots) == 2 + assert len(bads) == 0 + # check annotation instance + assert all(annot['description'] in ('BAD_flat', 'BAD_peak') + for annot in annots) + for annot in annots: + start_idx = 700 if annot['description'] == 'BAD_peak' else 800 + stop_idx = 899 if annot['description'] == 'BAD_peak' else -1 + _check_annotation(raw_, annot, meas_date, first_samp, start_idx, + stop_idx) + + # -- overlap between peak and peak on same channel -- + raw_ = raw.copy() + raw_._data[0, 700:900] = np.arange(0, 200 * 10, 10) + raw_._data[0, 800:] = np.arange(1000, 300 * 10, 10) + annots, bads = annotate_amplitude(raw_, peak=5, flat=None, bad_percent=50) + assert len(annots) == 1 + assert len(bads) == 0 + # check annotation instance + assert annots[0]['description'] == 'BAD_peak' + _check_annotation(raw_, annots[0], meas_date, first_samp, 700, -1) + + # -- overlap between flat and flat on different channel -- + raw_ = raw.copy() + raw_._data[0, 700:900] = 0. + raw_._data[1, 800:] = 0. + annots, bads = annotate_amplitude(raw_, peak=None, flat=0.01, + bad_percent=50) + assert len(annots) == 1 + assert len(bads) == 0 + # check annotation instance + assert annots[0]['description'] == 'BAD_flat' + _check_annotation(raw_, annots[0], meas_date, first_samp, 700, -1) + + +@pytest.mark.parametrize('meas_date', (None, date)) +@pytest.mark.parametrize('first_samp', (0, 10000)) +def test_annotate_amplitude_multiple_ch_types(meas_date, first_samp): + """Test cases with several channel types.""" + n_ch, n_times = 11, 1000 + data = np.random.RandomState(0).randn(n_ch, n_times) + assert not (np.diff(data, axis=-1) == 0).any() # nothing flat at first + info = create_info(n_ch, 1000., + ['eeg'] * 3 + ['mag'] * 2 + ['grad'] * 4 + ['eeg'] * 2) + # from annotate_flat: test first_samp != for gh-6295 + raw = RawArray(data, info, first_samp=first_samp) + raw.info['bads'] = [raw.ch_names[-1]] + raw.set_meas_date(meas_date) + + # -- 2 channel types both to annotate -- + raw_ = raw.copy() + raw_._data[1, 800:] = 0. + raw_._data[5, :200] = np.arange(0, 200 * 10, 10) + raw_._data[5, 200:] += raw_._data[5, 199] # add offset for next samples + annots, bads = annotate_amplitude(raw_, peak=5, flat=0, bad_percent=50) + assert len(annots) == 2 + assert len(bads) == 0 + # check annotation instance + assert all(annot['description'] in ('BAD_flat', 'BAD_peak') + for annot in annots) + for annot in annots: + start_idx = 0 if annot['description'] == 'BAD_peak' else 800 + stop_idx = 199 if annot['description'] == 'BAD_peak' else -1 + _check_annotation(raw_, annot, meas_date, first_samp, start_idx, + stop_idx) + + # -- 2 channel types, one flat picked, one not picked -- + raw_ = raw.copy() + raw_._data[1, 800:] = 0. + raw_._data[5, :200] = np.arange(0, 200 * 10, 10) + raw_._data[5, 200:] += raw_._data[5, 199] # add offset for next samples + annots, bads = annotate_amplitude(raw_, peak=5, flat=0, bad_percent=50, + picks='eeg') + assert len(annots) == 1 + assert len(bads) == 0 + # check annotation instance + _check_annotation(raw_, annots[0], meas_date, first_samp, 800, -1) + assert annots[0]['description'] == 'BAD_flat' + + # -- 2 channel types, one flat, one not picked, reverse -- + raw_ = raw.copy() + raw_._data[1, 800:] = 0. + raw_._data[5, :200] = np.arange(0, 200 * 10, 10) + raw_._data[5, 200:] += raw_._data[5, 199] # add offset for next samples + annots, bads = annotate_amplitude(raw_, peak=5, flat=0, bad_percent=50, + picks='grad') + assert len(annots) == 1 + assert len(bads) == 0 + # check annotation instance + _check_annotation(raw_, annots[0], meas_date, first_samp, 0, 199) + assert annots[0]['description'] == 'BAD_peak' + + +@testing.requires_testing_data +def test_flat_bad_acq_skip(): + """Test that acquisition skips are handled properly.""" + # -- file with a couple of skip and flat channels -- + raw = read_raw_fif(skip_fname, preload=True) + annots, bads = annotate_amplitude(raw, flat=0) + assert len(annots) == 0 + assert bads == [ # MaxFilter finds the same 21 channels + 'MEG%04d' % (int(num),) for num in + '141 331 421 431 611 641 1011 1021 1031 1241 1421 ' + '1741 1841 2011 2131 2141 2241 2531 2541 2611 2621'.split()] + + # -- overlap of flat segment with bad_acq_skip -- + n_ch, n_times = 11, 1000 + data = np.random.RandomState(0).randn(n_ch, n_times) + assert not (np.diff(data, axis=-1) == 0).any() # nothing flat at first + info = create_info(n_ch, 1000., 'eeg') + raw = RawArray(data, info, first_samp=0) + raw.info['bads'] = [raw.ch_names[-1]] + bad_acq_skip = Annotations([0.5], [0.2], ['bad_acq_skip'], orig_time=None) + raw.set_annotations(bad_acq_skip) + # add flat channel overlapping with the left edge of bad_acq_skip + raw_ = raw.copy() + raw_._data[0, 400:600] = 0. + annots, bads = annotate_amplitude(raw_, peak=None, flat=0, bad_percent=25) + assert len(annots) == 1 + assert len(bads) == 0 + # check annotation instance + assert annots[0]['description'] == 'BAD_flat' + _check_annotation(raw_, annots[0], None, 0, 400, 499) + + # add flat channel overlapping with the right edge of bad_acq_skip + raw_ = raw.copy() + raw_._data[0, 600:800] = 0. + annots, bads = annotate_amplitude(raw_, peak=None, flat=0, bad_percent=25) + assert len(annots) == 1 + assert len(bads) == 0 + # check annotation instance + assert annots[0]['description'] == 'BAD_flat' + _check_annotation(raw_, annots[0], None, 0, 700, 799) + + # add flat channel overlapping entirely with bad_acq_skip + raw_ = raw.copy() + raw_._data[0, 200:800] = 0. + annots, bads = annotate_amplitude(raw_, peak=None, flat=0, bad_percent=41) + assert len(annots) == 2 + assert len(bads) == 0 + # check annotation instance + annots = sorted(annots, key=lambda x: x['onset']) + assert all(annot['description'] == 'BAD_flat' for annot in annots) + _check_annotation(raw_, annots[0], None, 0, 200, 500) + _check_annotation(raw_, annots[1], None, 0, 700, 799) + + +def _check_annotation(raw, annot, meas_date, first_samp, start_idx, stop_idx): + """Util function to check an annotation.""" + assert meas_date == annot['orig_time'] + if meas_date is None: + assert np.isclose(raw.times[start_idx], annot['onset'], atol=1e-4) + assert np.isclose( + raw.times[stop_idx], annot['onset'] + annot['duration'], atol=1e-4) + else: + first_time = first_samp / raw.info['sfreq'] # because of meas_date + assert np.isclose( + raw.times[start_idx], annot['onset'] - first_time, atol=1e-4) + assert np.isclose( + raw.times[stop_idx], + annot['onset'] + annot['duration'] - first_time, + atol=1e-4) + + +def test_invalid_arguments(): + """Test error messages raised by invalid arguments.""" + n_ch, n_times = 2, 100 + data = np.random.RandomState(0).randn(n_ch, n_times) + info = create_info(n_ch, 100., 'eeg') + raw = RawArray(data, info, first_samp=0) + + # negative floats PTP + with pytest.raises(ValueError, + match="Argument 'flat' should define a positive " + "threshold. Provided: '-1'."): + annotate_amplitude(raw, peak=None, flat=-1) + with pytest.raises(ValueError, + match="Argument 'peak' should define a positive " + "threshold. Provided: '-1'."): + annotate_amplitude(raw, peak=-1, flat=None) + + # negative PTP threshold for one channel type + with pytest.raises(ValueError, + match="Argument 'flat' should define positive " + "thresholds. Provided for channel type " + "'eog': '-1'."): + annotate_amplitude(raw, peak=None, flat=dict(eeg=1, eog=-1)) + with pytest.raises(ValueError, + match="Argument 'peak' should define positive " + "thresholds. Provided for channel type " + "'eog': '-1'."): + annotate_amplitude(raw, peak=dict(eeg=1, eog=-1), flat=None) + + # test both PTP set to None + with pytest.raises(ValueError, + match="At least one of the arguments 'peak' or 'flat' " + "must not be None."): + annotate_amplitude(raw, peak=None, flat=None) + + # bad_percent outside [0, 100] + with pytest.raises(ValueError, + match="Argument 'bad_percent' should define a " + "percentage between 0% and 100%. Provided: " + "-1.0%."): + annotate_amplitude(raw, peak=dict(eeg=1), flat=None, bad_percent=-1) + + # min_duration negative + with pytest.raises(ValueError, + match="Argument 'min_duration' should define a " + "positive duration in seconds. Provided: " + "'-1.0' seconds."): + annotate_amplitude(raw, peak=dict(eeg=1), flat=None, min_duration=-1) + + # min_duration equal to the raw duration + with pytest.raises( + ValueError, + match=re.escape("Argument 'min_duration' should define a " + "positive duration in seconds shorter than the " + "raw duration (1.0 seconds). Provided: " + "'1.0' seconds.") + ): + annotate_amplitude(raw, peak=dict(eeg=1), flat=None, min_duration=1.) + + # min_duration longer than the raw duration + with pytest.raises( + ValueError, + match=re.escape("Argument 'min_duration' should define a " + "positive duration in seconds shorter than the " + "raw duration (1.0 seconds). Provided: " + "'10.0' seconds.") + ): + annotate_amplitude(raw, peak=dict(eeg=1), flat=None, min_duration=10) diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_annotate_nan.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_annotate_nan.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_annotate_nan.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_annotate_nan.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,53 @@ +# Author: Stefan Appelhoff +# +# License: BSD-3-Clause + +import os.path as op + +import numpy as np +from numpy.testing import assert_array_equal +import pytest + +import mne +from mne.preprocessing import annotate_nan + + +base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data') +raw_fname = op.join(base_dir, 'test_raw.fif') + + +@pytest.mark.parametrize('meas_date', (None, 'orig')) +def test_annotate_nan(meas_date): + """Tests automatic NaN annotation generation.""" + # Load data + raw = mne.io.read_raw_fif(raw_fname) + sfreq = 100 + raw.resample(sfreq) + if meas_date is None: + raw.set_meas_date(None) + + # No Nans, annotate returns empty annots + assert not np.isnan(raw._data).any() + annot_nan = annotate_nan(raw) + assert len(annot_nan) == 0 + + # but orig_time should be meas_date + assert annot_nan.orig_time == raw.info["meas_date"] + + # insert block of NaN from 1s to 3s for one channel + nan_ch_idx = 0 + raw._data[nan_ch_idx, 1 * sfreq:3 * sfreq] = np.nan + + # annotate_nan accurately finds this + annot_nan = annotate_nan(raw) + onset = np.array([1.]) + if raw.info["meas_date"]: + onset += raw.first_time + assert_array_equal(annot_nan.onset, onset) + assert_array_equal(annot_nan.duration, np.array([2])) + assert_array_equal(annot_nan.description, np.array(['BAD_NAN'])) + assert len(annot_nan.ch_names) == 1 + assert annot_nan.ch_names[0] == (raw.ch_names[nan_ch_idx],) + + # Set the NaN annotations to the raw object + raw.set_annotations(annot_nan) diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_artifact_detection.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_artifact_detection.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_artifact_detection.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_artifact_detection.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,46 +1,64 @@ # Author: Adonay Nunes # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op import numpy as np import pytest -from numpy.testing import assert_allclose + +from numpy.testing import assert_allclose, assert_array_equal from mne.chpi import read_head_pos from mne.datasets import testing from mne.io import read_raw_fif from mne.preprocessing import (annotate_movement, compute_average_dev_head_t, - annotate_muscle_zscore) -from mne import Annotations + annotate_muscle_zscore, annotate_break) +from mne import Annotations, events_from_annotations +from mne.tests.test_annotations import _assert_annotations_equal data_path = testing.data_path(download=False) sss_path = op.join(data_path, 'SSS') -pre = op.join(sss_path, 'test_move_anon_') -raw_fname = pre + 'raw.fif' -pos_fname = op.join(data_path, 'SSS', 'test_move_anon_raw.pos') +pos_fname = op.join(sss_path, 'test_move_anon_raw.pos') +raw_fname = op.join(sss_path, 'test_move_anon_raw.fif') @testing.requires_testing_data -def test_movement_annotation_head_correction(): +@pytest.mark.parametrize('meas_date', (None, 'orig')) +def test_movement_annotation_head_correction(meas_date): """Test correct detection movement artifact and dev_head_t.""" raw = read_raw_fif(raw_fname, allow_maxshield='yes').load_data() pos = read_head_pos(pos_fname) + if meas_date is None: + raw.set_meas_date(None) + else: + assert meas_date == 'orig' # Check 5 rotation segments are detected annot_rot, [] = annotate_movement(raw, pos, rotation_velocity_limit=5) - assert(annot_rot.duration.size == 5) + assert annot_rot.orig_time == raw.info["meas_date"] + assert annot_rot.duration.size == 5 # Check 2 translation vel. segments are detected annot_tra, [] = annotate_movement(raw, pos, translation_velocity_limit=.05) - assert(annot_tra.duration.size == 2) + assert annot_tra.duration.size == 2 # Check 1 movement distance segment is detected - annot_dis, disp = annotate_movement(raw, pos, mean_distance_limit=.02) - assert(annot_dis.duration.size == 1) + annot_dis, _ = annotate_movement(raw, pos, mean_distance_limit=.02) + assert annot_dis.duration.size == 1 # Check correct trans mat - raw.set_annotations(annot_rot + annot_tra + annot_dis) + annot_all_2 = annotate_movement( + raw, pos, rotation_velocity_limit=5, + translation_velocity_limit=.05, + mean_distance_limit=.02)[0] + assert (annot_rot.orig_time == + annot_tra.orig_time == + annot_dis.orig_time == + raw.info['meas_date']) + annot_all = annot_rot + annot_tra + annot_dis + _assert_annotations_equal(annot_all_2, annot_all) + assert annot_all.orig_time == raw.info['meas_date'] + raw.set_annotations(annot_all) dev_head_t = compute_average_dev_head_t(raw, pos) dev_head_t_ori = np.array([ @@ -53,30 +71,215 @@ # Smoke test skipping time due to previous annotations. raw.set_annotations(Annotations([raw.times[0]], 0.1, 'bad')) - annot_dis, disp = annotate_movement(raw, pos, mean_distance_limit=.02) - assert(annot_dis.duration.size == 1) + annot_dis, _ = annotate_movement(raw, pos, mean_distance_limit=.02) + assert annot_dis.duration.size == 1 @testing.requires_testing_data -def test_muscle_annotation(): +@pytest.mark.parametrize('meas_date', (None, 'orig')) +def test_muscle_annotation(meas_date, events): """Test correct detection muscle artifacts.""" raw = read_raw_fif(raw_fname, allow_maxshield='yes').load_data() + if meas_date is None: + raw.set_meas_date(None) raw.notch_filter([50, 110, 150]) # Check 2 muscle segments are detected annot_muscle, scores = annotate_muscle_zscore(raw, ch_type='mag', threshold=10) + assert annot_muscle.orig_time == raw.info["meas_date"] onset = annot_muscle.onset * raw.info['sfreq'] + if meas_date is not None: + onset -= raw.first_samp onset = onset.astype(int) - np.testing.assert_array_equal(scores[onset].astype(int), np.array([23, - 10])) - assert(annot_muscle.duration.size == 2) + assert_array_equal(scores[onset].astype(int), np.array([23, 10])) + assert annot_muscle.duration.size == 2 + raw.set_annotations(annot_muscle) @testing.requires_testing_data -def test_muscle_annotation_without_meeg_data(): +@pytest.mark.parametrize('meas_date', (None, 'orig')) +def test_muscle_annotation_without_meeg_data(meas_date): """Call annotate_muscle_zscore with data without meg or eeg.""" raw = read_raw_fif(raw_fname, allow_maxshield='yes') + if meas_date is None: + raw.set_meas_date(None) raw.crop(0, .1).load_data() raw.pick_types(meg=False, stim=True) with pytest.raises(ValueError, match="No M/EEG channel types found"): - annot_muscle, scores = annotate_muscle_zscore(raw, threshold=10) + annotate_muscle_zscore(raw, threshold=10) + + +@pytest.mark.parametrize('meas_date', (None, "orig")) +@testing.requires_testing_data +def test_annotate_breaks(meas_date): + """Test annotate_breaks.""" + raw = read_raw_fif(raw_fname, allow_maxshield='yes') + if meas_date is None: + raw.set_meas_date(None) + + annots = Annotations(onset=[12, 15, 16, 20, 21], + duration=[1, 1, 1, 2, 0.5], + description=['test'], + orig_time=raw.info['meas_date']) + + if raw.info['meas_date'] is None: + annots.onset -= raw.first_time + + raw.set_annotations(annots) + + min_break_duration = 0.5 + t_start_after_previous = 0.1 + t_stop_before_next = 0.1 + + expected_onsets = np.array( + [ + raw.first_time, + 13 + t_start_after_previous, + 17 + t_start_after_previous, + 22 + t_start_after_previous + ] + ) + + if raw.info['meas_date'] is None: + expected_onsets -= raw.first_time + + expected_durations = np.array( + [ + 12 - raw.first_time - t_stop_before_next, + 15 - 13 - t_start_after_previous - t_stop_before_next, + 20 - 17 - t_start_after_previous - t_stop_before_next, + raw._last_time - 22 - t_start_after_previous + ] + ) + + break_annots = annotate_break( + raw=raw, + min_break_duration=min_break_duration, + t_start_after_previous=t_start_after_previous, + t_stop_before_next=t_stop_before_next + ) + + assert break_annots.orig_time == raw.info["meas_date"] + assert_allclose(break_annots.onset, expected_onsets) + assert_allclose(break_annots.duration, expected_durations) + assert all(description == 'BAD_break' + for description in break_annots.description) + + # try setting the annotations, this should not omit anything + raw.set_annotations(break_annots) + current_annotations = raw.annotations + if raw.info['meas_date'] is None: + current_annotations.onset -= raw.first_time + raw.set_annotations(current_annotations + break_annots) + + # reset before next test + raw.set_annotations(annots) + + # `ignore` parameter should be respected + raw.annotations.description[0] = 'BAD_' + break_annots = annotate_break( + raw=raw, + min_break_duration=min_break_duration, + t_start_after_previous=t_start_after_previous, + t_stop_before_next=t_stop_before_next + ) + + assert_allclose(break_annots.onset, + expected_onsets[[True, False, True, True]]) + assert_allclose( + break_annots.duration, + [15 - raw.first_time - t_stop_before_next] + + list(expected_durations[2:]) + ) + + # try setting the annotations, this should not omit anything + raw.set_annotations(break_annots) + current_annotations = raw.annotations + if raw.info['meas_date'] is None: + current_annotations.onset -= raw.first_time + raw.set_annotations(current_annotations + break_annots) + + # Restore annotations for next test + raw.set_annotations(annots) + raw.annotations.description[0] = 'test' + + # Test with events + events, _ = events_from_annotations(raw=raw) + raw.set_annotations(None) + + expected_onsets = np.array( + [ + raw.first_time, + 12 + t_start_after_previous, + 15 + t_start_after_previous, + 16 + t_start_after_previous, + 20 + t_start_after_previous, + 21 + t_start_after_previous + ] + ) + + expected_durations = np.array( + [ + 12 - raw.first_time - t_stop_before_next, + 15 - 12 - t_start_after_previous - t_stop_before_next, + 16 - 15 - t_start_after_previous - t_stop_before_next, + 20 - 16 - t_start_after_previous - t_stop_before_next, + 21 - 20 - t_start_after_previous - t_stop_before_next, + raw._last_time - 21 - t_start_after_previous + ] + ) + + break_annots = annotate_break( + raw=raw, + events=events, + min_break_duration=min_break_duration, + t_start_after_previous=t_start_after_previous, + t_stop_before_next=t_stop_before_next + ) + + if raw.info['meas_date'] is None: + expected_onsets -= raw.first_time + + assert_allclose(break_annots.onset, expected_onsets) + assert_allclose(break_annots.duration, expected_durations) + + # try setting the annotations, this should not omit anything + raw.set_annotations(break_annots) + current_annotations = raw.annotations + if raw.info['meas_date'] is None: + current_annotations.onset -= raw.first_time + raw.set_annotations(current_annotations + break_annots) + + # reset before next test + raw.set_annotations(annots) + + # Not finding any break periods + break_annots = annotate_break( + raw=raw, + events=events, + min_break_duration=1000, + ) + + assert len(break_annots) == 0 + + # Implausible parameters (would produce break annot of duration < 0) + with pytest.raises(ValueError, match='must be greater than 0'): + annotate_break( + raw=raw, + min_break_duration=5, + t_start_after_previous=5, + t_stop_before_next=5 + ) + + # Empty events array + with pytest.raises(ValueError, match='events array must not be empty'): + annotate_break(raw=raw, events=np.array([])) + + # Invalid `ignore` value + with pytest.raises(TypeError, match='must be an instance of str'): + annotate_break(raw=raw, ignore=('foo', 1)) + + # No annotations to work with + raw.set_annotations(None) + with pytest.raises(ValueError, match='Could not find.*annotations'): + annotate_break(raw=raw) diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_csd.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_csd.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_csd.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_csd.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,11 +1,11 @@ # -*- coding: utf-8 -*- -"""Test the compute_current_source_density function. +"""Test the current source density and related functions. For each supported file format, implement a test. """ # Authors: Alex Rockhill # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op @@ -17,13 +17,15 @@ from scipy import linalg from mne.channels import make_dig_montage -from mne import create_info, EvokedArray, pick_types, Epochs +from mne import (create_info, EvokedArray, pick_types, Epochs, find_events, + read_epochs) from mne.io import read_raw_fif, RawArray from mne.io.constants import FIFF -from mne.utils import object_diff, run_tests_if_main +from mne.utils import object_diff from mne.datasets import testing -from mne.preprocessing import compute_current_source_density +from mne.preprocessing import (compute_current_source_density, + compute_bridged_electrodes) data_path = op.join(testing.data_path(download=False), 'preprocessing') eeg_fname = op.join(data_path, 'test_eeg.mat') @@ -174,7 +176,8 @@ # reset the only things that should change, and assert objects are the same assert raw_csd.info['custom_ref_applied'] == FIFF.FIFFV_MNE_CUSTOM_REF_CSD - raw_csd.info['custom_ref_applied'] = 0 + with raw_csd.info._unlock(): + raw_csd.info['custom_ref_applied'] = 0 for pick in picks: ch = raw_csd.info['chs'][pick] assert ch['coil_type'] == FIFF.FIFFV_COIL_EEG_CSD @@ -184,4 +187,40 @@ assert object_diff(raw.info, raw_csd.info) == '' -run_tests_if_main() +def test_csd_epochs(tmp_path): + """Test making epochs, saving to disk and loading.""" + raw = read_raw_fif(raw_fname) + raw.pick_types(eeg=True, stim=True).load_data() + events = find_events(raw) + epochs = Epochs(raw, events, reject=dict(eeg=1e-4), preload=True) + epochs = compute_current_source_density(epochs) + epo_fname = tmp_path / 'test_csd_epo.fif' + epochs.save(epo_fname) + epochs2 = read_epochs(epo_fname, preload=True) + assert_allclose(epochs._data, epochs2._data) + + +def test_compute_bridged_electrodes(): + """Test computing bridged electrodes.""" + # test I/O + raw = read_raw_fif(raw_fname).load_data() + raw.pick_types(meg=True) + with pytest.raises(RuntimeError, match='No EEG channels found'): + bridged_idx, ed_matrix = compute_bridged_electrodes(raw) + + # test output + epoch_duration = 3 + raw = read_raw_fif(raw_fname).load_data() + idx0 = raw.ch_names.index('EEG 001') + idx1 = raw.ch_names.index('EEG 002') + raw._data[idx1] = raw._data[idx0] + bridged_idx, ed_matrix = compute_bridged_electrodes( + raw, epoch_duration=epoch_duration) + assert bridged_idx == [(idx0, idx1)] + picks = pick_types(raw.info, meg=False, eeg=True) + assert ed_matrix.shape == \ + (raw.times.size // (epoch_duration * raw.info['sfreq']), + picks.size, picks.size) + picks = list(picks) + assert np.all(ed_matrix[:, picks.index(idx0), picks.index(idx1)] == 0) + assert np.all(np.isnan(ed_matrix[0][np.tril_indices(len(picks), -1)])) diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_css.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_css.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_css.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_css.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,37 @@ +# Author: John G Samuelsson + +import numpy as np + +from mne.preprocessing._css import cortical_signal_suppression +from mne import pick_types, read_evokeds +from mne.datasets import testing + +data_path = testing.data_path(download=False) +fname_evoked = data_path / 'MEG' / 'sample' / 'sample_audvis-ave.fif' + + +@testing.requires_testing_data +def test_cortical_signal_suppression(): + """Test that CSS is dampening the cortical signal and has right shape.""" + ave = read_evokeds(fname_evoked)[0] + eeg_ind = pick_types(ave.info, eeg=True) + mag_ind = pick_types(ave.info, meg='mag') + grad_ind = pick_types(ave.info, meg='grad') + ave.data[mag_ind][0, :] = np.sin(2 * np.pi * 40 * ave.times) * \ + np.mean(np.abs(ave.data[mag_ind][0, :])) + ave.data[mag_ind][1, :] = np.sin(2 * np.pi * 239 * ave.times) * \ + np.mean(np.abs(ave.data[mag_ind][1, :])) + ave.data[grad_ind][0, :] = np.sin(2 * np.pi * 40 * ave.times) * \ + np.mean(np.abs(ave.data[grad_ind][0, :])) + ave.data[eeg_ind][0, :] = np.sin(2 * np.pi * 40 * ave.times) * \ + np.mean(np.abs(ave.data[eeg_ind][0, :])) + ave.data[eeg_ind][1, :] = np.sin(2 * np.pi * 239 * ave.times) * \ + np.mean(np.abs(ave.data[eeg_ind][1, :])) + ave_f = cortical_signal_suppression(ave) + cort_power = np.sum(np.abs(ave.data[eeg_ind][0, :])) + deep_power = np.sum(np.abs(ave.data[eeg_ind][1, :])) + cort_power_f = np.sum(np.abs(ave_f.data[eeg_ind][0, :])) + deep_power_f = np.sum(np.abs(ave_f.data[eeg_ind][1, :])) + rel_SNR_gain = (deep_power_f / deep_power) / (cort_power_f / cort_power) + assert rel_SNR_gain > 0 + assert ave_f.data.shape == ave.data.shape diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_ctps.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_ctps.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_ctps.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_ctps.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Denis A. Engemann # -# License: BSD 3 clause +# License: BSD-3-Clause import numpy as np from numpy.testing import assert_array_equal diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_ecg.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_ecg.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_ecg.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_ecg.py 2022-08-03 15:48:14.000000000 +0000 @@ -73,7 +73,7 @@ assert 'MEG 2641' in ecg_epochs.ch_names # test with user provided ecg channel - raw.info['projs'] = list() + raw.del_proj() assert 'MEG 2641' in raw.ch_names with pytest.warns(RuntimeWarning, match='unit for channel'): raw.set_channel_types({'MEG 2641': 'ecg'}) diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_eeglab_infomax.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_eeglab_infomax.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_eeglab_infomax.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_eeglab_infomax.py 2022-08-03 15:48:14.000000000 +0000 @@ -10,15 +10,16 @@ from mne.io import read_raw_fif from mne import pick_types from mne.preprocessing.infomax_ import infomax -from mne.utils import random_permutation, run_tests_if_main +from mne.utils import random_permutation from mne.datasets import testing base_dir = op.join(op.dirname(__file__), 'data') +testing_path = testing.data_path(download=False) def generate_data_for_comparing_against_eeglab_infomax(ch_type, random_state): """Generate data.""" - data_dir = op.join(testing.data_path(download=False), 'MEG', 'sample') + data_dir = op.join(testing_path, 'MEG', 'sample') raw_fname = op.join(data_dir, 'sample_audvis_trunc_raw.fif') raw = read_raw_fif(raw_fname, preload=True) @@ -175,6 +176,3 @@ unmixing_eeglab)) assert_almost_equal(maximum_difference, 1e-12, decimal=10) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_eog.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_eog.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_eog.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_eog.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,5 @@ import os.path as op -import pytest - from mne import Annotations from mne.io import read_raw_fif from mne.preprocessing.eog import find_eog_events @@ -30,10 +28,6 @@ assert len(events_thr) == 5 # test different ways to specify the EOG channel(s) - with pytest.warns(DeprecationWarning, match='pass a list of channels'): - events = find_eog_events(raw, ch_name='EEG 060,EOG 061') - assert len(events) == 4 - events = find_eog_events(raw, ch_name=None) assert len(events) == 4 diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_fine_cal.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_fine_cal.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_fine_cal.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_fine_cal.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors: Mark Wronkiewicz # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op @@ -31,9 +31,9 @@ @pytest.mark.parametrize('fname', (cal_mf_fname, fine_cal_fname, fine_cal_fname_3d)) @testing.requires_testing_data -def test_fine_cal_io(tmpdir, fname): +def test_fine_cal_io(tmp_path, fname): """Test round trip reading/writing of fine calibration .dat file.""" - temp_fname = op.join(str(tmpdir), 'fine_cal_temp.dat') + temp_fname = op.join(str(tmp_path), 'fine_cal_temp.dat') # Load fine calibration file fine_cal_dict = read_fine_calibration(fname) @@ -102,7 +102,7 @@ # processing a very short (one 10-sec segment), downsampled (90 Hz) # file assert 66 < want_orig_max_angle < 68, want_orig_max_angle - assert 56 < got_orig_max_angle < 112, got_orig_max_angle + assert 56 < got_orig_max_angle < 113, got_orig_max_angle assert 53 < got_want_max_angle < 60, got_want_max_angle kwargs = dict(bad_condition='warning', cross_talk=ctc, coord_frame='meg') diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_flat.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_flat.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_flat.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_flat.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,84 +0,0 @@ -# Author: Eric Larson -# -# License: BSD (3-clause) - -import os.path as op -import numpy as np -import pytest - -from mne import create_info -from mne.datasets import testing -from mne.io import RawArray, read_raw_fif -from mne.preprocessing import annotate_flat - -data_path = testing.data_path(download=False) -skip_fname = op.join(data_path, 'misc', 'intervalrecording_raw.fif') - - -@pytest.mark.parametrize('first_samp', (0, 10000)) -def test_annotate_flat(first_samp): - """Test marking flat segments.""" - # Test if ECG analysis will work on data that is not preloaded - n_ch, n_times = 11, 1000 - data = np.random.RandomState(0).randn(n_ch, n_times) - assert not (np.diff(data, axis=-1) == 0).any() # nothing flat at first - info = create_info(n_ch, 1000., 'eeg') - info['meas_date'] = (1, 2) - # test first_samp != for gh-6295 - raw = RawArray(data, info, first_samp=first_samp) - raw.info['bads'] = [raw.ch_names[-1]] - - # - # First make a channel flat the whole time - # - raw_0 = raw.copy() - raw_0._data[0] = 0. - for kwargs, bads, want_times in [ - # Anything < 1 will mark spatially - (dict(bad_percent=100.), [], 0), - (dict(bad_percent=99.9), [raw.ch_names[0]], n_times), - (dict(), [raw.ch_names[0]], n_times)]: # default (1) - raw_time = raw_0.copy() - annot, got_bads = annotate_flat(raw_0, verbose='debug', **kwargs) - assert got_bads == bads - raw_time.set_annotations(raw_time.annotations + annot) - raw_time.info['bads'] += got_bads - n_good_times = raw_time.get_data(reject_by_annotation='omit').shape[1] - assert n_good_times == want_times - # - # Now make a channel flat for 20% of the time points - # - raw_0 = raw.copy() - n_good_times = int(round(0.8 * n_times)) - raw_0._data[0, n_good_times:] = 0. - threshold = 100 * (n_times - n_good_times) / n_times - for kwargs, bads, want_times in [ - # Should change behavior at bad_percent=20 - (dict(bad_percent=100), [], n_good_times), - (dict(bad_percent=threshold), [], n_good_times), - (dict(bad_percent=threshold - 1e-5), [raw.ch_names[0]], n_times), - (dict(), [raw.ch_names[0]], n_times)]: - annot, got_bads = annotate_flat(raw_0, verbose='debug', **kwargs) - assert got_bads == bads - raw_time = raw_0.copy() - raw_time.set_annotations(raw_time.annotations + annot) - raw_time.info['bads'] += got_bads - n_good_times = raw_time.get_data(reject_by_annotation='omit').shape[1] - assert n_good_times == want_times - - with pytest.raises(TypeError, match='must be an instance of BaseRaw'): - annotate_flat(0.) - with pytest.raises(ValueError, match='not convert string to float'): - annotate_flat(raw, 'x') - - -@testing.requires_testing_data -def test_flat_acq_skip(): - """Test that acquisition skips are handled properly.""" - raw = read_raw_fif(skip_fname).load_data() - annot, bads = annotate_flat(raw) - assert len(annot) == 0 - assert bads == [ # MaxFilter finds the same 21 channels - 'MEG%04d' % (int(num),) for num in - '141 331 421 431 611 641 1011 1021 1031 1241 1421 ' - '1741 1841 2011 2131 2141 2241 2531 2541 2611 2621'.split()] diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_ica.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_ica.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_ica.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_ica.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,13 +1,12 @@ # Author: Denis Engemann # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause -from itertools import product +from contextlib import nullcontext import os import os.path as op import shutil -from unittest import SkipTest import pytest import numpy as np @@ -21,7 +20,6 @@ EvokedArray, Annotations, pick_channels_regexp, make_ad_hoc_cov) from mne.cov import read_cov -from mne.fixes import nullcontext from mne.preprocessing import (ICA as _ICA, ica_find_ecg_events, ica_find_eog_events, read_ica) from mne.preprocessing.ica import (get_score_funcs, corrmap, _sort_components, @@ -30,7 +28,8 @@ from mne.io.pick import _DATA_CH_TYPES_SPLIT, get_channel_type_constants from mne.io.eeglab.eeglab import _check_load_mat from mne.rank import _compute_rank_int -from mne.utils import catch_logging, requires_sklearn, run_tests_if_main +from mne.utils import (catch_logging, requires_sklearn, _record_warnings, + check_version) from mne.datasets import testing from mne.event import make_fixed_length_events @@ -53,6 +52,8 @@ # if stop is too small pca may fail in some cases, but we're okay on this file start, stop = 0, 6 score_funcs_unsuited = ['pointbiserialr', 'ansari'] +pymatreader_mark = pytest.mark.skipif( + not check_version('pymatreader'), reason='Requires pymatreader') def ICA(*args, **kwargs): @@ -67,19 +68,18 @@ try: import picard # noqa, analysis:ignore except Exception as exp: - raise SkipTest("Picard is not installed (%s)." % (exp,)) + pytest.skip("Picard is not installed (%s)." % (exp,)) @requires_sklearn @pytest.mark.parametrize("method", ["fastica", "picard"]) -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') def test_ica_full_data_recovery(method): """Test recovery of full data when no source is rejected.""" # Most basic recovery _skip_check_picard(method) raw = read_raw_fif(raw_fname).crop(0.5, stop).load_data() - raw.info['projs'] = [] + with raw.info._unlock(): + raw.info['projs'] = [] events = read_events(event_name) picks = pick_types(raw.info, meg=True, stim=False, ecg=False, eog=False, exclude='bads')[:10] @@ -112,7 +112,7 @@ ica = ICA(n_components=n_components, method=method, random_state=0) - with pytest.warns(None): # sometimes warns + with _record_warnings(): # sometimes warns ica.fit(epochs, picks=picks) _assert_ica_attributes(ica, epochs.get_data(picks)) epochs2 = ica.apply(epochs.copy(), **kwargs) @@ -137,15 +137,13 @@ @pytest.mark.parametrize("method", ["fastica", "picard"]) -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') def test_ica_simple(method): """Test that ICA recovers the unmixing matrix in a simple case.""" if method == "fastica": try: import sklearn # noqa: F401 except ImportError: - raise SkipTest("scikit-learn not installed") + pytest.skip("scikit-learn not installed") _skip_check_picard(method) n_components = 3 n_samples = 1000 @@ -173,18 +171,21 @@ ica = ICA(n_components=2, max_iter=1, method='infomax', random_state=0) # not high-passed - epochs.info['highpass'] = 0. + with epochs.info._unlock(): + epochs.info['highpass'] = 0. with pytest.warns(RuntimeWarning, match='should be high-pass filtered'): ica.fit(epochs) # baselined - epochs.info['highpass'] = 1. + with epochs.info._unlock(): + epochs.info['highpass'] = 1. epochs.baseline = (epochs.tmin, 0) with pytest.warns(RuntimeWarning, match='epochs.*were baseline-corrected'): ica.fit(epochs) # cleaning baseline-corrected data - epochs.info['highpass'] = 1. + with epochs.info._unlock(): + epochs.info['highpass'] = 1. epochs.baseline = None ica.fit(epochs) @@ -198,15 +199,14 @@ @pytest.mark.parametrize('n_components', (None, 0.9999, 8, 9, 10)) @pytest.mark.parametrize('n_pca_components', [8, 9, 0.9999, 10]) @pytest.mark.filterwarnings('ignore:FastICA did not converge.*:UserWarning') -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') -def test_ica_noop(n_components, n_pca_components, tmpdir): +def test_ica_noop(n_components, n_pca_components, tmp_path): """Test that our ICA is stable even with a bad max_pca_components.""" data = np.random.RandomState(0).randn(10, 1000) info = create_info(10, 1000., 'eeg') raw = RawArray(data, info) raw.set_eeg_reference() - raw.info['highpass'] = 1.0 # fake high-pass filtering + with raw.info._unlock(): + raw.info['highpass'] = 1.0 # fake high-pass filtering assert np.linalg.matrix_rank(raw.get_data()) == 9 kwargs = dict(n_components=n_components, verbose=True) if isinstance(n_components, int) and \ @@ -242,7 +242,7 @@ assert_allclose(raw.get_data(), raw_new.get_data(), err_msg='Id failure') _assert_ica_attributes(ica, data) # and with I/O - fname = tmpdir.join('temp-ica.fif') + fname = tmp_path / 'temp-ica.fif' ica.save(fname) ica = read_ica(fname) raw_new = ica.apply(raw.copy()) @@ -252,28 +252,26 @@ @requires_sklearn @pytest.mark.parametrize("method, max_iter_default", [("fastica", 1000), - ("infomax", 500), ("picard", 500)]) + ("infomax", 500), + ("picard", 500)]) def test_ica_max_iter_(method, max_iter_default): """Test that ICA.max_iter is set to the right defaults.""" _skip_check_picard(method) - - # check that deprecated default max_iter=200 comes out for no input - with pytest.warns(DeprecationWarning, match='max_iter.*will be changed'): - ica = ICA(n_components=3, method=method) - assert ica.max_iter == 200 # check that new defaults come out for 'auto' ica = ICA(n_components=3, method=method, max_iter='auto') assert ica.max_iter == max_iter_default # check that user input comes out unchanged ica = ICA(n_components=3, method=method, max_iter=2000) assert ica.max_iter == 2000 + with pytest.raises(ValueError, match='Invalid'): + ICA(max_iter='foo') + with pytest.raises(TypeError, match='must be an instance'): + ICA(max_iter=1.) @requires_sklearn @pytest.mark.parametrize("method", ["infomax", "fastica", "picard"]) -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') -def test_ica_n_iter_(method, tmpdir): +def test_ica_n_iter_(method, tmp_path): """Test that ICA.n_iter_ is set after fitting.""" _skip_check_picard(method) @@ -293,7 +291,7 @@ assert_equal(ica.n_iter_, max_iter) # Test I/O roundtrip. - output_fname = tmpdir.join('test_ica-ica.fif') + output_fname = tmp_path / 'test_ica-ica.fif' _assert_ica_attributes(ica, raw.get_data('data'), limits=(5, 110)) ica.save(output_fname) ica = read_ica(output_fname) @@ -372,7 +370,7 @@ # infomax here just so we don't require sklearn ica = ICA(max_iter=1, noise_cov=noise_cov, method='infomax', n_components=10) - with pytest.warns(None): # convergence + with _record_warnings(): # convergence ica.fit(raw_fit) if cov: assert ica.pre_whitener_.shape == (len(raw.ch_names),) * 2 @@ -433,14 +431,13 @@ @pytest.mark.parametrize('n_components', (2, 0.6)) @pytest.mark.parametrize('noise_cov', (False, True)) @pytest.mark.parametrize('n_pca_components', [20]) -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') -def test_ica_core(method, n_components, noise_cov, n_pca_components): +def test_ica_core(method, n_components, noise_cov, n_pca_components, + browser_backend): """Test ICA on raw and epochs.""" _skip_check_picard(method) raw = read_raw_fif(raw_fname).crop(0, stop).load_data() - # XXX. The None cases helped revealing bugs but are time consuming. + # The None cases help reveal bugs but are time consuming. if noise_cov: noise_cov = read_cov(test_cov_name) noise_cov['projs'] = [] # avoid warnings @@ -464,6 +461,10 @@ 'meg' in ica print(ica) # to test repr + repr_ = ica.__repr__() + repr_html_ = ica._repr_html_() + assert repr_ == f'' + assert method in repr_html_ # test fit checker with pytest.raises(RuntimeError, match='No fit available'): @@ -479,6 +480,13 @@ with pytest.warns(UserWarning, match='did not converge'): ica.fit(raw) repr(ica) # to test repr + repr_ = ica.__repr__() + repr_html_ = ica._repr_html_() + assert 'raw data decomposition' in repr_ + assert f'{ica.n_components_} ICA components' in repr_ + assert 'Available PCA components' in repr_html_ + assert 'Explained variance' in repr_html_ + assert ('mag' in ica) # should now work without error # test re-fit @@ -493,13 +501,11 @@ print(raw_sources) # test for gh-6271 (scaling of ICA traces) - fig = raw_sources.plot() - assert len(fig.mne.ax_main.lines) in (4, 8) - for line in fig.mne.ax_main.lines: + fig = raw_sources.plot(clipping=None) + assert len(fig.mne.traces) in (2, 6) + for line in fig.mne.traces: y = line.get_ydata() - if len(y) > 2: # actual data, not markers - assert np.ptp(y) < 15 - plt.close('all') + assert np.ptp(y) < 15 sources = raw_sources[:, :][0] assert (sources.shape[0] == ica.n_components_) @@ -513,7 +519,7 @@ ####################################################################### # test epochs decomposition ica = ICA(noise_cov=noise_cov, n_components=n_components, method=method) - with pytest.warns(None): # sometimes warns + with _record_warnings(): # sometimes warns ica.fit(epochs) _assert_ica_attributes(ica, epochs.get_data(), limits=(0.2, 20)) data = epochs.get_data()[:, 0, :] @@ -589,9 +595,7 @@ @requires_sklearn @pytest.mark.slowtest @pytest.mark.parametrize("method", ["picard", "fastica"]) -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') -def test_ica_additional(method, tmpdir, short_raw_epochs): +def test_ica_additional(method, tmp_path, short_raw_epochs): """Test additional ICA functionality.""" _skip_check_picard(method) raw, epochs, epochs_eog = short_raw_epochs @@ -627,11 +631,10 @@ ica.find_bads_ecg(raw, threshold=None) with pytest.warns(RuntimeWarning, match='is longer than the signal'): ica.find_bads_ecg(raw, threshold=0.25) - # check invalid `measure` - with pytest.warns(RuntimeWarning, match='longer'): - with pytest.raises(ValueError, match='Unknown measure'): - ica.find_bads_ecg(raw, method='correlation', measure='unknown', - threshold='auto') + # check invalid measure argument + with pytest.raises(ValueError, match='Invalid value'): + ica.find_bads_ecg(raw, method='correlation', measure='unknown', + threshold='auto') # check passing a ch_name to find_bads_ecg with pytest.warns(RuntimeWarning, match='longer'): _, scores_1 = ica.find_bads_ecg(raw, threshold='auto') @@ -677,10 +680,11 @@ ica_different_channels = ICA(n_components=2, max_iter=1) with pytest.warns(Warning, match='converge'): ica_different_channels.fit(raw, picks=[2, 3, 4, 5]) - pytest.raises(ValueError, corrmap, [ica_different_channels, ica], (0, 0)) + with pytest.raises(ValueError, match='Not all ICA instances have the'): + corrmap([ica_different_channels, ica], (0, 0)) # test warnings on bad filenames - ica_badname = tmpdir.join('test-bad-name.fif.gz') + ica_badname = tmp_path / 'test-bad-name.fif.gz' with pytest.warns(RuntimeWarning, match='-ica.fif'): ica.save(ica_badname) with pytest.warns(RuntimeWarning, match='-ica.fif'): @@ -718,7 +722,9 @@ assert_equal(ica_sorted.labels_, dict(blink=[3], think=[2])) # epochs extraction from raw fit - pytest.raises(RuntimeError, ica.get_sources, epochs) + with pytest.warns(RuntimeWarning, match='could not be picked'), \ + pytest.raises(RuntimeError, match="match fitted data"): + ica.get_sources(epochs) # test filtering ica_raw = ica.get_sources(raw) @@ -731,7 +737,7 @@ ica_raw.notch_filter([10], trans_bandwidth=10, fir_design='firwin') assert ((d1 != ica_raw._data[0]).any()) - test_ica_fname = tmpdir.join('test-ica.fif') + test_ica_fname = tmp_path / 'test-ica.fif' ica.n_pca_components = 2 ica.method = 'fake' ica.save(test_ica_fname) @@ -754,7 +760,7 @@ ica_read.n_pca_components = 4 ica.exclude = [] - ica.save(test_ica_fname) + ica.save(test_ica_fname, overwrite=True) # also testing overwrite ica_read = read_ica(test_ica_fname) for attr in ['mixing_matrix_', 'unmixing_matrix_', 'pca_components_', 'pca_mean_', 'pca_explained_variance_', @@ -773,7 +779,7 @@ assert_array_almost_equal(_raw1[:, :][0], _raw2[:, :][0]) ica = ICA(n_components=2, method=method, max_iter=1) - with pytest.warns(None): # ICA does not converge + with _record_warnings(): # ICA does not converge ica.fit(raw, picks=few_picks) # check score funcs @@ -787,26 +793,8 @@ # check univariate stats scores = ica.score_sources(raw, start=0, stop=50, score_func=stats.skew) # check exception handling - pytest.raises(ValueError, ica.score_sources, raw, - target=np.arange(1)) - - params = [] - params += [(None, -1, slice(2), [0, 1])] # variance, kurtosis params - params += [(None, 'MEG 1531')] # ECG / EOG channel params - for idx, ch_name in product(*params): - ica.detect_artifacts(raw, start_find=0, stop_find=50, ecg_ch=ch_name, - eog_ch=ch_name, skew_criterion=idx, - var_criterion=idx, kurt_criterion=idx) - - # Make sure detect_artifacts marks the right components. - # For int criterion, the doc says "E.g. range(2) would return the two - # sources with the highest score". Assert that's what it does. - # Only test for skew, since it's always the same code. - ica.exclude = [] - ica.detect_artifacts(raw, start_find=0, stop_find=50, ecg_ch=None, - eog_ch=None, skew_criterion=0, - var_criterion=None, kurt_criterion=None) - assert np.abs(scores[ica.exclude]) == np.max(np.abs(scores)) + with pytest.raises(ValueError, match='Sources and target do not have'): + ica.score_sources(raw, target=np.arange(1)) evoked = epochs.average() evoked_data = evoked.data.copy() @@ -814,7 +802,8 @@ epochs_data = epochs.get_data().copy() with pytest.warns(RuntimeWarning, match='longer'): - idx, scores = ica.find_bads_ecg(raw, method='ctps', threshold='auto') + idx, scores = ica.find_bads_ecg(raw, method='ctps', threshold='auto', + start=0, stop=raw.times.size) assert_equal(len(scores), ica.n_components_) with pytest.warns(RuntimeWarning, match='longer'): idx, scores = ica.find_bads_ecg(raw, method='correlation', @@ -825,13 +814,18 @@ idx, scores = ica.find_bads_eog(raw) assert_equal(len(scores), ica.n_components_) - idx, scores = ica.find_bads_ecg(epochs, method='ctps', threshold='auto') + with pytest.raises(ValueError, match='integer .* start and stop'): + idx, scores = ica.find_bads_ecg(epochs, start=0, stop=1000) + + idx, scores = ica.find_bads_ecg(epochs, method='ctps', threshold='auto', + start=epochs.times[0], + stop=epochs.times[-1]) assert_equal(len(scores), ica.n_components_) - pytest.raises(ValueError, ica.find_bads_ecg, epochs.average(), - method='ctps', threshold='auto') - pytest.raises(ValueError, ica.find_bads_ecg, raw, - method='crazy-coupling') + with pytest.raises(ValueError, match='only Raw and Epochs input'): + ica.find_bads_ecg(epochs.average(), method='ctps', threshold='auto') + with pytest.raises(ValueError, match='Invalid value'): + ica.find_bads_ecg(raw, method='crazy-coupling') with pytest.warns(RuntimeWarning, match='longer'): idx, scores = ica.find_bads_eog(raw) @@ -846,6 +840,9 @@ idx, scores = ica.find_bads_eog(evoked, ch_name='MEG 1441') assert_equal(len(scores), ica.n_components_) + with pytest.raises(ValueError, match='integer .* start and stop'): + idx, scores = ica.find_bads_ecg(evoked, start=0, stop=1000) + idx, scores = ica.find_bads_ecg(evoked, method='correlation', threshold='auto') assert_equal(len(scores), ica.n_components_) @@ -866,8 +863,8 @@ scores = ica.score_sources(epochs, score_func=stats.skew) # check exception handling - pytest.raises(ValueError, ica.score_sources, epochs, - target=np.arange(1)) + with pytest.raises(ValueError, match='Sources and target do not have'): + ica.score_sources(epochs, target=np.arange(1)) # ecg functionality ecg_scores = ica.score_sources(raw, target='MEG 1531', @@ -889,9 +886,11 @@ # Test ica fiff export assert raw.last_samp - raw.first_samp + 1 == raw.n_times assert raw.n_times > 100 - ica_raw = ica.get_sources(raw, start=0, stop=100) + ica_raw = ica.get_sources(raw, start=100, stop=200) + assert ica_raw.first_samp == raw.first_samp + 100 assert ica_raw.n_times == 100 assert ica_raw.last_samp - ica_raw.first_samp + 1 == 100 + assert ica_raw._data.shape[1] == 100 assert_equal(len(ica_raw._filenames), 1) # API consistency ica_chans = [ch for ch in ica_raw.ch_names if 'ICA' in ch] assert (ica.n_components_ == len(ica_chans)) @@ -920,7 +919,7 @@ assert (ncomps_ == expected) ica = ICA(method=method) - with pytest.warns(None): # sometimes does not converge + with _record_warnings(): # sometimes does not converge ica.fit(raw, picks=few_picks) _assert_ica_attributes(ica, raw.get_data(few_picks)) with pytest.warns(RuntimeWarning, match='longer'): @@ -936,6 +935,14 @@ with pytest.warns(RuntimeWarning, match='longer'): ica.find_bads_ecg(raw, threshold='auto') + # test passing picks including the marked bad channels + raw_ = raw.copy() + raw_.pick_types(eeg=True) + raw_.info['bads'] = [raw_.ch_names[0]] + picks = pick_types(raw_.info, eeg=True, exclude=[]) + ica = ICA(n_components=0.99, max_iter='auto') + ica.fit(raw_, picks=picks, reject_by_annotation=True) + @requires_sklearn @pytest.mark.slowtest @@ -944,9 +951,7 @@ ('picard', test_cov_name), ('fastica', None), ]) -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') -def test_ica_cov(method, cov, tmpdir, short_raw_epochs): +def test_ica_cov(method, cov, tmp_path, short_raw_epochs): """Test ICA with cov.""" _skip_check_picard(method) raw, epochs, epochs_eog = short_raw_epochs @@ -954,11 +959,11 @@ cov = read_cov(cov) # test reading and writing - test_ica_fname = tmpdir.join('test-ica.fif') + test_ica_fname = tmp_path / 'test-ica.fif' kwargs = dict(n_pca_components=4) ica = ICA(noise_cov=cov, n_components=2, method=method, max_iter=1) - with pytest.warns(None): # ICA does not converge + with _record_warnings(): # ICA does not converge ica.fit(raw, picks=np.arange(10)) _assert_ica_attributes(ica) sources = ica.get_sources(epochs).get_data() @@ -970,7 +975,7 @@ for exclude in [[], [0], np.array([1, 2, 3])]: ica.exclude = exclude ica.labels_ = {'foo': [0]} - ica.save(test_ica_fname) + ica.save(test_ica_fname, overwrite=True) ica_read = read_ica(test_ica_fname) assert (list(ica.exclude) == ica_read.exclude) assert_equal(ica.labels_, ica_read.labels_) @@ -989,26 +994,7 @@ @requires_sklearn -@pytest.mark.parametrize("method", ("fastica", "picard", "infomax")) -@pytest.mark.parametrize("idx", (None, -1, slice(2), [0, 1])) -@pytest.mark.parametrize("ch_name", (None, 'MEG 1531')) -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') -def test_detect_artifacts_replacement_of_run_ica(method, idx, ch_name): - """Test replacement workflow for run_ica() function.""" - _skip_check_picard(method) - raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data() - ica = ICA(n_components=2, method=method) - ica.fit(raw) - ica.detect_artifacts(raw, start_find=0, stop_find=5, ecg_ch=ch_name, - eog_ch=ch_name, skew_criterion=idx, - var_criterion=idx, kurt_criterion=idx) - - -@requires_sklearn @pytest.mark.parametrize("method", ["fastica", "picard"]) -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') def test_ica_reject_buffer(method): """Test ICA data raw buffer rejection.""" _skip_check_picard(method) @@ -1029,8 +1015,6 @@ @requires_sklearn @pytest.mark.parametrize("method", ["fastica", "picard"]) -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') def test_ica_twice(method): """Test running ICA twice.""" _skip_check_picard(method) @@ -1040,25 +1024,23 @@ n_components = 0.99 n_pca_components = 0.9999 if method == 'fastica': - ctx = pytest.warns(None) # convergence, sometimes + ctx = _record_warnings # convergence, sometimes else: - ctx = nullcontext() + ctx = nullcontext ica1 = ICA(n_components=n_components, method=method) - with ctx: + with ctx(): ica1.fit(raw, picks=picks, decim=3) raw_new = ica1.apply(raw, n_pca_components=n_pca_components) ica2 = ICA(n_components=n_components, method=method) - with ctx: + with ctx(): ica2.fit(raw_new, picks=picks, decim=3) assert_equal(ica1.n_components_, ica2.n_components_) @requires_sklearn @pytest.mark.parametrize("method", ["fastica", "picard", "infomax"]) -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') -def test_fit_params(method, tmpdir): +def test_fit_methods(method, tmp_path): """Test fit_params for ICA.""" _skip_check_picard(method) fit_params = {} @@ -1070,8 +1052,8 @@ # Only picard and infomax support the "extended" keyword, so limit the # tests to those. if method in ['picard', 'infomax']: - tmpdir = str(tmpdir) - output_fname = op.join(tmpdir, 'test_ica-ica.fif') + tmp_path = str(tmp_path) + output_fname = op.join(tmp_path, 'test_ica-ica.fif') raw = read_raw_fif(raw_fname).crop(0.5, stop).load_data() n_components = 3 @@ -1093,11 +1075,34 @@ assert ica.fit_params == fit_params_after_instantiation +@pytest.mark.parametrize( + ('param_name', 'param_val'), + ( + ('start', 0), + ('stop', 500), + ('reject', dict(eeg=500e-6)), + ('flat', dict(eeg=1e-6)) + ) +) +def test_fit_params_epochs_vs_raw(param_name, param_val): + """Check that we get a warning when passing parameters that get ignored.""" + method = 'infomax' + n_components = 3 + max_iter = 1 + + raw = read_raw_fif(raw_fname).pick_types(meg=False, eeg=True) + events = read_events(event_name) + epochs = Epochs(raw, events=events) + ica = ICA(n_components=n_components, max_iter=max_iter, method=method) + + fit_params = {param_name: param_val} + with pytest.warns(RuntimeWarning, match='parameters.*will be ignored'): + ica.fit(inst=epochs, **fit_params) + + @requires_sklearn @pytest.mark.parametrize("method", ["fastica", "picard"]) @pytest.mark.parametrize("allow_ref_meg", [True, False]) -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') def test_bad_channels(method, allow_ref_meg): """Test exception when unsupported channels are used.""" _skip_check_picard(method) @@ -1110,8 +1115,10 @@ epochs = EpochsArray(data, info) # fake high-pass filtering - raw.info['highpass'] = 1.0 - epochs.info['highpass'] = 1.0 + with raw.info._unlock(): + raw.info['highpass'] = 1.0 + with epochs.info._unlock(): + epochs.info['highpass'] = 1.0 n_components = 0.9 data_chs = list(_DATA_CH_TYPES_SPLIT + ('eog',)) @@ -1139,15 +1146,15 @@ picks_bad2 = pick_types(inst.info, meg=True, **{str(ch): True}) - pytest.raises(ValueError, ica.fit, inst, picks=picks_bad1) - pytest.raises(ValueError, ica.fit, inst, picks=picks_bad2) - pytest.raises(ValueError, ica.fit, inst, picks=[]) + with pytest.raises(ValueError, match='Invalid channel type'): + ica.fit(inst, picks=picks_bad1) + ica.fit(inst, picks=picks_bad2) + with pytest.raises(ValueError, match='No appropriate channels found'): + ica.fit(inst, picks=[]) @requires_sklearn @pytest.mark.parametrize("method", ["fastica", "picard"]) -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') def test_eog_channel(method): """Test that EOG channel is included when performing ICA.""" _skip_check_picard(method) @@ -1180,9 +1187,7 @@ @requires_sklearn @pytest.mark.parametrize("method", ["fastica", "picard"]) -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') -def test_n_components_none(method, tmpdir): +def test_n_components_none(method, tmp_path): """Test n_components=None.""" _skip_check_picard(method) raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data() @@ -1194,10 +1199,10 @@ n_components = None random_state = 12345 - output_fname = tmpdir.join('test_ica-ica.fif') + output_fname = tmp_path / 'test_ica-ica.fif' ica = ICA(method=method, n_components=n_components, random_state=random_state) - with pytest.warns(None): + with _record_warnings(): ica.fit(epochs) _assert_ica_attributes(ica) ica.save(output_fname) @@ -1209,12 +1214,13 @@ assert ica.n_components_ == len(picks) +@pytest.mark.slowtest @requires_sklearn @testing.requires_testing_data def test_ica_ctf(): """Test run ICA computation on ctf data with/without compensation.""" method = 'fastica' - raw = read_raw_ctf(ctf_fname, preload=True) + raw = read_raw_ctf(ctf_fname).crop(0, 3).load_data() picks = sorted(set(range(0, len(raw.ch_names), 10)) | set(pick_types(raw.info, ref_meg=True))) raw.pick(picks) @@ -1228,7 +1234,7 @@ # test fit for inst in [raw, epochs]: ica = ICA(n_components=2, max_iter=2, method=method) - with pytest.warns(UserWarning, match='did not converge'): + with _record_warnings(): # convergence sometimes ica.fit(inst) _assert_ica_attributes(ica) @@ -1240,7 +1246,7 @@ # test mixed compensation case raw.apply_gradient_compensation(0) ica = ICA(n_components=2, max_iter=2, method=method) - with pytest.warns(UserWarning, match='did not converge'): + with _record_warnings(): # convergence sometimes ica.fit(raw) _assert_ica_attributes(ica) raw.apply_gradient_compensation(1) @@ -1316,12 +1322,17 @@ for key in ('ecg', 'eog', 'ref_meg', 'ecg/ECG-MAG'): assert key in ica.labels_ + scores = ica.find_bads_muscle(raw)[1] + assert 'muscle' in ica.labels_ + assert ica.labels_['muscle'] == [0] + assert_allclose(scores, [0.56, 0.01, 0.03, 0.00], atol=0.03) + @requires_sklearn @testing.requires_testing_data @pytest.mark.parametrize('fname, grade', [ (fif_fname, None), - (eeglab_fname, None), + pytest.param(eeglab_fname, None, marks=pymatreader_mark), (ctf_fname2, 0), (ctf_fname2, 1), ]) @@ -1358,7 +1369,7 @@ # test fit for inst in [raw, epochs]: ica = ICA(n_components=2, max_iter=2, method=method) - with pytest.warns(None): + with _record_warnings(): ica.fit(inst, picks=picks, verbose=True) _assert_ica_attributes(ica) @@ -1368,9 +1379,8 @@ ica.get_sources(inst) +@pymatreader_mark @testing.requires_testing_data -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') def test_read_ica_eeglab(): """Test read_ica_eeglab function.""" fname = op.join(test_base_dir, "EEGLAB", "test_raw.set") @@ -1402,15 +1412,14 @@ rtol=1e-05, atol=1e-08) +@pymatreader_mark @testing.requires_testing_data -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') -def test_read_ica_eeglab_mismatch(tmpdir): +def test_read_ica_eeglab_mismatch(tmp_path): """Test read_ica_eeglab function when there is a mismatch.""" fname_orig = op.join(test_base_dir, "EEGLAB", "test_raw.set") base = op.basename(fname_orig)[:-3] - shutil.copyfile(fname_orig[:-3] + 'fdt', tmpdir.join(base + 'fdt')) - fname = tmpdir.join(base) + shutil.copyfile(fname_orig[:-3] + 'fdt', tmp_path / (base + 'fdt')) + fname = tmp_path / base data = loadmat(fname_orig) w = data['EEG']['icaweights'][0][0] w[:] = np.random.RandomState(0).randn(*w.shape) @@ -1493,13 +1502,10 @@ method = 'infomax' for inst in [raw, epochs]: ica = ICA(n_components=2, max_iter=2, method=method) - with pytest.warns(None): + with _record_warnings(): ica.fit(inst, verbose=True) _assert_ica_attributes(ica) # test apply and get_sources for inst in [raw, epochs, evoked]: ica.apply(inst) ica.get_sources(inst) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_infomax.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_infomax.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_infomax.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_infomax.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Denis A. Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause # Parts of this code are taken from scikit-learn @@ -13,7 +13,7 @@ from scipy import linalg from mne.preprocessing.infomax_ import infomax -from mne.utils import requires_sklearn, run_tests_if_main +from mne.utils import requires_sklearn def center_and_norm(x, axis=-1): @@ -199,6 +199,3 @@ from sklearn.decomposition import PCA return PCA(n_components=2, whiten=True, svd_solver='randomized', random_state=rng) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_interpolate.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_interpolate.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_interpolate.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_interpolate.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,8 +1,9 @@ import os.path as op +import numpy as np import pytest -from mne.preprocessing.interpolate import equalize_bads +from mne.preprocessing import equalize_bads, interpolate_bridged_electrodes from mne import io, pick_types, read_events, Epochs base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data') @@ -58,3 +59,24 @@ for inst in insts_ok: assert set(inst.info['bads']) == set(bads_ok) + + +def test_interpolate_bridged_electrodes(): + """Test interpolate_bridged_electrodes function.""" + raw, epochs, evoked = _load_data() + for inst in (raw, epochs, evoked): + idx0 = inst.ch_names.index('EEG 001') + idx1 = inst.ch_names.index('EEG 002') + ch_names_orig = inst.ch_names.copy() + bads_orig = inst.info['bads'].copy() + inst2 = inst.copy() + inst2.info['bads'] = ['EEG 001', 'EEG 002'] + inst2.interpolate_bads() + data_interp_reg = inst2.get_data(picks=['EEG 001', 'EEG 002']) + inst = interpolate_bridged_electrodes(inst, [(idx0, idx1)]) + data_interp = inst.get_data(picks=['EEG 001', 'EEG 002']) + assert not any(['virtual' in ch for ch in inst.ch_names]) + assert inst.ch_names == ch_names_orig + assert inst.info['bads'] == bads_orig + # check closer to regular interpolation than original data + assert 1e-6 < np.mean(np.abs(data_interp - data_interp_reg)) < 5.4e-5 diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_maxwell.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_maxwell.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_maxwell.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_maxwell.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Author: Mark Wronkiewicz # -# License: BSD (3-clause) +# License: BSD-3-Clause from contextlib import contextmanager import os.path as op @@ -23,13 +23,16 @@ from mne.io import (read_raw_fif, read_info, read_raw_bti, read_raw_kit, BaseRaw, read_raw_ctf) from mne.io.constants import FIFF +from mne.preprocessing import (maxwell_filter, find_bad_channels_maxwell, + annotate_amplitude, compute_maxwell_basis, + maxwell_filter_prepare_emptyroom, + annotate_movement) from mne.preprocessing.maxwell import ( - maxwell_filter, _get_n_moments, _sss_basis_basic, _sh_complex_to_real, + _get_n_moments, _sss_basis_basic, _sh_complex_to_real, _sh_real_to_complex, _sh_negate, _bases_complex_to_real, _trans_sss_basis, - _bases_real_to_complex, _prep_mf_coils, find_bad_channels_maxwell, - compute_maxwell_basis) + _bases_real_to_complex, _prep_mf_coils) from mne.rank import _get_rank_sss, _compute_rank_int, compute_rank -from mne.utils import (assert_meg_snr, catch_logging, +from mne.utils import (assert_meg_snr, catch_logging, _record_warnings, object_diff, buggy_mkl_svd, use_log_level) io_path = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data') @@ -138,9 +141,9 @@ @pytest.mark.slowtest @testing.requires_testing_data -def test_movement_compensation(tmpdir): +def test_movement_compensation(tmp_path): """Test movement compensation.""" - temp_dir = str(tmpdir) + temp_dir = str(tmp_path) lims = (0, 4) raw = read_crop(raw_fname, lims).load_data() head_pos = read_head_pos(pos_fname) @@ -247,8 +250,7 @@ raw_sss_auto = maxwell_filter(raw_kit, origin=(0., 0., 0.04), ignore_ref=True, mag_scale='auto') assert_allclose(raw_sss._data, raw_sss_auto._data) - # XXX this KIT origin fit is terrible! Eventually we should get a - # corrected HSP file with proper coverage + # The KIT origin fit is terrible with pytest.warns(RuntimeWarning, match='more than 20 mm'): with catch_logging() as log: pytest.raises(RuntimeError, maxwell_filter, raw_kit, @@ -344,7 +346,7 @@ # Test our basis calculations info = read_info(raw_fname) with use_coil_def(elekta_def_fname): - coils = _prep_meg_channels(info, accurate=True, do_es=True)[0] + coils = _prep_meg_channels(info, do_es=True)[0] # Check against a known benchmark sss_data = loadmat(bases_fname) exp = dict(int_order=int_order, ext_order=ext_order) @@ -482,15 +484,12 @@ @testing.requires_testing_data -def test_maxwell_filter_additional(tmpdir): +def test_maxwell_filter_additional(tmp_path): """Test processing of Maxwell filtered data.""" # TODO: Future tests integrate with mne/io/tests/test_proc_history # Load testing data (raw, SSS std origin, SSS non-standard origin) - data_path = op.join(testing.data_path(download=False)) - file_name = 'test_move_anon' - raw_fname = op.join(data_path, 'SSS', file_name + '_raw.fif') # Use 2.0 seconds of data to get stable cov. estimate @@ -504,7 +503,7 @@ bad_condition='ignore') # Test io on processed data - tempdir = str(tmpdir) + tempdir = str(tmp_path) test_outname = op.join(tempdir, 'test_raw_sss.fif') raw_sss.save(test_outname) raw_sss_loaded = read_crop(test_outname).load_data() @@ -539,6 +538,7 @@ assert_meg_snr(raw_sss, read_crop(sss_bad_recon_fname), 300.) +@pytest.mark.slowtest @buggy_mkl_svd @testing.requires_testing_data def test_spatiotemporal(): @@ -734,7 +734,7 @@ @testing.requires_testing_data -def test_cross_talk(tmpdir): +def test_cross_talk(tmp_path): """Test Maxwell filter cross-talk cancellation.""" raw = read_crop(raw_fname, (0., 1.)) raw.info['bads'] = bads @@ -756,7 +756,7 @@ assert_array_equal(py_ctc['decoupler'].toarray(), mf_ctc['decoupler'].toarray()) # I/O roundtrip - tempdir = str(tmpdir) + tempdir = str(tmp_path) fname = op.join(tempdir, 'test_sss_raw.fif') sss_ctc.save(fname) sss_ctc_read = read_raw_fif(fname) @@ -902,7 +902,7 @@ @buggy_mkl_svd @pytest.mark.slowtest @testing.requires_testing_data -def test_shielding_factor(tmpdir): +def test_shielding_factor(tmp_path): """Test Maxwell filter shielding factor using empty room.""" raw_erm = read_crop(erm_fname).load_data().pick_types(meg=True) erm_power = raw_erm[pick_types(raw_erm.info, meg='mag')][0] @@ -1077,21 +1077,22 @@ _assert_shielding(raw_sss, erm_power_grad, 1.5, 1.6, 'grad') assert counts[0] == 3 with get_n_projected() as counts: - raw_sss = maxwell_filter(raw_erm, calibration=fine_cal_fname_3d, - cross_talk=ctc_fname, st_duration=1., - coord_frame='meg', regularize='in') + with _record_warnings(): # SVD convergence on arm64 + raw_sss = maxwell_filter(raw_erm, calibration=fine_cal_fname_3d, + cross_talk=ctc_fname, st_duration=1., + coord_frame='meg', regularize='in') # Our 3D cal has worse defaults for this ERM than the 1D file _assert_shielding(raw_sss, erm_power, 57, 58) assert counts[0] == 3 # Show it by rewriting the 3D as 1D and testing it - temp_dir = str(tmpdir) + temp_dir = str(tmp_path) temp_fname = op.join(temp_dir, 'test_cal.dat') with open(fine_cal_fname_3d, 'r') as fid: with open(temp_fname, 'w') as fid_out: for line in fid: fid_out.write(' '.join(line.strip().split(' ')[:14]) + '\n') with get_n_projected() as counts: - with pytest.warns(None): # SVD convergence sometimes + with _record_warnings(): # SVD convergence sometimes raw_sss = maxwell_filter(raw_erm, calibration=temp_fname, cross_talk=ctc_fname, st_duration=1., coord_frame='meg', regularize='in') @@ -1134,7 +1135,7 @@ mf_head_origin) for ii, rf in enumerate(raw_fnames): raw = read_crop(rf, (0., 1.)) - with pytest.warns(None): # sometimes the fit is bad + with _record_warnings(): # sometimes the fit is bad sss_py = maxwell_filter( raw, calibration=fine_cals[ii], cross_talk=ctcs[ii], st_duration=st_durs[ii], coord_frame=coord_frames[ii], @@ -1247,27 +1248,31 @@ @testing.requires_testing_data @pytest.mark.parametrize( ('fname', 'bads', 'annot', 'add_ch', 'ignore_ref', 'want_bads', - 'return_scores', 'h_freq'), [ + 'return_scores', 'h_freq', 'meas_date'), [ # Neuromag data tested against MF - (sample_fname, [], False, False, False, ['MEG 2443'], False, None), + (sample_fname, [], False, False, False, ['MEG 2443'], False, None, + 'raw'), # add 0111 to test picking, add annot to test it, and prepend chs for # idx (sample_fname, ['MEG 0111'], True, True, False, ['MEG 2443'], False, - None), + None, 'raw'), # CTF data seems to be sensitive to linalg lib (?) because some # channels are very close to the limit, so we just check that one shows # up (ctf_fname_continuous, [], False, False, False, {'BR1-4304'}, False, - None), + None, 'raw'), # faked (ctf_fname_continuous, [], False, False, True, ['MLC24-4304'], False, - None), + None, 'raw'), # For `return_scores=True` (sample_fname, ['MEG 0111'], True, True, False, ['MEG 2443'], True, - 50) + 50, 'raw'), + (sample_fname, ['MEG 0111'], True, True, False, ['MEG 2443'], True, + 50, None), ]) def test_find_bad_channels_maxwell(fname, bads, annot, add_ch, ignore_ref, - want_bads, return_scores, h_freq): + want_bads, return_scores, h_freq, + meas_date): """Test automatic bad channel detection.""" if fname.endswith('.ds'): raw = read_raw_ctf(fname).load_data() @@ -1276,6 +1281,10 @@ raw = read_raw_fif(fname) raw.fix_mag_coil_types().load_data().pick_types(meg=True, exclude=()) flat_idx = 1 + if meas_date is None: + raw.set_meas_date(None) + else: + assert meas_date == 'raw' raw.filter(None, 40) raw.info['bads'] = bads raw._data[flat_idx] = 0 # MaxFilter didn't have this but doesn't affect it @@ -1287,7 +1296,8 @@ if add_ch: raw_eeg = read_raw_fif(fname) raw_eeg.pick_types(meg=False, eeg=True, exclude=()).load_data() - raw_eeg.info['lowpass'] = 40. + with raw_eeg.info._unlock(): + raw_eeg.info['lowpass'] = 40. raw = raw_eeg.add_channels([raw]) # prepend the EEG channels assert 0 in pick_types(raw.info, meg=False, eeg=True) if ignore_ref: @@ -1370,6 +1380,50 @@ assert bads == set(want_bads) +def test_find_bads_maxwell_flat(): + """Test find_bads_maxwell when there are flat channels.""" + # See gh-9479 + raw = mne.io.read_raw_fif(raw_small_fname).load_data() + assert_allclose(raw.times[-1], 23.97, atol=1e-2) + noisy, flat = find_bad_channels_maxwell(raw, min_count=1) + assert noisy == ['MEG 1032', 'MEG 2313', 'MEG 2443'] + assert flat == [] + n = int(round(raw.info['sfreq'] * 10)) + assert (len(raw.times) - n) / raw.info['sfreq'] > 10 # at least 10 sec + with catch_logging() as log: + want_noisy, want_flat = find_bad_channels_maxwell( + raw.copy().crop(n / raw.info['sfreq'], None), min_count=1, + verbose='debug') + log = log.getvalue() + assert 'in 2 intervals ' in log + assert want_noisy == ['MEG 2313', 'MEG 2443'] + assert want_flat == [] + raw._data[:, :n] = 0 + with pytest.warns(RuntimeWarning, match='All-flat segment detected'): + with catch_logging() as log: + noisy, flat = find_bad_channels_maxwell( + raw, min_count=1, verbose='debug') + log = log.getvalue() + assert ' in 4 intervals ' in log + assert flat == raw.ch_names[:306] + assert noisy == [] # none found because all flat + # now do what we suggest in the warning + annot, _ = annotate_amplitude(raw, flat=0., bad_percent=100, + min_duration=1.) + assert_allclose(annot.duration, 10., atol=1e-2) # not even divisor sfreq + raw.info['bads'] = [] + raw.set_annotations(annot) + data_good = raw.get_data(reject_by_annotation='omit') + assert data_good.shape[1] / raw.info['sfreq'] / 5. > 2 # at least 10 sec + with catch_logging() as log: + noisy, flat = find_bad_channels_maxwell( + raw, min_count=1, skip_by_annotation='bad_flat', verbose='debug') + log = log.getvalue() + assert ' in 2 intervals ' in log, log + assert flat == want_flat + assert noisy == want_noisy + + @pytest.mark.parametrize('regularize, n', [ (None, 80), ('in', 71), @@ -1393,3 +1447,112 @@ xform = S[:, :n_use_in] @ pS[:n_use_in] got = xform @ raw.pick_types(meg=True, exclude='bads').get_data() assert_allclose(got, want) + + +@testing.requires_testing_data +@pytest.mark.parametrize('bads', ('from_raw', 'union', 'keep')) +def test_prepare_emptyroom_bads(bads): + """Test prepare_emptyroom.""" + raw = read_raw_fif(raw_fname, allow_maxshield='yes', verbose=False) + names = [name for name in raw.ch_names if 'EEG' not in name] + raw.pick_channels(names) + raw_er = read_raw_fif(erm_fname, allow_maxshield='yes', verbose=False) + raw_er.pick_channels(names) + assert raw.ch_names == raw_er.ch_names + assert raw_er.info['dev_head_t'] is None + assert raw.info['dev_head_t'] is not None + raw_er.set_montage(None) + + if bads == 'from_raw': + raw_bads_orig = ['MEG0113', 'MEG2313'] + raw_er_bads_orig = [] + elif bads == 'union': + raw_bads_orig = ['MEG0113'] + raw_er_bads_orig = ['MEG2313'] + elif bads == 'keep': + raw_bads_orig = [] + raw_er_bads_orig = ['MEG0113', 'MEG2313'] + + raw.info['bads'] = raw_bads_orig + raw_er.info['bads'] = raw_er_bads_orig + + raw_er_prepared = maxwell_filter_prepare_emptyroom( + raw_er=raw_er, + raw=raw, + bads=bads + ) + assert raw_er_prepared.info['bads'] == ['MEG0113', 'MEG2313'] + assert raw_er_prepared.info['dev_head_t'] == raw.info['dev_head_t'] + + montage_expected = raw.copy().pick_types(meg=True).get_montage() + assert raw_er_prepared.get_montage() == montage_expected + + # Ensure the originals were not modified + assert raw.info['bads'] == raw_bads_orig + assert raw_er.info['bads'] == raw_er_bads_orig + assert raw_er.info['dev_head_t'] is None + assert raw_er.get_montage() is None + + +@testing.requires_testing_data +@pytest.mark.slowtest # lots of params +@pytest.mark.parametrize('set_annot_when', ('before', 'after')) +@pytest.mark.parametrize('raw_meas_date', ('orig', None)) +@pytest.mark.parametrize('raw_er_meas_date', ('orig', None)) +def test_prepare_emptyroom_annot_first_samp(set_annot_when, raw_meas_date, + raw_er_meas_date): + """Test prepare_emptyroom.""" + raw = read_raw_fif(raw_fname, allow_maxshield='yes', verbose=False) + raw_er = read_raw_fif(erm_fname, allow_maxshield='yes', verbose=False) + names = raw.ch_names[:3] # make it faster + raw.pick_channels(names) + raw_er.pick_channels(names) + assert raw.ch_names == raw_er.ch_names + assert raw.info['meas_date'] != raw_er.info['meas_date'] + if raw_meas_date is None: + raw.set_meas_date(None) + if raw_er_meas_date is None: + raw_er.set_meas_date(None) + # to make life easier, make it the same duration + n_rep = max(int(np.ceil(len(raw.times) / len(raw_er.times))), 1) + raw_er = mne.concatenate_raws([raw_er] * n_rep).crop(0, raw.times[-1]) + assert_allclose(raw.times, raw_er.times) + raw_er_first_samp_orig = raw_er.first_samp + assert len(raw.annotations) == 0 + pos = mne.chpi.read_head_pos(pos_fname) + annot, _ = annotate_movement(raw, pos, 1.) + # Add an annotation right at the beginning and end to make sure nothing + # gets cropped + onset = raw.times[[0, -1]] + duration = 1. / raw.info['sfreq'] + annot.append(onset + raw.first_time * (raw.info['meas_date'] is not None), + duration, ['BAD_CUSTOM']) + want_annot = 7 # 5 from annotate_movement plus our first and last samps + if set_annot_when == 'before': + raw.set_annotations(annot) + meas_date = 'keep' + want_date = raw_er.info['meas_date'] + else: + assert set_annot_when == 'after' + meas_date = 'from_raw' + want_date = raw.info['meas_date'] + raw_er_prepared = maxwell_filter_prepare_emptyroom( + raw_er=raw_er, raw=raw, meas_date=meas_date, emit_warning=True) + assert raw_er.first_samp == raw_er_first_samp_orig + assert raw_er_prepared.info['meas_date'] == want_date + assert raw_er_prepared.first_samp == raw.first_samp + + # Ensure (movement) annotations carry over regardless of whether they're + # set before or after preparation + assert len(annot) == want_annot + if set_annot_when == 'after': + raw.set_annotations(annot) + raw_er_prepared.set_annotations(annot) + assert len(raw.annotations) == want_annot + prop_bad = np.isnan( + raw.get_data([0], reject_by_annotation='nan')).mean() + assert 0.3 < prop_bad < 0.4 + assert len(raw_er_prepared.annotations) == want_annot + prop_bad_er = np.isnan( + raw_er_prepared.get_data([0], reject_by_annotation='nan')).mean() + assert_allclose(prop_bad, prop_bad_er) diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_otp.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_otp.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_otp.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_otp.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Eric Larson # -# License: BSD 3 clause +# License: BSD-3-Clause import os.path as op @@ -14,7 +14,7 @@ from mne.io import RawArray, read_raw_fif from mne.io.pick import _pick_data_channels from mne.preprocessing import oversampled_temporal_projection -from mne.utils import run_tests_if_main, catch_logging +from mne.utils import catch_logging data_path = testing.data_path(download=False) erm_fname = op.join(data_path, 'SSS', 'test_move_anon_erm_raw.fif') @@ -99,6 +99,3 @@ raw = read_raw_fif(skip_fname, preload=True) raw.pick_channels(raw.ch_names[:10]) raw_otp = oversampled_temporal_projection(raw, duration=1.) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_peak_finder.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_peak_finder.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_peak_finder.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_peak_finder.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,6 @@ import pytest import numpy as np -from mne.utils import run_tests_if_main from mne.preprocessing import peak_finder @@ -38,6 +37,3 @@ # check values peak_inds, peak_mags = peak_finder([0, 2, 5, 0, 6, -1]) assert_array_equal(peak_inds, [2, 4]) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_realign.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_realign.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_realign.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_realign.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Author: Mark Wronkiewicz # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np from numpy.testing import assert_allclose diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_regress.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_regress.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_regress.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_regress.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_ssp.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_ssp.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_ssp.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_ssp.py 2022-08-03 15:48:14.000000000 +0000 @@ -120,7 +120,7 @@ with pytest.warns(RuntimeWarning, match='Attenuation'): projs, _ = compute_proj_eog( raw, n_eeg=2, bads=raw.ch_names[1:2], average=False, - avg_ref=True, no_proj=False, n_jobs=1, l_freq=None, h_freq=None, + avg_ref=True, no_proj=False, n_jobs=None, l_freq=None, h_freq=None, reject=None, tmax=dur_use, filter_length=100) raw_2 = short_raw.copy() with pytest.warns(RuntimeWarning, match='Attenuation'): diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_stim.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_stim.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_stim.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_stim.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Daniel Strohmeier # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op @@ -52,8 +52,7 @@ epochs = fix_stim_artifact(epochs, tmin=tmin, tmax=tmax, mode='window') data_from_epochs_fix = epochs.get_data()[:, :, tmin_samp:tmax_samp] - # XXX This is a very weird check... - assert np.all(data_from_epochs_fix) == 0. + assert not np.all(data_from_epochs_fix != 0) # use window before stimulus in raw event_idx = np.where(events[:, 2] == 1)[0][0] diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_xdawn.py python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_xdawn.py --- python-mne-0.23.4+dfsg/mne/preprocessing/tests/test_xdawn.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/tests/test_xdawn.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors: Alexandre Barachant # Jean-Remi King # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np import os.path as op diff -Nru python-mne-0.23.4+dfsg/mne/preprocessing/xdawn.py python-mne-1.1.0+dfsg/mne/preprocessing/xdawn.py --- python-mne-0.23.4+dfsg/mne/preprocessing/xdawn.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/preprocessing/xdawn.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Asish Panda # Jean-Remi King # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np diff -Nru python-mne-0.23.4+dfsg/mne/proj.py python-mne-1.1.0+dfsg/mne/proj.py --- python-mne-0.23.4+dfsg/mne/proj.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/proj.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,16 +1,17 @@ # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np from .epochs import Epochs -from .utils import check_fname, logger, verbose, _check_option +from .utils import check_fname, logger, verbose, _check_option, _check_fname +from .io.constants import FIFF from .io.open import fiff_open from .io.pick import pick_types, pick_types_forward from .io.proj import (Projection, _has_eeg_average_ref_proj, _read_proj, make_projector, make_eeg_average_ref_proj, _write_proj) -from .io.write import start_file, end_file +from .io.write import start_and_end_file from .event import make_fixed_length_events from .parallel import parallel_func from .cov import _check_n_samples @@ -48,7 +49,8 @@ return projs -def write_proj(fname, projs): +@verbose +def write_proj(fname, projs, *, overwrite=False, verbose=None): """Write projections to a FIF file. Parameters @@ -56,20 +58,24 @@ fname : str The name of file containing the projections vectors. It should end with -proj.fif or -proj.fif.gz. - projs : list The list of projection vectors. + %(overwrite)s + + .. versionadded:: 1.0 + %(verbose)s + + .. versionadded:: 1.0 See Also -------- read_proj """ + fname = _check_fname(fname, overwrite=overwrite) check_fname(fname, 'projection', ('-proj.fif', '-proj.fif.gz', '_proj.fif', '_proj.fif.gz')) - - with start_file(fname) as fid: + with start_and_end_file(fname) as fid: _write_proj(fid, projs) - end_file(fid) @verbose @@ -129,15 +135,16 @@ data=u[np.newaxis, :], nrow=1, ncol=u.size) this_desc = "%s-%s-PCA-%02d" % (desc, desc_prefix, k + 1) logger.info("Adding projection: %s" % this_desc) - proj = Projection(active=False, data=proj_data, - desc=this_desc, kind=1, explained_var=var) + proj = Projection( + active=False, data=proj_data, desc=this_desc, + kind=FIFF.FIFFV_PROJ_ITEM_FIELD, explained_var=var) projs.append(proj) return projs @verbose -def compute_proj_epochs(epochs, n_grad=2, n_mag=2, n_eeg=2, n_jobs=1, +def compute_proj_epochs(epochs, n_grad=2, n_mag=2, n_eeg=2, n_jobs=None, desc_prefix=None, meg='separate', verbose=None): """Compute SSP (signal-space projection) vectors on epoched data. @@ -193,7 +200,7 @@ def _compute_cov_epochs(epochs, n_jobs): """Compute epochs covariance.""" - parallel, p_fun, _ = parallel_func(np.dot, n_jobs) + parallel, p_fun, n_jobs = parallel_func(np.dot, n_jobs) data = parallel(p_fun(e, e.T) for e in epochs) n_epochs = len(data) if n_epochs == 0: @@ -254,8 +261,8 @@ @verbose def compute_proj_raw(raw, start=0, stop=None, duration=1, n_grad=2, n_mag=2, - n_eeg=0, reject=None, flat=None, n_jobs=1, meg='separate', - verbose=None): + n_eeg=0, reject=None, flat=None, n_jobs=None, + meg='separate', verbose=None): """Compute SSP (signal-space projection) vectors on continuous data. %(compute_ssp)s diff -Nru python-mne-0.23.4+dfsg/mne/rank.py python-mne-1.1.0+dfsg/mne/rank.py --- python-mne-0.23.4+dfsg/mne/rank.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/rank.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ """Some utility functions for rank estimation.""" # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np @@ -14,7 +14,7 @@ from .utils import (logger, _compute_row_norms, _pl, _validate_type, _apply_scaling_cov, _undo_scaling_cov, _scaled_array, warn, _check_rank, _on_missing, verbose, - _check_on_missing) + _check_on_missing, fill_doc) @verbose @@ -30,14 +30,14 @@ ---------- data : array Data to estimate the rank of (should be 2-dimensional). - %(rank_tol)s + %(tol_rank)s return_singular : bool If True, also return the singular values that were used to determine the rank. norm : bool If True, data will be scaled by their estimated row-wise norm. Else data are assumed to be scaled. Defaults to True. - %(rank_tol_kind)s + %(tol_kind_rank)s Returns ------- @@ -109,7 +109,7 @@ def _estimate_rank_raw(raw, picks=None, tol=1e-4, scalings='norm', with_ref_meg=False, tol_kind='absolute'): - """Aid the deprecation of raw.estimate_rank.""" + """Aid the transition away from raw.estimate_rank.""" if picks is None: picks = _picks_to_idx(raw.info, picks, with_ref_meg=with_ref_meg) # conveniency wrapper to expose the expert "tol" option + scalings options @@ -118,6 +118,7 @@ tol, False, tol_kind) +@fill_doc def _estimate_rank_meeg_signals(data, info, scalings, tol='auto', return_singular=False, tol_kind='absolute'): """Estimate rank for M/EEG data. @@ -126,8 +127,7 @@ ---------- data : np.ndarray of float, shape(n_channels, n_samples) The M/EEG signals. - info : Info - The measurement info. + %(info_not_none)s scalings : dict | 'norm' | np.ndarray | None The rescaling method to be applied. If dict, it will override the following default dict: @@ -175,8 +175,7 @@ ---------- data : np.ndarray of float, shape (n_channels, n_channels) The M/EEG covariance. - info : Info - The measurement info. + %(info_not_none)s scalings : dict | 'norm' | np.ndarray | None The rescaling method to be applied. If dict, it will override the following default dict: @@ -199,7 +198,7 @@ If return_singular is True, the singular values that were thresholded to determine the rank are also returned. """ - picks_list = _picks_by_type(info) + picks_list = _picks_by_type(info, exclude=[]) scalings = _handle_default('scalings_cov_rank', scalings) _apply_scaling_cov(data, picks_list, scalings) if data.shape[1] < data.shape[0]: @@ -256,7 +255,7 @@ def _info_rank(info, ch_type, picks, rank): - if ch_type == 'meg' and rank != 'full': + if ch_type in ['meg', 'mag', 'grad'] and rank != 'full': try: return _get_rank_sss(info) except ValueError: @@ -286,20 +285,18 @@ ---------- inst : instance of Raw, Epochs, or Covariance Raw measurements to compute the rank from or the covariance. - %(rank_None)s + %(rank_none)s scalings : dict | None (default None) Defaults to ``dict(mag=1e15, grad=1e13, eeg=1e6)``. These defaults will scale different channel types to comparable values. - info : instance of Info | None - The measurement info used to compute the covariance. It is - only necessary if inst is a Covariance object (since this does - not provide ``inst.info``). - %(rank_tol)s + %(info)s Only necessary if ``inst`` is a :class:`mne.Covariance` + object (since this does not provide ``inst.info``). + %(tol_rank)s proj : bool If True, all projs in ``inst`` and ``info`` will be applied or considered when ``rank=None`` or ``rank='info'``. - %(rank_tol_kind)s + %(tol_kind_rank)s %(on_rank_mismatch)s %(verbose)s @@ -325,8 +322,11 @@ inst_type = 'covariance' if info is None: raise ValueError('info cannot be None if inst is a Covariance.') + # Reset bads as it's already taken into account in inst['names'] + info = info.copy() + info['bads'] = [] inst = pick_channels_cov( - inst, set(inst['names']) & set(info['ch_names'])) + inst, set(inst['names']) & set(info['ch_names']), exclude=[]) if info['ch_names'] != inst['names']: info = pick_info(info, [info['ch_names'].index(name) for name in inst['names']]) @@ -388,8 +388,7 @@ assert rank_type == 'estimated' if isinstance(inst, (BaseRaw, BaseEpochs)): if isinstance(inst, BaseRaw): - data = inst.get_data(picks, None, None, - reject_by_annotation='omit') + data = inst.get_data(picks, reject_by_annotation='omit') else: # isinstance(inst, BaseEpochs): data = inst.get_data()[:, picks, :] data = np.concatenate(data, axis=1) diff -Nru python-mne-0.23.4+dfsg/mne/report/__init__.py python-mne-1.1.0+dfsg/mne/report/__init__.py --- python-mne-0.23.4+dfsg/mne/report/__init__.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/report/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ +from .report import Report, open_report, _ReportScraper diff -Nru python-mne-0.23.4+dfsg/mne/report/js_and_css/bootstrap.bundle.min.js python-mne-1.1.0+dfsg/mne/report/js_and_css/bootstrap.bundle.min.js --- python-mne-0.23.4+dfsg/mne/report/js_and_css/bootstrap.bundle.min.js 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/report/js_and_css/bootstrap.bundle.min.js 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,7 @@ +/*! + * Bootstrap v5.1.1 (https://getbootstrap.com/) + * Copyright 2011-2021 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors) + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) + */ +!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?module.exports=e():"function"==typeof define&&define.amd?define(e):(t="undefined"!=typeof globalThis?globalThis:t||self).bootstrap=e()}(this,(function(){"use strict";const t=t=>{let e=t.getAttribute("data-bs-target");if(!e||"#"===e){let i=t.getAttribute("href");if(!i||!i.includes("#")&&!i.startsWith("."))return null;i.includes("#")&&!i.startsWith("#")&&(i="#"+i.split("#")[1]),e=i&&"#"!==i?i.trim():null}return e},e=e=>{const i=t(e);return i&&document.querySelector(i)?i:null},i=e=>{const i=t(e);return i?document.querySelector(i):null},n=t=>{t.dispatchEvent(new Event("transitionend"))},s=t=>!(!t||"object"!=typeof t)&&(void 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t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){P.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach(t=>P.off(t,"mouseover",h)),this._popper&&this._popper.destroy(),this._menu.classList.remove("show"),this._element.classList.remove("show"),this._element.setAttribute("aria-expanded","false"),F.removeDataAttribute(this._menu,"popper"),P.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...F.getDataAttributes(this._element),...t},r("dropdown",t,this.constructor.DefaultType),"object"==typeof t.reference&&!s(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError("dropdown".toUpperCase()+': Option "reference" provided type "object" without a required 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null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map(t=>Number.parseInt(t,10)):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=U.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(a);i.length&&b(i,e,"ArrowDown"===t,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Oe.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=U.find('[data-bs-toggle="dropdown"]');for(let i=0,n=e.length;ie+t),this._setElementAttributes(".fixed-top, .fixed-bottom, .is-fixed, .sticky-top","paddingRight",e=>e+t),this._setElementAttributes(".sticky-top","marginRight",e=>e-t)}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=i(Number.parseFloat(s))+"px"})}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(".fixed-top, .fixed-bottom, .is-fixed, .sticky-top","paddingRight"),this._resetElementAttributes(".sticky-top","marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,t=>{const i=F.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(F.removeDataAttribute(t,e),t.style[e]=i)})}_applyManipulationCallback(t,e){s(t)?e(t):U.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const ke={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},Le={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"};class xe{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&d(this._getElement()),this._getElement().classList.add("show"),this._emulateAnimation(()=>{g(t)})):g(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove("show"),this._emulateAnimation(()=>{this.dispose(),g(t)})):g(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...ke,..."object"==typeof t?t:{}}).rootElement=o(t.rootElement),r("backdrop",t,Le),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),P.on(this._getElement(),"mousedown.bs.backdrop",()=>{g(this._config.clickCallback)}),this._isAppended=!0)}dispose(){this._isAppended&&(P.off(this._element,"mousedown.bs.backdrop"),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const De={trapElement:null,autofocus:!0},Se={trapElement:"element",autofocus:"boolean"};class Ne{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),P.off(document,".bs.focustrap"),P.on(document,"focusin.bs.focustrap",t=>this._handleFocusin(t)),P.on(document,"keydown.tab.bs.focustrap",t=>this._handleKeydown(t)),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,P.off(document,".bs.focustrap"))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=U.focusableChildren(i);0===n.length?i.focus():"backward"===this._lastTabNavDirection?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?"backward":"forward")}_getConfig(t){return t={...De,..."object"==typeof t?t:{}},r("focustrap",t,Se),t}}const Ie={backdrop:!0,keyboard:!0,focus:!0},Pe={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"};class je extends H{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=U.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new Ce}static get Default(){return Ie}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||P.trigger(this._element,"show.bs.modal",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add("modal-open"),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),P.on(this._dialog,"mousedown.dismiss.bs.modal",()=>{P.one(this._element,"mouseup.dismiss.bs.modal",t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)})}),this._showBackdrop(()=>this._showElement(t)))}hide(){if(!this._isShown||this._isTransitioning)return;if(P.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove("show"),P.off(this._element,"click.dismiss.bs.modal"),P.off(this._dialog,"mousedown.dismiss.bs.modal"),this._queueCallback(()=>this._hideModal(),this._element,t)}dispose(){[window,this._dialog].forEach(t=>P.off(t,".bs.modal")),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new xe({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ne({trapElement:this._element})}_getConfig(t){return t={...Ie,...F.getDataAttributes(this._element),..."object"==typeof t?t:{}},r("modal",t,Pe),t}_showElement(t){const e=this._isAnimated(),i=U.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&d(this._element),this._element.classList.add("show"),this._queueCallback(()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,P.trigger(this._element,"shown.bs.modal",{relatedTarget:t})},this._dialog,e)}_setEscapeEvent(){this._isShown?P.on(this._element,"keydown.dismiss.bs.modal",t=>{this._config.keyboard&&"Escape"===t.key?(t.preventDefault(),this.hide()):this._config.keyboard||"Escape"!==t.key||this._triggerBackdropTransition()}):P.off(this._element,"keydown.dismiss.bs.modal")}_setResizeEvent(){this._isShown?P.on(window,"resize.bs.modal",()=>this._adjustDialog()):P.off(window,"resize.bs.modal")}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide(()=>{document.body.classList.remove("modal-open"),this._resetAdjustments(),this._scrollBar.reset(),P.trigger(this._element,"hidden.bs.modal")})}_showBackdrop(t){P.on(this._element,"click.dismiss.bs.modal",t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())}),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(P.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains("modal-static")||(n||(i.overflowY="hidden"),t.add("modal-static"),this._queueCallback(()=>{t.remove("modal-static"),n||this._queueCallback(()=>{i.overflowY=""},this._dialog)},this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!p()||i&&!t&&p())&&(this._element.style.paddingLeft=e+"px"),(i&&!t&&!p()||!i&&t&&p())&&(this._element.style.paddingRight=e+"px")}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=je.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}P.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=i(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),P.one(e,"show.bs.modal",t=>{t.defaultPrevented||P.one(e,"hidden.bs.modal",()=>{a(this)&&this.focus()})});const n=U.findOne(".modal.show");n&&je.getInstance(n).hide(),je.getOrCreateInstance(e).toggle(this)})),B(je),m(je);const Me={backdrop:!0,keyboard:!0,scroll:!1},He={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"};class Be extends H{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return"offcanvas"}static get Default(){return Me}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||P.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new Ce).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add("show"),this._queueCallback(()=>{this._config.scroll||this._focustrap.activate(),P.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})},this._element,!0))}hide(){this._isShown&&(P.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove("show"),this._backdrop.hide(),this._queueCallback(()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new Ce).reset(),P.trigger(this._element,"hidden.bs.offcanvas")},this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Me,...F.getDataAttributes(this._element),..."object"==typeof t?t:{}},r("offcanvas",t,He),t}_initializeBackDrop(){return new xe({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ne({trapElement:this._element})}_addEventListeners(){P.on(this._element,"keydown.dismiss.bs.offcanvas",t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()})}static jQueryInterface(t){return this.each((function(){const e=Be.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}P.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=i(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;P.one(e,"hidden.bs.offcanvas",()=>{a(this)&&this.focus()});const n=U.findOne(".offcanvas.show");n&&n!==e&&Be.getInstance(n).hide(),Be.getOrCreateInstance(e).toggle(this)})),P.on(window,"load.bs.offcanvas.data-api",()=>U.find(".offcanvas.show").forEach(t=>Be.getOrCreateInstance(t).show())),B(Be),m(Be);const Re=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),We=/^(?:(?:https?|mailto|ftp|tel|file):|[^#&/:?]*(?:[#/?]|$))/i,ze=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,qe=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Re.has(i)||Boolean(We.test(t.nodeValue)||ze.test(t.nodeValue));const n=e.filter(t=>t instanceof RegExp);for(let t=0,e=n.length;t{qe(t,a)||i.removeAttribute(t.nodeName)})}return n.body.innerHTML}const Ue=new Set(["sanitize","allowList","sanitizeFn"]),$e={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ve={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},Ke={animation:!0,template:'',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},Xe={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"};class Ye extends H{constructor(t,e){if(void 0===me)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return Ke}static get NAME(){return"tooltip"}static get Event(){return Xe}static get DefaultType(){return $e}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains("show"))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),P.off(this._element.closest(".modal"),"hide.bs.modal",this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=P.trigger(this._element,this.constructor.Event.SHOW),e=c(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(".tooltip-inner").innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add("fade");const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;M.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),P.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=pe(this._element,n,this._getPopperConfig(r)),n.classList.add("show");const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach(t=>{P.on(t,"mouseover",h)});const d=this.tip.classList.contains("fade");this._queueCallback(()=>{const t=this._hoverState;this._hoverState=null,P.trigger(this._element,this.constructor.Event.SHOWN),"out"===t&&this._leave(null,this)},this.tip,d)}hide(){if(!this._popper)return;const t=this.getTipElement();if(P.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove("show"),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach(t=>P.off(t,"mouseover",h)),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains("fade");this._queueCallback(()=>{this._isWithActiveTrigger()||("show"!==this._hoverState&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),P.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())},this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove("fade","show"),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".tooltip-inner")}_sanitizeAndSetContent(t,e,i){const n=U.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return s(e)?(e=o(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Fe(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map(t=>Number.parseInt(t,10)):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ve[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach(t=>{if("click"===t)P.on(this._element,this.constructor.Event.CLICK,this._config.selector,t=>this.toggle(t));else if("manual"!==t){const e="hover"===t?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i="hover"===t?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;P.on(this._element,e,this._config.selector,t=>this._enter(t)),P.on(this._element,i,this._config.selector,t=>this._leave(t))}}),this._hideModalHandler=()=>{this._element&&this.hide()},P.on(this._element.closest(".modal"),"hide.bs.modal",this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?"focus":"hover"]=!0),e.getTipElement().classList.contains("show")||"show"===e._hoverState?e._hoverState="show":(clearTimeout(e._timeout),e._hoverState="show",e._config.delay&&e._config.delay.show?e._timeout=setTimeout(()=>{"show"===e._hoverState&&e.show()},e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?"focus":"hover"]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState="out",e._config.delay&&e._config.delay.hide?e._timeout=setTimeout(()=>{"out"===e._hoverState&&e.hide()},e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=F.getDataAttributes(this._element);return Object.keys(e).forEach(t=>{Ue.has(t)&&delete e[t]}),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:o(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),r("tooltip",t,this.constructor.DefaultType),t.sanitize&&(t.template=Fe(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map(t=>t.trim()).forEach(e=>t.classList.remove(e))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=Ye.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(Ye);const Qe={...Ye.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:''},Ge={...Ye.DefaultType,content:"(string|element|function)"},Ze={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class Je extends Ye{static get Default(){return Qe}static get NAME(){return"popover"}static get Event(){return Ze}static get DefaultType(){return Ge}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=Je.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(Je);const ti={offset:10,method:"auto",target:""},ei={offset:"number",method:"string",target:"(string|element)"},ii=".nav-link, .list-group-item, .dropdown-item";class ni extends H{constructor(t,e){super(t),this._scrollElement="BODY"===this._element.tagName?window:this._element,this._config=this._getConfig(e),this._offsets=[],this._targets=[],this._activeTarget=null,this._scrollHeight=0,P.on(this._scrollElement,"scroll.bs.scrollspy",()=>this._process()),this.refresh(),this._process()}static get Default(){return ti}static get NAME(){return"scrollspy"}refresh(){const t=this._scrollElement===this._scrollElement.window?"offset":"position",i="auto"===this._config.method?t:this._config.method,n="position"===i?this._getScrollTop():0;this._offsets=[],this._targets=[],this._scrollHeight=this._getScrollHeight(),U.find(ii,this._config.target).map(t=>{const s=e(t),o=s?U.findOne(s):null;if(o){const t=o.getBoundingClientRect();if(t.width||t.height)return[F[i](o).top+n,s]}return null}).filter(t=>t).sort((t,e)=>t[0]-e[0]).forEach(t=>{this._offsets.push(t[0]),this._targets.push(t[1])})}dispose(){P.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){return(t={...ti,...F.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target=o(t.target)||document.documentElement,r("scrollspy",t,ei),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return this._scrollElement.scrollHeight||Math.max(document.body.scrollHeight,document.documentElement.scrollHeight)}_getOffsetHeight(){return this._scrollElement===window?window.innerHeight:this._scrollElement.getBoundingClientRect().height}_process(){const t=this._getScrollTop()+this._config.offset,e=this._getScrollHeight(),i=this._config.offset+e-this._getOffsetHeight();if(this._scrollHeight!==e&&this.refresh(),t>=i){const t=this._targets[this._targets.length-1];this._activeTarget!==t&&this._activate(t)}else{if(this._activeTarget&&t0)return this._activeTarget=null,void this._clear();for(let e=this._offsets.length;e--;)this._activeTarget!==this._targets[e]&&t>=this._offsets[e]&&(void 0===this._offsets[e+1]||t`${e}[data-bs-target="${t}"],${e}[href="${t}"]`),i=U.findOne(e.join(","),this._config.target);i.classList.add("active"),i.classList.contains("dropdown-item")?U.findOne(".dropdown-toggle",i.closest(".dropdown")).classList.add("active"):U.parents(i,".nav, .list-group").forEach(t=>{U.prev(t,".nav-link, .list-group-item").forEach(t=>t.classList.add("active")),U.prev(t,".nav-item").forEach(t=>{U.children(t,".nav-link").forEach(t=>t.classList.add("active"))})}),P.trigger(this._scrollElement,"activate.bs.scrollspy",{relatedTarget:t})}_clear(){U.find(ii,this._config.target).filter(t=>t.classList.contains("active")).forEach(t=>t.classList.remove("active"))}static jQueryInterface(t){return this.each((function(){const e=ni.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}P.on(window,"load.bs.scrollspy.data-api",()=>{U.find('[data-bs-spy="scroll"]').forEach(t=>new ni(t))}),m(ni);class si extends H{static get NAME(){return"tab"}show(){if(this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE&&this._element.classList.contains("active"))return;let t;const e=i(this._element),n=this._element.closest(".nav, .list-group");if(n){const e="UL"===n.nodeName||"OL"===n.nodeName?":scope > li > .active":".active";t=U.find(e,n),t=t[t.length-1]}const s=t?P.trigger(t,"hide.bs.tab",{relatedTarget:this._element}):null;if(P.trigger(this._element,"show.bs.tab",{relatedTarget:t}).defaultPrevented||null!==s&&s.defaultPrevented)return;this._activate(this._element,n);const o=()=>{P.trigger(t,"hidden.bs.tab",{relatedTarget:this._element}),P.trigger(this._element,"shown.bs.tab",{relatedTarget:t})};e?this._activate(e,e.parentNode,o):o()}_activate(t,e,i){const n=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?U.children(e,".active"):U.find(":scope > li > .active",e))[0],s=i&&n&&n.classList.contains("fade"),o=()=>this._transitionComplete(t,n,i);n&&s?(n.classList.remove("show"),this._queueCallback(o,t,!0)):o()}_transitionComplete(t,e,i){if(e){e.classList.remove("active");const t=U.findOne(":scope > .dropdown-menu .active",e.parentNode);t&&t.classList.remove("active"),"tab"===e.getAttribute("role")&&e.setAttribute("aria-selected",!1)}t.classList.add("active"),"tab"===t.getAttribute("role")&&t.setAttribute("aria-selected",!0),d(t),t.classList.contains("fade")&&t.classList.add("show");let n=t.parentNode;if(n&&"LI"===n.nodeName&&(n=n.parentNode),n&&n.classList.contains("dropdown-menu")){const e=t.closest(".dropdown");e&&U.find(".dropdown-toggle",e).forEach(t=>t.classList.add("active")),t.setAttribute("aria-expanded",!0)}i&&i()}static jQueryInterface(t){return this.each((function(){const e=si.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}P.on(document,"click.bs.tab.data-api",'[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this)||si.getOrCreateInstance(this).show()})),m(si);const oi={animation:"boolean",autohide:"boolean",delay:"number"},ri={animation:!0,autohide:!0,delay:5e3};class ai extends H{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return oi}static get Default(){return ri}static get NAME(){return"toast"}show(){P.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove("hide"),d(this._element),this._element.classList.add("show"),this._element.classList.add("showing"),this._queueCallback(()=>{this._element.classList.remove("showing"),P.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()},this._element,this._config.animation))}hide(){this._element.classList.contains("show")&&(P.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add("showing"),this._queueCallback(()=>{this._element.classList.add("hide"),this._element.classList.remove("showing"),this._element.classList.remove("show"),P.trigger(this._element,"hidden.bs.toast")},this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains("show")&&this._element.classList.remove("show"),super.dispose()}_getConfig(t){return t={...ri,...F.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},r("toast",t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout(()=>{this.hide()},this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){P.on(this._element,"mouseover.bs.toast",t=>this._onInteraction(t,!0)),P.on(this._element,"mouseout.bs.toast",t=>this._onInteraction(t,!1)),P.on(this._element,"focusin.bs.toast",t=>this._onInteraction(t,!0)),P.on(this._element,"focusout.bs.toast",t=>this._onInteraction(t,!1))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ai.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return B(ai),m(ai),{Alert:R,Button:W,Carousel:Z,Collapse:et,Dropdown:Oe,Modal:je,Offcanvas:Be,Popover:Je,ScrollSpy:ni,Tab:si,Toast:ai,Tooltip:Ye}})); +//# sourceMappingURL=bootstrap.bundle.min.js.map \ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/report/js_and_css/bootstrap-icons/bootstrap-icons.mne.min.css python-mne-1.1.0+dfsg/mne/report/js_and_css/bootstrap-icons/bootstrap-icons.mne.min.css --- python-mne-0.23.4+dfsg/mne/report/js_and_css/bootstrap-icons/bootstrap-icons.mne.min.css 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/report/js_and_css/bootstrap-icons/bootstrap-icons.mne.min.css 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1 @@ 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\ No newline at end of file diff -Nru python-mne-0.23.4+dfsg/mne/report/js_and_css/bootstrap-icons/gen_css_for_mne.py python-mne-1.1.0+dfsg/mne/report/js_and_css/bootstrap-icons/gen_css_for_mne.py --- python-mne-0.23.4+dfsg/mne/report/js_and_css/bootstrap-icons/gen_css_for_mne.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/report/js_and_css/bootstrap-icons/gen_css_for_mne.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,60 @@ +""" +Generate a bootstrap-icons CSS file with embedded font. + +- Install rcssmin (for CSS minification) +- Download and extract bootstrap-icons +- Copy to this directory: + - bootstrap-icons.css + - bootstrap-icons.woff2 +- Run this script. It will generate bootstrap-icons.mne.css and + bootstrap-icons.mne.min.css +""" + +# Author: Richard Höchenberger +# +# License: BSD-3-Clause + + +from pathlib import Path +import base64 +import rcssmin + + +base_dir = Path('.') +css_path_in = base_dir / 'bootstrap-icons.css' +css_path_out = base_dir / 'bootstrap-icons.mne.css' +css_minified_path_out = base_dir / 'bootstrap-icons.mne.min.css' +font_path = base_dir / 'bootstrap-icons.woff2' + + +def main(): + """Start the CSS modification.""" + css_in = css_path_in.read_text(encoding='utf-8') + font_binary = font_path.read_bytes() + font_b64 = base64.b64encode(font_binary).decode('utf-8') + + css_out = [] + for css in css_in.split('\n'): + if 'src: url(' in css: + css = (f' src: url(data:font/woff2;charset=utf-8;' + f'base64,{font_b64}) format("woff2");') + elif 'url(' in css: + continue + + css_out.append(css) + + css_out = '\n'.join(css_out) + css_minified_out = rcssmin.cssmin(style=css_out) + + css_path_out.write_text( + data=css_out, + encoding='utf-8' + ) + css_minified_path_out.write_text( + data=css_minified_out, + encoding='utf-8' + ) + + +if __name__ == '__main__': + main() diff -Nru python-mne-0.23.4+dfsg/mne/report/js_and_css/bootstrap.min.css python-mne-1.1.0+dfsg/mne/report/js_and_css/bootstrap.min.css --- python-mne-0.23.4+dfsg/mne/report/js_and_css/bootstrap.min.css 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/report/js_and_css/bootstrap.min.css 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,7 @@ +@charset "UTF-8";/*! + * Bootstrap v5.1.1 (https://getbootstrap.com/) + * Copyright 2011-2021 The Bootstrap Authors + * Copyright 2011-2021 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) + 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PSD will be calculated on as many epochs as required to + cover at least this duration. Epochs will be picked across the + entire time range in equally-spaced distance. + + .. note:: + In rare edge cases, we may not be able to create a grid of + equally-spaced epochs that cover the entire requested time range. + In these situations, a warning will be emitted, informing you + about the duration that's actually being used. + + If ``True``, add PSD plots based on all ``epochs``. If ``False``, + do not add PSD plots. + %(projs_report)s + %(topomap_kwargs)s + drop_log_ignore : array-like of str + The drop reasons to ignore when creating the drop log bar plot. + All epochs for which a drop reason listed here appears in + ``epochs.drop_log`` will be excluded from the drop log plot. + %(tags_report)s + %(replace_report)s + + Notes + ----- + .. versionadded:: 0.24.0 + """ + tags = _check_tags(tags) + add_projs = self.projs if projs is None else projs + self._add_epochs( + epochs=epochs, + psd=psd, + add_projs=add_projs, + topomap_kwargs=topomap_kwargs, + drop_log_ignore=drop_log_ignore, + section=title, + tags=tags, + image_format=self.image_format, + replace=replace, + ) + + @fill_doc + def add_evokeds( + self, evokeds, *, titles=None, noise_cov=None, projs=None, + n_time_points=None, tags=('evoked',), replace=False, + topomap_kwargs=None, n_jobs=None + ): + """Add `~mne.Evoked` objects to the report. + + Parameters + ---------- + evokeds : path-like | instance of Evoked | list of Evoked + The evoked data to add to the report. Multiple `~mne.Evoked` + objects – as returned from `mne.read_evokeds` – can be passed as + a list. + titles : str | list of str | None + The titles corresponding to the evoked data. If ``None``, the + content of ``evoked.comment`` from each evoked will be used as + title. + noise_cov : path-like | instance of Covariance | None + A noise covariance matrix. If provided, will be used to whiten + the ``evokeds``. If ``None``, will fall back to the ``cov_fname`` + provided upon report creation. + %(projs_report)s + n_time_points : int | None + The number of equidistant time points to render. If ``None``, + will render each `~mne.Evoked` at 21 time points, unless the data + contains fewer time points, in which case all will be rendered. + %(tags_report)s + %(replace_report)s + %(topomap_kwargs)s + %(n_jobs)s + + Notes + ----- + .. versionadded:: 0.24.0 + """ + if isinstance(evokeds, Evoked): + evokeds = [evokeds] + elif isinstance(evokeds, list): + pass + else: + evoked_fname = evokeds + logger.debug(f'Evoked: Reading {evoked_fname}') + evokeds = read_evokeds(evoked_fname, verbose=False) + + if self.baseline is not None: + evokeds = [e.copy().apply_baseline(self.baseline) + for e in evokeds] + + if titles is None: + titles = [e.comment for e in evokeds] + elif isinstance(titles, str): + titles = [titles] + + if len(evokeds) != len(titles): + raise ValueError( + f'Number of evoked objects ({len(evokeds)}) must ' + f'match number of captions ({len(titles)})' + ) + + if noise_cov is None: + noise_cov = self.cov_fname + if noise_cov is not None and not isinstance(noise_cov, Covariance): + noise_cov = read_cov(fname=noise_cov) + tags = _check_tags(tags) + + add_projs = self.projs if projs is None else projs + + for evoked, title in zip(evokeds, titles): + self._add_evoked( + evoked=evoked, + noise_cov=noise_cov, + image_format=self.image_format, + add_projs=add_projs, + n_time_points=n_time_points, + tags=tags, + section=title, + topomap_kwargs=topomap_kwargs, + n_jobs=n_jobs, + replace=replace, + ) + + @fill_doc + def add_raw( + self, raw, title, *, psd=None, projs=None, butterfly=True, + scalings=None, tags=('raw',), replace=False, topomap_kwargs=None + ): + """Add `~mne.io.Raw` objects to the report. + + Parameters + ---------- + raw : path-like | instance of Raw + The data to add to the report. + title : str + The title corresponding to the ``raw`` object. + psd : bool | None + Whether to add PSD plots. Overrides the ``raw_psd`` parameter + passed when initializing the `~mne.Report`. If ``None``, use + ``raw_psd`` from `~mne.Report` creation. + %(projs_report)s + butterfly : bool | int + Whether to add butterfly plots of the data. Can be useful to + spot problematic channels. If ``True``, 10 equally-spaced 1-second + segments will be plotted. If an integer, specifies the number of + 1-second segments to plot. Larger numbers may take a considerable + amount of time if the data contains many sensors. You can disable + butterfly plots altogether by passing ``False``. + %(scalings)s + %(tags_report)s + %(replace_report)s + %(topomap_kwargs)s + + Notes + ----- + .. versionadded:: 0.24.0 + """ + tags = _check_tags(tags) + + if psd is None: + add_psd = dict() if self.raw_psd is True else self.raw_psd + elif psd is True: + add_psd = dict() + else: + add_psd = False + + add_projs = self.projs if projs is None else projs + + self._add_raw( + raw=raw, + add_psd=add_psd, + add_projs=add_projs, + butterfly=butterfly, + butterfly_scalings=scalings, + image_format=self.image_format, + tags=tags, + topomap_kwargs=topomap_kwargs, + section=title, + replace=replace, + ) + + @fill_doc + def add_stc( + self, stc, title, *, subject=None, subjects_dir=None, + n_time_points=None, tags=('source-estimate',), + replace=False, stc_plot_kwargs=None + ): + """Add a `~mne.SourceEstimate` (STC) to the report. + + Parameters + ---------- + stc : path-like | instance of SourceEstimate + The `~mne.SourceEstimate` to add to the report. + title : str + The title to add. + subject : str | None + The name of the FreeSurfer subject the STC belongs to. The name is + not stored with the STC data and therefore needs to be specified. + If ``None``, will use the value of ``subject`` passed on report + creation. + subjects_dir : path-like | None + The FreeSurfer ``SUBJECTS_DIR``. + n_time_points : int | None + The number of equidistant time points to render. If ``None``, + will render ``stc`` at 51 time points, unless the data + contains fewer time points, in which case all will be rendered. + %(tags_report)s + %(replace_report)s + %(stc_plot_kwargs_report)s + + Notes + ----- + .. versionadded:: 0.24.0 + """ + tags = _check_tags(tags) + self._add_stc( + stc=stc, + title=title, + tags=tags, + image_format=self.image_format, + subject=subject, + subjects_dir=subjects_dir, + n_time_points=n_time_points, + stc_plot_kwargs=stc_plot_kwargs, + section=None, + replace=replace, + ) + + @fill_doc + def add_forward( + self, forward, title, *, subject=None, subjects_dir=None, + tags=('forward-solution',), replace=False + ): + """Add a forward solution. + + Parameters + ---------- + forward : instance of Forward | path-like + The forward solution to add to the report. + title : str + The title corresponding to forward solution. + subject : str | None + The name of the FreeSurfer subject ``forward`` belongs to. If + provided, the sensitivity maps of the forward solution will + be visualized. If ``None``, will use the value of ``subject`` + passed on report creation. If supplied, also pass ``subjects_dir``. + subjects_dir : path-like | None + The FreeSurfer ``SUBJECTS_DIR``. + %(tags_report)s + %(replace_report)s + + Notes + ----- + .. versionadded:: 0.24.0 + """ + tags = _check_tags(tags) + + self._add_forward( + forward=forward, subject=subject, subjects_dir=subjects_dir, + title=title, image_format=self.image_format, section=None, + tags=tags, replace=replace, + ) + + @fill_doc + def add_inverse_operator( + self, inverse_operator, title, *, subject=None, + subjects_dir=None, trans=None, tags=('inverse-operator',), + replace=False + ): + """Add an inverse operator. + + Parameters + ---------- + inverse_operator : instance of InverseOperator | path-like + The inverse operator to add to the report. + title : str + The title corresponding to the inverse operator object. + subject : str | None + The name of the FreeSurfer subject ``inverse_op`` belongs to. If + provided, the source space the inverse solution is based on will + be visualized. If ``None``, will use the value of ``subject`` + passed on report creation. If supplied, also pass ``subjects_dir`` + and ``trans``. + subjects_dir : path-like | None + The FreeSurfer ``SUBJECTS_DIR``. + trans : path-like | instance of Transform | None + The ``head -> MRI`` transformation for ``subject``. + %(tags_report)s + %(replace_report)s + + Notes + ----- + .. versionadded:: 0.24.0 + """ + tags = _check_tags(tags) + + if ((subject is not None and trans is None) or + (trans is not None and subject is None)): + raise ValueError('Please pass subject AND trans, or neither.') + + self._add_inverse_operator( + inverse_operator=inverse_operator, subject=subject, + subjects_dir=subjects_dir, trans=trans, title=title, + image_format=self.image_format, section=None, tags=tags, + replace=replace, + ) + + @fill_doc + def add_trans( + self, trans, *, info, title, subject=None, subjects_dir=None, + alpha=None, tags=('coregistration',), replace=False + ): + """Add a coregistration visualization to the report. + + Parameters + ---------- + trans : path-like | instance of Transform + The ``head -> MRI`` transformation to render. + info : path-like | instance of Info + The `~mne.Info` corresponding to ``trans``. + title : str + The title to add. + subject : str | None + The name of the FreeSurfer subject the ``trans```` belong to. The + name is not stored with the ``trans`` and therefore needs to be + specified. If ``None``, will use the value of ``subject`` passed on + report creation. + subjects_dir : path-like | None + The FreeSurfer ``SUBJECTS_DIR``. + alpha : float | None + The level of opacity to apply to the head surface. If a float, must + be between 0 and 1 (inclusive), where 1 means fully opaque. If + ``None``, will use the MNE-Python default value. + %(tags_report)s + %(replace_report)s + + Notes + ----- + .. versionadded:: 0.24.0 + """ + tags = _check_tags(tags) + self._add_trans( + trans=trans, + info=info, + subject=subject, + subjects_dir=subjects_dir, + alpha=alpha, + title=title, + section=None, + tags=tags, + replace=replace, + ) + + @fill_doc + def add_covariance( + self, cov, *, info, title, tags=('covariance',), replace=False + ): + """Add covariance to the report. + + Parameters + ---------- + cov : path-like | instance of Covariance + The `~mne.Covariance` to add to the report. + info : path-like | instance of Info + The `~mne.Info` corresponding to ``cov``. + title : str + The title corresponding to the `~mne.Covariance` object. + %(tags_report)s + %(replace_report)s + + Notes + ----- + .. versionadded:: 0.24.0 + """ + tags = _check_tags(tags) + self._add_cov( + cov=cov, + info=info, + image_format=self.image_format, + section=title, + tags=tags, + replace=replace, + ) + + @fill_doc + def add_events( + self, events, title, *, event_id=None, sfreq, first_samp=0, + tags=('events',), replace=False + ): + """Add events to the report. + + Parameters + ---------- + events : path-like | array, shape (n_events, 3) + An MNE-Python events array. + title : str + The title corresponding to the events. + event_id : dict + A dictionary mapping event names (keys) to event codes (values). + sfreq : float + The sampling frequency used while recording. + first_samp : int + The first sample point in the recording. This corresponds to + ``raw.first_samp`` on files created with Elekta/Neuromag systems. + %(tags_report)s + %(replace_report)s + + Notes + ----- + .. versionadded:: 0.24.0 + """ + tags = _check_tags(tags) + self._add_events( + events=events, + event_id=event_id, + sfreq=sfreq, + first_samp=first_samp, + title=title, + section=None, + image_format=self.image_format, + tags=tags, + replace=replace, + ) + + @fill_doc + def add_projs(self, *, info, projs=None, title, topomap_kwargs=None, + tags=('ssp',), replace=False): + """Render (SSP) projection vectors. + + Parameters + ---------- + info : instance of Info | path-like + An `~mne.Info` structure or the path of a file containing one. This + is required to create the topographic plots. + projs : iterable of mne.Projection | path-like | None + The projection vectors to add to the report. Can be the path to a + file that will be loaded via `mne.read_proj`. If ``None``, the + projectors are taken from ``info['projs']``. + title : str + The title corresponding to the `~mne.Projection` object. + %(topomap_kwargs)s + %(tags_report)s + %(replace_report)s + + Notes + ----- + .. versionadded:: 0.24.0 + """ + tags = _check_tags(tags) + self._add_projs( + info=info, projs=projs, title=title, + image_format=self.image_format, section=None, tags=tags, + topomap_kwargs=topomap_kwargs, replace=replace + ) + + def _add_ica_overlay( + self, *, ica, inst, image_format, section, tags, replace + ): + if isinstance(inst, BaseRaw): + inst_ = inst + else: # Epochs + inst_ = inst.average() + + fig = ica.plot_overlay(inst=inst_, show=False) + del inst_ + tight_layout(fig=fig) + _constrain_fig_resolution( + fig, max_width=MAX_IMG_WIDTH, max_res=MAX_IMG_RES + ) + self._add_figure( + fig=fig, + title='Original and cleaned signal', + caption=None, + image_format=image_format, + section=section, + tags=tags, + replace=replace, + own_figure=True, + ) + + def _add_ica_properties( + self, *, ica, picks, inst, n_jobs, image_format, section, tags, replace + ): + ch_type = _get_ch_type(inst=ica.info, ch_type=None) + if not _check_ch_locs(info=ica.info, ch_type=ch_type): + ch_type_name = _handle_default("titles")[ch_type] + warn(f'No {ch_type_name} channel locations found, cannot ' + f'create ICA properties plots') + return + + figs = _plot_ica_properties_as_arrays( + ica=ica, inst=inst, picks=picks, n_jobs=n_jobs + ) + rel_explained_var = (ica.pca_explained_variance_ / + ica.pca_explained_variance_.sum()) + cum_explained_var = np.cumsum(rel_explained_var) + captions = [] + for idx, rel_var, cum_var in zip( + range(len(figs)), + rel_explained_var[:len(figs)], + cum_explained_var[:len(figs)] + ): + caption = ( + f'ICA component {idx}. ' + f'Variance explained: {round(100 * rel_var)}%' + ) + if idx == 0: + caption += '.' + else: + caption += f' ({round(100 * cum_var)}% cumulative).' + + captions.append(caption) + + title = 'ICA component properties' + # Only render a slider if we have more than 1 component. + if len(figs) == 1: + self._add_figure( + fig=figs[0], + title=title, + caption=captions[0], + image_format=image_format, + tags=tags, + section=section, + replace=replace, + own_figure=True, + ) + else: + self._add_slider( + figs=figs, + imgs=None, + title=title, + captions=captions, + start_idx=0, + image_format=image_format, + section=section, + tags=tags, + replace=replace, + own_figure=True, + ) + + def _add_ica_artifact_sources( + self, *, ica, inst, artifact_type, image_format, section, tags, replace + ): + with use_browser_backend('matplotlib'): + fig = ica.plot_sources(inst=inst, show=False) + _constrain_fig_resolution( + fig, max_width=MAX_IMG_WIDTH, max_res=MAX_IMG_RES + ) + self._add_figure( + fig=fig, + title=f'Original and cleaned {artifact_type} epochs', + caption=None, + image_format=image_format, + tags=tags, + section=section, + replace=replace, + own_figure=True, + ) + + def _add_ica_artifact_scores( + self, *, ica, scores, artifact_type, image_format, section, tags, + replace + ): + fig = ica.plot_scores(scores=scores, title=None, show=False) + _constrain_fig_resolution( + fig, max_width=MAX_IMG_WIDTH, max_res=MAX_IMG_RES + ) + self._add_figure( + fig=fig, + title=f'Scores for matching {artifact_type} patterns', + caption=None, + image_format=image_format, + tags=tags, + section=section, + replace=replace, + own_figure=True, + ) + + def _add_ica_components( + self, *, ica, picks, image_format, section, tags, replace + ): + ch_type = _get_ch_type(inst=ica.info, ch_type=None) + if not _check_ch_locs(info=ica.info, ch_type=ch_type): + ch_type_name = _handle_default("titles")[ch_type] + warn(f'No {ch_type_name} channel locations found, cannot ' + f'create ICA component plots') + return '' + + figs = ica.plot_components( + picks=picks, title='', colorbar=True, show=False + ) + if not isinstance(figs, list): + figs = [figs] + + for fig in figs: + tight_layout(fig=fig) + + title = 'ICA component topographies' + if len(figs) == 1: + fig = figs[0] + _constrain_fig_resolution( + fig, max_width=MAX_IMG_WIDTH, max_res=MAX_IMG_RES + ) + self._add_figure( + fig=fig, + title=title, + caption=None, + image_format=image_format, + tags=tags, + section=section, + replace=replace, + own_figure=True, + ) + else: + self._add_slider( + figs=figs, + imgs=None, + title=title, + captions=[None] * len(figs), + start_idx=0, + image_format=image_format, + section=section, + tags=tags, + replace=replace + ) + + def _add_ica( + self, *, ica, inst, picks, ecg_evoked, + eog_evoked, ecg_scores, eog_scores, title, image_format, + section, tags, n_jobs, replace + ): + if _path_like(ica): + ica = read_ica(ica) + + if ica.current_fit == 'unfitted': + raise RuntimeError( + 'ICA must be fitted before it can be added to the report.' + ) + + if inst is None: + pass # no-op + elif _path_like(inst): + # We cannot know which data type to expect, so let's first try to + # read a Raw, and if that fails, try to load Epochs + fname = str(inst) # could e.g. be a Path! + raw_kwargs = dict(fname=fname, preload=False) + if fname.endswith(('.fif', '.fif.gz')): + raw_kwargs['allow_maxshield'] = 'yes' + + try: + inst = read_raw(**raw_kwargs) + except ValueError: + try: + inst = read_epochs(fname) + except ValueError: + raise ValueError( + f'The specified file, {fname}, does not seem to ' + f'contain Raw data or Epochs' + ) + elif not inst.preload: + raise RuntimeError( + 'You passed an object to Report.add_ica() via the "inst" ' + 'parameter that was not preloaded. Please preload the data ' + 'via the load_data() method' + ) + + if _path_like(ecg_evoked): + ecg_evoked = read_evokeds(fname=ecg_evoked, condition=0) + + if _path_like(eog_evoked): + eog_evoked = read_evokeds(fname=eog_evoked, condition=0) + + # Summary table + self._add_html_repr( + inst=ica, + title='Info', + tags=tags, + section=section, + replace=replace, + div_klass='ica', + ) + + # Overlay plot + if inst: + self._add_ica_overlay( + ica=ica, inst=inst, image_format=image_format, section=section, + tags=tags, replace=replace, + ) + + # ECG artifact + if ecg_scores is not None: + self._add_ica_artifact_scores( + ica=ica, scores=ecg_scores, artifact_type='ECG', + image_format=image_format, section=section, tags=tags, + replace=replace, + ) + if ecg_evoked: + self._add_ica_artifact_sources( + ica=ica, inst=ecg_evoked, artifact_type='ECG', + image_format=image_format, section=section, tags=tags, + replace=replace, + ) + + # EOG artifact + if eog_scores is not None: + self._add_ica_artifact_scores( + ica=ica, scores=eog_scores, artifact_type='EOG', + image_format=image_format, section=section, tags=tags, + replace=replace, + ) + if eog_evoked: + self._add_ica_artifact_sources( + ica=ica, inst=eog_evoked, artifact_type='EOG', + image_format=image_format, section=section, tags=tags, + replace=replace, + ) + + # Component topography plots + self._add_ica_components( + ica=ica, picks=picks, image_format=image_format, section=section, + tags=tags, replace=replace, + ) + + # Properties plots + if inst: + self._add_ica_properties( + ica=ica, picks=picks, inst=inst, n_jobs=n_jobs, + image_format=image_format, section=section, tags=tags, + replace=replace, + ) + + @fill_doc + def add_ica( + self, ica, title, *, inst, picks=None, ecg_evoked=None, + eog_evoked=None, ecg_scores=None, eog_scores=None, n_jobs=None, + tags=('ica',), replace=False + ): + """Add (a fitted) `~mne.preprocessing.ICA` to the report. + + Parameters + ---------- + ica : path-like | instance of mne.preprocessing.ICA + The fitted ICA to add. + title : str + The title to add. + inst : path-like | mne.io.Raw | mne.Epochs | None + The data to use for visualization of the effects of ICA cleaning. + To only plot the ICA component topographies, explicitly pass + ``None``. + %(picks_ica)s If ``None``, plot all components. This only affects + the behavior of the component topography and properties plots. + ecg_evoked, eog_evoked : path-line | mne.Evoked | None + Evoked signal based on ECG and EOG epochs, respectively. If passed, + will be used to visualize the effects of artifact rejection. + ecg_scores, eog_scores : array of float | list of array of float | None + The scores produced by :meth:`mne.preprocessing.ICA.find_bads_ecg` + and :meth:`mne.preprocessing.ICA.find_bads_eog`, respectively. + If passed, will be used to visualize the scoring for each ICA + component. + %(n_jobs)s + %(tags_report)s + %(replace_report)s + + Notes + ----- + .. versionadded:: 0.24.0 + """ + tags = _check_tags(tags) + self._add_ica( + ica=ica, inst=inst, picks=picks, + ecg_evoked=ecg_evoked, eog_evoked=eog_evoked, + ecg_scores=ecg_scores, eog_scores=eog_scores, + title=title, image_format=self.image_format, + tags=tags, section=title, n_jobs=n_jobs, replace=replace, + ) + + def remove(self, *, title=None, tags=None, remove_all=False): + """Remove elements from the report. + + The element to remove is searched for by its title. Optionally, tags + may be specified as well to narrow down the search to elements that + have the supplied tags. + + Parameters + ---------- + title : str + The title of the element(s) to remove. + + .. versionadded:: 0.24.0 + tags : array-like of str | str | None + If supplied, restrict the operation to elements with the supplied + tags. + + .. versionadded:: 0.24.0 + remove_all : bool + Controls the behavior if multiple elements match the search + criteria. If ``False`` (default) only the element last added to the + report will be removed. If ``True``, all matches will be removed. + + .. versionadded:: 0.24.0 + + Returns + ------- + removed_index : int | tuple of int | None + The indices of the elements that were removed, or ``None`` if no + element matched the search criteria. A tuple will always be + returned if ``remove_all`` was set to ``True`` and at least one + element was removed. + + .. versionchanged:: 0.24.0 + Returns tuple if ``remove_all`` is ``True``. + """ + remove_idx = [] + for idx, element in enumerate(self._content): + if element.name == title: + if (tags is not None and + not all(t in element.tags for t in tags)): + continue + remove_idx.append(idx) + + if not remove_idx: + remove_idx = None + elif not remove_all: # only remove last occurrence + remove_idx = remove_idx[-1] + del self._content[remove_idx] + else: # remove all occurrences + remove_idx = tuple(remove_idx) + self._content = [e for idx, e in enumerate(self._content) + if idx not in remove_idx] + + return remove_idx + + @fill_doc + def _add_or_replace( + self, *, name, section, dom_id, tags, html, replace=False + ): + """Append HTML content report, or replace it if it already exists. + + Parameters + ---------- + name : str + The entry under which the content shall be listed in the table of + contents. If it already exists, the content will be replaced if + ``replace`` is ``True`` + %(section_report)s + dom_id : str + A unique element ``id`` in the DOM. + tags : tuple of str + The tags associated with the added element. + html : str + The HTML. + replace : bool + Whether to replace existing content. + """ + assert isinstance(html, str) # otherwise later will break + + new_content = _ContentElement( + name=name, + section=section, + dom_id=dom_id, + tags=tags, + html=html + ) + + existing_names = [element.name for element in self._content] + if name in existing_names and replace: + # Find and replace existing content, starting from the last element + for idx, content_element in enumerate(self._content[::-1]): + if content_element.name == name: + self._content[idx] = new_content + return + raise RuntimeError('This should never happen') + else: + # Simply append new content (no replace) + self._content.append(new_content) + + def _add_code(self, *, code, title, language, section, tags, replace): + if isinstance(code, Path): + code = Path(code).read_text() + + dom_id = self._get_dom_id() + html = _html_code_element( + tags=tags, + title=title, + id=dom_id, + code=code, + language=language + ) + self._add_or_replace( + dom_id=dom_id, + name=title, + section=section, + tags=tags, + html=html, + replace=replace + ) + + @fill_doc + def add_code( + self, code, title, *, language='python', tags=('code',), + replace=False + ): + """Add a code snippet (e.g., an analysis script) to the report. + + Parameters + ---------- + code : str | pathlib.Path + The code to add to the report as a string, or the path to a file + as a `pathlib.Path` object. + + .. note:: Paths must be passed as `pathlib.Path` object, since + strings will be treated as literal code. + title : str + The title corresponding to the code. + language : str + The programming language of ``code``. This will be used for syntax + highlighting. Can be ``'auto'`` to try to auto-detect the language. + %(tags_report)s + %(replace_report)s + + Notes + ----- + .. versionadded:: 0.24.0 + """ + tags = _check_tags(tags) + language = language.lower() + self._add_code( + code=code, title=title, language=language, section=None, tags=tags, + replace=replace, + ) + + @fill_doc + def add_sys_info(self, title, *, tags=('mne-sysinfo',), replace=False): + """Add a MNE-Python system information to the report. + + This is a convenience method that captures the output of + `mne.sys_info` and adds it to the report. + + Parameters + ---------- + title : str + The title to assign. + %(tags_report)s + %(replace_report)s + + Notes + ----- + .. versionadded:: 0.24.0 + """ + tags = _check_tags(tags) + + with contextlib.redirect_stdout(StringIO()) as f: + sys_info() + + info = f.getvalue() + self.add_code( + code=info, title=title, language='shell', tags=tags, + replace=replace + ) + + def _add_image( + self, *, img, title, caption, image_format, tags, section, replace, + ): + dom_id = self._get_dom_id() + html = _html_image_element( + img=img, div_klass='custom-image', img_klass='custom-image', + title=title, caption=caption, show=True, + image_format=image_format, id=dom_id, tags=tags + ) + self._add_or_replace( + dom_id=dom_id, + name=title, + section=section, + tags=tags, + html=html, + replace=replace + ) + + def _add_figure( + self, *, fig, title, caption, image_format, tags, section, replace, + own_figure + ): + img = _fig_to_img( + fig=fig, image_format=image_format, own_figure=own_figure + ) + self._add_image( + img=img, title=title, caption=caption, image_format=image_format, + tags=tags, section=section, replace=replace + ) + + @fill_doc + def add_figure( + self, fig, title, *, caption=None, image_format=None, + tags=('custom-figure',), section=None, replace=False + ): + """Add figures to the report. + + Parameters + ---------- + fig : matplotlib.figure.Figure | Figure3D | array | array-like of matplotlib.figure.Figure | array-like of Figure3D | array-like of array + One or more figures to add to the report. All figures must be an + instance of :class:`matplotlib.figure.Figure`, + :class:`mne.viz.Figure3D`, or :class:`numpy.ndarray`. If + multiple figures are passed, they will be added as "slides" + that can be navigated using buttons and a slider element. + title : str + The title corresponding to the figure(s). + caption : str | array-like of str | None + The caption(s) to add to the figure(s). + %(image_format_report)s + %(tags_report)s + %(section_report)s + %(replace_report)s + + Notes + ----- + .. versionadded:: 0.24.0 + """ # noqa E501 + tags = _check_tags(tags) + if image_format is None: + image_format = self.image_format + + if hasattr(fig, '__len__') and not isinstance(fig, np.ndarray): + figs = tuple(fig) + else: + figs = (fig,) + + for fig in figs: + if _path_like(fig): + raise TypeError( + f'It seems you passed a path to `add_figure`. However, ' + f'only Matplotlib figures, PyVista scenes, and NumPy ' + f'arrays are accepted. You may want to try `add_image` ' + f'instead. The provided path was: {fig}' + ) + del fig + + if isinstance(caption, str): + captions = (caption,) + elif caption is None and len(figs) == 1: + captions = [None] + elif caption is None and len(figs) > 1: + captions = [f'Figure {i+1}' for i in range(len(figs))] + else: + captions = tuple(caption) + + del caption + + assert figs + if len(figs) == 1: + self._add_figure( + title=title, fig=figs[0], caption=captions[0], + image_format=image_format, section=section, tags=tags, + replace=replace, own_figure=False + ) + else: + self._add_slider( + figs=figs, imgs=None, title=title, captions=captions, + start_idx=0, image_format=image_format, section=section, + tags=tags, own_figure=False, replace=replace + ) + + @fill_doc + def add_image( + self, image, title, *, caption=None, tags=('custom-image',), + section=None, replace=False + ): + """Add an image (e.g., PNG or JPEG pictures) to the report. + + Parameters + ---------- + image : path-like + The image to add. + title : str + Title corresponding to the images. + caption : str | None + If not ``None``, the caption to add to the image. + %(tags_report)s + %(section_report)s + %(replace_report)s + + Notes + ----- + .. versionadded:: 0.24.0 + """ + tags = _check_tags(tags) + img_bytes = Path(image).expanduser().read_bytes() + img_base64 = base64.b64encode(img_bytes).decode('ascii') + del img_bytes # Free memory + + img_format = Path(image).suffix.lower()[1:] # omit leading period + _check_option('Image format', value=img_format, + allowed_values=('png', 'gif', 'svg')) + + self._add_image( + img=img_base64, + title=title, + caption=caption, + image_format=img_format, + tags=tags, + section=section, + replace=replace + ) + + @fill_doc + def add_html( + self, html, title, *, tags=('custom-html',), replace=False + ): + """Add HTML content to the report. + + Parameters + ---------- + html : str + The HTML content to add. + title : str + The title corresponding to ``html``. + %(tags_report)s + %(replace_report)s + + Notes + ----- + .. versionadded:: 0.24.0 + """ + tags = _check_tags(tags) + dom_id = self._get_dom_id() + html_element = _html_element( + id=dom_id, html=html, title=title, tags=tags, + div_klass='custom-html' + ) + self._add_or_replace( + dom_id=dom_id, + name=title, + section=None, + tags=tags, + html=html_element, + replace=replace + ) + + @fill_doc + def add_bem( + self, subject, title, *, subjects_dir=None, decim=2, width=512, + n_jobs=None, tags=('bem',), replace=False + ): + """Render a visualization of the boundary element model (BEM) surfaces. + + Parameters + ---------- + subject : str + The FreeSurfer subject name. + title : str + The title corresponding to the BEM image. + %(subjects_dir)s + decim : int + Use this decimation factor for generating MRI/BEM images + (since it can be time consuming). + width : int + The width of the MRI images (in pixels). Larger values will have + clearer surface lines, but will create larger HTML files. + Typically a factor of 2 more than the number of MRI voxels along + each dimension (typically 512, default) is reasonable. + %(n_jobs)s + %(tags_report)s + %(replace_report)s + + Notes + ----- + .. versionadded:: 0.24.0 + """ + tags = _check_tags(tags) + width = _ensure_int(width, 'width') + self._add_bem( + subject=subject, subjects_dir=subjects_dir, + decim=decim, n_jobs=n_jobs, width=width, + image_format=self.image_format, section=title, tags=tags, + replace=replace + ) + + def _render_slider( + self, *, figs, imgs, title, captions, start_idx, + image_format, tags, klass, own_figure + ): + # This method only exists to make add_bem()'s life easier… + if figs is not None and imgs is not None: + raise ValueError('Must only provide either figs or imgs') + + if figs is not None and len(figs) != len(captions): + raise ValueError( + f'Number of captions ({len(captions)}) must be equal to the ' + f'number of figures ({len(figs)})' + ) + elif imgs is not None and len(imgs) != len(captions): + raise ValueError( + f'Number of captions ({len(captions)}) must be equal to the ' + f'number of images ({len(imgs)})' + ) + elif figs: # figs can be None if imgs is provided + imgs = [_fig_to_img(fig=fig, image_format=image_format, + own_figure=own_figure) + for fig in figs] + dom_id = self._get_dom_id() + html = _html_slider_element( + id=dom_id, + title=title, + captions=captions, + tags=tags, + images=imgs, + image_format=image_format, + start_idx=start_idx, + klass=klass + ) + return html, dom_id + + def _add_slider( + self, *, figs, imgs, title, captions, start_idx, + image_format, tags, section, replace, klass='', own_figure=True + ): + html, dom_id = self._render_slider( + figs=figs, imgs=imgs, title=title, captions=captions, + start_idx=start_idx, image_format=image_format, tags=tags, + klass=klass, own_figure=own_figure + ) + self._add_or_replace( + name=title, + section=section, + dom_id=dom_id, + tags=tags, + html=html, + replace=replace + ) + + ########################################################################### + # global rendering functions + @verbose + def _init_render(self, verbose=None): + """Initialize the renderer.""" + inc_fnames = [ + 'jquery-3.6.0.min.js', + 'bootstrap.bundle.min.js', + 'bootstrap.min.css', + 'bootstrap-table/bootstrap-table.min.js', + 'bootstrap-table/bootstrap-table.min.css', + 'bootstrap-table/bootstrap-table-copy-rows.min.js', + 'bootstrap-table/bootstrap-table-export.min.js', + 'bootstrap-table/tableExport.min.js', + 'bootstrap-icons/bootstrap-icons.mne.min.css', + 'highlightjs/highlight.min.js', + 'highlightjs/atom-one-dark-reasonable.min.css' + ] + + include = list() + for inc_fname in inc_fnames: + logger.info(f'Embedding : {inc_fname}') + fname = html_include_dir / inc_fname + file_content = fname.read_text(encoding='utf-8') + + if inc_fname.endswith('.js'): + include.append( + f'' + ) + elif inc_fname.endswith('.css'): + include.append( + f'' + ) + self.include = ''.join(include) + + def _iterate_files(self, *, fnames, cov, sfreq, raw_butterfly, + n_time_points_evokeds, n_time_points_stcs, on_error, + stc_plot_kwargs, topomap_kwargs): + """Parallel process in batch mode.""" + assert self.data_path is not None + + for fname in fnames: + logger.info( + f"Rendering : {op.join('…' + self.data_path[-20:], fname)}" + ) + + title = Path(fname).name + try: + if _endswith(fname, ['raw', 'sss', 'meg', 'nirs']): + self.add_raw( + raw=fname, title=title, psd=self.raw_psd, + projs=self.projs, butterfly=raw_butterfly + ) + elif _endswith(fname, 'fwd'): + self.add_forward( + forward=fname, title=title, subject=self.subject, + subjects_dir=self.subjects_dir + ) + elif _endswith(fname, 'inv'): + # XXX if we pass trans, we can plot the source space, too… + self.add_inverse_operator( + inverse_operator=fname, title=title + ) + elif _endswith(fname, 'ave'): + evokeds = read_evokeds(fname) + titles = [ + f'{Path(fname).name}: {e.comment}' + for e in evokeds + ] + self.add_evokeds( + evokeds=fname, titles=titles, noise_cov=cov, + n_time_points=n_time_points_evokeds, + topomap_kwargs=topomap_kwargs + ) + elif _endswith(fname, 'eve'): + if self.info_fname is not None: + sfreq = read_info(self.info_fname)['sfreq'] + else: + sfreq = None + self.add_events(events=fname, title=title, sfreq=sfreq) + elif _endswith(fname, 'epo'): + self.add_epochs(epochs=fname, title=title) + elif _endswith(fname, 'cov') and self.info_fname is not None: + self.add_covariance(cov=fname, info=self.info_fname, + title=title) + elif _endswith(fname, 'proj') and self.info_fname is not None: + self.add_projs(info=self.info_fname, projs=fname, + title=title, topomap_kwargs=topomap_kwargs) + # XXX TODO We could render ICA components here someday + # elif _endswith(fname, 'ica') and ica: + # pass + elif (_endswith(fname, 'trans') and + self.info_fname is not None and + self.subjects_dir is not None and + self.subject is not None): + self.add_trans( + trans=fname, info=self.info_fname, + subject=self.subject, subjects_dir=self.subjects_dir, + title=title + ) + elif (fname.endswith('-lh.stc') or + fname.endswith('-rh.stc') and + self.info_fname is not None and + self.subjects_dir is not None and + self.subject is not None): + self.add_stc( + stc=fname, title=title, subject=self.subject, + subjects_dir=self.subjects_dir, + n_time_points=n_time_points_stcs, + stc_plot_kwargs=stc_plot_kwargs + ) + except Exception as e: + if on_error == 'warn': + warn(f'Failed to process file {fname}:\n"{e}"') + elif on_error == 'raise': + raise + + @verbose + def parse_folder(self, data_path, pattern=None, n_jobs=None, mri_decim=2, + sort_content=True, *, on_error='warn', + image_format=None, render_bem=True, + n_time_points_evokeds=None, n_time_points_stcs=None, + raw_butterfly=True, stc_plot_kwargs=None, + topomap_kwargs=None, verbose=None): + r"""Render all the files in the folder. + + Parameters + ---------- + data_path : str + Path to the folder containing data whose HTML report will be + created. + pattern : None | str | list of str + Filename pattern(s) to include in the report. + For example, ``[\*raw.fif, \*ave.fif]`` will include `~mne.io.Raw` + as well as `~mne.Evoked` files. If ``None``, include all supported + file formats. + + .. versionchanged:: 0.23 + Include supported non-FIFF files by default. + %(n_jobs)s + mri_decim : int + Use this decimation factor for generating MRI/BEM images + (since it can be time consuming). + sort_content : bool + If ``True``, sort the content based on tags in the order: + raw -> events -> epochs -> evoked -> covariance -> coregistration + -> bem -> forward-solution -> inverse-operator -> source-estimate. + + .. versionadded:: 0.24.0 + on_error : str + What to do if a file cannot be rendered. Can be 'ignore', + 'warn' (default), or 'raise'. + %(image_format_report)s + + .. versionadded:: 0.15 + render_bem : bool + If True (default), try to render the BEM. + + .. versionadded:: 0.16 + n_time_points_evokeds, n_time_points_stcs : int | None + The number of equidistant time points to render for `~mne.Evoked` + and `~mne.SourceEstimate` data, respectively. If ``None``, + will render each `~mne.Evoked` at 21 and each `~mne.SourceEstimate` + at 51 time points, unless the respective data contains fewer time + points, in which call all will be rendered. + + .. versionadded:: 0.24.0 + raw_butterfly : bool + Whether to render butterfly plots for (decimated) `~mne.io.Raw` + data. + + .. versionadded:: 0.24.0 + %(stc_plot_kwargs_report)s + + .. versionadded:: 0.24.0 + %(topomap_kwargs)s + + .. versionadded:: 0.24.0 + %(verbose)s + """ + _validate_type(data_path, 'path-like', 'data_path') + data_path = str(data_path) + image_format = _check_image_format(self, image_format) + _check_option('on_error', on_error, ['ignore', 'warn', 'raise']) + + self.data_path = data_path + + if self.title is None: + self.title = f'MNE Report for {self.data_path[-20:]}' + + if pattern is None: + pattern = [f'*{ext}' for ext in SUPPORTED_READ_RAW_EXTENSIONS] + elif not isinstance(pattern, (list, tuple)): + pattern = [pattern] + + # iterate through the possible patterns + fnames = list() + for p in pattern: + data_path = _check_fname( + fname=self.data_path, overwrite='read', must_exist=True, + name='Directory or folder', need_dir=True + ) + fnames.extend(sorted(_recursive_search(data_path, p))) + + if not fnames and not render_bem: + raise RuntimeError(f'No matching files found in {self.data_path}') + + fnames_to_remove = [] + for fname in fnames: + # For split files, only keep the first one. + if _endswith(fname, ('raw', 'sss', 'meg')): + kwargs = dict(fname=fname, preload=False) + if fname.endswith(('.fif', '.fif.gz')): + kwargs['allow_maxshield'] = 'yes' + inst = read_raw(**kwargs) + + if len(inst.filenames) > 1: + fnames_to_remove.extend(inst.filenames[1:]) + # For STCs, only keep one hemisphere + elif fname.endswith('-lh.stc') or fname.endswith('-rh.stc'): + first_hemi_fname = fname + if first_hemi_fname.endswidth('-lh.stc'): + second_hemi_fname = (first_hemi_fname + .replace('-lh.stc', '-rh.stc')) + else: + second_hemi_fname = (first_hemi_fname + .replace('-rh.stc', '-lh.stc')) + + if (second_hemi_fname in fnames and + first_hemi_fname not in fnames_to_remove): + fnames_to_remove.extend(first_hemi_fname) + else: + continue + + fnames_to_remove = list(set(fnames_to_remove)) # Drop duplicates + for fname in fnames_to_remove: + if fname in fnames: + del fnames[fnames.index(fname)] + del fnames_to_remove + + if self.info_fname is not None: + info = read_info(self.info_fname, verbose=False) + sfreq = info['sfreq'] + else: + # only warn if relevant + if any(_endswith(fname, 'cov') for fname in fnames): + warn('`info_fname` not provided. Cannot render ' + '-cov.fif(.gz) files.') + if any(_endswith(fname, 'trans') for fname in fnames): + warn('`info_fname` not provided. Cannot render ' + '-trans.fif(.gz) files.') + if any(_endswith(fname, 'proj') for fname in fnames): + warn('`info_fname` not provided. Cannot render ' + '-proj.fif(.gz) files.') + info, sfreq = None, None + + cov = None + if self.cov_fname is not None: + cov = read_cov(self.cov_fname) + + # render plots in parallel; check that n_jobs <= # of files + logger.info(f'Iterating over {len(fnames)} potential files ' + f'(this may take some ') + parallel, p_fun, n_jobs = parallel_func( + self._iterate_files, n_jobs, max_jobs=len(fnames)) + parallel( + p_fun( + fnames=fname, cov=cov, sfreq=sfreq, + raw_butterfly=raw_butterfly, + n_time_points_evokeds=n_time_points_evokeds, + n_time_points_stcs=n_time_points_stcs, on_error=on_error, + stc_plot_kwargs=stc_plot_kwargs, topomap_kwargs=topomap_kwargs, + ) for fname in np.array_split(fnames, n_jobs) + ) + + # Render BEM + if render_bem: + if self.subjects_dir is not None and self.subject is not None: + logger.info('Rendering BEM') + self.add_bem( + subject=self.subject, subjects_dir=self.subjects_dir, + title='BEM surfaces', decim=mri_decim, n_jobs=n_jobs + ) + else: + warn('`subjects_dir` and `subject` not provided. Cannot ' + 'render MRI and -trans.fif(.gz) files.') + + if sort_content: + self._content = self._sort( + content=self._content, order=CONTENT_ORDER + ) + + def __getstate__(self): + """Get the state of the report as a dictionary.""" + state = dict() + for param_name in self._get_state_params(): + param_val = getattr(self, param_name) + + # Workaround as h5io doesn't support dataclasses + if param_name == '_content': + assert all(dataclasses.is_dataclass(val) for val in param_val) + param_val = [dataclasses.asdict(val) for val in param_val] + + state[param_name] = param_val + return state + + def __setstate__(self, state): + """Set the state of the report.""" + for param_name in self._get_state_params(): + param_val = state[param_name] + + # Workaround as h5io doesn't support dataclasses + if param_name == '_content': + param_val = [_ContentElement(**val) for val in param_val] + setattr(self, param_name, param_val) + return state + + @verbose + def save(self, fname=None, open_browser=True, overwrite=False, + sort_content=False, *, verbose=None): + """Save the report and optionally open it in browser. + + Parameters + ---------- + fname : path-like | None + Output filename. If the name ends with ``.h5`` or ``.hdf5``, the + report is saved in HDF5 format, so it can later be loaded again + with :func:`open_report`. For any other suffix, the report will be + saved in HTML format. If ``None`` and :meth:`Report.parse_folder` + was **not** called, the report is saved as ``report.html`` in the + current working directory. If ``None`` and + :meth:`Report.parse_folder` **was** used, the report is saved as + ``report.html`` inside the ``data_path`` supplied to + :meth:`Report.parse_folder`. + open_browser : bool + Whether to open the rendered HTML report in the default web browser + after saving. This is ignored when writing an HDF5 file. + %(overwrite)s + sort_content : bool + If ``True``, sort the content based on tags before saving in the + order: + raw -> events -> epochs -> evoked -> covariance -> coregistration + -> bem -> forward-solution -> inverse-operator -> source-estimate. + + .. versionadded:: 0.24.0 + %(verbose)s + + Returns + ------- + fname : str + The file name to which the report was saved. + """ + if fname is None: + if self.data_path is None: + self.data_path = os.getcwd() + warn(f'`data_path` not provided. Using {self.data_path} ' + f'instead') + fname = op.join(self.data_path, 'report.html') + + fname = _check_fname(fname, overwrite=overwrite, name=fname) + fname = op.realpath(fname) # resolve symlinks + + if sort_content: + self._content = self._sort( + content=self._content, order=CONTENT_ORDER + ) + + if not overwrite and op.isfile(fname): + msg = (f'Report already exists at location {fname}. ' + f'Overwrite it (y/[n])? ') + answer = _safe_input(msg, alt='pass overwrite=True') + if answer.lower() == 'y': + overwrite = True + + _, ext = op.splitext(fname) + is_hdf5 = ext.lower() in ['.h5', '.hdf5'] + + if overwrite or not op.isfile(fname): + logger.info(f'Saving report to : {fname}') + + if is_hdf5: + _, write_hdf5 = _import_h5io_funcs() + write_hdf5(fname, self.__getstate__(), overwrite=overwrite, + title='mnepython') + else: + # Add header, TOC, and footer. + header_html = _html_header_element( + title=self.title, include=self.include, lang=self.lang, + tags=self.tags, js=JAVASCRIPT, css=CSS, + mne_logo_img=mne_logo + ) + + # toc_html = _html_toc_element(content_elements=self._content) + _, dom_ids, titles, tags = self._content_as_html() + toc_html = _html_toc_element( + titles=titles, + dom_ids=dom_ids, + tags=tags + ) + + with warnings.catch_warnings(record=True): + warnings.simplefilter('ignore') + footer_html = _html_footer_element( + mne_version=MNE_VERSION, + date=time.strftime("%B %d, %Y") + ) + + html = [header_html, toc_html, *self.html, footer_html] + Path(fname).write_text(data=''.join(html), encoding='utf-8') + + building_doc = os.getenv('_MNE_BUILDING_DOC', '').lower() == 'true' + if open_browser and not is_hdf5 and not building_doc: + webbrowser.open_new_tab('file://' + fname) + + if self.fname is None: + self.fname = fname + return fname + + def __enter__(self): + """Do nothing when entering the context block.""" + return self + + def __exit__(self, type, value, traceback): + """Save the report when leaving the context block.""" + if self.fname is not None: + self.save(self.fname, open_browser=False, overwrite=True) + + @staticmethod + def _sort(content, order): + """Reorder content to reflect "natural" ordering.""" + content_unsorted = content.copy() + content_sorted = [] + content_sorted_idx = [] + del content + + # First arrange content with known tags in the predefined order + for tag in order: + for idx, content in enumerate(content_unsorted): + if tag in content.tags: + content_sorted_idx.append(idx) + content_sorted.append(content) + + # Now simply append the rest (custom tags) + content_remaining = [ + content for idx, content in enumerate(content_unsorted) + if idx not in content_sorted_idx + ] + + content_sorted = [*content_sorted, *content_remaining] + return content_sorted + + def _render_one_bem_axis(self, *, mri_fname, surfaces, + image_format, orientation, decim=2, n_jobs=None, + width=512, tags): + """Render one axis of bem contours (only PNG).""" + import nibabel as nib + + nim = nib.load(mri_fname) + data = _reorient_image(nim)[0] + axis = _mri_orientation(orientation)[0] + n_slices = data.shape[axis] + + sl = np.arange(0, n_slices, decim) + logger.debug(f'Rendering BEM {orientation} with {len(sl)} slices') + figs = _get_bem_contour_figs_as_arrays( + sl=sl, n_jobs=n_jobs, mri_fname=mri_fname, surfaces=surfaces, + orientation=orientation, src=None, show=False, + show_orientation='always', width=width + ) + + # Render the slider + captions = [f'Slice index: {i * decim}' for i in range(len(figs))] + start_idx = int(round(len(figs) / 2)) + html, _ = self._render_slider( + figs=figs, + imgs=None, + captions=captions, + title=orientation, + image_format=image_format, + start_idx=start_idx, + tags=tags, + klass='bem col-md', + own_figure=True, + ) + + return html + + def _add_html_repr( + self, *, inst, title, tags, section, replace, div_klass + ): + html = inst._repr_html_() + self._add_html_element( + html=html, + title=title, + tags=tags, + section=section, + replace=replace, + div_klass=div_klass, + ) + + def _add_raw_butterfly_segments( + self, *, raw: BaseRaw, n_segments, scalings, image_format, tags, + section, replace + ): + # Pick n_segments + 2 equally-spaced 1-second time slices, but omit + # the first and last slice, so we end up with n_segments slices + n = n_segments + 2 + times = np.linspace(raw.times[0], raw.times[-1], n)[1:-1] + t_starts = np.array([max(t - 0.5, 0) for t in times]) + t_stops = np.array([min(t + 0.5, raw.times[-1]) for t in times]) + durations = t_stops - t_starts + + # Remove annotations before plotting for better performance. + # Ensure we later restore raw.annotations even in case of an exception + orig_annotations = raw.annotations.copy() + + try: + raw.set_annotations(None) + + # Create the figure once and re-use it for performance reasons + with use_browser_backend('matplotlib'): + fig = raw.plot( + butterfly=True, show_scrollbars=False, start=t_starts[0], + duration=durations[0], scalings=scalings, show=False + ) + _constrain_fig_resolution( + fig, max_width=MAX_IMG_WIDTH, max_res=MAX_IMG_RES + ) + images = [_fig_to_img(fig=fig, image_format=image_format)] + + for start, duration in zip(t_starts[1:], durations[1:]): + fig.mne.t_start = start + fig.mne.duration = duration + fig._update_hscroll() + fig._redraw(annotations=False) + images.append(_fig_to_img(fig=fig, image_format=image_format)) + except Exception: + raise + finally: + raw.set_annotations(orig_annotations) + + del orig_annotations + + captions = [f'Segment {i+1} of {len(images)}' + for i in range(len(images))] + + self._add_slider( + figs=None, imgs=images, title='Time series', captions=captions, + start_idx=0, image_format=image_format, tags=tags, section=section, + replace=replace + ) + + def _add_raw( + self, *, raw, add_psd, add_projs, butterfly, + butterfly_scalings, image_format, tags, topomap_kwargs, + section, replace + ): + """Render raw.""" + if isinstance(raw, BaseRaw): + fname = raw.filenames[0] + else: + fname = str(raw) # could e.g. be a Path! + kwargs = dict(fname=fname, preload=False) + if fname.endswith(('.fif', '.fif.gz')): + kwargs['allow_maxshield'] = 'yes' + raw = read_raw(**kwargs) + + # Summary table + self._add_html_repr( + inst=raw, + title='Info', + tags=tags, + section=section, + replace=replace, + div_klass='raw' + ) + + # Butterfly plot + if butterfly: + n_butterfly_segments = 10 if butterfly is True else butterfly + self._add_raw_butterfly_segments( + raw=raw, scalings=butterfly_scalings, + n_segments=n_butterfly_segments, + image_format=image_format, tags=tags, replace=replace, + section=section + ) + + # PSD + if isinstance(add_psd, dict): + if raw.info['lowpass'] is not None: + fmax = raw.info['lowpass'] + 15 + # Must not exceed half the sampling frequency + if fmax > 0.5 * raw.info['sfreq']: + fmax = np.inf + else: + fmax = np.inf + + fig = raw.plot_psd(fmax=fmax, show=False, **add_psd) + tight_layout(fig=fig) + _constrain_fig_resolution( + fig, max_width=MAX_IMG_WIDTH, max_res=MAX_IMG_RES + ) + self._add_figure( + fig=fig, + title='PSD', + caption=None, + image_format=image_format, + tags=tags, + section=section, + replace=replace, + own_figure=True + ) + + # SSP projectors + if add_projs: + self._add_projs( + info=raw, projs=None, title='Projectors', + image_format=image_format, tags=tags, + topomap_kwargs=topomap_kwargs, section=section, + replace=replace + ) + + def _add_projs(self, *, info, projs, title, image_format, tags, section, + topomap_kwargs, replace): + if isinstance(info, Info): # no-op + pass + elif hasattr(info, 'info'): # try to get the file name + if isinstance(info, BaseRaw): + fname = info.filenames[0] + # elif isinstance(info, (Evoked, BaseEpochs)): + # fname = info.filename + else: + fname = '' + info = info.info + else: # read from a file + fname = info + info = read_info(fname, verbose=False) + + if projs is None: + projs = info['projs'] + elif not isinstance(projs, list): + fname = projs + projs = read_proj(fname) + + if not projs: + raise ValueError('No SSP projectors found') + + if not _check_ch_locs(info=info): + raise ValueError( + 'The provided data does not contain digitization ' + 'information (channel locations). However, this is ' + 'required for rendering the projectors.' + ) + + topomap_kwargs = self._validate_topomap_kwargs(topomap_kwargs) + fig = plot_projs_topomap( + projs=projs, info=info, colorbar=True, vlim='joint', + show=False, **topomap_kwargs + ) + # TODO This seems like a bad idea, better to provide a way to set a + # desired size in plot_projs_topomap, but that uses prepare_trellis... + # hard to see how (6, 4) could work in all number-of-projs by + # number-of-channel-types conditions... + fig.set_size_inches((6, 4)) + tight_layout(fig=fig) + _constrain_fig_resolution( + fig, max_width=MAX_IMG_WIDTH, max_res=MAX_IMG_RES + ) + self._add_figure( + fig=fig, + title=title, + caption=None, + image_format=image_format, + tags=tags, + section=section, + replace=replace, + own_figure=True, + ) + + def _add_forward( + self, *, forward, subject, subjects_dir, title, image_format, + section, tags, replace + ): + """Render forward solution.""" + if not isinstance(forward, Forward): + forward = read_forward_solution(forward) + + subject = self.subject if subject is None else subject + subjects_dir = (self.subjects_dir if subjects_dir is None + else subjects_dir) + + # XXX Todo + # Render sensitivity maps + if subject is not None: + sensitivity_maps_html = '' + else: + sensitivity_maps_html = '' + + dom_id = self._get_dom_id() + html = _html_forward_sol_element( + id=dom_id, + repr=forward._repr_html_(), + sensitivity_maps=sensitivity_maps_html, + title=title, + tags=tags + ) + self._add_or_replace( + name=title, + section=section, + dom_id=dom_id, + tags=tags, + html=html, + replace=replace, + ) + + def _add_inverse_operator( + self, *, inverse_operator, subject, + subjects_dir, trans, title, image_format, + section, tags, replace, + ): + """Render inverse operator.""" + if not isinstance(inverse_operator, InverseOperator): + inverse_operator = read_inverse_operator(inverse_operator) + + if trans is not None and not isinstance(trans, Transform): + trans = read_trans(trans) + + subject = self.subject if subject is None else subject + subjects_dir = (self.subjects_dir if subjects_dir is None + else subjects_dir) + + # XXX Todo Render source space? + # if subject is not None and trans is not None: + # src = inverse_operator['src'] + + # fig = plot_alignment( + # subject=subject, + # subjects_dir=subjects_dir, + # trans=trans, + # surfaces='white', + # src=src + # ) + # set_3d_view(fig, focalpoint=(0., 0., 0.06)) + # img = _fig_to_img(fig=fig, image_format=image_format) + + # dom_id = self._get_dom_id() + # src_img_html = _html_image_element( + # img=img, + # div_klass='inverse-operator source-space', + # img_klass='inverse-operator source-space', + # title='Source space', caption=None, show=True, + # image_format=image_format, id=dom_id, + # tags=tags + # ) + # else: + src_img_html = '' + + dom_id = self._get_dom_id() + html = _html_inverse_operator_element( + id=dom_id, + repr=inverse_operator._repr_html_(), + source_space=src_img_html, + title=title, + tags=tags, + ) + self._add_or_replace( + name=title, + section=section, + dom_id=dom_id, + tags=tags, + html=html, + replace=replace, + ) + + def _add_evoked_joint( + self, *, evoked, ch_types, image_format, section, tags, topomap_kwargs, + replace + ): + for ch_type in ch_types: + if not _check_ch_locs(info=evoked.info, ch_type=ch_type): + ch_type_name = _handle_default("titles")[ch_type] + warn(f'No {ch_type_name} channel locations found, cannot ' + f'create joint plot') + continue + + with use_log_level(False): + fig = evoked.copy().pick(ch_type, verbose=False).plot_joint( + ts_args=dict(gfp=True), + title=None, + show=False, + topomap_args=topomap_kwargs, + ) + + _constrain_fig_resolution( + fig, max_width=MAX_IMG_WIDTH, max_res=MAX_IMG_RES + ) + title = f'Time course ({_handle_default("titles")[ch_type]})' + self._add_figure( + fig=fig, + title=title, + caption=None, + image_format=image_format, + tags=tags, + section=section, + replace=replace, + own_figure=True, + ) + + def _plot_one_evoked_topomap_timepoint( + self, *, evoked, time, ch_types, vmin, vmax, topomap_kwargs + ): + import matplotlib.pyplot as plt + + fig, ax = plt.subplots( + 1, len(ch_types) * 2, + gridspec_kw={ + 'width_ratios': [8, 0.5] * len(ch_types) + }, + figsize=(2.5 * len(ch_types), 2) + ) + _constrain_fig_resolution( + fig, max_width=MAX_IMG_WIDTH, max_res=MAX_IMG_RES + ) + ch_type_ax_map = dict( + zip(ch_types, + [(ax[i], ax[i + 1]) for i in + range(0, 2 * len(ch_types) - 1, 2)]) + ) + + for ch_type in ch_types: + evoked.plot_topomap( + times=[time], ch_type=ch_type, + vmin=vmin[ch_type], vmax=vmax[ch_type], + axes=ch_type_ax_map[ch_type], show=False, + **topomap_kwargs + ) + ch_type_ax_map[ch_type][0].set_title(ch_type) + + tight_layout(fig=fig) + + with BytesIO() as buff: + fig.savefig( + buff, + format='png', + pad_inches=0 + ) + plt.close(fig) + buff.seek(0) + fig_array = plt.imread(buff, format='png') + return fig_array + + def _add_evoked_topomap_slider( + self, *, evoked, ch_types, n_time_points, image_format, section, tags, + topomap_kwargs, n_jobs, replace + ): + if n_time_points is None: + n_time_points = min(len(evoked.times), 21) + elif n_time_points > len(evoked.times): + raise ValueError( + f'The requested number of time points ({n_time_points}) ' + f'exceeds the time points in the provided Evoked object ' + f'({len(evoked.times)})' + ) + + if n_time_points == 1: # only a single time point, pick the first one + times = [evoked.times[0]] + else: + times = np.linspace( + start=evoked.tmin, + stop=evoked.tmax, + num=n_time_points + ) + + t_zero_idx = np.abs(times).argmin() # index closest to zero + + # global min and max values for each channel type + scalings = dict(eeg=1e6, grad=1e13, mag=1e15) + + vmax = dict() + vmin = dict() + for ch_type in ch_types: + if not _check_ch_locs(info=evoked.info, ch_type=ch_type): + ch_type_name = _handle_default("titles")[ch_type] + warn(f'No {ch_type_name} channel locations found, cannot ' + f'create topography plots') + continue + + vmax[ch_type] = (np.abs(evoked.copy() + .pick(ch_type, verbose=False) + .data) + .max()) * scalings[ch_type] + if ch_type == 'grad': + vmin[ch_type] = 0 + else: + vmin[ch_type] = -vmax[ch_type] + + if not (vmin and vmax): # we only had EEG data and no digpoints + return # No need to warn here, we did that above + else: + topomap_kwargs = self._validate_topomap_kwargs(topomap_kwargs) + parallel, p_fun, n_jobs = parallel_func( + func=self._plot_one_evoked_topomap_timepoint, + n_jobs=n_jobs, max_jobs=len(times), + ) + fig_arrays = parallel( + p_fun( + evoked=evoked, time=time, ch_types=ch_types, + vmin=vmin, vmax=vmax, topomap_kwargs=topomap_kwargs + ) for time in times + ) + + captions = [f'Time point: {round(t, 3):0.3f} s' for t in times] + self._add_slider( + figs=fig_arrays, + imgs=None, + captions=captions, + title='Topographies', + image_format=image_format, + start_idx=t_zero_idx, + section=section, + tags=tags, + replace=replace, + ) + + def _add_evoked_gfp( + self, *, evoked, ch_types, image_format, section, tags, replace + ): + # Make legend labels shorter by removing the multiplicative factors + pattern = r'\d\.\d* × ' + label = evoked.comment + if label is None: + label = '' + for match in re.findall(pattern=pattern, string=label): + label = label.replace(match, '') + + import matplotlib.pyplot as plt + fig, ax = plt.subplots(len(ch_types), 1, sharex=True) + if len(ch_types) == 1: + ax = [ax] + for idx, ch_type in enumerate(ch_types): + plot_compare_evokeds( + evokeds={ + label: evoked.copy().pick(ch_type, verbose=False) + }, + ci=None, truncate_xaxis=False, + truncate_yaxis=False, legend=False, + axes=ax[idx], show=False + ) + ax[idx].set_title(ch_type) + + # Hide x axis label for all but the last subplot + if idx < len(ch_types) - 1: + ax[idx].set_xlabel(None) + + tight_layout(fig=fig) + _constrain_fig_resolution( + fig, max_width=MAX_IMG_WIDTH, max_res=MAX_IMG_RES + ) + title = 'Global field power' + self._add_figure( + fig=fig, + title=title, + caption=None, + image_format=image_format, + section=section, + tags=tags, + replace=replace, + own_figure=True, + ) + + def _add_evoked_whitened( + self, *, evoked, noise_cov, image_format, section, tags, replace + ): + """Render whitened evoked.""" + fig = evoked.plot_white( + noise_cov=noise_cov, + show=False + ) + tight_layout(fig=fig) + _constrain_fig_resolution( + fig, max_width=MAX_IMG_WIDTH, max_res=MAX_IMG_RES + ) + title = 'Whitened' + + self._add_figure( + fig=fig, + title=title, + caption=None, + image_format=image_format, + tags=tags, + section=section, + replace=replace, + own_figure=True, + ) + + def _add_evoked( + self, *, evoked, noise_cov, add_projs, n_time_points, + image_format, section, tags, topomap_kwargs, n_jobs, replace + ): + ch_types = _get_ch_types(evoked) + self._add_evoked_joint( + evoked=evoked, ch_types=ch_types, + image_format=image_format, section=section, tags=tags, + topomap_kwargs=topomap_kwargs, replace=replace, + ) + self._add_evoked_topomap_slider( + evoked=evoked, ch_types=ch_types, + n_time_points=n_time_points, + image_format=image_format, + section=section, tags=tags, topomap_kwargs=topomap_kwargs, + n_jobs=n_jobs, replace=replace, + ) + self._add_evoked_gfp( + evoked=evoked, ch_types=ch_types, image_format=image_format, + section=section, tags=tags, replace=replace, + ) + + if noise_cov is not None: + self._add_evoked_whitened( + evoked=evoked, + noise_cov=noise_cov, + image_format=image_format, + section=section, + tags=tags, + replace=replace, + ) + + # SSP projectors + if add_projs: + self._add_projs( + info=evoked, + projs=None, + title='Projectors', + image_format=image_format, + section=section, + tags=tags, + topomap_kwargs=topomap_kwargs, + replace=replace, + ) + + logger.debug('Evoked: done') + + def _add_events( + self, *, events, event_id, sfreq, first_samp, title, section, + image_format, tags, replace + ): + """Render events.""" + if not isinstance(events, np.ndarray): + events = read_events(filename=events) + + fig = plot_events( + events=events, + event_id=event_id, + sfreq=sfreq, + first_samp=first_samp, + show=False + ) + _constrain_fig_resolution( + fig, max_width=MAX_IMG_WIDTH, max_res=MAX_IMG_RES + ) + self._add_figure( + fig=fig, + title=title, + caption=None, + image_format=image_format, + tags=tags, + section=section, + replace=replace, + own_figure=True, + ) + + def _add_epochs_psd( + self, *, epochs, psd, image_format, tags, section, replace + ): + epoch_duration = epochs.tmax - epochs.tmin + + if psd is True: # Entire time range -> all epochs + epochs_for_psd = epochs # Avoid creating a copy + else: # Only a subset of epochs + signal_duration = len(epochs) * epoch_duration + n_epochs_required = int( + np.ceil(psd / epoch_duration) + ) + if n_epochs_required > len(epochs): + raise ValueError( + f'You requested to calculate PSD on a duration of ' + f'{psd:.3f} sec, but all your epochs ' + f'are only {signal_duration:.1f} sec long' + ) + epochs_idx = np.round( + np.linspace( + start=0, + stop=len(epochs) - 1, + num=n_epochs_required + ) + ).astype(int) + # Edge case: there might be duplicate indices due to rounding? + epochs_idx_unique = np.unique(epochs_idx) + if len(epochs_idx_unique) != len(epochs_idx): + duration = round( + len(epochs_idx_unique) * epoch_duration, 1 + ) + warn(f'Using {len(epochs_idx_unique)} epochs, only ' + f'covering {duration:.1f} sec of data') + del duration + + epochs_for_psd = epochs[epochs_idx_unique] + + if epochs.info['lowpass'] is None: + fmax = np.inf + else: + fmax = epochs.info['lowpass'] + 15 + # Must not exceed half the sampling frequency + if fmax > 0.5 * epochs.info['sfreq']: + fmax = np.inf + + fig = epochs_for_psd.plot_psd(fmax=fmax, show=False) + _constrain_fig_resolution( + fig, max_width=MAX_IMG_WIDTH, max_res=MAX_IMG_RES + ) + duration = round(epoch_duration * len(epochs_for_psd), 1) + caption = ( + f'PSD calculated from {len(epochs_for_psd)} epochs ' + f'({duration:.1f} sec).' + ) + self._add_figure( + fig=fig, + image_format=image_format, + title='PSD', + caption=caption, + tags=tags, + section=section, + replace=replace, + own_figure=True, + ) + + def _add_html_element( + self, *, html, title, tags, section, replace, div_klass + ): + dom_id = self._get_dom_id() + html = _html_element( + div_klass=div_klass, + id=dom_id, + tags=tags, + title=title, + html=html + ) + self._add_or_replace( + name=title, + section=section, + dom_id=dom_id, + tags=tags, + html=html, + replace=replace + ) + + def _add_epochs_metadata(self, *, epochs, section, tags, replace): + metadata = epochs.metadata.copy() + + # Ensure we have a named index + if not metadata.index.name: + metadata.index.name = 'Epoch #' + + assert metadata.index.is_unique + index_name = metadata.index.name # store for later use + metadata = metadata.reset_index() # We want "proper" columns only + html = metadata.to_html( + border=0, + index=False, + show_dimensions=True, + justify='unset', + float_format=lambda x: f'{round(x, 3):.3f}', + classes='table table-hover table-striped ' + 'table-sm table-responsive small' + ) + del metadata + + # Massage the table such that it woks nicely with bootstrap-table + htmls = html.split('\n') + header_pattern = '(.*)' + + for idx, html in enumerate(htmls): + if '' + ) + continue + + col_headers = re.findall(pattern=header_pattern, string=html) + if col_headers: + # Make columns sortable + assert len(col_headers) == 1 + col_header = col_headers[0] + htmls[idx] = html.replace( + '', + f'' + ) + + html = '\n'.join(htmls) + self._add_html_element( + div_klass='epochs', + tags=tags, + title='Metadata', + html=html, + section=section, + replace=replace, + ) + + def _add_epochs( + self, *, epochs, psd, add_projs, topomap_kwargs, drop_log_ignore, + image_format, section, tags, replace + ): + """Render epochs.""" + if isinstance(epochs, BaseEpochs): + fname = epochs.filename + else: + fname = epochs + epochs = read_epochs(fname, preload=False) + + # Summary table + self._add_html_repr( + inst=epochs, + title='Info', + tags=tags, + section=section, + replace=replace, + div_klass='epochs', + ) + + # Metadata table + if epochs.metadata is not None: + self._add_epochs_metadata( + epochs=epochs, tags=tags, section=section, replace=replace + ) + + # ERP/ERF image(s) + ch_types = _get_ch_types(epochs) + epochs.load_data() + + for ch_type in ch_types: + with use_log_level(False): + figs = epochs.copy().pick(ch_type, verbose=False).plot_image( + show=False + ) + + assert len(figs) == 1 + fig = figs[0] + _constrain_fig_resolution( + fig, max_width=MAX_IMG_WIDTH, max_res=MAX_IMG_RES + ) + if ch_type in ('mag', 'grad'): + title_start = 'ERF image' + else: + assert 'eeg' in ch_type + title_start = 'ERP image' + + title = (f'{title_start} ' + f'({_handle_default("titles")[ch_type]})') + + self._add_figure( + fig=fig, + title=title, + caption=None, + image_format=image_format, + tags=tags, + section=section, + replace=replace, + own_figure=True, + ) + + # Drop log + if epochs._bad_dropped: + title = 'Drop log' + + if epochs.drop_log_stats(ignore=drop_log_ignore) == 0: # No drops + self._add_html_element( + html='No epochs exceeded the rejection thresholds. ' + 'Nothing was dropped.', + div_klass='epochs', + title=title, + tags=tags, + section=section, + replace=replace, + ) + else: + fig = epochs.plot_drop_log( + subject=self.subject, ignore=drop_log_ignore, show=False + ) + tight_layout(fig=fig) + _constrain_fig_resolution( + fig, max_width=MAX_IMG_WIDTH, max_res=MAX_IMG_RES + ) + self._add_figure( + fig=fig, + image_format=image_format, + title=title, + caption=None, + tags=tags, + section=section, + replace=replace, + own_figure=True, + ) + + if psd: + self._add_epochs_psd( + epochs=epochs, psd=psd, image_format=image_format, tags=tags, + section=section, replace=replace + ) + if add_projs: + self._add_projs( + info=epochs, + projs=None, + title='Projections', + image_format=image_format, + tags=tags, + topomap_kwargs=topomap_kwargs, + section=section, + replace=replace, + ) + + def _add_cov(self, *, cov, info, image_format, section, tags, replace): + """Render covariance matrix & SVD.""" + if not isinstance(cov, Covariance): + cov = read_cov(cov) + if not isinstance(info, Info): + info = read_info(info) + + fig_cov, fig_svd = plot_cov(cov=cov, info=info, show=False, + show_svd=True) + figs = [fig_cov, fig_svd] + titles = ( + 'Covariance matrix', + 'Singular values' + ) + for fig, title in zip(figs, titles): + _constrain_fig_resolution( + fig, max_width=MAX_IMG_WIDTH, max_res=MAX_IMG_RES + ) + self._add_figure( + fig=fig, + title=title, + caption=None, + image_format=image_format, + tags=tags, + section=section, + replace=replace, + own_figure=True, + ) + + def _add_trans( + self, *, trans, info, subject, subjects_dir, alpha, title, section, + tags, replace + ): + """Render trans (only PNG).""" + if not isinstance(trans, Transform): + trans = read_trans(trans) + if not isinstance(info, Info): + info = read_info(info) + + kwargs = dict(info=info, trans=trans, subject=subject, + subjects_dir=subjects_dir, dig=True, + meg=['helmet', 'sensors'], show_axes=True, + coord_frame='mri') + img, caption = _iterate_trans_views( + function=plot_alignment, alpha=alpha, **kwargs + ) + self._add_image( + img=img, + title=title, + section=section, + caption=caption, + image_format='png', + tags=tags, + replace=replace, + ) + + def _add_stc( + self, *, stc, title, subject, subjects_dir, n_time_points, + image_format, section, tags, stc_plot_kwargs, replace, + ): + """Render STC.""" + if isinstance(stc, SourceEstimate): + if subject is None: + subject = self.subject # supplied during Report init + if not subject: + subject = stc.subject # supplied when loading STC + if not subject: + raise ValueError( + 'Please specify the subject name, as it cannot ' + 'be found in stc.subject. You may wish to pass ' + 'the "subject" parameter to read_source_estimate()' + ) + else: + subject = subject + else: + fname = stc + stc = read_source_estimate(fname=fname, subject=subject) + + subjects_dir = (self.subjects_dir if subjects_dir is None + else subjects_dir) + + if n_time_points is None: + n_time_points = min(len(stc.times), 51) + elif n_time_points > len(stc.times): + raise ValueError( + f'The requested number of time points ({n_time_points}) ' + f'exceeds the time points in the provided STC object ' + f'({len(stc.times)})' + ) + if n_time_points == 1: # only a single time point, pick the first one + times = [stc.times[0]] + else: + times = np.linspace( + start=stc.times[0], + stop=stc.times[-1], + num=n_time_points + ) + t_zero_idx = np.abs(times).argmin() # index of time closest to zero + + # Plot using 3d backend if available, and use Matplotlib + # otherwise. + import matplotlib.pyplot as plt + stc_plot_kwargs = _handle_default( + 'report_stc_plot_kwargs', stc_plot_kwargs + ) + stc_plot_kwargs.update(subject=subject, subjects_dir=subjects_dir) + + if get_3d_backend() is not None: + brain = stc.plot(**stc_plot_kwargs) + brain._renderer.plotter.subplot(0, 0) + backend_is_3d = True + else: + backend_is_3d = False + + figs = [] + for t in times: + with warnings.catch_warnings(): + warnings.filterwarnings( + action='ignore', + message='More than 20 figures have been opened', + category=RuntimeWarning) + + if backend_is_3d: + brain.set_time(t) + fig, ax = plt.subplots(figsize=(4.5, 4.5)) + ax.imshow(brain.screenshot(time_viewer=True, mode='rgb')) + ax.axis('off') + tight_layout(fig=fig) + _constrain_fig_resolution( + fig, + max_width=stc_plot_kwargs['size'][0], + max_res=MAX_IMG_RES + ) + figs.append(fig) + plt.close(fig) + else: + fig_lh = plt.figure() + fig_rh = plt.figure() + + brain_lh = stc.plot( + views='lat', hemi='lh', + initial_time=t, + backend='matplotlib', + subject=subject, + subjects_dir=subjects_dir, + figure=fig_lh + ) + brain_rh = stc.plot( + views='lat', hemi='rh', + initial_time=t, + subject=subject, + subjects_dir=subjects_dir, + backend='matplotlib', + figure=fig_rh + ) + tight_layout(fig=fig_lh) # TODO is this necessary? + tight_layout(fig=fig_rh) # TODO is this necessary? + _constrain_fig_resolution( + fig_lh, + max_width=stc_plot_kwargs['size'][0], + max_res=MAX_IMG_RES + ) + _constrain_fig_resolution( + fig_rh, + max_width=stc_plot_kwargs['size'][0], + max_res=MAX_IMG_RES + ) + figs.append(brain_lh) + figs.append(brain_rh) + plt.close(fig_lh) + plt.close(fig_rh) + + if backend_is_3d: + brain.close() + else: + brain_lh.close() + brain_rh.close() + + captions = [f'Time point: {round(t, 3):0.3f} s' for t in times] + self._add_slider( + figs=figs, + imgs=None, + captions=captions, + title=title, + image_format=image_format, + start_idx=t_zero_idx, + section=section, + tags=tags, + replace=replace, + ) + + def _add_bem( + self, *, subject, subjects_dir, decim, n_jobs, width=512, + image_format, section, tags, replace + ): + """Render mri+bem (only PNG).""" + if subjects_dir is None: + subjects_dir = self.subjects_dir + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + + # Get the MRI filename + mri_fname = op.join(subjects_dir, subject, 'mri', 'T1.mgz') + if not op.isfile(mri_fname): + warn(f'MRI file "{mri_fname}" does not exist') + + # Get the BEM surface filenames + bem_path = op.join(subjects_dir, subject, 'bem') + + surfaces = _get_bem_plotting_surfaces(bem_path) + if not surfaces: + warn('No BEM surfaces found, rendering empty MRI') + + htmls = dict() + for orientation in _BEM_VIEWS: + htmls[orientation] = self._render_one_bem_axis( + mri_fname=mri_fname, surfaces=surfaces, + orientation=orientation, decim=decim, n_jobs=n_jobs, + width=width, image_format=image_format, + tags=tags + ) + + # Special handling to deal with our tests, where we monkey-patch + # _BEM_VIEWS to save time + html_slider_axial = htmls['axial'] if 'axial' in htmls else '' + html_slider_sagittal = htmls['sagittal'] if 'sagittal' in htmls else '' + html_slider_coronal = htmls['coronal'] if 'coronal' in htmls else '' + + dom_id = self._get_dom_id() + html = _html_bem_element( + id=dom_id, + div_klass='bem', + html_slider_axial=html_slider_axial, + html_slider_sagittal=html_slider_sagittal, + html_slider_coronal=html_slider_coronal, + tags=tags, + title=section, + ) + self._add_or_replace( + name=section, + section=None, # avoid nesting + dom_id=dom_id, + tags=tags, + html=html, + replace=replace, + ) + + +def _clean_tags(tags): + if isinstance(tags, str): + tags = (tags,) + + # Replace any whitespace characters with dashes + tags_cleaned = tuple(re.sub(r'[\s*]', '-', tag) for tag in tags) + return tags_cleaned + + +def _recursive_search(path, pattern): + """Auxiliary function for recursive_search of the directory.""" + filtered_files = list() + for dirpath, dirnames, files in os.walk(path): + for f in fnmatch.filter(files, pattern): + # only the following file types are supported + # this ensures equitable distribution of jobs + if f.endswith(VALID_EXTENSIONS): + filtered_files.append(op.realpath(op.join(dirpath, f))) + + return filtered_files + + +############################################################################### +# Scraper for sphinx-gallery + +_SCRAPER_TEXT = ''' +.. only:: builder_html + + .. container:: row + + .. rubric:: The `HTML document <{0}>`__ written by :meth:`mne.Report.save`: + + .. raw:: html + + + +''' # noqa: E501 +# Adapted from fa-file-code +_FA_FILE_CODE = '' # noqa: E501 + + +class _ReportScraper(object): + """Scrape Report outputs. + + Only works properly if conf.py is configured properly and the file + is written to the same directory as the example script. + """ + + def __init__(self): + self.app = None + self.files = dict() + + def __repr__(self): + return '' + + def __call__(self, block, block_vars, gallery_conf): + for report in block_vars['example_globals'].values(): + if (isinstance(report, Report) and + report.fname is not None and + report.fname.endswith('.html') and + gallery_conf['builder_name'] == 'html'): + # Thumbnail + image_path_iterator = block_vars['image_path_iterator'] + img_fname = next(image_path_iterator) + img_fname = img_fname.replace('.png', '.svg') + with open(img_fname, 'w') as fid: + fid.write(_FA_FILE_CODE) + # copy HTML file + html_fname = op.basename(report.fname) + out_dir = op.join( + self.app.builder.outdir, + op.relpath(op.dirname(block_vars['target_file']), + self.app.builder.srcdir)) + os.makedirs(out_dir, exist_ok=True) + out_fname = op.join(out_dir, html_fname) + assert op.isfile(report.fname) + self.files[report.fname] = out_fname + # embed links/iframe + data = _SCRAPER_TEXT.format(html_fname) + return data + return '' + + def copyfiles(self, *args, **kwargs): + for key, value in self.files.items(): + copyfile(key, value) diff -Nru python-mne-0.23.4+dfsg/mne/report/tests/test_report.py python-mne-1.1.0+dfsg/mne/report/tests/test_report.py --- python-mne-0.23.4+dfsg/mne/report/tests/test_report.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/report/tests/test_report.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,950 @@ +# -*- coding: utf-8 -*- +# Authors: Mainak Jas +# Teon Brooks +# +# License: BSD-3-Clause + +from pathlib import Path +import base64 +import copy +import glob +import pickle +from io import BytesIO +import os +import os.path as op +import re +import shutil + +import numpy as np +import pytest +from matplotlib import pyplot as plt + +from mne import (Epochs, read_events, read_evokeds, read_cov, + pick_channels_cov, create_info) +from mne.report import report as report_mod +from mne.report.report import CONTENT_ORDER +from mne.io import read_raw_fif, read_info, RawArray +from mne.datasets import testing +from mne.report import Report, open_report, _ReportScraper, report +from mne.utils import (requires_nibabel, Bunch, requires_version, + requires_sklearn) +from mne.viz import plot_alignment +from mne.io.write import DATE_NONE +from mne.preprocessing import ICA +from mne.epochs import make_metadata + + +data_dir = testing.data_path(download=False) +subjects_dir = op.join(data_dir, 'subjects') +sample_meg_dir = op.join(data_dir, 'MEG', 'sample') +raw_fname = op.join(sample_meg_dir, 'sample_audvis_trunc_raw.fif') +ms_fname = op.join(data_dir, 'SSS', 'test_move_anon_raw.fif') +events_fname = op.join(sample_meg_dir, 'sample_audvis_trunc_raw-eve.fif') +evoked_fname = op.join(sample_meg_dir, 'sample_audvis_trunc-ave.fif') +cov_fname = op.join(sample_meg_dir, 'sample_audvis_trunc-cov.fif') +ecg_proj_fname = op.join(sample_meg_dir, 'sample_audvis_ecg-proj.fif') +eog_proj_fname = op.join(sample_meg_dir, 'sample_audvis_eog-proj.fif') +fwd_fname = op.join( + sample_meg_dir, 'sample_audvis_trunc-meg-eeg-oct-6-fwd.fif' +) +trans_fname = op.join(sample_meg_dir, 'sample_audvis_trunc-trans.fif') +inv_fname = op.join( + sample_meg_dir, 'sample_audvis_trunc-meg-eeg-oct-6-meg-inv.fif' +) +stc_fname = op.join(sample_meg_dir, 'sample_audvis_trunc-meg') +mri_fname = op.join(subjects_dir, 'sample', 'mri', 'T1.mgz') +bdf_fname = op.realpath(op.join(op.dirname(__file__), '..', '..', 'io', + 'edf', 'tests', 'data', 'test.bdf')) +edf_fname = op.realpath(op.join(op.dirname(__file__), '..', '..', 'io', + 'edf', 'tests', 'data', 'test.edf')) + +base_dir = op.realpath(op.join(op.dirname(__file__), '..', '..', 'io', 'tests', + 'data')) +evoked_fname = op.join(base_dir, 'test-ave.fif') + +nirs_fname = op.join(data_dir, 'SNIRF', 'NIRx', 'NIRSport2', '1.0.3', + '2021-05-05_001.snirf') +stc_plot_kwargs = dict( # for speed + smoothing_steps=1, size=(300, 300), views='lat', hemi='lh') +topomap_kwargs = dict(res=8, contours=0, sensors=False) + + +def _get_example_figures(): + """Create two example figures.""" + fig1 = plt.plot([1, 2], [1, 2])[0].figure + fig2 = plt.plot([3, 4], [3, 4])[0].figure + return [fig1, fig2] + + +@pytest.fixture +def invisible_fig(monkeypatch): + """Make objects invisible to speed up draws.""" + orig = report._fig_to_img + + def _make_invisible(fig, **kwargs): + if isinstance(fig, plt.Figure): + for ax in fig.axes: + for attr in ('lines', 'collections', 'patches', 'images', + 'texts'): + for item in getattr(ax, attr): + item.set_visible(False) + ax.axis('off') + return orig(fig, **kwargs) + + monkeypatch.setattr(report, '_fig_to_img', _make_invisible) + yield + + +@pytest.mark.slowtest +@testing.requires_testing_data +def test_render_report(renderer_pyvistaqt, tmp_path, invisible_fig): + """Test rendering *.fif files for mne report.""" + tempdir = str(tmp_path) + raw_fname_new = op.join(tempdir, 'temp_raw.fif') + raw_fname_new_bids = op.join(tempdir, 'temp_meg.fif') + ms_fname_new = op.join(tempdir, 'temp_ms_raw.fif') + event_fname_new = op.join(tempdir, 'temp_raw-eve.fif') + cov_fname_new = op.join(tempdir, 'temp_raw-cov.fif') + proj_fname_new = op.join(tempdir, 'temp_ecg-proj.fif') + fwd_fname_new = op.join(tempdir, 'temp_raw-fwd.fif') + inv_fname_new = op.join(tempdir, 'temp_raw-inv.fif') + nirs_fname_new = op.join(tempdir, 'temp_raw-nirs.snirf') + for a, b in [[raw_fname, raw_fname_new], + [raw_fname, raw_fname_new_bids], + [ms_fname, ms_fname_new], + [events_fname, event_fname_new], + [cov_fname, cov_fname_new], + [ecg_proj_fname, proj_fname_new], + [fwd_fname, fwd_fname_new], + [inv_fname, inv_fname_new], + [nirs_fname, nirs_fname_new]]: + shutil.copyfile(a, b) + + # create and add -epo.fif and -ave.fif files + epochs_fname = op.join(tempdir, 'temp-epo.fif') + evoked_fname = op.join(tempdir, 'temp-ave.fif') + # Speed it up by picking channels + raw = read_raw_fif(raw_fname_new) + raw.pick_channels(['MEG 0111', 'MEG 0121', 'EEG 001', 'EEG 002']) + raw.del_proj() + raw.set_eeg_reference(projection=True).load_data() + epochs = Epochs(raw, read_events(events_fname), 1, -0.2, 0.2) + epochs.save(epochs_fname, overwrite=True) + # This can take forever, so let's make it fast + # Also, make sure crop range is wide enough to avoid rendering bug + evoked = epochs.average() + with pytest.warns(RuntimeWarning, match='tmax is not in time interval'): + evoked.crop(0.1, 0.2) + evoked.save(evoked_fname) + + report = Report(info_fname=raw_fname_new, subjects_dir=subjects_dir, + projs=False, image_format='png') + with pytest.warns(RuntimeWarning, match='Cannot render MRI'): + report.parse_folder(data_path=tempdir, on_error='raise', + n_time_points_evokeds=2, raw_butterfly=False, + stc_plot_kwargs=stc_plot_kwargs, + topomap_kwargs=topomap_kwargs) + assert repr(report) + + # Check correct paths and filenames + fnames = glob.glob(op.join(tempdir, '*.fif')) + fnames.extend(glob.glob(op.join(tempdir, '*.snirf'))) + + titles = [op.basename(x) for x in fnames if not x.endswith('-ave.fif')] + titles.append(f'{op.basename(evoked_fname)}: {evoked.comment}') + + _, _, content_titles, _ = report._content_as_html() + for title in titles: + assert title in content_titles + assert (''.join(report.html).find(title) != -1) + + assert len(content_titles) == len(fnames) + + # Check saving functionality + report.data_path = tempdir + fname = op.join(tempdir, 'report.html') + report.save(fname=fname, open_browser=False) + assert (op.isfile(fname)) + html = Path(fname).read_text(encoding='utf-8') + # Evoked in `evoked_fname` + assert f'{op.basename(evoked_fname)}: {evoked.comment}' in html + assert 'Topographies' in html + assert 'Global field power' in html + + # Check saving same report to new filename + report.save(fname=op.join(tempdir, 'report2.html'), open_browser=False) + assert (op.isfile(op.join(tempdir, 'report2.html'))) + + # Check overwriting file + report.save(fname=op.join(tempdir, 'report.html'), open_browser=False, + overwrite=True) + assert (op.isfile(op.join(tempdir, 'report.html'))) + + # Check pattern matching with multiple patterns + pattern = ['*proj.fif', '*eve.fif'] + with pytest.warns(RuntimeWarning, match='Cannot render MRI'): + report.parse_folder(data_path=tempdir, pattern=pattern, + raw_butterfly=False) + assert (repr(report)) + + fnames = glob.glob(op.join(tempdir, '*.raw')) + \ + glob.glob(op.join(tempdir, '*.raw')) + + content_names = [element.name for element in report._content] + for fname in fnames: + assert (op.basename(fname) in + [op.basename(x) for x in content_names]) + assert (''.join(report.html).find(op.basename(fname)) != -1) + + with pytest.raises(ValueError, match='Invalid value'): + Report(image_format='foo') + with pytest.raises(ValueError, match='Invalid value'): + Report(image_format=None) + + # ndarray support smoke test + report.add_figure(fig=np.zeros((2, 3, 3)), title='title') + + with pytest.raises(TypeError, match='It seems you passed a path'): + report.add_figure(fig='foo', title='title') + with pytest.raises(TypeError, match='.*MNEQtBrowser.*Figure3D.*got.*'): + report.add_figure(fig=1., title='title') + + +def test_render_mne_qt_browser(tmp_path, browser_backend): + """Test adding a mne_qt_browser (and matplotlib) raw plot.""" + report = Report() + info = create_info(1, 1000., 'eeg') + data = np.zeros((1, 1000)) + raw = RawArray(data, info) + fig = raw.plot() + name = fig.__class__.__name__ + if browser_backend.name == 'matplotlib': + assert 'MNEBrowseFigure' in name + else: + assert 'MNEQtBrowser' in name or 'PyQtGraphBrowser' in name + report.add_figure(fig, title='raw') + + +@testing.requires_testing_data +def test_render_report_extra(renderer_pyvistaqt, tmp_path, invisible_fig): + """Test SVG and projector rendering separately.""" + # ... otherwise things are very slow + tempdir = str(tmp_path) + raw_fname_new = op.join(tempdir, 'temp_raw.fif') + shutil.copyfile(raw_fname, raw_fname_new) + report = Report(info_fname=raw_fname_new, subjects_dir=subjects_dir, + projs=True, image_format='svg') + with pytest.warns(RuntimeWarning, match='Cannot render MRI'): + report.parse_folder(data_path=tempdir, on_error='raise', + n_time_points_evokeds=2, raw_butterfly=False, + stc_plot_kwargs=stc_plot_kwargs, + topomap_kwargs=topomap_kwargs) + assert repr(report) + report.data_path = tempdir + fname = op.join(tempdir, 'report.html') + report.save(fname=fname, open_browser=False) + assert op.isfile(fname) + html = Path(fname).read_text(encoding='utf-8') + # Projectors in Raw.info + assert 'Projectors' in html + + +def test_add_custom_css(tmp_path): + """Test adding custom CSS rules to the report.""" + tempdir = str(tmp_path) + fname = op.join(tempdir, 'report.html') + fig = plt.figure() # Empty figure + + report = Report() + report.add_figure(fig=fig, title='Test section') + custom_css = '.report_custom { color: red; }' + report.add_custom_css(css=custom_css) + + assert custom_css in report.include + report.save(fname, open_browser=False) + html = Path(fname).read_text(encoding='utf-8') + assert custom_css in html + + +def test_add_custom_js(tmp_path): + """Test adding custom JavaScript to the report.""" + tempdir = str(tmp_path) + fname = op.join(tempdir, 'report.html') + fig = plt.figure() # Empty figure + + report = Report() + report.add_figure(fig=fig, title='Test section') + custom_js = ('function hello() {\n' + ' alert("Hello, report!");\n' + '}') + report.add_custom_js(js=custom_js) + + assert custom_js in report.include + report.save(fname, open_browser=False) + html = Path(fname).read_text(encoding='utf-8') + assert custom_js in html + + +@testing.requires_testing_data +def test_render_non_fiff(tmp_path): + """Test rendering non-FIFF files for mne report.""" + tempdir = str(tmp_path) + fnames_in = [bdf_fname, edf_fname] + fnames_out = [] + for fname in fnames_in: + basename = op.basename(fname) + basename, ext = op.splitext(basename) + fname_out = f'{basename}_raw{ext}' + outpath = op.join(tempdir, fname_out) + shutil.copyfile(fname, outpath) + fnames_out.append(fname_out) + + report = Report() + report.parse_folder(data_path=tempdir, render_bem=False, on_error='raise', + raw_butterfly=False) + + # Check correct paths and filenames + _, _, content_titles, _ = report._content_as_html() + for fname in content_titles: + assert (op.basename(fname) in + [op.basename(x) for x in content_titles]) + + assert len(content_titles) == len(fnames_out) + + report.data_path = tempdir + fname = op.join(tempdir, 'report.html') + report.save(fname=fname, open_browser=False) + html = Path(fname).read_text(encoding='utf-8') + + assert 'test_raw.bdf' in html + assert 'test_raw.edf' in html + + +@testing.requires_testing_data +def test_report_raw_psd_and_date(tmp_path): + """Test report raw PSD and DATE_NONE functionality.""" + with pytest.raises(TypeError, match='dict'): + Report(raw_psd='foo') + + tempdir = str(tmp_path) + raw = read_raw_fif(raw_fname).crop(0, 1.).load_data() + raw.info['experimenter'] = 'mne test' + raw.info['subject_info'] = dict(id=123, his_id='sample') + + raw_fname_new = op.join(tempdir, 'temp_raw.fif') + raw.save(raw_fname_new) + report = Report(raw_psd=True) + report.parse_folder(data_path=tempdir, render_bem=False, + on_error='raise', raw_butterfly=False) + assert isinstance(report.html, list) + assert 'PSD' in ''.join(report.html) + assert 'Unknown' not in ''.join(report.html) + assert 'GMT' in ''.join(report.html) + + # test new anonymize functionality + report = Report() + raw.anonymize() + raw.save(raw_fname_new, overwrite=True) + report.parse_folder(data_path=tempdir, render_bem=False, + on_error='raise', raw_butterfly=False) + assert isinstance(report.html, list) + assert 'Unknown' not in ''.join(report.html) + + # DATE_NONE functionality + report = Report() + # old style (pre 0.20) date anonymization + with raw.info._unlock(): + raw.info['meas_date'] = None + for key in ('file_id', 'meas_id'): + value = raw.info.get(key) + if value is not None: + assert 'msecs' not in value + value['secs'] = DATE_NONE[0] + value['usecs'] = DATE_NONE[1] + raw.save(raw_fname_new, overwrite=True) + report.parse_folder(data_path=tempdir, render_bem=False, + on_error='raise', raw_butterfly=False) + assert isinstance(report.html, list) + assert 'Unknown' in ''.join(report.html) + + +@pytest.mark.slowtest # slow on Azure +@testing.requires_testing_data +def test_render_add_sections(renderer, tmp_path): + """Test adding figures/images to section.""" + from pyvista.plotting import plotting + tempdir = str(tmp_path) + report = Report(subjects_dir=subjects_dir) + # Check add_figure functionality + plt.close('all') + assert len(plt.get_fignums()) == 0 + fig = plt.plot([1, 2], [1, 2])[0].figure + assert len(plt.get_fignums()) == 1 + + report.add_figure(fig=fig, title='evoked response', image_format='svg') + assert 'caption' not in report._content[-1].html + assert len(plt.get_fignums()) == 1 + + report.add_figure(fig=fig, title='evoked with caption', caption='descr') + assert 'caption' in report._content[-1].html + assert len(plt.get_fignums()) == 1 + + # Check add_image with png + img_fname = op.join(tempdir, 'testimage.png') + fig.savefig(img_fname) + report.add_image(image=img_fname, title='evoked response') + + with pytest.raises(FileNotFoundError, match='No such file or directory'): + report.add_image(image='foobar.xxx', title='H') + + evoked = read_evokeds(evoked_fname, condition='Left Auditory', + baseline=(-0.2, 0.0)) + n_before = len(plotting._ALL_PLOTTERS) + fig = plot_alignment(evoked.info, trans_fname, subject='sample', + subjects_dir=subjects_dir) + n_after = n_before + 1 + assert n_after == len(plotting._ALL_PLOTTERS) + + report.add_figure(fig=fig, title='random image') + assert n_after == len(plotting._ALL_PLOTTERS) # not closed + assert (repr(report)) + fname = op.join(str(tmp_path), 'test.html') + report.save(fname, open_browser=False) + + assert len(report) == 4 + + +@pytest.mark.slowtest +@testing.requires_testing_data +@requires_nibabel() +def test_render_mri(renderer, tmp_path): + """Test rendering MRI for mne report.""" + tempdir = str(tmp_path) + trans_fname_new = op.join(tempdir, 'temp-trans.fif') + for a, b in [[trans_fname, trans_fname_new]]: + shutil.copyfile(a, b) + report = Report(info_fname=raw_fname, + subject='sample', subjects_dir=subjects_dir) + report.parse_folder(data_path=tempdir, mri_decim=30, pattern='*') + fname = op.join(tempdir, 'report.html') + report.save(fname, open_browser=False) + html = Path(fname).read_text(encoding='utf-8') + assert 'data-mne-tags=" bem "' in html + assert repr(report) + report.add_bem(subject='sample', title='extra', tags=('foo',), + subjects_dir=subjects_dir, decim=30) + report.save(fname, open_browser=False, overwrite=True) + html = Path(fname).read_text(encoding='utf-8') + assert 'data-mne-tags=" bem "' in html + assert 'data-mne-tags=" foo "' in html + + +@testing.requires_testing_data +@requires_nibabel() +@pytest.mark.parametrize('n_jobs', [ + 1, + pytest.param(2, marks=pytest.mark.slowtest), # 1.5 sec locally +]) +@pytest.mark.filterwarnings('ignore:No contour levels were.*:UserWarning') +def test_add_bem_n_jobs(n_jobs, monkeypatch): + """Test add_bem with n_jobs.""" + if n_jobs == 1: # in one case, do at init -- in the other, pass in + use_subjects_dir = None + else: + use_subjects_dir = subjects_dir + report = Report(subjects_dir=use_subjects_dir) + # implicitly test that subjects_dir is correctly preserved here + monkeypatch.setattr(report_mod, '_BEM_VIEWS', ('axial',)) + if use_subjects_dir is not None: + use_subjects_dir = None + report.add_bem( + subject='sample', title='sample', tags=('sample',), decim=15, + n_jobs=n_jobs, subjects_dir=subjects_dir + ) + assert len(report.html) == 1 + imgs = np.array([plt.imread(BytesIO(base64.b64decode(b)), 'png') + for b in re.findall(r'data:image/png;base64,(\S*)">', + report.html[0])]) + assert imgs.ndim == 4 # images, h, w, rgba + assert len(imgs) == 6 + imgs.shape = (len(imgs), -1) + norms = np.linalg.norm(imgs, axis=-1) + # should have down-up-down shape + corr = np.corrcoef(norms, np.hanning(len(imgs)))[0, 1] + assert 0.778 < corr < 0.80 + + +@testing.requires_testing_data +@requires_nibabel() +def test_render_mri_without_bem(tmp_path): + """Test rendering MRI without BEM for mne report.""" + tempdir = str(tmp_path) + os.mkdir(op.join(tempdir, 'sample')) + os.mkdir(op.join(tempdir, 'sample', 'mri')) + shutil.copyfile(mri_fname, op.join(tempdir, 'sample', 'mri', 'T1.mgz')) + report = Report(info_fname=raw_fname, + subject='sample', subjects_dir=tempdir) + with pytest.raises(RuntimeError, match='No matching files found'): + report.parse_folder(tempdir, render_bem=False) + with pytest.warns(RuntimeWarning, match='No BEM surfaces found'): + report.parse_folder(tempdir, render_bem=True, mri_decim=20) + assert 'BEM surfaces' in [element.name for element in report._content] + report.save(op.join(tempdir, 'report.html'), open_browser=False) + + +@testing.requires_testing_data +@requires_nibabel() +def test_add_html(): + """Test adding html str to mne report.""" + report = Report(info_fname=raw_fname, + subject='sample', subjects_dir=subjects_dir) + html = 'MNE-Python is AWESOME' + report.add_html(html=html, title='html') + assert (html in report.html[0]) + assert (repr(report)) + + +@testing.requires_testing_data +def test_multiple_figs(tmp_path): + """Test adding a slider with a series of figures to a Report.""" + tempdir = str(tmp_path) + report = Report(info_fname=raw_fname, + subject='sample', subjects_dir=subjects_dir) + figs = _get_example_figures() + report.add_figure(fig=figs, title='my title') + assert report._content[0].name == 'my title' + report.save(op.join(tempdir, 'report.html'), open_browser=False) + + with pytest.raises(ValueError): + report.add_figure(fig=figs, title='title', caption=['wug']) + + with pytest.raises(ValueError, + match='Number of captions.*must be equal to.*figures'): + report.add_figure(fig=figs, title='title', caption='wug') + + # Smoke test that SVG with unicode can be added + report = Report() + fig, ax = plt.subplots() + ax.set_xlabel('µ') + report.add_figure(fig=[fig] * 2, title='title', image_format='svg') + + +def test_validate_input(): + """Test Report input validation.""" + report = Report() + items = ['a', 'b', 'c'] + captions = ['Letter A', 'Letter B', 'Letter C'] + section = 'ABCs' + comments = ['First letter of the alphabet.', + 'Second letter of the alphabet', + 'Third letter of the alphabet'] + pytest.raises(ValueError, report._validate_input, items, captions[:-1], + section, comments=None) + pytest.raises(ValueError, report._validate_input, items, captions, section, + comments=comments[:-1]) + values = report._validate_input(items, captions, section, comments=None) + items_new, captions_new, comments_new = values + + +@requires_version('h5io') +def test_open_report(tmp_path): + """Test the open_report function.""" + tempdir = str(tmp_path) + hdf5 = op.join(tempdir, 'report.h5') + + # Test creating a new report through the open_report function + fig1 = _get_example_figures()[0] + this_sub_dir = tempdir + with open_report(hdf5, subjects_dir=this_sub_dir) as report: + assert report.subjects_dir == this_sub_dir + assert report.fname == hdf5 + report.add_figure(fig=fig1, title='evoked response') + # Exiting the context block should have triggered saving to HDF5 + assert op.exists(hdf5) + + # Load the HDF5 version of the report and check equivalence + report2 = open_report(hdf5) + assert report2.fname == hdf5 + assert report2.subjects_dir == report.subjects_dir + assert report2.html == report.html + assert report2.__getstate__() == report.__getstate__() + assert '_fname' not in report2.__getstate__() + + # Check parameters when loading a report + pytest.raises(ValueError, open_report, hdf5, foo='bar') # non-existing + pytest.raises(ValueError, open_report, hdf5, subjects_dir='foo') + open_report(hdf5, subjects_dir=this_sub_dir) # This should work + + # Check that the context manager doesn't swallow exceptions + with pytest.raises(ZeroDivisionError): + with open_report(hdf5, subjects_dir=this_sub_dir) as report: + 1 / 0 + + +def test_remove(): + """Test removing figures from a report.""" + r = Report() + fig1, fig2 = _get_example_figures() + r.add_figure(fig=fig1, title='figure1', tags=('slider',)) + r.add_figure(fig=[fig1, fig2], title='figure1', tags=('othertag',)) + r.add_figure(fig=fig2, title='figure1', tags=('slider',)) + r.add_figure(fig=fig2, title='figure2', tags=('slider',)) + + # Test removal by title + r2 = copy.deepcopy(r) + removed_index = r2.remove(title='figure1') + assert removed_index == 2 + assert len(r2.html) == 3 + assert r2.html[0] == r.html[0] + assert r2.html[1] == r.html[1] + assert r2.html[2] == r.html[3] + + # Test restricting to section + r2 = copy.deepcopy(r) + removed_index = r2.remove(title='figure1', tags=('othertag',)) + assert removed_index == 1 + assert len(r2.html) == 3 + assert r2.html[0] == r.html[0] + assert r2.html[1] == r.html[2] + assert r2.html[2] == r.html[3] + + +def test_add_or_replace(): + """Test replacing existing figures in a report.""" + r = Report() + fig1, fig2 = _get_example_figures() + r.add_figure(fig=fig1, title='duplicate', tags=('foo',)) + r.add_figure(fig=fig1, title='duplicate', tags=('foo',)) + r.add_figure(fig=fig1, title='duplicate', tags=('bar',)) + r.add_figure(fig=fig2, title='nonduplicate', tags=('foo',)) + # By default, replace=False, so all figures should be there + assert len(r.html) == 4 + + old_r = copy.deepcopy(r) + + # Replace last occurrence of `fig1` tagges as `foo` + r.add_figure( + fig=fig2, title='duplicate', tags=('foo',), replace=True + ) + assert len(r._content) == len(r.html) == 4 + assert r.html[1] != old_r.html[1] # This figure should have changed + # All other figures should be the same + assert r.html[0] == old_r.html[0] + assert r.html[2] == old_r.html[2] + assert r.html[3] == old_r.html[3] + + +def test_scraper(tmp_path): + """Test report scraping.""" + r = Report() + fig1, fig2 = _get_example_figures() + r.add_figure(fig=fig1, title='a') + r.add_figure(fig=fig2, title='b') + # Mock a Sphinx + sphinx_gallery config + app = Bunch(builder=Bunch(srcdir=str(tmp_path), + outdir=op.join(str(tmp_path), '_build', 'html'))) + scraper = _ReportScraper() + scraper.app = app + gallery_conf = dict(src_dir=app.builder.srcdir, builder_name='html') + img_fname = op.join(app.builder.srcdir, 'auto_examples', 'images', + 'sg_img.png') + target_file = op.join(app.builder.srcdir, 'auto_examples', 'sg.py') + os.makedirs(op.dirname(img_fname)) + os.makedirs(app.builder.outdir) + block_vars = dict(image_path_iterator=(img for img in [img_fname]), + example_globals=dict(a=1), target_file=target_file) + # Nothing yet + block = None + rst = scraper(block, block_vars, gallery_conf) + assert rst == '' + # Still nothing + block_vars['example_globals']['r'] = r + rst = scraper(block, block_vars, gallery_conf) + # Once it's saved, add it + assert rst == '' + fname = op.join(str(tmp_path), 'my_html.html') + r.save(fname, open_browser=False) + rst = scraper(block, block_vars, gallery_conf) + out_html = op.join(app.builder.outdir, 'auto_examples', 'my_html.html') + assert not op.isfile(out_html) + scraper.copyfiles() + assert op.isfile(out_html) + assert rst.count('"') == 6 + assert " -# Mainak Jas -# Teon Brooks -# -# License: BSD (3-clause) - -import base64 -from io import BytesIO -from mne.proj import read_proj -import os -import os.path as op -import fnmatch -import re -import codecs -from shutil import copyfile -import time -import warnings -import webbrowser -import numpy as np - -from . import read_evokeds, read_events, pick_types, read_cov -from .io import read_raw, read_info -from .io._read_raw import supported as extension_reader_map -from .io.pick import _DATA_CH_TYPES_SPLIT -from .source_space import _mri_orientation -from .utils import (logger, verbose, get_subjects_dir, warn, _ensure_int, - fill_doc, _check_option, _validate_type, _safe_input) -from .viz import (plot_events, plot_alignment, plot_cov, plot_projs_topomap, - plot_compare_evokeds, set_3d_view) -from .viz.misc import _plot_mri_contours, _get_bem_plotting_surfaces -from .viz.utils import _ndarray_to_fig, _figure_agg -from .forward import read_forward_solution -from .epochs import read_epochs -from . import dig_mri_distances -from .minimum_norm import read_inverse_operator -from .parallel import parallel_func, check_n_jobs - -from .externals.tempita import HTMLTemplate, Template -from .externals.h5io import read_hdf5, write_hdf5 - -_BEM_VIEWS = ('axial', 'sagittal', 'coronal') - - -# For raw files, we want to support different suffixes + extensions for all -# supported file formats -SUPPORTED_READ_RAW_EXTENSIONS = tuple(extension_reader_map.keys()) -RAW_EXTENSIONS = [] -for ext in SUPPORTED_READ_RAW_EXTENSIONS: - RAW_EXTENSIONS.append(f'raw{ext}') - if ext not in ('.bdf', '.edf', '.set', '.vhdr'): # EEG-only formats - RAW_EXTENSIONS.append(f'meg{ext}') - RAW_EXTENSIONS.append(f'eeg{ext}') - RAW_EXTENSIONS.append(f'ieeg{ext}') - -# Processed data will always be in (gzipped) FIFF format -VALID_EXTENSIONS = ('sss.fif', 'sss.fif.gz', - 'eve.fif', 'eve.fif.gz', - 'cov.fif', 'cov.fif.gz', - 'proj.fif', 'prof.fif.gz', - 'trans.fif', 'trans.fif.gz', - 'fwd.fif', 'fwd.fif.gz', - 'epo.fif', 'epo.fif.gz', - 'inv.fif', 'inv.fif.gz', - 'ave.fif', 'ave.fif.gz', - 'T1.mgz') + tuple(RAW_EXTENSIONS) -del RAW_EXTENSIONS - -SECTION_ORDER = ('raw', 'events', 'epochs', 'ssp', 'evoked', 'covariance', - 'trans', 'mri', 'forward', 'inverse') - - -############################################################################### -# PLOTTING FUNCTIONS - -def _fig_to_img(fig, image_format='png', scale=None, auto_close=True, - **kwargs): - """Plot figure and create a binary image.""" - # fig can be ndarray, mpl Figure, Mayavi Figure, or callable that produces - # a mpl Figure - import matplotlib.pyplot as plt - from matplotlib.figure import Figure - if isinstance(fig, np.ndarray): - fig = _ndarray_to_fig(fig) - elif callable(fig): - if auto_close: - plt.close('all') - fig = fig(**kwargs) - elif not isinstance(fig, Figure): - from .viz.backends.renderer import backend, MNE_3D_BACKEND_TESTING - backend._check_3d_figure(figure=fig) - if not MNE_3D_BACKEND_TESTING: - img = backend._take_3d_screenshot(figure=fig) - else: # Testing mode - img = np.zeros((2, 2, 3)) - - if auto_close: - backend._close_3d_figure(figure=fig) - fig = _ndarray_to_fig(img) - - output = BytesIO() - if scale is not None: - _scale_mpl_figure(fig, scale) - logger.debug('Saving figure %s with dpi %s' - % (fig.get_size_inches(), fig.get_dpi())) - with warnings.catch_warnings(record=True): - warnings.simplefilter('ignore') # incompatible axes - fig.savefig(output, format=image_format, dpi=fig.get_dpi(), - bbox_to_inches='tight') - plt.close(fig) - output = output.getvalue() - return (output.decode('utf-8') if image_format == 'svg' else - base64.b64encode(output).decode('ascii')) - - -def _scale_mpl_figure(fig, scale): - """Magic scaling helper. - - Keeps font-size and artist sizes constant - 0.5 : current font - 4pt - 2.0 : current font + 4pt - - XXX it's unclear why this works, but good to go for most cases - """ - scale = float(scale) - fig.set_size_inches(fig.get_size_inches() * scale) - fig.set_dpi(fig.get_dpi() * scale) - import matplotlib as mpl - if scale >= 1: - sfactor = scale ** 2 - else: - sfactor = -((1. / scale) ** 2) - for text in fig.findobj(mpl.text.Text): - fs = text.get_fontsize() - new_size = fs + sfactor - if new_size <= 0: - raise ValueError('could not rescale matplotlib fonts, consider ' - 'increasing "scale"') - text.set_fontsize(new_size) - - fig.canvas.draw() - - -def _figs_to_mrislices(sl, n_jobs, **kwargs): - import matplotlib.pyplot as plt - plt.close('all') - use_jobs = min(n_jobs, max(1, len(sl))) - parallel, p_fun, _ = parallel_func(_plot_mri_contours, use_jobs) - outs = parallel(p_fun(slices=s, **kwargs) - for s in np.array_split(sl, use_jobs)) - # deal with flip_z - flip_z = 1 - out = list() - for o in outs: - o, flip_z = o - out.extend(o[::flip_z]) - return out - - -def _iterate_trans_views(function, **kwargs): - """Auxiliary function to iterate over views in trans fig.""" - import matplotlib.pyplot as plt - from .viz.backends.renderer import MNE_3D_BACKEND_TESTING - from .viz._brain.view import views_dicts - - fig = function(**kwargs) - - views = ['frontal', 'lateral', 'medial'] - views += ['axial', 'rostral', 'coronal'] - - images = [] - for view in views: - if not MNE_3D_BACKEND_TESTING: - from .viz.backends.renderer import backend - set_3d_view(fig, **views_dicts['both'][view]) - backend._check_3d_figure(fig) - im = backend._take_3d_screenshot(figure=fig) - else: # Testing mode - im = np.zeros((2, 2, 3)) - images.append(im) - - images = np.concatenate( - [np.concatenate(images[:3], axis=1), - np.concatenate(images[3:], axis=1)], - axis=0) - - dists = dig_mri_distances(info=kwargs['info'], - trans=kwargs['trans'], - subject=kwargs['subject'], - subjects_dir=kwargs['subjects_dir']) - - fig2, ax = plt.subplots() - ax.imshow(images) - ax.set_title(f'Average distance from {len(dists)} digitized points to ' - f'head: {1e3 * np.mean(dists):.2f} mm') - ax.axis('off') - - if not MNE_3D_BACKEND_TESTING: - backend._close_all() - - img = _fig_to_img(fig2, image_format='png') - plt.close(fig2) - return img - -############################################################################### -# TOC FUNCTIONS - - -def _is_bad_fname(fname): - """Identify bad file naming patterns and highlight them in the TOC.""" - if fname.endswith('(whitened)'): - fname = fname[:-11] - - if not fname.endswith(VALID_EXTENSIONS + ('bem', 'custom')): - return 'red' - else: - return '' - - -def _get_fname(fname): - """Get fname without -#-.""" - if '-#-' in fname: - fname = fname.split('-#-')[0] - else: - fname = op.basename(fname) - fname = ' ... %s' % fname - return fname - - -def _endswith(fname, suffixes): - """Aux function to test if file name includes the specified suffixes.""" - if isinstance(suffixes, str): - suffixes = [suffixes] - for suffix in suffixes: - for ext in SUPPORTED_READ_RAW_EXTENSIONS: - if fname.endswith((f'-{suffix}{ext}', f'-{suffix}{ext}', - f'_{suffix}{ext}', f'_{suffix}{ext}')): - return True - return False - - -def _get_toc_property(fname): - """Assign class names to TOC elements to allow button-based toggling.""" - if _endswith(fname, 'eve'): - div_klass = 'events' - tooltip = fname - text = op.basename(fname) - elif _endswith(fname, 'ave'): - div_klass = 'evoked' - tooltip = fname - text = op.basename(fname) - elif _endswith(fname, 'cov'): - div_klass = 'covariance' - tooltip = fname - text = op.basename(fname) - elif _endswith(fname, 'proj'): - div_klass = 'ssp' - tooltip = fname - text = op.basename(fname) - elif _endswith(fname, ['raw', 'sss', 'meg']): - div_klass = 'raw' - tooltip = fname - text = op.basename(fname) - elif _endswith(fname, 'trans'): - div_klass = 'trans' - tooltip = fname - text = op.basename(fname) - elif _endswith(fname, 'fwd'): - div_klass = 'forward' - tooltip = fname - text = op.basename(fname) - elif _endswith(fname, 'inv'): - div_klass = 'inverse' - tooltip = fname - text = op.basename(fname) - elif _endswith(fname, 'epo'): - div_klass = 'epochs' - tooltip = fname - text = op.basename(fname) - elif fname.endswith(('.nii', '.nii.gz', '.mgh', '.mgz')): - div_klass = 'mri' - tooltip = 'MRI' - text = 'MRI' - elif fname.endswith(('bem')): - div_klass = 'bem' - tooltip = 'BEM' - text = 'BEM' - elif fname.endswith('(whitened)'): - div_klass = 'evoked' - tooltip = fname - text = op.basename(fname[:-11]) + '(whitened)' - else: - div_klass = fname.split('-#-')[1] - tooltip = fname.split('-#-')[0] - text = fname.split('-#-')[0] - - return div_klass, tooltip, text - - -def _iterate_files(report, fnames, info, cov, baseline, sfreq, on_error, - image_format, data_path): - """Parallel process in batch mode.""" - htmls, report_fnames, report_sectionlabels = [], [], [] - - def _update_html(html, report_fname, report_sectionlabel): - """Update the lists above.""" - htmls.append(html) - report_fnames.append(report_fname) - report_sectionlabels.append(report_sectionlabel) - - for fname in fnames: - logger.info("Rendering : %s" - % op.join('...' + report.data_path[-20:], - fname)) - try: - if _endswith(fname, ['raw', 'sss', 'meg']): - html = report._render_raw(fname, data_path) - report_fname = fname - report_sectionlabel = 'raw' - elif _endswith(fname, 'fwd'): - html = report._render_forward(fname, data_path) - report_fname = fname - report_sectionlabel = 'forward' - elif _endswith(fname, 'inv'): - html = report._render_inverse(fname, data_path) - report_fname = fname - report_sectionlabel = 'inverse' - elif _endswith(fname, 'ave'): - if cov is not None: - html = report._render_whitened_evoked(fname, cov, baseline, - image_format, - data_path) - report_fname = fname + ' (whitened)' - report_sectionlabel = 'evoked' - _update_html(html, report_fname, report_sectionlabel) - - html = report._render_evoked(fname, baseline, image_format, - data_path) - report_fname = fname - report_sectionlabel = 'evoked' - elif _endswith(fname, 'eve'): - html = report._render_eve(fname, sfreq, image_format, - data_path) - report_fname = fname - report_sectionlabel = 'events' - elif _endswith(fname, 'epo'): - html = report._render_epochs(fname, image_format, data_path) - report_fname = fname - report_sectionlabel = 'epochs' - elif _endswith(fname, 'cov') and report.info_fname is not None: - html = report._render_cov(fname, info, image_format, data_path) - report_fname = fname - report_sectionlabel = 'covariance' - elif _endswith(fname, 'proj') and report.info_fname is not None: - html = report._render_projs(proj_fname=fname, - info_fname=report.info_fname, - data_path=data_path) - report_fname = fname - report_sectionlabel = 'ssp' - elif (_endswith(fname, 'trans') and - report.info_fname is not None and report.subjects_dir - is not None and report.subject is not None): - html = report._render_trans(fname, report.data_path, info, - report.subject, - report.subjects_dir, - data_path) - report_fname = fname - report_sectionlabel = 'trans' - else: - html = None - report_fname = None - report_sectionlabel = None - except Exception as e: - if on_error == 'warn': - warn('Failed to process file %s:\n"%s"' % (fname, e)) - elif on_error == 'raise': - raise - html = None - report_fname = None - report_sectionlabel = None - - # Add SSP projectors found in the currently processed file, if any. - if (report.projs and report_fname is not None and - _endswith(fname, ['raw', 'sss', 'meg', 'epo', 'ave']) - and read_info(fname).get('projs')): - projs_html = report._render_projs(info_fname=fname, - data_path=data_path, - div_klass=report_sectionlabel) - html += f'\n\n{projs_html}' - - _update_html(html, report_fname, report_sectionlabel) - - return htmls, report_fnames, report_sectionlabels - - -def open_report(fname, **params): - """Read a saved report or, if it doesn't exist yet, create a new one. - - The returned report can be used as a context manager, in which case any - changes to the report are saved when exiting the context block. - - Parameters - ---------- - fname : str - The file containing the report, stored in the HDF5 format. If the file - does not exist yet, a new report is created that will be saved to the - specified file. - **params : kwargs - When creating a new report, any named parameters other than ``fname`` - are passed to the ``__init__`` function of the `Report` object. When - reading an existing report, the parameters are checked with the - loaded report and an exception is raised when they don't match. - - Returns - ------- - report : instance of Report - The report. - """ - if op.exists(fname): - # Check **params with the loaded report - state = read_hdf5(fname, title='mnepython') - for param in params.keys(): - if param not in state: - raise ValueError('The loaded report has no attribute %s' % - param) - if params[param] != state[param]: - raise ValueError("Attribute '%s' of loaded report does not " - "match the given parameter." % param) - report = Report() - report.__setstate__(state) - else: - report = Report(**params) - # Keep track of the filename in case the Report object is used as a context - # manager. - report._fname = fname - return report - - -############################################################################### -# IMAGE FUNCTIONS - -def _build_image_png(data, cmap='gray'): - """Build an image encoded in base64.""" - import matplotlib.pyplot as plt - - figsize = data.shape[::-1] - if figsize[0] == 1: - figsize = tuple(figsize[1:]) - data = data[:, :, 0] - fig = _figure_agg(figsize=figsize, dpi=100, frameon=False) - cmap = getattr(plt.cm, cmap, plt.cm.gray) - fig.figimage(data, cmap=cmap) - output = BytesIO() - fig.savefig(output, dpi=fig.get_dpi(), format='png') - return base64.b64encode(output.getvalue()).decode('ascii') - - -def _iterate_mri_slices(name, ind, global_id, slides_klass, data, cmap): - """Auxiliary function for parallel processing of mri slices.""" - img_klass = 'slideimg-%s' % name - - caption = u'Slice %s %s' % (name, ind) - slice_id = '%s-%s-%s' % (name, global_id, ind) - div_klass = 'span12 %s' % slides_klass - img = _build_image_png(data, cmap=cmap) - first = True if ind == 0 else False - html = _build_html_image(img, slice_id, div_klass, img_klass, caption, - first, image_format='png') - return ind, html - - -############################################################################### -# HTML functions - -def _build_html_image(img, id, div_klass, img_klass, caption=None, - show=True, image_format='png'): - """Build a html image from a slice array.""" - html = [] - add_style = u'' if show else u'style="display: none"' - html.append(u'
  • ' % (div_klass, id, add_style)) - html.append(u'
    ') - if image_format == 'png': - html.append(u'' - % (img_klass, img)) - else: - html.append(u'
    %s
    ' - % (img_klass, img)) - html.append(u'
    ') - if caption: - html.append(u'

    %s

    ' % caption) - html.append(u'
    • ') - return u'\n'.join(html) - - -slider_template = HTMLTemplate(u""" - -""") - -slider_full_template = Template(u""" -
  • -

    {{title}}

    -
    -
    • -
      -
      -
      -
        - {{image_html}} -
      - {{html}} -
      -
      -
    -
    -
    • -""") - - -def _build_html_slider(slices_range, slides_klass, slider_id, - start_value=None): - """Build an html slider for a given slices range and a slices klass.""" - if start_value is None: - start_value = slices_range[len(slices_range) // 2] - with warnings.catch_warnings(record=True): - warnings.simplefilter('ignore') - out = slider_template.substitute( - slider_id=slider_id, klass=slides_klass, - step=slices_range[1] - slices_range[0], - minvalue=slices_range[0], maxvalue=slices_range[-1], - startvalue=start_value) - return out - - -############################################################################### -# HTML scan renderer - -header_template = Template(u""" - - - - -{{include}} - - - - - - -""") # noqa: E501 - -footer_template = HTMLTemplate(u""" -
    -
    -

    -Created on {{date}}. -

    - - - -""") # noqa: E501 - -html_template = Template(u""" -
  • -

    {{caption}}

    -
    {{html}}
    -
    • -""") - -image_template = Template(u""" - -{{default interactive = False}} -{{default width = 50}} -{{default id = False}} -{{default image_format = 'png'}} -{{default scale = None}} -{{default comment = None}} - -
  • - -{{if caption}} -

    {{caption}}

    -{{endif}} -
    -{{if not interactive}} - {{if image_format == 'png'}} - {{if scale is not None}} - - {{else}} - - {{endif}} - {{elif image_format == 'gif'}} - {{if scale is not None}} - - {{else}} - - {{endif}} - {{elif image_format == 'svg'}} -
    - {{img}} -
    - {{endif}} - {{if comment is not None}} -

    -
    - -

    - {{comment}} -

    -
    - {{endif}} -{{else}} -
    {{interactive}}
    -{{endif}} -
    -
    • -""") - -repr_template = Template(u""" -
  • -

    {{caption}}

    -{{repr}} -
    • -""") - -toc_list = Template(u""" -
  • - {{if id}} - - {{endif}} - {{text}} -{{if id}}{{endif}} -
    • -""") - - -def _check_scale(scale): - """Ensure valid scale value is passed.""" - if np.isscalar(scale) and scale <= 0: - raise ValueError('scale must be positive, not %s' % scale) - - -def _check_image_format(rep, image_format): - """Ensure fmt is valid.""" - if rep is None: - _check_option('image_format', image_format, ['png', 'svg']) - elif image_format is not None: - _check_option('image_format', image_format, ['png', 'svg', None]) - else: # rep is not None and image_format is None - image_format = rep.image_format - return image_format - - -@fill_doc -class Report(object): - r"""Object for rendering HTML. - - Parameters - ---------- - info_fname : None | str - Name of the file containing the info dictionary. - %(subjects_dir)s - subject : str | None - Subject name. - title : str - Title of the report. - cov_fname : None | str - Name of the file containing the noise covariance. - %(baseline_report)s - Defaults to ``None``, i.e. no baseline correction. - image_format : str - Default image format to use (default is 'png'). - SVG uses vector graphics, so fidelity is higher but can increase - file size and browser image rendering time as well. - - .. versionadded:: 0.15 - - raw_psd : bool | dict - If True, include PSD plots for raw files. Can be False (default) to - omit, True to plot, or a dict to pass as ``kwargs`` to - :meth:`mne.io.Raw.plot_psd`. - - .. versionadded:: 0.17 - projs : bool - Whether to include topographic plots of SSP projectors, if present in - the data. Defaults to ``False``. - - .. versionadded:: 0.21 - %(verbose)s - - Attributes - ---------- - info_fname : None | str - Name of the file containing the info dictionary. - %(subjects_dir)s - subject : str | None - Subject name. - title : str - Title of the report. - cov_fname : None | str - Name of the file containing the noise covariance. - %(baseline_report)s - Defaults to ``None``, i.e. no baseline correction. - image_format : str - Default image format to use (default is 'png'). - SVG uses vector graphics, so fidelity is higher but can increase - file size and browser image rendering time as well. - - .. versionadded:: 0.15 - - raw_psd : bool | dict - If True, include PSD plots for raw files. Can be False (default) to - omit, True to plot, or a dict to pass as ``kwargs`` to - :meth:`mne.io.Raw.plot_psd`. - - .. versionadded:: 0.17 - projs : bool - Whether to include topographic plots of SSP projectors, if present in - the data. Defaults to ``False``. - - .. versionadded:: 0.21 - %(verbose)s - html : list of str - Contains items of html-page. - include : list of str - Dictionary containing elements included in head. - fnames : list of str - List of file names rendered. - sections : list of str - List of sections. - lang : str - language setting for the HTML file. - - Notes - ----- - See :ref:`tut-report` for an introduction to using ``mne.Report``. - - .. versionadded:: 0.8.0 - """ - - def __init__(self, info_fname=None, subjects_dir=None, - subject=None, title=None, cov_fname=None, baseline=None, - image_format='png', raw_psd=False, projs=False, verbose=None): - self.info_fname = str(info_fname) if info_fname is not None else None - self.cov_fname = str(cov_fname) if cov_fname is not None else None - self.baseline = baseline - if subjects_dir is not None: - subjects_dir = get_subjects_dir(subjects_dir) - self.subjects_dir = subjects_dir - self.subject = subject - self.title = title - self.image_format = _check_image_format(None, image_format) - self.projs = projs - self.verbose = verbose - - self._initial_id = 0 - self.html = [] - self.include = [] - self.fnames = [] # List of file names rendered - self.sections = [] # List of sections - self.lang = 'en-us' # language setting for the HTML file - self._sectionlabels = [] # Section labels - self._sectionvars = {} # Section variable names in js - # boolean to specify if sections should be ordered in natural - # order of processing (raw -> events ... -> inverse) - self._sort_sections = False - if not isinstance(raw_psd, bool) and not isinstance(raw_psd, dict): - raise TypeError('raw_psd must be bool or dict, got %s' - % (type(raw_psd),)) - self.raw_psd = raw_psd - self._init_render() # Initialize the renderer - - def __repr__(self): - """Print useful info about report.""" - s = ' 4: - s += '\n%s' % '\n'.join(fnames[:2]) - s += '\n ...\n' - s += '\n'.join(fnames[-2:]) - elif len(self.fnames) > 0: - s += '\n%s' % '\n'.join(fnames) - s += '\n>' - return s - - def __len__(self): - """Return the number of files processed by the report. - - Returns - ------- - n_files : int - The number of files processed. - """ - return len(self.fnames) - - def _get_id(self): - """Get id of plot.""" - self._initial_id += 1 - return self._initial_id - - def _validate_input(self, items, captions, section, comments=None): - """Validate input.""" - if not isinstance(items, (list, tuple)): - items = [items] - if not isinstance(captions, (list, tuple)): - captions = [captions] - if not isinstance(comments, (list, tuple)): - if comments is None: - comments = [comments] * len(captions) - else: - comments = [comments] - if len(comments) != len(items): - raise ValueError('Comments and report items must have the same ' - 'length or comments should be None, got %d and %d' - % (len(comments), len(items))) - elif len(captions) != len(items): - raise ValueError('Captions and report items must have the same ' - 'length, got %d and %d' - % (len(captions), len(items))) - - # Book-keeping of section names - if section not in self.sections: - self.sections.append(section) - self._sectionvars[section] = _clean_varnames(section) - - return items, captions, comments - - def add_custom_css(self, css): - """Add custom CSS to the report. - - Parameters - ---------- - css : str - Style definitions to add to the report. The content of this string - will be embedded between HTML ```` tags. - - Notes - ----- - .. versionadded:: 0.23 - """ - style = f'\n' - self.include += style - - def add_custom_js(self, js): - """Add custom JavaScript to the report. - - Parameters - ---------- - js : str - JavaScript code to add to the report. The content of this string - will be embedded between HTML ```` tags. - - Notes - ----- - .. versionadded:: 0.23 - """ - script = f'\n' - self.include += script - - def remove(self, caption, section=None): - """Remove a figure from the report. - - The figure to remove is searched for by its caption. When searching by - caption, the section label can be specified as well to narrow down the - search. If multiple figures match the search criteria, the last one - will be removed. - - Any empty sections will be removed as well. - - Parameters - ---------- - caption : str - If set, search for the figure by caption. - section : str | None - If set, limit the search to the section with the given label. - - Returns - ------- - removed_index : int | None - The integer index of the figure that was removed, or ``None`` if no - figure matched the search criteria. - """ - # Construct the search pattern - pattern = r'^%s-#-.*-#-custom$' % caption - - # Search for figures matching the search pattern, regardless of - # section - matches = [i for i, fname_ in enumerate(self.fnames) - if re.match(pattern, fname_)] - if section is not None: - # Narrow down the search to the given section - svar = self._sectionvars[section] - matches = [i for i in matches - if self._sectionlabels[i] == svar] - if len(matches) == 0: - return None - - # Remove last occurrence - index = max(matches) - - # Remove the figure - del self.fnames[index] - del self._sectionlabels[index] - del self.html[index] - - # Remove any (now) empty sections. - # We use a list() to copy the _sectionvars dictionary, since we are - # removing elements during the loop. - for section_, sectionlabel_ in list(self._sectionvars.items()): - if sectionlabel_ not in self._sectionlabels: - self.sections.remove(section_) - del self._sectionvars[section_] - - return index - - def _add_or_replace(self, fname, sectionlabel, html, replace=False): - """Append a figure to the report, or replace it if it already exists. - - Parameters - ---------- - fname : str - A unique identifier for the figure. If a figure with this - identifier has already been added, it will be replaced. - sectionlabel : str - The section to place the figure in. - html : str - The HTML that contains the figure. - replace : bool - Existing figures are only replaced if this is set to ``True``. - Defaults to ``False``. - """ - assert isinstance(html, str) # otherwise later will break - if replace and fname in self.fnames: - # Find last occurrence of the figure - ind = max([i for i, existing in enumerate(self.fnames) - if existing == fname]) - self.fnames[ind] = fname - self._sectionlabels[ind] = sectionlabel - self.html[ind] = html - else: - # Append new record - self.fnames.append(fname) - self._sectionlabels.append(sectionlabel) - self.html.append(html) - - def add_figs_to_section(self, figs, captions, section='custom', - scale=None, image_format=None, comments=None, - replace=False, auto_close=True): - """Append custom user-defined figures. - - Parameters - ---------- - figs : matplotlib.figure.Figure | mlab.Figure | array | list - A figure or a list of figures to add to the report. Each figure in - the list can be an instance of :class:`matplotlib.figure.Figure`, - :class:`mayavi.core.api.Scene`, or :class:`numpy.ndarray`. - captions : str | list of str - A caption or a list of captions to the figures. - section : str - Name of the section to place the figure in. If section already - exists, the figures will be appended to the end of the section. - scale : float | None | callable - Scale the images maintaining the aspect ratio. - If None, no scaling is applied. If float, scale will determine - the relative scaling (might not work for scale <= 1 depending on - font sizes). If function, should take a figure object as input - parameter. Defaults to None. - image_format : str | None - The image format to be used for the report, can be 'png' or 'svd'. - None (default) will use the default specified during Report - class construction. - comments : None | str | list of str - A string of text or a list of strings of text to be appended after - the figure. - replace : bool - If ``True``, figures already present that have the same caption - will be replaced. Defaults to ``False``. - auto_close : bool - If True, the plots are closed during the generation of the report. - Defaults to True. - """ - figs, captions, comments = self._validate_input(figs, captions, - section, comments) - image_format = _check_image_format(self, image_format) - _check_scale(scale) - for fig, caption, comment in zip(figs, captions, comments): - caption = 'custom plot' if caption == '' else caption - sectionvar = self._sectionvars[section] - global_id = self._get_id() - div_klass = self._sectionvars[section] - img_klass = self._sectionvars[section] - - img = _fig_to_img(fig, image_format, scale, auto_close) - html = image_template.substitute(img=img, id=global_id, - div_klass=div_klass, - img_klass=img_klass, - caption=caption, - show=True, - image_format=image_format, - comment=comment) - self._add_or_replace('%s-#-%s-#-custom' % (caption, sectionvar), - sectionvar, html, replace) - - def add_images_to_section(self, fnames, captions, scale=None, - section='custom', comments=None, replace=False): - """Append custom user-defined images. - - Parameters - ---------- - fnames : str | list of str - A filename or a list of filenames from which images are read. - Images can be PNG, GIF or SVG. - captions : str | list of str - A caption or a list of captions to the images. - scale : float | None - Scale the images maintaining the aspect ratio. - Defaults to None. If None, no scaling will be applied. - section : str - Name of the section. If section already exists, the images - will be appended to the end of the section. - comments : None | str | list of str - A string of text or a list of strings of text to be appended after - the image. - replace : bool - If ``True``, figures already present that have the same caption - will be replaced. Defaults to ``False``. - """ - # Note: using scipy.misc is equivalent because scipy internally - # imports PIL anyway. It's not possible to redirect image output - # to binary string using scipy.misc. - fnames, captions, comments = self._validate_input(fnames, captions, - section, comments) - _check_scale(scale) - - for fname, caption, comment in zip(fnames, captions, comments): - caption = 'custom plot' if caption == '' else caption - sectionvar = self._sectionvars[section] - global_id = self._get_id() - div_klass = self._sectionvars[section] - img_klass = self._sectionvars[section] - - image_format = os.path.splitext(fname)[1][1:] - image_format = image_format.lower() - - _check_option('image_format', image_format, ['png', 'gif', 'svg']) - - # Convert image to binary string. - with open(fname, 'rb') as f: - img = base64.b64encode(f.read()).decode('ascii') - html = image_template.substitute(img=img, id=global_id, - image_format=image_format, - div_klass=div_klass, - img_klass=img_klass, - caption=caption, - width=scale, - comment=comment, - show=True) - - self._add_or_replace('%s-#-%s-#-custom' % (caption, sectionvar), - sectionvar, html, replace) - - def add_htmls_to_section(self, htmls, captions, section='custom', - replace=False): - """Append htmls to the report. - - Parameters - ---------- - htmls : str | list of str - An html str or a list of html str. - captions : str | list of str - A caption or a list of captions to the htmls. - section : str - Name of the section. If section already exists, the images - will be appended to the end of the section. - replace : bool - If ``True``, figures already present that have the same caption - will be replaced. Defaults to ``False``. - - Notes - ----- - .. versionadded:: 0.9.0 - """ - htmls, captions, _ = self._validate_input(htmls, captions, section) - for html, caption in zip(htmls, captions): - caption = 'custom plot' if caption == '' else caption - sectionvar = self._sectionvars[section] - global_id = self._get_id() - div_klass = self._sectionvars[section] - - self._add_or_replace( - '%s-#-%s-#-custom' % (caption, sectionvar), sectionvar, - html_template.substitute(div_klass=div_klass, id=global_id, - caption=caption, html=html), replace) - - @verbose - def add_bem_to_section(self, subject, caption='BEM', section='bem', - decim=2, n_jobs=1, subjects_dir=None, - replace=False, width=512, verbose=None): - """Render a bem slider html str. - - Parameters - ---------- - subject : str - Subject name. - caption : str - A caption for the bem. - section : str - Name of the section. If section already exists, the bem - will be appended to the end of the section. - decim : int - Use this decimation factor for generating MRI/BEM images - (since it can be time consuming). - %(n_jobs)s - %(subjects_dir)s - replace : bool - If ``True``, figures already present that have the same caption - will be replaced. Defaults to ``False``. - width : int - The width of the MRI images (in pixels). Larger values will have - clearer surface lines, but will create larger HTML files. - Typically a factor of 2 more than the number of MRI voxels along - each dimension (typically 512, default) is reasonable. - - .. versionadded:: 0.23 - %(verbose_meth)s - - Notes - ----- - .. versionadded:: 0.9.0 - """ - width = _ensure_int(width, 'width') - caption = 'custom plot' if caption == '' else caption - html = self._render_bem(subject=subject, subjects_dir=subjects_dir, - decim=decim, n_jobs=n_jobs, section=section, - caption=caption, width=width) - html, caption, _ = self._validate_input(html, caption, section) - sectionvar = self._sectionvars[section] - # convert list->str - assert isinstance(html, list) - html = u''.join(html) - self._add_or_replace('%s-#-%s-#-custom' % (caption[0], sectionvar), - sectionvar, html, replace=replace) - - def add_slider_to_section(self, figs, captions=None, section='custom', - title='Slider', scale=None, image_format=None, - replace=False, auto_close=True): - """Render a slider of figs to the report. - - Parameters - ---------- - figs : list of Figure - Each figure in the list can be an instance of - :class:`matplotlib.figure.Figure`, - :class:`mayavi.core.api.Scene`, or :class:`numpy.ndarray`. - Must have at least 2 elements. - captions : list of str | list of float | None - A list of captions to the figures. If float, a str will be - constructed as ``%f s``. If None, it will default to - ``Data slice %d``. - section : str - Name of the section. If section already exists, the figures - will be appended to the end of the section. - title : str - The title of the slider. - scale : float | None | callable - Scale the images maintaining the aspect ratio. - If None, no scaling is applied. If float, scale will determine - the relative scaling (might not work for scale <= 1 depending on - font sizes). If function, should take a figure object as input - parameter. Defaults to None. - image_format : str | None - The image format to be used for the report, can be 'png' or 'svd'. - None (default) will use the default specified during Report - class construction. - replace : bool - If ``True``, figures already present that have the same caption - will be replaced. Defaults to ``False``. - auto_close : bool - If True, the plots are closed during the generation of the report. - Defaults to True. - - .. versionadded:: 0.23 - - Notes - ----- - .. versionadded:: 0.10.0 - """ - _check_scale(scale) - image_format = _check_image_format(self, image_format) - if isinstance(figs[0], list): - raise NotImplementedError('`add_slider_to_section` ' - 'can only add one slider at a time.') - if len(figs) < 2: - raise ValueError('figs must be at least length 2, got %s' - % (len(figs),)) - figs = [figs] - figs, _, _ = self._validate_input(figs, section, section) - figs = figs[0] - - sectionvar = self._sectionvars[section] - global_id = self._get_id() - name = 'slider' - - html = [] - slides_klass = '%s-%s' % (name, global_id) - div_klass = 'span12 %s' % slides_klass - - sl = np.arange(0, len(figs)) - slices = [] - img_klass = 'slideimg-%s' % name - - if captions is None: - captions = ['Data slice %d' % ii for ii in sl] - elif isinstance(captions, (list, tuple, np.ndarray)): - if len(figs) != len(captions): - raise ValueError('Captions must be the same length as the ' - 'number of slides.') - if isinstance(captions[0], (float, int)): - captions = ['%0.3f s' % caption for caption in captions] - else: - raise TypeError('Captions must be None or an iterable of ' - 'float, int, str, Got %s' % type(captions)) - for ii, (fig, caption) in enumerate(zip(figs, captions)): - img = _fig_to_img(fig, image_format, scale, auto_close) - slice_id = '%s-%s-%s' % (name, global_id, sl[ii]) - first = True if ii == 0 else False - slices.append(_build_html_image(img, slice_id, div_klass, - img_klass, caption, first, - image_format=image_format)) - # Render the slider - slider_id = 'select-%s-%s' % (name, global_id) - # Render the slices - image_html = u'\n'.join(slices) - html.append(_build_html_slider(sl, slides_klass, slider_id, - start_value=0)) - html = '\n'.join(html) - - slider_klass = sectionvar - - self._add_or_replace( - '%s-#-%s-#-custom' % (title, sectionvar), sectionvar, - slider_full_template.substitute(id=global_id, title=title, - div_klass=slider_klass, - slider_id=slider_id, html=html, - image_html=image_html), - replace=replace) - - ########################################################################### - # HTML rendering - def _render_one_axis(self, slices_iter, name, global_id, cmap, - n_elements, n_jobs): - """Render one axis of the array.""" - global_id = global_id or name - html = [] - html.append(u'
    ') - slides_klass = '%s-%s' % (name, global_id) - - use_jobs = min(n_jobs, max(1, n_elements)) - parallel, p_fun, _ = parallel_func(_iterate_mri_slices, use_jobs) - r = parallel(p_fun(name, ind, global_id, slides_klass, data, cmap) - for ind, data in slices_iter) - slices_range, slices = zip(*r) - - # Render the slider - slider_id = 'select-%s-%s' % (name, global_id) - html.append(u'
    ' % slider_id) - html.append(u'
      ') - # Render the slices - html.append(u'\n'.join(slices)) - html.append(u'
    ') - html.append(_build_html_slider(slices_range, slides_klass, slider_id)) - html.append(u'
    ') - return '\n'.join(html) - - ########################################################################### - # global rendering functions - @verbose - def _init_render(self, verbose=None): - """Initialize the renderer.""" - inc_fnames = ['jquery.js', 'jquery-ui.min.js', - 'bootstrap.min.js', 'jquery-ui.min.css', - 'bootstrap.min.css'] - - include = list() - for inc_fname in inc_fnames: - logger.info('Embedding : %s' % inc_fname) - fname = op.join(op.dirname(__file__), 'html', inc_fname) - with open(fname, 'rb') as fid: - file_content = fid.read().decode('utf-8') - if inc_fname.endswith('.js'): - include.append(u'') - elif inc_fname.endswith('.css'): - include.append(u'') - self.include = ''.join(include) - - @verbose - def parse_folder(self, data_path, pattern=None, n_jobs=1, mri_decim=2, - sort_sections=True, on_error='warn', image_format=None, - render_bem=True, verbose=None): - r"""Render all the files in the folder. - - Parameters - ---------- - data_path : str - Path to the folder containing data whose HTML report will be - created. - pattern : None | str | list of str - Filename pattern(s) to include in the report. - Example: [\*raw.fif, \*ave.fif] will include Raw as well as Evoked - files. If ``None``, include all supported file formats. - - .. versionchanged:: 0.23 - Include supported non-FIFF files by default. - %(n_jobs)s - mri_decim : int - Use this decimation factor for generating MRI/BEM images - (since it can be time consuming). - sort_sections : bool - If True, sort sections in the order: raw -> events -> epochs - -> evoked -> covariance -> trans -> mri -> forward -> inverse. - on_error : str - What to do if a file cannot be rendered. Can be 'ignore', - 'warn' (default), or 'raise'. - image_format : str | None - The image format to be used for the report, can be 'png' or 'svd'. - None (default) will use the default specified during Report - class construction. - - .. versionadded:: 0.15 - render_bem : bool - If True (default), try to render the BEM. - - .. versionadded:: 0.16 - %(verbose_meth)s - """ - _validate_type(data_path, 'path-like', 'data_path') - data_path = str(data_path) - image_format = _check_image_format(self, image_format) - _check_option('on_error', on_error, ['ignore', 'warn', 'raise']) - self._sort = sort_sections - - n_jobs = check_n_jobs(n_jobs) - self.data_path = data_path - - if self.title is None: - self.title = 'MNE Report for ...%s' % self.data_path[-20:] - - if pattern is None: - pattern = [f'*{ext}' for ext in SUPPORTED_READ_RAW_EXTENSIONS] - elif not isinstance(pattern, (list, tuple)): - pattern = [pattern] - - # iterate through the possible patterns - fnames = list() - for p in pattern: - fnames.extend(sorted(_recursive_search(self.data_path, p))) - - if not fnames and not render_bem: - raise RuntimeError(f'No matching files found in {self.data_path}') - - # For split files, only keep the first one. - fnames_to_remove = [] - for fname in fnames: - if _endswith(fname, ('raw', 'sss', 'meg')): - kwargs = dict(fname=fname, preload=False) - if fname.endswith(('.fif', '.fif.gz')): - kwargs['allow_maxshield'] = True - inst = read_raw(**kwargs) - else: - continue - - if len(inst.filenames) > 1: - fnames_to_remove.extend(inst.filenames[1:]) - - fnames_to_remove = list(set(fnames_to_remove)) # Drop duplicates - for fname in fnames_to_remove: - if fname in fnames: - del fnames[fnames.index(fname)] - del fnames_to_remove - - if self.info_fname is not None: - info = read_info(self.info_fname, verbose=False) - sfreq = info['sfreq'] - else: - # only warn if relevant - if any(_endswith(fname, 'cov') for fname in fnames): - warn('`info_fname` not provided. Cannot render ' - '-cov.fif(.gz) files.') - if any(_endswith(fname, 'trans') for fname in fnames): - warn('`info_fname` not provided. Cannot render ' - '-trans.fif(.gz) files.') - if any(_endswith(fname, 'proj') for fname in fnames): - warn('`info_fname` not provided. Cannot render ' - '-proj.fif(.gz) files.') - info, sfreq = None, None - - cov = None - if self.cov_fname is not None: - cov = read_cov(self.cov_fname) - baseline = self.baseline - - # render plots in parallel; check that n_jobs <= # of files - logger.info('Iterating over %s potential files (this may take some ' - 'time)' % len(fnames)) - use_jobs = min(n_jobs, max(1, len(fnames))) - parallel, p_fun, _ = parallel_func(_iterate_files, use_jobs) - r = parallel(p_fun(self, fname, info, cov, baseline, sfreq, on_error, - image_format, self.data_path) - for fname in np.array_split(fnames, use_jobs)) - htmls, report_fnames, report_sectionlabels = zip(*r) - - # combine results from n_jobs discarding plots not rendered - self.html = [html for html in sum(htmls, []) if html is not None] - self.fnames = [fname for fname in sum(report_fnames, []) if - fname is not None] - self._sectionlabels = [slabel for slabel in - sum(report_sectionlabels, []) - if slabel is not None] - - # find unique section labels - self.sections = sorted(set(self._sectionlabels)) - self._sectionvars = dict(zip(self.sections, self.sections)) - - # render mri - if render_bem: - if self.subjects_dir is not None and self.subject is not None: - logger.info('Rendering BEM') - self.fnames.append('bem') - self.add_bem_to_section( - self.subject, decim=mri_decim, n_jobs=n_jobs, - subjects_dir=self.subjects_dir) - else: - warn('`subjects_dir` and `subject` not provided. Cannot ' - 'render MRI and -trans.fif(.gz) files.') - - def _get_state_params(self): - """Obtain all fields that are in the state dictionary of this object. - - Returns - ------- - non_opt_params : list of str - All parameters that must be present in the state dictionary. - opt_params : list of str - All parameters that are optionally present in the state dictionary. - """ - # Note: self._fname is not part of the state - return (['baseline', 'cov_fname', 'fnames', 'html', 'include', - 'image_format', 'info_fname', '_initial_id', 'raw_psd', - '_sectionlabels', 'sections', '_sectionvars', 'projs', - '_sort_sections', 'subjects_dir', 'subject', 'title', - 'verbose'], - ['data_path', 'lang', '_sort']) - - def __getstate__(self): - """Get the state of the report as a dictionary.""" - state = dict() - non_opt_params, opt_params = self._get_state_params() - for param in non_opt_params: - state[param] = getattr(self, param) - for param in opt_params: - if hasattr(self, param): - state[param] = getattr(self, param) - return state - - def __setstate__(self, state): - """Set the state of the report.""" - non_opt_params, opt_params = self._get_state_params() - for param in non_opt_params: - setattr(self, param, state[param]) - for param in opt_params: - if param in state: - setattr(self, param, state[param]) - return state - - @verbose - def save(self, fname=None, open_browser=True, overwrite=False, *, - verbose=None): - """Save the report and optionally open it in browser. - - Parameters - ---------- - fname : str | None - File name of the report. If the file name ends in '.h5' or '.hdf5', - the report is saved in HDF5 format, so it can later be loaded again - with :func:`open_report`. If the file name ends in anything else, - the report is rendered to HTML. If ``None``, the report is saved to - 'report.html' in the current working directory. - Defaults to ``None``. - open_browser : bool - When saving to HTML, open the rendered HTML file browser after - saving if True. Defaults to True. - %(overwrite)s - %(verbose_meth)s - - Returns - ------- - fname : str - The file name to which the report was saved. - """ - if fname is None: - if not hasattr(self, 'data_path'): - self.data_path = os.getcwd() - warn('`data_path` not provided. Using %s instead' - % self.data_path) - fname = op.realpath(op.join(self.data_path, 'report.html')) - else: - fname = op.realpath(fname) - - if not overwrite and op.isfile(fname): - msg = ('Report already exists at location %s. ' - 'Overwrite it (y/[n])? ' - % fname) - answer = _safe_input(msg, alt='pass overwrite=True') - if answer.lower() == 'y': - overwrite = True - - _, ext = op.splitext(fname) - is_hdf5 = ext.lower() in ['.h5', '.hdf5'] - - if overwrite or not op.isfile(fname): - logger.info('Saving report to location %s' % fname) - - if is_hdf5: - write_hdf5(fname, self.__getstate__(), overwrite=overwrite, - title='mnepython') - else: - self._render_toc() - - # Annotate the HTML with a TOC and footer. - with warnings.catch_warnings(record=True): - warnings.simplefilter('ignore') - html = footer_template.substitute( - date=time.strftime("%B %d, %Y"), - current_year=time.strftime("%Y")) - self.html.append(html) - - # Writing to disk may fail. However, we need to make sure that - # the TOC and footer are removed regardless, otherwise they - # will be duplicated when the user attempts to save again. - try: - # Write HTML - with codecs.open(fname, 'w', 'utf-8') as fobj: - fobj.write(_fix_global_ids(u''.join(self.html))) - finally: - self.html.pop(0) - self.html.pop(0) - self.html.pop() - - building_doc = os.getenv('_MNE_BUILDING_DOC', '').lower() == 'true' - if open_browser and not is_hdf5 and not building_doc: - webbrowser.open_new_tab('file://' + fname) - - self.fname = fname - return fname - - def __enter__(self): - """Do nothing when entering the context block.""" - return self - - def __exit__(self, type, value, traceback): - """Save the report when leaving the context block.""" - if self._fname is not None: - self.save(self._fname, open_browser=False, overwrite=True) - - @staticmethod - def _gen_caption(prefix, fname, data_path, suffix=''): - if data_path is not None: - fname = op.relpath(fname, start=data_path) - - caption = f'{prefix}: {fname} {suffix}' - return caption.strip() - - @verbose - def _render_toc(self, verbose=None): - """Render the Table of Contents.""" - logger.info('Rendering : Table of Contents') - - html_toc = u'
    ' - html_toc += u'

    CONTENTS

    ' - - global_id = 1 - - # Reorder self.sections to reflect natural ordering - if self._sort_sections: - sections = list(set(self.sections) & set(SECTION_ORDER)) - custom = [section for section in self.sections if section - not in SECTION_ORDER] - order = [sections.index(section) for section in SECTION_ORDER if - section in sections] - self.sections = np.array(sections)[order].tolist() + custom - - # Sort by section - html, fnames, sectionlabels = [], [], [] - for section in self.sections: - logger.info('%s' % section) - for sectionlabel, this_html, fname in (zip(self._sectionlabels, - self.html, self.fnames)): - if self._sectionvars[section] == sectionlabel: - html.append(this_html) - fnames.append(fname) - sectionlabels.append(sectionlabel) - logger.info(_get_fname(fname)) - color = _is_bad_fname(fname) - div_klass, tooltip, text = _get_toc_property(fname) - - # loop through conditions for evoked - if _endswith(fname, 'ave') or fname.endswith('(whitened)'): - text = os.path.basename(fname) - if fname.endswith('(whitened)'): - fname = fname[:-11] - # XXX: remove redundant read_evokeds - evokeds = read_evokeds(fname, verbose=False) - - html_toc += toc_list.substitute( - div_klass=div_klass, id=None, tooltip=fname, - color='#428bca', text=text) - - html_toc += u'
    • ' - for ev in evokeds: - html_toc += toc_list.substitute( - div_klass=div_klass, id=global_id, - tooltip=fname, color=color, text=ev.comment) - global_id += 1 - html_toc += toc_list.substitute( - div_klass=div_klass, id=global_id, - tooltip=fname, color=color, text='GFP') - global_id += 1 - html_toc += u'
    • ' - - elif fname.endswith(VALID_EXTENSIONS + ('bem', 'custom')): - html_toc += toc_list.substitute(div_klass=div_klass, - id=global_id, - tooltip=tooltip, - color=color, - text=text) - global_id += 1 - - html_toc += u'\n
    ' - html_toc += u'
    ' - - # The sorted html (according to section) - self.html = html - self.fnames = fnames - self._sectionlabels = sectionlabels - - lang = getattr(self, 'lang', 'en-us') - sections = [section if section != 'mri' else 'MRI' - for section in self.sections] - html_header = header_template.substitute( - title=self.title, include=self.include, lang=lang, - sections=sections, sectionvars=self._sectionvars) - self.html.insert(0, html_header) # Insert header at position 0 - self.html.insert(1, html_toc) # insert TOC - - def _render_one_bem_axis(self, mri_fname, surfaces, global_id, - orientation='coronal', decim=2, n_jobs=1, - width=512): - """Render one axis of bem contours (only PNG).""" - import nibabel as nib - nim = nib.load(mri_fname) - (_, _, z), _, _ = _mri_orientation(nim, orientation) - n_slices = nim.shape[z] - - name = orientation - html = [] - html.append(u'
    ') - slides_klass = '%s-%s' % (name, global_id) - - sl = np.arange(0, n_slices, decim) - logger.debug(f'Rendering BEM {orientation} with {len(sl)} slices') - kwargs = dict(mri_fname=mri_fname, surfaces=surfaces, show=False, - orientation=orientation, img_output=True, src=None, - show_orientation=True, width=width) - imgs = _figs_to_mrislices(sl, n_jobs, **kwargs) - slices = [] - img_klass = 'slideimg-%s w-100' % name - div_klass = 'span12 %s' % slides_klass - for ii, img in enumerate(imgs): - slice_id = '%s-%s-%s' % (name, global_id, sl[ii]) - caption = u'Slice %s %s' % (name, sl[ii]) - first = True if ii == 0 else False - slices.append(_build_html_image(img, slice_id, div_klass, - img_klass, caption, first, - image_format='png')) - - # Render the slider - slider_id = 'select-%s-%s' % (name, global_id) - html.append(u'
    ' % slider_id) - html.append(u'
      ') - # Render the slices - html.append(u'\n'.join(slices)) - html.append(u'
    ') - html.append(_build_html_slider(sl, slides_klass, slider_id)) - html.append(u'
    ') - return '\n'.join(html) - - def _render_raw(self, raw_fname, data_path): - """Render raw (only text).""" - global_id = self._get_id() - - kwargs = dict(fname=raw_fname, preload=False) - if raw_fname.endswith(('.fif', '.fif.gz')): - kwargs['allow_maxshield'] = True - raw = read_raw(**kwargs) - extra = '(MaxShield on)' if raw.info.get('maxshield', False) else '' - caption = self._gen_caption(prefix='Raw', suffix=extra, - fname=raw_fname, data_path=data_path) - html = """
  • """ % (global_id) - html += raw._repr_html_(caption=caption) - html += "
    • " - - raw_psd = {} if self.raw_psd is True else self.raw_psd - if isinstance(raw_psd, dict): - n_ax = sum(kind in raw for kind in _DATA_CH_TYPES_SPLIT) - fig = _figure_agg(figsize=(6, 1 + 1.5 * n_ax), dpi=92) - axes = [fig.add_subplot(1, n_ax, ii + 1) for ii in range(n_ax)] - img = _fig_to_img(raw.plot_psd, self.image_format, - ax=axes, **raw_psd) - new_html = image_template.substitute( - img=img, div_klass='raw', img_klass='raw', - caption='PSD', show=True, image_format=self.image_format) - html += '\n\n' + new_html - return html - - def _render_projs(self, *, info_fname, data_path, proj_fname=None, - div_klass='ssp'): - """Render SSP projectors. - - Parameters - ---------- - info_fname : str - Path of a file containing the `~mne.Info` structure, which is - required to create the topographic plots. - data_path : str - The "base" directory of the data. This path will me omitted from - the captions. - proj_fname : str | None - Path of projectors file to load. If ``None``, read projectors from - the `~mne.Info` structure in ``info_fname``. - div_klass : str - The class of the generated ``
    `` container. - - """ - global_id = self._get_id() - div_klass = div_klass - img_klass = 'ssp' - info = read_info(info_fname, verbose=False) - projs = info['projs'] if proj_fname is None else read_proj(proj_fname) - - if div_klass == 'ssp': # "standalone" section - fname = info_fname if proj_fname is None else proj_fname - caption = self._gen_caption(prefix='SSP Projectors', fname=fname, - data_path=data_path) - else: # Part of another section - caption = 'SSP Projectors' - - img_kws = dict(projs=projs, info=info, colorbar=True, vlim='joint', - show=False) - img = _fig_to_img(plot_projs_topomap, self.image_format, **img_kws) - html = image_template.substitute( - img=img, div_klass=div_klass, img_klass=img_klass, - caption=caption, show=True, image_format=self.image_format, - id=global_id) - return html - - def _render_forward(self, fwd_fname, data_path): - """Render forward.""" - div_klass = 'forward' - caption = self._gen_caption(prefix='Forward', fname=fwd_fname, - data_path=data_path) - fwd = read_forward_solution(fwd_fname) - repr_fwd = re.sub('>', '', re.sub('<', '', repr(fwd))) - global_id = self._get_id() - html = repr_template.substitute(div_klass=div_klass, - id=global_id, - caption=caption, - repr=repr_fwd) - return html - - def _render_inverse(self, inv_fname, data_path): - """Render inverse.""" - div_klass = 'inverse' - caption = self._gen_caption(prefix='Inverse', fname=inv_fname, - data_path=data_path) - inv = read_inverse_operator(inv_fname) - repr_inv = re.sub('>', '', re.sub('<', '', repr(inv))) - global_id = self._get_id() - html = repr_template.substitute(div_klass=div_klass, - id=global_id, - caption=caption, - repr=repr_inv) - return html - - def _render_evoked(self, evoked_fname, baseline, image_format, data_path): - """Render evoked.""" - logger.debug('Evoked: Reading %s' % evoked_fname) - evokeds = read_evokeds(evoked_fname, baseline=baseline, verbose=False) - - html = [] - for ei, ev in enumerate(evokeds): - global_id = self._get_id() - kwargs = dict(show=False) - logger.debug('Evoked: Plotting instance %s/%s' - % (ei + 1, len(evokeds))) - with warnings.catch_warnings(): - warnings.filterwarnings( - 'ignore', - message='Channel locations not available.*', - category=RuntimeWarning) - img = _fig_to_img(ev.plot, image_format, spatial_colors=True, - **kwargs) - - caption = self._gen_caption(prefix='Evoked', - suffix=f'({ev.comment})', - fname=evoked_fname, - data_path=data_path) - html.append(image_template.substitute( - img=img, id=global_id, div_klass='evoked', - img_klass='evoked', caption=caption, show=True, - image_format=image_format)) - has_types = [] - if len(pick_types(ev.info, meg=False, eeg=True)) > 0: - has_types.append('eeg') - if len(pick_types(ev.info, meg='grad', eeg=False, - ref_meg=False)) > 0: - has_types.append('grad') - if len(pick_types(ev.info, meg='mag', eeg=False)) > 0: - has_types.append('mag') - for ch_type in has_types: - logger.debug(' Topomap type %s' % ch_type) - img = _fig_to_img(ev.plot_topomap, image_format, - ch_type=ch_type, **kwargs) - caption = u'Topomap (ch_type = %s)' % ch_type - html.append(image_template.substitute( - img=img, div_klass='evoked', img_klass='evoked', - caption=caption, show=True, image_format=image_format)) - - # Plot GFP comparison. - figs = plot_compare_evokeds(evokeds=evokeds, ci=None, - show_sensors=True, **kwargs) - for fig in figs: - img = _fig_to_img(fig, image_format) - caption = self._gen_caption(prefix='Evoked', - suffix='(GFPs)', - fname=evoked_fname, - data_path=data_path) - global_id = self._get_id() - html.append(image_template.substitute( - img=img, id=global_id, div_klass='evoked', - img_klass='evoked', caption=caption, show=True, - image_format=image_format)) - - logger.debug('Evoked: done') - return '\n'.join(html) - - def _render_eve(self, eve_fname, sfreq, image_format, data_path): - """Render events.""" - global_id = self._get_id() - events = read_events(eve_fname) - kwargs = dict(events=events, sfreq=sfreq, show=False) - img = _fig_to_img(plot_events, image_format, **kwargs) - caption = self._gen_caption(prefix='Events', fname=eve_fname, - data_path=data_path) - html = image_template.substitute( - img=img, id=global_id, div_klass='events', img_klass='events', - caption=caption, show=True, image_format=image_format) - return html - - def _render_epochs(self, epo_fname, image_format, data_path): - """Render epochs.""" - global_id = self._get_id() - epochs = read_epochs(epo_fname) - kwargs = dict(subject=self.subject, show=False) - img = _fig_to_img(epochs.plot_drop_log, image_format, **kwargs) - caption = self._gen_caption(prefix='Epochs', fname=epo_fname, - data_path=data_path) - show = True - html = image_template.substitute( - img=img, id=global_id, div_klass='epochs', img_klass='epochs', - caption=caption, show=show, image_format=image_format) - html += epochs._repr_html_() - return html - - def _render_cov(self, cov_fname, info_fname, image_format, data_path, - show_svd=True): - """Render cov.""" - global_id = self._get_id() - cov = read_cov(cov_fname) - fig, svd = plot_cov(cov, info_fname, show=False, show_svd=show_svd) - html = [] - figs = [fig] - captions = [self._gen_caption(prefix='Covariance', - suffix=f'(n_samples: {cov.nfree})', - fname=cov_fname, data_path=data_path)] - if svd is not None: - figs.append(svd) - captions.append('Singular values of the noise covariance') - for fig, caption in zip(figs, captions): - img = _fig_to_img(fig, image_format) - show = True - html.append(image_template.substitute( - img=img, id=global_id, div_klass='covariance', - img_klass='covariance', caption=caption, show=show, - image_format=image_format)) - return '\n'.join(html) - - def _render_whitened_evoked(self, evoked_fname, noise_cov, baseline, - image_format, data_path): - """Render whitened evoked.""" - evokeds = read_evokeds(evoked_fname, verbose=False) - html = [] - for ev in evokeds: - ev = read_evokeds(evoked_fname, ev.comment, baseline=baseline, - verbose=False) - global_id = self._get_id() - kwargs = dict(noise_cov=noise_cov, show=False) - img = _fig_to_img(ev.plot_white, image_format, **kwargs) - caption = self._gen_caption(prefix='Whitened evoked', - suffix=f'({ev.comment})', - fname=evoked_fname, - data_path=data_path) - show = True - html.append(image_template.substitute( - img=img, id=global_id, div_klass='evoked', - img_klass='evoked', caption=caption, show=show, - image_format=image_format)) - return '\n'.join(html) - - def _render_trans(self, trans, path, info, subject, subjects_dir, - data_path): - """Render trans (only PNG).""" - kwargs = dict(info=info, trans=trans, subject=subject, - subjects_dir=subjects_dir, dig=True, - meg=['helmet', 'sensors'], - coord_frame='mri') - try: - img = _iterate_trans_views(function=plot_alignment, - surfaces=['head-dense'], **kwargs) - except IOError: - img = _iterate_trans_views(function=plot_alignment, - surfaces=['head'], **kwargs) - - if img is not None: - global_id = self._get_id() - caption = self._gen_caption(prefix='Trans', fname=trans, - data_path=data_path) - html = image_template.substitute( - img=img, id=global_id, div_klass='trans', - img_klass='trans', caption=caption, width=75, - show=True, image_format='png') - return html - - def _render_bem(self, subject, subjects_dir, decim, n_jobs, - section='bem', caption='BEM', width=512): - """Render mri+bem (only PNG).""" - if subjects_dir is None: - subjects_dir = self.subjects_dir - subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) - - # Get the MRI filename - mri_fname = op.join(subjects_dir, subject, 'mri', 'T1.mgz') - if not op.isfile(mri_fname): - warn('MRI file "%s" does not exist' % mri_fname) - - # Get the BEM surface filenames - bem_path = op.join(subjects_dir, subject, 'bem') - - surfaces = _get_bem_plotting_surfaces(bem_path) - if len(surfaces) == 0: - warn('No BEM surfaces found, rendering empty MRI') - - html = [] - - global_id = self._get_id() - klass = _clean_varnames(section) - if section == 'bem': # special case for bulitin one - if 'bem' not in self.sections: - self.sections.append('bem') - self._sectionvars['bem'] = 'bem' - klass = 'bem' - name = caption - html += u'
  • \n' % (klass, global_id) - html += u'

    %s

    \n' % name # all other captions are h4 - for view in _BEM_VIEWS: - html += self._render_one_bem_axis(mri_fname, surfaces, global_id, - view, decim, n_jobs, width) - html += u'
    • \n' - return ''.join(html) - - -def _clean_varnames(s): - # Remove invalid characters - s = re.sub('[^0-9a-zA-Z_]', '', s) - - # add report_ at the beginning so that the javascript class names - # are valid ones - return 'report_' + s - - -def _recursive_search(path, pattern): - """Auxiliary function for recursive_search of the directory.""" - filtered_files = list() - for dirpath, dirnames, files in os.walk(path): - for f in fnmatch.filter(files, pattern): - # only the following file types are supported - # this ensures equitable distribution of jobs - if f.endswith(VALID_EXTENSIONS): - filtered_files.append(op.realpath(op.join(dirpath, f))) - - return filtered_files - - -def _fix_global_ids(html): - """Fix the global_ids after reordering in _render_toc().""" - html = re.sub(r'id="\d+"', 'id="###"', html) - global_id = 1 - while len(re.findall('id="###"', html)) > 0: - html = re.sub('id="###"', 'id="%s"' % global_id, html, count=1) - global_id += 1 - return html - - -############################################################################### -# Scraper for sphinx-gallery - -_SCRAPER_TEXT = ''' -.. only:: builder_html - - .. container:: row - - .. rubric:: The `HTML document <{0}>`__ written by :meth:`mne.Report.save`: - - .. raw:: html - - - -''' # noqa: E501 -# Adapted from fa-file-code -_FA_FILE_CODE = '' # noqa: E501 - - -class _ReportScraper(object): - """Scrape Report outputs. - - Only works properly if conf.py is configured properly and the file - is written to the same directory as the example script. - """ - - def __init__(self): - self.app = None - self.files = dict() - - def __repr__(self): - return '' - - def __call__(self, block, block_vars, gallery_conf): - for report in block_vars['example_globals'].values(): - if (isinstance(report, Report) and hasattr(report, 'fname') and - report.fname.endswith('.html') and - gallery_conf['builder_name'] == 'html'): - # Thumbnail - image_path_iterator = block_vars['image_path_iterator'] - img_fname = next(image_path_iterator) - img_fname = img_fname.replace('.png', '.svg') - with open(img_fname, 'w') as fid: - fid.write(_FA_FILE_CODE) - # copy HTML file - html_fname = op.basename(report.fname) - out_dir = op.join( - self.app.builder.outdir, - op.relpath(op.dirname(block_vars['target_file']), - self.app.builder.srcdir)) - os.makedirs(out_dir, exist_ok=True) - out_fname = op.join(out_dir, html_fname) - assert op.isfile(report.fname) - self.files[report.fname] = out_fname - # embed links/iframe - data = _SCRAPER_TEXT.format(html_fname) - return data - return '' - - def copyfiles(self, *args, **kwargs): - for key, value in self.files.items(): - copyfile(key, value) diff -Nru python-mne-0.23.4+dfsg/mne/simulation/evoked.py python-mne-1.1.0+dfsg/mne/simulation/evoked.py --- python-mne-0.23.4+dfsg/mne/simulation/evoked.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/simulation/evoked.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Daniel Strohmeier # Martin Luessi # -# License: BSD (3-clause) +# License: BSD-3-Clause import math import numpy as np @@ -15,7 +15,7 @@ @verbose -def simulate_evoked(fwd, stc, info, cov, nave=30, iir_filter=None, +def simulate_evoked(fwd, stc, info, cov=None, nave=30, iir_filter=None, random_state=None, use_cps=True, verbose=None): """Generate noisy evoked data. @@ -31,10 +31,9 @@ A forward solution. stc : SourceEstimate object The source time courses. - info : dict - Measurement info to generate the evoked. - cov : Covariance object - The noise covariance. + %(info_not_none)s Used to generate the evoked. + cov : Covariance object | None + The noise covariance. If None, no noise is added. nave : int Number of averaged epochs (defaults to 30). @@ -70,11 +69,14 @@ .. versionadded:: 0.10.0 """ evoked = apply_forward(fwd, stc, info, use_cps=use_cps) + if cov is None: + return evoked + if nave < np.inf: noise = _simulate_noise_evoked(evoked, cov, iir_filter, random_state) evoked.data += noise.data / math.sqrt(nave) evoked.nave = np.int64(nave) - if cov is not None and cov.get('projs', None): + if cov.get('projs', None): evoked.add_proj(cov['projs']).apply_proj() return evoked diff -Nru python-mne-0.23.4+dfsg/mne/simulation/metrics.py python-mne-1.1.0+dfsg/mne/simulation/metrics.py --- python-mne-0.23.4+dfsg/mne/simulation/metrics.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/simulation/metrics.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors: Yousra Bekhti # Mark Wronkiewicz # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np diff -Nru python-mne-0.23.4+dfsg/mne/simulation/raw.py python-mne-1.1.0+dfsg/mne/simulation/raw.py --- python-mne-0.23.4+dfsg/mne/simulation/raw.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/simulation/raw.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Yousra Bekhti # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause from collections.abc import Iterable @@ -16,10 +16,9 @@ from ..cov import make_ad_hoc_cov, read_cov, Covariance from ..bem import fit_sphere_to_headshape, make_sphere_model, read_bem_solution from ..io import RawArray, BaseRaw, Info -from ..chpi import (read_head_pos, head_pos_to_trans_rot_t, _get_hpi_info, +from ..chpi import (read_head_pos, head_pos_to_trans_rot_t, get_chpi_info, _get_hpi_initial_fit) from ..io.constants import FIFF -from ..fixes import einsum from ..forward import (_magnetic_dipole_field_vec, _merge_meg_eeg_fwds, _stc_src_sel, convert_forward_solution, _prepare_for_forward, _transform_orig_meg_coils, @@ -32,25 +31,21 @@ from ..surface import _CheckInside from ..utils import (logger, verbose, check_random_state, _pl, _validate_type, _check_preload) -from ..parallel import check_n_jobs from .source import SourceSimulator def _check_cov(info, cov): """Check that the user provided a valid covariance matrix for the noise.""" + _validate_type(cov, (Covariance, None, dict, str, 'path-like'), 'cov') if isinstance(cov, Covariance) or cov is None: pass elif isinstance(cov, dict): cov = make_ad_hoc_cov(info, cov, verbose=False) - elif isinstance(cov, str): + else: if cov == 'simple': cov = make_ad_hoc_cov(info, None, verbose=False) else: cov = read_cov(cov, verbose=False) - else: - raise TypeError('Covariance matrix type not recognized. Valid input ' - 'types are: instance of Covariance, dict, str, None. ' - ', got %s' % (cov,)) return cov @@ -130,7 +125,7 @@ @verbose def simulate_raw(info, stc=None, trans=None, src=None, bem=None, head_pos=None, - mindist=1.0, interp='cos2', n_jobs=1, use_cps=True, + mindist=1.0, interp='cos2', n_jobs=None, use_cps=True, forward=None, first_samp=0, max_iter=10000, verbose=None): u"""Simulate raw data. @@ -139,8 +134,7 @@ Parameters ---------- - info : instance of Info - The channel information to use for simulation. + %(info_not_none)s Used for simulation. .. versionchanged:: 0.18 Support for :class:`mne.Info`. @@ -245,7 +239,6 @@ .. footbibliography:: """ # noqa: E501 _validate_type(info, Info, 'info') - raw_verbose = verbose if len(pick_types(info, meg=False, stim=True)) == 0: event_ch = None @@ -253,7 +246,6 @@ event_ch = pick_channels(info['ch_names'], _get_stim_channel(None, info))[0] - n_jobs = check_n_jobs(n_jobs) if forward is not None: if any(x is not None for x in (trans, src, bem, head_pos)): raise ValueError('If forward is not None then trans, src, bem, ' @@ -319,7 +311,7 @@ None if n == 0 else verts) if event_ch is not None: this_data[event_ch, :] = stim_data[:n_doing] - this_data[meeg_picks] = einsum('svt,vt->st', fwd, stc_data) + this_data[meeg_picks] = np.einsum('svt,vt->st', fwd, stc_data) try: stc_counted = next(stc_enum) except StopIteration: @@ -333,13 +325,12 @@ raw_data = np.concatenate(raw_datas, axis=-1) raw = RawArray(raw_data, info, first_samp=first_samp, verbose=False) raw.set_annotations(raw.annotations) - raw.verbose = raw_verbose - logger.info('Done') + logger.info('[done]') return raw @verbose -def add_eog(raw, head_pos=None, interp='cos2', n_jobs=1, random_state=None, +def add_eog(raw, head_pos=None, interp='cos2', n_jobs=None, random_state=None, verbose=None): """Add blink noise to raw data. @@ -393,7 +384,7 @@ @verbose -def add_ecg(raw, head_pos=None, interp='cos2', n_jobs=1, random_state=None, +def add_ecg(raw, head_pos=None, interp='cos2', n_jobs=None, random_state=None, verbose=None): """Add ECG noise to raw data. @@ -528,7 +519,7 @@ proc_lims = np.concatenate([np.arange(0, len(used), 10000), [len(used)]]) for start, stop in zip(proc_lims[:-1], proc_lims[1:]): fwd, _ = interper.feed(stop - start) - data[picks, start:stop] += einsum( + data[picks, start:stop] += np.einsum( 'svt,vt->st', fwd, exg_data[:, start:stop]) assert not used[start:stop].any() used[start:stop] = True @@ -536,7 +527,7 @@ @verbose -def add_chpi(raw, head_pos=None, interp='cos2', n_jobs=1, verbose=None): +def add_chpi(raw, head_pos=None, interp='cos2', n_jobs=None, verbose=None): """Add cHPI activations to raw data. Parameters @@ -564,7 +555,7 @@ if len(meg_picks) == 0: raise RuntimeError('Cannot add cHPI if no MEG picks are present') dev_head_ts, offsets = _check_head_pos(head_pos, info, first_samp, times) - hpi_freqs, hpi_pick, hpi_ons = _get_hpi_info(info) + hpi_freqs, hpi_pick, hpi_ons = get_chpi_info(info, on_missing='raise') hpi_rrs = _get_hpi_initial_fit(info, verbose='error') hpi_nns = hpi_rrs / np.sqrt(np.sum(hpi_rrs * hpi_rrs, axis=1))[:, np.newaxis] @@ -575,7 +566,8 @@ sinusoids = 70e-9 * np.sin(2 * np.pi * hpi_freqs[:, np.newaxis] * (np.arange(len(times)) / info['sfreq'])) info = pick_info(info, meg_picks) - info.update(projs=[], bads=[]) # Ensure no 'projs' or 'bads' + with info._unlock(): + info.update(projs=[], bads=[]) # Ensure no 'projs' or 'bads' megcoils, _, _, _ = _prep_meg_channels(info, ignore_ref=False) used = np.zeros(len(raw.times), bool) dev_head_ts.append(dev_head_ts[-1]) # ZOH after time ends @@ -584,7 +576,7 @@ lims = np.concatenate([offsets, [len(raw.times)]]) for start, stop in zip(lims[:-1], lims[1:]): fwd, = interper.feed(stop - start) - data[meg_picks, start:stop] += einsum( + data[meg_picks, start:stop] += np.einsum( 'svt,vt->st', fwd, sinusoids[:, start:stop]) assert not used[start:stop].any() used[start:stop] = True @@ -691,7 +683,8 @@ """Calculate a forward solution for a subject.""" logger.info('Setting up forward solutions') info = pick_info(info, picks) - info.update(projs=[], bads=[]) # Ensure no 'projs' or 'bads' + with info._unlock(): + info.update(projs=[], bads=[]) # Ensure no 'projs' or 'bads' mri_head_t, trans = _get_trans(trans) megcoils, meg_info, compcoils, megnames, eegels, eegnames, rr, info, \ update_kwargs, bem = _prepare_for_forward( diff -Nru python-mne-0.23.4+dfsg/mne/simulation/source.py python-mne-1.1.0+dfsg/mne/simulation/source.py --- python-mne-0.23.4+dfsg/mne/simulation/source.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/simulation/source.py 2022-08-03 15:48:14.000000000 +0000 @@ -6,7 +6,7 @@ # Samuel Deslauriers-Gauthier # Ivana Kojcic # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np @@ -330,7 +330,7 @@ If None, it is computed using existing events and waveform lengths. first_samp : int First sample from which the simulation takes place, as an integer. - Comparable to the ``first_samp`` property of `~mne.io.Raw` objects. + Comparable to the :term:`first_samp` property of `~mne.io.Raw` objects. Default is 0. Attributes diff -Nru python-mne-0.23.4+dfsg/mne/simulation/tests/test_evoked.py python-mne-1.1.0+dfsg/mne/simulation/tests/test_evoked.py --- python-mne-0.23.4+dfsg/mne/simulation/tests/test_evoked.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/simulation/tests/test_evoked.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Author: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op @@ -17,7 +17,7 @@ from mne.io import read_raw_fif from mne.io.pick import pick_channels_cov from mne.cov import regularize, whiten_evoked -from mne.utils import run_tests_if_main, catch_logging, check_version +from mne.utils import catch_logging, check_version data_path = testing.data_path(download=False) fwd_fname = op.join(data_path, 'MEG', 'sample', @@ -142,7 +142,8 @@ # See gh-5940 evoked = read_evokeds(ave_fname, 0, baseline=(None, 0)) evoked.info['bads'] = ['MEG 2443'] - evoked.info['lowpass'] = 20 # fake for decim + with evoked.info._unlock(): + evoked.info['lowpass'] = 20 # fake for decim picks = pick_types(evoked.info, meg=True, eeg=False) picks = picks[::16] evoked.pick_channels([evoked.ch_names[pick] for pick in picks]) @@ -160,4 +161,23 @@ assert r > 0.98 -run_tests_if_main() +@testing.requires_testing_data +def test_order(): + """Test that order does not matter.""" + fwd = read_forward_solution(fwd_fname) + fwd = convert_forward_solution(fwd, force_fixed=True, use_cps=False) + evoked = read_evokeds(ave_fname)[0].pick_types(meg=True, eeg=True) + assert 'meg' in evoked + assert 'eeg' in evoked + meg_picks = pick_types(evoked.info, meg=True) + eeg_picks = pick_types(evoked.info, eeg=True) + # MEG then EEG + assert (eeg_picks > meg_picks.max()).all() + times = np.arange(10) / 1000. + stc = simulate_sparse_stc(fwd['src'], 1, times=times, random_state=0) + evoked_sim = simulate_evoked(fwd, stc, evoked.info, nave=np.inf) + reorder = np.concatenate([eeg_picks, meg_picks]) + evoked.reorder_channels([evoked.ch_names[pick] for pick in reorder]) + evoked_sim_2 = simulate_evoked(fwd, stc, evoked.info, nave=np.inf) + want_data = evoked_sim.data[reorder] + assert_allclose(evoked_sim_2.data, want_data) diff -Nru python-mne-0.23.4+dfsg/mne/simulation/tests/test_metrics.py python-mne-1.1.0+dfsg/mne/simulation/tests/test_metrics.py --- python-mne-0.23.4+dfsg/mne/simulation/tests/test_metrics.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/simulation/tests/test_metrics.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Author: Yousra Bekhti # Mark Wronkiewicz # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op @@ -13,7 +13,6 @@ from mne import read_source_spaces from mne.datasets import testing from mne.simulation import simulate_sparse_stc, source_estimate_quantification -from mne.utils import run_tests_if_main data_path = testing.data_path(download=False) src_fname = op.join(data_path, 'subjects', 'sample', 'bem', @@ -45,6 +44,3 @@ pytest.raises(ValueError, source_estimate_quantification, stc1, stc_bad) pytest.raises(ValueError, source_estimate_quantification, stc1, stc2, metric='foo') - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/simulation/tests/test_raw.py python-mne-1.1.0+dfsg/mne/simulation/tests/test_raw.py --- python-mne-0.23.4+dfsg/mne/simulation/tests/test_raw.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/simulation/tests/test_raw.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Yousra Bekhti # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op from copy import deepcopy @@ -20,7 +20,7 @@ make_bem_solution) from mne.bem import _surfaces_to_bem from mne.chpi import (read_head_pos, compute_chpi_amplitudes, - compute_chpi_locs, compute_head_pos, _get_hpi_info) + compute_chpi_locs, compute_head_pos, get_chpi_info) from mne.tests.test_chpi import _assert_quats from mne.datasets import testing from mne.simulation import (simulate_sparse_stc, simulate_raw, add_eog, @@ -184,8 +184,9 @@ raw.info.normalize_proj() ecg = RawArray(np.zeros((1, len(raw.times))), create_info(['ECG 063'], raw.info['sfreq'], 'ecg')) - for key in ('dev_head_t', 'highpass', 'lowpass', 'dig'): - ecg.info[key] = raw.info[key] + with ecg.info._unlock(): + for key in ('dev_head_t', 'highpass', 'lowpass', 'dig'): + ecg.info[key] = raw.info[key] raw.add_channels([ecg]) src = read_source_spaces(src_fname) @@ -208,12 +209,12 @@ return head_pos_sim -def test_simulate_raw_sphere(raw_data, tmpdir): +def test_simulate_raw_sphere(raw_data, tmp_path): """Test simulation of raw data with sphere model.""" seed = 42 raw, src, stc, trans, sphere = raw_data assert len(pick_types(raw.info, meg=False, ecg=True)) == 1 - tempdir = str(tmpdir) + tempdir = str(tmp_path) # head pos head_pos_sim = _get_head_pos_sim(raw) @@ -319,7 +320,8 @@ simulate_raw(info, stc, trans, src, sphere, head_pos=head_pos_sim_err) raw_bad = raw.copy() - raw_bad.info['dig'] = None + with raw_bad.info._unlock(): + raw_bad.info['dig'] = None with pytest.raises(RuntimeError, match='Cannot fit headshape'): add_eog(raw_bad) @@ -395,6 +397,7 @@ assert raw_sim.n_times == ss.n_times +@pytest.mark.slowtest # slow on Windows Azure def test_simulate_round_trip(raw_data): """Test simulate_raw round trip calculations.""" # Check a diagonal round-trip @@ -477,7 +480,7 @@ # need to trim extra samples off this one raw_chpi = add_chpi(raw_sim.copy(), head_pos=pos_fname, interp='zero') # test cHPI indication - hpi_freqs, hpi_pick, hpi_ons = _get_hpi_info(raw.info) + hpi_freqs, hpi_pick, hpi_ons = get_chpi_info(raw.info, on_missing='raise') assert_allclose(raw_sim[hpi_pick][0], 0.) assert_allclose(raw_chpi[hpi_pick][0], hpi_ons.sum()) # test that the cHPI signals make some reasonable values diff -Nru python-mne-0.23.4+dfsg/mne/simulation/tests/test_source.py python-mne-1.1.0+dfsg/mne/simulation/tests/test_source.py --- python-mne-0.23.4+dfsg/mne/simulation/tests/test_source.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/simulation/tests/test_source.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Author: Kostiantyn Maksymenko # Samuel Deslauriers-Gauthier # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op @@ -15,7 +15,7 @@ convert_forward_solution) from mne.label import Label from mne.simulation import simulate_stc, simulate_sparse_stc, SourceSimulator -from mne.utils import run_tests_if_main, check_version +from mne.utils import check_version data_path = testing.data_path(download=False) @@ -412,6 +412,3 @@ assert len(stcs) == 2 assert stcs[0].data.shape[1] == 6 assert stcs[1].data.shape[1] == 4 - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/source_estimate.py python-mne-1.1.0+dfsg/mne/source_estimate.py --- python-mne-0.23.4+dfsg/mne/source_estimate.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/source_estimate.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Martin Luessi # Mads Jensen # -# License: BSD (3-clause) +# License: BSD-3-Clause import contextlib import copy @@ -16,27 +16,28 @@ from .cov import Covariance from .evoked import _get_peak from .filter import resample +from .fixes import _safe_svd +from ._freesurfer import (_import_nibabel, _get_mri_info_data, + _get_atlas_values, read_freesurfer_lut) from .io.constants import FIFF +from .io.pick import pick_types from .surface import (read_surface, _get_ico_surface, mesh_edges, _project_onto_surface) from .source_space import (_ensure_src, _get_morph_src_reordering, _ensure_src_subject, SourceSpaces, _get_src_nn, - _import_nibabel, _get_mri_info_data, - _get_atlas_values, _check_volume_labels, - read_freesurfer_lut) + _check_volume_labels) from .transforms import _get_trans, apply_trans from .utils import (get_subjects_dir, _check_subject, logger, verbose, _pl, _time_mask, warn, copy_function_doc_to_method_doc, fill_doc, _check_option, _validate_type, _check_src_normal, _check_stc_units, _check_pandas_installed, _check_pandas_index_arguments, _convert_times, _ensure_int, - _build_data_frame, _check_time_format, _check_path_like, - sizeof_fmt, object_size) + _build_data_frame, _check_time_format, _path_like, + sizeof_fmt, object_size, _check_fname, _import_h5io_funcs) from .viz import (plot_source_estimates, plot_vector_source_estimates, plot_volume_source_estimates) from .io.base import TimeMixin from .io.meas_info import Info -from .externals.h5io import read_hdf5, write_hdf5 def _read_stc(filename): @@ -101,26 +102,20 @@ data : 2D array The data matrix (nvert * ntime). """ - fid = open(filename, 'wb') - - # write start time in ms - fid.write(np.array(1000 * tmin, dtype='>f4').tobytes()) - # write sampling rate in ms - fid.write(np.array(1000 * tstep, dtype='>f4').tobytes()) - # write number of vertices - fid.write(np.array(vertices.shape[0], dtype='>u4').tobytes()) - # write the vertex indices - fid.write(np.array(vertices, dtype='>u4').tobytes()) - - # write the number of timepts - fid.write(np.array(data.shape[1], dtype='>u4').tobytes()) - # - # write the data - # - fid.write(np.array(data.T, dtype='>f4').tobytes()) - - # close the file - fid.close() + filename + with open(filename, 'wb') as fid: + # write start time in ms + fid.write(np.array(1000 * tmin, dtype='>f4').tobytes()) + # write sampling rate in ms + fid.write(np.array(1000 * tstep, dtype='>f4').tobytes()) + # write number of vertices + fid.write(np.array(vertices.shape[0], dtype='>u4').tobytes()) + # write the vertex indices + fid.write(np.array(vertices, dtype='>u4').tobytes()) + # write the number of timepts + fid.write(np.array(data.shape[1], dtype='>u4').tobytes()) + # write the data + fid.write(np.array(data.T, dtype='>f4').tobytes()) def _read_3(fid): @@ -198,23 +193,19 @@ """ assert (len(vertices) == len(data)) - fid = open(filename, 'wb') - - # write 2 zero bytes - fid.write(np.zeros((2), dtype=np.uint8).tobytes()) - - # write number of vertices/sources (3 byte integer) - vertices_n = len(vertices) - _write_3(fid, vertices_n) - - # write the vertices and data - for i in range(vertices_n): - _write_3(fid, vertices[i]) - # XXX: without float() endianness is wrong, not sure why - fid.write(np.array(float(data[i]), dtype='>f4').tobytes()) + with open(filename, 'wb') as fid: + # write 2 zero bytes + fid.write(np.zeros((2), dtype=np.uint8).tobytes()) + + # write number of vertices/sources (3 byte integer) + vertices_n = len(vertices) + _write_3(fid, vertices_n) - # close the file - fid.close() + # write the vertices and data + for i in range(vertices_n): + _write_3(fid, vertices[i]) + # XXX: without float() endianness is wrong, not sure why + fid.write(np.array(float(data[i]), dtype='>f4').tobytes()) def read_source_estimate(fname, subject=None): @@ -253,13 +244,16 @@ """ # noqa: E501 fname_arg = fname _validate_type(fname, 'path-like', 'fname') - fname = str(fname) + + # expand `~` without checking whether the file actually exists – we'll + # take care of that later, as it's complicated by the different suffixes + # STC files can have + fname = _check_fname(fname=fname, overwrite='read', must_exist=False) # make sure corresponding file(s) can be found ftype = None if op.exists(fname): - if fname.endswith('-vl.stc') or fname.endswith('-vol.stc') or \ - fname.endswith('-vl.w') or fname.endswith('-vol.w'): + if fname.endswith(('-vl.stc', '-vol.stc', '-vl.w', '-vol.w')): ftype = 'volume' elif fname.endswith('.stc'): ftype = 'surface' @@ -336,6 +330,7 @@ kwargs['tstep'] = 1.0 ftype = 'surface' elif ftype == 'h5': + read_hdf5, _ = _import_h5io_funcs() kwargs = read_hdf5(fname + '.h5', title='mnepython') ftype = kwargs.pop('src_type', 'surface') if isinstance(kwargs['vertices'], np.ndarray): @@ -509,7 +504,6 @@ self._tmin = tmin self._tstep = tstep self.vertices = vertices - self.verbose = verbose self._kernel = kernel self._sens_data = sens_data self._kernel_removed = False @@ -566,15 +560,15 @@ Parameters ---------- - %(eltc_labels)s - %(eltc_src)s - %(eltc_mode)s - %(eltc_allow_empty)s - %(verbose_meth)s + %(labels_eltc)s + %(src_eltc)s + %(mode_eltc)s + %(allow_empty_eltc)s + %(verbose)s Returns ------- - %(eltc_returns)s + %(label_tc_el_returns)s See Also -------- @@ -597,7 +591,7 @@ %(baseline_stc)s Defaults to ``(None, 0)``, i.e. beginning of the the data until time point zero. - %(verbose_meth)s + %(verbose)s Returns ------- @@ -612,7 +606,7 @@ return self @verbose - def save(self, fname, ftype='h5', verbose=None): + def save(self, fname, ftype='h5', *, overwrite=False, verbose=None): """Save the full source estimate to an HDF5 file. Parameters @@ -622,20 +616,25 @@ '-stc.h5'. ftype : str File format to use. Currently, the only allowed values is "h5". - %(verbose_meth)s + %(overwrite)s + + .. versionadded:: 1.0 + %(verbose)s """ - _validate_type(fname, 'path-like', 'fname') - fname = str(fname) + fname = _check_fname(fname=fname, overwrite=True) # check below if ftype != 'h5': raise ValueError('%s objects can only be written as HDF5 files.' % (self.__class__.__name__,)) + _, write_hdf5 = _import_h5io_funcs() if not fname.endswith('.h5'): fname += '-stc.h5' + fname = _check_fname(fname=fname, overwrite=overwrite) write_hdf5(fname, dict(vertices=self.vertices, data=self.data, tmin=self.tmin, tstep=self.tstep, subject=self.subject, src_type=self._src_type), - title='mnepython', overwrite=True) + title='mnepython', + overwrite=True) @copy_function_doc_to_method_doc(plot_source_estimates) def plot(self, subject=None, surface='inflated', hemi='lh', @@ -707,7 +706,7 @@ return self # return self for chaining methods @verbose - def resample(self, sfreq, npad='auto', window='boxcar', n_jobs=1, + def resample(self, sfreq, npad='auto', window='boxcar', n_jobs=None, verbose=None): """Resample data. @@ -725,7 +724,7 @@ window : str | tuple Window to use in resampling. See :func:`scipy.signal.resample`. %(n_jobs)s - %(verbose_meth)s + %(verbose)s Returns ------- @@ -1201,9 +1200,10 @@ return stcs - @fill_doc + @verbose def to_data_frame(self, index=None, scalings=None, - long_format=False, time_format='ms'): + long_format=False, time_format=None, *, + verbose=None): """Export data in tabular structure as a pandas DataFrame. Vertices are converted to columns in the DataFrame. By default, @@ -1212,13 +1212,14 @@ Parameters ---------- - %(df_index_evk)s + %(index_df_evk)s Defaults to ``None``. - %(df_scalings)s - %(df_longform_stc)s - %(df_time_format)s + %(scalings_df)s + %(long_format_df_stc)s + %(time_format_df)s .. versionadded:: 0.20 + %(verbose)s Returns ------- @@ -1295,13 +1296,9 @@ Vertex numbers corresponding to the data. The first element of the list contains vertices of left hemisphere and the second element contains vertices of right hemisphere. - tmin : scalar - Time point of the first sample in data. - tstep : scalar - Time step between successive samples in data. - subject : str | None - The subject name. While not necessary, it is safer to set the - subject parameter to avoid analysis errors. + %(tmin)s + %(tstep)s + %(subject_optional)s %(verbose)s Attributes @@ -1464,7 +1461,7 @@ The original subject. For most source spaces this shouldn't need to be provided, since it is stored in the source space itself. %(subjects_dir)s - %(verbose_meth)s + %(verbose)s Returns ------- @@ -1551,13 +1548,9 @@ Vertex numbers corresponding to the data. The first element of the list contains vertices of left hemisphere and the second element contains vertices of right hemisphere. - tmin : scalar - Time point of the first sample in data. - tstep : scalar - Time step between successive samples in data. - subject : str | None - The subject name. While not necessary, it is safer to set the - subject parameter to avoid analysis errors. + %(tmin)s + %(tstep)s + %(subject_optional)s %(verbose)s Attributes @@ -1582,7 +1575,7 @@ """ @verbose - def save(self, fname, ftype='stc', verbose=None): + def save(self, fname, ftype='stc', *, overwrite=False, verbose=None): """Save the source estimates to a file. Parameters @@ -1595,10 +1588,12 @@ ftype : str File format to use. Allowed values are "stc" (default), "w", and "h5". The "w" format only supports a single time point. - %(verbose_meth)s + %(overwrite)s + + .. versionadded:: 1.0 + %(verbose)s """ - _validate_type(fname, 'path-like', 'fname') - fname = str(fname) + fname = _check_fname(fname=fname, overwrite=True) # checked below _check_option('ftype', ftype, ['stc', 'w', 'h5']) lh_data = self.data[:len(self.lh_vertno)] @@ -1611,23 +1606,23 @@ "in HDF5 format instead, or cast the data to " "real numbers before saving.") logger.info('Writing STC to disk...') - _write_stc(fname + '-lh.stc', tmin=self.tmin, tstep=self.tstep, + fname_l = _check_fname(fname + '-lh.stc', overwrite=overwrite) + fname_r = _check_fname(fname + '-rh.stc', overwrite=overwrite) + _write_stc(fname_l, tmin=self.tmin, tstep=self.tstep, vertices=self.lh_vertno, data=lh_data) - _write_stc(fname + '-rh.stc', tmin=self.tmin, tstep=self.tstep, + _write_stc(fname_r, tmin=self.tmin, tstep=self.tstep, vertices=self.rh_vertno, data=rh_data) - elif ftype == 'w': if self.shape[1] != 1: raise ValueError('w files can only contain a single time ' 'point') logger.info('Writing STC to disk (w format)...') - _write_w(fname + '-lh.w', vertices=self.lh_vertno, - data=lh_data[:, 0]) - _write_w(fname + '-rh.w', vertices=self.rh_vertno, - data=rh_data[:, 0]) - + fname_l = _check_fname(fname + '-lh.w', overwrite=overwrite) + fname_r = _check_fname(fname + '-rh.w', overwrite=overwrite) + _write_w(fname_l, vertices=self.lh_vertno, data=lh_data[:, 0]) + _write_w(fname_r, vertices=self.rh_vertno, data=rh_data[:, 0]) elif ftype == 'h5': - super().save(fname) + super().save(fname, overwrite=overwrite) logger.info('[done]') @verbose @@ -1643,8 +1638,7 @@ Parameters ---------- - info : instance Info - The measurement info. + %(info_not_none)s fwd : instance of Forward The forward solution used to create the source estimate. cov : instance of Covariance @@ -1810,8 +1804,7 @@ """ data_mag = np.linalg.norm(self.data, axis=1) return self._scalar_class( - data_mag, self.vertices, self.tmin, self.tstep, self.subject, - self.verbose) + data_mag, self.vertices, self.tmin, self.tstep, self.subject) def _get_src_normals(self, src, use_cps): normals = np.vstack([_get_src_nn(s, use_cps, v) for s, v in @@ -1899,8 +1892,7 @@ 'directions.shape', directions.shape, [(self.data.shape[0], 3)]) data_norm = np.matmul(directions[:, np.newaxis], self.data)[:, 0] stc = self._scalar_class( - data_norm, self.vertices, self.tmin, self.tstep, self.subject, - self.verbose) + data_norm, self.vertices, self.tmin, self.tstep, self.subject) return stc, directions @copy_function_doc_to_method_doc(plot_vector_source_estimates) @@ -1983,16 +1975,16 @@ Parameters ---------- - %(eltc_labels)s - %(eltc_src)s - %(eltc_mode)s - %(eltc_allow_empty)s - %(eltc_mri_resolution)s - %(verbose_meth)s + %(labels_eltc)s + %(src_eltc)s + %(mode_eltc)s + %(allow_empty_eltc)s + %(mri_resolution_eltc)s + %(verbose)s Returns ------- - %(eltc_returns)s + %(label_tc_el_returns)s See Also -------- @@ -2024,7 +2016,7 @@ src : instance of SourceSpaces The volumetric source space. It must be a single, whole-brain volume. - %(verbose_meth)s + %(verbose)s Returns ------- @@ -2053,8 +2045,9 @@ tstep=self.tstep, subject=self.subject) return label_stc + @verbose def save_as_volume(self, fname, src, dest='mri', mri_resolution=False, - format='nifti1'): + format='nifti1', *, overwrite=False, verbose=None): """Save a volume source estimate in a NIfTI file. Parameters @@ -2076,6 +2069,12 @@ Either 'nifti1' (default) or 'nifti2'. .. versionadded:: 0.17 + %(overwrite)s + + .. versionadded:: 1.0 + %(verbose)s + + .. versionadded:: 1.0 Returns ------- @@ -2088,7 +2087,7 @@ """ import nibabel as nib _validate_type(fname, 'path-like', 'fname') - fname = str(fname) + fname = _check_fname(fname=fname, overwrite=overwrite) img = self.as_volume(src, dest=dest, mri_resolution=mri_resolution, format=format) nib.save(img, fname) @@ -2140,15 +2139,10 @@ a tuple with two arrays: "kernel" shape (n_vertices, n_sensors) and "sens_data" shape (n_sensors, n_times). In this case, the source space data corresponds to ``np.dot(kernel, sens_data)``. - vertices : array of shape (n_dipoles,) - The indices of the dipoles in the source space. - tmin : scalar - Time point of the first sample in data. - tstep : scalar - Time step between successive samples in data. - subject : str | None - The subject name. While not necessary, it is safer to set the - subject parameter to avoid analysis errors. + %(vertices_volume)s + %(tmin)s + %(tstep)s + %(subject_optional)s %(verbose)s Attributes @@ -2157,8 +2151,7 @@ The subject name. times : array of shape (n_times,) The time vector. - vertices : array of shape (n_dipoles,) - The indices of the dipoles in the source space. + %(vertices_volume)s data : array of shape (n_dipoles, n_times) The data in source space. shape : tuple @@ -2177,7 +2170,7 @@ """ @verbose - def save(self, fname, ftype='stc', verbose=None): + def save(self, fname, ftype='stc', *, overwrite=False, verbose=None): """Save the source estimates to a file. Parameters @@ -2188,10 +2181,13 @@ ftype : str File format to use. Allowed values are "stc" (default), "w", and "h5". The "w" format only supports a single time point. - %(verbose_meth)s + %(overwrite)s + + .. versionadded:: 1.0 + %(verbose)s """ - _validate_type(fname, 'path-like', 'fname') - fname = str(fname) + # check overwrite individually below + fname = _check_fname(fname=fname, overwrite=True) # checked below _check_option('ftype', ftype, ['stc', 'w', 'h5']) if ftype != 'h5' and len(self.vertices) != 1: raise ValueError('Can only write to .stc or .w if a single volume ' @@ -2201,17 +2197,19 @@ ', use .h5 instead') if ftype == 'stc': logger.info('Writing STC to disk...') - if not (fname.endswith('-vl.stc') or fname.endswith('-vol.stc')): + if not fname.endswith(('-vl.stc', '-vol.stc')): fname += '-vl.stc' + fname = _check_fname(fname, overwrite=overwrite) _write_stc(fname, tmin=self.tmin, tstep=self.tstep, vertices=self.vertices[0], data=self.data) elif ftype == 'w': logger.info('Writing STC to disk (w format)...') - if not (fname.endswith('-vl.w') or fname.endswith('-vol.w')): + if not fname.endswith(('-vl.w', '-vol.w')): fname += '-vl.w' + fname = _check_fname(fname, overwrite=overwrite) _write_w(fname, vertices=self.vertices[0], data=self.data) elif ftype == 'h5': - super().save(fname, 'h5') + super().save(fname, 'h5', overwrite=overwrite) logger.info('[done]') @@ -2225,15 +2223,10 @@ data : array of shape (n_dipoles, 3, n_times) The data in source space. Each dipole contains three vectors that denote the dipole strength in X, Y and Z directions over time. - vertices : array of shape (n_dipoles,) - The indices of the dipoles in the source space. - tmin : scalar - Time point of the first sample in data. - tstep : scalar - Time step between successive samples in data. - subject : str | None - The subject name. While not necessary, it is safer to set the - subject parameter to avoid analysis errors. + %(vertices_volume)s + %(tmin)s + %(tstep)s + %(subject_optional)s %(verbose)s Attributes @@ -2242,8 +2235,7 @@ The subject name. times : array of shape (n_times,) The time vector. - vertices : array of shape (n_dipoles,) - The indices of the dipoles in the source space. + %(vertices_volume)s data : array of shape (n_dipoles, n_times) The data in source space. shape : tuple @@ -2309,13 +2301,9 @@ Vertex numbers corresponding to the data. The first element of the list contains vertices of left hemisphere and the second element contains vertices of right hemisphere. - tmin : float - Time point of the first sample in data. - tstep : float - Time step between successive samples in data. - subject : str | None - The subject name. While not necessary, it is safer to set the - subject parameter to avoid analysis errors. + %(tmin)s + %(tstep)s + %(subject_optional)s %(verbose)s Attributes @@ -2378,7 +2366,7 @@ klass = SourceEstimate return klass( self.data[:self._n_surf_vert], self.vertices[:2], - self.tmin, self.tstep, self.subject, self.verbose) + self.tmin, self.tstep, self.subject) def volume(self): """Return the volume surface source estimate. @@ -2394,7 +2382,7 @@ klass = VolSourceEstimate return klass( self.data[self._n_surf_vert:], self.vertices[2:], - self.tmin, self.tstep, self.subject, self.verbose) + self.tmin, self.tstep, self.subject) @fill_doc @@ -2411,13 +2399,9 @@ vertices : list of array Vertex numbers corresponding to the data. The list contains arrays with one array per source space. - tmin : scalar - Time point of the first sample in data. - tstep : scalar - Time step between successive samples in data. - subject : str | None - The subject name. While not necessary, it is safer to set the - subject parameter to avoid analysis errors. + %(tmin)s + %(tstep)s + %(subject_optional)s %(verbose)s Attributes @@ -2459,13 +2443,9 @@ denote the dipole strength in X, Y and Z directions over time. vertices : list of array, shape (n_src,) Vertex numbers corresponding to the data. - tmin : scalar - Time point of the first sample in data. - tstep : scalar - Time step between successive samples in data. - subject : str | None - The subject name. While not necessary, it is safer to set the - subject parameter to avoid analysis errors. + %(tmin)s + %(tstep)s + %(subject_optional)s %(verbose)s Attributes @@ -2830,8 +2810,7 @@ def _pca_flip(flip, data): - from scipy import linalg - U, s, V = linalg.svd(data, full_matrices=False) + U, s, V = _safe_svd(data, full_matrices=False) # determine sign-flip sign = np.sign(np.dot(U[:, 0], flip)) # use average power in label for scaling @@ -2991,7 +2970,7 @@ extra = ' when using a volume source space' _import_nibabel('use volume atlas labels') _validate_type(labels, ('path-like', list, tuple), 'labels' + extra) - if _check_path_like(labels): + if _path_like(labels): mri = labels infer_labels = True else: @@ -3176,18 +3155,18 @@ ---------- stcs : SourceEstimate | list (or generator) of SourceEstimate The source estimates from which to extract the time course. - %(eltc_labels)s - %(eltc_src)s - %(eltc_mode)s - %(eltc_allow_empty)s + %(labels_eltc)s + %(src_eltc)s + %(mode_eltc)s + %(allow_empty_eltc)s return_generator : bool If True, a generator instead of a list is returned. - %(eltc_mri_resolution)s + %(mri_resolution_eltc)s %(verbose)s Returns ------- - %(eltc_returns)s + %(label_tc_el_returns)s Notes ----- @@ -3224,7 +3203,8 @@ @verbose def stc_near_sensors(evoked, trans, subject, distance=0.01, mode='sum', - project=True, subjects_dir=None, src=None, verbose=None): + project=True, subjects_dir=None, src=None, picks=None, + surface='pial', verbose=None): """Create a STC from ECoG, sEEG and DBS sensor data. Parameters @@ -3237,12 +3217,16 @@ distance : float Distance (m) defining the activation "ball" of the sensor. mode : str - Can be "sum" to do a linear sum of weights, "nearest" to + Can be "sum" to do a linear sum of weights, "weighted" to make this + a weighted sum, "nearest" to use only the weight of the nearest sensor, or "single" to do a distance-weight of the nearest sensor. Default is "sum". See Notes. + + .. versionchanged:: 0.24 + Added "weighted" option. project : bool - If True, project the electrodes to the nearest ``'pial`` surface + If True, project the sensors to the nearest ``'pial`` surface vertex before computing distances. Only used when doing a surface projection. %(subjects_dir)s @@ -3250,7 +3234,16 @@ The source space. .. warning:: If a surface source space is used, make sure that - ``surf='pial'`` was used during construction. + ``surface='pial'`` was used during construction, + or that you set ``surface='pial'`` here. + %(picks_base)s good sEEG, ECoG, and DBS channels. + + .. versionadded:: 0.24 + surface : str | None + The surface to use if ``src=None``. Default is the pial surface. + If None, the source space surface will be used. + + .. versionadded:: 0.24.1 %(verbose)s Returns @@ -3275,16 +3268,19 @@ Activation is the sum across each sensor weighted by the fractional ``distance`` from each sensor. A sensor with zero distance gets weight 1 and a sensor at ``distance`` meters away (or larger) gets weight 0. - If ``distance`` is less than the distance between any two electrodes, - this will be the same as ``'nearest'``. + If ``distance`` is less than half the distance between any two + sensors, this will be the same as ``'single'``. - ``'single'`` - Same as ``'sum'`` except that only the nearest electrode is used, - rather than summing across electrodes within the ``distance`` radius. + Same as ``'sum'`` except that only the nearest sensor is used, + rather than summing across sensors within the ``distance`` radius. As ``'nearest'`` for vertices with distance zero to the projected sensor. - ``'nearest'`` The value is given by the value of the nearest sensor, up to a ``distance`` (beyond which it is zero). + - ``'weighted'`` + The value is given by the same as ``sum`` but the total weight for + each vertex is 1. (i.e., it's a weighted sum based on proximity). If creating a Volume STC, ``src`` must be passed in, and this function will project sEEG and DBS sensors to nearby surrounding vertices. @@ -3298,10 +3294,16 @@ _validate_type(evoked, Evoked, 'evoked') _validate_type(mode, str, 'mode') _validate_type(src, (None, SourceSpaces), 'src') - _check_option('mode', mode, ('sum', 'single', 'nearest')) + _check_option('mode', mode, ('sum', 'single', 'nearest', 'weighted')) # create a copy of Evoked using ecog, seeg and dbs - evoked = evoked.copy().pick_types(ecog=True, seeg=True, dbs=True) + if picks is None: + picks = pick_types(evoked.info, ecog=True, seeg=True, dbs=True) + evoked = evoked.copy().pick(picks) + frames = set(evoked.info['chs'][pick]['coord_frame'] for pick in picks) + if not frames == {FIFF.FIFFV_COORD_HEAD}: + raise RuntimeError('Channels must be in the head coordinate frame, ' + f'got {sorted(frames)}') # get channel positions that will be used to pinpoint where # in the Source space we will use the evoked data @@ -3322,17 +3324,27 @@ subject = _check_subject(None, subject, raise_error=False) subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + if surface is not None: + surf_rr = [read_surface(op.join(subjects_dir, subject, 'surf', + f'{hemi}.{surface}'))[0] / 1000. + for hemi in ('lh', 'rh')] if src is None: # fake a full surface one - rrs = [read_surface(op.join(subjects_dir, subject, - 'surf', f'{hemi}.pial'))[0] - for hemi in ('lh', 'rh')] + _validate_type(surface, str, 'surface', 'when src is None') src = SourceSpaces([ - dict(rr=rr / 1000., vertno=np.arange(len(rr)), type='surf', + dict(rr=rr, vertno=np.arange(len(rr)), type='surf', coord_frame=FIFF.FIFFV_COORD_MRI) - for rr in rrs]) - del rrs + for rr in surf_rr]) + rrs = np.concatenate([s_rr[s['vertno']] for s_rr, s in + zip(surf_rr, src)]) keep_all = False else: + if surface is None: + rrs = np.concatenate([s['rr'][s['vertno']] for s in src]) + if src[0]['coord_frame'] == FIFF.FIFFV_COORD_HEAD: + rrs = apply_trans(trans, rrs) + else: + rrs = np.concatenate([s_rr[s['vertno']] for s_rr, s in + zip(surf_rr, src)]) keep_all = True # ensure it's a usable one klass = dict( @@ -3342,15 +3354,12 @@ ) _check_option('src.kind', src.kind, sorted(klass.keys())) klass = klass[src.kind] - rrs = np.concatenate([s['rr'][s['vertno']] for s in src]) - if src[0]['coord_frame'] == FIFF.FIFFV_COORD_HEAD: - rrs = apply_trans(trans, rrs) # projection will only occur with surfaces logger.info( f'Projecting data from {len(pos)} sensor{_pl(pos)} onto {len(rrs)} ' f'{src.kind} vertices: {mode} mode') if project and src.kind == 'surface': - logger.info(' Projecting electrodes onto surface') + logger.info(' Projecting sensors onto surface') pos = _project_onto_surface(pos, dict(rr=rrs), project_rrs=True, method='nearest')[2] @@ -3375,24 +3384,28 @@ vals = w[range_, idx] if mode == 'single' else 1. w.fill(0) w[range_, idx] = vals + elif mode == 'weighted': + norms = w.sum(-1, keepdims=True) + norms[norms == 0] = 1. + w /= norms missing = np.where(~np.any(w, axis=0))[0] if len(missing): warn(f'Channel{_pl(missing)} missing in STC: ' f'{", ".join(evoked.ch_names[mi] for mi in missing)}') nz_data = w @ evoked.data - if not keep_all: - assert src.kind == 'surface' - data = nz_data - offset = len(src[0]['vertno']) - vertices = [vertices[vertices < offset], - vertices[vertices >= offset] - offset] - else: + if keep_all: data = np.zeros( (sum(len(s['vertno']) for s in src), len(evoked.times)), dtype=nz_data.dtype) data[vertices] = nz_data vertices = [s['vertno'].copy() for s in src] + else: + assert src.kind == 'surface' + data = nz_data + offset = len(src[0]['vertno']) + vertices = [vertices[vertices < offset], + vertices[vertices >= offset] - offset] return klass(data, vertices, evoked.times[0], 1. / evoked.info['sfreq'], subject=subject, verbose=verbose) diff -Nru python-mne-0.23.4+dfsg/mne/source_space.py python-mne-1.1.0+dfsg/mne/source_space.py --- python-mne-0.23.4+dfsg/mne/source_space.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/source_space.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,101 +1,48 @@ # Authors: Matti Hämäläinen # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause # Many of the computations in this code were derived from Matti Hämäläinen's # C code. from copy import deepcopy from functools import partial -from gzip import GzipFile import os import os.path as op import numpy as np from .io.constants import FIFF -from .io.meas_info import create_info, Info, read_fiducials +from .io.meas_info import create_info, Info from .io.tree import dir_tree_find from .io.tag import find_tag, read_tag from .io.open import fiff_open from .io.write import (start_block, end_block, write_int, write_float_sparse_rcs, write_string, write_float_matrix, write_int_matrix, - write_coord_trans, start_file, end_file, write_id) + write_coord_trans, start_and_end_file, write_id) from .io.pick import channel_type, _picks_to_idx -from .bem import read_bem_surfaces +from .bem import read_bem_surfaces, ConductorModel from .fixes import _get_img_fdata from .surface import (read_surface, _create_surf_spacing, _get_ico_surface, _tessellate_sphere_surf, _get_surf_neighbors, _normalize_vectors, _triangle_neighbors, mesh_dist, complete_surface_info, _compute_nearest, fast_cross_3d, _CheckInside) +# keep get_mni_fiducials here just for easy backward compat +from ._freesurfer import (_get_mri_info_data, _get_atlas_values, # noqa: F401 + read_freesurfer_lut, get_mni_fiducials, _check_mri) from .utils import (get_subjects_dir, check_fname, logger, verbose, fill_doc, _ensure_int, check_version, _get_call_line, warn, - _check_fname, _check_path_like, has_nibabel, _check_sphere, + _check_fname, _path_like, _check_sphere, _validate_type, _check_option, _is_numeric, _pl, _suggest, object_size, sizeof_fmt) -from .parallel import parallel_func, check_n_jobs +from .parallel import parallel_func from .transforms import (invert_transform, apply_trans, _print_coord_trans, combine_transforms, _get_trans, _coord_frame_name, Transform, _str_to_frame, - _ensure_trans, read_ras_mni_t) - - -def read_freesurfer_lut(fname=None): - """Read a Freesurfer-formatted LUT. - - Parameters - ---------- - fname : str | None - The filename. Can be None to read the standard Freesurfer LUT. - - Returns - ------- - atlas_ids : dict - Mapping from label names to IDs. - colors : dict - Mapping from label names to colors. - """ - lut = _get_lut(fname) - names, ids = lut['name'], lut['id'] - colors = np.array([lut['R'], lut['G'], lut['B'], lut['A']], float).T - atlas_ids = dict(zip(names, ids)) - colors = dict(zip(names, colors)) - return atlas_ids, colors - - -def _get_lut(fname=None): - """Get a FreeSurfer LUT.""" - _validate_type(fname, ('path-like', None), 'fname') - if fname is None: - fname = op.join(op.dirname(__file__), 'data', 'FreeSurferColorLUT.txt') - _check_fname(fname, 'read', must_exist=True) - dtype = [('id', ' 0 - return lut - - -def _get_lut_id(lut, label): - """Convert a label to a LUT ID number.""" - assert isinstance(label, str) - mask = (lut['name'] == label) - assert mask.sum() == 1 - return lut['id'][mask] + _ensure_trans) _src_kind_dict = { @@ -108,8 +55,14 @@ class SourceSpaces(list): """Represent a list of source space. - Currently implemented as a list of dictionaries containing the source - space information + This class acts like a list of dictionaries containing the source + space information, one entry in the list per source space type. See + Notes for details. + + .. warning:: + This class should not be created or modified by the end user. Use + :func:`mne.setup_source_space`, :func:`mne.setup_volume_source_space`, + or :func:`mne.read_source_spaces` to create :class:`SourceSpaces`. Parameters ---------- @@ -121,9 +74,126 @@ Attributes ---------- + kind : str + The kind of source space, one of + ``{'surface', 'volume', 'discrete', 'mixed'}``. info : dict Dictionary with information about the creation of the source space file. Has keys 'working_dir' and 'command_line'. + + See Also + -------- + mne.setup_source_space : Setup a surface source space. + mne.setup_volume_source_space : Setup a volume source space. + mne.read_source_spaces : Read source spaces from a file. + + Notes + ----- + Each element in SourceSpaces (e.g., ``src[0]``) is a dictionary. For + example, a surface source space will have ``len(src) == 2``, one entry for + each hemisphere. A volume source space will have ``len(src) == 1`` if it + uses a single monolithic grid, or ``len(src) == len(volume_label)`` when + created with a list-of-atlas-labels. A mixed source space consists of both + surface and volumetric source spaces in a single SourceSpaces object. + + Each of those dictionaries can be accessed using standard Python + :class:`python:dict` access using the string keys listed below (e.g., + ``src[0]['type'] == 'surf'``). The relevant key/value pairs depend on + the source space type: + + **Relevant to all source spaces** + + The following are always present: + + id : int + The FIF ID, either ``FIFF.FIFFV_MNE_SURF_LEFT_HEMI`` or + ``FIFF.FIFFV_MNE_SURF_RIGHT_HEMI`` for surfaces, or + ``FIFF.FIFFV_MNE_SURF_UNKNOWN`` for volume source spaces. + type : str + The type of source space, one of ``{'surf', 'vol', 'discrete'}``. + np : int + Number of vertices in the dense surface or complete volume. + coord_frame : int + The coordinate frame, usually ``FIFF.FIFFV_COORD_MRI``. + rr : ndarray, shape (np, 3) + The dense surface or complete volume vertex locations. + nn : ndarray, shape (np, 3) + The dense surface or complete volume normals. + nuse : int + The number of points in the subsampled surface. + inuse : ndarray, shape (np,) + An integer array defining whether each dense surface vertex is used + (``1``) or unused (``0``). + vertno : ndarray, shape (n_src,) + The vertex numbers of the dense surface or complete volume that are + used (i.e., ``np.where(src[0]['inuse'])[0]``). + subject_his_id : str + The FreeSurfer subject name. + + **Surface source spaces** + + Surface source spaces created using :func:`mne.setup_source_space` can have + the following additional entries (which will be missing, or have values of + ``None`` or ``0`` for volumetric source spaces): + + ntri : int + Number of triangles in the dense surface triangulation. + tris : ndarray, shape (ntri, 3) + The dense surface triangulation. + nuse_tri : int + The number of triangles in the subsampled surface. + use_tris : ndarray, shape (nuse_tri, 3) + The subsampled surface triangulation. + dist : scipy.sparse.csr_matrix, shape (n_src, n_src) | None + The distances (euclidean for volume, along the cortical surface for + surfaces) between source points. + dist_limit : float + The maximum distance allowed for inclusion in ``nearest``. + pinfo : dict + Information about the patch of cortex represented by a vertex in + the subsampled surface. + patch_inds : list of ndarray + For each vertex in the subsampled surface, the indices of the + vertices in the dense surface that it represents (i.e., is closest + to of all subsampled indices). + nearest : ndarray, shape (np,) + For each vertex on the dense surface, this gives the vertex index + on the subsampled surface that it's closest to. + nearest_dist : ndarray, shape (np,) + The distances corresponding to ``nearest``. + + **Volume source spaces** + + Volume source spaces created using :func:`mne.setup_volume_source_space` + can have the following additional entries (which will be missing, or + have values of ``None`` or ``0`` for surface source spaces): + + mri_width, mri_height, mri_depth : int + The MRI dimensions (in voxels). + neighbor_vert : ndarray + The 26-neighborhood information for each vertex. + interpolator : scipy.sparse.csr_matrix | None + The linear interpolator to go from the subsampled volume vertices + to the high-resolution volume. + shape : tuple of int + The shape of the subsampled grid. + mri_ras_t : instance of :class:`~mne.transforms.Transform` + The transformation from MRI surface RAS (``FIFF.FIFFV_COORD_MRI``) + to MRI scanner RAS (``FIFF.FIFFV_MNE_COORD_RAS``). + src_mri_t : instance of :class:`~mne.transforms.Transform` + The transformation from subsampled source space voxel to MRI + surface RAS. + vox_mri_t : instance of :class:`~mne.transforms.Transform` + The transformation from the original MRI voxel + (``FIFF.FIFFV_MNE_COORD_MRI_VOXEL``) space to MRI surface RAS. + mri_volume_name : str + The MRI volume name, e.g. ``'subjects_dir/subject/mri/T1.mgz'``. + seg_name : str + The MRI atlas segmentation name (e.g., ``'Left-Cerebellum-Cortex'`` + from the parameter ``volume_label``). + + Source spaces also have some attributes that are accessible via ``.`` + access, like ``src.kind``. """ def __init__(self, source_spaces, info=None): # noqa: D102 @@ -189,11 +259,11 @@ produced during coregistration. If trans is None, an identity matrix is assumed. This is only needed when the source space is in head coordinates. - %(verbose_meth)s + %(verbose)s Returns ------- - fig : instance of mayavi.mlab.Figure + fig : instance of Figure3D The figure. """ from .viz import plot_alignment @@ -330,9 +400,9 @@ fname : str File to write. %(overwrite)s - %(verbose_meth)s + %(verbose)s """ - write_source_spaces(fname, self, overwrite) + write_source_spaces(fname, self, overwrite=overwrite) @verbose def export_volume(self, fname, include_surfaces=True, @@ -376,7 +446,7 @@ %(overwrite)s .. versionadded:: 0.19 - %(verbose_meth)s + %(verbose)s Notes ----- @@ -428,7 +498,7 @@ # use lookup table to assign values to source spaces logger.info('Reading FreeSurfer lookup table') # read the lookup table - lut = _get_lut() + lut, _ = read_freesurfer_lut() # Setup a dictionary of source types src_types = dict(volume=[], surface_discrete=[]) @@ -492,7 +562,7 @@ # find the color value for this volume use_id = 1. if mri_resolution is True or use_lut: - id_ = _get_lut_id(lut, vs['seg_name']) + id_ = lut[vs['seg_name']] if use_lut: use_id = id_ @@ -521,7 +591,7 @@ FIFF.FIFFV_MNE_SURF_LEFT_HEMI: 'Left', FIFF.FIFFV_MNE_SURF_RIGHT_HEMI: 'Right', }[src['id']] + '-Cerebral-Cortex' - val = _get_lut_id(lut, surf_name) + val = lut[surf_name] else: assert src['type'] == 'discrete' if not include_discrete: @@ -576,7 +646,7 @@ # save the mgh image img = nib.freesurfer.mghformat.MGHImage(img, affine) else: - raise(ValueError('Unrecognized file extension')) + raise ValueError('Unrecognized file extension') # write image to file nib.save(img, fname) @@ -683,6 +753,7 @@ write_source_spaces, setup_source_space, setup_volume_source_space """ # be more permissive on read than write (fwd/inv can contain src) + fname = _check_fname(fname, overwrite='read', must_exist=True) check_fname(fname, 'source space', ('-src.fif', '-src.fif.gz', '_src.fif', '_src.fif.gz', '-fwd.fif', '-fwd.fif.gz', @@ -761,9 +832,9 @@ tag = read_tag(fid, d.pos) trans = tag.data if trans['from'] == FIFF.FIFFV_MNE_COORD_MRI_VOXEL: - res['vox_mri_t'] = tag.data + res['vox_mri_t'] = trans if trans['to'] == FIFF.FIFFV_MNE_COORD_RAS: - res['mri_ras_t'] = tag.data + res['mri_ras_t'] = trans tag = find_tag(fid, mri, FIFF.FIFF_MNE_SOURCE_SPACE_INTERPOLATOR) if tag is not None: @@ -835,7 +906,7 @@ if tag is None: raise ValueError('Vertex data not found') - res['rr'] = tag.data.astype(np.float64) # double precision for mayavi + res['rr'] = tag.data.astype(np.float64) if res['rr'].shape[0] != res['np']: raise ValueError('Vertex information is incorrect') @@ -1052,7 +1123,7 @@ @verbose -def write_source_spaces(fname, src, overwrite=False, verbose=None): +def write_source_spaces(fname, src, *, overwrite=False, verbose=None): """Write source spaces to a file. Parameters @@ -1060,7 +1131,7 @@ fname : str The name of the file, which should end with -src.fif or -src.fif.gz. - src : SourceSpaces + src : instance of SourceSpaces The source spaces (as returned by read_source_spaces). %(overwrite)s %(verbose)s @@ -1069,11 +1140,16 @@ -------- read_source_spaces """ + _validate_type(src, SourceSpaces, 'src') check_fname(fname, 'source space', ('-src.fif', '-src.fif.gz', '_src.fif', '_src.fif.gz')) _check_fname(fname, overwrite=overwrite) - fid = start_file(fname) + with start_and_end_file(fname) as fid: + _write_source_spaces(fid, src) + + +def _write_source_spaces(fid, src): start_block(fid, FIFF.FIFFB_MNE) if src.info: @@ -1090,10 +1166,9 @@ end_block(fid, FIFF.FIFFB_MNE_ENV) - _write_source_spaces_to_fid(fid, src, verbose) + _write_source_spaces_to_fid(fid, src) end_block(fid, FIFF.FIFFB_MNE) - end_file(fid) def _write_one_source_space(fid, this, verbose=None): @@ -1193,212 +1268,6 @@ write_string(fid, FIFF.FIFF_COMMENT, this['seg_name']) -############################################################################## -# Head to MRI volume conversion - - -@verbose -def head_to_mri(pos, subject, mri_head_t, subjects_dir=None, - verbose=None): - """Convert pos from head coordinate system to MRI ones. - - This function converts to MRI RAS coordinates and not to surface - RAS. - - Parameters - ---------- - pos : array, shape (n_pos, 3) - The coordinates (in m) in head coordinate system. - %(subject)s - mri_head_t : instance of Transform - MRI<->Head coordinate transformation. - %(subjects_dir)s - %(verbose)s - - Returns - ------- - coordinates : array, shape (n_pos, 3) - The MRI RAS coordinates (in mm) of pos. - - Notes - ----- - This function requires nibabel. - """ - subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) - t1_fname = op.join(subjects_dir, subject, 'mri', 'T1.mgz') - head_mri_t = _ensure_trans(mri_head_t, 'head', 'mri') - _, _, mri_ras_t, _, _ = _read_mri_info(t1_fname) - head_ras_t = combine_transforms(head_mri_t, mri_ras_t, 'head', 'ras') - return 1e3 * apply_trans(head_ras_t, pos) # mm - - -############################################################################## -# Surface to MNI conversion - -@verbose -def vertex_to_mni(vertices, hemis, subject, subjects_dir=None, verbose=None): - """Convert the array of vertices for a hemisphere to MNI coordinates. - - Parameters - ---------- - vertices : int, or list of int - Vertex number(s) to convert. - hemis : int, or list of int - Hemisphere(s) the vertices belong to. - %(subject)s - subjects_dir : str, or None - Path to SUBJECTS_DIR if it is not set in the environment. - %(verbose)s - - Returns - ------- - coordinates : array, shape (n_vertices, 3) - The MNI coordinates (in mm) of the vertices. - """ - singleton = False - if not isinstance(vertices, list) and not isinstance(vertices, np.ndarray): - singleton = True - vertices = [vertices] - - if not isinstance(hemis, list) and not isinstance(hemis, np.ndarray): - hemis = [hemis] * len(vertices) - - if not len(hemis) == len(vertices): - raise ValueError('hemi and vertices must match in length') - - subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) - - surfs = [op.join(subjects_dir, subject, 'surf', '%s.white' % h) - for h in ['lh', 'rh']] - - # read surface locations in MRI space - rr = [read_surface(s)[0] for s in surfs] - - # take point locations in MRI space and convert to MNI coordinates - xfm = read_talxfm(subject, subjects_dir) - xfm['trans'][:3, 3] *= 1000. # m->mm - data = np.array([rr[h][v, :] for h, v in zip(hemis, vertices)]) - if singleton: - data = data[0] - return apply_trans(xfm['trans'], data) - - -############################################################################## -# Volume to MNI conversion - -@verbose -def head_to_mni(pos, subject, mri_head_t, subjects_dir=None, - verbose=None): - """Convert pos from head coordinate system to MNI ones. - - Parameters - ---------- - pos : array, shape (n_pos, 3) - The coordinates (in m) in head coordinate system. - %(subject)s - mri_head_t : instance of Transform - MRI<->Head coordinate transformation. - %(subjects_dir)s - %(verbose)s - - Returns - ------- - coordinates : array, shape (n_pos, 3) - The MNI coordinates (in mm) of pos. - - Notes - ----- - This function requires either nibabel. - """ - subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) - - # before we go from head to MRI (surface RAS) - head_mni_t = combine_transforms( - _ensure_trans(mri_head_t, 'head', 'mri'), - read_talxfm(subject, subjects_dir), 'head', 'mni_tal') - return apply_trans(head_mni_t, pos) * 1000. - - -@verbose -def read_talxfm(subject, subjects_dir=None, verbose=None): - """Compute MRI-to-MNI transform from FreeSurfer talairach.xfm file. - - Parameters - ---------- - %(subject)s - %(subjects_dir)s - %(verbose)s - - Returns - ------- - mri_mni_t : instance of Transform - The affine transformation from MRI to MNI space for the subject. - """ - # Adapted from freesurfer m-files. Altered to deal with Norig - # and Torig correctly - subjects_dir = get_subjects_dir(subjects_dir) - # Setup the RAS to MNI transform - ras_mni_t = read_ras_mni_t(subject, subjects_dir) - ras_mni_t['trans'][:3, 3] /= 1000. # mm->m - - # We want to get from Freesurfer surface RAS ('mri') to MNI ('mni_tal'). - # This file only gives us RAS (non-zero origin) ('ras') to MNI ('mni_tal'). - # Se we need to get the ras->mri transform from the MRI headers. - - # To do this, we get Norig and Torig - # (i.e. vox_ras_t and vox_mri_t, respectively) - path = op.join(subjects_dir, subject, 'mri', 'orig.mgz') - if not op.isfile(path): - path = op.join(subjects_dir, subject, 'mri', 'T1.mgz') - if not op.isfile(path): - raise IOError('mri not found: %s' % path) - _, _, mri_ras_t, _, _ = _read_mri_info(path) - mri_mni_t = combine_transforms(mri_ras_t, ras_mni_t, 'mri', 'mni_tal') - return mri_mni_t - - -def _read_mri_info(path, units='m', return_img=False): - if has_nibabel(): - import nibabel - mgz = nibabel.load(path) - hdr = mgz.header - n_orig = hdr.get_vox2ras() - t_orig = hdr.get_vox2ras_tkr() - dims = hdr.get_data_shape() - zooms = hdr.get_zooms()[:3] - else: - mgz = None - hdr = _get_mgz_header(path) - n_orig = hdr['vox2ras'] - t_orig = hdr['vox2ras_tkr'] - dims = hdr['dims'] - zooms = hdr['zooms'] - - # extract the MRI_VOXEL to RAS (non-zero origin) transform - vox_ras_t = Transform('mri_voxel', 'ras', n_orig) - - # extract the MRI_VOXEL to MRI transform - vox_mri_t = Transform('mri_voxel', 'mri', t_orig) - - # construct the MRI to RAS (non-zero origin) transform - mri_ras_t = combine_transforms( - invert_transform(vox_mri_t), vox_ras_t, 'mri', 'ras') - - assert units in ('m', 'mm') - if units == 'm': - conv = np.array([[1e-3, 1e-3, 1e-3, 1]]).T - # scaling and translation terms - vox_ras_t['trans'] *= conv - vox_mri_t['trans'] *= conv - # just the translation term - mri_ras_t['trans'][:, 3:4] *= conv - - out = (vox_ras_t, vox_mri_t, mri_ras_t, dims, zooms) - if return_img: - out += (mgz,) - return out - - ############################################################################### # Creation and decimation @@ -1453,7 +1322,7 @@ @verbose def setup_source_space(subject, spacing='oct6', surface='white', - subjects_dir=None, add_dist=True, n_jobs=1, + subjects_dir=None, add_dist=True, n_jobs=None, *, verbose=None): """Set up bilateral hemisphere surface-based source space with subsampling. @@ -1559,19 +1428,6 @@ return src -def _check_mri(mri, subject, subjects_dir): - _validate_type(mri, 'path-like', 'mri') - if not op.isfile(mri): - if subject is None: - raise FileNotFoundError( - 'MRI file %r not found and no subject provided' % (mri,)) - subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) - mri = op.join(subjects_dir, subject, 'mri', mri) - if not op.isfile(mri): - raise FileNotFoundError('MRI file %r not found' % (mri,)) - return mri - - def _check_volume_labels(volume_label, mri, name='volume_label'): _validate_type(mri, 'path-like', 'mri when %s is not None' % (name,)) mri = _check_fname(mri, overwrite='read', must_exist=True) @@ -1640,10 +1496,10 @@ Only used if ``bem`` and ``surface`` are both None. Can also be a spherical ConductorModel, which will use the origin and radius. None (the default) uses a head-digitization fit. - bem : str | None | ConductorModel + bem : path-like | None | ConductorModel Define source space bounds using a BEM file (specifically the inner skull surface) or a ConductorModel for a 1-layer of 3-layers BEM. - surface : str | dict | None + surface : path-like | dict | None Define source space bounds using a FreeSurfer surface file. Can also be a dictionary with entries ``'rr'`` and ``'tris'``, such as those returned by :func:`mne.read_surface`. @@ -1721,6 +1577,14 @@ subjects_dir = get_subjects_dir(subjects_dir) _validate_type( volume_label, (str, list, tuple, dict, None), 'volume_label') + _validate_type(bem, ('path-like', ConductorModel, None), 'bem') + _validate_type(surface, ('path-like', dict, None), 'surface') + if bem is not None and not isinstance(bem, ConductorModel): + bem = _check_fname(bem, overwrite='read', must_exist=True, + name='bem filename') + if surface is not None and not isinstance(surface, dict): + surface = _check_fname(surface, overwrite='read', must_exist=True, + name='surface filename') if bem is not None and surface is not None: raise ValueError('Only one of "bem" and "surface" should be ' @@ -1756,7 +1620,7 @@ # let's make sure we have geom info complete_surface_info(surface, copy=False, verbose=False) surf_extra = 'dict()' - elif isinstance(surface, str): + else: if not op.isfile(surface): raise IOError('surface file "%s" not found' % surface) surf_extra = surface @@ -1913,8 +1777,9 @@ rr = np.array(pos['rr'], float) nn = np.array(pos['nn'], float) if not (rr.ndim == nn.ndim == 2 and nn.shape[0] == nn.shape[0] and - rr.shape[1] == nn.shape[1]): - raise RuntimeError('"rr" and "nn" must both be 2D arrays with ' + rr.shape[1] == nn.shape[1] and np.isfinite(rr).all() and + np.isfinite(nn).all()): + raise RuntimeError('"rr" and "nn" must both be finite 2D arrays with ' 'the same number of rows and 3 columns') npts = rr.shape[0] _normalize_vectors(nn) @@ -1927,93 +1792,13 @@ # Ready to make the source space sp = dict(coord_frame=coord_frame, type='discrete', nuse=npts, np=npts, inuse=np.ones(npts, int), vertno=np.arange(npts), rr=rr, nn=nn, - id=-1) + id=FIFF.FIFFV_MNE_SURF_UNKNOWN) return sp -def _import_nibabel(why='use MRI files'): - try: - import nibabel as nib - except ImportError as exp: - msg = 'nibabel is required to %s, got:\n%s' % (why, exp) - else: - msg = '' - if msg: - raise ImportError(msg) - return nib - - -def _mri_orientation(img, orientation): - """Get MRI orientation information from an image. - - Parameters - ---------- - img : instance of SpatialImage - The MRI image. - orientation : str - Orientation that you want. Can be "axial", "saggital", or "coronal". - - Returns - ------- - xyz : tuple, shape (3,) - The dimension indices for X, Y, and Z. - flips : tuple, shape (3,) - Whether each dimension requires a flip. - order : tuple, shape (3,) - The resulting order of the data if the given ``xyz`` and ``flips`` - are used. - - Notes - ----- - .. versionadded:: 0.21 - """ - import nibabel as nib - _validate_type(img, nib.spatialimages.SpatialImage) - _check_option('orientation', orientation, ('coronal', 'axial', 'sagittal')) - axcodes = ''.join(nib.orientations.aff2axcodes(img.affine)) - flips = {o: (1 if o in axcodes else -1) for o in 'RAS'} - axcodes = axcodes.replace('L', 'R').replace('P', 'A').replace('I', 'S') - order = dict( - coronal=('R', 'S', 'A'), - axial=('R', 'A', 'S'), - sagittal=('A', 'S', 'R'), - )[orientation] - xyz = tuple(axcodes.index(c) for c in order) - flips = tuple(flips[c] for c in order) - return xyz, flips, order - - -def _get_mri_info_data(mri, data): - # Read the segmentation data using nibabel - if data: - _import_nibabel('load MRI atlas data') - out = dict() - _, out['vox_mri_t'], out['mri_ras_t'], dims, _, mgz = _read_mri_info( - mri, return_img=True) - out.update( - mri_width=dims[0], mri_height=dims[1], - mri_depth=dims[1], mri_volume_name=mri) - if data: - assert mgz is not None - out['mri_vox_t'] = invert_transform(out['vox_mri_t']) - out['data'] = np.asarray(mgz.dataobj) - return out - - -def _get_atlas_values(vol_info, rr): - # Transform MRI coordinates (where our surfaces live) to voxels - rr_vox = apply_trans(vol_info['mri_vox_t'], rr) - good = ((rr_vox >= -.5) & - (rr_vox < np.array(vol_info['data'].shape, int) - 0.5)).all(-1) - idx = np.round(rr_vox[good].T).astype(np.int64) - values = np.full(rr.shape[0], np.nan) - values[good] = vol_info['data'][tuple(idx)] - return values - - def _make_volume_source_space(surf, grid, exclude, mindist, mri=None, - volume_labels=None, do_neighbors=True, n_jobs=1, - vol_info={}, single_volume=False): + volume_labels=None, do_neighbors=True, + n_jobs=None, vol_info={}, single_volume=False): """Make a source space which covers the volume bounded by surf.""" # Figure out the grid size in the MRI coordinate frame if 'rr' in surf: @@ -2059,7 +1844,8 @@ rr = np.array([x * grid, y * grid, z * grid]).T sp = dict(np=npts, nn=np.zeros((npts, 3)), rr=rr, inuse=np.ones(npts, bool), type='vol', nuse=npts, - coord_frame=FIFF.FIFFV_COORD_MRI, id=-1, shape=ns) + coord_frame=FIFF.FIFFV_COORD_MRI, id=FIFF.FIFFV_MNE_SURF_UNKNOWN, + shape=ns) sp['nn'][:, 2] = 1.0 assert sp['rr'].shape[0] == npts @@ -2111,6 +1897,10 @@ n_good = good.sum() logger.info(' Selected %d voxel%s from %s' % (n_good, _pl(n_good), volume_label)) + if n_good == 0: + warn('Found no usable vertices in volume label ' + f'{repr(volume_label)} (id={id_}) using a ' + f'{grid * 1000:0.1f} mm grid') # Update source info sp['inuse'][sp['vertno'][~good]] = False sp['vertno'] = sp['vertno'][good] @@ -2248,43 +2038,6 @@ return jj + width * kk + pp * (width * height) -def _get_mgz_header(fname): - """Adapted from nibabel to quickly extract header info.""" - if not fname.endswith('.mgz'): - raise IOError('Filename must end with .mgz') - header_dtd = [('version', '>i4'), ('dims', '>i4', (4,)), - ('type', '>i4'), ('dof', '>i4'), ('goodRASFlag', '>i2'), - ('delta', '>f4', (3,)), ('Mdc', '>f4', (3, 3)), - ('Pxyz_c', '>f4', (3,))] - header_dtype = np.dtype(header_dtd) - with GzipFile(fname, 'rb') as fid: - hdr_str = fid.read(header_dtype.itemsize) - header = np.ndarray(shape=(), dtype=header_dtype, - buffer=hdr_str) - # dims - dims = header['dims'].astype(int) - dims = dims[:3] if len(dims) == 4 else dims - # vox2ras_tkr - delta = header['delta'] - ds = np.array(delta, float) - ns = np.array(dims * ds) / 2.0 - v2rtkr = np.array([[-ds[0], 0, 0, ns[0]], - [0, 0, ds[2], -ns[2]], - [0, -ds[1], 0, ns[1]], - [0, 0, 0, 1]], dtype=np.float32) - # ras2vox - d = np.diag(delta) - pcrs_c = dims / 2.0 - Mdc = header['Mdc'].T - pxyz_0 = header['Pxyz_c'] - np.dot(Mdc, np.dot(d, pcrs_c)) - M = np.eye(4, 4) - M[0:3, 0:3] = np.dot(Mdc, d) - M[0:3, 3] = pxyz_0.T - header = dict(dims=dims, vox2ras_tkr=v2rtkr, vox2ras=M, - zooms=header['delta']) - return header - - def _src_vol_dims(s): w, h, d = [s[f'mri_{key}'] for key in ('width', 'height', 'depth')] return w, h, d, np.prod([w, h, d]) @@ -2479,7 +2232,7 @@ @verbose -def _filter_source_spaces(surf, limit, mri_head_t, src, n_jobs=1, +def _filter_source_spaces(surf, limit, mri_head_t, src, n_jobs=None, verbose=None): """Remove all source space points closer than a given limit (in mm).""" if src[0]['coord_frame'] == FIFF.FIFFV_COORD_HEAD and mri_head_t is None: @@ -2551,6 +2304,7 @@ # Adjust the patch inds as well if necessary if omit_limit + omit_outside > 0: _adjust_patch_info(s) + return check_inside @verbose @@ -2571,7 +2325,7 @@ _check_option( 'kind', kind, (None, 'surface', 'volume', 'mixed', 'discrete')) msg = 'src must be a string or instance of SourceSpaces%s' % (extra,) - if _check_path_like(src): + if _path_like(src): src = str(src) if not op.isfile(src): raise IOError('Source space file "%s" not found' % src) @@ -2609,7 +2363,8 @@ @verbose -def add_source_space_distances(src, dist_limit=np.inf, n_jobs=1, verbose=None): +def add_source_space_distances(src, dist_limit=np.inf, n_jobs=None, *, + verbose=None): """Compute inter-source distances along the cortical surface. This function will also try to add patch info for the source space. @@ -2650,7 +2405,6 @@ """ from scipy.sparse import csr_matrix from scipy.sparse.csgraph import dijkstra - n_jobs = check_n_jobs(n_jobs) src = _ensure_src(src) dist_limit = float(dist_limit) if dist_limit < 0: @@ -2666,7 +2420,7 @@ raise RuntimeError('Currently all source spaces must be of surface ' 'type') - parallel, p_fun, _ = parallel_func(_do_src_distances, n_jobs) + parallel, p_fun, n_jobs = parallel_func(_do_src_distances, n_jobs) min_dists = list() min_idxs = list() msg = 'patch information' if patch_only else 'source space distances' @@ -2748,63 +2502,6 @@ return d, min_idx, min_dist -def get_volume_labels_from_aseg(mgz_fname, return_colors=False, - atlas_ids=None): - """Return a list of names and colors of segmented volumes. - - Parameters - ---------- - mgz_fname : str - Filename to read. Typically aseg.mgz or some variant in the freesurfer - pipeline. - return_colors : bool - If True returns also the labels colors. - atlas_ids : dict | None - A lookup table providing a mapping from region names (str) to ID values - (int). Can be None to use the standard Freesurfer LUT. - - .. versionadded:: 0.21.0 - - Returns - ------- - label_names : list of str - The names of segmented volumes included in this mgz file. - label_colors : list of str - The RGB colors of the labels included in this mgz file. - - See Also - -------- - read_freesurfer_lut - - Notes - ----- - .. versionchanged:: 0.21.0 - The label names are now sorted in the same order as their corresponding - values in the MRI file. - - .. versionadded:: 0.9.0 - """ - import nibabel as nib - atlas = nib.load(mgz_fname) - data = np.asarray(atlas.dataobj) # don't need float here - want = np.unique(data) - if atlas_ids is None: - atlas_ids, colors = read_freesurfer_lut() - elif return_colors: - raise ValueError('return_colors must be False if atlas_ids are ' - 'provided') - # restrict to the ones in the MRI, sorted by label name - keep = np.in1d(list(atlas_ids.values()), want) - keys = sorted((key for ki, key in enumerate(atlas_ids.keys()) if keep[ki]), - key=lambda x: atlas_ids[x]) - if return_colors: - colors = [colors[k] for k in keys] - out = keys, colors - else: - out = keys - return out - - # XXX this should probably be deprecated because it returns surface Labels, # and probably isn't the way to go moving forward # XXX this also assumes that the first two source spaces are surf without @@ -2827,12 +2524,10 @@ List of Label of segmented volumes included in src space. """ from . import Label - from . import get_volume_labels_from_aseg + from ._freesurfer import get_volume_labels_from_aseg # Read the aseg file aseg_fname = op.join(subjects_dir, subject, 'mri', 'aseg.mgz') - if not op.isfile(aseg_fname): - raise IOError('aseg file "%s" not found' % aseg_fname) all_labels_aseg = get_volume_labels_from_aseg( aseg_fname, return_colors=True) @@ -3190,7 +2885,7 @@ if use_cps and s.get('patch_inds') is not None: nn = np.empty((len(vertices), 3)) for vp, p in enumerate(np.searchsorted(s['vertno'], vertices)): - assert(s['vertno'][p] == vertices[vp]) + assert s['vertno'][p] == vertices[vp] # Project out the surface normal and compute SVD nn[vp] = np.sum( s['nn'][s['pinfo'][s['patch_inds'][p]], :], axis=0) @@ -3210,8 +2905,7 @@ src : instance of SourceSpaces The object with vertex positions for which to compute distances to sensors. - info : instance of Info - Measurement information with sensor positions to which distances shall + %(info)s Must contain sensor positions to which distances shall be computed. %(picks_good_data)s %(trans_not_none)s @@ -3236,7 +2930,7 @@ # get vertex position in same coordinates as for sensors below src_pos = np.vstack([ - apply_trans(src_trans, s['rr'][s['inuse'].astype(np.bool)]) + apply_trans(src_trans, s['rr'][s['inuse'].astype(bool)]) for s in src ]) @@ -3262,49 +2956,38 @@ return depths -@verbose -def get_mni_fiducials(subject, subjects_dir=None, verbose=None): - """Estimate fiducials for a subject. +def get_decimated_surfaces(src): + """Get the decimated surfaces from a source space. Parameters ---------- - %(subject)s - %(subjects_dir)s - %(verbose)s + src : instance of SourceSpaces | path-like + The source space with decimated surfaces. Returns ------- - fids_mri : list - List of estimated fiducials (each point in a dict), in the order - LPA, nasion, RPA. + surfaces : list of dict + The decimated surfaces present in the source space. Each dict + which contains 'rr' and 'tris' keys for vertices positions and + triangle indices. Notes ----- - This takes the ``fsaverage-fiducials.fif`` file included with MNE—which - contain the LPA, nasion, and RPA for the ``fsaverage`` subject—and - transforms them to the given FreeSurfer subject's MRI space. - The MRI of ``fsaverage`` is already in MNI Talairach space, so applying - the inverse of the given subject's MNI Talairach affine transformation - (``$SUBJECTS_DIR/$SUBJECT/mri/transforms/talairach.xfm``) is used - to estimate the subject's fiducial locations. - - For more details about the coordinate systems and transformations involved, - see https://surfer.nmr.mgh.harvard.edu/fswiki/CoordinateSystems and - :ref:`plot_source_alignment`. + .. versionadded:: 1.0 """ - # Eventually we might want to allow using the MNI Talairach with-skull - # transformation rather than the standard brain-based MNI Talaranch - # transformation, and/or project the points onto the head surface - # (if available). - fname_fids_fs = os.path.join(os.path.dirname(__file__), 'data', - 'fsaverage', 'fsaverage-fiducials.fif') - - # Read fsaverage fiducials file and subject Talairach. - fids, coord_frame = read_fiducials(fname_fids_fs) - assert coord_frame == FIFF.FIFFV_COORD_MRI - if subject == 'fsaverage': - return fids # special short-circuit for fsaverage - mni_mri_t = invert_transform(read_talxfm(subject, subjects_dir)) - for f in fids: - f['r'] = apply_trans(mni_mri_t, f['r']) - return fids + src = _ensure_src(src) + surfaces = [] + for s in src: + if s['type'] != 'surf': + continue + rr = s['rr'] + use_tris = s['use_tris'] + vertno = s['vertno'] + ss = {} + ss['rr'] = rr[vertno] + reindex = np.full(len(rr), -1, int) + reindex[vertno] = np.arange(len(vertno)) + ss['tris'] = reindex[use_tris] + assert (ss['tris'] >= 0).all() + surfaces.append(ss) + return surfaces diff -Nru python-mne-0.23.4+dfsg/mne/stats/_adjacency.py python-mne-1.1.0+dfsg/mne/stats/_adjacency.py --- python-mne-0.23.4+dfsg/mne/stats/_adjacency.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/stats/_adjacency.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,8 +1,9 @@ # -*- coding: utf-8 -*- # Authors: Eric Larson +# Stefan Appelhoff # -# License: Simplified BSD +# License: BSD-3-Clause import numpy as np @@ -18,16 +19,45 @@ *structure : list The adjacency along each dimension. Each entry can be: - - ndarray or sparse matrix + - ndarray or scipy.sparse.spmatrix A square binary adjacency matrix for the given dimension. + For example created by :func:`mne.channels.find_ch_adjacency`. - int The number of elements along the given dimension. A lattice - adjacency will be generated. + adjacency will be generated, which is a binary matrix + reflecting that element N of an array is adjacent to + elements at indices N - 1 and N + 1. Returns ------- adjacency : scipy.sparse.coo_matrix, shape (n_features, n_features) - The adjacency matrix. + The square adjacency matrix, where the shape ``n_features`` + corresponds to the product of the length of all dimensions. + For example ``len(times) * len(freqs) * len(chans)``. + + See Also + -------- + mne.channels.find_ch_adjacency + mne.channels.read_ch_adjacency + + Notes + ----- + For 4-dimensional data with shape ``(n_obs, n_times, n_freqs, n_chans)``, + you can specify **no** connections among elements in a particular + dimension by passing a matrix of zeros. For example: + + >>> import numpy as np + >>> from scipy.sparse import diags + >>> from mne.stats import combine_adjacency + >>> n_times, n_freqs, n_chans = (50, 7, 16) + >>> chan_adj = diags([1., 1.], offsets=(-1, 1), shape=(n_chans, n_chans)) + >>> combine_adjacency( + ... n_times, # regular lattice adjacency for times + ... np.zeros((n_freqs, n_freqs)), # no adjacency between freq. bins + ... chan_adj, # custom matrix, or use mne.channels.find_ch_adjacency + ... ) # doctest: +NORMALIZE_WHITESPACE + <5600x5600 sparse matrix of type '' + with 27076 stored elements in COOrdinate format> """ from scipy import sparse structure = list(structure) @@ -35,15 +65,11 @@ name = f'structure[{di}]' _validate_type(dim, ('int-like', np.ndarray, sparse.spmatrix), name) if isinstance(dim, int_like): - dim = int(dim) - # Don't add the diagonal, because we explicitly remove it later: - # dim = sparse.eye(dim, format='coo') - # dim += sparse.eye(dim.shape[0], k=1, format='coo') - # dim += sparse.eye(dim.shape[0], k=-1, format='coo') - ii, jj = np.arange(0, dim - 1), np.arange(1, dim) - edges = np.vstack([np.hstack([ii, jj]), np.hstack([jj, ii])]) - dim = sparse.coo_matrix( - (np.ones(edges.shape[1]), edges), (dim, dim), float) + # Don't add the diagonal, because we explicitly remove it later + dim = sparse.diags([1, 1], + offsets=(-1, 1), + shape=(int(dim), int(dim)), + dtype=float).tocoo() else: _check_option(f'{name}.ndim', dim.ndim, [2]) if dim.shape[0] != dim.shape[1]: diff -Nru python-mne-0.23.4+dfsg/mne/stats/cluster_level.py python-mne-1.1.0+dfsg/mne/stats/cluster_level.py --- python-mne-0.23.4+dfsg/mne/stats/cluster_level.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/stats/cluster_level.py 2022-08-03 15:48:14.000000000 +0000 @@ -13,7 +13,7 @@ import numpy as np from .parametric import f_oneway, ttest_1samp_no_p -from ..parallel import parallel_func, check_n_jobs +from ..parallel import parallel_func from ..fixes import jit, has_numba from ..utils import (split_list, logger, verbose, ProgressBar, warn, _pl, check_random_state, _check_option, _validate_type) @@ -562,6 +562,8 @@ for ci, c in enumerate(components): if isinstance(c, np.ndarray): # mask components[ci] = np.where(c.reshape(shape)) + elif isinstance(c, slice): + components[ci] = np.arange(c.start, c.stop) else: assert isinstance(c, tuple), type(c) c = list(c) # tuple->list @@ -831,7 +833,6 @@ adjacency, n_jobs, seed, max_step, exclude, step_down_p, t_power, out_type, check_disjoint, buffer_size): - n_jobs = check_n_jobs(n_jobs) """Aux Function. Note. X is required to be a list. Depending on the length of X @@ -904,7 +905,7 @@ partitions = _get_partitions_from_adjacency(adjacency, n_times) else: partitions = None - logger.info('Running initial clustering') + logger.info('Running initial clustering …') out = _find_clusters(t_obs, threshold, tail, adjacency, max_step=max_step, include=include, partitions=partitions, t_power=t_power, @@ -918,7 +919,7 @@ if isinstance(threshold, dict): t_obs = cluster_stats.reshape(t_obs.shape) * np.sign(t_obs) - logger.info('Found %d clusters' % len(clusters)) + logger.info(f'Found {len(clusters)} cluster{_pl(clusters)}') # convert clusters to old format if adjacency is not None and adjacency is not False: @@ -953,7 +954,7 @@ orders = [rng.permutation(len(X_full)) for _ in range(n_permutations - 1)] del rng - parallel, my_do_perm_func, _ = parallel_func( + parallel, my_do_perm_func, n_jobs = parallel_func( do_perm_func, n_jobs, verbose=False) if len(clusters) == 0: @@ -977,8 +978,11 @@ this_include = include else: this_include = step_down_include - logger.info('Permuting %d times%s...' % (len(orders), extra)) - with ProgressBar(len(orders)) as progress_bar: + + with ProgressBar( + iterable=range(len(orders)), + mesg=f'Permuting{extra}' + ) as progress_bar: H0 = parallel( my_do_perm_func(X_full, slices, threshold, tail, adjacency, stat_fun, max_step, this_include, partitions, @@ -994,7 +998,7 @@ orig = abs(cluster_stats).max() H0.insert(0, [orig]) H0 = np.concatenate(H0) - logger.info('Computing cluster p-values') + logger.debug('Computing cluster p-values') cluster_pv = _pval_from_histogram(cluster_stats, H0, tail) # figure out how many new ones will be removed for step-down @@ -1013,7 +1017,7 @@ 'cluster%s to exclude from subsequent iterations' % (n_step_downs, n_removed, a_text, _pl(n_removed))) - logger.info('Done.') + # The clusters should have the same shape as the samples clusters = _reshape_clusters(clusters, sample_shape) return t_obs, clusters, cluster_pv, H0 @@ -1056,7 +1060,7 @@ @verbose def permutation_cluster_test( X, threshold=None, n_permutations=1024, tail=0, stat_fun=None, - adjacency=None, n_jobs=1, seed=None, max_step=1, exclude=None, + adjacency=None, n_jobs=None, seed=None, max_step=1, exclude=None, step_down_p=0, t_power=1, out_type='indices', check_disjoint=False, buffer_size=1000, verbose=None): """Cluster-level statistical permutation test. @@ -1066,12 +1070,12 @@ permutations and cluster-level correction. Each element of the list ``X`` should contain the data for one group of observations (e.g., 2D arrays for time series, 3D arrays for time-frequency power values). Permutations are - generated with random partitions of the data. See - :footcite:`MarisOostenveld2007` for details. + generated with random partitions of the data. For details, see + :footcite:p:`MarisOostenveld2007,Sassenhagen2019`. Parameters ---------- - X : list of array, shape (n_observations, p[, q]) + X : list of array, shape (n_observations, p[, q][, r]) The data to be clustered. Each array in ``X`` should contain the observations for one group. The first dimension of each array is the number of observations from that group; remaining dimensions comprise @@ -1083,28 +1087,25 @@ dimension represented in the ``adjacency`` parameter (e.g., spectral data should be provided as ``(observations, frequencies, channels/vertices)``). - %(clust_thresh_f)s - %(clust_nperm_int)s - %(clust_tail)s - %(clust_stat_f)s - %(clust_adj_n)s + %(threshold_clust_f)s + %(n_permutations_clust_int)s + %(tail_clust)s + %(stat_fun_clust_f)s + %(adjacency_clust_n)s %(n_jobs)s %(seed)s - %(clust_maxstep)s - exclude : bool array or None - Mask to apply to the data to exclude certain points from clustering - (e.g., medial wall vertices). Should be the same shape as X. If None, - no points are excluded. - %(clust_stepdown)s - %(clust_power_f)s - %(clust_out)s - %(clust_disjoint)s - %(clust_buffer)s + %(max_step_clust)s + %(exclude_clust)s + %(step_down_p_clust)s + %(f_power_clust)s + %(out_type_clust)s + %(check_disjoint_clust)s + %(buffer_size_clust)s %(verbose)s Returns ------- - F_obs : array, shape (n_tests,) + F_obs : array, shape (p[, q][, r]) Statistic (F by default) observed for all variables. clusters : list List type defined by out_type above. @@ -1113,6 +1114,10 @@ H0 : array, shape (n_permutations,) Max cluster level stats observed under permutation. + Notes + ----- + %(threshold_clust_f_notes)s + References ---------- .. footbibliography:: @@ -1129,42 +1134,41 @@ @verbose def permutation_cluster_1samp_test( X, threshold=None, n_permutations=1024, tail=0, stat_fun=None, - adjacency=None, n_jobs=1, seed=None, max_step=1, + adjacency=None, n_jobs=None, seed=None, max_step=1, exclude=None, step_down_p=0, t_power=1, out_type='indices', check_disjoint=False, buffer_size=1000, verbose=None): """Non-parametric cluster-level paired t-test. + For details, see :footcite:p:`MarisOostenveld2007,Sassenhagen2019`. + Parameters ---------- - X : array, shape (n_observations, p[, q]) + X : array, shape (n_observations, p[, q][, r]) The data to be clustered. The first dimension should correspond to the difference between paired samples (observations) in two conditions. - The subarrays ``X[k]`` can be 1D (e.g., time series) or 2D (e.g., - time-frequency image) associated with the kth observation. For - spatiotemporal data, see also - :func:`mne.stats.spatio_temporal_cluster_1samp_test`. - %(clust_thresh_t)s - %(clust_nperm_all)s - %(clust_tail)s - %(clust_stat_t)s - %(clust_adj_1)s + The subarrays ``X[k]`` can be 1D (e.g., time series), 2D (e.g., + time series over channels), or 3D (e.g., time-frequencies over + channels) associated with the kth observation. For spatiotemporal data, + see also :func:`mne.stats.spatio_temporal_cluster_1samp_test`. + %(threshold_clust_t)s + %(n_permutations_clust_all)s + %(tail_clust)s + %(stat_fun_clust_t)s + %(adjacency_clust_1)s %(n_jobs)s %(seed)s - %(clust_maxstep)s - exclude : bool array or None - Mask to apply to the data to exclude certain points from clustering - (e.g., medial wall vertices). Should be the same shape as X. If None, - no points are excluded. - %(clust_stepdown)s - %(clust_power_t)s - %(clust_out)s - %(clust_disjoint)s - %(clust_buffer)s + %(max_step_clust)s + %(exclude_clust)s + %(step_down_p_clust)s + %(t_power_clust)s + %(out_type_clust)s + %(check_disjoint_clust)s + %(buffer_size_clust)s %(verbose)s Returns ------- - t_obs : array, shape (n_tests,) + t_obs : array, shape (p[, q][, r]) T-statistic observed for all variables. clusters : list List type defined by out_type above. @@ -1188,12 +1192,15 @@ computes a 1-sample t-test (by default) and uses sign flipping (always) to perform permutations. This might not be suitable for the case where there is truly a single observation under test; see :ref:`disc-stats`. + %(threshold_clust_t_notes)s - If ``n_permutations >= 2 ** (n_samples - (tail == 0))``, - ``n_permutations`` and ``seed`` will be ignored since an exact test - (full permutation test) will be performed. + If ``n_permutations`` exceeds the maximum number of possible permutations + given the number of observations, then ``n_permutations`` and ``seed`` + will be ignored since an exact test (full permutation test) will be + performed (this is the case when + ``n_permutations >= 2 ** (n_observations - (tail == 0))``). - If no initial clusters are found, i.e., all points in the true + If no initial clusters are found because all points in the true distribution are below the threshold, then ``clusters``, ``cluster_pv``, and ``H0`` will all be empty arrays. @@ -1213,7 +1220,7 @@ @verbose def spatio_temporal_cluster_1samp_test( X, threshold=None, n_permutations=1024, tail=0, - stat_fun=None, adjacency=None, n_jobs=1, seed=None, + stat_fun=None, adjacency=None, n_jobs=None, seed=None, max_step=1, spatial_exclude=None, step_down_p=0, t_power=1, out_type='indices', check_disjoint=False, buffer_size=1000, verbose=None): @@ -1221,34 +1228,38 @@ This function provides a convenient wrapper for :func:`mne.stats.permutation_cluster_1samp_test`, for use with data - organized in the form (observations × time × space). See - :footcite:`MarisOostenveld2007` for details. + organized in the form (observations × time × space), + (observations × frequencies × space), or optionally + (observations × time × frequencies × space). For details, see + :footcite:p:`MarisOostenveld2007,Sassenhagen2019`. Parameters ---------- - X : array, shape (n_observations, n_times, n_vertices) + X : array, shape (n_observations, p[, q], n_vertices) The data to be clustered. The first dimension should correspond to the difference between paired samples (observations) in two conditions. - %(clust_thresh_t)s - %(clust_nperm_all)s - %(clust_tail)s - %(clust_stat_t)s - %(clust_adj_st1)s + The second, and optionally third, dimensions correspond to the + time or time-frequency data. And, the last dimension should be spatial. + %(threshold_clust_t)s + %(n_permutations_clust_all)s + %(tail_clust)s + %(stat_fun_clust_t)s + %(adjacency_clust_st1)s %(n_jobs)s %(seed)s - %(clust_maxstep)s + %(max_step_clust)s spatial_exclude : list of int or None List of spatial indices to exclude from clustering. - %(clust_stepdown)s - %(clust_power_t)s - %(clust_out)s - %(clust_disjoint)s - %(clust_buffer)s + %(step_down_p_clust)s + %(t_power_clust)s + %(out_type_clust)s + %(check_disjoint_clust)s + %(buffer_size_clust)s %(verbose)s Returns ------- - t_obs : array, shape (n_times * n_vertices,) + t_obs : array, shape (p[, q], n_vertices) T-statistic observed for all variables. clusters : list List type defined by out_type above. @@ -1257,15 +1268,18 @@ H0 : array, shape (n_permutations,) Max cluster level stats observed under permutation. + Notes + ----- + %(threshold_clust_t_notes)s + References ---------- .. footbibliography:: """ - n_samples, n_times, n_vertices = X.shape # convert spatial_exclude before passing on if necessary if spatial_exclude is not None: - exclude = _st_mask_from_s_inds(n_times, n_vertices, - spatial_exclude, True) + exclude = _st_mask_from_s_inds( + np.prod(X.shape[1:-1]), X.shape[-1], spatial_exclude, True) else: exclude = None return permutation_cluster_1samp_test( @@ -1279,7 +1293,7 @@ @verbose def spatio_temporal_cluster_test( X, threshold=None, n_permutations=1024, tail=0, stat_fun=None, - adjacency=None, n_jobs=1, seed=None, max_step=1, + adjacency=None, n_jobs=None, seed=None, max_step=1, spatial_exclude=None, step_down_p=0, t_power=1, out_type='indices', check_disjoint=False, buffer_size=1000, verbose=None): @@ -1287,38 +1301,41 @@ This function provides a convenient wrapper for :func:`mne.stats.permutation_cluster_test`, for use with data - organized in the form (observations × time × space). - See :footcite:`MarisOostenveld2007` for more information. + organized in the form (observations × time × space), + (observations × time × space), or optionally + (observations × time × frequencies × space). For more information, + see :footcite:p:`MarisOostenveld2007,Sassenhagen2019`. Parameters ---------- - X : list of array, shape (n_observations, n_times, n_vertices) + X : list of array, shape (n_observations, p[, q], n_vertices) The data to be clustered. Each array in ``X`` should contain the observations for one group. The first dimension of each array is the - number of observations from that group (and may vary between groups); - the remaining dimensions (times and vertices) should match across all - groups. - %(clust_thresh_f)s - %(clust_nperm_int)s - %(clust_tail)s - %(clust_stat_f)s - %(clust_adj_stn)s + number of observations from that group (and may vary between groups). + The second, and optionally third, dimensions correspond to the + time or time-frequency data. And, the last dimension should be spatial. + All dimensions except the first should match across all groups. + %(threshold_clust_f)s + %(n_permutations_clust_int)s + %(tail_clust)s + %(stat_fun_clust_f)s + %(adjacency_clust_stn)s %(n_jobs)s %(seed)s - %(clust_maxstep)s + %(max_step_clust)s spatial_exclude : list of int or None List of spatial indices to exclude from clustering. - %(clust_stepdown)s - %(clust_power_f)s - %(clust_out)s - %(clust_disjoint)s - %(clust_buffer)s + %(step_down_p_clust)s + %(f_power_clust)s + %(out_type_clust)s + %(check_disjoint_clust)s + %(buffer_size_clust)s %(verbose)s Returns ------- - t_obs : array, shape (n_times * n_vertices,) - Statistic (t by default) observed for all variables. + F_obs : array, shape (p[, q], n_vertices) + Statistic (F by default) observed for all variables. clusters : list List type defined by out_type above. cluster_pv: array @@ -1326,15 +1343,18 @@ H0 : array, shape (n_permutations,) Max cluster level stats observed under permutation. + Notes + ----- + %(threshold_clust_f_notes)s + References ---------- .. footbibliography:: """ - n_samples, n_times, n_vertices = X[0].shape # convert spatial_exclude before passing on if necessary if spatial_exclude is not None: - exclude = _st_mask_from_s_inds(n_times, n_vertices, - spatial_exclude, True) + exclude = _st_mask_from_s_inds( + np.prod(X[0].shape[1:-1]), X[0].shape[-1], spatial_exclude, True) else: exclude = None return permutation_cluster_test( diff -Nru python-mne-0.23.4+dfsg/mne/stats/multi_comp.py python-mne-1.1.0+dfsg/mne/stats/multi_comp.py --- python-mne-0.23.4+dfsg/mne/stats/multi_comp.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/stats/multi_comp.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # # Code borrowed from statsmodels # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np diff -Nru python-mne-0.23.4+dfsg/mne/stats/parametric.py python-mne-1.1.0+dfsg/mne/stats/parametric.py --- python-mne-0.23.4+dfsg/mne/stats/parametric.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/stats/parametric.py 2022-08-03 15:48:14.000000000 +0000 @@ -43,14 +43,6 @@ To use the "hat" adjustment method :footcite:`RidgwayEtAl2012`, a value of ``sigma=1e-3`` may be a reasonable choice. - You can use the conversion from ``scipy.stats.distributions.t.ppf``:: - - thresh = -scipy.stats.distributions.t.ppf(p_thresh, n_samples - 1) / 2. - - to convert a desired p-value threshold to 2-tailed t-value threshold. - For one-tailed tests, ``thresh`` in the above should be multiplied by 2 - (and for ``tail=-1``, multiplied by ``-1``). - References ---------- .. footbibliography:: @@ -144,12 +136,12 @@ 1. The samples are independent 2. Each sample is from a normally distributed population - 3. The population standard deviations of the groups are all equal. This - property is known as homocedasticity. + 3. The population standard deviations of the groups are all equal. This + property is known as homoscedasticity. If these assumptions are not true for a given set of data, it may still be possible to use the Kruskal-Wallis H-test (:func:`scipy.stats.kruskal`) - although with some loss of power + although with some loss of power. The algorithm is from Heiman :footcite:`Heiman2002`, pp.394-7. @@ -378,9 +370,11 @@ """ from scipy.stats import f + out_reshape = (-1,) if data.ndim == 2: # general purpose support, e.g. behavioural data data = data[:, :, np.newaxis] - elif data.ndim > 3: # let's allow for some magic here. + elif data.ndim > 3: # let's allow for some magic here + out_reshape = data.shape[2:] data = data.reshape( data.shape[0], data.shape[1], np.prod(data.shape[2:])) @@ -421,7 +415,8 @@ pvalues.append(pvals) # handle single effect returns - return [np.squeeze(np.asarray(vv)) for vv in (fvalues, pvalues)] + return [np.squeeze(np.asarray([v.reshape(out_reshape) for v in vv])) + for vv in (fvalues, pvalues)] def _parametric_ci(arr, ci=.95): diff -Nru python-mne-0.23.4+dfsg/mne/stats/permutations.py python-mne-1.1.0+dfsg/mne/stats/permutations.py --- python-mne-0.23.4+dfsg/mne/stats/permutations.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/stats/permutations.py 2022-08-03 15:48:14.000000000 +0000 @@ -21,7 +21,7 @@ @verbose -def permutation_t_test(X, n_permutations=10000, tail=0, n_jobs=1, +def permutation_t_test(X, n_permutations=10000, tail=0, n_jobs=None, seed=None, verbose=None): """One sample/paired sample permutation test based on a t-statistic. diff -Nru python-mne-0.23.4+dfsg/mne/stats/regression.py python-mne-1.1.0+dfsg/mne/stats/regression.py --- python-mne-0.23.4+dfsg/mne/stats/regression.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/stats/regression.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,7 +4,7 @@ # Jona Sassenhagen # Marijn van Vliet # -# License: BSD (3-clause) +# License: BSD-3-Clause from inspect import isgenerator from collections import namedtuple @@ -248,7 +248,7 @@ if solver == 'cholesky': def solver(X, y): a = (X.T * X).toarray() # dot product of sparse matrices - return linalg.solve(a, X.T * y, sym_pos=True, + return linalg.solve(a, X.T * y, assume_a='pos', overwrite_a=True, overwrite_b=True).T elif callable(solver): pass @@ -288,7 +288,8 @@ picks = _picks_to_idx(raw.info, picks) info = pick_info(raw.info, picks) decim = int(decim) - info["sfreq"] /= decim + with info._unlock(): + info["sfreq"] /= decim data, times = raw[:] data = data[picks, ::decim] if len(set(events[:, 0])) < len(events[:, 0]): diff -Nru python-mne-0.23.4+dfsg/mne/stats/tests/test_cluster_level.py python-mne-1.1.0+dfsg/mne/stats/tests/test_cluster_level.py --- python-mne-0.23.4+dfsg/mne/stats/tests/test_cluster_level.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/stats/tests/test_cluster_level.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors: Eric Larson # Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause from functools import partial import os @@ -14,14 +14,14 @@ from mne import (SourceEstimate, VolSourceEstimate, MixedSourceEstimate, SourceSpaces) -from mne.parallel import _force_serial from mne.stats import ttest_ind_no_p, combine_adjacency from mne.stats.cluster_level import (permutation_cluster_test, f_oneway, permutation_cluster_1samp_test, spatio_temporal_cluster_test, spatio_temporal_cluster_1samp_test, ttest_1samp_no_p, summarize_clusters_stc) -from mne.utils import catch_logging, check_version, requires_sklearn +from mne.utils import (catch_logging, check_version, requires_sklearn, + _record_warnings) n_space = 50 @@ -102,9 +102,9 @@ buffer_size=None, out_type='mask') -def test_cache_dir(tmpdir, numba_conditional): +def test_cache_dir(tmp_path, numba_conditional): """Test use of cache dir.""" - tempdir = str(tmpdir) + tempdir = str(tmp_path) orig_dir = os.getenv('MNE_CACHE_DIR', None) orig_size = os.getenv('MNE_MEMMAP_MIN_SIZE', None) rng = np.random.RandomState(0) @@ -249,7 +249,7 @@ # test with 2 jobs and buffer_size enabled buffer_size = condition1.shape[1] // 10 - with pytest.warns(None): # sometimes "independently" + with _record_warnings(): # sometimes "independently" T_obs_neg_buff, _, cluster_p_values_neg_buff, _ = \ permutation_cluster_1samp_test( -condition1, n_permutations=100, tail=-1, out_type='mask', @@ -271,7 +271,7 @@ @requires_sklearn -def test_cluster_permutation_with_adjacency(numba_conditional): +def test_cluster_permutation_with_adjacency(numba_conditional, monkeypatch): """Test cluster level permutations with adjacency matrix.""" from sklearn.feature_extraction.image import grid_to_graph condition1_1d, condition2_1d, condition1_2d, condition2_2d = \ @@ -370,10 +370,11 @@ sums_5 = np.sort(sums_5) assert_array_almost_equal(sums_4, sums_5) - if not _force_serial: - pytest.raises(ValueError, spatio_temporal_func, X1d_3, - n_permutations=1, adjacency=adjacency, - max_step=1, threshold=1.67, n_jobs=-1000) + monkeypatch.delenv('MNE_FORCE_SERIAL', raising=False) + with pytest.raises(ValueError, match='must not be less'): + spatio_temporal_func( + X1d_3, n_permutations=1, adjacency=adjacency, + max_step=1, threshold=1.67, n_jobs=-1000) # not enough TFCE params with pytest.raises(KeyError, match='threshold, if dict, must have'): @@ -381,7 +382,7 @@ X1d_3, adjacency=adjacency, threshold=dict(me='hello')) # too extreme a start threshold - with pytest.warns(None) as w: + with _record_warnings() as w: spatio_temporal_func(X1d_3, adjacency=adjacency, threshold=dict(start=10, step=1)) if not did_warn: @@ -410,7 +411,8 @@ spatio_temporal_func( X1d_3, adjacency=adjacency, threshold=[]) with pytest.raises(ValueError, match='Invalid value for the \'tail\''): - with pytest.warns(None): # sometimes ignoring tail + # sometimes ignoring tail + with _record_warnings(): spatio_temporal_func( X1d_3, adjacency=adjacency, tail=2) @@ -669,6 +671,10 @@ permutation_cluster_1samp_test( data, tail=1, out_type='mask', threshold=dict(start=1, step=-0.5)) + # Should work with 2D data too + permutation_cluster_1samp_test(X=data[..., 0], + threshold=dict(start=0, step=0.2)) + # 1D gives slices, 2D+ gives boolean masks @pytest.mark.parametrize('shape', ((11,), (11, 3), (11, 1, 2))) diff -Nru python-mne-0.23.4+dfsg/mne/stats/tests/test_parametric.py python-mne-1.1.0+dfsg/mne/stats/tests/test_parametric.py --- python-mne-0.23.4+dfsg/mne/stats/tests/test_parametric.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/stats/tests/test_parametric.py 2022-08-03 15:48:14.000000000 +0000 @@ -90,6 +90,12 @@ assert (fvals_ >= 0).all() assert fvals_.size == n_effects + # check time-frequency input + n_subj, n_freqs, n_times, n_levels = (5, 10, 101, 4) + data = rng.random_sample([n_subj, n_levels, n_freqs, n_times]) + fvals, pvals = f_mway_rm(data, _effects[n_levels]) + assert fvals.shape[1:] == pvals.shape[1:] == (n_freqs, n_times) + data = rng.random_sample([n_subj, n_levels, 1]) pytest.raises(ValueError, f_mway_rm, data, _effects[n_levels], effects='C', correction=correction) diff -Nru python-mne-0.23.4+dfsg/mne/stats/tests/test_permutations.py python-mne-1.1.0+dfsg/mne/stats/tests/test_permutations.py --- python-mne-0.23.4+dfsg/mne/stats/tests/test_permutations.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/stats/tests/test_permutations.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause from numpy.testing import assert_array_equal, assert_allclose import numpy as np @@ -9,7 +9,7 @@ from mne.stats import permutation_cluster_1samp_test from mne.stats.permutations import (permutation_t_test, _ci, bootstrap_confidence_interval) -from mne.utils import run_tests_if_main, check_version +from mne.utils import check_version def test_permutation_t_test(): @@ -79,6 +79,3 @@ if check_version('numpy', '1.17'): random_state = np.random.default_rng(0) bootstrap_confidence_interval(arr, random_state=random_state) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/stats/tests/test_regression.py python-mne-1.1.0+dfsg/mne/stats/tests/test_regression.py --- python-mne-0.23.4+dfsg/mne/stats/tests/test_regression.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/stats/tests/test_regression.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Denis A. Engemann # Jona Sassenhagen # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op @@ -17,13 +17,13 @@ from mne.datasets import testing from mne.stats.regression import linear_regression, linear_regression_raw from mne.io import RawArray -from mne.utils import requires_sklearn, run_tests_if_main +from mne.utils import requires_sklearn data_path = testing.data_path(download=False) stc_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc-meg-lh.stc') -raw_fname = data_path + '/MEG/sample/sample_audvis_trunc_raw.fif' -event_fname = data_path + '/MEG/sample/sample_audvis_trunc_raw-eve.fif' +raw_fname = data_path / 'MEG' / 'sample' / 'sample_audvis_trunc_raw.fif' +event_fname = data_path / 'MEG' / 'sample' / 'sample_audvis_trunc_raw-eve.fif' @testing.requires_testing_data @@ -88,7 +88,8 @@ # a sampling of frequency where rounding and truncation yield # different results checks conversion from samples to times is # consistent across Epochs and linear_regression_raw - raw.info['sfreq'] = 128 + with raw.info._unlock(): + raw.info['sfreq'] = 128 events = mne.read_events(event_fname) event_id = dict(audio_l=1, audio_r=2) @@ -151,6 +152,3 @@ solver=solT) pytest.raises(ValueError, linear_regression_raw, raw, events, solver='err') pytest.raises(TypeError, linear_regression_raw, raw, events, solver=0) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/surface.py python-mne-1.1.0+dfsg/mne/surface.py --- python-mne-0.23.4+dfsg/mne/surface.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/surface.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,38 +3,35 @@ # Matti Hämäläinen # Denis A. Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause # Many of the computations in this code were derived from Matti Hämäläinen's # C code. from copy import deepcopy -from distutils.version import LooseVersion +from functools import partial, lru_cache +from collections import OrderedDict from glob import glob -from functools import partial -import os from os import path as op -import warnings from struct import pack +import time +import warnings import numpy as np +from .channels.channels import _get_meg_system +from .fixes import (_serialize_volume_info, _get_read_geometry, jit, + prange, bincount) from .io.constants import FIFF -from .io.open import fiff_open from .io.pick import pick_types -from .io.tree import dir_tree_find -from .io.tag import find_tag -from .io.write import (write_int, start_file, end_block, start_block, end_file, - write_string, write_float_sparse_rcs) -from .channels.channels import _get_meg_system from .parallel import parallel_func from .transforms import (transform_surface_to, _pol_to_cart, _cart_to_sph, - _get_trans, apply_trans, Transform) + _get_trans, apply_trans, Transform, _frame_to_str, + apply_volume_registration) from .utils import (logger, verbose, get_subjects_dir, warn, _check_fname, _check_option, _ensure_int, _TempDir, run_subprocess, - _check_freesurfer_home) -from .fixes import (_serialize_volume_info, _get_read_geometry, einsum, jit, - prange, bincount) + _check_freesurfer_home, _hashable_ndarray, fill_doc, + _validate_type, _require_version, _pl) ############################################################################### @@ -42,7 +39,7 @@ @verbose def get_head_surf(subject, source=('bem', 'head'), subjects_dir=None, - verbose=None): + on_defects='raise', verbose=None): """Load the subject head surface. Parameters @@ -59,6 +56,9 @@ subjects_dir : str, or None Path to the SUBJECTS_DIR. If None, the path is obtained by using the environment variable SUBJECTS_DIR. + %(on_defects)s + + .. versionadded:: 1.0 %(verbose)s Returns @@ -67,10 +67,12 @@ The head surface. """ return _get_head_surface(subject=subject, source=source, - subjects_dir=subjects_dir) + subjects_dir=subjects_dir, on_defects=on_defects) -def _get_head_surface(subject, source, subjects_dir, raise_error=True): +# TODO this should be refactored with mne._freesurfer._get_head_surface +def _get_head_surface(subject, source, subjects_dir, on_defects, + raise_error=True): """Load the subject head surface.""" from .bem import read_bem_surfaces # Load the head surface from the BEM @@ -87,6 +89,7 @@ if op.exists(this_head): surf = read_bem_surfaces(this_head, True, FIFF.FIFFV_BEM_SURF_ID_HEAD, + on_defects=on_defects, verbose=False) else: # let's do a more sophisticated search @@ -100,6 +103,7 @@ try: surf = read_bem_surfaces(this_head, True, FIFF.FIFFV_BEM_SURF_ID_HEAD, + on_defects=on_defects, verbose=False) except ValueError: pass @@ -124,8 +128,7 @@ Parameters ---------- - info : instance of Info - Measurement info. + %(info_not_none)s trans : dict The head<->MRI transformation, usually obtained using read_trans(). Can be None, in which case the surface will @@ -319,8 +322,8 @@ out = _find_nearest_tri_pts(rrs, pt_tris, pt_lens, reproject=True, **surf_geom) if project_rrs: # - out += (einsum('ij,ijk->ik', out[0], - surf['rr'][surf['tris'][out[1]]]),) + out += (np.einsum('ij,ijk->ik', out[0], + surf['rr'][surf['tris'][out[1]]]),) if return_nn: out += (surf_geom['nn'][out[1]],) else: # nearest neighbor @@ -328,9 +331,12 @@ idx = _compute_nearest(surf['rr'], rrs) out = (None, None, surf['rr'][idx]) if return_nn: + surf_geom = _get_tri_supp_geom(surf) nn = _accumulate_normals(surf['tris'].astype(int), surf_geom['nn'], len(surf['rr'])) - out += (nn[idx],) + nn = nn[idx] + _normalize_vectors(nn) + out += (nn,) return out @@ -349,7 +355,7 @@ @verbose def complete_surface_info(surf, do_neighbor_vert=False, copy=True, - verbose=None): + do_neighbor_tri=True, *, verbose=None): """Complete surface information. Parameters @@ -357,9 +363,11 @@ surf : dict The surface. do_neighbor_vert : bool - If True, add neighbor vertex information. + If True (default False), add neighbor vertex information. copy : bool If True (default), make a copy. If False, operate in-place. + do_neighbor_tri : bool + If True (default), compute triangle neighbors. %(verbose)s Returns @@ -389,27 +397,28 @@ # Find neighboring triangles, accumulate vertex normals, normalize logger.info(' Triangle neighbors and vertex normals...') - surf['neighbor_tri'] = _triangle_neighbors(surf['tris'], surf['np']) surf['nn'] = _accumulate_normals(surf['tris'].astype(int), surf['tri_nn'], surf['np']) _normalize_vectors(surf['nn']) # Check for topological defects - zero, fewer = list(), list() - for ni, n in enumerate(surf['neighbor_tri']): - if len(n) < 3: - if len(n) == 0: - zero.append(ni) - else: - fewer.append(ni) - surf['neighbor_tri'][ni] = np.array([], int) - if len(zero) > 0: - logger.info(' Vertices do not have any neighboring ' - 'triangles: [%s]' % ', '.join(str(z) for z in zero)) - if len(fewer) > 0: - logger.info(' Vertices have fewer than three neighboring ' - 'triangles, removing neighbors: [%s]' - % ', '.join(str(f) for f in fewer)) + if do_neighbor_tri: + surf['neighbor_tri'] = _triangle_neighbors(surf['tris'], surf['np']) + zero, fewer = list(), list() + for ni, n in enumerate(surf['neighbor_tri']): + if len(n) < 3: + if len(n) == 0: + zero.append(ni) + else: + fewer.append(ni) + surf['neighbor_tri'][ni] = np.array([], int) + if len(zero) > 0: + logger.info(' Vertices do not have any neighboring ' + 'triangles: [%s]' % ', '.join(str(z) for z in zero)) + if len(fewer) > 0: + logger.info(' Vertices have fewer than three neighboring ' + 'triangles, removing neighbors: [%s]' + % ', '.join(str(f) for f in fewer)) # Determine the neighboring vertices and fix errors if do_neighbor_vert is True: @@ -543,7 +552,7 @@ @verbose -def _points_outside_surface(rr, surf, n_jobs=1, verbose=None): +def _points_outside_surface(rr, surf, n_jobs=None, verbose=None): """Check whether points are outside a surface. Parameters @@ -560,56 +569,141 @@ """ rr = np.atleast_2d(rr) assert rr.shape[1] == 3 - assert n_jobs > 0 - parallel, p_fun, _ = parallel_func(_get_solids, n_jobs) + parallel, p_fun, n_jobs = parallel_func(_get_solids, n_jobs) tot_angles = parallel(p_fun(surf['rr'][tris], rr) for tris in np.array_split(surf['tris'], n_jobs)) return np.abs(np.sum(tot_angles, axis=0) / (2 * np.pi) - 1.0) > 1e-5 +def _surface_to_polydata(surf): + import pyvista as pv + vertices = np.array(surf['rr']) + if 'tris' not in surf: + return pv.PolyData(vertices) + else: + triangles = np.array(surf['tris']) + triangles = np.c_[np.full(len(triangles), 3), triangles] + return pv.PolyData(vertices, triangles) + + +def _polydata_to_surface(pd, normals=True): + from pyvista import PolyData + if not isinstance(pd, PolyData): + pd = PolyData(pd) + out = dict(rr=pd.points, tris=pd.faces.reshape(-1, 4)[:, 1:]) + if normals: + out['nn'] = pd.point_normals + return out + + class _CheckInside(object): """Efficiently check if points are inside a surface.""" - def __init__(self, surf): - from scipy.spatial import Delaunay + @verbose + def __init__(self, surf, *, mode='old', verbose=None): + assert mode in ('pyvista', 'old') + self.mode = mode + t0 = time.time() self.surf = surf + if self.mode == 'pyvista': + self._init_pyvista() + else: + self._init_old() + logger.debug( + f'Setting up {mode} interior check for {len(self.surf["rr"])} ' + f'points took {(time.time() - t0) * 1000:0.1f} ms') + + def _init_old(self): + from scipy.spatial import Delaunay self.inner_r = None - self.cm = surf['rr'].mean(0) - if not _points_outside_surface( - self.cm[np.newaxis], surf)[0]: # actually inside - # Immediately cull some points from the checks - self.inner_r = np.linalg.norm(surf['rr'] - self.cm, axis=-1).min() + self.cm = self.surf['rr'].mean(0) # We could use Delaunay or ConvexHull here, Delaunay is slightly slower # to construct but faster to evaluate # See https://stackoverflow.com/questions/16750618/whats-an-efficient-way-to-find-if-a-point-lies-in-the-convex-hull-of-a-point-cl # noqa - self.del_tri = Delaunay(surf['rr']) + self.del_tri = Delaunay(self.surf['rr']) + if self.del_tri.find_simplex(self.cm) >= 0: + # Immediately cull some points from the checks + dists = np.linalg.norm(self.surf['rr'] - self.cm, axis=-1) + self.inner_r = dists.min() + self.outer_r = dists.max() + + def _init_pyvista(self): + if not isinstance(self.surf, dict): + self.pdata = self.surf + self.surf = _polydata_to_surface(self.pdata) + else: + self.pdata = _surface_to_polydata(self.surf).clean() @verbose - def __call__(self, rr, n_jobs=1, verbose=None): - inside = np.ones(len(rr), bool) # innocent until proven guilty - idx = np.arange(len(rr)) + def __call__(self, rr, n_jobs=None, verbose=None): + n_orig = len(rr) + logger.info(f'Checking surface interior status for ' + f'{n_orig} point{_pl(n_orig, " ")}...') + t0 = time.time() + if self.mode == 'pyvista': + inside = self._call_pyvista(rr) + else: + inside = self._call_old(rr, n_jobs) + n = inside.sum() + logger.info( + f' Total {n}/{n_orig} point{_pl(n, " ")} inside the surface') + logger.info( + f'Interior check completed in {(time.time() - t0) * 1000:0.1f} ms') + return inside + def _call_pyvista(self, rr): + pdata = _surface_to_polydata(dict(rr=rr)) + out = pdata.select_enclosed_points(self.pdata, check_surface=False) + return out['SelectedPoints'].astype(bool) + + def _call_old(self, rr, n_jobs): + n_orig = len(rr) + prec = int(np.ceil(np.log10(max(n_orig, 10)))) + inside = np.ones(n_orig, bool) # innocent until proven guilty + idx = np.arange(n_orig) # Limit to indices that can plausibly be outside the surf + # but are not definitely outside it if self.inner_r is not None: - mask = np.linalg.norm(rr - self.cm, axis=-1) >= self.inner_r + dists = np.linalg.norm(rr - self.cm, axis=-1) + in_mask = dists < self.inner_r + n = (in_mask).sum() + n_pad = str(n).rjust(prec) + logger.info( + f' Found {n_pad}/{n_orig} point{_pl(n, " ")} ' + f'inside an interior sphere of radius ' + f'{1000 * self.inner_r:6.1f} mm') + out_mask = dists > self.outer_r + inside[out_mask] = False + n = (out_mask).sum() + n_pad = str(n).rjust(prec) + logger.info( + f' Found {n_pad}/{n_orig} point{_pl(n, " ")} ' + f'outside an exterior sphere of radius ' + f'{1000 * self.outer_r:6.1f} mm') + mask = (~in_mask) & (~out_mask) # not definitely inside or outside idx = idx[mask] rr = rr[mask] - logger.info(' Skipping interior check for %d sources that fit ' - 'inside a sphere of radius %6.1f mm' - % ((~mask).sum(), self.inner_r * 1000)) # Use qhull as our first pass (*much* faster than our check) del_outside = self.del_tri.find_simplex(rr) < 0 - omit_outside = sum(del_outside) + n = sum(del_outside) inside[idx[del_outside]] = False idx = idx[~del_outside] rr = rr[~del_outside] - logger.info(' Skipping solid angle check for %d points using Qhull' - % (omit_outside,)) + n_pad = str(n).rjust(prec) + check_pad = str(len(del_outside)).rjust(prec) + logger.info( + f' Found {n_pad}/{check_pad} point{_pl(n, " ")} outside using ' + 'surface Qhull') # use our more accurate check solid_outside = _points_outside_surface(rr, self.surf, n_jobs) - omit_outside += np.sum(solid_outside) + n = np.sum(solid_outside) + n_pad = str(n).rjust(prec) + check_pad = str(len(solid_outside)).rjust(prec) + logger.info( + f' Found {n_pad}/{check_pad} point{_pl(n, " ")} outside using ' + 'solid angles') inside[idx[solid_outside]] = False return inside @@ -733,11 +827,14 @@ ret += (dict(),) if return_dict: - ret += (dict(rr=ret[0], tris=ret[1], ntri=len(ret[1]), use_tris=ret[1], - np=len(ret[0])),) + ret += (_rr_tris_dict(ret[0], ret[1]),) return ret +def _rr_tris_dict(rr, tris): + return dict(rr=rr, tris=tris, ntri=len(tris), use_tris=tris, np=len(rr)) + + def _read_mri_surface(fname): surf = read_surface(fname, return_dict=True)[2] surf['rr'] /= 1000. @@ -1101,7 +1198,7 @@ if file_format == 'freesurfer': try: import nibabel as nib - has_nibabel = LooseVersion(nib.__version__) > LooseVersion('2.1.0') + has_nibabel = True except ImportError: has_nibabel = False if has_nibabel: @@ -1143,11 +1240,11 @@ def _decimate_surface_vtk(points, triangles, n_triangles): """Aux function.""" try: - from vtk.util.numpy_support import \ + from vtkmodules.util.numpy_support import \ numpy_to_vtk, numpy_to_vtkIdTypeArray - from vtk.numpy_interface.dataset_adapter import WrapDataObject - from vtk import \ - vtkPolyData, vtkQuadricDecimation, vtkPoints, vtkCellArray + from vtkmodules.vtkCommonDataModel import vtkPolyData, vtkCellArray + from vtkmodules.vtkCommonCore import vtkPoints + from vtkmodules.vtkFiltersCore import vtkQuadricDecimation except ImportError: raise ValueError('This function requires the VTK package to be ' 'installed') @@ -1176,10 +1273,9 @@ reduction = 1 - (float(n_triangles) / len(triangles)) decimate.SetTargetReduction(reduction) decimate.Update() - out = WrapDataObject(decimate.GetOutput()) - rrs = out.Points - tris = out.Polygons.reshape(-1, 4)[:, 1:] - return rrs, tris + + out = _polydata_to_surface(decimate.GetOutput(), normals=False) + return out['rr'], out['tris'] def _decimate_surface_sphere(rr, tris, n_triangles): @@ -1232,7 +1328,7 @@ @verbose -def decimate_surface(points, triangles, n_triangles, method='quadric', +def decimate_surface(points, triangles, n_triangles, method='quadric', *, verbose=None): """Decimate surface data. @@ -1291,147 +1387,7 @@ ############################################################################### -# Morph maps - -# XXX this morphing related code should probably be moved to morph.py - -@verbose -def read_morph_map(subject_from, subject_to, subjects_dir=None, xhemi=False, - verbose=None): - """Read morph map. - - Morph maps can be generated with mne_make_morph_maps. If one isn't - available, it will be generated automatically and saved to the - ``subjects_dir/morph_maps`` directory. - - Parameters - ---------- - subject_from : str - Name of the original subject as named in the SUBJECTS_DIR. - subject_to : str - Name of the subject on which to morph as named in the SUBJECTS_DIR. - subjects_dir : str - Path to SUBJECTS_DIR is not set in the environment. - xhemi : bool - Morph across hemisphere. Currently only implemented for - ``subject_to == subject_from``. See notes of - :func:`mne.compute_source_morph`. - %(verbose)s - - Returns - ------- - left_map, right_map : ~scipy.sparse.csr_matrix - The morph maps for the 2 hemispheres. - """ - subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) - - # First check for morph-map dir existence - mmap_dir = op.join(subjects_dir, 'morph-maps') - if not op.isdir(mmap_dir): - try: - os.mkdir(mmap_dir) - except Exception: - warn('Could not find or make morph map directory "%s"' % mmap_dir) - - # filename components - if xhemi: - if subject_to != subject_from: - raise NotImplementedError( - "Morph-maps between hemispheres are currently only " - "implemented for subject_to == subject_from") - map_name_temp = '%s-%s-xhemi' - log_msg = 'Creating morph map %s -> %s xhemi' - else: - map_name_temp = '%s-%s' - log_msg = 'Creating morph map %s -> %s' - - map_names = [map_name_temp % (subject_from, subject_to), - map_name_temp % (subject_to, subject_from)] - - # find existing file - for map_name in map_names: - fname = op.join(mmap_dir, '%s-morph.fif' % map_name) - if op.exists(fname): - return _read_morph_map(fname, subject_from, subject_to) - # if file does not exist, make it - logger.info('Morph map "%s" does not exist, creating it and saving it to ' - 'disk' % fname) - logger.info(log_msg % (subject_from, subject_to)) - mmap_1 = _make_morph_map(subject_from, subject_to, subjects_dir, xhemi) - if subject_to == subject_from: - mmap_2 = None - else: - logger.info(log_msg % (subject_to, subject_from)) - mmap_2 = _make_morph_map(subject_to, subject_from, subjects_dir, - xhemi) - _write_morph_map(fname, subject_from, subject_to, mmap_1, mmap_2) - return mmap_1 - - -def _read_morph_map(fname, subject_from, subject_to): - """Read a morph map from disk.""" - f, tree, _ = fiff_open(fname) - with f as fid: - # Locate all maps - maps = dir_tree_find(tree, FIFF.FIFFB_MNE_MORPH_MAP) - if len(maps) == 0: - raise ValueError('Morphing map data not found') - - # Find the correct ones - left_map = None - right_map = None - for m in maps: - tag = find_tag(fid, m, FIFF.FIFF_MNE_MORPH_MAP_FROM) - if tag.data == subject_from: - tag = find_tag(fid, m, FIFF.FIFF_MNE_MORPH_MAP_TO) - if tag.data == subject_to: - # Names match: which hemishere is this? - tag = find_tag(fid, m, FIFF.FIFF_MNE_HEMI) - if tag.data == FIFF.FIFFV_MNE_SURF_LEFT_HEMI: - tag = find_tag(fid, m, FIFF.FIFF_MNE_MORPH_MAP) - left_map = tag.data - logger.info(' Left-hemisphere map read.') - elif tag.data == FIFF.FIFFV_MNE_SURF_RIGHT_HEMI: - tag = find_tag(fid, m, FIFF.FIFF_MNE_MORPH_MAP) - right_map = tag.data - logger.info(' Right-hemisphere map read.') - - if left_map is None or right_map is None: - raise ValueError('Could not find both hemispheres in %s' % fname) - - return left_map, right_map - - -def _write_morph_map(fname, subject_from, subject_to, mmap_1, mmap_2): - """Write a morph map to disk.""" - try: - fid = start_file(fname) - except Exception as exp: - warn('Could not write morph-map file "%s" (error: %s)' - % (fname, exp)) - return - - assert len(mmap_1) == 2 - hemis = [FIFF.FIFFV_MNE_SURF_LEFT_HEMI, FIFF.FIFFV_MNE_SURF_RIGHT_HEMI] - for m, hemi in zip(mmap_1, hemis): - start_block(fid, FIFF.FIFFB_MNE_MORPH_MAP) - write_string(fid, FIFF.FIFF_MNE_MORPH_MAP_FROM, subject_from) - write_string(fid, FIFF.FIFF_MNE_MORPH_MAP_TO, subject_to) - write_int(fid, FIFF.FIFF_MNE_HEMI, hemi) - write_float_sparse_rcs(fid, FIFF.FIFF_MNE_MORPH_MAP, m) - end_block(fid, FIFF.FIFFB_MNE_MORPH_MAP) - # don't write mmap_2 if it is identical (subject_to == subject_from) - if mmap_2 is not None: - assert len(mmap_2) == 2 - for m, hemi in zip(mmap_2, hemis): - start_block(fid, FIFF.FIFFB_MNE_MORPH_MAP) - write_string(fid, FIFF.FIFF_MNE_MORPH_MAP_FROM, subject_to) - write_string(fid, FIFF.FIFF_MNE_MORPH_MAP_TO, subject_from) - write_int(fid, FIFF.FIFF_MNE_HEMI, hemi) - write_float_sparse_rcs(fid, FIFF.FIFF_MNE_MORPH_MAP, m) - end_block(fid, FIFF.FIFFB_MNE_MORPH_MAP) - end_file(fid) - +# Geometry @jit() def _get_tri_dist(p, q, p0, q0, a, b, c, dist): # pragma: no cover @@ -1451,9 +1407,9 @@ r12 = surf['rr'][surf['tris'][:, 1], :] - r1 r13 = surf['rr'][surf['tris'][:, 2], :] - r1 r1213 = np.ascontiguousarray(np.array([r12, r13]).swapaxes(0, 1)) - a = einsum('ij,ij->i', r12, r12) - b = einsum('ij,ij->i', r13, r13) - c = einsum('ij,ij->i', r12, r13) + a = np.einsum('ij,ij->i', r12, r12) + b = np.einsum('ij,ij->i', r13, r13) + c = np.einsum('ij,ij->i', r12, r13) mat = np.ascontiguousarray(np.rollaxis(np.array([[b, -c], [-c, a]]), 2)) norm = (a * b - c * c) norm[norm == 0] = 1. # avoid divide by zero @@ -1464,74 +1420,6 @@ a=a, b=b, c=c, mat=mat, nn=nn) -def _make_morph_map(subject_from, subject_to, subjects_dir, xhemi): - """Construct morph map from one subject to another. - - Note that this is close, but not exactly like the C version. - For example, parts are more accurate due to double precision, - so expect some small morph-map differences! - - Note: This seems easily parallelizable, but the overhead - of pickling all the data structures makes it less efficient - than just running on a single core :( - """ - subjects_dir = get_subjects_dir(subjects_dir) - if xhemi: - reg = '%s.sphere.left_right' - hemis = (('lh', 'rh'), ('rh', 'lh')) - else: - reg = '%s.sphere.reg' - hemis = (('lh', 'lh'), ('rh', 'rh')) - - return [_make_morph_map_hemi(subject_from, subject_to, subjects_dir, - reg % hemi_from, reg % hemi_to) - for hemi_from, hemi_to in hemis] - - -def _make_morph_map_hemi(subject_from, subject_to, subjects_dir, reg_from, - reg_to): - """Construct morph map for one hemisphere.""" - from scipy.sparse import csr_matrix, eye as speye - # add speedy short-circuit for self-maps - if subject_from == subject_to and reg_from == reg_to: - fname = op.join(subjects_dir, subject_from, 'surf', reg_from) - n_pts = len(read_surface(fname, verbose=False)[0]) - return speye(n_pts, n_pts, format='csr') - - # load surfaces and normalize points to be on unit sphere - fname = op.join(subjects_dir, subject_from, 'surf', reg_from) - from_rr, from_tri = read_surface(fname, verbose=False) - fname = op.join(subjects_dir, subject_to, 'surf', reg_to) - to_rr = read_surface(fname, verbose=False)[0] - _normalize_vectors(from_rr) - _normalize_vectors(to_rr) - - # from surface: get nearest neighbors, find triangles for each vertex - nn_pts_idx = _compute_nearest(from_rr, to_rr, method='cKDTree') - from_pt_tris = _triangle_neighbors(from_tri, len(from_rr)) - from_pt_tris = [from_pt_tris[pt_idx].astype(int) for pt_idx in nn_pts_idx] - from_pt_lens = np.cumsum([0] + [len(x) for x in from_pt_tris]) - from_pt_tris = np.concatenate(from_pt_tris) - assert from_pt_tris.ndim == 1 - assert from_pt_lens[-1] == len(from_pt_tris) - - # find triangle in which point lies and assoc. weights - tri_inds = [] - weights = [] - tri_geom = _get_tri_supp_geom(dict(rr=from_rr, tris=from_tri)) - weights, tri_inds = _find_nearest_tri_pts( - to_rr, from_pt_tris, from_pt_lens, run_all=False, reproject=False, - **tri_geom) - - nn_idx = from_tri[tri_inds] - weights = np.array(weights) - - row_ind = np.repeat(np.arange(len(to_rr)), 3) - this_map = csr_matrix((weights.ravel(), (row_ind, nn_idx.ravel())), - shape=(len(to_rr), len(from_rr))) - return this_map - - @jit(parallel=True) def _find_nearest_tri_pts(rrs, pt_triss, pt_lens, a, b, c, nn, r1, r12, r13, r1213, mat, @@ -1662,9 +1550,15 @@ Returns ------- - edges : sparse matrix + edges : scipy.sparse.spmatrix The adjacency matrix. """ + tris = _hashable_ndarray(tris) + return _mesh_edges(tris=tris) + + +@lru_cache(maxsize=10) +def _mesh_edges(tris=None): from scipy.sparse import coo_matrix if np.max(tris) > len(np.unique(tris)): raise ValueError( @@ -1804,7 +1698,8 @@ @verbose def dig_mri_distances(info, trans, subject, subjects_dir=None, - dig_kinds='auto', exclude_frontal=False, verbose=None): + dig_kinds='auto', exclude_frontal=False, + on_defects='raise', verbose=None): """Compute distances between head shape points and the scalp surface. This function is useful to check that coregistration is correct. @@ -1813,9 +1708,7 @@ Parameters ---------- - info : instance of Info - The measurement info that contains the head shape - points in ``info['dig']``. + %(info_not_none)s Must contain the head shape points in ``info['dig']``. trans : str | instance of Transform The head<->MRI transform. If str is passed it is the path to file on disk. @@ -1827,6 +1720,9 @@ %(dig_kinds)s %(exclude_frontal)s Default is False. + %(on_defects)s + + .. versionadded:: 1.0 %(verbose)s Returns @@ -1844,10 +1740,494 @@ """ from .bem import get_fitting_dig pts = get_head_surf(subject, ('head-dense', 'head', 'bem'), - subjects_dir=subjects_dir)['rr'] + subjects_dir=subjects_dir, on_defects=on_defects)['rr'] trans = _get_trans(trans, fro="mri", to="head")[0] pts = apply_trans(trans, pts) info_dig = get_fitting_dig( info, dig_kinds, exclude_frontal=exclude_frontal) dists = _compute_nearest(pts, info_dig, return_dists=True)[1] return dists + + +def _mesh_borders(tris, mask): + assert isinstance(mask, np.ndarray) and mask.ndim == 1 + edges = mesh_edges(tris) + edges = edges.tocoo() + border_edges = mask[edges.row] != mask[edges.col] + return np.unique(edges.row[border_edges]) + + +def _marching_cubes(image, level, smooth=0, fill_hole_size=None): + """Compute marching cubes on a 3D image.""" + # vtkDiscreteMarchingCubes would be another option, but it merges + # values at boundaries which is not what we want + # https://kitware.github.io/vtk-examples/site/Cxx/Medical/GenerateModelsFromLabels/ # noqa: E501 + # Also vtkDiscreteFlyingEdges3D should be faster. + # If we ever want not-discrete (continuous/float) marching cubes, + # we should probably use vtkFlyingEdges3D rather than vtkMarchingCubes. + from vtkmodules.vtkCommonDataModel import \ + vtkImageData, vtkDataSetAttributes + from vtkmodules.vtkFiltersCore import vtkThreshold + from vtkmodules.vtkFiltersGeneral import vtkDiscreteFlyingEdges3D + from vtkmodules.vtkFiltersGeometry import vtkGeometryFilter + from vtkmodules.util.numpy_support import vtk_to_numpy, numpy_to_vtk + from scipy.ndimage import binary_dilation + + if image.ndim != 3: + raise ValueError(f'3D data must be supplied, got {image.shape}') + + level = np.array(level) + if level.ndim != 1 or level.size == 0 or level.dtype.kind not in 'ui': + raise TypeError( + 'level must be non-empty numeric or 1D array-like of int, ' + f'got {level.ndim}D array-like of {level.dtype} with ' + f'{level.size} elements') + + # fill holes + if fill_hole_size is not None: + image = image.copy() # don't modify original + for val in level: + bin_image = image == val + mask = image == 0 # don't go into other areas + bin_image = binary_dilation(bin_image, iterations=fill_hole_size, + mask=mask) + image[bin_image] = val + + # force double as passing integer types directly can be problematic! + image_shape = image.shape + data_vtk = numpy_to_vtk(image.ravel().astype(float), deep=True) + del image + + mc = vtkDiscreteFlyingEdges3D() + # create image + imdata = vtkImageData() + imdata.SetDimensions(image_shape) + imdata.SetSpacing([1, 1, 1]) + imdata.SetOrigin([0, 0, 0]) + imdata.GetPointData().SetScalars(data_vtk) + + # compute marching cubes on smoothed data + mc.SetNumberOfContours(len(level)) + for li, lev in enumerate(level): + mc.SetValue(li, lev) + mc.SetInputData(imdata) + mc.Update() + mc = _vtk_smooth(mc.GetOutput(), smooth) + + # get verts and triangles + selector = vtkThreshold() + selector.SetInputData(mc) + dsa = vtkDataSetAttributes() + selector.SetInputArrayToProcess( + 0, 0, 0, imdata.FIELD_ASSOCIATION_POINTS, dsa.SCALARS) + geometry = vtkGeometryFilter() + geometry.SetInputConnection(selector.GetOutputPort()) + + out = list() + for val in level: + try: + selector.SetLowerThreshold + except AttributeError: + selector.ThresholdBetween(val, val) + else: + # default SetThresholdFunction is between, so: + selector.SetLowerThreshold(val) + selector.SetUpperThreshold(val) + geometry.Update() + polydata = geometry.GetOutput() + rr = vtk_to_numpy(polydata.GetPoints().GetData()) + tris = vtk_to_numpy( + polydata.GetPolys().GetConnectivityArray()).reshape(-1, 3) + rr = np.ascontiguousarray(rr[:, ::-1]) + tris = np.ascontiguousarray(tris[:, ::-1]) + out.append((rr, tris)) + return out + + +def _vtk_smooth(pd, smooth): + _validate_type(smooth, 'numeric', smooth) + smooth = float(smooth) + if not 0 <= smooth < 1: + raise ValueError('smoothing factor must be between 0 (inclusive) and ' + f'1 (exclusive), got {smooth}') + if smooth == 0: + return pd + from vtkmodules.vtkFiltersCore import vtkWindowedSincPolyDataFilter + logger.info(f' Smoothing by a factor of {smooth}') + return_ndarray = False + if isinstance(pd, dict): + pd = _surface_to_polydata(pd) + return_ndarray = True + smoother = vtkWindowedSincPolyDataFilter() + smoother.SetInputData(pd) + smoother.SetNumberOfIterations(100) + smoother.BoundarySmoothingOff() + smoother.FeatureEdgeSmoothingOff() + smoother.SetFeatureAngle(120.0) + smoother.SetPassBand(1 - smooth) + smoother.NonManifoldSmoothingOn() + smoother.NormalizeCoordinatesOff() + smoother.Update() + out = smoother.GetOutput() + if return_ndarray: + out = _polydata_to_surface(out, normals=False) + return out + + +def _warn_missing_chs(info, dig_image, after_warp, verbose=None): + """Warn that channels are missing.""" + # ensure that each electrode contact was marked in at least one voxel + missing = set(np.arange(1, len(info.ch_names) + 1)).difference( + set(np.unique(np.array(dig_image.dataobj)))) + missing_ch = [info.ch_names[idx - 1] for idx in missing] + if missing_ch and verbose != 'error': + warn(f'Channel{_pl(missing_ch)} ' + f'{", ".join(repr(ch) for ch in missing_ch)} not assigned ' + 'voxels ' + + (f' after applying {after_warp}' if after_warp else '')) + + +@verbose +def warp_montage_volume(montage, base_image, reg_affine, sdr_morph, + subject_from, subject_to='fsaverage', + subjects_dir_from=None, subjects_dir_to=None, + thresh=0.5, max_peak_dist=1, voxels_max=100, + use_min=False, verbose=None): + """Warp a montage to a template with image volumes using SDR. + + Find areas of the input volume with intensity greater than + a threshold surrounding local extrema near the channel location. + Monotonicity from the peak is enforced to prevent channels + bleeding into each other. + + .. note:: This is likely only applicable for channels inside the brain + (intracranial electrodes). + + Parameters + ---------- + montage : instance of mne.channels.DigMontage + The montage object containing the channels. + base_image : path-like | nibabel.spatialimages.SpatialImage + Path to a volumetric scan (e.g. CT) of the subject. Can be in any + format readable by nibabel. Can also be a nibabel image object. + Local extrema (max or min) should be nearby montage channel locations. + %(reg_affine)s + %(sdr_morph)s + subject_from : str + The name of the subject used for the Freesurfer reconstruction. + subject_to : str + The name of the subject to use as a template to morph to + (e.g. 'fsaverage'). + subjects_dir_from : path-like | None + The path to the Freesurfer ``recon-all`` directory for the + ``subject_from`` subject. The ``SUBJECTS_DIR`` environment + variable will be used when ``None``. + subjects_dir_to : path-like | None + The path to the Freesurfer ``recon-all`` directory for the + ``subject_to`` subject. ``subject_dir_from`` will be used + when ``None``. + thresh : float + The threshold relative to the peak to determine the size + of the sensors on the volume. + max_peak_dist : int + The number of voxels away from the channel location to + look in the ``image``. This will depend on the accuracy of + the channel locations, the default (one voxel in all directions) + will work only with localizations that are that accurate. + voxels_max : int + The maximum number of voxels for each channel. + use_min : bool + Whether to hypointensities in the volume as channel locations. + Default False uses hyperintensities. + %(verbose)s + + Returns + ------- + montage_warped : mne.channels.DigMontage + The modified montage object containing the channels. + image_from : nibabel.spatialimages.SpatialImage + An image in Freesurfer surface RAS space with voxel values + corresponding to the index of the channel. The background + is 0s and this index starts at 1. + image_to : nibabel.spatialimages.SpatialImage + The warped image with voxel values corresponding to the index + of the channel. The background is 0s and this index starts at 1. + """ + _require_version('nibabel', 'SDR morph', '2.1.0') + _require_version('dipy', 'SDR morph', '0.10.1') + from .channels import DigMontage, make_dig_montage + from ._freesurfer import _check_subject_dir + import nibabel as nib + + _validate_type(montage, DigMontage, 'montage') + _validate_type(base_image, nib.spatialimages.SpatialImage, 'base_image') + _validate_type(thresh, float, 'thresh') + if thresh < 0 or thresh >= 1: + raise ValueError(f'`thresh` must be between 0 and 1, got {thresh}') + _validate_type(max_peak_dist, int, 'max_peak_dist') + _validate_type(voxels_max, int, 'voxels_max') + _validate_type(use_min, bool, 'use_min') + + # first, make sure we have the necessary freesurfer surfaces + _check_subject_dir(subject_from, subjects_dir_from) + if subjects_dir_to is None: # assume shared + subjects_dir_to = subjects_dir_from + _check_subject_dir(subject_to, subjects_dir_to) + + # load image and make sure it's in surface RAS + if not isinstance(base_image, nib.spatialimages.SpatialImage): + base_image = nib.load(base_image) + fs_from_img = nib.load( + op.join(subjects_dir_from, subject_from, 'mri', 'brain.mgz')) + if not np.allclose(base_image.affine, fs_from_img.affine, atol=1e-6): + raise RuntimeError('The `base_image` is not aligned to Freesurfer ' + 'surface RAS space. This space is required as ' + 'it is the space where the anatomical ' + 'segmentation and reconstructed surfaces are') + + # get montage channel coordinates + ch_dict = montage.get_positions() + if ch_dict['coord_frame'] != 'mri': + bad_coord_frames = np.unique([d['coord_frame'] for d in montage.dig]) + bad_coord_frames = ', '.join([ + _frame_to_str[cf] if cf in _frame_to_str else str(cf) + for cf in bad_coord_frames]) + raise RuntimeError('Coordinate frame not supported, expected ' + f'"mri", got {bad_coord_frames}') + ch_names = list(ch_dict['ch_pos'].keys()) + ch_coords = np.array([ch_dict['ch_pos'][name] for name in ch_names]) + + # convert to freesurfer voxel space + ch_coords = apply_trans( + np.linalg.inv(fs_from_img.header.get_vox2ras_tkr()), ch_coords * 1000) + + # take channel coordinates and use the image to transform them + # into a volume where all the voxels over a threshold nearby + # are labeled with an index + image_data = np.array(base_image.dataobj) + if use_min: + image_data *= -1 + image_from = np.zeros(base_image.shape, dtype=int) + for i, ch_coord in enumerate(ch_coords): + if np.isnan(ch_coord).any(): + continue + # this looks up to a voxel away, it may be marked imperfectly + volume = _voxel_neighbors(ch_coord, image_data, thresh=thresh, + max_peak_dist=max_peak_dist, + voxels_max=voxels_max) + for voxel in volume: + if image_from[voxel] != 0: + # some voxels ambiguous because the contacts are bridged on + # the image so assign the voxel to the nearest contact location + dist_old = np.sqrt( + (ch_coords[image_from[voxel] - 1] - voxel)**2).sum() + dist_new = np.sqrt((ch_coord - voxel)**2).sum() + if dist_new < dist_old: + image_from[voxel] = i + 1 + else: + image_from[voxel] = i + 1 + + # apply the mapping + image_from = nib.spatialimages.SpatialImage(image_from, fs_from_img.affine) + _warn_missing_chs(montage, image_from, after_warp=False) + + template_brain = nib.load( + op.join(subjects_dir_to, subject_to, 'mri', 'brain.mgz')) + + image_to = apply_volume_registration( + image_from, template_brain, reg_affine, sdr_morph, + interpolation='nearest') + + after_warp = \ + 'SDR warp' if sdr_morph is not None else 'affine transformation' + _warn_missing_chs(montage, image_to, after_warp=after_warp) + + # recover the contact positions as the center of mass + warped_data = np.asanyarray(image_to.dataobj) + for val, ch_coord in enumerate(ch_coords, 1): + ch_coord[:] = np.mean(np.where(warped_data == val), axis=1) + + # convert back to surface RAS of the template + fs_to_img = nib.load( + op.join(subjects_dir_to, subject_to, 'mri', 'brain.mgz')) + ch_coords = apply_trans( + fs_to_img.header.get_vox2ras_tkr(), ch_coords) / 1000 + + # make warped montage + montage_warped = make_dig_montage( + dict(zip(ch_names, ch_coords)), coord_frame='mri') + return montage_warped, image_from, image_to + + +_VOXELS_MAX = 1000 # define constant to avoid runtime issues + + +@fill_doc +def get_montage_volume_labels(montage, subject, subjects_dir=None, + aseg='aparc+aseg', dist=2): + """Get regions of interest near channels from a Freesurfer parcellation. + + .. note:: This is applicable for channels inside the brain + (intracranial electrodes). + + Parameters + ---------- + %(montage)s + %(subject)s + %(subjects_dir)s + %(aseg)s + dist : float + The distance in mm to use for identifying regions of interest. + + Returns + ------- + labels : dict + The regions of interest labels within ``dist`` of each channel. + colors : dict + The Freesurfer lookup table colors for the labels. + """ + from .channels import DigMontage + from ._freesurfer import read_freesurfer_lut, _get_aseg + + _validate_type(montage, DigMontage, 'montage') + _validate_type(dist, (int, float), 'dist') + + if dist < 0 or dist > 10: + raise ValueError('`dist` must be between 0 and 10') + + aseg, aseg_data = _get_aseg(aseg, subject, subjects_dir) + + # read freesurfer lookup table + lut, fs_colors = read_freesurfer_lut() + label_lut = {v: k for k, v in lut.items()} + + # assert that all the values in the aseg are in the labels + assert all([idx in label_lut for idx in np.unique(aseg_data)]) + + # get transform to surface RAS for distance units instead of voxels + vox2ras_tkr = aseg.header.get_vox2ras_tkr() + + ch_dict = montage.get_positions() + if ch_dict['coord_frame'] != 'mri': + raise RuntimeError('Coordinate frame not supported, expected ' + '"mri", got ' + str(ch_dict['coord_frame'])) + ch_coords = np.array(list(ch_dict['ch_pos'].values())) + + # convert to freesurfer voxel space + ch_coords = apply_trans( + np.linalg.inv(aseg.header.get_vox2ras_tkr()), ch_coords * 1000) + labels = OrderedDict() + for ch_name, ch_coord in zip(montage.ch_names, ch_coords): + if np.isnan(ch_coord).any(): + labels[ch_name] = list() + else: + voxels = _voxel_neighbors( + ch_coord, aseg_data, dist=dist, vox2ras_tkr=vox2ras_tkr, + voxels_max=_VOXELS_MAX) + label_idxs = set([aseg_data[tuple(voxel)].astype(int) + for voxel in voxels]) + labels[ch_name] = [label_lut[idx] for idx in label_idxs] + + all_labels = set([label for val in labels.values() for label in val]) + colors = {label: tuple(fs_colors[label][:3] / 255) + (1.,) + for label in all_labels} + return labels, colors + + +def _get_neighbors(loc, image, voxels, thresh, dist_params): + """Find all the neighbors above a threshold near a voxel.""" + neighbors = set() + for axis in range(len(loc)): + for i in (-1, 1): + next_loc = np.array(loc) + next_loc[axis] += i + if thresh is not None: + assert dist_params is None + # must be above thresh, monotonically decreasing from + # the peak and not already found + next_loc = tuple(next_loc) + if image[next_loc] > thresh and \ + image[next_loc] < image[loc] and \ + next_loc not in voxels: + neighbors.add(next_loc) + else: + assert thresh is None + dist, seed_fs_ras, vox2ras_tkr = dist_params + next_loc_fs_ras = apply_trans(vox2ras_tkr, next_loc + 0.5) + if np.linalg.norm(seed_fs_ras - next_loc_fs_ras) <= dist: + neighbors.add(tuple(next_loc)) + return neighbors + + +def _voxel_neighbors(seed, image, thresh=None, max_peak_dist=1, + use_relative=True, dist=None, vox2ras_tkr=None, + voxels_max=100): + """Find voxels above a threshold contiguous with a seed location. + + Parameters + ---------- + seed : tuple | ndarray + The location in image coordinated to seed the algorithm. + image : ndarray + The image to search. + thresh : float + The threshold to use as a cutoff for what qualifies as a neighbor. + Will be relative to the peak if ``use_relative`` or absolute if not. + max_peak_dist : int + The maximum number of voxels to search for the peak near + the seed location. + use_relative : bool + If ``True``, the threshold will be relative to the peak, if + ``False``, the threshold will be absolute. + dist : float + The distance in mm to include surrounding voxels. + vox2ras_tkr : ndarray + The voxel to surface RAS affine. Must not be None if ``dist`` + if not None. + voxels_max : int + The maximum size of the output ``voxels``. + + Returns + ------- + voxels : set + The set of locations including the ``seed`` voxel and + surrounding that meet the criteria. + + .. note:: Either ``dist`` or ``thesh`` may be used but not both. + When ``thresh`` is used, first a peak nearby the seed + location is found and then voxels are only included if they + decrease monotonically from the peak. When ``dist`` is used, + only voxels within ``dist`` mm of the seed are included. + """ + seed = np.array(seed).round().astype(int) + assert ((dist is not None) + (thresh is not None)) == 1 + if thresh is not None: + dist_params = None + check_grid = image[tuple([ + slice(idx - max_peak_dist, idx + max_peak_dist + 1) + for idx in seed])] + peak = np.array(np.unravel_index( + np.argmax(check_grid), check_grid.shape)) - max_peak_dist + seed + voxels = neighbors = set([tuple(peak)]) + if use_relative: + thresh *= image[tuple(peak)] + else: + assert vox2ras_tkr is not None + seed_fs_ras = apply_trans(vox2ras_tkr, seed + 0.5) # center of voxel + dist_params = (dist, seed_fs_ras, vox2ras_tkr) + voxels = neighbors = set([tuple(seed)]) + while neighbors and len(voxels) <= voxels_max: + next_neighbors = set() + for next_loc in neighbors: + voxel_neighbors = _get_neighbors(next_loc, image, voxels, + thresh, dist_params) + # prevent looping back to already visited voxels + voxel_neighbors = voxel_neighbors.difference(voxels) + # add voxels not already visited to search next + next_neighbors = next_neighbors.union(voxel_neighbors) + # add new voxels that match the criteria to the overall set + voxels = voxels.union(voxel_neighbors) + if len(voxels) > voxels_max: + break + neighbors = next_neighbors # start again checking all new neighbors + return voxels diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_annotations.py python-mne-1.1.0+dfsg/mne/tests/test_annotations.py --- python-mne-0.23.4+dfsg/mne/tests/test_annotations.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_annotations.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,10 +1,10 @@ # Authors: Jaakko Leppakangas # Robert Luke # -# License: BSD 3 clause +# License: BSD-3-Clause from collections import OrderedDict -from datetime import datetime, timezone +from datetime import datetime, timezone, timedelta from itertools import repeat import sys @@ -21,10 +21,10 @@ from mne import (create_info, read_annotations, annotations_from_events, events_from_annotations) from mne import Epochs, Annotations -from mne.utils import (requires_version, - catch_logging, requires_pandas) -from mne.utils import (assert_and_remove_boundary_annot, _raw_annot, - _dt_to_stamp, _stamp_to_dt, check_version) +from mne.utils import (requires_version, catch_logging, requires_pandas, + assert_and_remove_boundary_annot, _raw_annot, + _dt_to_stamp, _stamp_to_dt, check_version, + _record_warnings) from mne.io import read_raw_fif, RawArray, concatenate_raws from mne.annotations import (_sync_onset, _handle_meas_date, _read_annotations_txt_parse_header) @@ -36,6 +36,10 @@ first_samps = pytest.mark.parametrize('first_samp', (0, 10000)) +data_path = testing.data_path(download=False) +edf_reduced = op.join(data_path, 'EDF', 'test_reduced.edf') +edf_annot_only = op.join(data_path, 'EDF', 'SC4001EC-Hypnogram.edf') + needs_pandas = pytest.mark.skipif( not check_version('pandas'), reason='Needs pandas') @@ -62,8 +66,8 @@ def test_basics(): """Test annotation class.""" raw = read_raw_fif(fif_fname) - assert raw.annotations is not None # XXX to be fixed in #5416 - assert len(raw.annotations.onset) == 0 # XXX to be fixed in #5416 + assert raw.annotations is not None + assert len(raw.annotations.onset) == 0 pytest.raises(IOError, read_annotations, fif_fname) onset = np.array(range(10)) duration = np.ones(10) @@ -106,10 +110,10 @@ assert_array_equal(raw.annotations.description, np.repeat('test', 10)) -def test_annot_sanitizing(tmpdir): +def test_annot_sanitizing(tmp_path): """Test description sanitizing.""" annot = Annotations([0], [1], ['a;:b']) - fname = str(tmpdir.join('custom-annot.fif')) + fname = tmp_path / 'custom-annot.fif' annot.save(fname) annot_read = read_annotations(fname) _assert_annotations_equal(annot, annot_read) @@ -126,7 +130,8 @@ info = create_info(ch_names=['MEG1', 'MEG2'], ch_types=['grad'] * 2, sfreq=sfreq) meas_date = _handle_meas_date(np.pi) - info['meas_date'] = meas_date + with info._unlock(): + info['meas_date'] = meas_date raws = [] for first_samp in [12300, 100, 12]: raw = RawArray(data.copy(), info, first_samp=first_samp) @@ -169,7 +174,7 @@ assert raw.annotations.orig_time == orig_time -def test_crop(tmpdir): +def test_crop(tmp_path): """Test cropping with annotations.""" raw = read_raw_fif(fif_fname) events = mne.find_events(raw) @@ -221,7 +226,7 @@ assert_array_almost_equal(raw.annotations.onset, expected_onset, decimal=2) # Test IO - tempdir = str(tmpdir) + tempdir = str(tmp_path) fname = op.join(tempdir, 'test-annot.fif') raw.annotations.save(fname) annot_read = read_annotations(fname) @@ -247,8 +252,23 @@ raw.set_annotations(None) raw.save(fname, overwrite=True) raw_read = read_raw_fif(fname) - assert raw_read.annotations is not None # XXX to be fixed in #5416 - assert len(raw_read.annotations.onset) == 0 # XXX to be fixed in #5416 + assert raw_read.annotations is not None + assert len(raw_read.annotations.onset) == 0 + # test saving and reloading cropped annotations in raw instance + info = create_info([f'EEG{i+1}' for i in range(3)], + ch_types=['eeg'] * 3, sfreq=50) + raw = RawArray(np.zeros((3, 50 * 20)), info) + annotation = mne.Annotations([8, 12, 15], [2] * 3, [1, 2, 3]) + raw = raw.set_annotations(annotation) + raw_copied = raw.copy().crop(5, 18) + fname = op.join(tempdir, 'test_raw.fif') + raw_copied.save(fname, overwrite=True) + raw_loaded = mne.io.read_raw(str(fname)) + for attr in ('onset', 'duration'): + assert_allclose(getattr(raw.annotations, attr), + getattr(raw_copied.annotations, attr)) + assert_allclose(getattr(raw_copied.annotations, attr), + getattr(raw_loaded.annotations, attr)) @first_samps @@ -258,7 +278,8 @@ raw = RawArray(data=np.empty([10, 10], dtype=np.float64), info=create_info(ch_names=10, sfreq=1.), first_samp=first_samp) - raw.info['meas_date'] = _handle_meas_date(0) + with raw.info._unlock(): + raw.info['meas_date'] = _handle_meas_date(0) raw.set_annotations(Annotations(description='foo', onset=[0], duration=[10], orig_time=None)) assert raw.annotations.orig_time == raw.info['meas_date'] @@ -349,6 +370,17 @@ assert np.unique(annot.description).size == 5 +@testing.requires_testing_data +@pytest.mark.parametrize('fname, n_annot', [ + (edf_annot_only, 154), + (edf_reduced, 5), +]) +def test_read_edf_annotations(fname, n_annot): + """Test reading EDF annotations.""" + annot = read_annotations(fname) + assert len(annot) == n_annot + + @first_samps def test_raw_reject(first_samp): """Test raw data getter with annotation reject.""" @@ -368,10 +400,10 @@ # with orig_time and complete overlap raw = read_raw_fif(fif_fname) - raw.set_annotations(Annotations(onset=[1, 4, 5] + raw._first_time, - duration=[1, 3, 1], - description='BAD', - orig_time=raw.info['meas_date'])) + raw.set_annotations(Annotations( + onset=np.array([1, 4, 5], float) + raw._first_time, + duration=[1, 3, 1], description='BAD', + orig_time=raw.info['meas_date'])) t_stop = 18. assert raw.times[-1] > t_stop n_stop = int(round(t_stop * raw.info['sfreq'])) @@ -393,7 +425,7 @@ assert_array_equal(raw.get_data(), raw[:][0]) # Test _sync_onset - times = [10, -88, 190] + times = np.array([10, -88, 190], float) onsets = _sync_onset(raw, times) assert_array_almost_equal(onsets, times - raw.first_samp / raw.info['sfreq']) @@ -522,8 +554,10 @@ def test_annotation_concat(): """Test if two Annotations objects can be concatenated.""" - a = Annotations([1, 2, 3], [5, 5, 8], ["a", "b", "c"]) - b = Annotations([11, 12, 13], [1, 2, 2], ["x", "y", "z"]) + a = Annotations([1, 2, 3], [5, 5, 8], ["a", "b", "c"], + ch_names=[['1'], ['2'], []]) + b = Annotations([11, 12, 13], [1, 2, 2], ["x", "y", "z"], + ch_names=[[], ['3'], []]) # test + operator (does not modify a or b) c = a + b @@ -534,6 +568,10 @@ assert_equal(len(b), 3) assert_equal(len(c), 6) + # c should have updated channel names + want_names = np.array([('1',), ('2',), (), (), ('3',), ()], dtype='O') + assert_array_equal(c.ch_names, want_names) + # test += operator (modifies a in place) a += b assert_array_equal(a.onset, [1, 2, 3, 11, 12, 13]) @@ -565,14 +603,14 @@ assert_array_equal(a_.duration, a.duration) # cropping with left shifted window - with pytest.warns(None) as w: + with _record_warnings() as w: a_ = a.copy().crop(tmin=0, tmax=4.2) assert_array_equal(a_.onset, [1., 2., 3., 4.]) assert_allclose(a_.duration, [3.2, 2.2, 1.2, 0.2]) assert len(w) == 0 # cropping with right shifted window - with pytest.warns(None) as w: + with _record_warnings() as w: a_ = a.copy().crop(tmin=17.8, tmax=22) assert_array_equal(a_.onset, [17.8, 17.8]) assert_allclose(a_.duration, [0.2, 1.2]) @@ -584,7 +622,7 @@ assert_array_equal(a_.duration, [0, 1, 1, 1, 1, 1, 1, 1, 1]) # cropping with out-of-bounds window - with pytest.warns(None) as w: + with _record_warnings() as w: a_ = a.copy().crop(tmin=42, tmax=100) assert_array_equal(a_.onset, []) assert_array_equal(a_.duration, []) @@ -689,9 +727,9 @@ """Test events and annotations onset are the same.""" raw = _raw_annot(meas_date=1, orig_time=1.5) # sec 0 1 2 3 - # raw . |--------XXXXXXXXX - # annot . |---XX - # raw.annot . |--------XX + # raw . |--------xxxxxxxxx + # annot . |---xx + # raw.annot . |--------xx # latency . 0 1 2 # . 0 0 @@ -789,20 +827,22 @@ def _assert_annotations_equal(a, b, tol=0): __tracebackhide__ = True - assert_allclose(a.onset, b.onset, rtol=0, atol=tol) - assert_allclose(a.duration, b.duration, rtol=0, atol=tol) - assert_array_equal(a.description, b.description) - assert_array_equal(a.ch_names, b.ch_names) + assert_allclose( + a.onset, b.onset, rtol=0, atol=tol, err_msg='onset') + assert_allclose( + a.duration, b.duration, rtol=0, atol=tol, err_msg='duration') + assert_array_equal(a.description, b.description, err_msg='description') + assert_array_equal(a.ch_names, b.ch_names, err_msg='ch_names') a_orig_time = a.orig_time b_orig_time = b.orig_time - assert a_orig_time == b_orig_time + assert a_orig_time == b_orig_time, 'orig_time' _ORIG_TIME = datetime.fromtimestamp(1038942071.7201, timezone.utc) @pytest.fixture(scope='function', params=('ch_names', 'fmt')) -def dummy_annotation_file(tmpdir_factory, ch_names, fmt): +def dummy_annotation_file(tmp_path_factory, ch_names, fmt): """Create csv file for testing.""" if fmt == 'csv': content = ("onset,duration,description\n" @@ -830,9 +870,10 @@ content[-1] += ',MEG0111:MEG2563' content = '\n'.join(content) - fname = tmpdir_factory.mktemp('data').join(f'annotations-annot.{fmt}') + fname = tmp_path_factory.mktemp('data') / f'annotations-annot.{fmt}' if isinstance(content, str): - fname.write(content) + with open(fname, "w") as f: + f.write(content) else: content.save(fname) return fname @@ -844,7 +885,7 @@ 'txt', 'fif' ]) -def test_io_annotation(dummy_annotation_file, tmpdir, fmt, ch_names): +def test_io_annotation(dummy_annotation_file, tmp_path, fmt, ch_names): """Test CSV, TXT, and FIF input/output (which support ch_names).""" annot = read_annotations(dummy_annotation_file) assert annot.orig_time == _ORIG_TIME @@ -856,7 +897,7 @@ tol=1e-6) # Now test writing - fname = tmpdir.join(f'annotations-annot.{fmt}') + fname = tmp_path / f'annotations-annot.{fmt}' annot.save(fname) annot2 = read_annotations(fname) _assert_annotations_equal(annot, annot2) @@ -869,14 +910,15 @@ @requires_version('pandas') -def test_broken_csv(tmpdir): +def test_broken_csv(tmp_path): """Test broken .csv that does not use timestamps.""" content = ("onset,duration,description\n" "1.,1.0,AA\n" "3.,2.425,BB") - fname = tmpdir.join('annotations_broken.csv') - fname.write(content) + fname = tmp_path / 'annotations_broken.csv' + with open(fname, "w") as f: + f.write(content) with pytest.warns(RuntimeWarning, match='save your CSV as a TXT'): read_annotations(fname) @@ -884,7 +926,7 @@ # Test for IO with .txt files @pytest.fixture(scope='function', params=('ch_names',)) -def dummy_annotation_txt_file(tmpdir_factory, ch_names): +def dummy_annotation_txt_file(tmp_path_factory, ch_names): """Create txt file for testing.""" content = ("3.14, 42, AA \n" "6.28, 48, BB") @@ -894,13 +936,14 @@ content[1] = content[1].strip() + ', MEG0111:MEG2563' content = '\n'.join(content) - fname = tmpdir_factory.mktemp('data').join('annotations.txt') - fname.write(content) + fname = tmp_path_factory.mktemp('data') / 'annotations.txt' + with open(fname, "w") as f: + f.write(content) return fname @pytest.mark.parametrize('ch_names', (False, True)) -def test_io_annotation_txt(dummy_annotation_txt_file, tmpdir_factory, +def test_io_annotation_txt(dummy_annotation_txt_file, tmp_path_factory, ch_names): """Test TXT input/output without meas_date.""" annot = read_annotations(str(dummy_annotation_txt_file)) @@ -912,7 +955,7 @@ annot, Annotations([3.14, 6.28], [42., 48], ['AA', 'BB'], **kwargs)) # Now test writing - fname = str(tmpdir_factory.mktemp('data').join('annotations.txt')) + fname = tmp_path_factory.mktemp('data') / 'annotations.txt' annot.save(fname) annot2 = read_annotations(fname) _assert_annotations_equal(annot, annot2) @@ -945,7 +988,7 @@ assert _handle_meas_date(meas_date) == out -def test_read_annotation_txt_header(tmpdir): +def test_read_annotation_txt_header(tmp_path): """Test TXT orig_time recovery.""" content = ("# A something \n" "# orig_time : 42\n" @@ -953,30 +996,33 @@ "# orig_time : 42\n" "# C\n" "Done") - fname = tmpdir.join('header.txt') - fname.write(content) + fname = tmp_path / 'header.txt' + with open(fname, "w") as f: + f.write(content) orig_time = _read_annotations_txt_parse_header(fname) want = datetime.fromtimestamp(1038942071.7201, timezone.utc) assert orig_time == want -def test_read_annotation_txt_one_segment(tmpdir): +def test_read_annotation_txt_one_segment(tmp_path): """Test empty TXT input/output.""" content = ("# MNE-Annotations\n" "# onset, duration, description\n" "3.14, 42, AA") - fname = tmpdir.join('one-annotations.txt') - fname.write(content) + fname = tmp_path / 'one-annotations.txt' + with open(fname, "w") as f: + f.write(content) annot = read_annotations(fname) _assert_annotations_equal(annot, Annotations(3.14, 42, ['AA'])) -def test_read_annotation_txt_empty(tmpdir): +def test_read_annotation_txt_empty(tmp_path): """Test empty TXT input/output.""" content = ("# MNE-Annotations\n" "# onset, duration, description\n") - fname = tmpdir.join('empty-annotations.txt') - fname.write(content) + fname = tmp_path / 'empty-annotations.txt' + with open(fname, "w") as f: + f.write(content) annot = read_annotations(fname) _assert_annotations_equal(annot, Annotations([], [], [])) @@ -1067,7 +1113,7 @@ assert_array_equal(annot.duration, duration) -def test_date_none(tmpdir): +def test_date_none(tmp_path): """Test that DATE_NONE is used properly.""" # Regression test for gh-5908 n_chans = 139 @@ -1078,7 +1124,7 @@ info = create_info(ch_names=ch_names, ch_types=ch_types, sfreq=2048) assert info['meas_date'] is None raw = RawArray(data=data, info=info) - fname = op.join(str(tmpdir), 'test-raw.fif') + fname = op.join(str(tmp_path), 'test-raw.fif') raw.save(fname) raw_read = read_raw_fif(fname, preload=True) assert raw_read.info['meas_date'] is None @@ -1121,6 +1167,26 @@ assert crop_annot.orig_time == orig_dt # orig_time does not change +def test_crop_with_none(windows_like_datetime): + """Test cropping with None in arguments.""" + orig_time_stamp = 100 + annot = Annotations(description='foo', onset=np.arange(5, 10, 1), + duration=[1], orig_time=orig_time_stamp) + annot.crop(tmin=None, tmax=None) + assert len(annot) == 5 + annot.crop(tmin=(7.5 + orig_time_stamp), tmax=None) + assert len(annot) == 3 + + +def test_crop_wo_orig_time(windows_like_datetime): + """Test cropping without orig_time.""" + orig_time_stamp = 100 + annot = Annotations(description='foo', onset=np.arange(5, 10, 1), + duration=[1], orig_time=orig_time_stamp) + annot.crop(tmin=(7.5), tmax=None, use_orig_time=False) + assert len(annot) == 3 + + def test_allow_nan_durations(): """Deal with "n/a" strings in BIDS events with nan durations.""" raw = RawArray(data=np.empty([2, 10], dtype=np.float64), @@ -1284,3 +1350,274 @@ raw_2.set_annotations(annot, on_missing='warn') assert raw_2.annotations is not annot_pruned _assert_annotations_equal(raw_2.annotations, annot_pruned) + + +def test_annotation_rename(): + """Test annotation renaming works.""" + a = Annotations([1, 2, 3], [5, 5, 8], ["a", "b", "c"]) + assert isinstance(a.description, np.ndarray) + assert len(a) == 3 + assert "a" in a.description + assert "b" in a.description + assert "c" in a.description + assert "new_name" not in a.description + + a = Annotations([1, 2, 3], [5, 5, 8], ["a", "b", "c"]) + a.rename({"a": "new_name"}) + assert isinstance(a.description, np.ndarray) + assert len(a) == 3 + assert "a" not in a.description + assert "new_name" in a.description + assert np.where([d == "new_name" for d in a.description])[0] == 0 + + a = Annotations([1, 2, 3], [5, 5, 8], ["a", "b", "c"]) + a.rename({"a": "new_name", "b": "new name b"}) + assert len(a) == 3 + assert "a" not in a.description + assert "new_name" in a.description + assert "b" not in a.description + assert "new name b" in a.description + assert np.where([d == "new_name" for d in a.description])[0] == 0 + assert np.where([d == "new name b" for d in a.description])[0] == 1 + + a = Annotations([1, 2, 3], [5, 5, 8], ["a", "b", "c"]) + a.rename({"b": "new_name", "c": "new name c"}) + assert isinstance(a.description, np.ndarray) + assert len(a) == 3 + assert "b" not in a.description + assert "new_name" in a.description + assert "c" not in a.description + assert "new name c" in a.description + assert "a" in a.description + assert np.where([d == "new_name" for d in a.description])[0] == 1 + assert np.where([d == "new name c" for d in a.description])[0] == 2 + assert len(np.where([d == "new name b" for d in a.description])[0]) == 0 + + a = Annotations([1, 2, 3], [5, 5, 8], ["a", "b", "c"]) + with pytest.raises(ValueError, match="not present in data"): + a.rename({"aaa": "does not exist"}) + with pytest.raises(ValueError, match="[' a']"): + a.rename({" a": "does not exist"}) + with pytest.raises(TypeError, match="dict, got instead"): + a.rename("wrong") + with pytest.raises(TypeError, match="dict, got instead"): + a.rename(["wrong"]) + with pytest.raises(TypeError, match="dict, got instead"): + a.rename({"wrong"}) + + +def test_annotation_duration_setting(): + """Test annotation duration setting works.""" + a = Annotations([1, 2, 3], [5, 5, 8], ["a", "b", "c"]) + assert isinstance(a.duration, np.ndarray) + assert len(a) == 3 + assert a.duration[0] == 5 + assert a.duration[2] == 8 + a.set_durations({"a": 3}) + assert a.duration[0] == 3 + assert a.duration[2] == 8 + a.set_durations({"a": 313, "c": 18}) + assert a.duration[0] == 313 + assert a.duration[2] == 18 + a.set_durations({"a": 1, "b": 13}) + assert a.duration[0] == 1 + assert a.duration[1] == 13 + + a = Annotations([1, 2, 3], [5, 5, 8], ["a", "b", "c"]) + assert len(a) == 3 + assert a.duration[0] == 5 + assert a.duration[2] == 8 + a.set_durations(7.2) + assert isinstance(a.duration, np.ndarray) + assert a.duration[0] == 7.2 + assert a.duration[2] == 7.2 + a.set_durations(2) + assert a.duration[0] == 2 + + with pytest.raises(ValueError, match="not present in data"): + a.set_durations({"aaa": 2.2}) + with pytest.raises(TypeError, match=" got instead"): + a.set_durations({"aaa", 2.2}) + + +@pytest.mark.parametrize('meas_date', (None, 1)) +@pytest.mark.parametrize('set_meas_date', ('before', 'after')) +@pytest.mark.parametrize('first_samp', (0, 100, 3000)) +def test_annot_noop(meas_date, first_samp, set_meas_date): + """Show some unintuitive behavior of annotations.""" + sfreq = 1000. + info = create_info(1, sfreq, 'eeg') + onset = 0.5 + annot_kwargs = dict() + if set_meas_date == 'before': + with info._unlock(): + info['meas_date'] = _handle_meas_date(meas_date) + if meas_date is not None: + onset += first_samp / sfreq + annot_kwargs['orig_time'] = meas_date + raw = RawArray(np.zeros((1, 2000)), info, first_samp=first_samp) + annot = Annotations(onset, 0.1, 'bad', **annot_kwargs) + raw.set_annotations(annot, verbose='debug') + if set_meas_date == 'after': + raw.set_meas_date(meas_date) + first_annot = raw.annotations + if meas_date is None: + first_annot.onset -= raw.first_time + raw.set_annotations(first_annot, verbose='debug') # should be a no-op... + second_annot = raw.annotations + want = first_annot.onset[0] + # it has been shifted when meas_date is None! + if meas_date is None: + want = want + raw.first_time + assert_allclose(second_annot.onset[0], want) + + +@pytest.mark.parametrize('setting', ('before', 'after')) +@pytest.mark.parametrize('meas_date', ('first', 'second', 'both', None)) +@pytest.mark.parametrize('first_samp_2', (0, 320)) +@pytest.mark.parametrize('first_samp_1', (160, 0)) +def test_annot_concat_crop(meas_date, first_samp_1, first_samp_2, setting): + """Test that annotation and cropping works properly.""" + n_ch = 2 + sfreq = 160 + duration = 0.1 + meas_date_1 = meas_date_2 = None + assert meas_date in (None, 'first', 'second', 'both') + if meas_date in ('first', 'both'): + meas_date_1 = datetime(2022, 1, 1, 0, 0, 0, tzinfo=timezone.utc) + if meas_date in ('second', 'both'): + meas_date_2 = datetime(2022, 1, 1, 0, 0, 0, tzinfo=timezone.utc) + del meas_date + + def _create_raw(eeg, sfreq, onset, description, meas_date, first_samp, + setting): + info = mne.create_info(eeg.shape[0], ch_types='eeg', sfreq=sfreq) + raw = mne.io.RawArray(eeg, info, first_samp=first_samp) + if setting == 'before': + annot = mne.Annotations(onset, duration, description) + raw = raw.set_annotations(annot) + raw.set_meas_date(meas_date) + else: + assert setting == 'after' + raw.set_meas_date(meas_date) + delta = first_samp / sfreq if meas_date is not None else 0 + annot = mne.Annotations( + onset + delta, duration, description, orig_time=meas_date) + raw = raw.set_annotations(annot) + return raw + + data_1 = np.array( + [list(range(40)) * 4 * 10] * n_ch) * 5 * 1e-7 + onset_1 = np.array([2.5, 5, 6, 7, 8]) + description_1 = [12, 'on', 1, 2, 'off'] + raw_1 = _create_raw(data_1, sfreq, onset_1, description_1, meas_date_1, + first_samp_1, setting) + assert_allclose(raw_1.annotations.onset, onset_1 + first_samp_1 / sfreq) + + data_2 = np.array( + [([1e-5] * int(sfreq / 2) + [0] * int(sfreq / 2)) * 10] * n_ch) + onset_2 = np.array([1.5, 2, 2.7, 5]) + description_2 = ['on', 3, 4, 'off'] + raw_2 = _create_raw(data_2, sfreq, onset_2, description_2, meas_date_2, + first_samp_2, setting) + assert_allclose(raw_2.annotations.onset, onset_2 + first_samp_2 / sfreq) + + onset = np.concatenate( + [onset_1, np.round(onset_2 + len(raw_1.times) / sfreq, 6)]) + assert onset[0] == 2.5 + assert_allclose(raw_1.annotations.onset[0], 2.5 + first_samp_1 / sfreq) + onset = np.round(onset + first_samp_1 / sfreq, 6) + want_annot = mne.Annotations( + onset=onset, duration=duration, + description=description_1 + description_2, orig_time=meas_date_1) + raw_copy = concatenate_raws([raw_1.copy()]) + assert_allclose(raw_copy.annotations.onset[0], 2.5 + first_samp_1 / sfreq) + raw = concatenate_raws([raw_1, raw_2]) + assert raw.first_samp == raw_1.first_samp == first_samp_1 + del raw_1, raw_2 + assert_allclose(raw.annotations.onset[0], 2.5 + first_samp_1 / sfreq) + assert raw.info['meas_date'] == meas_date_1 + gap_idx = len(description_1) + assert list(raw.annotations.description[gap_idx:gap_idx + 2]) == \ + ['BAD boundary', 'EDGE boundary'] + raw.annotations.delete([gap_idx, gap_idx + 1]) + start_idx = np.where(raw.annotations.description == 'on')[0] + end_idx = np.where(raw.annotations.description == 'off')[0] + tmins = raw.annotations.onset[start_idx] + tmaxs = raw.annotations.onset[end_idx] + tmins -= raw.first_time + tmaxs -= raw.first_time + assert len(tmins) == len(tmaxs) == 2 + assert raw.info['meas_date'] == meas_date_1 + _assert_annotations_equal(raw.annotations, want_annot) + # test a round-trip set -- see test_annot_noop for why we need conditional + if meas_date_1 is None: + want_annot.onset -= first_samp_1 / sfreq + raw.set_annotations(want_annot) + if meas_date_1 is None: # put it back to what it was before + want_annot.onset += first_samp_1 / sfreq + _assert_annotations_equal(raw.annotations, want_annot) + want_descs = list() + for start, stop in zip(start_idx, end_idx): + want_descs.append(list(raw.annotations.description[start:stop + 1])) + + for tmin, tmax, descs in zip(tmins, tmaxs, want_descs): + sess = raw.copy() + _assert_annotations_equal(sess.annotations, raw.annotations) + _assert_annotations_equal(sess.annotations, want_annot) + # let's manually print what the logger.debug should say if it's + # doing something correctly + if meas_date_1 is not None: + md = raw.info['meas_date'] + print(f'\nmeas_info set to {md}') + md = md + timedelta(seconds=raw._first_time) + print(f'Data starts at {md}') + md = md + timedelta(seconds=tmin) + print(f'Cropping data to {md}') + md = raw.info['meas_date'] + \ + timedelta(seconds=raw.annotations.onset[1]) + print(f'Second annot at {md}') + assert sess.first_samp == first_samp_1 + sess.crop(tmin, tmax, verbose='debug') + want_first_samp = first_samp_1 + int(round(tmin * sfreq)) + assert sess.first_samp == want_first_samp + assert sess.annotations.orig_time == meas_date_1 + assert list(sess.annotations.description) == descs + + +@pytest.mark.parametrize('first_samp', (0, 10000)) +@pytest.mark.parametrize('meas_date', (None, 24 * 60 * 60)) +def test_annot_meas_date_first_samp_crop(meas_date, first_samp): + """Test yet another meas_date / first_samp issue.""" + sfreq = 1000. + info = mne.create_info(1, sfreq, 'eeg') + raw = mne.io.RawArray( + np.random.RandomState(0).randn(1, 3000), info, first_samp=first_samp) + raw.set_meas_date(meas_date) + onset = np.array([0, 1, 2], float) + if meas_date is not None: + onset += first_samp / sfreq + annot = mne.Annotations( + onset=onset, + duration=[0.1, 0.2, 0.3], + description=["a", "b", "c"], + orig_time=raw.info['meas_date']) + assert len(annot) == 3 + raw.set_annotations(annot) + assert len(raw.annotations) == 3 + raw_crop = raw.copy().crop(0, 1.5, verbose='debug') + assert len(raw_crop.annotations) == 2 + assert_array_equal(raw_crop.annotations.description, annot.description[:2]) + assert_array_equal(raw_crop.annotations.duration, annot.duration[:2]) + # these two should be the equivalent + raw_crop = raw.copy().crop(2, 2.5, verbose='debug') + raw_crop_2 = raw.copy().crop(1, None).crop(1, 1.5) + assert_allclose(raw_crop.get_data(), raw_crop_2.get_data()) + assert raw_crop.first_samp == raw_crop_2.first_samp + want_onset = onset[2:] + if meas_date is None: + want_onset = want_onset + raw.first_time + for this_raw in (raw_crop, raw_crop_2): + assert len(this_raw.annotations) == 1 + assert_allclose(this_raw.annotations.onset, want_onset) + assert_allclose(this_raw.annotations.duration, annot.duration[2:]) diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_bem.py python-mne-1.1.0+dfsg/mne/tests/test_bem.py --- python-mne-0.23.4+dfsg/mne/tests/test_bem.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_bem.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors: Marijn van Vliet # -# License: BSD 3 clause +# License: BSD-3-Clause from copy import deepcopy from os import makedirs @@ -12,6 +12,7 @@ import pytest from numpy.testing import assert_equal, assert_allclose +import mne from mne import (make_bem_model, read_bem_surfaces, write_bem_surfaces, make_bem_solution, read_bem_solution, write_bem_solution, make_sphere_model, Transform, Info, write_surface, @@ -20,11 +21,12 @@ from mne.io.constants import FIFF from mne.transforms import translation from mne.datasets import testing -from mne.utils import (run_tests_if_main, catch_logging, requires_h5py) +from mne.utils import catch_logging, check_version from mne.bem import (_ico_downsample, _get_ico_map, _order_surfaces, _assert_complete_surface, _assert_inside, - _check_surface_size, _bem_find_surface) -from mne.surface import read_surface + _check_surface_size, _bem_find_surface, + make_scalp_surfaces, distance_to_bem) +from mne.surface import read_surface, _get_ico_surface from mne.io import read_info fname_raw = op.join(op.dirname(__file__), '..', 'io', 'tests', 'data', @@ -68,13 +70,17 @@ err_msg='Mismatch: %s' % key) +h5py_mark = pytest.mark.skipif(not check_version('h5py'), reason='Needs h5py') + + @testing.requires_testing_data -@requires_h5py -@pytest.mark.parametrize('ext', ('fif', 'h5')) -def test_io_bem(tmpdir, ext): +@pytest.mark.parametrize('ext', [ + 'fif', + pytest.param('h5', marks=h5py_mark), +]) +def test_io_bem(tmp_path, ext): """Test reading and writing of bem surfaces and solutions.""" - import h5py - temp_bem = op.join(str(tmpdir), f'temp-bem.{ext}') + temp_bem = op.join(str(tmp_path), f'temp-bem.{ext}') # model with pytest.raises(ValueError, match='BEM data not found'): read_bem_surfaces(fname_raw) @@ -87,6 +93,7 @@ write_bem_surfaces(temp_bem, surf[0]) write_bem_surfaces(temp_bem, surf[0], overwrite=True) if ext == 'h5': + import h5py with h5py.File(temp_bem, 'r'): # make sure it's valid pass surf_read = read_bem_surfaces(temp_bem, patch_stats=False) @@ -95,7 +102,7 @@ # solution with pytest.raises(RuntimeError, match='No BEM solution found'): read_bem_solution(fname_bem_3) - temp_sol = op.join(str(tmpdir), f'temp-sol.{ext}') + temp_sol = op.join(str(tmp_path), f'temp-sol.{ext}') sol = read_bem_solution(fname_bem_sol_3) assert 'BEM' in repr(sol) write_bem_solution(temp_sol, sol) @@ -138,12 +145,12 @@ @testing.requires_testing_data @pytest.mark.parametrize('kwargs, fname', [ - [dict(), fname_bem_3], + pytest.param(dict(), fname_bem_3, marks=pytest.mark.slowtest), # Azure [dict(conductivity=[0.3]), fname_bem_1], ]) -def test_make_bem_model(tmpdir, kwargs, fname): +def test_make_bem_model(tmp_path, kwargs, fname): """Test BEM model creation from Python with I/O.""" - fname_temp = tmpdir.join('temp-bem.fif') + fname_temp = tmp_path / 'temp-bem.fif' with catch_logging() as log: model = make_bem_model('sample', ico=2, subjects_dir=subjects_dir, verbose=True, **kwargs) @@ -166,25 +173,25 @@ @testing.requires_testing_data -def test_bem_model_topology(tmpdir): +def test_bem_model_topology(tmp_path): """Test BEM model topological checks.""" # bad topology (not enough neighboring tris) - makedirs(tmpdir.join('foo', 'bem')) + makedirs(tmp_path / 'foo' / 'bem') for fname in ('inner_skull', 'outer_skull', 'outer_skin'): fname += '.surf' copy(op.join(subjects_dir, 'sample', 'bem', fname), - str(tmpdir.join('foo', 'bem', fname))) - outer_fname = tmpdir.join('foo', 'bem', 'outer_skull.surf') + tmp_path / 'foo' / 'bem' / fname) + outer_fname = tmp_path / 'foo' / 'bem' / 'outer_skull.surf' rr, tris = read_surface(outer_fname) tris = tris[:-1] write_surface(outer_fname, rr, tris[:-1], overwrite=True) with pytest.raises(RuntimeError, match='Surface outer skull is not compl'): - make_bem_model('foo', None, subjects_dir=tmpdir) + make_bem_model('foo', None, subjects_dir=tmp_path) # Now get past this error to reach gh-6127 (not enough neighbor tris) rr_bad = np.concatenate([rr, np.mean(rr, axis=0, keepdims=True)], axis=0) write_surface(outer_fname, rr_bad, tris, overwrite=True) - with pytest.raises(RuntimeError, match='Surface outer skull.*triangles'): - make_bem_model('foo', None, subjects_dir=tmpdir) + with pytest.raises(ValueError, match='Surface outer skull.*triangles'): + make_bem_model('foo', None, subjects_dir=tmp_path) @pytest.mark.slowtest @@ -193,7 +200,7 @@ [(0.3,), fname_bem_sol_1], [(0.3, 0.006, 0.3), fname_bem_sol_3], ]) -def test_bem_solution(tmpdir, cond, fname): +def test_bem_solution(tmp_path, cond, fname): """Test making a BEM solution from Python with I/O.""" # test degenerate conditions surf = read_bem_surfaces(fname_bem_1)[0] @@ -218,7 +225,7 @@ pytest.raises(RuntimeError, _check_surface_size, surfs[1]) # actually test functionality - fname_temp = op.join(str(tmpdir), 'temp-bem-sol.fif') + fname_temp = op.join(str(tmp_path), 'temp-bem-sol.fif') # use a model and solution made in Python for model_type in ('python', 'c'): if model_type == 'python': @@ -390,22 +397,23 @@ pytest.raises(TypeError, fit_sphere_to_headshape, 1, units='m') +@pytest.mark.slowtest # ~2 min on Azure Windows @testing.requires_testing_data -def test_io_head_bem(tmpdir): +def test_io_head_bem(tmp_path): """Test reading and writing of defective head surfaces.""" head = read_bem_surfaces(fname_dense_head)[0] - fname_defect = op.join(str(tmpdir), 'temp-head-defect.fif') + fname_defect = op.join(str(tmp_path), 'temp-head-defect.fif') # create defects head['rr'][0] = np.array([-0.01487014, -0.04563854, -0.12660208]) head['tris'][0] = np.array([21919, 21918, 21907]) - with pytest.raises(RuntimeError, match='topological defects:'): + with pytest.raises(ValueError, match='topological defects:'): write_head_bem(fname_defect, head['rr'], head['tris']) with pytest.warns(RuntimeWarning, match='topological defects:'): write_head_bem(fname_defect, head['rr'], head['tris'], on_defects='warn') # test on_defects in read_bem_surfaces - with pytest.raises(RuntimeError, match='topological defects:'): + with pytest.raises(ValueError, match='topological defects:'): read_bem_surfaces(fname_defect) with pytest.warns(RuntimeWarning, match='topological defects:'): head_defect = read_bem_surfaces(fname_defect, on_defects='warn')[0] @@ -415,4 +423,96 @@ assert np.allclose(head['tris'], head_defect['tris']) -run_tests_if_main() +@pytest.mark.slowtest # ~4 sec locally +def test_make_scalp_surfaces_topology(tmp_path, monkeypatch): + """Test topology checks for make_scalp_surfaces.""" + pytest.importorskip('pyvista') + subjects_dir = tmp_path + subject = 'test' + surf_dir = subjects_dir / subject / 'surf' + makedirs(surf_dir) + surf = _get_ico_surface(2) + surf['rr'] *= 100 # mm + write_surface(surf_dir / 'lh.seghead', surf['rr'], surf['tris']) + + # make it so that decimation really messes up the mesh just by deleting + # the last N tris + def _decimate_surface(points, triangles, n_triangles): + assert len(triangles) >= n_triangles + return points, triangles[:n_triangles] + + monkeypatch.setattr(mne.bem, 'decimate_surface', _decimate_surface) + # TODO: These two errors should probably have the same class... + + # Not enough neighbors + monkeypatch.setattr(mne.bem, '_tri_levels', dict(sparse=315)) + with pytest.raises(ValueError, match='.*have fewer than three.*'): + make_scalp_surfaces(subject, subjects_dir, force=False, verbose=True) + monkeypatch.setattr(mne.bem, '_tri_levels', dict(sparse=319)) + # Incomplete surface (sum of solid angles) + with pytest.raises(RuntimeError, match='.*is not complete.*'): + make_scalp_surfaces( + subject, subjects_dir, force=False, verbose=True, overwrite=True) + bem_dir = subjects_dir / subject / 'bem' + sparse_path = (bem_dir / f'{subject}-head-sparse.fif') + assert not sparse_path.is_file() + + # These are ignorable + monkeypatch.setattr(mne.bem, '_tri_levels', dict(sparse=315)) + with pytest.warns(RuntimeWarning, match='.*have fewer than three.*'): + make_scalp_surfaces( + subject, subjects_dir, force=True, overwrite=True) + surf, = read_bem_surfaces(sparse_path, on_defects='ignore') + assert len(surf['tris']) == 315 + monkeypatch.setattr(mne.bem, '_tri_levels', dict(sparse=319)) + with pytest.warns(RuntimeWarning, match='.*is not complete.*'): + make_scalp_surfaces( + subject, subjects_dir, force=True, overwrite=True) + surf, = read_bem_surfaces(sparse_path, on_defects='ignore') + assert len(surf['tris']) == 319 + + +@pytest.mark.parametrize("bem_type", ["bem", "sphere"]) +@pytest.mark.parametrize("n_pos", [1, 10]) +@testing.requires_testing_data +def test_distance_to_bem(bem_type, n_pos): + """Test distance_to_bem.""" + # Test spherical ConductorModels + if bem_type == "sphere": + bem = make_sphere_model(r0=np.array([0, 0, 0]), verbose=0) + r = bem['layers'][0]['rad'] + true_dist = np.array([r, 0., 0., 0., 0., 0., 0., 0., 0., 0.]) + else: + bem = read_bem_solution(fname_bem_sol_1) + r = 0.05 + true_dist = np.array([ + 0.01708097, 0.00256595, 0.01022884, 0.02306622, 0.02927288, + 0.04491787, 0.00990493, 0.02244751, 0.04819345, 0.01928304 + ]) + + pos = np.array( + [ + [0.0, 0.0, 0.0], + [r, 0.0, 0.0], + [-r, 0.0, 0.0], + [0.0, r, 0.0], + [0.0, -r, 0.0], + [0.0, 0.0, r], + [-r / np.sqrt(2.), r / np.sqrt(2.), 0.0], + [-r / np.sqrt(2.), -r / np.sqrt(2.), 0.0], + [0, -r / np.sqrt(2.), r / np.sqrt(2.)], + [r / np.sqrt(3.), r / np.sqrt(3.), r / np.sqrt(3.)] + ] + ) + + if n_pos == 1: + pos = pos[0, :] + true_dist = true_dist[0] + + dist = distance_to_bem(pos, bem) + if n_pos == 1: + assert isinstance(dist, float) + else: + assert isinstance(dist, np.ndarray) + + assert_allclose(dist, true_dist, rtol=1e-6, atol=1e-6) diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_chpi.py python-mne-1.1.0+dfsg/mne/tests/test_chpi.py --- python-mne-0.23.4+dfsg/mne/tests/test_chpi.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_chpi.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,11 +1,12 @@ # Author: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op import numpy as np -from numpy.testing import assert_allclose, assert_array_less +from numpy.testing import (assert_allclose, assert_array_less, + assert_array_equal) from scipy.interpolate import interp1d from scipy.spatial.distance import cdist import pytest @@ -16,11 +17,11 @@ RawArray, read_raw_kit) from mne.io.constants import FIFF from mne.chpi import (compute_chpi_amplitudes, compute_chpi_locs, - compute_head_pos, _setup_ext_proj, + compute_chpi_snr, compute_head_pos, _setup_ext_proj, _chpi_locs_to_times_dig, _compute_good_distances, extract_chpi_locs_ctf, head_pos_to_trans_rot_t, read_head_pos, write_head_pos, filter_chpi, - _get_hpi_info, _get_hpi_initial_fit, + get_chpi_info, _get_hpi_initial_fit, extract_chpi_locs_kit) from mne.datasets import testing from mne.simulation import add_chpi @@ -64,7 +65,7 @@ raw = read_raw_fif(chpi_fif_fname, allow_maxshield='yes') with catch_logging() as log: _get_hpi_initial_fit(raw.info, adjust=True, verbose='debug') - _get_hpi_info(raw.info, verbose='debug') + get_chpi_info(raw.info, on_missing='raise', verbose='debug') # Ran MaxFilter (with -list, -v, -movecomp, etc.), and got: msg = ['HPIFIT: 5 coils digitized in order 5 1 4 3 2', 'HPIFIT: 3 coils accepted: 1 2 4', @@ -92,15 +93,15 @@ 'Note: HPI coil 5 isotrak is adjusted by 3.2 mm!'] + msg[-2:] with catch_logging() as log: _get_hpi_initial_fit(raw.info, adjust=True, verbose='debug') - _get_hpi_info(raw.info, verbose='debug') + get_chpi_info(raw.info, on_missing='raise', verbose='debug') log = log.getvalue().splitlines() assert set(log) == set(msg), '\n' + '\n'.join(set(msg) - set(log)) @testing.requires_testing_data -def test_read_write_head_pos(tmpdir): +def test_read_write_head_pos(tmp_path): """Test reading and writing head position quaternion parameters.""" - temp_name = op.join(str(tmpdir), 'temp.pos') + temp_name = op.join(str(tmp_path), 'temp.pos') # This isn't a 100% valid quat matrix but it should be okay for tests head_pos_rand = np.random.RandomState(0).randn(20, 10) # This one is valid @@ -118,9 +119,9 @@ @testing.requires_testing_data -def test_hpi_info(tmpdir): +def test_hpi_info(tmp_path): """Test getting HPI info.""" - temp_name = op.join(str(tmpdir), 'temp_raw.fif') + temp_name = op.join(str(tmp_path), 'temp_raw.fif') for fname in (chpi_fif_fname, sss_fif_fname): raw = read_raw_fif(fname, allow_maxshield='yes').crop(0, 0.1) assert len(raw.info['hpi_subsystem']) > 0 @@ -128,6 +129,32 @@ info = read_info(temp_name) assert len(info['hpi_subsystem']) == len(raw.info['hpi_subsystem']) + # test get_chpi_info() + info = read_info(chpi_fif_fname) + hpi_freqs, stim_ch_idx, hpi_on_codes = get_chpi_info(info) + + assert_allclose(hpi_freqs, np.array([83., 143., 203., 263., 323.])) + assert stim_ch_idx == 378 + assert_allclose(hpi_on_codes, np.array([256, 512, 1024, 2048, 4096])) + + # test get_chpi_info() if no proper cHPI info is available + with info._unlock(): + info['hpi_subsystem'] = None + info['hpi_meas'] = [] + info['hpi_results'] = [] + + with pytest.raises(ValueError, match='No appropriate cHPI information'): + get_chpi_info(info) + + with pytest.warns(RuntimeWarning, match='No appropriate cHPI information'): + get_chpi_info(info, on_missing='warn') + + hpi_freqs, stim_ch_idx, hpi_on_codes = get_chpi_info(info, + on_missing='ignore') + assert_array_equal([], hpi_freqs) + assert stim_ch_idx is None + assert_array_equal([], hpi_on_codes) + def _assert_quats(actual, desired, dist_tol=0.003, angle_tol=5., err_rtol=0.5, gof_rtol=0.001, vel_atol=2e-3): # 2 mm/s @@ -194,7 +221,8 @@ """Decimate raw data (with aliasing) in cHPI-fitting compatible way.""" raw_dec = RawArray( raw._data[:, ::decim], raw.info, first_samp=raw.first_samp // decim) - raw_dec.info['sfreq'] /= decim + with raw_dec.info._unlock(): + raw_dec.info['sfreq'] /= decim for coil in raw_dec.info['hpi_meas'][0]['hpi_coils']: if coil['coil_freq'] > raw_dec.info['sfreq']: coil['coil_freq'] = np.mod(coil['coil_freq'], @@ -249,11 +277,11 @@ _assert_quats(py_quats, mf_quats, dist_tol=0.001, angle_tol=0.7) # degenerate conditions raw_no_chpi = read_raw_fif(sample_fname) - with pytest.raises(RuntimeError, match='cHPI information not found'): + with pytest.raises(ValueError, match='No appropriate cHPI information'): _calculate_chpi_positions(raw_no_chpi) raw_bad = raw.copy() del raw_bad.info['hpi_meas'][0]['hpi_coils'][0]['coil_freq'] - with pytest.raises(RuntimeError, match='cHPI information not found'): + with pytest.raises(ValueError, match='No appropriate cHPI information'): _calculate_chpi_positions(raw_bad) raw_bad = raw.copy() for d in raw_bad.info['dig']: @@ -277,13 +305,31 @@ assert '0/5 good HPI fits' in log_file.getvalue() # half the rate cuts off cHPI coils - raw.info['lowpass'] /= 2. + with raw.info._unlock(): + raw.info['lowpass'] /= 2. with pytest.raises(RuntimeError, match='above the'): _calculate_chpi_positions(raw) @testing.requires_testing_data @pytest.mark.slowtest +def test_calculate_chpi_snr(): + """Test cHPI SNR calculation.""" + raw = read_raw_fif(chpi_fif_fname, allow_maxshield='yes') + result = compute_chpi_snr(raw) + # make sure all the entries are there + keys = {f'{ch_type}_{key}' for ch_type in ('mag', 'grad') for key in + ('snr', 'power', 'resid')} + assert set(result) == keys.union({'times', 'freqs'}) + # make sure the values are plausible, given the sample data file + assert result['mag_snr'].min() > 1 + assert result['mag_snr'].max() < 40 + assert result['grad_snr'].min() > 1 + assert result['grad_snr'].max() < 40 + + +@testing.requires_testing_data +@pytest.mark.slowtest def test_calculate_chpi_positions_artemis(): """Test on 5k artemis data.""" raw = read_raw_artemis123(art_fname, preload=True) @@ -392,15 +438,17 @@ dtype=np.int32)}], 'ncoil': ncoil} - info['hpi_subsystem'] = hpi_subsystem - for fi, freq in enumerate(coil_freq): - info['hpi_meas'][0]['hpi_coils'][fi]['coil_freq'] = freq + with info._unlock(): + info['hpi_subsystem'] = hpi_subsystem + for fi, freq in enumerate(coil_freq): + info['hpi_meas'][0]['hpi_coils'][fi]['coil_freq'] = freq + info['sfreq'] = 100. # this will speed it up a lot picks = pick_types(info, meg=True, stim=True, eeg=False, exclude=[]) - info['sfreq'] = 100. # this will speed it up a lot info = pick_info(info, picks) info['chs'][info['ch_names'].index('STI 001')]['ch_name'] = 'STI201' info._update_redundant() - info['projs'] = [] + with info._unlock(): + info['projs'] = [] info_trans = info['dev_head_t']['trans'].copy() @@ -496,7 +544,9 @@ times, cHPI_digs = _calculate_chpi_coil_locs(raw, verbose='debug') assert len(np.setdiff1d(times, raw.times + raw.first_time)) == 0 - assert_allclose(times[5], 1.5, atol=1e-3) + # Should be somewhere around 1.5 sec, depending on coil GOF values + # around 0.98 it can change + assert_allclose(times[5], 1.5, atol=2e-1) assert_allclose(cHPI_digs[5][0]['gof'], 0.995, atol=5e-3) assert_allclose(cHPI_digs[5][0]['r'], [-0.0157, 0.0655, 0.0018], atol=1e-3) @@ -579,8 +629,9 @@ assert raw.info['line_freq'] is None with pytest.raises(RuntimeError, match='line_freq.*consider setting it'): filter_chpi(raw, t_window=0.2) - raw.info['line_freq'] = 60. - with pytest.raises(RuntimeError, match='cHPI information not found'): + with raw.info._unlock(): + raw.info['line_freq'] = 60. + with pytest.raises(ValueError, match='No appropriate cHPI information'): filter_chpi(raw, t_window=0.2) # but this is allowed with catch_logging() as log: @@ -597,10 +648,11 @@ # it can strip out some values of info, which we emulate here: raw = read_raw_fif(chpi_fif_fname, allow_maxshield='yes') raw = raw.crop(0, 1).load_data().resample(600., npad='auto') - raw.info['lowpass'] = 200. - del raw.info['maxshield'] - del raw.info['hpi_results'][0]['moments'] - del raw.info['hpi_subsystem']['event_channel'] + with raw.info._unlock(): + raw.info['lowpass'] = 200. + del raw.info['maxshield'] + del raw.info['hpi_results'][0]['moments'] + del raw.info['hpi_subsystem']['event_channel'] with catch_logging() as log: filter_chpi(raw, t_window='auto', verbose=True) with pytest.raises(ValueError, match='must be > 0'): diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_coreg.py python-mne-1.1.0+dfsg/mne/tests/test_coreg.py --- python-mne-0.23.4+dfsg/mne/tests/test_coreg.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_coreg.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,34 +2,41 @@ from glob import glob import os import os.path as op -from shutil import copyfile, copytree +from shutil import copyfile import pytest import numpy as np from numpy.testing import (assert_array_almost_equal, assert_allclose, - assert_array_equal) + assert_array_equal, assert_array_less) import mne from mne.datasets import testing from mne.transforms import (Transform, apply_trans, rotation, translation, - scaling) + scaling, read_trans, _angle_between_quats, + rot_to_quat, invert_transform) from mne.coreg import (fit_matched_points, create_default_subject, scale_mri, _is_mri_subject, scale_labels, scale_source_space, - coregister_fiducials, get_mni_fiducials) -from mne.io import read_fiducials + coregister_fiducials, get_mni_fiducials, Coregistration) +from mne.io import read_fiducials, read_info from mne.io.constants import FIFF -from mne.utils import (run_tests_if_main, requires_nibabel, modified_env, - check_version) +from mne.utils import (requires_nibabel, check_version, catch_logging, + _record_warnings) from mne.source_space import write_source_spaces +from mne.channels import DigMontage data_path = testing.data_path(download=False) +subjects_dir = os.path.join(data_path, 'subjects') +fid_fname = op.join(subjects_dir, 'sample', 'bem', 'sample-fiducials.fif') +raw_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif') +trans_fname = op.join(data_path, 'MEG', 'sample', + 'sample_audvis_trunc-trans.fif') @pytest.fixture -def few_surfaces(): +def few_surfaces(monkeypatch): """Set the _MNE_FEW_SURFACES env var.""" - with modified_env(_MNE_FEW_SURFACES='true'): - yield + monkeypatch.setenv('_MNE_FEW_SURFACES', 'true') + yield def test_coregister_fiducials(): @@ -57,15 +64,16 @@ assert_array_almost_equal(trans_est['trans'], trans['trans']) +@requires_nibabel() @pytest.mark.slowtest # can take forever on OSX Travis @testing.requires_testing_data @pytest.mark.parametrize('scale', (.9, [1, .2, .8])) -def test_scale_mri(tmpdir, few_surfaces, scale): +def test_scale_mri(tmp_path, few_surfaces, scale): """Test creating fsaverage and scaling it.""" # create fsaverage using the testing "fsaverage" instead of the FreeSurfer # one - tempdir = str(tmpdir) - fake_home = testing.data_path() + tempdir = str(tmp_path) + fake_home = data_path create_default_subject(subjects_dir=tempdir, fs_home=fake_home, verbose=True) assert _is_mri_subject('fsaverage', tempdir), "Creating fsaverage failed" @@ -78,7 +86,7 @@ # copy MRI file from sample data (shouldn't matter that it's incorrect, # so here choose a small one) - path_from = op.join(testing.data_path(), 'subjects', 'sample', 'mri', + path_from = op.join(fake_home, 'subjects', 'sample', 'mri', 'T1.mgz') path_to = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz') copyfile(path_from, path_to) @@ -103,7 +111,7 @@ # scale fsaverage write_source_spaces(path % 'ico-0', src, overwrite=True) - with pytest.warns(None): # sometimes missing nibabel + with _record_warnings(): # sometimes missing nibabel scale_mri('fsaverage', 'flachkopf', scale, True, subjects_dir=tempdir, verbose='debug') assert _is_mri_subject('flachkopf', tempdir), "Scaling failed" @@ -159,21 +167,11 @@ @pytest.mark.slowtest # can take forever on OSX Travis @testing.requires_testing_data @requires_nibabel() -def test_scale_mri_xfm(tmpdir, few_surfaces): +def test_scale_mri_xfm(tmp_path, few_surfaces, subjects_dir_tmp_few): """Test scale_mri transforms and MRI scaling.""" # scale fsaverage - tempdir = str(tmpdir) - fake_home = testing.data_path() - # add fsaverage - create_default_subject(subjects_dir=tempdir, fs_home=fake_home, - verbose=True) - # add sample (with few files) - sample_dir = op.join(tempdir, 'sample') - os.mkdir(sample_dir) - os.mkdir(op.join(sample_dir, 'bem')) - for dirname in ('mri', 'surf'): - copytree(op.join(fake_home, 'subjects', 'sample', dirname), - op.join(sample_dir, dirname)) + tempdir = str(subjects_dir_tmp_few) + sample_dir = subjects_dir_tmp_few / 'sample' subject_to = 'flachkopf' spacing = 'oct2' for subject_from in ('fsaverage', 'sample'): @@ -227,6 +225,17 @@ mni = mne.vertex_to_mni(vertices, hemis, subject_to, subjects_dir=tempdir) assert_allclose(mni, mni_from, atol=1e-3) # 0.001 mm + # Check head_to_mni (the `trans` here does not really matter) + trans = rotation(0.001, 0.002, 0.003) @ translation(0.01, 0.02, 0.03) + trans = Transform('head', 'mri', trans) + pos_head_from = np.random.RandomState(0).randn(4, 3) + pos_mni_from = mne.head_to_mni( + pos_head_from, subject_from, trans, tempdir) + pos_mri_from = apply_trans(trans, pos_head_from) + pos_mri = pos_mri_from * scale + pos_head = apply_trans(invert_transform(trans), pos_mri) + pos_mni = mne.head_to_mni(pos_head, subject_to, trans, tempdir) + assert_allclose(pos_mni, pos_mni_from, atol=1e-3) def test_fit_matched_points(): @@ -268,9 +277,6 @@ @requires_nibabel() def test_get_mni_fiducials(): """Test get_mni_fiducials.""" - subjects_dir = op.join(data_path, 'subjects') - fid_fname = op.join(subjects_dir, 'sample', 'bem', - 'sample-fiducials.fif') fids, coord_frame = read_fiducials(fid_fname) assert coord_frame == FIFF.FIFFV_COORD_MRI assert [f['ident'] for f in fids] == list(range(1, 4)) @@ -281,4 +287,190 @@ assert (dists < 8).all(), dists -run_tests_if_main() +@pytest.mark.slowtest +@testing.requires_testing_data +@pytest.mark.parametrize( + 'scale_mode,ref_scale,grow_hair,fiducials,fid_match', [ + (None, [1., 1., 1.], 0., None, 'nearest'), + (None, [1., 1., 1.], 0., 'estimated', 'nearest'), + (None, [1., 1., 1.], 2., 'auto', 'nearest'), + ('uniform', [1., 1., 1.], 0., None, 'nearest'), + ('3-axis', [1., 1., 1.], 0., 'auto', 'nearest'), + ('uniform', [0.8, 0.8, 0.8], 0., 'auto', 'nearest'), + ('3-axis', [0.8, 1.2, 1.2], 0., 'auto', 'matched')]) +def test_coregistration(scale_mode, ref_scale, grow_hair, fiducials, + fid_match): + """Test automated coregistration.""" + subject = 'sample' + if fiducials is None: + fiducials, coord_frame = read_fiducials(fid_fname) + assert coord_frame == FIFF.FIFFV_COORD_MRI + info = read_info(raw_fname) + for d in info['dig']: + d['r'] = d['r'] * ref_scale + trans = read_trans(trans_fname) + coreg = Coregistration(info, subject=subject, subjects_dir=subjects_dir, + fiducials=fiducials) + assert np.allclose(coreg._last_parameters, coreg._parameters) + assert len(coreg.fiducials.dig) == 3 + for dig_point in coreg.fiducials.dig: + assert dig_point['coord_frame'] == FIFF.FIFFV_COORD_MRI + assert dig_point['kind'] == FIFF.FIFFV_POINT_CARDINAL + + coreg.set_fid_match(fid_match) + default_params = list(coreg._default_parameters) + coreg.set_rotation(default_params[:3]) + coreg.set_translation(default_params[3:6]) + coreg.set_scale(default_params[6:9]) + coreg.set_grow_hair(grow_hair) + coreg.set_scale_mode(scale_mode) + # Identity transform + errs_id = coreg.compute_dig_mri_distances() + is_scaled = ref_scale != [1., 1., 1.] + id_max = 0.03 if is_scaled and scale_mode == '3-axis' else 0.02 + assert 0.005 < np.median(errs_id) < id_max + # Fiducial transform + scale + coreg.fit_fiducials(verbose=True) + assert coreg._extra_points_filter is None + coreg.omit_head_shape_points(distance=0.02) + assert coreg._extra_points_filter is not None + errs_fid = coreg.compute_dig_mri_distances() + assert_array_less(0, errs_fid) + if is_scaled or scale_mode is not None: + fid_max = 0.05 + fid_med = 0.02 + else: + fid_max = 0.03 + fid_med = 0.01 + assert_array_less(errs_fid, fid_max) + assert 0.001 < np.median(errs_fid) < fid_med + assert not np.allclose(coreg._parameters, default_params) + coreg.omit_head_shape_points(distance=-1) + coreg.omit_head_shape_points(distance=5. / 1000) + assert coreg._extra_points_filter is not None + # ICP transform + scale + coreg.fit_icp(verbose=True) + assert isinstance(coreg.trans, Transform) + errs_icp = coreg.compute_dig_mri_distances() + assert_array_less(0, errs_icp) + if is_scaled or scale_mode == '3-axis': + icp_max = 0.015 + else: + icp_max = 0.01 + assert_array_less(errs_icp, icp_max) + assert 0.001 < np.median(errs_icp) < 0.004 + assert np.rad2deg(_angle_between_quats( + rot_to_quat(coreg.trans['trans'][:3, :3]), + rot_to_quat(trans['trans'][:3, :3]))) < 13 + if scale_mode is None: + atol = 1e-7 + else: + atol = 0.35 + assert_allclose(coreg._scale, ref_scale, atol=atol) + coreg.reset() + assert_allclose(coreg._parameters, default_params) + + +@pytest.mark.slowtest +@testing.requires_testing_data +def test_coreg_class_gui_match(): + """Test that using Coregistration matches mne coreg.""" + fiducials, _ = read_fiducials(fid_fname) + info = read_info(raw_fname) + coreg = Coregistration(info, subject='sample', subjects_dir=subjects_dir, + fiducials=fiducials) + assert_allclose(coreg.trans['trans'], np.eye(4), atol=1e-6) + # mne coreg -s sample -d subjects -f MEG/sample/sample_audvis_trunc_raw.fif + # then "Fit Fid.", Save... to get trans, read_trans: + want_trans = [ + [9.99428809e-01, 2.94733196e-02, 1.65350307e-02, -8.76054692e-04], + [-1.92420650e-02, 8.98512006e-01, -4.38526988e-01, 9.39774036e-04], + [-2.77817696e-02, 4.37958330e-01, 8.98565888e-01, -8.29207990e-03], + [0, 0, 0, 1]] + coreg.set_fid_match('matched') + coreg.fit_fiducials(verbose=True) + assert_allclose(coreg.trans['trans'], want_trans, atol=1e-6) + # Set ICP iterations to one, click "Fit ICP" + want_trans = [ + [9.99512792e-01, 2.80128177e-02, 1.37659665e-02, 6.08855276e-04], + [-1.91694051e-02, 8.98992002e-01, -4.37545270e-01, 9.66848747e-04], + [-2.46323701e-02, 4.37068194e-01, 8.99091005e-01, -1.44129358e-02], + [0, 0, 0, 1]] + coreg.fit_icp(1, verbose=True) + assert_allclose(coreg.trans['trans'], want_trans, atol=1e-6) + # Set ICP iterations to 20, click "Fit ICP" + with catch_logging() as log: + coreg.fit_icp(20, verbose=True) + log = log.getvalue() + want_trans = [ + [9.97582495e-01, 2.12266613e-02, 6.61706254e-02, -5.07694029e-04], + [1.81089472e-02, 8.39900672e-01, -5.42437911e-01, 7.81218382e-03], + [-6.70908988e-02, 5.42324841e-01, 8.37485850e-01, -2.50057746e-02], + [0, 0, 0, 1]] + assert_allclose(coreg.trans['trans'], want_trans, atol=1e-6) + assert 'ICP 19' in log + assert 'ICP 20' not in log # converged on 19 + # Change to uniform scale mode, "Fit Fiducials" in scale UI + coreg.set_scale_mode('uniform') + coreg.fit_fiducials() + want_scale = [0.975] * 3 + want_trans = [ + [9.99428809e-01, 2.94733196e-02, 1.65350307e-02, -9.25998494e-04], + [-1.92420650e-02, 8.98512006e-01, -4.38526988e-01, -1.03350170e-03], + [-2.77817696e-02, 4.37958330e-01, 8.98565888e-01, -9.03170835e-03], + [0, 0, 0, 1]] + assert_allclose(coreg.scale, want_scale, atol=5e-4) + assert_allclose(coreg.trans['trans'], want_trans, atol=1e-6) + # Click "Fit ICP" in scale UI + with catch_logging() as log: + coreg.fit_icp(20, verbose=True) + log = log.getvalue() + assert 'ICP 18' in log + assert 'ICP 19' not in log + want_scale = [1.036] * 3 + want_trans = [ + [9.98992383e-01, 1.72388796e-02, 4.14364934e-02, 6.19427126e-04], + [6.80460501e-03, 8.54430079e-01, -5.19521892e-01, 5.58008114e-03], + [-4.43605632e-02, 5.19280374e-01, 8.53451848e-01, -2.03358755e-02], + [0, 0, 0, 1]] + assert_allclose(coreg.scale, want_scale, atol=5e-4) + assert_allclose(coreg.trans['trans'], want_trans, atol=1e-6) + # Change scale mode to 3-axis, click "Fit ICP" in scale UI + coreg.set_scale_mode('3-axis') + with catch_logging() as log: + coreg.fit_icp(20, verbose=True) + log = log.getvalue() + assert 'ICP 7' in log + assert 'ICP 8' not in log + want_scale = [1.025, 1.010, 1.121] + want_trans = [ + [9.98387098e-01, 2.04762165e-02, 5.29526398e-02, 4.97257097e-05], + [1.13287698e-02, 8.42087150e-01, -5.39222538e-01, 7.09863892e-03], + [-5.56319728e-02, 5.38952649e-01, 8.40496957e-01, -1.46372067e-02], + [0, 0, 0, 1]] + assert_allclose(coreg.scale, want_scale, atol=5e-4) + assert_allclose(coreg.trans['trans'], want_trans, atol=1e-6) + + +@testing.requires_testing_data +@pytest.mark.parametrize( + 'drop_point_kind', (FIFF.FIFFV_POINT_CARDINAL, FIFF.FIFFV_POINT_HPI, + FIFF.FIFFV_POINT_EXTRA, FIFF.FIFFV_POINT_EEG)) +def test_coreg_class_init(drop_point_kind): + """Test that Coregistration can be instantiated with various digs.""" + fiducials, _ = read_fiducials(fid_fname) + info = read_info(raw_fname) + + dig_list = [] + eeg_chans = [] + for pt in info['dig']: + if pt['kind'] != drop_point_kind: + dig_list.append(pt) + if pt['kind'] == FIFF.FIFFV_POINT_EEG: + eeg_chans.append(f"EEG {pt['ident']:03d}") + + this_info = info.copy() + this_info.set_montage(DigMontage(dig=dig_list, ch_names=eeg_chans), + on_missing='ignore') + Coregistration(this_info, subject='sample', + subjects_dir=subjects_dir, fiducials=fiducials) diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_cov.py python-mne-1.1.0+dfsg/mne/tests/test_cov.py --- python-mne-0.23.4+dfsg/mne/tests/test_cov.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_cov.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,10 +1,11 @@ # Author: Alexandre Gramfort # Denis Engemann # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op import itertools as itt +import sys from numpy.testing import (assert_array_almost_equal, assert_array_equal, assert_equal, assert_allclose) @@ -29,7 +30,8 @@ from mne.io.pick import _DATA_CH_TYPES_SPLIT, pick_info from mne.preprocessing import maxwell_filter from mne.rank import _compute_rank_int -from mne.utils import requires_sklearn, catch_logging, assert_snr +from mne.utils import (requires_sklearn, catch_logging, assert_snr, + _record_warnings) base_dir = op.join(op.dirname(__file__), '..', 'io', 'tests', 'data') cov_fname = op.join(base_dir, 'test-cov.fif') @@ -56,6 +58,7 @@ raw.del_proj() with pytest.warns(RuntimeWarning, match='Too few samples'): cov = compute_raw_covariance(raw) + assert cov['names'] == raw.ch_names W, _, C = compute_whitener(cov, raw.info, pca=pca, return_colorer=True, verbose='error') n_channels = len(raw.ch_names) @@ -75,6 +78,26 @@ assert pca is False assert_allclose(round_trip, np.eye(n_channels), atol=0.05) + raw.info['bads'] = [raw.ch_names[0]] + picks = pick_types(raw.info, meg=True, eeg=True, exclude=[]) + with pytest.warns(RuntimeWarning, match='Too few samples'): + cov2 = compute_raw_covariance(raw, picks=picks) + cov3 = compute_raw_covariance(raw, picks=None) + assert_allclose(cov2['data'][1:, 1:], cov3['data']) + W2, _, C2 = compute_whitener(cov2, raw.info, pca=pca, return_colorer=True, + picks=picks, verbose='error') + W3, _, C3 = compute_whitener(cov3, raw.info, pca=pca, return_colorer=True, + picks=None, verbose='error') + # this tol is not great, but Windows needs it + rtol = 1e-3 if sys.platform.startswith('win') else 1e-11 + assert_allclose(W, W2, rtol=rtol) + assert_allclose(C, C2, rtol=rtol) + n_channels = len(raw.ch_names) - len(raw.info['bads']) + n_reduced = len(raw.ch_names) - len(raw.info['bads']) + rank = n_channels - len(raw.info['projs']) + n_reduced = rank if pca is True else n_channels + assert W3.shape == C3.shape[::-1] == (n_reduced, n_channels) + def test_cov_mismatch(): """Test estimation with MEG<->Head mismatch.""" @@ -93,7 +116,7 @@ epochs_2.info['dev_head_t'] = None pytest.raises(ValueError, compute_covariance, [epochs, epochs_2]) compute_covariance([epochs, epochs_2], on_mismatch='ignore') - with pytest.raises(RuntimeWarning, match='transform mismatch'): + with pytest.warns(RuntimeWarning, match='transform mismatch'): compute_covariance([epochs, epochs_2], on_mismatch='warn') with pytest.raises(ValueError, match='Invalid value'): compute_covariance(epochs, on_mismatch='x') @@ -171,9 +194,9 @@ cov_orig['data'], atol=1e-20) -def test_ad_hoc_cov(tmpdir): +def test_ad_hoc_cov(tmp_path): """Test ad hoc cov creation and I/O.""" - out_fname = tmpdir.join('test-cov.fif') + out_fname = tmp_path / 'test-cov.fif' evoked = read_evokeds(ave_fname)[0] cov = make_ad_hoc_cov(evoked.info) cov.save(out_fname) @@ -182,7 +205,7 @@ assert_array_almost_equal(cov['data'], cov2['data']) std = dict(grad=2e-13, mag=10e-15, eeg=0.1e-6) cov = make_ad_hoc_cov(evoked.info, std) - cov.save(out_fname) + cov.save(out_fname, overwrite=True) assert 'Covariance' in repr(cov) cov2 = read_cov(out_fname) assert_array_almost_equal(cov['data'], cov2['data']) @@ -196,13 +219,13 @@ cov._get_square() -def test_io_cov(tmpdir): +def test_io_cov(tmp_path): """Test IO for noise covariance matrices.""" cov = read_cov(cov_fname) cov['method'] = 'empirical' cov['loglik'] = -np.inf - cov.save(tmpdir.join('test-cov.fif')) - cov2 = read_cov(tmpdir.join('test-cov.fif')) + cov.save(tmp_path / 'test-cov.fif') + cov2 = read_cov(tmp_path / 'test-cov.fif') assert_array_almost_equal(cov.data, cov2.data) assert_equal(cov['method'], cov2['method']) assert_equal(cov['loglik'], cov2['loglik']) @@ -210,32 +233,36 @@ cov2 = read_cov(cov_gz_fname) assert_array_almost_equal(cov.data, cov2.data) - cov2.save(tmpdir.join('test-cov.fif.gz')) - cov2 = read_cov(tmpdir.join('test-cov.fif.gz')) + cov2.save(tmp_path / 'test-cov.fif.gz') + cov2 = read_cov(tmp_path / 'test-cov.fif.gz') assert_array_almost_equal(cov.data, cov2.data) cov['bads'] = ['EEG 039'] cov_sel = pick_channels_cov(cov, exclude=cov['bads']) assert cov_sel['dim'] == (len(cov['data']) - len(cov['bads'])) assert cov_sel['data'].shape == (cov_sel['dim'], cov_sel['dim']) - cov_sel.save(tmpdir.join('test-cov.fif')) + cov_sel.save(tmp_path / 'test-cov.fif', overwrite=True) cov2 = read_cov(cov_gz_fname) assert_array_almost_equal(cov.data, cov2.data) - cov2.save(tmpdir.join('test-cov.fif.gz')) - cov2 = read_cov(tmpdir.join('test-cov.fif.gz')) + cov2.save(tmp_path / 'test-cov.fif.gz', overwrite=True) + cov2 = read_cov(tmp_path / 'test-cov.fif.gz') assert_array_almost_equal(cov.data, cov2.data) # test warnings on bad filenames - cov_badname = tmpdir.join('test-bad-name.fif.gz') + cov_badname = tmp_path / 'test-bad-name.fif.gz' with pytest.warns(RuntimeWarning, match='-cov.fif'): write_cov(cov_badname, cov) with pytest.warns(RuntimeWarning, match='-cov.fif'): read_cov(cov_badname) -@pytest.mark.parametrize('method', (None, 'empirical', 'shrunk')) -def test_cov_estimation_on_raw(method, tmpdir): +@pytest.mark.parametrize('method', [ + None, + 'empirical', + pytest.param('shrunk', marks=pytest.mark.slowtest), +]) +def test_cov_estimation_on_raw(method, tmp_path): """Test estimation from raw (typically empty room).""" if method == 'shrunk': try: @@ -249,7 +276,7 @@ # The pure-string uses the more efficient numpy-based method, the # the list gets triaged to compute_covariance (should be equivalent # but use more memory) - with pytest.warns(None): # can warn about EEG ref + with _record_warnings(): # can warn about EEG ref cov = compute_raw_covariance( raw, tstep=None, method=method, rank='full', method_params=method_params) @@ -272,15 +299,15 @@ assert_snr(cov.data, other.data, 1e6) # tstep=0.2 (default) - with pytest.warns(None): # can warn about EEG ref + with _record_warnings(): # can warn about EEG ref cov = compute_raw_covariance(raw, method=method, rank='full', method_params=method_params) assert_equal(cov.nfree, cov_mne.nfree - 120) # cutoff some samples assert_snr(cov.data, cov_mne.data, 170) # test IO when computation done in Python - cov.save(tmpdir.join('test-cov.fif')) # test saving - cov_read = read_cov(tmpdir.join('test-cov.fif')) + cov.save(tmp_path / 'test-cov.fif') # test saving + cov_read = read_cov(tmp_path / 'test-cov.fif') assert cov_read.ch_names == cov.ch_names assert cov_read.nfree == cov.nfree assert_array_almost_equal(cov.data, cov_read.data) @@ -304,7 +331,7 @@ pytest.raises(ValueError, compute_raw_covariance, raw, tstep=None, method='empirical', reject=dict(eog=200e-6)) # but this should work - with pytest.warns(None): # sklearn + with _record_warnings(): # sklearn cov = compute_raw_covariance( raw.copy().crop(0, 10.), tstep=None, method=method, reject=dict(eog=1000e-6), method_params=method_params, @@ -316,12 +343,12 @@ def test_cov_estimation_on_raw_reg(): """Test estimation from raw with regularization.""" raw = read_raw_fif(raw_fname, preload=True) - raw.info['sfreq'] /= 10. + with raw.info._unlock(): + raw.info['sfreq'] /= 10. raw = RawArray(raw._data[:, ::10].copy(), raw.info) # decimate for speed cov_mne = read_cov(erm_cov_fname) with pytest.warns(RuntimeWarning, match='Too few samples'): - # XXX don't use "shrunk" here, for some reason it makes Travis 2.7 - # hang... "diagonal_fixed" is much faster. Use long epochs for speed. + # "diagonal_fixed" is much faster. Use long epochs for speed. cov = compute_raw_covariance(raw, tstep=5., method='diagonal_fixed') assert_snr(cov.data, cov_mne.data, 5) @@ -337,7 +364,7 @@ @pytest.mark.slowtest @pytest.mark.parametrize('rank', ('full', None)) -def test_cov_estimation_with_triggers(rank, tmpdir): +def test_cov_estimation_with_triggers(rank, tmp_path): """Test estimation from raw with triggers.""" raw = read_raw_fif(raw_fname) raw.set_eeg_reference(projection=True).load_data() @@ -384,8 +411,8 @@ keep_sample_mean=False, method='shrunk', rank=rank) # test IO when computation done in Python - cov.save(tmpdir.join('test-cov.fif')) # test saving - cov_read = read_cov(tmpdir.join('test-cov.fif')) + cov.save(tmp_path / 'test-cov.fif') # test saving + cov_read = read_cov(tmp_path / 'test-cov.fif') _assert_cov(cov, cov_read, 1e-5) # cov with list of epochs with different projectors @@ -546,12 +573,12 @@ covs = compute_covariance(epochs, method='auto', method_params=method_params, return_estimators=True, rank=rank) - # make sure regularization produces structured differencess + # make sure regularization produces structured differences diag_mask = np.eye(len(epochs.ch_names)).astype(bool) off_diag_mask = np.invert(diag_mask) for cov_a, cov_b in itt.combinations(covs, 2): if (cov_a['method'] == 'diagonal_fixed' and - # here we have diagnoal or no regularization. + # here we have diagonal or no regularization. cov_b['method'] == 'empirical' and rank == 'full'): assert not np.any(cov_a['data'][diag_mask] == @@ -617,7 +644,7 @@ # ignore warnings about rank mismatches: sometimes we will intentionally # violate the computed/info assumption, such as when using SSS with # `rank='full'` - with pytest.warns(None): + with _record_warnings(): return _compute_rank_int(cov, info=info, proj=proj) @@ -699,7 +726,8 @@ # test that rank=306 is same as rank='full' epochs_meg = epochs.copy().pick_types(meg=True) assert len(epochs_meg.ch_names) == 306 - epochs_meg.info.update(bads=[], projs=[]) + with epochs_meg.info._unlock(): + epochs_meg.info.update(bads=[], projs=[]) cov_full = compute_covariance(epochs_meg, method='oas', rank='full', verbose='error') assert _cov_rank(cov_full, epochs_meg.info) == 306 @@ -787,7 +815,8 @@ """Test risky rank options.""" info = read_info(ave_fname) info = pick_info(info, pick_types(info, meg=True)) - info['projs'] = [] + with info._unlock(): + info['projs'] = [] # need a square version because the diag one takes shortcuts in # compute_whitener (users shouldn't even need this function so it's # private) diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_defaults.py python-mne-1.1.0+dfsg/mne/tests/test_defaults.py --- python-mne-0.23.4+dfsg/mne/tests/test_defaults.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_defaults.py 2022-08-03 15:48:14.000000000 +0000 @@ -40,3 +40,19 @@ else: want_scale = _get_scaling(key, units[key]) assert_allclose(scale, want_scale, rtol=1e-12) + + +@pytest.mark.parametrize('key', ('si_units', 'color', 'scalings', + 'scalings_plot_raw')) +def test_consistency(key): + """Test defaults consistency.""" + units = set(_handle_default('units')) + other = set(_handle_default(key)) + au_keys = set('stim exci syst resp ias chpi'.split()) + assert au_keys.intersection(units) == set() + if key in ('color', 'scalings_plot_raw'): + assert au_keys.issubset(other) + other = other.difference(au_keys) + else: + assert au_keys.intersection(other) == set() + assert units == other, key diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_dipole.py python-mne-1.1.0+dfsg/mne/tests/test_dipole.py --- python-mne-0.23.4+dfsg/mne/tests/test_dipole.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_dipole.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,8 +1,13 @@ +# Author: Eric Larson +# +# License: BSD-3-Clause + import os import os.path as op import numpy as np -from numpy.testing import assert_allclose, assert_array_equal +from numpy.testing import (assert_allclose, assert_array_equal, + assert_array_less) import matplotlib.pyplot as plt import pytest @@ -12,11 +17,12 @@ transform_surface_to, make_sphere_model, pick_types, pick_info, EvokedArray, read_source_spaces, make_ad_hoc_cov, make_forward_solution, Dipole, DipoleFixed, Epochs, - make_fixed_length_events, Evoked) + make_fixed_length_events, Evoked, head_to_mni) from mne.dipole import get_phantom_dipoles, _BDIP_ERROR_KEYS from mne.simulation import simulate_evoked from mne.datasets import testing -from mne.utils import run_tests_if_main, requires_mne, run_subprocess +from mne.utils import (requires_mne, run_subprocess, requires_nibabel, + _record_warnings) from mne.proj import make_eeg_average_ref_proj from mne.io import read_raw_fif, read_raw_ctf @@ -25,7 +31,6 @@ from mne.surface import _compute_nearest from mne.bem import _bem_find_surface, read_bem_solution from mne.transforms import apply_trans, _get_trans -from mne.source_space import head_to_mni data_path = testing.data_path(download=False) meg_path = op.join(data_path, 'MEG', 'sample') @@ -71,11 +76,11 @@ @testing.requires_testing_data -def test_io_dipoles(tmpdir): +def test_io_dipoles(tmp_path): """Test IO for .dip files.""" dipole = read_dipole(fname_dip) assert 'Dipole ' in repr(dipole) # test repr - out_fname = op.join(str(tmpdir), 'temp.dip') + out_fname = op.join(str(tmp_path), 'temp.dip') dipole.save(out_fname) dipole_new = read_dipole(out_fname) _compare_dipoles(dipole, dipole_new) @@ -93,16 +98,18 @@ # for now our CTF phantom fitting tutorials will have to do # (otherwise we need to add that to the testing dataset, which is # a bit too big) - fit_dipole(evoked, cov, sphere, rank=dict(meg=len(evoked.data))) + fit_dipole(evoked, cov, sphere, rank=dict(meg=len(evoked.data)), + tol=1e-3, accuracy='accurate') @pytest.mark.slowtest @testing.requires_testing_data +@requires_nibabel() @requires_mne -def test_dipole_fitting(tmpdir): +def test_dipole_fitting(tmp_path): """Test dipole fitting.""" amp = 100e-9 - tempdir = str(tmpdir) + tempdir = str(tmp_path) rng = np.random.RandomState(0) fname_dtemp = op.join(tempdir, 'test.dip') fname_sim = op.join(tempdir, 'test-ave.fif') @@ -148,6 +155,15 @@ subjects_dir=subjects_dir) assert_allclose(dip_mni_pos, head_to_mni_dip_pos, rtol=1e-3, atol=0) + # Test finding label for dip.pos in an aseg, also tests `to_mri` + target_labels = ['Left-Cerebral-Cortex', 'Unknown', 'Left-Cerebral-Cortex', + 'Right-Cerebral-Cortex', 'Left-Cerebral-Cortex', + 'Unknown', 'Unknown', 'Unknown', + 'Right-Cerebral-White-Matter', 'Right-Cerebral-Cortex'] + labels = dip.to_volume_labels(fname_trans, subject='fsaverage', + aseg="aseg", subjects_dir=subjects_dir) + assert labels == target_labels + # Sanity check: do our residuals have less power than orig data? data_rms = np.sqrt(np.sum(evoked.data ** 2, axis=0)) resi_rms = np.sqrt(np.sum(residual.data ** 2, axis=0)) @@ -193,7 +209,7 @@ @testing.requires_testing_data -def test_dipole_fitting_fixed(tmpdir): +def test_dipole_fitting_fixed(tmp_path): """Test dipole fitting with a fixed position.""" tpeak = 0.073 sphere = make_sphere_model(head_radius=0.1) @@ -229,7 +245,7 @@ assert_allclose(dip_fixed.info['chs'][0]['loc'][3:6], ori) assert_allclose(dip_fixed.data[1, t_idx], gof) assert_allclose(resid.data, resid_fixed.data[:, [t_idx]]) - _check_roundtrip_fixed(dip_fixed, tmpdir) + _check_roundtrip_fixed(dip_fixed, tmp_path) # bad resetting evoked.info['bads'] = [evoked.ch_names[3]] dip_fixed, resid_fixed = fit_dipole(evoked, cov, sphere, pos=pos, ori=ori) @@ -297,7 +313,7 @@ bem = read_bem_solution(fname_bem) dip, residual = fit_dipole(evoked, cov, bem, fname_trans, - min_dist=min_dist) + min_dist=min_dist, tol=1e-4) assert isinstance(residual, Evoked) dist = _compute_depth(dip, fname_bem, fname_trans, subject, subjects_dir) @@ -305,8 +321,8 @@ # Constraints are not exact, so bump the minimum slightly assert (min_dist - 0.1 < (dist[0] * 1000.) < (min_dist + 1.)) - pytest.raises(ValueError, fit_dipole, evoked, cov, fname_bem, fname_trans, - -1.) + with pytest.raises(ValueError, match='min_dist should be positive'): + fit_dipole(evoked, cov, fname_bem, fname_trans, -1.) def _compute_depth(dip, fname_bem, fname_trans, subject, subjects_dir): @@ -361,17 +377,17 @@ # make sure that our median is sub-mm and the large majority are very # close (we expect some to be off by a bit e.g. because they are # radial) - assert ((np.percentile(ds, [50, 90]) < [0.0005, perc_90]).all()) + assert_array_less(np.percentile(ds, [50, 90]), [0.0005, perc_90]) @testing.requires_testing_data -def test_dipole_fixed(tmpdir): +def test_dipole_fixed(tmp_path): """Test reading a fixed-position dipole (from Xfit).""" dip = read_dipole(fname_xfit_dip) # print the representation of the object DipoleFixed assert 'DipoleFixed ' in repr(dip) - _check_roundtrip_fixed(dip, tmpdir) + _check_roundtrip_fixed(dip, tmp_path) with pytest.warns(RuntimeWarning, match='extra fields'): dip_txt = read_dipole(fname_xfit_dip_txt) assert_allclose(dip.info['chs'][0]['loc'][:3], dip_txt.pos[0]) @@ -381,9 +397,9 @@ assert_allclose(dip_txt_seq.gof, [27.3, 46.4, 43.7, 41., 37.3, 32.5]) -def _check_roundtrip_fixed(dip, tmpdir): +def _check_roundtrip_fixed(dip, tmp_path): """Check roundtrip IO for fixed dipoles.""" - tempdir = str(tmpdir) + tempdir = str(tmp_path) dip.save(op.join(tempdir, 'test-dip.fif.gz')) dip_read = read_dipole(op.join(tempdir, 'test-dip.fif.gz')) assert_allclose(dip_read.data, dip_read.data) @@ -408,14 +424,14 @@ @testing.requires_testing_data -def test_confidence(tmpdir): +def test_confidence(tmp_path): """Test confidence limits.""" evoked = read_evokeds(fname_evo_full, 'Left Auditory', baseline=(None, 0)) evoked.crop(0.08, 0.08).pick_types(meg=True) # MEG-only cov = make_ad_hoc_cov(evoked.info) sphere = make_sphere_model((0., 0., 0.04), 0.08) dip_py = fit_dipole(evoked, cov, sphere)[0] - fname_test = op.join(str(tmpdir), 'temp-dip.txt') + fname_test = op.join(str(tmp_path), 'temp-dip.txt') dip_py.save(fname_test) dip_read = read_dipole(fname_test) with pytest.warns(RuntimeWarning, match="'noise/ft/cm', 'prob'"): @@ -442,16 +458,16 @@ (fname_dip, fname_bdip), (fname_dip_xfit, fname_bdip_xfit), ]) -def test_bdip(fname_dip_, fname_bdip_, tmpdir): +def test_bdip(fname_dip_, fname_bdip_, tmp_path): """Test bdip I/O.""" # use text as veridical - with pytest.warns(None): # ignored fields + with _record_warnings(): # ignored fields dip = read_dipole(fname_dip_) # read binary orig_size = os.stat(fname_bdip_).st_size bdip = read_dipole(fname_bdip_) # test round-trip by writing and reading, too - fname = tmpdir.join('test.bdip') + fname = tmp_path / 'test.bdip' bdip.save(fname) bdip_read = read_dipole(fname) write_size = os.stat(str(fname)).st_size @@ -491,6 +507,3 @@ # Test whether indexing works this_bdip0 = this_bdip[0] _check_dipole(this_bdip0, 1) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_docstring_parameters.py python-mne-1.1.0+dfsg/mne/tests/test_docstring_parameters.py --- python-mne-0.23.4+dfsg/mne/tests/test_docstring_parameters.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_docstring_parameters.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,15 +1,21 @@ +# -*- coding: utf-8 -*- +# Author: Eric Larson +# +# License: BSD-3-Clause + +from collections import Counter import inspect from inspect import getsource import os.path as op +from pathlib import Path from pkgutil import walk_packages import re import sys -from unittest import SkipTest import pytest import mne -from mne.utils import run_tests_if_main, requires_numpydoc, _pl +from mne.utils import requires_numpydoc, _pl, _record_warnings public_modules = [ # the list of modules users need to access for all functionality @@ -18,7 +24,6 @@ 'mne.beamformer', 'mne.channels', 'mne.chpi', - 'mne.connectivity', 'mne.cov', 'mne.cuda', 'mne.datasets', @@ -27,8 +32,10 @@ 'mne.datasets.sample', 'mne.decoding', 'mne.dipole', + 'mne.export', 'mne.filter', 'mne.forward', + 'mne.gui', 'mne.inverse_sparse', 'mne.io', 'mne.io.kit', @@ -63,16 +70,12 @@ # functions to ignore args / docstring of docstring_ignores = { - 'mne.externals', 'mne.fixes', 'mne.io.write', 'mne.io.meas_info.Info', - 'mne.utils.docs.deprecated', } char_limit = 800 # XX eventually we should probably get this lower tab_ignores = [ - 'mne.externals.tqdm._tqdm.__main__', - 'mne.externals.tqdm._tqdm.cli', 'mne.channels.tests.test_montage', 'mne.io.curry.tests.test_curry', ] @@ -106,9 +109,7 @@ from numpydoc.validate import validate name = _func_name(func, cls) skip = (not name.startswith('mne.') or - any(re.match(d, name) for d in docstring_ignores) or - 'deprecation_wrapped' in getattr( - getattr(func, '__code__', None), 'co_name', '')) + any(re.match(d, name) for d in docstring_ignores)) if skip: return list() if cls is not None: @@ -127,21 +128,14 @@ def test_docstring_parameters(): """Test module docstring formatting.""" from numpydoc import docscrape - # skip modules that require mayavi if mayavi is not installed - public_modules_ = public_modules[:] - try: - import mayavi # noqa: F401 analysis:ignore - public_modules_.append('mne.gui') - except ImportError: - pass incorrect = [] - for name in public_modules_: + for name in public_modules: # Assert that by default we import all public names with `import mne` if name not in ('mne', 'mne.gui'): extra = name.split('.')[1] assert hasattr(mne, extra) - with pytest.warns(None): # traits warnings + with _record_warnings(): # traits warnings module = __import__(name, globals()) for submod in name.split('.')[1:]: module = getattr(module, submod) @@ -155,7 +149,8 @@ method = getattr(cls, method_name) incorrect += check_parameters_match(method, cls=cls) if hasattr(cls, '__call__') and \ - 'of type object' not in str(cls.__call__): + 'of type object' not in str(cls.__call__) and \ + 'of ABCMeta object' not in str(cls.__call__): incorrect += check_parameters_match(cls.__call__, cls) functions = inspect.getmembers(module, inspect.isfunction) for fname, func in functions: @@ -171,21 +166,12 @@ def test_tabs(): """Test that there are no tabs in our source files.""" - # avoid importing modules that require mayavi if mayavi is not installed - ignore = tab_ignores[:] - try: - import mayavi # noqa: F401 analysis:ignore - except ImportError: - ignore.extend('mne.gui.' + name for name in - ('_coreg_gui', '_fiducials_gui', '_file_traits', '_help', - '_kit2fiff_gui', '_marker_gui', '_viewer')) - for _, modname, ispkg in walk_packages(mne.__path__, prefix='mne.'): # because we don't import e.g. mne.tests w/mne - if not ispkg and modname not in ignore: + if not ispkg and modname not in tab_ignores: # mod = importlib.import_module(modname) # not py26 compatible! try: - with pytest.warns(None): + with _record_warnings(): __import__(modname) except Exception: # can't import properly continue @@ -221,7 +207,6 @@ apply_maxfilter apply_trans channel_type -check_n_jobs combine_kit_markers combine_tfr combine_transforms @@ -265,25 +250,16 @@ def test_documented(): """Test that public functions and classes are documented.""" - # skip modules that require mayavi if mayavi is not installed - public_modules_ = public_modules[:] - try: - import mayavi # noqa: F401, analysis:ignore - except ImportError: - pass - else: - public_modules_.append('mne.gui') - doc_dir = op.abspath(op.join(op.dirname(__file__), '..', '..', 'doc')) doc_file = op.join(doc_dir, 'python_reference.rst') if not op.isfile(doc_file): - raise SkipTest('Documentation file not found: %s' % doc_file) + pytest.skip('Documentation file not found: %s' % doc_file) api_files = ( - 'connectivity', 'covariance', 'creating_from_arrays', 'datasets', + 'covariance', 'creating_from_arrays', 'datasets', 'decoding', 'events', 'file_io', 'forward', 'inverse', 'logging', 'most_used_classes', 'mri', 'preprocessing', 'reading_raw_data', 'realtime', 'report', 'sensor_space', 'simulation', 'source_space', - 'statistics', 'time_frequency', 'visualization') + 'statistics', 'time_frequency', 'visualization', 'export') known_names = list() for api_file in api_files: with open(op.join(doc_dir, f'{api_file}.rst'), 'rb') as fid: @@ -297,8 +273,8 @@ known_names = set(known_names) missing = [] - for name in public_modules_: - with pytest.warns(None): # traits warnings + for name in public_modules: + with _record_warnings(): # traits warnings module = __import__(name, globals()) for submod in name.split('.')[1:]: module = getattr(module, submod) @@ -309,10 +285,10 @@ if not name.startswith('_') and name not in known_names: from_mod = inspect.getmodule(cf).__name__ if (from_mod.startswith('mne') and - not from_mod.startswith('mne.externals') and not any(from_mod.startswith(x) for x in documented_ignored_mods) and - name not in documented_ignored_names): + name not in documented_ignored_names and + not hasattr(cf, '_deprecated_original')): missing.append('%s (%s.%s)' % (name, from_mod, name)) if len(missing) > 0: raise AssertionError('\n\nFound new public members missing from ' @@ -320,4 +296,20 @@ '\n* '.join(sorted(set(missing)))) -run_tests_if_main() +def test_docdict_order(): + """Test that docdict is alphabetical.""" + docs_path = Path(__file__).parent.parent / 'utils' / 'docs.py' + assert docs_path.is_file(), docs_path + with open(docs_path, 'r') as fid: + docs = fid.read() + entries = re.findall(r'docdict\[["\']([a-z_\-0-9]+)["\']\] = ', docs) + # We can always modify the upper or lower bounds here as entries are made. + # This just makes sure our regex is reasonable. + # As of 2022/03/02 we're at 346 + assert 340 < len(entries) < 500 + # no dups (verbosely describe the error) + for key, count in Counter(entries).items(): + assert count == 1, key + # this should be redundant, but why not + assert len(set(entries)) == len(entries) + assert sorted(entries) == entries diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_epochs.py python-mne-1.1.0+dfsg/mne/tests/test_epochs.py --- python-mne-0.23.4+dfsg/mne/tests/test_epochs.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_epochs.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,12 +3,13 @@ # Denis Engemann # Stefan Appelhoff # -# License: BSD (3-clause) +# License: BSD-3-Clause from copy import deepcopy -from distutils.version import LooseVersion +from datetime import timedelta from functools import partial from io import BytesIO +import os import os.path as op import pickle @@ -17,7 +18,6 @@ assert_allclose, assert_equal, assert_array_less) import numpy as np from numpy.fft import rfft, rfftfreq -import matplotlib.pyplot as plt import scipy.signal import mne @@ -25,23 +25,23 @@ equalize_channels, pick_types, pick_channels, read_evokeds, write_evokeds, create_info, make_fixed_length_events, make_fixed_length_epochs, combine_evoked) +from mne.annotations import _handle_meas_date from mne.baseline import rescale from mne.datasets import testing from mne.chpi import read_head_pos, head_pos_to_trans_rot_t from mne.event import merge_events -from mne.io import RawArray, read_raw_fif, read_epochs_eeglab +from mne.io import RawArray, read_raw_fif from mne.io.constants import FIFF from mne.io.proj import _has_eeg_average_ref_proj -from mne.io.write import write_int, INT32_MAX, _get_split_size +from mne.io.write import write_int, INT32_MAX, _get_split_size, write_float from mne.preprocessing import maxwell_filter from mne.epochs import ( bootstrap, equalize_epoch_counts, combine_event_ids, add_channels_epochs, EpochsArray, concatenate_epochs, BaseEpochs, average_movements, _handle_event_repeated, make_metadata) -from mne.utils import (requires_pandas, object_diff, +from mne.utils import (requires_pandas, object_diff, use_log_level, catch_logging, _FakeNoPandas, - assert_meg_snr, check_version, _dt_to_stamp, - _check_eeglabio_installed) + assert_meg_snr, check_version, _dt_to_stamp) data_path = testing.data_path(download=False) fname_raw_testing = op.join(data_path, 'MEG', 'sample', @@ -61,6 +61,34 @@ rng = np.random.RandomState(42) +def _create_epochs_with_annotations(): + """Create test dataset of Epochs with Annotations.""" + # set up a test dataset + data = rng.randn(1, 600) + sfreq = 100. + info = create_info(ch_names=['MEG1'], ch_types=['grad'], sfreq=sfreq) + raw = RawArray(data, info) + + # epoch onsets will be at 0.5, 2.5, 4.5s and will be one second long + events = np.zeros((3, 3), dtype=int) + events[:, 0] = (np.array([0.5, 2.5, 4.5]) * sfreq).astype(int) + + # make annotations to test various kinds of overlap + # onset dur descr + annots = [(0.3, 0.0, 'no_overlap'), + (0.4, 0.1, 'coincident_onset'), # only edge coincides + (0.4, 0.2, 'straddles_onset'), + (1.4, 0.2, 'straddles_offset'), + (1.5, 0.0, 'coincident_offset'), # only edge coincides, zero-dur + (2.6, 0.0, 'within_epoch'), + (4.4, 1.2, 'surround_epoch'), + (3.4, 1.2, 'multiple')] + annots = Annotations(*zip(*annots)) + raw.set_annotations(annots) + epochs = Epochs(raw, events=events, tmin=0, tmax=1, baseline=None) + return epochs, raw, events + + def test_event_repeated(): """Test epochs takes into account repeated events.""" n_samples = 100 @@ -184,6 +212,55 @@ flat = dict(grad=1e-15, mag=1e-15) +def test_get_data(): + """Test the .get_data() method.""" + raw, events, picks = _get_data() + event_id = {'a/1': 1, 'a/2': 2, 'b/1': 3, 'b/2': 4} + epochs = Epochs(raw, events, event_id, preload=True) + + # Testing with respect to units param + # more tests in mne/io/tests/test_raw.py::test_get_data_units + # EEG is already in V, so no conversion should take place + d1 = epochs.get_data(picks="eeg", units=None) + d2 = epochs.get_data(picks="eeg", units="V") + assert_array_equal(d1, d2) + + with pytest.raises(ValueError, match="is not a valid unit for eeg"): + epochs.get_data(picks="eeg", units="") + + with pytest.raises(ValueError, match="cannot be str if there is more"): + epochs.get_data(picks=["eeg", "meg"], units="V") + + # Check combination of units with item param, scale only one ch_type + d3 = epochs.get_data(item=[1, 2, 3], units={"grad": "fT/cm"}) + assert d3.shape[0] == 3 + + grad_idxs = np.array([i == "grad" for i in epochs.get_channel_types()]) + eeg_idxs = np.array([i == "eeg" for i in epochs.get_channel_types()]) + assert_array_equal( + d3[:, grad_idxs, :], + epochs.get_data("grad", item=[1, 2, 3]) * 1e13 # T/m to fT/cm + ) + assert_array_equal( + d3[:, eeg_idxs, :], + epochs.get_data("eeg", item=[1, 2, 3]) + ) + + # Test tmin/tmax + data = epochs.get_data(tmin=0) + assert np.all(data.shape[-1] == + epochs._data.shape[-1] - + np.nonzero(epochs.times == 0)[0]) + + assert epochs.get_data(tmin=0, tmax=0).size == 0 + + with pytest.raises(TypeError, match='tmin .* float, None'): + epochs.get_data(tmin=[1], tmax=1) + + with pytest.raises(TypeError, match='tmax .* float, None'): + epochs.get_data(tmin=1, tmax=np.ones(5)) + + def test_hierarchical(): """Test hierarchical access.""" raw, events, picks = _get_data() @@ -432,6 +509,15 @@ assert_equal(epochs.drop_log[4][0], 'BAD') raw.set_annotations(None) + # rejection with all None / False arguments: no loading / dropping + epochs = Epochs(raw, events, event_id, tmin, tmax, picks=[0], + reject=None, flat=None, reject_by_annotation=False, + reject_tmin=None, reject_tmax=None) + with catch_logging() as log: + epochs.drop_bad(verbose='debug') + log = log.getvalue() + assert 'is a noop' in log + def test_reject_by_annotations_reject_tmin_reject_tmax(): """Test reject_by_annotations with reject_tmin and reject_tmax defined.""" @@ -497,7 +583,8 @@ data = rng.randn(n_epochs, n_channels, n_times) events = np.array([np.arange(n_epochs), [0] * n_epochs, [1] * n_epochs]).T info = create_info(n_channels, sfreq, 'eeg') - info['lowpass'] = sfreq_new / float(decim) + with info._unlock(): + info['lowpass'] = sfreq_new / float(decim) epochs = EpochsArray(data, info, events) data_epochs = epochs.copy().decimate(decim).get_data() data_epochs_2 = epochs.copy().decimate(decim, offset=1).get_data() @@ -513,7 +600,8 @@ raw.info.normalize_proj() del picks sfreq_new = raw.info['sfreq'] / decim - raw.info['lowpass'] = sfreq_new / 12. # suppress aliasing warnings + with raw.info._unlock(): + raw.info['lowpass'] = sfreq_new / 12. # suppress aliasing warnings pytest.raises(ValueError, epochs.decimate, -1) pytest.raises(ValueError, epochs.decimate, 2, offset=-1) pytest.raises(ValueError, epochs.decimate, 2, offset=2) @@ -610,12 +698,12 @@ pytest.raises(NotImplementedError, epochs.get_data) # events have wrong dtype (float) with pytest.raises(TypeError, match='events should be a NumPy array'): - BaseEpochs(raw.info, None, np.ones((1, 3), float), event_id, tmin, - tmax) + BaseEpochs(raw.info, None, np.ones((1, 3), float), event_id, + tmin, tmax) # events have wrong shape with pytest.raises(ValueError, match='events must be of shape'): - BaseEpochs(raw.info, None, np.ones((1, 3, 2), int), event_id, tmin, - tmax) + BaseEpochs(raw.info, None, np.ones((1, 3, 2), int), event_id, + tmin, tmax) # events are tuple (like returned by mne.events_from_annotations) with pytest.raises(TypeError, match='events should be a NumPy array'): BaseEpochs(raw.info, None, (np.ones((1, 3), int), {'foo': 1})) @@ -644,7 +732,7 @@ np.mean(data_filt[:, :, stop_mask]) * 5) -def test_filter(tmpdir): +def test_filter(tmp_path): """Test filtering.""" h_freq = 40. raw, events = _get_data()[:2] @@ -671,7 +759,7 @@ np.mean(data_filt_fft[:, :, stop_mask]) * 10) # smoke test for filtering I/O data (gh-5614) - temp_fname = op.join(str(tmpdir), 'test-epo.fif') + temp_fname = op.join(str(tmp_path), 'test-epo.fif') epochs_orig.save(temp_fname, overwrite=True) epochs = mne.read_epochs(temp_fname) epochs.filter(None, h_freq) @@ -737,7 +825,7 @@ @pytest.mark.parametrize('preload', (True, False)) -def test_epochs_baseline(preload): +def test_epochs_baseline_basic(preload, tmp_path): """Test baseline and rescaling modes with and without preloading.""" data = np.array([[2, 3], [2, 3]], float) info = create_info(2, 1000., ('eeg', 'misc')) @@ -747,6 +835,7 @@ epochs = mne.Epochs(raw, events, None, 0, 1e-3, baseline=None, preload=preload) epochs.drop_bad() + epochs_nobl = epochs.copy() epochs_data = epochs.get_data() assert epochs_data.shape == (1, 2, 2) expected = data.copy() @@ -780,6 +869,19 @@ else: epochs.apply_baseline(None) assert epochs.baseline is None + # gh-10139 + fname = tmp_path / 'test-epo.fif' + epochs.apply_baseline((None, None)) + assert_allclose(epochs.get_data('eeg').mean(-1), 0, atol=1e-20) + assert epochs_nobl.baseline is None + for ep in (epochs, epochs_nobl): + ep.save(fname, overwrite=True) + ep = mne.read_epochs(fname, preload=preload) + ep.apply_baseline((0, 0)) + assert_allclose(ep.get_data('eeg').mean(-1), 0.5, atol=1e-20) + ep.save(fname, overwrite=True) + ep = mne.read_epochs(fname, preload=preload) + assert_allclose(ep.get_data('eeg').mean(-1), 0.5, atol=1e-20) def test_epochs_bad_baseline(): @@ -863,7 +965,7 @@ assert evoked -def test_io_epochs_basic(tmpdir): +def test_io_epochs_basic(tmp_path): """Test epochs from raw files with IO as fif file.""" raw, events, picks = _get_data(preload=True) baseline = (None, 0) @@ -895,8 +997,9 @@ epochs_dec = Epochs(raw, events, event_id, tmin, tmax, picks=picks, decim=2) - # decim without lowpass - epochs_dec.info['lowpass'] = None + # decim without + with epochs_dec.info._unlock(): + epochs_dec.info['lowpass'] = None with pytest.warns(RuntimeWarning, match='aliasing'): epochs_dec.decimate(2) @@ -915,8 +1018,12 @@ assert (evoked_dec.info['sfreq'] == evoked.info['sfreq'] / 4) -@pytest.mark.parametrize('proj', (True, 'delayed', False)) -def test_epochs_io_proj(tmpdir, proj): +@pytest.mark.parametrize('proj', [ + pytest.param(True, marks=pytest.mark.slowtest), + pytest.param('delayed', marks=pytest.mark.slowtest), + False, +]) +def test_epochs_io_proj(tmp_path, proj): """Test epochs I/O with projection.""" # Test event access on non-preloaded data (#2345) @@ -928,7 +1035,7 @@ events[::2, 1] = 1 events[1::2, 2] = 2 event_ids = dict(a=1, b=2) - temp_fname = op.join(str(tmpdir), 'test-epo.fif') + temp_fname = tmp_path / 'test-epo.fif' epochs = Epochs(raw, events, event_ids, tmin, tmax, picks=picks, proj=proj, reject=reject, flat=dict(), @@ -960,20 +1067,31 @@ del epochs_copy +@pytest.mark.slowtest @pytest.mark.parametrize('preload', (False, True)) -def test_epochs_io_preload(tmpdir, preload): +def test_epochs_io_preload(tmp_path, preload): """Test epochs I/O with preloading.""" # due to reapplication of the proj matrix, this is our quality limit # for some tests tols = dict(atol=1e-3, rtol=1e-20) - raw, events, picks = _get_data(preload=True) - tempdir = str(tmpdir) + raw, events, picks = _get_data(preload=preload) + tempdir = str(tmp_path) temp_fname = op.join(tempdir, 'test-epo.fif') temp_fname_no_bl = op.join(tempdir, 'test_no_bl-epo.fif') baseline = (None, 0) - epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, - baseline=baseline, preload=True) + with catch_logging() as log: + epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, + baseline=baseline, preload=True, verbose=True) + log = log.getvalue() + msg = 'Not setting metadata' + assert log.count(msg) == 1, f'\nto find:\n{msg}\n\nlog:\n{log}' + load_msg = 'Loading data for 7 events and 421 original time points ...' + if preload: + load_msg = ('Using data from preloaded Raw for 7 events and 421 ' + 'original time points ...') + assert log.count(load_msg) == 1, f'\nto find:\n{load_msg}\n\nlog:\n{log}' + evoked = epochs.average() epochs.save(temp_fname, overwrite=True) @@ -1123,7 +1241,7 @@ not check_version('pandas'), reason='Requires Pandas')) ]) @pytest.mark.parametrize('concat', (False, True)) -def test_split_saving(tmpdir, split_size, n_epochs, n_files, size, metadata, +def test_split_saving(tmp_path, split_size, n_epochs, n_files, size, metadata, concat): """Test saving split epochs.""" # See gh-5102 @@ -1148,19 +1266,46 @@ epochs = concatenate_epochs([epochs[ii] for ii in range(len(epochs))]) epochs_data = epochs.get_data() assert len(epochs) == n_epochs - fname = str(tmpdir.join('test-epo.fif')) + fname = tmp_path / 'test-epo.fif' epochs.save(fname, split_size=split_size, overwrite=True) got_size = _get_split_size(split_size) assert got_size == size _assert_splits(fname, n_files, size) - assert not op.isfile(f'{fname[:-4]}-{n_files + 1}.fif') + assert not op.isfile(f'{str(fname)[:-4]}-{n_files + 1}.fif') for preload in (True, False): epochs2 = mne.read_epochs(fname, preload=preload) assert_allclose(epochs2.get_data(), epochs_data) assert_array_equal(epochs.events, epochs2.events) + # Check that if BIDS is used and no split is needed it defaults to + # simple writing without _split- entity. + split_fname = str(tmp_path / 'test_epo.fif') + split_fname_neuromag_part1 = split_fname.replace( + 'epo.fif', f'epo-{n_files + 1}.fif') + split_fname_bids_part1 = split_fname.replace( + '_epo', f'_split-{n_files + 1:02d}_epo') + + epochs.save(split_fname, split_naming='bids', verbose=True) + assert op.isfile(split_fname) + assert not op.isfile(split_fname_bids_part1) + for split_naming in ('neuromag', 'bids'): + with pytest.raises(FileExistsError, match='Destination file'): + epochs.save(split_fname, split_naming=split_naming, verbose=True) + os.remove(split_fname) + # we don't test for reserved files as it's not implemented here + + epochs.save(split_fname, split_size='1.4MB', verbose=True) + # check that the filenames match the intended pattern + assert op.isfile(split_fname) + assert op.isfile(split_fname_neuromag_part1) + # check that filenames are being formatted correctly for BIDS + epochs.save(split_fname, split_size='1.4MB', split_naming='bids', + overwrite=True, verbose=True) + assert op.isfile(split_fname_bids_part1) + -def test_split_many_reset(tmpdir): +@pytest.mark.slowtest +def test_split_many_reset(tmp_path): """Test splitting with many events and using reset.""" data = np.zeros((1000, 1, 1024)) # 1 ch, 1024 samples assert data[0, 0].nbytes == 8192 # 8 kB per epoch @@ -1169,7 +1314,7 @@ epochs = EpochsArray(data, info, tmin=0., selection=selection) assert len(epochs.drop_log) == 101000 assert len(epochs) == len(data) == len(epochs.events) - fname = str(tmpdir.join('temp-epo.fif')) + fname = tmp_path / 'temp-epo.fif' for split_size in ('0.5MB', '1MB', '2MB'): # tons of overhead from sel with pytest.raises(ValueError, match='too small to safely'): epochs.save(fname, split_size=split_size, verbose='debug') @@ -1183,7 +1328,7 @@ mb = 3 * 1024 * 1024 _assert_splits(fname, 6, mb) # reset, then it should work - fname = str(tmpdir.join('temp-reset-epo.fif')) + fname = tmp_path / 'temp-reset-epo.fif' epochs.reset_drop_log_selection() epochs.save(fname, split_size=split_size, verbose='debug') _assert_splits(fname, 4, mb) @@ -1194,8 +1339,8 @@ def _assert_splits(fname, n, size): __tracebackhide__ = True assert n >= 0 - next_fnames = [fname] + [ - fname[:-4] + '-%d.fif' % ii for ii in range(1, n + 2)] + next_fnames = [str(fname)] + [ + str(fname)[:-4] + '-%d.fif' % ii for ii in range(1, n + 2)] bad_fname = next_fnames.pop(-1) for ii, this_fname in enumerate(next_fnames[:-1]): assert op.isfile(this_fname), f'Missing file: {this_fname}' @@ -1207,9 +1352,9 @@ assert not op.isfile(bad_fname), f'Errantly wrote {bad_fname}' -def test_epochs_proj(tmpdir): +def test_epochs_proj(tmp_path): """Test handling projection (apply proj in Raw or in Epochs).""" - tempdir = str(tmpdir) + tempdir = str(tmp_path) raw, events, picks = _get_data() exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more this_picks = pick_types(raw.info, meg=True, eeg=False, stim=True, @@ -1245,7 +1390,8 @@ assert (not _has_eeg_average_ref_proj(epochs.info['projs'])) # make sure we don't add avg ref when a custom ref has been applied - raw.info['custom_ref_applied'] = True + with raw.info._unlock(): + raw.info['custom_ref_applied'] = True epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=this_picks, proj=True) assert (not _has_eeg_average_ref_proj(epochs.info['projs'])) @@ -1255,7 +1401,8 @@ proj = raw.info['projs'] epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=this_picks, proj=False) - epochs.info['projs'] = [] + with epochs.info._unlock(): + epochs.info['projs'] = [] data = epochs.copy().add_proj(proj).apply_proj().get_data() # save and reload data fname_epo = op.join(tempdir, 'temp-epo.fif') @@ -1305,16 +1452,24 @@ assert_equal(evoked_avg.nave, evoked1.nave + evoked2.nave) -def test_evoked_io_from_epochs(tmpdir): +def test_evoked_io_from_epochs(tmp_path): """Test IO of evoked data made from epochs.""" - tempdir = str(tmpdir) + tempdir = str(tmp_path) raw, events, picks = _get_data() - raw.info['lowpass'] = 40 # avoid aliasing warnings + with raw.info._unlock(): + raw.info['lowpass'] = 40 # avoid aliasing warnings # offset our tmin so we don't get exactly a zero value when decimating - epochs = Epochs(raw, events[:4], event_id, tmin + 0.011, tmax, - picks=picks, decim=5) + with catch_logging() as log: + epochs = Epochs(raw, events[:4], event_id, tmin + 0.011, tmax, + picks=picks, decim=5, preload=True, verbose=True) + log = log.getvalue() + load_msg = ('Loading data for 1 events and 415 original time points ' + '(prior to decimation) ...') + assert log.count(load_msg) == 1, f'\nto find:\n{load_msg}\n\nlog:\n{log}' evoked = epochs.average() - evoked.info['proj_name'] = '' # Test that empty string shortcuts to None. + with evoked.info._unlock(): + # Test that empty string shortcuts to None. + evoked.info['proj_name'] = '' fname_temp = op.join(tempdir, 'evoked-ave.fif') evoked.save(fname_temp) evoked2 = read_evokeds(fname_temp)[0] @@ -1329,7 +1484,7 @@ picks=picks, baseline=baseline, decim=5) evoked = epochs.average() assert_allclose(evoked.baseline, baseline) - evoked.save(fname_temp) + evoked.save(fname_temp, overwrite=True) evoked2 = read_evokeds(fname_temp)[0] assert_allclose(evoked.data, evoked2.data, rtol=1e-4, atol=1e-20) assert_allclose(evoked.times, evoked2.times, rtol=1e-4, atol=1e-20) @@ -1355,20 +1510,20 @@ for picks in (None, 'all'): evoked = epochs.average(picks) evokeds.append(evoked) - evoked.save(fname_temp) + evoked.save(fname_temp, overwrite=True) evoked2 = read_evokeds(fname_temp)[0] start = 1 if picks is None else 0 for ev in (evoked, evoked2): assert ev.ch_names == epochs.ch_names[start:] assert_allclose(ev.data, epochs.get_data().mean(0)[start:]) with pytest.raises(ValueError, match='.*nchan.* must match'): - write_evokeds(fname_temp, evokeds) + write_evokeds(fname_temp, evokeds, overwrite=True) -def test_evoked_standard_error(tmpdir): +def test_evoked_standard_error(tmp_path): """Test calculation and read/write of standard error.""" raw, events, picks = _get_data() - tempdir = str(tmpdir) + tempdir = str(tmp_path) epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks) evoked = [epochs.average(), epochs.standard_error()] write_evokeds(op.join(tempdir, 'evoked-ave.fif'), evoked) @@ -1393,9 +1548,9 @@ assert ave.first == ave2.first -def test_reject_epochs(tmpdir): +def test_reject_epochs(tmp_path): """Test of epochs rejection.""" - tempdir = str(tmpdir) + tempdir = str(tmp_path) temp_fname = op.join(tempdir, 'test-epo.fif') raw, events, picks = _get_data() @@ -1460,6 +1615,33 @@ epochs.save(temp_fname, overwrite=True) read_epochs(temp_fname) + # Ensure repeated rejection works, even if applied to only a subset of the + # previously-used channel types + epochs = Epochs(raw, events1, event_id, tmin, tmax, + reject=reject, flat=flat) + + new_reject = reject.copy() + new_flat = flat.copy() + del new_reject['grad'], new_reject['eeg'], new_reject['eog'] + del new_flat['mag'] + + # No changes expected + epochs_cleaned = epochs.copy().drop_bad(reject=new_reject, flat=new_flat) + assert epochs_cleaned.reject == epochs.reject + assert epochs_cleaned.flat == epochs.flat + + new_reject['mag'] /= 2 + new_flat['grad'] *= 2 + # Only the newly-provided thresholds should be updated, the existing ones + # should be kept + epochs_cleaned = epochs.copy().drop_bad(reject=new_reject, flat=new_flat) + assert epochs_cleaned.reject == dict(mag=new_reject['mag'], + grad=reject['grad'], + eeg=reject['eeg'], + eog=reject['eog']) + assert epochs_cleaned.flat == dict(grad=new_flat['grad'], + mag=flat['mag']) + def test_preload_epochs(): """Test preload of epochs.""" @@ -1491,7 +1673,7 @@ start_index = 1 end_index = n_good_events - 1 - assert((end_index - start_index) > 0) + assert (end_index - start_index) > 0 for preload in [True, False]: epochs2 = Epochs(raw, events[:20], event_id, tmin, tmax, picks=picks, @@ -1548,9 +1730,9 @@ assert_array_almost_equal(evoked.times, c_evoked.times, 12) -def test_crop(tmpdir): +def test_crop(tmp_path): """Test of crop of epochs.""" - tempdir = str(tmpdir) + tempdir = str(tmp_path) temp_fname = op.join(tempdir, 'test-epo.fif') raw, events, picks = _get_data() @@ -1621,6 +1803,14 @@ assert np.isclose(epochs_cropped.tmax, epochs_cropped.reject_tmax) del epochs_cropped + # Test that repeated cropping is idempotent + epoch_crop = epochs.copy() + epoch_crop.crop(None, 0.4, include_tmax=False) + n_times = len(epoch_crop.times) + with pytest.warns(RuntimeWarning, match='tmax is set to'): + epoch_crop.crop(None, 0.4, include_tmax=False) + assert len(epoch_crop.times) == n_times + # Cropping & I/O roundtrip epochs.crop(0, 0.1) epochs.save(temp_fname) @@ -1664,7 +1854,7 @@ # use parallel epochs = epochs_o.copy() - epochs.resample(sfreq_normal * 2, n_jobs=1, npad=0) + epochs.resample(sfreq_normal * 2, n_jobs=None, npad=0) assert (np.allclose(data_up, epochs._data, rtol=1e-8, atol=1e-16)) # test copy flag @@ -1890,7 +2080,8 @@ new_shapes = [epochs[key].events.shape[0] for key in ['a', 'b', 'c', 'd']] assert_equal(new_shapes[0] + new_shapes[1], new_shapes[2]) assert_equal(new_shapes[3], old_shapes[3]) - pytest.raises(KeyError, epochs.equalize_event_counts, [1, 'a']) + with pytest.raises(ValueError, match='keys must be strings, got'): + epochs.equalize_event_counts([1, 'a']) # now let's combine conditions old_shapes = new_shapes @@ -1898,7 +2089,8 @@ new_shapes = [epochs[key].events.shape[0] for key in ['a', 'b', 'c', 'd']] assert_equal(old_shapes[0] + old_shapes[1], new_shapes[0] + new_shapes[1]) assert_equal(new_shapes[0] + new_shapes[1], new_shapes[2] + new_shapes[3]) - pytest.raises(ValueError, combine_event_ids, epochs, ['a', 'b'], {'ab': 1}) + with pytest.raises(ValueError, match='value must not already exist'): + combine_event_ids(epochs, ['a', 'b'], {'ab': 1}) combine_event_ids(epochs, ['a', 'b'], {'ab': np.int32(12)}, copy=False) caught = 0 @@ -1923,11 +2115,19 @@ es = [epochs.copy().equalize_event_counts(c)[0] for c in (cond1, cond2)] assert_array_equal(es[0].events[:, 0], es[1].events[:, 0]) - cond1, cond2 = ['a', ['b', 'b/y']], [['a/x', 'a/y'], 'x'] - for c in (cond1, cond2): # error b/c tag and id mix/non-orthogonal tags - pytest.raises(ValueError, epochs.equalize_event_counts, c) - pytest.raises(KeyError, epochs.equalize_event_counts, - ["a/no_match", "b"]) + with pytest.raises(ValueError, match='mix hierarchical and regular'): + epochs.equalize_event_counts(['a', ['b', 'b/y']]) + with pytest.raises(ValueError, match='overlapping. Provide an orthogonal'): + epochs.equalize_event_counts([['a/x', 'a/y'], 'x']) + with pytest.raises(KeyError, match='not found in the epoch object'): + epochs.equalize_event_counts(["a/no_match", "b"]) + # test equalization with only one epoch in each cond + epo = epochs[[0, 1, 5]] + assert len(epo['x']) == 2 + assert len(epo['y']) == 1 + epo_, drop_inds = epo.equalize_event_counts() + assert len(epo_) == 2 + assert drop_inds.shape == (1,) # test equalization with no events of one type epochs.drop(np.arange(10)) assert_equal(len(epochs['a/x']), 0) @@ -1951,9 +2151,9 @@ epochs.equalize_event_counts(1.5) -def test_access_by_name(tmpdir): +def test_access_by_name(tmp_path): """Test accessing epochs by event name and on_missing for rare events.""" - tempdir = str(tmpdir) + tempdir = str(tmp_path) raw, events, picks = _get_data() # Test various invalid inputs @@ -2025,6 +2225,7 @@ assert_equal(epochs[['a', 'b']]['a']._name, 'a') +@pytest.mark.slowtest @requires_pandas def test_to_data_frame(): """Test epochs Pandas exporter.""" @@ -2046,7 +2247,7 @@ expected = ('condition', 'epoch', 'time', 'channel', 'ch_type', 'value') assert set(expected) == set(df_long.columns) assert set(epochs.ch_names) == set(df_long['channel']) - assert(len(df_long) == epochs.get_data().size) + assert len(df_long) == epochs.get_data().size # test long format w/ index df_long = epochs.to_data_frame(long_format=True, index=['epoch']) del df_wide, df_long @@ -2177,7 +2378,8 @@ raw.info.normalize_proj() del picks n_epochs = 2 # number we expect after rejection - raw.info['lowpass'] = 40. # fake the LP info so no warnings + with raw.info._unlock(): + raw.info['lowpass'] = 40. # fake the LP info so no warnings for decim in (1, 3): proj_data = Epochs(raw, events, event_id, tmin, tmax, proj=True, reject=reject, decim=decim) @@ -2203,7 +2405,8 @@ epochs = EpochsArray(comp, raw.info, tmin=use_tmin, event_id=1, events=fake_events, proj=proj) - epochs.info['sfreq'] /= decim + with epochs.info._unlock(): + epochs.info['sfreq'] /= decim assert_equal(len(epochs), n_epochs) assert (raw.proj is False) assert (epochs.proj is @@ -2257,34 +2460,38 @@ assert_array_equal(events[epochs['1'].selection], events1[[0, 1, 3, 5, 6]]) -def test_drop_epochs_mult(): - """Test that subselecting epochs or making less epochs is equivalent.""" +@pytest.mark.parametrize('preload', (True, False)) +def test_drop_epochs_mult(preload): + """Test that subselecting epochs or making fewer epochs is similar.""" raw, events, picks = _get_data() - for preload in [True, False]: - epochs1 = Epochs(raw, events, {'a': 1, 'b': 2}, - tmin, tmax, picks=picks, reject=reject, - preload=preload)['a'] - epochs2 = Epochs(raw, events, {'a': 1}, - tmin, tmax, picks=picks, reject=reject, - preload=preload) - - if preload: - # In the preload case you cannot know the bads if already ignored - assert_equal(len(epochs1.drop_log), len(epochs2.drop_log)) - for d1, d2 in zip(epochs1.drop_log, epochs2.drop_log): - if d1 == ('IGNORED',): - assert (d2 == ('IGNORED',)) - if d1 != ('IGNORED',) and d1 != []: - assert ((d2 == d1) or (d2 == ('IGNORED',))) - if d1 == []: - assert (d2 == []) - assert_array_equal(epochs1.events, epochs2.events) - assert_array_equal(epochs1.selection, epochs2.selection) - else: - # In the non preload is should be exactly the same - assert_equal(epochs1.drop_log, epochs2.drop_log) - assert_array_equal(epochs1.events, epochs2.events) - assert_array_equal(epochs1.selection, epochs2.selection) + assert_array_equal(events[14], [33712, 0, 1]) # event type a + epochs1 = Epochs(raw, events, {'a': 1, 'b': 2}, + tmin, tmax, picks=picks, reject=reject, + preload=preload) + epochs2 = Epochs(raw, events, {'a': 1}, + tmin, tmax, picks=picks, reject=reject, + preload=preload) + epochs1 = epochs1['a'] + assert_array_equal(epochs1.events, epochs2.events) + assert_array_equal(epochs1.selection, epochs2.selection) + + if preload: + # In the preload case you cannot know the bads if already ignored + assert len(epochs1.drop_log) == len(epochs2.drop_log) + for di, (d1, d2) in enumerate(zip(epochs1.drop_log, epochs2.drop_log)): + assert isinstance(d1, tuple) + assert isinstance(d2, tuple) + msg = (f'\nepochs1.drop_log[{di}] = {d1}, ' + f'\nepochs2.drop_log[{di}] = {d2}') + if 'IGNORED' in d1: + assert 'IGNORED' in d2, msg + if 'IGNORED' not in d1 and d1 != (): + assert ((d2 == d1) or (d2 == ('IGNORED',))), msg + if d1 == (): + assert (d2 == ()), msg + else: + # In the non preload is should be exactly the same + assert epochs1.drop_log == epochs2.drop_log def test_contains(): @@ -2293,18 +2500,20 @@ # Add seeg channel seeg = RawArray(np.zeros((1, len(raw.times))), create_info(['SEEG 001'], raw.info['sfreq'], 'seeg')) - for key in ('dev_head_t', 'highpass', 'lowpass', - 'dig', 'description', 'acq_pars', 'experimenter', - 'proj_name'): - seeg.info[key] = raw.info[key] + with seeg.info._unlock(): + for key in ('dev_head_t', 'highpass', 'lowpass', + 'dig', 'description', 'acq_pars', 'experimenter', + 'proj_name'): + seeg.info[key] = raw.info[key] raw.add_channels([seeg]) # Add dbs channel dbs = RawArray(np.zeros((1, len(raw.times))), create_info(['DBS 001'], raw.info['sfreq'], 'dbs')) - for key in ('dev_head_t', 'highpass', 'lowpass', - 'dig', 'description', 'acq_pars', 'experimenter', - 'proj_name'): - dbs.info[key] = raw.info[key] + with dbs.info._unlock(): + for key in ('dev_head_t', 'highpass', 'lowpass', + 'dig', 'description', 'acq_pars', 'experimenter', + 'proj_name'): + dbs.info[key] = raw.info[key] raw.add_channels([dbs]) tests = [(('mag', False, False, False), ('grad', 'eeg', 'seeg', 'dbs')), (('grad', False, False, False), ('mag', 'eeg', 'seeg', 'dbs')), @@ -2456,30 +2665,35 @@ pytest.raises(RuntimeError, add_channels_epochs, [epochs_meg, epochs_eeg]) epochs_meg2 = epochs_meg.copy() - epochs_meg2.info['sfreq'] = None + with epochs_meg2.info._unlock(): + epochs_meg2.info['sfreq'] = None pytest.raises(RuntimeError, add_channels_epochs, [epochs_meg2, epochs_eeg]) epochs_meg2 = epochs_meg.copy() - epochs_meg2.info['sfreq'] += 10 + with epochs_meg2.info._unlock(): + epochs_meg2.info['sfreq'] += 10 pytest.raises(RuntimeError, add_channels_epochs, [epochs_meg2, epochs_eeg]) epochs_meg2 = epochs_meg.copy() - epochs_meg2.info['chs'][1]['ch_name'] = epochs_meg2.info['ch_names'][0] + with epochs_meg2.info._unlock(): + epochs_meg2.info['chs'][1]['ch_name'] = epochs_meg2.info['ch_names'][0] epochs_meg2.info._update_redundant() with pytest.warns(RuntimeWarning, match='not unique'): pytest.raises(RuntimeError, add_channels_epochs, [epochs_meg2, epochs_eeg]) epochs_meg2 = epochs_meg.copy() - epochs_meg2.info['dev_head_t']['to'] += 1 + with epochs_meg2.info._unlock(): + epochs_meg2.info['dev_head_t']['to'] += 1 pytest.raises(ValueError, add_channels_epochs, [epochs_meg2, epochs_eeg]) epochs_meg2 = epochs_meg.copy() - epochs_meg2.info['dev_head_t']['to'] += 1 + with epochs_meg2.info._unlock(): + epochs_meg2.info['dev_head_t']['to'] += 1 pytest.raises(ValueError, add_channels_epochs, [epochs_meg2, epochs_eeg]) epochs_meg2 = epochs_meg.copy() - epochs_meg2.info['expimenter'] = 'foo' + epochs_meg2.info['experimenter'] = 'foo' pytest.raises(RuntimeError, add_channels_epochs, [epochs_meg2, epochs_eeg]) epochs_meg2 = epochs_meg.copy() @@ -2524,9 +2738,9 @@ assert_allclose(new_proj[n_meg:, n_meg:], np.eye(n_eeg), atol=1e-12) -def test_array_epochs(tmpdir): +def test_array_epochs(tmp_path, browser_backend): """Test creating epochs from array.""" - tempdir = str(tmpdir) + tempdir = str(tmp_path) # creating data = rng.random_sample((10, 20, 300)) @@ -2562,7 +2776,6 @@ # plotting epochs[0].plot() - plt.close('all') # indexing assert_array_equal(np.unique(epochs['1'].events[:, 2]), np.array([1])) @@ -2646,14 +2859,14 @@ epochs2 = epochs.copy() concatenate_epochs([epochs, epochs2]) # should work epochs2.info['dev_head_t']['trans'][:3, 3] += 0.0001 - with pytest.raises(ValueError, match='dev_head_t.*must match'): + with pytest.raises(ValueError, match=r"info\['dev_head_t'\] differs"): concatenate_epochs([epochs, epochs2]) with pytest.raises(TypeError, match='must be a list or tuple'): concatenate_epochs('foo') with pytest.raises(TypeError, match='must be an instance of Epochs'): concatenate_epochs([epochs, 'foo']) epochs2.info['dev_head_t'] = None - with pytest.raises(ValueError, match='dev_head_t.*must match'): + with pytest.raises(ValueError, match=r"info\['dev_head_t'\] differs"): concatenate_epochs([epochs, epochs2]) epochs.info['dev_head_t'] = None concatenate_epochs([epochs, epochs2]) # should work @@ -2666,7 +2879,21 @@ with pytest.raises(ValueError, match='identical keys'): concatenate_epochs([epochs1, epochs2]) + # check concatenating epochs where one of the objects is empty + epochs2 = epochs.copy()[:0] + with pytest.warns(RuntimeWarning, match='was empty'): + concatenate_epochs([epochs, epochs2]) + + # check concatenating epochs results are chronologically ordered + epochs2 = epochs.copy().load_data() + # Ensure first event is at 0 + epochs2.events[:, 0] -= np.min(epochs2.events[:, 0]) + with pytest.warns(RuntimeWarning, match='not chronologically ordered'): + concatenate_epochs([epochs, epochs2], add_offset=False) + concatenate_epochs([epochs, epochs2], add_offset=True) + +@pytest.mark.slowtest def test_concatenate_epochs_large(): """Test concatenating epochs on large data.""" raw, events, picks = _get_data() @@ -2713,7 +2940,8 @@ # Now test errors epoch_badsf = epoch_eeg.copy() - epoch_badsf.info['sfreq'] = 3.1415927 + with epoch_badsf.info._unlock(): + epoch_badsf.info['sfreq'] = 3.1415927 epoch_eeg = epoch_eeg.crop(-.1, .1) epoch_meg.load_data() @@ -2748,23 +2976,33 @@ @requires_pandas -def test_metadata(tmpdir): +def test_metadata(tmp_path): """Test metadata support with pandas.""" - from pandas import DataFrame + from pandas import DataFrame, Series, NA data = np.random.randn(10, 2, 2000) chs = ['a', 'b'] info = create_info(chs, 1000) meta = np.array([[1.] * 5 + [3.] * 5, - ['a'] * 2 + ['b'] * 3 + ['c'] * 3 + ['µ'] * 2]).T + ['a'] * 2 + ['b'] * 3 + ['c'] * 3 + ['µ'] * 2], + dtype='object').T meta = DataFrame(meta, columns=['num', 'letter']) meta['num'] = np.array(meta['num'], float) events = np.arange(meta.shape[0]) events = np.column_stack([events, np.zeros([len(events), 2])]).astype(int) events[5:, -1] = 1 event_id = {'zero': 0, 'one': 1} - epochs = EpochsArray(data, info, metadata=meta, - events=events, event_id=event_id) + with catch_logging() as log: + epochs = EpochsArray(data, info, metadata=meta, + events=events, event_id=event_id, verbose=True) + log = log.getvalue() + msg = 'Adding metadata with 2 columns' + assert log.count(msg) == 1, f'\nto find:\n{msg}\n\nlog:\n{log}' + with use_log_level(True): + with catch_logging() as log: + epochs.metadata = meta + log = log.getvalue().strip() + assert log == 'Replacing existing metadata with 2 columns', f'{log}' indices = np.arange(len(epochs)) # expected indices assert_array_equal(epochs.metadata.index, indices) @@ -2814,7 +3052,7 @@ # I/O # Make sure values don't change with I/O - tempdir = str(tmpdir) + tempdir = str(tmp_path) temp_fname = op.join(tempdir, 'tmp-epo.fif') temp_one_fname = op.join(tempdir, 'tmp-one-epo.fif') with catch_logging() as log: @@ -2925,6 +3163,23 @@ assert len(epochs) == 1 assert len(epochs.metadata) == 1 + # gh-10705: support boolean columns + metadata = DataFrame( + {"A": Series([True, True, True, False, False, NA], dtype="boolean")} + ) + rng = np.random.default_rng() + epochs = mne.EpochsArray( + data=rng.standard_normal(size=(6, 8, 500)), + info=mne.create_info(8, 250, "eeg"), + event_id={"A": 1}, + metadata=metadata + ) + + assert len(epochs["A"]) == 6 # epochs of event type A + assert len(epochs["A == True"]) == 3 # epochs for which column A == True + assert len(epochs["not A"]) == 2 # epochs for which column A == False + assert len(epochs["A.isna()"]) == 1 # epochs for NA in column A + def assert_metadata_equal(got, exp): """Assert metadata are equal.""" @@ -2942,9 +3197,6 @@ assert isinstance(exp, pandas.DataFrame) assert isinstance(got, pandas.DataFrame) assert set(got.columns) == set(exp.columns) - if LooseVersion(pandas.__version__) < LooseVersion('0.25'): - # Old Pandas does not necessarily order them properly - got = got[exp.columns] check = (got == exp) assert check.all().all() @@ -3034,9 +3286,9 @@ assert (epochs._repr_html_()) # test _repr_html_ -def test_save_overwrite(tmpdir): +def test_save_overwrite(tmp_path): """Test saving with overwrite functionality.""" - tempdir = str(tmpdir) + tempdir = str(tmp_path) raw = mne.io.RawArray(np.random.RandomState(0).randn(100, 10000), mne.create_info(100, 1000.)) @@ -3075,14 +3327,14 @@ @pytest.mark.parametrize('preload', (True, False)) @pytest.mark.parametrize('is_complex', (True, False)) @pytest.mark.parametrize('fmt, rtol', [('single', 2e-6), ('double', 1e-10)]) -def test_save_complex_data(tmpdir, preload, is_complex, fmt, rtol): +def test_save_complex_data(tmp_path, preload, is_complex, fmt, rtol): """Test whether epochs of hilbert-transformed data can be saved.""" raw, events = _get_data()[:2] raw.load_data() if is_complex: raw.apply_hilbert(envelope=False, n_fft=None) epochs = Epochs(raw, events[:1], preload=True)[0] - temp_fname = op.join(str(tmpdir), 'test-epo.fif') + temp_fname = op.join(str(tmp_path), 'test-epo.fif') epochs.save(temp_fname, fmt=fmt) data = epochs.get_data().copy() epochs_read = read_epochs(temp_fname, proj=False, preload=preload) @@ -3097,47 +3349,21 @@ assert_allclose(data_read, data, rtol=rtol) -@pytest.mark.skipif(not _check_eeglabio_installed(strict=False), - reason='eeglabio not installed') -@pytest.mark.parametrize('preload', (True, False)) -def test_export_eeglab(tmpdir, preload): - """Test saving an Epochs instance to EEGLAB's set format.""" - raw, events = _get_data()[:2] - raw.load_data() - epochs = Epochs(raw, events, preload=preload) - temp_fname = op.join(str(tmpdir), 'test.set') - epochs.export(temp_fname) - epochs.drop_channels([ch for ch in ['epoc', 'STI 014'] - if ch in epochs.ch_names]) - epochs_read = read_epochs_eeglab(temp_fname) - assert epochs.ch_names == epochs_read.ch_names - cart_coords = np.array([d['loc'][:3] - for d in epochs.info['chs']]) # just xyz - cart_coords_read = np.array([d['loc'][:3] - for d in epochs_read.info['chs']]) - assert_allclose(cart_coords, cart_coords_read) - assert_array_equal(epochs.events[:, 0], - epochs_read.events[:, 0]) # latency - assert epochs.event_id.keys() == epochs_read.event_id.keys() # just keys - assert_allclose(epochs.times, epochs_read.times) - assert_allclose(epochs.get_data(), epochs_read.get_data()) - - -def test_no_epochs(tmpdir): +def test_no_epochs(tmp_path): """Test that having the first epoch bad does not break writing.""" # a regression noticed in #5564 raw, events = _get_data()[:2] reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6) raw.info['bads'] = ['MEG 2443', 'EEG 053'] epochs = mne.Epochs(raw, events, reject=reject) - epochs.save(op.join(str(tmpdir), 'sample-epo.fif'), overwrite=True) + epochs.save(op.join(str(tmp_path), 'sample-epo.fif'), overwrite=True) assert 0 not in epochs.selection assert len(epochs) > 0 # and with no epochs remaining raw.info['bads'] = [] epochs = mne.Epochs(raw, events, reject=reject) with pytest.warns(RuntimeWarning, match='no data'): - epochs.save(op.join(str(tmpdir), 'sample-epo.fif'), overwrite=True) + epochs.save(op.join(str(tmp_path), 'sample-epo.fif'), overwrite=True) assert len(epochs) == 0 # all dropped @@ -3167,9 +3393,14 @@ n_epochs, n_channels, n_times = 5, 10, 20 sfreq = 1000. data = rng.randn(n_epochs, n_channels, n_times) + events = np.array([np.arange(n_epochs), [0] * n_epochs, [1] * n_epochs]).T + # Add second event type + events[-2:, 2] = 2 + event_id = dict(first=1, second=2) + info = create_info(n_channels, sfreq, 'eeg') - epochs = EpochsArray(data, info, events) + epochs = EpochsArray(data, info, events, event_id=event_id) for method in ('mean', 'median'): if method == "mean": @@ -3182,6 +3413,14 @@ evoked_data = epochs.average(method=method).data assert_array_equal(evoked_data, fun(data)) + # Test averaging by event type + ev = epochs.average(by_event_type=True) + assert len(ev) == 2 + assert ev[0].comment == 'first' + assert_array_equal(ev[0].data, np.mean(data[:-2], axis=0)) + assert ev[1].comment == 'second' + assert_array_equal(ev[1].data, np.mean(data[-2:], axis=0)) + @pytest.mark.parametrize('relative', (True, False)) def test_shift_time(relative): @@ -3276,9 +3515,9 @@ @pytest.mark.parametrize('kind', ('file', 'bytes')) @pytest.mark.parametrize('preload', (True, False)) -def test_file_like(kind, preload, tmpdir): +def test_file_like(kind, preload, tmp_path): """Test handling with file-like objects.""" - tempdir = str(tmpdir) + tempdir = str(tmp_path) raw = mne.io.RawArray(np.random.RandomState(0).randn(100, 10000), mne.create_info(100, 1000.)) events = mne.make_fixed_length_events(raw, 1) @@ -3353,8 +3592,12 @@ with pytest.raises(ValueError, match='overlap must be'): make_fixed_length_epochs(raw, duration=1, overlap=1.1) + # id + epochs = make_fixed_length_epochs(raw, duration=1, preload=True, id=2) + assert '2' in epochs.event_id and len(epochs.event_id) == 1 -def test_epochs_huge_events(tmpdir): + +def test_epochs_huge_events(tmp_path): """Test epochs with event numbers that are too large.""" data = np.zeros((1, 1, 1000)) info = create_info(1, 1000., 'eeg') @@ -3367,7 +3610,7 @@ epochs = EpochsArray(data, info) epochs.events = events with pytest.raises(TypeError, match='exceeds maximum'): - epochs.save(tmpdir.join('temp-epo.fif')) + epochs.save(tmp_path / 'temp-epo.fif') def _old_bad_write(fid, kind, arr): @@ -3377,7 +3620,7 @@ return write_int(fid, kind, arr) -def test_concat_overflow(tmpdir, monkeypatch): +def test_concat_overflow(tmp_path, monkeypatch): """Test overflow events during concat.""" data = np.zeros((2, 10, 1000)) events = np.array([[0, 0, 1], [INT32_MAX, 0, 2]]) @@ -3387,7 +3630,7 @@ with pytest.warns(RuntimeWarning, match='consecutive increasing'): epochs = mne.concatenate_epochs((epochs_1, epochs_2)) assert_array_less(0, epochs.events[:, 0]) - fname = tmpdir.join('temp-epo.fif') + fname = tmp_path / 'temp-epo.fif' epochs.save(fname) epochs = read_epochs(fname) assert_array_less(0, epochs.events[:, 0]) @@ -3401,7 +3644,7 @@ assert_array_less(epochs.events[:, 0], INT32_MAX + 1) -def test_epochs_baseline_after_cropping(tmpdir): +def test_epochs_baseline_after_cropping(tmp_path): """Epochs.baseline should be retained if baseline period was cropped.""" sfreq = 1000 tstep = 1. / sfreq @@ -3441,7 +3684,7 @@ epochs_orig.get_data().squeeze()[200:]) # Test I/O roundtrip. - epochs_fname = tmpdir.join('temp-cropped-epo.fif') + epochs_fname = tmp_path / 'temp-cropped-epo.fif' epochs_cropped.save(epochs_fname) epochs_cropped_read = mne.read_epochs(epochs_fname) @@ -3505,3 +3748,304 @@ epochs_bis = epochs.copy().rename_channels(lambda ch: ch + '_bis') epochs_final.add_channels([epochs_bis], force_update_info=True) epochs_final.drop_channels(epochs.ch_names) + + +@requires_pandas +@pytest.mark.parametrize('first_samp', [0, 10]) +@pytest.mark.parametrize( + 'meas_date, orig_date', [ + [None, None], + [np.pi, None], + [np.pi, timedelta(seconds=1)] + ] +) +def test_epoch_annotations(first_samp, meas_date, orig_date, tmp_path): + """Test Epoch Annotations from RawArray with dates. + + Tests the following cases crossed with each other: + - with and without first_samp + - with and without meas_date + - with and without an orig_time set in Annotations + """ + from pandas.testing import assert_frame_equal + + data = np.random.randn(2, 400) * 10e-12 + info = create_info(ch_names=['MEG1', 'MEG2'], ch_types='grad', + sfreq=100.) + + # create a Raw object with a first_samp + raw = RawArray(data.copy(), info, first_samp=first_samp) + meas_date = _handle_meas_date(meas_date) + raw.set_meas_date(meas_date) + + # handle orig_date + if orig_date is not None: + orig_date = meas_date + orig_date + ant_dur = 0.1 + ants = Annotations( + onset=[1.1, 1.2, 2.1], + duration=[ant_dur, ant_dur, ant_dur], + description=['x', 'y', 'z'], + orig_time=orig_date + ) + raw.set_annotations(ants) + epochs = make_fixed_length_epochs(raw, duration=1, overlap=0.5) + + # add Annotations to Epochs metadata + epochs.add_annotations_to_metadata() + metadata = epochs.metadata + assert 'annot_onset' in metadata.columns + assert 'annot_duration' in metadata.columns + assert 'annot_description' in metadata.columns + + # Test that writing and reading back these new metadata works + temp_fname = op.join(str(tmp_path), 'test-epo.fif') + epochs.save(temp_fname) + epochs_read = mne.read_epochs(temp_fname) + assert_metadata_equal(epochs.metadata, epochs_read.metadata) + + # check that the annotations themselves should be equivalent + # because first_samp offsetting occurs in Raw + assert_array_equal( + raw.annotations.onset, + epochs.annotations.onset + ) + assert_array_equal(raw.annotations.duration, + epochs.annotations.duration) + assert_array_equal(raw.annotations.description, + epochs.annotations.description) + + # compare Epoch annotations with expected values + epoch_ants = epochs.get_annotations_per_epoch() + if orig_date is None: + expected_annot_times = [ + [], + [[.6, ant_dur, 'x'], [.7, ant_dur, 'y']], + [[.1, ant_dur, 'x'], [.2, ant_dur, 'y']], + [[.6, ant_dur, 'z']], + [[.1, ant_dur, 'z']], + [], + [] + ] + else: + expected_annot_times = [ + [], + [], + [], + [[.6, ant_dur, 'x'], [.7, ant_dur, 'y']], + [[.1, ant_dur, 'x'], [.2, ant_dur, 'y']], + [[.6, ant_dur, 'z']], + [[.1, ant_dur, 'z']], + ] + assert len(expected_annot_times) == len(epoch_ants) + for (x, y) in zip(epoch_ants, expected_annot_times): + if orig_date is not None: + # when orig_date is set + first_samp, those will offset + # the onset when Raw sets annotations. These should + # then be offset accordingly when Epochs look for annotations + assert_array_almost_equal([_x[0] for _x in x], [ + _y[0] - raw._first_time for _y in y]) + else: + # onset relative to Epoch start + assert_array_almost_equal([_x[0] for _x in x], [_y[0] for _y in y]) + + # duration + assert_array_equal([_x[1] for _x in x], [_y[1] for _y in y]) + + # description should be exactly the same + assert_array_equal([_x[2] for _x in x], [_y[2] for _y in y]) + + # metadata should match after resampling + epochs.load_data() + epochs.add_annotations_to_metadata(overwrite=True) + metadata = epochs.metadata.copy() + epochs.resample(epochs.info['sfreq'] * 1.5) + epochs.add_annotations_to_metadata(overwrite=True) + new_metadata = epochs.metadata + assert_frame_equal(metadata, new_metadata) + + +def test_epoch_annotations_cases(): + """Test Epoch Annotations different cases. + + Here, we test the following cases crossed: + - annotation start is before/after epoch start + - annotation end is before/after epoch end + - 1 annotation that is fully outside all epochs (make sure it is dropped) + - 1 annotation that spans multiple epochs (make sure it shows up in both) + + In addition, tests functionality when Epochs are loaded vs not. + """ + # set up a test dataset + epochs, raw, events = _create_epochs_with_annotations() + epoch_ants = epochs.get_annotations_per_epoch() + + # assert 'no_overlap' is not in any Epoch + assert all('no_overlap' not in np.array(sublist) for sublist in epoch_ants) + + # assert 'coincident_onset' is not in any Epoch + assert all('coincident_onset' not in np.array(sublist) + for sublist in epoch_ants) + + # all coincident and straddling events should be only in the first Epoch + first_epoch_ant = np.array(epoch_ants[0]) + assert all(x in first_epoch_ant for x in [ + 'coincident_offset', 'straddles_onset', 'straddles_offset', + ]) + assert all(x not in np.array(sublist) + for sublist in epoch_ants[1:] + for x in [ + 'coincident_offset', 'straddles_onset', 'straddles_offset', + ]) + + # 'within_epoch' should be in the second Epoch only + second_epoch_ant = np.array(epoch_ants[1]) + third_epoch_ant = np.array(epoch_ants[2]) + assert 'within_epoch' in second_epoch_ant + assert 'within_epoch' not in first_epoch_ant + assert all('within_epoch' not in np.array(sublist) + for sublist in epoch_ants[2:]) + + # 'surround_epoch' should be in the third Epoch only + assert 'surround_epoch' in third_epoch_ant + assert all('surround_epoch' not in np.array(sublist) + for sublist in epoch_ants[:-1]) + + # 'multiple' should be in 2nd and 3rd Epoch + assert 'multiple' not in first_epoch_ant + assert 'multiple' in second_epoch_ant + assert 'multiple' in third_epoch_ant + + # if we drop the first Epoch, then some Annotations will now not + # be part of Epoch Annotations, and others will be shifted + epochs = Epochs(raw, events=events, tmin=0, tmax=1, baseline=None) + epochs = epochs.drop(0) + epoch_ants = epochs.get_annotations_per_epoch() + assert all(x not in np.array(sublist) for sublist in epoch_ants for x in [ + 'coincident_offset', 'straddles_onset', 'straddles_offset']) + + # 'multiple' should be in 1st and 2nd Epoch now + first_epoch_ant = np.array(epoch_ants[0]) + second_epoch_ant = np.array(epoch_ants[1]) + assert 'multiple' in first_epoch_ant + assert 'multiple' in second_epoch_ant + + # test that concatenation does not preserve annotations + old_epochs = epochs.copy() + with pytest.warns(RuntimeWarning, match='Annotations'): + concatenate_epochs([epochs, old_epochs]) + + # concatenation should not change the *input* Epochs' annotations + assert epochs.annotations == old_epochs.annotations + + +@pytest.mark.parametrize('meas_date', (None, (1, 2))) +@pytest.mark.parametrize('first_samp', (0, 10000)) +@pytest.mark.parametrize('decim', (1, 2)) +def test_epochs_annotations_backwards_compat(monkeypatch, tmp_path, meas_date, + first_samp, decim): + """Test backwards compatibility with Epochs saved without annotations.""" + # loading an earlier saved file should work + def no_sfreq_write_float(a, b, c): + if b == FIFF.FIFF_MNE_EPOCHS_RAW_SFREQ: + return + return write_float(a, b, c) + + # force 'write_float' to not write raw_sfreq + monkeypatch.setattr(mne.epochs, 'write_float', no_sfreq_write_float) + + # create a test epochs dataset + sfreq, n_epochs = 10., 4 + data = np.linspace(0, 1, n_epochs * int(sfreq))[np.newaxis] + info = create_info(ch_names=1, ch_types='eeg', sfreq=sfreq) + with info._unlock(): + info['lowpass'] = 1. + raw = RawArray(data, info, first_samp) + raw.set_meas_date(meas_date) + # Add a single annotation that occurs between 1 # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op import os @@ -16,9 +16,8 @@ read_evokeds, Epochs, create_info, compute_raw_covariance, Annotations) from mne.io import read_raw_fif, RawArray -from mne.utils import run_tests_if_main from mne.event import (define_target_events, merge_events, AcqParserFIF, - shift_time_events) + shift_time_events, match_event_names) from mne.datasets import testing base_dir = op.join(op.dirname(__file__), '..', 'io', 'tests', 'data') @@ -111,11 +110,11 @@ assert_array_equal(events, events_good) -def test_io_events(tmpdir): +def test_io_events(tmp_path): """Test IO for events.""" # Test binary fif IO events = read_events(fname) # Use as the gold standard - fname_temp = tmpdir.join('events-eve.fif') + fname_temp = tmp_path / 'events-eve.fif' write_events(fname_temp, events) events2 = read_events(fname_temp) assert_array_almost_equal(events, events2) @@ -123,13 +122,13 @@ # Test binary fif.gz IO events2 = read_events(fname_gz) # Use as the gold standard assert_array_almost_equal(events, events2) - fname_temp += '.gz' + fname_temp = str(fname_temp) + '.gz' write_events(fname_temp, events2) events2 = read_events(fname_temp) assert_array_almost_equal(events, events2) # Test new format text file IO - fname_temp = str(tmpdir.join('events.eve')) + fname_temp = tmp_path / 'events.eve' write_events(fname_temp, events) events2 = read_events(fname_temp) assert_array_almost_equal(events, events2) @@ -140,12 +139,12 @@ # Test old format text file IO events2 = read_events(fname_old_txt) assert_array_almost_equal(events, events2) - write_events(fname_temp, events) + write_events(fname_temp, events, overwrite=True) events2 = read_events(fname_temp) assert_array_almost_equal(events, events2) # Test event selection - fname_temp = tmpdir.join('events-eve.fif') + fname_temp = tmp_path / 'events-eve.fif' a = read_events(fname_temp, include=1) b = read_events(fname_temp, include=[1]) c = read_events(fname_temp, exclude=[2, 3, 4, 5, 32]) @@ -157,24 +156,24 @@ # test reading file with mask=None events2 = events.copy() events2[:, -1] = range(events2.shape[0]) - write_events(fname_temp, events2) + write_events(fname_temp, events2, overwrite=True) events3 = read_events(fname_temp, mask=None) assert_array_almost_equal(events2, events3) # Test binary file IO for 1 event events = read_events(fname_1) # Use as the new gold standard - write_events(fname_temp, events) + write_events(fname_temp, events, overwrite=True) events2 = read_events(fname_temp) assert_array_almost_equal(events, events2) # Test text file IO for 1 event - fname_temp = str(tmpdir.join('events.eve')) - write_events(fname_temp, events) + fname_temp = tmp_path / 'events.eve' + write_events(fname_temp, events, overwrite=True) events2 = read_events(fname_temp) assert_array_almost_equal(events, events2) # test warnings on bad filenames - fname2 = tmpdir.join('test-bad-name.fif') + fname2 = tmp_path / 'test-bad-name.fif' with pytest.warns(RuntimeWarning, match='-eve.fif'): write_events(fname2, events) with pytest.warns(RuntimeWarning, match='-eve.fif'): @@ -217,8 +216,9 @@ assert_array_equal(events11, events22) # Reset some data for ease of comparison - raw._first_samps[0] = 0 - raw.info['sfreq'] = 1000 + raw._cropped_samp = 0 + with raw.info._unlock(): + raw.info['sfreq'] = 1000 stim_channel = 'STI 014' stim_channel_idx = pick_channels(raw.info['ch_names'], @@ -381,12 +381,12 @@ # test initial_event argument info = create_info(['MYSTI'], 1000, 'stim') data = np.zeros((1, 1000)) - raw = RawArray(data, info) + raw = RawArray(data, info, first_samp=7) data[0, :10] = 100 data[0, 30:40] = 200 - assert_array_equal(find_events(raw, 'MYSTI'), [[30, 0, 200]]) + assert_array_equal(find_events(raw, 'MYSTI'), [[37, 0, 200]]) assert_array_equal(find_events(raw, 'MYSTI', initial_event=True), - [[0, 0, 100], [30, 0, 200]]) + [[7, 0, 100], [37, 0, 200]]) # test error message for raw without stim channels raw = read_raw_fif(raw_fname, preload=True) @@ -461,7 +461,7 @@ # Make sure it gets used properly by compute_raw_covariance cov = compute_raw_covariance(raw, tstep=None) expected = np.cov(data[:, :21216]) - np.testing.assert_allclose(cov['data'], expected, atol=1e-12) + assert_allclose(cov['data'], expected, atol=1e-12) # overlaps events = make_fixed_length_events(raw, 1, duration=1) @@ -593,4 +593,48 @@ assert all(new_events[:, 0] == EXPECTED) -run_tests_if_main() +def test_match_event_names(): + """Test event name / event group matching.""" + event_names = [ + 'auditory/left', + 'auditory/right', + 'visual/left', + 'visual/right' + ] + + keys = ['auditory', 'left'] + expected_matches = ['auditory/left', 'auditory/right', 'visual/left'] + matches = match_event_names(event_names=event_names, keys=keys) + assert matches == expected_matches + + # `keys` order shouldn't matter + keys = ['left', 'auditory'] + matches = match_event_names(event_names=event_names, keys=keys) + assert matches == expected_matches + + # Test `keys` is string + keys = 'left' + expected_matches = ['auditory/left', 'visual/left'] + matches = match_event_names(event_names=event_names, keys=keys) + assert matches == expected_matches + + # Test `keys` is invalid type + for keys in (123, [123, 456]): + with pytest.raises(ValueError, match='keys must be strings'): + match_event_names(event_names=event_names, keys=keys) + + # Test no matches + keys = 'laboratory' + with pytest.raises(KeyError, match='could not be found'): + match_event_names(event_names=event_names, keys=keys) + + with pytest.warns(RuntimeWarning, match='could not be found'): + matches = match_event_names( + event_names=event_names, keys=keys, on_missing='warn' + ) + assert matches == [] + + matches = match_event_names( + event_names=event_names, keys=keys, on_missing='ignore' + ) + assert matches == [] diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_evoked.py python-mne-1.1.0+dfsg/mne/tests/test_evoked.py --- python-mne-0.23.4+dfsg/mne/tests/test_evoked.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_evoked.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,7 +3,7 @@ # Andrew Dykstra # Mads Jensen # -# License: BSD (3-clause) +# License: BSD-3-Clause from copy import deepcopy import os.path as op @@ -21,7 +21,7 @@ from mne.evoked import _get_peak, Evoked, EvokedArray from mne.io import read_raw_fif from mne.io.constants import FIFF -from mne.utils import _TempDir, requires_pandas, grand_average +from mne.utils import requires_pandas, grand_average base_dir = op.join(op.dirname(__file__), '..', 'io', 'tests', 'data') fname = op.join(base_dir, 'test-ave.fif') @@ -30,6 +30,45 @@ event_name = op.join(base_dir, 'test-eve.fif') +def test_get_data(): + """Test the get_data method for Evoked.""" + evoked = read_evokeds(fname, 0) + d1 = evoked.get_data() + d2 = evoked.data + assert_array_equal(d1, d2) + + eeg_idxs = np.array([i == "eeg" for i in evoked.get_channel_types()]) + assert_array_equal( + evoked.data[eeg_idxs], + evoked.get_data(picks="eeg") + ) + + # Get a specific time window using tmin and tmax + d3 = evoked.get_data(tmin=0) + assert np.all(d3.shape[1] == + evoked.data.shape[1] - + np.nonzero(evoked.times == 0)[0]) + + assert evoked.get_data(tmin=0, tmax=0).size == 0 + + with pytest.raises(TypeError, match='tmin .* float, None'): + evoked.get_data(tmin=[1], tmax=1) + + with pytest.raises(TypeError, match='tmax .* float, None'): + evoked.get_data(tmin=1, tmax=np.ones(5)) + + # Test units + # more tests in mne/io/tests/test_raw.py::test_get_data_units + # EEG is already in V, so no conversion should take place + d1 = evoked.get_data(picks="eeg", units=None) + d2 = evoked.get_data(picks="eeg", units="V") + assert_array_equal(d1, d2) + + # Convert to µV + d3 = evoked.get_data(picks="eeg", units="µV") + assert_array_equal(d1 * 1e6, d3) + + def test_decim(): """Test evoked decimation.""" rng = np.random.RandomState(0) @@ -40,25 +79,28 @@ sfreq_new = sfreq / decim data = rng.randn(n_channels, n_times) info = create_info(n_channels, sfreq, 'eeg') - info['lowpass'] = sfreq_new / float(decim) + with info._unlock(): + info['lowpass'] = sfreq_new / float(decim) evoked = EvokedArray(data, info, tmin=-1) + zero_idx = evoked.times.tolist().index(0) evoked_dec = evoked.copy().decimate(decim) evoked_dec_2 = evoked.copy().decimate(decim, offset=1) evoked_dec_3 = evoked.decimate(dec_1).decimate(dec_2) - assert_array_equal(evoked_dec.data, data[:, ::decim]) - assert_array_equal(evoked_dec_2.data, data[:, 1::decim]) - assert_array_equal(evoked_dec.data, evoked_dec_3.data) + start_samp = zero_idx - decim + assert_array_equal(evoked_dec.data, data[:, start_samp::decim]) + # this has +1 because offset=1 when decimating ↓↓↓↓↓↓↓↓↓↓↓↓↓↓ + assert_array_equal(evoked_dec_2.data, data[:, (start_samp + 1)::decim]) # Check proper updating of various fields - assert evoked_dec.first == -2 + assert evoked_dec.first == -1 assert evoked_dec.last == 1 - assert_array_equal(evoked_dec.times, [-1, -0.4, 0.2, 0.8]) - assert evoked_dec_2.first == -2 + assert_array_equal(evoked_dec.times, [-0.6, 0.0, 0.6]) + assert evoked_dec_2.first == -1 assert evoked_dec_2.last == 1 - assert_array_equal(evoked_dec_2.times, [-0.9, -0.3, 0.3, 0.9]) - assert evoked_dec_3.first == -2 + assert_array_equal(evoked_dec_2.times, [-0.5, 0.1, 0.7]) + assert evoked_dec_3.first == -1 assert evoked_dec_3.last == 1 - assert_array_equal(evoked_dec_3.times, [-1, -0.4, 0.2, 0.8]) + assert_array_equal(evoked_dec_3.times, [-0.6, 0.0, 0.6]) # make sure the time nearest zero is also sample number 0. for ev in (evoked_dec, evoked_dec_2, evoked_dec_3): @@ -73,7 +115,8 @@ raw = read_raw_fif(raw_fname) events = read_events(event_name) sfreq_new = raw.info['sfreq'] / decim - raw.info['lowpass'] = sfreq_new / 4. # suppress aliasing warnings + with raw.info._unlock(): + raw.info['lowpass'] = sfreq_new / 4. # suppress aliasing warnings picks = pick_types(raw.info, meg=True, eeg=True, exclude=()) epochs = Epochs(raw, events, 1, -0.2, 0.5, picks=picks, preload=True) for offset in (0, 1): @@ -135,14 +178,14 @@ @pytest.mark.parametrize('aspect_kind', _aspect_kinds()) -def test_evoked_aspects(aspect_kind, tmpdir): +def test_evoked_aspects(aspect_kind, tmp_path): """Test handling of evoked aspects.""" # gh-6359 ave = read_evokeds(fname, 0) ave._aspect_kind = aspect_kind assert 'Evoked' in repr(ave) # for completeness let's try a round-trip - temp_fname = op.join(str(tmpdir), 'test-ave.fif') + temp_fname = op.join(str(tmp_path), 'test-ave.fif') ave.save(temp_fname) ave_2 = read_evokeds(temp_fname, condition=0) assert_allclose(ave.data, ave_2.data) @@ -150,15 +193,15 @@ @pytest.mark.slowtest -def test_io_evoked(tmpdir): +def test_io_evoked(tmp_path): """Test IO for evoked data (fif + gz) with integer and str args.""" ave = read_evokeds(fname, 0) ave_double = ave.copy() ave_double.comment = ave.comment + ' doubled nave' ave_double.nave = ave.nave * 2 - write_evokeds(tmpdir.join('evoked-ave.fif'), [ave, ave_double]) - ave2, ave_double = read_evokeds(op.join(tmpdir, 'evoked-ave.fif')) + write_evokeds(tmp_path / 'evoked-ave.fif', [ave, ave_double]) + ave2, ave_double = read_evokeds(op.join(tmp_path, 'evoked-ave.fif')) assert ave2.nave * 2 == ave_double.nave # This not being assert_array_equal due to windows rounding @@ -187,8 +230,8 @@ aves1 = read_evokeds(fname)[1::2] aves2 = read_evokeds(fname, [1, 3]) aves3 = read_evokeds(fname, ['Right Auditory', 'Right visual']) - write_evokeds(tmpdir.join('evoked-ave.fif'), aves1) - aves4 = read_evokeds(tmpdir.join('evoked-ave.fif')) + write_evokeds(tmp_path / 'evoked-ave.fif', aves1, overwrite=True) + aves4 = read_evokeds(tmp_path / 'evoked-ave.fif') for aves in [aves2, aves3, aves4]: for [av1, av2] in zip(aves1, aves): assert_array_almost_equal(av1.data, av2.data) @@ -203,20 +246,20 @@ # test saving and reading complex numbers in evokeds ave_complex = ave.copy() ave_complex._data = 1j * ave_complex.data - fname_temp = str(tmpdir.join('complex-ave.fif')) + fname_temp = str(tmp_path / 'complex-ave.fif') ave_complex.save(fname_temp) ave_complex = read_evokeds(fname_temp)[0] assert_allclose(ave.data, ave_complex.data.imag) # test warnings on bad filenames - fname2 = tmpdir.join('test-bad-name.fif') + fname2 = tmp_path / 'test-bad-name.fif' with pytest.warns(RuntimeWarning, match='-ave.fif'): write_evokeds(fname2, ave) with pytest.warns(RuntimeWarning, match='-ave.fif'): read_evokeds(fname2) # test writing when order of bads doesn't match - fname3 = tmpdir.join('test-bad-order-ave.fif') + fname3 = tmp_path / 'test-bad-order-ave.fif' condition = 'Left Auditory' ave4 = read_evokeds(fname, condition) ave4.info['bads'] = ave4.ch_names[:3] @@ -228,9 +271,10 @@ pytest.raises(TypeError, Evoked, fname) # MaxShield - fname_ms = tmpdir.join('test-ave.fif') + fname_ms = tmp_path / 'test-ave.fif' assert (ave.info['maxshield'] is False) - ave.info['maxshield'] = True + with ave.info._unlock(): + ave.info['maxshield'] = True ave.save(fname_ms) pytest.raises(ValueError, read_evokeds, fname_ms) with pytest.warns(RuntimeWarning, match='Elekta'): @@ -240,25 +284,26 @@ assert (all(ave.info['maxshield'] is True for ave in aves)) -def test_shift_time_evoked(): +def test_shift_time_evoked(tmp_path): """Test for shifting of time scale.""" - tempdir = _TempDir() + tempdir = str(tmp_path) # Shift backward ave = read_evokeds(fname, 0).shift_time(-0.1, relative=True) - write_evokeds(op.join(tempdir, 'evoked-ave.fif'), ave) + fname_temp = op.join(tempdir, 'evoked-ave.fif') + write_evokeds(fname_temp, ave) # Shift forward twice the amount - ave_bshift = read_evokeds(op.join(tempdir, 'evoked-ave.fif'), 0) + ave_bshift = read_evokeds(fname_temp, 0) ave_bshift.shift_time(0.2, relative=True) - write_evokeds(op.join(tempdir, 'evoked-ave.fif'), ave_bshift) + write_evokeds(fname_temp, ave_bshift, overwrite=True) # Shift backward again - ave_fshift = read_evokeds(op.join(tempdir, 'evoked-ave.fif'), 0) + ave_fshift = read_evokeds(fname_temp, 0) ave_fshift.shift_time(-0.1, relative=True) - write_evokeds(op.join(tempdir, 'evoked-ave.fif'), ave_fshift) + write_evokeds(fname_temp, ave_fshift, overwrite=True) ave_normal = read_evokeds(fname, 0) - ave_relative = read_evokeds(op.join(tempdir, 'evoked-ave.fif'), 0) + ave_relative = read_evokeds(fname_temp, 0) assert_allclose(ave_normal.data, ave_relative.data, atol=1e-16, rtol=1e-3) assert_array_almost_equal(ave_normal.times, ave_relative.times, 8) @@ -269,9 +314,9 @@ # Absolute time shift ave = read_evokeds(fname, 0) ave.shift_time(-0.3, relative=False) - write_evokeds(op.join(tempdir, 'evoked-ave.fif'), ave) + write_evokeds(fname_temp, ave, overwrite=True) - ave_absolute = read_evokeds(op.join(tempdir, 'evoked-ave.fif'), 0) + ave_absolute = read_evokeds(fname_temp, 0) assert_allclose(ave_normal.data, ave_absolute.data, atol=1e-16, rtol=1e-3) assert_equal(ave_absolute.first, int(-0.3 * ave.info['sfreq'])) @@ -289,14 +334,14 @@ # should shift by 0 samples ave.shift_time(1e-6) assert_array_equal(first_last, np.array([ave.first, ave.last])) - write_evokeds(op.join(tempdir, 'evoked-ave.fif'), ave) - ave_loaded = read_evokeds(op.join(tempdir, 'evoked-ave.fif'), 0) + write_evokeds(fname_temp, ave, overwrite=True) + ave_loaded = read_evokeds(fname_temp, 0) assert_array_almost_equal(ave.times, ave_loaded.times, 8) # should shift by 57 samples ave.shift_time(57. / ave.info['sfreq']) assert_array_equal(first_last + 57, np.array([ave.first, ave.last])) - write_evokeds(op.join(tempdir, 'evoked-ave.fif'), ave) - ave_loaded = read_evokeds(op.join(tempdir, 'evoked-ave.fif'), 0) + write_evokeds(fname_temp, ave, overwrite=True) + ave_loaded = read_evokeds(fname_temp, 0) assert_array_almost_equal(ave.times, ave_loaded.times, 8) @@ -307,23 +352,24 @@ assert evoked.times[-1] == evoked.tmax -def test_evoked_resample(): +def test_evoked_resample(tmp_path): """Test resampling evoked data.""" - tempdir = _TempDir() + tempdir = str(tmp_path) # upsample, write it out, read it in ave = read_evokeds(fname, 0) orig_lp = ave.info['lowpass'] sfreq_normal = ave.info['sfreq'] ave.resample(2 * sfreq_normal, npad=100) assert ave.info['lowpass'] == orig_lp - write_evokeds(op.join(tempdir, 'evoked-ave.fif'), ave) - ave_up = read_evokeds(op.join(tempdir, 'evoked-ave.fif'), 0) + fname_temp = op.join(tempdir, 'evoked-ave.fif') + write_evokeds(fname_temp, ave) + ave_up = read_evokeds(fname_temp, 0) # compare it to the original ave_normal = read_evokeds(fname, 0) # and compare the original to the downsampled upsampled version - ave_new = read_evokeds(op.join(tempdir, 'evoked-ave.fif'), 0) + ave_new = read_evokeds(fname_temp, 0) ave_new.resample(sfreq_normal, npad=100) assert ave.info['lowpass'] == orig_lp @@ -389,7 +435,7 @@ expected = ('time', 'channel', 'ch_type', 'value') assert set(expected) == set(df_long.columns) assert set(ave.ch_names) == set(df_long['channel']) - assert(len(df_long) == ave.data.size) + assert len(df_long) == ave.data.size del df_wide, df_long # test scalings df = ave.to_data_frame(index='time') @@ -441,13 +487,24 @@ def test_get_peak(): """Test peak getter.""" evoked = read_evokeds(fname, condition=0, proj=True) - pytest.raises(ValueError, evoked.get_peak, ch_type='mag', tmin=1) - pytest.raises(ValueError, evoked.get_peak, ch_type='mag', tmax=0.9) - pytest.raises(ValueError, evoked.get_peak, ch_type='mag', tmin=0.02, - tmax=0.01) - pytest.raises(ValueError, evoked.get_peak, ch_type='mag', mode='foo') - pytest.raises(RuntimeError, evoked.get_peak, ch_type=None, mode='foo') - pytest.raises(ValueError, evoked.get_peak, ch_type='misc', mode='foo') + + with pytest.raises(ValueError, match='tmin.*must be <= tmax'): + evoked.get_peak(ch_type='mag', tmin=1) + + with pytest.raises(ValueError, match='tmax.*is out of bounds'): + evoked.get_peak(ch_type='mag', tmax=0.9) + + with pytest.raises(ValueError, match='tmin.*must be <= tmax'): + evoked.get_peak(ch_type='mag', tmin=0.02, tmax=0.01) + + with pytest.raises(ValueError, match="Invalid.*'mode' parameter"): + evoked.get_peak(ch_type='mag', mode='foo') + + with pytest.raises(RuntimeError, match='Multiple data channel types'): + evoked.get_peak(ch_type=None, mode='foo') + + with pytest.raises(ValueError, match='Channel type.*not found'): + evoked.get_peak(ch_type='misc', mode='foo') ch_name, time_idx = evoked.get_peak(ch_type='mag') assert (ch_name in evoked.ch_names) @@ -460,8 +517,12 @@ assert_equal(ch_name, 'MEG 1421') assert_allclose(max_amp, 7.17057e-13, rtol=1e-5) - pytest.raises(ValueError, evoked.get_peak, ch_type='mag', - merge_grads=True) + with pytest.raises(ValueError, match='must be "grad" for merge_grads'): + evoked.get_peak(ch_type='mag', merge_grads=True) + + with pytest.raises(ValueError, match='Negative mode.*does not make sense'): + evoked.get_peak(ch_type='grad', merge_grads=True, mode='neg') + ch_name, time_idx = evoked.get_peak(ch_type='grad', merge_grads=True) assert_equal(ch_name, 'MEG 244X') @@ -485,8 +546,38 @@ assert_equal(time_idx, 2) assert_allclose(max_amp, 2.) - pytest.raises(ValueError, _get_peak, data + 1e3, times, mode='neg') - pytest.raises(ValueError, _get_peak, data - 1e3, times, mode='pos') + # Check behavior if `mode` doesn't match the available data + evoked_all_pos = evoked.copy().crop(0, 0.1).pick('EEG 001') + evoked_all_neg = evoked.copy().crop(0, 0.1).pick('EEG 001') + + evoked_all_pos.data = np.abs(evoked_all_pos.data) # all values positive + evoked_all_neg.data = -np.abs(evoked_all_neg.data) # all negative + + with pytest.raises(ValueError, match='No negative values'): + evoked_all_pos.get_peak(mode='neg') + + with pytest.raises(ValueError, match='No positive values'): + evoked_all_neg.get_peak(mode='pos') + + # Test interaction between `mode` and `tmin` / `tmax` + # For the test, create an Evoked where half of the values are negative + # and the rest is positive + evoked_neg_and_pos = evoked_all_neg.copy() + time_sep_neg_and_pos = 0.05 + idx_time_sep_neg_and_pos = evoked_neg_and_pos.time_as_index( + time_sep_neg_and_pos + )[0] + evoked_neg_and_pos.data[:, idx_time_sep_neg_and_pos:] *= -1 + + with pytest.raises(ValueError, match='No positive values'): + evoked_neg_and_pos.get_peak( + mode='pos', + # subtract 1 time instant, otherwise were off-by-one + tmax=time_sep_neg_and_pos - 1 / evoked_neg_and_pos.info['sfreq'] + ) + + with pytest.raises(ValueError, match='No negative values'): + evoked_neg_and_pos.get_peak(mode='neg', tmin=time_sep_neg_and_pos) def test_drop_channels_mixin(): @@ -621,9 +712,9 @@ assert evoked1.ch_names == evoked3.ch_names -def test_array_epochs(): +def test_array_epochs(tmp_path): """Test creating evoked from array.""" - tempdir = _TempDir() + tempdir = str(tmp_path) # creating rng = np.random.RandomState(42) @@ -680,6 +771,10 @@ assert_array_equal(evoked.time_as_index([-.1, .1], use_rounding=True), [0, len(evoked.times) - 1]) evoked.crop(evoked.tmin, evoked.tmax, include_tmax=False) + n_times = len(evoked.times) + with pytest.warns(RuntimeWarning, match='tmax is set to'): + evoked.crop(tmin, tmax, include_tmax=False) + assert len(evoked.times) == n_times assert_allclose(evoked.times[[0, -1]], [tmin, tmax - delta], atol=atol) @@ -689,7 +784,8 @@ hpi_coils = [{'event_bits': []}, {'event_bits': np.array([256, 0, 256, 256])}, {'event_bits': np.array([512, 0, 512, 512])}] - evoked.info['hpi_subsystem'] = dict(hpi_coils=hpi_coils, ncoil=2) + with evoked.info._unlock(): + evoked.info['hpi_subsystem'] = dict(hpi_coils=hpi_coils, ncoil=2) evoked_eeg = evoked.copy().pick_types(meg=False, eeg=True) evoked_meg = evoked.copy().pick_types(meg=True) evoked_stim = evoked.copy().pick_types(meg=False, stim=True) @@ -706,7 +802,8 @@ # Now test errors evoked_badsf = evoked_eeg.copy() - evoked_badsf.info['sfreq'] = 3.1415927 + with evoked_badsf.info._unlock(): + evoked_badsf.info['sfreq'] = 3.1415927 evoked_eeg = evoked_eeg.crop(-.1, .1) pytest.raises(RuntimeError, evoked_meg.add_channels, [evoked_badsf]) @@ -715,7 +812,7 @@ pytest.raises(TypeError, evoked_meg.add_channels, evoked_badsf) -def test_evoked_baseline(tmpdir): +def test_evoked_baseline(tmp_path): """Test evoked baseline.""" evoked = read_evokeds(fname, condition=0, baseline=None) @@ -758,7 +855,7 @@ evoked.apply_baseline(baseline) assert_allclose(evoked.baseline, baseline) - tmp_fname = tmpdir / 'test-ave.fif' + tmp_fname = tmp_path / 'test-ave.fif' evoked.save(tmp_fname) evoked_read = read_evokeds(tmp_fname, condition=0) assert_allclose(evoked_read.baseline, evoked.baseline) @@ -811,6 +908,6 @@ return data * multiplier mult = -1 - applied = evoked.apply_function(fun, n_jobs=1, multiplier=mult) + applied = evoked.apply_function(fun, n_jobs=None, multiplier=mult) assert np.shape(applied.data) == np.shape(evoked_data) assert np.equal(applied.data, evoked_data * mult).all() diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_filter.py python-mne-1.1.0+dfsg/mne/tests/test_filter.py --- python-mne-0.23.4+dfsg/mne/tests/test_filter.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_filter.py 2022-08-03 15:48:14.000000000 +0000 @@ -7,7 +7,7 @@ import pytest from scipy.signal import resample as sp_resample, butter, freqz, sosfreqz -from mne import create_info +from mne import create_info, Epochs from numpy.fft import fft, fftfreq from mne.io import RawArray, read_raw_fif from mne.io.pick import _DATA_CH_TYPES_SPLIT @@ -17,8 +17,7 @@ estimate_ringing_samples, create_filter, _length_factors) -from mne.utils import (sum_squared, run_tests_if_main, - catch_logging, requires_mne, run_subprocess) +from mne.utils import sum_squared, catch_logging, requires_mne, run_subprocess def test_filter_array(): @@ -29,9 +28,9 @@ @requires_mne -def test_mne_c_design(tmpdir): +def test_mne_c_design(tmp_path): """Test MNE-C filter design.""" - tempdir = str(tmpdir) + tempdir = str(tmp_path) temp_fname = op.join(tempdir, 'test_raw.fif') out_fname = op.join(tempdir, 'test_c_raw.fif') x = np.zeros((1, 10001)) @@ -283,10 +282,10 @@ def test_n_jobs(n_jobs): """Test resampling against SciPy.""" x = np.random.RandomState(0).randn(4, 100) - y1 = resample(x, 2, 1, n_jobs=1) + y1 = resample(x, 2, 1, n_jobs=None) y2 = resample(x, 2, 1, n_jobs=n_jobs) assert_allclose(y1, y2) - y1 = filter_data(x, 100., 0, 40, n_jobs=1) + y1 = filter_data(x, 100., 0, 40, n_jobs=None) y2 = filter_data(x, 100., 0, 40, n_jobs=n_jobs) assert_allclose(y1, y2) @@ -328,6 +327,31 @@ assert data.shape == (1, 63) +def test_resample_below_1_sample(): + """Test resampling doesn't yield datapoints.""" + # Raw + x = np.zeros((1, 100)) + sfreq = 1000. + raw = RawArray(x, create_info(1, sfreq, 'eeg')) + raw.resample(5) + assert len(raw.times) == 1 + assert raw.get_data().shape[1] == 1 + + # Epochs + x = np.zeros((1, 10000)) + sfreq = 1000. + raw = RawArray(x, create_info(1, sfreq, 'eeg')) + events = np.array([[400, 0, 1], + [2000, 0, 1], + [3000, 0, 1]]) + epochs = Epochs(raw, events, {'test': 1}, 0, 0.2, proj=False, + picks='eeg', baseline=None, preload=True, + verbose=False) + epochs.resample(1) + assert len(epochs.times) == 1 + assert epochs.get_data().shape[2] == 1 + + @pytest.mark.slowtest def test_filters(): """Test low-, band-, high-pass, and band-stop filters plus resampling.""" @@ -342,9 +366,12 @@ pytest.raises((ValueError, TypeError), filter_data, a, sfreq, 4, 8, None, fl, 1.0, 1.0, fir_design='firwin') - for nj in ['blah', 0.5]: - pytest.raises(ValueError, filter_data, a, sfreq, 4, 8, None, 1000, - 1.0, 1.0, n_jobs=nj, phase='zero', fir_design='firwin') + with pytest.raises(TypeError, match='got # -# License: BSD (3-clause) +# License: BSD-3-Clause import sys from mne.utils import run_subprocess @@ -14,7 +14,7 @@ # check scipy (Numba imports it to check the version) ok_scipy_submodules = set(['scipy', 'numpy', # these appear in old scipy - 'version', 'sparse', 'misc']) + 'version']) scipy_submodules = set(x.split('.')[1] for x in sys.modules.keys() if x.startswith('scipy.') and '__' not in x and not x.split('.')[1].startswith('_') @@ -25,8 +25,9 @@ # check sklearn and others for x in sys.modules.keys(): - for key in ('sklearn', 'pandas', 'mayavi', 'pyvista', 'matplotlib', - 'dipy', 'nibabel', 'cupy', 'picard', 'pyvistaqt'): + for key in ('sklearn', 'pandas', 'pyvista', 'matplotlib', + 'dipy', 'nibabel', 'cupy', 'picard', 'pyvistaqt', 'pooch', + 'tqdm', 'jinja2'): if x.startswith(key): x = '.'.join(x.split('.')[:2]) out |= {x} diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_label.py python-mne-1.1.0+dfsg/mne/tests/test_label.py --- python-mne-0.23.4+dfsg/mne/tests/test_label.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_label.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,10 @@ -from itertools import product +# Author: Eric Larson +# +# License: BSD-3-Clause +from contextlib import nullcontext import glob +from itertools import product import os import os.path as op import pickle @@ -10,7 +14,7 @@ from scipy import sparse from numpy.testing import (assert_array_equal, assert_array_almost_equal, - assert_equal) + assert_equal, assert_allclose, assert_array_less) import pytest from mne.datasets import testing @@ -20,12 +24,13 @@ read_surface, random_parcellation, morph_labels, labels_to_stc) from mne.label import (Label, _blend_colors, label_sign_flip, _load_vert_pos, - select_sources) -from mne.utils import (_TempDir, requires_sklearn, get_subjects_dir, - run_tests_if_main, check_version) -from mne.label import _n_colors, _read_annot, _read_annot_cands + select_sources, _n_colors, _read_annot, + _read_annot_cands) from mne.source_space import SourceSpaces from mne.source_estimate import mesh_edges +from mne.surface import _mesh_borders +from mne.utils import (requires_sklearn, get_subjects_dir, check_version, + _record_warnings) data_path = testing.data_path(download=False) @@ -266,7 +271,7 @@ # Check that we can auto-fill patch info quickly for one condition for s in src: s['nearest'] = None - with pytest.warns(None): + with _record_warnings(): label_src = label_src.fill(src) else: label_src = label_src.fill(src) @@ -306,9 +311,9 @@ @testing.requires_testing_data -def test_label_io(): +def test_label_io(tmp_path): """Test IO of label files.""" - tempdir = _TempDir() + tempdir = str(tmp_path) label = read_label(label_fname) # label attributes @@ -341,10 +346,10 @@ @testing.requires_testing_data -def test_annot_io(): +def test_annot_io(tmp_path): """Test I/O from and to *.annot files.""" # copy necessary files from fsaverage to tempdir - tempdir = _TempDir() + tempdir = str(tmp_path) subject = 'fsaverage' label_src = os.path.join(subjects_dir, 'fsaverage', 'label') surf_src = os.path.join(subjects_dir, 'fsaverage', 'surf') @@ -444,7 +449,7 @@ @testing.requires_testing_data -def test_read_labels_from_annot(tmpdir): +def test_read_labels_from_annot(tmp_path): """Test reading labels from FreeSurfer parcellation.""" # test some invalid inputs pytest.raises(ValueError, read_labels_from_annot, 'sample', hemi='bla', @@ -454,7 +459,7 @@ with pytest.raises(IOError, match='does not exist'): _read_annot_cands('foo') with pytest.raises(IOError, match='no candidate'): - _read_annot(str(tmpdir)) + _read_annot(str(tmp_path)) # read labels using hemi specification labels_lh = read_labels_from_annot('sample', hemi='lh', @@ -499,9 +504,10 @@ regexp='.*-.{4,}_.{3,3}-L', subjects_dir=subjects_dir)[0] assert (label.name == 'G_oc-temp_med-Lingual-lh') - pytest.raises(RuntimeError, read_labels_from_annot, 'sample', parc='aparc', - annot_fname=annot_fname, regexp='JackTheRipper', - subjects_dir=subjects_dir) + with pytest.raises(RuntimeError, match='did not match any of'): + read_labels_from_annot( + 'sample', parc='aparc', annot_fname=annot_fname, regexp='foo', + subjects_dir=subjects_dir) @testing.requires_testing_data @@ -517,9 +523,9 @@ @testing.requires_testing_data -def test_write_labels_to_annot(): +def test_write_labels_to_annot(tmp_path): """Test writing FreeSurfer parcellation from labels.""" - tempdir = _TempDir() + tempdir = str(tmp_path) labels = read_labels_from_annot('sample', subjects_dir=subjects_dir) @@ -776,13 +782,13 @@ verts = [np.arange(10242), np.arange(10242)] for hemi in ['lh', 'rh']: label.hemi = hemi - with pytest.warns(None): # morph map maybe missing + with _record_warnings(): # morph map maybe missing label.morph(None, 'fsaverage', 5, verts, subjects_dir, 2) pytest.raises(TypeError, label.morph, None, 1, 5, verts, subjects_dir, 2) pytest.raises(TypeError, label.morph, None, 'fsaverage', 5.5, verts, subjects_dir, 2) - with pytest.warns(None): # morph map maybe missing + with _record_warnings(): # morph map maybe missing label.smooth(subjects_dir=subjects_dir) # make sure this runs @@ -970,9 +976,6 @@ surf='foo') -run_tests_if_main() - - @testing.requires_testing_data def test_select_sources(): """Test the selection of sources for simulation.""" @@ -1019,3 +1022,51 @@ name='mne') assert (label.name == 'mne') assert (label.hemi == 'rh') + + +@testing.requires_testing_data +@pytest.mark.parametrize('fname, area', [ + (real_label_fname, 0.657e-3), + (v1_label_fname, 3.245e-3), +]) +def test_label_geometry(fname, area): + """Test label geometric computations.""" + label = read_label(fname, subject='sample') + got_area = label.compute_area(subjects_dir=subjects_dir) + assert_allclose(got_area, area, rtol=1e-3) + # using a sparse label emits a warning + label_sparse = label.restrict(src_fname) + assert 0 < len(label_sparse.vertices) < len(label.vertices) + with pytest.warns(RuntimeWarning, match='No complete triangles'): + assert label_sparse.compute_area(subjects_dir=subjects_dir) == 0. + if not check_version('scipy', '1.3'): + ctx = pytest.raises(RuntimeError, match='required to calculate') + stop = True + else: + ctx = nullcontext() + stop = False + with ctx: + dist, outside = label.distances_to_outside(subjects_dir=subjects_dir) + if stop: + return + rr, tris = read_surface( + op.join(subjects_dir, 'sample', 'surf', 'lh.white')) + mask = np.zeros(len(rr), bool) + mask[label.vertices] = 1 + border_mask = np.in1d(label.vertices, _mesh_borders(tris, mask)) + # The distances of the border vertices is smaller than that of non-border + lo, mi, hi = np.percentile(dist[border_mask], (0, 50, 100)) + assert 0.1e-3 < lo < 0.5e-3 < mi < 1.0e-3 < hi < 2.0e-3 + lo, mi, hi = np.percentile(dist[~border_mask], (0, 50, 100)) + assert 0.5e-3 < lo < 1.0e-3 < mi < 9.0e-3 < hi < 25e-3 + # check that the distances are close but uniformly <= than euclidean + assert not np.in1d(outside, label.vertices).any() + border_dist = dist[border_mask] + border_euc = 1e-3 * np.linalg.norm( + rr[label.vertices[border_mask]] - rr[outside[border_mask]], axis=1) + assert_allclose(border_dist, border_euc, atol=1e-4) + inside_dist = dist[~border_mask] + inside_euc = 1e-3 * np.linalg.norm( + rr[label.vertices[~border_mask]] - rr[outside[~border_mask]], axis=1) + assert_array_less(inside_euc, inside_dist) + assert_array_less(0.25 * inside_dist, inside_euc) diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_line_endings.py python-mne-1.1.0+dfsg/mne/tests/test_line_endings.py --- python-mne-0.23.4+dfsg/mne/tests/test_line_endings.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_line_endings.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,16 +1,15 @@ # Author: Eric Larson # Adapted from vispy # -# License: BSD (3-clause) +# License: BSD-3-Clause import os -from unittest import SkipTest from os import path as op import sys import pytest -from mne.utils import run_tests_if_main, _TempDir, _get_root_dir +from mne.utils import _get_root_dir skip_files = ( @@ -31,7 +30,7 @@ def _assert_line_endings(dir_): """Check line endings for a directory.""" if sys.platform == 'win32': - raise SkipTest('Skipping line endings check on Windows') + pytest.skip('Skipping line endings check on Windows') report = list() good_exts = ('.py', '.dat', '.sel', '.lout', '.css', '.js', '.lay', '.txt', '.elc', '.csd', '.sfp', '.json', '.hpts', '.vmrk', '.vhdr', @@ -57,9 +56,9 @@ % (len(report), '\n'.join(report))) -def test_line_endings(): +def test_line_endings(tmp_path): """Test line endings of mne-python.""" - tempdir = _TempDir() + tempdir = str(tmp_path) with open(op.join(tempdir, 'foo'), 'wb') as fid: fid.write('bad\r\ngood\n'.encode('ascii')) _assert_line_endings(tempdir) @@ -71,6 +70,3 @@ pytest.raises(AssertionError, _assert_line_endings, tempdir) # now check mne _assert_line_endings(_get_root_dir()) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_misc.py python-mne-1.1.0+dfsg/mne/tests/test_misc.py --- python-mne-0.23.4+dfsg/mne/tests/test_misc.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_misc.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,7 @@ +# Authors: Alexandre Gramfort +# +# License: BSD-3-Clause + import os.path as op from mne.misc import parse_config diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_morph_map.py python-mne-1.1.0+dfsg/mne/tests/test_morph_map.py --- python-mne-0.23.4+dfsg/mne/tests/test_morph_map.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_morph_map.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,60 @@ +# Authors: Alexandre Gramfort +# +# License: BSD-3-Clause + +import os +import os.path as op +from shutil import copyfile + +import pytest +import numpy as np +from numpy.testing import assert_allclose +from scipy import sparse + +from mne.datasets import testing +from mne.utils import catch_logging, _record_warnings +from mne import read_morph_map + +data_path = testing.data_path(download=False) +subjects_dir = op.join(data_path, 'subjects') + + +@pytest.mark.slowtest +@testing.requires_testing_data +def test_make_morph_maps(tmp_path): + """Test reading and creating morph maps.""" + # make a new fake subjects_dir + tempdir = str(tmp_path) + for subject in ('sample', 'sample_ds', 'fsaverage_ds'): + os.mkdir(op.join(tempdir, subject)) + os.mkdir(op.join(tempdir, subject, 'surf')) + regs = ('reg', 'left_right') if subject == 'fsaverage_ds' else ('reg',) + for hemi in ['lh', 'rh']: + for reg in regs: + args = [subject, 'surf', hemi + '.sphere.' + reg] + copyfile(op.join(subjects_dir, *args), + op.join(tempdir, *args)) + + for subject_from, subject_to, xhemi in ( + ('fsaverage_ds', 'sample_ds', False), + ('fsaverage_ds', 'fsaverage_ds', True)): + # trigger the creation of morph-maps dir and create the map + with catch_logging() as log: + mmap = read_morph_map(subject_from, subject_to, tempdir, + xhemi=xhemi, verbose=True) + log = log.getvalue() + assert 'does not exist' in log + assert 'Creating' in log + mmap2 = read_morph_map(subject_from, subject_to, subjects_dir, + xhemi=xhemi) + assert len(mmap) == len(mmap2) + for m1, m2 in zip(mmap, mmap2): + # deal with sparse matrix stuff + diff = (m1 - m2).data + assert_allclose(diff, np.zeros_like(diff), atol=1e-3, rtol=0) + + # This will also trigger creation, but it's trivial + with _record_warnings(): + mmap = read_morph_map('sample', 'sample', subjects_dir=tempdir) + for mm in mmap: + assert (mm - sparse.eye(mm.shape[0], mm.shape[0])).sum() == 0 diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_morph.py python-mne-1.1.0+dfsg/mne/tests/test_morph.py --- python-mne-0.23.4+dfsg/mne/tests/test_morph.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_morph.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # Author: Tommy Clausner # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op import pytest @@ -9,7 +9,7 @@ from numpy.testing import (assert_array_less, assert_allclose, assert_array_equal) from scipy.spatial.distance import cdist -from scipy.sparse import csr_matrix +from scipy.sparse import csr_matrix, eye as speye import mne from mne import (SourceEstimate, VolSourceEstimate, VectorSourceEstimate, @@ -18,17 +18,16 @@ read_forward_solution, grade_to_vertices, setup_volume_source_space, make_forward_solution, make_sphere_model, make_ad_hoc_cov, VolVectorSourceEstimate, - read_freesurfer_lut) + get_volume_labels_from_aseg, read_freesurfer_lut) from mne.datasets import testing from mne.fixes import _get_img_fdata +from mne._freesurfer import _get_mri_info_data, _get_atlas_values from mne.minimum_norm import (apply_inverse, read_inverse_operator, make_inverse_operator) -from mne.source_space import (get_volume_labels_from_aseg, _get_mri_info_data, - _get_atlas_values, _add_interpolator, - _grid_interp) +from mne.source_space import _add_interpolator, _grid_interp from mne.transforms import quat_to_rot from mne.utils import (requires_nibabel, check_version, requires_version, - requires_dipy, requires_h5py, catch_logging) + requires_dipy, catch_logging, _record_warnings) from mne.fixes import _get_args # Setup paths @@ -68,7 +67,7 @@ def test_sourcemorph_consistency(): """Test SourceMorph class consistency.""" - assert _get_args(SourceMorph.__init__)[1:] == \ + assert _get_args(SourceMorph.__init__)[1:-1] == \ mne.morph._SOURCE_MORPH_ATTRIBUTES @@ -194,7 +193,7 @@ with pytest.raises(ValueError, match='required to use nearest'): morph = compute_source_morph(stc, 'sample', 'fsaverage', **kwargs) return - with pytest.warns(None) as w: + with _record_warnings() as w: morph = compute_source_morph(stc, 'sample', 'fsaverage', **kwargs) w = [ww for ww in w if 'vertices not included' in str(ww.message)] assert len(w) == n_warn @@ -210,6 +209,20 @@ assert_power_preserved(stc, stc_back) +@testing.requires_testing_data +def test_surface_source_morph_shortcut(): + """Test that our shortcut for smooth=0 works.""" + stc = mne.read_source_estimate(fname_smorph) + morph_identity = compute_source_morph( + stc, 'sample', 'sample', spacing=stc.vertices, smooth=0, + subjects_dir=subjects_dir) + stc_back = morph_identity.apply(stc) + assert_allclose(stc_back.data, stc.data, rtol=1e-4) + abs_sum = morph_identity.morph_mat - speye(len(stc.data), format='csc') + abs_sum = np.abs(abs_sum.data).sum() + assert abs_sum < 1e-4 + + def assert_power_preserved(orig, new, limits=(1., 1.05)): """Assert that the power is preserved during a round-trip morph.""" __tracebackhide__ = True @@ -223,9 +236,9 @@ assert min_ < power_ratio < max_, f'Power ratio {kind} = {power_ratio}' -@requires_h5py +@requires_version('h5io') @testing.requires_testing_data -def test_surface_vector_source_morph(tmpdir): +def test_surface_vector_source_morph(tmp_path): """Test surface and vector source estimate morph.""" inverse_operator_surf = read_inverse_operator(fname_inv_surf) @@ -257,9 +270,9 @@ assert 'surface' in repr(source_morph_surf) # check loading and saving for surf - source_morph_surf.save(tmpdir.join('42.h5')) + source_morph_surf.save(tmp_path / '42.h5') - source_morph_surf_r = read_source_morph(tmpdir.join('42.h5')) + source_morph_surf_r = read_source_morph(tmp_path / '42.h5') assert (all([read == saved for read, saved in zip(sorted(source_morph_surf_r.__dict__), @@ -275,12 +288,12 @@ source_morph_surf.apply(stc_vol) -@requires_h5py +@requires_version('h5io') @requires_nibabel() @requires_dipy() @pytest.mark.slowtest @testing.requires_testing_data -def test_volume_source_morph_basic(tmpdir): +def test_volume_source_morph_basic(tmp_path): """Test volume source estimate morph, special cases and exceptions.""" import nibabel as nib inverse_operator_vol = read_inverse_operator(fname_inv_vol) @@ -343,14 +356,14 @@ subjects_dir=subjects_dir) # two different ways of saving - source_morph_vol.save(tmpdir.join('vol')) + source_morph_vol.save(tmp_path / 'vol') # check loading - source_morph_vol_r = read_source_morph(tmpdir.join('vol-morph.h5')) + source_morph_vol_r = read_source_morph(tmp_path / 'vol-morph.h5') # check for invalid file name handling () with pytest.raises(IOError, match='not found'): - read_source_morph(tmpdir.join('42')) + read_source_morph(tmp_path / '42') # check morph stc_vol_morphed = source_morph_vol.apply(stc_vol) @@ -460,7 +473,7 @@ assert_allclose(img_vol, img_vol_2) -@requires_h5py +@requires_version('h5io') @requires_nibabel() @requires_dipy() @pytest.mark.slowtest @@ -474,7 +487,7 @@ ('sample', 'fsaverage', 10, 12, float, True), # morph_mat ]) def test_volume_source_morph_round_trip( - tmpdir, subject_from, subject_to, lower, upper, dtype, morph_mat, + tmp_path, subject_from, subject_to, lower, upper, dtype, morph_mat, monkeypatch): """Test volume source estimate morph round-trips well.""" import nibabel as nib @@ -564,7 +577,7 @@ stc_from_unit_rt = morph_to_from.apply(morph_from_to.apply(stc_from_unit)) assert_power_preserved(stc_from_unit, stc_from_unit_rt, limits=limits) if morph_mat: - fname = tmpdir.join('temp-morph.h5') + fname = tmp_path / 'temp-morph.h5' morph_to_from.save(fname) morph_to_from = read_source_morph(fname) assert morph_to_from.vol_morph_mat is None @@ -671,7 +684,7 @@ spacing=6, subjects_dir=subjects_dir) del stc_to1 - with pytest.raises(ValueError, match='smooth.* has to be at least 1'): + with pytest.raises(ValueError, match='smooth.* has to be at least 0'): compute_source_morph( stc_from, subject_from, subject_to, spacing=5, smooth=-1, subjects_dir=subjects_dir) @@ -746,7 +759,7 @@ (slice(1, 2), 51, 204, 12), (slice(0, 2), 88, 324, 20), ]) -def test_volume_labels_morph(tmpdir, sl, n_real, n_mri, n_orig): +def test_volume_labels_morph(tmp_path, sl, n_real, n_mri, n_orig): """Test generating a source space from volume label.""" import nibabel as nib n_use = (sl.stop - sl.start) // (sl.step or 1) @@ -833,6 +846,7 @@ @requires_nibabel() @requires_dipy() +@pytest.mark.slowtest @pytest.mark.parametrize('vector', (False, True)) def test_mixed_source_morph(_mixed_morph_srcs, vector): """Test mixed source space morphing.""" @@ -866,7 +880,6 @@ # Now actually morph stc_fs = morph.apply(stc) - img = stc_fs.volume().as_volume(src_fs, mri_resolution=False) vol_info = _get_mri_info_data(fname_aseg_fs, data=True) rrs = np.concatenate([src_fs[2]['rr'][sp['vertno']] for sp in src_fs[2:]]) n_want = np.in1d(_get_atlas_values(vol_info, rrs), ids).sum() diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_parallel.py python-mne-1.1.0+dfsg/mne/tests/test_parallel.py --- python-mne-0.23.4+dfsg/mne/tests/test_parallel.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_parallel.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,40 @@ +# -*- coding: utf-8 -*- +# Author: Eric Larson +# +# License: BSD-3-Clause + +from contextlib import nullcontext +import multiprocessing +import os + +import pytest + +from mne.parallel import parallel_func + + +@pytest.mark.parametrize('n_jobs', [ + None, 1, -1, 'loky 2', 'threading 3', 'multiprocessing 4', +]) +def test_parallel_func(n_jobs): + """Test Parallel wrapping.""" + joblib = pytest.importorskip('joblib') + if os.getenv('MNE_FORCE_SERIAL', '').lower() in ('true', '1'): + pytest.skip('MNE_FORCE_SERIAL cannot be set') + + def fun(x): + return x * 2 + + if isinstance(n_jobs, str): + backend, n_jobs = n_jobs.split() + n_jobs = want_jobs = int(n_jobs) + ctx = joblib.parallel_backend(backend, n_jobs) + n_jobs = None + else: + ctx = nullcontext() + if n_jobs is not None and n_jobs < 0: + want_jobs = multiprocessing.cpu_count() + 1 + n_jobs + else: + want_jobs = 1 + with ctx: + parallel, p_fun, got_jobs = parallel_func(fun, n_jobs) + assert got_jobs == want_jobs diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_proj.py python-mne-1.1.0+dfsg/mne/tests/test_proj.py --- python-mne-0.23.4+dfsg/mne/tests/test_proj.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_proj.py 2022-08-03 15:48:14.000000000 +0000 @@ -20,7 +20,7 @@ from mne.proj import (read_proj, write_proj, make_eeg_average_ref_proj, _has_eeg_average_ref_proj) from mne.rank import _compute_rank_int -from mne.utils import run_tests_if_main +from mne.utils import _record_warnings base_dir = op.join(op.dirname(__file__), '..', 'io', 'tests', 'data') raw_fname = op.join(base_dir, 'test_raw.fif') @@ -88,7 +88,7 @@ def _check_warnings(raw, events, picks=None, count=3): """Count warnings.""" - with pytest.warns(None) as w: + with _record_warnings() as w: Epochs(raw, events, dict(aud_l=1, vis_l=3), -0.2, 0.5, picks=picks, preload=True, proj=True) assert len(w) == count @@ -144,9 +144,9 @@ sensitivity_map(fwd) -def test_compute_proj_epochs(tmpdir): +def test_compute_proj_epochs(tmp_path): """Test SSP computation on epochs.""" - tempdir = str(tmpdir) + tempdir = str(tmp_path) event_id, tmin, tmax = 1, -0.2, 0.3 raw = read_raw_fif(raw_fname, preload=True) @@ -158,7 +158,7 @@ baseline=None, proj=False) evoked = epochs.average() - projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=1) + projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0) write_proj(op.join(tempdir, 'test-proj.fif.gz'), projs) for p_fname in [proj_fname, proj_gz_fname, op.join(tempdir, 'test-proj.fif.gz')]: @@ -182,6 +182,7 @@ corr = np.corrcoef(p1_data, p2_data)[0, 1] assert_array_almost_equal(corr, 1.0, 5) if p2['explained_var']: + assert isinstance(p2['explained_var'], float) assert_array_almost_equal(p1['explained_var'], p2['explained_var']) @@ -193,7 +194,8 @@ assert U.shape[1] == 2 # test that you can save them - epochs.info['projs'] += projs + with epochs.info._unlock(): + epochs.info['projs'] += projs evoked = epochs.average() evoked.save(op.join(tempdir, 'foo-ave.fif')) @@ -204,7 +206,7 @@ # XXX : test something # test parallelization - projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=1, + projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, desc_prefix='foobar') assert all('foobar' in x['desc'] for x in projs) projs = activate_proj(projs) @@ -223,13 +225,13 @@ with pytest.raises(TypeError, match='projs'): write_proj(fname, 'foo') with pytest.raises(TypeError, match=r'projs\[0\] must be .*'): - write_proj(fname, ['foo']) + write_proj(fname, ['foo'], overwrite=True) @pytest.mark.slowtest -def test_compute_proj_raw(tmpdir): +def test_compute_proj_raw(tmp_path): """Test SSP computation on raw.""" - tempdir = str(tmpdir) + tempdir = str(tmp_path) # Test that the raw projectors work raw_time = 2.5 # Do shorter amount for speed raw = read_raw_fif(raw_fname).crop(0, raw_time) @@ -247,7 +249,8 @@ assert U.shape[1] == 2 # test that you can save them - raw.info['projs'] += projs + with raw.info._unlock(): + raw.info['projs'] += projs raw.save(op.join(tempdir, 'foo_%d_raw.fif' % ii), overwrite=True) # Test that purely continuous (no duration) raw projection works @@ -263,7 +266,8 @@ assert U.shape[1] == 2 # test that you can save them - raw.info['projs'] += projs + with raw.info._unlock(): + raw.info['projs'] += projs raw.save(op.join(tempdir, 'foo_rawproj_continuous_raw.fif')) # test resampled-data projector, upsampling instead of downsampling @@ -334,10 +338,11 @@ reref = raw.copy() reref.add_proj(car) reref.apply_proj() - assert_array_almost_equal(reref._data[eeg].mean(axis=0), 0, decimal=19) + assert_array_almost_equal(reref._data[eeg].mean(axis=0), 0, decimal=18) # Error when custom reference has already been applied - raw.info['custom_ref_applied'] = True + with raw.info._unlock(): + raw.info['custom_ref_applied'] = True pytest.raises(RuntimeError, make_eeg_average_ref_proj, raw.info) # test that an average EEG ref is not added when doing proj @@ -374,7 +379,8 @@ # Custom ref flag set raw = read_raw_fif(raw_fname) - raw.info['custom_ref_applied'] = True + with raw.info._unlock(): + raw.info['custom_ref_applied'] = True assert not _needs_eeg_average_ref_proj(raw.info) @@ -403,6 +409,3 @@ else: mag_names = ch_names[2::3] assert this_raw.info['projs'][3]['data']['col_names'] == mag_names - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_rank.py python-mne-1.1.0+dfsg/mne/tests/test_rank.py --- python-mne-0.23.4+dfsg/mne/tests/test_rank.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_rank.py 2022-08-03 15:48:14.000000000 +0000 @@ -187,6 +187,7 @@ assert rank[ch_type] == expected_rank +@pytest.mark.slowtest # ~3 sec apiece on Azure means overall it's slow @testing.requires_testing_data @pytest.mark.parametrize('fname, rank_orig', ((hp_fif_fname, 120), (mf_fif_fname, 67))) @@ -204,7 +205,11 @@ assert raw.info['projs'] == [] mf = raw.info['proc_history'][0]['max_info'] assert mf['sss_info']['nfree'] == rank_orig - assert _get_rank_sss(raw) == rank_orig + + assert compute_rank(raw, 'info')['meg'] == rank_orig + assert compute_rank(raw.copy().pick('grad'), 'info')['grad'] == rank_orig + assert compute_rank(raw.copy().pick('mag'), 'info')['mag'] == rank_orig + mult = 1 + (meg == 'separate') rank = rank_orig - mult * n_proj if n_proj > 0: @@ -257,3 +262,42 @@ rank_new = _compute_rank_int(raw, 'info') assert rank_new == rank assert_array_equal(raw[:][0], data_orig) + + +def test_explicit_bads_pick(): + """Test when bads channels are explicitly passed + default picks=None.""" + raw = read_raw_fif(raw_fname).crop(0, 5).load_data() + raw.pick_types(eeg=True, meg=True, ref_meg=True) + + # Covariance + # Default picks=None + raw.info['bads'] = list() + noise_cov_1 = compute_raw_covariance(raw, picks=None) + rank = compute_rank(noise_cov_1, info=raw.info) + assert rank == dict(meg=303, eeg=60) + assert raw.info['bads'] == [] + + raw.info['bads'] = ['EEG 002', 'EEG 012', 'EEG 015', 'MEG 0122'] + noise_cov = compute_raw_covariance(raw, picks=None) + rank = compute_rank(noise_cov, info=raw.info) + assert rank == dict(meg=302, eeg=57) + assert raw.info['bads'] == ['EEG 002', 'EEG 012', 'EEG 015', 'MEG 0122'] + + # Explicit picks + picks = pick_types(raw.info, meg=True, eeg=True, exclude=[]) + noise_cov_2 = compute_raw_covariance(raw, picks=picks) + rank = compute_rank(noise_cov_2, info=raw.info) + assert rank == dict(meg=303, eeg=60) + assert raw.info['bads'] == ['EEG 002', 'EEG 012', 'EEG 015', 'MEG 0122'] + + assert_array_equal(noise_cov_1['data'], noise_cov_2['data']) + assert noise_cov_1['names'] == noise_cov_2['names'] + + # Raw + raw.info['bads'] = list() + rank = compute_rank(raw) + assert rank == dict(meg=303, eeg=60) + + raw.info['bads'] = ['EEG 002', 'EEG 012', 'EEG 015', 'MEG 0122'] + rank = compute_rank(raw) + assert rank == dict(meg=302, eeg=57) diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_read_vectorview_selection.py python-mne-1.1.0+dfsg/mne/tests/test_read_vectorview_selection.py --- python-mne-0.23.4+dfsg/mne/tests/test_read_vectorview_selection.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_read_vectorview_selection.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,7 +4,6 @@ from mne import read_vectorview_selection from mne.io import read_raw_fif -from mne.utils import run_tests_if_main test_path = op.join(op.split(__file__)[0], '..', 'io', 'tests', 'data') raw_fname = op.join(test_path, 'test_raw.fif') @@ -47,6 +46,3 @@ with pytest.raises(TypeError, match='must be an instance of Info or None'): read_vectorview_selection(name, info='foo') - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_report.py python-mne-1.1.0+dfsg/mne/tests/test_report.py --- python-mne-0.23.4+dfsg/mne/tests/test_report.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_report.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,648 +0,0 @@ -# -*- coding: utf-8 -*- -# Authors: Mainak Jas -# Teon Brooks -# -# License: BSD (3-clause) - -import base64 -import copy -import glob -import pickle -from io import BytesIO -import os -import os.path as op -import re -import shutil -import pathlib - -import numpy as np -import pytest -from matplotlib import pyplot as plt - -from mne import Epochs, read_events, read_evokeds, report as report_mod -from mne.io import read_raw_fif -from mne.datasets import testing -from mne.report import Report, open_report, _ReportScraper -from mne.utils import (requires_nibabel, Bunch, - run_tests_if_main, requires_h5py) -from mne.viz import plot_alignment -from mne.io.write import DATE_NONE - -data_dir = testing.data_path(download=False) -subjects_dir = op.join(data_dir, 'subjects') -report_dir = op.join(data_dir, 'MEG', 'sample') -raw_fname = op.join(report_dir, 'sample_audvis_trunc_raw.fif') -ms_fname = op.join(data_dir, 'SSS', 'test_move_anon_raw.fif') -event_fname = op.join(report_dir, 'sample_audvis_trunc_raw-eve.fif') -cov_fname = op.join(report_dir, 'sample_audvis_trunc-cov.fif') -proj_fname = op.join(report_dir, 'sample_audvis_ecg-proj.fif') -fwd_fname = op.join(report_dir, 'sample_audvis_trunc-meg-eeg-oct-6-fwd.fif') -trans_fname = op.join(report_dir, 'sample_audvis_trunc-trans.fif') -inv_fname = op.join(report_dir, - 'sample_audvis_trunc-meg-eeg-oct-6-meg-inv.fif') -mri_fname = op.join(subjects_dir, 'sample', 'mri', 'T1.mgz') -bdf_fname = op.realpath(op.join(op.dirname(__file__), '..', 'io', - 'edf', 'tests', 'data', 'test.bdf')) -edf_fname = op.realpath(op.join(op.dirname(__file__), '..', 'io', - 'edf', 'tests', 'data', 'test.edf')) - -base_dir = op.realpath(op.join(op.dirname(__file__), '..', 'io', 'tests', - 'data')) -evoked_fname = op.join(base_dir, 'test-ave.fif') - - -def _get_example_figures(): - """Create two example figures.""" - fig1 = plt.plot([1, 2], [1, 2])[0].figure - fig2 = plt.plot([3, 4], [3, 4])[0].figure - return [fig1, fig2] - - -@pytest.mark.slowtest -@testing.requires_testing_data -def test_render_report(renderer, tmpdir): - """Test rendering *.fif files for mne report.""" - tempdir = str(tmpdir) - raw_fname_new = op.join(tempdir, 'temp_raw.fif') - raw_fname_new_bids = op.join(tempdir, 'temp_meg.fif') - ms_fname_new = op.join(tempdir, 'temp_ms_raw.fif') - event_fname_new = op.join(tempdir, 'temp_raw-eve.fif') - cov_fname_new = op.join(tempdir, 'temp_raw-cov.fif') - proj_fname_new = op.join(tempdir, 'temp_ecg-proj.fif') - fwd_fname_new = op.join(tempdir, 'temp_raw-fwd.fif') - inv_fname_new = op.join(tempdir, 'temp_raw-inv.fif') - for a, b in [[raw_fname, raw_fname_new], - [raw_fname, raw_fname_new_bids], - [ms_fname, ms_fname_new], - [event_fname, event_fname_new], - [cov_fname, cov_fname_new], - [proj_fname, proj_fname_new], - [fwd_fname, fwd_fname_new], - [inv_fname, inv_fname_new]]: - shutil.copyfile(a, b) - - # create and add -epo.fif and -ave.fif files - epochs_fname = op.join(tempdir, 'temp-epo.fif') - evoked_fname = op.join(tempdir, 'temp-ave.fif') - # Speed it up by picking channels - raw = read_raw_fif(raw_fname_new, preload=True) - raw.pick_channels(['MEG 0111', 'MEG 0121', 'EEG 001', 'EEG 002']) - raw.del_proj() - raw.set_eeg_reference(projection=True) - epochs = Epochs(raw, read_events(event_fname), 1, -0.2, 0.2) - epochs.save(epochs_fname, overwrite=True) - # This can take forever, so let's make it fast - # Also, make sure crop range is wide enough to avoid rendering bug - evoked = epochs.average() - with pytest.warns(RuntimeWarning, match='tmax is not in Evoked'): - evoked.crop(0.1, 0.2) - evoked.save(evoked_fname) - - report = Report(info_fname=raw_fname_new, subjects_dir=subjects_dir, - projs=True) - with pytest.warns(RuntimeWarning, match='Cannot render MRI'): - report.parse_folder(data_path=tempdir, on_error='raise') - assert repr(report) - - # Check correct paths and filenames - fnames = glob.glob(op.join(tempdir, '*.fif')) - for fname in fnames: - assert (op.basename(fname) in - [op.basename(x) for x in report.fnames]) - assert (''.join(report.html).find(op.basename(fname)) != -1) - - assert len(report.fnames) == len(fnames) - assert len(report.html) == len(report.fnames) - assert len(report.fnames) == len(report) - - # Check saving functionality - report.data_path = tempdir - fname = op.join(tempdir, 'report.html') - report.save(fname=fname, open_browser=False) - assert (op.isfile(fname)) - with open(fname, 'rb') as fid: - html = fid.read().decode('utf-8') - assert '(MaxShield on)' in html - # Projectors in Raw.info - assert '

    SSP Projectors

    ' in html - # Projectors in `proj_fname_new` - assert f'SSP Projectors: {op.basename(proj_fname_new)}' in html - # Evoked in `evoked_fname` - assert f'Evoked: {op.basename(evoked_fname)} ({evoked.comment})' in html - assert 'Topomap (ch_type =' in html - assert f'Evoked: {op.basename(evoked_fname)} (GFPs)' in html - - assert len(report.html) == len(fnames) - assert len(report.html) == len(report.fnames) - - # Check saving same report to new filename - report.save(fname=op.join(tempdir, 'report2.html'), open_browser=False) - assert (op.isfile(op.join(tempdir, 'report2.html'))) - - # Check overwriting file - report.save(fname=op.join(tempdir, 'report.html'), open_browser=False, - overwrite=True) - assert (op.isfile(op.join(tempdir, 'report.html'))) - - # Check pattern matching with multiple patterns - pattern = ['*raw.fif', '*eve.fif'] - with pytest.warns(RuntimeWarning, match='Cannot render MRI'): - report.parse_folder(data_path=tempdir, pattern=pattern) - assert (repr(report)) - - fnames = glob.glob(op.join(tempdir, '*.raw')) + \ - glob.glob(op.join(tempdir, '*.raw')) - for fname in fnames: - assert (op.basename(fname) in - [op.basename(x) for x in report.fnames]) - assert (''.join(report.html).find(op.basename(fname)) != -1) - - pytest.raises(ValueError, Report, image_format='foo') - pytest.raises(ValueError, Report, image_format=None) - - # SVG rendering - report = Report(info_fname=raw_fname_new, subjects_dir=subjects_dir, - image_format='svg') - tempdir = pathlib.Path(tempdir) # test using pathlib.Path - with pytest.warns(RuntimeWarning, match='Cannot render MRI'): - report.parse_folder(data_path=tempdir, on_error='raise') - - # ndarray support smoke test - report.add_figs_to_section(np.zeros((2, 3, 3)), 'caption', 'section') - - with pytest.raises(TypeError, match='figure must be a'): - report.add_figs_to_section('foo', 'caption', 'section') - with pytest.raises(TypeError, match='figure must be a'): - report.add_figs_to_section(['foo'], 'caption', 'section') - - -def test_add_custom_css(tmpdir): - """Test adding custom CSS rules to the report.""" - tempdir = str(tmpdir) - fname = op.join(tempdir, 'report.html') - fig = plt.figure() # Empty figure - - report = Report() - report.add_figs_to_section(figs=fig, captions='Test section') - custom_css = '.report_custom { color: red; }' - report.add_custom_css(css=custom_css) - - assert custom_css in report.include - report.save(fname, open_browser=False) - with open(fname, 'rb') as fid: - html = fid.read().decode('utf-8') - assert custom_css in html - - -def test_add_custom_js(tmpdir): - """Test adding custom JavaScript to the report.""" - tempdir = str(tmpdir) - fname = op.join(tempdir, 'report.html') - fig = plt.figure() # Empty figure - - report = Report() - report.add_figs_to_section(figs=fig, captions='Test section') - custom_js = ('function hello() {\n' - ' alert("Hello, report!");\n' - '}') - report.add_custom_js(js=custom_js) - - assert custom_js in report.include - report.save(fname, open_browser=False) - with open(fname, 'rb') as fid: - html = fid.read().decode('utf-8') - assert custom_js in html - - -@testing.requires_testing_data -def test_render_non_fiff(tmpdir): - """Test rendering non-FIFF files for mne report.""" - tempdir = str(tmpdir) - fnames_in = [bdf_fname, edf_fname] - fnames_out = [] - for fname in fnames_in: - basename = op.basename(fname) - basename, ext = op.splitext(basename) - fname_out = f'{basename}_raw{ext}' - outpath = op.join(tempdir, fname_out) - shutil.copyfile(fname, outpath) - fnames_out.append(fname_out) - - report = Report() - report.parse_folder(data_path=tempdir, render_bem=False, on_error='raise') - - # Check correct paths and filenames - for fname in fnames_out: - assert (op.basename(fname) in - [op.basename(x) for x in report.fnames]) - - assert len(report.fnames) == len(fnames_out) - assert len(report.html) == len(report.fnames) - assert len(report.fnames) == len(report) - - report.data_path = tempdir - fname = op.join(tempdir, 'report.html') - report.save(fname=fname, open_browser=False) - with open(fname, 'rb') as fid: - html = fid.read().decode('utf-8') - - assert '

    Raw: test_raw.bdf

    ' in html - assert '

    Raw: test_raw.edf

    ' in html - - -@testing.requires_testing_data -def test_report_raw_psd_and_date(tmpdir): - """Test report raw PSD and DATE_NONE functionality.""" - with pytest.raises(TypeError, match='dict'): - Report(raw_psd='foo') - - tempdir = str(tmpdir) - raw = read_raw_fif(raw_fname).crop(0, 1.).load_data() - raw_fname_new = op.join(tempdir, 'temp_raw.fif') - raw.save(raw_fname_new) - report = Report(raw_psd=True) - report.parse_folder(data_path=tempdir, render_bem=False, - on_error='raise') - assert isinstance(report.html, list) - assert 'PSD' in ''.join(report.html) - assert 'GMT' in ''.join(report.html) - - # test new anonymize functionality - report = Report() - raw.anonymize() - raw.save(raw_fname_new, overwrite=True) - report.parse_folder(data_path=tempdir, render_bem=False, - on_error='raise') - assert isinstance(report.html, list) - assert 'GMT' in ''.join(report.html) - - # DATE_NONE functionality - report = Report() - # old style (pre 0.20) date anonymization - raw.info['meas_date'] = None - for key in ('file_id', 'meas_id'): - value = raw.info.get(key) - if value is not None: - assert 'msecs' not in value - value['secs'] = DATE_NONE[0] - value['usecs'] = DATE_NONE[1] - raw.save(raw_fname_new, overwrite=True) - report.parse_folder(data_path=tempdir, render_bem=False, - on_error='raise') - assert isinstance(report.html, list) - assert 'GMT' not in ''.join(report.html) - - -@testing.requires_testing_data -def test_render_add_sections(renderer, tmpdir): - """Test adding figures/images to section.""" - tempdir = str(tmpdir) - report = Report(subjects_dir=subjects_dir) - # Check add_figs_to_section functionality - fig = plt.plot([1, 2], [1, 2])[0].figure - report.add_figs_to_section(figs=fig, # test non-list input - captions=['evoked response'], scale=1.2, - image_format='svg') - pytest.raises(ValueError, report.add_figs_to_section, figs=[fig, fig], - captions='H') - pytest.raises(ValueError, report.add_figs_to_section, figs=fig, - captions=['foo'], scale=0, image_format='svg') - pytest.raises(ValueError, report.add_figs_to_section, figs=fig, - captions=['foo'], scale=1e-10, image_format='svg') - # need to recreate because calls above change size - fig = plt.plot([1, 2], [1, 2])[0].figure - - # Check add_images_to_section with png - img_fname = op.join(tempdir, 'testimage.png') - fig.savefig(img_fname) - report.add_images_to_section(fnames=[img_fname], - captions=['evoked response']) - - report.add_images_to_section(fnames=[img_fname], - captions=['evoked response']) - - pytest.raises(ValueError, report.add_images_to_section, - fnames=[img_fname, img_fname], captions='H') - - pytest.raises(ValueError, report.add_images_to_section, - fnames=['foobar.xxx'], captions='H') - - evoked = read_evokeds(evoked_fname, condition='Left Auditory', - baseline=(-0.2, 0.0)) - fig = plot_alignment(evoked.info, trans_fname, subject='sample', - subjects_dir=subjects_dir) - - report.add_figs_to_section(figs=fig, # test non-list input - captions='random image', scale=1.2) - assert (repr(report)) - fname = op.join(str(tmpdir), 'test.html') - report.save(fname, open_browser=False) - with open(fname, 'r') as fid: - html = fid.read() - assert html.count('
  • ', - report.html[0])]) - assert imgs.ndim == 4 # images, h, w, rgba - assert len(imgs) == 6 - imgs.shape = (len(imgs), -1) - norms = np.linalg.norm(imgs, axis=-1) - # should have down-up-down shape - corr = np.corrcoef(norms, np.hanning(len(imgs)))[0, 1] - assert 0.78 < corr < 0.80 - - -@testing.requires_testing_data -@requires_nibabel() -def test_render_mri_without_bem(tmpdir): - """Test rendering MRI without BEM for mne report.""" - tempdir = str(tmpdir) - os.mkdir(op.join(tempdir, 'sample')) - os.mkdir(op.join(tempdir, 'sample', 'mri')) - shutil.copyfile(mri_fname, op.join(tempdir, 'sample', 'mri', 'T1.mgz')) - report = Report(info_fname=raw_fname, - subject='sample', subjects_dir=tempdir) - with pytest.raises(RuntimeError, match='No matching files found'): - report.parse_folder(tempdir, render_bem=False) - with pytest.warns(RuntimeWarning, match='No BEM surfaces found'): - report.parse_folder(tempdir, render_bem=True, mri_decim=20) - assert 'bem' in report.fnames - report.save(op.join(tempdir, 'report.html'), open_browser=False) - - -@testing.requires_testing_data -@requires_nibabel() -def test_add_htmls_to_section(): - """Test adding html str to mne report.""" - report = Report(info_fname=raw_fname, - subject='sample', subjects_dir=subjects_dir) - html = 'MNE-Python is AWESOME' - caption, section = 'html', 'html_section' - report.add_htmls_to_section(html, caption, section) - idx = report._sectionlabels.index('report_' + section) - html_compare = report.html[idx] - assert (html in html_compare) - assert (repr(report)) - - -def test_add_slider_to_section(tmpdir): - """Test adding a slider with a series of images to mne report.""" - tempdir = str(tmpdir) - report = Report(info_fname=raw_fname, - subject='sample', subjects_dir=subjects_dir) - section = 'slider_section' - figs = _get_example_figures() - report.add_slider_to_section(figs, section=section, title='my title') - assert report.fnames[0] == 'my title-#-report_slider_section-#-custom' - report.save(op.join(tempdir, 'report.html'), open_browser=False) - - pytest.raises(NotImplementedError, report.add_slider_to_section, - [figs, figs]) - pytest.raises(ValueError, report.add_slider_to_section, figs, ['wug']) - pytest.raises(TypeError, report.add_slider_to_section, figs, 'wug') - # need at least 2 - pytest.raises(ValueError, report.add_slider_to_section, figs[:1], 'wug') - - # Smoke test that SVG w/unicode can be added - report = Report() - fig, ax = plt.subplots() - ax.set_xlabel('µ') - report.add_slider_to_section([fig] * 2, image_format='svg') - - -def test_validate_input(): - """Test Report input validation.""" - report = Report() - items = ['a', 'b', 'c'] - captions = ['Letter A', 'Letter B', 'Letter C'] - section = 'ABCs' - comments = ['First letter of the alphabet.', - 'Second letter of the alphabet', - 'Third letter of the alphabet'] - pytest.raises(ValueError, report._validate_input, items, captions[:-1], - section, comments=None) - pytest.raises(ValueError, report._validate_input, items, captions, section, - comments=comments[:-1]) - values = report._validate_input(items, captions, section, comments=None) - items_new, captions_new, comments_new = values - assert len(comments_new) == len(items) - - -@requires_h5py -def test_open_report(tmpdir): - """Test the open_report function.""" - tempdir = str(tmpdir) - hdf5 = op.join(tempdir, 'report.h5') - - # Test creating a new report through the open_report function - fig1 = _get_example_figures()[0] - with open_report(hdf5, subjects_dir=subjects_dir) as report: - assert report.subjects_dir == subjects_dir - assert report._fname == hdf5 - report.add_figs_to_section(figs=fig1, captions=['evoked response']) - # Exiting the context block should have triggered saving to HDF5 - assert op.exists(hdf5) - - # Load the HDF5 version of the report and check equivalence - report2 = open_report(hdf5) - assert report2._fname == hdf5 - assert report2.subjects_dir == report.subjects_dir - assert report2.html == report.html - assert report2.__getstate__() == report.__getstate__() - assert '_fname' not in report2.__getstate__() - - # Check parameters when loading a report - pytest.raises(ValueError, open_report, hdf5, foo='bar') # non-existing - pytest.raises(ValueError, open_report, hdf5, subjects_dir='foo') - open_report(hdf5, subjects_dir=subjects_dir) # This should work - - # Check that the context manager doesn't swallow exceptions - with pytest.raises(ZeroDivisionError): - with open_report(hdf5, subjects_dir=subjects_dir) as report: - 1 / 0 - - -def test_remove(): - """Test removing figures from a report.""" - r = Report() - fig1, fig2 = _get_example_figures() - r.add_figs_to_section(fig1, 'figure1', 'mysection') - r.add_slider_to_section([fig1, fig2], title='figure1', - section='othersection') - r.add_figs_to_section(fig2, 'figure1', 'mysection') - r.add_figs_to_section(fig2, 'figure2', 'mysection') - - # Test removal by caption - r2 = copy.deepcopy(r) - removed_index = r2.remove(caption='figure1') - assert removed_index == 2 - assert len(r2.html) == 3 - assert r2.html[0] == r.html[0] - assert r2.html[1] == r.html[1] - assert r2.html[2] == r.html[3] - - # Test restricting to section - r2 = copy.deepcopy(r) - removed_index = r2.remove(caption='figure1', section='othersection') - assert removed_index == 1 - assert len(r2.html) == 3 - assert r2.html[0] == r.html[0] - assert r2.html[1] == r.html[2] - assert r2.html[2] == r.html[3] - - # Test removal of empty sections - r2 = copy.deepcopy(r) - r2.remove(caption='figure1', section='othersection') - assert r2.sections == ['mysection'] - assert r2._sectionvars == {'mysection': 'report_mysection'} - - -def test_add_or_replace(): - """Test replacing existing figures in a report.""" - r = Report() - fig1, fig2 = _get_example_figures() - r.add_figs_to_section(fig1, 'duplicate', 'mysection') - r.add_figs_to_section(fig1, 'duplicate', 'mysection') - r.add_figs_to_section(fig1, 'duplicate', 'othersection') - r.add_figs_to_section(fig2, 'nonduplicate', 'mysection') - # By default, replace=False, so all figures should be there - assert len(r.html) == 4 - - old_r = copy.deepcopy(r) - - # Re-add fig1 with replace=True, it should overwrite the last occurrence of - # fig1 in section 'mysection'. - r.add_figs_to_section(fig2, 'duplicate', 'mysection', replace=True) - assert len(r.html) == 4 - assert r.html[1] != old_r.html[1] # This figure should have changed - # All other figures should be the same - assert r.html[0] == old_r.html[0] - assert r.html[2] == old_r.html[2] - assert r.html[3] == old_r.html[3] - - -def test_scraper(tmpdir): - """Test report scraping.""" - r = Report() - fig1, fig2 = _get_example_figures() - r.add_figs_to_section(fig1, 'a', 'mysection') - r.add_figs_to_section(fig2, 'b', 'mysection') - # Mock a Sphinx + sphinx_gallery config - app = Bunch(builder=Bunch(srcdir=str(tmpdir), - outdir=op.join(str(tmpdir), '_build', 'html'))) - scraper = _ReportScraper() - scraper.app = app - gallery_conf = dict(src_dir=app.builder.srcdir, builder_name='html') - img_fname = op.join(app.builder.srcdir, 'auto_examples', 'images', - 'sg_img.png') - target_file = op.join(app.builder.srcdir, 'auto_examples', 'sg.py') - os.makedirs(op.dirname(img_fname)) - os.makedirs(app.builder.outdir) - block_vars = dict(image_path_iterator=(img for img in [img_fname]), - example_globals=dict(a=1), target_file=target_file) - # Nothing yet - block = None - rst = scraper(block, block_vars, gallery_conf) - assert rst == '' - # Still nothing - block_vars['example_globals']['r'] = r - rst = scraper(block, block_vars, gallery_conf) - # Once it's saved, add it - assert rst == '' - fname = op.join(str(tmpdir), 'my_html.html') - r.save(fname, open_browser=False) - rst = scraper(block, block_vars, gallery_conf) - out_html = op.join(app.builder.outdir, 'auto_examples', 'my_html.html') - assert not op.isfile(out_html) - scraper.copyfiles() - assert op.isfile(out_html) - assert rst.count('"') == 6 - assert "numpy + with _record_warnings(): # nib<->numpy img = nib.load(str(vol_fname)) assert (img.shape == src[0]['shape'] + (len(stc.times),)) - with pytest.warns(None): # nib<->numpy + with _record_warnings(): # nib<->numpy t1_img = nib.load(fname_t1) - stc.save_as_volume(tmpdir.join('stc.nii.gz'), src, - dest='mri', mri_resolution=True) - with pytest.warns(None): # nib<->numpy + stc.save_as_volume(vol_fname, src, dest='mri', mri_resolution=True, + overwrite=True) + with _record_warnings(): # nib<->numpy img = nib.load(str(vol_fname)) assert (img.shape == t1_img.shape + (len(stc.times),)) assert_allclose(img.affine, t1_img.affine, atol=1e-5) @@ -408,11 +412,11 @@ assert stc.data.shape == (len(data), 1) -def test_io_stc(tmpdir): +def test_io_stc(tmp_path): """Test IO for STC files.""" stc = _fake_stc() - stc.save(tmpdir.join("tmp.stc")) - stc2 = read_source_estimate(tmpdir.join("tmp.stc")) + stc.save(tmp_path / "tmp.stc") + stc2 = read_source_estimate(tmp_path / "tmp.stc") assert_array_almost_equal(stc.data, stc2.data) assert_array_almost_equal(stc.tmin, stc2.tmin) @@ -423,23 +427,28 @@ # test warning for complex data stc2.data = stc2.data.astype(np.complex128) with pytest.raises(ValueError, match='Cannot save complex-valued STC'): - stc2.save(tmpdir.join('complex.stc')) + stc2.save(tmp_path / 'complex.stc') -@requires_h5py +@requires_version('h5io') @pytest.mark.parametrize('is_complex', (True, False)) @pytest.mark.parametrize('vector', (True, False)) -def test_io_stc_h5(tmpdir, is_complex, vector): +def test_io_stc_h5(tmp_path, is_complex, vector): """Test IO for STC files using HDF5.""" if vector: stc = _fake_vec_stc(is_complex=is_complex) else: stc = _fake_stc(is_complex=is_complex) - pytest.raises(ValueError, stc.save, tmpdir.join('tmp'), - ftype='foo') - out_name = tmpdir.join('tmp') + match = 'can only be written' if vector else "Invalid value for the 'ftype" + with pytest.raises(ValueError, match=match): + stc.save(tmp_path / 'tmp.h5', ftype='foo') + out_name = str(tmp_path / 'tmp') stc.save(out_name, ftype='h5') - stc.save(out_name, ftype='h5') # test overwrite + # test overwrite + assert op.isfile(out_name + '-stc.h5') + with pytest.raises(FileExistsError, match='Destination file exists'): + stc.save(out_name, ftype='h5') + stc.save(out_name, ftype='h5', overwrite=True) stc3 = read_source_estimate(out_name) stc4 = read_source_estimate(out_name + '-stc') stc5 = read_source_estimate(out_name + '-stc.h5') @@ -455,14 +464,14 @@ assert_array_equal(v1, v2) -def test_io_w(tmpdir): +def test_io_w(tmp_path): """Test IO for w files.""" stc = _fake_stc(n_time=1) - w_fname = tmpdir.join('fake') + w_fname = tmp_path / 'fake' stc.save(w_fname, ftype='w') src = read_source_estimate(w_fname) - src.save(tmpdir.join('tmp'), ftype='w') - src2 = read_source_estimate(tmpdir.join('tmp-lh.w')) + src.save(tmp_path / 'tmp', ftype='w') + src2 = read_source_estimate(tmp_path / 'tmp-lh.w') assert_array_almost_equal(src.data, src2.data) assert_array_almost_equal(src.lh_vertno, src2.lh_vertno) assert_array_almost_equal(src.rh_vertno, src2.rh_vertno) @@ -689,7 +698,8 @@ with pytest.raises(ValueError, match='when using a vector'): extract_label_time_course(stcs, labels, src, mode=mode) continue - label_tc = extract_label_time_course(stcs, labels, src, mode=mode) + with _record_warnings(): # SVD convergence on arm64 + label_tc = extract_label_time_course(stcs, labels, src, mode=mode) label_tc_method = [stc.extract_label_time_course(labels, src, mode=mode) for stc in stcs] @@ -883,7 +893,7 @@ assert_allclose(_varexp(label_tc, label_tc), 1.) ve = _varexp(stc_back.data, stcs[0].data) assert 0.83 < ve < 0.85 - with pytest.warns(None): # ignore warnings about no output + with _record_warnings(): # ignore no output label_tc_rt = extract_label_time_course( stc_back, labels, src=src, mri_resolution=mri_res, allow_empty=True) @@ -1087,7 +1097,7 @@ assert_array_equal(df.values.T[2:], stc.data) # test long format df_long = stc.to_data_frame(long_format=True) - assert(len(df_long) == stc.data.size) + assert len(df_long) == stc.data.size expected = ('subject', 'time', 'source', 'value') assert set(expected) == set(df_long.columns) @@ -1141,7 +1151,7 @@ with pytest.raises(ValueError, match='out of bounds'): stc.get_peak(tmax=90) with pytest.raises(ValueError, - match='smaller or equal' if n_times > 1 else 'out of'): + match='must be <=' if n_times > 1 else 'out of'): stc.get_peak(tmin=0.002, tmax=0.001) vert_idx, time_idx = stc.get_peak() @@ -1166,9 +1176,9 @@ stc.get_peak(hemi='rh') -@requires_h5py +@requires_version('h5io') @testing.requires_testing_data -def test_mixed_stc(tmpdir): +def test_mixed_stc(tmp_path): """Test source estimate from mixed source space.""" N = 90 # number of sources T = 2 # number of time points @@ -1184,7 +1194,7 @@ stc = MixedSourceEstimate(data, vertno, 0, 1) # make sure error is raised for plotting surface with volume source - fname = tmpdir.join('mixed-stc.h5') + fname = tmp_path / 'mixed-stc.h5' stc.save(fname) stc_out = read_source_estimate(fname) assert_array_equal(stc_out.vertices, vertno) @@ -1194,7 +1204,7 @@ assert isinstance(stc_out, MixedSourceEstimate) -@requires_h5py +@requires_version('h5io') @pytest.mark.parametrize('klass, kind', [ (VectorSourceEstimate, 'surf'), (VolVectorSourceEstimate, 'vol'), @@ -1203,7 +1213,7 @@ ]) @pytest.mark.parametrize('dtype', [ np.float32, np.float64, np.complex64, np.complex128]) -def test_vec_stc_basic(tmpdir, klass, kind, dtype): +def test_vec_stc_basic(tmp_path, klass, kind, dtype): """Test (vol)vector source estimate.""" nn = np.array([ [1, 0, 0], @@ -1276,7 +1286,7 @@ assert_allclose(got_directions, directions * flips) assert_allclose(projected.data, amplitudes * flips) - out_name = tmpdir.join('temp.h5') + out_name = tmp_path / 'temp.h5' stc.save(out_name) stc_read = read_source_estimate(out_name) assert_allclose(stc.data, stc_read.data) @@ -1528,20 +1538,21 @@ @testing.requires_testing_data -def test_stc_near_sensors(tmpdir): +def test_stc_near_sensors(tmp_path): """Test stc_near_sensors.""" info = read_info(fname_evoked) # pick the left EEG sensors picks = pick_types(info, meg=False, eeg=True, exclude=()) picks = [pick for pick in picks if info['chs'][pick]['loc'][0] < 0] pick_info(info, picks, copy=False) - info['projs'] = [] + with info._unlock(): + info['projs'] = [] info['bads'] = [] assert info['nchan'] == 33 evoked = EvokedArray(np.eye(info['nchan']), info) trans = read_trans(fname_fwd) assert trans['to'] == FIFF.FIFFV_COORD_HEAD - this_dir = str(tmpdir) + this_dir = str(tmp_path) # testing does not have pial, so fake it os.makedirs(op.join(this_dir, 'sample', 'surf')) for hemi in ('lh', 'rh'): @@ -1550,7 +1561,7 @@ # here we use a distance is smaller than the inter-sensor distance kwargs = dict(subject='sample', trans=trans, subjects_dir=this_dir, verbose=True, distance=0.005) - with pytest.raises(ValueError, match='No channels'): + with pytest.raises(ValueError, match='No appropriate channels'): stc_near_sensors(evoked, **kwargs) evoked.set_channel_types({ch_name: 'ecog' for ch_name in evoked.ch_names}) with catch_logging() as log: @@ -1607,14 +1618,43 @@ # and now with volumetric projection src = read_source_spaces(fname_vsrc) with catch_logging() as log: - stc_vol = stc_near_sensors(evoked, trans, 'sample', src=src, - subjects_dir=subjects_dir, verbose=True, - distance=0.033) + stc_vol = stc_near_sensors( + evoked, trans, 'sample', src=src, surface=None, + subjects_dir=subjects_dir, distance=0.033, verbose=True) assert isinstance(stc_vol, VolSourceEstimate) log = log.getvalue() assert '4157 volume vertices' in log +@requires_version('pymatreader') +@testing.requires_testing_data +def test_stc_near_sensors_picks(): + """Test using picks with stc_near_sensors.""" + info = mne.io.read_raw_nirx(fname_nirx).info + evoked = mne.EvokedArray(np.ones((len(info['ch_names']), 1)), info) + src = mne.read_source_spaces(fname_src_fs) + kwargs = dict( + evoked=evoked, subject='fsaverage', trans='fsaverage', + subjects_dir=subjects_dir, src=src, surface=None, project=True) + with pytest.raises(ValueError, match='No appropriate channels'): + stc_near_sensors(**kwargs) + picks = np.arange(len(info['ch_names'])) + data = stc_near_sensors(picks=picks, **kwargs).data + assert len(data) == 20484 + assert (data >= 0).all() + data = data[data > 0] + n_pts = len(data) + assert 500 < n_pts < 600 + lo, hi = np.percentile(data, (5, 95)) + assert 0.01 < lo < 0.1 + assert 1.3 < hi < 1.7 # > 1 + data = stc_near_sensors(picks=picks, mode='weighted', **kwargs).data + assert (data >= 0).all() + data = data[data > 0] + assert len(data) == n_pts + assert_array_equal(data, 1.) # values preserved + + def _make_morph_map_hemi_same(subject_from, subject_to, subjects_dir, reg_from, reg_to): return _make_morph_map_hemi(subject_from, subject_from, subjects_dir, @@ -1624,13 +1664,14 @@ @requires_nibabel() @testing.requires_testing_data @pytest.mark.parametrize('kind', ( - pytest.param('volume', marks=[requires_version('dipy')]), + pytest.param('volume', marks=[requires_version('dipy'), + pytest.mark.slowtest]), 'surface', )) @pytest.mark.parametrize('scale', ((1.0, 0.8, 1.2), 1., 0.9)) -def test_scale_morph_labels(kind, scale, monkeypatch, tmpdir): +def test_scale_morph_labels(kind, scale, monkeypatch, tmp_path): """Test label extraction, morphing, and MRI scaling relationships.""" - tempdir = str(tmpdir) + tempdir = str(tmp_path) subject_from = 'sample' subject_to = 'small' testing_dir = op.join(subjects_dir, subject_from) @@ -1679,7 +1720,7 @@ op.join(from_dir, 'bem', 'sample-vol20-src.fif'), src_from) scale_mri(subject_from, subject_to, scale, subjects_dir=tempdir, annot=True, skip_fiducials=True, verbose=True, - overwrite=True) # XXX + overwrite=True) if kind == 'surface': src_to = read_source_spaces( op.join(tempdir, subject_to, 'bem', @@ -1687,7 +1728,7 @@ labels_to = read_labels_from_annot( subject_to, subjects_dir=tempdir) # Save time since we know these subjects are identical - monkeypatch.setattr(mne.surface, '_make_morph_map_hemi', + monkeypatch.setattr(mne.morph_map, '_make_morph_map_hemi', _make_morph_map_hemi_same) else: src_to = read_source_spaces( @@ -1751,7 +1792,7 @@ want_affine = np.eye(4) want_affine.ravel()[::5][:3] = 1. / np.array(scale, float) # just a scaling (to within 1% if zooms=None, 20% with zooms=10) - assert_allclose(want_affine[:, :3], got_affine[:, :3], atol=2e-1) + assert_allclose(want_affine[:, :3], got_affine[:, :3], atol=0.4) assert got_affine[3, 3] == 1. # little translation (to within `limit` mm) move = np.linalg.norm(got_affine[:3, 3]) @@ -1770,10 +1811,10 @@ min_, max_ = 0.57, 0.67 elif scale == 1: # min_, max_ = 0.85, 0.875 # zooms='auto' values - min_, max_ = 0.72, 0.75 + min_, max_ = 0.72, 0.76 else: # min_, max_ = 0.84, 0.855 # zooms='auto' values - min_, max_ = 0.61, 0.62 + min_, max_ = 0.46, 0.63 assert min_ < corr <= max_, scale else: assert_allclose( diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_source_space.py python-mne-1.1.0+dfsg/mne/tests/test_source_space.py --- python-mne-0.23.4+dfsg/mne/tests/test_source_space.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_source_space.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Authors: Alexandre Gramfort # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import os.path as op from shutil import copytree @@ -14,24 +14,20 @@ assert_array_less) from mne.datasets import testing import mne -from mne import (read_source_spaces, vertex_to_mni, write_source_spaces, +from mne import (read_source_spaces, write_source_spaces, setup_source_space, setup_volume_source_space, add_source_space_distances, read_bem_surfaces, morph_source_spaces, SourceEstimate, make_sphere_model, - head_to_mni, compute_source_morph, pick_types, - read_bem_solution, read_freesurfer_lut, read_talxfm, + compute_source_morph, pick_types, + read_bem_solution, read_freesurfer_lut, read_trans) from mne.fixes import _get_img_fdata -from mne.utils import (requires_nibabel, run_subprocess, - modified_env, requires_mne, run_tests_if_main, - check_version) +from mne.utils import (requires_nibabel, run_subprocess, _record_warnings, + requires_mne, check_version) from mne.surface import _accumulate_normals, _triangle_neighbors -from mne.source_space import _get_mgz_header from mne.source_estimate import _get_src_type -from mne.transforms import apply_trans, _get_trans -from mne.source_space import (get_volume_labels_from_aseg, - get_volume_labels_from_src, - _compare_source_spaces, +from mne.source_space import (get_volume_labels_from_src, + _compare_source_spaces, get_decimated_surfaces, compute_distance_to_sensors) from mne.io.pick import _picks_to_idx from mne.io.constants import FIFF @@ -120,18 +116,6 @@ compute_distance_to_sensors(src, info, use_picks, trans) -@testing.requires_testing_data -@requires_nibabel() -def test_mgz_header(): - """Test MGZ header reading.""" - import nibabel - header = _get_mgz_header(fname_mri) - mri_hdr = nibabel.load(fname_mri).header - assert_allclose(mri_hdr.get_data_shape(), header['dims']) - assert_allclose(mri_hdr.get_vox2ras_tkr(), header['vox2ras_tkr']) - assert_allclose(mri_hdr.get_ras2vox(), np.linalg.inv(header['vox2ras'])) - - def _read_small_src(remove=True): src = read_source_spaces(fname_small) if remove: @@ -179,7 +163,7 @@ @testing.requires_testing_data -def test_add_source_space_distances_limited(tmpdir): +def test_add_source_space_distances_limited(tmp_path): """Test adding distances to source space with a dist_limit.""" src = read_source_spaces(fname) src_new = read_source_spaces(fname) @@ -188,7 +172,7 @@ n_do = 200 # limit this for speed src_new[0]['vertno'] = src_new[0]['vertno'][:n_do].copy() src_new[1]['vertno'] = src_new[1]['vertno'][:n_do].copy() - out_name = tmpdir.join('temp-src.fif') + out_name = tmp_path / 'temp-src.fif' add_source_space_distances(src_new, dist_limit=0.007) write_source_spaces(out_name, src_new) src_new = read_source_spaces(out_name) @@ -213,7 +197,7 @@ @pytest.mark.slowtest @testing.requires_testing_data -def test_add_source_space_distances(tmpdir): +def test_add_source_space_distances(tmp_path): """Test adding distances to source space.""" src = read_source_spaces(fname) src_new = read_source_spaces(fname) @@ -222,7 +206,7 @@ n_do = 19 # limit this for speed src_new[0]['vertno'] = src_new[0]['vertno'][:n_do].copy() src_new[1]['vertno'] = src_new[1]['vertno'][:n_do].copy() - out_name = tmpdir.join('temp-src.fif') + out_name = tmp_path / 'temp-src.fif' n_jobs = 2 assert n_do % n_jobs != 0 with pytest.raises(ValueError, match='non-negative'): @@ -257,15 +241,15 @@ @testing.requires_testing_data @requires_mne -def test_discrete_source_space(tmpdir): +def test_discrete_source_space(tmp_path): """Test setting up (and reading/writing) discrete source spaces.""" src = read_source_spaces(fname) v = src[0]['vertno'] # let's make a discrete version with the C code, and with ours - temp_name = tmpdir.join('temp-src.fif') + temp_name = tmp_path / 'temp-src.fif' # save - temp_pos = tmpdir.join('temp-pos.txt') + temp_pos = tmp_path / 'temp-pos.txt' np.savetxt(str(temp_pos), np.c_[src[0]['rr'][v], src[0]['nn'][v]]) # let's try the spherical one (no bem or surf supplied) run_subprocess(['mne_volume_source_space', '--meters', @@ -286,20 +270,25 @@ _compare_source_spaces(src_c, src_c2) # now do MRI - pytest.raises(ValueError, setup_volume_source_space, 'sample', - pos=pos_dict, mri=fname_mri) + with pytest.raises(ValueError, match='Cannot create interpolation'): + setup_volume_source_space('sample', pos=pos_dict, mri=fname_mri) assert repr(src_new).split('~')[0] == repr(src_c).split('~')[0] assert ' kB' in repr(src_new) assert src_new.kind == 'discrete' assert _get_src_type(src_new, None) == 'discrete' + with pytest.raises(RuntimeError, match='finite'): + setup_volume_source_space( + pos=dict(rr=[[0, 0, float('inf')]], nn=[[0, 1, 0]])) + +@requires_nibabel() @pytest.mark.slowtest @testing.requires_testing_data -def test_volume_source_space(tmpdir): +def test_volume_source_space(tmp_path): """Test setting up volume source spaces.""" src = read_source_spaces(fname_vol) - temp_name = tmpdir.join('temp-src.fif') + temp_name = tmp_path / 'temp-src.fif' surf = read_bem_surfaces(fname_bem, s_id=FIFF.FIFFV_BEM_SURF_ID_BRAIN) surf['rr'] *= 1e3 # convert to mm bem_sol = read_bem_solution(fname_bem_3_sol) @@ -317,7 +306,7 @@ del src_new src_new = read_source_spaces(temp_name) _compare_source_spaces(src, src_new, mode='approx') - with pytest.raises(IOError, match='surface file.*not found'): + with pytest.raises(IOError, match='surface file.*not exist'): setup_volume_source_space( 'sample', surface='foo', mri=fname_mri, subjects_dir=subjects_dir) bem['surfs'][-1]['coord_frame'] = FIFF.FIFFV_COORD_HEAD @@ -348,13 +337,13 @@ @testing.requires_testing_data @requires_mne -def test_other_volume_source_spaces(tmpdir): +def test_other_volume_source_spaces(tmp_path): """Test setting up other volume source spaces.""" # these are split off because they require the MNE tools, and # Travis doesn't seem to like them # let's try the spherical one (no bem or surf supplied) - temp_name = tmpdir.join('temp-src.fif') + temp_name = tmp_path / 'temp-src.fif' run_subprocess(['mne_volume_source_space', '--grid', '7.0', '--src', temp_name, @@ -438,7 +427,7 @@ @pytest.mark.slowtest @testing.requires_testing_data -def test_setup_source_space(tmpdir): +def test_setup_source_space(tmp_path): """Test setting up ico, oct, and all source spaces.""" fname_ico = op.join(data_path, 'subjects', 'fsaverage', 'bem', 'fsaverage-ico-5-src.fif') @@ -463,7 +452,7 @@ # ico 5 (fsaverage) - write to temp file src = read_source_spaces(fname_ico) - with pytest.warns(None): # sklearn equiv neighbors + with _record_warnings(): # sklearn equiv neighbors src_new = setup_source_space('fsaverage', spacing='ico5', subjects_dir=subjects_dir, add_dist=False) _compare_source_spaces(src, src_new, mode='approx') @@ -474,8 +463,8 @@ # oct-6 (sample) - auto filename + IO src = read_source_spaces(fname) - temp_name = tmpdir.join('temp-src.fif') - with pytest.warns(None): # sklearn equiv neighbors + temp_name = tmp_path / 'temp-src.fif' + with _record_warnings(): # sklearn equiv neighbors src_new = setup_source_space('sample', spacing='oct6', subjects_dir=subjects_dir, add_dist=False) write_source_spaces(temp_name, src_new, overwrite=True) @@ -500,22 +489,24 @@ @pytest.mark.slowtest @pytest.mark.timeout(60) @pytest.mark.parametrize('spacing', [2, 7]) -def test_setup_source_space_spacing(tmpdir, spacing): +def test_setup_source_space_spacing(tmp_path, spacing, monkeypatch): """Test setting up surface source spaces using a given spacing.""" - copytree(op.join(subjects_dir, 'sample'), str(tmpdir.join('sample'))) + copytree(op.join(subjects_dir, 'sample'), tmp_path / 'sample') args = [] if spacing == 7 else ['--spacing', str(spacing)] - with modified_env(SUBJECTS_DIR=str(tmpdir), SUBJECT='sample'): - run_subprocess(['mne_setup_source_space'] + args) - src = read_source_spaces(tmpdir.join('sample', 'bem', - 'sample-%d-src.fif' % spacing)) - src_new = setup_source_space('sample', spacing=spacing, add_dist=False, - subjects_dir=subjects_dir) + monkeypatch.setenv('SUBJECTS_DIR', str(tmp_path)) + monkeypatch.setenv('SUBJECT', 'sample') + run_subprocess(['mne_setup_source_space'] + args) + src = read_source_spaces( + tmp_path / 'sample' / 'bem' / ('sample-%d-src.fif' % spacing) + ) + # No need to pass subjects_dir here because we've setenv'ed it + src_new = setup_source_space('sample', spacing=spacing, add_dist=False) _compare_source_spaces(src, src_new, mode='approx', nearest=True) # Degenerate conditions with pytest.raises(TypeError, match='spacing must be.*got.*float.*'): - setup_source_space('sample', 7., subjects_dir=subjects_dir) + setup_source_space('sample', 7.) with pytest.raises(ValueError, match='spacing must be >= 2, got 1'): - setup_source_space('sample', 1, subjects_dir=subjects_dir) + setup_source_space('sample', 1) @testing.requires_testing_data @@ -542,16 +533,16 @@ @pytest.mark.slowtest @testing.requires_testing_data -def test_write_source_space(tmpdir): +def test_write_source_space(tmp_path): """Test reading and writing of source spaces.""" src0 = read_source_spaces(fname, patch_stats=False) - temp_fname = tmpdir.join('tmp-src.fif') + temp_fname = tmp_path / 'tmp-src.fif' write_source_spaces(temp_fname, src0) src1 = read_source_spaces(temp_fname, patch_stats=False) _compare_source_spaces(src0, src1) # test warnings on bad filenames - src_badname = tmpdir.join('test-bad-name.fif.gz') + src_badname = tmp_path / 'test-bad-name.fif.gz' with pytest.warns(RuntimeWarning, match='-src.fif'): write_source_spaces(src_badname, src0) with pytest.warns(RuntimeWarning, match='-src.fif'): @@ -559,114 +550,9 @@ @testing.requires_testing_data -def test_vertex_to_mni(): - """Test conversion of vertices to MNI coordinates.""" - # obtained using "tksurfer (sample) (l/r)h white" - vertices = [100960, 7620, 150549, 96761] - coords = np.array([[-60.86, -11.18, -3.19], [-36.46, -93.18, -2.36], - [-38.00, 50.08, -10.61], [47.14, 8.01, 46.93]]) - hemis = [0, 0, 0, 1] - coords_2 = vertex_to_mni(vertices, hemis, 'sample', subjects_dir) - # less than 1mm error - assert_allclose(coords, coords_2, atol=1.0) - - -@testing.requires_testing_data -def test_head_to_mni(): - """Test conversion of aseg vertices to MNI coordinates.""" - # obtained using freeview - coords = np.array([[22.52, 11.24, 17.72], [22.52, 5.46, 21.58], - [16.10, 5.46, 22.23], [21.24, 8.36, 22.23]]) / 1000. - - xfm = read_talxfm('sample', subjects_dir) - coords_MNI = apply_trans(xfm['trans'], coords) * 1000. - - mri_head_t, _ = _get_trans(trans_fname, 'mri', 'head', allow_none=False) - - # obtained from sample_audvis-meg-oct-6-mixed-fwd.fif - coo_right_amygdala = np.array([[0.01745682, 0.02665809, 0.03281873], - [0.01014125, 0.02496262, 0.04233755], - [0.01713642, 0.02505193, 0.04258181], - [0.01720631, 0.03073877, 0.03850075]]) - coords_MNI_2 = head_to_mni(coo_right_amygdala, 'sample', mri_head_t, - subjects_dir) - # less than 1mm error - assert_allclose(coords_MNI, coords_MNI_2, atol=10.0) - - -@testing.requires_testing_data -def test_vertex_to_mni_fs_nibabel(monkeypatch): - """Test equivalence of vert_to_mni for nibabel and freesurfer.""" - n_check = 1000 - subject = 'sample' - vertices = rng.randint(0, 100000, n_check) - hemis = rng.randint(0, 1, n_check) - coords = vertex_to_mni(vertices, hemis, subject, subjects_dir) - monkeypatch.setattr(mne.source_space, 'has_nibabel', lambda: False) - coords_2 = vertex_to_mni(vertices, hemis, subject, subjects_dir) - # less than 0.1 mm error - assert_allclose(coords, coords_2, atol=0.1) - - -@testing.requires_testing_data -@requires_nibabel() -@pytest.mark.parametrize('fname', [ - None, - op.join(op.dirname(mne.__file__), 'data', 'FreeSurferColorLUT.txt'), -]) -def test_read_freesurfer_lut(fname, tmpdir): - """Test reading volume label names.""" - atlas_ids, colors = read_freesurfer_lut(fname) - assert list(atlas_ids).count('Brain-Stem') == 1 - assert len(colors) == len(atlas_ids) == 1266 - label_names, label_colors = get_volume_labels_from_aseg( - aseg_fname, return_colors=True) - assert isinstance(label_names, list) - assert isinstance(label_colors, list) - assert label_names.count('Brain-Stem') == 1 - for c in label_colors: - assert isinstance(c, np.ndarray) - assert c.shape == (4,) - assert len(label_names) == len(label_colors) == 46 - with pytest.raises(ValueError, match='must be False'): - get_volume_labels_from_aseg( - aseg_fname, return_colors=True, atlas_ids=atlas_ids) - label_names_2 = get_volume_labels_from_aseg( - aseg_fname, atlas_ids=atlas_ids) - assert label_names == label_names_2 - # long name (only test on one run) - if fname is not None: - return - fname = str(tmpdir.join('long.txt')) - names = ['Anterior_Cingulate_and_Medial_Prefrontal_Cortex-' + hemi - for hemi in ('lh', 'rh')] - ids = np.arange(1, len(names) + 1) - colors = [(id_,) * 4 for id_ in ids] - with open(fname, 'w') as fid: - for name, id_, color in zip(names, ids, colors): - out_color = ' '.join('%3d' % x for x in color) - line = '%d %s %s\n' % (id_, name, out_color) - fid.write(line) - lut, got_colors = read_freesurfer_lut(fname) - assert len(lut) == len(got_colors) == len(names) == len(ids) - for name, id_, color in zip(names, ids, colors): - assert name in lut - assert name in got_colors - assert_array_equal(got_colors[name][:3], color[:3]) - assert lut[name] == id_ - with open(fname, 'w') as fid: - for name, id_, color in zip(names, ids, colors): - out_color = ' '.join('%3d' % x for x in color[:3]) # wrong length! - line = '%d %s %s\n' % (id_, name, out_color) - fid.write(line) - with pytest.raises(RuntimeError, match='formatted'): - read_freesurfer_lut(fname) - - -@testing.requires_testing_data @requires_nibabel() @pytest.mark.parametrize('pass_ids', (True, False)) -def test_source_space_from_label(tmpdir, pass_ids): +def test_source_space_from_label(tmp_path, pass_ids): """Test generating a source space from volume label.""" aseg_short = 'aseg.mgz' atlas_ids, _ = read_freesurfer_lut() @@ -706,7 +592,7 @@ assert src[0]['nuse'] == 404 # for our given pos and label # test reading and writing - out_name = tmpdir.join('temp-src.fif') + out_name = tmp_path / 'temp-src.fif' write_source_spaces(out_name, src) src_from_file = read_source_spaces(out_name) _compare_source_spaces(src, src_from_file, mode='approx') @@ -731,10 +617,11 @@ for si, s in enumerate(src): assert_allclose(src_full[0]['rr'], s['rr'], atol=1e-6) # also check single_volume=True -- should be the same result - src_single = setup_volume_source_space( - src[0]['subject_his_id'], 7., 'aseg.mgz', bem=fname_bem, - volume_label=volume_labels, single_volume=True, add_interpolator=False, - subjects_dir=subjects_dir) + with pytest.warns(RuntimeWarning, match='Found no usable.*Left-vessel.*'): + src_single = setup_volume_source_space( + src[0]['subject_his_id'], 7., 'aseg.mgz', bem=fname_bem, + volume_label=volume_labels, single_volume=True, + add_interpolator=False, subjects_dir=subjects_dir) assert len(src_single) == 1 assert 'Unknown+Left-Cerebral-White-Matter+Left-' in repr(src_single) assert_array_equal(src_full[0]['vertno'], src_single[0]['vertno']) @@ -782,7 +669,7 @@ @testing.requires_testing_data @requires_nibabel() -def test_combine_source_spaces(tmpdir): +def test_combine_source_spaces(tmp_path): """Test combining source spaces.""" import nibabel as nib rng = np.random.RandomState(2) @@ -820,7 +707,7 @@ assert len(src) == 4 # test reading and writing - src_out_name = tmpdir.join('temp-src.fif') + src_out_name = tmp_path / 'temp-src.fif' src.save(src_out_name) src_from_file = read_source_spaces(src_out_name) _compare_source_spaces(src, src_from_file, mode='approx') @@ -832,7 +719,7 @@ assert (coord_frames == FIFF.FIFFV_COORD_MRI).all() # test errors for export_volume - image_fname = tmpdir.join('temp-image.mgz') + image_fname = tmp_path / 'temp-image.mgz' # source spaces with no volume with pytest.raises(ValueError, match='at least one volume'): @@ -846,7 +733,7 @@ del disc2 # unrecognized file type - bad_image_fname = tmpdir.join('temp-image.png') + bad_image_fname = tmp_path / 'temp-image.png' # vertices outside vol space warning pytest.raises(ValueError, src.export_volume, bad_image_fname, verbose='error') @@ -859,7 +746,7 @@ src_mixed_coord.export_volume(image_fname, verbose='error') # now actually write it - fname_img = tmpdir.join('img.nii') + fname_img = tmp_path / 'img.nii' for mri_resolution in (False, 'sparse', True): for src, up in ((vol, 705), (srf + vol, 27272), @@ -875,7 +762,7 @@ assert n_src == n_want, src # gh-8004 - temp_aseg = tmpdir.join('aseg.mgz') + temp_aseg = tmp_path / 'aseg.mgz' aseg_img = nib.load(aseg_fname) aseg_affine = aseg_img.affine aseg_affine[:3, :3] *= 0.7 @@ -999,7 +886,21 @@ src, subjects_dir=subjects_dir) -run_tests_if_main() +@testing.requires_testing_data +@pytest.mark.parametrize('src, n, nv', [ + (fname_fs, 2, 10242), (fname_src, 2, 258), (fname_vol, 0, 0), +]) +def test_get_decimated_surfaces(src, n, nv): + """Test get_decimated_surfaces.""" + surfaces = get_decimated_surfaces(src) + assert len(surfaces) == n + if n == 0: + return + for s in surfaces: + assert set(s) == {'rr', 'tris'} + assert len(s['rr']) == nv + assert_array_equal(np.unique(s['tris']), np.arange(nv)) + # The following code was used to generate small-src.fif.gz. # Unfortunately the C code bombs when trying to add source space distances, @@ -1014,7 +915,8 @@ data_path = mne.datasets.sample.data_path() src = mne.setup_source_space('sample', fname=None, spacing='oct5') hemis = ['lh', 'rh'] -fnames = [data_path + '/subjects/sample/surf/%s.decimated' % h for h in hemis] +fnames = [ + str(data_path) + '/subjects/sample/surf/%s.decimated' % h for h in hemis] vs = list() for s, fname in zip(src, fnames): @@ -1027,7 +929,8 @@ mne.write_surface(fname, coords, faces) # we need to move sphere surfaces -spheres = [data_path + '/subjects/sample/surf/%s.sphere' % h for h in hemis] +spheres = [ + str(data_path) + '/subjects/sample/surf/%s.sphere' % h for h in hemis] for s in spheres: os.rename(s, s + '.bak') try: diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_surface.py python-mne-1.1.0+dfsg/mne/tests/test_surface.py --- python-mne-0.23.4+dfsg/mne/tests/test_surface.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_surface.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,24 +1,31 @@ -import os -import os.path as op -from shutil import copyfile +# Authors: Alexandre Gramfort +# +# License: BSD-3-Clause -import numpy as np -from scipy import sparse +import os.path as op import pytest +import numpy as np from numpy.testing import assert_array_equal, assert_allclose, assert_equal -from mne.datasets import testing from mne import (read_surface, write_surface, decimate_surface, pick_types, - dig_mri_distances) -from mne.surface import (read_morph_map, _compute_nearest, _tessellate_sphere, - fast_cross_3d, get_head_surf, read_curvature, - get_meg_helmet_surf, _normal_orth, _read_patch) -from mne.utils import (_TempDir, requires_vtk, catch_logging, - run_tests_if_main, object_diff, requires_freesurfer) + dig_mri_distances, get_montage_volume_labels) +from mne.channels import make_dig_montage +from mne.coreg import get_mni_fiducials +from mne.datasets import testing from mne.io import read_info from mne.io.constants import FIFF -from mne.transforms import _get_trans +from mne.surface import (_compute_nearest, _tessellate_sphere, fast_cross_3d, + get_head_surf, read_curvature, get_meg_helmet_surf, + _normal_orth, _read_patch, _marching_cubes, + _voxel_neighbors, warp_montage_volume, + _project_onto_surface, _get_ico_surface) +from mne.transforms import (_get_trans, compute_volume_registration, + apply_trans) +from mne.utils import (catch_logging, object_diff, + requires_freesurfer, requires_nibabel, requires_dipy, + _record_warnings) + data_path = testing.data_path(download=False) subjects_dir = op.join(data_path, 'subjects') @@ -27,6 +34,7 @@ fname_trans = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc-trans.fif') fname_raw = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif') +fname_t1 = op.join(subjects_dir, 'fsaverage', 'mri', 'T1.mgz') rng = np.random.RandomState(0) @@ -112,61 +120,20 @@ assert_array_equal(nn1, nn3) -@pytest.mark.slowtest @testing.requires_testing_data -def test_make_morph_maps(): - """Test reading and creating morph maps.""" - # make a new fake subjects_dir - tempdir = _TempDir() - for subject in ('sample', 'sample_ds', 'fsaverage_ds'): - os.mkdir(op.join(tempdir, subject)) - os.mkdir(op.join(tempdir, subject, 'surf')) - regs = ('reg', 'left_right') if subject == 'fsaverage_ds' else ('reg',) - for hemi in ['lh', 'rh']: - for reg in regs: - args = [subject, 'surf', hemi + '.sphere.' + reg] - copyfile(op.join(subjects_dir, *args), - op.join(tempdir, *args)) - - for subject_from, subject_to, xhemi in ( - ('fsaverage_ds', 'sample_ds', False), - ('fsaverage_ds', 'fsaverage_ds', True)): - # trigger the creation of morph-maps dir and create the map - with catch_logging() as log: - mmap = read_morph_map(subject_from, subject_to, tempdir, - xhemi=xhemi, verbose=True) - log = log.getvalue() - assert 'does not exist' in log - assert 'Creating' in log - mmap2 = read_morph_map(subject_from, subject_to, subjects_dir, - xhemi=xhemi) - assert_equal(len(mmap), len(mmap2)) - for m1, m2 in zip(mmap, mmap2): - # deal with sparse matrix stuff - diff = (m1 - m2).data - assert_allclose(diff, np.zeros_like(diff), atol=1e-3, rtol=0) - - # This will also trigger creation, but it's trivial - with pytest.warns(None): - mmap = read_morph_map('sample', 'sample', subjects_dir=tempdir) - for mm in mmap: - assert (mm - sparse.eye(mm.shape[0], mm.shape[0])).sum() == 0 - - -@testing.requires_testing_data -def test_io_surface(): +def test_io_surface(tmp_path): """Test reading and writing of Freesurfer surface mesh files.""" - tempdir = _TempDir() + tempdir = str(tmp_path) fname_quad = op.join(data_path, 'subjects', 'bert', 'surf', 'lh.inflated.nofix') fname_tri = op.join(data_path, 'subjects', 'sample', 'bem', 'inner_skull.surf') for fname in (fname_quad, fname_tri): - with pytest.warns(None): # no volume info + with _record_warnings(): # no volume info pts, tri, vol_info = read_surface(fname, read_metadata=True) write_surface(op.join(tempdir, 'tmp'), pts, tri, volume_info=vol_info, overwrite=True) - with pytest.warns(None): # no volume info + with _record_warnings(): # no volume info c_pts, c_tri, c_vol_info = read_surface(op.join(tempdir, 'tmp'), read_metadata=True) assert_array_equal(pts, c_pts) @@ -202,9 +169,9 @@ assert np.logical_or(bin_curv == 0, bin_curv == 1).all() -@requires_vtk def test_decimate_surface_vtk(): """Test triangular surface decimation.""" + pytest.importorskip('pyvista') points = np.array([[-0.00686118, -0.10369860, 0.02615170], [-0.00713948, -0.10370162, 0.02614874], [-0.00686208, -0.10368247, 0.02588313], @@ -260,4 +227,212 @@ assert_allclose(ori[2], nn, atol=1e-12) -run_tests_if_main() +# 0.06 sec locally even with all these params +@pytest.mark.parametrize('dtype', (np.float64, np.uint16, '>i4')) +@pytest.mark.parametrize('value', (1, 12)) +@pytest.mark.parametrize('smooth', (0, 0.9)) +def test_marching_cubes(dtype, value, smooth): + """Test creating surfaces via marching cubes.""" + pytest.importorskip('pyvista') + data = np.zeros((50, 50, 50), dtype=dtype) + data[20:30, 20:30, 20:30] = value + level = [value] + out = _marching_cubes(data, level, smooth=smooth) + assert len(out) == 1 + verts, triangles = out[0] + # verts and faces are rather large so use checksum + rtol = 1e-2 if smooth else 1e-9 + assert_allclose(verts.sum(axis=0), [14700, 14700, 14700], rtol=rtol) + assert_allclose(triangles.sum(axis=0), [363402, 360865, 350588]) + # test fill holes + data[24:27, 24:27, 24:27] = 0 + verts, triangles = _marching_cubes(data, level, smooth=smooth, + fill_hole_size=2)[0] + # check that no surfaces in the middle + assert np.linalg.norm(verts - np.array([25, 25, 25]), axis=1).min() > 4 + # problematic values + with pytest.raises(TypeError, match='1D array-like'): + _marching_cubes(data, ['foo']) + with pytest.raises(TypeError, match='1D array-like'): + _marching_cubes(data, [[1]]) + with pytest.raises(TypeError, match='1D array-like'): + _marching_cubes(data, [1.]) + with pytest.raises(ValueError, match='must be between 0'): + _marching_cubes(data, [1], smooth=1.) + with pytest.raises(ValueError, match='3D data'): + _marching_cubes(data[0], [1]) + + +@requires_nibabel() +@testing.requires_testing_data +def test_get_montage_volume_labels(): + """Test finding ROI labels near montage channel locations.""" + ch_coords = np.array([[-8.7040273, 17.99938754, 10.29604017], + [-14.03007764, 19.69978401, 12.07236939], + [-21.1130506, 21.98310911, 13.25658887]]) + ch_pos = dict(zip(['1', '2', '3'], ch_coords / 1000)) # mm -> m + montage = make_dig_montage(ch_pos, coord_frame='mri') + labels, colors = get_montage_volume_labels( + montage, 'sample', subjects_dir, aseg='aseg', dist=1) + assert labels == {'1': ['Unknown'], '2': ['Left-Cerebral-Cortex'], + '3': ['Left-Cerebral-Cortex']} + assert 'Unknown' in colors + assert 'Left-Cerebral-Cortex' in colors + np.testing.assert_almost_equal( + colors['Left-Cerebral-Cortex'], + (0.803921568627451, 0.24313725490196078, 0.3058823529411765, 1.0)) + np.testing.assert_almost_equal( + colors['Unknown'], (0.0, 0.0, 0.0, 1.0)) + + # test inputs + with pytest.raises(RuntimeError, + match='`aseg` file path must end with "aseg"'): + get_montage_volume_labels(montage, 'sample', subjects_dir, aseg='foo') + fail_montage = make_dig_montage(ch_pos, coord_frame='head') + with pytest.raises(RuntimeError, + match='Coordinate frame not supported'): + get_montage_volume_labels( + fail_montage, 'sample', subjects_dir, aseg='aseg') + with pytest.raises(ValueError, match='between 0 and 10'): + get_montage_volume_labels(montage, 'sample', subjects_dir, dist=11) + + +def test_voxel_neighbors(): + """Test finding points above a threshold near a seed location.""" + locs = np.array(np.meshgrid(*[np.linspace(-1, 1, 101)] * 3)) + image = 1 - np.linalg.norm(locs, axis=0) + true_volume = set([tuple(coord) for coord in + np.array(np.where(image > 0.95)).T]) + volume = _voxel_neighbors( + np.array([-0.3, 0.6, 0.5]) + (np.array(image.shape[0]) - 1) / 2, + image, thresh=0.95, use_relative=False) + assert volume.difference(true_volume) == set() + assert true_volume.difference(volume) == set() + + +@requires_nibabel() +@requires_dipy() +@pytest.mark.slowtest +@testing.requires_testing_data +def test_warp_montage_volume(): + """Test warping an montage based on intracranial electrode positions.""" + import nibabel as nib + subject_brain = nib.load( + op.join(subjects_dir, 'sample', 'mri', 'brain.mgz')) + template_brain = nib.load( + op.join(subjects_dir, 'fsaverage', 'mri', 'brain.mgz')) + zooms = dict(translation=10, rigid=10, sdr=10) + reg_affine, sdr_morph = compute_volume_registration( + subject_brain, template_brain, zooms=zooms, + niter=[3, 3, 3], + pipeline=('translation', 'rigid', 'sdr')) + # make an info object with three channels with positions + ch_coords = np.array([[-8.7040273, 17.99938754, 10.29604017], + [-14.03007764, 19.69978401, 12.07236939], + [-21.1130506, 21.98310911, 13.25658887]]) + ch_pos = dict(zip(['1', '2', '3'], ch_coords / 1000)) # mm -> m + lpa, nasion, rpa = get_mni_fiducials('sample', subjects_dir) + montage = make_dig_montage(ch_pos, lpa=lpa['r'], nasion=nasion['r'], + rpa=rpa['r'], coord_frame='mri') + # make fake image based on the info + CT_data = np.zeros(subject_brain.shape) + # convert to voxels + ch_coords_vox = apply_trans( + np.linalg.inv(subject_brain.header.get_vox2ras_tkr()), ch_coords) + for (x, y, z) in ch_coords_vox.round().astype(int): + # make electrode contact hyperintensities + # first, make the surrounding voxels high intensity + CT_data[x - 1:x + 2, y - 1:y + 2, z - 1:z + 2] = 500 + # then, make the center even higher intensity + CT_data[x, y, z] = 1000 + CT = nib.Nifti1Image(CT_data, subject_brain.affine) + ch_coords = np.array([[-8.7040273, 17.99938754, 10.29604017], + [-14.03007764, 19.69978401, 12.07236939], + [-21.1130506, 21.98310911, 13.25658887]]) + ch_pos = dict(zip(['1', '2', '3'], ch_coords / 1000)) # mm -> m + lpa, nasion, rpa = get_mni_fiducials('sample', subjects_dir) + montage = make_dig_montage(ch_pos, lpa=lpa['r'], nasion=nasion['r'], + rpa=rpa['r'], coord_frame='mri') + montage_warped, image_from, image_to = warp_montage_volume( + montage, CT, reg_affine, sdr_morph, 'sample', + subjects_dir_from=subjects_dir, thresh=0.99) + # checked with nilearn plot from `tut-ieeg-localize` + # check montage in surface RAS + ground_truth_warped = np.array([[-0.009, -0.00133333, -0.033], + [-0.01445455, 0.00127273, -0.03163636], + [-0.022, 0.00285714, -0.031]]) + for i, d in enumerate(montage_warped.dig): + assert np.linalg.norm( # off by less than 1.5 cm + d['r'] - ground_truth_warped[i]) < 0.015 + # check image_from + for idx, contact in enumerate(range(1, len(ch_pos) + 1)): + voxels = np.array(np.where(np.array(image_from.dataobj) == contact)).T + assert ch_coords_vox.round()[idx] in voxels + assert ch_coords_vox.round()[idx] + 5 not in voxels + # check image_to, too many, just check center + ground_truth_warped_voxels = np.array( + [[135.5959596, 161.97979798, 123.83838384], + [143.11111111, 159.71428571, 125.61904762], + [150.53982301, 158.38053097, 127.31858407]]) + for i in range(len(montage.ch_names)): + assert np.linalg.norm( + np.array(np.where(np.array(image_to.dataobj) == i + 1) + ).mean(axis=1) - ground_truth_warped_voxels[i]) < 8 + # test inputs + with pytest.raises(ValueError, match='`thresh` must be between 0 and 1'): + warp_montage_volume( + montage, CT, reg_affine, sdr_morph, 'sample', thresh=11.) + with pytest.raises(ValueError, match='subject folder is incorrect'): + warp_montage_volume( + montage, CT, reg_affine, sdr_morph, subject_from='foo', + subjects_dir_from=subjects_dir) + CT_unaligned = nib.Nifti1Image(CT_data, template_brain.affine) + with pytest.raises(RuntimeError, match='not aligned to Freesurfer'): + warp_montage_volume(montage, CT_unaligned, reg_affine, + sdr_morph, 'sample', + subjects_dir_from=subjects_dir) + bad_montage = montage.copy() + for d in bad_montage.dig: + d['coord_frame'] = 99 + with pytest.raises(RuntimeError, match='Coordinate frame not supported'): + warp_montage_volume(bad_montage, CT, reg_affine, + sdr_morph, 'sample', + subjects_dir_from=subjects_dir) + + # check channel not warped + ch_pos_doubled = ch_pos.copy() + ch_pos_doubled.update(zip(['4', '5', '6'], ch_coords / 1000)) + doubled_montage = make_dig_montage( + ch_pos_doubled, lpa=lpa['r'], nasion=nasion['r'], + rpa=rpa['r'], coord_frame='mri') + with pytest.warns(RuntimeWarning, match='not assigned'): + warp_montage_volume(doubled_montage, CT, reg_affine, + None, 'sample', subjects_dir_from=subjects_dir) + + +@testing.requires_testing_data +@pytest.mark.parametrize('ret_nn', (False, True)) +@pytest.mark.parametrize('method', ('accurate', 'nearest')) +def test_project_onto_surface(method, ret_nn): + """Test _project_onto_surface (gh-10930).""" + locs = np.random.default_rng(0).normal(size=(10, 3)) + locs *= 2 / np.linalg.norm(locs, axis=1)[:, None] # lie on a sphere rad=2 + surf = _get_ico_surface(3) + assert len(surf['rr']) == 642 + assert_allclose(np.linalg.norm(surf['rr'], axis=1), 1., rtol=1e-3) # unit + # project + weights, tri_idx, *out = _project_onto_surface( + locs, surf, project_rrs=True, return_nn=ret_nn, method=method) + locs /= 2. # back to unit + assert_allclose(np.linalg.norm(locs, axis=1), 1., rtol=1e-5) + assert len(out) == 2 if ret_nn else 1 + # for a sphere, both the rr (out[0]) and nn (out[1], if exists) should + # both be very similar to each other and to our unit-length `locs` + for kind, comp in zip(('rr', 'nn'), out): + assert_allclose( + np.linalg.norm(comp, axis=1), 1., atol=0.05, + err_msg=f'{kind} not unit vectors for {method}') + cos = np.sum(locs * comp, axis=1) + assert_allclose( + cos, 1., atol=0.05, # ico > 3 would be even better tol + err_msg=f'{kind} not in same direction as locs for {method}') diff -Nru python-mne-0.23.4+dfsg/mne/tests/test_transforms.py python-mne-1.1.0+dfsg/mne/tests/test_transforms.py --- python-mne-0.23.4+dfsg/mne/tests/test_transforms.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/tests/test_transforms.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,16 +1,22 @@ +# -*- coding: utf-8 -*- +# Author: Alexandre Gramfort +# +# License: BSD-3-Clause + import os import os.path as op import pytest import numpy as np from numpy.testing import (assert_array_equal, assert_equal, assert_allclose, - assert_array_less) + assert_array_less, assert_almost_equal) +import itertools import mne from mne.datasets import testing +from mne.fixes import _get_img_fdata from mne import read_trans, write_trans from mne.io import read_info -from mne.utils import _TempDir, run_tests_if_main from mne.transforms import (invert_transform, _get_trans, rotation, rotation3d, rotation_angles, _find_trans, combine_transforms, apply_trans, translation, @@ -22,12 +28,15 @@ rotation3d_align_z_axis, _read_fs_xfm, _write_fs_xfm, _quat_real, _fit_matched_points, _quat_to_euler, _euler_to_quat, - _quat_to_affine) + _quat_to_affine, _compute_r2, _validate_pipeline) +from mne.utils import requires_nibabel, requires_dipy data_path = testing.data_path(download=False) fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc-trans.fif') fname_eve = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw-eve.fif') +subjects_dir = op.join(data_path, 'subjects') +fname_t1 = op.join(subjects_dir, 'fsaverage', 'mri', 'T1.mgz') base_dir = op.join(op.dirname(__file__), '..', 'io', 'tests', 'data') fname_trans = op.join(base_dir, 'sample-audvis-raw-trans.txt') @@ -66,9 +75,9 @@ @testing.requires_testing_data -def test_io_trans(): +def test_io_trans(tmp_path): """Test reading and writing of trans files.""" - tempdir = _TempDir() + tempdir = str(tmp_path) os.mkdir(op.join(tempdir, 'sample')) pytest.raises(RuntimeError, _find_trans, 'sample', subjects_dir=tempdir) trans0 = read_trans(fname) @@ -232,9 +241,9 @@ m4 = rotation(x, y, z) assert_array_equal(m, m4[:3, :3]) back = rotation_angles(m) - assert_equal(back, rot) + assert_almost_equal(actual=back, desired=rot, decimal=12) back4 = rotation_angles(m4) - assert_equal(back4, rot) + assert_almost_equal(actual=back4, desired=rot, decimal=12) def test_rotation3d_align_z_axis(): @@ -366,35 +375,35 @@ @testing.requires_testing_data -def test_fs_xfm(): +@pytest.mark.parametrize('subject', ('fsaverage', 'sample')) +def test_fs_xfm(subject, tmp_path): """Test reading and writing of Freesurfer transforms.""" - for subject in ('fsaverage', 'sample'): - fname = op.join(data_path, 'subjects', subject, 'mri', 'transforms', - 'talairach.xfm') - xfm, kind = _read_fs_xfm(fname) - if subject == 'fsaverage': - assert_allclose(xfm, np.eye(4), atol=1e-5) # fsaverage is in MNI - assert kind == 'MNI Transform File' - tempdir = _TempDir() - fname_out = op.join(tempdir, 'out.xfm') - _write_fs_xfm(fname_out, xfm, kind) - xfm_read, kind_read = _read_fs_xfm(fname_out) - assert kind_read == kind - assert_allclose(xfm, xfm_read, rtol=1e-5, atol=1e-5) - # Some wacky one - xfm[:3] = np.random.RandomState(0).randn(3, 4) - _write_fs_xfm(fname_out, xfm, 'foo') - xfm_read, kind_read = _read_fs_xfm(fname_out) - assert kind_read == 'foo' - assert_allclose(xfm, xfm_read, rtol=1e-5, atol=1e-5) - # degenerate conditions - with open(fname_out, 'w') as fid: - fid.write('foo') - with pytest.raises(ValueError, match='Failed to find'): - _read_fs_xfm(fname_out) - _write_fs_xfm(fname_out, xfm[:2], 'foo') - with pytest.raises(ValueError, match='Could not find'): - _read_fs_xfm(fname_out) + fname = op.join(data_path, 'subjects', subject, 'mri', 'transforms', + 'talairach.xfm') + xfm, kind = _read_fs_xfm(fname) + if subject == 'fsaverage': + assert_allclose(xfm, np.eye(4), atol=1e-5) # fsaverage is in MNI + assert kind == 'MNI Transform File' + tempdir = str(tmp_path) + fname_out = op.join(tempdir, 'out.xfm') + _write_fs_xfm(fname_out, xfm, kind) + xfm_read, kind_read = _read_fs_xfm(fname_out) + assert kind_read == kind + assert_allclose(xfm, xfm_read, rtol=1e-5, atol=1e-5) + # Some wacky one + xfm[:3] = np.random.RandomState(0).randn(3, 4) + _write_fs_xfm(fname_out, xfm, 'foo') + xfm_read, kind_read = _read_fs_xfm(fname_out) + assert kind_read == 'foo' + assert_allclose(xfm, xfm_read, rtol=1e-5, atol=1e-5) + # degenerate conditions + with open(fname_out, 'w') as fid: + fid.write('foo') + with pytest.raises(ValueError, match='Failed to find'): + _read_fs_xfm(fname_out) + _write_fs_xfm(fname_out, xfm[:2], 'foo') + with pytest.raises(ValueError, match='Could not find'): + _read_fs_xfm(fname_out) @pytest.fixture() @@ -510,4 +519,44 @@ assert_allclose(quat_rot, euler_rot, atol=1e-14) -run_tests_if_main() +@requires_nibabel() +@requires_dipy() +@pytest.mark.slowtest +@testing.requires_testing_data +def test_volume_registration(): + """Test volume registration.""" + import nibabel as nib + from dipy.align import resample + T1 = nib.load(fname_t1) + affine = np.eye(4) + affine[0, 3] = 10 + T1_resampled = resample(moving=T1.get_fdata(), + static=T1.get_fdata(), + moving_affine=T1.affine, + static_affine=T1.affine, + between_affine=np.linalg.inv(affine)) + for pipeline, cval in zip(('rigids', ('translation', 'sdr')), (0., '1%')): + reg_affine, sdr_morph = mne.transforms.compute_volume_registration( + T1_resampled, T1, pipeline=pipeline, zooms=10, niter=[5]) + assert_allclose(affine, reg_affine, atol=0.01) + T1_aligned = mne.transforms.apply_volume_registration( + T1_resampled, T1, reg_affine, sdr_morph, cval=cval) + r2 = _compute_r2(_get_img_fdata(T1_aligned), _get_img_fdata(T1)) + assert 99.9 < r2 + with pytest.raises(ValueError, match='cval'): + mne.transforms.apply_volume_registration( + T1_resampled, T1, reg_affine, sdr_morph, cval='bad') + + # check that all orders of the pipeline work + for pipeline_len in range(1, 5): + for pipeline in itertools.combinations( + ('translation', 'rigid', 'affine', 'sdr'), pipeline_len): + _validate_pipeline(pipeline) + _validate_pipeline(list(pipeline)) + + with pytest.raises(ValueError, match='Steps in pipeline are out of order'): + _validate_pipeline(('sdr', 'affine')) + + with pytest.raises(ValueError, + match='Steps in pipeline should not be repeated'): + _validate_pipeline(('affine', 'affine')) diff -Nru python-mne-0.23.4+dfsg/mne/time_frequency/ar.py python-mne-1.1.0+dfsg/mne/time_frequency/ar.py --- python-mne-0.23.4+dfsg/mne/time_frequency/ar.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/time_frequency/ar.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors: Alexandre Gramfort # The statsmodels folks for AR yule_walker # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np diff -Nru python-mne-0.23.4+dfsg/mne/time_frequency/csd.py python-mne-1.1.0+dfsg/mne/time_frequency/csd.py --- python-mne-0.23.4+dfsg/mne/time_frequency/csd.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/time_frequency/csd.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,22 +3,21 @@ # Susanna Aro # Roman Goj # -# License: BSD (3-clause) +# License: BSD-3-Clause import copy as cp import numbers import numpy as np + from .tfr import _cwt_array, morlet, _get_nfft -from ..fixes import _import_fft from ..io.pick import pick_channels, _picks_to_idx from ..utils import (logger, verbose, warn, copy_function_doc_to_method_doc, - ProgressBar) + ProgressBar, _check_fname, _import_h5io_funcs) from ..viz.misc import plot_csd from ..time_frequency.multitaper import (_compute_mt_params, _mt_spectra, _csd_from_mt, _psd_from_mt_adaptive) from ..parallel import parallel_func -from ..externals.h5io import read_hdf5, write_hdf5 def pick_channels_csd(csd, include=[], exclude=[], ordered=False, copy=True): @@ -443,7 +442,8 @@ projs=self.projs, ) - def save(self, fname): + @verbose + def save(self, fname, *, overwrite=False, verbose=None): """Save the CSD to an HDF5 file. Parameters @@ -451,15 +451,24 @@ fname : str The name of the file to save the CSD to. The extension '.h5' will be appended if the given filename doesn't have it already. + %(overwrite)s + + .. versionadded:: 1.0 + %(verbose)s + + .. versionadded:: 1.0 See Also -------- read_csd : For reading CSD objects from a file. """ + _, write_hdf5 = _import_h5io_funcs() if not fname.endswith('.h5'): fname += '.h5' - write_hdf5(fname, self.__getstate__(), overwrite=True, title='conpy') + fname = _check_fname(fname, overwrite=overwrite) + write_hdf5(fname, self.__getstate__(), overwrite=True, + title='conpy') def copy(self): """Return copy of the CrossSpectralDensity object. @@ -592,6 +601,7 @@ -------- CrossSpectralDensity.save : For saving CSD objects. """ + read_hdf5, _ = _import_h5io_funcs() if not fname.endswith('.h5'): fname += '.h5' @@ -601,7 +611,7 @@ @verbose def csd_fourier(epochs, fmin=0, fmax=np.inf, tmin=None, tmax=None, picks=None, - n_fft=None, projs=None, n_jobs=1, verbose=None): + n_fft=None, projs=None, n_jobs=None, *, verbose=None): """Estimate cross-spectral density from an array using short-time fourier. Parameters @@ -651,7 +661,7 @@ @verbose def csd_array_fourier(X, sfreq, t0=0, fmin=0, fmax=np.inf, tmin=None, tmax=None, ch_names=None, n_fft=None, projs=None, - n_jobs=1, verbose=None): + n_jobs=None, *, verbose=None): """Estimate cross-spectral density from an array using short-time fourier. Parameters @@ -699,7 +709,7 @@ csd_morlet csd_multitaper """ - rfftfreq = _import_fft('rfftfreq') + from scipy.fft import rfftfreq X, times, tmin, tmax, fmin, fmax = _prepare_csd_array( X, sfreq, t0, tmin, tmax, fmin, fmax) @@ -732,7 +742,7 @@ @verbose def csd_multitaper(epochs, fmin=0, fmax=np.inf, tmin=None, tmax=None, picks=None, n_fft=None, bandwidth=None, adaptive=False, - low_bias=True, projs=None, n_jobs=1, verbose=None): + low_bias=True, projs=None, n_jobs=None, *, verbose=None): """Estimate cross-spectral density from epochs using a multitaper method. Parameters @@ -791,8 +801,8 @@ @verbose def csd_array_multitaper(X, sfreq, t0=0, fmin=0, fmax=np.inf, tmin=None, tmax=None, ch_names=None, n_fft=None, bandwidth=None, - adaptive=False, low_bias=True, projs=None, n_jobs=1, - verbose=None): + adaptive=False, low_bias=True, projs=None, + n_jobs=None, *, verbose=None): """Estimate cross-spectral density from an array using a multitaper method. Parameters @@ -847,7 +857,7 @@ csd_morlet csd_multitaper """ - rfftfreq = _import_fft('rfftfreq') + from scipy.fft import rfftfreq X, times, tmin, tmax, fmin, fmax = _prepare_csd_array( X, sfreq, t0, tmin, tmax, fmin, fmax) @@ -882,7 +892,7 @@ @verbose def csd_morlet(epochs, frequencies, tmin=None, tmax=None, picks=None, - n_cycles=7, use_fft=True, decim=1, projs=None, n_jobs=1, + n_cycles=7, use_fft=True, decim=1, projs=None, n_jobs=None, *, verbose=None): """Estimate cross-spectral density from epochs using Morlet wavelets. @@ -942,7 +952,7 @@ @verbose def csd_array_morlet(X, sfreq, frequencies, t0=0, tmin=None, tmax=None, ch_names=None, n_cycles=7, use_fft=True, decim=1, - projs=None, n_jobs=1, verbose=None): + projs=None, n_jobs=None, *, verbose=None): """Estimate cross-spectral density from an array using Morlet wavelets. Parameters @@ -1096,7 +1106,8 @@ @verbose def _execute_csd_function(X, times, frequencies, csd_function, params, n_fft, - ch_names=None, projs=None, n_jobs=1, verbose=None): + ch_names=None, projs=None, n_jobs=None, *, + verbose=None): """Estimate cross-spectral density with a given function. This function will apply the given CSD function in parallel across epochs. @@ -1140,7 +1151,8 @@ # Prepare the function that does the actual CSD computation for parallel # execution. - parallel, my_csd, _ = parallel_func(csd_function, n_jobs, verbose=verbose) + parallel, my_csd, n_jobs = parallel_func( + csd_function, n_jobs, verbose=verbose) # Compute CSD for each trial n_blocks = int(np.ceil(n_epochs / float(n_jobs))) diff -Nru python-mne-0.23.4+dfsg/mne/time_frequency/multitaper.py python-mne-1.1.0+dfsg/mne/time_frequency/multitaper.py --- python-mne-0.23.4+dfsg/mne/time_frequency/multitaper.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/time_frequency/multitaper.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,12 +1,11 @@ # Author : Martin Luessi mluessi@nmr.mgh.harvard.edu (2012) -# License : BSD 3-clause +# License : BSD-3-Clause # Parts of this code were copied from NiTime http://nipy.sourceforge.net/nitime import operator import numpy as np -from ..fixes import _import_fft from ..parallel import parallel_func from ..utils import sum_squared, warn, verbose, logger, _check_option @@ -53,16 +52,16 @@ Notes ----- - Tridiagonal form of DPSS calculation from: + Tridiagonal form of DPSS calculation from :footcite:`Slepian1978`. - Slepian, D. Prolate spheroidal wave functions, Fourier analysis, and - uncertainty V: The discrete case. Bell System Technical Journal, - Volume 57 (1978), 1371430 + References + ---------- + .. footbibliography:: """ from scipy import interpolate + from scipy.fft import rfft, irfft from scipy.signal.windows import dpss as sp_dpss from ..filter import next_fast_len - rfft, irfft = _import_fft(('rfft', 'irfft')) # This np.int32 business works around a weird Windows bug, see # gh-5039 and https://github.com/scipy/scipy/pull/8608 Kmax = np.int32(operator.index(Kmax)) @@ -300,7 +299,7 @@ freqs : array The frequency points in Hz of the spectra """ - rfft, rfftfreq = _import_fft(('rfft', 'rfftfreq')) + from scipy.fft import rfft, rfftfreq if n_fft is None: n_fft = x.shape[-1] @@ -319,7 +318,7 @@ x_mt[idx] = rfft(sig[..., np.newaxis, :] * dpss, n=n_fft) # Adjust DC and maybe Nyquist, depending on one-sided transform x_mt[..., 0] /= np.sqrt(2.) - if x.shape[1] % 2 == 0: + if n_fft % 2 == 0: x_mt[..., -1] /= np.sqrt(2.) return x_mt, freqs @@ -363,9 +362,9 @@ @verbose -def psd_array_multitaper(x, sfreq, fmin=0, fmax=np.inf, bandwidth=None, +def psd_array_multitaper(x, sfreq, fmin=0.0, fmax=np.inf, bandwidth=None, adaptive=False, low_bias=True, normalization='length', - n_jobs=1, verbose=None): + output='power', n_jobs=None, verbose=None): """Compute power spectral density (PSD) using a multi-taper method. Parameters @@ -386,20 +385,25 @@ low_bias : bool Only use tapers with more than 90%% spectral concentration within bandwidth. - normalization : str - Either "full" or "length" (default). If "full", the PSD will - be normalized by the sampling rate as well as the length of - the signal (as in nitime). + %(normalization)s + output : str + The format of the returned ``psds`` array. Can be either ``'complex'`` + or ``'power'``. If ``'power'``, the power spectral density is returned. + If ``output='complex'``, the complex fourier coefficients are returned + per taper. %(n_jobs)s %(verbose)s Returns ------- - psds : ndarray, shape (..., n_freqs) or - The power spectral densities. All dimensions up to the last will - be the same as input. + psds : ndarray, shape (..., n_freqs) or (..., n_tapers, n_freqs) + The power spectral densities. All dimensions up to the last (or the + last two if ``output='complex'``) will be the same as input. freqs : array The frequency points in Hz of the PSD. + weights : ndarray + The weights used for averaging across tapers. Only returned if + ``output='complex'``. See Also -------- @@ -412,7 +416,7 @@ ----- .. versionadded:: 0.14.0 """ - rfftfreq = _import_fft('rfftfreq') + from scipy.fft import rfftfreq _check_option('normalization', normalization, ['length', 'full']) # Reshape data so its 2-D for parallelization @@ -424,43 +428,57 @@ dpss, eigvals, adaptive = _compute_mt_params( n_times, sfreq, bandwidth, low_bias, adaptive) + n_tapers = len(dpss) + weights = np.sqrt(eigvals)[np.newaxis, :, np.newaxis] # decide which frequencies to keep freqs = rfftfreq(n_times, 1. / sfreq) freq_mask = (freqs >= fmin) & (freqs <= fmax) freqs = freqs[freq_mask] + n_freqs = len(freqs) + + if output == 'complex': + psd = np.zeros((x.shape[0], n_tapers, n_freqs), dtype='complex') + else: + psd = np.zeros((x.shape[0], n_freqs)) - psd = np.zeros((x.shape[0], freq_mask.sum())) # Let's go in up to 50 MB chunks of signals to save memory - n_chunk = max(50000000 // (len(freq_mask) * len(eigvals) * 16), n_jobs) + n_chunk = max(50000000 // (len(freq_mask) * len(eigvals) * 16), 1) offsets = np.concatenate((np.arange(0, x.shape[0], n_chunk), [x.shape[0]])) for start, stop in zip(offsets[:-1], offsets[1:]): x_mt = _mt_spectra(x[start:stop], dpss, sfreq)[0] - if not adaptive: - weights = np.sqrt(eigvals)[np.newaxis, :, np.newaxis] - psd[start:stop] = _psd_from_mt(x_mt[:, :, freq_mask], weights) + if output == 'power': + if not adaptive: + psd[start:stop] = _psd_from_mt(x_mt[:, :, freq_mask], weights) + else: + parallel, my_psd_from_mt_adaptive, n_jobs = \ + parallel_func(_psd_from_mt_adaptive, n_jobs) + n_splits = min(stop - start, n_jobs) + out = parallel(my_psd_from_mt_adaptive(x, eigvals, freq_mask) + for x in np.array_split(x_mt, n_splits)) + psd[start:stop] = np.concatenate(out) else: - n_splits = min(stop - start, n_jobs) - parallel, my_psd_from_mt_adaptive, n_jobs = \ - parallel_func(_psd_from_mt_adaptive, n_splits) - out = parallel(my_psd_from_mt_adaptive(x, eigvals, freq_mask) - for x in np.array_split(x_mt, n_splits)) - psd[start:stop] = np.concatenate(out) + psd[start:stop] = x_mt[:, :, freq_mask] if normalization == 'full': psd /= sfreq # Combining/reshaping to original data shape - psd.shape = dshape + (-1,) + last_dims = (n_freqs,) if output == 'power' else (n_tapers, n_freqs) + psd.shape = dshape + last_dims if ndim_in == 1: psd = psd[0] - return psd, freqs + + if output == 'complex': + return psd, freqs, weights + else: + return psd, freqs @verbose def tfr_array_multitaper(epoch_data, sfreq, freqs, n_cycles=7.0, zero_mean=True, time_bandwidth=None, use_fft=True, - decim=1, output='complex', n_jobs=1, + decim=1, output='complex', n_jobs=None, verbose=None): """Compute Time-Frequency Representation (TFR) using DPSS tapers. @@ -497,9 +515,9 @@ is done after the convolutions. output : str, default 'complex' - * 'complex' : single trial complex. + * 'complex' : single trial per taper complex values. * 'power' : single trial power. - * 'phase' : single trial phase. + * 'phase' : single trial per taper phase. * 'avg_power' : average of single trial power. * 'itc' : inter-trial coherence. * 'avg_power_itc' : average of single trial power and inter-trial @@ -512,11 +530,13 @@ Returns ------- out : array - Time frequency transform of epoch_data. If output is in ['complex', - 'phase', 'power'], then shape of out is (n_epochs, n_chans, n_freqs, - n_times), else it is (n_chans, n_freqs, n_times). If output is - 'avg_power_itc', the real values code for 'avg_power' and the - imaginary values code for the 'itc': out = avg_power + i * itc. + Time frequency transform of epoch_data. If ``output in ['complex', + 'phase']``, then the shape of ``out`` is ``(n_epochs, n_chans, + n_tapers, n_freqs, n_times)``; if output is 'power', the shape of + ``out`` is ``(n_epochs, n_chans, n_freqs, n_times)``, else it is + ``(n_chans, n_freqs, n_times)``. If output is 'avg_power_itc', the real + values in ``out`` contain the average power and the imaginary values + contain the ITC: ``out = avg_power + i * itc``. See Also -------- diff -Nru python-mne-0.23.4+dfsg/mne/time_frequency/psd.py python-mne-1.1.0+dfsg/mne/time_frequency/psd.py --- python-mne-0.23.4+dfsg/mne/time_frequency/psd.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/time_frequency/psd.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ # Authors : Alexandre Gramfort, alexandre.gramfort@inria.fr (2011) # Denis A. Engemann -# License : BSD 3-clause +# License : BSD-3-Clause from functools import partial import numpy as np @@ -11,6 +11,39 @@ from .multitaper import psd_array_multitaper +# adapted from SciPy +# https://github.com/scipy/scipy/blob/f71e7fad717801c4476312fe1e23f2dfbb4c9d7f/scipy/signal/_spectral_py.py#L2019 # noqa: E501 +def _median_biases(n): + # Compute the biases for 0 to max(n, 1) terms included in a median calc + biases = np.ones(n + 1) + # The original SciPy code is: + # + # def _median_bias(n): + # ii_2 = 2 * np.arange(1., (n - 1) // 2 + 1) + # return 1 + np.sum(1. / (ii_2 + 1) - 1. / ii_2) + # + # This is a sum over (n-1)//2 terms. + # The ii_2 terms here for different n are: + # + # n=0: [] # 0 terms + # n=1: [] # 0 terms + # n=2: [] # 0 terms + # n=3: [2] # 1 term + # n=4: [2] # 1 term + # n=5: [2, 4] # 2 terms + # n=6: [2, 4] # 2 terms + # ... + # + # We can get the terms for 0 through n using a cumulative summation and + # indexing: + if n >= 3: + ii_2 = 2 * np.arange(1, (n - 1) // 2 + 1) + sums = 1 + np.cumsum(1. / (ii_2 + 1) - 1. / ii_2) + idx = np.arange(2, n) // 2 - 1 + biases[3:] = sums[idx] + return biases + + def _decomp_aggregate_mask(epoch, func, average, freq_sl): _, _, spect = func(epoch) spect = spect[..., freq_sl, :] @@ -18,7 +51,9 @@ if average == 'mean': spect = np.nanmean(spect, axis=-1) elif average == 'median': - spect = np.nanmedian(spect, axis=-1) + biases = _median_biases(spect.shape[-1]) + idx = (~np.isnan(spect)).sum(-1) + spect = np.nanmedian(spect, axis=-1) / biases[idx] return spect @@ -60,8 +95,10 @@ from ..epochs import BaseEpochs from ..evoked import Evoked if not isinstance(inst, (BaseEpochs, BaseRaw, Evoked)): - raise ValueError('epochs must be an instance of Epochs, Raw, or' - 'Evoked. Got type {}'.format(type(inst))) + raise ValueError( + f'inst must be an instance of Epochs, Raw, or Evoked. Got ' + f'{type(inst)}' + ) time_mask = _time_mask(inst.times, tmin, tmax, sfreq=inst.info['sfreq']) picks = _picks_to_idx(inst.info, picks, 'data', with_ref_meg=False) @@ -84,8 +121,8 @@ @verbose def psd_array_welch(x, sfreq, fmin=0, fmax=np.inf, n_fft=256, n_overlap=0, - n_per_seg=None, n_jobs=1, average='mean', window='hamming', - verbose=None): + n_per_seg=None, n_jobs=None, average='mean', + window='hamming', *, verbose=None): """Compute power spectral density (PSD) using Welch's method. Parameters @@ -108,10 +145,10 @@ Length of each Welch segment (windowed with a Hamming window). Defaults to None, which sets n_per_seg equal to n_fft. %(n_jobs)s - %(average-psd)s + %(average_psd)s .. versionadded:: 0.19.0 - %(window-psd)s + %(window_psd)s .. versionadded:: 0.22.0 %(verbose)s @@ -153,7 +190,6 @@ freqs = freqs[freq_sl] # Parallelize across first N-1 dimensions - x_splits = np.array_split(x, n_jobs) logger.debug( f'Spectogram using {n_fft}-point FFT on {n_per_seg} samples with ' f'{n_overlap} overlap and {window} window') @@ -162,6 +198,7 @@ parallel, my_spect_func, n_jobs = parallel_func(_spect_func, n_jobs=n_jobs) func = partial(spectrogram, noverlap=n_overlap, nperseg=n_per_seg, nfft=n_fft, fs=sfreq, window=window) + x_splits = np.array_split(x, n_jobs) f_spect = parallel(my_spect_func(d, func=func, freq_sl=freq_sl, average=average) for d in x_splits) @@ -175,8 +212,8 @@ @verbose def psd_welch(inst, fmin=0, fmax=np.inf, tmin=None, tmax=None, n_fft=256, - n_overlap=0, n_per_seg=None, picks=None, proj=False, n_jobs=1, - reject_by_annotation=True, average='mean', window='hamming', + n_overlap=0, n_per_seg=None, picks=None, proj=False, n_jobs=None, + reject_by_annotation=True, average='mean', window='hamming', *, verbose=None): """Compute the power spectral density (PSD) using Welch's method. @@ -213,10 +250,10 @@ %(reject_by_annotation_raw)s .. versionadded:: 0.15.0 - %(average-psd)s + %(average_psd)s .. versionadded:: 0.19.0 - %(window-psd)s + %(window_psd)s .. versionadded:: 0.22.0 %(verbose)s @@ -257,12 +294,13 @@ def psd_multitaper(inst, fmin=0, fmax=np.inf, tmin=None, tmax=None, bandwidth=None, adaptive=False, low_bias=True, normalization='length', picks=None, proj=False, - n_jobs=1, verbose=None): + n_jobs=None, reject_by_annotation=False, *, verbose=None): """Compute the power spectral density (PSD) using multitapers. Calculates spectral density for orthogonal tapers, then averages them - together for each channel/epoch. See [1] for a description of the tapers - and [2] for the general method. + together for each channel/epoch. See :footcite:`Slepian1978` for a + description of the tapers and :footcite:`PercivalWalden1993` for the + general method. Parameters ---------- @@ -285,14 +323,12 @@ low_bias : bool Only use tapers with more than 90%% spectral concentration within bandwidth. - normalization : str - Either "full" or "length" (default). If "full", the PSD will - be normalized by the sampling rate as well as the length of - the signal (as in nitime). + %(normalization)s %(picks_good_data_noref)s proj : bool Apply SSP projection vectors. If inst is ndarray this is not used. %(n_jobs)s + %(reject_by_annotation_raw)s %(verbose)s Returns @@ -318,16 +354,11 @@ References ---------- - .. [1] Slepian, D. "Prolate spheroidal wave functions, Fourier analysis, - and uncertainty V: The discrete case." Bell System Technical - Journal, vol. 57, 1978. - - .. [2] Percival D.B. and Walden A.T. "Spectral Analysis for Physical - Applications: Multitaper and Conventional Univariate Techniques." - Cambridge University Press, 1993. + .. footbibliography:: """ # Prep data - data, sfreq = _check_psd_data(inst, tmin, tmax, picks, proj) + data, sfreq = _check_psd_data(inst, tmin, tmax, picks, proj, + reject_by_annotation=reject_by_annotation) return psd_array_multitaper(data, sfreq, fmin=fmin, fmax=fmax, bandwidth=bandwidth, adaptive=adaptive, low_bias=low_bias, normalization=normalization, diff -Nru python-mne-0.23.4+dfsg/mne/time_frequency/_stft.py python-mne-1.1.0+dfsg/mne/time_frequency/_stft.py --- python-mne-0.23.4+dfsg/mne/time_frequency/_stft.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/time_frequency/_stft.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,6 @@ from math import ceil import numpy as np -from ..fixes import _import_fft from ..utils import logger, verbose @@ -34,7 +33,7 @@ istft stftfreq """ - rfft = _import_fft('rfft') + from scipy.fft import rfft if not np.isrealobj(x): raise ValueError("x is not a real valued array") @@ -119,7 +118,7 @@ stft """ # Errors and warnings - irfft = _import_fft('irfft') + from scipy.fft import irfft X = np.asarray(X) if X.ndim < 2: raise ValueError(f'X must have ndim >= 2, got {X.ndim}') @@ -197,7 +196,7 @@ stft istft """ - rfftfreq = _import_fft('rfftfreq') + from scipy.fft import rfftfreq freqs = rfftfreq(wsize) if sfreq is not None: freqs *= float(sfreq) diff -Nru python-mne-0.23.4+dfsg/mne/time_frequency/_stockwell.py python-mne-1.1.0+dfsg/mne/time_frequency/_stockwell.py --- python-mne-0.23.4+dfsg/mne/time_frequency/_stockwell.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/time_frequency/_stockwell.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,17 +1,15 @@ # Authors : Denis A. Engemann # Alexandre Gramfort # -# License : BSD 3-clause +# License : BSD-3-Clause from copy import deepcopy import numpy as np -# XXX explore cuda optimization at some point. -from ..fixes import _import_fft from ..io.pick import _pick_data_channels, pick_info from ..utils import verbose, warn, fill_doc, _validate_type -from ..parallel import parallel_func, check_n_jobs +from ..parallel import parallel_func from .tfr import AverageTFR, _get_data @@ -42,7 +40,7 @@ def _precompute_st_windows(n_samp, start_f, stop_f, sfreq, width): """Precompute stockwell Gaussian windows (in the freq domain).""" - fft, fftfreq = _import_fft(('fft', 'fftfreq')) + from scipy.fft import fft, fftfreq tw = fftfreq(n_samp, 1. / sfreq) / n_samp tw = np.r_[tw[:1], tw[1:][::-1]] @@ -62,7 +60,7 @@ def _st(x, start_f, windows): """Compute ST based on Ali Moukadem MATLAB code (used in tests).""" - fft, ifft = _import_fft(('fft', 'ifft')) + from scipy.fft import fft, ifft n_samp = x.shape[-1] ST = np.empty(x.shape[:-1] + (len(windows), n_samp), dtype=np.complex128) # do the work @@ -76,7 +74,7 @@ def _st_power_itc(x, start_f, compute_itc, zero_pad, decim, W): """Aux function.""" - fft, ifft = _import_fft(('fft', 'ifft')) + from scipy.fft import fft, ifft n_samp = x.shape[-1] n_out = (n_samp - zero_pad) n_out = n_out // decim + bool(n_out % decim) @@ -103,7 +101,7 @@ @fill_doc def tfr_array_stockwell(data, sfreq, fmin=None, fmax=None, n_fft=None, - width=1.0, decim=1, return_itc=False, n_jobs=1): + width=1.0, decim=1, return_itc=False, n_jobs=None): """Compute power and intertrial coherence using Stockwell (S) transform. Same computation as `~mne.time_frequency.tfr_stockwell`, but operates on @@ -158,7 +156,7 @@ ---------- .. footbibliography:: """ - fftfreq = _import_fft('fftfreq') + from scipy.fft import fftfreq _validate_type(data, np.ndarray, 'data') if data.ndim != 3: raise ValueError( @@ -183,7 +181,7 @@ psd = np.empty((n_channels, n_freq, n_out)) itc = np.empty((n_channels, n_freq, n_out)) if return_itc else None - parallel, my_st, _ = parallel_func(_st_power_itc, n_jobs) + parallel, my_st, n_jobs = parallel_func(_st_power_itc, n_jobs) tfrs = parallel(my_st(data[:, c, :], start_f, return_itc, zero_pad, decim, W) for c in range(n_channels)) @@ -197,7 +195,7 @@ @verbose def tfr_stockwell(inst, fmin=None, fmax=None, n_fft=None, - width=1.0, decim=1, return_itc=False, n_jobs=1, + width=1.0, decim=1, return_itc=False, n_jobs=None, verbose=None): """Compute Time-Frequency Representation (TFR) using Stockwell Transform. @@ -259,7 +257,6 @@ picks = _pick_data_channels(inst.info) info = pick_info(inst.info, picks) data = data[:, picks, :] - n_jobs = check_n_jobs(n_jobs) power, itc, freqs = tfr_array_stockwell(data, sfreq=info['sfreq'], fmin=fmin, fmax=fmax, n_fft=n_fft, width=width, decim=decim, diff -Nru python-mne-0.23.4+dfsg/mne/time_frequency/tests/test_ar.py python-mne-1.1.0+dfsg/mne/time_frequency/tests/test_ar.py --- python-mne-0.23.4+dfsg/mne/time_frequency/tests/test_ar.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/time_frequency/tests/test_ar.py 2022-08-03 15:48:14.000000000 +0000 @@ -6,7 +6,7 @@ from mne import io from mne.time_frequency.ar import _yule_walker, fit_iir_model_raw -from mne.utils import requires_version, run_tests_if_main +from mne.utils import requires_version raw_fname = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data', @@ -48,6 +48,3 @@ for order in (2, 5, 10): coeffs = fit_iir_model_raw(raw, order)[1] assert_allclose(coeffs, iir + [0.] * (order - 2), atol=5e-2) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/time_frequency/tests/test_csd.py python-mne-1.1.0+dfsg/mne/time_frequency/tests/test_csd.py --- python-mne-0.23.4+dfsg/mne/time_frequency/tests/test_csd.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/time_frequency/tests/test_csd.py 2022-08-03 15:48:14.000000000 +0000 @@ -8,7 +8,7 @@ import mne from mne.channels import equalize_channels -from mne.utils import sum_squared, run_tests_if_main, _TempDir, requires_h5py +from mne.utils import sum_squared, requires_version from mne.time_frequency import (csd_fourier, csd_multitaper, csd_morlet, csd_array_fourier, csd_array_multitaper, csd_array_morlet, @@ -220,11 +220,11 @@ raises(IndexError, csd.mean().get_data, index=15) -@requires_h5py -def test_csd_save(): +@requires_version('h5io') +def test_csd_save(tmp_path): """Test saving and loading a CrossSpectralDensity.""" csd = _make_csd() - tempdir = _TempDir() + tempdir = str(tmp_path) fname = op.join(tempdir, 'csd.h5') csd.save(fname) csd2 = read_csd(fname) @@ -236,10 +236,10 @@ assert csd._is_sum == csd2._is_sum -def test_csd_pickle(): +def test_csd_pickle(tmp_path): """Test pickling and unpickling a CrossSpectralDensity.""" csd = _make_csd() - tempdir = _TempDir() + tempdir = str(tmp_path) fname = op.join(tempdir, 'csd.dat') with open(fname, 'wb') as f: pickle.dump(csd, f) @@ -536,7 +536,8 @@ # Test baselining warning epochs_nobase = epochs.copy() epochs_nobase.baseline = None - epochs_nobase.info['highpass'] = 0 + with epochs_nobase.info._unlock(): + epochs_nobase.info['highpass'] = 0 with pytest.warns(RuntimeWarning, match='baseline'): csd = csd_morlet(epochs_nobase, frequencies=[10], decim=20) @@ -549,6 +550,3 @@ assert csd1.ch_names == ['CH1', 'CH2'] assert csd2.ch_names == ['CH1', 'CH2'] - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/time_frequency/tests/test_multitaper.py python-mne-1.1.0+dfsg/mne/time_frequency/tests/test_multitaper.py --- python-mne-0.23.4+dfsg/mne/time_frequency/tests/test_multitaper.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/time_frequency/tests/test_multitaper.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,12 +1,10 @@ -from distutils.version import LooseVersion - import numpy as np import pytest from numpy.testing import assert_array_almost_equal from mne.time_frequency import psd_multitaper from mne.time_frequency.multitaper import dpss_windows -from mne.utils import requires_nitime +from mne.utils import requires_nitime, _record_warnings from mne.io import RawArray from mne import create_info @@ -20,7 +18,7 @@ Kmax = int(2 * half_nbw) dpss, eigs = dpss_windows(N, half_nbw, Kmax, low_bias=False) - with pytest.warns(None): # conversions + with _record_warnings(): # conversions dpss_ni, eigs_ni = ni.algorithms.dpss_windows(N, half_nbw, Kmax) assert_array_almost_equal(dpss, dpss_ni) @@ -28,7 +26,7 @@ dpss, eigs = dpss_windows(N, half_nbw, Kmax, interp_from=200, low_bias=False) - with pytest.warns(None): # conversions + with _record_warnings(): # conversions dpss_ni, eigs_ni = ni.algorithms.dpss_windows(N, half_nbw, Kmax, interp_from=200) @@ -48,13 +46,12 @@ raw = RawArray(data, info) pytest.raises(ValueError, psd_multitaper, raw, sfreq, normalization='foo') - ni_5 = (LooseVersion(ni.__version__) >= LooseVersion('0.5')) - norm = 'full' if ni_5 else 'length' + norm = 'full' for adaptive, n_jobs in zip((False, True, True), (1, 1, 2)): psd, freqs = psd_multitaper(raw, adaptive=adaptive, n_jobs=n_jobs, normalization=norm) - with pytest.warns(None): # nitime integers + with _record_warnings(): # nitime integers freqs_ni, psd_ni, _ = ni.algorithms.spectral.multi_taper_psd( data, sfreq, adaptive=adaptive, jackknife=False) assert_array_almost_equal(psd, psd_ni, decimal=4) diff -Nru python-mne-0.23.4+dfsg/mne/time_frequency/tests/test_psd.py python-mne-1.1.0+dfsg/mne/time_frequency/tests/test_psd.py --- python-mne-0.23.4+dfsg/mne/time_frequency/tests/test_psd.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/time_frequency/tests/test_psd.py 2022-08-03 15:48:14.000000000 +0000 @@ -7,7 +7,10 @@ from mne import pick_types, Epochs, read_events from mne.io import RawArray, read_raw_fif from mne.utils import catch_logging -from mne.time_frequency import psd_welch, psd_multitaper, psd_array_welch +from mne.time_frequency import (psd_welch, psd_array_welch, psd_multitaper, + psd_array_multitaper) +from mne.time_frequency.multitaper import _psd_from_mt +from mne.time_frequency.psd import _median_biases base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data') raw_fname = op.join(base_dir, 'test_raw.fif') @@ -112,6 +115,15 @@ with pytest.raises(ValueError, match='No frequencies found'): psd_array_welch(np.zeros((1, 1000)), 1000., fmin=10, fmax=1) + # -- psd_array_multitaper -- + psd_complex, freq, weights = psd_array_multitaper( + raw._data[:4, :500], raw.info['sfreq'], output='complex') + psd, freq = psd_array_multitaper( + raw._data[:4, :500], raw.info['sfreq'], output='power') + assert psd_complex.ndim == 3 # channels x tapers x freqs + psd_from_complex = _psd_from_mt(psd_complex, weights) + assert_allclose(psd_from_complex, psd) + # -- Epochs/Evoked -- events = read_events(event_fname) events[:, 0] -= first_samp @@ -176,6 +188,12 @@ assert (psds_ev.shape == (len(kws['picks']), len(freqs))) +# Copied from SciPy +def _median_bias(n): + ii_2 = 2 * np.arange(1., (n - 1) // 2 + 1) + return 1 + np.sum(1. / (ii_2 + 1) - 1. / ii_2) + + @pytest.mark.parametrize('kind', ('raw', 'epochs', 'evoked')) def test_psd_welch_average_kwarg(kind): """Test `average` kwarg of psd_welch().""" @@ -193,8 +211,10 @@ tmin, tmax = -0.5, 0.5 fmin, fmax = 0, np.inf - n_fft = 256 - n_per_seg = 128 + # make these small so that sometimes we get an odd number, sometimes an + # even number of estimates + n_fft = 64 + n_per_seg = 32 n_overlap = 0 event_id = 2 @@ -231,8 +251,29 @@ assert psds_mean.shape == psds_unagg.shape[:-1] assert_allclose(psds_mean, psds_unagg.mean(axis=-1)) - # Compare with manual median calculation - assert_allclose(psds_median, np.median(psds_unagg, axis=-1)) + # Compare with manual median calculation (_median_bias copied from SciPy) + + bias = _median_bias(psds_unagg.shape[-1]) + assert_allclose(psds_median, np.median(psds_unagg, axis=-1) / bias) + if kind == 'epochs': + want_shape = (3, 2, 33, 18) + elif kind == 'evoked': + want_shape = (2, 33, 18) + else: + assert kind == 'raw' + want_shape = (2, 33, 9) + assert psds_unagg.shape == want_shape + + +@pytest.mark.parametrize('n', (2, 3, 5, 8, 12, 13, 14, 15)) +def test_median_biases(n): + """Test vectorization of median_biases.""" + want_biases = np.concatenate( + ([1., 1.], [_median_bias(ii) for ii in range(2, n + 1)])) + got_biases = _median_biases(n) + assert_allclose(want_biases, got_biases) + assert_allclose(got_biases[n], _median_bias(n)) + assert_allclose(got_biases[:3], 1.) @pytest.mark.slowtest @@ -253,7 +294,7 @@ # Compute psds with the new implementation using Welch psds_welch, freqs_welch = psd_welch(raw, tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax, proj=False, picks=picks, - n_fft=n_fft, n_jobs=1) + n_fft=n_fft, n_jobs=None) # Compute psds with plt.psd start, stop = raw.time_as_index([tmin, tmax]) diff -Nru python-mne-0.23.4+dfsg/mne/time_frequency/tests/test_stft.py python-mne-1.1.0+dfsg/mne/time_frequency/tests/test_stft.py --- python-mne-0.23.4+dfsg/mne/time_frequency/tests/test_stft.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/time_frequency/tests/test_stft.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors : Alexandre Gramfort # Eric Larson # -# License : BSD 3-clause +# License : BSD-3-Clause import pytest import numpy as np diff -Nru python-mne-0.23.4+dfsg/mne/time_frequency/tests/test_stockwell.py python-mne-1.1.0+dfsg/mne/time_frequency/tests/test_stockwell.py --- python-mne-0.23.4+dfsg/mne/time_frequency/tests/test_stockwell.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/time_frequency/tests/test_stockwell.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ # Authors : Denis A. Engemann # Alexandre Gramfort # -# License : BSD 3-clause +# License : BSD-3-Clause import os.path as op @@ -20,7 +20,7 @@ _st_power_itc) from mne.time_frequency import AverageTFR, tfr_array_stockwell -from mne.utils import run_tests_if_main +from mne.utils import _record_warnings base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data') raw_fname = op.join(base_dir, 'test_raw.fif') @@ -43,7 +43,7 @@ for last_dim in (127, 128): data = np.zeros((2, 10, last_dim)) - with pytest.warns(None): # n_fft sometimes + with _record_warnings(): # n_fft sometimes x_in, n_fft, zero_pad = _check_input_st(data, None) assert_equal(x_in.shape, (2, 10, 128)) @@ -141,6 +141,3 @@ assert_allclose(itc, np.ones_like(itc)) assert power.shape == (1, len(freqs), data.shape[-1]) assert_array_less(0, power) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/time_frequency/tests/test_tfr.py python-mne-1.1.0+dfsg/mne/time_frequency/tests/test_tfr.py --- python-mne-0.23.4+dfsg/mne/time_frequency/tests/test_tfr.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/time_frequency/tests/test_tfr.py 2022-08-03 15:48:14.000000000 +0000 @@ -11,8 +11,8 @@ from mne import (Epochs, read_events, pick_types, create_info, EpochsArray, Info, Transform) from mne.io import read_raw_fif -from mne.utils import (_TempDir, run_tests_if_main, requires_h5py, - requires_pandas, grand_average, catch_logging) +from mne.utils import (requires_version, requires_pandas, grand_average, + catch_logging) from mne.time_frequency.tfr import (morlet, tfr_morlet, _make_dpss, tfr_multitaper, AverageTFR, read_tfrs, write_tfrs, combine_tfr, cwt, _compute_tfr, @@ -131,6 +131,21 @@ # computed within the method. assert_allclose(epochs_amplitude_2.data**2, epochs_power_picks.data) + # test that averaging power across tapers when multitaper with + # output='complex' gives the same as output='power' + epoch_data = epochs.get_data() + multitaper_power = tfr_array_multitaper( + epoch_data, epochs.info['sfreq'], freqs, n_cycles, + output="power") + multitaper_complex = tfr_array_multitaper( + epoch_data, epochs.info['sfreq'], freqs, n_cycles, + output="complex") + + taper_dim = 2 + power_from_complex = (multitaper_complex * multitaper_complex.conj() + ).real.mean(axis=taper_dim) + assert_allclose(power_from_complex, multitaper_power) + print(itc) # test repr print(itc.ch_names) # test property itc += power # test add @@ -139,6 +154,7 @@ itc = ret / 23 # test dic power = power.apply_baseline(baseline=(-0.1, 0), mode='logratio') + assert power.baseline == (-0.1, 0) assert 'meg' in power assert 'grad' in power @@ -404,12 +420,27 @@ assert tfr.data.shape[-2] == 2 -@requires_h5py +def test_decim(): + """Test TFR decimation.""" + data = np.zeros((3, 3, 3, 1000)) + times = np.linspace(0, 1, 1000) + freqs = np.array([.10, .20, .30]) + info = mne.create_info(['MEG 001', 'MEG 002', 'MEG 003'], 1000., + ['mag', 'mag', 'mag']) + with info._unlock(): + info['lowpass'] = 100 + tfr = EpochsTFR(info, data=data, times=times, freqs=freqs) + tfr.decimate(3) + assert tfr.times.size == 1000 // 3 + 1 + assert tfr.data.shape == ((3, 3, 3, 1000 // 3 + 1)) + + +@requires_version('h5io') @requires_pandas -def test_io(): +def test_io(tmp_path): """Test TFR IO capacities.""" from pandas import DataFrame - tempdir = _TempDir() + tempdir = str(tmp_path) fname = op.join(tempdir, 'test-tfr.h5') data = np.zeros((3, 2, 3)) times = np.array([.1, .2, .3]) @@ -417,9 +448,9 @@ info = mne.create_info(['MEG 001', 'MEG 002', 'MEG 003'], 1000., ['mag', 'mag', 'mag']) - info['meas_date'] = datetime.datetime(year=2020, month=2, day=5, - tzinfo=datetime.timezone.utc) - info._check_consistency() + with info._unlock(check_after=True): + info['meas_date'] = datetime.datetime(year=2020, month=2, day=5, + tzinfo=datetime.timezone.utc) tfr = AverageTFR(info, data=data, times=times, freqs=freqs, nave=20, comment='test', method='crazy-tfr') tfr.save(fname) @@ -437,7 +468,8 @@ tfr.comment = None # test old meas_date - info['meas_date'] = (1, 2) + with info._unlock(): + info['meas_date'] = (1, 2) tfr.save(fname, overwrite=True) assert_equal(read_tfrs(fname, condition=0).comment, tfr.comment) tfr.comment = 'test-A' @@ -517,7 +549,7 @@ freqs_x = freqs[:-1] with pytest.raises(ValueError, match="frequencies and data size don't"): tfr = EpochsTFR(info, data=data, times=times_x, freqs=freqs_x) - del(tfr) + del tfr def test_plot(): @@ -682,8 +714,9 @@ # Now test errors tfr_badsf = tfr_eeg.copy() - tfr_badsf.info['sfreq'] = 3.1415927 - tfr_eeg = tfr_eeg.crop(-.1, .1) + with tfr_badsf.info._unlock(): + tfr_badsf.info['sfreq'] = 3.1415927 + tfr_eeg = tfr_eeg.crop(.1, .1) pytest.raises(RuntimeError, tfr_meg.add_channels, [tfr_badsf]) pytest.raises(AssertionError, tfr_meg.add_channels, [tfr_eeg]) @@ -719,17 +752,16 @@ (tfr_array_multitaper, tfr_array_morlet), (False, True), (False, True), ('complex', 'power', 'phase', 'avg_power_itc', 'avg_power', 'itc')): - # Check exception - if (func == tfr_array_multitaper) and (output == 'phase'): - pytest.raises(NotImplementedError, func, data, sfreq=sfreq, - freqs=freqs, output=output) - continue # Check runs out = func(data, sfreq=sfreq, freqs=freqs, use_fft=use_fft, zero_mean=zero_mean, n_cycles=2., output=output) # Check shapes - shape = np.r_[data.shape[:2], len(freqs), data.shape[2]] + if func == tfr_array_multitaper and output in ['complex', 'phase']: + n_tapers = 3 + shape = np.r_[data.shape[:2], n_tapers, len(freqs), data.shape[2]] + else: + shape = np.r_[data.shape[:2], len(freqs), data.shape[2]] if ('avg' in output) or ('itc' in output): assert_array_equal(shape[1:], out.shape) else: @@ -760,9 +792,6 @@ # No time_bandwidth param in morlet pytest.raises(ValueError, _compute_tfr, data, freqs, sfreq, method='morlet', time_bandwidth=1) - # No phase in multitaper XXX Check ? - pytest.raises(NotImplementedError, _compute_tfr, data, freqs, sfreq, - method='multitaper', output='phase') # Inter-trial coherence tests out = _compute_tfr(data, freqs, sfreq, output='itc', n_cycles=2.) @@ -778,10 +807,11 @@ _decim = slice(None, None, decim) if isinstance(decim, int) else decim n_time = len(np.arange(data.shape[2])[_decim]) shape = np.r_[data.shape[:2], len(freqs), n_time] + for method in ('multitaper', 'morlet'): # Single trials out = _compute_tfr(data, freqs, sfreq, method=method, decim=decim, - n_cycles=2.) + output='power', n_cycles=2.) assert_array_equal(shape, out.shape) # Averages out = _compute_tfr(data, freqs, sfreq, method=method, decim=decim, @@ -796,14 +826,17 @@ sfreq = 1000. t = np.arange(1000) / sfreq f = 50. - data = np.sin(2 * np.pi * 50. * t) + data = np.sin(2 * np.pi * f * t) data *= np.hanning(data.size) data = data[np.newaxis, np.newaxis] - freqs = np.arange(10, 111, 10) + freqs = np.arange(10, 111, 4) assert f in freqs + + # previous n_cycles=2 gives weird results for multitaper + n_cycles = freqs * 0.25 tfr = _compute_tfr(data, freqs, sfreq, method=method, decim=decim, - n_cycles=2)[0, 0] - assert freqs[np.argmax(np.abs(tfr).mean(-1))] == f + n_cycles=n_cycles, output='power')[0, 0] + assert freqs[np.argmax(tfr.mean(-1))] == f def test_averaging_epochsTFR(): @@ -840,13 +873,83 @@ [np.mean, np.median, np.mean], ['mean', 'median', lambda x: np.mean(x, axis=0)]): avgpower = power.average(method=method) - np.testing.assert_array_equal(func(power.data, axis=0), - avgpower.data) + assert_array_equal(func(power.data, axis=0), avgpower.data) with pytest.raises(RuntimeError, match='You passed a function that ' 'resulted in data'): power.average(method=np.mean) +@pytest.mark.parametrize('copy', [True, False]) +def test_averaging_freqsandtimes_epochsTFR(copy): + """Test that EpochsTFR averaging freqs methods work.""" + # Setup for reading the raw data + event_id = 1 + tmin = -0.2 + tmax = 0.498 # Allows exhaustive decimation testing + + freqs = np.arange(6, 20, 5) # define frequencies of interest + n_cycles = freqs / 4. + + raw = read_raw_fif(raw_fname) + # only pick a few events for speed + events = read_events(event_fname)[:4] + + include = [] + exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more + + # picks MEG gradiometers + picks = pick_types(raw.info, meg='grad', eeg=False, + stim=False, include=include, exclude=exclude) + picks = picks[:2] + + epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks) + + # Obtain EpochsTFR + power = tfr_morlet(epochs, freqs=freqs, n_cycles=n_cycles, + average=False, use_fft=True, + return_itc=False) + + # Test average methods for freqs and times + for idx, (func, method) in enumerate(zip( + [np.mean, np.median, np.mean, np.mean], + ['mean', 'median', lambda x: np.mean(x, axis=2), + lambda x: np.mean(x, axis=3)])): + if idx == 3: + with pytest.raises(RuntimeError, match='You passed a function'): + avgpower = power.copy().average(method=method, dim='freqs', + copy=copy) + continue + avgpower = power.copy().average(method=method, dim='freqs', copy=copy) + assert_array_equal(func(power.data, axis=2, keepdims=True), + avgpower.data) + assert avgpower.freqs == np.mean(power.freqs) + assert isinstance(avgpower, EpochsTFR) + + # average over epochs + avgpower = avgpower.average() + assert isinstance(avgpower, AverageTFR) + + # Test average methods for freqs and times + for idx, (func, method) in enumerate(zip( + [np.mean, np.median, np.mean, np.mean], + ['mean', 'median', lambda x: np.mean(x, axis=3), + lambda x: np.mean(x, axis=2)])): + if idx == 3: + with pytest.raises(RuntimeError, match='You passed a function'): + avgpower = power.copy().average(method=method, dim='times', + copy=copy) + continue + avgpower = power.copy().average(method=method, dim='times', copy=copy) + assert_array_equal(func(power.data, axis=-1, keepdims=True), + avgpower.data) + assert avgpower.times == np.mean(power.times) + assert isinstance(avgpower, EpochsTFR) + + # average over epochs + avgpower = avgpower.average() + assert isinstance(avgpower, AverageTFR) + + @requires_pandas def test_getitem_epochsTFR(): """Test GetEpochsMixin in the context of EpochsTFR.""" @@ -877,7 +980,7 @@ # With 0.5 s time windows, this gives 8 Hz smoothing kwargs = dict(freqs=freqs, n_cycles=n_cycles, use_fft=True, time_bandwidth=time_bandwidth, return_itc=False, - average=False, n_jobs=1) + average=False, n_jobs=None) power = tfr_multitaper(epochs, **kwargs) # Check that power and epochs metadata is the same @@ -965,7 +1068,7 @@ 'value') assert set(expected) == set(df_long.columns) assert set(tfr.ch_names) == set(df_long['channel']) - assert(len(df_long) == tfr.data.size) + assert len(df_long) == tfr.data.size # test long format w/ index df_long = tfr.to_data_frame(long_format=True, index=['freq']) del df_wide, df_long @@ -975,8 +1078,8 @@ assert_array_equal(df.values[:, 0], data[:, 0, :, :].reshape(1, -1).squeeze()) # compare arbitrary observation: - assert df.loc[('he', slice(None), freqs[1], times[2] * srate), - ch_names[3]].iloc[0] == data[1, 3, 1, 2] + assert (df.loc[('he', slice(None), freqs[1], times[2]), ch_names[3]].iat[0] + == data[1, 3, 1, 2]) # Check also for AverageTFR: tfr = tfr.average() @@ -995,7 +1098,7 @@ expected = ('freq', 'time', 'channel', 'ch_type', 'value') assert set(expected) == set(df_long.columns) assert set(tfr.ch_names) == set(df_long['channel']) - assert(len(df_long) == tfr.data.size) + assert len(df_long) == tfr.data.size # test long format w/ index df_long = tfr.to_data_frame(long_format=True, index=['freq']) del df_wide, df_long @@ -1005,8 +1108,7 @@ assert_array_equal(df.values[:, 0], data[0, :, :].reshape(1, -1).squeeze()) # compare arbitrary observation: - assert df.loc[(freqs[1], times[2] * srate), ch_names[3]] == \ - data[3, 1, 2] + assert df.loc[(freqs[1], times[2]), ch_names[3]] == data[3, 1, 2] @requires_pandas @@ -1067,6 +1169,3 @@ df = tfr.to_data_frame(time_format=time_format) dtypes = {None: np.float64, 'ms': np.int64, 'timedelta': Timedelta} assert isinstance(df['time'].iloc[0], dtypes[time_format]) - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/time_frequency/tfr.py python-mne-1.1.0+dfsg/mne/time_frequency/tfr.py --- python-mne-0.23.4+dfsg/mne/time_frequency/tfr.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/time_frequency/tfr.py 2022-08-03 15:48:14.000000000 +0000 @@ -7,7 +7,7 @@ # Clement Moutard # Jean-Remi King # -# License : BSD (3-clause) +# License : BSD-3-Clause from copy import deepcopy from functools import partial @@ -16,26 +16,25 @@ from .multitaper import dpss_windows -from ..baseline import rescale -from ..fixes import _import_fft +from ..baseline import rescale, _check_baseline from ..filter import next_fast_len from ..parallel import parallel_func from ..utils import (logger, verbose, _time_mask, _freq_mask, check_fname, - sizeof_fmt, GetEpochsMixin, _prepare_read_metadata, - fill_doc, _prepare_write_metadata, _check_event_id, - _gen_events, SizeMixin, _is_numeric, _check_option, - _validate_type, _check_combine, _check_pandas_installed, - _check_pandas_index_arguments, _check_time_format, - _convert_times, _build_data_frame) -from ..channels.channels import ContainsMixin, UpdateChannelsMixin + sizeof_fmt, GetEpochsMixin, TimeMixin, + _prepare_read_metadata, fill_doc, _prepare_write_metadata, + _check_event_id, _gen_events, SizeMixin, _is_numeric, + _check_option, _validate_type, _check_combine, + _check_pandas_installed, _check_pandas_index_arguments, + _check_time_format, _convert_times, _build_data_frame, + warn, _import_h5io_funcs) +from ..channels.channels import UpdateChannelsMixin from ..channels.layout import _merge_ch_data, _pair_grad_sensors from ..io.pick import (pick_info, _picks_to_idx, channel_type, _pick_inst, _get_channel_types) -from ..io.meas_info import Info -from ..viz.utils import (figure_nobar, plt_show, _setup_cmap, warn, +from ..io.meas_info import Info, ContainsMixin +from ..viz.utils import (figure_nobar, plt_show, _setup_cmap, _connection_line, _prepare_joint_axes, _setup_vmin_vmax, _set_title_multiple_electrodes) -from ..externals.h5io import write_hdf5, read_hdf5 def morlet(sfreq, freqs, n_cycles=7.0, sigma=None, zero_mean=False): @@ -219,7 +218,7 @@ out : array, shape (n_signals, n_freqs, n_time_decim) The time-frequency transform of the signals. """ - fft, ifft = _import_fft(('fft', 'ifft')) + from scipy.fft import fft, ifft _check_option('mode', mode, ['same', 'valid', 'full']) decim = _check_decim(decim) X = np.asarray(X) @@ -274,7 +273,7 @@ def _compute_tfr(epoch_data, freqs, sfreq=1.0, method='morlet', n_cycles=7.0, zero_mean=None, time_bandwidth=None, - use_fft=True, decim=1, output='complex', n_jobs=1, + use_fft=True, decim=1, output='complex', n_jobs=None, verbose=None): """Compute time-frequency transforms. @@ -332,10 +331,13 @@ ------- out : array Time frequency transform of epoch_data. If output is in ['complex', - 'phase', 'power'], then shape of out is (n_epochs, n_chans, n_freqs, - n_times), else it is (n_chans, n_freqs, n_times). If output is - 'avg_power_itc', the real values code for 'avg_power' and the - imaginary values code for the 'itc': out = avg_power + i * itc + 'phase', 'power'], then shape of ``out`` is ``(n_epochs, n_chans, + n_freqs, n_times)``, else it is ``(n_chans, n_freqs, n_times)``. + However, using multitaper method and output ``'complex'`` or + ``'phase'`` results in shape of ``out`` being ``(n_epochs, n_chans, + n_tapers, n_freqs, n_times)``. If output is ``'avg_power_itc'``, the + real values in the ``output`` contain average power' and the imaginary + values contain the ITC: ``out = avg_power + i * itc``. """ # Check data epoch_data = np.asarray(epoch_data) @@ -371,6 +373,7 @@ # Initialize output n_freqs = len(freqs) + n_tapers = len(Ws) n_epochs, n_chans, n_times = epoch_data[:, :, decim].shape if output in ('power', 'phase', 'avg_power', 'itc'): dtype = np.float64 @@ -381,17 +384,19 @@ if ('avg_' in output) or ('itc' in output): out = np.empty((n_chans, n_freqs, n_times), dtype) + elif output in ['complex', 'phase'] and method == 'multitaper': + out = np.empty((n_chans, n_tapers, n_epochs, n_freqs, n_times), dtype) else: out = np.empty((n_chans, n_epochs, n_freqs, n_times), dtype) # Parallel computation all_Ws = sum([list(W) for W in Ws], list()) _get_nfft(all_Ws, epoch_data, use_fft) - parallel, my_cwt, _ = parallel_func(_time_frequency_loop, n_jobs) + parallel, my_cwt, n_jobs = parallel_func(_time_frequency_loop, n_jobs) # Parallelization is applied across channels. tfrs = parallel( - my_cwt(channel, Ws, output, use_fft, 'same', decim) + my_cwt(channel, Ws, output, use_fft, 'same', decim, method) for channel in epoch_data.transpose(1, 0, 2)) # FIXME: to avoid overheads we should use np.array_split() @@ -400,7 +405,10 @@ if ('avg_' not in output) and ('itc' not in output): # This is to enforce that the first dimension is for epochs - out = out.transpose(1, 0, 2, 3) + if output in ['complex', 'phase'] and method == 'multitaper': + out = out.transpose(2, 0, 1, 3, 4) + else: + out = out.transpose(1, 0, 2, 3) return out @@ -429,11 +437,6 @@ % type(zero_mean)) freqs = np.asarray(freqs) - if (method == 'multitaper') and (output == 'phase'): - raise NotImplementedError( - 'This function is not optimized to compute the phase using the ' - 'multitaper method. Use np.angle of the complex output instead.') - # Check n_cycles if isinstance(n_cycles, (int, float)): n_cycles = float(n_cycles) @@ -473,7 +476,8 @@ return freqs, sfreq, zero_mean, n_cycles, time_bandwidth, decim -def _time_frequency_loop(X, Ws, output, use_fft, mode, decim): +def _time_frequency_loop(X, Ws, output, use_fft, mode, decim, + method=None): """Aux. function to _compute_tfr. Loops time-frequency transform across wavelets and epochs. @@ -500,6 +504,9 @@ See numpy.convolve. decim : slice The decimation slice: e.g. power[:, decim] + method : str | None + Used only for multitapering to create tapers dimension in the output + if ``output in ['complex', 'phase']``. """ # Set output type dtype = np.float64 @@ -508,15 +515,19 @@ # Init outputs decim = _check_decim(decim) + n_tapers = len(Ws) n_epochs, n_times = X[:, decim].shape n_freqs = len(Ws[0]) if ('avg_' in output) or ('itc' in output): tfrs = np.zeros((n_freqs, n_times), dtype=dtype) + elif output in ['complex', 'phase'] and method == 'multitaper': + tfrs = np.zeros((n_tapers, n_epochs, n_freqs, n_times), + dtype=dtype) else: tfrs = np.zeros((n_epochs, n_freqs, n_times), dtype=dtype) # Loops across tapers. - for W in Ws: + for taper_idx, W in enumerate(Ws): # No need to check here, it's done earlier (outside parallel part) nfft = _get_nfft(W, X, use_fft, check=False) coefs = _cwt_gen( @@ -544,6 +555,8 @@ # Stack or add if ('avg_' in output) or ('itc' in output): tfrs += tfr + elif output in ['complex', 'phase'] and method == 'multitaper': + tfrs[taper_idx, epoch_idx] += tfr else: tfrs[epoch_idx] += tfr @@ -558,7 +571,8 @@ tfrs /= n_epochs # Normalization by number of taper - tfrs /= len(Ws) + if n_tapers > 1 and output not in ['complex', 'phase']: + tfrs /= n_tapers return tfrs @@ -640,7 +654,8 @@ out = _compute_tfr(data, freqs, info['sfreq'], method=method, output=output, decim=decim, **tfr_params) times = inst.times[decim].copy() - info['sfreq'] /= decim.step + with info._unlock(): + info['sfreq'] /= decim.step if average: if return_itc: @@ -674,7 +689,7 @@ @verbose def tfr_morlet(inst, freqs, n_cycles, use_fft=False, return_itc=True, decim=1, - n_jobs=1, picks=None, zero_mean=True, average=True, + n_jobs=None, picks=None, zero_mean=True, average=True, output='power', verbose=None): """Compute Time-Frequency Representation (TFR) using Morlet wavelets. @@ -710,7 +725,7 @@ Make sure the wavelet has a mean of zero. .. versionadded:: 0.13.0 - %(tfr_average)s + %(average_tfr)s output : str Can be "power" (default) or "complex". If "complex", then average must be False. @@ -743,7 +758,7 @@ @verbose def tfr_array_morlet(epoch_data, sfreq, freqs, n_cycles=7.0, zero_mean=False, use_fft=True, decim=1, output='complex', - n_jobs=1, verbose=None): + n_jobs=None, verbose=None): """Compute Time-Frequency Representation (TFR) using Morlet wavelets. Same computation as `~mne.time_frequency.tfr_morlet`, but operates on @@ -818,7 +833,7 @@ @verbose def tfr_multitaper(inst, freqs, n_cycles, time_bandwidth=4.0, use_fft=True, return_itc=True, decim=1, - n_jobs=1, picks=None, average=True, verbose=None): + n_jobs=None, picks=None, average=True, verbose=None): """Compute Time-Frequency Representation (TFR) using DPSS tapers. Same computation as `~mne.time_frequency.tfr_array_multitaper`, but @@ -855,7 +870,7 @@ .. note:: Decimation may create aliasing artifacts. %(n_jobs)s %(picks_good_data)s - %(tfr_average)s + %(average_tfr)s %(verbose)s Returns @@ -886,9 +901,12 @@ # TFR(s) class -class _BaseTFR(ContainsMixin, UpdateChannelsMixin, SizeMixin): +class _BaseTFR(ContainsMixin, UpdateChannelsMixin, SizeMixin, TimeMixin): """Base TFR class.""" + def __init__(self): + self.baseline = None + @property def data(self): return self._data @@ -928,13 +946,7 @@ inst : instance of AverageTFR The modified instance. """ - if tmin is not None or tmax is not None: - time_mask = _time_mask( - self.times, tmin, tmax, sfreq=self.info['sfreq'], - include_tmax=include_tmax) - - else: - time_mask = slice(None) + super().crop(tmin=tmin, tmax=tmax, include_tmax=include_tmax) if fmin is not None or fmax is not None: freq_mask = _freq_mask(self.freqs, sfreq=self.info['sfreq'], @@ -942,14 +954,12 @@ else: freq_mask = slice(None) - self.times = self.times[time_mask] self.freqs = self.freqs[freq_mask] # Deal with broadcasting (boolean arrays do not broadcast, but indices # do, so we need to convert freq_mask to make use of broadcasting) - if isinstance(time_mask, np.ndarray) and \ - isinstance(freq_mask, np.ndarray): - freq_mask = np.where(freq_mask)[0][:, np.newaxis] - self.data = self.data[..., freq_mask, time_mask] + if isinstance(freq_mask, np.ndarray): + freq_mask = np.where(freq_mask)[0] + self._data = self._data[..., freq_mask, :] return self def copy(self): @@ -990,14 +1000,16 @@ - dividing by the mean of baseline values, taking the log, and dividing by the standard deviation of log baseline values ('zlogratio') - %(verbose_meth)s + %(verbose)s Returns ------- inst : instance of AverageTFR The modified instance. """ # noqa: E501 - rescale(self.data, self.times, baseline, mode, copy=False) + self.baseline = _check_baseline(baseline, times=self.times, + sfreq=self.info['sfreq']) + rescale(self.data, self.times, self.baseline, mode, copy=False) return self @verbose @@ -1017,9 +1029,9 @@ """ write_tfrs(fname, self, overwrite=overwrite) - @fill_doc + @verbose def to_data_frame(self, picks=None, index=None, long_format=False, - time_format='ms'): + time_format=None, *, verbose=None): """Export data in tabular structure as a pandas DataFrame. Channels are converted to columns in the DataFrame. By default, @@ -1032,15 +1044,16 @@ Parameters ---------- %(picks_all)s - %(df_index_epo)s + %(index_df_epo)s Valid string values are ``'time'``, ``'freq'``, ``'epoch'``, and ``'condition'`` for ``EpochsTFR`` and ``'time'`` and ``'freq'`` for ``AverageTFR``. Defaults to ``None``. - %(df_longform_epo)s - %(df_time_format)s + %(long_format_df_epo)s + %(time_format_df)s .. versionadded:: 0.23 + %(verbose)s Returns ------- @@ -1101,8 +1114,7 @@ Parameters ---------- - info : Info - The measurement info. + %(info_not_none)s data : ndarray, shape (n_channels, n_freqs, n_times) The data. times : ndarray, shape (n_times,) @@ -1119,8 +1131,7 @@ Attributes ---------- - info : instance of Info - Measurement info. + %(info_not_none)s ch_names : list The names of the channels. nave : int @@ -1140,6 +1151,7 @@ @verbose def __init__(self, info, data, times, freqs, nave, comment=None, method=None, verbose=None): # noqa: D102 + super().__init__() self.info = info if data.ndim != 3: raise ValueError('data should be 3d. Got %d.' % data.ndim) @@ -1154,7 +1166,8 @@ raise ValueError("Number of times and data size don't match" " (%d != %d)." % (n_times, len(times))) self.data = data - self.times = np.array(times, dtype=float) + self._set_times(np.array(times, dtype=float)) + self._raw_times = self.times.copy() self.freqs = np.array(freqs, dtype=float) self.nave = nave self.comment = comment @@ -1167,7 +1180,7 @@ cmap='RdBu_r', dB=False, colorbar=True, show=True, title=None, axes=None, layout=None, yscale='auto', mask=None, mask_style=None, mask_cmap="Greys", mask_alpha=0.1, combine=None, - exclude=[], verbose=None): + exclude=[], cnorm=None, verbose=None): """Plot TFRs as a two-dimensional image(s). Parameters @@ -1292,7 +1305,10 @@ exclude : list of str | 'bads' Channels names to exclude from being shown. If 'bads', the bad channels are excluded. Defaults to an empty list. - %(verbose_meth)s + %(cnorm)s + + .. versionadded:: 0.24 + %(verbose)s Returns ------- @@ -1306,7 +1322,7 @@ axes=axes, layout=layout, yscale=yscale, mask=mask, mask_style=mask_style, mask_cmap=mask_cmap, mask_alpha=mask_alpha, combine=combine, - exclude=exclude, verbose=verbose) + exclude=exclude, cnorm=cnorm, verbose=verbose) @verbose def _plot(self, picks=None, baseline=None, mode='mean', tmin=None, @@ -1316,7 +1332,7 @@ mask_style=None, mask_cmap="Greys", mask_alpha=.25, combine=None, exclude=None, copy=True, source_plot_joint=False, topomap_args=dict(), ch_type=None, - verbose=None): + cnorm=None, verbose=None): """Plot TFRs as a two-dimensional image(s). See self.plot() for parameters description. @@ -1380,7 +1396,7 @@ tfr=data[idx: idx + 1], freq=tfr.freqs, x_label='Time (s)', y_label='Frequency (Hz)', colorbar=colorbar, cmap=cmap, yscale=yscale, mask=mask, mask_style=mask_style, - mask_cmap=mask_cmap, mask_alpha=mask_alpha) + mask_cmap=mask_cmap, mask_alpha=mask_alpha, cnorm=cnorm) if title == 'auto': if len(tfr.info['ch_names']) == 1 or combine is None: @@ -1484,7 +1500,7 @@ to style the image. ``axes`` and ``show`` are ignored. Beyond that, if ``None``, no customizable arguments will be passed. Defaults to ``None``. - %(verbose_meth)s + %(verbose)s Returns ------- @@ -1990,7 +2006,7 @@ The axes to plot to. If None the axes is defined automatically. show : bool Call pyplot.show() at the end. - %(topomap_outlines)s + %(outlines_topomap)s contours : int | array of float The number of contour lines to draw. If 0, no contours will be drawn. When an integer, matplotlib ticker locator is used to find @@ -1998,7 +2014,7 @@ inaccurate, use array for accuracy). If an array, the values represent the levels for the contours. If colorbar=True, the ticks in colorbar correspond to the contour levels. Defaults to 6. - %(topomap_sphere_auto)s + %(sphere_topomap_auto)s Returns ------- @@ -2082,8 +2098,7 @@ Parameters ---------- - info : Info - The measurement info. + %(info_not_none)s data : ndarray, shape (n_epochs, n_channels, n_freqs, n_times) The data. times : ndarray, shape (n_times,) @@ -2119,8 +2134,7 @@ Attributes ---------- - info : instance of Info - Measurement info. + %(info_not_none)s ch_names : list The names of the channels. data : ndarray, shape (n_epochs, n_channels, n_freqs, n_times) @@ -2169,6 +2183,7 @@ events=None, event_id=None, selection=None, drop_log=None, metadata=None, verbose=None): # noqa: D102 + super().__init__() self.info = info if data.ndim != 4: raise ValueError('data should be 4d. Got %d.' % data.ndim) @@ -2202,7 +2217,9 @@ (len(dl) == 0 for dl in drop_log)) event_id = _check_event_id(event_id, events) self.data = data - self.times = np.array(times, dtype=float) + self._set_times(np.array(times, dtype=float)) + self._raw_times = self.times.copy() # needed for decimate + self._decim = 1 self.freqs = np.array(freqs, dtype=float) self.events = events self.event_id = event_id @@ -2231,7 +2248,7 @@ epochs.data = np.abs(self.data) return epochs - def average(self, method='mean'): + def average(self, method='mean', dim='epochs', copy=False): """Average the data across epochs. Parameters @@ -2243,10 +2260,16 @@ returns an array of shape (n_channels, n_freqs, n_time). Note that due to file type limitations, the kind for all these will be "average". + dim : 'epochs' | 'freqs' | 'times' + The dimension along which to combine the data. + copy : bool + Whether to return a copy of the modified instance, + or modify in place. Ignored when ``dim='epochs'`` + because a new instance must be returned. Returns ------- - ave : instance of AverageTFR + ave : instance of AverageTFR | EpochsTFR The averaged data. Notes @@ -2260,21 +2283,49 @@ https://github.com/scipy/scipy/pull/12676#issuecomment-783370228 """ + _check_option('dim', dim, ('epochs', 'freqs', 'times')) + axis = dict(epochs=0, freqs=2, times=self.data.ndim - 1)[dim] + # return a lambda function for computing a combination metric # over epochs - func = _check_combine(mode=method) + func = _check_combine(mode=method, axis=axis) data = func(self.data) - if data.shape != self._data.shape[1:]: + n_epochs, n_channels, n_freqs, n_times = self.data.shape + freqs, times = self.freqs, self.times + + if dim == 'freqs': + freqs = np.mean(self.freqs, keepdims=True) + n_freqs = 1 + elif dim == 'times': + times = np.mean(self.times, keepdims=True) + n_times = 1 + if dim == 'epochs': + expected_shape = self._data.shape[1:] + else: + expected_shape = (n_epochs, n_channels, n_freqs, n_times) + data = np.expand_dims(data, axis=axis) + + if data.shape != expected_shape: raise RuntimeError( - 'You passed a function that resulted in data of shape {}, ' - 'but it should be {}.'.format( - data.shape, self._data.shape[1:])) - - return AverageTFR(info=self.info.copy(), data=data, - times=self.times.copy(), freqs=self.freqs.copy(), - nave=self.data.shape[0], method=self.method, - comment=self.comment) + f'You passed a function that resulted in data of shape ' + f'{data.shape}, but it should be {expected_shape}.') + + if dim == 'epochs': + return AverageTFR(info=self.info.copy(), data=data, + times=times, freqs=freqs, + nave=self.data.shape[0], method=self.method, + comment=self.comment) + elif copy: + return EpochsTFR(info=self.info.copy(), data=data, + times=times, freqs=freqs, method=self.method, + comment=self.comment, metadata=self.metadata, + events=self.events, event_id=self.event_id) + else: + self.data = data + self._set_times(times) + self.freqs = freqs + return self def combine_tfr(all_tfr, weights='nave'): @@ -2384,6 +2435,9 @@ copy = baseline is not None data = rescale(data, times, baseline, mode, copy=copy) + if np.iscomplexobj(data): + data = np.sqrt((data * data.conj()).real) + # crop time itmin, itmax = None, None idx = np.where(_time_mask(times, tmin, tmax, sfreq=sfreq))[0] @@ -2408,7 +2462,7 @@ data = data[:, ifmin:ifmax, itmin:itmax] if dB: - data = 10 * np.log10((data * data.conj()).real) + data = 20 * np.log10(data) vmin, vmax = _setup_vmin_vmax(data, vmin, vmax) return data, times, freqs, vmin, vmax @@ -2451,6 +2505,7 @@ ----- .. versionadded:: 0.9.0 """ + _, write_hdf5 = _import_h5io_funcs() out = [] if not isinstance(tfr, (list, tuple)): tfr = [tfr] @@ -2502,6 +2557,7 @@ .. versionadded:: 0.9.0 """ check_fname(fname, 'tfr', ('-tfr.h5', '_tfr.h5')) + read_hdf5, _ = _import_h5io_funcs() logger.info('Reading %s ...' % fname) tfr_data = read_hdf5(fname, title='mnepython', slash='replace') @@ -2597,7 +2653,7 @@ tfr.data, tfr.times, tfr.freqs, tmin, tmax, fmin, fmax, mode, baseline, vmin, vmax, dB, tfr.info['sfreq'], copy=False) - tfr.times = times + tfr._set_times(times) tfr.freqs = freqs tfr.data = data diff -Nru python-mne-0.23.4+dfsg/mne/transforms.py python-mne-1.1.0+dfsg/mne/transforms.py --- python-mne-0.23.4+dfsg/mne/transforms.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/transforms.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,7 +4,7 @@ # Authors: Alexandre Gramfort # Christian Brodbeck # -# License: BSD (3-clause) +# License: BSD-3-Clause import os import os.path as op @@ -13,13 +13,15 @@ import numpy as np from copy import deepcopy -from .fixes import einsum, jit, mean +from .fixes import jit, mean, _get_img_fdata from .io.constants import FIFF from .io.open import fiff_open from .io.tag import read_tag -from .io.write import start_file, end_file, write_coord_trans +from .io.write import start_and_end_file, write_coord_trans +from .defaults import _handle_default from .utils import (check_fname, logger, verbose, _ensure_int, _validate_type, - _check_path_like, get_subjects_dir, fill_doc, _check_fname) + _path_like, get_subjects_dir, fill_doc, _check_fname, + _check_option, _require_version, wrapped_stdout) # transformation from anterior/left/superior coordinate system to @@ -62,7 +64,9 @@ """Convert string or int coord frame into int.""" if isinstance(cf, str): if cf not in _str_to_frame: - raise ValueError('Unknown cf %s' % cf) + raise ValueError( + f'Unknown coordinate frame {cf}, ' + 'expected "' + '", "'.join(_str_to_frame.keys()) + '"') cf = _str_to_frame[cf] else: cf = _ensure_int(cf, 'coordinate frame', 'a str or int') @@ -95,16 +99,17 @@ to = _to_const(to) trans = np.eye(4) if trans is None else np.asarray(trans, np.float64) if trans.shape != (4, 4): - raise ValueError('Transformation must be shape (4, 4) not %s' - % (trans.shape,)) + raise ValueError( + f'Transformation must be shape (4, 4) not {trans.shape}') self['from'] = fro self['to'] = to self['trans'] = trans def __repr__(self): # noqa: D105 - return ('%s>\n%s' - % (_coord_frame_name(self['from']), - _coord_frame_name(self['to']), self['trans'])) + with np.printoptions(suppress=True): # suppress scientific notation + return '{to}>\n{trans}'.format( + fro=_coord_frame_name(self['from']), + to=_coord_frame_name(self['to']), trans=self['trans']) def __eq__(self, other, rtol=0., atol=0.): """Check for equality. @@ -182,8 +187,8 @@ level='info'): # Units gives the units of the transformation. This always prints in mm. log_func = getattr(logger, level) - log_func(prefix + '%s -> %s' - % (_coord_frame_name(t['from']), _coord_frame_name(t['to']))) + log_func(prefix + '{fro} -> {to}'.format( + fro=_coord_frame_name(t['from']), to=_coord_frame_name(t['to']))) for ti, tt in enumerate(t['trans']): scale = 1000. if (ti != 3 and units != 'mm') else 1. text = ' mm' if ti != 3 else '' @@ -328,10 +333,10 @@ target_z_axis[1] * target_z_axis[1]) # assert that r is a rotation matrix r^t * r = I and det(r) = 1 - assert(np.any((r.dot(r.T) - np.identity(3)) < 1E-12)) - assert((np.linalg.det(r) - 1.0) < 1E-12) + assert np.any((r.dot(r.T) - np.identity(3)) < 1E-12) + assert (np.linalg.det(r) - 1.0) < 1E-12 # assert that r maps [0 0 1] on the device z axis (target_z_axis) - assert(np.linalg.norm(target_z_axis - r.dot([0, 0, 1])) < 1e-12) + assert np.linalg.norm(target_z_axis - r.dot([0, 0, 1])) < 1e-12 return r @@ -414,8 +419,8 @@ to_str = _frame_to_str[to] to_const = to del to - err_str = ('trans must be a Transform between %s<->%s, got' - % (from_str, to_str)) + err_str = 'trans must be a Transform between ' \ + f'{from_str}<->{to_str}, got' if not isinstance(trans, (list, tuple)): trans = [trans] # Ensure that we have exactly one match @@ -423,15 +428,16 @@ misses = list() for ti, this_trans in enumerate(trans): if not isinstance(this_trans, Transform): - raise ValueError('%s None' % err_str) + raise ValueError(f'{err_str} None') if {this_trans['from'], this_trans['to']} == {from_const, to_const}: idx.append(ti) else: - misses += ['%s->%s' % (_frame_to_str[this_trans['from']], - _frame_to_str[this_trans['to']])] + misses += ['{fro}->{to}'.format( + fro=_frame_to_str[this_trans['from']], + to=_frame_to_str[this_trans['to']])] if len(idx) != 1: - raise ValueError('%s %s' % (err_str, ', '.join(misses))) + raise ValueError(f'{err_str} ' + ', '.join(misses)) trans = trans[idx[0]] if trans['from'] != from_const: trans = invert_transform(trans) @@ -444,13 +450,13 @@ if allow_none: types += (None,) _validate_type(trans, types, 'trans') - if _check_path_like(trans): + if _path_like(trans): trans = str(trans) if trans == 'fsaverage': trans = op.join(op.dirname(__file__), 'data', 'fsaverage', 'fsaverage-trans.fif') if not op.isfile(trans): - raise IOError('trans file "%s" not found' % trans) + raise IOError(f'trans file "{trans}" not found') if op.splitext(trans)[1] in ['.fif', '.gz']: fro_to_t = read_trans(trans) else: @@ -458,8 +464,7 @@ # these are usually actually in to_fro form t = np.genfromtxt(trans) if t.ndim != 2 or t.shape != (4, 4): - raise RuntimeError('File "%s" did not have 4x4 entries' - % trans) + raise RuntimeError(f'File "{trans}" did not have 4x4 entries') fro_to_t = Transform(to, fro, t) elif isinstance(trans, Transform): fro_to_t = trans @@ -495,31 +500,32 @@ fro = _to_const(fro) to = _to_const(to) if t_first['from'] != fro: - raise RuntimeError('From mismatch: %s ("%s") != %s ("%s")' - % (t_first['from'], - _coord_frame_name(t_first['from']), - fro, _coord_frame_name(fro))) + raise RuntimeError( + 'From mismatch: {fro1} ("{cf1}") != {fro2} ("{cf2}")'.format( + fro1=t_first['from'], cf1=_coord_frame_name(t_first['from']), + fro2=fro, cf2=_coord_frame_name(fro))) if t_first['to'] != t_second['from']: - raise RuntimeError('Transform mismatch: t1["to"] = %s ("%s"), ' - 't2["from"] = %s ("%s")' - % (t_first['to'], _coord_frame_name(t_first['to']), - t_second['from'], - _coord_frame_name(t_second['from']))) + raise RuntimeError('Transform mismatch: t1["to"] = {to1} ("{cf1}"), ' + 't2["from"] = {fro2} ("{cf2}")'.format( + to1=t_first['to'], + cf1=_coord_frame_name(t_first['to']), + fro2=t_second['from'], + cf2=_coord_frame_name(t_second['from']))) if t_second['to'] != to: - raise RuntimeError('To mismatch: %s ("%s") != %s ("%s")' - % (t_second['to'], - _coord_frame_name(t_second['to']), - to, _coord_frame_name(to))) + raise RuntimeError( + 'To mismatch: {to1} ("{cf1}") != {to2} ("{cf2}")'.format( + to1=t_second['to'], cf1=_coord_frame_name(t_second['to']), + to2=to, cf2=_coord_frame_name(to))) return Transform(fro, to, np.dot(t_second['trans'], t_first['trans'])) @verbose def read_trans(fname, return_all=False, verbose=None): - """Read a -trans.fif file. + """Read a ``-trans.fif`` file. Parameters ---------- - fname : str + fname : path-like The name of the file. return_all : bool If True, return all transformations in the file. @@ -538,6 +544,7 @@ write_trans mne.transforms.Transform """ + fname = _check_fname(fname, overwrite='read', must_exist=True) fid, tree, directory = fiff_open(fname) trans = list() @@ -552,15 +559,18 @@ return trans if return_all else trans[0] -def write_trans(fname, trans): +@verbose +def write_trans(fname, trans, *, overwrite=False, verbose=None): """Write a -trans.fif file. Parameters ---------- - fname : str - The name of the file, which should end in '-trans.fif'. + fname : path-like + The name of the file, which should end in ``-trans.fif``. trans : dict Trans file data, as returned by read_trans. + %(overwrite)s + %(verbose)s See Also -------- @@ -568,9 +578,9 @@ """ check_fname(fname, 'trans', ('-trans.fif', '-trans.fif.gz', '_trans.fif', '_trans.fif.gz')) - fid = start_file(fname) - write_coord_trans(fid, trans) - end_file(fid) + fname = _check_fname(fname=fname, overwrite=overwrite) + with start_and_end_file(fname) as fid: + write_coord_trans(fid, trans) def invert_transform(trans): @@ -679,6 +689,22 @@ return trans +def _get_transforms_to_coord_frame(info, trans, coord_frame='mri'): + """Get the transforms to a coordinate frame from device, head and mri.""" + head_mri_t = _get_trans(trans, 'head', 'mri')[0] + dev_head_t = _get_trans(info['dev_head_t'], 'meg', 'head')[0] + mri_dev_t = invert_transform(combine_transforms( + dev_head_t, head_mri_t, 'meg', 'mri')) + to_cf_t = dict( + meg=_ensure_trans([dev_head_t, mri_dev_t, Transform('meg', 'meg')], + fro='meg', to=coord_frame), + head=_ensure_trans([dev_head_t, head_mri_t, Transform('head', 'head')], + fro='head', to=coord_frame), + mri=_ensure_trans([head_mri_t, mri_dev_t, Transform('mri', 'mri')], + fro='mri', to=coord_frame)) + return to_cf_t + + ############################################################################### # Spherical coordinates and harmonics @@ -695,8 +721,8 @@ sph_pts : ndarray, shape (n_points, 3) Array containing points in spherical coordinates (rad, azimuth, polar) """ - assert cart.ndim == 2 and cart.shape[1] == 3 cart = np.atleast_2d(cart) + assert cart.ndim == 2 and cart.shape[1] == 3 out = np.empty((len(cart), 3)) out[:, 0] = np.sqrt(np.sum(cart * cart, axis=1)) norm = np.where(out[:, 0] > 0, out[:, 0], 1) # protect against / 0 @@ -720,8 +746,8 @@ Array containing points in Cartesian coordinates (x, y, z) """ - assert sph_pts.ndim == 2 and sph_pts.shape[1] == 3 sph_pts = np.atleast_2d(sph_pts) + assert sph_pts.ndim == 2 and sph_pts.shape[1] == 3 cart_pts = np.empty((len(sph_pts), 3)) cart_pts[:, 2] = sph_pts[:, 0] * np.cos(sph_pts[:, 2]) xy = sph_pts[:, 0] * np.sin(sph_pts[:, 2]) @@ -782,7 +808,7 @@ trans = np.array([[c_as * s_ps, -s_as, c_as * c_ps], [s_as * s_ps, c_as, c_ps * s_as], [c_ps, np.zeros_like(c_as), -s_ps]]) - cart_grads = einsum('ijk,kj->ki', trans, sph_grads) + cart_grads = np.einsum('ijk,kj->ki', trans, sph_grads) return cart_grads @@ -1255,11 +1281,12 @@ return affine -def _angle_between_quats(x, y): +def _angle_between_quats(x, y=None): """Compute the ang between two quaternions w/3-element representations.""" # z = conj(x) * y # conjugate just negates all but the first element in a 4-element quat, # so it's just a negative for us + y = np.zeros(3) if y is None else y z = _quat_mult(-x, y) z0 = _quat_real(z) return 2 * np.arctan2(np.linalg.norm(z, axis=-1), z0) @@ -1521,3 +1548,269 @@ quat[..., 2] = cphi * ctheta * spsi - sphi * stheta * cpsi quat *= mult return quat + + +############################################################################### +# Affine Registration and SDR + +_ORDERED_STEPS = ('translation', 'rigid', 'affine', 'sdr') + + +def _validate_zooms(zooms): + _validate_type(zooms, (dict, list, tuple, 'numeric', None), 'zooms') + zooms = _handle_default('transform_zooms', zooms) + for key, val in zooms.items(): + _check_option('zooms key', key, _ORDERED_STEPS) + if val is not None: + val = tuple( + float(x) for x in np.array(val, dtype=float).ravel()) + _check_option(f'len(zooms[{repr(key)})', len(val), (1, 3)) + if len(val) == 1: + val = val * 3 + for this_zoom in val: + if this_zoom <= 1: + raise ValueError(f'Zooms must be > 1, got {this_zoom}') + zooms[key] = val + return zooms + + +def _validate_niter(niter): + _validate_type(niter, (dict, list, tuple, None), 'niter') + niter = _handle_default('transform_niter', niter) + for key, value in niter.items(): + _check_option('niter key', key, _ORDERED_STEPS) + _check_option(f'len(niter[{repr(key)}])', len(value), (1, 2, 3)) + return niter + + +def _validate_pipeline(pipeline): + _validate_type(pipeline, (str, list, tuple), 'pipeline') + pipeline_defaults = dict( + all=_ORDERED_STEPS, + rigids=_ORDERED_STEPS[:_ORDERED_STEPS.index('rigid') + 1], + affines=_ORDERED_STEPS[:_ORDERED_STEPS.index('affine') + 1]) + if isinstance(pipeline, str): # use defaults + _check_option('pipeline', pipeline, ('all', 'rigids', 'affines'), + extra='when str') + pipeline = pipeline_defaults[pipeline] + for ii, step in enumerate(pipeline): + name = f'pipeline[{ii}]' + _validate_type(step, str, name) + _check_option(name, step, _ORDERED_STEPS) + ordered_pipeline = tuple(sorted( + pipeline, key=lambda x: _ORDERED_STEPS.index(x))) + if tuple(pipeline) != ordered_pipeline: + raise ValueError( + f'Steps in pipeline are out of order, expected {ordered_pipeline} ' + f'but got {pipeline} instead') + if len(set(pipeline)) != len(pipeline): + raise ValueError('Steps in pipeline should not be repeated') + return tuple(pipeline) + + +def _compute_r2(a, b): + return 100 * (a.ravel() @ b.ravel()) / \ + (np.linalg.norm(a) * np.linalg.norm(b)) + + +def _reslice_normalize(img, zooms): + from dipy.align.reslice import reslice + img_zooms = img.header.get_zooms()[:3] + img_affine = img.affine + img = _get_img_fdata(img) + if zooms is not None: + img, img_affine = reslice(img, img_affine, img_zooms, zooms) + img /= img.max() # normalize + return img, img_affine + + +@verbose +def compute_volume_registration(moving, static, pipeline='all', zooms=None, + niter=None, verbose=None): + """Align two volumes using an affine and, optionally, SDR. + + Parameters + ---------- + %(moving)s + %(static)s + %(pipeline)s + zooms : float | tuple | dict | None + The voxel size of volume for each spatial dimension in mm. + If None (default), MRIs won't be resliced (slow, but most accurate). + Can be a tuple to provide separate zooms for each dimension (X/Y/Z), + or a dict with keys ``['translation', 'rigid', 'affine', 'sdr']`` + (each with values that are float`, tuple, or None) to provide separate + reslicing/accuracy for the steps. + %(niter)s + %(verbose)s + + Returns + ------- + %(reg_affine)s + %(sdr_morph)s + + Notes + ----- + This function is heavily inspired by and extends + :func:`dipy.align.affine_registration + `. + + .. versionadded:: 0.24 + """ + return _compute_volume_registration( + moving, static, pipeline, zooms, niter)[:2] + + +def _compute_volume_registration(moving, static, pipeline, zooms, niter): + _require_version('nibabel', 'SDR morph', '2.1.0') + _require_version('dipy', 'SDR morph', '0.10.1') + import nibabel as nib + with np.testing.suppress_warnings(): + from dipy.align.imaffine import AffineMap + from dipy.align import (affine_registration, center_of_mass, + translation, rigid, affine, + imwarp, metrics) + + # input validation + _validate_type(moving, nib.spatialimages.SpatialImage, 'moving') + _validate_type(static, nib.spatialimages.SpatialImage, 'static') + original_zoom = np.mean(moving.header.get_zooms()[:3]) + zooms = _validate_zooms(zooms) + niter = _validate_niter(niter) + pipeline = _validate_pipeline(pipeline) + + logger.info('Computing registration...') + + # affine optimizations + reg_affine = None + sdr_morph = None + pipeline_options = dict(translation=[center_of_mass, translation], + rigid=[rigid], affine=[affine]) + sigmas_mm = np.array([3.0, 1.0, 0.0]) # default for affine_registration + sigma_diff_mm = 2.0 + factors = [4, 2, 1] + current_zoom = None + for i, step in enumerate(pipeline): + # reslice image with zooms + if i == 0 or zooms[step] != zooms[pipeline[i - 1]]: + if zooms[step] is not None: + logger.info(f'Reslicing to zooms={zooms[step]} for {step} ...') + current_zoom = np.mean(zooms[step]) + else: + logger.info(f'Using original zooms for {step} ...') + current_zoom = original_zoom + static_zoomed, static_affine = _reslice_normalize( + static, zooms[step]) + moving_zoomed, moving_affine = _reslice_normalize( + moving, zooms[step]) + logger.info(f'Optimizing {step}:') + if step == 'sdr': # happens last + sigma_diff_vox = sigma_diff_mm / current_zoom + affine_map = AffineMap(reg_affine, # apply registration here + static_zoomed.shape, static_affine, + moving_zoomed.shape, moving_affine) + moving_zoomed = affine_map.transform(moving_zoomed) + metric = metrics.CCMetric( + dim=3, sigma_diff=sigma_diff_vox, + radius=max(int(np.ceil(2 * sigma_diff_vox)), 1)) + sdr = imwarp.SymmetricDiffeomorphicRegistration( + metric, niter[step]) + with wrapped_stdout(indent=' ', cull_newlines=True): + sdr_morph = sdr.optimize(static_zoomed, moving_zoomed, + static_affine, static_affine) + moved_zoomed = sdr_morph.transform(moving_zoomed) + else: + sigmas_vox = list(sigmas_mm / current_zoom) + with wrapped_stdout(indent=' ', cull_newlines=True): + moved_zoomed, reg_affine = affine_registration( + moving_zoomed, static_zoomed, moving_affine, static_affine, + nbins=32, metric='MI', pipeline=pipeline_options[step], + level_iters=niter[step], sigmas=sigmas_vox, + factors=factors, starting_affine=reg_affine) + + # report some useful information + if step in ('translation', 'rigid'): + dist = np.linalg.norm(reg_affine[:3, 3]) + angle = np.rad2deg(_angle_between_quats( + np.zeros(3), rot_to_quat(reg_affine[:3, :3]))) + logger.info(f' Translation: {dist:6.1f} mm') + if step == 'rigid': + logger.info(f' Rotation: {angle:6.1f}°') + assert moved_zoomed.shape == static_zoomed.shape, step + r2 = _compute_r2(static_zoomed, moved_zoomed) + logger.info(f' R²: {r2:6.1f}%') + return (reg_affine, sdr_morph, static_zoomed.shape, static_affine, + moving_zoomed.shape, moving_affine) + + +@verbose +def apply_volume_registration(moving, static, reg_affine, sdr_morph=None, + interpolation='linear', cval=0., + verbose=None): + """Apply volume registration. + + Uses registration parameters computed by + :func:`~mne.transforms.compute_volume_registration`. + + Parameters + ---------- + %(moving)s + %(static)s + %(reg_affine)s + %(sdr_morph)s + interpolation : str + Interpolation to be used during the interpolation. + Can be "linear" (default) or "nearest". + cval : float | str + The constant value to assume exists outside the bounds of the + ``moving`` image domain. Can be a string percentage like ``'1%%'`` + to use the given percentile of image data as the constant value. + %(verbose)s + + Returns + ------- + reg_img : instance of SpatialImage + The image after affine (and SDR, if provided) registration. + + Notes + ----- + .. versionadded:: 0.24 + """ + _require_version('nibabel', 'SDR morph', '2.1.0') + _require_version('dipy', 'SDR morph', '0.10.1') + from nibabel.spatialimages import SpatialImage + from dipy.align.imwarp import DiffeomorphicMap + from dipy.align.imaffine import AffineMap + _validate_type(moving, SpatialImage, 'moving') + _validate_type(static, SpatialImage, 'static') + _validate_type(reg_affine, np.ndarray, 'reg_affine') + _check_option('reg_affine.shape', reg_affine.shape, ((4, 4),)) + _validate_type(sdr_morph, (DiffeomorphicMap, None), 'sdr_morph') + _validate_type(cval, ('numeric', str), 'cval') + perc = None + if isinstance(cval, str): + if not cval.endswith('%'): + raise ValueError(f'cval must end with % if str, got {cval}') + perc = float(cval[:-1]) + logger.info('Applying affine registration ...') + moving_affine = moving.affine + moving = np.asarray(moving.dataobj, dtype=float) + if perc is not None: + cval = np.percentile(moving, perc) + logger.info(f'Using a lower bound at the {perc} percentile: {cval}') + moving -= cval + static, static_affine = np.asarray(static.dataobj), static.affine + affine_map = AffineMap(reg_affine, + static.shape, static_affine, + moving.shape, moving_affine) + reg_data = affine_map.transform(moving, interpolation=interpolation) + if sdr_morph is not None: + logger.info('Appling SDR warp ...') + reg_data = sdr_morph.transform( + reg_data, interpolation=interpolation, + image_world2grid=np.linalg.inv(static_affine), + out_shape=static.shape, out_grid2world=static_affine) + reg_data += cval + reg_img = SpatialImage(reg_data, static_affine) + logger.info('[done]') + return reg_img diff -Nru python-mne-0.23.4+dfsg/mne/utils/_bunch.py python-mne-1.1.0+dfsg/mne/utils/_bunch.py --- python-mne-0.23.4+dfsg/mne/utils/_bunch.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/_bunch.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,7 +4,7 @@ # Eric Larson # Joan Massich # -# License: BSD (3-clause) +# License: BSD-3-Clause from copy import deepcopy @@ -26,10 +26,10 @@ class BunchConst(Bunch): """Class to prevent us from re-defining constants (DRY).""" - def __setattr__(self, attr, val): # noqa: D105 - if attr != '__dict__' and hasattr(self, attr): - raise AttributeError('Attribute "%s" already set' % attr) - super().__setattr__(attr, val) + def __setitem__(self, key, val): # noqa: D105 + if key != '__dict__' and key in self: + raise AttributeError(f'Attribute {repr(key)} already set') + super().__setitem__(key, val) ############################################################################### diff -Nru python-mne-0.23.4+dfsg/mne/utils/check.py python-mne-1.1.0+dfsg/mne/utils/check.py --- python-mne-0.23.4+dfsg/mne/utils/check.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/check.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,28 +2,31 @@ """The check functions.""" # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause from builtins import input # no-op here but facilitates testing from difflib import get_close_matches -from distutils.version import LooseVersion +from importlib import import_module import operator import os import os.path as op from pathlib import Path +import re import sys import warnings +import numbers import numpy as np -from ..fixes import _median_complex -from ._logging import warn, logger +from ..fixes import _median_complex, _compare_version +from ._logging import warn, logger, verbose -def _ensure_int(x, name='unknown', must_be='an int'): +def _ensure_int(x, name='unknown', must_be='an int', *, extra=''): """Ensure a variable is an integer.""" # This is preferred over numbers.Integral, see: # https://github.com/scipy/scipy/pull/7351#issuecomment-299713159 + extra = f' {extra}' if extra else extra try: # someone passing True/False is much more likely to be an error than # intentional usage @@ -31,7 +34,7 @@ raise TypeError() x = int(operator.index(x)) except TypeError: - raise TypeError('%s must be %s, got %s' % (name, must_be, type(x))) + raise TypeError(f'{name} must be {must_be}{extra}, got {type(x)}') return x @@ -63,7 +66,8 @@ % (fname, filetype, print_endings)) -def check_version(library, min_version='0.0'): +def check_version(library, min_version='0.0', *, strip=True, + return_version=False): r"""Check minimum library version required. Parameters @@ -74,35 +78,91 @@ The minimum version string. Anything that matches ``'(\d+ | [a-z]+ | \.)'``. Can also be empty to skip version check (just check for library presence). + strip : bool + If True (default), then PEP440 development markers like ``.devN`` + will be stripped from the version. This makes it so that + ``check_version('mne', '1.1')`` will be ``True`` even when on version + ``'1.1.dev0'`` (prerelease/dev version). This option is provided for + backward compatibility with the behavior of ``LooseVersion``, and + diverges from how modern parsing in ``packaging.version.parse`` works. + + .. versionadded:: 1.0 + return_version : bool + If True (default False), also return the version (can be None if the + library is missing). + + .. versionadded:: 1.0 Returns ------- ok : bool True if the library exists with at least the specified version. + version : str | None + The version. Only returned when ``return_version=True``. """ ok = True + version = None try: - library = __import__(library) + library = import_module(library) except ImportError: ok = False else: - if min_version and \ - LooseVersion(library.__version__) < LooseVersion(min_version): - ok = False - return ok + check_version = min_version and min_version != '0.0' + get_version = check_version or return_version + if get_version: + version = library.__version__ + if strip: + version = _strip_dev(version) + if check_version: + if _compare_version(version, '<', min_version): + ok = False + out = (ok, version) if return_version else ok + return out + + +def _strip_dev(version): + # First capturing group () is what we want to keep, at the beginning: + # + # - at least one numeral, then + # - repeats of {dot, at least one numeral} + # + # The rest (consume to the end of the string) is the stuff we want to cut + # off: + # + # - A period (maybe), then + # - "dev", "rc", or "+", then + # - numerals, periods, dashes, and "a" through "g" (hex chars) + # + # Thanks https://www.regextester.com ! + exp = r'^([0-9]+(?:\.[0-9]+)*)\.?(?:dev|rc|\+)[0-9+a-g\.\-]+$' + match = re.match(exp, version) + return match.groups()[0] if match is not None else version def _require_version(lib, what, version='0.0'): """Require library for a purpose.""" - if not check_version(lib, version): + ok, got = check_version(lib, version, return_version=True) + if not ok: extra = f' (version >= {version})' if version != '0.0' else '' - raise ImportError(f'The {lib} package{extra} is required to {what}') + why = 'package was not found' if got is None else f'got {repr(got)}' + raise ImportError(f'The {lib} package{extra} is required to {what}, ' + f'{why}') + + +def _import_h5py(): + _require_version('h5py', 'read MATLAB files >= v7.3') + import h5py + return h5py + + +def _import_h5io_funcs(): + h5io = _soft_import('h5io', 'HDF5-based I/O') + return h5io.read_hdf5, h5io.write_hdf5 -def _check_mayavi_version(min_version='4.3.0'): - """Check mayavi version.""" - if not check_version('mayavi', min_version): - raise RuntimeError("Need mayavi >= %s" % min_version) +def _import_pymatreader_funcs(purpose): + pymatreader = _soft_import('pymatreader', purpose) + return pymatreader.read_mat # adapted from scikit-learn utils/validation.py @@ -150,10 +210,17 @@ return event_id +@verbose def _check_fname(fname, overwrite=False, must_exist=False, name='File', - need_dir=False): - """Check for file existence.""" + need_dir=False, *, verbose=None): + """Check for file existence, and return string of its absolute path.""" _validate_type(fname, 'path-like', name) + fname = str( + Path(fname) + .expanduser() + .absolute() + ) + if op.exists(fname): if not overwrite: raise FileExistsError('Destination file exists. Please use option ' @@ -176,7 +243,8 @@ f'{name} does not have read permissions: {fname}') elif must_exist: raise FileNotFoundError(f'{name} does not exist: {fname}') - return str(op.abspath(fname)) + + return fname def _check_subject(first, second, *, raise_error=True, @@ -236,7 +304,8 @@ # pick channels for t_info in [info1, info2]: if t_info['comps']: - t_info['comps'] = [] + with t_info._unlock(): + t_info['comps'] = [] picks = pick_channels(t_info['ch_names'], ch_names) pick_info(t_info, picks, copy=False) # "or 0" here aliases None -> 0, as they are equivalent @@ -250,43 +319,69 @@ % (name1, grade1, name2, grade2)) -def _check_pylsl_installed(strict=True): - """Aux function.""" +def _soft_import(name, purpose, strict=True): + """Import soft dependencies, providing informative errors on failure. + + Parameters + ---------- + name : str + Name of the module to be imported. For example, 'pandas'. + purpose : str + A very brief statement (formulated as a noun phrase) explaining what + functionality the package provides to MNE-Python. + strict : bool + Whether to raise an error if module import fails. + """ + # so that error msg lines are aligned + def indent(x): + return x.rjust(len(x) + 14) + + # Mapping import namespaces to their pypi package name + pip_name = dict( + sklearn='scikit-learn', + EDFlib='EDFlib-Python', + mne_bids='mne-bids', + mne_nirs='mne-nirs', + mne_features='mne-features', + mne_qt_browser='mne-qt-browser', + mne_connectivity='mne-connectivity', + pyvista='pyvistaqt').get(name, name) + try: - import pylsl - return pylsl - except ImportError: - if strict is True: - raise RuntimeError('For this functionality to work, the pylsl ' - 'library is required.') + mod = import_module(name) + return mod + except (ImportError, ModuleNotFoundError): + if strict: + raise RuntimeError( + f'For {purpose} to work, the {name} module is needed, ' + + 'but it could not be imported.\n' + + '\n'.join((indent('use the following installation method ' + 'appropriate for your environment:'), + indent(f"'pip install {pip_name}'"), + indent(f"'conda install -c conda-forge {pip_name}'") + ))) else: return False def _check_pandas_installed(strict=True): """Aux function.""" - try: - import pandas - return pandas - except ImportError: - if strict is True: - raise RuntimeError('For this functionality to work, the Pandas ' - 'library is required.') - else: - return False + return _soft_import('pandas', 'dataframe integration', strict=strict) def _check_eeglabio_installed(strict=True): """Aux function.""" - try: - import eeglabio - return eeglabio - except ImportError: - if strict is True: - raise RuntimeError('For this functionality to work, the eeglabio ' - 'library is required.') - else: - return False + return _soft_import('eeglabio', 'exporting to EEGLab', strict=strict) + + +def _check_edflib_installed(strict=True): + """Aux function.""" + return _soft_import('EDFlib', 'exporting to EDF', strict=strict) + + +def _check_pybv_installed(strict=True): + """Aux function.""" + return _soft_import('pybv', 'exporting to BrainVision', strict=strict) def _check_pandas_index_arguments(index, valid): @@ -322,14 +417,31 @@ return time_format -def _check_ch_locs(chs): +def _check_ch_locs(info, picks=None, ch_type=None): """Check if channel locations exist. Parameters ---------- - chs : dict - The channels from info['chs'] + info : Info | None + `~mne.Info` instance. + picks : list of int + Channel indices to consider. If provided, ``ch_type`` must be ``None``. + ch_type : str | None + The channel type to restrict the check to. If ``None``, check all + channel types. If provided, ``picks`` must be ``None``. """ + from ..io.pick import _picks_to_idx, pick_info + + if picks is not None and ch_type is not None: + raise ValueError('Either picks or ch_type may be provided, not both') + + if picks is not None: + info = pick_info(info=info, sel=picks) + elif ch_type is not None: + picks = _picks_to_idx(info=info, picks=ch_type, none=ch_type) + info = pick_info(info=info, sel=picks) + + chs = info['chs'] locs3d = np.array([ch['loc'][:3] for ch in chs]) return not ((locs3d == 0).all() or (~np.isfinite(locs3d)).all() or @@ -337,7 +449,7 @@ def _is_numeric(n): - return isinstance(n, (np.integer, np.floating, int, float)) + return isinstance(n, numbers.Number) class _IntLike(object): @@ -352,7 +464,7 @@ int_like = _IntLike() -path_like = (str, Path) +path_like = (str, Path, os.PathLike) class _Callable(object): @@ -368,19 +480,10 @@ 'int-like': (int_like,), 'callable': (_Callable(),), } -try: - _multi['path-like'] += (os.PathLike,) -except AttributeError: # only on 3.6+ - try: - # At least make PyTest work - from py._path.common import PathBase - except Exception: # no py.path - pass - else: - _multi['path-like'] += (PathBase,) -def _validate_type(item, types=None, item_name=None, type_name=None): +def _validate_type(item, types=None, item_name=None, type_name=None, *, + extra=''): """Validate that `item` is an instance of `types`. Parameters @@ -389,16 +492,20 @@ The thing to be checked. types : type | str | tuple of types | tuple of str The types to be checked against. - If str, must be one of {'int', 'str', 'numeric', 'info', 'path-like', - 'callable'}. + If str, must be one of {'int', 'int-like', 'str', 'numeric', 'info', + 'path-like', 'callable'}. + If a tuple of str is passed, use 'int-like' and not 'int' for + integers. item_name : str | None Name of the item to show inside the error message. type_name : str | None Possible types to show inside the error message that the checked item can be. + extra : str + Extra text to append to the warning. """ if types == "int": - _ensure_int(item, name=item_name) + _ensure_int(item, name=item_name, extra=extra) return # terminate prematurely elif types == "info": from mne.io import Info as types @@ -409,6 +516,7 @@ check_types = sum(((type(None),) if type_ is None else (type_,) if not isinstance(type_, str) else _multi[type_] for type_ in types), ()) + extra = f' {extra}' if extra else extra if not isinstance(item, check_types): if type_name is None: type_name = ['None' if cls_ is None else cls_.__name__ @@ -422,11 +530,12 @@ type_name[-1] = 'or ' + type_name[-1] type_name = ', '.join(type_name) _item_name = 'Item' if item_name is None else item_name - raise TypeError(f"{_item_name} must be an instance of {type_name}, " - f"got {type(item)} instead") + raise TypeError( + f"{_item_name} must be an instance of {type_name}{extra}, " + f"got {type(item)} instead.") -def _check_path_like(item): +def _path_like(item): """Validate that `item` is `path-like`. Parameters @@ -571,6 +680,41 @@ return _handle_default(kind, depth) +def _check_dict_keys(mapping, valid_keys, + key_description, valid_key_source): + """Check that the keys in dictionary are valid against a set list. + + Return the input dictionary if it is valid, + otherwise raise a ValueError with a readable error message. + + Parameters + ---------- + mapping : dict + The user-provided dict whose keys we want to check. + valid_keys : iterable + The valid keys. + key_description : str + Description of the keys in ``mapping``, e.g., "channel name(s)" or + "annotation(s)". + valid_key_source : str + Description of the ``valid_keys`` source, e.g., "info dict" or + "annotations in the data". + + Returns + ------- + mapping + If all keys are valid the input dict is returned unmodified. + """ + missing = set(mapping) - set(valid_keys) + if len(missing): + _is = 'are' if len(missing) > 1 else 'is' + msg = (f'Invalid {key_description} {missing} {_is} not present in ' + f'{valid_key_source}') + raise ValueError(msg) + + return mapping + + def _check_option(parameter, value, allowed_values, extra=''): """Check the value of a parameter against a list of valid options. @@ -603,7 +747,7 @@ return value # Prepare a nice error message for the user - extra = ' ' + extra if extra else extra + extra = f' {extra}' if extra else extra msg = ("Invalid value for the '{parameter}' parameter{extra}. " '{options}, but got {value!r} instead.') allowed_values = list(allowed_values) # e.g., if a dict was given @@ -611,8 +755,11 @@ options = f'The only allowed value is {repr(allowed_values[0])}' else: options = 'Allowed values are ' - options += ', '.join([f'{repr(v)}' for v in allowed_values[:-1]]) - options += f', and {repr(allowed_values[-1])}' + if len(allowed_values) == 2: + options += ' and '.join(repr(v) for v in allowed_values) + else: + options += ', '.join(repr(v) for v in allowed_values[:-1]) + options += f', and {repr(allowed_values[-1])}' raise ValueError(msg.format(parameter=parameter, options=options, value=value, extra=extra)) @@ -626,16 +773,16 @@ return True -def _check_combine(mode, valid=('mean', 'median', 'std')): +def _check_combine(mode, valid=('mean', 'median', 'std'), axis=0): if mode == "mean": def fun(data): - return np.mean(data, axis=0) + return np.mean(data, axis=axis) elif mode == "std": def fun(data): - return np.std(data, axis=0) + return np.std(data, axis=axis) elif mode == "median" or mode == np.median: def fun(data): - return _median_complex(data, axis=0) + return _median_complex(data, axis=axis) elif callable(mode): fun = mode else: @@ -664,23 +811,26 @@ % (1e9 * max_cur)) -def _check_pyqt5_version(): - bad = True +def _check_qt_version(*, return_api=False): + """Check if Qt is installed.""" try: - from PyQt5.Qt import PYQT_VERSION_STR as version + from qtpy import QtCore, API_NAME as api except Exception: - version = 'unknown' + api = version = None else: - if LooseVersion(version) >= LooseVersion('5.10'): - bad = False - bad &= sys.platform == 'darwin' - if bad: - warn('macOS users should use PyQt5 >= 5.10 for GUIs, got %s. ' - 'Please upgrade e.g. with:\n\n' - ' pip install "PyQt5>=5.10,<5.14"\n' - % (version,)) - - return version + try: # pyside + version = QtCore.__version__ + except AttributeError: + version = QtCore.QT_VERSION_STR + if sys.platform == 'darwin' and api in ('PyQt5', 'PySide2'): + if not _compare_version(version, '>=', '5.10'): + warn(f'macOS users should use {api} >= 5.10 for GUIs, ' + f'got {version}. Please upgrade e.g. with:\n\n' + f' pip install "{api}>=5.10"\n') + if return_api: + return version, api + else: + return version def _check_sphere(sphere, info=None, sphere_units='m'): @@ -696,13 +846,86 @@ pass else: sphere = 'auto' + if isinstance(sphere, str): - if sphere != 'auto': - raise ValueError('sphere, if str, must be "auto", got %r' - % (sphere)) - R, r0, _ = fit_sphere_to_headshape(info, verbose=False, units='m') - sphere = tuple(r0) + (R,) - sphere_units = 'm' + if sphere not in ('auto', 'eeglab'): + raise ValueError( + f'sphere, if str, must be "auto" or "eeglab", got {sphere}' + ) + assert info is not None + + if sphere == 'auto': + R, r0, _ = fit_sphere_to_headshape(info, verbose=False, units='m') + sphere = tuple(r0) + (R,) + sphere_units = 'm' + elif sphere == 'eeglab': + # We need coordinates for the 2D plane formed by + # Fpz<->Oz and T7<->T8, as this plane will be the horizon (i.e. it + # will determine the location of the head circle). + # + # We implement some special-handling in case Fpz is missing, as + # this seems to be a quite common situation in numerous EEG labs. + montage = info.get_montage() + if montage is None: + raise ValueError( + 'No montage was set on your data, but sphere="eeglab" ' + 'can only work if digitization points for the EEG ' + 'channels are available. Consider calling set_montage() ' + 'to apply a montage.' + ) + ch_pos = montage.get_positions()['ch_pos'] + horizon_ch_names = ('Fpz', 'Oz', 'T7', 'T8') + + if 'FPz' in ch_pos: # "fix" naming + ch_pos['Fpz'] = ch_pos['FPz'] + del ch_pos['FPz'] + elif 'Fpz' not in ch_pos and 'Oz' in ch_pos: + logger.info( + 'Approximating Fpz location by mirroring Oz along ' + 'the X and Y axes.' + ) + # This assumes Fpz and Oz have the same Z coordinate + ch_pos['Fpz'] = ch_pos['Oz'] * [-1, -1, 1] + + for ch_name in horizon_ch_names: + if ch_name not in ch_pos: + msg = ( + f'sphere="eeglab" requires digitization points of ' + f'the following electrode locations in the data: ' + f'{", ".join(horizon_ch_names)}, but could not find: ' + f'{ch_name}' + ) + if ch_name == 'Fpz': + msg += ( + ', and was unable to approximate its location ' + 'from Oz' + ) + raise ValueError(msg) + + # Calculate the radius from: T7<->T8, Fpz<->Oz + radius = np.abs([ + ch_pos['T7'][0], # X axis + ch_pos['T8'][0], # X axis + ch_pos['Fpz'][1], # Y axis + ch_pos['Oz'][1] # Y axis + ]).mean() + + # Calculate the center of the head sphere + # Use 4 digpoints for each of the 3 axes to hopefully get a better + # approximation than when using just 2 digpoints. + sphere_locs = dict() + for idx, axis in enumerate(('X', 'Y', 'Z')): + sphere_locs[axis] = np.mean([ + ch_pos['T7'][idx], + ch_pos['T8'][idx], + ch_pos['Fpz'][idx], + ch_pos['Oz'][idx] + ]) + sphere = ( + sphere_locs['X'], sphere_locs['Y'], sphere_locs['Z'], radius + ) + sphere_units = 'm' + del sphere_locs, radius, montage, ch_pos elif isinstance(sphere, ConductorModel): if not sphere['is_sphere'] or len(sphere['layers']) == 0: raise ValueError('sphere, if a ConductorModel, must be spherical ' @@ -773,57 +996,39 @@ def _ensure_events(events): - events_type = type(events) - with warnings.catch_warnings(record=True): - warnings.simplefilter('ignore') # deprecation for object array - events = np.asarray(events) + err_msg = f'events should be a NumPy array of integers, got {type(events)}' + if check_version('numpy', '1.24'): + # NumPy >= 1.24 raises ValueError + try: + events = np.asarray(events) + except ValueError as np_err: + if str(np_err).startswith( + 'setting an array element with a sequence. The requested ' + 'array has an inhomogeneous shape'): + raise TypeError(err_msg) + else: + raise + else: + # NumPy < 1.24 gives VisibleDeprecationWarning + with warnings.catch_warnings(record=True): + warnings.simplefilter('ignore') + events = np.asarray(events) + # now that we're sure that events is an array... if not np.issubdtype(events.dtype, np.integer): - raise TypeError('events should be a NumPy array of integers, ' - f'got {events_type}') + raise TypeError(err_msg) if events.ndim != 2 or events.shape[1] != 3: raise ValueError( f'events must be of shape (N, 3), got {events.shape}') return events -def _infer_check_export_fmt(fmt, fname, supported_formats): - """Infer export format from filename extension if auto. - - Raises error if fmt is auto and no file extension found, - then checks format against supported formats, raises error if format is not - supported. - - Parameters - ---------- - fmt : str - Format of the export, will only infer the format from filename if fmt - is auto. - fname : str - Name of the target export file, only used when fmt is auto. - supported_formats : dict of str : tuple/list - Dictionary containing supported formats (as keys) and each format's - corresponding file extensions in a tuple/list (e.g. 'eeglab': ('set',)) - """ - _validate_type(fmt, str, 'fmt') - fmt = fmt.lower() - if fmt == "auto": - fmt = op.splitext(fname)[1] - if fmt: - fmt = fmt[1:].lower() - # find fmt in supported formats dict's tuples - fmt = next((k for k, v in supported_formats.items() if fmt in v), - fmt) # default to original fmt for raising error later - else: - raise ValueError(f"Couldn't infer format from filename {fname}" - " (no extension found)") - - if fmt not in supported_formats: - supported = [] - for format, extensions in supported_formats.items(): - ext_str = ', '.join(f'*.{ext}' for ext in extensions) - supported.append(f'{format} ({ext_str})') - - supported_str = ', '.join(supported) - raise ValueError(f"Format '{fmt}' is not supported. " - f"Supported formats are {supported_str}.") - return fmt +def _to_rgb(*args, name='color', alpha=False): + from matplotlib.colors import colorConverter + func = colorConverter.to_rgba if alpha else colorConverter.to_rgb + try: + return func(*args) + except ValueError: + args = args[0] if len(args) == 1 else args + raise ValueError( + f'Invalid RGB{"A" if alpha else ""} argument(s) for {name}: ' + f'{repr(args)}') from None diff -Nru python-mne-0.23.4+dfsg/mne/utils/config.py python-mne-1.1.0+dfsg/mne/utils/config.py --- python-mne-0.23.4+dfsg/mne/utils/config.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/config.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ """The config functions.""" # Authors: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import atexit from functools import partial @@ -11,13 +11,16 @@ import os.path as op import platform import shutil +import subprocess import sys import tempfile import re import numpy as np -from .check import _validate_type, _check_pyqt5_version +from .check import (_validate_type, _check_qt_version, _check_option, + _check_fname) +from .docs import fill_doc from ._logging import warn, logger @@ -66,10 +69,20 @@ # List the known configuration values known_config_types = ( 'MNE_3D_OPTION_ANTIALIAS', + 'MNE_3D_OPTION_DEPTH_PEELING', + 'MNE_3D_OPTION_MULTI_SAMPLES', + 'MNE_3D_OPTION_SMOOTH_SHADING', + 'MNE_3D_OPTION_THEME', 'MNE_BROWSE_RAW_SIZE', + 'MNE_BROWSER_BACKEND', + 'MNE_BROWSER_OVERVIEW_MODE', + 'MNE_BROWSER_PRECOMPUTE', + 'MNE_BROWSER_THEME', + 'MNE_BROWSER_USE_OPENGL', 'MNE_CACHE_DIR', 'MNE_COREG_ADVANCED_RENDERING', 'MNE_COREG_COPY_ANNOT', + 'MNE_COREG_FULLSCREEN', 'MNE_COREG_GUESS_MRI_SUBJECT', 'MNE_COREG_HEAD_HIGH_RES', 'MNE_COREG_HEAD_OPACITY', @@ -77,7 +90,6 @@ 'MNE_COREG_INTERACTION', 'MNE_COREG_MARK_INSIDE', 'MNE_COREG_PREPARE_BEM', - 'MNE_COREG_PROJECT_EEG', 'MNE_COREG_ORIENT_TO_SURFACE', 'MNE_COREG_SCALE_LABELS', 'MNE_COREG_SCALE_BY_DISTANCE', @@ -132,6 +144,8 @@ # These allow for partial matches, e.g. 'MNE_STIM_CHANNEL_1' is okay key known_config_wildcards = ( 'MNE_STIM_CHANNEL', + 'MNE_DATASETS_FNIRS', + 'MNE_NIRS', ) @@ -276,7 +290,7 @@ value = str(value) if key not in known_config_types and not \ - any(k in key for k in known_config_wildcards): + any(key.startswith(k) for k in known_config_wildcards): warn('Setting non-standard config type: "%s"' % key) # Read all previous values @@ -358,11 +372,21 @@ value : str | None The SUBJECTS_DIR value. """ + _validate_type(item=subjects_dir, types=('path-like', None), + item_name='subjects_dir', type_name='str or path-like') + if subjects_dir is None: subjects_dir = get_config('SUBJECTS_DIR', raise_error=raise_error) + if subjects_dir is not None: + subjects_dir = _check_fname( + fname=subjects_dir, overwrite='read', must_exist=True, + need_dir=True, name='subjects_dir' + ) + return subjects_dir +@fill_doc def _get_stim_channel(stim_channel, info, raise_error=True): """Determine the appropriate stim_channel. @@ -374,8 +398,7 @@ ---------- stim_channel : str | list of str | None The stim channel selected by the user. - info : instance of Info - An information structure containing information about the channels. + %(info_not_none)s Returns ------- @@ -393,7 +416,7 @@ stim_channel = list() ch_count = 0 ch = get_config('MNE_STIM_CHANNEL') - while(ch is not None and ch in info['ch_names']): + while ch is not None and ch in info['ch_names']: stim_channel.append(ch) ch_count += 1 ch = get_config('MNE_STIM_CHANNEL_%d' % ch_count) @@ -448,7 +471,25 @@ return libs -def sys_info(fid=None, show_paths=False): +_gpu_cmd = """\ +from pyvista import GPUInfo; \ +gi = GPUInfo(); \ +print(gi.version); \ +print(gi.renderer)""" + + +def _get_gpu_info(): + # Once https://github.com/pyvista/pyvista/pull/2250 is merged and PyVista + # does a release, we can triage based on version > 0.33.2 + proc = subprocess.run( + [sys.executable, '-c', _gpu_cmd], check=False, capture_output=True) + out = proc.stdout.decode().strip().replace('\r', '').split('\n') + if proc.returncode or len(out) != 2: + return None, None + return out + + +def sys_info(fid=None, show_paths=False, *, dependencies='user'): """Print the system information for debugging. This function is useful for printing system information @@ -461,6 +502,11 @@ Can be None to use :data:`sys.stdout`. show_paths : bool If True, print paths for each module. + dependencies : 'user' | 'developer' + Show dependencies relevant for users (default) or for developers + (i.e., output includes additional dependencies). + + .. versionadded:: 0.24 Examples -------- @@ -487,12 +533,15 @@ dipy: 1.1.1 cupy: Not found pandas: 1.0.5 - mayavi: Not found pyvista: 0.25.3 {pyvistaqt=0.1.1, OpenGL 3.3 (Core Profile) Mesa 18.3.6 via llvmpipe (LLVM 7.0, 256 bits)} vtk: 9.0.1 - PyQt5: 5.15.0 + qtpy: 2.0.1 {PySide6=6.2.4} + pyqtgraph: 0.12.4 + pooch: v1.5.1 """ # noqa: E501 - ljust = 15 + _validate_type(dependencies, str) + _check_option('dependencies', dependencies, ('user', 'developer')) + ljust = 21 if dependencies == 'developer' else 18 platform_str = platform.platform() if platform.system() == 'Darwin' and sys.version_info[:2] < (3, 8): # platform.platform() in Python < 3.8 doesn't call @@ -505,78 +554,73 @@ platform_str = f'macOS-{macos_ver}-{macos_architecture}' del macos_ver, macos_architecture - out = 'Platform:'.ljust(ljust) + platform_str + '\n' - out += 'Python:'.ljust(ljust) + str(sys.version).replace('\n', ' ') + '\n' - out += 'Executable:'.ljust(ljust) + sys.executable + '\n' - out += 'CPU:'.ljust(ljust) + ('%s: ' % platform.processor()) + out = partial(print, end='', file=fid) + out('Platform:'.ljust(ljust) + platform_str + '\n') + out('Python:'.ljust(ljust) + str(sys.version).replace('\n', ' ') + '\n') + out('Executable:'.ljust(ljust) + sys.executable + '\n') + out('CPU:'.ljust(ljust) + f'{platform.processor()}: ') try: import multiprocessing except ImportError: - out += ('number of processors unavailable ' + - '(requires "multiprocessing" package)\n') + out('number of processors unavailable ' + '(requires "multiprocessing" package)\n') else: - out += '%s cores\n' % multiprocessing.cpu_count() - out += 'Memory:'.ljust(ljust) + out(f'{multiprocessing.cpu_count()} cores\n') + out('Memory:'.ljust(ljust)) try: import psutil except ImportError: - out += 'Unavailable (requires "psutil" package)' + out('Unavailable (requires "psutil" package)') else: - out += '%0.1f GB\n' % (psutil.virtual_memory().total / float(2 ** 30),) - out += '\n' + out(f'{psutil.virtual_memory().total / float(2 ** 30):0.1f} GB\n') + out('\n') libs = _get_numpy_libs() - has_3d = False - for mod_name in ('mne', 'numpy', 'scipy', 'matplotlib', '', 'sklearn', + use_mod_names = ('mne', 'numpy', 'scipy', 'matplotlib', '', 'sklearn', 'numba', 'nibabel', 'nilearn', 'dipy', 'cupy', 'pandas', - 'mayavi', 'pyvista', 'vtk', 'PyQt5'): + 'pyvista', 'pyvistaqt', 'ipyvtklink', 'vtk', + 'qtpy', 'ipympl', 'pyqtgraph', 'pooch', '', 'mne_bids', + 'mne_nirs', 'mne_features', 'mne_qt_browser', + 'mne_connectivity', 'mne_icalabel') + if dependencies == 'developer': + use_mod_names += ( + '', 'sphinx', 'sphinx_gallery', 'numpydoc', 'pydata_sphinx_theme', + 'pytest', 'nbclient') + for mod_name in use_mod_names: if mod_name == '': - out += '\n' - continue - if mod_name == 'PyQt5' and not has_3d: + out('\n') continue - out += ('%s:' % mod_name).ljust(ljust) + out(f'{mod_name}:'.ljust(ljust)) try: mod = __import__(mod_name) - if mod_name == 'mayavi': - # the real test - from mayavi import mlab # noqa, analysis:ignore except Exception: - out += 'Not found\n' + out('Not found\n') else: - extra = (' (%s)' % op.dirname(mod.__file__)) if show_paths else '' - if mod_name == 'numpy': - extra += ' {%s}%s' % (libs, extra) - elif mod_name == 'matplotlib': - extra += ' {backend=%s}%s' % (mod.get_backend(), extra) - elif mod_name == 'pyvista': - extras = list() - try: - from pyvistaqt import __version__ - except Exception: - pass - else: - extras += [f'pyvistaqt={__version__}'] - try: - from pyvista import GPUInfo - except ImportError: - pass - else: - gi = GPUInfo() - extras += [f'OpenGL {gi.version} via {gi.renderer}'] - if extras: - extra += f' {{{", ".join(extras)}}}' - elif mod_name in ('mayavi', 'vtk'): - has_3d = True if mod_name == 'vtk': - version = mod.vtkVersion() + vtk_version = mod.vtkVersion() # 9.0 dev has VersionFull but 9.0 doesn't for attr in ('GetVTKVersionFull', 'GetVTKVersion'): - if hasattr(version, attr): - version = getattr(version, attr)() - break - elif mod_name == 'PyQt5': - version = _check_pyqt5_version() + if hasattr(vtk_version, attr): + version = getattr(vtk_version, attr)() + if version != '': + out(version) + break + else: + out('unknown') else: - version = mod.__version__ - out += '%s%s\n' % (version, extra) - print(out, end='', file=fid) + out(mod.__version__) + if mod_name == 'numpy': + out(f' {{{libs}}}') + elif mod_name == 'qtpy': + version, api = _check_qt_version(return_api=True) + out(f' {{{api}={version}}}') + elif mod_name == 'matplotlib': + out(f' {{backend={mod.get_backend()}}}') + elif mod_name == 'pyvista': + version, renderer = _get_gpu_info() + if version is None: + out(' {OpenGL could not be initialized}') + else: + out(f' {{OpenGL {version} via {renderer}}}') + if show_paths: + out(f'\n{" " * ljust}•{op.dirname(mod.__file__)}') + out('\n') diff -Nru python-mne-0.23.4+dfsg/mne/utils/dataframe.py python-mne-1.1.0+dfsg/mne/utils/dataframe.py --- python-mne-0.23.4+dfsg/mne/utils/dataframe.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/dataframe.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,15 +2,16 @@ """inst.to_data_frame() helper functions.""" # Authors: Daniel McCloy # -# License: BSD (3-clause) +# License: BSD-3-Clause import numpy as np -from ._logging import logger +from ._logging import logger, verbose from ..defaults import _handle_default -def _set_pandas_dtype(df, columns, dtype): +@verbose +def _set_pandas_dtype(df, columns, dtype, verbose=None): """Try to set the right columns to dtype.""" for column in columns: df[column] = df[column].astype(dtype) @@ -46,8 +47,10 @@ return times +@verbose def _build_data_frame(inst, data, picks, long_format, mindex, index, - default_index, col_names=None, col_kind='channel'): + default_index, col_names=None, col_kind='channel', + verbose=None): """Build DataFrame from MNE-object-derived data array.""" # private function; pandas already checked in calling function from pandas import DataFrame diff -Nru python-mne-0.23.4+dfsg/mne/utils/docs.py python-mne-1.1.0+dfsg/mne/utils/docs.py --- python-mne-0.23.4+dfsg/mne/utils/docs.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/docs.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,114 +2,177 @@ """The documentation functions.""" # Authors: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause from copy import deepcopy import inspect import os import os.path as op +import re import sys import warnings import webbrowser +from decorator import FunctionMaker + +from ._bunch import BunchConst from ..defaults import HEAD_SIZE_DEFAULT -from ..externals.doccer import indentcount_lines + + +def _reflow_param_docstring(docstring, has_first_line=True, width=75): + """Reflow text to a nice width for terminals. + + WARNING: does not handle gracefully things like .. versionadded:: + """ + maxsplit = docstring.count('\n') - 1 if has_first_line else -1 + merged = ' '.join(line.strip() for line in + docstring.rsplit('\n', maxsplit=maxsplit)) + reflowed = '\n '.join(re.findall(fr'.{{1,{width}}}(?:\s+|$)', merged)) + if has_first_line: + reflowed = reflowed.replace('\n \n', '\n', 1) + return reflowed ############################################################################## # Define our standard documentation entries +# +# To reduce redundancy across functions, please standardize the format to +# ``argument_optional_keywords``. For example ``tmin_raw`` for an entry that +# is specific to ``raw`` and since ``tmin`` is used other places, needs to +# be disambiguated. This way the entries will be easy to find since they +# are alphabetized (you can look up by the name of the argument). This way +# the same ``docdict`` entries are easier to reuse. -docdict = dict() +docdict = BunchConst() -# Verbose -docdict['verbose'] = """ -verbose : bool, str, int, or None - If not None, override default verbose level (see :func:`mne.verbose` - and :ref:`Logging documentation ` for more). - If used, it should be passed as a keyword-argument only.""" -docdict['verbose_meth'] = (docdict['verbose'] + ' Defaults to self.verbose.') +# %% +# A -# Preload -docdict['preload'] = """ -preload : bool or str (default False) - Preload data into memory for data manipulation and faster indexing. - If True, the data will be preloaded into memory (fast, requires - large amount of memory). If preload is a string, preload is the - file name of a memory-mapped file which is used to store the data - on the hard drive (slower, requires less memory).""" -docdict['preload_concatenate'] = """ -preload : bool, str, or None (default None) - Preload data into memory for data manipulation and faster indexing. - If True, the data will be preloaded into memory (fast, requires - large amount of memory). If preload is a string, preload is the - file name of a memory-mapped file which is used to store the data - on the hard drive (slower, requires less memory). If preload is - None, preload=True or False is inferred using the preload status - of the instances passed in. +docdict['accept'] = """ +accept : bool + If True (default False), accept the license terms of this dataset. """ -# Raw -_on_missing_base = """\ -Can be ``'raise'`` (default) to raise an error, ``'warn'`` to emit a - warning, or ``'ignore'`` to ignore when""" -docdict['on_split_missing'] = """ -on_split_missing : str - %s split file is missing. +docdict['add_ch_type_export_params'] = """ +add_ch_type : bool + Whether to incorporate the channel type into the signal label (e.g. whether + to store channel "Fz" as "EEG Fz"). Only used for EDF format. Default is + ``False``. +""" - .. versionadded:: 0.22 -""" % (_on_missing_base,) +docdict['add_data_kwargs'] = """ +add_data_kwargs : dict | None + Additional arguments to brain.add_data (e.g., + ``dict(time_label_size=10)``). +""" -# Cropping -docdict['include_tmax'] = """ -include_tmax : bool - If True (default), include tmax. If False, exclude tmax (similar to how - Python indexing typically works). +docdict['add_frames'] = """ +add_frames : int | None + If int, enable (>=1) or disable (0) the printing of stack frame + information using formatting. Default (None) does not change the + formatting. This can add overhead so is meant only for debugging. +""" - .. versionadded:: 0.19 +docdict['adjacency_clust'] = """ +adjacency : scipy.sparse.spmatrix | None | False + Defines adjacency between locations in the data, where "locations" can be + spatial vertices, frequency bins, time points, etc. For spatial vertices, + see: :func:`mne.channels.find_ch_adjacency`. If ``False``, assumes + no adjacency (each location is treated as independent and unconnected). + If ``None``, a regular lattice adjacency is assumed, connecting + each {sp} location to its neighbor(s) along the last dimension + of {{eachgrp}} ``{{x}}``{lastdim}. + If ``adjacency`` is a matrix, it is assumed to be symmetric (only the + upper triangular half is used) and must be square with dimension equal to + ``{{x}}.shape[-1]`` {parone} or ``{{x}}.shape[-1] * {{x}}.shape[-2]`` + {partwo} or (optionally) + ``{{x}}.shape[-1] * {{x}}.shape[-2] * {{x}}.shape[-3]`` + {parthree}.{memory} """ -docdict['notes_tmax_included_by_default'] = """ -Unlike Python slices, MNE time intervals by default include **both** -their end points; ``crop(tmin, tmax)`` returns the interval -``tmin <= t <= tmax``. Pass ``include_tmax=False`` to specify the half-open -interval ``tmin <= t < tmax`` instead. + +mem = (' If spatial adjacency is uniform in time, it is recommended to use ' + 'a square matrix with dimension ``{x}.shape[-1]`` (n_vertices) to save ' + 'memory and computation, and to use ``max_step`` to define the extent ' + 'of temporal adjacency to consider when clustering.') +comb = ' The function `mne.stats.combine_adjacency` may be useful for 4D data.' +st = dict(sp='spatial', lastdim='', parone='(n_vertices)', + partwo='(n_times * n_vertices)', + parthree='(n_times * n_freqs * n_vertices)', memory=mem) +tf = dict(sp='', lastdim=' (or the last two dimensions if ``{x}`` is 2D)', + parone='(for 2D data)', partwo='(for 3D data)', + parthree='(for 4D data)', memory=comb) +nogroups = dict(eachgrp='', x='X') +groups = dict(eachgrp='each group ', x='X[k]') +docdict['adjacency_clust_1'] = \ + docdict['adjacency_clust'].format(**tf).format(**nogroups) +docdict['adjacency_clust_n'] = \ + docdict['adjacency_clust'].format(**tf).format(**groups) +docdict['adjacency_clust_st1'] = \ + docdict['adjacency_clust'].format(**st).format(**nogroups) +docdict['adjacency_clust_stn'] = \ + docdict['adjacency_clust'].format(**st).format(**groups) + +docdict['adjust_dig_chpi'] = """ +adjust_dig : bool + If True, adjust the digitization locations used for fitting based on + the positions localized at the start of the file. """ -docdict['raw_tmin'] = """ -tmin : float - Start time of the raw data to use in seconds (must be >= 0). + +docdict['agg_fun_psd_topo'] = """ +agg_fun : callable + The function used to aggregate over frequencies. Defaults to + :func:`numpy.sum` if ``normalize=True``, else :func:`numpy.mean`. """ -docdict['raw_tmax'] = """ -tmax : float - End time of the raw data to use in seconds (cannot exceed data duration). + +docdict['align_view'] = """ +align : bool + If True, consider view arguments relative to canonical MRI + directions (closest to MNI for the subject) rather than native MRI + space. This helps when MRIs are not in standard orientation (e.g., + have large rotations). """ -# Raw -docdict['standardize_names'] = """ -standardize_names : bool - If True, standardize MEG and EEG channel names to be - ``"MEG ###"`` and ``"EEG ###"``. If False (default), native - channel names in the file will be used when possible. +docdict['allow_empty_eltc'] = """ +allow_empty : bool | str + ``False`` (default) will emit an error if there are labels that have no + vertices in the source estimate. ``True`` and ``'ignore'`` will return + all-zero time courses for labels that do not have any vertices in the + source estimate, and True will emit a warning while and "ignore" will + just log a message. + + .. versionchanged:: 0.21.0 + Support for "ignore". """ -docdict['event_color'] = """ -event_color : color object | dict | None - Color(s) to use for events. To show all events in the same color, pass any - matplotlib-compatible color. To color events differently, pass a `dict` - that maps event names or integer event numbers to colors (must include - entries for *all* events, or include a "fallback" entry with key ``-1``). - If ``None``, colors are chosen from the current Matplotlib color cycle. +docdict['alpha'] = """ +alpha : float in [0, 1] + Alpha level to control opacity. """ -docdict['browse_group_by'] = """ -group_by : str - How to group channels. ``'type'`` groups by channel type, - ``'original'`` plots in the order of ch_names, ``'selection'`` uses - Elekta's channel groupings (only works for Neuromag data), - ``'position'`` groups the channels by the positions of the sensors. - ``'selection'`` and ``'position'`` modes allow custom selections by - using a lasso selector on the topomap. In butterfly mode, ``'type'`` - and ``'original'`` group the channels by type, whereas ``'selection'`` - and ``'position'`` use regional grouping. ``'type'`` and ``'original'`` - modes are ignored when ``order`` is not ``None``. Defaults to ``'type'``. +docdict['anonymize_info_notes'] = """ +Removes potentially identifying information if it exists in ``info``. +Specifically for each of the following we use: + +- meas_date, file_id, meas_id + A default value, or as specified by ``daysback``. +- subject_info + Default values, except for 'birthday' which is adjusted + to maintain the subject age. +- experimenter, proj_name, description + Default strings. +- utc_offset + ``None``. +- proj_id + Zeros. +- proc_history + Dates use the ``meas_date`` logic, and experimenter a default string. +- helium_info, device_info + Dates use the ``meas_date`` logic, meta info uses defaults. + +If ``info['meas_date']`` is ``None``, it will remain ``None`` during processing +the above fields. + +Operates in place. """ # raw/epochs/evoked apply_function method @@ -130,751 +193,599 @@ """ applyfun_preload = (' The object has to have the data loaded e.g. with ' '``preload=True`` or ``self.load_data()``.') -docdict['applyfun_summary_raw'] = \ - applyfun_summary.format('raw', applyfun_preload) docdict['applyfun_summary_epochs'] = \ applyfun_summary.format('epochs', applyfun_preload) docdict['applyfun_summary_evoked'] = \ applyfun_summary.format('evoked', '') -# apply_function params: fun -applyfun_fun = """ -fun : callable - A function to be applied to the channels. The first argument of - fun has to be a timeseries (:class:`numpy.ndarray`). The function must - operate on an array of shape ``(n_times,)`` {}. - The function must return an :class:`~numpy.ndarray` shaped like its input. -""" -docdict['applyfun_fun'] = applyfun_fun.format( - ' if ``channel_wise=True`` and ``(len(picks), n_times)`` otherwise') -docdict['applyfun_fun_evoked'] = applyfun_fun.format( - ' because it will apply channel-wise') -docdict['applyfun_dtype'] = """ -dtype : numpy.dtype - Data type to use after applying the function. If None - (default) the data type is not modified. +docdict['applyfun_summary_raw'] = \ + applyfun_summary.format('raw', applyfun_preload) + +docdict['area_alpha_plot_psd'] = """ +area_alpha : float + Alpha for the area. """ -chwise = """ -channel_wise : bool - Whether to apply the function to each channel {}individually. If ``False``, - the function will be applied to all {}channels at once. Default ``True``. + +docdict['area_mode_plot_psd'] = """ +area_mode : str | None + Mode for plotting area. If 'std', the mean +/- 1 STD (across channels) + will be plotted. If 'range', the min and max (across channels) will be + plotted. Bad channels will be excluded from these calculations. + If None, no area will be plotted. If average=False, no area is plotted. """ -docdict['applyfun_chwise'] = chwise.format('', '') -docdict['applyfun_chwise_epo'] = chwise.format('in each epoch ', 'epochs and ') -docdict['kwarg_fun'] = """ -**kwargs : dict - Additional keyword arguments to pass to ``fun``. + +docdict['aseg'] = """ +aseg : str + The anatomical segmentation file. Default ``aparc+aseg``. This may + be any anatomical segmentation file in the mri subdirectory of the + Freesurfer subject directory. """ -# Epochs -docdict['proj_epochs'] = """ -proj : bool | 'delayed' - Apply SSP projection vectors. If proj is 'delayed' and reject is not - None the single epochs will be projected before the rejection - decision, but used in unprojected state if they are kept. - This way deciding which projection vectors are good can be postponed - to the evoked stage without resulting in lower epoch counts and - without producing results different from early SSP application - given comparable parameters. Note that in this case baselining, - detrending and temporal decimation will be postponed. - If proj is False no projections will be applied which is the - recommended value if SSPs are not used for cleaning the data. +docdict['average_plot_psd'] = """ +average : bool + If False, the PSDs of all channels is displayed. No averaging + is done and parameters area_mode and area_alpha are ignored. When + False, it is possible to paint an area (hold left mouse button and + drag) to plot a topomap. """ -# Reject by annotation -docdict['reject_by_annotation_all'] = """ -reject_by_annotation : bool - Whether to omit bad segments from the data before fitting. If ``True`` - (default), annotated segments whose description begins with ``'bad'`` are - omitted. If ``False``, no rejection based on annotations is performed. +docdict['average_psd'] = """ +average : str | None + How to average the segments. If ``mean`` (default), calculate the + arithmetic mean. If ``median``, calculate the median, corrected for + its bias relative to the mean. If ``None``, returns the unaggregated + segments. """ -docdict['reject_by_annotation_epochs'] = """ -reject_by_annotation : bool - Whether to reject based on annotations. If ``True`` (default), epochs - overlapping with segments whose description begins with ``'bad'`` are - rejected. If ``False``, no rejection based on annotations is performed. + +docdict['average_tfr'] = """ +average : bool, default True + If ``False`` return an `EpochsTFR` containing separate TFRs for each + epoch. If ``True`` return an `AverageTFR` containing the average of all + TFRs across epochs. + + .. note:: + Using ``average=True`` is functionally equivalent to using + ``average=False`` followed by ``EpochsTFR.average()``, but is + more memory efficient. + + .. versionadded:: 0.13.0 """ -docdict['reject_by_annotation_raw'] = docdict['reject_by_annotation_all'] + """ - Has no effect if ``inst`` is not a :class:`mne.io.Raw` object. + +docdict['axes_psd_topo'] = """ +axes : list of Axes | None + List of axes to plot consecutive topographies to. If ``None`` the axes + will be created automatically. Defaults to ``None``. """ -docdict['annot_ch_names'] = """ -ch_names : list | None - List of lists of channel names associated with the annotations. - Empty entries are assumed to be associated with no specific channel, - i.e., with all channels or with the time slice itself. None (default) is - the same as passing all empty lists. For example, this creates three - annotations, associating the first with the time interval itself, the - second with two channels, and the third with a single channel:: - Annotations(onset=[0, 3, 10], duration=[1, 0.25, 0.5], - description=['Start', 'BAD_flux', 'BAD_noise'], - ch_names=[[], ['MEG0111', 'MEG2563'], ['MEG1443']]) +docdict['axes_topomap'] = """ +axes : instance of Axes | list | None + The axes to plot to. If list, the list must be a list of Axes of the + same length as ``times`` (unless ``times`` is None). If instance of + Axes, ``times`` must be a float or a list of one float. + Defaults to None. """ -# General plotting -docdict["show"] = """ -show : bool - Show figure if True. +docdict['azimuth'] = """ +azimuth : float + The azimuthal angle of the camera rendering the view in degrees. """ -docdict["title_None"] = """ -title : str | None - Title. If None (default), no title is displayed. + +# %% +# B + +docdict['bad_condition_maxwell_cond'] = """ +bad_condition : str + How to deal with ill-conditioned SSS matrices. Can be "error" + (default), "warning", "info", or "ignore". """ -docdict["plot_proj"] = """ -proj : bool | 'interactive' | 'reconstruct' - If true SSP projections are applied before display. If 'interactive', - a check box for reversible selection of SSP projection vectors will - be shown. If 'reconstruct', projection vectors will be applied and then - M/EEG data will be reconstructed via field mapping to reduce the signal - bias caused by projection. - .. versionchanged:: 0.21 - Support for 'reconstruct' was added. +docdict['bands_psd_topo'] = """ +bands : list of tuple | None + The frequencies or frequency ranges to plot. Length-2 tuples specify + a single frequency and a subplot title (e.g., + ``(6.5, 'presentation rate')``); length-3 tuples specify lower and + upper band edges and a subplot title. If ``None`` (the default), + expands to:: + + bands = [(0, 4, 'Delta'), (4, 8, 'Theta'), (8, 12, 'Alpha'), + (12, 30, 'Beta'), (30, 45, 'Gamma')] + + In bands where a single frequency is provided, the topomap will reflect + the single frequency bin that is closest to the provided value. """ -docdict["topomap_ch_type"] = """ -ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None - The channel type to plot. For 'grad', the gradiometers are collected in - pairs and the RMS for each pair is plotted. - If None, then channels are chosen in the order given above. + +docdict['base_estimator'] = """ +base_estimator : object + The base estimator to iteratively fit on a subset of the dataset. """ -docdict["topomap_vmin_vmax"] = """ -vmin, vmax : float | callable | None - Lower and upper bounds of the colormap, in the same units as the data. - If ``vmin`` and ``vmax`` are both ``None``, they are set at ± the - maximum absolute value of the data (yielding a colormap with midpoint - at 0). If only one of ``vmin``, ``vmax`` is ``None``, will use - ``min(data)`` or ``max(data)``, respectively. If callable, should - accept a :class:`NumPy array ` of data and return a - float. -""" -docdict["topomap_cmap"] = """ -cmap : matplotlib colormap | (colormap, bool) | 'interactive' | None - Colormap to use. If tuple, the first value indicates the colormap to - use and the second value is a boolean defining interactivity. In - interactive mode the colors are adjustable by clicking and dragging the - colorbar with left and right mouse button. Left mouse button moves the - scale up and down and right mouse button adjusts the range (zoom). - The mouse scroll can also be used to adjust the range. Hitting space - bar resets the range. Up and down arrows can be used to change the - colormap. If None (default), 'Reds' is used for all positive data, - otherwise defaults to 'RdBu_r'. If 'interactive', translates to - (None, True). - .. warning:: Interactive mode works smoothly only for a small amount - of topomaps. Interactive mode is disabled by default for more than - 2 topomaps. +_baseline_rescale_base = """ +baseline : None | tuple of length 2 + The time interval to consider as "baseline" when applying baseline + correction. If ``None``, do not apply baseline correction. + If a tuple ``(a, b)``, the interval is between ``a`` and ``b`` + (in seconds), including the endpoints. + If ``a`` is ``None``, the **beginning** of the data is used; and if ``b`` + is ``None``, it is set to the **end** of the interval. + If ``(None, None)``, the entire time interval is used. + + .. note:: The baseline ``(a, b)`` includes both endpoints, i.e. all + timepoints ``t`` such that ``a <= t <= b``. """ -docdict["topomap_sensors"] = """ -sensors : bool | str - Add markers for sensor locations to the plot. Accepts matplotlib plot - format string (e.g., 'r+' for red plusses). If True (default), - circles will be used. + +docdict['baseline_epochs'] = f"""{_baseline_rescale_base} + Correction is applied **to each epoch and channel individually** in the + following way: + + 1. Calculate the mean signal of the baseline period. + 2. Subtract this mean from the **entire** epoch. + """ -docdict["topomap_colorbar"] = """ -colorbar : bool - Plot a colorbar in the rightmost column of the figure. + +docdict['baseline_evoked'] = f"""{_baseline_rescale_base} + Correction is applied **to each channel individually** in the following + way: + + 1. Calculate the mean signal of the baseline period. + 2. Subtract this mean from the **entire** ``Evoked``. + """ -docdict["topomap_scalings"] = """ -scalings : dict | float | None - The scalings of the channel types to be applied for plotting. - If None, defaults to ``dict(eeg=1e6, grad=1e13, mag=1e15)``. + +docdict['baseline_report'] = f"""{_baseline_rescale_base} + Correction is applied in the following way **to each channel:** + + 1. Calculate the mean signal of the baseline period. + 2. Subtract this mean from the **entire** time period. + + For `~mne.Epochs`, this algorithm is run **on each epoch individually.** """ -docdict["topomap_units"] = """ -units : dict | str | None - The unit of the channel type used for colorbar label. If - scale is None the unit is automatically determined. + +docdict['baseline_rescale'] = _baseline_rescale_base + +docdict['baseline_stc'] = f"""{_baseline_rescale_base} + Correction is applied **to each source individually** in the following + way: + + 1. Calculate the mean signal of the baseline period. + 2. Subtract this mean from the **entire** source estimate data. + + .. note:: Baseline correction is appropriate when signal and noise are + approximately additive, and the noise level can be estimated from + the baseline interval. This can be the case for non-normalized + source activities (e.g. signed and unsigned MNE), but it is not + the case for normalized estimates (e.g. signal-to-noise ratios, + dSPM, sLORETA). + """ -docdict["topomap_res"] = """ -res : int - The resolution of the topomap image (n pixels along each side). + +docdict['border_topomap'] = """ +border : float | 'mean' + Value to extrapolate to on the topomap borders. If ``'mean'`` (default), + then each extrapolated point has the average value of its neighbours. + + .. versionadded:: 0.20 """ -docdict["topomap_size"] = """ -size : float - Side length per topomap in inches. + +docdict['brain_kwargs'] = """ +brain_kwargs : dict | None + Additional arguments to the :class:`mne.viz.Brain` constructor (e.g., + ``dict(silhouette=True)``). """ -docdict["topomap_cbar_fmt"] = """ -cbar_fmt : str - String format for colorbar values. + +docdict['browser'] = """ +fig : matplotlib.figure.Figure | mne_qt_browser.figure.MNEQtBrowser + Browser instance. """ -docdict["topomap_mask"] = """ -mask : ndarray of bool, shape (n_channels, n_times) | None - The channels to be marked as significant at a given time point. - Indices set to ``True`` will be considered. Defaults to ``None``. + +docdict['buffer_size_clust'] = """ +buffer_size : int | None + Block size to use when computing test statistics. This can significantly + reduce memory usage when ``n_jobs > 1`` and memory sharing between + processes is enabled (see :func:`mne.set_cache_dir`), because ``X`` will be + shared between processes and each process only needs to allocate space for + a small block of locations at a time. """ -docdict["topomap_mask_params"] = """ -mask_params : dict | None - Additional plotting parameters for plotting significant sensors. - Default (None) equals:: - dict(marker='o', markerfacecolor='w', markeredgecolor='k', - linewidth=0, markersize=4) +docdict['by_event_type'] = """ +by_event_type : bool + When ``False`` (the default) all epochs are processed together and a single + :class:`~mne.Evoked` object is returned. When ``True``, epochs are first + grouped by event type (as specified using the ``event_id`` parameter) and a + list is returned containing a separate :class:`~mne.Evoked` object for each + event type. The ``.comment`` attribute is set to the label of the event + type. + + .. versionadded:: 0.24.0 """ -docdict['topomap_outlines'] = """ -outlines : 'head' | 'skirt' | dict | None - The outlines to be drawn. If 'head', the default head scheme will be - drawn. If 'skirt' the head scheme will be drawn, but sensors are - allowed to be plotted outside of the head circle. If dict, each key - refers to a tuple of x and y positions, the values in 'mask_pos' will - serve as image mask. - Alternatively, a matplotlib patch object can be passed for advanced - masking options, either directly or as a function that returns patches - (required for multi-axis plots). If None, nothing will be drawn. - Defaults to 'head'. + +# %% +# C + +docdict['calibration_maxwell_cal'] = """ +calibration : str | None + Path to the ``'.dat'`` file with fine calibration coefficients. + File can have 1D or 3D gradiometer imbalance correction. + This file is machine/site-specific. """ -docdict['topomap_contours'] = """ -contours : int | array of float - The number of contour lines to draw. If 0, no contours will be drawn. - When an integer, matplotlib ticker locator is used to find suitable - values for the contour thresholds (may sometimes be inaccurate, use - array for accuracy). If an array, the values represent the levels for - the contours. The values are in µV for EEG, fT for magnetometers and - fT/m for gradiometers. If colorbar=True, the ticks in colorbar - correspond to the contour levels. Defaults to 6. + +docdict['cbar_fmt_psd_topo'] = """ +cbar_fmt : str + Format string for the colorbar tick labels. If ``'auto'``, is equivalent + to '%0.3f' if ``dB=False`` and '%0.1f' if ``dB=True``. Defaults to + ``'auto'``. """ -docdict['topomap_image_interp'] = """ -image_interp : str - The image interpolation to be used. All matplotlib options are - accepted. + +docdict['cbar_fmt_topomap'] = """ +cbar_fmt : str + String format for colorbar values. """ -docdict['topomap_average'] = """ -average : float | None - The time window around a given time to be used for averaging (seconds). - For example, 0.01 would translate into window that starts 5 ms before - and ends 5 ms after a given time point. Defaults to None, which means - no averaging. + +docdict['center'] = """ +center : float or None + If not None, center of a divergent colormap, changes the meaning of + fmin, fmax and fmid. """ -docdict['topomap_axes'] = """ -axes : instance of Axes | list | None - The axes to plot to. If list, the list must be a list of Axes of the - same length as ``times`` (unless ``times`` is None). If instance of - Axes, ``times`` must be a float or a list of one float. - Defaults to None. + +docdict['ch_name_ecg'] = """ +ch_name : None | str + The name of the channel to use for ECG peak detection. + If ``None`` (default), ECG channel is used if present. If ``None`` and + **no** ECG channel is present, a synthetic ECG channel is created from + the cross-channel average. This synthetic channel can only be created from + MEG channels. """ -docdict['topomap_extrapolate'] = """ -extrapolate : str - Options: - - ``'box'`` - Extrapolate to four points placed to form a square encompassing all - data points, where each side of the square is three times the range - of the data in the respective dimension. - - ``'local'`` (default) - Extrapolate only to nearby points (approximately to points closer than - median inter-electrode distance). This will also set the - mask to be polygonal based on the convex hull of the sensors. - - ``'head'`` - Extrapolate out to the edges of the clipping circle. This will be on - the head circle when the sensors are contained within the head circle, - but it can extend beyond the head when sensors are plotted outside - the head circle. +docdict['ch_name_eog'] = """ +ch_name : str | list of str | None + The name of the channel(s) to use for EOG peak detection. If a string, + can be an arbitrary channel. This doesn't have to be a channel of + ``eog`` type; it could, for example, also be an ordinary EEG channel + that was placed close to the eyes, like ``Fp1`` or ``Fp2``. - .. versionchanged:: 0.21 + Multiple channel names can be passed as a list of strings. - - The default was changed to ``'local'`` - - ``'local'`` was changed to use a convex hull mask - - ``'head'`` was changed to extrapolate out to the clipping circle. + If ``None`` (default), use the channel(s) in ``raw`` with type ``eog``. """ -docdict['topomap_border'] = """ -border : float | 'mean' - Value to extrapolate to on the topomap borders. If ``'mean'`` (default), - then each extrapolated point has the average value of its neighbours. - .. versionadded:: 0.20 +docdict['ch_names_annot'] = """ +ch_names : list | None + List of lists of channel names associated with the annotations. + Empty entries are assumed to be associated with no specific channel, + i.e., with all channels or with the time slice itself. None (default) is + the same as passing all empty lists. For example, this creates three + annotations, associating the first with the time interval itself, the + second with two channels, and the third with a single channel:: + + Annotations(onset=[0, 3, 10], duration=[1, 0.25, 0.5], + description=['Start', 'BAD_flux', 'BAD_noise'], + ch_names=[[], ['MEG0111', 'MEG2563'], ['MEG1443']]) """ -docdict['topomap_sphere'] = """ -sphere : float | array-like | instance of ConductorModel - The sphere parameters to use for the cartoon head. - Can be array-like of shape (4,) to give the X/Y/Z origin and radius in - meters, or a single float to give the radius (origin assumed 0, 0, 0). - Can also be a spherical ConductorModel, which will use the origin and - radius. Can also be None (default) which is an alias for %s. - Currently the head radius does not affect plotting. - .. versionadded:: 0.20 -""" % (HEAD_SIZE_DEFAULT,) -docdict['topomap_sphere_auto'] = """ -sphere : float | array-like | str | None - The sphere parameters to use for the cartoon head. - Can be array-like of shape (4,) to give the X/Y/Z origin and radius in - meters, or a single float to give the radius (origin assumed 0, 0, 0). - Can also be a spherical ConductorModel, which will use the origin and - radius. Can be "auto" to use a digitization-based fit. - Can also be None (default) to use 'auto' when enough extra digitization - points are available, and %s otherwise. - Currently the head radius does not affect plotting. +docdict['ch_type_evoked_topomap'] = """ +ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None + The channel type to plot. For 'grad', the gradiometers are collected in + pairs and the RMS for each pair is plotted. + If None, then channels are chosen in the order given above. +""" - .. versionadded:: 0.20 -""" % (HEAD_SIZE_DEFAULT,) -docdict['topomap_ch_type'] = """ +docdict['ch_type_set_eeg_reference'] = """ +ch_type : list of str | str + The name of the channel type to apply the reference to. + Valid channel types are ``'auto'``, ``'eeg'``, ``'ecog'``, ``'seeg'``, + ``'dbs'``. If ``'auto'``, the first channel type of eeg, ecog, seeg or dbs + that is found (in that order) will be selected. + + .. versionadded:: 0.19 +""" + +docdict['ch_type_topomap'] = """ ch_type : str The channel type being plotted. Determines the ``'auto'`` extrapolation mode. .. versionadded:: 0.21 """ -docdict['topomap_show_names'] = """ -show_names : bool | callable - If True, show channel names on top of the map. If a callable is - passed, channel names will be formatted using the callable; e.g., to - delete the prefix 'MEG ' from all channel names, pass the function - ``lambda x: x.replace('MEG ', '')``. If ``mask`` is not None, only - significant sensors will be shown. + +chwise = """ +channel_wise : bool + Whether to apply the function to each channel {}individually. If ``False``, + the function will be applied to all {}channels at once. Default ``True``. """ +docdict['channel_wise_applyfun'] = chwise.format('', '') +docdict['channel_wise_applyfun_epo'] = chwise.format( + 'in each epoch ', 'epochs and ') -# PSD topomaps -docdict["psd_topo_vlim_joint"] = """ -vlim : tuple of length 2 | 'joint' - Colormap limits to use. If a :class:`tuple` of floats, specifies the - lower and upper bounds of the colormap (in that order); providing - ``None`` for either entry will set the corresponding boundary at the - min/max of the data (separately for each topomap). Elements of the - :class:`tuple` may also be callable functions which take in a - :class:`NumPy array ` and return a scalar. - If ``vlim='joint'``, will compute the colormap limits jointly across - all topomaps of the same channel type, using the min/max of the data. - Defaults to ``(None, None)``. - .. versionadded:: 0.21 +docdict['check_disjoint_clust'] = """ +check_disjoint : bool + Whether to check if the connectivity matrix can be separated into disjoint + sets before clustering. This may lead to faster clustering, especially if + the second dimension of ``X`` (usually the "time" dimension) is large. """ -docdict['psd_topo_agg_fun'] = """ -agg_fun : callable - The function used to aggregate over frequencies. Defaults to - :func:`numpy.sum` if ``normalize=True``, else :func:`numpy.mean`. + +docdict['chpi_amplitudes'] = """ +chpi_amplitudes : dict + The time-varying cHPI coil amplitudes, with entries + "times", "proj", and "slopes". """ -docdict['psd_topo_dB'] = """ -dB : bool - If ``True``, transform data to decibels (with ``10 * np.log10(data)``) - following the application of ``agg_fun``. Ignored if ``normalize=True``. + +docdict['chpi_locs'] = """ +chpi_locs : dict + The time-varying cHPI coils locations, with entries + "times", "rrs", "moments", and "gofs". """ -docdict['psd_topo_cmap'] = """ -cmap : matplotlib colormap | (colormap, bool) | 'interactive' | None - Colormap to use. If :class:`tuple`, the first value indicates the colormap - to use and the second value is a boolean defining interactivity. In - interactive mode the colors are adjustable by clicking and dragging the - colorbar with left and right mouse button. Left mouse button moves the - scale up and down and right mouse button adjusts the range. Hitting - space bar resets the range. Up and down arrows can be used to change - the colormap. If ``None``, ``'Reds'`` is used for data that is either - all-positive or all-negative, and ``'RdBu_r'`` is used otherwise. - ``'interactive'`` is equivalent to ``(None, True)``. Defaults to ``None``. -""" -docdict['psd_topo_cbar_fmt'] = """ -cbar_fmt : str - Format string for the colorbar tick labels. If ``'auto'``, is equivalent - to '%0.3f' if ``dB=False`` and '%0.1f' if ``dB=True``. Defaults to - ``'auto'``. -""" -docdict['psd_topo_normalize'] = """ -normalize : bool - If True, each band will be divided by the total power. Defaults to - False. -""" -docdict['psd_topo_bands'] = """ -bands : list of tuple | None - The frequencies or frequency ranges to plot. Length-2 tuples specify - a single frequency and a subplot title (e.g., - ``(6.5, 'presentation rate')``); length-3 tuples specify lower and - upper band edges and a subplot title. If ``None`` (the default), - expands to:: - bands = [(0, 4, 'Delta'), (4, 8, 'Theta'), (8, 12, 'Alpha'), - (12, 30, 'Beta'), (30, 45, 'Gamma')] +docdict['clim'] = """ +clim : str | dict + Colorbar properties specification. If 'auto', set clim automatically + based on data percentiles. If dict, should contain: - In bands where a single frequency is provided, the topomap will reflect - the single frequency bin that is closest to the provided value. -""" -docdict['psd_topo_axes'] = """ -axes : list of Axes | None - List of axes to plot consecutive topographies to. If ``None`` the axes - will be created automatically. Defaults to ``None``. + ``kind`` : 'value' | 'percent' + Flag to specify type of limits. + ``lims`` : list | np.ndarray | tuple of float, 3 elements + Lower, middle, and upper bounds for colormap. + ``pos_lims`` : list | np.ndarray | tuple of float, 3 elements + Lower, middle, and upper bound for colormap. Positive values + will be mirrored directly across zero during colormap + construction to obtain negative control points. + + .. note:: Only one of ``lims`` or ``pos_lims`` should be provided. + Only sequential colormaps should be used with ``lims``, and + only divergent colormaps should be used with ``pos_lims``. """ -# Picks -picks_header = 'picks : str | list | slice | None' -picks_intro = ('Channels to include. Slices and lists of integers will be ' - 'interpreted as channel indices.') -_reminder = ("Note that channels in ``info['bads']`` *will be included* if " - "their {}indices are explicitly provided.\n") -reminder = _reminder.format('names or ') -reminder_nostr = _reminder.format('') -noref = f'(excluding reference MEG channels). {reminder}' -picks_base = f"""{picks_header} - {picks_intro} In lists, channel *type* strings - (e.g., ``['meg', 'eeg']``) will pick channels of those - types, channel *name* strings (e.g., ``['MEG0111', 'MEG2623']`` - will pick the given channels. Can also be the string values - "all" to pick all channels, or "data" to pick :term:`data channels`. - None (default) will pick""" -docdict['picks_header'] = picks_header # these get reused as stubs in a -docdict['picks_base'] = picks_base # couple places (e.g., BaseEpochs) -docdict['picks_all'] = f'{picks_base} all channels. {reminder}' -docdict['picks_all_data'] = f'{picks_base} all data channels. {reminder}' -docdict['picks_good_data'] = f'{picks_base} good data channels. {reminder}' -docdict['picks_all_data_noref'] = f'{picks_base} all data channels {noref}' -docdict['picks_good_data_noref'] = f'{picks_base} good data channels {noref}' -docdict['picks_nostr'] = f"""picks : list | slice | None - {picks_intro} None (default) will pick all channels. {reminder_nostr}""" +docdict['clim_onesided'] = """ +clim : str | dict + Colorbar properties specification. If 'auto', set clim automatically + based on data percentiles. If dict, should contain: -# Filtering -docdict['l_freq'] = """ -l_freq : float | None - For FIR filters, the lower pass-band edge; for IIR filters, the lower - cutoff frequency. If None the data are only low-passed. -""" -docdict['h_freq'] = """ -h_freq : float | None - For FIR filters, the upper pass-band edge; for IIR filters, the upper - cutoff frequency. If None the data are only high-passed. -""" -docdict['filter_length'] = """ -filter_length : str | int - Length of the FIR filter to use (if applicable): + ``kind`` : 'value' | 'percent' + Flag to specify type of limits. + ``lims`` : list | np.ndarray | tuple of float, 3 elements + Lower, middle, and upper bound for colormap. - * **'auto' (default)**: The filter length is chosen based - on the size of the transition regions (6.6 times the reciprocal - of the shortest transition band for fir_window='hamming' - and fir_design="firwin2", and half that for "firwin"). - * **str**: A human-readable time in - units of "s" or "ms" (e.g., "10s" or "5500ms") will be - converted to that number of samples if ``phase="zero"``, or - the shortest power-of-two length at least that duration for - ``phase="zero-double"``. - * **int**: Specified length in samples. For fir_design="firwin", - this should not be used. + Unlike :meth:`stc.plot `, it cannot use + ``pos_lims``, as the surface plot must show the magnitude. """ -docdict['filter_length_notch'] = docdict['filter_length'] + """ - When ``method=='spectrum_fit'``, this sets the effective window duration - over which fits are computed. See :func:`mne.filter.create_filter` - for options. Longer window lengths will give more stable frequency - estimates, but require (potentially much) more processing and are not able - to adapt as well to non-stationarities. - The default in 0.21 is None, but this will change to ``'10s'`` in 0.22. +docdict['cmap_psd_topo'] = """ +cmap : matplotlib colormap | (colormap, bool) | 'interactive' | None + Colormap to use. If :class:`tuple`, the first value indicates the colormap + to use and the second value is a boolean defining interactivity. In + interactive mode the colors are adjustable by clicking and dragging the + colorbar with left and right mouse button. Left mouse button moves the + scale up and down and right mouse button adjusts the range. Hitting + space bar resets the range. Up and down arrows can be used to change + the colormap. If ``None``, ``'Reds'`` is used for data that is either + all-positive or all-negative, and ``'RdBu_r'`` is used otherwise. + ``'interactive'`` is equivalent to ``(None, True)``. Defaults to ``None``. """ -docdict['l_trans_bandwidth'] = """ -l_trans_bandwidth : float | str - Width of the transition band at the low cut-off frequency in Hz - (high pass or cutoff 1 in bandpass). Can be "auto" - (default) to use a multiple of ``l_freq``:: - min(max(l_freq * 0.25, 2), l_freq) +docdict['cmap_topomap'] = """ +cmap : matplotlib colormap | (colormap, bool) | 'interactive' | None + Colormap to use. If tuple, the first value indicates the colormap to + use and the second value is a boolean defining interactivity. In + interactive mode the colors are adjustable by clicking and dragging the + colorbar with left and right mouse button. Left mouse button moves the + scale up and down and right mouse button adjusts the range (zoom). + The mouse scroll can also be used to adjust the range. Hitting space + bar resets the range. Up and down arrows can be used to change the + colormap. If None (default), 'Reds' is used for all positive data, + otherwise defaults to 'RdBu_r'. If 'interactive', translates to + (None, True). - Only used for ``method='fir'``. + .. warning:: Interactive mode works smoothly only for a small amount + of topomaps. Interactive mode is disabled by default for more than + 2 topomaps. """ -docdict['h_trans_bandwidth'] = """ -h_trans_bandwidth : float | str - Width of the transition band at the high cut-off frequency in Hz - (low pass or cutoff 2 in bandpass). Can be "auto" - (default in 0.14) to use a multiple of ``h_freq``:: - - min(max(h_freq * 0.25, 2.), info['sfreq'] / 2. - h_freq) - Only used for ``method='fir'``. +docdict['cmap_topomap_simple'] = """ +cmap : matplotlib colormap | None + Colormap to use. If None, 'Reds' is used for all positive data, + otherwise defaults to 'RdBu_r'. """ -docdict['phase'] = """ -phase : str - Phase of the filter, only used if ``method='fir'``. - Symmetric linear-phase FIR filters are constructed, and if ``phase='zero'`` - (default), the delay of this filter is compensated for, making it - non-causal. If ``phase=='zero-double'``, - then this filter is applied twice, once forward, and once backward - (also making it non-causal). If 'minimum', then a minimum-phase filter will - be constricted and applied, which is causal but has weaker stop-band - suppression. - .. versionadded:: 0.13 +docdict['cnorm'] = """ +cnorm : matplotlib.colors.Normalize | None + Colormap normalization, default None means linear normalization. If not + None, ``vmin`` and ``vmax`` arguments are ignored. See Notes for more + details. """ -docdict['fir_design'] = """ -fir_design : str - Can be "firwin" (default) to use :func:`scipy.signal.firwin`, - or "firwin2" to use :func:`scipy.signal.firwin2`. "firwin" uses - a time-domain design technique that generally gives improved - attenuation using fewer samples than "firwin2". - .. versionadded:: 0.15 +docdict['color_matplotlib'] = """ +color : color + A list of anything matplotlib accepts: string, RGB, hex, etc. """ -docdict['fir_window'] = """ -fir_window : str - The window to use in FIR design, can be "hamming" (default), - "hann" (default in 0.13), or "blackman". - .. versionadded:: 0.15 -""" -docdict['pad-fir'] = """ -pad : str - The type of padding to use. Supports all :func:`numpy.pad` ``mode`` - options. Can also be "reflect_limited", which pads with a - reflected version of each vector mirrored on the first and last - values of the vector, followed by zeros. Only used for ``method='fir'``. -""" -docdict['method-fir'] = """ -method : str - 'fir' will use overlap-add FIR filtering, 'iir' will use IIR - forward-backward filtering (via filtfilt). -""" -docdict['n_jobs-fir'] = """ -n_jobs : int | str - Number of jobs to run in parallel. Can be 'cuda' if ``cupy`` - is installed properly and method='fir'. -""" -docdict['n_jobs-cuda'] = """ -n_jobs : int | str - Number of jobs to run in parallel. Can be 'cuda' if ``cupy`` - is installed properly. -""" -docdict['iir_params'] = """ -iir_params : dict | None - Dictionary of parameters to use for IIR filtering. - If iir_params is None and method="iir", 4th order Butterworth will be used. - For more information, see :func:`mne.filter.construct_iir_filter`. -""" -docdict['npad'] = """ -npad : int | str - Amount to pad the start and end of the data. - Can also be "auto" to use a padding that will result in - a power-of-two size (can be much faster). -""" -docdict['window-resample'] = """ -window : str | tuple - Frequency-domain window to use in resampling. - See :func:`scipy.signal.resample`. -""" -docdict['average-psd'] = """ -average : str | None - How to average the segments. If ``mean`` (default), calculate the - arithmetic mean. If ``median``, calculate the median, corrected for - its bias relative to the mean. If ``None``, returns the unaggregated - segments. +docdict['color_plot_psd'] = """ +color : str | tuple + A matplotlib-compatible color to use. Has no effect when + spatial_colors=True. """ -docdict['window-psd'] = """ -window : str | float | tuple - Windowing function to use. See :func:`scipy.signal.get_window`. + +docdict['colorbar_topomap'] = """ +colorbar : bool + Plot a colorbar in the rightmost column of the figure. """ -docdict['decim'] = """ -decim : int - Factor by which to subsample the data. - .. warning:: Low-pass filtering is not performed, this simply selects - every Nth sample (where N is the value passed to - ``decim``), i.e., it compresses the signal (see Notes). - If the data are not properly filtered, aliasing artifacts - may occur. +docdict['colormap'] = """ +colormap : str | np.ndarray of float, shape(n_colors, 3 | 4) + Name of colormap to use or a custom look up table. If array, must + be (n x 3) or (n x 4) array for with RGB or RGBA values between + 0 and 255. """ -docdict['decim_offset'] = """ -offset : int - Apply an offset to where the decimation starts relative to the - sample corresponding to t=0. The offset is in samples at the - current sampling rate. - .. versionadded:: 0.12 +docdict['combine'] = """ +combine : None | str | callable + How to combine information across channels. If a :class:`str`, must be + one of 'mean', 'median', 'std' (standard deviation) or 'gfp' (global + field power). """ -docdict['decim_notes'] = """ -For historical reasons, ``decim`` / "decimation" refers to simply subselecting -samples from a given signal. This contrasts with the broader signal processing -literature, where decimation is defined as (quoting -:footcite:`OppenheimEtAl1999`, p. 172; which cites -:footcite:`CrochiereRabiner1983`): - "... a general system for downsampling by a factor of M is the one shown - in Figure 4.23. Such a system is called a decimator, and downsampling - by lowpass filtering followed by compression [i.e, subselecting samples] - has been termed decimation (Crochiere and Rabiner, 1983)." +docdict['compute_proj_ecg'] = """This function will: -Hence "decimation" in MNE is what is considered "compression" in the signal -processing community. +#. Filter the ECG data channel. -Decimation can be done multiple times. For example, -``inst.decimate(2).decimate(2)`` will be the same as -``inst.decimate(4)``. -""" +#. Find ECG R wave peaks using :func:`mne.preprocessing.find_ecg_events`. -# cHPI -docdict['chpi_t_window'] = """ -t_window : float - Time window to use to estimate the amplitudes, default is - 0.2 (200 ms). -""" -docdict['chpi_ext_order'] = """ -ext_order : int - The external order for SSS-like interfence suppression. - The SSS bases are used as projection vectors during fitting. +#. Filter the raw data. - .. versionchanged:: 0.20 - Added ``ext_order=1`` by default, which should improve - detection of true HPI signals. -""" -docdict['chpi_adjust_dig'] = """ -adjust_dig : bool - If True, adjust the digitization locations used for fitting based on - the positions localized at the start of the file. -""" -docdict['chpi_amplitudes'] = """ -chpi_amplitudes : dict - The time-varying cHPI coil amplitudes, with entries - "times", "proj", and "slopes". -""" -docdict['chpi_locs'] = """ -chpi_locs : dict - The time-varying cHPI coils locations, with entries - "times", "rrs", "moments", and "gofs". -""" +#. Create `~mne.Epochs` around the R wave peaks, capturing the heartbeats. -# EEG reference: set_eeg_reference -docdict['set_eeg_reference_ref_channels'] = """ -ref_channels : list of str | str - Can be: +#. Optionally average the `~mne.Epochs` to produce an `~mne.Evoked` if + ``average=True`` was passed (default). - - The name(s) of the channel(s) used to construct the reference. - - ``'average'`` to apply an average reference (default) - - ``'REST'`` to use the Reference Electrode Standardization Technique - infinity reference :footcite:`Yao2001`. - - An empty list, in which case MNE will not attempt any re-referencing of - the data +#. Calculate SSP projection vectors on that data to capture the artifacts.""" + +docdict['compute_proj_eog'] = """This function will: + +#. Filter the EOG data channel. + +#. Find the peaks of eyeblinks in the EOG data using + :func:`mne.preprocessing.find_eog_events`. + +#. Filter the raw data. + +#. Create `~mne.Epochs` around the eyeblinks. + +#. Optionally average the `~mne.Epochs` to produce an `~mne.Evoked` if + ``average=True`` was passed (default). + +#. Calculate SSP projection vectors on that data to capture the artifacts.""" + +docdict['compute_ssp'] = """This function aims to find those SSP vectors that +will project out the ``n`` most prominent signals from the data for each +specified sensor type. Consequently, if the provided input data contains high +levels of noise, the produced SSP vectors can then be used to eliminate that +noise from the data. """ -docdict['set_eeg_reference_projection'] = """ -projection : bool - If ``ref_channels='average'`` this argument specifies if the - average reference should be computed as a projection (True) or not - (False; default). If ``projection=True``, the average reference is - added as a projection and is not applied to the data (it can be - applied afterwards with the ``apply_proj`` method). If - ``projection=False``, the average reference is directly applied to - the data. If ``ref_channels`` is not ``'average'``, ``projection`` - must be set to ``False`` (the default in this case). + +docdict['contours_topomap'] = """ +contours : int | array of float + The number of contour lines to draw. If 0, no contours will be drawn. + When an integer, matplotlib ticker locator is used to find suitable + values for the contour thresholds (may sometimes be inaccurate, use + array for accuracy). If an array, the values represent the levels for + the contours. The values are in µV for EEG, fT for magnetometers and + fT/m for gradiometers. If colorbar=True, the ticks in colorbar + correspond to the contour levels. Defaults to 6. """ -docdict['set_eeg_reference_ch_type'] = """ -ch_type : 'auto' | 'eeg' | 'ecog' | 'seeg' | 'dbs' - The name of the channel type to apply the reference to. If 'auto', - the first channel type of eeg, ecog, seeg or dbs that is found (in that - order) will be selected. - .. versionadded:: 0.19 +docdict['coord_frame_maxwell'] = """ +coord_frame : str + The coordinate frame that the ``origin`` is specified in, either + ``'meg'`` or ``'head'``. For empty-room recordings that do not have + a head<->meg transform ``info['dev_head_t']``, the MEG coordinate + frame should be used. """ -docdict['set_eeg_reference_forward'] = """ -forward : instance of Forward | None - Forward solution to use. Only used with ``ref_channels='REST'``. - .. versionadded:: 0.21 +docdict['copy_df'] = """ +copy : bool + If ``True``, data will be copied. Otherwise data may be modified in place. + Defaults to ``True``. """ -docdict['set_eeg_reference_see_also_notes'] = """ -See Also --------- -mne.set_bipolar_reference : Convenience function for creating bipolar - references. -Notes ------ -Some common referencing schemes and the corresponding value for the -``ref_channels`` parameter: +docdict['create_ecg_epochs'] = """This function will: -- Average reference: - A new virtual reference electrode is created by averaging the current - EEG signal by setting ``ref_channels='average'``. Bad EEG channels are - automatically excluded if they are properly set in ``info['bads']``. +#. Filter the ECG data channel. -- A single electrode: - Set ``ref_channels`` to a list containing the name of the channel that - will act as the new reference, for example ``ref_channels=['Cz']``. +#. Find ECG R wave peaks using :func:`mne.preprocessing.find_ecg_events`. -- The mean of multiple electrodes: - A new virtual reference electrode is created by computing the average - of the current EEG signal recorded from two or more selected channels. - Set ``ref_channels`` to a list of channel names, indicating which - channels to use. For example, to apply an average mastoid reference, - when using the 10-20 naming scheme, set ``ref_channels=['M1', 'M2']``. +#. Create `~mne.Epochs` around the R wave peaks, capturing the heartbeats. +""" -- REST - The given EEG electrodes are referenced to a point at infinity using the - lead fields in ``forward``, which helps standardize the signals. +docdict['create_eog_epochs'] = """This function will: -1. If a reference is requested that is not the average reference, this - function removes any pre-existing average reference projections. +#. Filter the EOG data channel. -2. During source localization, the EEG signal should have an average - reference. +#. Find the peaks of eyeblinks in the EOG data using + :func:`mne.preprocessing.find_eog_events`. -3. In order to apply a reference, the data must be preloaded. This is not - necessary if ``ref_channels='average'`` and ``projection=True``. +#. Create `~mne.Epochs` around the eyeblinks. +""" -4. For an average or REST reference, bad EEG channels are automatically - excluded if they are properly set in ``info['bads']``. +docdict['cross_talk_maxwell'] = """ +cross_talk : str | None + Path to the FIF file with cross-talk correction information. +""" -.. versionadded:: 0.9.0 +# %% +# D -References ----------- -.. footbibliography:: +docdict['dB_plot_psd'] = """ +dB : bool + Plot Power Spectral Density (PSD), in units (amplitude**2/Hz (dB)) if + ``dB=True``, and ``estimate='power'`` or ``estimate='auto'``. Plot PSD + in units (amplitude**2/Hz) if ``dB=False`` and, + ``estimate='power'``. Plot Amplitude Spectral Density (ASD), in units + (amplitude/sqrt(Hz)), if ``dB=False`` and ``estimate='amplitude'`` or + ``estimate='auto'``. Plot ASD, in units (amplitude/sqrt(Hz) (db)), if + ``dB=True`` and ``estimate='amplitude'``. """ -# ICA -docdict['n_pca_components_apply'] = """ -n_pca_components : int | float | None - The number of PCA components to be kept, either absolute (int) - or fraction of the explained variance (float). If None (default), - the ``ica.n_pca_components`` from initialization will be used in 0.22; - in 0.23 all components will be used. +docdict['dB_psd_topo'] = """ +dB : bool + If ``True``, transform data to decibels (with ``10 * np.log10(data)``) + following the application of ``agg_fun``. Ignored if ``normalize=True``. """ -# Maxwell filtering -docdict['maxwell_origin'] = """ -origin : array-like, shape (3,) | str - Origin of internal and external multipolar moment space in meters. - The default is ``'auto'``, which means ``(0., 0., 0.)`` when - ``coord_frame='meg'``, and a head-digitization-based - origin fit using :func:`~mne.bem.fit_sphere_to_headshape` - when ``coord_frame='head'``. If automatic fitting fails (e.g., due - to having too few digitization points), - consider separately calling the fitting function with different - options or specifying the origin manually. -""" -docdict['maxwell_int'] = """ -int_order : int - Order of internal component of spherical expansion. -""" -docdict['maxwell_ext'] = """ -ext_order : int - Order of external component of spherical expansion. -""" -docdict['maxwell_cal'] = """ -calibration : str | None - Path to the ``'.dat'`` file with fine calibration coefficients. - File can have 1D or 3D gradiometer imbalance correction. - This file is machine/site-specific. -""" -docdict['maxwell_cross'] = """ -cross_talk : str | None - Path to the FIF file with cross-talk correction information. -""" -docdict['maxwell_coord'] = """ -coord_frame : str - The coordinate frame that the ``origin`` is specified in, either - ``'meg'`` or ``'head'``. For empty-room recordings that do not have - a head<->meg transform ``info['dev_head_t']``, the MEG coordinate - frame should be used. +docdict['daysback_anonymize_info'] = """ +daysback : int | None + Number of days to subtract from all dates. + If ``None`` (default), the acquisition date, ``info['meas_date']``, + will be set to ``January 1ˢᵗ, 2000``. This parameter is ignored if + ``info['meas_date']`` is ``None`` (i.e., no acquisition date has been set). """ -docdict['maxwell_reg'] = """ -regularize : str | None - Basis regularization type, must be "in" or None. - "in" is the same algorithm as the "-regularize in" option in - MaxFilter™. + +docdict['dbs'] = """ +dbs : bool + If True (default), show DBS (deep brain stimulation) electrodes. """ -docdict['maxwell_ref'] = """ -ignore_ref : bool - If True, do not include reference channels in compensation. This - option should be True for KIT files, since Maxwell filtering - with reference channels is not currently supported. + +docdict['decim'] = """ +decim : int + Factor by which to subsample the data. + + .. warning:: Low-pass filtering is not performed, this simply selects + every Nth sample (where N is the value passed to + ``decim``), i.e., it compresses the signal (see Notes). + If the data are not properly filtered, aliasing artifacts + may occur. """ -docdict['maxwell_cond'] = """ -bad_condition : str - How to deal with ill-conditioned SSS matrices. Can be "error" - (default), "warning", "info", or "ignore". + +docdict['decim_notes'] = """ +For historical reasons, ``decim`` / "decimation" refers to simply subselecting +samples from a given signal. This contrasts with the broader signal processing +literature, where decimation is defined as (quoting +:footcite:`OppenheimEtAl1999`, p. 172; which cites +:footcite:`CrochiereRabiner1983`): + + "... a general system for downsampling by a factor of M is the one shown + in Figure 4.23. Such a system is called a decimator, and downsampling + by lowpass filtering followed by compression [i.e, subselecting samples] + has been termed decimation (Crochiere and Rabiner, 1983)." + +Hence "decimation" in MNE is what is considered "compression" in the signal +processing community. + +Decimation can be done multiple times. For example, +``inst.decimate(2).decimate(2)`` will be the same as +``inst.decimate(4)``. """ -docdict['maxwell_pos'] = """ -head_pos : array | None - If array, movement compensation will be performed. - The array should be of shape (N, 10), holding the position - parameters as returned by e.g. ``read_head_pos``. + +docdict['depth'] = """ +depth : None | float | dict + How to weight (or normalize) the forward using a depth prior. + If float (default 0.8), it acts as the depth weighting exponent (``exp``) + to use None is equivalent to 0, meaning no depth weighting is performed. + It can also be a :class:`dict` containing keyword arguments to pass to + :func:`mne.forward.compute_depth_prior` (see docstring for details and + defaults). This is effectively ignored when ``method='eLORETA'``. + + .. versionchanged:: 0.20 + Depth bias ignored for ``method='eLORETA'``. """ -docdict['maxwell_dest'] = """ + +docdict['destination_maxwell_dest'] = """ destination : str | array-like, shape (3,) | None The destination location for the head. Can be ``None``, which will not change the head position, or a string path to a FIF file @@ -884,48 +795,326 @@ as ``--trans default`` would in MaxFilter™ (i.e., to the default head location). """ -docdict['maxwell_st_fixed_only'] = """ -st_fixed : bool - If True (default), do tSSS using the median head position during the - ``st_duration`` window. This is the default behavior of MaxFilter - and has been most extensively tested. - - .. versionadded:: 0.12 -st_only : bool - If True, only tSSS (temporal) projection of MEG data will be - performed on the output data. The non-tSSS parameters (e.g., - ``int_order``, ``calibration``, ``head_pos``, etc.) will still be - used to form the SSS bases used to calculate temporal projectors, - but the output MEG data will *only* have temporal projections - performed. Noise reduction from SSS basis multiplication, - cross-talk cancellation, movement compensation, and so forth - will not be applied to the data. This is useful, for example, when - evoked movement compensation will be performed with - :func:`~mne.epochs.average_movements`. - .. versionadded:: 0.12 +docdict['detrend_epochs'] = """ +detrend : int | None + If 0 or 1, the data channels (MEG and EEG) will be detrended when + loaded. 0 is a constant (DC) detrend, 1 is a linear detrend. None + is no detrending. Note that detrending is performed before baseline + correction. If no DC offset is preferred (zeroth order detrending), + either turn off baseline correction, as this may introduce a DC + shift, or set baseline correction to use the entire time interval + (will yield equivalent results but be slower). """ -docdict['maxwell_mag'] = """ -mag_scale : float | str - The magenetometer scale-factor used to bring the magnetometers - to approximately the same order of magnitude as the gradiometers - (default 100.), as they have different units (T vs T/m). - Can be ``'auto'`` to use the reciprocal of the physical distance - between the gradiometer pickup loops (e.g., 0.0168 m yields - 59.5 for VectorView). + +docdict['df_return'] = """ +df : instance of pandas.DataFrame + A dataframe suitable for usage with other statistical/plotting/analysis + packages. """ -docdict['maxwell_skip'] = """ -skip_by_annotation : str | list of str - If a string (or list of str), any annotation segment that begins - with the given string will not be included in filtering, and - segments on either side of the given excluded annotated segment - will be filtered separately (i.e., as independent signals). - The default ``('edge', 'bad_acq_skip')`` will separately filter - any segments that were concatenated by :func:`mne.concatenate_raws` - or :meth:`mne.io.Raw.append`, or separated during acquisition. - To disable, provide an empty list. + +docdict['dig_kinds'] = """ +dig_kinds : list of str | str + Kind of digitization points to use in the fitting. These can be any + combination of ('cardinal', 'hpi', 'eeg', 'extra'). Can also + be 'auto' (default), which will use only the 'extra' points if + enough (more than 4) are available, and if not, uses 'extra' and + 'eeg' points. +""" + +docdict['dipole'] = """ +dipole : instance of Dipole | list of Dipole + Dipole object containing position, orientation and amplitude of + one or more dipoles. Multiple simultaneous dipoles may be defined by + assigning them identical times. Alternatively, multiple simultaneous + dipoles may also be specified as a list of Dipole objects. + + .. versionchanged:: 1.1 + Added support for a list of :class:`mne.Dipole` instances. +""" + +docdict['distance'] = """ +distance : float | None + The distance from the camera rendering the view to the focalpoint + in plot units (either m or mm). +""" + +docdict['dtype_applyfun'] = """ +dtype : numpy.dtype + Data type to use after applying the function. If None + (default) the data type is not modified. +""" + +# %% +# E + +docdict['ecog'] = """ +ecog : bool + If True (default), show ECoG sensors. +""" + +docdict['eeg'] = """ +eeg : bool | str | list + String options are: + + - "original" (default; equivalent to ``True``) + Shows EEG sensors using their digitized locations (after + transformation to the chosen ``coord_frame``) + - "projected" + The EEG locations projected onto the scalp, as is done in + forward modeling + + Can also be a list of these options, or an empty list (``[]``, + equivalent of ``False``). +""" + +docdict['elevation'] = """ +elevation : float + The The zenith angle of the camera rendering the view in degrees. +""" + +docdict['eltc_mode_notes'] = """ +Valid values for ``mode`` are: + +- ``'max'`` + Maximum value across vertices at each time point within each label. +- ``'mean'`` + Average across vertices at each time point within each label. Ignores + orientation of sources for standard source estimates, which varies + across the cortical surface, which can lead to cancellation. + Vector source estimates are always in XYZ / RAS orientation, and are thus + already geometrically aligned. +- ``'mean_flip'`` + Finds the dominant direction of source space normal vector orientations + within each label, applies a sign-flip to time series at vertices whose + orientation is more than 180° different from the dominant direction, and + then averages across vertices at each time point within each label. +- ``'pca_flip'`` + Applies singular value decomposition to the time courses within each label, + and uses the first right-singular vector as the representative label time + course. This signal is scaled so that its power matches the average + (per-vertex) power within the label, and sign-flipped by multiplying by + ``np.sign(u @ flip)``, where ``u`` is the first left-singular vector and + ``flip`` is the same sign-flip vector used when ``mode='mean_flip'``. This + sign-flip ensures that extracting time courses from the same label in + similar STCs does not result in 180° direction/phase changes. +- ``'auto'`` (default) + Uses ``'mean_flip'`` when a standard source estimate is applied, and + ``'mean'`` when a vector source estimate is supplied. + + .. versionadded:: 0.21 + Support for ``'auto'``, vector, and volume source estimates. + +The only modes that work for vector and volume source estimates are ``'mean'``, +``'max'``, and ``'auto'``. +""" + +docdict['emit_warning'] = """ +emit_warning : bool + Whether to emit warnings when cropping or omitting annotations. +""" + +docdict['epochs_preload'] = """ + Load all epochs from disk when creating the object + or wait before accessing each epoch (more memory + efficient but can be slower). +""" + +docdict['epochs_reject_tmin_tmax'] = """ +reject_tmin, reject_tmax : float | None + Start and end of the time window used to reject epochs based on + peak-to-peak (PTP) amplitudes as specified via ``reject`` and ``flat``. + The default ``None`` corresponds to the first and last time points of the + epochs, respectively. + + .. note:: This parameter controls the time period used in conjunction with + both, ``reject`` and ``flat``. +""" + +docdict['epochs_tmin_tmax'] = """ +tmin, tmax : float + Start and end time of the epochs in seconds, relative to the time-locked + event. The closest or matching samples corresponding to the start and end + time are included. Defaults to ``-0.2`` and ``0.5``, respectively. +""" + +docdict['estimate_plot_psd'] = """ +estimate : str, {'auto', 'power', 'amplitude'} + Can be "power" for power spectral density (PSD), "amplitude" for + amplitude spectrum density (ASD), or "auto" (default), which uses + "power" when dB is True and "amplitude" otherwise. +""" + +docdict['event_color'] = """ +event_color : color object | dict | None + Color(s) to use for :term:`events`. To show all :term:`events` in the same + color, pass any matplotlib-compatible color. To color events differently, + pass a `dict` that maps event names or integer event numbers to colors + (must include entries for *all* events, or include a "fallback" entry with + key ``-1``). If ``None``, colors are chosen from the current Matplotlib + color cycle. +""" + +docdict['event_id'] = """ +event_id : int | list of int | dict | None + The id of the :term:`events` to consider. If dict, the keys can later be + used to access associated :term:`events`. Example: + dict(auditory=1, visual=3). If int, a dict will be created with the id as + string. If a list, all :term:`events` with the IDs specified in the list + are used. If None, all :term:`events` will be used and a dict is created + with string integer names corresponding to the event id integers.""" + +docdict['event_id_ecg'] = """ +event_id : int + The index to assign to found ECG events. +""" + +docdict['event_repeated_epochs'] = """ +event_repeated : str + How to handle duplicates in ``events[:, 0]``. Can be ``'error'`` + (default), to raise an error, 'drop' to only retain the row occurring + first in the :term:`events`, or ``'merge'`` to combine the coinciding + events (=duplicates) into a new event (see Notes for details). + + .. versionadded:: 0.19 +""" + +docdict['events'] = """ +events : array of int, shape (n_events, 3) + The array of :term:`events`. The first column contains the event time in + samples, with :term:`first_samp` included. The third column contains the + event id.""" + +docdict['events_epochs'] = """ +events : array of int, shape (n_events, 3) + The array of :term:`events`. The first column contains the event time in + samples, with :term:`first_samp` included. The third column contains the + event id. + If some events don't match the events of interest as specified by event_id, + they will be marked as ``IGNORED`` in the drop log.""" + +docdict['evoked_by_event_type_returns'] = """ +evoked : instance of Evoked | list of Evoked + The averaged epochs. + When ``by_event_type=True`` was specified, a list is returned containing a + separate :class:`~mne.Evoked` object for each event type. The list has the + same order as the event types as specified in the ``event_id`` + dictionary. +""" + +docdict['exclude_clust'] = """ +exclude : bool array or None + Mask to apply to the data to exclude certain points from clustering + (e.g., medial wall vertices). Should be the same shape as ``X``. + If ``None``, no points are excluded. """ -docdict['maxwell_extended'] = """ + +docdict['exclude_frontal'] = """ +exclude_frontal : bool + If True, exclude points that have both negative Z values + (below the nasion) and positivy Y values (in front of the LPA/RPA). +""" + +docdict['export_edf_note'] = """ +For EDF exports, only channels measured in Volts are allowed; in MNE-Python +this means channel types 'eeg', 'ecog', 'seeg', 'emg', 'eog', 'ecg', 'dbs', +'bio', and 'misc'. 'stim' channels are dropped. Although this function +supports storing channel types in the signal label (e.g. ``EEG Fz`` or +``MISC E``), other software may not support this (optional) feature of +the EDF standard. + +If ``add_ch_type`` is True, then channel types are written based on what +they are currently set in MNE-Python. One should double check that all +their channels are set correctly. You can call +:attr:`raw.set_channel_types ` to set +channel types. + +In addition, EDF does not support storing a montage. You will need +to store the montage separately and call :attr:`raw.set_montage() +`. +""" + +docdict['export_eeglab_note'] = """ +For EEGLAB exports, channel locations are expanded to full EEGLAB format. +For more details see :func:`eeglabio.utils.cart_to_eeglab`. +""" + +_export_fmt_params_base = """\ +Format of the export. Defaults to ``'auto'``, which will infer the format + from the filename extension. See supported formats above for more + information.""" + +docdict['export_fmt_params_epochs'] = """ +fmt : 'auto' | 'eeglab' + {} +""".format(_export_fmt_params_base) + +docdict['export_fmt_params_evoked'] = """ +fmt : 'auto' | 'mff' + {} +""".format(_export_fmt_params_base) + +docdict['export_fmt_params_raw'] = """ +fmt : 'auto' | 'brainvision' | 'edf' | 'eeglab' + {} +""".format(_export_fmt_params_base) + +docdict['export_fmt_support_epochs'] = """\ +Supported formats: + - EEGLAB (``.set``, uses :mod:`eeglabio`) +""" + +docdict['export_fmt_support_evoked'] = """\ +Supported formats: + - MFF (``.mff``, uses :func:`mne.export.export_evokeds_mff`) +""" + +docdict['export_fmt_support_raw'] = """\ +Supported formats: + - BrainVision (``.vhdr``, ``.vmrk``, ``.eeg``, uses `pybv `_) + - EEGLAB (``.set``, uses :mod:`eeglabio`) + - EDF (``.edf``, uses `EDFlib-Python `_) +""" # noqa: E501 + +docdict['export_warning'] = """\ +.. warning:: + Since we are exporting to external formats, there's no guarantee that all + the info will be preserved in the external format. See Notes for details. +""" + +_export_warning_note_base = """\ +Export to external format may not preserve all the information from the +instance. To save in native MNE format (``.fif``) without information loss, +use :meth:`mne.{0}.save` instead. +Export does not apply projector(s). Unapplied projector(s) will be lost. +Consider applying projector(s) before exporting with +:meth:`mne.{0}.apply_proj`.""" + +docdict['export_warning_note_epochs'] = \ + _export_warning_note_base.format('Epochs') + +docdict['export_warning_note_evoked'] = \ + _export_warning_note_base.format('Evoked') + +docdict['export_warning_note_raw'] = \ + _export_warning_note_base.format('io.Raw') + +docdict['ext_order_chpi'] = """ +ext_order : int + The external order for SSS-like interfence suppression. + The SSS bases are used as projection vectors during fitting. + + .. versionchanged:: 0.20 + Added ``ext_order=1`` by default, which should improve + detection of true HPI signals. +""" + +docdict['ext_order_maxwell'] = """ +ext_order : int + Order of external component of spherical expansion. +""" + +docdict['extended_proj_maxwell'] = """ extended_proj : list The empty-room projection vectors used to extend the external SSS basis (i.e., use eSSS). @@ -933,8 +1122,1395 @@ .. versionadded:: 0.21 """ -# Rank -docdict['rank'] = """ +docdict['extrapolate_topomap'] = """ +extrapolate : str + Options: + + - ``'box'`` + Extrapolate to four points placed to form a square encompassing all + data points, where each side of the square is three times the range + of the data in the respective dimension. + - ``'local'`` (default for MEG sensors) + Extrapolate only to nearby points (approximately to points closer than + median inter-electrode distance). This will also set the + mask to be polygonal based on the convex hull of the sensors. + - ``'head'`` (default for non-MEG sensors) + Extrapolate out to the edges of the clipping circle. This will be on + the head circle when the sensors are contained within the head circle, + but it can extend beyond the head when sensors are plotted outside + the head circle. + + .. versionchanged:: 0.21 + + - The default was changed to ``'local'`` for MEG sensors. + - ``'local'`` was changed to use a convex hull mask + - ``'head'`` was changed to extrapolate out to the clipping circle. +""" + +# %% +# F + +docdict['f_power_clust'] = """ +t_power : float + Power to raise the statistical values (usually F-values) by before + summing (sign will be retained). Note that ``t_power=0`` will give a + count of locations in each cluster, ``t_power=1`` will weight each location + by its statistical score. +""" + +docdict['fiducials'] = """ +fiducials : list | dict | str + The fiducials given in the MRI (surface RAS) coordinate + system. If a dictionary is provided, it must contain the **keys** + ``'lpa'``, ``'rpa'``, and ``'nasion'``, with **values** being the + respective coordinates in meters. + If a list, it must be a list of ``DigPoint`` instances as returned by the + :func:`mne.io.read_fiducials` function. + If ``'estimated'``, the fiducials are derived from the ``fsaverage`` + template. If ``'auto'`` (default), tries to find the fiducials + in a file with the canonical name + (``{subjects_dir}/{subject}/bem/{subject}-fiducials.fif``) + and if absent, falls back to ``'estimated'``. +""" + +docdict['filter_length'] = """ +filter_length : str | int + Length of the FIR filter to use (if applicable): + + * **'auto' (default)**: The filter length is chosen based + on the size of the transition regions (6.6 times the reciprocal + of the shortest transition band for fir_window='hamming' + and fir_design="firwin2", and half that for "firwin"). + * **str**: A human-readable time in + units of "s" or "ms" (e.g., "10s" or "5500ms") will be + converted to that number of samples if ``phase="zero"``, or + the shortest power-of-two length at least that duration for + ``phase="zero-double"``. + * **int**: Specified length in samples. For fir_design="firwin", + this should not be used. +""" + +docdict['filter_length_ecg'] = """ +filter_length : str | int | None + Number of taps to use for filtering. +""" + +docdict['filter_length_notch'] = """ +filter_length : str | int + Length of the FIR filter to use (if applicable): + + * **'auto' (default)**: The filter length is chosen based + on the size of the transition regions (6.6 times the reciprocal + of the shortest transition band for fir_window='hamming' + and fir_design="firwin2", and half that for "firwin"). + * **str**: A human-readable time in + units of "s" or "ms" (e.g., "10s" or "5500ms") will be + converted to that number of samples if ``phase="zero"``, or + the shortest power-of-two length at least that duration for + ``phase="zero-double"``. + * **int**: Specified length in samples. For fir_design="firwin", + this should not be used. + + When ``method=='spectrum_fit'``, this sets the effective window duration + over which fits are computed. See :func:`mne.filter.create_filter` + for options. Longer window lengths will give more stable frequency + estimates, but require (potentially much) more processing and are not able + to adapt as well to non-stationarities. + + The default in 0.21 is None, but this will change to ``'10s'`` in 0.22. +""" + +docdict['fir_design'] = """ +fir_design : str + Can be "firwin" (default) to use :func:`scipy.signal.firwin`, + or "firwin2" to use :func:`scipy.signal.firwin2`. "firwin" uses + a time-domain design technique that generally gives improved + attenuation using fewer samples than "firwin2". + + .. versionadded:: 0.15 +""" + +docdict['fir_window'] = """ +fir_window : str + The window to use in FIR design, can be "hamming" (default), + "hann" (default in 0.13), or "blackman". + + .. versionadded:: 0.15 +""" + +_flat_common = """\ + Reject epochs based on **minimum** peak-to-peak signal amplitude (PTP). + Valid **keys** can be any channel type present in the object. The + **values** are floats that set the minimum acceptable PTP. If the PTP + is smaller than this threshold, the epoch will be dropped. If ``None`` + then no rejection is performed based on flatness of the signal.""" + +docdict['flat'] = f""" +flat : dict | None +{_flat_common} + + .. note:: To constrain the time period used for estimation of signal + quality, pass the ``reject_tmin`` and ``reject_tmax`` parameters. +""" + +docdict['flat_drop_bad'] = f""" +flat : dict | str | None +{_flat_common} + If ``'existing'``, then the flat parameters set during epoch creation are + used. +""" + +docdict['fmin_fmid_fmax'] = """ +fmin : float + Minimum value in colormap (uses real fmin if None). +fmid : float + Intermediate value in colormap (fmid between fmin and + fmax if None). +fmax : float + Maximum value in colormap (uses real max if None). +""" + +docdict['fname_epochs'] = """ +fname : path-like | file-like + The epochs to load. If a filename, should end with ``-epo.fif`` or + ``-epo.fif.gz``. If a file-like object, preloading must be used. +""" + +docdict['fname_export_params'] = """ +fname : str + Name of the output file. +""" + +docdict['fnirs'] = """ +fnirs : str | list | bool | None + Can be "channels", "pairs", "detectors", and/or "sources" to show the + fNIRS channel locations, optode locations, or line between + source-detector pairs, or a combination like ``('pairs', 'channels')``. + True translates to ``('pairs',)``. +""" + +docdict['focalpoint'] = """ +focalpoint : tuple, shape (3,) | None + The focal point of the camera rendering the view: (x, y, z) in + plot units (either m or mm). +""" + +docdict['forward_set_eeg_reference'] = """ +forward : instance of Forward | None + Forward solution to use. Only used with ``ref_channels='REST'``. + + .. versionadded:: 0.21 +""" + +docdict['fullscreen'] = """ +fullscreen : bool + Whether to start in fullscreen (``True``) or windowed mode + (``False``). +""" + +applyfun_fun_base = """ +fun : callable + A function to be applied to the channels. The first argument of + fun has to be a timeseries (:class:`numpy.ndarray`). The function must + operate on an array of shape ``(n_times,)`` {}. + The function must return an :class:`~numpy.ndarray` shaped like its input. +""" +docdict['fun_applyfun'] = applyfun_fun_base .format( + ' if ``channel_wise=True`` and ``(len(picks), n_times)`` otherwise') +docdict['fun_applyfun_evoked'] = applyfun_fun_base .format( + ' because it will apply channel-wise') + +docdict['fwd'] = """ +fwd : instance of Forward + The forward solution. If present, the orientations of the dipoles + present in the forward solution are displayed. +""" + +# %% +# G + +docdict['get_peak_parameters'] = """ +tmin : float | None + The minimum point in time to be considered for peak getting. +tmax : float | None + The maximum point in time to be considered for peak getting. +mode : {'pos', 'neg', 'abs'} + How to deal with the sign of the data. If 'pos' only positive + values will be considered. If 'neg' only negative values will + be considered. If 'abs' absolute values will be considered. + Defaults to 'abs'. +vert_as_index : bool + Whether to return the vertex index (True) instead of of its ID + (False, default). +time_as_index : bool + Whether to return the time index (True) instead of the latency + (False, default). +""" + +docdict['group_by_browse'] = """ +group_by : str + How to group channels. ``'type'`` groups by channel type, + ``'original'`` plots in the order of ch_names, ``'selection'`` uses + Elekta's channel groupings (only works for Neuromag data), + ``'position'`` groups the channels by the positions of the sensors. + ``'selection'`` and ``'position'`` modes allow custom selections by + using a lasso selector on the topomap. In butterfly mode, ``'type'`` + and ``'original'`` group the channels by type, whereas ``'selection'`` + and ``'position'`` use regional grouping. ``'type'`` and ``'original'`` + modes are ignored when ``order`` is not ``None``. Defaults to ``'type'``. +""" + +# %% +# H + +docdict['h_freq'] = """ +h_freq : float | None + For FIR filters, the upper pass-band edge; for IIR filters, the upper + cutoff frequency. If None the data are only high-passed. +""" + +docdict['h_trans_bandwidth'] = """ +h_trans_bandwidth : float | str + Width of the transition band at the high cut-off frequency in Hz + (low pass or cutoff 2 in bandpass). Can be "auto" + (default in 0.14) to use a multiple of ``h_freq``:: + + min(max(h_freq * 0.25, 2.), info['sfreq'] / 2. - h_freq) + + Only used for ``method='fir'``. +""" + +docdict['head_pos'] = """ +head_pos : None | str | dict | tuple | array + Name of the position estimates file. Should be in the format of + the files produced by MaxFilter. If dict, keys should + be the time points and entries should be 4x4 ``dev_head_t`` + matrices. If None, the original head position (from + ``info['dev_head_t']``) will be used. If tuple, should have the + same format as data returned by ``head_pos_to_trans_rot_t``. + If array, should be of the form returned by + :func:`mne.chpi.read_head_pos`. +""" + +docdict['head_pos_maxwell'] = """ +head_pos : array | None + If array, movement compensation will be performed. + The array should be of shape (N, 10), holding the position + parameters as returned by e.g. ``read_head_pos``. +""" + +docdict['head_source'] = """ +head_source : str | list of str + Head source(s) to use. See the ``source`` option of + :func:`mne.get_head_surf` for more information. +""" + +docdict['hitachi_notes'] = """ +Hitachi does not encode their channel positions, so you will need to +create a suitable mapping using :func:`mne.channels.make_standard_montage` +or :func:`mne.channels.make_dig_montage` like (for a 3x5/ETG-7000 example): + +>>> mon = mne.channels.make_standard_montage('standard_1020') +>>> need = 'S1 D1 S2 D2 S3 D3 S4 D4 S5 D5 S6 D6 S7 D7 S8'.split() +>>> have = 'F3 FC3 C3 CP3 P3 F5 FC5 C5 CP5 P5 F7 FT7 T7 TP7 P7'.split() +>>> mon.rename_channels(dict(zip(have, need))) +>>> raw.set_montage(mon) # doctest: +SKIP + +The 3x3 (ETG-100) is laid out as two separate layouts:: + + S1--D1--S2 S6--D6--S7 + | | | | | | + D2--S3--D3 D7--S8--D8 + | | | | | | + S4--D4--S5 S9--D9--S10 + +The 3x5 (ETG-7000) is laid out as:: + + S1--D1--S2--D2--S3 + | | | | | + D3--S4--D4--S5--D5 + | | | | | + S6--D6--S7--D7--S8 + +The 4x4 (ETG-7000) is laid out as:: + + S1--D1--S2--D2 + | | | | + D3--S3--D4--S4 + | | | | + S5--D5--S6--D6 + | | | | + D7--S7--D8--S8 + +The 3x11 (ETG-4000) is laid out as:: + + S1--D1--S2--D2--S3--D3--S4--D4--S5--D5--S6 + | | | | | | | | | | | + D6--S7--D7--S8--D8--S9--D9--S10-D10-S11-D11 + | | | | | | | | | | | + S12-D12-S13-D13-S14-D14-S16-D16-S17-D17-S18 + +For each layout, the channels come from the (left-to-right) neighboring +source-detector pairs in the first row, then between the first and second row, +then the second row, etc. + +.. versionadded:: 0.24 +""" + +# %% +# I + +docdict['idx_pctf'] = """ +idx : list of int | list of Label + Source for indices for which to compute PSFs or CTFs. If mode is None, + PSFs/CTFs will be returned for all indices. If mode is not None, the + corresponding summary measure will be computed across all PSFs/CTFs + available from idx. + Can be: + + * list of integers : Compute PSFs/CTFs for all indices to source space + vertices specified in idx. + * list of Label : Compute PSFs/CTFs for source space vertices in + specified labels. +""" + +docdict['ignore_ref_maxwell'] = """ +ignore_ref : bool + If True, do not include reference channels in compensation. This + option should be True for KIT files, since Maxwell filtering + with reference channels is not currently supported. +""" + +docdict['iir_params'] = """ +iir_params : dict | None + Dictionary of parameters to use for IIR filtering. + If iir_params is None and method="iir", 4th order Butterworth will be used. + For more information, see :func:`mne.filter.construct_iir_filter`. +""" + +docdict['image_format_report'] = """ +image_format : 'png' | 'svg' | 'gif' | None + The image format to be used for the report, can be ``'png'``, + ``'svg'``, or ``'gif'``. + None (default) will use the default specified during `~mne.Report` + instantiation. +""" + +docdict['image_interp_topomap'] = """ +image_interp : str + The image interpolation to be used. Options are ``'cubic'`` (default) + to use :class:`scipy.interpolate.CloughTocher2DInterpolator`, + ``'nearest'`` to use :class:`scipy.spatial.Voronoi` or + ``'linear'`` to use :class:`scipy.interpolate.LinearNDInterpolator`. +""" + +docdict['include_tmax'] = """ +include_tmax : bool + If True (default), include tmax. If False, exclude tmax (similar to how + Python indexing typically works). + + .. versionadded:: 0.19 +""" + +_index_df_base = """ +index : {} | None + Kind of index to use for the DataFrame. If ``None``, a sequential + integer index (:class:`pandas.RangeIndex`) will be used. If ``'time'``, a + :class:`pandas.Float64Index`, :class:`pandas.Int64Index`, {}or + :class:`pandas.TimedeltaIndex` will be used + (depending on the value of ``time_format``). {} +""" + +docdict['index_df'] = _index_df_base + +datetime = ':class:`pandas.DatetimeIndex`, ' +multiindex = ('If a list of two or more string values, a ' + ':class:`pandas.MultiIndex` will be created. ') +raw = ("'time'", datetime, '') +epo = ('str | list of str', '', multiindex) +evk = ("'time'", '', '') + +docdict['index_df_epo'] = _index_df_base.format(*epo) +docdict['index_df_evk'] = _index_df_base.format(*evk) +docdict['index_df_raw'] = _index_df_base.format(*raw) + +_info_base = ('The :class:`mne.Info` object with information about the ' + 'sensors and methods of measurement.') + +docdict['info'] = f""" +info : mne.Info | None + {_info_base} +""" + +docdict['info_not_none'] = f""" +info : mne.Info + {_info_base} +""" + +docdict['info_str'] = f""" +info : mne.Info | path-like + {_info_base} If ``path-like``, it should be a :class:`str` or + :class:`pathlib.Path` to a file with measurement information + (e.g. :class:`mne.io.Raw`). +""" + +docdict['int_order_maxwell'] = """ +int_order : int + Order of internal component of spherical expansion. +""" + +docdict['interaction_scene'] = """ +interaction : 'trackball' | 'terrain' + How interactions with the scene via an input device (e.g., mouse or + trackpad) modify the camera position. If ``'terrain'``, one axis is + fixed, enabling "turntable-style" rotations. If ``'trackball'``, + movement along all axes is possible, which provides more freedom of + movement, but you may incidentally perform unintentional rotations along + some axes. +""" + +docdict['interaction_scene_none'] = """ +interaction : 'trackball' | 'terrain' | None + How interactions with the scene via an input device (e.g., mouse or + trackpad) modify the camera position. If ``'terrain'``, one axis is + fixed, enabling "turntable-style" rotations. If ``'trackball'``, + movement along all axes is possible, which provides more freedom of + movement, but you may incidentally perform unintentional rotations along + some axes. + If ``None``, the setting stored in the MNE-Python configuration file is + used. +""" + +docdict['interp'] = """ +interp : str + Either 'hann', 'cos2' (default), 'linear', or 'zero', the type of + forward-solution interpolation to use between forward solutions + at different head positions. +""" + +docdict['interpolation_brain_time'] = """ +interpolation : str | None + Interpolation method (:class:`scipy.interpolate.interp1d` parameter). + Must be one of 'linear', 'nearest', 'zero', 'slinear', 'quadratic', + or 'cubic'. +""" + +docdict['inversion_bf'] = """ +inversion : 'single' | 'matrix' + This determines how the beamformer deals with source spaces in "free" + orientation. Such source spaces define three orthogonal dipoles at each + source point. When ``inversion='single'``, each dipole is considered + as an individual source and the corresponding spatial filter is + computed for each dipole separately. When ``inversion='matrix'``, all + three dipoles at a source vertex are considered as a group and the + spatial filters are computed jointly using a matrix inversion. While + ``inversion='single'`` is more stable, ``inversion='matrix'`` is more + precise. See section 5 of :footcite:`vanVlietEtAl2018`. + Defaults to ``'matrix'``. +""" + +# %% +# K + +docdict['keep_his_anonymize_info'] = """ +keep_his : bool + If ``True``, ``his_id`` of ``subject_info`` will **not** be overwritten. + Defaults to ``False``. + + .. warning:: This could mean that ``info`` is not fully + anonymized. Use with caution. +""" + +docdict['kwargs_fun'] = """ +**kwargs : dict + Additional keyword arguments to pass to ``fun``. +""" + +# %% +# L + +docdict['l_freq'] = """ +l_freq : float | None + For FIR filters, the lower pass-band edge; for IIR filters, the lower + cutoff frequency. If None the data are only low-passed. +""" + +docdict['l_freq_ecg_filter'] = """ +l_freq : float + Low pass frequency to apply to the ECG channel while finding events. +h_freq : float + High pass frequency to apply to the ECG channel while finding events. +""" + +docdict['l_trans_bandwidth'] = """ +l_trans_bandwidth : float | str + Width of the transition band at the low cut-off frequency in Hz + (high pass or cutoff 1 in bandpass). Can be "auto" + (default) to use a multiple of ``l_freq``:: + + min(max(l_freq * 0.25, 2), l_freq) + + Only used for ``method='fir'``. +""" + +docdict['label_tc_el_returns'] = """ +label_tc : array | list (or generator) of array, shape (n_labels[, n_orient], n_times) + Extracted time course for each label and source estimate. +""" # noqa: E501 + +docdict['labels_eltc'] = """ +labels : Label | BiHemiLabel | list | tuple | str + If using a surface or mixed source space, this should be the + :class:`~mne.Label`'s for which to extract the time course. + If working with whole-brain volume source estimates, this must be one of: + + - a string path to a FreeSurfer atlas for the subject (e.g., their + 'aparc.a2009s+aseg.mgz') to extract time courses for all volumes in the + atlas + - a two-element list or tuple, the first element being a path to an atlas, + and the second being a list or dict of ``volume_labels`` to extract + (see :func:`mne.setup_volume_source_space` for details). + + .. versionchanged:: 0.21.0 + Support for volume source estimates. +""" + +docdict['line_alpha_plot_psd'] = """ +line_alpha : float | None + Alpha for the PSD line. Can be None (default) to use 1.0 when + ``average=True`` and 0.1 when ``average=False``. +""" + +_long_format_df_base = """ +long_format : bool + If True, the DataFrame is returned in long format where each row is one + observation of the signal at a unique combination of time point{}. + {}Defaults to ``False``. +""" + +ch_type = ('For convenience, a ``ch_type`` column is added to facilitate ' + 'subsetting the resulting DataFrame. ') +raw = (' and channel', ch_type) +epo = (', channel, epoch number, and condition', ch_type) +stc = (' and vertex', '') + +docdict['long_format_df_epo'] = _long_format_df_base.format(*epo) +docdict['long_format_df_raw'] = _long_format_df_base.format(*raw) +docdict['long_format_df_stc'] = _long_format_df_base.format(*stc) + +docdict['loose'] = """ +loose : float | 'auto' | dict + Value that weights the source variances of the dipole components + that are parallel (tangential) to the cortical surface. Can be: + + - float between 0 and 1 (inclusive) + If 0, then the solution is computed with fixed orientation. + If 1, it corresponds to free orientations. + - ``'auto'`` (default) + Uses 0.2 for surface source spaces (unless ``fixed`` is True) and + 1.0 for other source spaces (volume or mixed). + - dict + Mapping from the key for a given source space type (surface, volume, + discrete) to the loose value. Useful mostly for mixed source spaces. +""" + +# %% +# M + +docdict['mag_scale_maxwell'] = """ +mag_scale : float | str + The magenetometer scale-factor used to bring the magnetometers + to approximately the same order of magnitude as the gradiometers + (default 100.), as they have different units (T vs T/m). + Can be ``'auto'`` to use the reciprocal of the physical distance + between the gradiometer pickup loops (e.g., 0.0168 m yields + 59.5 for VectorView). +""" + +docdict['mapping_rename_channels_duplicates'] = """ +mapping : dict | callable + A dictionary mapping the old channel to a new channel name + e.g. {'EEG061' : 'EEG161'}. Can also be a callable function + that takes and returns a string. + + .. versionchanged:: 0.10.0 + Support for a callable function. +allow_duplicates : bool + If True (default False), allow duplicates, which will automatically + be renamed with ``-N`` at the end. + + .. versionadded:: 0.22.0 +""" + +_mask_base = """ +mask : ndarray of bool, shape {shape} | None + Array indicating channel{shape_appendix} to highlight with a distinct + plotting style{example}. Array elements set to ``True`` will be plotted + with the parameters given in ``mask_params``. Defaults to ``None``, + equivalent to an array of all ``False`` elements. +""" + +docdict['mask_evoked_topomap'] = _mask_base.format( + shape='(n_channels, n_times)', shape_appendix='-time combinations', + example=' (useful for, e.g. marking which channels at which times a ' + 'statistical test of the data reaches significance)') + +docdict['mask_params_topomap'] = """ +mask_params : dict | None + Additional plotting parameters for plotting significant sensors. + Default (None) equals:: + + dict(marker='o', markerfacecolor='w', markeredgecolor='k', + linewidth=0, markersize=4) +""" + +docdict['mask_patterns_topomap'] = _mask_base.format( + shape='(n_channels, n_patterns)', shape_appendix='-pattern combinations', + example='') + +docdict['mask_topomap'] = _mask_base.format( + shape='(n_channels,)', shape_appendix='(s)', example='') + +docdict['match_alias'] = """ +match_alias : bool | dict + Whether to use a lookup table to match unrecognized channel location names + to their known aliases. If True, uses the mapping in + ``mne.io.constants.CHANNEL_LOC_ALIASES``. If a :class:`dict` is passed, it + will be used instead, and should map from non-standard channel names to + names in the specified ``montage``. Default is ``False``. + + .. versionadded:: 0.23 +""" + +docdict['match_case'] = """ +match_case : bool + If True (default), channel name matching will be case sensitive. + + .. versionadded:: 0.20 +""" + +docdict['max_step_clust'] = """ +max_step : int + Maximum distance between samples along the second axis of ``X`` to be + considered adjacent (typically the second axis is the "time" dimension). + Only used when ``adjacency`` has shape (n_vertices, n_vertices), that is, + when adjacency is only specified for sensors (e.g., via + :func:`mne.channels.find_ch_adjacency`), and not via sensors **and** + further dimensions such as time points (e.g., via an additional call of + :func:`mne.stats.combine_adjacency`). +""" + +docdict['measure'] = """ +measure : 'zscore' | 'correlation' + Which method to use for finding outliers among the components: + + - ``'zscore'`` (default) is the iterative z-scoring method. This method + computes the z-score of the component's scores and masks the components + with a z-score above threshold. This process is repeated until no + supra-threshold component remains. + - ``'correlation'`` is an absolute raw correlation threshold ranging from 0 + to 1. + + .. versionadded:: 0.21""" + +docdict['meg'] = """ +meg : str | list | bool | None + Can be "helmet", "sensors" or "ref" to show the MEG helmet, sensors or + reference sensors respectively, or a combination like + ``('helmet', 'sensors')`` (same as None, default). True translates to + ``('helmet', 'sensors', 'ref')``. +""" + +docdict['metadata_epochs'] = """ +metadata : instance of pandas.DataFrame | None + A :class:`pandas.DataFrame` specifying metadata about each epoch. + If given, ``len(metadata)`` must equal ``len(events)``. The DataFrame + may only contain values of type (str | int | float | bool). + If metadata is given, then pandas-style queries may be used to select + subsets of data, see :meth:`mne.Epochs.__getitem__`. + When a subset of the epochs is created in this (or any other + supported) manner, the metadata object is subsetted accordingly, and + the row indices will be modified to match ``epochs.selection``. + + .. versionadded:: 0.16 +""" + +docdict['method_fir'] = """ +method : str + 'fir' will use overlap-add FIR filtering, 'iir' will use IIR + forward-backward filtering (via filtfilt). +""" + +docdict['mode_eltc'] = """ +mode : str + Extraction mode, see Notes. +""" + +docdict['mode_pctf'] = """ +mode : None | 'mean' | 'max' | 'svd' + Compute summary of PSFs/CTFs across all indices specified in 'idx'. + Can be: + + * None : Output individual PSFs/CTFs for each specific vertex + (Default). + * 'mean' : Mean of PSFs/CTFs across vertices. + * 'max' : PSFs/CTFs with maximum norm across vertices. Returns the + n_comp largest PSFs/CTFs. + * 'svd' : SVD components across PSFs/CTFs across vertices. Returns the + n_comp first SVD components. +""" + +docdict['montage'] = """ +montage : None | str | DigMontage + A montage containing channel positions. If a string or + :class:`~mne.channels.DigMontage` is + specified, the existing channel information will be updated with the + channel positions from the montage. Valid strings are the names of the + built-in montages that ship with MNE-Python; you can list those via + :func:`mne.channels.get_builtin_montages`. + If ``None`` (default), the channel positions will be removed from the + :class:`~mne.Info`. +""" + +docdict['montage_types'] = """EEG/sEEG/ECoG/DBS/fNIRS""" + +docdict['moving'] = """ +moving : instance of SpatialImage + The image to morph ("from" volume). +""" + +docdict['mri_resolution_eltc'] = """ +mri_resolution : bool + If True (default), the volume source space will be upsampled to the + original MRI resolution via trilinear interpolation before the atlas values + are extracted. This ensnures that each atlas label will contain source + activations. When False, only the original source space points are used, + and some atlas labels thus may not contain any source space vertices. + + .. versionadded:: 0.21.0 +""" + +# %% +# N + +docdict['n_comp_pctf_n'] = """ +n_comp : int + Number of PSF/CTF components to return for mode='max' or mode='svd'. + Default n_comp=1. +""" + +docdict['n_jobs'] = """ +n_jobs : int | None + The number of jobs to run in parallel. If ``-1``, it is set + to the number of CPU cores. Requires the :mod:`joblib` package. + ``None`` (default) is a marker for 'unset' that will be interpreted + as ``n_jobs=1`` (sequential execution) unless the call is performed under + a :func:`joblib:joblib.parallel_backend` context manager that sets another + value for ``n_jobs``. +""" + +docdict['n_jobs_cuda'] = """ +n_jobs : int | str + Number of jobs to run in parallel. Can be 'cuda' if ``cupy`` + is installed properly. +""" + +docdict['n_jobs_fir'] = """ +n_jobs : int | str + Number of jobs to run in parallel. Can be 'cuda' if ``cupy`` + is installed properly and method='fir'. +""" + +docdict['n_pca_components_apply'] = """ +n_pca_components : int | float | None + The number of PCA components to be kept, either absolute (int) + or fraction of the explained variance (float). If None (default), + the ``ica.n_pca_components`` from initialization will be used in 0.22; + in 0.23 all components will be used. +""" + +docdict['n_permutations_clust_all'] = """ +n_permutations : int | 'all' + The number of permutations to compute. Can be 'all' to perform + an exact test. +""" + +docdict['n_permutations_clust_int'] = """ +n_permutations : int + The number of permutations to compute. +""" + +docdict['nirx_notes'] = """ +This function has only been tested with NIRScout and NIRSport devices, +and with the NIRStar software version 15 and above and Aurora software +2021 and above. + +The NIRSport device can detect if the amplifier is saturated. +Starting from NIRStar 14.2, those saturated values are replaced by NaNs +in the standard .wlX files. +The raw unmodified measured values are stored in another file +called .nosatflags_wlX. As NaN values can cause unexpected behaviour with +mathematical functions the default behaviour is to return the +saturated data. +""" + +docdict['niter'] = """ +niter : dict | tuple | None + For each phase of the volume registration, ``niter`` is the number of + iterations per successive stage of optimization. If a tuple is + provided, it will be used for all steps (except center of mass, which does + not iterate). It should have length 3 to + correspond to ``sigmas=[3.0, 1.0, 0.0]`` and ``factors=[4, 2, 1]`` in + the pipeline (see :func:`dipy.align.affine_registration + ` for details). + If a dictionary is provided, number of iterations can be set for each + step as a key. Steps not in the dictionary will use the default value. + The default (None) is equivalent to: + + niter=dict(translation=(100, 100, 10), + rigid=(100, 100, 10), + affine=(100, 100, 10), + sdr=(5, 5, 3)) +""" + +docdict['norm_pctf'] = """ +norm : None | 'max' | 'norm' + Whether and how to normalise the PSFs and CTFs. This will be applied + before computing summaries as specified in 'mode'. + Can be: + + * None : Use un-normalized PSFs/CTFs (Default). + * 'max' : Normalize to maximum absolute value across all PSFs/CTFs. + * 'norm' : Normalize to maximum norm across all PSFs/CTFs. +""" + +docdict['normalization'] = """normalization : 'full' | 'length' + Normalization strategy. If "full", the PSD will be normalized by the + sampling rate as well as the length of the signal (as in + :ref:`Nitime `). Default is ``'length'``.""" + +docdict['normalize_psd_topo'] = """ +normalize : bool + If True, each band will be divided by the total power. Defaults to + False. +""" + +docdict['notes_tmax_included_by_default'] = """ +Unlike Python slices, MNE time intervals by default include **both** +their end points; ``crop(tmin, tmax)`` returns the interval +``tmin <= t <= tmax``. Pass ``include_tmax=False`` to specify the half-open +interval ``tmin <= t < tmax`` instead. +""" + +docdict['npad'] = """ +npad : int | str + Amount to pad the start and end of the data. + Can also be "auto" to use a padding that will result in + a power-of-two size (can be much faster). +""" + +# %% +# O + +docdict['offset_decim'] = """ +offset : int + Apply an offset to where the decimation starts relative to the + sample corresponding to t=0. The offset is in samples at the + current sampling rate. + + .. versionadded:: 0.12 +""" + +docdict['on_defects'] = """ +on_defects : 'raise' | 'warn' | 'ignore' + What to do if the surface is found to have topological defects. + Can be ``'raise'`` (default) to raise an error, ``'warn'`` to emit a + warning, or ``'ignore'`` to ignore when one or more defects are found. + Note that a lot of computations in MNE-Python assume the surfaces to be + topologically correct, topological defects may still make other + computations (e.g., `mne.make_bem_model` and `mne.make_bem_solution`) + fail irrespective of this parameter. +""" + +docdict['on_header_missing'] = """ +on_header_missing : str + Can be ``'raise'`` (default) to raise an error, ``'warn'`` to emit a + warning, or ``'ignore'`` to ignore when the FastSCAN header is missing. + + .. versionadded:: 0.22 +""" + +_on_missing_base = """\ +Can be ``'raise'`` (default) to raise an error, ``'warn'`` to emit a + warning, or ``'ignore'`` to ignore when""" + + +docdict['on_mismatch_info'] = f""" +on_mismatch : 'raise' | 'warn' | 'ignore' + {_on_missing_base} the device-to-head transformation differs between + instances. + + .. versionadded:: 0.24 +""" + +docdict['on_missing_ch_names'] = f""" +on_missing : 'raise' | 'warn' | 'ignore' + {_on_missing_base} entries in ch_names are not present in the raw instance. + + .. versionadded:: 0.23.0 +""" + +docdict['on_missing_chpi'] = f""" +on_missing : 'raise' | 'warn' | 'ignore' + {_on_missing_base} no cHPI information can be found. If ``'ignore'`` or + ``'warn'``, all return values will be empty arrays or ``None``. If + ``'raise'``, an exception will be raised. +""" + +docdict['on_missing_epochs'] = """ +on_missing : 'raise' | 'warn' | 'ignore' + What to do if one or several event ids are not found in the recording. + Valid keys are 'raise' | 'warn' | 'ignore' + Default is ``'raise'``. If ``'warn'``, it will proceed but + warn; if ``'ignore'``, it will proceed silently. + + .. note:: + If none of the event ids are found in the data, an error will be + automatically generated irrespective of this parameter. +""" + +docdict['on_missing_events'] = f""" +on_missing : 'raise' | 'warn' | 'ignore' + {_on_missing_base} event numbers from ``event_id`` are missing from + :term:`events`. When numbers from :term:`events` are missing from + ``event_id`` they will be ignored and a warning emitted; consider + using ``verbose='error'`` in this case. + + .. versionadded:: 0.21 +""" + +docdict['on_missing_fwd'] = f""" +on_missing : 'raise' | 'warn' | 'ignore' + {_on_missing_base} ``stc`` has vertices that are not in ``fwd``. +""" + +docdict['on_missing_montage'] = f""" +on_missing : 'raise' | 'warn' | 'ignore' + {_on_missing_base} channels have missing coordinates. + + .. versionadded:: 0.20.1 +""" + +docdict['on_rank_mismatch'] = """ +on_rank_mismatch : str + If an explicit MEG value is passed, what to do when it does not match + an empirically computed rank (only used for covariances). + Can be 'raise' to raise an error, 'warn' (default) to emit a warning, or + 'ignore' to ignore. + + .. versionadded:: 0.23 +""" + +docdict['on_split_missing'] = f""" +on_split_missing : str + {_on_missing_base} split file is missing. + + .. versionadded:: 0.22 +""" + +docdict['origin_maxwell'] = """ +origin : array-like, shape (3,) | str + Origin of internal and external multipolar moment space in meters. + The default is ``'auto'``, which means ``(0., 0., 0.)`` when + ``coord_frame='meg'``, and a head-digitization-based + origin fit using :func:`~mne.bem.fit_sphere_to_headshape` + when ``coord_frame='head'``. If automatic fitting fails (e.g., due + to having too few digitization points), + consider separately calling the fitting function with different + options or specifying the origin manually. +""" + +docdict['out_type_clust'] = """ +out_type : 'mask' | 'indices' + Output format of clusters within a list. + If ``'mask'``, returns a list of boolean arrays, + each with the same shape as the input data (or slices if the shape is 1D + and adjacency is None), with ``True`` values indicating locations that are + part of a cluster. If ``'indices'``, returns a list of tuple of ndarray, + where each ndarray contains the indices of locations that together form the + given cluster along the given dimension. Note that for large datasets, + ``'indices'`` may use far less memory than ``'mask'``. + Default is ``'indices'``. +""" + +docdict['outlines_topomap'] = """ +outlines : 'head' | 'skirt' | dict | None + The outlines to be drawn. If 'head', the default head scheme will be + drawn. If 'skirt' the head scheme will be drawn, but sensors are + allowed to be plotted outside of the head circle. If dict, each key + refers to a tuple of x and y positions, the values in 'mask_pos' will + serve as image mask. + Alternatively, a matplotlib patch object can be passed for advanced + masking options, either directly or as a function that returns patches + (required for multi-axis plots). If None, nothing will be drawn. + Defaults to 'head'. +""" + +docdict['overview_mode'] = """ +overview_mode : str | None + Can be "channels", "empty", or "hidden" to set the overview bar mode + for the ``'qt'`` backend. If None (default), the config option + ``MNE_BROWSER_OVERVIEW_MODE`` will be used, defaulting to "channels" + if it's not found. +""" + +docdict['overwrite'] = """ +overwrite : bool + If True (default False), overwrite the destination file if it + exists. +""" + +# %% +# P + +_pad_base = """ +pad : str + The type of padding to use. Supports all :func:`numpy.pad` ``mode`` + options. Can also be ``"reflect_limited"``, which pads with a + reflected version of each vector mirrored on the first and last values + of the vector, followed by zeros. +""" + +docdict['pad'] = _pad_base + +docdict['pad_fir'] = _pad_base + """ + Only used for ``method='fir'``. +""" + +docdict['pca_vars_pctf'] = """ +pca_vars : array, shape (n_comp,) | list of array + The explained variances of the first n_comp SVD components across the + PSFs/CTFs for the specified vertices. Arrays for multiple labels are + returned as list. Only returned if mode='svd' and return_pca_vars=True. +""" + +docdict['phase'] = """ +phase : str + Phase of the filter, only used if ``method='fir'``. + Symmetric linear-phase FIR filters are constructed, and if ``phase='zero'`` + (default), the delay of this filter is compensated for, making it + non-causal. If ``phase='zero-double'``, + then this filter is applied twice, once forward, and once backward + (also making it non-causal). If ``'minimum'``, then a minimum-phase filter + will be constricted and applied, which is causal but has weaker stop-band + suppression. + + .. versionadded:: 0.13 +""" + +docdict['physical_range_export_params'] = """ +physical_range : str | tuple + The physical range of the data. If 'auto' (default), then + it will infer the physical min and max from the data itself, + taking the minimum and maximum values per channel type. + If it is a 2-tuple of minimum and maximum limit, then those + physical ranges will be used. Only used for exporting EDF files. +""" + +_pick_ori_novec = """ + Options: + + - ``None`` + Pooling is performed by taking the norm of loose/free + orientations. In case of a fixed source space no norm is computed + leading to signed source activity. + - ``"normal"`` + Only the normal to the cortical surface is kept. This is only + implemented when working with loose orientations. +""" + +docdict['pick_ori'] = """ +pick_ori : None | "normal" | "vector" +""" + _pick_ori_novec + """ + - ``"vector"`` + No pooling of the orientations is done, and the vector result + will be returned in the form of a :class:`mne.VectorSourceEstimate` + object. +""" + +docdict['pick_ori_bf'] = """ +pick_ori : None | str + For forward solutions with fixed orientation, None (default) must be + used and a scalar beamformer is computed. For free-orientation forward + solutions, a vector beamformer is computed and: + + - ``None`` + Orientations are pooled after computing a vector beamformer (Default). + - ``'normal'`` + Filters are computed for the orientation tangential to the + cortical surface. + - ``'max-power'`` + Filters are computed for the orientation that maximizes power. +""" + +docdict['pick_ori_novec'] = """ +pick_ori : None | "normal" +""" + _pick_ori_novec + +_picks_types = 'str | list | slice | None' +_picks_header = f'picks : {_picks_types}' +_picks_desc = 'Channels to include.' +_picks_int = ('Slices and lists of integers will be interpreted as channel ' + 'indices.') +_picks_str = """In lists, channel *type* strings + (e.g., ``['meg', 'eeg']``) will pick channels of those + types, channel *name* strings (e.g., ``['MEG0111', 'MEG2623']`` + will pick the given channels. Can also be the string values + "all" to pick all channels, or "data" to pick :term:`data channels`. + None (default) will pick""" +_reminder = ("Note that channels in ``info['bads']`` *will be included* if " + "their {}indices are explicitly provided.") +reminder = _reminder.format('names or ') +reminder_nostr = _reminder.format('') +noref = f'(excluding reference MEG channels). {reminder}' +picks_base = f"""{_picks_header} + {_picks_desc} {_picks_int} {_picks_str}""" +docdict['picks_all'] = _reflow_param_docstring( + f'{picks_base} all channels. {reminder}') +docdict['picks_all_data'] = _reflow_param_docstring( + f'{picks_base} all data channels. {reminder}') +docdict['picks_all_data_noref'] = _reflow_param_docstring( + f'{picks_base} all data channels {noref}') +docdict['picks_base'] = _reflow_param_docstring(picks_base) +docdict['picks_good_data'] = _reflow_param_docstring( + f'{picks_base} good data channels. {reminder}') +docdict['picks_good_data_noref'] = _reflow_param_docstring( + f'{picks_base} good data channels {noref}') +docdict['picks_header'] = _picks_header +docdict['picks_ica'] = """ +picks : int | list of int | slice | None + Indices of the independent components (ICs) to visualize. + If an integer, represents the index of the IC to pick. + Multiple ICs can be selected using a list of int or a slice. + The indices are 0-indexed, so ``picks=1`` will + pick the second IC: ``ICA001``. +""" +docdict['picks_nostr'] = f"""picks : list | slice | None + {_picks_desc} {_picks_int} + None (default) will pick all channels. {reminder_nostr}""" + +docdict['picks_plot_projs_joint_trace'] = f"""\ +picks_trace : {_picks_types} + Channels to show alongside the projected time courses. Typically + these are the ground-truth channels for an artifact (e.g., ``'eog'`` or + ``'ecg'``). {_picks_int} {_picks_str} no channels. +""" + +docdict['picks_plot_psd_good_data'] = \ + f'{picks_base} good data channels. {reminder}'[:-2] + """ + Cannot be None if ``ax`` is supplied.If both ``picks`` and ``ax`` are None + separate subplots will be created for each standard channel type + (``mag``, ``grad``, and ``eeg``). +""" + +docdict['pipeline'] = """ +pipeline : str | tuple + The volume registration steps to perform (a ``str`` for a single step, + or ``tuple`` for a set of sequential steps). The following steps can be + performed, and do so by matching mutual information between the images + (unless otherwise noted): + + ``'translation'`` + Translation. + + ``'rigid'`` + Rigid-body, i.e., rotation and translation. + + ``'affine'`` + A full affine transformation, which includes translation, rotation, + scaling, and shear. + + ``'sdr'`` + Symmetric diffeomorphic registration :footcite:`AvantsEtAl2008`, a + non-linear similarity-matching algorithm. + + The following string shortcuts can also be used: + + ``'all'`` (default) + All steps will be performed above in the order above, i.e., + ``('translation', 'rigid', 'affine', 'sdr')``. + + ``'rigids'`` + The rigid steps (first two) will be performed, which registers + the volume without distorting its underlying structure, i.e., + ``('translation', 'rigid')``. This is useful for + example when registering images from the same subject, such as + CT and MR images. + + ``'affines'`` + The affine steps (first three) will be performed, i.e., omitting + the SDR step. +""" + +docdict['plot_psd_doc'] = """ +Plot the power spectral density across channels. + +Different channel types are drawn in sub-plots. When the data have been +processed with a bandpass, lowpass or highpass filter, dashed lines (╎) +indicate the boundaries of the filter. The line noise frequency is +also indicated with a dashed line (⋮) +""" + +docdict['precompute'] = """ +precompute : bool | str + Whether to load all data (not just the visible portion) into RAM and + apply preprocessing (e.g., projectors) to the full data array in a separate + processor thread, instead of window-by-window during scrolling. The default + None uses the ``MNE_BROWSER_PRECOMPUTE`` variable, which defaults to + ``'auto'``. ``'auto'`` compares available RAM space to the expected size of + the precomputed data, and precomputes only if enough RAM is available. + This is only used with the Qt backend. + + .. versionadded:: 0.24 + .. versionchanged:: 1.0 + Support for the MNE_BROWSER_PRECOMPUTE config variable. +""" + +docdict['preload'] = """ +preload : bool or str (default False) + Preload data into memory for data manipulation and faster indexing. + If True, the data will be preloaded into memory (fast, requires + large amount of memory). If preload is a string, preload is the + file name of a memory-mapped file which is used to store the data + on the hard drive (slower, requires less memory).""" + +docdict['preload_concatenate'] = """ +preload : bool, str, or None (default None) + Preload data into memory for data manipulation and faster indexing. + If True, the data will be preloaded into memory (fast, requires + large amount of memory). If preload is a string, preload is the + file name of a memory-mapped file which is used to store the data + on the hard drive (slower, requires less memory). If preload is + None, preload=True or False is inferred using the preload status + of the instances passed in. +""" + +docdict['proj_epochs'] = """ +proj : bool | 'delayed' + Apply SSP projection vectors. If proj is 'delayed' and reject is not + None the single epochs will be projected before the rejection + decision, but used in unprojected state if they are kept. + This way deciding which projection vectors are good can be postponed + to the evoked stage without resulting in lower epoch counts and + without producing results different from early SSP application + given comparable parameters. Note that in this case baselining, + detrending and temporal decimation will be postponed. + If proj is False no projections will be applied which is the + recommended value if SSPs are not used for cleaning the data. +""" + +docdict['proj_plot'] = """ +proj : bool | 'interactive' | 'reconstruct' + If true SSP projections are applied before display. If 'interactive', + a check box for reversible selection of SSP projection vectors will + be shown. If 'reconstruct', projection vectors will be applied and then + M/EEG data will be reconstructed via field mapping to reduce the signal + bias caused by projection. + + .. versionchanged:: 0.21 + Support for 'reconstruct' was added. +""" + +docdict['proj_topomap_kwargs'] = """ +cmap : matplotlib colormap | (colormap, bool) | 'interactive' | None + Colormap to use. If tuple, the first value indicates the colormap to + use and the second value is a boolean defining interactivity. In + interactive mode (only works if ``colorbar=True``) the colors are + adjustable by clicking and dragging the colorbar with left and right + mouse button. Left mouse button moves the scale up and down and right + mouse button adjusts the range. Hitting space bar resets the range. Up + and down arrows can be used to change the colormap. If None (default), + 'Reds' is used for all positive data, otherwise defaults to 'RdBu_r'. + If 'interactive', translates to (None, True). +sensors : bool | str + Add markers for sensor locations to the plot. Accepts matplotlib plot + format string (e.g., 'r+' for red plusses). If True, a circle will be + used (via .add_artist). Defaults to True. +colorbar : bool + Plot a colorbar. +res : int + The resolution of the topomap image (n pixels along each side). +size : scalar + Side length of the topomaps in inches (only applies when plotting + multiple topomaps at a time). +show : bool + Show figure if True. + +outlines : 'head' | 'skirt' | dict | None + The outlines to be drawn. If 'head', the default head scheme will be + drawn. If 'skirt' the head scheme will be drawn, but sensors are + allowed to be plotted outside of the head circle. If dict, each key + refers to a tuple of x and y positions, the values in 'mask_pos' will + serve as image mask. + Alternatively, a matplotlib patch object can be passed for advanced + masking options, either directly or as a function that returns patches + (required for multi-axis plots). If None, nothing will be drawn. + Defaults to 'head'. + +contours : int | array of float + The number of contour lines to draw. If 0, no contours will be drawn. + When an integer, matplotlib ticker locator is used to find suitable + values for the contour thresholds (may sometimes be inaccurate, use + array for accuracy). If an array, the values represent the levels for + the contours. Defaults to 6. +""" + docdict['image_interp_topomap'] + """ +axes : instance of Axes | list | None + The axes to plot to. If list, the list must be a list of Axes of + the same length as the number of projectors. If instance of Axes, + there must be only one projector. Defaults to None. +vlim : tuple of length 2 | 'joint' + Colormap limits to use. If :class:`tuple`, specifies the lower and + upper bounds of the colormap (in that order); providing ``None`` for + either of these will set the corresponding boundary at the min/max of + the data (separately for each projector). The keyword value ``'joint'`` + will compute the colormap limits jointly across all provided + projectors of the same channel type, using the min/max of the projector + data. If vlim is ``'joint'``, ``info`` must not be ``None``. Defaults + to ``(None, None)``. +""" + +docdict['projection_set_eeg_reference'] = """ +projection : bool + If ``ref_channels='average'`` this argument specifies if the + average reference should be computed as a projection (True) or not + (False; default). If ``projection=True``, the average reference is + added as a projection and is not applied to the data (it can be + applied afterwards with the ``apply_proj`` method). If + ``projection=False``, the average reference is directly applied to + the data. If ``ref_channels`` is not ``'average'``, ``projection`` + must be set to ``False`` (the default in this case). +""" + +docdict['projs_report'] = """ +projs : bool | None + Whether to add SSP projector plots if projectors are present in + the data. If ``None``, use ``projs`` from `~mne.Report` creation. +""" + +# %% +# R + +docdict['random_state'] = """ +random_state : None | int | instance of ~numpy.random.RandomState + A seed for the NumPy random number generator (RNG). If ``None`` (default), + the seed will be obtained from the operating system + (see :class:`~numpy.random.RandomState` for details), meaning it will most + likely produce different output every time this function or method is run. + To achieve reproducible results, pass a value here to explicitly initialize + the RNG with a defined state. +""" + +_rank_base = """ rank : None | 'info' | 'full' | dict This controls the rank computation that can be read from the measurement info or estimated from the data. When a noise covariance @@ -978,85 +2554,16 @@ only the gradiometer rank would be determined, while the specified magnetometer and EEG ranks would be taken for granted. """ -docdict['rank_None'] = docdict['rank'] + "\n The default is ``None``." -docdict['rank_info'] = docdict['rank'] + "\n The default is ``'info'``." -docdict['rank_tol'] = """ -tol : float | 'auto' - Tolerance for singular values to consider non-zero in - calculating the rank. The singular values are calculated - in this method such that independent data are expected to - have singular value around one. Can be 'auto' to use the - same thresholding as :func:`scipy.linalg.orth`. -""" -docdict['rank_tol_kind'] = """ -tol_kind : str - Can be: "absolute" (default) or "relative". Only used if ``tol`` is a - float, because when ``tol`` is a string the mode is implicitly relative. - After applying the chosen scale factors / normalization to the data, - the singular values are computed, and the rank is then taken as: - - - ``'absolute'`` - The number of singular values ``s`` greater than ``tol``. - This mode can fail if your data do not adhere to typical - data scalings. - - ``'relative'`` - The number of singular values ``s`` greater than ``tol * s.max()``. - This mode can fail if you have one or more large components in the - data (e.g., artifacts). - - .. versionadded:: 0.21.0 -""" - -# Inverses -docdict['depth'] = """ -depth : None | float | dict - How to weight (or normalize) the forward using a depth prior. - If float (default 0.8), it acts as the depth weighting exponent (``exp``) - to use None is equivalent to 0, meaning no depth weighting is performed. - It can also be a :class:`dict` containing keyword arguments to pass to - :func:`mne.forward.compute_depth_prior` (see docstring for details and - defaults). This is effectively ignored when ``method='eLORETA'``. - .. versionchanged:: 0.20 - Depth bias ignored for ``method='eLORETA'``. -""" -docdict['loose'] = """ -loose : float | 'auto' | dict - Value that weights the source variances of the dipole components - that are parallel (tangential) to the cortical surface. Can be: +docdict['rank'] = _rank_base +docdict['rank_info'] = _rank_base + "\n The default is ``'info'``." +docdict['rank_none'] = _rank_base + "\n The default is ``None``." - - float between 0 and 1 (inclusive) - If 0, then the solution is computed with fixed orientation. - If 1, it corresponds to free orientations. - - ``'auto'`` (default) - Uses 0.2 for surface source spaces (unless ``fixed`` is True) and - 1.0 for other source spaces (volume or mixed). - - dict - Mapping from the key for a given source space type (surface, volume, - discrete) to the loose value. Useful mostly for mixed source spaces. +docdict['raw_epochs'] = """ +raw : Raw object + An instance of `~mne.io.Raw`. """ -_pick_ori_novec = """ - Options: - - ``None`` - Pooling is performed by taking the norm of loose/free - orientations. In case of a fixed source space no norm is computed - leading to signed source activity. - - ``"normal"`` - Only the normal to the cortical surface is kept. This is only - implemented when working with loose orientations. -""" -docdict['pick_ori-novec'] = """ -pick_ori : None | "normal" -""" + _pick_ori_novec -docdict['pick_ori'] = """ -pick_ori : None | "normal" | "vector" -""" + _pick_ori_novec + """ - - ``"vector"`` - No pooling of the orientations is done, and the vector result - will be returned in the form of a :class:`mne.VectorSourceEstimate` - object. -""" docdict['reduce_rank'] = """ reduce_rank : bool If True, the rank of the denominator of the beamformer formula (i.e., @@ -1068,489 +2575,311 @@ Support for reducing rank in all modes (previously only supported ``pick='max_power'`` with weight normalization). """ -docdict['on_rank_mismatch'] = """ -on_rank_mismatch : str - If an explicit MEG value is passed, what to do when it does not match - an empirically computed rank (only used for covariances). - Can be 'raise' to raise an error, 'warn' (default) to emit a warning, or - 'ignore' to ignore. - - .. versionadded:: 0.23 -""" -docdict['weight_norm'] = """ -weight_norm : str | None - Can be: - - - ``None`` - The unit-gain LCMV beamformer :footcite:`SekiharaNagarajan2008` will be - computed. - - ``'unit-noise-gain'`` - The unit-noise gain minimum variance beamformer will be computed - (Borgiotti-Kaplan beamformer) :footcite:`SekiharaNagarajan2008`, - which is not rotation invariant when ``pick_ori='vector'``. - This should be combined with - :meth:`stc.project('pca') ` to follow - the definition in :footcite:`SekiharaNagarajan2008`. - - ``'nai'`` - The Neural Activity Index :footcite:`VanVeenEtAl1997` will be computed, - which simply scales all values from ``'unit-noise-gain'`` by a fixed - value. - - ``'unit-noise-gain-invariante'`` - Compute a rotation-invariant normalization using the matrix square - root. This differs from ``'unit-noise-gain'`` only when - ``pick_ori='vector'``, creating a solution that: - - 1. Is rotation invariant (``'unit-noise-gain'`` is not); - 2. Satisfies the first requirement from - :footcite:`SekiharaNagarajan2008` that ``w @ w.conj().T == I``, - whereas ``'unit-noise-gain'`` has non-zero off-diagonals; but - 3. Does not satisfy the second requirement that ``w @ G.T = θI``, - which arguably does not make sense for a rotation-invariant - solution. -""" -docdict['bf_pick_ori'] = """ -pick_ori : None | str - For forward solutions with fixed orientation, None (default) must be - used and a scalar beamformer is computed. For free-orientation forward - solutions, a vector beamformer is computed and: - - - ``None`` - Orientations are pooled after computing a vector beamformer (Default). - - ``'normal'`` - Filters are computed for the orientation tangential to the - cortical surface. - - ``'max-power'`` - Filters are computed for the orientation that maximizes power. -""" -docdict['bf_inversion'] = """ -inversion : 'single' | 'matrix' - This determines how the beamformer deals with source spaces in "free" - orientation. Such source spaces define three orthogonal dipoles at each - source point. When ``inversion='single'``, each dipole is considered - as an individual source and the corresponding spatial filter is - computed for each dipole separately. When ``inversion='matrix'``, all - three dipoles at a source vertex are considered as a group and the - spatial filters are computed jointly using a matrix inversion. While - ``inversion='single'`` is more stable, ``inversion='matrix'`` is more - precise. See section 5 of :footcite:`vanVlietEtAl2018`. - Defaults to ``'matrix'``. -""" -docdict['use_cps'] = """ -use_cps : bool - Whether to use cortical patch statistics to define normal orientations for - surfaces (default True). -""" -docdict['use_cps_restricted'] = docdict['use_cps'] + """ - Only used when the inverse is free orientation (``loose=1.``), - not in surface orientation, and ``pick_ori='normal'``. -""" -docdict['pctf_mode'] = """ -mode : None | 'mean' | 'max' | 'svd' - Compute summary of PSFs/CTFs across all indices specified in 'idx'. - Can be: - * None : Output individual PSFs/CTFs for each specific vertex - (Default). - * 'mean' : Mean of PSFs/CTFs across vertices. - * 'max' : PSFs/CTFs with maximum norm across vertices. Returns the - n_comp largest PSFs/CTFs. - * 'svd' : SVD components across PSFs/CTFs across vertices. Returns the - n_comp first SVD components. +docdict['ref_channels'] = """ +ref_channels : str | list of str + Name of the electrode(s) which served as the reference in the + recording. If a name is provided, a corresponding channel is added + and its data is set to 0. This is useful for later re-referencing. """ -docdict['pctf_idx'] = """ -idx : list of int | list of Label - Source for indices for which to compute PSFs or CTFs. If mode is None, - PSFs/CTFs will be returned for all indices. If mode is not None, the - corresponding summary measure will be computed across all PSFs/CTFs - available from idx. - Can be: - * list of integers : Compute PSFs/CTFs for all indices to source space - vertices specified in idx. - * list of Label : Compute PSFs/CTFs for source space vertices in - specified labels. -""" -docdict['pctf_n_comp'] = """ -n_comp : int - Number of PSF/CTF components to return for mode='max' or mode='svd'. - Default n_comp=1. -""" -docdict['pctf_norm'] = """ -norm : None | 'max' | 'norm' - Whether and how to normalise the PSFs and CTFs. This will be applied - before computing summaries as specified in 'mode'. +docdict['ref_channels_set_eeg_reference'] = """ +ref_channels : list of str | str Can be: - - * None : Use un-normalized PSFs/CTFs (Default). - * 'max' : Normalize to maximum absolute value across all PSFs/CTFs. - * 'norm' : Normalize to maximum norm across all PSFs/CTFs. -""" -docdict['pctf_return_pca_vars'] = """ -return_pca_vars : bool - Whether or not to return the explained variances across the specified - vertices for individual SVD components. This is only valid if - mode='svd'. - Default return_pca_vars=False. -""" -docdict['pctf_pca_vars'] = """ -pca_vars : array, shape (n_comp,) | list of array - The explained variances of the first n_comp SVD components across the - PSFs/CTFs for the specified vertices. Arrays for multiple labels are - returned as list. Only returned if mode='svd' and return_pca_vars=True. -""" -docdict['pctf_stcs'] = """ -stcs : instance of SourceEstimate | list of instances of SourceEstimate - PSFs or CTFs as STC objects. - All PSFs/CTFs will be returned as successive samples in STC objects, - in the order they are specified in idx. STCs for different labels will - be returned as a list. -""" - -# Forward -docdict['on_missing_fwd'] = """ -on_missing : str - %s ``stc`` has vertices that are not in ``fwd``. -""" % (_on_missing_base,) -docdict['dig_kinds'] = """ -dig_kinds : list of str | str - Kind of digitization points to use in the fitting. These can be any - combination of ('cardinal', 'hpi', 'eeg', 'extra'). Can also - be 'auto' (default), which will use only the 'extra' points if - enough (more than 4) are available, and if not, uses 'extra' and - 'eeg' points. -""" -docdict['exclude_frontal'] = """ -exclude_frontal : bool - If True, exclude points that have both negative Z values - (below the nasion) and positivy Y values (in front of the LPA/RPA). -""" -_trans_base = """\ -If str, the path to the head<->MRI transform ``*-trans.fif`` file produced - during coregistration. Can also be ``'fsaverage'`` to use the built-in - fsaverage transformation.""" -docdict['trans_not_none'] = """ -trans : str | dict | instance of Transform - %s -""" % (_trans_base,) -docdict['trans'] = """ -trans : str | dict | instance of Transform | None - %s - If trans is None, an identity matrix is assumed. - - .. versionchanged:: 0.19 - Support for 'fsaverage' argument. -""" % (_trans_base,) -docdict['subjects_dir'] = """ -subjects_dir : str | None - The path to the FreeSurfer subjects reconstructions. - It corresponds to FreeSurfer environment variable ``SUBJECTS_DIR``. -""" -docdict['subject'] = """ -subject : str - The FreeSurfer subject name. -""" - -# Simulation -docdict['interp'] = """ -interp : str - Either 'hann', 'cos2' (default), 'linear', or 'zero', the type of - forward-solution interpolation to use between forward solutions - at different head positions. -""" -docdict['head_pos'] = """ -head_pos : None | str | dict | tuple | array - Name of the position estimates file. Should be in the format of - the files produced by MaxFilter. If dict, keys should - be the time points and entries should be 4x4 ``dev_head_t`` - matrices. If None, the original head position (from - ``info['dev_head_t']``) will be used. If tuple, should have the - same format as data returned by ``head_pos_to_trans_rot_t``. - If array, should be of the form returned by - :func:`mne.chpi.read_head_pos`. -""" -docdict['n_jobs'] = """ -n_jobs : int - The number of jobs to run in parallel (default 1). - Requires the joblib package. -""" - -# Random state -docdict['random_state'] = """ -random_state : None | int | instance of ~numpy.random.RandomState - If ``random_state`` is an :class:`int`, it will be used as a seed for - :class:`~numpy.random.RandomState`. If ``None``, the seed will be - obtained from the operating system (see - :class:`~numpy.random.RandomState` for details). Default is - ``None``. + + - The name(s) of the channel(s) used to construct the reference. + - ``'average'`` to apply an average reference (default) + - ``'REST'`` to use the Reference Electrode Standardization Technique + infinity reference :footcite:`Yao2001`. + - An empty list, in which case MNE will not attempt any re-referencing of + the data """ -docdict['seed'] = """ -seed : None | int | instance of ~numpy.random.RandomState - If ``seed`` is an :class:`int`, it will be used as a seed for - :class:`~numpy.random.RandomState`. If ``None``, the seed will be - obtained from the operating system (see - :class:`~numpy.random.RandomState` for details). Default is - ``None``. +docdict['reg_affine'] = """ +reg_affine : ndarray of float, shape (4, 4) + The affine that registers one volume to another. """ -# Visualization -docdict['combine'] = """ -combine : None | str | callable - How to combine information across channels. If a :class:`str`, must be - one of 'mean', 'median', 'std' (standard deviation) or 'gfp' (global - field power). +docdict['regularize_maxwell_reg'] = """ +regularize : str | None + Basis regularization type, must be "in" or None. + "in" is the same algorithm as the "-regularize in" option in + MaxFilter™. """ -docdict['show_scrollbars'] = """ -show_scrollbars : bool - Whether to show scrollbars when the plot is initialized. Can be toggled - after initialization by pressing :kbd:`z` ("zen mode") while the plot - window is focused. Default is ``True``. - .. versionadded:: 0.19.0 +_reject_by_annotation_base = """ +reject_by_annotation : bool + Whether to omit bad segments from the data before fitting. If ``True`` + (default), annotated segments whose description begins with ``'bad'`` are + omitted. If ``False``, no rejection based on annotations is performed. """ -# PSD plotting -docdict["plot_psd_doc"] = """ -Plot the power spectral density across channels. +docdict['reject_by_annotation_all'] = _reject_by_annotation_base -Different channel types are drawn in sub-plots. When the data have been -processed with a bandpass, lowpass or highpass filter, dashed lines (╎) -indicate the boundaries of the filter. The line noise frequency is -also indicated with a dashed line (⋮) +docdict['reject_by_annotation_epochs'] = """ +reject_by_annotation : bool + Whether to reject based on annotations. If ``True`` (default), epochs + overlapping with segments whose description begins with ``'bad'`` are + rejected. If ``False``, no rejection based on annotations is performed. """ -docdict['plot_psd_picks_good_data'] = docdict['picks_good_data'][:-2] + """ - Cannot be None if ``ax`` is supplied.If both ``picks`` and ``ax`` are None - separate subplots will be created for each standard channel type - (``mag``, ``grad``, and ``eeg``). + +docdict['reject_by_annotation_raw'] = _reject_by_annotation_base + """ + Has no effect if ``inst`` is not a :class:`mne.io.Raw` object. """ -docdict["plot_psd_color"] = """ -color : str | tuple - A matplotlib-compatible color to use. Has no effect when - spatial_colors=True. + +_reject_common = """\ + Reject epochs based on **maximum** peak-to-peak signal amplitude (PTP), + i.e. the absolute difference between the lowest and the highest signal + value. In each individual epoch, the PTP is calculated for every channel. + If the PTP of any one channel exceeds the rejection threshold, the + respective epoch will be dropped. + + The dictionary keys correspond to the different channel types; valid + **keys** can be any channel type present in the object. + + Example:: + + reject = dict(grad=4000e-13, # unit: T / m (gradiometers) + mag=4e-12, # unit: T (magnetometers) + eeg=40e-6, # unit: V (EEG channels) + eog=250e-6 # unit: V (EOG channels) + ) + + .. note:: Since rejection is based on a signal **difference** + calculated for each channel separately, applying baseline + correction does not affect the rejection procedure, as the + difference will be preserved. """ -docdict["plot_psd_xscale"] = """ -xscale : str - Can be 'linear' (default) or 'log'. + +docdict['reject_drop_bad'] = f""" +reject : dict | str | None +{_reject_common} + If ``reject`` is ``None``, no rejection is performed. If ``'existing'`` + (default), then the rejection parameters set at instantiation are used. """ -docdict["plot_psd_area_mode"] = """ -area_mode : str | None - Mode for plotting area. If 'std', the mean +/- 1 STD (across channels) - will be plotted. If 'range', the min and max (across channels) will be - plotted. Bad channels will be excluded from these calculations. - If None, no area will be plotted. If average=False, no area is plotted. + +docdict['reject_epochs'] = f""" +reject : dict | None +{_reject_common} + .. note:: To constrain the time period used for estimation of signal + quality, pass the ``reject_tmin`` and ``reject_tmax`` parameters. + + If ``reject`` is ``None`` (default), no rejection is performed. """ -docdict["plot_psd_area_alpha"] = """ -area_alpha : float - Alpha for the area. + +docdict['replace_report'] = """ +replace : bool + If ``True``, content already present that has the same ``title`` will be + replaced. Defaults to ``False``, which will cause duplicate entries in the + table of contents if an entry for ``title`` already exists. """ -docdict["plot_psd_dB"] = """ -dB : bool - Plot Power Spectral Density (PSD), in units (amplitude**2/Hz (dB)) if - ``dB=True``, and ``estimate='power'`` or ``estimate='auto'``. Plot PSD - in units (amplitude**2/Hz) if ``dB=False`` and, - ``estimate='power'``. Plot Amplitude Spectral Density (ASD), in units - (amplitude/sqrt(Hz)), if ``dB=False`` and ``estimate='amplitude'`` or - ``estimate='auto'``. Plot ASD, in units (amplitude/sqrt(Hz) (db)), if - ``dB=True`` and ``estimate='amplitude'``. + +docdict['res_topomap'] = """ +res : int + The resolution of the topomap image (n pixels along each side). """ -docdict["plot_psd_estimate"] = """ -estimate : str, {'auto', 'power', 'amplitude'} - Can be "power" for power spectral density (PSD), "amplitude" for - amplitude spectrum density (ASD), or "auto" (default), which uses - "power" when dB is True and "amplitude" otherwise. + +docdict['return_pca_vars_pctf'] = """ +return_pca_vars : bool + Whether or not to return the explained variances across the specified + vertices for individual SVD components. This is only valid if + mode='svd'. + Default return_pca_vars=False. """ -docdict["plot_psd_average"] = """ -average : bool - If False, the PSDs of all channels is displayed. No averaging - is done and parameters area_mode and area_alpha are ignored. When - False, it is possible to paint an area (hold left mouse button and - drag) to plot a topomap. + +docdict['roll'] = """ +roll : float | None + The roll of the camera rendering the view in degrees. +""" + +# %% +# S + +docdict['saturated'] = """saturated : str + Replace saturated segments of data with NaNs, can be: + + ``"ignore"`` + The measured data is returned, even if it contains measurements + while the amplifier was saturated. + ``"nan"`` + The returned data will contain NaNs during time segments + when the amplifier was saturated. + ``"annotate"`` (default) + The returned data will contain annotations specifying + sections the saturate segments. + + This argument will only be used if there is no .nosatflags file + (only if a NIRSport device is used and saturation occurred). + + .. versionadded:: 0.24 +""" + +docdict['scalings'] = """ +scalings : 'auto' | dict | None + Scaling factors for the traces. If a dictionary where any + value is ``'auto'``, the scaling factor is set to match the 99.5th + percentile of the respective data. If ``'auto'``, all scalings (for all + channel types) are set to ``'auto'``. If any values are ``'auto'`` and the + data is not preloaded, a subset up to 100 MB will be loaded. If ``None``, + defaults to:: + + dict(mag=1e-12, grad=4e-11, eeg=20e-6, eog=150e-6, ecg=5e-4, + emg=1e-3, ref_meg=1e-12, misc=1e-3, stim=1, + resp=1, chpi=1e-4, whitened=1e2) + + .. note:: + A particular scaling value ``s`` corresponds to half of the visualized + signal range around zero (i.e. from ``0`` to ``+s`` or from ``0`` to + ``-s``). For example, the default scaling of ``20e-6`` (20µV) for EEG + signals means that the visualized range will be 40 µV (20 µV in the + positive direction and 20 µV in the negative direction). """ -docdict["plot_psd_line_alpha"] = """ -line_alpha : float | None - Alpha for the PSD line. Can be None (default) to use 1.0 when - ``average=True`` and 0.1 when ``average=False``. + +docdict['scalings_df'] = """ +scalings : dict | None + Scaling factor applied to the channels picked. If ``None``, defaults to + ``dict(eeg=1e6, mag=1e15, grad=1e13)`` — i.e., converts EEG to µV, + magnetometers to fT, and gradiometers to fT/cm. """ -docdict["plot_psd_spatial_colors"] = """ -spatial_colors : bool - Whether to use spatial colors. Only used when ``average=False``. + +docdict['scalings_topomap'] = """ +scalings : dict | float | None + The scalings of the channel types to be applied for plotting. + If None, defaults to ``dict(eeg=1e6, grad=1e13, mag=1e15)``. """ -# plot_projs_topomap -docdict["proj_topomap_kwargs"] = """ -cmap : matplotlib colormap | (colormap, bool) | 'interactive' | None - Colormap to use. If tuple, the first value indicates the colormap to - use and the second value is a boolean defining interactivity. In - interactive mode (only works if ``colorbar=True``) the colors are - adjustable by clicking and dragging the colorbar with left and right - mouse button. Left mouse button moves the scale up and down and right - mouse button adjusts the range. Hitting space bar resets the range. Up - and down arrows can be used to change the colormap. If None (default), - 'Reds' is used for all positive data, otherwise defaults to 'RdBu_r'. - If 'interactive', translates to (None, True). -sensors : bool | str - Add markers for sensor locations to the plot. Accepts matplotlib plot - format string (e.g., 'r+' for red plusses). If True, a circle will be - used (via .add_artist). Defaults to True. -colorbar : bool - Plot a colorbar. -res : int - The resolution of the topomap image (n pixels along each side). -size : scalar - Side length of the topomaps in inches (only applies when plotting - multiple topomaps at a time). -show : bool - Show figure if True. -%(topomap_outlines)s -contours : int | array of float - The number of contour lines to draw. If 0, no contours will be drawn. - When an integer, matplotlib ticker locator is used to find suitable - values for the contour thresholds (may sometimes be inaccurate, use - array for accuracy). If an array, the values represent the levels for - the contours. Defaults to 6. -image_interp : str - The image interpolation to be used. All matplotlib options are - accepted. -axes : instance of Axes | list | None - The axes to plot to. If list, the list must be a list of Axes of - the same length as the number of projectors. If instance of Axes, - there must be only one projector. Defaults to None. -vlim : tuple of length 2 | 'joint' - Colormap limits to use. If :class:`tuple`, specifies the lower and - upper bounds of the colormap (in that order); providing ``None`` for - either of these will set the corresponding boundary at the min/max of - the data (separately for each projector). The keyword value ``'joint'`` - will compute the colormap limits jointly across all provided - projectors of the same channel type, using the min/max of the projector - data. If vlim is ``'joint'``, ``info`` must not be ``None``. Defaults - to ``(None, None)``. -""" % docdict +docdict['scoring'] = """ +scoring : callable | str | None + Score function (or loss function) with signature + ``score_func(y, y_pred, **kwargs)``. + Note that the "predict" method is automatically identified if scoring is + a string (e.g. ``scoring='roc_auc'`` calls ``predict_proba``), but is + **not** automatically set if ``scoring`` is a callable (e.g. + ``scoring=sklearn.metrics.roc_auc_score``). +""" + +docdict['sdr_morph'] = """ +sdr_morph : instance of dipy.align.DiffeomorphicMap + The class that applies the the symmetric diffeomorphic registration + (SDR) morph. +""" + +docdict['section_report'] = """ +section : str | None + The name of the section (or content block) to add the content to. This + feature is useful for grouping multiple related content elements + together under a single, collapsible section. Each content element will + retain its own title and functionality, but not appear separately in the + table of contents. Hence, using sections is a way to declutter the table + of contents, and to easy navigation of the report. -# Montage -docdict["montage"] = """ -montage : None | str | DigMontage - A montage containing channel positions. If str or DigMontage is - specified, the channel info will be updated with the channel - positions. Default is None. For valid :class:`str` values see documentation - of :func:`mne.channels.make_standard_montage`. See also the documentation - of :class:`mne.channels.DigMontage` for more information. + .. versionadded:: 1.1 """ -docdict["match_case"] = """ -match_case : bool - If True (default), channel name matching will be case sensitive. - .. versionadded:: 0.20 +docdict['seed'] = """ +seed : None | int | instance of ~numpy.random.RandomState + A seed for the NumPy random number generator (RNG). If ``None`` (default), + the seed will be obtained from the operating system + (see :class:`~numpy.random.RandomState` for details), meaning it will most + likely produce different output every time this function or method is run. + To achieve reproducible results, pass a value here to explicitly initialize + the RNG with a defined state. """ -docdict["match_alias"] = """ -match_alias : bool | dict - Whether to use a lookup table to match unrecognized channel location names - to their known aliases. If True, uses the mapping in - ``mne.io.constants.CHANNEL_LOC_ALIASES``. If a :class:`dict` is passed, it - will be used instead, and should map from non-standard channel names to - names in the specified ``montage``. Default is ``False``. - .. versionadded:: 0.23 +docdict['seeg'] = """ +seeg : bool + If True (default), show sEEG electrodes. """ -docdict['on_header_missing'] = """ -on_header_missing : str - %s the FastSCAN header is missing. - .. versionadded:: 0.22 -""" % (_on_missing_base,) -docdict['on_missing_events'] = """ -on_missing : str - %s event numbers from ``event_id`` are missing from ``events``. - When numbers from ``events`` are missing from ``event_id`` they will be - ignored and a warning emitted; consider using ``verbose='error'`` in - this case. +docdict['sensors_topomap'] = """ +sensors : bool | str + Add markers for sensor locations to the plot. Accepts matplotlib plot + format string (e.g., 'r+' for red plusses). If True (default), + circles will be used. +""" - .. versionadded:: 0.21 -""" % (_on_missing_base,) -docdict['on_missing_montage'] = """ -on_missing : str - %s channels have missing coordinates. +docdict['set_eeg_reference_see_also_notes'] = """ +See Also +-------- +mne.set_bipolar_reference : Convenience function for creating bipolar + references. - .. versionadded:: 0.20.1 -""" % (_on_missing_base,) -docdict['on_missing_ch_names'] = """ -on_missing : str - %s entries in ch_names are not present in the raw instance. +Notes +----- +Some common referencing schemes and the corresponding value for the +``ref_channels`` parameter: - .. versionadded:: 0.23.0 -""" % (_on_missing_base,) -docdict['rename_channels_mapping_duplicates'] = """ -mapping : dict | callable - A dictionary mapping the old channel to a new channel name - e.g. {'EEG061' : 'EEG161'}. Can also be a callable function - that takes and returns a string. +- Average reference: + A new virtual reference electrode is created by averaging the current + EEG signal by setting ``ref_channels='average'``. Bad EEG channels are + automatically excluded if they are properly set in ``info['bads']``. - .. versionchanged:: 0.10.0 - Support for a callable function. -allow_duplicates : bool - If True (default False), allow duplicates, which will automatically - be renamed with ``-N`` at the end. +- A single electrode: + Set ``ref_channels`` to a list containing the name of the channel that + will act as the new reference, for example ``ref_channels=['Cz']``. - .. versionadded:: 0.22.0 -""" +- The mean of multiple electrodes: + A new virtual reference electrode is created by computing the average + of the current EEG signal recorded from two or more selected channels. + Set ``ref_channels`` to a list of channel names, indicating which + channels to use. For example, to apply an average mastoid reference, + when using the 10-20 naming scheme, set ``ref_channels=['M1', 'M2']``. -# Brain plotting -docdict["clim"] = """ -clim : str | dict - Colorbar properties specification. If 'auto', set clim automatically - based on data percentiles. If dict, should contain: +- REST + The given EEG electrodes are referenced to a point at infinity using the + lead fields in ``forward``, which helps standardize the signals. - ``kind`` : 'value' | 'percent' - Flag to specify type of limits. - ``lims`` : list | np.ndarray | tuple of float, 3 elements - Lower, middle, and upper bounds for colormap. - ``pos_lims`` : list | np.ndarray | tuple of float, 3 elements - Lower, middle, and upper bound for colormap. Positive values - will be mirrored directly across zero during colormap - construction to obtain negative control points. +1. If a reference is requested that is not the average reference, this + function removes any pre-existing average reference projections. - .. note:: Only one of ``lims`` or ``pos_lims`` should be provided. - Only sequential colormaps should be used with ``lims``, and - only divergent colormaps should be used with ``pos_lims``. -""" -docdict["clim_onesided"] = """ -clim : str | dict - Colorbar properties specification. If 'auto', set clim automatically - based on data percentiles. If dict, should contain: +2. During source localization, the EEG signal should have an average + reference. + +3. In order to apply a reference, the data must be preloaded. This is not + necessary if ``ref_channels='average'`` and ``projection=True``. + +4. For an average or REST reference, bad EEG channels are automatically + excluded if they are properly set in ``info['bads']``. - ``kind`` : 'value' | 'percent' - Flag to specify type of limits. - ``lims`` : list | np.ndarray | tuple of float, 3 elements - Lower, middle, and upper bound for colormap. +.. versionadded:: 0.9.0 - Unlike :meth:`stc.plot `, it cannot use - ``pos_lims``, as the surface plot must show the magnitude. +References +---------- +.. footbibliography:: """ -docdict["colormap"] = """ -colormap : str | np.ndarray of float, shape(n_colors, 3 | 4) - Name of colormap to use or a custom look up table. If array, must - be (n x 3) or (n x 4) array for with RGB or RGBA values between - 0 and 255. + +docdict['show'] = """ +show : bool + Show the figure if ``True``. """ -docdict["transparent"] = """ -transparent : bool | None - If True: use a linear transparency between fmin and fmid - and make values below fmin fully transparent (symmetrically for - divergent colormaps). None will choose automatically based on colormap - type. + +docdict['show_names_topomap'] = """ +show_names : bool | callable + If True, show channel names on top of the map. If a callable is + passed, channel names will be formatted using the callable; e.g., to + delete the prefix 'MEG ' from all channel names, pass the function + ``lambda x: x.replace('MEG ', '')``. If ``mask`` is not None, only + significant sensors will be shown. """ -docdict["brain_time_interpolation"] = """ -interpolation : str | None - Interpolation method (:class:`scipy.interpolate.interp1d` parameter). - Must be one of 'linear', 'nearest', 'zero', 'slinear', 'quadratic', - or 'cubic'. + +docdict['show_scalebars'] = """ +show_scalebars : bool + Whether to show scale bars when the plot is initialized. Can be toggled + after initialization by pressing :kbd:`s` while the plot window is focused. + Default is ``True``. """ -docdict["brain_screenshot_time_viewer"] = """ -time_viewer : bool - If True, include time viewer traces. Only used if - ``time_viewer=True`` and ``separate_canvas=False``. + +docdict['show_scrollbars'] = """ +show_scrollbars : bool + Whether to show scrollbars when the plot is initialized. Can be toggled + after initialization by pressing :kbd:`z` ("zen mode") while the plot + window is focused. Default is ``True``. + + .. versionadded:: 0.19.0 """ -docdict["show_traces"] = """ + +docdict['show_traces'] = """ show_traces : bool | str | float If True, enable interactive picking of a point on the surface of the brain and plot its time course. @@ -1563,33 +2892,85 @@ .. versionadded:: 0.20.0 """ -docdict["time_label"] = """ -time_label : str | callable | None - Format of the time label (a format string, a function that maps - floating point time values to strings, or None for no label). The - default is ``'auto'``, which will use ``time=%0.2f ms`` if there - is more than one time point. + +docdict['size_topomap'] = """ +size : float + Side length per topomap in inches. """ -docdict["fmin_fmid_fmax"] = """ -fmin : float - Minimum value in colormap (uses real fmin if None). -fmid : float - Intermediate value in colormap (fmid between fmin and - fmax if None). -fmax : float - Maximum value in colormap (uses real max if None). + +docdict['skip_by_annotation_maxwell'] = """ +skip_by_annotation : str | list of str + If a string (or list of str), any annotation segment that begins + with the given string will not be included in filtering, and + segments on either side of the given excluded annotated segment + will be filtered separately (i.e., as independent signals). + The default ``('edge', 'bad_acq_skip')`` will separately filter + any segments that were concatenated by :func:`mne.concatenate_raws` + or :meth:`mne.io.Raw.append`, or separated during acquisition. + To disable, provide an empty list. """ -docdict["thresh"] = """ -thresh : None or float - Not supported yet. - If not None, values below thresh will not be visible. + +docdict['smooth'] = """ +smooth : float in [0, 1) + The smoothing factor to be applied. Default 0 is no smoothing. """ -docdict["center"] = """ -center : float or None - If not None, center of a divergent colormap, changes the meaning of - fmin, fmax and fmid. + +docdict['spatial_colors_plot_psd'] = """ +spatial_colors : bool + Whether to use spatial colors. Only used when ``average=False``. """ -docdict["src_volume_options"] = """ + +_sphere_header = ( + 'sphere : float | array-like | instance of ConductorModel | None') +_sphere_desc = ( + 'The sphere parameters to use for the head outline. Can be array-like of ' + 'shape (4,) to give the X/Y/Z origin and radius in meters, or a single ' + 'float to give just the radius (origin assumed 0, 0, 0). Can also be an ' + 'instance of a spherical :class:`~mne.bem.ConductorModel` to use the ' + 'origin and radius from that object.' +) +_sphere_topo = _reflow_param_docstring( + f"""{_sphere_desc} ``None`` (the default) is equivalent to + (0, 0, 0, {HEAD_SIZE_DEFAULT}). + Currently the head radius does not affect plotting.""", + has_first_line=False) +_sphere_topo_auto = _reflow_param_docstring( + f"""{_sphere_desc} If ``'auto'`` the sphere is fit to digitization points. + If ``'eeglab'`` the head circle is defined by EEG electrodes ``'Fpz'``, + ``'Oz'``, ``'T7'``, and ``'T8'`` (if ``'Fpz'`` is not present, it will + be approximated from the coordinates of ``'Oz'``). ``None`` (the default) + is equivalent to ``'auto'`` when enough extra digitization points are + available, and (0, 0, 0, {HEAD_SIZE_DEFAULT}) otherwise. Currently the head + radius does not affect plotting.""", has_first_line=False) +docdict['sphere_topomap'] = f""" +{_sphere_header} + {_sphere_topo} + + .. versionadded:: 0.20 +""" + +docdict['sphere_topomap_auto'] = f"""\ +{_sphere_header} | 'auto' | 'eeglab' + {_sphere_topo_auto} + + .. versionadded:: 0.20 + .. versionchanged:: 1.1 Added ``'eeglab'`` option. +""" + +docdict['split_naming'] = """ +split_naming : 'neuromag' | 'bids' + When splitting files, append a filename partition with the appropriate + naming schema: for ``'neuromag'``, a split file ``fname.fif`` will be named + ``fname.fif``, ``fname-1.fif``, ``fname-2.fif`` etc.; while for ``'bids'``, + it will be named ``fname_split-01.fif``, ``fname_split-02.fif``, etc. +""" + +docdict['src_eltc'] = """ +src : instance of SourceSpaces + The source spaces for the source time courses. +""" + +docdict['src_volume_options'] = """ src : instance of SourceSpaces | None The source space corresponding to the source estimate. Only necessary if the STC is a volume or mixed source estimate. @@ -1619,207 +3000,77 @@ A float input (default 1.) or None will be used for the ``'resolution'`` entry. """ -docdict['view_layout'] = """ -view_layout : str - Can be "vertical" (default) or "horizontal". When using "horizontal" mode, - the PyVista backend must be used and hemi cannot be "split". -""" -docdict['add_data_kwargs'] = """ -add_data_kwargs : dict | None - Additional arguments to brain.add_data (e.g., - ``dict(time_label_size=10)``). -""" -docdict['brain_kwargs'] = """ -brain_kwargs : dict | None - Additional arguments to the :class:`mne.viz.Brain` constructor (e.g., - ``dict(silhouette=True)``). -""" -docdict['views'] = """ -views : str | list - View to use. Can be any of:: - - ['lateral', 'medial', 'rostral', 'caudal', 'dorsal', 'ventral', - 'frontal', 'parietal', 'axial', 'sagittal', 'coronal'] - - Three letter abbreviations (e.g., ``'lat'``) are also supported. - Using multiple views (list) is not supported for mpl backend. -""" -# STC label time course -docdict['eltc_labels'] = """ -labels : Label | BiHemiLabel | list | tuple | str - If using a surface or mixed source space, this should be the - :class:`~mne.Label`'s for which to extract the time course. - If working with whole-brain volume source estimates, this must be one of: +docdict['st_fixed_maxwell_only'] = """ +st_fixed : bool + If True (default), do tSSS using the median head position during the + ``st_duration`` window. This is the default behavior of MaxFilter + and has been most extensively tested. - - a string path to a FreeSurfer atlas for the subject (e.g., their - 'aparc.a2009s+aseg.mgz') to extract time courses for all volumes in the - atlas - - a two-element list or tuple, the first element being a path to an atlas, - and the second being a list or dict of ``volume_labels`` to extract - (see :func:`mne.setup_volume_source_space` for details). + .. versionadded:: 0.12 +st_only : bool + If True, only tSSS (temporal) projection of MEG data will be + performed on the output data. The non-tSSS parameters (e.g., + ``int_order``, ``calibration``, ``head_pos``, etc.) will still be + used to form the SSS bases used to calculate temporal projectors, + but the output MEG data will *only* have temporal projections + performed. Noise reduction from SSS basis multiplication, + cross-talk cancellation, movement compensation, and so forth + will not be applied to the data. This is useful, for example, when + evoked movement compensation will be performed with + :func:`~mne.epochs.average_movements`. - .. versionchanged:: 0.21.0 - Support for volume source estimates. -""" -docdict['eltc_src'] = """ -src : instance of SourceSpaces - The source spaces for the source time courses. -""" -docdict['eltc_mode'] = """ -mode : str - Extraction mode, see Notes. + .. versionadded:: 0.12 """ -docdict['eltc_allow_empty'] = """ -allow_empty : bool | str - ``False`` (default) will emit an error if there are labels that have no - vertices in the source estimate. ``True`` and ``'ignore'`` will return - all-zero time courses for labels that do not have any vertices in the - source estimate, and True will emit a warning while and "ignore" will - just log a message. - .. versionchanged:: 0.21.0 - Support for "ignore". +docdict['standardize_names'] = """ +standardize_names : bool + If True, standardize MEG and EEG channel names to be + ``"MEG ###"`` and ``"EEG ###"``. If False (default), native + channel names in the file will be used when possible. """ -docdict -docdict['eltc_mri_resolution'] = """ -mri_resolution : bool - If True (default), the volume source space will be upsampled to the - original MRI resolution via trilinear interpolation before the atlas values - are extracted. This ensnures that each atlas label will contain source - activations. When False, only the original source space points are used, - and some atlas labels thus may not contain any source space vertices. - .. versionadded:: 0.21.0 +_stat_fun_clust_base = """ +stat_fun : callable | None + Function called to calculate the test statistic. Must accept 1D-array as + input and return a 1D array. If ``None`` (the default), uses + `mne.stats.{}`. """ -docdict['eltc_returns'] = """ -label_tc : array | list (or generator) of array, shape (n_labels[, n_orient], n_times) - Extracted time course for each label and source estimate. -""" # noqa: E501 -docdict['eltc_mode_notes'] = """ -Valid values for ``mode`` are: -- ``'max'`` - Maximum value across vertices at each time point within each label. -- ``'mean'`` - Average across vertices at each time point within each label. Ignores - orientation of sources for standard source estimates, which varies - across the cortical surface, which can lead to cancellation. - Vector source estimates are always in XYZ / RAS orientation, and are thus - already geometrically aligned. -- ``'mean_flip'`` - Finds the dominant direction of source space normal vector orientations - within each label, applies a sign-flip to time series at vertices whose - orientation is more than 180° different from the dominant direction, and - then averages across vertices at each time point within each label. -- ``'pca_flip'`` - Applies singular value decomposition to the time courses within each label, - and uses the first right-singular vector as the representative label time - course. This signal is scaled so that its power matches the average - (per-vertex) power within the label, and sign-flipped by multiplying by - ``np.sign(u @ flip)``, where ``u`` is the first left-singular vector and - ``flip`` is the same sign-flip vector used when ``mode='mean_flip'``. This - sign-flip ensures that extracting time courses from the same label in - similar STCs does not result in 180° direction/phase changes. -- ``'auto'`` (default) - Uses ``'mean_flip'`` when a standard source estimate is applied, and - ``'mean'`` when a vector source estimate is supplied. +docdict['stat_fun_clust_f'] = _stat_fun_clust_base.format('f_oneway') - .. versionadded:: 0.21 - Support for ``'auto'``, vector, and volume source estimates. +docdict['stat_fun_clust_t'] = _stat_fun_clust_base.format('ttest_1samp_no_p') -The only modes that work for vector and volume source estimates are ``'mean'``, -``'max'``, and ``'auto'``. -""" -docdict['get_peak_parameters'] = """ -tmin : float | None - The minimum point in time to be considered for peak getting. -tmax : float | None - The maximum point in time to be considered for peak getting. -mode : {'pos', 'neg', 'abs'} - How to deal with the sign of the data. If 'pos' only positive - values will be considered. If 'neg' only negative values will - be considered. If 'abs' absolute values will be considered. - Defaults to 'abs'. -vert_as_index : bool - Whether to return the vertex index (True) instead of of its ID - (False, default). -time_as_index : bool - Whether to return the time index (True) instead of the latency - (False, default). +docdict['static'] = """ +static : instance of SpatialImage + The image to align with ("to" volume). """ -# Clustering -docdict['clust_thresh'] = """ -threshold : float | dict | None - If numeric, vertices with data values more extreme than ``threshold`` will - be used to form clusters. If threshold is ``None``, {} will be chosen - automatically that corresponds to a p-value of 0.05 for the given number of - observations (only valid when using {}). If ``threshold`` is a - :class:`dict` (with keys ``'start'`` and ``'step'``) then threshold-free - cluster enhancement (TFCE) will be used (see the - :ref:`TFCE example ` and :footcite:`SmithNichols2009`). -""" -f_test = ('an F-threshold', 'an F-statistic') -t_test = ('a t-threshold', 'a t-statistic') -docdict['clust_thresh_f'] = docdict['clust_thresh'].format(*f_test) -docdict['clust_thresh_t'] = docdict['clust_thresh'].format(*t_test) -docdict['clust_nperm'] = """ -n_permutations : int{} - The number of permutations to compute.{} -""" -nperm_all = (" | 'all'", " Can be 'all' to perform an exact test.") -docdict['clust_nperm_all'] = docdict['clust_nperm'].format(*nperm_all) -docdict['clust_nperm_int'] = docdict['clust_nperm'].format('', '') -docdict['clust_tail'] = """ -tail : int - If tail is 1, the statistic is thresholded above threshold. - If tail is -1, the statistic is thresholded below threshold. - If tail is 0, the statistic is thresholded on both sides of - the distribution. -""" -docdict['clust_stat'] = """ -stat_fun : callable | None - Function called to calculate the test statistic. Must accept 1D-array as - input and return a 1D array. If ``None`` (the default), uses - :func:`mne.stats.{}`. +docdict['stc_plot_kwargs_report'] = """ +stc_plot_kwargs : dict + Dictionary of keyword arguments to pass to + :class:`mne.SourceEstimate.plot`. Only used when plotting in 3D + mode. """ -docdict['clust_stat_f'] = docdict['clust_stat'].format('f_oneway') -docdict['clust_stat_t'] = docdict['clust_stat'].format('ttest_1samp_no_p') -docdict['clust_adj'] = """ -adjacency : scipy.sparse.spmatrix | None | False - Defines adjacency between locations in the data, where "locations" can - be spatial vertices, frequency bins, etc. If ``False``, assumes no - adjacency (each location is treated as independent and unconnected). - If ``None``, a regular lattice adjacency is assumed, connecting - each {sp} location to its neighbor(s) along the last dimension - of {{eachgrp}} ``{{x}}``{lastdim}. - If ``adjacency`` is a matrix, it is assumed to be symmetric (only the - upper triangular half is used) and must be square with dimension equal to - ``{{x}}.shape[-1]`` {parone} or ``{{x}}.shape[-1] * {{x}}.shape[-2]`` - {partwo}.{memory} + +docdict['stcs_pctf'] = """ +stcs : instance of SourceEstimate | list of instances of SourceEstimate + PSFs or CTFs as STC objects. + All PSFs/CTFs will be returned as successive samples in STC objects, + in the order they are specified in idx. STCs for different labels will + be returned as a list. """ -mem = (' If spatial adjacency is uniform in time, it is recommended to use ' - 'a square matrix with dimension ``{x}.shape[-1]`` (n_vertices) to save ' - 'memory and computation, and to use ``max_step`` to define the extent ' - 'of temporal adjacency to consider when clustering.') -st = dict(sp='spatial', lastdim='', parone='(n_vertices)', - partwo='(n_times * n_vertices)', memory=mem) -tf = dict(sp='', lastdim=' (or the last two dimensions if ``{x}`` is 2D)', - parone='', partwo='', memory='') -nogroups = dict(eachgrp='', x='X') -groups = dict(eachgrp='each group ', x='X[k]') -docdict['clust_adj_st1'] = docdict['clust_adj'].format(**st).format(**nogroups) -docdict['clust_adj_stn'] = docdict['clust_adj'].format(**st).format(**groups) -docdict['clust_adj_1'] = docdict['clust_adj'].format(**tf).format(**nogroups) -docdict['clust_adj_n'] = docdict['clust_adj'].format(**tf).format(**groups) -docdict['clust_maxstep'] = """ -max_step : int - Maximum distance along the second dimension (typically this is the "time" - axis) between samples that are considered "connected". Only used - when ``connectivity`` has shape (n_vertices, n_vertices). + +docdict['std_err_by_event_type_returns'] = """ +std_err : instance of Evoked | list of Evoked + The standard error over epochs. + When ``by_event_type=True`` was specified, a list is returned containing a + separate :class:`~mne.Evoked` object for each event type. The list has the + same order as the event types as specified in the ``event_id`` + dictionary. """ -docdict['clust_stepdown'] = """ + +docdict['step_down_p_clust'] = """ step_down_p : float To perform a step-down-in-jumps test, pass a p-value for clusters to exclude from each successive iteration. Default is zero, perform no @@ -1827,481 +3078,455 @@ Setting this to a reasonable value, e.g. 0.05, can increase sensitivity but costs computation time. """ -docdict['clust_power'] = """ -t_power : float - Power to raise the statistical values (usually {}-values) by before - summing (sign will be retained). Note that ``t_power=0`` will give a - count of locations in each cluster, ``t_power=1`` will weight each location - by its statistical score. -""" -docdict['clust_power_t'] = docdict['clust_power'].format('t') -docdict['clust_power_f'] = docdict['clust_power'].format('F') -docdict['clust_out'] = """ -out_type : 'mask' | 'indices' - Output format of clusters within a list. - If ``'mask'``, returns a list of boolean arrays, - each with the same shape as the input data (or slices if the shape is 1D - and adjacency is None), with ``True`` values indicating locations that are - part of a cluster. If ``'indices'``, returns a list of tuple of ndarray, - where each ndarray contains the indices of locations that together form the - given cluster along the given dimension. Note that for large datasets, - ``'indices'`` may use far less memory than ``'mask'``. - Default is ``'indices'``. -""" -docdict['clust_disjoint'] = """ -check_disjoint : bool - Whether to check if the connectivity matrix can be separated into disjoint - sets before clustering. This may lead to faster clustering, especially if - the second dimension of ``X`` (usually the "time" dimension) is large. -""" -docdict['clust_buffer'] = """ -buffer_size : int | None - Block size to use when computing test statistics. This can significantly - reduce memory usage when n_jobs > 1 and memory sharing between processes is - enabled (see :func:`mne.set_cache_dir`), because ``X`` will be shared - between processes and each process only needs to allocate space for a small - block of locations at a time. -""" -# DataFrames -docdict['df_index'] = """ -index : {} | None - Kind of index to use for the DataFrame. If ``None``, a sequential - integer index (:class:`pandas.RangeIndex`) will be used. If ``'time'``, a - :class:`pandas.Float64Index`, :class:`pandas.Int64Index`, {}or - :class:`pandas.TimedeltaIndex` will be used - (depending on the value of ``time_format``). {} +docdict['subject'] = """ +subject : str + The FreeSurfer subject name. """ -datetime = ':class:`pandas.DatetimeIndex`, ' -multiindex = ('If a list of two or more string values, a ' - ':class:`pandas.MultiIndex` will be created. ') -raw = ("'time'", datetime, '') -epo = ('str | list of str', '', multiindex) -evk = ("'time'", '', '') -docdict['df_index_raw'] = docdict['df_index'].format(*raw) -docdict['df_index_epo'] = docdict['df_index'].format(*epo) -docdict['df_index_evk'] = docdict['df_index'].format(*evk) -docdict['df_tf'] = """ -time_format : str | None - Desired time format. If ``None``, no conversion is applied, and time values - remain as float values in seconds. If ``'ms'``, time values will be rounded - to the nearest millisecond and converted to integers. If ``'timedelta'``, - time values will be converted to :class:`pandas.Timedelta` values. {} - Default is ``'ms'`` in version 0.22, and will change to ``None`` in - version 0.23. -""" # XXX make sure we deal with this deprecation in 0.23 -raw_tf = ("If ``'datetime'``, time values will be converted to " - ":class:`pandas.Timestamp` values, relative to " - "``raw.info['meas_date']`` and offset by ``raw.first_samp``. ") -docdict['df_time_format_raw'] = docdict['df_tf'].format(raw_tf) -docdict['df_time_format'] = docdict['df_tf'].format('') -docdict['df_scalings'] = """ -scalings : dict | None - Scaling factor applied to the channels picked. If ``None``, defaults to - ``dict(eeg=1e6, mag=1e15, grad=1e13)`` — i.e., converts EEG to µV, - magnetometers to fT, and gradiometers to fT/cm. + +docdict['subject_label'] = """ +subject : str | None + Subject which this label belongs to. Should only be specified if it is not + specified in the label. """ -docdict['df_copy'] = """ -copy : bool - If ``True``, data will be copied. Otherwise data may be modified in place. - Defaults to ``True``. + +docdict['subject_none'] = """ +subject : str | None + The FreeSurfer subject name. """ -docdict['df_longform'] = """ -long_format : bool - If True, the DataFrame is returned in long format where each row is one - observation of the signal at a unique combination of time point{}. - {}Defaults to ``False``. + +docdict['subject_optional'] = """ +subject : str + The FreeSurfer subject name. While not necessary, it is safer to set the + subject parameter to avoid analysis errors. """ -ch_type = ('For convenience, a ``ch_type`` column is added to facilitate ' - 'subsetting the resulting DataFrame. ') -raw = (' and channel', ch_type) -epo = (', channel, epoch number, and condition', ch_type) -stc = (' and vertex', '') -docdict['df_longform_raw'] = docdict['df_longform'].format(*raw) -docdict['df_longform_epo'] = docdict['df_longform'].format(*epo) -docdict['df_longform_stc'] = docdict['df_longform'].format(*stc) -docdict['df_return'] = """ -df : instance of pandas.DataFrame - A dataframe suitable for usage with other statistical/plotting/analysis - packages. + +docdict['subjects_dir'] = """ +subjects_dir : path-like | None + The path to the directory containing the FreeSurfer subjects + reconstructions. If ``None``, defaults to the ``SUBJECTS_DIR`` environment + variable. """ -# Dipole -docdict['dipole_locs_fig_title'] = """ -title : str | None - The title of the figure if ``mode='orthoview'`` (ignored for all other - modes). If ``None``, dipole number and its properties (amplitude, - orientation etc.) will be shown. Defaults to ``None``. +docdict['surface'] = """surface : str + The surface along which to do the computations, defaults to ``'white'`` + (the gray-white matter boundary). """ -# TFRs -docdict['tfr_average'] = """ -average : bool, default True - If ``False`` return an `EpochsTFR` containing separate TFRs for each - epoch. If ``True`` return an `AverageTFR` containing the average of all - TFRs across epochs. +# %% +# T - .. note:: - Using ``average=True`` is functionally equivalent to using - ``average=False`` followed by ``EpochsTFR.average()``, but is - more memory efficient. +docdict['t_power_clust'] = """ +t_power : float + Power to raise the statistical values (usually t-values) by before + summing (sign will be retained). Note that ``t_power=0`` will give a + count of locations in each cluster, ``t_power=1`` will weight each location + by its statistical score. +""" - .. versionadded:: 0.13.0 +docdict['t_window_chpi_t'] = """ +t_window : float + Time window to use to estimate the amplitudes, default is + 0.2 (200 ms). """ -# Anonymization -docdict['anonymize_info_parameters'] = """ -daysback : int | None - Number of days to subtract from all dates. - If ``None`` (default), the acquisition date, ``info['meas_date']``, - will be set to ``January 1ˢᵗ, 2000``. This parameter is ignored if - ``info['meas_date']`` is ``None`` (i.e., no acquisition date has been set). -keep_his : bool - If ``True``, ``his_id`` of ``subject_info`` will **not** be overwritten. - Defaults to ``False``. +docdict['tags_report'] = """ +tags : array-like of str | str + Tags to add for later interactive filtering. Must not contain spaces. +""" - .. warning:: This could mean that ``info`` is not fully - anonymized. Use with caution. +docdict['tail_clust'] = """ +tail : int + If tail is 1, the statistic is thresholded above threshold. + If tail is -1, the statistic is thresholded below threshold. + If tail is 0, the statistic is thresholded on both sides of + the distribution. """ -docdict['anonymize_info_notes'] = """ -Removes potentially identifying information if it exists in ``info``. -Specifically for each of the following we use: -- meas_date, file_id, meas_id - A default value, or as specified by ``daysback``. -- subject_info - Default values, except for 'birthday' which is adjusted - to maintain the subject age. -- experimenter, proj_name, description - Default strings. -- utc_offset - ``None``. -- proj_id - Zeros. -- proc_history - Dates use the ``meas_date`` logic, and experimenter a default string. -- helium_info, device_info - Dates use the ``meas_date`` logic, meta info uses defaults. +_theme = """\ +theme : str | path-like + Can be "auto", "light", or "dark" or a path-like to a + custom stylesheet. For Dark-Mode and automatic Dark-Mode-Detection, + :mod:`qdarkstyle` and + `darkdetect `__, + respectively, are required.\ + If None (default), the config option {config_option} will be used, + defaulting to "auto" if it's not found.\ +""" + +docdict['theme_3d'] = """ +{theme} +""".format(theme=_theme.format(config_option='MNE_3D_OPTION_THEME')) + +docdict['theme_pg'] = """ +{theme} + Only supported by the ``'qt'`` backend. +""".format(theme=_theme.format(config_option='MNE_BROWSER_THEME')) -If ``info['meas_date']`` is ``None``, it will remain ``None`` during processing -the above fields. +docdict['thresh'] = """ +thresh : None or float + Not supported yet. + If not None, values below thresh will not be visible. +""" -Operates in place. +_threshold_clust_base = """ +threshold : float | dict | None + The so-called "cluster forming threshold" in the form of a test statistic + (note: this is not an alpha level / "p-value"). + If numeric, vertices with data values more extreme than ``threshold`` will + be used to form clusters. If ``None``, {} will be chosen + automatically that corresponds to a p-value of 0.05 for the given number of + observations (only valid when using {}). If ``threshold`` is a + :class:`dict` (with keys ``'start'`` and ``'step'``) then threshold-free + cluster enhancement (TFCE) will be used (see the + :ref:`TFCE example ` and :footcite:`SmithNichols2009`). + See Notes for an example on how to compute a threshold based on + a particular p-value for one-tailed or two-tailed tests. """ -# Baseline -docdict['rescale_baseline'] = """ -baseline : None | tuple of length 2 - The time interval to consider as "baseline" when applying baseline - correction. If ``None``, do not apply baseline correction. - If a tuple ``(a, b)``, the interval is between ``a`` and ``b`` - (in seconds), including the endpoints. - If ``a`` is ``None``, the **beginning** of the data is used; and if ``b`` - is ``None``, it is set to the **end** of the interval. - If ``(None, None)``, the entire time interval is used. +f_test = ('an F-threshold', 'an F-statistic') +docdict['threshold_clust_f'] = _threshold_clust_base.format(*f_test) - .. note:: The baseline ``(a, b)`` includes both endpoints, i.e. all - timepoints ``t`` such that ``a <= t <= b``. +docdict['threshold_clust_f_notes'] = """ +For computing a ``threshold`` based on a p-value, use the conversion +from :meth:`scipy.stats.rv_continuous.ppf`:: + + pval = 0.001 # arbitrary + dfn = n_conditions - 1 # degrees of freedom numerator + dfd = n_observations - n_conditions # degrees of freedom denominator + thresh = scipy.stats.f.ppf(1 - pval, dfn=dfn, dfd=dfd) # F distribution """ -docdict['baseline_epochs'] = """%(rescale_baseline)s - Correction is applied **to each epoch and channel individually** in the - following way: - 1. Calculate the mean signal of the baseline period. - 2. Subtract this mean from the **entire** epoch. +t_test = ('a t-threshold', 'a t-statistic') +docdict['threshold_clust_t'] = _threshold_clust_base.format(*t_test) -""" % docdict -docdict['baseline_evoked'] = """%(rescale_baseline)s - Correction is applied **to each channel individually** in the following - way: +docdict['threshold_clust_t_notes'] = """ +For computing a ``threshold`` based on a p-value, use the conversion +from :meth:`scipy.stats.rv_continuous.ppf`:: - 1. Calculate the mean signal of the baseline period. - 2. Subtract this mean from the **entire** ``Evoked``. + pval = 0.001 # arbitrary + df = n_observations - 1 # degrees of freedom for the test + thresh = scipy.stats.t.ppf(1 - pval / 2, df) # two-tailed, t distribution -""" % docdict -docdict['baseline_stc'] = """%(rescale_baseline)s - Correction is applied **to each source individually** in the following - way: +For a one-tailed test (``tail=1``), don't divide the p-value by 2. +For testing the lower tail (``tail=-1``), don't subtract ``pval`` from 1. +""" - 1. Calculate the mean signal of the baseline period. - 2. Subtract this mean from the **entire** source estimate data. +docdict['time_format'] = """ +time_format : 'float' | 'clock' + Style of time labels on the horizontal axis. If ``'float'``, labels will be + number of seconds from the start of the recording. If ``'clock'``, + labels will show "clock time" (hours/minutes/seconds) inferred from + ``raw.info['meas_date']``. Default is ``'float'``. - .. note:: Baseline correction is appropriate when signal and noise are - approximately additive, and the noise level can be estimated from - the baseline interval. This can be the case for non-normalized - source activities (e.g. signed and unsigned MNE), but it is not - the case for normalized estimates (e.g. signal-to-noise ratios, - dSPM, sLORETA). + .. versionadded:: 0.24 +""" -""" % docdict -docdict['baseline_report'] = """%(rescale_baseline)s - Correction is applied in the following way **to each channel:** +_time_format_df_base = """ +time_format : str | None + Desired time format. If ``None``, no conversion is applied, and time values + remain as float values in seconds. If ``'ms'``, time values will be rounded + to the nearest millisecond and converted to integers. If ``'timedelta'``, + time values will be converted to :class:`pandas.Timedelta` values. {} + Default is ``None``. +""" - 1. Calculate the mean signal of the baseline period. - 2. Subtract this mean from the **entire** time period. +docdict['time_format_df'] = _time_format_df_base.format('') - For `~mne.Epochs`, this algorithm is run **on each epoch individually.** -""" % docdict +_raw_tf = ("If ``'datetime'``, time values will be converted to " + ":class:`pandas.Timestamp` values, relative to " + "``raw.info['meas_date']`` and offset by ``raw.first_samp``. ") +docdict['time_format_df_raw'] = _time_format_df_base.format(_raw_tf) -# Epochs -docdict['epochs_tmin_tmax'] = """ -tmin, tmax : float - Start and end time of the epochs in seconds, relative to the time-locked - event. Defaults to -0.2 and 0.5, respectively. +docdict['time_label'] = """ +time_label : str | callable | None + Format of the time label (a format string, a function that maps + floating point time values to strings, or None for no label). The + default is ``'auto'``, which will use ``time=%0.2f ms`` if there + is more than one time point. """ -docdict['epochs_reject_tmin_tmax'] = """ -reject_tmin, reject_tmax : float | None - Start and end of the time window used to reject epochs. The default - ``None`` corresponds to the first and last time points of the epochs, - respectively. + +docdict['time_unit'] = """\ +time_unit : str + The units for the time axis, can be "s" (default) or "ms". """ -docdict['epochs_events_event_id'] = """ -events : array of int, shape (n_events, 3) - The events typically returned by the read_events function. - If some events don't match the events of interest as specified - by event_id, they will be marked as 'IGNORED' in the drop log. -event_id : int | list of int | dict | None - The id of the event to consider. If dict, - the keys can later be used to access associated events. Example: - dict(auditory=1, visual=3). If int, a dict will be created with - the id as string. If a list, all events with the IDs specified - in the list are used. If None, all events will be used with - and a dict is created with string integer names corresponding - to the event id integers. + +docdict['time_viewer_brain_screenshot'] = """ +time_viewer : bool + If True, include time viewer traces. Only used if + ``time_viewer=True`` and ``separate_canvas=False``. """ -docdict['epochs_preload'] = """ - Load all epochs from disk when creating the object - or wait before accessing each epoch (more memory - efficient but can be slower). + +docdict['title_none'] = """ +title : str | None + The title of the generated figure. If ``None`` (default), no title is + displayed. """ -docdict['epochs_detrend'] = """ -detrend : int | None - If 0 or 1, the data channels (MEG and EEG) will be detrended when - loaded. 0 is a constant (DC) detrend, 1 is a linear detrend. None - is no detrending. Note that detrending is performed before baseline - correction. If no DC offset is preferred (zeroth order detrending), - either turn off baseline correction, as this may introduce a DC - shift, or set baseline correction to use the entire time interval - (will yield equivalent results but be slower). + +docdict['tmax_raw'] = """ +tmax : float + End time of the raw data to use in seconds (cannot exceed data duration). """ -docdict['epochs_metadata'] = """ -metadata : instance of pandas.DataFrame | None - A :class:`pandas.DataFrame` specifying metadata about each epoch. - If given, ``len(metadata)`` must equal ``len(events)``. The DataFrame - may only contain values of type (str | int | float | bool). - If metadata is given, then pandas-style queries may be used to select - subsets of data, see :meth:`mne.Epochs.__getitem__`. - When a subset of the epochs is created in this (or any other - supported) manner, the metadata object is subsetted accordingly, and - the row indices will be modified to match ``epochs.selection``. - .. versionadded:: 0.16 +docdict['tmin'] = """ +tmin : scalar + Time point of the first sample in data. """ -docdict['epochs_event_repeated'] = """ -event_repeated : str - How to handle duplicates in ``events[:, 0]``. Can be ``'error'`` - (default), to raise an error, 'drop' to only retain the row occurring - first in the ``events``, or ``'merge'`` to combine the coinciding - events (=duplicates) into a new event (see Notes for details). - .. versionadded:: 0.19 +docdict['tmin_raw'] = """ +tmin : float + Start time of the raw data to use in seconds (must be >= 0). +""" + +docdict['tol_kind_rank'] = """ +tol_kind : str + Can be: "absolute" (default) or "relative". Only used if ``tol`` is a + float, because when ``tol`` is a string the mode is implicitly relative. + After applying the chosen scale factors / normalization to the data, + the singular values are computed, and the rank is then taken as: + + - ``'absolute'`` + The number of singular values ``s`` greater than ``tol``. + This mode can fail if your data do not adhere to typical + data scalings. + - ``'relative'`` + The number of singular values ``s`` greater than ``tol * s.max()``. + This mode can fail if you have one or more large components in the + data (e.g., artifacts). + + .. versionadded:: 0.21.0 """ -docdict['epochs_raw'] = """ -raw : Raw object - An instance of `~mne.io.Raw`. + +docdict['tol_rank'] = """ +tol : float | 'auto' + Tolerance for singular values to consider non-zero in + calculating the rank. The singular values are calculated + in this method such that independent data are expected to + have singular value around one. Can be 'auto' to use the + same thresholding as :func:`scipy.linalg.orth`. """ -docdict['epochs_on_missing'] = """ -on_missing : str - What to do if one or several event ids are not found in the recording. - Valid keys are 'raise' | 'warn' | 'ignore' - Default is 'raise'. If on_missing is 'warn' it will proceed but - warn, if 'ignore' it will proceed silently. Note. - If none of the event ids are found in the data, an error will be - automatically generated irrespective of this parameter. -""" -reject_common = """ - Reject epochs based on peak-to-peak signal amplitude (PTP), i.e. the - absolute difference between the lowest and the highest signal value. In - each individual epoch, the PTP is calculated for every channel. If the - PTP of any one channel exceeds the rejection threshold, the respective - epoch will be dropped. - The dictionary keys correspond to the different channel types; valid - keys are: ``'grad'``, ``'mag'``, ``'eeg'``, ``'eog'``, and ``'ecg'``. +docdict['topomap_kwargs'] = """ +topomap_kwargs : dict | None + Keyword arguments to pass to the topomap-generating functions. +""" - Example:: +_trans_base = """\ +If str, the path to the head<->MRI transform ``*-trans.fif`` file produced + during coregistration. Can also be ``'fsaverage'`` to use the built-in + fsaverage transformation.""" - reject = dict(grad=4000e-13, # unit: T / m (gradiometers) - mag=4e-12, # unit: T (magnetometers) - eeg=40e-6, # unit: V (EEG channels) - eog=250e-6 # unit: V (EOG channels) - ) +docdict['trans'] = f""" +trans : path-like | dict | instance of Transform | None + {_trans_base} + If trans is None, an identity matrix is assumed. - .. note:: Since rejection is based on a signal **difference** - calculated for each channel separately, applying baseline - correction does not affect the rejection procedure, as the - difference will be preserved. -""" -docdict['reject_epochs'] = f""" -reject : dict | None -{reject_common} - If ``reject`` is ``None`` (default), no rejection is performed. -""" -docdict['reject_drop_bad'] = f""" -reject : dict | str | None -{reject_common} - If ``reject`` is ``None``, no rejection is performed. If ``'existing'`` - (default), then the rejection parameters set at instantiation are used. -""" -flat_common = """ - Rejection parameters based on flatness of signal. - Valid **keys** are ``'grad'``, ``'mag'``, ``'eeg'``, ``'eog'``, ``'ecg'``. - The **values** are floats that set the minimum acceptable peak-to-peak - amplitude (PTP). If the PTP is smaller than this threshold, the epoch will - be dropped. If ``None`` then no rejection is performed based on flatness - of the signal.""" -docdict['flat'] = f""" -flat : dict | None -{flat_common} -""" -docdict['flat_drop_bad'] = f""" -flat : dict | str | None -{flat_common} - If ``'existing'``, then the flat parameters set during epoch creation are - used. + .. versionchanged:: 0.19 + Support for 'fsaverage' argument. """ -# ECG detection -docdict['ecg_event_id'] = """ -event_id : int - The index to assign to found ECG events. -""" -docdict['ecg_ch_name'] = """ -ch_name : None | str - The name of the channel to use for ECG peak detection. - If ``None`` (default), ECG channel is used if present. If ``None`` and - **no** ECG channel is present, a synthetic ECG channel is created from - the cross-channel average. This synthetic channel can only be created from - MEG channels. -""" -docdict['ecg_filter_freqs'] = """ -l_freq : float - Low pass frequency to apply to the ECG channel while finding events. -h_freq : float - High pass frequency to apply to the ECG channel while finding events. -""" -docdict['ecg_filter_length'] = """ -filter_length : str | int | None - Number of taps to use for filtering. +docdict['trans_not_none'] = """ +trans : str | dict | instance of Transform + %s +""" % (_trans_base,) + +docdict['transparent'] = """ +transparent : bool | None + If True: use a linear transparency between fmin and fmid + and make values below fmin fully transparent (symmetrically for + divergent colormaps). None will choose automatically based on colormap + type. """ -docdict['ecg_tstart'] = """ + +docdict['tstart_ecg'] = """ tstart : float Start ECG detection after ``tstart`` seconds. Useful when the beginning of the run is noisy. """ -docdict['create_ecg_epochs'] = """This function will: - -#. Filter the ECG data channel. - -#. Find ECG R wave peaks using :func:`mne.preprocessing.find_ecg_events`. -#. Filter the raw data. +docdict['tstep'] = """ +tstep : scalar + Time step between successive samples in data. +""" + +# %% +# U + +docdict['units'] = """ +units : str | dict | None + Specify the unit(s) that the data should be returned in. If + ``None`` (default), the data is returned in the + channel-type-specific default units, which are SI units (see + :ref:`units` and :term:`data channels`). If a string, must be a + sub-multiple of SI units that will be used to scale the data from + all channels of the type associated with that unit. This only works + if the data contains one channel type that has a unit (unitless + channel types are left unchanged). For example if there are only + EEG and STIM channels, ``units='uV'`` will scale EEG channels to + micro-Volts while STIM channels will be unchanged. Finally, if a + dictionary is provided, keys must be channel types, and values must + be units to scale the data of that channel type to. For example + ``dict(grad='fT/cm', mag='fT')`` will scale the corresponding types + accordingly, but all other channel types will remain in their + channel-type-specific default unit. +""" -#. Create `~mne.Epochs` around the R wave peaks, capturing the heartbeats. +docdict['units_topomap'] = """ +units : dict | str | None + The unit of the channel type used for colorbar label. If + scale is None the unit is automatically determined. """ -# EOG detection -docdict['create_eog_epochs'] = """This function will: +docdict['use_cps'] = """ +use_cps : bool + Whether to use cortical patch statistics to define normal orientations for + surfaces (default True). +""" -#. Filter the EOG data channel. +docdict['use_cps_restricted'] = """ +use_cps : bool + Whether to use cortical patch statistics to define normal orientations for + surfaces (default True). -#. Find the peaks of eyeblinks in the EOG data using - :func:`mne.preprocessing.find_eog_events`. + Only used when the inverse is free orientation (``loose=1.``), + not in surface orientation, and ``pick_ori='normal'``. +""" -#. Filter the raw data. +docdict['use_opengl'] = """ +use_opengl : bool | None + Whether to use OpenGL when rendering the plot (requires ``pyopengl``). + May increase performance, but effect is dependent on system CPU and + graphics hardware. Only works if using the Qt backend. Default is + None, which will use False unless the user configuration variable + ``MNE_BROWSER_USE_OPENGL`` is set to ``'true'``, + see :func:`mne.set_config`. -#. Create `~mne.Epochs` around the eyeblinks. + .. versionadded:: 0.24 """ -docdict['eog_ch_name'] = """ -ch_name : str | list of str | None - The name of the channel(s) to use for EOG peak detection. If a string, - can be an arbitrary channel. This doesn't have to be a channel of - ``eog`` type; it could, for example, also be an ordinary EEG channel - that was placed close to the eyes, like ``Fp1`` or ``Fp2``. - Multiple channel names can be passed as a list of strings. +# %% +# V - If ``None`` (default), use the channel(s) in ``raw`` with type ``eog``. +docdict['verbose'] = """ +verbose : bool | str | int | None + Control verbosity of the logging output. If ``None``, use the default + verbosity level. See the :ref:`logging documentation ` and + :func:`mne.verbose` for details. Should only be passed as a keyword + argument.""" + +docdict['vertices_volume'] = """ +vertices : list of array of int + The indices of the dipoles in the source space. Should be a single + array of shape (n_dipoles,) unless there are subvolumes. +""" + +docdict['view'] = """ +view : str | None + The name of the view to show (e.g. "lateral"). Other arguments + take precedence and modify the camera starting from the ``view``. + See :meth:`Brain.show_view ` for valid + string shortcut options. """ -# SSP -docdict['compute_ssp'] = """This function aims to find those SSP vectors that -will project out the ``n`` most prominent signals from the data for each -specified sensor type. Consequently, if the provided input data contains high -levels of noise, the produced SSP vectors can then be used to eliminate that -noise from the data. +docdict['view_layout'] = """ +view_layout : str + Can be "vertical" (default) or "horizontal". When using "horizontal" mode, + the PyVista backend must be used and hemi cannot be "split". """ -compute_proj_common = """ -#. Optionally average the `~mne.Epochs` to produce an `~mne.Evoked` if - ``average=True`` was passed (default). - -#. Calculate SSP projection vectors on that data to capture the artifacts.""" -docdict['compute_proj_ecg'] = f"""%(create_ecg_epochs)s {compute_proj_common} -""" % docdict -docdict['compute_proj_eog'] = f"""%(create_eog_epochs)s {compute_proj_common} -""" % docdict -# BEM -docdict['on_defects'] = """ -on_defects : str - What to do if the surface is found to have topological defects. Can be - ``'raise'`` (default) to raise an error, or ``'warn'`` to emit a warning. - Note that a lot of computations in MNE-Python assume the surfaces to be - topologically correct, topological defects may still make other - computations (e.g., ``mne.make_bem_model`` and ``mne.make_bem_solution``) - fail irrespective of this parameter. +docdict['views'] = """ +views : str | list + View to use. Using multiple views (list) is not supported for mpl + backend. See :meth:`Brain.show_view ` for + valid string options. """ -# Export -docdict['export_warning'] = """ -.. warning:: - Since we are exporting to external formats, there's no guarantee that all - the info will be preserved in the external format. To save in native MNE - format (``.fif``) without information loss, use :func:`save` instead. -""" -docdict['export_params_fname'] = """ -fname : str - Name of the output file. +docdict['vlim_psd_topo_joint'] = """ +vlim : tuple of length 2 | 'joint' + Colormap limits to use. If a :class:`tuple` of floats, specifies the + lower and upper bounds of the colormap (in that order); providing + ``None`` for either entry will set the corresponding boundary at the + min/max of the data (separately for each topomap). Elements of the + :class:`tuple` may also be callable functions which take in a + :class:`NumPy array ` and return a scalar. + If ``vlim='joint'``, will compute the colormap limits jointly across + all topomaps of the same channel type, using the min/max of the data. + Defaults to ``(None, None)``. + + .. versionadded:: 0.21 """ -docdict['export_params_fmt'] = """ -fmt : 'auto' | 'eeglab' - Format of the export. Defaults to ``'auto'``, which will infer the format - from the filename extension. See supported formats above for more - information. + +docdict['vmin_vmax_topomap'] = """ +vmin, vmax : float | callable | None + Lower and upper bounds of the colormap, in the same units as the data. + If ``vmin`` and ``vmax`` are both ``None``, they are set at ± the + maximum absolute value of the data (yielding a colormap with midpoint + at 0). If only one of ``vmin``, ``vmax`` is ``None``, will use + ``min(data)`` or ``max(data)``, respectively. If callable, should + accept a :class:`NumPy array ` of data and return a + float. """ -docdict['export_eeglab_note'] = """ -For EEGLAB exports, channel locations are expanded to full EEGLAB format. -For more details see :func:`eeglabio.utils.cart_to_eeglab`. + +# %% +# W + +docdict['weight_norm'] = """ +weight_norm : str | None + Can be: + + - ``None`` + The unit-gain LCMV beamformer :footcite:`SekiharaNagarajan2008` will be + computed. + - ``'unit-noise-gain'`` + The unit-noise gain minimum variance beamformer will be computed + (Borgiotti-Kaplan beamformer) :footcite:`SekiharaNagarajan2008`, + which is not rotation invariant when ``pick_ori='vector'``. + This should be combined with + :meth:`stc.project('pca') ` to follow + the definition in :footcite:`SekiharaNagarajan2008`. + - ``'nai'`` + The Neural Activity Index :footcite:`VanVeenEtAl1997` will be computed, + which simply scales all values from ``'unit-noise-gain'`` by a fixed + value. + - ``'unit-noise-gain-invariant'`` + Compute a rotation-invariant normalization using the matrix square + root. This differs from ``'unit-noise-gain'`` only when + ``pick_ori='vector'``, creating a solution that: + + 1. Is rotation invariant (``'unit-noise-gain'`` is not); + 2. Satisfies the first requirement from + :footcite:`SekiharaNagarajan2008` that ``w @ w.conj().T == I``, + whereas ``'unit-noise-gain'`` has non-zero off-diagonals; but + 3. Does not satisfy the second requirement that ``w @ G.T = θI``, + which arguably does not make sense for a rotation-invariant + solution. """ -# Other -docdict['accept'] = """ -accept : bool - If True (default False), accept the license terms of this dataset. +docdict['window_psd'] = """ +window : str | float | tuple + Windowing function to use. See :func:`scipy.signal.get_window`. """ -docdict['overwrite'] = """ -overwrite : bool - If True (default False), overwrite the destination file if it - exists. + +docdict['window_resample'] = """ +window : str | tuple + Frequency-domain window to use in resampling. + See :func:`scipy.signal.resample`. """ -docdict['ref_channels'] = """ -ref_channels : str | list of str - Name of the electrode(s) which served as the reference in the - recording. If a name is provided, a corresponding channel is added - and its data is set to 0. This is useful for later re-referencing. +# %% +# X + +docdict['xscale_plot_psd'] = """ +xscale : str + Can be 'linear' (default) or 'log'. """ +# %% +# Y + +# %% +# Z + docdict_indented = {} @@ -2326,7 +3551,7 @@ if len(lines) < 2: icount = 0 else: - icount = indentcount_lines(lines[1:]) + icount = _indentcount_lines(lines[1:]) # Insert this indent to dictionary docstrings try: indented = docdict_indented[icount] @@ -2676,33 +3901,21 @@ warnings.filterwarnings('always', category=DeprecationWarning, module='mne') -class deprecated(object): - """Mark a function or class as deprecated (decorator). - - Issue a warning when the function is called/the class is instantiated and - adds a warning to the docstring. - - The optional extra argument will be appended to the deprecation message - and the docstring. Note: to use this with the default value for extra, put - in an empty of parentheses:: - - >>> from mne.utils import deprecated - >>> deprecated() # doctest: +ELLIPSIS - - - >>> @deprecated() - ... def some_function(): pass +class deprecated: + """Mark a function, class, or method as deprecated (decorator). + Originally adapted from sklearn and + http://wiki.python.org/moin/PythonDecoratorLibrary, then modified to make + arguments populate properly following our verbose decorator methods based + on decorator. Parameters ---------- - extra: string - To be added to the deprecation messages. + extra : str + Extra information beyond just saying the class/function/method + is deprecated. """ - # Adapted from http://wiki.python.org/moin/PythonDecoratorLibrary, - # but with many changes. - def __init__(self, extra=''): # noqa: D102 self.extra = extra @@ -2713,6 +3926,11 @@ ---------- obj : object Object to call. + + Returns + ------- + obj : object + The modified object. """ if isinstance(obj, type): return self._decorate_class(obj) @@ -2720,44 +3938,39 @@ return self._decorate_fun(obj) def _decorate_class(self, cls): - msg = "Class %s is deprecated" % cls.__name__ - if self.extra: - msg += "; %s" % self.extra - - # FIXME: we should probably reset __new__ for full generality - init = cls.__init__ - - def deprecation_wrapped(*args, **kwargs): - warnings.warn(msg, category=DeprecationWarning) - return init(*args, **kwargs) - cls.__init__ = deprecation_wrapped - - deprecation_wrapped.__name__ = '__init__' - deprecation_wrapped.__doc__ = self._update_doc(init.__doc__) - deprecation_wrapped.deprecated_original = init - + msg = f"Class {cls.__name__} is deprecated" + cls.__init__ = self._make_fun(cls.__init__, msg) return cls def _decorate_fun(self, fun): """Decorate function fun.""" - msg = "Function %s is deprecated" % fun.__name__ + msg = f"Function {fun.__name__} is deprecated" + return self._make_fun(fun, msg) + + def _make_fun(self, function, msg): if self.extra: msg += "; %s" % self.extra - def deprecation_wrapped(*args, **kwargs): - warnings.warn(msg, category=DeprecationWarning) - return fun(*args, **kwargs) - - deprecation_wrapped.__name__ = fun.__name__ - deprecation_wrapped.__dict__ = fun.__dict__ - deprecation_wrapped.__doc__ = self._update_doc(fun.__doc__) - - return deprecation_wrapped + body = f"""\ +def %(name)s(%(signature)s):\n + import warnings + warnings.warn({repr(msg)}, category=DeprecationWarning) + return _function_(%(shortsignature)s)""" + evaldict = dict(_function_=function) + fm = FunctionMaker( + function, None, None, None, None, function.__module__) + attrs = dict(__wrapped__=function, __qualname__=function.__qualname__, + __globals__=function.__globals__) + dep = fm.make(body, evaldict, addsource=True, **attrs) + dep.__doc__ = self._update_doc(dep.__doc__) + dep._deprecated_original = function + return dep def _update_doc(self, olddoc): newdoc = ".. warning:: DEPRECATED" if self.extra: newdoc = "%s: %s" % (newdoc, self.extra) + newdoc += '.' if olddoc: # Get the spacing right to avoid sphinx warnings n_space = 4 @@ -2782,3 +3995,73 @@ f'{dep_name} has been deprecated in favor of {func.__name__} and will ' f'be removed in {removed_in}.' )(deepcopy(func)) + + +############################################################################### +# The following tools were adapted (mostly trimmed) from SciPy's doccer.py + + +def _docformat(docstring, docdict=None, funcname=None): + """Fill a function docstring from variables in dictionary. + + Adapt the indent of the inserted docs + + Parameters + ---------- + docstring : string + docstring from function, possibly with dict formatting strings + docdict : dict, optional + dictionary with keys that match the dict formatting strings + and values that are docstring fragments to be inserted. The + indentation of the inserted docstrings is set to match the + minimum indentation of the ``docstring`` by adding this + indentation to all lines of the inserted string, except the + first + + Returns + ------- + outstring : string + string with requested ``docdict`` strings inserted + """ + if not docstring: + return docstring + if docdict is None: + docdict = {} + if not docdict: + return docstring + lines = docstring.expandtabs().splitlines() + # Find the minimum indent of the main docstring, after first line + if len(lines) < 2: + icount = 0 + else: + icount = _indentcount_lines(lines[1:]) + indent = ' ' * icount + # Insert this indent to dictionary docstrings + indented = {} + for name, dstr in docdict.items(): + lines = dstr.expandtabs().splitlines() + try: + newlines = [lines[0]] + for line in lines[1:]: + newlines.append(indent + line) + indented[name] = '\n'.join(newlines) + except IndexError: + indented[name] = dstr + funcname = docstring.split('\n')[0] if funcname is None else funcname + try: + return docstring % indented + except (TypeError, ValueError, KeyError) as exp: + raise RuntimeError('Error documenting %s:\n%s' + % (funcname, str(exp))) + + +def _indentcount_lines(lines): + """Compute minimum indent for all lines in line list.""" + indentno = sys.maxsize + for line in lines: + stripped = line.lstrip() + if stripped: + indentno = min(indentno, len(line) - len(stripped)) + if indentno == sys.maxsize: + return 0 + return indentno diff -Nru python-mne-0.23.4+dfsg/mne/utils/fetching.py python-mne-1.1.0+dfsg/mne/utils/fetching.py --- python-mne-0.23.4+dfsg/mne/utils/fetching.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/fetching.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,136 +2,9 @@ """File downloading functions.""" # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import os -import shutil -import time - -from .progressbar import ProgressBar -from .numerics import hashfunc -from .misc import sizeof_fmt -from ._logging import logger, verbose - - -# Adapted from nilearn - - -def _get_http(url, temp_file_name, initial_size, timeout, verbose_bool): - """Safely (resume a) download to a file from http(s).""" - from urllib import request - from urllib.error import HTTPError, URLError - # Actually do the reading - response = None - extra = '' - if initial_size > 0: - logger.debug(' Resuming at %s' % (initial_size,)) - req = request.Request( - url, headers={'Range': 'bytes=%s-' % (initial_size,)}) - try: - response = request.urlopen(req, timeout=timeout) - content_range = response.info().get('Content-Range', None) - if (content_range is None or not content_range.startswith( - 'bytes %s-' % (initial_size,))): - raise IOError('Server does not support resuming') - except (KeyError, HTTPError, URLError, IOError): - initial_size = 0 - response = None - else: - extra = ', resuming at %s' % (sizeof_fmt(initial_size),) - if response is None: - response = request.urlopen(request.Request(url), timeout=timeout) - file_size = int(response.headers.get('Content-Length', '0').strip()) - file_size += initial_size - url = response.geturl() - logger.info('Downloading %s (%s%s)' % (url, sizeof_fmt(file_size), extra)) - del url - mode = 'ab' if initial_size > 0 else 'wb' - progress = ProgressBar(file_size, initial_size, unit='B', - mesg='Downloading', unit_scale=True, - unit_divisor=1024) - del file_size - chunk_size = 8192 # 2 ** 13 - with open(temp_file_name, mode) as local_file: - while True: - t0 = time.time() - chunk = response.read(chunk_size) - dt = time.time() - t0 - if dt < 0.01: - chunk_size *= 2 - elif dt > 0.1 and chunk_size > 8192: - chunk_size = chunk_size // 2 - if not chunk: - break - local_file.write(chunk) - progress.update_with_increment_value(len(chunk)) - - -@verbose -def _fetch_file(url, file_name, print_destination=True, resume=True, - hash_=None, timeout=30., hash_type='md5', verbose=None): - """Load requested file, downloading it if needed or requested. - - Parameters - ---------- - url: string - The url of file to be downloaded. - file_name: string - Name, along with the path, of where downloaded file will be saved. - print_destination: bool, optional - If true, destination of where file was saved will be printed after - download finishes. - resume: bool, optional - If true, try to resume partially downloaded files. - hash_ : str | None - The hash of the file to check. If None, no checking is - performed. - timeout : float - The URL open timeout. - hash_type : str - The type of hashing to use such as "md5" or "sha1" - %(verbose)s - """ - # Adapted from NISL: - # https://github.com/nisl/tutorial/blob/master/nisl/datasets.py - from urllib import parse - if hash_ is not None and (not isinstance(hash_, str) or - len(hash_) != 32) and hash_type == 'md5': - raise ValueError('Bad hash value given, should be a 32-character ' - 'string:\n%s' % (hash_,)) - temp_file_name = file_name + ".part" - verbose_bool = (logger.level <= 20) # 20 is info - scheme = parse.urlparse(url).scheme - if scheme not in ('http', 'https'): - raise NotImplementedError('Cannot use scheme %r' % (scheme,)) - try: - # Triage resume - if not os.path.exists(temp_file_name): - resume = False - if resume: - with open(temp_file_name, 'rb', buffering=0) as local_file: - local_file.seek(0, 2) - initial_size = local_file.tell() - del local_file - else: - initial_size = 0 - _get_http(url, temp_file_name, initial_size, timeout, verbose_bool) - - # check hash sum eg md5sum - if hash_ is not None: - logger.info('Verifying hash %s.' % (hash_,)) - hashsum = hashfunc(temp_file_name, hash_type=hash_type) - if hash_ != hashsum: - raise RuntimeError('Hash mismatch for downloaded file %s, ' - 'expected %s but got %s' - % (temp_file_name, hash_, hashsum)) - shutil.move(temp_file_name, file_name) - if print_destination is True: - logger.info('File saved as %s.\n' % file_name) - except Exception: - logger.error('Error while fetching file %s.' - ' Dataset fetching aborted.' % url) - raise def _url_to_local_path(url, path): diff -Nru python-mne-0.23.4+dfsg/mne/utils/__init__.py python-mne-1.1.0+dfsg/mne/utils/__init__.py --- python-mne-0.23.4+dfsg/mne/utils/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,54 +3,56 @@ # is changed here! _doc_special_members = ('__contains__', '__getitem__', '__iter__', '__len__', '__add__', '__sub__', '__mul__', '__div__', - '__neg__', '__hash__') + '__neg__') from ._bunch import Bunch, BunchConst, BunchConstNamed from .check import (check_fname, check_version, check_random_state, _check_fname, _check_subject, _check_pandas_installed, - _check_pandas_index_arguments, _check_mayavi_version, + _check_pandas_index_arguments, _check_event_id, _check_ch_locs, _check_compensation_grade, - _check_if_nan, _is_numeric, _ensure_int, _check_preload, - _validate_type, _check_info_inv, _check_pylsl_installed, + _check_if_nan, _is_numeric, _ensure_int, _check_preload, + _validate_type, _check_info_inv, _check_channels_spatial_filter, _check_one_ch_type, _check_rank, _check_option, _check_depth, _check_combine, - _check_path_like, _check_src_normal, _check_stc_units, - _check_pyqt5_version, _check_sphere, _check_time_format, + _path_like, _check_src_normal, _check_stc_units, + _check_qt_version, _check_sphere, _check_time_format, _check_freesurfer_home, _suggest, _require_version, _on_missing, _check_on_missing, int_like, _safe_input, _check_all_same_channel_names, path_like, _ensure_events, - _check_eeglabio_installed, _infer_check_export_fmt) + _check_eeglabio_installed, _check_pybv_installed, + _check_edflib_installed, _to_rgb, _soft_import, + _check_dict_keys, + _import_h5py, _import_h5io_funcs, + _import_pymatreader_funcs) from .config import (set_config, get_config, get_config_path, set_cache_dir, set_memmap_min_size, get_subjects_dir, _get_stim_channel, sys_info, _get_extra_data_path, _get_root_dir, _get_numpy_libs) from .docs import (copy_function_doc_to_method_doc, copy_doc, linkcode_resolve, open_docs, deprecated, fill_doc, deprecated_alias, - copy_base_doc_to_subclass_doc) -from .fetching import _fetch_file, _url_to_local_path + copy_base_doc_to_subclass_doc, docdict as _docdict) +from .fetching import _url_to_local_path from ._logging import (verbose, logger, set_log_level, set_log_file, use_log_level, catch_logging, warn, filter_out_warnings, - ETSContext, wrapped_stdout, _get_call_line, + wrapped_stdout, _get_call_line, _record_warnings, ClosingStringIO) from .misc import (run_subprocess, _pl, _clean_names, pformat, _file_like, _explain_exception, _get_argvalues, sizeof_fmt, running_subprocess, _DefaultEventParser, - _assert_no_instances) + _assert_no_instances, _resource_path) from .progressbar import ProgressBar -from ._testing import (run_tests_if_main, run_command_if_main, - requires_sklearn, - requires_version, requires_nibabel, requires_mayavi, - requires_good_network, requires_mne, requires_pandas, - requires_h5py, traits_test, requires_pysurfer, +from ._testing import (run_command_if_main, requires_sklearn, + requires_version, requires_nibabel, requires_mne, + requires_good_network, requires_pandas, requires_h5py, ArgvSetter, SilenceStdout, has_freesurfer, has_mne_c, - _TempDir, has_nibabel, _import_mlab, buggy_mkl_svd, - requires_numpydoc, requires_vtk, requires_freesurfer, + _TempDir, has_nibabel, buggy_mkl_svd, + requires_numpydoc, requires_freesurfer, requires_nitime, requires_dipy, - requires_neuromag2ft, requires_pylsl, assert_object_equal, - assert_and_remove_boundary_annot, _raw_annot, - assert_dig_allclose, assert_meg_snr, assert_snr, - assert_stcs_equal, modified_env, _click_ch_name, - _close_event) + requires_neuromag2ft, requires_pylsl, + assert_object_equal, assert_and_remove_boundary_annot, + _raw_annot, assert_dig_allclose, assert_meg_snr, + assert_snr, assert_stcs_equal, modified_env, + _click_ch_name) from .numerics import (hashfunc, _compute_row_norms, _reg_pinv, random_permutation, _reject_data_segments, compute_corr, _get_inst_data, array_split_idx, @@ -62,9 +64,11 @@ _mask_to_onsets_offsets, _array_equal_nan, _julian_to_cal, _cal_to_julian, _dt_to_julian, _julian_to_dt, _dt_to_stamp, _stamp_to_dt, - _check_dt, _ReuseCycle, _arange_div) -from .mixin import (SizeMixin, GetEpochsMixin, _prepare_read_metadata, - _prepare_write_metadata, _FakeNoPandas, ShiftTimeMixin) + _check_dt, _ReuseCycle, _arange_div, _hashable_ndarray, + _custom_lru_cache) +from .mixin import (SizeMixin, GetEpochsMixin, TimeMixin, + _prepare_read_metadata, _prepare_write_metadata, + _FakeNoPandas) from .linalg import (_svd_lwork, _repeated_svd, _sym_mat_pow, sqrtm_sym, eigh, _get_blas_funcs) from .dataframe import (_set_pandas_dtype, _scale_dataframe_data, diff -Nru python-mne-0.23.4+dfsg/mne/utils/linalg.py python-mne-1.1.0+dfsg/mne/utils/linalg.py --- python-mne-0.23.4+dfsg/mne/utils/linalg.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/linalg.py 2022-08-03 15:48:14.000000000 +0000 @@ -20,7 +20,7 @@ """ # Authors: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import functools @@ -50,11 +50,15 @@ def _svd_lwork(shape, dtype=np.float64): """Set up SVD calculations on identical-shape float64/complex128 arrays.""" from scipy import linalg + try: + ds = linalg._decomp_svd + except AttributeError: # < 1.8.0 + ds = linalg.decomp_svd gesdd_lwork, gesvd_lwork = _get_lapack_funcs( dtype, ('gesdd_lwork', 'gesvd_lwork')) - sdd_lwork = linalg.decomp_svd._compute_lwork( + sdd_lwork = ds._compute_lwork( gesdd_lwork, *shape, compute_uv=True, full_matrices=False) - svd_lwork = linalg.decomp_svd._compute_lwork( + svd_lwork = ds._compute_lwork( gesvd_lwork, *shape, compute_uv=True, full_matrices=False) return sdd_lwork, svd_lwork diff -Nru python-mne-0.23.4+dfsg/mne/utils/_logging.py python-mne-1.1.0+dfsg/mne/utils/_logging.py --- python-mne-0.23.4+dfsg/mne/utils/_logging.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/_logging.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,9 +2,11 @@ """Some utility functions.""" # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause import contextlib +from decorator import FunctionMaker +import importlib import inspect from io import StringIO import re @@ -15,7 +17,6 @@ from typing import Any, Callable, TypeVar from .docs import fill_doc -from ..externals.decorator import FunctionMaker logger = logging.getLogger('mne') # one selection here used across mne-python @@ -101,29 +102,16 @@ except TypeError: # nothing to add pass - # Anything using verbose should either have `verbose=None` in the signature - # or have a `self.verbose` attribute (if in a method). This code path - # will raise an error if neither is the case. + # Anything using verbose should have `verbose=None` in the signature. + # This code path will raise an error if this is not the case. body = """\ def %(name)s(%(signature)s):\n try: - verbose - except UnboundLocalError: - try: - verbose = self.verbose - except NameError: - raise RuntimeError('Function %%s does not accept verbose parameter' - %% (_function_,)) - except AttributeError: - raise RuntimeError('Method %%s class does not have self.verbose' - %% (_function_,)) - else: - if verbose is None: - try: - verbose = self.verbose - except (NameError, AttributeError): - pass - if verbose is not None: + do_level_change = verbose is not None + except (NameError, UnboundLocalError): + raise RuntimeError('Function/method %%s does not accept verbose ' + 'parameter' %% (_function_,)) from None + if do_level_change: with _use_log_level_(verbose): return _function_(%(shortsignature)s) else: @@ -131,34 +119,58 @@ evaldict = dict( _use_log_level_=use_log_level, _function_=function) fm = FunctionMaker(function, None, None, None, None, function.__module__) - attrs = dict(__wrapped__=function, __qualname__=function.__qualname__) + attrs = dict(__wrapped__=function, __qualname__=function.__qualname__, + __globals__=function.__globals__) return fm.make(body, evaldict, addsource=True, **attrs) -class use_log_level(object): - """Context handler for logging level. +@fill_doc +class use_log_level: + """Context manager for logging level. Parameters ---------- - level : int - The level to use. - add_frames : int | None - Number of stack frames to include. + %(verbose)s + %(add_frames)s + + See Also + -------- + mne.verbose + + Notes + ----- + See the :ref:`logging documentation ` for details. + + Examples + -------- + >>> from mne import use_log_level + >>> from mne.utils import logger + >>> with use_log_level(False): + ... # Most MNE logger messages are "info" level, False makes them not + ... # print: + ... logger.info('This message will not be printed') + >>> with use_log_level(True): + ... # Using verbose=True in functions, methods, or this context manager + ... # will ensure they are printed + ... logger.info('This message will be printed!') + This message will be printed! """ - def __init__(self, level, add_frames=None): # noqa: D102 - self.level = level - self.add_frames = add_frames - self.old_frames = _filter.add_frames + def __init__(self, verbose, *, add_frames=None): # noqa: D102 + self._level = verbose + self._add_frames = add_frames + self._old_frames = _filter.add_frames def __enter__(self): # noqa: D105 - self.old_level = set_log_level(self.level, True, self.add_frames) + self._old_level = set_log_level( + self._level, return_old_level=True, add_frames=self._add_frames) def __exit__(self, *args): # noqa: D105 - add_frames = self.old_frames if self.add_frames is not None else None - set_log_level(self.old_level, add_frames=add_frames) + add_frames = self._old_frames if self._add_frames is not None else None + set_log_level(self._old_level, add_frames=add_frames) +@fill_doc def set_log_level(verbose=None, return_old_level=False, add_frames=None): """Set the logging level. @@ -173,10 +185,7 @@ it doesn't exist, defaults to INFO. return_old_level : bool If True, return the old verbosity level. - add_frames : int | None - If int, enable (>=1) or disable (0) the printing of stack frame - information using formatting. Default (None) does not change the - formatting. This can add overhead so is meant only for debugging. + %(add_frames)s Returns ------- @@ -283,20 +292,38 @@ stdout when complete. """ + def __init__(self, verbose=None): + self.verbose = verbose + def __enter__(self): # noqa: D105 + if self.verbose is not None: + self._ctx = use_log_level(self.verbose) + else: + self._ctx = contextlib.nullcontext() self._data = ClosingStringIO() self._lh = logging.StreamHandler(self._data) self._lh.setFormatter(logging.Formatter('%(message)s')) self._lh._mne_file_like = True # monkey patch for warn() use _remove_close_handlers(logger) logger.addHandler(self._lh) + self._ctx.__enter__() return self._data def __exit__(self, *args): # noqa: D105 + self._ctx.__exit__(*args) logger.removeHandler(self._lh) set_log_file(None) +@contextlib.contextmanager +def _record_warnings(): + # this is a helper that mostly acts like pytest.warns(None) did before + # pytest 7 + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + yield w + + class WrapStdOut(object): """Dynamically wrap to sys.stdout. @@ -316,7 +343,8 @@ _verbose_dec_re = re.compile('^$') -def warn(message, category=RuntimeWarning, module='mne'): +def warn(message, category=RuntimeWarning, module='mne', + ignore_namespaces=('mne',)): """Emit a warning with trace outside the mne namespace. This function takes arguments like warnings.warn, and sends messages @@ -333,9 +361,13 @@ The warning class. Defaults to ``RuntimeWarning``. module : str The name of the module emitting the warning. + ignore_namespaces : list of str + Namespaces to ignore when traversing the stack. + + .. versionadded:: 0.24 """ - import mne - root_dir = op.dirname(mne.__file__) + root_dirs = [importlib.import_module(ns) for ns in ignore_namespaces] + root_dirs = [op.dirname(ns.__file__) for ns in root_dirs] frame = None if logger.level <= logging.WARN: frame = inspect.currentframe() @@ -346,7 +378,7 @@ if not _verbose_dec_re.search(fname): # treat tests as scripts # and don't capture unittest/case.py (assert_raises) - if not (fname.startswith(root_dir) or + if not (any(fname.startswith(rd) for rd in root_dirs) or ('unittest' in fname and 'case' in fname)) or \ op.basename(op.dirname(fname)) == 'tests': break @@ -392,28 +424,6 @@ match : str | None text or regex that matches the error message to filter out - - Examples - -------- - This can be used as:: - - >>> import pytest - >>> import warnings - >>> from mne.utils import filter_out_warnings - >>> with pytest.warns(None) as recwarn: - ... warnings.warn("value must be 0 or None", UserWarning) - >>> filter_out_warnings(recwarn, match=".* 0 or None") - >>> assert len(recwarn.list) == 0 - - >>> with pytest.warns(None) as recwarn: - ... warnings.warn("value must be 42", UserWarning) - >>> filter_out_warnings(recwarn, match=r'.* must be \d+$') - >>> assert len(recwarn.list) == 0 - - >>> with pytest.warns(None) as recwarn: - ... warnings.warn("this is not here", UserWarning) - >>> filter_out_warnings(recwarn, match=r'.* must be \d+$') - >>> assert len(recwarn.list) == 1 """ regexp = re.compile('.*' if match is None else match) is_category = [w.category == category if category is not None else True @@ -427,20 +437,6 @@ warn_record._list.pop(i) -class ETSContext(object): - """Add more meaningful message to errors generated by ETS Toolkit.""" - - def __enter__(self): # noqa: D105 - pass - - def __exit__(self, type, value, traceback): # noqa: D105 - if isinstance(value, SystemExit) and value.code.\ - startswith("This program needs access to the screen"): - value.code += ("\nThis can probably be solved by setting " - "ETS_TOOLKIT=qt4. On bash, type\n\n $ export " - "ETS_TOOLKIT=qt4\n\nand run the command again.") - - @contextlib.contextmanager def wrapped_stdout(indent='', cull_newlines=False): """Wrap stdout writes to logger.info, with an optional indent prefix. diff -Nru python-mne-0.23.4+dfsg/mne/utils/misc.py python-mne-1.1.0+dfsg/mne/utils/misc.py --- python-mne-0.23.4+dfsg/mne/utils/misc.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/misc.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ """Some miscellaneous utility functions.""" # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause from contextlib import contextmanager import fnmatch @@ -24,10 +24,10 @@ from ._logging import logger, verbose, warn -def _pl(x, non_pl=''): +def _pl(x, non_pl='', pl='s'): """Determine if plural should be used.""" len_x = x if isinstance(x, (int, np.generic)) else len(x) - return non_pl if len_x == 1 else 's' + return non_pl if len_x == 1 else pl def _explain_exception(start=-1, stop=None, prefix='> '): @@ -123,7 +123,7 @@ # non-blocking adapted from https://stackoverflow.com/questions/375427/non-blocking-read-on-a-subprocess-pipe-in-python#4896288 # noqa: E501 out_q = Queue() err_q = Queue() - with running_subprocess(command, *args, **kwargs) as p: + with running_subprocess(command, *args, **kwargs) as p, p.stdout, p.stderr: out_t = Thread(target=_enqueue_output, args=(p.stdout, out_q)) err_t = Thread(target=_enqueue_output, args=(p.stderr, err_q)) out_t.daemon = True @@ -149,12 +149,16 @@ break else: err = err.decode('utf-8') + # Leave this as logger.warning rather than warn(...) to + # mirror the logger.info above for stdout. This function + # is basically just a version of subprocess.call, and + # shouldn't emit Python warnings due to stderr outputs + # (the calling function can check for stderr output and + # emit a warning if it wants). logger.warning(err) all_err += err if do_break: break - p.stdout.close() - p.stderr.close() output = (all_out, all_err) if return_code: @@ -322,6 +326,14 @@ return all(callable(getattr(obj, name, None)) for name in ('read', 'seek')) +def _fullname(obj): + klass = obj.__class__ + module = klass.__module__ + if module == 'builtins': + return klass.__qualname__ + return module + '.' + klass.__qualname__ + + def _assert_no_instances(cls, when=''): __tracebackhide__ = True n = 0 @@ -334,6 +346,9 @@ except Exception: # such as a weakref check = False if check: + if cls.__name__ == 'Brain': + ref.append( + f'Brain._cleaned = {getattr(obj, "_cleaned", None)}') rr = gc.get_referrers(obj) count = 0 for r in rr: @@ -343,11 +358,51 @@ not inspect.isframe(r): if isinstance(r, (list, dict)): rep = f'len={len(r)}' + r_ = gc.get_referrers(r) + types = (_fullname(x) for x in r_) + types = "/".join(sorted(set( + x for x in types if x is not None))) + rep += f', {len(r_)} referrers: {types}' + del r_ else: rep = repr(r)[:100].replace('\n', ' ') - ref.append(f'{r.__class__.__name__}: {rep}') + # If it's a __closure__, get more information + if rep.startswith(' 0 - assert n == 0, f'{n} {when}:\n' + '\n'.join(ref) + del obj + del objs + gc.collect() + assert n == 0, f'\n{n} {cls.__name__} @ {when}:\n' + '\n'.join(ref) + + +def _resource_path(submodule, filename): + """Return a full system path to a package resource (AKA a file). + + Parameters + ---------- + submodule : str + An import-style module or submodule name + (e.g., "mne.datasets.testing"). + filename : str + The file whose full path you want. + + Returns + ------- + path : str + The full system path to the requested file. + """ + try: + from importlib.resources import files + return files(submodule).joinpath(filename) + except ImportError: + from pkg_resources import resource_filename + return resource_filename(submodule, filename) diff -Nru python-mne-0.23.4+dfsg/mne/utils/mixin.py python-mne-1.1.0+dfsg/mne/utils/mixin.py --- python-mne-0.23.4+dfsg/mne/utils/mixin.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/mixin.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ """Some utility functions.""" # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause from collections import OrderedDict from copy import deepcopy @@ -13,7 +13,7 @@ from .check import _check_pandas_installed, _check_preload, _validate_type from ._logging import warn, verbose -from .numerics import object_size, object_hash +from .numerics import object_size, object_hash, _time_mask logger = logging.getLogger('mne') # one selection here used across mne-python @@ -232,12 +232,14 @@ def _keys_to_idx(self, keys): """Find entries in event dict.""" + from ..event import match_event_names # avoid circular import + keys = keys if isinstance(keys, (list, tuple)) else [keys] try: # Assume it's a condition name return np.where(np.any( np.array([self.events[:, 2] == self.event_id[k] - for k in _hid_match(self.event_id, keys)]), + for k in match_event_names(self.event_id, keys)]), axis=0))[0] except KeyError as err: # Could we in principle use metadata with these Epochs and keys? @@ -252,12 +254,15 @@ self._check_metadata(metadata=md) try: # Try metadata - mask = self.metadata.eval(keys[0], engine='python').values + vals = self.metadata.reset_index().query( + keys[0], + engine='python' + ).index.values except Exception as exp: msg += (' The epochs.metadata Pandas query did not ' 'yield any results: %s' % (exp.args[0],)) else: - return np.where(mask)[0] + return vals else: # If not, warn this might be a problem msg += (' The epochs.metadata Pandas query could not ' @@ -418,83 +423,221 @@ self._metadata = metadata -def _prepare_write_metadata(metadata): - """Convert metadata to JSON for saving.""" - if metadata is not None: - if not isinstance(metadata, list): - metadata = metadata.to_json(orient='records') - else: # Pandas DataFrame - metadata = json.dumps(metadata) - assert isinstance(metadata, str) - return metadata +def _check_decim(info, decim, offset, check_filter=True): + """Check decimation parameters.""" + if decim < 1 or decim != int(decim): + raise ValueError('decim must be an integer > 0') + decim = int(decim) + new_sfreq = info['sfreq'] / float(decim) + offset = int(offset) + if not 0 <= offset < decim: + raise ValueError(f'decim must be at least 0 and less than {decim}, ' + f'got {offset}') + if check_filter: + lowpass = info['lowpass'] + if decim > 1 and lowpass is None: + warn('The measurement information indicates data is not low-pass ' + f'filtered. The decim={decim} parameter will result in a ' + f'sampling frequency of {new_sfreq} Hz, which can cause ' + 'aliasing artifacts.') + elif decim > 1 and new_sfreq < 3 * lowpass: + warn('The measurement information indicates a low-pass frequency ' + f'of {lowpass} Hz. The decim={decim} parameter will result ' + f'in a sampling frequency of {new_sfreq} Hz, which can ' + 'cause aliasing artifacts.') # > 50% nyquist lim + return decim, offset, new_sfreq -def _prepare_read_metadata(metadata): - """Convert saved metadata back from JSON.""" - if metadata is not None: - pd = _check_pandas_installed(strict=False) - # use json.loads because this preserves ordering - # (which is necessary for round-trip equivalence) - metadata = json.loads(metadata, object_pairs_hook=OrderedDict) - assert isinstance(metadata, list) - if pd: - metadata = pd.DataFrame.from_records(metadata) - assert isinstance(metadata, pd.DataFrame) - return metadata +class TimeMixin(object): + """Class to handle operations on time for MNE objects.""" + @property + def times(self): + """Time vector in seconds.""" + return self._times_readonly + + def _set_times(self, times): + """Set self._times_readonly (and make it read only).""" + # naming used to indicate that it shouldn't be + # changed directly, but rather via this method + self._times_readonly = times.copy() + self._times_readonly.flags['WRITEABLE'] = False -def _hid_match(event_id, keys): - """Match event IDs using HID selection. + @property + def tmin(self): + """First time point.""" + return self.times[0] - Parameters - ---------- - event_id : dict - The event ID dictionary. - keys : list | str - The event ID or subset (for HID), or list of such items. - - Returns - ------- - use_keys : list - The full keys that fit the selection criteria. - """ - # form the hierarchical event ID mapping - use_keys = [] - for key in keys: - if not isinstance(key, str): - raise KeyError('keys must be strings, got %s (%s)' - % (type(key), key)) - use_keys.extend(k for k in event_id.keys() - if set(key.split('/')).issubset(k.split('/'))) - if len(use_keys) == 0: - raise KeyError('Event "{}" is not in Epochs. Event_ids must be one of ' - '"{}"'.format(key, ', '.join(event_id.keys()))) - use_keys = list(set(use_keys)) # deduplicate if necessary - return use_keys + @property + def tmax(self): + """Last time point.""" + return self.times[-1] + @verbose + def crop(self, tmin=None, tmax=None, include_tmax=True, verbose=None): + """Crop data to a given time interval. -class _FakeNoPandas(object): # noqa: D101 - def __enter__(self): # noqa: D105 + Parameters + ---------- + tmin : float | None + Start time of selection in seconds. + tmax : float | None + End time of selection in seconds. + %(include_tmax)s + %(verbose)s - def _check(strict=True): - if strict: - raise RuntimeError('Pandas not installed') + Returns + ------- + inst : instance of Raw, Epochs, Evoked, AverageTFR, or SourceEstimate + The cropped time-series object, modified in-place. + + Notes + ----- + %(notes_tmax_included_by_default)s + """ + if tmin is None: + tmin = self.tmin + elif tmin < self.tmin: + warn(f'tmin is not in time interval. tmin is set to ' + f'{type(self)}.tmin ({self.tmin:g} sec)') + tmin = self.tmin + + if tmax is None: + tmax = self.tmax + elif tmax > self.tmax: + warn(f'tmax is not in time interval. tmax is set to ' + f'{type(self)}.tmax ({self.tmax:g} sec)') + tmax = self.tmax + include_tmax = True + + mask = _time_mask(self.times, tmin, tmax, sfreq=self.info['sfreq'], + include_tmax=include_tmax) + self._set_times(self.times[mask]) + self._raw_times = self._raw_times[mask] + self._update_first_last() + self._data = self._data[..., mask] + + return self + + @verbose + def decimate(self, decim, offset=0, verbose=None): + """Decimate the time-series data. + + Parameters + ---------- + %(decim)s + %(offset_decim)s + %(verbose)s + + Returns + ------- + inst : MNE-object + The decimated object. + + See Also + -------- + mne.Epochs.resample + mne.io.Raw.resample + + Notes + ----- + %(decim_notes)s + + If ``decim`` is 1, this method does not copy the underlying data. + + .. versionadded:: 0.10.0 + + References + ---------- + .. footbibliography:: + """ + # if epochs have frequencies, they are not in time (EpochsTFR) + # and so do not need to be checked whether they have been + # appropriately filtered to avoid aliasing + decim, offset, new_sfreq = _check_decim( + self.info, decim, offset, check_filter=not hasattr(self, 'freqs')) + start_idx = int(round(-self._raw_times[0] * (self.info['sfreq'] * + self._decim))) + self._decim *= decim + i_start = start_idx % self._decim + offset + decim_slice = slice(i_start, None, self._decim) + with self.info._unlock(): + self.info['sfreq'] = new_sfreq + + if self.preload: + if decim != 1: + self._data = self._data[..., decim_slice].copy() + self._raw_times = self._raw_times[decim_slice].copy() else: - return False + self._data = np.ascontiguousarray(self._data) + self._decim_slice = slice(None) + self._decim = 1 + else: + self._decim_slice = decim_slice + self._set_times(self._raw_times[self._decim_slice]) + self._update_first_last() + return self - import mne - self._old_check = _check_pandas_installed - mne.epochs._check_pandas_installed = _check - mne.utils.mixin._check_pandas_installed = _check + # Overridden method signature does not match call... + def time_as_index(self, times, use_rounding=False): # lgtm + """Convert time to indices. - def __exit__(self, *args): # noqa: D105 - import mne - mne.epochs._check_pandas_installed = self._old_check - mne.utils.mixin._check_pandas_installed = self._old_check + Parameters + ---------- + times : list-like | float | int + List of numbers or a number representing points in time. + use_rounding : bool + If True, use rounding (instead of truncation) when converting + times to indices. This can help avoid non-unique indices. + + Returns + ------- + index : ndarray + Indices corresponding to the times supplied. + """ + from ..source_estimate import _BaseSourceEstimate + if isinstance(self, _BaseSourceEstimate): + sfreq = 1. / self.tstep + else: + sfreq = self.info['sfreq'] + index = (np.atleast_1d(times) - self.times[0]) * sfreq + if use_rounding: + index = np.round(index) + return index.astype(int) + + def _handle_tmin_tmax(self, tmin, tmax): + """Convert seconds to index into data. + + Parameters + ---------- + tmin : int | float | None + Start time of data to get in seconds. + tmax : int | float | None + End time of data to get in seconds. + + Returns + ------- + start : int + Integer index into data corresponding to tmin. + stop : int + Integer index into data corresponding to tmax. + """ + _validate_type(tmin, types=('numeric', None), item_name='tmin', + type_name="int, float, None") + _validate_type(tmax, types=('numeric', None), item_name='tmax', + type_name='int, float, None') + + # handle tmin/tmax as start and stop indices into data array + n_times = self.times.size + start = 0 if tmin is None else self.time_as_index(tmin)[0] + stop = n_times if tmax is None else self.time_as_index(tmax)[0] + + # truncate start/stop to the open interval [0, n_times] + start = min(max(0, start), n_times) + stop = min(max(0, stop), n_times) -class ShiftTimeMixin(object): - """Class for shift_time method (Epochs, Evoked, and DipoleFixed).""" + return start, stop def shift_time(self, tshift, relative=True): """Shift time scale in epoched or evoked data. @@ -512,8 +655,8 @@ Returns ------- - epochs : instance of Epochs - The modified Epochs instance. + epochs : MNE-object + The modified instance. Notes ----- @@ -521,18 +664,59 @@ data sample by an arbitrary amount. It does *not* resample the signal or change the *data* values in any way. """ - from ..epochs import BaseEpochs _check_preload(self, 'shift_time') start = tshift + (self.times[0] if relative else 0.) new_times = start + np.arange(len(self.times)) / self.info['sfreq'] - if isinstance(self, BaseEpochs): - self._set_times(new_times) - else: - self.times = new_times - self._update_first_last() + self._set_times(new_times) + self._update_first_last() return self def _update_first_last(self): """Update self.first and self.last (sample indices).""" self.first = int(round(self.times[0] * self.info['sfreq'])) self.last = len(self.times) + self.first - 1 + + +def _prepare_write_metadata(metadata): + """Convert metadata to JSON for saving.""" + if metadata is not None: + if not isinstance(metadata, list): + metadata = metadata.to_json(orient='records') + else: # Pandas DataFrame + metadata = json.dumps(metadata) + assert isinstance(metadata, str) + return metadata + + +def _prepare_read_metadata(metadata): + """Convert saved metadata back from JSON.""" + if metadata is not None: + pd = _check_pandas_installed(strict=False) + # use json.loads because this preserves ordering + # (which is necessary for round-trip equivalence) + metadata = json.loads(metadata, object_pairs_hook=OrderedDict) + assert isinstance(metadata, list) + if pd: + metadata = pd.DataFrame.from_records(metadata) + assert isinstance(metadata, pd.DataFrame) + return metadata + + +class _FakeNoPandas(object): # noqa: D101 + def __enter__(self): # noqa: D105 + + def _check(strict=True): + if strict: + raise RuntimeError('Pandas not installed') + else: + return False + + import mne + self._old_check = _check_pandas_installed + mne.epochs._check_pandas_installed = _check + mne.utils.mixin._check_pandas_installed = _check + + def __exit__(self, *args): # noqa: D105 + import mne + mne.epochs._check_pandas_installed = self._old_check + mne.utils.mixin._check_pandas_installed = self._old_check diff -Nru python-mne-0.23.4+dfsg/mne/utils/numerics.py python-mne-1.1.0+dfsg/mne/utils/numerics.py --- python-mne-0.23.4+dfsg/mne/utils/numerics.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/numerics.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,20 +3,19 @@ # Authors: Alexandre Gramfort # Clemens Brunner # -# License: BSD (3-clause) +# License: BSD-3-Clause -from contextlib import contextmanager import hashlib -from io import BytesIO, StringIO -from math import sqrt import numbers import operator import os -import os.path as op -from math import ceil import shutil import sys +from contextlib import contextmanager from datetime import datetime, timedelta, timezone +from io import BytesIO, StringIO +from math import ceil, sqrt +from pathlib import Path import numpy as np @@ -427,7 +426,7 @@ # adapted from sphinx-gallery assert fname.endswith('.new') fname_old = fname[:-4] - if op.isfile(fname_old) and hashfunc(fname) == hashfunc(fname_old): + if os.path.isfile(fname_old) and hashfunc(fname) == hashfunc(fname_old): os.remove(fname) else: shutil.move(fname, fname_old) @@ -605,6 +604,18 @@ return grand_average +class _HashableNdarray(np.ndarray): + def __hash__(self): + return object_hash(self) + + def __eq__(self, other): + return NotImplementedError # defer to hash + + +def _hashable_ndarray(x): + return x.view(_HashableNdarray) + + def object_hash(x, h=None): """Hash a reasonable python object. @@ -621,6 +632,7 @@ digest : int The digest resulting from the hash. """ + from scipy import sparse if h is None: h = hashlib.md5() if hasattr(x, 'keys'): @@ -642,6 +654,13 @@ h.update(x.tobytes()) elif isinstance(x, datetime): object_hash(_dt_to_stamp(x)) + elif sparse.issparse(x): + h.update(str(type(x)).encode('utf-8')) + if not isinstance(x, (sparse.csr_matrix, sparse.csc_matrix)): + raise RuntimeError(f'Unsupported sparse type {type(x)}') + h.update(x.data.tobytes()) + h.update(x.indices.tobytes()) + h.update(x.indptr.tobytes()) elif hasattr(x, '__len__'): # all other list-like types h.update(str(type(x)).encode('utf-8')) @@ -677,7 +696,7 @@ id_ = id(x) if id_ in memo: return 0 # do not add already existing ones - if isinstance(x, (bytes, str, int, float, type(None))): + if isinstance(x, (bytes, str, int, float, type(None), Path)): size = sys.getsizeof(x) elif isinstance(x, np.ndarray): # On newer versions of NumPy, just doing sys.getsizeof(x) works, @@ -713,15 +732,19 @@ return keys -def _array_equal_nan(a, b): +def _array_equal_nan(a, b, allclose=False): try: - np.testing.assert_array_equal(a, b) + if allclose: + func = np.testing.assert_allclose + else: + func = np.testing.assert_array_equal + func(a, b) except AssertionError: return False return True -def object_diff(a, b, pre=''): +def object_diff(a, b, pre='', *, allclose=False): """Compute all differences between two python variables. Parameters @@ -733,6 +756,8 @@ Must be same type as ``a``. pre : str String to prepend to each line. + allclose : bool + If True (default False), use assert_allclose. Returns ------- @@ -759,15 +784,17 @@ out += pre + ' right missing key %s\n' % key else: out += object_diff(a[key], b[key], - pre=(pre + '[%s]' % repr(key))) + pre=(pre + '[%s]' % repr(key)), + allclose=allclose) elif isinstance(a, (list, tuple)): if len(a) != len(b): out += pre + ' length mismatch (%s, %s)\n' % (len(a), len(b)) else: for ii, (xx1, xx2) in enumerate(zip(a, b)): - out += object_diff(xx1, xx2, pre + '[%s]' % ii) + out += object_diff( + xx1, xx2, pre + '[%s]' % ii, allclose=allclose) elif isinstance(a, float): - if not _array_equal_nan(a, b): + if not _array_equal_nan(a, b, allclose): out += pre + ' value mismatch (%s, %s)\n' % (a, b) elif isinstance(a, (str, int, bytes, np.generic)): if a != b: @@ -776,7 +803,7 @@ if b is not None: out += pre + ' left is None, right is not (%s)\n' % (b) elif isinstance(a, np.ndarray): - if not _array_equal_nan(a, b): + if not _array_equal_nan(a, b, allclose): out += pre + ' array mismatch\n' elif isinstance(a, (StringIO, BytesIO)): if a.getvalue() != b.getvalue(): @@ -796,7 +823,8 @@ out += pre + (' sparse matrix a and b differ on %s ' 'elements' % c.nnz) elif hasattr(a, '__getstate__'): - out += object_diff(a.__getstate__(), b.__getstate__(), pre) + out += object_diff(a.__getstate__(), b.__getstate__(), pre, + allclose=allclose) else: raise RuntimeError(pre + ': unsupported type %s (%s)' % (type(a), a)) return out @@ -1021,7 +1049,7 @@ if len(stamp) == 1: # In case there is no microseconds information stamp.append(0) return (datetime.fromtimestamp(0, tz=timezone.utc) + - timedelta(0, stamp[0], stamp[1])) # day, sec, µs + timedelta(seconds=stamp[0], microseconds=stamp[1])) class _ReuseCycle(object): @@ -1076,3 +1104,29 @@ return out else: # pragma: no cover _arange_div = _arange_div_fallback + + +_LRU_CACHES = dict() +_LRU_CACHE_MAXSIZES = dict() + + +def _custom_lru_cache(maxsize): + def dec(fun): + fun_hash = hash(fun) + this_cache = _LRU_CACHES[fun_hash] = dict() + _LRU_CACHE_MAXSIZES[fun_hash] = maxsize + + def cache_fun(*args): + hash_ = object_hash(args) + if hash_ in this_cache: + this_val = this_cache.pop(hash_) + else: + this_val = fun(*args) + this_cache[hash_] = this_val # (re)insert in last pos + while len(this_cache) > _LRU_CACHE_MAXSIZES[fun_hash]: + for key in this_cache: # just an easy way to get first element + this_cache.pop(key) + break # first in, first out + return this_val + return cache_fun + return dec diff -Nru python-mne-0.23.4+dfsg/mne/utils/progressbar.py python-mne-1.1.0+dfsg/mne/utils/progressbar.py --- python-mne-0.23.4+dfsg/mne/utils/progressbar.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/progressbar.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ """Some utility functions.""" # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause from collections.abc import Iterable import os @@ -46,12 +46,18 @@ **kwargs): # noqa: D102 # The following mimics this, but with configurable module to use # from ..externals.tqdm import auto - from ..externals import tqdm + import tqdm which_tqdm = get_config('MNE_TQDM', 'tqdm.auto') _check_option('MNE_TQDM', which_tqdm[:5], ('tqdm', 'tqdm.', 'off'), extra='beginning') logger.debug(f'Using ProgressBar with {which_tqdm}') if which_tqdm not in ('tqdm', 'off'): + try: + __import__(which_tqdm) + except Exception as exc: + raise ValueError( + f'Unknown tqdm backend {repr(which_tqdm)}, got: {exc}' + ) from None tqdm = getattr(tqdm, which_tqdm.split('.', 1)[1]) tqdm = tqdm.tqdm defaults = dict( diff -Nru python-mne-0.23.4+dfsg/mne/utils/_testing.py python-mne-1.1.0+dfsg/mne/utils/_testing.py --- python-mne-0.23.4+dfsg/mne/utils/_testing.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/_testing.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,10 +2,9 @@ """Testing functions.""" # Authors: Alexandre Gramfort # -# License: BSD (3-clause) +# License: BSD-3-Clause from contextlib import contextmanager -from distutils.version import LooseVersion from functools import partial, wraps import os import inspect @@ -15,21 +14,15 @@ import tempfile import traceback from unittest import SkipTest -import warnings import numpy as np from numpy.testing import assert_array_equal, assert_allclose from ._logging import warn, ClosingStringIO +from .check import check_version from .numerics import object_diff -def nottest(f): - """Mark a function as not a test (decorator).""" - f.__test__ = False - return f - - def _explain_exception(start=-1, stop=None, prefix='> '): """Explain an exception.""" # start=-1 means "only the most recent caller" @@ -72,7 +65,7 @@ def requires_dipy(): """Check for dipy.""" import pytest - # for some strange reason on CIs we cane get: + # for some strange reason on CIs we can get: # # can get weird ImportError: dlopen: cannot load any more object # with static TLS @@ -83,19 +76,24 @@ from dipy.align.reslice import reslice # noqa, analysis:ignore from dipy.align.imaffine import AffineMap # noqa, analysis:ignore from dipy.align.imwarp import DiffeomorphicMap # noqa, analysis:ignore - except Exception: + except Exception as exc: have = False + why = str(exc) else: + why = '' have = True - return pytest.mark.skipif(not have, reason='Requires dipy >= 0.10.1') + return pytest.mark.skipif( + not have, reason=f'Requires dipy >= 0.10.1, got: {why}') def requires_version(library, min_version='0.0'): """Check for a library version.""" import pytest + reason = f'Requires {library}' + if min_version != '0.0': + reason += f' version >= {min_version}' return pytest.mark.skipif(not check_version(library, min_version), - reason=('Requires %s version >= %s' - % (library, min_version))) + reason=reason) def requires_module(function, name, call=None): @@ -114,18 +112,6 @@ return pytest.mark.skipif(skip, reason=reason)(function) -_pandas_call = """ -import pandas -version = LooseVersion(pandas.__version__) -if version < '0.8.0': - raise ImportError -""" - -_mayavi_call = """ -with warnings.catch_warnings(record=True): # traits - from mayavi import mlab -""" - _mne_call = """ if not has_mne_c(): raise ImportError @@ -141,10 +127,9 @@ raise ImportError """ -requires_pandas = partial(requires_module, name='pandas', call=_pandas_call) +requires_pandas = partial(requires_module, name='pandas') requires_pylsl = partial(requires_module, name='pylsl') requires_sklearn = partial(requires_module, name='sklearn') -requires_mayavi = partial(requires_module, name='mayavi', call=_mayavi_call) requires_mne = partial(requires_module, name='MNE-C', call=_mne_call) @@ -168,16 +153,12 @@ requires_neuromag2ft = partial(requires_module, name='neuromag2ft', call=_n2ft_call) -requires_vtk = partial(requires_module, name='vtk') -requires_pysurfer = partial(requires_module, name='PySurfer', - call="""import warnings -with warnings.catch_warnings(record=True): - from surfer import Brain""") requires_good_network = partial( requires_module, name='good network connection', call='if int(os.environ.get("MNE_SKIP_NETWORK_TESTS", 0)):\n' ' raise ImportError') requires_nitime = partial(requires_module, name='nitime') +# just keep this in case downstream packages need it (no coverage hit here) requires_h5py = partial(requires_module, name='h5py') @@ -186,83 +167,6 @@ return requires_version('numpydoc', '1.0')(func) # validate needs 1.0 -def check_version(library, min_version): - r"""Check minimum library version required. - - Parameters - ---------- - library : str - The library name to import. Must have a ``__version__`` property. - min_version : str - The minimum version string. Anything that matches - ``'(\d+ | [a-z]+ | \.)'``. Can also be empty to skip version - check (just check for library presence). - - Returns - ------- - ok : bool - True if the library exists with at least the specified version. - """ - ok = True - try: - library = __import__(library) - except ImportError: - ok = False - else: - if min_version: - this_version = LooseVersion( - getattr(library, '__version__', '0.0').lstrip('v')) - if this_version < min_version: - ok = False - return ok - - -def _check_mayavi_version(min_version='4.3.0'): - """Check mayavi version.""" - if not check_version('mayavi', min_version): - raise RuntimeError("Need mayavi >= %s" % min_version) - - -def _import_mlab(): - """Quietly import mlab.""" - with warnings.catch_warnings(record=True): - from mayavi import mlab - return mlab - - -@contextmanager -def traits_test_context(): - """Context to raise errors in trait handlers.""" - from traits.api import push_exception_handler - - push_exception_handler(reraise_exceptions=True) - try: - yield - finally: - push_exception_handler(reraise_exceptions=False) - - -def traits_test(test_func): - """Raise errors in trait handlers (decorator).""" - @wraps(test_func) - def dec(*args, **kwargs): - with traits_test_context(): - return test_func(*args, **kwargs) - return dec - - -@nottest -def run_tests_if_main(): - """Run tests in a given file if it is run as a script.""" - local_vars = inspect.currentframe().f_back.f_locals - if local_vars.get('__name__', '') != '__main__': - return - import pytest - code = pytest.main([local_vars['__file__'], '-v']) - if code: - raise AssertionError('pytest finished with errors (%d)' % (code,)) - - def run_command_if_main(): """Run a given command if it's __main__.""" local_vars = inspect.currentframe().f_back.f_locals @@ -377,8 +281,8 @@ raw = RawArray(data=np.empty((10, 10)), info=info, first_samp=10) if meas_date is not None: meas_date = _handle_meas_date(meas_date) - raw.info['meas_date'] = meas_date - raw.info._check_consistency() + with raw.info._unlock(check_after=True): + raw.info['meas_date'] = meas_date annot = Annotations([.5], [.2], ['dummy'], orig_time) raw.set_annotations(annotations=annot) return raw @@ -517,6 +421,9 @@ **kwargs : dict The key/value pairs of environment variables to replace. """ + warn('modified_env is deprecated and will be removed in 1.1. In tests, ' + 'use monkeypatch from pytest instead. In subprocess calls, pass ' + 'modified environments directly.', DeprecationWarning) orig_env = dict() for key, val in d.items(): orig_env[key] = os.getenv(key) @@ -545,13 +452,3 @@ y = bbox.intervaly.mean() _fake_click(fig, fig.mne.ax_main, (x, y), xform='pix', button=button) - - -def _close_event(fig): - """Force calling of the MPL figure close event.""" - # XXX workaround: plt.close() doesn't spawn close_event on Agg backend - # (check MPL github issue #18609; scheduled to be fixed by MPL 3.4) - try: - fig.canvas.close_event() - except ValueError: # old mpl with Qt - pass # pragma: no cover diff -Nru python-mne-0.23.4+dfsg/mne/utils/tests/test_bunch.py python-mne-1.1.0+dfsg/mne/utils/tests/test_bunch.py --- python-mne-0.23.4+dfsg/mne/utils/tests/test_bunch.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/tests/test_bunch.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Authors: Clemens Brunner # Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause import pickle from mne.utils import BunchConstNamed diff -Nru python-mne-0.23.4+dfsg/mne/utils/tests/test_check.py python-mne-1.1.0+dfsg/mne/utils/tests/test_check.py --- python-mne-0.23.4+dfsg/mne/utils/tests/test_check.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/tests/test_check.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,7 @@ # Authors: MNE Developers # Stefan Appelhoff # -# License: BSD (3-clause) +# License: BSD-3-Clause import os import os.path as op import sys @@ -14,12 +14,11 @@ import mne from mne import read_vectorview_selection from mne.datasets import testing -from mne.io.pick import pick_channels_cov -from mne.utils import (check_random_state, _check_fname, check_fname, - _check_subject, requires_mayavi, traits_test, - _check_mayavi_version, _check_info_inv, _check_option, - check_version, _check_path_like, _validate_type, - _suggest, _on_missing, requires_nibabel, _safe_input) +from mne.io.pick import pick_channels_cov, _picks_to_idx +from mne.utils import (check_random_state, _check_fname, check_fname, _suggest, + _check_subject, _check_info_inv, _check_option, Bunch, + check_version, _path_like, _validate_type, _on_missing, + requires_nibabel, _safe_input, _check_ch_locs) data_path = testing.data_path(download=False) base_dir = op.join(data_path, 'MEG', 'sample') @@ -31,12 +30,12 @@ @testing.requires_testing_data -def test_check(tmpdir): +def test_check(tmp_path): """Test checking functions.""" pytest.raises(ValueError, check_random_state, 'foo') pytest.raises(TypeError, _check_fname, 1) _check_fname(Path('./foo')) - fname = str(tmpdir.join('foo')) + fname = tmp_path / 'foo' with open(fname, 'wb'): pass assert op.isfile(fname) @@ -65,22 +64,14 @@ @pytest.mark.parametrize('suffix', ('_meg.fif', '_eeg.fif', '_ieeg.fif', '_meg.fif.gz', '_eeg.fif.gz', '_ieeg.fif.gz')) -def test_check_fname_suffixes(suffix, tmpdir): +def test_check_fname_suffixes(suffix, tmp_path): """Test checking for valid filename suffixes.""" - new_fname = str(tmpdir.join(op.basename(fname_raw) - .replace('_raw.fif', suffix))) + new_fname = tmp_path / op.basename(fname_raw).replace('_raw.fif', suffix) raw = mne.io.read_raw_fif(fname_raw).crop(0, 0.1) raw.save(new_fname) mne.io.read_raw_fif(new_fname) -@requires_mayavi -@traits_test -def test_check_mayavi(): - """Test mayavi version check.""" - pytest.raises(RuntimeError, _check_mayavi_version, '100.0.0') - - def _get_data(): """Read in data used in tests.""" # read forward model @@ -176,15 +167,15 @@ assert _check_option('option', 'bad', ['valid']) -def test_check_path_like(): - """Test _check_path_like().""" +def test_path_like(): + """Test _path_like().""" str_path = str(base_dir) pathlib_path = Path(base_dir) no_path = dict(foo='bar') - assert _check_path_like(str_path) is True - assert _check_path_like(pathlib_path) is True - assert _check_path_like(no_path) is False + assert _path_like(str_path) is True + assert _path_like(pathlib_path) is True + assert _path_like(no_path) is False def test_validate_type(): @@ -231,3 +222,81 @@ with pytest.raises(RuntimeError, match='Could not use input'): _safe_input('whatever', alt='nothing') assert _safe_input('whatever', use='nothing') == 'nothing' + + +@testing.requires_testing_data +def test_check_ch_locs(): + """Test _check_ch_locs behavior.""" + info = mne.io.read_info(fname_raw) + assert _check_ch_locs(info=info) + + for picks in ([0], [0, 1], None): + assert _check_ch_locs(info=info, picks=picks) + + for ch_type in ('meg', 'mag', 'grad', 'eeg'): + assert _check_ch_locs(info=info, ch_type=ch_type) + + # drop locations for EEG + picks_eeg = _picks_to_idx(info=info, picks='eeg') + for idx in picks_eeg: + info['chs'][idx]['loc'][:3] = np.nan + + # EEG tests should fail now + assert _check_ch_locs(info=info, picks=picks_eeg) is False + assert _check_ch_locs(info=info, ch_type='eeg') is False + + # tests for other (and "all") channels should still pass + assert _check_ch_locs(info=info) + assert _check_ch_locs(info=info, ch_type='mag') + + +# Check a bunch of version schemes as of 2022/03/01 +# We don't have to get this 100% generalized, but it would be nice if all +# of these worked. +@pytest.mark.parametrize('version, want, have_unstripped', [ + # test some dev cases + ('1.23.0.dev0+782.g1168868df6', '1.23', False), # NumPy + ('1.9.0.dev0+1485.b06254e', '1.9', False), # SciPy + ('3.6.0.dev1651+g30d6161406', '3.6', False), # matplotlib + ('1.1.dev0', '1.1', False), # sklearn + ('0.56.0dev0+39.gef1ba4c10', '0.56', False), # numba + ('9.1.0.rc1', '9.1', False), # VTK + ('0.3dev0', '0.3', False), # mne-connectivity + ('0.2.2.dev0', '0.2.2', False), # mne-qt-browser + ('3.2.2+150.g1e93bd5d', '3.2.2', True), # nibabel + # test some stable cases + ('1.2.3', '1.2.3', True), + ('1.2', '1.2', True), + ('1', '1', True), +]) +def test_strip_dev(version, want, have_unstripped, monkeypatch): + """Test that stripping dev works.""" + monkeypatch.setattr( + mne.utils.check, 'import_module', + lambda x: Bunch(__version__=version)) + got_have_unstripped, same_version = check_version( + version, want, strip=False, return_version=True) + assert same_version == version + assert got_have_unstripped is have_unstripped + have, simpler_version = check_version( + 'foo', want, return_version=True) # strip=True is the default + assert have, (simpler_version, version) + + def looks_stable(version): + try: + [int(x) for x in version.split('.')] + except ValueError: + return False + else: + return True + + if looks_stable(version): + assert 'dev' not in version + assert 'rc' not in version + assert simpler_version == version + else: + assert simpler_version != version + assert 'dev' not in simpler_version + assert 'rc' not in simpler_version + assert not simpler_version.endswith('.') + assert looks_stable(simpler_version) diff -Nru python-mne-0.23.4+dfsg/mne/utils/tests/test_config.py python-mne-1.1.0+dfsg/mne/utils/tests/test_config.py --- python-mne-0.23.4+dfsg/mne/utils/tests/test_config.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/tests/test_config.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,12 +5,12 @@ from mne.utils import (set_config, get_config, get_config_path, set_memmap_min_size, _get_stim_channel, sys_info, - ClosingStringIO) + ClosingStringIO, get_subjects_dir) -def test_config(tmpdir): +def test_config(tmp_path): """Test mne-python config file support.""" - tempdir = str(tmpdir) + tempdir = str(tmp_path) key = '_MNE_PYTHON_CONFIG_TESTING' value = '123456' value2 = '123' @@ -86,4 +86,28 @@ assert ('numpy:' in out) if platform.system() == 'Darwin': - assert 'Platform: macOS-' in out + assert 'Platform: macOS-' in out + elif platform.system() == 'Linux': + assert 'Platform: Linux' in out + + +def test_get_subjects_dir(tmp_path, monkeypatch): + """Test get_subjects_dir().""" + subjects_dir = tmp_path / 'foo' + subjects_dir.mkdir() + + # String + assert get_subjects_dir(str(subjects_dir)) == str(subjects_dir) + + # Path + assert get_subjects_dir(subjects_dir) == str(subjects_dir) + + # `None` + monkeypatch.setenv('_MNE_FAKE_HOME_DIR', str(tmp_path)) + monkeypatch.delenv('SUBJECTS_DIR', raising=False) + assert get_subjects_dir() is None + + # Expand `~` + monkeypatch.setenv('HOME', str(tmp_path)) + monkeypatch.setenv('USERPROFILE', str(tmp_path)) # Windows + assert get_subjects_dir('~/foo') == str(subjects_dir) diff -Nru python-mne-0.23.4+dfsg/mne/utils/tests/test_docs.py python-mne-1.1.0+dfsg/mne/utils/tests/test_docs.py --- python-mne-0.23.4+dfsg/mne/utils/tests/test_docs.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/tests/test_docs.py 2022-08-03 15:48:14.000000000 +0000 @@ -13,7 +13,7 @@ doc = obj.__doc__ assert 'verbose : ' in doc if obj is add_channels_epochs: - assert 'keyword-argument only. Defaults to True if' in doc + assert 'passed as a keyword' in doc def test_deprecated_alias(): @@ -28,23 +28,35 @@ assert 'deprecated' not in new_func.__doc__ -@deprecated('message') +@deprecated('bad func') def deprecated_func(): """Do something.""" pass -@deprecated('message') +@deprecated('bad class') class deprecated_class(object): def __init__(self): pass + @deprecated('bad method') + def bad(self): + pass + def test_deprecated(): """Test deprecated function.""" - pytest.deprecated_call(deprecated_func) - pytest.deprecated_call(deprecated_class) + assert 'DEPRECATED' in deprecated_func.__doc__ + with pytest.deprecated_call(match='bad func'): + deprecated_func() + assert 'DEPRECATED' in deprecated_class.__init__.__doc__ + with pytest.deprecated_call(match='bad class'): + dep = deprecated_class() + assert 'DEPRECATED' in deprecated_class.bad.__doc__ + assert 'DEPRECATED' in dep.bad.__doc__ + with pytest.deprecated_call(match='bad method'): + dep.bad() def test_copy_doc(): diff -Nru python-mne-0.23.4+dfsg/mne/utils/tests/test_fetching.py python-mne-1.1.0+dfsg/mne/utils/tests/test_fetching.py --- python-mne-0.23.4+dfsg/mne/utils/tests/test_fetching.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/tests/test_fetching.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,44 +0,0 @@ -import os -import os.path as op - -import pytest - -from mne.utils import (_fetch_file, requires_good_network, catch_logging, - sizeof_fmt) - - -@pytest.mark.timeout(60) -@requires_good_network -@pytest.mark.parametrize('url', ( - 'https://raw.githubusercontent.com/mne-tools/mne-python/main/README.rst', -)) -def test_fetch_file(url, tmpdir): - """Test URL retrieval.""" - tempdir = str(tmpdir) - archive_name = op.join(tempdir, "download_test") - with catch_logging() as log: - _fetch_file(url, archive_name, timeout=30., verbose=True) - log = log.getvalue() - assert ', resuming at' not in log - with open(archive_name, 'rb') as fid: - data = fid.read() - stop = len(data) // 2 - assert 0 < stop < len(data) - with open(archive_name + '.part', 'wb') as fid: - fid.write(data[:stop]) - with catch_logging() as log: - _fetch_file(url, archive_name, timeout=30., verbose=True) - log = log.getvalue() - assert ', resuming at %s' % sizeof_fmt(stop) in log - with pytest.raises(Exception, match='Cannot use'): - _fetch_file('NOT_AN_ADDRESS', op.join(tempdir, 'test'), verbose=False) - resume_name = op.join(tempdir, "download_resume") - # touch file - with open(resume_name + '.part', 'w'): - os.utime(resume_name + '.part', None) - _fetch_file(url, resume_name, resume=True, timeout=30., - verbose=False) - with pytest.raises(ValueError, match='Bad hash value'): - _fetch_file(url, archive_name, hash_='a', verbose=False) - with pytest.raises(RuntimeError, match='Hash mismatch'): - _fetch_file(url, archive_name, hash_='a' * 32, verbose=False) diff -Nru python-mne-0.23.4+dfsg/mne/utils/tests/test_linalg.py python-mne-1.1.0+dfsg/mne/utils/tests/test_linalg.py --- python-mne-0.23.4+dfsg/mne/utils/tests/test_linalg.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/tests/test_linalg.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,15 +1,16 @@ """Test linalg utilities.""" # Authors: Britta Westner # -# License: BSD (3-clause) +# License: BSD-3-Clause -from distutils.version import LooseVersion import numpy as np from numpy.testing import assert_allclose, assert_array_equal from scipy import linalg import pytest -from mne.utils import _sym_mat_pow, _reg_pinv, requires_version +from mne.utils import (_sym_mat_pow, _reg_pinv, requires_version, + _record_warnings) +from mne.fixes import _compare_version @requires_version('numpy', '1.17') # pinv bugs @@ -28,7 +29,7 @@ ]) def test_pos_semidef_inv(ndim, dtype, n, deficient, reduce_rank, psdef, func): """Test positive semidefinite matrix inverses.""" - if LooseVersion(np.__version__) >= LooseVersion('1.19'): + if _compare_version(np.__version__, '>=', '1.19'): svd = np.linalg.svd else: from mne.fixes import svd @@ -41,7 +42,7 @@ if deficient: vec = np.ones(n) / np.sqrt(n) proj -= np.outer(vec, vec) - with pytest.warns(None): # intentionally discard imag + with _record_warnings(): # intentionally discard imag mat = mat.astype(dtype) # now make it conjugate symmetric or positive semi-definite if psdef: diff -Nru python-mne-0.23.4+dfsg/mne/utils/tests/test_logging.py python-mne-1.1.0+dfsg/mne/utils/tests/test_logging.py --- python-mne-0.23.4+dfsg/mne/utils/tests/test_logging.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/tests/test_logging.py 2022-08-03 15:48:14.000000000 +0000 @@ -6,11 +6,11 @@ import numpy as np import pytest -from mne import read_evokeds, Epochs -from mne.io import read_raw_fif +from mne import read_evokeds, Epochs, create_info +from mne.io import read_raw_fif, RawArray from mne.utils import (warn, set_log_level, set_log_file, filter_out_warnings, verbose, _get_call_line, use_log_level, catch_logging, - logger) + logger, check) from mne.utils._logging import _frame_info base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data') @@ -63,12 +63,12 @@ for line in lines] -def test_logging_options(tmpdir): +def test_logging_options(tmp_path): """Test logging (to file).""" with use_log_level(None): # just ensure it's set back with pytest.raises(ValueError, match="Invalid value for the 'verbose"): set_log_level('foo') - tempdir = str(tmpdir) + tempdir = str(tmp_path) test_name = op.join(tempdir, 'test.log') with open(fname_log, 'r') as old_log_file: # [:-1] used to strip an extra "No baseline correction applied" @@ -169,13 +169,31 @@ assert log == '' -def test_warn(capsys): +def test_warn(capsys, tmp_path, monkeypatch): """Test the smart warn() function.""" with pytest.warns(RuntimeWarning, match='foo'): warn('foo') captured = capsys.readouterr() assert captured.out == '' # gh-5592 assert captured.err == '' # this is because pytest.warns took it already + # test ignore_namespaces + bad_name = tmp_path / 'bad.fif' + raw = RawArray(np.zeros((1, 1)), create_info(1, 1000., 'eeg')) + with pytest.warns(RuntimeWarning, match='filename') as ws: + raw.save(bad_name) + assert len(ws) == 1 + assert 'test_logging.py' in ws[0].filename # this file (it's in tests/) + + def warn_wrap(msg): + warn(msg, ignore_namespaces=()) + + monkeypatch.setattr(check, 'warn', warn_wrap) + with pytest.warns(RuntimeWarning, match='filename') as ws: + raw.save(bad_name, overwrite=True) + + assert len(ws) == 1 + assert 'test_logging.py' not in ws[0].filename # this file + assert '_logging.py' in ws[0].filename # where `mne.utils.warn` lives def test_get_call_line(): @@ -207,33 +225,18 @@ class Okay: @verbose - def meth_1(self): # allowed because it should just use self.verbose - logger.info('meth_1') - - @verbose def meth_2(self, verbose=None): logger.info('meth_2') o = Okay() - with pytest.raises(RuntimeError, match=r'does not have self\.verbose'): - o.meth_1() # should raise, no verbose attr yet - o.verbose = False with catch_logging() as log: - o.meth_1() - o.meth_2() + o.meth_2(verbose=False) log = log.getvalue() assert log == '' with catch_logging() as log: o.meth_2(verbose=True) log = log.getvalue() assert 'meth_2' in log - o.verbose = True - with catch_logging() as log: - o.meth_1() - o.meth_2() - log = log.getvalue() - assert 'meth_1' in log - assert 'meth_2' in log with catch_logging() as log: o.meth_2(verbose=False) log = log.getvalue() diff -Nru python-mne-0.23.4+dfsg/mne/utils/tests/test_numerics.py python-mne-1.1.0+dfsg/mne/utils/tests/test_numerics.py --- python-mne-0.23.4+dfsg/mne/utils/tests/test_numerics.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/tests/test_numerics.py 2022-08-03 15:48:14.000000000 +0000 @@ -21,7 +21,9 @@ _undo_scaling_array, _PCA, requires_sklearn, _array_equal_nan, _julian_to_cal, _cal_to_julian, _dt_to_julian, _julian_to_dt, grand_average, - _ReuseCycle, requires_version, numerics) + _ReuseCycle, requires_version, numerics, + _custom_lru_cache) +from mne.utils.numerics import _LRU_CACHES, _LRU_CACHE_MAXSIZES base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data') @@ -53,9 +55,9 @@ pytest.raises(TypeError, _get_inst_data, 'foo') -def test_hashfunc(tmpdir): +def test_hashfunc(tmp_path): """Test md5/sha1 hash calculations.""" - tempdir = str(tmpdir) + tempdir = str(tmp_path) fname1 = op.join(tempdir, 'foo') fname2 = op.join(tempdir, 'bar') with open(fname1, 'wb') as fid: @@ -539,3 +541,50 @@ want = np.arange(n) / d got = numerics._arange_div(n, d) assert_allclose(got, want) + + +def test_custom_lru_cache(): + """Test our _custom_lru_cache implementation.""" + n_calls = [0, 0] + start_size = len(_LRU_CACHES) + + @_custom_lru_cache(2) + def my_fun(*args): + n_calls[0] += 1 + return ', '.join(arg.__class__.__name__ for arg in args) + + assert len(_LRU_CACHES) == start_size + 1 + fun_hash = list(_LRU_CACHES)[-1] + assert _LRU_CACHE_MAXSIZES[fun_hash] == 2 + + @_custom_lru_cache(1) + def my_fun_2(*args): + n_calls[1] += 1 + return ', '.join(arg.__class__.__name__ for arg in args) + + assert len(_LRU_CACHES) == start_size + 2 + fun_2_hash = list(_LRU_CACHES)[-1] + assert _LRU_CACHE_MAXSIZES[fun_2_hash] == 1 + + assert n_calls == [0, 0] + assert my_fun(1, 2, 3) == 'int, int, int' + assert n_calls == [1, 0] + assert my_fun_2(1, 2, 3.) == 'int, int, float' + assert n_calls == [1, 1] + # repeated calls use cached version + assert my_fun(1, 2, 3) == 'int, int, int' + assert n_calls == [1, 1] + assert my_fun_2(1, 2, 3.) == 'int, int, float' + assert n_calls == [1, 1] + assert len(_LRU_CACHES[fun_hash]) == 1 + assert len(_LRU_CACHES[fun_2_hash]) == 1 + assert my_fun(1, np.array([2]), 3) == 'int, ndarray, int' + assert n_calls == [2, 1] + assert len(_LRU_CACHES[fun_hash]) == 2 + assert my_fun_2(1, sparse.eye(1, format='csc')) == 'int, csc_matrix' + assert n_calls == [2, 2] + assert len(_LRU_CACHES[fun_2_hash]) == 1 # other got popped + # we could add support for this eventually, but don't bother for now + with pytest.raises(RuntimeError, match='Unsupported sparse type'): + my_fun_2(1, sparse.eye(1, format='coo')) + assert n_calls == [2, 2] # never did any computation diff -Nru python-mne-0.23.4+dfsg/mne/utils/tests/test_progressbar.py python-mne-1.1.0+dfsg/mne/utils/tests/test_progressbar.py --- python-mne-0.23.4+dfsg/mne/utils/tests/test_progressbar.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/tests/test_progressbar.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,3 +1,8 @@ +# -*- coding: utf-8 -*- +# Authors: Eric Larson +# +# License: BSD-3-Clause + import os.path as op import numpy as np @@ -6,10 +11,10 @@ from mne.parallel import parallel_func from mne.utils import (ProgressBar, array_split_idx, use_log_level, - modified_env, catch_logging) + catch_logging) -def test_progressbar(): +def test_progressbar(monkeypatch): """Test progressbar class.""" a = np.arange(10) pbar = ProgressBar(a) @@ -28,19 +33,21 @@ iter_func(pbar) # Make sure different progress bars can be used - with catch_logging() as log, modified_env(MNE_TQDM='tqdm'), \ - use_log_level('debug'), ProgressBar(np.arange(3)) as pbar: + monkeypatch.setenv('MNE_TQDM', 'tqdm') + with catch_logging('debug') as log, ProgressBar(np.arange(3)) as pbar: for p in pbar: pass log = log.getvalue() assert 'Using ProgressBar with tqdm\n' in log - with modified_env(MNE_TQDM='broken'), pytest.raises(ValueError): + monkeypatch.setenv('MNE_TQDM', 'broken') + with pytest.raises(ValueError, match='Invalid value for the'): ProgressBar(np.arange(3)) - with modified_env(MNE_TQDM='tqdm.broken'), pytest.raises(AttributeError): + monkeypatch.setenv('MNE_TQDM', 'tqdm.broken') + with pytest.raises(ValueError, match='Unknown tqdm'): ProgressBar(np.arange(3)) # off - with catch_logging() as log, modified_env(MNE_TQDM='off'), \ - use_log_level('debug'), ProgressBar(np.arange(3)) as pbar: + monkeypatch.setenv('MNE_TQDM', 'off') + with catch_logging('debug') as log, ProgressBar(np.arange(3)) as pbar: for p in pbar: pass log = log.getvalue() diff -Nru python-mne-0.23.4+dfsg/mne/utils/tests/test_testing.py python-mne-1.1.0+dfsg/mne/utils/tests/test_testing.py --- python-mne-0.23.4+dfsg/mne/utils/tests/test_testing.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/utils/tests/test_testing.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,10 +1,17 @@ +# -*- coding: utf-8 -*- +# Authors: Eric Larson +# +# License: BSD-3-Clause + +import os import os.path as op import numpy as np import pytest from mne.datasets import testing -from mne.utils import (_TempDir, _url_to_local_path, buggy_mkl_svd) +from mne.utils import (_TempDir, _url_to_local_path, buggy_mkl_svd, + modified_env) def test_buggy_mkl(): @@ -15,7 +22,8 @@ def foo(a, b): raise np.linalg.LinAlgError('SVD did not converge') with pytest.warns(RuntimeWarning, match='convergence error'): - pytest.raises(SkipTest, foo, 1, 2) + with pytest.raises(SkipTest): + foo(1, 2) @buggy_mkl_svd def bar(c, d, e): @@ -32,18 +40,37 @@ assert (not op.isdir(x)) -def test_datasets(monkeypatch, tmpdir): +def test_datasets(monkeypatch, tmp_path): """Test dataset config.""" # gh-4192 - fake_path = tmpdir.mkdir('MNE-testing-data') - with open(fake_path.join('version.txt'), 'w') as fid: + fake_path = tmp_path / 'MNE-testing-data' + fake_path.mkdir() + with open(fake_path / 'version.txt', 'w') as fid: fid.write('9999.9999') - monkeypatch.setenv('_MNE_FAKE_HOME_DIR', str(tmpdir)) - monkeypatch.setenv('MNE_DATASETS_TESTING_PATH', str(tmpdir)) - assert testing.data_path(download=False, verbose='debug') == str(fake_path) + monkeypatch.setenv('_MNE_FAKE_HOME_DIR', str(tmp_path)) + monkeypatch.setenv('MNE_DATASETS_TESTING_PATH', str(tmp_path)) + got_path = str(testing.data_path(download=False, verbose='debug')) + assert got_path == str(fake_path) def test_url_to_local_path(): """Test URL to local path.""" assert _url_to_local_path('http://google.com/home/why.html', '.') == \ op.join('.', 'home', 'why.html') + + +@pytest.mark.filterwarnings('ignore:.*monkeypatch.*:DeprecationWarning') +def test_modified_env(monkeypatch): + """Test modified_env.""" + monkeypatch.setenv('_MNE_1', 'true') + monkeypatch.setenv('_MNE_2', 'truer') + assert os.getenv('_MNE_1') == 'true' + assert os.getenv('_MNE_2') == 'truer' + assert os.getenv('_MNE_3') is None + with modified_env(_MNE_1='false', _MNE_2=None, _MNE_3='falsest'): + assert os.getenv('_MNE_1') == 'false' + assert os.getenv('_MNE_2') is None + assert os.getenv('_MNE_3') == 'falsest' + assert os.getenv('_MNE_1') == 'true' + assert os.getenv('_MNE_2') == 'truer' + assert os.getenv('_MNE_3') is None diff -Nru python-mne-0.23.4+dfsg/mne/_version.py python-mne-1.1.0+dfsg/mne/_version.py --- python-mne-0.23.4+dfsg/mne/_version.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/_version.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ """The version number.""" # Authors: Eric Larson # -# License: BSD (3-clause) +# License: BSD-3-Clause -__version__ = '0.23.4' +__version__ = '1.1.0' diff -Nru python-mne-0.23.4+dfsg/mne/viz/_3d_overlay.py python-mne-1.1.0+dfsg/mne/viz/_3d_overlay.py --- python-mne-0.23.4+dfsg/mne/viz/_3d_overlay.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/_3d_overlay.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,191 @@ +# -*- coding: utf-8 -*- +"""Classes to handle overlapping surfaces.""" + +# Authors: Guillaume Favelier +# Eric Larson +# +# License: Simplified BSD + +from collections import OrderedDict + +import numpy as np + +from ..utils import logger + + +class _Overlay(object): + def __init__(self, scalars, colormap, rng, opacity, name): + self._scalars = scalars + self._colormap = colormap + assert rng is not None + self._rng = rng + self._opacity = opacity + self._name = name + + def to_colors(self): + from ._3d import _get_cmap + from matplotlib.colors import Colormap, ListedColormap + + if isinstance(self._colormap, str): + cmap = _get_cmap(self._colormap) + elif isinstance(self._colormap, Colormap): + cmap = self._colormap + else: + cmap = ListedColormap( + self._colormap / 255., name=str(type(self._colormap))) + logger.debug( + f'Color mapping {repr(self._name)} with {cmap.name} ' + f'colormap and range {self._rng}') + + rng = self._rng + assert rng is not None + scalars = self._norm(rng) + + colors = cmap(scalars) + if self._opacity is not None: + colors[:, 3] *= self._opacity + return colors + + def _norm(self, rng): + if rng[0] == rng[1]: + factor = 1 if rng[0] == 0 else 1e-6 * rng[0] + else: + factor = rng[1] - rng[0] + return (self._scalars - rng[0]) / factor + + +class _LayeredMesh(object): + def __init__(self, renderer, vertices, triangles, normals): + self._renderer = renderer + self._vertices = vertices + self._triangles = triangles + self._normals = normals + + self._polydata = None + self._actor = None + self._is_mapped = False + + self._current_colors = None + self._cached_colors = None + self._overlays = OrderedDict() + + self._default_scalars = np.ones(vertices.shape) + self._default_scalars_name = 'Data' + + def map(self): + kwargs = { + "color": None, + "pickable": True, + "rgba": True, + } + mesh_data = self._renderer.mesh( + x=self._vertices[:, 0], + y=self._vertices[:, 1], + z=self._vertices[:, 2], + triangles=self._triangles, + normals=self._normals, + scalars=self._default_scalars, + **kwargs + ) + self._actor, self._polydata = mesh_data + self._is_mapped = True + + def _compute_over(self, B, A): + assert A.ndim == B.ndim == 2 + assert A.shape[1] == B.shape[1] == 4 + A_w = A[:, 3:] # * 1 + B_w = B[:, 3:] * (1 - A_w) + C = A.copy() + C[:, :3] *= A_w + C[:, :3] += B[:, :3] * B_w + C[:, 3:] += B_w + C[:, :3] /= C[:, 3:] + return np.clip(C, 0, 1, out=C) + + def _compose_overlays(self): + B = cache = None + for overlay in self._overlays.values(): + A = overlay.to_colors() + if B is None: + B = A + else: + cache = B + B = self._compute_over(cache, A) + return B, cache + + def add_overlay(self, scalars, colormap, rng, opacity, name): + overlay = _Overlay( + scalars=scalars, + colormap=colormap, + rng=rng, + opacity=opacity, + name=name, + ) + self._overlays[name] = overlay + colors = overlay.to_colors() + if self._current_colors is None: + self._current_colors = colors + else: + # save previous colors to cache + self._cached_colors = self._current_colors + self._current_colors = self._compute_over( + self._cached_colors, colors) + + # apply the texture + self._apply() + + def remove_overlay(self, names): + to_update = False + if not isinstance(names, list): + names = [names] + for name in names: + if name in self._overlays: + del self._overlays[name] + to_update = True + if to_update: + self.update() + + def _apply(self): + if self._current_colors is None or self._renderer is None: + return + self._renderer._set_mesh_scalars( + mesh=self._polydata, + scalars=self._current_colors, + name=self._default_scalars_name, + ) + + def update(self, colors=None): + if colors is not None and self._cached_colors is not None: + self._current_colors = self._compute_over( + self._cached_colors, colors) + else: + self._current_colors, self._cached_colors = \ + self._compose_overlays() + self._apply() + + def _clean(self): + mapper = self._actor.GetMapper() + mapper.SetLookupTable(None) + self._actor.SetMapper(None) + self._actor = None + self._polydata = None + self._renderer = None + + def update_overlay(self, name, scalars=None, colormap=None, + opacity=None, rng=None): + overlay = self._overlays.get(name, None) + if overlay is None: + return + if scalars is not None: + overlay._scalars = scalars + if colormap is not None: + overlay._colormap = colormap + if opacity is not None: + overlay._opacity = opacity + if rng is not None: + overlay._rng = rng + # partial update: use cache if possible + if name == list(self._overlays.keys())[-1]: + self.update(colors=overlay.to_colors()) + else: # full update + self.update() diff -Nru python-mne-0.23.4+dfsg/mne/viz/_3d.py python-mne-1.1.0+dfsg/mne/viz/_3d.py --- python-mne-0.23.4+dfsg/mne/viz/_3d.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/_3d.py 2022-08-03 15:48:14.000000000 +0000 @@ -12,37 +12,42 @@ from itertools import cycle import os.path as op -import sys import warnings from collections.abc import Iterable from functools import partial +from dataclasses import dataclass +from typing import Optional import numpy as np +from ._dipole import (_check_concat_dipoles, _plot_dipole_mri_outlines, + _plot_dipole_3d) from ..defaults import DEFAULTS -from ..fixes import einsum, _crop_colorbar, _get_img_fdata, _get_args +from ..fixes import _crop_colorbar, _get_img_fdata +from .._freesurfer import (_read_mri_info, _check_mri, _get_head_surface, + _get_skull_surface) from ..io import _loc_to_coil_trans -from ..io.pick import pick_types, _picks_to_idx +from ..io.pick import (pick_types, channel_type, pick_info, + _FNIRS_CH_TYPES_SPLIT, _MEG_CH_TYPES_SPLIT) from ..io.constants import FIFF from ..io.meas_info import read_fiducials, create_info from ..source_space import (_ensure_src, _create_surf_spacing, _check_spacing, - _read_mri_info, SourceSpaces, read_freesurfer_lut) + SourceSpaces, read_freesurfer_lut) from ..surface import (get_meg_helmet_surf, _read_mri_surface, _DistanceQuery, - transform_surface_to, _project_onto_surface, - _reorder_ccw) -from ..transforms import (_find_trans, apply_trans, rot_to_quat, - combine_transforms, _get_trans, _ensure_trans, - invert_transform, Transform, rotation, - read_ras_mni_t, _print_coord_trans) + _project_onto_surface, _reorder_ccw, _CheckInside) +from ..transforms import (apply_trans, rot_to_quat, combine_transforms, + _get_trans, _ensure_trans, Transform, rotation, + read_ras_mni_t, _print_coord_trans, _find_trans, + transform_surface_to, _frame_to_str, + _get_transforms_to_coord_frame) from ..utils import (get_subjects_dir, logger, _check_subject, verbose, warn, has_nibabel, check_version, fill_doc, _pl, get_config, - _ensure_int, _validate_type, _check_option) + _ensure_int, _validate_type, _check_option, _to_rgb) +from ._3d_overlay import _LayeredMesh from .utils import (mne_analyze_colormap, _get_color_list, plt_show, tight_layout, figure_nobar, _check_time_unit) -from .misc import _check_mri -from ..bem import (ConductorModel, _bem_find_surface, - read_bem_surfaces, _ensure_bem_surfaces) +from ..bem import ConductorModel, _bem_find_surface, _ensure_bem_surfaces verbose_dec = verbose @@ -77,6 +82,7 @@ return np.array([]) +@fill_doc def plot_head_positions(pos, mode='traces', cmap='viridis', direction='z', show=True, destination=None, info=None, color='k', axes=None): @@ -103,10 +109,8 @@ details. .. versionadded:: 0.16 - info : instance of mne.Info | None - Measurement information. If provided, will be used to show the - destination position when ``destination is None``, and for - showing the MEG sensors. + %(info)s If provided, will be used to show the destination position when + ``destination is None``, and for showing the MEG sensors. .. versionadded:: 0.16 color : color object @@ -150,8 +154,8 @@ trans, rot, t = head_pos_to_trans_rot_t(pos) # also ensures pos is okay # trans, rot, and t are for dev_head_t, but what we really want # is head_dev_t (i.e., where the head origin is in device coords) - use_trans = einsum('ijk,ik->ij', rot[:, :3, :3].transpose([0, 2, 1]), - -trans) * 1000 + use_trans = np.einsum('ijk,ik->ij', rot[:, :3, :3].transpose([0, 2, 1]), + -trans) * 1000 use_rot = rot.transpose([0, 2, 1]) use_quats = -pos[:, 1:4] # inverse (like doing rot.T) surf = rrs = lims = None @@ -167,7 +171,7 @@ transform_surface_to(surf, 'meg', info['dev_head_t'], copy=False) surf['rr'] *= 1000. - helmet_color = (0.0, 0.0, 0.6) + helmet_color = DEFAULTS['coreg']['helmet_color'] if mode == 'traces': if axes is None: axes = plt.subplots(3, 2, sharex=True)[1] @@ -301,8 +305,8 @@ @verbose def plot_evoked_field(evoked, surf_maps, time=None, time_label='t = %0.0f ms', - n_jobs=1, fig=None, vmax=None, n_contours=21, - verbose=None): + n_jobs=None, fig=None, vmax=None, n_contours=21, + *, interaction='terrain', verbose=None): """Plot MEG/EEG fields on head surface and helmet in 3D. Parameters @@ -317,7 +321,7 @@ time_label : str | None How to print info about the time instant visualized. %(n_jobs)s - fig : instance of mayavi.core.api.Scene | None + fig : instance of Figure3D | None If None (default), a new figure will be created, otherwise it will plot into the given figure. @@ -330,18 +334,23 @@ The number of contours. .. versionadded:: 0.21 + %(interaction_scene)s + Defaults to ``'terrain'``. + + .. versionadded:: 1.1 %(verbose)s Returns ------- - fig : instance of mayavi.mlab.Figure - The mayavi figure. + fig : instance of Figure3D + The figure. """ # Update the backend from .backends.renderer import _get_renderer types = [t for t in ['eeg', 'grad', 'mag'] if t in evoked] _validate_type(vmax, (None, 'numeric'), 'vmax') n_contours = _ensure_int(n_contours, 'n_contours') + _check_option('interaction', interaction, ['trackball', 'terrain']) time_idx = None if time is None: @@ -355,12 +364,13 @@ # Plot them alphas = [1.0, 0.5] colors = [(0.6, 0.6, 0.6), (1.0, 1.0, 1.0)] - colormap = mne_analyze_colormap(format='mayavi') + colormap = mne_analyze_colormap(format='vtk') colormap_lines = np.concatenate([np.tile([0., 0., 255., 255.], (127, 1)), np.tile([0., 0., 0., 255.], (2, 1)), np.tile([255., 0., 0., 255.], (127, 1))]) renderer = _get_renderer(fig, bgcolor=(0.0, 0.0, 0.0), size=(600, 600)) + renderer.set_interaction(interaction) for ii, this_map in enumerate(surf_maps): surf = this_map['surf'] @@ -394,18 +404,37 @@ vmax = np.max(np.abs(data)) vmax = float(vmax) alpha = alphas[ii] - renderer.surface(surface=surf, color=colors[ii], - opacity=alpha) - + mesh = _LayeredMesh( + renderer=renderer, + vertices=surf['rr'], + triangles=surf['tris'], + normals=surf['nn'], + ) + mesh.map() + color = _to_rgb(colors[ii], alpha=True) + cmap = np.array([(0, 0, 0, 0,), color]) + ctable = np.round(cmap * 255).astype(np.uint8) + mesh.add_overlay( + scalars=np.ones(len(data)), + colormap=ctable, + rng=[0, 1], + opacity=alpha, + name='surf', + ) # Now show our field pattern - renderer.surface(surface=surf, vmin=-vmax, vmax=vmax, - scalars=data, colormap=colormap, - polygon_offset=-1) + mesh.add_overlay( + scalars=data, + colormap=colormap, + rng=[-vmax, vmax], + opacity=1., + name='field', + ) # And the field lines on top - renderer.contour(surface=surf, scalars=data, contours=n_contours, - vmin=-vmax, vmax=vmax, opacity=alpha, - colormap=colormap_lines) + if n_contours > 0: + renderer.contour(surface=surf, scalars=data, contours=n_contours, + vmin=-vmax, vmax=vmax, opacity=alpha, + colormap=colormap_lines) if time_label is not None: if '%' in time_label: @@ -418,22 +447,18 @@ @verbose def plot_alignment(info=None, trans=None, subject=None, subjects_dir=None, - surfaces='auto', coord_frame='head', + surfaces='auto', coord_frame='auto', meg=None, eeg='original', fwd=None, dig=False, ecog=True, src=None, mri_fiducials=False, bem=None, seeg=True, fnirs=True, show_axes=False, dbs=True, - fig=None, interaction='trackball', verbose=None): + fig=None, interaction='terrain', verbose=None): """Plot head, sensor, and source space alignment in 3D. Parameters ---------- - info : dict | None - The measurement info. - If None (default), no sensor information will be shown. + %(info)s If None (default), no sensor information will be shown. %(trans)s - subject : str | None - The subject name corresponding to FreeSurfer environment - variable SUBJECT. Can be omitted if ``src`` is provided. + %(subject)s Can be omitted if ``src`` is provided. %(subjects_dir)s surfaces : str | list | dict Surfaces to plot. Supported values: @@ -454,34 +479,20 @@ Defaults to 'auto', which will look for a head surface and plot it if found. - .. note:: For single layer BEMs it is recommended to use 'brain'. - coord_frame : str - Coordinate frame to use, 'head', 'meg', or 'mri'. - meg : str | list | bool | None - Can be "helmet", "sensors" or "ref" to show the MEG helmet, sensors or - reference sensors respectively, or a combination like - ``('helmet', 'sensors')`` (same as None, default). True translates to - ``('helmet', 'sensors', 'ref')``. - eeg : bool | str | list - String options are: - - - "original" (default; equivalent to ``True``) - Shows EEG sensors using their digitized locations (after - transformation to the chosen ``coord_frame``) - - "projected" - The EEG locations projected onto the scalp, as is done in forward - modeling - - Can also be a list of these options, or an empty list (``[]``, - equivalent of ``False``). - fwd : instance of Forward - The forward solution. If present, the orientations of the dipoles - present in the forward solution are displayed. + .. note:: For single layer BEMs it is recommended to use ``'brain'``. + coord_frame : 'auto' | 'head' | 'meg' | 'mri' + The coordinate frame to use. If ``'auto'`` (default), chooses ``'mri'`` + if ``trans`` was passed, and ``'head'`` otherwise. + + .. versionchanged:: 1.0 + Defaults to ``'auto'``. + %(meg)s + %(eeg)s + %(fwd)s dig : bool | 'fiducials' If True, plot the digitization points; 'fiducials' to plot fiducial points only. - ecog : bool - If True (default), show ECoG sensors. + %(ecog)s src : instance of SourceSpaces | None If not None, also plot the source space points. mri_fiducials : bool | str @@ -499,14 +510,8 @@ for ``'$SUBJECT*$SOURCE.fif'`` in the same directory. For ``'outer_skin'``, the subjects bem and bem/flash folders are searched. Defaults to None. - seeg : bool - If True (default), show sEEG electrodes. - fnirs : str | list | bool | None - Can be "channels", "pairs", "detectors", and/or "sources" to show the - fNIRS channel locations, optode locations, or line between - source-detector pairs, or a combination like ``('pairs', 'channels')``. - True translates to ``('pairs',)``. - + %(seeg)s + %(fnirs)s .. versionadded:: 0.20 show_axes : bool If True (default False), coordinate frame axis indicators will be @@ -517,24 +522,23 @@ * MEG in blue (if MEG sensors are present). .. versionadded:: 0.16 - dbs : bool - If True (default), show DBS (deep brain stimulation) electrodes. - fig : mayavi.mlab.Figure | None - Mayavi Scene in which to plot the alignment. + %(dbs)s + fig : Figure3D | None + PyVista scene in which to plot the alignment. If ``None``, creates a new 600x600 pixel figure with black background. .. versionadded:: 0.16 - interaction : str - Can be "trackball" (default) or "terrain", i.e. a turntable-style - camera. + %(interaction_scene)s .. versionadded:: 0.16 + .. versionchanged:: 1.0 + Defaults to ``'terrain'``. %(verbose)s Returns ------- - fig : instance of mayavi.mlab.Figure - The mayavi figure. + fig : instance of Figure3D + The figure. See Also -------- @@ -552,55 +556,18 @@ .. versionadded:: 0.15 """ - from ..forward import _create_meg_coils, Forward - from ..coreg import get_mni_fiducials # Update the backend from .backends.renderer import _get_renderer - if eeg is False: - eeg = list() - elif eeg is True: - eeg = 'original' - if meg is None: - meg = ('helmet', 'sensors') - # only consider warning if the value is explicit - warn_meg = False - else: - warn_meg = True - - if meg is True: - meg = ('helmet', 'sensors', 'ref') - elif meg is False: - meg = list() - elif isinstance(meg, str): - meg = [meg] - if isinstance(eeg, str): - eeg = [eeg] - - if fnirs is True: - fnirs = ['pairs'] - elif fnirs is False: - fnirs = list() - elif isinstance(fnirs, str): - fnirs = [fnirs] - + meg, eeg, fnirs, warn_meg = _handle_sensor_types(meg, eeg, fnirs) _check_option('interaction', interaction, ['trackball', 'terrain']) - for kind, var in zip(('eeg', 'meg', 'fnirs'), (eeg, meg, fnirs)): - if not isinstance(var, (list, tuple)) or \ - not all(isinstance(x, str) for x in var): - raise TypeError('%s must be list or tuple of str, got %s' - % (kind, type(var))) - for xi, x in enumerate(meg): - _check_option('meg[%d]' % xi, x, ('helmet', 'sensors', 'ref')) - for xi, x in enumerate(eeg): - _check_option('eeg[%d]' % xi, x, ('original', 'projected')) - for xi, x in enumerate(fnirs): - _check_option('fnirs[%d]' % xi, x, ('channels', 'pairs', - 'sources', 'detectors')) info = create_info(1, 1000., 'misc') if info is None else info _validate_type(info, "info") + # Handle surfaces: + if surfaces == 'auto' and trans is None: + surfaces = list() # if no `trans` can't plot mri surfaces if isinstance(surfaces, str): surfaces = [surfaces] if isinstance(surfaces, dict): @@ -620,430 +587,198 @@ surfaces = list(surfaces) for si, s in enumerate(surfaces): _validate_type(s, "str", f"surfaces[{si}]") - brain = sorted( - set(surfaces) & set(['brain', 'pial', 'white', 'inflated'])) bem = _ensure_bem_surfaces(bem, extra_allow=(ConductorModel, None)) assert isinstance(bem, ConductorModel) or bem is None - _check_option('coord_frame', coord_frame, ['head', 'meg', 'mri']) + _check_option('coord_frame', coord_frame, ['head', 'meg', 'mri', 'auto']) + if coord_frame == 'auto': + coord_frame = 'head' if trans is None else 'mri' + if src is not None: src = _ensure_src(src) src_subject = src._subject subject = src_subject if subject is None else subject if src_subject is not None and subject != src_subject: - raise ValueError('subject ("%s") did not match the subject name ' - ' in src ("%s")' % (subject, src_subject)) - - if fwd is not None: - _validate_type(fwd, [Forward]) - fwd_rr = fwd['source_rr'] - if fwd['source_ori'] == FIFF.FIFFV_MNE_FIXED_ORI: - fwd_nn = fwd['source_nn'].reshape(-1, 1, 3) - else: - fwd_nn = fwd['source_nn'].reshape(-1, 3, 3) - - ref_meg = 'ref' in meg - meg_picks = pick_types(info, meg=True, ref_meg=ref_meg) - eeg_picks = pick_types(info, meg=False, eeg=True, ref_meg=False) - fnirs_picks = pick_types(info, meg=False, eeg=False, ref_meg=False, - fnirs=True) - other_bools = dict(ecog=ecog, seeg=seeg, dbs=dbs, - fnirs=(('channels' in fnirs) | - ('sources' in fnirs) | - ('detectors' in fnirs))) - del ecog, seeg, dbs - other_keys = sorted(other_bools.keys()) - other_picks = {key: pick_types(info, meg=False, ref_meg=False, - **{key: True}) for key in other_keys} - + raise ValueError(f'subject ("{subject}") did not match the ' + f'subject name in src ("{src_subject}")') + # configure transforms if trans == 'auto': - # let's try to do this in MRI coordinates so they're easy to plot subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) trans = _find_trans(subject, subjects_dir) - head_mri_t, _ = _get_trans(trans, 'head', 'mri') - dev_head_t, _ = _get_trans(info['dev_head_t'], 'meg', 'head') - del trans - # Figure out our transformations - if coord_frame == 'meg': - head_trans = invert_transform(dev_head_t) - meg_trans = Transform('meg', 'meg') - mri_trans = invert_transform(combine_transforms( - dev_head_t, head_mri_t, 'meg', 'mri')) - elif coord_frame == 'mri': - head_trans = head_mri_t - meg_trans = combine_transforms(dev_head_t, head_mri_t, 'meg', 'mri') - mri_trans = Transform('mri', 'mri') - else: # coord_frame == 'head' - head_trans = Transform('head', 'head') - meg_trans = dev_head_t - mri_trans = invert_transform(head_mri_t) + picks = pick_types(info, meg=('sensors' in meg), ref_meg=('ref' in meg), + eeg=(len(eeg) > 0), ecog=ecog, seeg=seeg, dbs=dbs, + fnirs=(len(fnirs) > 0)) + if trans is None: + # Some stuff is natively in head coords, others in MRI coords + msg = ('A head<->mri transformation matrix (trans) is required ' + f'to plot %s in {coord_frame} coordinates, ' + '`trans=None` is not allowed') + if fwd is not None: + fwd_frame = _frame_to_str[fwd['coord_frame']] + if fwd_frame != coord_frame: + raise ValueError( + msg % f'a {fwd_frame}-coordinate forward solution') + if src is not None: + src_frame = _frame_to_str[src[0]['coord_frame']] + if src_frame != coord_frame: + raise ValueError( + msg % f'a {src_frame}-coordinate source space') + if mri_fiducials is not False and coord_frame != 'mri': + raise ValueError(msg % 'mri fiducials') + # only enforce needing `trans` if there are channels in "head"/"device" + if picks.size and coord_frame == 'mri': + raise ValueError(msg % 'sensors') + # if only plotting sphere model no trans needed + if bem is not None: + if not bem['is_sphere']: + if coord_frame != 'mri': + raise ValueError(msg % 'a BEM') + elif surfaces not in (['brain'], []): # can only plot these + raise ValueError(msg % (', '.join(surfaces) + ' surfaces')) + elif len(surfaces) > 0 and coord_frame != 'mri': + raise ValueError(msg % (', '.join(surfaces) + ' surfaces')) + trans = Transform('head', 'mri') # not used so just use identity + # get transforms + head_mri_t = _get_trans(trans, 'head', 'mri')[0] + to_cf_t = _get_transforms_to_coord_frame( + info, head_mri_t, coord_frame=coord_frame) + # Surfaces: # both the head and helmet will be in MRI coordinates after this surfs = dict() - # Head: - head = False - for s in surfaces: - if s in ('auto', 'head', 'outer_skin', 'head-dense', 'seghead'): - if head: - raise ValueError('Can only supply one head-like surface name') - surfaces.pop(surfaces.index(s)) - head = True - head_surf = None - # Try the BEM if applicable - if s in ('auto', 'head', 'outer_skin'): - if bem is not None: - head_missing = ( - 'Could not find the surface for ' - 'head in the provided BEM model, ' - 'looking in the subject directory.') - try: - head_surf = _bem_find_surface(bem, 'head') - except RuntimeError: - logger.info(head_missing) - if head_surf is None: - if subject is None: - if s == 'auto': - # ignore - continue - raise ValueError('To plot the head surface, the BEM/sphere' - ' model must contain a head surface ' - 'or "subject" must be provided (got ' - 'None)') - subject_dir = op.join( - get_subjects_dir(subjects_dir, raise_error=True), subject) - if s in ('head-dense', 'seghead'): - try_fnames = [ - op.join(subject_dir, 'bem', '%s-head-dense.fif' - % subject), - op.join(subject_dir, 'surf', 'lh.seghead'), - ] - else: - try_fnames = [ - op.join(subject_dir, 'bem', 'outer_skin.surf'), - op.join(subject_dir, 'bem', 'flash', - 'outer_skin.surf'), - op.join(subject_dir, 'bem', '%s-head.fif' - % subject), - ] - for fname in try_fnames: - if op.exists(fname): - logger.info('Using %s for head surface.' - % (op.basename(fname),)) - if op.splitext(fname)[-1] == '.fif': - head_surf = read_bem_surfaces(fname)[0] - else: - head_surf = _read_mri_surface(fname) - break - else: - raise IOError('No head surface found for subject ' - '%s after trying:\n%s' - % (subject, '\n'.join(try_fnames))) - surfs['head'] = head_surf - - # Skull: - skull = list() - for name in ('outer_skull', 'inner_skull'): - if name in surfaces: - surfaces.pop(surfaces.index(name)) - if bem is None: - fname = op.join( - get_subjects_dir(subjects_dir, raise_error=True), - subject, 'bem', name + '.surf') - if not op.isfile(fname): - raise ValueError('bem is None and the the %s file cannot ' - 'be found:\n%s' % (name, fname)) - surf = _read_mri_surface(fname) - else: - surf = _bem_find_surface(bem, name).copy() - surf['name'] = name - skull.append(surf) - assert all(isinstance(s, dict) for s in skull) - - if mri_fiducials: - if mri_fiducials is True: - subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) - if subject is None: - raise ValueError("Subject needs to be specified to " - "automatically find the fiducials file.") - mri_fiducials = op.join(subjects_dir, subject, 'bem', - subject + '-fiducials.fif') - if isinstance(mri_fiducials, str): - if mri_fiducials == 'estimated': - mri_fiducials = get_mni_fiducials(subject, subjects_dir) - else: - mri_fiducials, cf = read_fiducials(mri_fiducials) - if cf != FIFF.FIFFV_COORD_MRI: - raise ValueError("Fiducials are not in MRI space") - fid_loc = _fiducial_coords(mri_fiducials, FIFF.FIFFV_COORD_MRI) - fid_loc = apply_trans(mri_trans, fid_loc) - else: - fid_loc = [] - - if 'helmet' in meg and len(meg_picks) > 0: - surfs['helmet'] = get_meg_helmet_surf(info, head_mri_t) - assert surfs['helmet']['coord_frame'] == FIFF.FIFFV_COORD_MRI - - # Brain: + # Brain surface: + brain = sorted( + set(surfaces) & set(['brain', 'pial', 'white', 'inflated'])) if len(brain) > 1: - raise ValueError('Only one brain surface can be plotted. ' - 'Got %s.' % brain) - elif len(brain) == 0: - brain = False - else: # exactly 1 - brain = brain[0] + raise ValueError( + f'Only one brain surface can be plotted, got {brain}.') + brain = brain[0] if brain else False + if brain is not False: surfaces.pop(surfaces.index(brain)) - if brain in user_alpha: - user_alpha['lh'] = user_alpha['rh'] = user_alpha.pop(brain) if bem is not None and bem['is_sphere'] and brain == 'brain': surfs['lh'] = _bem_find_surface(bem, 'brain') else: brain = 'pial' if brain == 'brain' else brain subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) for hemi in ['lh', 'rh']: - fname = op.join(subjects_dir, subject, 'surf', - '%s.%s' % (hemi, brain)) - surfs[hemi] = _read_mri_surface(fname) - brain = True + brain_fname = op.join(subjects_dir, subject, 'surf', + f'{hemi}.{brain}') + if not op.isfile(brain_fname): + raise RuntimeError( + f'No brain surface found for subject {subject}, ' + f'expected {brain_fname} to exist') + surfs[hemi] = _read_mri_surface(brain_fname) + + # Head surface: + head_keys = ('auto', 'head', 'outer_skin', 'head-dense', 'seghead') + head = [s for s in surfaces if s in head_keys] + if len(head) > 1: + raise ValueError('Can only supply one head-like surface name, ' + f'got {head}') + head = head[0] if head else False + if head is not False: + surfaces.pop(surfaces.index(head)) + elif 'projected' in eeg: + raise ValueError('A head surface is required to project EEG, ' + '"head", "outer_skin", "head-dense" or "seghead" ' + 'must be in surfaces or surfaces must be "auto"') + + # Skull surface: + skulls = [s for s in surfaces if s in ('outer_skull', 'inner_skull')] + for skull_name in skulls: + surfaces.pop(surfaces.index(skull_name)) + skull = _get_skull_surface( + skull_name.split('_')[0], subject, subjects_dir, bem=bem) + skull['name'] = skull_name # set name for alpha + surfs[skull_name] = skull # we've looked through all of them, raise if some remain if len(surfaces) > 0: - raise ValueError('Unknown surface type%s: %s' - % (_pl(surfaces), surfaces,)) + raise ValueError(f'Unknown surface type{_pl(surfaces)}: {surfaces}') - skull_alpha = dict() - skull_colors = dict() - hemi_val = 0.5 - no_deep = all(len(other_picks[key]) == 0 for key in ('dbs', 'seeg')) + # set colors and alphas + defaults = DEFAULTS['coreg'] + no_deep = not (dbs or seeg) or \ + pick_types(info, dbs=True, seeg=True).size == 0 max_alpha = 1.0 if no_deep else 0.75 + hemi_val = 0.5 if src is None or (brain and any(s['type'] == 'surf' for s in src)): hemi_val = max_alpha - alphas = np.linspace(max_alpha / 2., 0, 5)[:len(skull) + 1] - - for idx, this_skull in enumerate(skull): - name = this_skull['name'] - skull_alpha[name] = alphas[idx + 1] - skull_colors[name] = (0.95 - idx * 0.2, 0.85, 0.95 - idx * 0.2) - surfs[name] = this_skull - - if src is None and brain is False and len(skull) == 0 and not show_axes: + alpha_range = np.linspace(max_alpha / 2., 0, 5)[:len(skulls) + 1] + if src is None and brain is False and len(skulls) == 0 and not show_axes: head_alpha = max_alpha else: - head_alpha = alphas[0] - - for key in surfs: - # Surfs can sometimes be in head coords (e.g., if coming from sphere) - surf = surfs[key] - assert isinstance(surf, dict), f'{key}: {type(surf)}' - surfs[key] = transform_surface_to(surfs[key], coord_frame, - [mri_trans, head_trans], copy=True) - - if fwd is not None: - fwd_rr, fwd_nn = _update_coord_frame(fwd, fwd_rr, fwd_nn, - mri_trans, head_trans) - - # determine points - meg_rrs, meg_tris = list(), list() - hpi_loc = list() - ext_loc = list() - car_loc = list() - eeg_loc = list() - eegp_loc = list() - other_loc = dict() - if len(eeg) > 0: - eeg_loc = np.array([info['chs'][k]['loc'][:3] for k in eeg_picks]) - if len(eeg_loc) > 0: - eeg_loc = apply_trans(head_trans, eeg_loc) - # XXX do projections here if necessary - if 'projected' in eeg: - eegp_loc, eegp_nn = _project_onto_surface( - eeg_loc, surfs['head'], project_rrs=True, - return_nn=True)[2:4] - if 'original' not in eeg: - eeg_loc = list() - del eeg - if 'sensors' in meg: - coil_transs = [_loc_to_coil_trans(info['chs'][pick]['loc']) - for pick in meg_picks] - coils = _create_meg_coils([info['chs'][pick] for pick in meg_picks], - acc='normal') - offset = 0 - for coil, coil_trans in zip(coils, coil_transs): - rrs, tris = _sensor_shape(coil) - rrs = apply_trans(coil_trans, rrs) - meg_rrs.append(rrs) - meg_tris.append(tris + offset) - offset += len(meg_rrs[-1]) - if len(meg_rrs) == 0: - if warn_meg: - warn('MEG sensors not found. Cannot plot MEG locations.') - else: - meg_rrs = apply_trans(meg_trans, np.concatenate(meg_rrs, axis=0)) - meg_tris = np.concatenate(meg_tris, axis=0) - del meg - if dig: - if dig == 'fiducials': - hpi_loc = ext_loc = [] - elif dig is not True: - raise ValueError("dig needs to be True, False or 'fiducials', " - "not %s" % repr(dig)) - else: - hpi_loc = np.array([ - d['r'] for d in (info['dig'] or []) - if (d['kind'] == FIFF.FIFFV_POINT_HPI and - d['coord_frame'] == FIFF.FIFFV_COORD_HEAD)]) - ext_loc = np.array([ - d['r'] for d in (info['dig'] or []) - if (d['kind'] == FIFF.FIFFV_POINT_EXTRA and - d['coord_frame'] == FIFF.FIFFV_COORD_HEAD)]) - car_loc = _fiducial_coords(info['dig'], FIFF.FIFFV_COORD_HEAD) - # Transform from head coords if necessary - if coord_frame == 'meg': - for loc in (hpi_loc, ext_loc, car_loc): - loc[:] = apply_trans(invert_transform(info['dev_head_t']), loc) - elif coord_frame == 'mri': - for loc in (hpi_loc, ext_loc, car_loc): - loc[:] = apply_trans(head_mri_t, loc) - if len(car_loc) == len(ext_loc) == len(hpi_loc) == 0: - warn('Digitization points not found. Cannot plot digitization.') - del dig - for key, picks in other_picks.items(): - if other_bools[key] and len(picks): - title = DEFAULTS["titles"][key] if key != 'fnirs' else 'fNIRS' - if key != 'fnirs' or 'channels' in fnirs: - other_loc[key] = [ - info['chs'][pick]['loc'][:3] for pick in picks - ] - # deal with NaN - other_loc[key] = np.array([loc for loc in other_loc[key] - if np.isfinite(loc).all()], float) - logger.info( - f'Plotting {len(other_loc[key])} {title}' - f' location{_pl(other_loc[key])}') - if key == 'fnirs': - if 'sources' in fnirs: - other_loc['source'] = np.array( - [info['chs'][pick]['loc'][3:6] - for pick in picks]) - logger.info('Plotting %d %s source%s' - % (len(other_loc['source']), - title, _pl(other_loc['source']))) - if 'detectors' in fnirs: - other_loc['detector'] = np.array( - [info['chs'][pick]['loc'][6:9] - for pick in picks]) - logger.info('Plotting %d %s detector%s' - % (len(other_loc['detector']), - title, _pl(other_loc['detector']))) - for v in other_loc.values(): - v[:] = apply_trans(head_trans, v) - other_keys = sorted(other_loc) # re-sort and only keep non-empty - del other_bools - - # initialize figure - renderer = _get_renderer(fig, bgcolor=(0.5, 0.5, 0.5), size=(800, 800)) - if interaction == 'terrain': - renderer.set_interaction('terrain') - - # plot surfaces - alphas = dict(head=head_alpha, helmet=0.25, lh=hemi_val, rh=hemi_val) - alphas.update(skull_alpha) + head_alpha = alpha_range[0] + alphas = dict(lh=hemi_val, rh=hemi_val) + colors = dict(lh=(0.5,) * 3, rh=(0.5,) * 3) + for idx, name in enumerate(skulls): + alphas[name] = alpha_range[idx + 1] + colors[name] = (0.95 - idx * 0.2, 0.85, 0.95 - idx * 0.2) + if brain is not False and brain in user_alpha: + alphas['lh'] = alphas['rh'] = user_alpha.pop(brain) # replace default alphas with specified user_alpha for k, v in user_alpha.items(): if v is not None: alphas[k] = v - colors = dict(head=DEFAULTS['coreg']['head_color'], - helmet=(0.0, 0.0, 0.6), lh=(0.5,) * 3, - rh=(0.5,) * 3) - colors.update(skull_colors) - for key, surf in surfs.items(): - renderer.surface(surface=surf, color=colors[key], - opacity=alphas[key], - backface_culling=(key != 'helmet')) + if k in head_keys and v is not None: + head_alpha = v + fid_colors = tuple( + defaults[f'{key}_color'] for key in ('lpa', 'nasion', 'rpa')) + + # initialize figure + renderer = _get_renderer(fig, name=f'Sensor alignment: {subject}', + bgcolor=(0.5, 0.5, 0.5), size=(800, 800)) + renderer.set_interaction(interaction) + + # plot head + _, _, head_surf = _plot_head_surface( + renderer, head, subject, subjects_dir, bem, coord_frame, + to_cf_t, alpha=head_alpha) + + # plot helmet + if 'helmet' in meg and pick_types(info, meg=True).size > 0: + _, _, src_surf = _plot_helmet( + renderer, info, to_cf_t, head_mri_t, coord_frame) + + # plot surfaces if brain and 'lh' not in surfs: # one layer sphere assert bem['coord_frame'] == FIFF.FIFFV_COORD_HEAD center = bem['r0'].copy() - center = apply_trans(head_trans, center) + center = apply_trans(to_cf_t['head'], center) renderer.sphere(center, scale=0.01, color=colors['lh'], opacity=alphas['lh']) if show_axes: - axes = [(head_trans, (0.9, 0.3, 0.3))] # always show head - if not np.allclose(head_mri_t['trans'], np.eye(4)): # Show MRI - axes.append((mri_trans, (0.6, 0.6, 0.6))) - if len(meg_picks) > 0: # Show MEG - axes.append((meg_trans, (0., 0.6, 0.6))) - for ax in axes: - x, y, z = np.tile(ax[0]['trans'][:3, 3], 3).reshape((3, 3)).T - u, v, w = ax[0]['trans'][:3, :3] - renderer.sphere(center=np.column_stack((x[0], y[0], z[0])), - color=ax[1], scale=3e-3) - renderer.quiver3d(x=x, y=y, z=z, u=u, v=v, w=w, mode='arrow', - scale=2e-2, color=ax[1], - scale_mode='scalar', resolution=20, - scalars=[0.33, 0.66, 1.0]) + _plot_axes(renderer, info, to_cf_t, head_mri_t) # plot points - defaults = DEFAULTS['coreg'] - datas = [eeg_loc, - hpi_loc, - ext_loc] + list(other_loc[key] for key in other_keys) - colors = [defaults['eeg_color'], - defaults['hpi_color'], - defaults['extra_color'] - ] + [defaults[key + '_color'] for key in other_keys] - alphas = [0.8, - 0.5, - 0.25] + [0.8] * len(other_keys) - scales = [defaults['eeg_scale'], - defaults['hpi_scale'], - defaults['extra_scale'] - ] + [defaults[key + '_scale'] for key in other_keys] - assert len(datas) == len(colors) == len(alphas) == len(scales) - fid_colors = tuple( - defaults[f'{key}_color'] for key in ('lpa', 'nasion', 'rpa')) - glyphs = ['sphere'] * len(datas) - for kind, loc in (('dig', car_loc), ('mri', fid_loc)): - if len(loc) > 0: - datas.extend(loc[:, np.newaxis]) - colors.extend(fid_colors) - alphas.extend(3 * (defaults[f'{kind}_fid_opacity'],)) - scales.extend(3 * (defaults[f'{kind}_fid_scale'],)) - glyphs.extend(3 * (('oct' if kind == 'mri' else 'sphere'),)) - for data, color, alpha, scale, glyph in zip( - datas, colors, alphas, scales, glyphs): - if len(data) > 0: - if glyph == 'oct': - transform = np.eye(4) - transform[:3, :3] = mri_trans['trans'][:3, :3] * scale - # rotate around Z axis 45 deg first - transform = transform @ rotation(0, 0, np.pi / 4) - renderer.quiver3d( - x=data[:, 0], y=data[:, 1], z=data[:, 2], - u=1., v=0., w=0., color=color, mode='oct', - scale=1., opacity=alpha, backface_culling=True, - solid_transform=transform) - else: - assert glyph == 'sphere' - assert data.ndim == 2 and data.shape[1] == 3, data.shape - renderer.sphere(center=data, color=color, scale=scale, - opacity=alpha, backface_culling=True) - if len(eegp_loc) > 0: - renderer.quiver3d( - x=eegp_loc[:, 0], y=eegp_loc[:, 1], z=eegp_loc[:, 2], - u=eegp_nn[:, 0], v=eegp_nn[:, 1], w=eegp_nn[:, 2], - color=defaults['eegp_color'], mode='cylinder', - scale=defaults['eegp_scale'], opacity=0.6, - glyph_height=defaults['eegp_height'], - glyph_center=(0., -defaults['eegp_height'], 0), - glyph_resolution=20, - backface_culling=True) - if len(meg_rrs) > 0: - color, alpha = (0., 0.25, 0.5), 0.25 - surf = dict(rr=meg_rrs, tris=meg_tris) - renderer.surface(surface=surf, color=color, - opacity=alpha, backface_culling=True) + _check_option('dig', dig, (True, False, 'fiducials')) + if dig: + if dig is True: + _plot_hpi_coils(renderer, info, to_cf_t) + _plot_head_shape_points(renderer, info, to_cf_t) + _plot_head_fiducials(renderer, info, to_cf_t, fid_colors) + + if mri_fiducials: + _plot_mri_fiducials(renderer, mri_fiducials, subjects_dir, subject, + to_cf_t, fid_colors) + + for key, surf in surfs.items(): + # Surfs can sometimes be in head coords (e.g., if coming from sphere) + assert isinstance(surf, dict), f'{key}: {type(surf)}' + surf = transform_surface_to( + surf, coord_frame, [to_cf_t['mri'], to_cf_t['head']], copy=True) + renderer.surface(surface=surf, color=colors[key], + opacity=alphas[key], + backface_culling=(key != 'helmet')) + + # plot sensors (NB snapshot_brain_montage relies on the last thing being + # plotted being the sensors, so we need to do this after the surfaces) + if picks.size > 0: + _plot_sensors(renderer, info, to_cf_t, picks, meg, eeg, fnirs, + warn_meg, head_surf, 'm') if src is not None: atlas_ids, colors = read_freesurfer_lut() @@ -1051,8 +786,11 @@ src_rr = ss['rr'][ss['inuse'].astype(bool)] src_nn = ss['nn'][ss['inuse'].astype(bool)] - src_rr, src_nn = _update_coord_frame(src[0], src_rr, src_nn, - mri_trans, head_trans) + # update coordinate frame + src_trans = to_cf_t[_frame_to_str[src[0]['coord_frame']]] + src_rr = apply_trans(src_trans, src_rr) + src_nn = apply_trans(src_trans, src_nn, move=False) + # volume sources if ss['type'] == 'vol': seg_name = ss.get('seg_name', None) @@ -1076,24 +814,8 @@ backface_culling=True) if fwd is not None: - red = (1.0, 0.0, 0.0) - green = (0.0, 1.0, 0.0) - blue = (0.0, 0.0, 1.0) - for ori, color in zip(range(fwd_nn.shape[1]), (red, green, blue)): - renderer.quiver3d(fwd_rr[:, 0], - fwd_rr[:, 1], - fwd_rr[:, 2], - fwd_nn[:, ori, 0], - fwd_nn[:, ori, 1], - fwd_nn[:, ori, 2], - color=color, mode='arrow', scale=1.5e-3) - if 'pairs' in fnirs and len(fnirs_picks) > 0: - origin = apply_trans(head_trans, np.array( - [info['chs'][k]['loc'][3:6] for k in fnirs_picks])) - destination = apply_trans(head_trans, np.array( - [info['chs'][k]['loc'][6:9] for k in fnirs_picks])) - logger.info(f'Plotting {origin.shape[0]} fNIRS pair{_pl(origin)}') - renderer.tube(origin=origin, destination=destination) + _plot_forward(renderer, fwd, + to_cf_t[_frame_to_str[fwd['coord_frame']]]) renderer.set_camera(azimuth=90, elevation=90, distance=0.6, focalpoint=(0., 0., 0.)) @@ -1101,6 +823,446 @@ return renderer.scene() +def _handle_sensor_types(meg, eeg, fnirs): + """Handle plotting inputs for sensors types.""" + if eeg is True: + eeg = ['original'] + elif eeg is False: + eeg = list() + + warn_meg = meg is not None # only warn if the value is explicit + if meg is True: + meg = ['helmet', 'sensors', 'ref'] + elif meg is None: + meg = ['helmet', 'sensors'] + elif meg is False: + meg = list() + + if fnirs is True: + fnirs = ['pairs'] + elif fnirs is False: + fnirs = list() + + if isinstance(meg, str): + meg = [meg] + if isinstance(eeg, str): + eeg = [eeg] + if isinstance(fnirs, str): + fnirs = [fnirs] + + for kind, var in zip(('eeg', 'meg', 'fnirs'), (eeg, meg, fnirs)): + if not isinstance(var, (list, tuple)) or \ + not all(isinstance(x, str) for x in var): + raise TypeError( + f'{kind} must be list or tuple of str, got {type(kind)}') + for xi, x in enumerate(meg): + _check_option(f'meg[{xi}]', x, ('helmet', 'sensors', 'ref')) + for xi, x in enumerate(eeg): + _check_option(f'eeg[{xi}]', x, ('original', 'projected')) + for xi, x in enumerate(fnirs): + _check_option(f'fnirs[{xi}]', x, ('channels', 'pairs', + 'sources', 'detectors')) + return meg, eeg, fnirs, warn_meg + + +@verbose +def _ch_pos_in_coord_frame(info, to_cf_t, warn_meg=True, verbose=None): + """Transform positions from head/device/mri to a coordinate frame.""" + from ..forward import _create_meg_coils + chs = dict(ch_pos=dict(), sources=dict(), detectors=dict()) + unknown_chs = list() # prepare for chs with unknown coordinate frame + type_counts = dict() + for idx in range(info['nchan']): + ch_type = channel_type(info, idx) + if ch_type in type_counts: + type_counts[ch_type] += 1 + else: + type_counts[ch_type] = 1 + type_slices = dict(ch_pos=slice(0, 3)) + if ch_type in _FNIRS_CH_TYPES_SPLIT: + # add sensors and detectors too for fNIRS + type_slices.update(sources=slice(3, 6), detectors=slice(6, 9)) + for type_name, type_slice in type_slices.items(): + if ch_type in _MEG_CH_TYPES_SPLIT + ('ref_meg',): + coil_trans = _loc_to_coil_trans(info['chs'][idx]['loc']) + # Here we prefer accurate geometry in case we need to + # ConvexHull the coil, we want true 3D geometry (and not, for + # example, a straight line / 1D geometry) + this_coil = [info['chs'][idx]] + try: + coil = _create_meg_coils(this_coil, acc='accurate')[0] + except RuntimeError: # we don't have an accurate one + coil = _create_meg_coils(this_coil, acc='normal')[0] + # store verts as ch_coord + ch_coord, triangles = _sensor_shape(coil) + ch_coord = apply_trans(coil_trans, ch_coord) + if len(ch_coord) == 0 and warn_meg: + warn(f'MEG sensor {info.ch_names[idx]} not found.') + else: + ch_coord = info['chs'][idx]['loc'][type_slice] + ch_coord_frame = info['chs'][idx]['coord_frame'] + if ch_coord_frame not in (FIFF.FIFFV_COORD_UNKNOWN, + FIFF.FIFFV_COORD_DEVICE, + FIFF.FIFFV_COORD_HEAD, + FIFF.FIFFV_COORD_MRI): + raise RuntimeError( + f'Channel {info.ch_names[idx]} has coordinate frame ' + f'{ch_coord_frame}, must be "meg", "head" or "mri".') + # set unknown as head first + if ch_coord_frame == FIFF.FIFFV_COORD_UNKNOWN: + unknown_chs.append(info.ch_names[idx]) + ch_coord_frame = FIFF.FIFFV_COORD_HEAD + ch_coord = apply_trans( + to_cf_t[_frame_to_str[ch_coord_frame]], ch_coord) + if ch_type in _MEG_CH_TYPES_SPLIT + ('ref_meg',): + chs[type_name][info.ch_names[idx]] = (ch_coord, triangles) + else: + chs[type_name][info.ch_names[idx]] = ch_coord + if unknown_chs: + warn(f'Got coordinate frame "unknown" for {unknown_chs}, assuming ' + '"head" coordinates.') + logger.info('Channel types::\t' + ', '.join( + [f'{ch_type}: {count}' for ch_type, count in type_counts.items()])) + return chs['ch_pos'], chs['sources'], chs['detectors'] + + +def _plot_head_surface(renderer, head, subject, subjects_dir, bem, + coord_frame, to_cf_t, alpha, color=None): + """Render a head surface in a 3D scene.""" + color = DEFAULTS['coreg']['head_color'] if color is None else color + actor = None + src_surf = dst_surf = None + if head is not False: + src_surf = _get_head_surface(head, subject, subjects_dir, bem=bem) + src_surf = transform_surface_to( + src_surf, coord_frame, [to_cf_t['mri'], to_cf_t['head']], + copy=True) + actor, dst_surf = renderer.surface( + surface=src_surf, color=color, opacity=alpha, + backface_culling=False) + return actor, dst_surf, src_surf + + +def _plot_helmet(renderer, info, to_cf_t, head_mri_t, coord_frame, + alpha=0.25, color=None): + color = DEFAULTS['coreg']['helmet_color'] if color is None else color + src_surf = get_meg_helmet_surf(info, head_mri_t) + assert src_surf['coord_frame'] == FIFF.FIFFV_COORD_MRI + src_surf = transform_surface_to( + src_surf, coord_frame, [to_cf_t['mri'], to_cf_t['head']], copy=True) + actor, dst_surf = renderer.surface( + surface=src_surf, color=color, opacity=alpha, + backface_culling=False) + return actor, dst_surf, src_surf + + +def _plot_axes(renderer, info, to_cf_t, head_mri_t): + """Render different axes a 3D scene.""" + axes = [(to_cf_t['head'], (0.9, 0.3, 0.3))] # always show head + if not np.allclose(head_mri_t['trans'], np.eye(4)): # Show MRI + axes.append((to_cf_t['mri'], (0.6, 0.6, 0.6))) + if pick_types(info, meg=True).size > 0: # Show MEG + axes.append((to_cf_t['meg'], (0., 0.6, 0.6))) + actors = list() + for ax in axes: + x, y, z = np.tile(ax[0]['trans'][:3, 3], 3).reshape((3, 3)).T + u, v, w = ax[0]['trans'][:3, :3] + actor, _ = renderer.sphere(center=np.column_stack((x[0], y[0], z[0])), + color=ax[1], scale=3e-3) + actors.append(actor) + actor, _ = renderer.quiver3d(x=x, y=y, z=z, u=u, v=v, w=w, + mode='arrow', scale=2e-2, color=ax[1], + scale_mode='scalar', resolution=20, + scalars=[0.33, 0.66, 1.0]) + actors.append(actor) + return actors + + +def _plot_head_fiducials(renderer, info, to_cf_t, fid_colors): + defaults = DEFAULTS['coreg'] + car_loc = _fiducial_coords(info['dig'], FIFF.FIFFV_COORD_HEAD) + car_loc = apply_trans(to_cf_t['head'], car_loc) + if len(car_loc) == 0: + warn('Digitization points not found. Cannot plot digitization.') + actors = list() + for color, data in zip(fid_colors, car_loc): + actor, _ = renderer.sphere(center=data, color=color, + scale=defaults['dig_fid_scale'], + opacity=defaults['dig_fid_opacity'], + backface_culling=True) + actors.append(actor) + return actors + + +def _plot_mri_fiducials(renderer, mri_fiducials, subjects_dir, subject, + to_cf_t, fid_colors): + from ..coreg import get_mni_fiducials + defaults = DEFAULTS['coreg'] + if mri_fiducials is True: + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + if subject is None: + raise ValueError("Subject needs to be specified to " + "automatically find the fiducials file.") + mri_fiducials = op.join(subjects_dir, subject, 'bem', + subject + '-fiducials.fif') + if isinstance(mri_fiducials, str): + if mri_fiducials == 'estimated': + mri_fiducials = get_mni_fiducials(subject, subjects_dir) + else: + mri_fiducials, cf = read_fiducials(mri_fiducials) + if cf != FIFF.FIFFV_COORD_MRI: + raise ValueError("Fiducials are not in MRI space") + if isinstance(mri_fiducials, np.ndarray): + fid_loc = mri_fiducials + else: + fid_loc = _fiducial_coords(mri_fiducials, FIFF.FIFFV_COORD_MRI) + fid_loc = apply_trans(to_cf_t['mri'], fid_loc) + transform = np.eye(4) + transform[:3, :3] = to_cf_t['mri']['trans'][:3, :3] * \ + defaults['mri_fid_scale'] + # rotate around Z axis 45 deg first + transform = transform @ rotation(0, 0, np.pi / 4) + actors = list() + for color, data in zip(fid_colors, fid_loc): + actor, _ = renderer.quiver3d( + x=data[0], y=data[1], z=data[2], + u=1., v=0., w=0., color=color, mode='oct', + scale=1., opacity=defaults['mri_fid_opacity'], + backface_culling=True, solid_transform=transform) + actors.append(actor) + return actors + + +def _plot_hpi_coils(renderer, info, to_cf_t, opacity=0.5, scale=None, + orient_glyphs=False, scale_by_distance=False, + surf=None, check_inside=None, nearest=None): + defaults = DEFAULTS['coreg'] + scale = defaults['hpi_scale'] if scale is None else scale + hpi_loc = np.array([ + d['r'] for d in (info['dig'] or []) + if (d['kind'] == FIFF.FIFFV_POINT_HPI and + d['coord_frame'] == FIFF.FIFFV_COORD_HEAD)]) + hpi_loc = apply_trans(to_cf_t['head'], hpi_loc) + actor, _ = _plot_glyphs(renderer=renderer, loc=hpi_loc, + color=defaults['hpi_color'], scale=scale, + opacity=opacity, orient_glyphs=orient_glyphs, + scale_by_distance=scale_by_distance, surf=surf, + backface_culling=True, check_inside=check_inside, + nearest=nearest) + return actor + + +def _get_nearest(nearest, check_inside, project_to_trans, proj_rr): + idx = nearest.query(proj_rr)[1] + proj_pts = apply_trans( + project_to_trans, nearest.data[idx]) + proj_nn = apply_trans( + project_to_trans, check_inside.surf['nn'][idx], + move=False) + return proj_pts, proj_nn + + +def _orient_glyphs(pts, surf, project_to_surface=False, mark_inside=False, + check_inside=None, nearest=None): + if check_inside is None: + check_inside = _CheckInside(surf, mode='pyvista') + if nearest is None: + nearest = _DistanceQuery(surf['rr']) + project_to_trans = np.eye(4) + inv_trans = np.linalg.inv(project_to_trans) + proj_rr = apply_trans(inv_trans, pts) + proj_pts, proj_nn = _get_nearest( + nearest, check_inside, project_to_trans, proj_rr) + vec = pts - proj_pts # point to the surface + nn = proj_nn + if mark_inside and not project_to_surface: + scalars = (~check_inside(proj_rr)).astype(int) + else: + scalars = np.ones(len(pts)) + dist = np.linalg.norm(vec, axis=-1, keepdims=True) + vectors = (250 * dist + 1) * nn + return scalars, vectors, proj_pts + + +def _plot_glyphs(renderer, loc, color, scale, opacity=1, mode="cylinder", + orient_glyphs=False, scale_by_distance=False, + project_points=False, mark_inside=False, surf=None, + backface_culling=False, check_inside=None, nearest=None): + if surf is not None and len(loc) > 0: + defaults = DEFAULTS['coreg'] + scalars, vectors, proj_pts = _orient_glyphs(loc, surf, project_points, + mark_inside, check_inside, + nearest) + if mark_inside: + from matplotlib.colors import ListedColormap + color = np.append(color, 1) + colormap = ListedColormap(np.array([(0, 0, 0, 1,), color])) + color = None + clim = [0, 1] + else: + scalars = None + colormap = None + clim = None + mode = 'cylinder' if orient_glyphs else 'sphere' + scale_mode = 'vector' if scale_by_distance else 'none' + x, y, z = proj_pts.T if project_points else loc.T + u, v, w = vectors.T + return renderer.quiver3d( + x, y, z, u, v, w, color=color, scale=scale, + mode=mode, glyph_height=defaults['eegp_height'], + glyph_center=(0., -defaults['eegp_height'], 0), + resolution=16, glyph_resolution=16, + glyph_radius=None, opacity=opacity, scale_mode=scale_mode, + scalars=scalars, colormap=colormap, clim=clim) + else: + return renderer.sphere(center=loc, color=color, scale=scale, + opacity=opacity, + backface_culling=backface_culling) + + +@verbose +def _plot_head_shape_points(renderer, info, to_cf_t, opacity=0.25, + orient_glyphs=False, scale_by_distance=False, + mark_inside=False, surf=None, mask=None, + check_inside=None, nearest=None, verbose=False): + defaults = DEFAULTS['coreg'] + ext_loc = np.array([ + d['r'] for d in (info['dig'] or []) + if (d['kind'] == FIFF.FIFFV_POINT_EXTRA and + d['coord_frame'] == FIFF.FIFFV_COORD_HEAD)]) + ext_loc = apply_trans(to_cf_t['head'], ext_loc) + ext_loc = ext_loc[mask] if mask is not None else ext_loc + actor, _ = _plot_glyphs(renderer=renderer, loc=ext_loc, + color=defaults['extra_color'], + scale=defaults['extra_scale'], opacity=opacity, + orient_glyphs=orient_glyphs, + scale_by_distance=scale_by_distance, + mark_inside=mark_inside, surf=surf, + backface_culling=True, check_inside=check_inside, + nearest=nearest) + return actor + + +def _plot_forward(renderer, fwd, fwd_trans, fwd_scale=1, scale=1.5e-3, + alpha=1): + from ..forward import Forward + _validate_type(fwd, [Forward]) + n_dipoles = fwd['source_rr'].shape[0] + fwd_rr = fwd['source_rr'] + if fwd['source_ori'] == FIFF.FIFFV_MNE_FIXED_ORI: + fwd_nn = fwd['source_nn'].reshape(-1, 1, 3) + else: + fwd_nn = fwd['source_nn'].reshape(-1, 3, 3) + # update coordinate frame + fwd_rr = apply_trans(fwd_trans, fwd_rr) * fwd_scale + fwd_nn = apply_trans(fwd_trans, fwd_nn, move=False) + red = (1.0, 0.0, 0.0) + green = (0.0, 1.0, 0.0) + blue = (0.0, 0.0, 1.0) + actors = list() + for ori, color in zip(range(fwd_nn.shape[1]), (red, green, blue)): + actor, _ = renderer.quiver3d( + *fwd_rr.T, *fwd_nn[:, ori].T, + color=color, mode='arrow', scale_mode='scalar', + scalars=np.ones(n_dipoles), scale=scale, opacity=alpha) + actors.append(actor) + return actors + + +def _plot_sensors(renderer, info, to_cf_t, picks, meg, eeg, fnirs, + warn_meg, head_surf, units, sensor_opacity=0.8, + orient_glyphs=False, scale_by_distance=False, + project_points=False, surf=None, check_inside=None, + nearest=None): + """Render sensors in a 3D scene.""" + defaults = DEFAULTS['coreg'] + ch_pos, sources, detectors = _ch_pos_in_coord_frame( + pick_info(info, picks), to_cf_t=to_cf_t, warn_meg=warn_meg) + + actors = dict(meg=list(), ref_meg=list(), eeg=list(), fnirs=list(), + ecog=list(), seeg=list(), dbs=list()) + locs = dict(eeg=list(), fnirs=list(), ecog=list(), seeg=list(), + source=list(), detector=list()) + scalar = 1 if units == 'm' else 1e3 + for ch_name, ch_coord in ch_pos.items(): + ch_type = channel_type(info, info.ch_names.index(ch_name)) + # for default picking + if ch_type in _FNIRS_CH_TYPES_SPLIT: + ch_type = 'fnirs' + elif ch_type in _MEG_CH_TYPES_SPLIT: + ch_type = 'meg' + # only plot sensor locations if channels/original in selection + plot_sensors = (ch_type != 'fnirs' or 'channels' in fnirs) and \ + (ch_type != 'eeg' or 'original' in eeg) + color = defaults[ch_type + '_color'] + # plot sensors + if isinstance(ch_coord, tuple): # is meg, plot coil + verts, triangles = ch_coord + actor, _ = renderer.surface( + surface=dict(rr=verts * scalar, tris=triangles), + color=color, opacity=0.25, backface_culling=True) + actors[ch_type].append(actor) + else: + if plot_sensors: + locs[ch_type].append(ch_coord) + if ch_name in sources and 'sources' in fnirs: + locs['source'].append(sources[ch_name]) + if ch_name in detectors and 'detectors' in fnirs: + locs['detector'].append(detectors[ch_name]) + if ch_name in sources and ch_name in detectors and \ + 'pairs' in fnirs: + actor, _ = renderer.tube( # array of origin and dest points + origin=sources[ch_name][np.newaxis] * scalar, + destination=detectors[ch_name][np.newaxis] * scalar, + radius=0.001 * scalar) + actors[ch_type].append(actor) + + # add sensors + for sensor_type in locs.keys(): + if len(locs[sensor_type]) > 0: + sens_loc = np.array(locs[sensor_type]) + sens_loc = sens_loc[~np.isnan(sens_loc).any(axis=1)] + color = defaults[sensor_type + '_color'] + scale = defaults[sensor_type + '_scale'] + actor, _ = _plot_glyphs(renderer=renderer, loc=sens_loc * scalar, + color=color, scale=scale * scalar, + opacity=sensor_opacity, + orient_glyphs=orient_glyphs, + scale_by_distance=scale_by_distance, + project_points=project_points, + surf=surf, check_inside=check_inside, + nearest=nearest) + if sensor_type in ('source', 'detector'): + sensor_type = 'fnirs' + actors[sensor_type].append(actor) + + # add projected eeg + eeg_indices = pick_types(info, eeg=True) + if eeg_indices.size > 0 and 'projected' in eeg: + logger.info('Projecting sensors to the head surface') + eeg_loc = np.array([ + ch_pos[info.ch_names[idx]] for idx in eeg_indices]) + eeg_loc = eeg_loc[~np.isnan(eeg_loc).any(axis=1)] + eegp_loc, eegp_nn = _project_onto_surface( + eeg_loc, head_surf, project_rrs=True, + return_nn=True)[2:4] + del eeg_loc + eegp_loc *= scalar + scale = defaults['eegp_scale'] * scalar + actor, _ = renderer.quiver3d( + x=eegp_loc[:, 0], y=eegp_loc[:, 1], z=eegp_loc[:, 2], + u=eegp_nn[:, 0], v=eegp_nn[:, 1], w=eegp_nn[:, 2], + color=defaults['eegp_color'], mode='cylinder', + scale=scale, opacity=0.6, + glyph_height=defaults['eegp_height'], + glyph_center=(0., -defaults['eegp_height'] / 2., 0), + glyph_resolution=20, + backface_culling=True) + actors['eeg'].append(actor) + + return actors + + def _make_tris_fan(n_vert): """Make tris given a number of vertices of a circle-like obj.""" tris = np.zeros((n_vert - 2, 3), int) @@ -1111,7 +1273,11 @@ def _sensor_shape(coil): """Get the sensor shape vertices.""" - from scipy.spatial import ConvexHull + from scipy.spatial import ConvexHull, Delaunay + try: + from scipy.spatial import QhullError + except ImportError: # scipy < 1.8 + from scipy.spatial.qhull import QhullError id_ = coil['type'] & 0xFFFF pad = True # Square figure eight @@ -1184,11 +1350,19 @@ np.concatenate([circle.imag, -circle.imag])]).T tris = np.concatenate([_make_tris_fan(n_pts + 1), _make_tris_fan(n_pts + 1) + n_pts + 1]) - # 3D convex hull (will fail for 2D geometry, can extend later if needed) else: + # 3D convex hull (will fail for 2D geometry) rrs = coil['rmag_orig'].copy() + try: + tris = _reorder_ccw(rrs, ConvexHull(rrs).simplices) + except QhullError: # 2D geometry likely + logger.debug('Falling back to planar geometry') + u, _, _ = np.linalg.svd(rrs.T, full_matrices=False) + u[:, 2] = 0 + rr_rot = rrs @ u + tris = Delaunay(rr_rot[:, :2]).simplices + tris = np.concatenate((tris, tris[:, ::-1])) pad = False - tris = _reorder_ccw(rrs, ConvexHull(rrs).simplices) # Go from (x,y) -> (x,y,z) if pad: @@ -1576,8 +1750,8 @@ If True, link the vertices picked with the mouse. Defaults to False. """ from .backends.renderer import _get_3d_backend - if _get_3d_backend() != 'pyvista': - raise NotImplementedError("Expected 3d backend is pyvista but" + if _get_3d_backend() != 'pyvistaqt': + raise NotImplementedError("Expected 3d backend is pyvistaqt but" " {} was given.".format(_get_3d_backend())) from ._brain import Brain, _LinkViewer if not isinstance(brains, Iterable): @@ -1610,10 +1784,6 @@ if isinstance(stc, _BaseSurfaceSourceEstimate): return False else: - if backend_name == 'mayavi': - raise RuntimeError( - 'Must use the PyVista 3D backend to plot a mixed or volume ' - 'source estimate') _validate_type(src, SourceSpaces, 'src', 'src when stc is a mixed or volume source estimate') if isinstance(stc, _BaseMixedSourceEstimate): @@ -1644,10 +1814,8 @@ ---------- stc : SourceEstimate The source estimates to plot. - subject : str | None - The subject name corresponding to FreeSurfer environment - variable SUBJECT. If None stc.subject will be used. If that - is None, the environment will be used. + %(subject_none)s + If ``None``, ``stc.subject`` will be used. surface : str The type of surface (inflated, white etc.). hemi : str @@ -1672,10 +1840,10 @@ .. versionchanged:: 0.20.0 "auto" mode added. %(subjects_dir)s - figure : instance of mayavi.core.api.Scene | instance of matplotlib.figure.Figure | list | int | None + figure : instance of Figure3D | instance of matplotlib.figure.Figure | list | int | None If None, a new figure will be created. If multiple views or a split view is requested, this must be a list of the appropriate - length. If int is provided it will be used to identify the Mayavi + length. If int is provided it will be used to identify the PyVista figure by it's id or create a new figure with the given id. If an instance of matplotlib figure, mpl backend is used for plotting. %(views)s @@ -1684,6 +1852,8 @@ and using the PyVista backend, ``views='flat'`` is also supported to plot cortex as a flatmap. + Using multiple views (list) is not supported by the matplotlib backend. + .. versionchanged:: 0.21.0 Support for flatmaps. colorbar : bool @@ -1691,9 +1861,9 @@ %(clim)s cortex : str or tuple Specifies how binarized curvature values are rendered. - Either the name of a preset PySurfer cortex colorscheme (one of + Either the name of a preset Brain cortex colorscheme (one of 'classic', 'bone', 'low_contrast', or 'high_contrast'), or the name of - mayavi colormap, or a tuple with values (colormap, min, max, reverse) + a colormap, or a tuple with values (colormap, min, max, reverse) to fully specify the curvature colors. Has no effect with mpl backend. size : float or tuple of float The size of the window, in pixels. can be one number to specify @@ -1710,17 +1880,18 @@ time_unit : 's' | 'ms' Whether time is represented in seconds ("s", default) or milliseconds ("ms"). - backend : 'auto' | 'mayavi' | 'pyvista' | 'matplotlib' + backend : 'auto' | 'pyvistaqt' | 'matplotlib' Which backend to use. If ``'auto'`` (default), tries to plot with - pyvista, but resorts to matplotlib if no 3d backend is available. + pyvistaqt, but resorts to matplotlib if no 3d backend is available. .. versionadded:: 0.15.0 spacing : str + Only affects the matplotlib backend. The spacing to use for the source space. Can be ``'ico#'`` for a recursively subdivided icosahedron, ``'oct#'`` for a recursively subdivided octahedron, or ``'all'`` for all points. In general, you can - speed up the plotting by selecting a sparser source space. Has no - effect with mayavi backend. Defaults to 'oct6'. + speed up the plotting by selecting a sparser source space. + Defaults to 'oct6'. .. versionadded:: 0.15.0 title : str | None @@ -1758,7 +1929,7 @@ raise_error=True) subject = _check_subject(stc.subject, subject) _check_option('backend', backend, - ['auto', 'matplotlib', 'mayavi', 'pyvista', 'notebook']) + ['auto', 'matplotlib', 'pyvistaqt', 'notebook']) plot_mpl = backend == 'matplotlib' if not plot_mpl: if backend == 'auto': @@ -1802,14 +1973,13 @@ backend = _get_3d_backend() del _get_3d_backend - using_mayavi = backend == "mayavi" Brain = get_brain_class() views = _check_views(surface, views, hemi, stc, backend) _check_option('hemi', hemi, ['lh', 'rh', 'split', 'both']) _check_option('view_layout', view_layout, ('vertical', 'horizontal')) time_label, times = _handle_time(time_label, time_unit, stc.times) show_traces, time_viewer = _check_st_tv( - show_traces, time_viewer, using_mayavi, times) + show_traces, time_viewer, times) # convert control points to locations in colormap use = stc.magnitude().data if vec else stc.data @@ -1818,12 +1988,8 @@ volume = _check_volume(stc, src, surface, backend) - # XXX we should only need to do this for PySurfer/Mayavi, the PyVista - # plotter should be smart enough to do this separation in the cmap-to-ctab - # conversion. But this will need to be another refactoring that will - # hopefully restore this line: - # - # if using_mayavi: + # XXX we should not need to do this for PyVista, the plotter should be + # smart enough to do this separation in the cmap-to-ctab conversion _separate_map(mapdata) colormap = mapdata['colormap'] diverging = 'pos_lims' in mapdata['clim'] @@ -1851,23 +2017,12 @@ } if brain_kwargs is not None: kwargs.update(brain_kwargs) - if backend in ['pyvista', 'notebook']: - kwargs["show"] = False - kwargs["view_layout"] = view_layout - else: - kwargs.update(_check_pysurfer_antialias(Brain)) - if view_layout != 'vertical': - raise ValueError('view_layout must be "vertical" when using the ' - 'mayavi backend') + kwargs["show"] = False + kwargs["view_layout"] = view_layout with warnings.catch_warnings(record=True): # traits warnings brain = Brain(**kwargs) del kwargs - if using_mayavi: - # Here we patch to avoid segfault: - # https://github.com/mne-tools/mne-python/pull/8828 - brain.close = lambda *args, **kwargs: brain._close(False) - if scale_factor is None: # Configure the glyphs scale directly width = np.mean([np.ptp(brain.geo[hemi].coords[:, 1]) @@ -1877,7 +2032,6 @@ if transparent is None: transparent = True center = 0. if diverging else None - sd_kwargs = dict(transparent=transparent, center=center, verbose=False) kwargs = { "array": stc, "colormap": colormap, @@ -1887,7 +2041,6 @@ "colorbar": colorbar, "vector_alpha": vector_alpha, "scale_factor": scale_factor, - "verbose": False, "initial_time": initial_time, "transparent": transparent, "center": center, @@ -1897,7 +2050,7 @@ "clim": clim, "src": src, "volume_options": volume_options, - "verbose": False, + "verbose": None, } if add_data_kwargs is not None: kwargs.update(add_data_kwargs) @@ -1909,18 +2062,8 @@ continue # no data use_kwargs = kwargs.copy() use_kwargs.update(hemi=hemi) - if using_mayavi: - del use_kwargs['clim'], use_kwargs['src'] - del use_kwargs['volume_options'] - use_kwargs.update( - min=use_kwargs.pop('fmin'), mid=use_kwargs.pop('fmid'), - max=use_kwargs.pop('fmax'), array=getattr(stc, hemi + '_data'), - vertices=vertices) with warnings.catch_warnings(record=True): # traits warnings brain.add_data(**use_kwargs) - if using_mayavi: - brain.scale_data_colormap(fmin=scale_pts[0], fmid=scale_pts[1], - fmax=scale_pts[2], **sd_kwargs) if volume: use_kwargs = kwargs.copy() @@ -1928,44 +2071,16 @@ brain.add_data(**use_kwargs) del kwargs - need_peeling = (brain_alpha < 1.0 and - sys.platform != 'darwin' and - vec) - if using_mayavi: - for hemi in hemis: - for b in brain._brain_list: - for layer in b['brain'].data.values(): - glyphs = layer['glyphs'] - if glyphs is None: - continue - glyphs.glyph.glyph.scale_factor = scale_factor - glyphs.glyph.glyph.clamping = False - glyphs.glyph.glyph.range = (0., 1.) - - # depth peeling patch - if need_peeling: - for ff in brain._figures: - for f in ff: - if f.scene is not None and sys.platform != 'darwin': - f.scene.renderer.use_depth_peeling = True - elif need_peeling: - brain.enable_depth_peeling() - if time_viewer: - if using_mayavi: - from surfer import TimeViewer - TimeViewer(brain) - else: # PyVista - brain.setup_time_viewer(time_viewer=time_viewer, - show_traces=show_traces) + brain.setup_time_viewer(time_viewer=time_viewer, + show_traces=show_traces) else: - if not using_mayavi: - brain.show() + brain.show() return brain -def _check_st_tv(show_traces, time_viewer, using_mayavi, times): +def _check_st_tv(show_traces, time_viewer, times): # time_viewer and show_traces _check_option('time_viewer', time_viewer, (True, False, 'auto')) _validate_type(show_traces, (str, bool, 'numeric'), 'show_traces') @@ -1974,19 +2089,15 @@ ('auto', 'separate', 'vertex', 'label'), extra='when a string') if time_viewer == 'auto': - time_viewer = not using_mayavi + time_viewer = True if show_traces == 'auto': show_traces = ( - not using_mayavi and time_viewer and times is not None and len(times) > 1 ) if show_traces and not time_viewer: raise ValueError('show_traces cannot be used when time_viewer=False') - if using_mayavi and show_traces: - raise NotImplementedError("show_traces=True is not available " - "for the mayavi 3d backend.") return show_traces, time_viewer @@ -2037,10 +2148,8 @@ .. versionchanged:: 0.18 Support for :class:`~nibabel.spatialimages.SpatialImage`. - subject : str | None - The subject name corresponding to FreeSurfer environment - variable SUBJECT. If None stc.subject will be used. If that - is None, the environment will be used. + %(subject_none)s + If ``None``, ``stc.subject`` will be used. %(subjects_dir)s mode : str The plotting mode to use. Either 'stat_map' (default) or 'glass_brain'. @@ -2107,7 +2216,6 @@ >>> fig = stc_vol_sample.plot(morph) # doctest: +SKIP """ # noqa: E501 from matplotlib import pyplot as plt, colors - from matplotlib.cbook import mplDeprecation import nibabel as nib from ..source_estimate import VolSourceEstimate from ..morph import SourceMorph @@ -2154,7 +2262,7 @@ % (stc.vertices[0][loc_idx], dist)) return loc_idx - ax_name = dict(x='X (saggital)', y='Y (coronal)', z='Z (axial)') + ax_name = dict(x='X (sagittal)', y='Y (coronal)', z='Z (axial)') def _click_to_cut_coords(event, params): """Get voxel coordinates from mouse click.""" @@ -2236,6 +2344,9 @@ plot_map_callback( params['img_idx'], title='', cut_coords=cut_coords) + def _update_vertlabel(loc_idx): + vert_legend.get_texts()[0].set_text(f'{stc.vertices[0][loc_idx]}') + @verbose_dec def _onclick(event, params, verbose=None): """Manage clicks on the plot.""" @@ -2261,6 +2372,7 @@ ax_time.lines[0].set_ydata(ydata) else: ax_time.lines[0].set_ydata(0.) + _update_vertlabel(loc_idx) params['fig'].canvas.draw() if mode == 'glass_brain': @@ -2324,10 +2436,12 @@ axes.set(xticks=[], yticks=[]) marker = 'o' if len(stc.times) == 1 else None ydata = stc.data[loc_idx] - ax_time.plot(stc.times, ydata, color='k', marker=marker) + h = ax_time.plot(stc.times, ydata, color='k', marker=marker)[0] if len(stc.times) > 1: ax_time.set(xlim=stc.times[[0, -1]]) ax_time.set(xlabel='Time (s)', ylabel='Activation') + vert_legend = ax_time.legend([h], [''], title='Vertex') + _update_vertlabel(loc_idx) lx = ax_time.axvline(stc.times[time_idx], color='g') fig.tight_layout() @@ -2392,7 +2506,7 @@ if params.get('fig_anat') is not None and plot_kwargs['colorbar']: params['fig_anat']._cbar.ax.clear() with warnings.catch_warnings(record=True): # nilearn bug; ax recreated - warnings.simplefilter('ignore', mplDeprecation) + warnings.simplefilter('ignore', DeprecationWarning) params['fig_anat'] = partial( plot_func, **plot_kwargs)(*args, **kwargs) params['fig_anat']._cbar.outline.set_visible(False) @@ -2424,18 +2538,8 @@ return fig -def _check_pysurfer_antialias(Brain): - antialias = _get_3d_option('antialias') - kwargs = dict() - if not antialias: - if 'antialias' not in _get_args(Brain): - raise ValueError('To turn off antialiasing, PySurfer needs to be ' - 'updated to version 0.11+') - kwargs['antialias'] = antialias - return kwargs - - def _check_views(surf, views, hemi, stc=None, backend=None): + from ._brain.view import views_dicts from ..source_estimate import SourceEstimate _validate_type(views, (list, tuple, str), 'views') views = [views] if isinstance(views, str) else list(views) @@ -2449,12 +2553,16 @@ _validate_type(stc, SourceEstimate, 'stc', 'SourceEstimate when a flatmap is used') if backend is not None: - if backend not in ('pyvista', 'notebook'): + if backend not in ('pyvistaqt', 'notebook'): raise RuntimeError('The PyVista 3D backend must be used to ' 'plot a flatmap') if (views == ['flat']) ^ (surf == 'flat'): # exactly only one of the two raise ValueError('surface="flat" must be used with views="flat", got ' f'surface={repr(surf)} and views={repr(views)}') + _check_option('hemi', hemi, ('split', 'both', 'lh', 'rh', 'vol', None)) + use_hemi = 'lh' if hemi == 'split' or hemi is None else hemi + for vi, v in enumerate(views): + _check_option(f'views[{vi}]', v, sorted(views_dicts[use_hemi])) return views @@ -2474,7 +2582,7 @@ view_layout='vertical', add_data_kwargs=None, brain_kwargs=None, verbose=None): - """Plot VectorSourceEstimate with PySurfer. + """Plot VectorSourceEstimate with PyVista. A "glass brain" is drawn and all dipoles defined in the source estimate are shown using arrows, depicting the direction and magnitude of the @@ -2485,10 +2593,8 @@ ---------- stc : VectorSourceEstimate | MixedVectorSourceEstimate The vector source estimate to plot. - subject : str | None - The subject name corresponding to FreeSurfer environment - variable SUBJECT. If None stc.subject will be used. If that - is None, the environment will be used. + %(subject_none)s + If ``None``, ``stc.subject`` will be used. hemi : str, 'lh' | 'rh' | 'split' | 'both' The hemisphere to display. %(colormap)s @@ -2516,10 +2622,10 @@ subjects_dir : str The path to the freesurfer subjects reconstructions. It corresponds to Freesurfer environment variable SUBJECTS_DIR. - figure : instance of mayavi.core.api.Scene | list | int | None + figure : instance of Figure3D | list | int | None If None, a new figure will be created. If multiple views or a split view is requested, this must be a list of the appropriate - length. If int is provided it will be used to identify the Mayavi + length. If int is provided it will be used to identify the PyVista figure by it's id or create a new figure with the given id. %(views)s colorbar : bool @@ -2527,9 +2633,9 @@ %(clim_onesided)s cortex : str or tuple Specifies how binarized curvature values are rendered. - either the name of a preset PySurfer cortex colorscheme (one of + either the name of a preset Brain cortex colorscheme (one of 'classic', 'bone', 'low_contrast', or 'high_contrast'), or the - name of mayavi colormap, or a tuple with values (colormap, min, + name of a colormap, or a tuple with values (colormap, min, max, reverse) to fully specify the curvature colors. size : float or tuple of float The size of the window, in pixels. can be one number to specify @@ -2600,7 +2706,7 @@ src : dict The source space. stcs : instance of SourceEstimate or list of instances of SourceEstimate - The source estimates (up to 3). + The source estimates. colors : list List of colors. linewidth : int @@ -2618,7 +2724,7 @@ high_resolution : bool If True, plot on the original (non-downsampled) cortical mesh. fig_name : str - Mayavi figure name. + PyVista figure name. fig_number : int Matplotlib figure number. labels : ndarray or list of ndarray @@ -2635,18 +2741,36 @@ List of floating point scale factors for the markers. %(verbose)s **kwargs : kwargs - Keyword arguments to pass to mlab.triangular_mesh. + Keyword arguments to pass to renderer.mesh. Returns ------- - surface : instance of mayavi.mlab.pipeline.surface - The triangular mesh surface. + surface : instance of Figure3D + The 3D figure containing the triangular mesh surface. """ import matplotlib.pyplot as plt - from matplotlib.colors import ColorConverter # Update the backend from .backends.renderer import _get_renderer + linestyles = [ + ('solid', 'solid'), # noqa: E241 + ('dashed', 'dashed'), # noqa: E241 + ('dotted', 'dotted'), # noqa: E241 + ('dashdot', 'dashdot'), # noqa: E241 + ('loosely dotted', (0, (1, 10))), # noqa: E241 + ('dotted', (0, (1, 1))), # noqa: E241 + ('densely dotted', (0, (1, 1))), # noqa: E241 + ('loosely dashed', (0, (5, 10))), # noqa: E241 + ('dashed', (0, (5, 5))), # noqa: E241 + ('densely dashed', (0, (5, 1))), # noqa: E241 + ('loosely dashdotted', (0, (3, 10, 1, 10))), # noqa: E241 + ('dashdotted', (0, (3, 5, 1, 5))), # noqa: E241 + ('densely dashdotted', (0, (3, 1, 1, 1))), # noqa: E241 + ('dashdotdotted', (0, (3, 5, 1, 5, 1, 5))), # noqa: E241 + ('loosely dashdotdotted', (0, (3, 10, 1, 10, 1, 10))), # noqa: E241 + ('densely dashdotdotted', (0, (3, 1, 1, 1, 1, 1))) # noqa: E241 + ] + known_modes = ['cone', 'sphere'] if not isinstance(modes, (list, tuple)) or \ not all(mode in known_modes for mode in modes): @@ -2660,7 +2784,8 @@ if colors is None: colors = _get_color_list() - linestyles = ['-', '--', ':'] + linestyles = cycle(linestyles) + linestyles = [next(linestyles)[1] for _ in range(len(stcs))] # Show 3D lh_points = src[0]['rr'] @@ -2686,14 +2811,11 @@ for stc in stcs] unique_vertnos = np.unique(np.concatenate(vertnos).ravel()) - color_converter = ColorConverter() - renderer = _get_renderer(bgcolor=bgcolor, size=(600, 600), name=fig_name) - surface = renderer.mesh(x=points[:, 0], y=points[:, 1], - z=points[:, 2], triangles=use_faces, - color=brain_color, opacity=opacity, - backface_culling=True, shading=True, - normals=normals, **kwargs) + renderer.mesh( + x=points[:, 0], y=points[:, 1], z=points[:, 2], triangles=use_faces, + color=brain_color, opacity=opacity, backface_culling=True, + shading=True, normals=normals, **kwargs) # Show time courses fig = plt.figure(fig_number) @@ -2729,7 +2851,7 @@ x, y, z = points[v] nx, ny, nz = normals[v] renderer.quiver3d(x=x, y=y, z=z, u=nx, v=ny, w=nz, - color=color_converter.to_rgb(c), + color=_to_rgb(c), mode=mode, scale=scale_factor) for k in ind: @@ -2748,22 +2870,21 @@ plt_show(show) renderer.show() - return surface + return renderer.scene() @verbose def plot_dipole_locations(dipoles, trans=None, subject=None, subjects_dir=None, mode='orthoview', coord_frame='mri', idx='gof', show_all=True, ax=None, block=False, show=True, - scale=5e-3, color=None, highlight_color='r', - fig=None, verbose=None, title=None): + scale=None, color=None, *, highlight_color='r', + fig=None, title=None, head_source='seghead', + surf='pial', width=None, verbose=None): """Plot dipole locations. If mode is set to 'arrow' or 'sphere', only the location of the first time point of each dipole is shown else use the show_all parameter. - The option mode='orthoview' was added in version 0.14. - Parameters ---------- dipoles : list of instances of Dipole | Dipole @@ -2772,21 +2893,33 @@ The mri to head trans. Can be None with mode set to '3d'. subject : str | None - The subject name corresponding to FreeSurfer environment - variable SUBJECT. - Can be None with mode set to '3d'. - subjects_dir : None | str - The path to the freesurfer subjects reconstructions. - It corresponds to Freesurfer environment variable SUBJECTS_DIR. - The default is None. + The FreeSurfer subject name (will be used to set the FreeSurfer + environment variable ``SUBJECT``). + Can be ``None`` with mode set to ``'3d'``. + %(subjects_dir)s mode : str - Can be ``'arrow'``, ``'sphere'`` or ``'orthoview'``. + Can be: - .. versionadded:: 0.19.0 + ``'arrow'`` or ``'sphere'`` + Plot in 3D mode using PyVista with the given glyph type. + ``'orthoview'`` + Plot in matplotlib ``Axes3D`` using matplotlib with MRI slices + shown on the sides of a cube, with the dipole(s) shown as arrows + extending outward from a dot (i.e., the arrows pivot on the tail). + ``'outlines'`` + Plot in matplotlib ``Axes`` using a quiver of arrows for the + dipoles in three axes (axial, coronal, and sagittal views), + with the arrow pivoting in the middle of the arrow. + + .. versionchanged:: 1.1 + Added support for ``'outlines'``. coord_frame : str - Coordinate frame to use, 'head' or 'mri'. Defaults to 'mri'. + Coordinate frame to use: 'head' or 'mri'. Can also be 'mri_rotated' + when mode equals ``'outlines'``. Defaults to 'mri'. .. versionadded:: 0.14.0 + .. versionchanged:: 1.1 + Added support for ``'mri_rotated'``. idx : int | 'gof' | 'amplitude' Index of the initially plotted dipole. Can also be 'gof' to plot the dipole with highest goodness of fit value or 'amplitude' to plot the @@ -2803,9 +2936,10 @@ Only used if ``mode='orthoview'``. .. versionadded:: 0.14.0 - ax : instance of matplotlib Axes3D | None + ax : instance of matplotlib Axes3D | list of matplotlib Axes | None Axes to plot into. If None (default), axes will be created. - Only used if mode equals 'orthoview'. + If mode equals ``'orthoview'``, must be a single ``Axes3D``. + If mode equals ``'outlines'``, must be a list of three ``Axes``. .. versionadded:: 0.14.0 block : bool @@ -2818,11 +2952,14 @@ Show figure if True. Defaults to True. Only used if mode equals 'orthoview'. scale : float - The scale of the dipoles if ``mode`` is 'arrow' or 'sphere'. + The scale (size in meters) of the dipoles if ``mode`` is not + ``'orthoview'``. The default is 0.03 when mode is ``'outlines'`` and + 0.005 otherwise. color : tuple The color of the dipoles. The default (None) will use ``'y'`` if mode is ``'orthoview'`` and - ``show_all`` is True, else 'r'. + ``show_all`` is True, else 'r'. Can also be a list of colors to use + when mode is ``'outlines'``. .. versionchanged:: 0.19.0 Color is now passed in orthoview mode. @@ -2831,59 +2968,71 @@ ``show_all=True``. .. versionadded:: 0.19.0 - fig : mayavi.mlab.Figure | None - 3D Scene in which to plot the alignment. + fig : instance of Figure3D | None + 3D figure in which to plot the alignment. If ``None``, creates a new 600x600 pixel figure with black background. + Only used when mode is ``'arrow'`` or ``'sphere'``. .. versionadded:: 0.19.0 - %(verbose)s - %(dipole_locs_fig_title)s + title : str | None + The title of the figure if ``mode='orthoview'`` (ignored for all other + modes). If ``None``, dipole number and its properties (amplitude, + orientation etc.) will be shown. Defaults to ``None``. .. versionadded:: 0.21.0 + %(head_source)s + Only used when mode equals ``'outlines'``. + + .. versionadded:: 1.1 + surf : str | None + Brain surface to show outlines for, can be ``'white'``, ``'pial'``, or + ``None``. Only used when mode is ``'outlines'``. + + .. versionadded:: 1.1 + width : float | None + Width of the matplotlib quiver arrow, see + :meth:`matplotlib:matplotlib.axes.Axes.quiver`. If None (default), + when mode is ``'outlines'`` 0.015 will be used, and when mode is + ``'orthoview'`` the matplotlib default is used. + %(verbose)s Returns ------- - fig : instance of mayavi.mlab.Figure or matplotlib.figure.Figure - The mayavi figure or matplotlib Figure. + fig : instance of Figure3D or matplotlib.figure.Figure + The PyVista figure or matplotlib Figure. Notes ----- .. versionadded:: 0.9.0 """ + _validate_type(mode, str, 'mode') + _validate_type(coord_frame, str, 'coord_frame') + _check_option('mode', mode, ('orthoview', 'outlines', 'arrow', 'sphere')) + if mode in ('orthoview', 'outlines'): + subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) + kwargs = dict( + trans=trans, subject=subject, subjects_dir=subjects_dir, + coord_frame=coord_frame, ax=ax, block=block, show=show, color=color, + title=title, width=width) + dipoles = _check_concat_dipoles(dipoles) if mode == 'orthoview': fig = _plot_dipole_mri_orthoview( - dipoles, trans=trans, subject=subject, subjects_dir=subjects_dir, - coord_frame=coord_frame, idx=idx, show_all=show_all, - ax=ax, block=block, show=show, color=color, - highlight_color=highlight_color, title=title) - elif mode in ['arrow', 'sphere']: - from .backends.renderer import _get_renderer - color = (1., 0., 0.) if color is None else color - renderer = _get_renderer(fig=fig, size=(600, 600)) - pos = dipoles.pos - ori = dipoles.ori - if coord_frame != 'head': - trans = _get_trans(trans, fro='head', to=coord_frame)[0] - pos = apply_trans(trans, pos) - ori = apply_trans(trans, ori) - - renderer.sphere(center=pos, color=color, scale=scale) - if mode == 'arrow': - x, y, z = pos.T - u, v, w = ori.T - renderer.quiver3d(x, y, z, u, v, w, scale=3 * scale, - color=color, mode='arrow') - renderer.show() - fig = renderer.scene() - else: - raise ValueError('Mode must be "cone", "arrow" or orthoview", ' - 'got %s.' % (mode,)) + dipoles, idx=idx, show_all=show_all, + highlight_color=highlight_color, **kwargs) + elif mode == 'outlines': + fig = _plot_dipole_mri_outlines( + dipoles, head_source=head_source, surf=surf, scale=scale, **kwargs) + else: + assert mode in ('arrow', 'sphere'), mode + fig = _plot_dipole_3d( + dipoles, trans=trans, coord_frame=coord_frame, color=color, + fig=fig, scale=scale, mode=mode) return fig def snapshot_brain_montage(fig, montage, hide_sensors=True): - """Take a snapshot of a Mayavi Scene and project channels onto 2d coords. + """Take a snapshot of a PyVista Scene and project channels onto 2d coords. Note that this will take the raw values for 3d coordinates of each channel, without applying any transforms. If brain images are flipped up/dn upon @@ -2892,7 +3041,7 @@ Parameters ---------- - fig : instance of ~mayavi.core.api.Scene + fig : instance of Figure3D The figure on which you've plotted electrodes using :func:`mne.viz.plot_alignment`. montage : instance of DigMontage or Info | dict @@ -2901,6 +3050,8 @@ field of ``chs``. dict should have ch:xyz mappings. hide_sensors : bool Whether to remove the spheres in the scene before taking a snapshot. + The sensors will always be shown in the final figure. If you want an + image of just the brain, use :class:`mne.viz.Brain` instead. Returns ------- @@ -2943,109 +3094,18 @@ return proj.xy, im -@fill_doc -def plot_sensors_connectivity(info, con, picks=None, - cbar_label='Connectivity'): - """Visualize the sensor connectivity in 3D. - - Parameters - ---------- - info : dict | None - The measurement info. - con : array, shape (n_channels, n_channels) - The computed connectivity measure(s). - %(picks_good_data)s - Indices of selected channels. - cbar_label : str - Label for the colorbar. - - Returns - ------- - fig : instance of mayavi.mlab.Figure - The mayavi figure. - """ - _validate_type(info, "info") - - from .backends.renderer import _get_renderer - - renderer = _get_renderer(size=(600, 600), bgcolor=(0.5, 0.5, 0.5)) - - picks = _picks_to_idx(info, picks) - if len(picks) != len(con): - raise ValueError('The number of channels picked (%s) does not ' - 'correspond to the size of the connectivity data ' - '(%s)' % (len(picks), len(con))) - - # Plot the sensor locations - sens_loc = [info['chs'][k]['loc'][:3] for k in picks] - sens_loc = np.array(sens_loc) - - renderer.sphere(np.c_[sens_loc[:, 0], sens_loc[:, 1], sens_loc[:, 2]], - color=(1, 1, 1), opacity=1, scale=0.005) - - # Get the strongest connections - n_con = 20 # show up to 20 connections - min_dist = 0.05 # exclude sensors that are less than 5cm apart - threshold = np.sort(con, axis=None)[-n_con] - ii, jj = np.where(con >= threshold) - - # Remove close connections - con_nodes = list() - con_val = list() - for i, j in zip(ii, jj): - if np.linalg.norm(sens_loc[i] - sens_loc[j]) > min_dist: - con_nodes.append((i, j)) - con_val.append(con[i, j]) - - con_val = np.array(con_val) - - # Show the connections as tubes between sensors - vmax = np.max(con_val) - vmin = np.min(con_val) - for val, nodes in zip(con_val, con_nodes): - x1, y1, z1 = sens_loc[nodes[0]] - x2, y2, z2 = sens_loc[nodes[1]] - tube = renderer.tube(origin=np.c_[x1, y1, z1], - destination=np.c_[x2, y2, z2], - scalars=np.c_[val, val], - vmin=vmin, vmax=vmax, - reverse_lut=True) - - renderer.scalarbar(source=tube, title=cbar_label) - - # Add the sensor names for the connections shown - nodes_shown = list(set([n[0] for n in con_nodes] + - [n[1] for n in con_nodes])) - - for node in nodes_shown: - x, y, z = sens_loc[node] - renderer.text3d(x, y, z, text=info['ch_names'][picks[node]], - scale=0.005, - color=(0, 0, 0)) - - renderer.set_camera(azimuth=-88.7, elevation=40.8, - distance=0.76, - focalpoint=np.array([-3.9e-4, -8.5e-3, -1e-2])) - renderer.show() - return renderer.scene() - - def _plot_dipole_mri_orthoview(dipole, trans, subject, subjects_dir=None, coord_frame='head', idx='gof', show_all=True, ax=None, block=False, show=True, color=None, - highlight_color='r', title=None): + highlight_color='r', title=None, width=None): """Plot dipoles on top of MRI slices in 3-D.""" import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D - from .. import Dipole if not has_nibabel(): raise ImportError('This function requires nibabel.') _check_option('coord_frame', coord_frame, ['head', 'mri']) - if not isinstance(dipole, Dipole): - from ..dipole import _concatenate_dipoles - dipole = _concatenate_dipoles(dipole) if idx == 'gof': idx = np.argmax(dipole.gof) elif idx == 'amplitude': @@ -3061,7 +3121,8 @@ if ax is None: fig, ax = plt.subplots(1, subplot_kw=dict(projection='3d')) else: - _validate_type(ax, Axes3D, "ax", "Axes3D") + _validate_type(ax, Axes3D, "ax", "Axes3D", + extra='when mode is "orthoview"') fig = ax.get_figure() gridx, gridy = np.meshgrid(np.linspace(-dd, dd, dims), @@ -3071,7 +3132,7 @@ 'ori': ori, 'coord_frame': coord_frame, 'show_all': show_all, 'pos': pos, 'color': color, 'highlight_color': highlight_color, - 'title': title} + 'title': title, 'width': width} _plot_dipole(**params) ax.view_init(elev=30, azim=-140) @@ -3139,11 +3200,9 @@ def _plot_dipole(ax, data, vox, idx, dipole, gridx, gridy, ori, coord_frame, - show_all, pos, color, highlight_color, title): + show_all, pos, color, highlight_color, title, width): """Plot dipoles.""" import matplotlib.pyplot as plt - from matplotlib.colors import ColorConverter - color_converter = ColorConverter() xidx, yidx, zidx = np.round(vox[idx]).astype(int) xslice = data[xidx] yslice = data[:, yidx] @@ -3152,8 +3211,9 @@ ori = ori[idx] if color is None: color = 'y' if show_all else 'r' - color = np.array(color_converter.to_rgba(color)) - highlight_color = np.array(color_converter.to_rgba(highlight_color)) + color = np.array(_to_rgb(color, alpha=True)) + highlight_color = np.array(_to_rgb( + highlight_color, name='highlight_color', alpha=True)) if show_all: colors = np.repeat(color[np.newaxis], len(vox), axis=0) colors[idx] = highlight_color @@ -3180,6 +3240,8 @@ np.repeat(xyz[idx, 1], len(zz)), zs=zz, zorder=1, linestyle='-', color=highlight_color) q_kwargs = dict(length=50, color=highlight_color, pivot='tail') + if width is not None: + q_kwargs['width'] = width ax.quiver(xyz[idx, 0], xyz[idx, 1], xyz[idx, 2], ori[0], ori[1], ori[2], **q_kwargs) dims = np.array([(len(data) / -2.), (len(data) / 2.)]) @@ -3235,16 +3297,6 @@ _plot_dipole(**params) -def _update_coord_frame(obj, rr, nn, mri_trans, head_trans): - if obj['coord_frame'] == FIFF.FIFFV_COORD_MRI: - rr = apply_trans(mri_trans, rr) - nn = apply_trans(mri_trans, nn, move=False) - elif obj['coord_frame'] == FIFF.FIFFV_COORD_HEAD: - rr = apply_trans(head_trans, rr) - nn = apply_trans(head_trans, nn, move=False) - return rr, nn - - @fill_doc def plot_brain_colorbar(ax, clim, colormap='auto', transparent=True, orientation='vertical', label='Activation', @@ -3294,34 +3346,80 @@ return cbar -_3d_options = dict() -_3d_default = dict(antialias='true') +@dataclass() +class _3d_Options: + antialias: Optional[bool] + depth_peeling: Optional[bool] + smooth_shading: Optional[bool] + multi_samples: Optional[int] + + +_3d_options = _3d_Options( + antialias=None, + depth_peeling=None, + smooth_shading=None, + multi_samples=None, +) +_3d_default = _3d_Options( + antialias='true', + depth_peeling='true', + smooth_shading='true', + multi_samples='4', +) -def set_3d_options(antialias=None): +def set_3d_options(antialias=None, depth_peeling=None, smooth_shading=None, *, + multi_samples=None): """Set 3D rendering options. Parameters ---------- antialias : bool | None - If not None, set the default full-screen anti-aliasing setting. + If bool, whether to enable or disable full-screen anti-aliasing. False is useful when renderers have problems (such as software - MESA renderers). This option can also be controlled using an - environment variable, e.g., ``MNE_3D_OPTION_ANTIALIAS=false``. + MESA renderers). If None, use the default setting. This option + can also be controlled using an environment variable, e.g., + ``MNE_3D_OPTION_ANTIALIAS=false``. + depth_peeling : bool | None + If bool, whether to enable or disable accurate transparency. + False is useful when renderers have problems (for instance + while X forwarding on remote servers). If None, use the default + setting. This option can also be controlled using an environment + variable, e.g., ``MNE_3D_OPTION_DEPTH_PEELING=false``. + smooth_shading : bool | None + If bool, whether to enable or disable smooth color transitions + between polygons. False is useful on certain configurations + where this type of shading is not supported or for performance + reasons. This option can also be controlled using an environment + variable, e.g., ``MNE_3D_OPTION_SMOOTH_SHADING=false``. + multi_samples : int + Number of multi-samples. Should be 1 for MESA for volumetric rendering + to work properly. + + .. versionadded:: 1.1 Notes ----- .. versionadded:: 0.21.0 """ if antialias is not None: - _3d_options['antialias'] = str(bool(antialias)).lower() + _3d_options.antialias = bool(antialias) + if depth_peeling is not None: + _3d_options.depth_peeling = bool(depth_peeling) + if smooth_shading is not None: + _3d_options.smooth_shading = bool(smooth_shading) + if multi_samples is not None: + _3d_options.multi_samples = int(multi_samples) def _get_3d_option(key): - try: - opt = _3d_options[key] - except KeyError: - opt = get_config(f'MNE_3D_OPTION_{key.upper()}', _3d_default[key]) - opt = opt.lower() - _check_option(f'3D option {key}', opt, ('true', 'false')) - return opt == 'true' + _validate_type(key, 'str', 'key') + opt = getattr(_3d_options, key) + if opt is None: # parse get_config (and defaults) + default_value = getattr(_3d_default, key) + opt = get_config(f'MNE_3D_OPTION_{key.upper()}', default_value) + if key == 'multi_samples': + opt = int(opt) + else: + opt = opt.lower() == 'true' + return opt diff -Nru python-mne-0.23.4+dfsg/mne/viz/backends/_abstract.py python-mne-1.1.0+dfsg/mne/viz/backends/_abstract.py --- python-mne-0.23.4+dfsg/mne/viz/backends/_abstract.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/backends/_abstract.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,19 +5,26 @@ # # License: Simplified BSD -import warnings from abc import ABC, abstractmethod, abstractclassmethod +from contextlib import nullcontext +import warnings + from ..utils import tight_layout -from ...fixes import nullcontext class _AbstractRenderer(ABC): + @abstractclassmethod def __init__(self, fig=None, size=(600, 600), bgcolor=(0., 0., 0.), - name=None, show=False, shape=(1, 1)): + name=None, show=False, shape=(1, 1), splash=False): """Set up the scene.""" pass + @property + @abstractmethod + def _kind(self): + pass + @abstractclassmethod def subplot(self, x, y): """Set the active subplot.""" @@ -34,6 +41,35 @@ pass @abstractclassmethod + def legend(self, labels, border=False, size=0.1, face='triangle', + loc='upper left'): + """Add a legend to the scene. + + Parameters + ---------- + labels : list of tuples + Each entry must contain two strings, (label, color), + where ``label`` is the name of the item to add, and + ``color`` is the color of the label to add. + border : bool + Controls if there will be a border around the legend. + The default is False. + size : float + The size of the entire figure window. + loc : str + The location of the legend. + face : str + Face shape of legend face. One of the following: + + * None: ``None`` + * Line: ``"-"`` or ``"line"`` + * Triangle: ``"^"`` or ``'triangle'`` + * Circle: ``"o"`` or ``'circle'`` + * Rectangle: ``"r"`` or ``'rectangle'`` + """ + pass + + @abstractclassmethod def mesh(self, x, y, z, triangles, color, opacity=1.0, shading=False, backface_culling=False, scalars=None, colormap=None, vmin=None, vmax=None, interpolate_before_map=True, @@ -190,7 +226,7 @@ If True, enable backface culling on the sphere(s). radius : float | None Replace the glyph scaling by a fixed radius value for each - sphere (not supported by mayavi). + sphere. """ pass @@ -231,6 +267,8 @@ Returns ------- + actor : + The actor in the scene. surface : Handle of the tube in the scene. """ @@ -296,6 +334,13 @@ If None, the max of the data will be used line_width : float The width of the 2d arrows. + + Returns + ------- + actor : + The actor in the scene. + surface : + Handle of the quiver in the scene. """ pass @@ -430,11 +475,6 @@ pass @abstractclassmethod - def enable_depth_peeling(self): - """Enable depth peeling.""" - pass - - @abstractclassmethod def remove_mesh(self, mesh_data): """Remove the given mesh from the scene. @@ -448,15 +488,11 @@ class _AbstractToolBar(ABC): @abstractmethod - def _tool_bar_load_icons(self): - pass - - @abstractmethod def _tool_bar_initialize(self, name="default", window=None): pass @abstractmethod - def _tool_bar_add_button(self, name, desc, func, icon_name=None, + def _tool_bar_add_button(self, name, desc, func, *, icon_name=None, shortcut=None): pass @@ -473,17 +509,18 @@ pass @abstractmethod - def _tool_bar_add_file_button(self, name, desc, func, shortcut=None): + def _tool_bar_add_file_button(self, name, desc, func, *, shortcut=None): pass @abstractmethod - def _tool_bar_set_theme(self, theme): + def _tool_bar_add_play_button(self, name, desc, func, *, shortcut=None): pass class _AbstractDock(ABC): @abstractmethod - def _dock_initialize(self, window=None): + def _dock_initialize(self, window=None, name="Controls", + area="left", max_width=None): pass @abstractmethod @@ -499,7 +536,7 @@ pass @abstractmethod - def _dock_add_stretch(self, layout): + def _dock_add_stretch(self, layout=None): pass @abstractmethod @@ -507,34 +544,63 @@ pass @abstractmethod - def _dock_add_label(self, value, align=False, layout=None): + def _dock_add_label( + self, value, *, align=False, layout=None, selectable=False + ): + pass + + @abstractmethod + def _dock_add_button( + self, name, callback, *, style='pushbutton', icon=None, tooltip=None, + layout=None + ): pass @abstractmethod - def _dock_add_button(self, name, callback, layout=None): + def _dock_named_layout(self, name, *, layout=None, compact=True): pass @abstractmethod - def _dock_named_layout(self, name, layout, compact): + def _dock_add_slider(self, name, value, rng, callback, *, + compact=True, double=False, tooltip=None, + layout=None): pass @abstractmethod - def _dock_add_slider(self, name, value, rng, callback, - compact=True, double=False, layout=None): + def _dock_add_check_box(self, name, value, callback, *, tooltip=None, + layout=None): pass @abstractmethod - def _dock_add_spin_box(self, name, value, rng, callback, - compact=True, double=True, layout=None): + def _dock_add_spin_box(self, name, value, rng, callback, *, + compact=True, double=True, step=None, + tooltip=None, layout=None): pass @abstractmethod - def _dock_add_combo_box(self, name, value, rng, - callback, compact=True, layout=None): + def _dock_add_combo_box(self, name, value, rng, callback, *, compact=True, + tooltip=None, layout=None): pass @abstractmethod - def _dock_add_group_box(self, name, layout=None): + def _dock_add_radio_buttons(self, value, rng, callback, *, vertical=True, + layout=None): + pass + + @abstractmethod + def _dock_add_group_box(self, name, *, collapse=None, layout=None): + pass + + @abstractmethod + def _dock_add_text(self, name, value, placeholder, *, callback=None, + layout=None): + pass + + @abstractmethod + def _dock_add_file_button( + self, name, desc, func, *, filter=None, initial_directory=None, + save=False, is_directory=False, icon=False, tooltip=None, layout=None + ): pass @@ -558,7 +624,7 @@ pass @abstractmethod - def _status_bar_add_label(self, value, stretch=0): + def _status_bar_add_label(self, value, *, stretch=0): pass @abstractmethod @@ -572,7 +638,29 @@ class _AbstractPlayback(ABC): @abstractmethod - def _playback_initialize(self, func, timeout): + def _playback_initialize(self, func, timeout, value, rng, + time_widget, play_widget): + pass + + +class _AbstractKeyPress(ABC): + @abstractmethod + def _keypress_initialize(self, widget=None): + pass + + @abstractmethod + def _keypress_add(self, shortcut, callback): + pass + + @abstractmethod + def _keypress_trigger(self, shortcut): + pass + + +class _AbstractDialog(ABC): + @abstractmethod + def _dialog_create(self, title, text, info_text, callback, *, + icon='Warning', buttons=[], modal=True, window=None): pass @@ -582,7 +670,26 @@ pass @abstractmethod - def _layout_add_widget(self, layout, widget, stretch=0): + def _layout_add_widget(self, layout, widget, stretch=0, + *, row=None, col=None): + pass + + @abstractmethod + def _layout_create(self, orientation='vertical'): + pass + + +class _AbstractWidgetList(ABC): + @abstractmethod + def set_enabled(self, state): + pass + + @abstractmethod + def get_value(self, idx): + pass + + @abstractmethod + def set_value(self, idx, value): pass @@ -615,9 +722,46 @@ pass @abstractmethod + def set_enabled(self, state): + pass + + @abstractmethod + def is_enabled(self): + pass + + @abstractmethod def update(self, repaint=True): pass + @abstractmethod + def get_tooltip(self): + pass + + @abstractmethod + def set_tooltip(self, tooltip: str): + pass + + @abstractmethod + def set_style(self, style): + pass + + +class _AbstractAction(ABC): + def __init__(self, action): + self._action = action + + @abstractmethod + def trigger(self): + pass + + @abstractmethod + def set_icon(self): + pass + + @abstractmethod + def set_shortcut(self): + pass + class _AbstractMplInterface(ABC): @abstractmethod @@ -649,17 +793,19 @@ self.canvas.mpl_connect( event + '_event', getattr(self, 'on_' + event)) - def plot(self, x, y, label, **kwargs): + def plot(self, x, y, label, update=True, **kwargs): """Plot a curve.""" line, = self.axes.plot( x, y, label=label, **kwargs) - self.update_plot() + if update: + self.update_plot() return line - def plot_time_line(self, x, label, **kwargs): + def plot_time_line(self, x, label, update=True, **kwargs): """Plot the vertical line.""" line = self.axes.axvline(x, label=label, **kwargs) - self.update_plot() + if update: + self.update_plot() return line def update_plot(self): @@ -740,7 +886,10 @@ class _AbstractWindow(ABC): - def _window_initialize(self): + def _window_initialize( + self, *, window=None, central_layout=None, fullscreen=False + ): + self._icons = dict() self._window = None self._interactor = None self._mplcanvas = None @@ -749,7 +898,15 @@ self._interactor_fraction = None @abstractmethod - def _window_close_connect(self, func): + def _window_load_icons(self): + pass + + @abstractmethod + def _window_close_connect(self, func, *, after=True): + pass + + @abstractmethod + def _window_close_disconnect(self, after=True): pass @abstractmethod @@ -799,3 +956,37 @@ @abstractmethod def _window_set_theme(self, theme): pass + + @abstractmethod + def _window_create(self): + pass + + +class Figure3D(ABC): + """Class that refers to a 3D figure. + + .. note:: + This class is not meant to be instantiated directly, use + :func:`mne.viz.create_3d_figure` instead. + """ + + # Here we use _init rather than __init__ so that users are less tempted to + # instantiate the class directly. It also helps us + # document the class more easily, as we don't have to say what all the + # params are in public docs. + + @abstractclassmethod + def _init(self, fig=None, size=(600, 600), bgcolor=(0., 0., 0.), + name=None, show=False, shape=(1, 1), splash=False): + pass + + @property + def plotter(self): + """The native 3D plotting widget. + + Returns + ------- + plotter : instance of pyvista.Plotter + The plotter. Useful for interacting with the native 3D library. + """ + return self._plotter diff -Nru python-mne-0.23.4+dfsg/mne/viz/backends/_notebook.py python-mne-1.1.0+dfsg/mne/viz/backends/_notebook.py --- python-mne-0.23.4+dfsg/mne/viz/backends/_notebook.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/backends/_notebook.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,43 +4,226 @@ # # License: Simplified BSD -from contextlib import contextmanager +import os +from contextlib import contextmanager, nullcontext + from IPython.display import display -from ipywidgets import (Button, Dropdown, FloatSlider, FloatText, HBox, - IntSlider, IntText, Text, VBox, IntProgress) +from ipywidgets import (Button, Dropdown, FloatSlider, BoundedFloatText, HBox, + IntSlider, IntText, Text, VBox, IntProgress, Play, + Checkbox, RadioButtons, HTML, Accordion, jsdlink, + Layout, Select, GridBox) -from ...fixes import nullcontext from ._abstract import (_AbstractDock, _AbstractToolBar, _AbstractMenuBar, _AbstractStatusBar, _AbstractLayout, _AbstractWidget, _AbstractWindow, _AbstractMplCanvas, _AbstractPlayback, - _AbstractBrainMplCanvas, _AbstractMplInterface) + _AbstractBrainMplCanvas, _AbstractMplInterface, + _AbstractWidgetList, _AbstractAction, _AbstractDialog, + _AbstractKeyPress) from ._pyvista import _PyVistaRenderer, _close_all, _set_3d_view, _set_3d_title # noqa: F401,E501, analysis:ignore +# modified from: +# https://gist.github.com/elkhadiy/284900b3ea8a13ed7b777ab93a691719 +class _FilePicker: + def __init__(self, rows=20, directory_only=False, ignore_dotfiles=True): + self._callback = None + self._directory_only = directory_only + self._ignore_dotfiles = ignore_dotfiles + self._empty_selection = True + self._selected_dir = os.getcwd() + self._item_layout = Layout(width='auto') + self._nb_rows = rows + self._file_selector = Select( + options=self._get_selector_options(), + rows=min(len(os.listdir(self._selected_dir)), self._nb_rows), + layout=self._item_layout + ) + self._open_button = Button( + description='Open', + layout=Layout(flex='auto 1 auto', width='auto') + ) + self._select_button = Button( + description='Select', + layout=Layout(flex='auto 1 auto', width='auto') + ) + self._cancel_button = Button( + description='Cancel', + layout=Layout(flex='auto 1 auto', width='auto') + ) + self._parent_button = Button( + icon='chevron-up', + layout=Layout(flex='auto 1 auto', width='auto') + ) + self._selection = Text( + value=os.path.join( + self._selected_dir, self._file_selector.value), + disabled=True, + layout=Layout(flex='1 1 auto', width='auto') + ) + self._filename = Text( + value='', + layout=Layout(flex='1 1 auto', width='auto') + ) + self._parent_button.on_click(self._parent_button_clicked) + self._open_button.on_click(self._open_button_clicked) + self._select_button.on_click(self._select_button_clicked) + self._cancel_button.on_click(self._cancel_button_clicked) + self._file_selector.observe(self._update_path) + + self._widget = VBox([ + HBox([ + self._parent_button, HTML(value='Look in:'), self._selection, + ]), + self._file_selector, + HBox([ + HTML(value='File name'), self._filename, self._open_button, + self._select_button, self._cancel_button, + ]), + ]) + + def _get_selector_options(self): + options = os.listdir(self._selected_dir) + if self._ignore_dotfiles: + tmp = list() + for el in options: + if el[0] != '.': + tmp.append(el) + options = tmp + if self._directory_only: + tmp = list() + for el in options: + if os.path.isdir(os.path.join(self._selected_dir, el)): + tmp.append(el) + options = tmp + if not options: + options = [''] + self._empty_selection = True + else: + self._empty_selection = False + return options + + def _update_selector_options(self): + self._file_selector.options = self._get_selector_options() + self._file_selector.rows = min( + len(os.listdir(self._selected_dir)), self._nb_rows) + self._selection.value = os.path.join( + self._selected_dir, self._file_selector.value + ) + self._filename.value = self._file_selector.value + + def show(self): + self._update_selector_options() + self._widget.layout.display = "block" + + def hide(self): + self._widget.layout.display = "none" + + def set_directory_only(self, state): + self._directory_only = state + + def set_ignore_dotfiles(self, state): + self._ignore_dotfiles = state + + def connect(self, callback): + self._callback = callback + + def _open_button_clicked(self, button): + if self._empty_selection: + return + if os.path.isdir(self._selection.value): + self._selected_dir = self._selection.value + self._file_selector.options = self._get_selector_options() + self._file_selector.rows = min( + len(os.listdir(self._selected_dir)), self._nb_rows) + + def _select_button_clicked(self, button): + if self._empty_selection: + return + result = os.path.join(self._selected_dir, self._filename.value) + if self._callback is not None: + self._callback(result) + # the picker is shared so only one connection is allowed at a time + self._callback = None # reset the callback + self.hide() + + def _cancel_button_clicked(self, button): + self._callback = None # reset the callback + self.hide() + + def _parent_button_clicked(self, button): + self._selected_dir, _ = os.path.split(self._selected_dir) + self._update_selector_options() + + def _update_path(self, change): + self._selection.value = os.path.join( + self._selected_dir, self._file_selector.value + ) + self._filename.value = self._file_selector.value + + +class _IpyKeyPress(_AbstractKeyPress): + def _keypress_initialize(self, widget=None): + pass + + def _keypress_add(self, shortcut, callback): + pass + + def _keypress_trigger(self, shortcut): + pass + + +class _IpyDialog(_AbstractDialog): + def _dialog_create(self, title, text, info_text, callback, *, + icon='Warning', buttons=[], modal=True, window=None): + pass + + class _IpyLayout(_AbstractLayout): def _layout_initialize(self, max_width): self._layout_max_width = max_width - def _layout_add_widget(self, layout, widget, stretch=0): + def _layout_add_widget(self, layout, widget, stretch=0, + *, row=None, col=None): widget.layout.margin = "2px 0px 2px 0px" - widget.layout.min_width = "0px" - children = list(layout.children) + if not isinstance(widget, Play): + widget.layout.min_width = "0px" + if isinstance(layout, Accordion): + box = layout.children[0] + else: + box = layout + children = list(box.children) children.append(widget) - layout.children = tuple(children) + box.children = tuple(children) # Fix columns if self._layout_max_width is not None and isinstance(widget, HBox): children = widget.children - width = int(self._layout_max_width / len(children)) - for child in children: - child.layout.width = f"{width}px" + if len(children) > 0: + width = int(self._layout_max_width / len(children)) + for child in children: + child.layout.width = f"{width}px" + + def _layout_create(self, orientation='vertical'): + if orientation == 'vertical': + layout = VBox() + elif orientation == 'horizontal': + layout = HBox() + else: + assert orientation == 'grid' + layout = GridBox() + return layout class _IpyDock(_AbstractDock, _IpyLayout): - def _dock_initialize(self, window=None): - self._dock_width = 300 - self._dock = self._dock_layout = VBox() + def _dock_initialize(self, window=None, name="Controls", + area="left", max_width=None): + if self._docks is None: + self._docks = dict() + current_dock = VBox() + self._dock_width = 302 + self._dock = self._dock_layout = current_dock self._dock.layout.width = f"{self._dock_width}px" self._layout_initialize(self._dock_width) + self._docks[area] = (self._dock, self._dock_layout) def _dock_finalize(self): pass @@ -51,25 +234,38 @@ def _dock_hide(self): self._dock_layout.layout.visibility = "hidden" - def _dock_add_stretch(self, layout): + def _dock_add_stretch(self, layout=None): pass def _dock_add_layout(self, vertical=True): return VBox() if vertical else HBox() - def _dock_add_label(self, value, align=False, layout=None): + def _dock_add_label( + self, value, *, align=False, layout=None, selectable=False + ): layout = self._dock_layout if layout is None else layout - widget = Text(value=value, disabled=True) + widget = HTML(value=value, disabled=True) self._layout_add_widget(layout, widget) return _IpyWidget(widget) - def _dock_add_button(self, name, callback, layout=None): - widget = Button(description=name) + def _dock_add_button( + self, name, callback, *, style='pushbutton', icon=None, tooltip=None, + layout=None + ): + layout = self._dock_layout if layout is None else layout + kwargs = dict() + if style == 'pushbutton': + kwargs["description"] = name + if tooltip is not None: + kwargs["tooltip"] = tooltip + widget = Button(**kwargs) widget.on_click(lambda x: callback()) + if icon is not None: + widget.icon = icon self._layout_add_widget(layout, widget) return _IpyWidget(widget) - def _dock_named_layout(self, name, layout, compact): + def _dock_named_layout(self, name, *, layout=None, compact=True): layout = self._dock_layout if layout is None else layout if name is not None: hlayout = self._dock_add_layout(not compact) @@ -79,9 +275,11 @@ layout = hlayout return layout - def _dock_add_slider(self, name, value, rng, callback, - compact=True, double=False, layout=None): - layout = self._dock_named_layout(name, layout, compact) + def _dock_add_slider(self, name, value, rng, callback, *, + compact=True, double=False, tooltip=None, + layout=None): + layout = self._dock_named_layout( + name=name, layout=layout, compact=compact) klass = FloatSlider if double else IntSlider widget = klass( value=value, @@ -93,23 +291,41 @@ self._layout_add_widget(layout, widget) return _IpyWidget(widget) - def _dock_add_spin_box(self, name, value, rng, callback, - compact=True, double=True, layout=None): - layout = self._dock_named_layout(name, layout, compact) - klass = FloatText if double else IntText + def _dock_add_check_box(self, name, value, callback, *, tooltip=None, + layout=None): + layout = self._dock_layout if layout is None else layout + widget = Checkbox( + value=value, + description=name, + indent=False, + disabled=False + ) + hbox = HBox([widget]) # fix stretching to the right + widget.observe(_generate_callback(callback), names='value') + self._layout_add_widget(layout, hbox) + return _IpyWidget(widget) + + def _dock_add_spin_box(self, name, value, rng, callback, *, + compact=True, double=True, step=None, + tooltip=None, layout=None): + layout = self._dock_named_layout( + name=name, layout=layout, compact=compact) + klass = BoundedFloatText if double else IntText widget = klass( value=value, min=rng[0], max=rng[1], - readout=False, ) + if step is not None: + widget.step = step widget.observe(_generate_callback(callback), names='value') self._layout_add_widget(layout, widget) return _IpyWidget(widget) - def _dock_add_combo_box(self, name, value, rng, - callback, compact=True, layout=None): - layout = self._dock_named_layout(name, layout, compact) + def _dock_add_combo_box(self, name, value, rng, callback, *, compact=True, + tooltip=None, layout=None): + layout = self._dock_named_layout( + name=name, layout=layout, compact=compact) widget = Dropdown( value=value, options=rng, @@ -118,12 +334,68 @@ self._layout_add_widget(layout, widget) return _IpyWidget(widget) - def _dock_add_group_box(self, name, layout=None): + def _dock_add_radio_buttons(self, value, rng, callback, *, vertical=True, + layout=None): + # XXX: vertical=False is not supported yet + layout = self._dock_layout if layout is None else layout + widget = RadioButtons( + options=rng, + value=value, + disabled=False, + ) + widget.observe(_generate_callback(callback), names='value') + self._layout_add_widget(layout, widget) + return _IpyWidgetList(widget) + + def _dock_add_group_box(self, name, *, collapse=None, layout=None): layout = self._dock_layout if layout is None else layout - hlayout = VBox() + if collapse is None: + hlayout = VBox([HTML("" + name + "")]) + else: + assert isinstance(collapse, bool) + vbox = VBox() + hlayout = Accordion([vbox]) + hlayout.set_title(0, name) + if collapse: + hlayout.selected_index = None + else: + hlayout.selected_index = 0 self._layout_add_widget(layout, hlayout) return hlayout + def _dock_add_text(self, name, value, placeholder, *, callback=None, + layout=None): + layout = self._dock_layout if layout is None else layout + widget = Text(value=value, placeholder=placeholder) + if callback is not None: + widget.observe(_generate_callback(callback), names='value') + self._layout_add_widget(layout, widget) + return _IpyWidget(widget) + + def _dock_add_file_button( + self, name, desc, func, *, filter=None, initial_directory=None, + save=False, is_directory=False, icon=False, tooltip=None, layout=None + ): + layout = self._dock_layout if layout is None else layout + + def callback(): + self._file_picker.set_directory_only(is_directory) + self._file_picker.connect(func) + self._file_picker.show() + + if icon: + kwargs = dict(style='toolbutton', icon='folder') + else: + kwargs = dict() + widget = self._dock_add_button( + name=desc, + callback=callback, + tooltip=tooltip, + layout=layout, + **kwargs + ) + return widget + def _generate_callback(callback, to_float=False): def func(data): @@ -133,48 +405,34 @@ class _IpyToolBar(_AbstractToolBar, _IpyLayout): - def _tool_bar_load_icons(self): - self.icons = dict() - self.icons["help"] = "question" - self.icons["play"] = None - self.icons["pause"] = None - self.icons["reset"] = "history" - self.icons["scale"] = "magic" - self.icons["clear"] = "trash" - self.icons["movie"] = "video-camera" - self.icons["restore"] = "replay" - self.icons["screenshot"] = "camera" - self.icons["visibility_on"] = "eye" - self.icons["visibility_off"] = "eye" - def _tool_bar_initialize(self, name="default", window=None): self.actions = dict() self._tool_bar = self._tool_bar_layout = HBox() self._layout_initialize(None) - def _tool_bar_add_button(self, name, desc, func, icon_name=None, + def _tool_bar_add_button(self, name, desc, func, *, icon_name=None, shortcut=None): icon_name = name if icon_name is None else icon_name - icon = self.icons[icon_name] + icon = self._icons[icon_name] if icon is None: return widget = Button(tooltip=desc, icon=icon) widget.on_click(lambda x: func()) self._layout_add_widget(self._tool_bar_layout, widget) - self.actions[name] = widget + self.actions[name] = _IpyAction(widget) def _tool_bar_update_button_icon(self, name, icon_name): - self.actions[name].icon = self.icons[icon_name] + self.actions[name].set_icon(self._icons[icon_name]) def _tool_bar_add_text(self, name, value, placeholder): widget = Text(value=value, placeholder=placeholder) self._layout_add_widget(self._tool_bar_layout, widget) - self.actions[name] = widget + self.actions[name] = _IpyAction(widget) def _tool_bar_add_spacer(self): pass - def _tool_bar_add_file_button(self, name, desc, func, shortcut=None): + def _tool_bar_add_file_button(self, name, desc, func, *, shortcut=None): def callback(): fname = self.actions[f"{name}_field"].value func(None if len(fname) == 0 else fname) @@ -189,34 +447,59 @@ func=callback, ) - def _tool_bar_set_theme(self, theme): - pass + def _tool_bar_add_play_button(self, name, desc, func, *, shortcut=None): + widget = Play(interval=500) + self._layout_add_widget(self._tool_bar_layout, widget) + self.actions[name] = _IpyAction(widget) + return _IpyWidget(widget) class _IpyMenuBar(_AbstractMenuBar): def _menu_initialize(self, window=None): - pass + self._menus = dict() + self._menu_actions = dict() + self._menu_desc2button = dict() # only for notebook + self._menu_bar = self._menu_bar_layout = HBox() + self._layout_initialize(None) def _menu_add_submenu(self, name, desc): - pass + widget = Dropdown(value=desc, options=[desc]) + self._menus[name] = widget + self._menu_actions[name] = dict() + + def callback(input_desc): + if input_desc == desc: + return + button_name = self._menu_desc2button[input_desc] + if button_name in self._menu_actions[name]: + self._menu_actions[name][button_name].trigger() + widget.value = desc + widget.observe(_generate_callback(callback), names='value') + self._layout_add_widget(self._menu_bar_layout, widget) def _menu_add_button(self, menu_name, name, desc, func): - pass + menu = self._menus[menu_name] + options = list(menu.options) + options.append(desc) + menu.options = options + self._menu_actions[menu_name][name] = _IpyAction(func) + # associate the description with the name given by the user + self._menu_desc2button[desc] = name class _IpyStatusBar(_AbstractStatusBar, _IpyLayout): def _status_bar_initialize(self, window=None): - self._status_bar = self._status_bar_layout = HBox() + self._status_bar = HBox() self._layout_initialize(None) - def _status_bar_add_label(self, value, stretch=0): + def _status_bar_add_label(self, value, *, stretch=0): widget = Text(value=value, disabled=True) - self._layout_add_widget(self._status_bar_layout, widget) + self._layout_add_widget(self._status_bar, widget) return _IpyWidget(widget) def _status_bar_add_progress_bar(self, stretch=0): widget = IntProgress() - self._layout_add_widget(self._status_bar_layout, widget) + self._layout_add_widget(self._status_bar, widget) return _IpyWidget(widget) def _status_bar_update(self): @@ -224,8 +507,15 @@ class _IpyPlayback(_AbstractPlayback): - def _playback_initialize(self, func, timeout): - pass + def _playback_initialize(self, func, timeout, value, rng, + time_widget, play_widget): + play = play_widget._widget + play.min = rng[0] + play.max = rng[1] + play.value = value + slider = time_widget._widget + jsdlink((play, 'value'), (slider, 'value')) + jsdlink((slider, 'value'), (play, 'value')) class _IpyMplInterface(_AbstractMplInterface): @@ -250,7 +540,31 @@ class _IpyWindow(_AbstractWindow): - def _window_close_connect(self, func): + def _window_initialize( + self, *, window=None, central_layout=None, fullscreen=False + ): + super()._window_initialize() + self._window_load_icons() + + def _window_load_icons(self): + # from: https://fontawesome.com/icons + self._icons["help"] = "question" + self._icons["play"] = None + self._icons["pause"] = None + self._icons["reset"] = "history" + self._icons["scale"] = "magic" + self._icons["clear"] = "trash" + self._icons["movie"] = "video-camera" + self._icons["restore"] = "replay" + self._icons["screenshot"] = "camera" + self._icons["visibility_on"] = "eye" + self._icons["visibility_off"] = "eye" + self._icons["folder"] = "folder" + + def _window_close_connect(self, func, *, after=True): + pass + + def _window_close_disconnect(self, after=True): pass def _window_get_dpi(self): @@ -291,10 +605,50 @@ def _window_set_theme(self, theme): pass + def _window_create(self): + pass + # XXX: this could be a VBox if _Renderer.show is refactored + + +class _IpyWidgetList(_AbstractWidgetList): + def __init__(self, src): + self._src = src + if isinstance(self._src, RadioButtons): + self._widgets = _IpyWidget(self._src) + else: + self._widgets = list() + for widget in self._src: + if not isinstance(widget, _IpyWidget): + widget = _IpyWidget(widget) + self._widgets.append(widget) + + def set_enabled(self, state): + if isinstance(self._src, RadioButtons): + self._widgets.set_enabled(state) + else: + for widget in self._widgets: + widget.set_enabled(state) + + def get_value(self, idx): + if isinstance(self._src, RadioButtons): + # for consistency, we do not use get_value() + return self._widgets._widget.options[idx] + else: + return self._widgets[idx].get_value() + + def set_value(self, idx, value): + if isinstance(self._src, RadioButtons): + self._widgets.set_value(value) + else: + self._widgets[idx].set_value(value) + class _IpyWidget(_AbstractWidget): def set_value(self, value): - self._widget.value = value + if isinstance(self._widget, Button): + self._widget.click() + else: + self._widget.value = value def get_value(self): return self._widget.value @@ -309,57 +663,98 @@ def hide(self): self._widget.layout.visibility = "hidden" + def set_enabled(self, state): + self._widget.disabled = not state + + def is_enabled(self): + return not self._widget.disabled + def update(self, repaint=True): pass + def get_tooltip(self): + assert hasattr(self._widget, 'tooltip') + return self._widget.tooltip + + def set_tooltip(self, tooltip): + assert hasattr(self._widget, 'tooltip') + self._widget.tooltip = tooltip + + def set_style(self, style): + for key, val in style.items(): + setattr(self._widget.layout, key, val) + + +class _IpyAction(_AbstractAction): + def trigger(self): + if callable(self._action): + self._action() + else: # standard Button widget + self._action.click() + + def set_icon(self, icon): + self._action.icon = icon + + def set_shortcut(self, shortcut): + pass + class _Renderer(_PyVistaRenderer, _IpyDock, _IpyToolBar, _IpyMenuBar, - _IpyStatusBar, _IpyWindow, _IpyPlayback): + _IpyStatusBar, _IpyWindow, _IpyPlayback, _IpyDialog, + _IpyKeyPress): + _kind = 'notebook' + def __init__(self, *args, **kwargs): - self._dock = None + self._docks = None + self._menu_bar = None self._tool_bar = None self._status_bar = None + self._file_picker = _FilePicker(rows=10) kwargs["notebook"] = True + fullscreen = kwargs.pop('fullscreen', False) super().__init__(*args, **kwargs) + self._window_initialize(fullscreen=fullscreen) def _update(self): if self.figure.display is not None: self.figure.display.update_canvas() - def _create_default_tool_bar(self): - self._tool_bar_load_icons() + def _display_default_tool_bar(self): self._tool_bar_initialize() self._tool_bar_add_file_button( name="screenshot", desc="Take a screenshot", func=self.screenshot, ) + display(self._tool_bar) def show(self): - # default tool bar - if self._tool_bar is None: - self._create_default_tool_bar() - display(self._tool_bar) + # menu bar + if self._menu_bar is not None: + display(self._menu_bar) + # tool bar + if self._tool_bar is not None: + display(self._tool_bar) + else: + self._display_default_tool_bar() # viewer - try: - # pyvista<0.30.0 - viewer = self.plotter.show( - use_ipyvtk=True, return_viewer=True) - except RuntimeError: - # pyvista>=0.30.0 - viewer = self.plotter.show( - jupyter_backend="ipyvtk_simple", return_viewer=True) + viewer = self.plotter.show( + jupyter_backend="ipyvtklink", return_viewer=True) viewer.layout.width = None # unlock the fixed layout - # main widget - if self._dock is None: - main_widget = viewer - else: - main_widget = HBox([self._dock, viewer]) - display(main_widget) + rendering_row = list() + if self._docks is not None and "left" in self._docks: + rendering_row.append(self._docks["left"][0]) + rendering_row.append(viewer) + if self._docks is not None and "right" in self._docks: + rendering_row.append(self._docks["right"][0]) + display(HBox(rendering_row)) self.figure.display = viewer # status bar if self._status_bar is not None: display(self._status_bar) + # file picker + self._file_picker.hide() + display(self._file_picker._widget) return self.scene() diff -Nru python-mne-0.23.4+dfsg/mne/viz/backends/_pysurfer_mayavi.py python-mne-1.1.0+dfsg/mne/viz/backends/_pysurfer_mayavi.py --- python-mne-0.23.4+dfsg/mne/viz/backends/_pysurfer_mayavi.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/backends/_pysurfer_mayavi.py 1970-01-01 00:00:00.000000000 +0000 @@ -1,553 +0,0 @@ -""" -Core visualization operations based on Mayavi. - -Actual implementation of _Renderer and _Projection classes. -""" - -# Authors: Alexandre Gramfort -# Denis Engemann -# Martin Luessi -# Eric Larson -# Mainak Jas -# Mark Wronkiewicz -# Joan Massich -# Guillaume Favelier -# -# License: Simplified BSD - -from contextlib import contextmanager -import warnings -import numpy as np - -from mayavi.core.scene import Scene -from mayavi.core.ui.mayavi_scene import MayaviScene -from tvtk.pyface.tvtk_scene import TVTKScene - -from ._abstract import _AbstractRenderer -from ._utils import _check_color, _alpha_blend_background, ALLOWED_QUIVER_MODES -from ..utils import _save_ndarray_img -from ...surface import _normalize_vectors -from ...utils import (_import_mlab, _validate_type, SilenceStdout, - copy_base_doc_to_subclass_doc, _check_option) - - -class _Projection(object): - """Class storing projection information. - - Attributes - ---------- - xy : array - Result of 2d projection of 3d data. - pts : Source - Mayavi source handle. - """ - - def __init__(self, xy=None, pts=None): - """Store input projection information into attributes.""" - self.xy = xy - self.pts = pts - - def visible(self, state): - """Modify visibility attribute of the source.""" - if self.pts is not None: - self.pts.visible = state - - -@copy_base_doc_to_subclass_doc -class _Renderer(_AbstractRenderer): - """Class managing rendering scene. - - Attributes - ---------- - mlab: mayavi.mlab - Main Mayavi access point. - fig: mlab.Figure - Mayavi scene handle. - """ - - def __init__(self, fig=None, size=(600, 600), bgcolor='black', - name=None, show=False, shape=(1, 1), smooth_shading=True): - if bgcolor is not None: - bgcolor = _check_color(bgcolor) - self.mlab = _import_mlab() - self.shape = shape - if fig is None: - self.fig = _mlab_figure(figure=name, bgcolor=bgcolor, size=size) - elif isinstance(fig, int): - self.fig = _mlab_figure(figure=fig, bgcolor=bgcolor, size=size) - else: - self.fig = fig - self.fig._window_size = size - _toggle_mlab_render(self.fig, show) - - @property - def figure(self): # cross-compat w/PyVista - return self.fig - - def subplot(self, x, y): - pass - - def scene(self): - return self.fig - - def set_interaction(self, interaction): - from tvtk.api import tvtk - if self.fig.scene is not None: - self.fig.scene.interactor.interactor_style = \ - getattr(tvtk, f'InteractorStyle{interaction.capitalize()}')() - - def mesh(self, x, y, z, triangles, color, opacity=1.0, shading=False, - backface_culling=False, scalars=None, colormap=None, - vmin=None, vmax=None, interpolate_before_map=True, - representation='surface', line_width=1., normals=None, - polygon_offset=None, **kwargs): - # normals and pickable are unused - kwargs.pop('pickable', None) - del normals - - if color is not None: - color = _check_color(color) - if color is not None and isinstance(color, np.ndarray) \ - and color.ndim > 1: - if color.shape[1] == 3: - vertex_color = np.c_[color, np.ones(len(color))] * 255.0 - else: - vertex_color = color * 255.0 - # create a lookup table to enable one color per vertex - scalars = np.arange(len(color)) - color = None - else: - vertex_color = None - with warnings.catch_warnings(record=True): # traits - surface = self.mlab.triangular_mesh(x, y, z, triangles, - color=color, - scalars=scalars, - opacity=opacity, - figure=self.fig, - vmin=vmin, - vmax=vmax, - representation=representation, - line_width=line_width, - **kwargs) - - l_m = surface.module_manager.scalar_lut_manager - if vertex_color is not None: - l_m.lut.table = vertex_color - elif isinstance(colormap, np.ndarray): - if colormap.dtype == np.uint8: - l_m.lut.table = colormap - elif colormap.dtype == np.float64: - l_m.load_lut_from_list(colormap) - else: - raise TypeError('Expected type for colormap values are' - ' np.float64 or np.uint8: ' - '{} was given'.format(colormap.dtype)) - elif colormap is not None: - from matplotlib.cm import get_cmap - l_m.load_lut_from_list( - get_cmap(colormap)(np.linspace(0, 1, 256))) - else: - assert color is not None - surface.actor.property.shading = shading - surface.actor.property.backface_culling = backface_culling - return surface - - def contour(self, surface, scalars, contours, width=1.0, opacity=1.0, - vmin=None, vmax=None, colormap=None, - normalized_colormap=False, kind='line', color=None): - mesh = _create_mesh_surf(surface, self.fig, scalars=scalars) - with warnings.catch_warnings(record=True): # traits - cont = self.mlab.pipeline.contour_surface( - mesh, contours=contours, line_width=width, vmin=vmin, - vmax=vmax, opacity=opacity, figure=self.fig) - cont.module_manager.scalar_lut_manager.lut.table = colormap - return cont - - def surface(self, surface, color=None, opacity=1.0, - vmin=None, vmax=None, colormap=None, - normalized_colormap=False, scalars=None, - backface_culling=False, polygon_offset=None): - if color is not None: - color = _check_color(color) - if normalized_colormap: - colormap = colormap * 255.0 - # Make a solid surface - mesh = _create_mesh_surf(surface, self.fig, scalars=scalars) - with warnings.catch_warnings(record=True): # traits - surface = self.mlab.pipeline.surface( - mesh, color=color, opacity=opacity, vmin=vmin, vmax=vmax, - figure=self.fig) - if colormap is not None: - surface.module_manager.scalar_lut_manager.lut.table = colormap - surface.actor.property.backface_culling = backface_culling - - def sphere(self, center, color, scale, opacity=1.0, - resolution=8, backface_culling=False, - radius=None): - color = _check_color(color) - center = np.atleast_2d(center) - x, y, z = center.T - surface = self.mlab.points3d(x, y, z, color=color, - resolution=resolution, - scale_factor=scale, opacity=opacity, - figure=self.fig) - surface.actor.property.backface_culling = backface_culling - - def tube(self, origin, destination, radius=0.001, color='white', - scalars=None, vmin=None, vmax=None, colormap='RdBu', - normalized_colormap=False, reverse_lut=False): - color = _check_color(color) - origin = np.atleast_2d(origin) - destination = np.atleast_2d(destination) - if scalars is None: - # TODO: iterating over each tube rather than plotting in - # one call may be slow. - # See https://github.com/mne-tools/mne-python/issues/7644 - for idx in range(origin.shape[0]): - surface = self.mlab.plot3d([origin[idx, 0], - destination[idx, 0]], - [origin[idx, 1], - destination[idx, 1]], - [origin[idx, 2], - destination[idx, 2]], - tube_radius=radius, - color=color, - figure=self.fig) - else: - for idx in range(origin.shape[0]): - surface = self.mlab.plot3d([origin[idx, 0], - destination[idx, 0]], - [origin[idx, 1], - destination[idx, 1]], - [origin[idx, 2], - destination[idx, 2]], - [scalars[idx, 0], - scalars[idx, 1]], - tube_radius=radius, - vmin=vmin, - vmax=vmax, - colormap=colormap, - figure=self.fig) - surface.module_manager.scalar_lut_manager.reverse_lut = reverse_lut - return surface - - def quiver3d(self, x, y, z, u, v, w, color, scale, mode, resolution=8, - glyph_height=None, glyph_center=None, glyph_resolution=None, - opacity=1.0, scale_mode='none', scalars=None, - backface_culling=False, colormap=None, vmin=None, vmax=None, - line_width=2., name=None, solid_transform=None): - _check_option('mode', mode, ALLOWED_QUIVER_MODES) - color = _check_color(color) - with warnings.catch_warnings(record=True): # traits - if mode in ('arrow', '2darrow'): - self.mlab.quiver3d(x, y, z, u, v, w, mode=mode, - color=color, scale_factor=scale, - scale_mode=scale_mode, - resolution=resolution, scalars=scalars, - opacity=opacity, figure=self.fig) - elif mode in ('cone', 'sphere', 'oct'): - use_mode = 'sphere' if mode == 'oct' else mode - quiv = self.mlab.quiver3d(x, y, z, u, v, w, color=color, - mode=use_mode, scale_factor=scale, - opacity=opacity, figure=self.fig) - if mode == 'sphere': - quiv.glyph.glyph_source.glyph_source.center = 0., 0., 0. - elif mode == 'oct': - _oct_glyph(quiv.glyph.glyph_source, solid_transform) - else: - assert mode == 'cylinder', mode # should be guaranteed above - quiv = self.mlab.quiver3d(x, y, z, u, v, w, mode=mode, - color=color, scale_factor=scale, - opacity=opacity, figure=self.fig) - if glyph_height is not None: - quiv.glyph.glyph_source.glyph_source.height = glyph_height - if glyph_center is not None: - quiv.glyph.glyph_source.glyph_source.center = glyph_center - if glyph_resolution is not None: - quiv.glyph.glyph_source.glyph_source.resolution = \ - glyph_resolution - quiv.actor.property.backface_culling = backface_culling - - def text2d(self, x_window, y_window, text, size=14, color='white', - justification=None): - if color is not None: - color = _check_color(color) - size = 14 if size is None else size - with warnings.catch_warnings(record=True): # traits - text = self.mlab.text(x_window, y_window, text, color=color, - figure=self.fig) - text.property.font_size = size - text.actor.text_scale_mode = 'viewport' - if isinstance(justification, str): - text.property.justification = justification - - def text3d(self, x, y, z, text, scale, color='white'): - color = _check_color(color) - with warnings.catch_warnings(record=True): # traits - self.mlab.text3d(x, y, z, text, scale=scale, color=color, - figure=self.fig) - - def scalarbar(self, source, color="white", title=None, n_labels=4, - bgcolor=None): - with warnings.catch_warnings(record=True): # traits - bar = self.mlab.scalarbar(source, title=title, nb_labels=n_labels) - if color is not None: - bar.label_text_property.color = _check_color(color) - if bgcolor is not None: - from tvtk.api import tvtk - bgcolor = np.asarray(bgcolor) - bgcolor = np.append(bgcolor, 1.0) * 255. - cmap = source.module_manager.scalar_lut_manager - lut = cmap.lut - ctable = lut.table.to_array() - cbar_lut = tvtk.LookupTable() - cbar_lut.deep_copy(lut) - vals = _alpha_blend_background(ctable, bgcolor) - cbar_lut.table.from_array(vals) - cmap.scalar_bar.lookup_table = cbar_lut - return bar - - def show(self): - if self.fig is not None: - _toggle_mlab_render(self.fig, True) - - def close(self): - _close_3d_figure(figure=self.fig) - - def set_camera(self, azimuth=None, elevation=None, distance=None, - focalpoint=None, roll=None, reset_camera=None, - rigid=None): - _set_3d_view(figure=self.fig, azimuth=azimuth, - elevation=elevation, distance=distance, - focalpoint=focalpoint, roll=roll) - - def reset_camera(self): - renderer = getattr(self.fig.scene, 'renderer', None) - if renderer is not None: - renderer.reset_camera() - - def screenshot(self, mode='rgb', filename=None): - return _take_3d_screenshot(figure=self.fig, mode=mode, - filename=filename) - - def project(self, xyz, ch_names): - xy = _3d_to_2d(self.fig, xyz) - xy = dict(zip(ch_names, xy)) - pts = self.fig.children[-1] - - return _Projection(xy=xy, pts=pts) - - def enable_depth_peeling(self): - if self.fig.scene is not None: - self.fig.scene.renderer.use_depth_peeling = True - - def remove_mesh(self, surface): - if self.fig.scene is not None: - self.fig.scene.renderer.remove_actor(surface.actor) - - -def _mlab_figure(**kwargs): - """Create a Mayavi figure using our defaults.""" - from .._3d import _get_3d_option - fig = _import_mlab().figure(**kwargs) - # If using modern VTK/Mayavi, improve rendering with FXAA - antialias = _get_3d_option('antialias') - if antialias and hasattr(getattr(fig.scene, 'renderer', None), 'use_fxaa'): - fig.scene.renderer.use_fxaa = True - return fig - - -def _toggle_mlab_render(fig, render): - mlab = _import_mlab() - if mlab.options.backend != 'test': - fig.scene.disable_render = not render - - -def _create_mesh_surf(surf, fig=None, scalars=None, vtk_normals=True): - """Create Mayavi mesh from MNE surf.""" - mlab = _import_mlab() - x, y, z = surf['rr'].T - with warnings.catch_warnings(record=True): # traits - mesh = mlab.pipeline.triangular_mesh_source( - x, y, z, surf['tris'], scalars=scalars, figure=fig) - if vtk_normals: - mesh = mlab.pipeline.poly_data_normals(mesh) - mesh.filter.compute_cell_normals = False - mesh.filter.consistency = False - mesh.filter.non_manifold_traversal = False - mesh.filter.splitting = False - else: - # make absolutely sure these are normalized for Mayavi - nn = surf['nn'].copy() - _normalize_vectors(nn) - mesh.data.point_data.normals = nn - mesh.data.cell_data.normals = None - return mesh - - -def _3d_to_2d(fig, xyz): - """Convert 3d points to a 2d perspective using a Mayavi Scene.""" - _validate_type(fig, Scene, "fig", "Scene") - xyz = np.column_stack([xyz, np.ones(xyz.shape[0])]) - - # Transform points into 'unnormalized' view coordinates - comb_trans_mat = _get_world_to_view_matrix(fig.scene) - view_coords = np.dot(comb_trans_mat, xyz.T).T - - # Divide through by the fourth element for normalized view coords - norm_view_coords = view_coords / (view_coords[:, 3].reshape(-1, 1)) - - # Transform from normalized view coordinates to display coordinates. - view_to_disp_mat = _get_view_to_display_matrix(fig.scene) - xy = np.dot(view_to_disp_mat, norm_view_coords.T).T - - # Pull the first two columns since they're meaningful for 2d plotting - xy = xy[:, :2] - return xy - - -def _get_world_to_view_matrix(scene): - """Return the 4x4 matrix to transform xyz space to the current view. - - This is a concatenation of the model view and perspective transforms. - """ - _validate_type(scene, (MayaviScene, TVTKScene), "scene", - "TVTKScene/MayaviScene") - cam = scene.camera - - # The VTK method needs the aspect ratio and near and far - # clipping planes in order to return the proper transform. - scene_size = tuple(scene.get_size()) - clip_range = cam.clipping_range - aspect_ratio = float(scene_size[0]) / scene_size[1] - - # Get the vtk matrix object using the aspect ratio we defined - vtk_comb_trans_mat = cam.get_composite_projection_transform_matrix( - aspect_ratio, clip_range[0], clip_range[1]) - vtk_comb_trans_mat = vtk_comb_trans_mat.to_array() - return vtk_comb_trans_mat - - -def _get_view_to_display_matrix(scene): - """Return the 4x4 matrix to convert view coordinates to display coords. - - It's assumed that the view should take up the entire window and that the - origin of the window is in the upper left corner. - """ - _validate_type(scene, (MayaviScene, TVTKScene), "scene", - "TVTKScene/MayaviScene") - - # normalized view coordinates have the origin in the middle of the space - # so we need to scale by width and height of the display window and shift - # by half width and half height. The matrix accomplishes that. - x, y = tuple(scene.get_size()) - view_to_disp_mat = np.array([[x / 2.0, 0., 0., x / 2.0], - [0., -y / 2.0, 0., y / 2.0], - [0., 0., 1., 0.], - [0., 0., 0., 1.]]) - return view_to_disp_mat - - -def _close_all(): - from mayavi import mlab - mlab.close(all=True) - - -def _set_3d_view(figure, azimuth, elevation, focalpoint, distance, roll=None, - reset_camera=True): - from mayavi import mlab - with warnings.catch_warnings(record=True): # traits - with SilenceStdout(): - mlab.view(azimuth, elevation, distance, - focalpoint=focalpoint, figure=figure, roll=roll) - mlab.draw(figure) - - -def _set_3d_title(figure, title, size=40): - from mayavi import mlab - text = mlab.title(text='', figure=figure) - text.property.vertical_justification = 'top' - text.property.font_size = size - mlab.draw(figure) - - -def _check_3d_figure(figure): - try: - import mayavi # noqa F401 - except Exception: - raise TypeError('figure must be a mayavi scene but the' - 'mayavi package is not found.') - else: - from mayavi.core.scene import Scene - if not isinstance(figure, Scene): - raise TypeError('figure must be a mayavi scene.') - - -def _save_figure(img, filename): - _save_ndarray_img(filename, img) - - -def _close_3d_figure(figure): - from mayavi import mlab - mlab.close(figure) - - -def _take_3d_screenshot(figure, mode='rgb', filename=None): - from mayavi import mlab - from mne.viz.backends.renderer import MNE_3D_BACKEND_TESTING - if MNE_3D_BACKEND_TESTING: - ndim = 3 if mode == 'rgb' else 4 - if figure.scene is None: - figure_size = figure._window_size - else: - figure_size = figure.scene._renwin.size - img = np.zeros(tuple(figure_size) + (ndim,), np.uint8) - else: - from pyface.api import GUI - gui = GUI() - gui.process_events() - with warnings.catch_warnings(record=True): # traits - img = mlab.screenshot(figure, mode=mode) - if isinstance(filename, str): - _save_figure(img, filename) - return img - - -@contextmanager -def _testing_context(interactive): - mlab = _import_mlab() - orig_backend = mlab.options.backend - mlab.options.backend = 'test' - try: - yield - finally: - mlab.options.backend = orig_backend - - -def _oct_glyph(glyph_source, transform): - from tvtk.api import tvtk - from tvtk.common import configure_input - from traits.api import Array - gs = tvtk.PlatonicSolidSource() - - # Workaround for: - # File "mayavi/components/glyph_source.py", line 231, in _glyph_position_changed # noqa: E501 - # g.center = 0.0, 0.0, 0.0 - # traits.trait_errors.TraitError: Cannot set the undefined 'center' attribute of a 'TransformPolyDataFilter' object. # noqa: E501 - class SafeTransformPolyDataFilter(tvtk.TransformPolyDataFilter): - center = Array(shape=(3,), value=np.zeros(3)) - - gs.solid_type = 'octahedron' - if transform is not None: - # glyph: mayavi.modules.vectors.Vectors - # glyph.glyph: vtkGlyph3D - # glyph.glyph.glyph: mayavi.components.glyph.Glyph - assert transform.shape == (4, 4) - tr = tvtk.Transform() - tr.set_matrix(transform.ravel()) - trp = SafeTransformPolyDataFilter() - configure_input(trp, gs) - trp.transform = tr - trp.update() - gs = trp - glyph_source.glyph_source = gs diff -Nru python-mne-0.23.4+dfsg/mne/viz/backends/_pyvista.py python-mne-1.1.0+dfsg/mne/viz/backends/_pyvista.py --- python-mne-0.23.4+dfsg/mne/viz/backends/_pyvista.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/backends/_pyvista.py 2022-08-03 15:48:14.000000000 +0000 @@ -12,20 +12,20 @@ # License: Simplified BSD from contextlib import contextmanager -from distutils.version import LooseVersion import os +import re import sys import warnings import numpy as np -import vtk -from ._abstract import _AbstractRenderer +from ._abstract import _AbstractRenderer, Figure3D from ._utils import (_get_colormap_from_array, _alpha_blend_background, - ALLOWED_QUIVER_MODES, _init_qt_resources) -from ...fixes import _get_args + ALLOWED_QUIVER_MODES, _init_mne_qtapp) +from ...fixes import _get_args, _point_data, _cell_data, _compare_version from ...transforms import apply_trans -from ...utils import copy_base_doc_to_subclass_doc, _check_option +from ...utils import (copy_base_doc_to_subclass_doc, _check_option, + _require_version, _validate_type) with warnings.catch_warnings(): @@ -37,36 +37,63 @@ except ImportError: from pyvista import BackgroundPlotter from pyvista.plotting.plotting import _ALL_PLOTTERS -VTK9 = LooseVersion(getattr(vtk, 'VTK_VERSION', '9.0')) >= LooseVersion('9.0') + +from vtkmodules.vtkCommonCore import ( + vtkCommand, vtkLookupTable, VTK_UNSIGNED_CHAR) +from vtkmodules.vtkCommonDataModel import VTK_VERTEX, vtkPiecewiseFunction +from vtkmodules.vtkCommonTransforms import vtkTransform +from vtkmodules.vtkFiltersCore import vtkCellDataToPointData, vtkGlyph3D +from vtkmodules.vtkFiltersGeneral import ( + vtkTransformPolyDataFilter, vtkMarchingContourFilter) +from vtkmodules.vtkFiltersHybrid import vtkPolyDataSilhouette +from vtkmodules.vtkFiltersSources import ( + vtkSphereSource, vtkConeSource, vtkCylinderSource, vtkArrowSource, + vtkPlatonicSolidSource, vtkGlyphSource2D) +from vtkmodules.vtkImagingCore import vtkImageReslice +from vtkmodules.vtkRenderingCore import ( + vtkMapper, vtkActor, vtkCellPicker, vtkColorTransferFunction, + vtkPolyDataMapper, vtkVolume, vtkCoordinate, vtkDataSetMapper) +from vtkmodules.vtkRenderingVolumeOpenGL2 import vtkSmartVolumeMapper +from vtkmodules.util.numpy_support import numpy_to_vtk +try: + from vtkmodules.vtkCommonCore import VTK_VERSION +except Exception: # some bad versions of VTK + VTK_VERSION = '9.0' + +VTK9 = _compare_version(VTK_VERSION, '>=', '9.0') _FIGURES = dict() -class _Figure(object): - def __init__(self, - plotter=None, - show=False, - title='PyVista Scene', - size=(600, 600), - shape=(1, 1), - background_color='black', - smooth_shading=True, - off_screen=False, - notebook=False): - self.plotter = plotter +class PyVistaFigure(Figure3D): + """PyVista-based 3D Figure. + + This class is not meant to be instantiated directly, use + :func:`mne.viz.create_3d_figure` instead. + """ + + def __init__(self): + pass + + def _init(self, plotter=None, show=False, title='PyVista Scene', + size=(600, 600), shape=(1, 1), background_color='black', + smooth_shading=True, off_screen=False, notebook=False, + splash=False, multi_samples=None): + self._plotter = plotter self.display = None self.background_color = background_color self.smooth_shading = smooth_shading self.notebook = notebook + self.title = title + self.splash = splash self.store = dict() self.store['window_size'] = size self.store['shape'] = shape self.store['off_screen'] = off_screen self.store['border'] = False - # multi_samples > 1 is broken on macOS + Intel Iris + volume rendering - self.store['multi_samples'] = 1 if sys.platform == 'darwin' else 4 + self.store['multi_samples'] = multi_samples if not self.notebook: self.store['show'] = show @@ -74,35 +101,40 @@ self.store['auto_update'] = False self.store['menu_bar'] = False self.store['toolbar'] = False + self.store['update_app_icon'] = False + self._plotter_class = BackgroundPlotter + if 'app_window_class' in _get_args(BackgroundPlotter): + from ._qt import _MNEMainWindow + self.store['app_window_class'] = _MNEMainWindow + else: + self._plotter_class = Plotter self._nrows, self._ncols = self.store['shape'] self._azimuth = self._elevation = None - def build(self): - if self.notebook: - plotter_class = Plotter - else: - plotter_class = BackgroundPlotter - + def _build(self): if self.plotter is None: if not self.notebook: - from PyQt5.QtWidgets import QApplication - app = QApplication.instance() - if app is None: - app = QApplication(["MNE"]) + out = _init_mne_qtapp( + enable_icon=hasattr(self._plotter_class, 'set_icon'), + splash=self.splash) + # replace it with the Qt object + if self.splash: + self.splash = out[1] + app = out[0] + else: + app = out self.store['app'] = app - plotter = plotter_class(**self.store) + plotter = self._plotter_class(**self.store) plotter.background_color = self.background_color - self.plotter = plotter - if not self.notebook and hasattr(plotter_class, 'set_icon'): - _init_qt_resources() - _process_events(plotter) - plotter.set_icon(":/mne-icon.png") + self._plotter = plotter + if self.plotter.iren is not None: + self.plotter.iren.initialize() _process_events(self.plotter) _process_events(self.plotter) return self.plotter - def is_active(self): + def _is_active(self): if self.plotter is None: return False return hasattr(self.plotter, 'ren_win') @@ -119,14 +151,16 @@ Scene sensors handle. """ - def __init__(self, xy=None, pts=None): + def __init__(self, *, xy, pts, plotter): """Store input projection information into attributes.""" self.xy = xy self.pts = pts + self.plotter = plotter def visible(self, state): """Modify visibility attribute of the sensors.""" self.pts.SetVisibility(state) + self.plotter.render() @copy_base_doc_to_subclass_doc @@ -143,20 +177,30 @@ def __init__(self, fig=None, size=(600, 600), bgcolor='black', name="PyVista Scene", show=False, shape=(1, 1), - notebook=None, smooth_shading=True): - from .renderer import MNE_3D_BACKEND_TESTING + notebook=None, smooth_shading=True, splash=False, + multi_samples=None): from .._3d import _get_3d_option - figure = _Figure(show=show, title=name, size=size, shape=shape, - background_color=bgcolor, notebook=notebook, - smooth_shading=smooth_shading) + _require_version('pyvista', 'use 3D rendering', '0.32') + multi_samples = _get_3d_option('multi_samples') + # multi_samples > 1 is broken on macOS + Intel Iris + volume rendering + if sys.platform == 'darwin': + multi_samples = 1 + figure = PyVistaFigure() + figure._init( + show=show, title=name, size=size, shape=shape, + background_color=bgcolor, notebook=notebook, + smooth_shading=smooth_shading, splash=splash, + multi_samples=multi_samples) self.font_family = "arial" self.tube_n_sides = 20 - antialias = _get_3d_option('antialias') - self.antialias = antialias and not MNE_3D_BACKEND_TESTING + self.antialias = _get_3d_option('antialias') + self.depth_peeling = _get_3d_option('depth_peeling') + # smooth_shading=True fails on MacOS CIs + self.smooth_shading = _get_3d_option('smooth_shading') if isinstance(fig, int): saved_fig = _FIGURES.get(fig) # Restore only active plotter - if saved_fig is not None and saved_fig.is_active(): + if saved_fig is not None and saved_fig._is_active(): self.figure = saved_fig else: self.figure = figure @@ -172,29 +216,33 @@ with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=FutureWarning) - if MNE_3D_BACKEND_TESTING: - self.tube_n_sides = 3 - # smooth_shading=True fails on MacOS CIs - self.figure.smooth_shading = False + # pyvista theme may enable depth peeling by default so + # we disable it initially to better control the value afterwards with _disabled_depth_peeling(): - self.plotter = self.figure.build() + self.plotter = self.figure._build() self._hide_axes() - self._enable_aa() + self._enable_antialias() + self._enable_depth_peeling() # FIX: https://github.com/pyvista/pyvistaqt/pull/68 - if LooseVersion(pyvista.__version__) >= '0.27.0': - if not hasattr(self.plotter, "iren"): - self.plotter.iren = None + if not hasattr(self.plotter, "iren"): + self.plotter.iren = None self.update_lighting() @property def _all_plotters(self): - return [self.figure.plotter] + if self.figure.plotter is not None: + return [self.figure.plotter] + else: + return list() @property def _all_renderers(self): - return self.figure.plotter.renderers + if self.figure.plotter is not None: + return self.figure.plotter.renderers + else: + return list() def _hide_axes(self): for renderer in self._all_renderers: @@ -252,12 +300,22 @@ if hasattr(self.plotter, 'enable_rubber_band_2d_style'): self.plotter.enable_rubber_band_2d_style() else: - style = vtk.vtkInteractorStyleRubberBand2D() + from vtkmodules.vtkInteractionStyle import\ + vtkInteractorStyleRubberBand2D + style = vtkInteractorStyleRubberBand2D() self.plotter.interactor.SetInteractorStyle(style) else: for renderer in self._all_renderers: renderer.disable_parallel_projection() - getattr(self.plotter, f'enable_{interaction}_style')() + kwargs = dict() + if interaction == 'terrain': + kwargs['mouse_wheel_zooms'] = True + getattr(self.plotter, f'enable_{interaction}_style')(**kwargs) + + def legend(self, labels, border=False, size=0.1, face='triangle', + loc='upper left'): + return self.plotter.add_legend( + labels, size=(size, size), face=face, loc=loc) def polydata(self, mesh, color=None, opacity=1.0, normals=None, backface_culling=False, scalars=None, colormap=None, @@ -281,7 +339,7 @@ from matplotlib.colors import ListedColormap colormap = ListedColormap(colormap) if normals is not None: - mesh.point_arrays["Normals"] = normals + _point_data(mesh)["Normals"] = normals mesh.GetPointData().SetActiveNormals("Normals") else: _compute_normals(mesh) @@ -294,7 +352,7 @@ rgba=rgba, opacity=opacity, cmap=colormap, backface_culling=backface_culling, rng=[vmin, vmax], show_scalar_bar=False, - smooth_shading=self.figure.smooth_shading, + smooth_shading=self.smooth_shading, interpolate_before_map=interpolate_before_map, style=representation, line_width=line_width, **kwargs, ) @@ -314,7 +372,7 @@ polygon_offset=None, **kwargs): with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=FutureWarning) - vertices = np.c_[x, y, z] + vertices = np.c_[x, y, z].astype(float) triangles = np.c_[np.full(len(triangles), 3), triangles] mesh = PolyData(vertices, triangles) return self.polydata( @@ -347,7 +405,7 @@ n_triangles = len(triangles) triangles = np.c_[np.full(n_triangles, 3), triangles] mesh = PolyData(vertices, triangles) - mesh.point_arrays['scalars'] = scalars + _point_data(mesh)['scalars'] = scalars contour = mesh.contour(isosurfaces=contours) line_width = width if kind == 'tube': @@ -362,7 +420,7 @@ rng=[vmin, vmax], cmap=colormap, opacity=opacity, - smooth_shading=self.figure.smooth_shading + smooth_shading=self.smooth_shading ) return actor, contour @@ -379,7 +437,7 @@ mesh = PolyData(vertices, triangles) colormap = _get_colormap_from_array(colormap, normalized_colormap) if scalars is not None: - mesh.point_arrays['scalars'] = scalars + _point_data(mesh)['scalars'] = scalars return self.polydata( mesh=mesh, color=color, @@ -396,24 +454,30 @@ def sphere(self, center, color, scale, opacity=1.0, resolution=8, backface_culling=False, radius=None): + from vtkmodules.vtkFiltersSources import vtkSphereSource factor = 1.0 if radius is not None else scale + center = np.array(center, dtype=float) + if len(center) == 0: + return None, None + _check_option('center.ndim', center.ndim, (1, 2)) + _check_option('center.shape[-1]', center.shape[-1], (3,)) with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=FutureWarning) - sphere = vtk.vtkSphereSource() + sphere = vtkSphereSource() sphere.SetThetaResolution(resolution) sphere.SetPhiResolution(resolution) if radius is not None: sphere.SetRadius(radius) sphere.Update() geom = sphere.GetOutput() - mesh = PolyData(np.array(center)) + mesh = PolyData(center) glyph = mesh.glyph(orient=False, scale=False, factor=factor, geom=geom) actor = _add_mesh( self.plotter, mesh=glyph, color=color, opacity=opacity, backface_culling=backface_culling, - smooth_shading=self.figure.smooth_shading + smooth_shading=self.smooth_shading ) return actor, glyph @@ -426,13 +490,13 @@ for (pointa, pointb) in zip(origin, destination): line = Line(pointa, pointb) if scalars is not None: - line.point_arrays['scalars'] = scalars[0, :] + _point_data(line)['scalars'] = scalars[0, :] scalars = 'scalars' color = None else: scalars = None tube = line.tube(radius, n_sides=self.tube_n_sides) - _add_mesh( + actor = _add_mesh( plotter=self.plotter, mesh=tube, scalars=scalars, @@ -441,16 +505,16 @@ color=color, show_scalar_bar=False, cmap=cmap, - smooth_shading=self.figure.smooth_shading, + smooth_shading=self.smooth_shading, ) - return tube + return actor, tube def quiver3d(self, x, y, z, u, v, w, color, scale, mode, resolution=8, glyph_height=None, glyph_center=None, glyph_resolution=None, - opacity=1.0, scale_mode='none', scalars=None, + opacity=1.0, scale_mode='none', scalars=None, colormap=None, backface_culling=False, line_width=2., name=None, - glyph_width=None, glyph_depth=None, - solid_transform=None): + glyph_width=None, glyph_depth=None, glyph_radius=0.15, + solid_transform=None, *, clim=None): _check_option('mode', mode, ALLOWED_QUIVER_MODES) with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=FutureWarning) @@ -458,43 +522,43 @@ vectors = np.c_[u, v, w] points = np.vstack(np.c_[x, y, z]) n_points = len(points) - cell_type = np.full(n_points, vtk.VTK_VERTEX) + cell_type = np.full(n_points, VTK_VERTEX) cells = np.c_[np.full(n_points, 1), range(n_points)] args = (cells, cell_type, points) if not VTK9: args = (np.arange(n_points) * 3,) + args grid = UnstructuredGrid(*args) - grid.point_arrays['vec'] = vectors - if scale_mode == 'scalar': - grid.point_arrays['mag'] = np.array(scalars) - scale = 'mag' - else: - scale = False + if scalars is None: + scalars = np.ones((n_points,)) + _point_data(grid)['scalars'] = np.array(scalars) + _point_data(grid)['vec'] = vectors if mode == '2darrow': return _arrow_glyph(grid, factor), grid elif mode == 'arrow': alg = _glyph( grid, orient='vec', - scalars=scale, + scalars='scalars', factor=factor ) mesh = pyvista.wrap(alg.GetOutput()) else: tr = None if mode == 'cone': - glyph = vtk.vtkConeSource() + glyph = vtkConeSource() glyph.SetCenter(0.5, 0, 0) - glyph.SetRadius(0.15) + if glyph_radius is not None: + glyph.SetRadius(glyph_radius) elif mode == 'cylinder': - glyph = vtk.vtkCylinderSource() - glyph.SetRadius(0.15) + glyph = vtkCylinderSource() + if glyph_radius is not None: + glyph.SetRadius(glyph_radius) elif mode == 'oct': - glyph = vtk.vtkPlatonicSolidSource() + glyph = vtkPlatonicSolidSource() glyph.SetSolidTypeToOctahedron() else: assert mode == 'sphere', mode # guaranteed above - glyph = vtk.vtkSphereSource() + glyph = vtkSphereSource() if mode == 'cylinder': if glyph_height is not None: glyph.SetHeight(glyph_height) @@ -502,32 +566,37 @@ glyph.SetCenter(glyph_center) if glyph_resolution is not None: glyph.SetResolution(glyph_resolution) - tr = vtk.vtkTransform() + tr = vtkTransform() tr.RotateWXYZ(90, 0, 0, 1) elif mode == 'oct': if solid_transform is not None: assert solid_transform.shape == (4, 4) - tr = vtk.vtkTransform() + tr = vtkTransform() tr.SetMatrix( solid_transform.astype(np.float64).ravel()) if tr is not None: # fix orientation glyph.Update() - trp = vtk.vtkTransformPolyDataFilter() + trp = vtkTransformPolyDataFilter() trp.SetInputData(glyph.GetOutput()) trp.SetTransform(tr) glyph = trp glyph.Update() geom = glyph.GetOutput() - mesh = grid.glyph(orient='vec', scale=scale, factor=factor, - geom=geom) - _add_mesh( + mesh = grid.glyph(orient='vec', scale=scale_mode == 'vector', + factor=factor, geom=geom) + actor = _add_mesh( self.plotter, mesh=mesh, color=color, opacity=opacity, - backface_culling=backface_culling + scalars=None, + colormap=colormap, + show_scalar_bar=False, + backface_culling=backface_culling, + clim=clim, ) + return actor, mesh def text2d(self, x_window, y_window, text, size=14, color='white', justification=None): @@ -537,7 +606,6 @@ warnings.filterwarnings("ignore", category=FutureWarning) actor = self.plotter.add_text(text, position=position, font_size=size, - font=self.font_family, color=color, viewport=True) if isinstance(justification, str): @@ -551,13 +619,14 @@ raise ValueError('Expected values for `justification`' 'are `left`, `center` or `right` but ' 'got {} instead.'.format(justification)) + _hide_testing_actor(actor) return actor def text3d(self, x, y, z, text, scale, color='white'): with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=FutureWarning) kwargs = dict( - points=[x, y, z], + points=np.array([x, y, z]).astype(float), labels=[text], point_size=scale, text_color=color, @@ -567,13 +636,15 @@ ) if 'always_visible' in _get_args(self.plotter.add_point_labels): kwargs['always_visible'] = True - self.plotter.add_point_labels(**kwargs) + actor = self.plotter.add_point_labels(**kwargs) + _hide_testing_actor(actor) + return actor def scalarbar(self, source, color="white", title=None, n_labels=4, bgcolor=None, **extra_kwargs): - if isinstance(source, vtk.vtkMapper): + if isinstance(source, vtkMapper): mapper = source - elif isinstance(source, vtk.vtkActor): + elif isinstance(source, vtkActor): mapper = source.GetMapper() else: mapper = None @@ -585,7 +656,9 @@ label_font_size=22, font_family=self.font_family, background_color=bgcolor, mapper=mapper) kwargs.update(extra_kwargs) - return self.plotter.add_scalar_bar(**kwargs) + actor = self.plotter.add_scalar_bar(**kwargs) + _hide_testing_actor(actor) + return actor def show(self): self.plotter.show() @@ -593,12 +666,15 @@ def close(self): _close_3d_figure(figure=self.figure) + def get_camera(self): + return _get_3d_view(self.figure) + def set_camera(self, azimuth=None, elevation=None, distance=None, focalpoint='auto', roll=None, reset_camera=True, - rigid=None): + rigid=None, update=True): _set_3d_view(self.figure, azimuth=azimuth, elevation=elevation, distance=distance, focalpoint=focalpoint, roll=roll, - reset_camera=reset_camera, rigid=rigid) + reset_camera=reset_camera, rigid=rigid, update=update) def reset_camera(self): self.plotter.reset_camera() @@ -613,14 +689,16 @@ # pts = self.fig.children[-1] pts = self.plotter.renderer.GetActors().GetLastItem() - return _Projection(xy=xy, pts=pts) + return _Projection(xy=xy, pts=pts, plotter=self.plotter) - def enable_depth_peeling(self): + def _enable_depth_peeling(self): + if not self.depth_peeling: + return if not self.figure.store['off_screen']: for renderer in self._all_renderers: renderer.enable_depth_peeling() - def _enable_aa(self): + def _enable_antialias(self): """Enable it everywhere except Azure.""" if not self.antialias: return @@ -630,10 +708,13 @@ # before the window has actually been made "active"...? # For Mayavi we have an "on activated" event or so, we should look into # using this for Azure at some point, too. - if os.getenv('AZURE_CI_WINDOWS', 'false').lower() == 'true': - return - if self.figure.is_active(): - if sys.platform != 'darwin': + if self.figure._is_active(): + # macOS, Azure + bad_system = ( + sys.platform == 'darwin' or + os.getenv('AZURE_CI_WINDOWS', 'false').lower() == 'true') + bad_system |= _is_mesa(self.plotter) + if not bad_system: for renderer in self._all_renderers: renderer.enable_anti_aliasing() for plotter in self._all_plotters: @@ -655,9 +736,10 @@ self.plotter.enable() def _actor(self, mapper=None): - actor = vtk.vtkActor() + actor = vtkActor() if mapper is not None: actor.SetMapper(mapper) + _hide_testing_actor(actor) return actor def _process_events(self): @@ -669,18 +751,13 @@ on_button_press, on_button_release, on_pick): - try: - # pyvista<0.30.0 - add_obs = self.plotter.iren.AddObserver - except AttributeError: - # pyvista>=0.30.0 - add_obs = self.plotter.iren.add_observer - add_obs(vtk.vtkCommand.RenderEvent, on_mouse_move) - add_obs(vtk.vtkCommand.LeftButtonPressEvent, on_button_press) - add_obs(vtk.vtkCommand.EndInteractionEvent, on_button_release) - self.plotter.picker = vtk.vtkCellPicker() + add_obs = self.plotter.iren.add_observer + add_obs(vtkCommand.RenderEvent, on_mouse_move) + add_obs(vtkCommand.LeftButtonPressEvent, on_button_press) + add_obs(vtkCommand.EndInteractionEvent, on_button_release) + self.plotter.picker = vtkCellPicker() self.plotter.picker.AddObserver( - vtk.vtkCommand.EndPickEvent, + vtkCommand.EndPickEvent, on_pick ) self.plotter.picker.SetVolumeOpacityIsovalue(0.) @@ -690,11 +767,10 @@ # issubdtype from `complex` to `np.complexfloating` is deprecated. with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=FutureWarning) - mesh.point_arrays[name] = scalars + _point_data(mesh)[name] = scalars def _set_colormap_range(self, actor, ctable, scalar_bar, rng=None, background_color=None): - from vtk.util.numpy_support import numpy_to_vtk if rng is not None: mapper = actor.GetMapper() mapper.SetScalarRange(*rng) @@ -705,30 +781,31 @@ if background_color is not None: background_color = np.array(background_color) * 255 ctable = _alpha_blend_background(ctable, background_color) - lut.SetTable(numpy_to_vtk(ctable, - array_type=vtk.VTK_UNSIGNED_CHAR)) + lut.SetTable(numpy_to_vtk(ctable, array_type=VTK_UNSIGNED_CHAR)) lut.SetRange(*rng) def _set_volume_range(self, volume, ctable, alpha, scalar_bar, rng): - import vtk - from vtk.util.numpy_support import numpy_to_vtk - color_tf = vtk.vtkColorTransferFunction() - opacity_tf = vtk.vtkPiecewiseFunction() + color_tf = vtkColorTransferFunction() + opacity_tf = vtkPiecewiseFunction() for loc, color in zip(np.linspace(*rng, num=len(ctable)), ctable): color_tf.AddRGBPoint(loc, *(color[:-1] / 255.)) opacity_tf.AddPoint(loc, color[-1] * alpha / 255.) color_tf.ClampingOn() opacity_tf.ClampingOn() - volume.GetProperty().SetColor(color_tf) - volume.GetProperty().SetScalarOpacity(opacity_tf) + prop = volume.GetProperty() + prop.SetColor(color_tf) + prop.SetScalarOpacity(opacity_tf) + prop.ShadeOn() + prop.SetInterpolationTypeToLinear() if scalar_bar is not None: - lut = vtk.vtkLookupTable() + lut = vtkLookupTable() lut.SetRange(*rng) lut.SetTable(numpy_to_vtk(ctable)) scalar_bar.SetLookupTable(lut) def _sphere(self, center, color, radius): - sphere = vtk.vtkSphereSource() + from vtkmodules.vtkFiltersSources import vtkSphereSource + sphere = vtkSphereSource() sphere.SetThetaResolution(8) sphere.SetPhiResolution(8) sphere.SetRadius(radius) @@ -749,33 +826,36 @@ grid.dimensions = dimensions + 1 # inject data on the cells grid.origin = origin grid.spacing = spacing - grid.cell_arrays['values'] = scalars + _cell_data(grid)['values'] = scalars # Add contour of enclosed volume (use GetOutput instead of # GetOutputPort below to avoid updating) - grid_alg = vtk.vtkCellDataToPointData() - grid_alg.SetInputDataObject(grid) - grid_alg.SetPassCellData(False) - grid_alg.Update() + if surface_alpha > 0 or resolution is not None: + grid_alg = vtkCellDataToPointData() + grid_alg.SetInputDataObject(grid) + grid_alg.SetPassCellData(False) + grid_alg.Update() + else: + grid_alg = None if surface_alpha > 0: - grid_surface = vtk.vtkMarchingContourFilter() + grid_surface = vtkMarchingContourFilter() grid_surface.ComputeNormalsOn() grid_surface.ComputeScalarsOff() grid_surface.SetInputData(grid_alg.GetOutput()) grid_surface.SetValue(0, 0.1) grid_surface.Update() - grid_mesh = vtk.vtkPolyDataMapper() + grid_mesh = vtkPolyDataMapper() grid_mesh.SetInputData(grid_surface.GetOutput()) else: grid_mesh = None - mapper = vtk.vtkSmartVolumeMapper() + mapper = vtkSmartVolumeMapper() if resolution is None: # native mapper.SetScalarModeToUseCellData() mapper.SetInputDataObject(grid) else: - upsampler = vtk.vtkImageReslice() + upsampler = vtkImageReslice() upsampler.SetInterpolationModeToLinear() # default anyway upsampler.SetOutputSpacing(*([resolution] * 3)) upsampler.SetInputConnection(grid_alg.GetOutputPort()) @@ -783,20 +863,20 @@ # Additive, AverageIntensity, and Composite might also be reasonable remap = dict(composite='Composite', mip='MaximumIntensity') getattr(mapper, f'SetBlendModeTo{remap[blending]}')() - volume_pos = vtk.vtkVolume() + volume_pos = vtkVolume() volume_pos.SetMapper(mapper) dist = grid.length / (np.mean(grid.dimensions) - 1) volume_pos.GetProperty().SetScalarOpacityUnitDistance(dist) if center is not None and blending == 'mip': # We need to create a minimum intensity projection for the neg half - mapper_neg = vtk.vtkSmartVolumeMapper() + mapper_neg = vtkSmartVolumeMapper() if resolution is None: # native mapper_neg.SetScalarModeToUseCellData() mapper_neg.SetInputDataObject(grid) else: mapper_neg.SetInputConnection(upsampler.GetOutputPort()) mapper_neg.SetBlendModeToMinimumIntensity() - volume_neg = vtk.vtkVolume() + volume_neg = vtkVolume() volume_neg.SetMapper(mapper_neg) volume_neg.GetProperty().SetScalarOpacityUnitDistance(dist) else: @@ -806,27 +886,29 @@ def _silhouette(self, mesh, color=None, line_width=None, alpha=None, decimate=None): mesh = mesh.decimate(decimate) if decimate is not None else mesh - silhouette_filter = vtk.vtkPolyDataSilhouette() + silhouette_filter = vtkPolyDataSilhouette() silhouette_filter.SetInputData(mesh) silhouette_filter.SetCamera(self.plotter.renderer.GetActiveCamera()) silhouette_filter.SetEnableFeatureAngle(0) - silhouette_mapper = vtk.vtkPolyDataMapper() + silhouette_mapper = vtkPolyDataMapper() silhouette_mapper.SetInputConnection( silhouette_filter.GetOutputPort()) - _, prop = self.plotter.add_actor( + actor, prop = self.plotter.add_actor( silhouette_mapper, reset_camera=False, name=None, - culling=False, pickable=False) + culling=False, pickable=False, render=False) if color is not None: prop.SetColor(*color) if alpha is not None: prop.SetOpacity(alpha) if line_width is not None: prop.SetLineWidth(line_width) + _hide_testing_actor(actor) + return actor def _compute_normals(mesh): """Patch PyVista compute_normals.""" - if 'Normals' not in mesh.point_arrays: + if 'Normals' not in _point_data(mesh): mesh.compute_normals( cell_normals=False, consistent_normals=False, @@ -837,8 +919,6 @@ def _add_mesh(plotter, *args, **kwargs): """Patch PyVista add_mesh.""" - from . import renderer - _process_events(plotter) mesh = kwargs.get('mesh') if 'smooth_shading' in kwargs: smooth_shading = kwargs.pop('smooth_shading') @@ -846,15 +926,20 @@ smooth_shading = True # disable rendering pass for add_mesh, render() # is called in show() - if 'render' not in kwargs and 'render' in _get_args(plotter.add_mesh): + if 'render' not in kwargs: kwargs['render'] = False actor = plotter.add_mesh(*args, **kwargs) - if smooth_shading and 'Normals' in mesh.point_arrays: + if smooth_shading and 'Normals' in _point_data(mesh): prop = actor.GetProperty() prop.SetInterpolationToPhong() + _hide_testing_actor(actor) + return actor + + +def _hide_testing_actor(actor): + from . import renderer if renderer.MNE_3D_BACKEND_TESTING: actor.SetVisibility(False) - return actor def _deg2rad(deg): @@ -883,8 +968,7 @@ def _3d_to_2d(plotter, xyz): # https://vtk.org/Wiki/VTK/Examples/Cxx/Utilities/Coordinate - import vtk - coordinate = vtk.vtkCoordinate() + coordinate = vtkCoordinate() coordinate.SetCoordinateSystemToWorld() xy = list() for coord in xyz: @@ -894,28 +978,6 @@ return xy -def _get_world_to_view_matrix(plotter): - cam = plotter.renderer.camera - - scene_size = plotter.window_size - clip_range = cam.GetClippingRange() - aspect_ratio = float(scene_size[0]) / scene_size[1] - - vtk_comb_trans_mat = cam.GetCompositeProjectionTransformMatrix( - aspect_ratio, clip_range[0], clip_range[1]) - vtk_comb_trans_mat = _mat_to_array(vtk_comb_trans_mat) - return vtk_comb_trans_mat - - -def _get_view_to_display_matrix(size): - x, y = size - view_to_disp_mat = np.array([[x / 2.0, 0., 0., x / 2.0], - [0., -y / 2.0, 0., y / 2.0], - [0., 0., 1., 0.], - [0., 0., 0., 1.]]) - return view_to_disp_mat - - def _close_all(): with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=DeprecationWarning) @@ -931,8 +993,19 @@ return r, theta, phi +def _get_3d_view(figure): + position = np.array(figure.plotter.camera_position[0]) + focalpoint = np.array(figure.plotter.camera_position[1]) + _, theta, phi = _get_camera_direction(focalpoint, position) + azimuth, elevation = _rad2deg(phi), _rad2deg(theta) + return (figure.plotter.camera.GetRoll(), + figure.plotter.camera.GetDistance(), + azimuth, elevation, focalpoint) + + def _set_3d_view(figure, azimuth=None, elevation=None, focalpoint='auto', - distance=None, roll=None, reset_camera=True, rigid=None): + distance=None, roll=None, reset_camera=True, rigid=None, + update=True): rigid = np.eye(4) if rigid is None else rigid position = np.array(figure.plotter.camera_position[0]) bounds = np.array(figure.plotter.renderer.ComputeVisiblePropBounds()) @@ -993,8 +1066,9 @@ if roll is not None: figure.plotter.camera.SetRoll(figure.plotter.camera.GetRoll() + roll) - figure.plotter.update() - _process_events(figure.plotter) + if update: + figure.plotter.update() + _process_events(figure.plotter) def _set_3d_title(figure, title, size=16): @@ -1007,20 +1081,21 @@ def _check_3d_figure(figure): - if not isinstance(figure, _Figure): - raise TypeError('figure must be an instance of _Figure.') + _validate_type(figure, PyVistaFigure, 'figure') def _close_3d_figure(figure): with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=FutureWarning) + # copy the plotter locally because figure.plotter is modified + plotter = figure.plotter # close the window - figure.plotter.close() - _process_events(figure.plotter) + plotter.close() # additional cleaning following signal_close + _process_events(plotter) # free memory and deregister from the scraper - figure.plotter.deep_clean() - del _ALL_PLOTTERS[figure.plotter._id_name] - _process_events(figure.plotter) + plotter.deep_clean() # remove internal references + del _ALL_PLOTTERS[plotter._id_name] + _process_events(plotter) def _take_3d_screenshot(figure, mode='rgb', filename=None): @@ -1040,19 +1115,19 @@ def _add_camera_callback(camera, callback): - camera.AddObserver(vtk.vtkCommand.ModifiedEvent, callback) + camera.AddObserver(vtkCommand.ModifiedEvent, callback) def _arrow_glyph(grid, factor): - glyph = vtk.vtkGlyphSource2D() + glyph = vtkGlyphSource2D() glyph.SetGlyphTypeToArrow() glyph.FilledOff() glyph.Update() # fix position - tr = vtk.vtkTransform() + tr = vtkTransform() tr.Translate(0.5, 0., 0.) - trp = vtk.vtkTransformPolyDataFilter() + trp = vtkTransformPolyDataFilter() trp.SetInputConnection(glyph.GetOutputPort()) trp.SetTransform(tr) trp.Update() @@ -1065,7 +1140,7 @@ factor=factor, geom=trp.GetOutputPort(), ) - mapper = vtk.vtkDataSetMapper() + mapper = vtkDataSetMapper() mapper.SetInputConnection(alg.GetOutputPort()) return mapper @@ -1073,10 +1148,10 @@ def _glyph(dataset, scale_mode='scalar', orient=True, scalars=True, factor=1.0, geom=None, tolerance=0.0, absolute=False, clamping=False, rng=None): if geom is None: - arrow = vtk.vtkArrowSource() + arrow = vtkArrowSource() arrow.Update() geom = arrow.GetOutputPort() - alg = vtk.vtkGlyph3D() + alg = vtkGlyph3D() alg.SetSourceConnection(geom) if isinstance(scalars, str): dataset.active_scalars_name = scalars @@ -1099,21 +1174,27 @@ return alg -def _require_minimum_version(version_required): - from distutils.version import LooseVersion - version = LooseVersion(pyvista.__version__) - if version < version_required: - raise ImportError('pyvista>={} is required for this module but the ' - 'version found is {}'.format(version_required, - version)) - - @contextmanager def _disabled_depth_peeling(): - from pyvista import rcParams - depth_peeling_enabled = rcParams["depth_peeling"]["enabled"] - rcParams["depth_peeling"]["enabled"] = False + try: + from pyvista import global_theme + except Exception: # workaround for older PyVista + from pyvista import rcParams + depth_peeling = rcParams['depth_peeling'] + else: + depth_peeling = global_theme.depth_peeling + depth_peeling_enabled = depth_peeling["enabled"] + depth_peeling["enabled"] = False try: yield finally: - rcParams["depth_peeling"]["enabled"] = depth_peeling_enabled + depth_peeling["enabled"] = depth_peeling_enabled + + +def _is_mesa(plotter): + # MESA (could use GPUInfo / _get_gpu_info here, but it takes + # > 700 ms to make a new window + report capabilities!) + # CircleCI's is: "Mesa 20.0.8 via llvmpipe (LLVM 10.0.0, 256 bits)" + gpu_info = plotter.ren_win.ReportCapabilities() + gpu_info = re.findall("OpenGL renderer string:(.+)\n", gpu_info) + return ' mesa ' in ' '.join(gpu_info).lower().split() diff -Nru python-mne-0.23.4+dfsg/mne/viz/backends/_qt.py python-mne-1.1.0+dfsg/mne/viz/backends/_qt.py --- python-mne-0.23.4+dfsg/mne/viz/backends/_qt.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/backends/_qt.py 2022-08-03 15:48:14.000000000 +0000 @@ -6,20 +6,23 @@ # License: Simplified BSD from contextlib import contextmanager +import os +import platform +import sys +import weakref import pyvista -try: - from pyvista.plotting.qt_plotting import FileDialog -except ImportError: - from pyvistaqt.plotting import FileDialog - -from PyQt5.QtCore import Qt, pyqtSignal, QLocale -from PyQt5.QtGui import QIcon, QImage, QPixmap, QCursor -from PyQt5.QtWidgets import (QComboBox, QDockWidget, QDoubleSpinBox, QGroupBox, - QHBoxLayout, QLabel, QToolButton, QMenuBar, - QSlider, QSpinBox, QVBoxLayout, QWidget, - QSizePolicy, QScrollArea, QStyle, QProgressBar, - QStyleOptionSlider, QLayout) +from pyvistaqt.plotting import FileDialog, MainWindow + +from qtpy.QtCore import Qt, Signal, QLocale, QObject, QLibraryInfo +from qtpy.QtGui import QIcon, QCursor +from qtpy.QtWidgets import (QComboBox, QDockWidget, QDoubleSpinBox, QGroupBox, + QHBoxLayout, QLabel, QToolButton, QMenuBar, + QSlider, QSpinBox, QVBoxLayout, QWidget, + QSizePolicy, QScrollArea, QStyle, QProgressBar, + QStyleOptionSlider, QLayout, QCheckBox, + QButtonGroup, QRadioButton, QLineEdit, QGridLayout, + QFileDialog, QPushButton, QMessageBox) from ._pyvista import _PyVistaRenderer from ._pyvista import (_close_all, _close_3d_figure, _check_3d_figure, # noqa: F401,E501 analysis:ignore @@ -27,28 +30,161 @@ from ._abstract import (_AbstractDock, _AbstractToolBar, _AbstractMenuBar, _AbstractStatusBar, _AbstractLayout, _AbstractWidget, _AbstractWindow, _AbstractMplCanvas, _AbstractPlayback, - _AbstractBrainMplCanvas, _AbstractMplInterface) -from ._utils import _init_qt_resources, _qt_disable_paint -from ..utils import logger + _AbstractBrainMplCanvas, _AbstractMplInterface, + _AbstractWidgetList, _AbstractAction, _AbstractDialog, + _AbstractKeyPress) +from ._utils import (_qt_disable_paint, _qt_get_stylesheet, _qt_is_dark, + _qt_detect_theme, _qt_raise_window) +from ..utils import safe_event +from ...utils import _check_option, get_config +from ...fixes import _compare_version + +# Adapted from matplotlib +if (sys.platform == 'darwin' and + _compare_version(platform.mac_ver()[0], '>=', '10.16') and + QLibraryInfo.version().segments() <= [5, 15, 2]): + os.environ.setdefault("QT_MAC_WANTS_LAYER", "1") + + +class _QtKeyPress(_AbstractKeyPress): + _widget_id = 0 + _callbacks = dict() + _to_qt = dict( + escape=Qt.Key_Escape, + up=Qt.Key_Up, + down=Qt.Key_Down, + left=Qt.Key_Left, + right=Qt.Key_Right, + comma=Qt.Key_Comma, + period=Qt.Key_Period, + page_up=Qt.Key_PageUp, + page_down=Qt.Key_PageDown, + ) + + def _keypress_initialize(self, widget=None): + widget = self._window if widget is None else widget + self._widget_id = _QtKeyPress._widget_id + _QtKeyPress._widget_id += 1 + _QtKeyPress._callbacks[self._widget_id] = dict() + + def keyPressEvent(event): + text = event.text() + widget_callbacks = _QtKeyPress._callbacks[self._widget_id] + if text in widget_callbacks: + callback = widget_callbacks[text] + callback() + else: + key = event.key() + if key in widget_callbacks: + callback = widget_callbacks[key] + callback() + + widget.keyPressEvent = keyPressEvent + + def _keypress_add(self, shortcut, callback): + widget_callbacks = _QtKeyPress._callbacks[self._widget_id] + if len(shortcut) > 1: # special key + shortcut = _QtKeyPress._to_qt[shortcut] + widget_callbacks[shortcut] = callback + + def _keypress_trigger(self, shortcut): + widget_callbacks = _QtKeyPress._callbacks[self._widget_id] + if len(shortcut) > 1: # special key + shortcut = _QtKeyPress._to_qt[shortcut] + widget_callbacks[shortcut]() + + +class _QtDialog(_AbstractDialog): + # from QMessageBox.StandardButtons + supported_button_names = [ + "Ok", "Open", "Save", "Cancel", "Close", "Discard", "Apply", + "Reset", "RestoreDefaults", "Help", "SaveAll", "Yes", + "YesToAll", "No", "NoToAll", "Abort", "Retry", "Ignore" + ] + # from QMessageBox.Icon + supported_icon_names = [ + "NoIcon", "Question", "Information", "Warning", "Critical" + ] + + def _dialog_create(self, title, text, info_text, callback, *, + icon='Warning', buttons=[], modal=True, window=None): + window = self._window if window is None else window + widget = QMessageBox(window) + widget.setWindowTitle(title) + widget.setText(text) + # icon is one of _QtDialog.supported_icon_names + icon_id = getattr(QMessageBox, icon) + widget.setIcon(icon_id) + widget.setInformativeText(info_text) + + if not buttons: + buttons = ["Ok"] + + button_ids = list() + for button in buttons: + # button is one of _QtDialog.supported_button_names + button_id = getattr(QMessageBox, button) + button_ids.append(button_id) + standard_buttons = default_button = button_ids[0] + for button_id in button_ids[1:]: + standard_buttons |= button_id + widget.setStandardButtons(standard_buttons) + widget.setDefaultButton(default_button) + + @safe_event + def func(button): + button_id = widget.standardButton(button) + for button_name in _QtDialog.supported_button_names: + if button_id == getattr(QMessageBox, button_name): + widget.setCursor(QCursor(Qt.WaitCursor)) + try: + callback(button_name) + finally: + widget.unsetCursor() + break + + widget.buttonClicked.connect(func) + return _QtDialogWidget(widget, modal) class _QtLayout(_AbstractLayout): def _layout_initialize(self, max_width): pass - def _layout_add_widget(self, layout, widget, stretch=0): + def _layout_add_widget(self, layout, widget, stretch=0, + *, row=None, col=None): + """Add a widget to an existing layout.""" if isinstance(widget, QLayout): layout.addLayout(widget) else: - layout.addWidget(widget, stretch) + if isinstance(layout, QGridLayout): + layout.addWidget(widget, row, col) + else: + layout.addWidget(widget, stretch) + + def _layout_create(self, orientation='vertical'): + if orientation == 'vertical': + layout = QVBoxLayout() + elif orientation == 'horizontal': + layout = QHBoxLayout() + else: + assert orientation == 'grid' + layout = QGridLayout() + return layout class _QtDock(_AbstractDock, _QtLayout): - def _dock_initialize(self, window=None): + def _dock_initialize(self, window=None, name="Controls", + area="left", max_width=None): window = self._window if window is None else window + qt_area = getattr(Qt, f'{area.capitalize()}DockWidgetArea') self._dock, self._dock_layout = _create_dock_widget( - self._window, "Controls", Qt.LeftDockWidgetArea) - window.setCorner(Qt.BottomLeftCorner, Qt.LeftDockWidgetArea) + window, name, qt_area, max_width=max_width + ) + if area == "left": + window.setCorner(Qt.BottomLeftCorner, Qt.LeftDockWidgetArea) + else: + window.setCorner(Qt.BottomRightCorner, Qt.RightDockWidgetArea) def _dock_finalize(self): self._dock.setMinimumSize(self._dock.sizeHint().width(), 0) @@ -60,33 +196,57 @@ def _dock_hide(self): self._dock.hide() - def _dock_add_stretch(self, layout): + def _dock_add_stretch(self, layout=None): + layout = self._dock_layout if layout is None else layout layout.addStretch() def _dock_add_layout(self, vertical=True): layout = QVBoxLayout() if vertical else QHBoxLayout() return layout - def _dock_add_label(self, value, align=False, layout=None): + def _dock_add_label( + self, value, *, align=False, layout=None, selectable=False + ): layout = self._dock_layout if layout is None else layout widget = QLabel() if align: widget.setAlignment(Qt.AlignCenter) widget.setText(value) + widget.setWordWrap(True) + if selectable: + widget.setTextInteractionFlags(Qt.TextSelectableByMouse) self._layout_add_widget(layout, widget) return _QtWidget(widget) - def _dock_add_button(self, name, callback, layout=None): - layout = self._dock_layout if layout is None else layout - # If we want one with text instead of an icon, we should use - # QPushButton(name) - widget = QToolButton() + def _dock_add_button( + self, name, callback, *, style='pushbutton', icon=None, tooltip=None, + layout=None + ): + _check_option( + parameter='style', + value=style, + allowed_values=('toolbutton', 'pushbutton') + ) + if style == 'toolbutton': + widget = QToolButton() + widget.setText(name) + else: + widget = QPushButton(name) + # Don't change text color upon button press + widget.setStyleSheet( + 'QPushButton:pressed {color: none;}' + ) + if icon is not None: + widget.setIcon(self._icons[icon]) + + _set_widget_tooltip(widget, tooltip) widget.clicked.connect(callback) - widget.setText(name) + + layout = self._dock_layout if layout is None else layout self._layout_add_widget(layout, widget) return _QtWidget(widget) - def _dock_named_layout(self, name, layout, compact): + def _dock_named_layout(self, name, *, layout=None, compact=True): layout = self._dock_layout if layout is None else layout if name is not None: hlayout = self._dock_add_layout(not compact) @@ -96,40 +256,64 @@ layout = hlayout return layout - def _dock_add_slider(self, name, value, rng, callback, - compact=True, double=False, layout=None): - layout = self._dock_named_layout(name, layout, compact) + def _dock_add_slider(self, name, value, rng, callback, *, + compact=True, double=False, tooltip=None, + layout=None): + layout = self._dock_named_layout( + name=name, layout=layout, compact=compact) slider_class = QFloatSlider if double else QSlider cast = float if double else int widget = slider_class(Qt.Horizontal) + _set_widget_tooltip(widget, tooltip) widget.setMinimum(cast(rng[0])) widget.setMaximum(cast(rng[1])) widget.setValue(cast(value)) - widget.valueChanged.connect(callback) + if double: + widget.floatValueChanged.connect(callback) + else: + widget.valueChanged.connect(callback) + self._layout_add_widget(layout, widget) + return _QtWidget(widget) + + def _dock_add_check_box(self, name, value, callback, *, tooltip=None, + layout=None): + layout = self._dock_layout if layout is None else layout + widget = QCheckBox(name) + _set_widget_tooltip(widget, tooltip) + widget.setChecked(value) + widget.stateChanged.connect(callback) self._layout_add_widget(layout, widget) return _QtWidget(widget) - def _dock_add_spin_box(self, name, value, rng, callback, - compact=True, double=True, layout=None): - layout = self._dock_named_layout(name, layout, compact) + def _dock_add_spin_box(self, name, value, rng, callback, *, + compact=True, double=True, step=None, + tooltip=None, layout=None): + layout = self._dock_named_layout( + name=name, layout=layout, compact=compact) value = value if double else int(value) widget = QDoubleSpinBox() if double else QSpinBox() + _set_widget_tooltip(widget, tooltip) widget.setAlignment(Qt.AlignCenter) widget.setMinimum(rng[0]) widget.setMaximum(rng[1]) - inc = (rng[1] - rng[0]) / 20. - inc = max(int(round(inc)), 1) if not double else inc widget.setKeyboardTracking(False) - widget.setSingleStep(inc) + if step is None: + inc = (rng[1] - rng[0]) / 20. + inc = max(int(round(inc)), 1) if not double else inc + widget.setSingleStep(inc) + else: + widget.setSingleStep(step) widget.setValue(value) widget.valueChanged.connect(callback) self._layout_add_widget(layout, widget) return _QtWidget(widget) - def _dock_add_combo_box(self, name, value, rng, - callback, compact=True, layout=None): - layout = self._dock_named_layout(name, layout, compact) + def _dock_add_combo_box(self, name, value, rng, callback, *, compact=True, + tooltip=None, layout=None): + layout = self._dock_named_layout( + name=name, layout=layout, compact=compact) widget = QComboBox() + _set_widget_tooltip(widget, tooltip) widget.addItems(rng) widget.setCurrentText(value) widget.currentTextChanged.connect(callback) @@ -137,7 +321,25 @@ self._layout_add_widget(layout, widget) return _QtWidget(widget) - def _dock_add_group_box(self, name, layout=None): + def _dock_add_radio_buttons(self, value, rng, callback, *, vertical=True, + layout=None): + layout = self._dock_layout if layout is None else layout + group_layout = QVBoxLayout() if vertical else QHBoxLayout() + group = QButtonGroup() + for val in rng: + button = QRadioButton(val) + if val == value: + button.setChecked(True) + group.addButton(button) + self._layout_add_widget(group_layout, button) + + def func(button): + callback(button.text()) + group.buttonClicked.connect(func) + self._layout_add_widget(layout, group_layout) + return _QtWidgetList(group) + + def _dock_add_group_box(self, name, *, collapse=None, layout=None): layout = self._dock_layout if layout is None else layout hlayout = QVBoxLayout() widget = QGroupBox(name) @@ -145,11 +347,61 @@ self._layout_add_widget(layout, widget) return hlayout + def _dock_add_text(self, name, value, placeholder, *, callback=None, + layout=None): + layout = self._dock_layout if layout is None else layout + widget = QLineEdit(value) + widget.setPlaceholderText(placeholder) + self._layout_add_widget(layout, widget) + if callback is not None: + widget.textChanged.connect(callback) + return _QtWidget(widget) + + def _dock_add_file_button( + self, name, desc, func, *, filter=None, initial_directory=None, + save=False, is_directory=False, icon=False, tooltip=None, layout=None + ): + layout = self._dock_layout if layout is None else layout + + def callback(): + if is_directory: + name = QFileDialog.getExistingDirectory( + directory=initial_directory + ) + elif save: + name = QFileDialog.getSaveFileName( + directory=initial_directory, + filter=filter + ) + else: + name = QFileDialog.getOpenFileName( + directory=initial_directory, + filter=filter + ) + name = name[0] if isinstance(name, tuple) else name + # handle the cancel button + if len(name) == 0: + return + func(name) + + if icon: + kwargs = dict(style='toolbutton', icon='folder') + else: + kwargs = dict() + button_widget = self._dock_add_button( + name=desc, + callback=callback, + tooltip=tooltip, + layout=layout, + **kwargs + ) + return button_widget # It's already a _QtWidget instance + class QFloatSlider(QSlider): """Slider that handles float values.""" - valueChanged = pyqtSignal(float) + floatValueChanged = Signal(float) def __init__(self, ori, parent=None): """Initialize the slider.""" @@ -164,7 +416,7 @@ super().valueChanged.connect(self._convert) def _convert(self, value): - self.valueChanged.emit(value / self._precision) + self.floatValueChanged.emit(value / self._precision) def minimum(self): """Get the minimum.""" @@ -220,37 +472,23 @@ class _QtToolBar(_AbstractToolBar, _QtLayout): - def _tool_bar_load_icons(self): - _init_qt_resources() - self.icons = dict() - self.icons["help"] = QIcon(":/help.svg") - self.icons["play"] = QIcon(":/play.svg") - self.icons["pause"] = QIcon(":/pause.svg") - self.icons["reset"] = QIcon(":/reset.svg") - self.icons["scale"] = QIcon(":/scale.svg") - self.icons["clear"] = QIcon(":/clear.svg") - self.icons["movie"] = QIcon(":/movie.svg") - self.icons["restore"] = QIcon(":/restore.svg") - self.icons["screenshot"] = QIcon(":/screenshot.svg") - self.icons["visibility_on"] = QIcon(":/visibility_on.svg") - self.icons["visibility_off"] = QIcon(":/visibility_off.svg") - def _tool_bar_initialize(self, name="default", window=None): self.actions = dict() window = self._window if window is None else window self._tool_bar = window.addToolBar(name) self._tool_bar_layout = self._tool_bar.layout() - def _tool_bar_add_button(self, name, desc, func, icon_name=None, + def _tool_bar_add_button(self, name, desc, func, *, icon_name=None, shortcut=None): icon_name = name if icon_name is None else icon_name - icon = self.icons[icon_name] - self.actions[name] = self._tool_bar.addAction(icon, desc, func) + icon = self._icons[icon_name] + self.actions[name] = _QtAction(self._tool_bar.addAction( + icon, desc, func)) if shortcut is not None: - self.actions[name].setShortcut(shortcut) + self.actions[name].set_shortcut(shortcut) def _tool_bar_update_button_icon(self, name, icon_name): - self.actions[name].setIcon(self.icons[icon_name]) + self.actions[name].set_icon(self._icons[icon_name]) def _tool_bar_add_text(self, name, value, placeholder): pass @@ -260,10 +498,15 @@ spacer.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Preferred) self._tool_bar.addWidget(spacer) - def _tool_bar_add_file_button(self, name, desc, func, shortcut=None): - def callback(): + def _tool_bar_add_file_button(self, name, desc, func, *, shortcut=None): + weakself = weakref.ref(self) + + def callback(weakself=weakself): + weakself = weakself() + if weakself is None: + return return FileDialog( - self.plotter.app_window, + weakself._window, callback=func, ) @@ -274,16 +517,9 @@ shortcut=shortcut, ) - def _tool_bar_set_theme(self, theme): - if theme == 'auto': - theme = _detect_theme() - - if theme == 'dark': - for icon_key in self.icons: - icon = self.icons[icon_key] - image = icon.pixmap(80).toImage() - image.invertPixels(mode=QImage.InvertRgb) - self.icons[icon_key] = QIcon(QPixmap.fromImage(image)) + def _tool_bar_add_play_button(self, name, desc, func, *, shortcut=None): + self._tool_bar_add_button( + name=name, desc=desc, func=func, icon_name=None, shortcut=shortcut) class _QtMenuBar(_AbstractMenuBar): @@ -297,40 +533,42 @@ def _menu_add_submenu(self, name, desc): self._menus[name] = self._menu_bar.addMenu(desc) + self._menu_actions[name] = dict() def _menu_add_button(self, menu_name, name, desc, func): menu = self._menus[menu_name] - self._menu_actions[name] = menu.addAction(desc, func) + self._menu_actions[menu_name][name] = \ + _QtAction(menu.addAction(desc, func)) class _QtStatusBar(_AbstractStatusBar, _QtLayout): def _status_bar_initialize(self, window=None): window = self._window if window is None else window self._status_bar = window.statusBar() - self._status_bar_layout = self._status_bar.layout() - def _status_bar_add_label(self, value, stretch=0): + def _status_bar_add_label(self, value, *, stretch=0): widget = QLabel(value) - self._layout_add_widget(self._status_bar_layout, widget, stretch) + self._layout_add_widget(self._status_bar.layout(), widget, stretch) return _QtWidget(widget) def _status_bar_add_progress_bar(self, stretch=0): widget = QProgressBar() - self._layout_add_widget(self._status_bar_layout, widget, stretch) + self._layout_add_widget(self._status_bar.layout(), widget, stretch) return _QtWidget(widget) def _status_bar_update(self): - self._status_bar_layout.update() + self._status_bar.layout().update() class _QtPlayback(_AbstractPlayback): - def _playback_initialize(self, func, timeout): + def _playback_initialize(self, func, timeout, value, rng, + time_widget, play_widget): self.figure.plotter.add_callback(func, timeout) class _QtMplInterface(_AbstractMplInterface): def _mpl_initialize(self): - from PyQt5 import QtWidgets + from qtpy import QtWidgets from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg self.canvas = FigureCanvasQTAgg(self.fig) FigureCanvasQTAgg.setSizePolicy( @@ -359,14 +597,82 @@ class _QtWindow(_AbstractWindow): - def _window_initialize(self): + def _window_initialize( + self, *, window=None, central_layout=None, fullscreen=False + ): super()._window_initialize() self._interactor = self.figure.plotter.interactor - self._window = self.figure.plotter.app_window + if window is None: + self._window = self.figure.plotter.app_window + else: + self._window = window + + if fullscreen: + self._window.setWindowState(Qt.WindowFullScreen) + + if central_layout is not None: + central_widget = self._window.centralWidget() + if central_widget is None: + central_widget = QWidget() + self._window.setCentralWidget(central_widget) + central_widget.setLayout(central_layout) + self._window_load_icons() + self._window_set_theme() self._window.setLocale(QLocale(QLocale.Language.English)) + self._window.signal_close.connect(self._window_clean) + self._window_before_close_callbacks = list() + self._window_after_close_callbacks = list() + + # patch closeEvent + def closeEvent(event): + # functions to call before closing + accept_close_event = True + for callback in self._window_before_close_callbacks: + ret = callback() + # check if one of the callbacks ignores the close event + if isinstance(ret, bool) and not ret: + accept_close_event = False + + if accept_close_event: + self._window.signal_close.emit() + event.accept() + else: + event.ignore() + + # functions to call after closing + for callback in self._window_after_close_callbacks: + callback() + self._window.closeEvent = closeEvent + + def _window_load_icons(self): + self._icons["help"] = QIcon.fromTheme("help") + self._icons["play"] = QIcon.fromTheme("play") + self._icons["pause"] = QIcon.fromTheme("pause") + self._icons["reset"] = QIcon.fromTheme("reset") + self._icons["scale"] = QIcon.fromTheme("scale") + self._icons["clear"] = QIcon.fromTheme("clear") + self._icons["movie"] = QIcon.fromTheme("movie") + self._icons["restore"] = QIcon.fromTheme("restore") + self._icons["screenshot"] = QIcon.fromTheme("screenshot") + self._icons["visibility_on"] = QIcon.fromTheme("visibility_on") + self._icons["visibility_off"] = QIcon.fromTheme("visibility_off") + self._icons["folder"] = QIcon.fromTheme("folder") + + def _window_clean(self): + self.figure._plotter = None + self._interactor = None + + def _window_close_connect(self, func, *, after=True): + if after: + self._window_after_close_callbacks.append(func) + else: + self._window_before_close_callbacks.append(func) - def _window_close_connect(self, func): - self._window.signal_close.connect(func) + def _window_close_disconnect(self, after=True): + if after: + self._window_after_close_callbacks.clear() + else: + self._window_before_close_callbacks.clear() def _window_get_dpi(self): return self._window.windowHandle().screen().logicalDotsPerInch() @@ -396,10 +702,11 @@ dock_layout.addWidget(canvas) def _window_get_cursor(self): - return self._interactor.cursor() + return self._window.cursor() def _window_set_cursor(self, cursor): self._interactor.setCursor(cursor) + self._window.setCursor(cursor) def _window_new_cursor(self, name): return QCursor(getattr(Qt, name)) @@ -450,44 +757,74 @@ self._process_events() self._process_events() - def _window_set_theme(self, theme): - if theme == 'auto': - theme = _detect_theme() - - if theme == 'dark': - try: - import qdarkstyle - except ModuleNotFoundError: - logger.info('For Dark-Mode "qdarkstyle" has to be installed! ' - 'You can install it with `pip install qdarkstyle`') - stylesheet = None - else: - stylesheet = qdarkstyle.load_stylesheet() - elif theme != 'light': - with open(theme, 'r') as file: - stylesheet = file.read() + def _window_set_theme(self, theme=None): + if theme is None: + default_theme = _qt_detect_theme() else: - stylesheet = None - + default_theme = theme + theme = get_config('MNE_3D_OPTION_THEME', default_theme) + stylesheet = _qt_get_stylesheet(theme) self._window.setStyleSheet(stylesheet) + if _qt_is_dark(self._window): + QIcon.setThemeName('dark') + else: + QIcon.setThemeName('light') + + def _window_create(self): + return _MNEMainWindow() + + +class _QtWidgetList(_AbstractWidgetList): + def __init__(self, src): + self._src = src + self._widgets = list() + if isinstance(self._src, QButtonGroup): + widgets = self._src.buttons() + else: + widgets = src + for widget in widgets: + if not isinstance(widget, _QtWidget): + widget = _QtWidget(widget) + self._widgets.append(widget) + + def set_enabled(self, state): + for widget in self._widgets: + widget.set_enabled(state) + + def get_value(self, idx): + return self._widgets[idx].get_value() + + def set_value(self, idx, value): + if isinstance(self._src, QButtonGroup): + self._widgets[idx].set_value(True) + else: + self._widgets[idx].set_value(value) class _QtWidget(_AbstractWidget): def set_value(self, value): - if hasattr(self._widget, "setValue"): - self._widget.setValue(value) - elif hasattr(self._widget, "setCurrentText"): - self._widget.setCurrentText(value) + if isinstance(self._widget, (QRadioButton, QToolButton, QPushButton)): + self._widget.click() else: - assert hasattr(self._widget, "setText") - self._widget.setText(value) + if hasattr(self._widget, "setValue"): + self._widget.setValue(value) + elif hasattr(self._widget, "setCurrentText"): + self._widget.setCurrentText(value) + elif hasattr(self._widget, "setChecked"): + self._widget.setChecked(value) + else: + assert hasattr(self._widget, "setText") + self._widget.setText(value) def get_value(self): if hasattr(self._widget, "value"): return self._widget.value() elif hasattr(self._widget, "currentText"): return self._widget.currentText() - elif hasattr(self._widget, "text"): + elif hasattr(self._widget, "checkState"): + return self._widget.checkState() != Qt.Unchecked + else: + assert hasattr(self._widget, "text") return self._widget.text() def set_range(self, rng): @@ -499,17 +836,82 @@ def hide(self): self._widget.hide() + def set_enabled(self, state): + self._widget.setEnabled(state) + + def is_enabled(self): + return self._widget.isEnabled() + def update(self, repaint=True): self._widget.update() if repaint: self._widget.repaint() + def get_tooltip(self): + assert hasattr(self._widget, 'toolTip') + return self._widget.toolTip() + + def set_tooltip(self, tooltip): + assert hasattr(self._widget, 'setToolTip') + self._widget.setToolTip(tooltip) + + def set_style(self, style): + stylesheet = "" + for key, val in style.items(): + stylesheet = stylesheet + f"{key}:{val};" + self._widget.setStyleSheet(stylesheet) + + +class _QtDialogCommunicator(QObject): + signal_show = Signal() + + def __init__(self, parent=None): + super().__init__(parent) + + +class _QtDialogWidget(_QtWidget): + def __init__(self, widget, modal): + super().__init__(widget) + self._modal = modal + self._communicator = _QtDialogCommunicator() + self._communicator.signal_show.connect(self.show) + + def trigger(self, button): + button_id = getattr(QMessageBox, button) + for current_button in self._widget.buttons(): + if self._widget.standardButton(current_button) == button_id: + current_button.click() + + def show(self, thread=False): + if thread: + self._communicator.signal_show.emit() + else: + if self._modal: + self._widget.exec() + else: + self._widget.show() + + +class _QtAction(_AbstractAction): + def trigger(self): + self._action.trigger() + + def set_icon(self, icon): + self._action.setIcon(icon) + + def set_shortcut(self, shortcut): + self._action.setShortcut(shortcut) + class _Renderer(_PyVistaRenderer, _QtDock, _QtToolBar, _QtMenuBar, - _QtStatusBar, _QtWindow, _QtPlayback): + _QtStatusBar, _QtWindow, _QtPlayback, _QtDialog, + _QtKeyPress): + _kind = 'qt' + def __init__(self, *args, **kwargs): + fullscreen = kwargs.pop('fullscreen', False) super().__init__(*args, **kwargs) - self._window_initialize() + self._window_initialize(fullscreen=fullscreen) def show(self): super().show() @@ -518,11 +920,27 @@ self.plotter.app_window.show() self._update() for plotter in self._all_plotters: + plotter.updateGeometry() plotter._render() + # Ideally we would just put a `splash.finish(plotter.window())` in the + # same place that we initialize this (_init_qt_app call). However, + # the window show event is triggered (closing the splash screen) well + # before the window actually appears for complex scenes like the coreg + # GUI. Therefore, we close after all these events have been processed + # here. self._process_events() + splash = getattr(self.figure, 'splash', False) + if splash: + splash.close() + _qt_raise_window(self.plotter.app_window) + +def _set_widget_tooltip(widget, tooltip): + if tooltip is not None: + widget.setToolTip(tooltip) -def _create_dock_widget(window, name, area): + +def _create_dock_widget(window, name, area, *, max_width=None): # create dock widget dock = QDockWidget(name) # add scroll area @@ -539,18 +957,14 @@ widget.setLayout(dock_layout) # Fix resize grip size # https://stackoverflow.com/a/65050468/2175965 - dock.setStyleSheet("QDockWidget { margin: 4px; }") + styles = ['margin: 4px;'] + if max_width is not None: + styles.append(f'max-width: {max_width};') + style_sheet = 'QDockWidget { ' + ' \n'.join(styles) + '\n}' + dock.setStyleSheet(style_sheet) return dock, dock_layout -def _detect_theme(): - try: - import darkdetect - return darkdetect.theme().lower() - except Exception: - return 'light' - - @contextmanager def _testing_context(interactive): from . import renderer @@ -570,3 +984,15 @@ pyvista.OFF_SCREEN = orig_offscreen renderer.MNE_3D_BACKEND_TESTING = orig_testing renderer.MNE_3D_BACKEND_INTERACTIVE = orig_interactive + + +# In theory we should be able to do this later (e.g., in _pyvista.py when +# initializing), but at least on Qt6 this has to be done earlier. So let's do +# it immediately upon instantiation of the QMainWindow class. +# TODO: This should eventually allow us to handle +# https://github.com/mne-tools/mne-python/issues/9182 + +class _MNEMainWindow(MainWindow): + def __init__(self, parent=None, title=None, size=None): + super().__init__(parent, title, size) + self.setAttribute(Qt.WA_ShowWithoutActivating, True) diff -Nru python-mne-0.23.4+dfsg/mne/viz/backends/renderer.py python-mne-1.1.0+dfsg/mne/viz/backends/renderer.py --- python-mne-0.23.4+dfsg/mne/viz/backends/renderer.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/backends/renderer.py 2022-08-03 15:48:14.000000000 +0000 @@ -7,14 +7,12 @@ # # License: Simplified BSD -import sys -import os from contextlib import contextmanager import importlib from ._utils import VALID_3D_BACKENDS -from ...utils import (logger, verbose, get_config, _check_option, - _require_version) +from ...utils import (logger, verbose, get_config, _check_option, fill_doc, + _validate_type) MNE_3D_BACKEND = None MNE_3D_BACKEND_TESTING = False @@ -22,8 +20,7 @@ _backend_name_map = dict( - mayavi='._pysurfer_mayavi', - pyvista='._qt', + pyvistaqt='._qt', notebook='._notebook', ) backend = None @@ -37,13 +34,20 @@ def _get_renderer(*args, **kwargs): - set_3d_backend(_get_3d_backend(), verbose=False) + _get_3d_backend() return backend._Renderer(*args, **kwargs) +def _check_3d_backend_name(backend_name): + _validate_type(backend_name, str, 'backend_name') + backend_name = 'pyvistaqt' if backend_name == 'pyvista' else backend_name + _check_option('backend_name', backend_name, VALID_3D_BACKENDS) + return backend_name + + @verbose def set_3d_backend(backend_name, verbose=None): - """Set the backend for MNE. + """Set the 3D backend for MNE. The backend will be set as specified and operations will use that backend. @@ -52,86 +56,94 @@ ---------- backend_name : str The 3d backend to select. See Notes for the capabilities of each - backend. + backend (``'pyvistaqt'`` and ``'notebook'``). + + .. versionchanged:: 0.24 + The ``'pyvista'`` backend was renamed ``'pyvistaqt'``. %(verbose)s + Returns + ------- + old_backend_name : str | None + The old backend that was in use. + Notes ----- + To use PyVista, set ``backend_name`` to ``pyvistaqt`` but the value + ``pyvista`` is still supported for backward compatibility. + This table shows the capabilities of each backend ("✓" for full support, and "-" for partial support): .. table:: :widths: auto - +--------------------------------------+--------+---------+----------+ - | **3D function:** | mayavi | pyvista | notebook | - +======================================+========+=========+==========+ - | :func:`plot_vector_source_estimates` | ✓ | ✓ | ✓ | - +--------------------------------------+--------+---------+----------+ - | :func:`plot_source_estimates` | ✓ | ✓ | ✓ | - +--------------------------------------+--------+---------+----------+ - | :func:`plot_alignment` | ✓ | ✓ | ✓ | - +--------------------------------------+--------+---------+----------+ - | :func:`plot_sparse_source_estimates` | ✓ | ✓ | ✓ | - +--------------------------------------+--------+---------+----------+ - | :func:`plot_evoked_field` | ✓ | ✓ | ✓ | - +--------------------------------------+--------+---------+----------+ - | :func:`plot_sensors_connectivity` | ✓ | ✓ | ✓ | - +--------------------------------------+--------+---------+----------+ - | :func:`snapshot_brain_montage` | ✓ | ✓ | ✓ | - +--------------------------------------+--------+---------+----------+ - | :func:`link_brains` | | ✓ | | - +--------------------------------------+--------+---------+----------+ - +--------------------------------------+--------+---------+----------+ - | **Feature:** | - +--------------------------------------+--------+---------+----------+ - | Large data | ✓ | ✓ | ✓ | - +--------------------------------------+--------+---------+----------+ - | Opacity/transparency | ✓ | ✓ | ✓ | - +--------------------------------------+--------+---------+----------+ - | Support geometric glyph | ✓ | ✓ | ✓ | - +--------------------------------------+--------+---------+----------+ - | Smooth shading | ✓ | ✓ | ✓ | - +--------------------------------------+--------+---------+----------+ - | Subplotting | ✓ | ✓ | ✓ | - +--------------------------------------+--------+---------+----------+ - | Inline plot in Jupyter Notebook | ✓ | | ✓ | - +--------------------------------------+--------+---------+----------+ - | Inline plot in JupyterLab | ✓ | | ✓ | - +--------------------------------------+--------+---------+----------+ - | Inline plot in Google Colab | | | | - +--------------------------------------+--------+---------+----------+ - | Toolbar | | ✓ | ✓ | - +--------------------------------------+--------+---------+----------+ + +--------------------------------------+-----------+----------+ + | **3D function:** | pyvistaqt | notebook | + +======================================+===========+==========+ + | :func:`plot_vector_source_estimates` | ✓ | ✓ | + +--------------------------------------+-----------+----------+ + | :func:`plot_source_estimates` | ✓ | ✓ | + +--------------------------------------+-----------+----------+ + | :func:`plot_alignment` | ✓ | ✓ | + +--------------------------------------+-----------+----------+ + | :func:`plot_sparse_source_estimates` | ✓ | ✓ | + +--------------------------------------+-----------+----------+ + | :func:`plot_evoked_field` | ✓ | ✓ | + +--------------------------------------+-----------+----------+ + | :func:`snapshot_brain_montage` | ✓ | ✓ | + +--------------------------------------+-----------+----------+ + | :func:`link_brains` | ✓ | | + +--------------------------------------+-----------+----------+ + +--------------------------------------+-----------+----------+ + | **Feature:** | + +--------------------------------------+-----------+----------+ + | Large data | ✓ | ✓ | + +--------------------------------------+-----------+----------+ + | Opacity/transparency | ✓ | ✓ | + +--------------------------------------+-----------+----------+ + | Support geometric glyph | ✓ | ✓ | + +--------------------------------------+-----------+----------+ + | Smooth shading | ✓ | ✓ | + +--------------------------------------+-----------+----------+ + | Subplotting | ✓ | ✓ | + +--------------------------------------+-----------+----------+ + | Inline plot in Jupyter Notebook | | ✓ | + +--------------------------------------+-----------+----------+ + | Inline plot in JupyterLab | | ✓ | + +--------------------------------------+-----------+----------+ + | Inline plot in Google Colab | | | + +--------------------------------------+-----------+----------+ + | Toolbar | ✓ | ✓ | + +--------------------------------------+-----------+----------+ """ global MNE_3D_BACKEND - try: - MNE_3D_BACKEND - except NameError: - MNE_3D_BACKEND = backend_name - _check_option('backend_name', backend_name, VALID_3D_BACKENDS) + old_backend_name = MNE_3D_BACKEND + backend_name = _check_3d_backend_name(backend_name) if MNE_3D_BACKEND != backend_name: _reload_backend(backend_name) MNE_3D_BACKEND = backend_name - - # Qt5 macOS 11 compatibility - if sys.platform == 'darwin' and 'QT_MAC_WANTS_LAYER' not in os.environ: - os.environ['QT_MAC_WANTS_LAYER'] = '1' + return old_backend_name def get_3d_backend(): - """Return the backend currently used. + """Return the 3D backend currently used. Returns ------- backend_used : str | None The 3d backend currently in use. If no backend is found, returns ``None``. + + .. versionchanged:: 0.24 + The ``'pyvista'`` backend has been renamed ``'pyvistaqt'``, so + ``'pyvista'`` is no longer returned by this function. """ try: backend = _get_3d_backend() - except RuntimeError: - return None + except RuntimeError as exc: + backend = None + logger.info(str(exc)) return backend @@ -141,22 +153,32 @@ if MNE_3D_BACKEND is None: MNE_3D_BACKEND = get_config(key='MNE_3D_BACKEND', default=None) if MNE_3D_BACKEND is None: # try them in order + errors = dict() for name in VALID_3D_BACKENDS: try: _reload_backend(name) - except ImportError: - continue + except ImportError as exc: + errors[name] = str(exc) else: MNE_3D_BACKEND = name break else: - raise RuntimeError(f'Could not load any valid 3D backend: ' - f'{", ".join(VALID_3D_BACKENDS)}') + + raise RuntimeError( + 'Could not load any valid 3D backend\n' + + "\n".join(f'{key}: {val}' for key, val in errors.items()) + + "\n".join(('\n\n install pyvistaqt, using pip or conda:', + "'pip install pyvistaqt'", + "'conda install -c conda-forge pyvistaqt'", + '\n or install ipywidgets, ' + + 'if using a notebook backend', + "'pip install ipywidgets'", + "'conda install -c conda-forge ipywidgets'"))) + else: - _check_option('MNE_3D_BACKEND', MNE_3D_BACKEND, VALID_3D_BACKENDS) + MNE_3D_BACKEND = _check_3d_backend_name(MNE_3D_BACKEND) _reload_backend(MNE_3D_BACKEND) - else: - _check_option('MNE_3D_BACKEND', MNE_3D_BACKEND, VALID_3D_BACKENDS) + MNE_3D_BACKEND = _check_3d_backend_name(MNE_3D_BACKEND) return MNE_3D_BACKEND @@ -169,18 +191,18 @@ Parameters ---------- - backend_name : {'mayavi', 'pyvista', 'notebook'} + backend_name : {'pyvistaqt', 'notebook'} The 3d backend to use in the context. """ - old_backend = _get_3d_backend() - set_3d_backend(backend_name) + old_backend = set_3d_backend(backend_name) try: yield finally: - try: - set_3d_backend(old_backend) - except Exception: - pass + if old_backend is not None: + try: + set_3d_backend(old_backend) + except Exception: + pass @contextmanager @@ -194,17 +216,24 @@ interactive : bool If True, ensure interactive elements are accessible. """ + with _actors_invisible(): + with use_3d_backend(backend_name): + with backend._testing_context(interactive): + yield + + +@contextmanager +def _actors_invisible(): global MNE_3D_BACKEND_TESTING orig_testing = MNE_3D_BACKEND_TESTING MNE_3D_BACKEND_TESTING = True try: - with use_3d_backend(backend_name): - with backend._testing_context(interactive): - yield + yield finally: MNE_3D_BACKEND_TESTING = orig_testing +@fill_doc def set_3d_view(figure, azimuth=None, elevation=None, focalpoint=None, distance=None, roll=None, reset_camera=True): @@ -214,16 +243,11 @@ ---------- figure : object The scene which is modified. - azimuth : float - The azimuthal angle of the view. - elevation : float - The zenith angle of the view. - focalpoint : tuple, shape (3,) - The focal point of the view: (x, y, z). - distance : float - The distance to the focal point. - roll : float - The view roll. + %(azimuth)s + %(elevation)s + %(focalpoint)s + %(distance)s + %(roll)s reset_camera : bool If True, reset the camera properties beforehand. """ @@ -249,7 +273,7 @@ def create_3d_figure(size, bgcolor=(0, 0, 0), smooth_shading=True, - handle=None, scene=True): + handle=None, *, scene=True, show=False): """Return an empty figure based on the current 3d backend. .. warning:: Proceed with caution when the renderer object is @@ -268,20 +292,25 @@ handle : int | None The figure identifier. scene : bool - Specify if the returned object is the scene. If False, - the renderer object is returned. Defaults to True. + If True (default), the returned object is the Figure3D. If False, + an advanced, undocumented Renderer object is returned (the API is not + stable or documented, so this is not recommended). + show : bool + If True, show the renderer immediately. + + .. versionadded:: 1.0 Returns ------- - figure : object - The requested empty scene or the renderer object if - ``scene=False``. + figure : instance of Figure3D or ``Renderer`` + The requested empty figure or renderer, depending on ``scene``. """ renderer = _get_renderer( fig=handle, size=size, bgcolor=bgcolor, smooth_shading=smooth_shading, + show=show, ) if scene: return renderer.scene() @@ -289,6 +318,22 @@ return renderer +def close_3d_figure(figure): + """Close the given scene. + + Parameters + ---------- + figure : object + The scene which needs to be closed. + """ + backend._close_3d_figure(figure) + + +def close_all_3d_figures(): + """Close all the scenes of the current 3d backend.""" + backend._close_all() + + def get_brain_class(): """Return the proper Brain class based on the current 3d backend. @@ -297,9 +342,5 @@ brain : object The Brain class corresponding to the current 3d backend. """ - if get_3d_backend() == "mayavi": - from surfer import Brain - _require_version('surfer', 'stc.plot', '0.9') - else: # PyVista - from ...viz._brain import Brain + from ...viz._brain import Brain return Brain diff -Nru python-mne-0.23.4+dfsg/mne/viz/backends/tests/test_renderer.py python-mne-1.1.0+dfsg/mne/viz/backends/tests/test_renderer.py --- python-mne-0.23.4+dfsg/mne/viz/backends/tests/test_renderer.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/backends/tests/test_renderer.py 2022-08-03 15:48:14.000000000 +0000 @@ -6,32 +6,27 @@ # License: Simplified BSD import os +import sys import pytest import numpy as np -from mne.viz.backends.tests._utils import (skips_if_not_mayavi, - skips_if_not_pyvista) +from mne.utils import run_subprocess +from mne.viz import set_3d_backend, get_3d_backend, Figure3D +from mne.viz.backends.renderer import _get_renderer +from mne.viz.backends.tests._utils import skips_if_not_pyvistaqt from mne.viz.backends._utils import ALLOWED_QUIVER_MODES -@pytest.fixture -def backend_mocker(): - """Help to test set up 3d backend.""" - from mne.viz.backends import renderer - del renderer.MNE_3D_BACKEND - yield - renderer.MNE_3D_BACKEND = None - - @pytest.mark.parametrize('backend', [ - pytest.param('mayavi', marks=skips_if_not_mayavi), - pytest.param('pyvista', marks=skips_if_not_pyvista), + pytest.param('pyvistaqt', marks=skips_if_not_pyvistaqt), pytest.param('foo', marks=pytest.mark.xfail(raises=ValueError)), ]) -def test_backend_environment_setup(backend, backend_mocker, monkeypatch): +def test_backend_environment_setup(backend, monkeypatch): """Test set up 3d backend based on env.""" monkeypatch.setenv("MNE_3D_BACKEND", backend) + monkeypatch.setattr( + 'mne.viz.backends.renderer.MNE_3D_BACKEND', None) assert os.environ['MNE_3D_BACKEND'] == backend # just double-check # reload the renderer to check if the 3d backend selection by @@ -45,15 +40,16 @@ def test_3d_functions(renderer): """Test figure management functions.""" fig = renderer.create_3d_figure((300, 300)) - # Mayavi actually needs something in the display to set the title + assert isinstance(fig, Figure3D) wrap_renderer = renderer.backend._Renderer(fig=fig) wrap_renderer.sphere(np.array([0., 0., 0.]), 'w', 1.) renderer.backend._check_3d_figure(fig) - renderer.backend._set_3d_view(figure=fig, azimuth=None, elevation=None, - focalpoint=(0., 0., 0.), distance=None) - renderer.backend._set_3d_title(figure=fig, title='foo') + renderer.set_3d_view(figure=fig, azimuth=None, elevation=None, + focalpoint=(0., 0., 0.), distance=None) + renderer.set_3d_title(figure=fig, title='foo') renderer.backend._take_3d_screenshot(figure=fig) - renderer.backend._close_all() + renderer.close_3d_figure(fig) + renderer.close_all_3d_figures() def test_3d_backend(renderer): @@ -152,9 +148,9 @@ # use tube rend.tube(origin=np.array([[0, 0, 0]]), destination=np.array([[0, 1, 0]])) - tube = rend.tube(origin=np.array([[1, 0, 0]]), - destination=np.array([[1, 1, 0]]), - scalars=np.array([[1.0, 1.0]])) + _, tube = rend.tube(origin=np.array([[1, 0, 0]]), + destination=np.array([[1, 1, 0]]), + scalars=np.array([[1.0, 1.0]])) # scalar bar rend.scalarbar(source=tube, title="Scalar Bar", @@ -177,3 +173,32 @@ orig_backend = renderer.MNE_3D_BACKEND assert renderer.get_3d_backend() == orig_backend assert renderer.get_3d_backend() == orig_backend + + +def test_renderer(renderer, monkeypatch): + """Test that renderers are available on demand.""" + backend = renderer.get_3d_backend() + cmd = [sys.executable, '-uc', + 'import mne; mne.viz.create_3d_figure((800, 600), show=True); ' + 'backend = mne.viz.get_3d_backend(); ' + 'assert backend == %r, backend' % (backend,)] + monkeypatch.setenv('MNE_3D_BACKEND', backend) + run_subprocess(cmd) + + +def test_set_3d_backend_bad(monkeypatch, tmp_path): + """Test that the error emitted when a bad backend name is used.""" + match = "Allowed values are 'pyvistaqt' and 'notebook'" + with pytest.raises(ValueError, match=match): + set_3d_backend('invalid') + + # gh-9607 + def fail(x): + raise ModuleNotFoundError(x) + monkeypatch.setattr('mne.viz.backends.renderer._reload_backend', fail) + monkeypatch.setattr( + 'mne.viz.backends.renderer.MNE_3D_BACKEND', None) + match = 'Could not load any valid 3D.*\npyvistaqt: .*' + assert get_3d_backend() is None + with pytest.raises(RuntimeError, match=match): + _get_renderer() diff -Nru python-mne-0.23.4+dfsg/mne/viz/backends/tests/test_utils.py python-mne-1.1.0+dfsg/mne/viz/backends/tests/test_utils.py --- python-mne-0.23.4+dfsg/mne/viz/backends/tests/test_utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/backends/tests/test_utils.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,8 +5,18 @@ # # License: Simplified BSD +import sys +from colorsys import rgb_to_hls +from contextlib import nullcontext + +import numpy as np import pytest -from mne.viz.backends._utils import _get_colormap_from_array, _check_color + +from mne import create_info +from mne.io import RawArray +from mne.viz.backends._utils import (_get_colormap_from_array, _check_color, + _qt_is_dark, _pixmap_to_ndarray) +from mne.utils import _check_qt_version def test_get_colormap_from_array(): @@ -39,3 +49,49 @@ _check_color(['foo', 'bar', 'foo']) with pytest.raises(TypeError, match='Expected type'): _check_color(None) + + +@pytest.mark.parametrize('theme', ('auto', 'light', 'dark')) +def test_theme_colors(pg_backend, theme, monkeypatch, tmp_path): + """Test that theme colors propagate properly.""" + darkdetect = pytest.importorskip('darkdetect') + monkeypatch.setenv('_MNE_FAKE_HOME_DIR', str(tmp_path)) + monkeypatch.delenv('MNE_BROWSER_THEME', raising=False) + # make it seem like the system is always in light mode + monkeypatch.setattr(darkdetect, 'theme', lambda: 'light') + raw = RawArray(np.zeros((1, 1000)), create_info(1, 1000., 'eeg')) + _, api = _check_qt_version(return_api=True) + if api in ('PyQt6', 'PySide6') and theme == 'dark': + ctx = pytest.warns(RuntimeWarning, match='not yet supported') + return_early = True + else: + ctx = nullcontext() + return_early = False + with ctx: + fig = raw.plot(theme=theme) + if return_early: + return # we could add a ton of conditionals below, but KISS + is_dark = _qt_is_dark(fig) + # on Darwin these checks get complicated, so don't bother for now + if sys.platform != 'darwin': + if theme == 'dark': + assert is_dark, theme + elif theme == 'light': + assert not is_dark, theme + else: + got_dark = darkdetect.theme().lower() == 'dark' + assert is_dark is got_dark + + def assert_correct_darkness(widget, want_dark): + __tracebackhide__ = True # noqa + # This should work, but it just picks up the parent in the errant case! + bgcolor = widget.palette().color(widget.backgroundRole()).getRgbF()[:3] + dark = rgb_to_hls(*bgcolor)[1] < 0.5 + assert dark == want_dark, f'{widget} dark={dark} want_dark={want_dark}' + # ... so we use a more direct test + colors = _pixmap_to_ndarray(widget.grab())[:, :, :3] + dark = colors.mean() < 0.5 + assert dark == want_dark, f'{widget} dark={dark} want_dark={want_dark}' + + for widget in (fig.mne.toolbar, fig.statusBar()): + assert_correct_darkness(widget, is_dark) diff -Nru python-mne-0.23.4+dfsg/mne/viz/backends/tests/_utils.py python-mne-1.1.0+dfsg/mne/viz/backends/tests/_utils.py --- python-mne-0.23.4+dfsg/mne/viz/backends/tests/_utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/backends/tests/_utils.py 2022-08-03 15:48:14.000000000 +0000 @@ -10,7 +10,7 @@ def has_pyvista(): - """Check that pyvista is installed.""" + """Check that PyVista is installed.""" try: with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=DeprecationWarning) @@ -20,20 +20,12 @@ return False -def has_mayavi(): - """Check that mayavi is installed.""" +def has_pyvistaqt(): + """Check that PyVistaQt is installed.""" try: - with warnings.catch_warnings(record=True): # traits - from mayavi import mlab # noqa: F401 - return True - except ImportError: - return False - - -def has_pyqt5(): - """Check if PyQt5 is installed.""" - try: - import PyQt5 # noqa: F401 + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=DeprecationWarning) + import pyvistaqt # noqa: F401 return True except ImportError: return False @@ -48,7 +40,5 @@ return False -skips_if_not_mayavi = pytest.mark.skipif( - not has_mayavi(), reason='requires mayavi') -skips_if_not_pyvista = pytest.mark.skipif( - not has_pyvista(), reason='requires pyvista') +skips_if_not_pyvistaqt = pytest.mark.skipif( + not has_pyvistaqt(), reason='requires pyvistaqt') diff -Nru python-mne-0.23.4+dfsg/mne/viz/backends/_utils.py python-mne-1.1.0+dfsg/mne/viz/backends/_utils.py --- python-mne-0.23.4+dfsg/mne/viz/backends/_utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/backends/_utils.py 2022-08-03 15:48:14.000000000 +0000 @@ -6,15 +6,24 @@ # Guillaume Favelier # # License: Simplified BSD - +import collections.abc +from colorsys import rgb_to_hls from contextlib import contextmanager +import functools +import platform +import signal +import sys + +from pathlib import Path import numpy as np -import collections.abc -from ...externals.decorator import decorator + +VALID_BROWSE_BACKENDS = ( + 'qt', + 'matplotlib', +) VALID_3D_BACKENDS = ( - 'pyvista', # default 3d backend - 'mayavi', + 'pyvistaqt', # default 3d backend 'notebook', ) ALLOWED_QUIVER_MODES = ('2darrow', 'arrow', 'cone', 'cylinder', 'sphere', @@ -67,18 +76,12 @@ return (use_table * alphas) + background_color * (1 - alphas) -@decorator -def run_once(fun, *args, **kwargs): - """Run the function only once.""" - if not hasattr(fun, "_has_run"): - fun._has_run = True - return fun(*args, **kwargs) - - -@run_once -def _init_qt_resources(): - from ...icons import resources - resources.qInitResources() +@functools.lru_cache(1) +def _qt_init_icons(): + from qtpy.QtGui import QIcon + icons_path = f"{Path(__file__).parent.parent.parent}/icons" + QIcon.setThemeSearchPaths([icons_path]) + return icons_path @contextmanager @@ -89,3 +92,240 @@ yield finally: widget.paintEvent = paintEvent + + +def _init_mne_qtapp(enable_icon=True, pg_app=False, splash=False): + """Get QApplication-instance for MNE-Python. + + Parameter + --------- + enable_icon: bool + If to set an MNE-icon for the app. + pg_app: bool + If to create the QApplication with pyqtgraph. For an until know + undiscovered reason the pyqtgraph-browser won't show without + mkQApp from pyqtgraph. + splash : bool | str + If not False, display a splash screen. If str, set the message + to the given string. + + Returns + ------- + app : ``qtpy.QtWidgets.QApplication`` + Instance of QApplication. + splash : ``qtpy.QtWidgets.QSplashScreen`` + Instance of QSplashScreen. Only returned if splash is True or a + string. + """ + from qtpy.QtCore import Qt + from qtpy.QtGui import QIcon, QPixmap, QGuiApplication + from qtpy.QtWidgets import QApplication, QSplashScreen + app_name = 'MNE-Python' + organization_name = 'MNE' + + # Fix from cbrnr/mnelab for app name in menu bar + # This has to come *before* the creation of the QApplication to work. + # It also only affects the title bar, not the application dock. + # There seems to be no way to change the application dock from "python" + # at runtime. + if sys.platform.startswith("darwin"): + try: + # set bundle name on macOS (app name shown in the menu bar) + from Foundation import NSBundle + bundle = NSBundle.mainBundle() + info = bundle.localizedInfoDictionary() or bundle.infoDictionary() + info["CFBundleName"] = app_name + except ModuleNotFoundError: + pass + + if pg_app: + from pyqtgraph import mkQApp + app = mkQApp(app_name) + else: + app = QApplication.instance() or QApplication(sys.argv or [app_name]) + app.setApplicationName(app_name) + app.setOrganizationName(organization_name) + try: + app.setAttribute(Qt.AA_UseHighDpiPixmaps) # works on PyQt5 and PySide2 + except AttributeError: + pass # not required on PyQt6 and PySide6 anyway + + if enable_icon or splash: + icons_path = _qt_init_icons() + + if enable_icon: + # Set icon + kind = 'bigsur_' if platform.mac_ver()[0] >= '10.16' else 'default_' + app.setWindowIcon(QIcon(f"{icons_path}/mne_{kind}icon.png")) + + out = app + if splash: + pixmap = QPixmap(f"{icons_path}/mne_splash.png") + pixmap.setDevicePixelRatio( + QGuiApplication.primaryScreen().devicePixelRatio()) + qsplash = QSplashScreen(pixmap, Qt.WindowStaysOnTopHint) + qsplash.setAttribute(Qt.WA_ShowWithoutActivating, True) + if isinstance(splash, str): + alignment = int(Qt.AlignBottom | Qt.AlignHCenter) + qsplash.showMessage( + splash, alignment=alignment, color=Qt.white) + qsplash.show() + app.processEvents() + out = (out, qsplash) + + return out + + +# https://stackoverflow.com/questions/5160577/ctrl-c-doesnt-work-with-pyqt +def _qt_app_exec(app): + # adapted from matplotlib + old_signal = signal.getsignal(signal.SIGINT) + is_python_signal_handler = old_signal is not None + if is_python_signal_handler: + signal.signal(signal.SIGINT, signal.SIG_DFL) + try: + # Make IPython Console accessible again in Spyder + app.lastWindowClosed.connect(app.quit) + app.exec_() + finally: + # reset the SIGINT exception handler + if is_python_signal_handler: + signal.signal(signal.SIGINT, old_signal) + + +def _qt_detect_theme(): + from ..utils import logger + try: + import darkdetect + theme = darkdetect.theme().lower() + except ModuleNotFoundError: + logger.info('For automatic theme detection, "darkdetect" has to' + ' be installed! You can install it with ' + '`pip install darkdetect`') + theme = 'light' + except Exception: + theme = 'light' + return theme + + +def _qt_get_stylesheet(theme): + from ...fixes import _compare_version + from ...utils import logger, warn, _validate_type, _check_qt_version + _validate_type(theme, ('path-like',), 'theme') + theme = str(theme) + orig_theme = theme + system_theme = None + stylesheet = '' + extra_msg = '' + if theme == 'auto': + theme = system_theme = _qt_detect_theme() + if theme in ('dark', 'light'): + if system_theme is None: + system_theme = _qt_detect_theme() + qt_version, api = _check_qt_version(return_api=True) + # On macOS, we shouldn't need to set anything when the requested theme + # matches that of the current OS state + if sys.platform == 'darwin': + extra_msg = f'when in {system_theme} mode on macOS' + # But before 5.13, we need to patch some mistakes + if sys.platform == 'darwin' and theme == system_theme: + if theme == 'dark' and _compare_version(qt_version, '<', '5.13'): + # Taken using "Digital Color Meter" on macOS 12.2.1 looking at + # Meld, and also adapting (MIT-licensed) + # https://github.com/ColinDuquesnoy/QDarkStyleSheet/blob/master/qdarkstyle/dark/style.qss # noqa: E501 + # Something around rgb(51, 51, 51) worked as the bgcolor here, + # but it's easy enough just to set it transparent and inherit + # the bgcolor of the window (which is the same). We also take + # the separator images from QDarkStyle (MIT). + icons_path = _qt_init_icons() + stylesheet = """\ +QStatusBar { + border: 1px solid rgb(76, 76, 75); + background: transparent; +} +QStatusBar QLabel { + background: transparent; +} +QToolBar { + background-color: transparent; + border-bottom: 1px solid rgb(99, 99, 99); +} +QToolBar::separator:horizontal { + width: 16px; + image: url("%(icons_path)s/toolbar_separator_horizontal@2x.png"); +} +QToolBar::separator:vertical { + height: 16px; + image: url("%(icons_path)s/toolbar_separator_vertical@2x.png"); +} +QToolBar::handle:horizontal { + width: 16px; + image: url("%(icons_path)s/toolbar_move_horizontal@2x.png"); +} +QToolBar::handle:vertical { + height: 16px; + image: url("%(icons_path)s/toolbar_move_vertical@2x.png"); +} +""" % dict(icons_path=icons_path) + else: + # Here we are on non-macOS (or on macOS but our sys theme does not + # match the requested theme) + if api in ('PySide6', 'PyQt6'): + if orig_theme != 'auto' and not \ + (theme == system_theme == 'light'): + warn(f'Setting theme={repr(theme)} is not yet supported ' + f'for {api} in qdarkstyle, it will be ignored') + else: + try: + import qdarkstyle + except ModuleNotFoundError: + logger.info( + f'To use {theme} mode{extra_msg}, "qdarkstyle" has to ' + 'be installed! You can install it with:\n' + 'pip install qdarkstyle\n') + else: + klass = getattr(getattr(qdarkstyle, theme).palette, + f'{theme.capitalize()}Palette') + stylesheet = qdarkstyle.load_stylesheet(klass) + else: + try: + file = open(theme, 'r') + except IOError: + warn('Requested theme file not found, will use light instead: ' + f'{repr(theme)}') + else: + with file as fid: + stylesheet = fid.read() + + return stylesheet + + +def _qt_raise_window(widget): + # Set raise_window like matplotlib if possible + try: + from matplotlib import rcParams + raise_window = rcParams['figure.raise_window'] + except ImportError: + raise_window = True + if raise_window: + widget.activateWindow() + widget.raise_() + + +def _qt_is_dark(widget): + # Ideally this would use CIELab, but this should be good enough + win = widget.window() + bgcolor = win.palette().color(win.backgroundRole()).getRgbF()[:3] + return rgb_to_hls(*bgcolor)[1] < 0.5 + + +def _pixmap_to_ndarray(pixmap): + img = pixmap.toImage() + img = img.convertToFormat(img.Format_RGBA8888) + ptr = img.bits() + count = img.height() * img.width() * 4 + if hasattr(ptr, 'setsize'): # PyQt + ptr.setsize(count) + data = np.frombuffer(ptr, dtype=np.uint8, count=count).copy() + data.shape = (img.height(), img.width(), 4) + return data / 255. diff -Nru python-mne-0.23.4+dfsg/mne/viz/_brain/_brain.py python-mne-1.1.0+dfsg/mne/viz/_brain/_brain.py --- python-mne-0.23.4+dfsg/mne/viz/_brain/_brain.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/_brain/_brain.py 2022-08-03 15:48:14.000000000 +0000 @@ -12,14 +12,13 @@ from io import BytesIO import os import os.path as op -import sys import time import copy import traceback import warnings +import weakref import numpy as np -from collections import OrderedDict from .colormap import calculate_lut from .surface import _Surface @@ -28,194 +27,30 @@ UpdateLUT, UpdateColorbarScale) from ..utils import (_show_help_fig, _get_color_list, concatenate_images, - _generate_default_filename, _save_ndarray_img) -from .._3d import _process_clim, _handle_time, _check_views - -from ...externals.decorator import decorator -from ...defaults import _handle_default -from ...surface import mesh_edges -from ...source_space import SourceSpaces, vertex_to_mni, read_talxfm -from ...transforms import apply_trans, invert_transform + _generate_default_filename, _save_ndarray_img, safe_event) +from .._3d import (_process_clim, _handle_time, _check_views, + _handle_sensor_types, _plot_sensors, _plot_forward) +from .._3d_overlay import _LayeredMesh +from ...defaults import _handle_default, DEFAULTS +from ...fixes import _point_data, _cell_data +from ..._freesurfer import (vertex_to_mni, read_talxfm, read_freesurfer_lut, + _get_head_surface, _get_skull_surface, + _estimate_talxfm_rigid) +from ...io.pick import pick_types +from ...io.meas_info import Info +from ...surface import (mesh_edges, _mesh_borders, _marching_cubes, + get_meg_helmet_surf) +from ...source_space import SourceSpaces +from ...transforms import (Transform, apply_trans, _frame_to_str, + _get_trans, _get_transforms_to_coord_frame) from ...utils import (_check_option, logger, verbose, fill_doc, _validate_type, use_log_level, Bunch, _ReuseCycle, warn, - get_subjects_dir) + get_subjects_dir, _check_fname, _to_rgb, _ensure_int) _ARROW_MOVE = 10 # degrees per press -@decorator -def safe_event(fun, *args, **kwargs): - """Protect against PyQt5 exiting on event-handling errors.""" - try: - return fun(*args, **kwargs) - except Exception: - traceback.print_exc(file=sys.stderr) - - -class _Overlay(object): - def __init__(self, scalars, colormap, rng, opacity, name): - self._scalars = scalars - self._colormap = colormap - assert rng is not None - self._rng = rng - self._opacity = opacity - self._name = name - - def to_colors(self): - from .._3d import _get_cmap - from matplotlib.colors import ListedColormap - - if isinstance(self._colormap, str): - kind = self._colormap - cmap = _get_cmap(self._colormap) - else: - cmap = ListedColormap(self._colormap / 255.) - kind = str(type(self._colormap)) - logger.debug( - f'Color mapping {repr(self._name)} with {kind} ' - f'colormap and range {self._rng}') - - rng = self._rng - assert rng is not None - scalars = _norm(self._scalars, rng) - - colors = cmap(scalars) - if self._opacity is not None: - colors[:, 3] *= self._opacity - return colors - - -def _norm(x, rng): - if rng[0] == rng[1]: - factor = 1 if rng[0] == 0 else 1e-6 * rng[0] - else: - factor = rng[1] - rng[0] - return (x - rng[0]) / factor - - -class _LayeredMesh(object): - def __init__(self, renderer, vertices, triangles, normals): - self._renderer = renderer - self._vertices = vertices - self._triangles = triangles - self._normals = normals - - self._polydata = None - self._actor = None - self._is_mapped = False - - self._cache = None - self._overlays = OrderedDict() - - self._default_scalars = np.ones(vertices.shape) - self._default_scalars_name = 'Data' - - def map(self): - kwargs = { - "color": None, - "pickable": True, - "rgba": True, - } - mesh_data = self._renderer.mesh( - x=self._vertices[:, 0], - y=self._vertices[:, 1], - z=self._vertices[:, 2], - triangles=self._triangles, - normals=self._normals, - scalars=self._default_scalars, - **kwargs - ) - self._actor, self._polydata = mesh_data - self._is_mapped = True - - def _compute_over(self, B, A): - assert A.ndim == B.ndim == 2 - assert A.shape[1] == B.shape[1] == 4 - A_w = A[:, 3:] # * 1 - B_w = B[:, 3:] * (1 - A_w) - C = A.copy() - C[:, :3] *= A_w - C[:, :3] += B[:, :3] * B_w - C[:, 3:] += B_w - C[:, :3] /= C[:, 3:] - return np.clip(C, 0, 1, out=C) - - def _compose_overlays(self): - B = None - for overlay in self._overlays.values(): - A = overlay.to_colors() - if B is None: - B = A - else: - B = self._compute_over(B, A) - return B - - def add_overlay(self, scalars, colormap, rng, opacity, name): - overlay = _Overlay( - scalars=scalars, - colormap=colormap, - rng=rng, - opacity=opacity, - name=name, - ) - self._overlays[name] = overlay - colors = overlay.to_colors() - - # save colors in cache - if self._cache is None: - self._cache = colors - else: - self._cache = self._compute_over(self._cache, colors) - - # update the texture - self._update() - - def remove_overlay(self, names): - if not isinstance(names, list): - names = [names] - for name in names: - if name in self._overlays: - del self._overlays[name] - self.update() - - def _update(self): - if self._cache is None or self._renderer is None: - return - self._renderer._set_mesh_scalars( - mesh=self._polydata, - scalars=self._cache, - name=self._default_scalars_name, - ) - - def update(self): - self._cache = self._compose_overlays() - self._update() - - def _clean(self): - mapper = self._actor.GetMapper() - mapper.SetLookupTable(None) - self._actor.SetMapper(None) - self._actor = None - self._polydata = None - self._renderer = None - - def update_overlay(self, name, scalars=None, colormap=None, - opacity=None, rng=None): - overlay = self._overlays.get(name, None) - if overlay is None: - return - if scalars is not None: - overlay._scalars = scalars - if colormap is not None: - overlay._colormap = colormap - if opacity is not None: - overlay._opacity = opacity - if rng is not None: - overlay._rng = rng - self.update() - - @fill_doc class Brain(object): """Class for visualizing a brain. @@ -238,12 +73,23 @@ FreeSurfer surface mesh name (ie 'white', 'inflated', etc.). title : str Title for the window. - cortex : str or None - Specifies how the cortical surface is rendered. - The name of one of the preset cortex styles can be: - ``'classic'`` (default), ``'high_contrast'``, - ``'low_contrast'``, or ``'bone'`` or a valid color name. - Setting this to ``None`` is equivalent to ``(0.5, 0.5, 0.5)``. + cortex : str, list, dict + Specifies how the cortical surface is rendered. Options: + + 1. The name of one of the preset cortex styles: + ``'classic'`` (default), ``'high_contrast'``, + ``'low_contrast'``, or ``'bone'``. + 2. A single color-like argument to render the cortex as a single + color, e.g. ``'red'`` or ``(0.1, 0.4, 1.)``. + 3. A list of two color-like used to render binarized curvature + values for gyral (first) and sulcal (second). regions, e.g., + ``['red', 'blue']`` or ``[(1, 0, 0), (0, 0, 1)]``. + 4. A dict containing keys ``'vmin', 'vmax', 'colormap'`` with + values used to render the binarized curvature (where 0 is gyral, + 1 is sulcal). + + .. versionchanged:: 0.24 + Add support for non-string arguments. alpha : float in [0, 1] Alpha level to control opacity of the cortical surface. size : int | array-like, shape (2,) @@ -255,16 +101,14 @@ Color of the foreground (will be used for colorbars and text). None (default) will use black or white depending on the value of ``background``. - figure : list of Figure | None | int + figure : list of Figure | None If None (default), a new window will be created with the appropriate - views. For single view plots, the figure can be specified as int to - retrieve the corresponding Mayavi window. + views. subjects_dir : str | None If not None, this directory will be used as the subjects directory instead of the value set using the SUBJECTS_DIR environment variable. - views : list | str - The views to use. + %(views)s offset : bool | str If True, shifts the right- or left-most x coordinate of the left and right surfaces, respectively, to be at zero. This is useful for viewing @@ -291,18 +135,16 @@ and ``decimate`` (level of decimation between 0 and 1 or None) of the brain's silhouette to display. If True, the default values are used and if False, no silhouette will be displayed. Defaults to False. - theme : str | path-like - Can be "auto" (default), "light", or "dark" or a path-like to a - custom stylesheet. For Dark-Mode and automatic Dark-Mode-Detection, - :mod:`qdarkstyle` respectively and `darkdetect - `__ is required. + %(theme_3d)s show : bool Display the window as soon as it is ready. Defaults to True. + block : bool + If True, start the Qt application event loop. Default to False. Attributes ---------- geo : dict - A dictionary of pysurfer.Surface objects for each hemisphere. + A dictionary of PyVista surface objects for each hemisphere. overlays : dict The overlays. @@ -314,79 +156,100 @@ .. table:: :widths: auto - +---------------------------+--------------+---------------+ - | 3D function: | surfer.Brain | mne.viz.Brain | - +===========================+==============+===============+ - | add_annotation | ✓ | ✓ | - +---------------------------+--------------+---------------+ - | add_data | ✓ | ✓ | - +---------------------------+--------------+---------------+ - | add_foci | ✓ | ✓ | - +---------------------------+--------------+---------------+ - | add_label | ✓ | ✓ | - +---------------------------+--------------+---------------+ - | add_text | ✓ | ✓ | - +---------------------------+--------------+---------------+ - | close | ✓ | ✓ | - +---------------------------+--------------+---------------+ - | data | ✓ | ✓ | - +---------------------------+--------------+---------------+ - | foci | ✓ | | - +---------------------------+--------------+---------------+ - | labels | ✓ | ✓ | - +---------------------------+--------------+---------------+ - | remove_foci | ✓ | | - +---------------------------+--------------+---------------+ - | remove_labels | ✓ | ✓ | - +---------------------------+--------------+---------------+ - | remove_annotations | - | ✓ | - +---------------------------+--------------+---------------+ - | scale_data_colormap | ✓ | | - +---------------------------+--------------+---------------+ - | save_image | ✓ | ✓ | - +---------------------------+--------------+---------------+ - | save_movie | ✓ | ✓ | - +---------------------------+--------------+---------------+ - | screenshot | ✓ | ✓ | - +---------------------------+--------------+---------------+ - | show_view | ✓ | ✓ | - +---------------------------+--------------+---------------+ - | TimeViewer | ✓ | ✓ | - +---------------------------+--------------+---------------+ - | enable_depth_peeling | | ✓ | - +---------------------------+--------------+---------------+ - | get_picked_points | | ✓ | - +---------------------------+--------------+---------------+ - | add_data(volume) | | ✓ | - +---------------------------+--------------+---------------+ - | view_layout | | ✓ | - +---------------------------+--------------+---------------+ - | flatmaps | | ✓ | - +---------------------------+--------------+---------------+ - | vertex picking | | ✓ | - +---------------------------+--------------+---------------+ - | label picking | | ✓ | - +---------------------------+--------------+---------------+ + +-------------------------------------+--------------+---------------+ + | 3D function: | surfer.Brain | mne.viz.Brain | + +=====================================+==============+===============+ + | :meth:`add_annotation` | ✓ | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`add_data` | ✓ | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`add_dipole` | | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`add_foci` | ✓ | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`add_forward` | | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`add_head` | | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`add_label` | ✓ | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`add_sensors` | | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`add_skull` | | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`add_text` | ✓ | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`add_volume_labels` | | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`close` | ✓ | ✓ | + +-------------------------------------+--------------+---------------+ + | data | ✓ | ✓ | + +-------------------------------------+--------------+---------------+ + | foci | ✓ | | + +-------------------------------------+--------------+---------------+ + | labels | ✓ | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`remove_data` | | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`remove_dipole` | | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`remove_forward` | | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`remove_head` | | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`remove_labels` | ✓ | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`remove_annotations` | - | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`remove_sensors` | | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`remove_skull` | | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`remove_text` | | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`remove_volume_labels` | | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`save_image` | ✓ | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`save_movie` | ✓ | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`screenshot` | ✓ | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`show_view` | ✓ | ✓ | + +-------------------------------------+--------------+---------------+ + | TimeViewer | ✓ | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`get_picked_points` | | ✓ | + +-------------------------------------+--------------+---------------+ + | :meth:`add_data(volume) ` | | ✓ | + +-------------------------------------+--------------+---------------+ + | view_layout | | ✓ | + +-------------------------------------+--------------+---------------+ + | flatmaps | | ✓ | + +-------------------------------------+--------------+---------------+ + | vertex picking | | ✓ | + +-------------------------------------+--------------+---------------+ + | label picking | | ✓ | + +-------------------------------------+--------------+---------------+ """ - def __init__(self, subject_id, hemi, surf, title=None, + def __init__(self, subject_id, hemi='both', surf='pial', title=None, cortex="classic", alpha=1.0, size=800, background="black", foreground=None, figure=None, subjects_dir=None, views='auto', offset='auto', show_toolbar=False, offscreen=False, interaction='trackball', units='mm', - view_layout='vertical', silhouette=False, theme='auto', - show=True): + view_layout='vertical', silhouette=False, theme=None, + show=True, block=False): from ..backends.renderer import backend, _get_renderer - from .._3d import _get_cmap - from matplotlib.colors import colorConverter + if hemi is None: + hemi = 'vol' + hemi = self._check_hemi(hemi, extras=('both', 'split', 'vol')) if hemi in ('both', 'split'): self._hemis = ('lh', 'rh') - elif hemi in ('lh', 'rh'): - self._hemis = (hemi, ) else: - raise KeyError('hemi has to be either "lh", "rh", "split", ' - 'or "both"') + assert hemi in ('lh', 'rh', 'vol') + self._hemis = (hemi, ) self._view_layout = _check_option('view_layout', view_layout, ('vertical', 'horizontal')) @@ -398,19 +261,13 @@ self._title = title self._interaction = 'trackball' - if isinstance(background, str): - background = colorConverter.to_rgb(background) - self._bg_color = background + self._bg_color = _to_rgb(background, name='background') if foreground is None: foreground = 'w' if sum(self._bg_color) < 2 else 'k' - if isinstance(foreground, str): - foreground = colorConverter.to_rgb(foreground) - self._fg_color = foreground - - if isinstance(views, str): - views = [views] + self._fg_color = _to_rgb(foreground, name='foreground') + del background, foreground views = _check_views(surf, views, hemi) - col_dict = dict(lh=1, rh=1, both=1, split=2) + col_dict = dict(lh=1, rh=1, both=1, split=2, vol=1) shape = (len(views), col_dict[hemi]) if self._view_layout == 'horizontal': shape = shape[::-1] @@ -423,9 +280,9 @@ size = size if len(size) == 2 else size * 2 # 1-tuple to 2-tuple subjects_dir = get_subjects_dir(subjects_dir) - self.theme = theme - self.time_viewer = False + self._hash = time.time_ns() + self._block = block self._hemi = hemi self._units = units self._alpha = float(alpha) @@ -438,9 +295,11 @@ self._labels = {'lh': list(), 'rh': list()} self._unnamed_label_id = 0 # can only grow self._annots = {'lh': list(), 'rh': list()} - self._layered_meshes = {} + self._layered_meshes = dict() + self._actors = dict() self._elevation_rng = [15, 165] # range of motion of camera on theta self._lut_locked = None + self._cleaned = False # default values for silhouette self._silhouette = { 'color': self._bg_color, @@ -466,7 +325,7 @@ geo_kwargs = self._cortex_colormap(cortex) # evaluate at the midpoint of the used colormap val = -geo_kwargs['vmin'] / (geo_kwargs['vmax'] - geo_kwargs['vmin']) - self._brain_color = _get_cmap(geo_kwargs['colormap'])(val) + self._brain_color = geo_kwargs['colormap'](val) # load geometry for one or both hemispheres as necessary _validate_type(offset, (str, bool), 'offset') @@ -475,9 +334,9 @@ offset = (surf in ('inflated', 'flat')) offset = None if (not offset or hemi != 'both') else 0.0 logger.debug(f'Hemi offset: {offset}') - + _validate_type(theme, (str, None), 'theme') self._renderer = _get_renderer(name=self._title, size=size, - bgcolor=background, + bgcolor=self._bg_color, shape=shape, fig=figure) self._renderer._window_close_connect(self._clean) @@ -485,16 +344,19 @@ self.plotter = self._renderer.plotter self._setup_canonical_rotation() - for h in self._hemis: + + # plot hemis + for h in ('lh', 'rh'): + if h not in self._hemis: + continue # don't make surface if not chosen # Initialize a Surface object as the geometry - geo = _Surface(subject_id, h, surf, subjects_dir, offset, - units=self._units, x_dir=self._rigid[0, :3]) + geo = _Surface(self._subject_id, h, surf, self._subjects_dir, + offset, units=self._units, x_dir=self._rigid[0, :3]) # Load in the geometry and curvature geo.load_geometry() geo.load_curvature() self.geo[h] = geo - for ri, ci, v in self._iter_views(h): - self._renderer.subplot(ri, ci) + for _, _, v in self._iter_views(h): if self._layered_meshes.get(h) is None: mesh = _LayeredMesh( renderer=self._renderer, @@ -515,7 +377,7 @@ mesh._polydata._hemi = h else: actor = self._layered_meshes[h]._actor - self._renderer.plotter.add_actor(actor) + self._renderer.plotter.add_actor(actor, render=False) if self.silhouette: mesh = self._layered_meshes[h] self._renderer._silhouette( @@ -525,7 +387,8 @@ alpha=self._silhouette["alpha"], decimate=self._silhouette["decimate"], ) - self._renderer.set_camera(**views_dicts[h][v]) + self._renderer.set_camera(update=False, reset_camera=False, + **views_dicts[h][v]) self.interaction = interaction self._closed = False @@ -540,19 +403,14 @@ self._renderer.set_interaction("rubber_band_2d") def _setup_canonical_rotation(self): - from ...coreg import fit_matched_points, _trans_from_params self._rigid = np.eye(4) try: - xfm = read_talxfm(self._subject_id, self._subjects_dir) + xfm = _estimate_talxfm_rigid(self._subject_id, self._subjects_dir) except Exception: - return - # XYZ+origin + halfway - pts_tal = np.concatenate([np.eye(4)[:, :3], np.eye(3) * 0.5]) - pts_subj = apply_trans(invert_transform(xfm), pts_tal) - # we fit with scaling enabled, but then discard it (we just need - # the rigid-body components) - params = fit_matched_points(pts_subj, pts_tal, scale=3, out='params') - self._rigid[:] = _trans_from_params((True, True, False), params[:6]) + logger.info('Could not estimate rigid Talairach alignment, ' + 'using identity matrix') + else: + self._rigid[:] = xfm def setup_time_viewer(self, time_viewer=True, show_traces=True): """Configure the time viewer parameters. @@ -582,13 +440,14 @@ 'Left': Decrease camera azimuth angle 'Right': Increase camera azimuth angle """ + from ..backends._utils import _qt_app_exec if self.time_viewer: return if not self._data: raise ValueError("No data to visualize. See ``add_data``.") self.time_viewer = time_viewer self.orientation = list(_lh_views_dict.keys()) - self.default_smoothing_range = [0, 15] + self.default_smoothing_range = [-1, 15] # Default configuration self.playback = False @@ -676,14 +535,17 @@ # finally, show the MplCanvas if self.show_traces: self.mpl_canvas.show() + if self._block: + _qt_app_exec(self._renderer.figure.store["app"]) @safe_event def _clean(self): # resolve the reference cycle + self._renderer._window_close_disconnect() self.clear_glyphs() self.remove_annotations() # clear init actors - for hemi in self._hemis: + for hemi in self._layered_meshes: self._layered_meshes[hemi]._clean() self._clear_callbacks() self._clear_widgets() @@ -709,6 +571,7 @@ 'picked_renderer', 'act_data_smooth', '_scalar_bar', 'actions', 'widgets', 'geo', '_data'): setattr(self, key, None) + self._cleaned = True def toggle_interface(self, value=None): """Toggle the interface. @@ -951,12 +814,12 @@ hemis_ref = self._hemis orientation_data = [None] * len(rends) for hemi in hemis_ref: - for ri, ci, view in self._iter_views(hemi): + for ri, ci, v in self._iter_views(hemi): idx = self._renderer._loc_to_index((ri, ci)) - if view == 'flat': + if v == 'flat': _data = None else: - _data = dict(default=view, hemi=hemi, row=ri, col=ci) + _data = dict(default=v, hemi=hemi, row=ri, col=ci) orientation_data[idx] = _data self.callbacks["orientation"] = ShowView( brain=self, @@ -1011,6 +874,7 @@ name="↺", callback=self.restore_user_scaling, layout=hlayout, + style='toolbutton', ) for key, char, val in (("fminus", "➖", 1.2 ** -0.25), ("fplus", "➕", 1.2 ** 0.25)): @@ -1022,6 +886,7 @@ name=char, callback=self.callbacks[key], layout=hlayout, + style='toolbutton', ) self._renderer._layout_add_widget(layout, hlayout) @@ -1040,9 +905,13 @@ return layout = self._renderer._dock_add_group_box(name) + weakself = weakref.ref(self) # setup candidate annots - def _set_annot(annot): + def _set_annot(annot, weakself=weakself): + self = weakself() + if self is None: + return self.clear_glyphs() self.remove_labels() self.remove_annotations() @@ -1057,7 +926,10 @@ self._renderer._update() # setup label extraction parameters - def _set_label_mode(mode): + def _set_label_mode(mode, weakself=weakself): + self = weakself() + if self is None: + return if self.traces_mode != 'label': return glyphs = copy.deepcopy(self.picked_patches) @@ -1128,7 +1000,14 @@ self._renderer._dock_finalize() def _configure_playback(self): - self._renderer._playback_initialize(self._play, self.refresh_rate_ms) + self._renderer._playback_initialize( + func=self._play, + timeout=self.refresh_rate_ms, + value=self._data['time_idx'], + rng=[0, len(self._data['time']) - 1], + time_widget=self.widgets["time"], + play_widget=self.widgets["play"], + ) def _configure_mplcanvas(self): # Get the fractional components for the brain and mpl @@ -1170,7 +1049,7 @@ alpha=0.5, ls=':') # now plot the time line - self.plot_time_line() + self.plot_time_line(update=False) # then the picked points for idx, hemi in enumerate(['lh', 'rh', 'vol']): @@ -1199,7 +1078,7 @@ else: mesh = self._layered_meshes[hemi]._polydata vertex_id = vertices[ind[0]] - self._add_vertex_glyph(hemi, mesh, vertex_id) + self._add_vertex_glyph(hemi, mesh, vertex_id, update=False) def _configure_picking(self): # get data for each hemi @@ -1227,27 +1106,42 @@ ) def _configure_tool_bar(self): - self._renderer._tool_bar_load_icons() - self._renderer._tool_bar_set_theme(self.theme) self._renderer._tool_bar_initialize(name="Toolbar") + weakself = weakref.ref(self) + + def save_image(filename, weakself=weakself): + self = weakself() + if self is None: + return + self.save_image(filename) + self._renderer._tool_bar_add_file_button( name="screenshot", desc="Take a screenshot", - func=self.save_image, + func=save_image, ) + + def save_movie(filename, weakself=weakself): + self = weakself() + if self is None: + return + self.save_movie( + filename=filename, + time_dilation=(1. / self.playback_speed)) + self._renderer._tool_bar_add_file_button( name="movie", desc="Save movie...", - func=self.save_movie, + func=save_movie, shortcut="ctrl+shift+s", ) self._renderer._tool_bar_add_button( name="visibility", - desc="Toggle Visibility", + desc="Toggle Controls", func=self.toggle_interface, icon_name="visibility_on" ) - self._renderer._tool_bar_add_button( + self.widgets["play"] = self._renderer._tool_bar_add_play_button( name="play", desc="Play/Pause", func=self.toggle_playback, @@ -1345,7 +1239,14 @@ if self._mouse_no_mvt > 0: x, y = vtk_picker.GetEventPosition() # programmatically detect the picked renderer - self.picked_renderer = self.plotter.iren.FindPokedRenderer(x, y) + try: + # pyvista<0.30.0 + self.picked_renderer = \ + self.plotter.iren.FindPokedRenderer(x, y) + except AttributeError: + # pyvista>=0.30.0 + self.picked_renderer = \ + self.plotter.iren.interactor.FindPokedRenderer(x, y) # trigger the pick self.plotter.picker.Pick(x, y, 0, self.picked_renderer) self._mouse_no_mvt = 0 @@ -1401,7 +1302,7 @@ grid = mesh = self._data[hemi]['grid'] vertices = self._data[hemi]['vertices'] coords = self._data[hemi]['grid_coords'][vertices] - scalars = grid.cell_arrays['values'][vertices] + scalars = _cell_data(grid)['values'][vertices] spacing = np.array(grid.GetSpacing()) max_dist = np.linalg.norm(spacing) / 2. origin = vtk_picker.GetRenderer().GetActiveCamera().GetPosition() @@ -1417,7 +1318,7 @@ # useful for debugging the ray by mapping it into the volume: # dists = dists - dists.min() # dists = (1. - dists / dists.max()) * self._cmap_range[1] - # grid.cell_arrays['values'][vertices] = dists * mask + # _cell_data(grid)['values'][vertices] = dists * mask idx = idx[np.argmax(np.abs(scalars[idx]))] vertex_id = vertices[idx] # Naive way: convert pos directly to idx; i.e., apply mri_src_t @@ -1462,7 +1363,7 @@ self._layered_meshes[hemi].remove_overlay(label.name) self.picked_patches[hemi].remove(label_id) - def _add_vertex_glyph(self, hemi, mesh, vertex_id): + def _add_vertex_glyph(self, hemi, mesh, vertex_id, update=True): if vertex_id in self.picked_points[hemi]: return @@ -1470,7 +1371,7 @@ if self.act_data_smooth[hemi][0] is None: return color = next(self.color_cycle) - line = self.plot_time_course(hemi, vertex_id, color) + line = self.plot_time_course(hemi, vertex_id, color, update=update) if hemi == 'vol': ijk = np.unravel_index( vertex_id, np.array(mesh.GetDimensions()) - 1, order='F') @@ -1498,8 +1399,7 @@ actors = list() spheres = list() - for ri, ci, _ in self._iter_views(hemi): - self.plotter.subplot(ri, ci) + for _ in self._iter_views(hemi): # Using _sphere() instead of renderer.sphere() for 2 reasons: # 1) renderer.sphere() fails on Windows in a scenario where a lot # of picking requests are done in a short span of time (could be @@ -1547,9 +1447,11 @@ self.color_cycle.restore(color) for sphere in spheres: # remove all actors - self.plotter.remove_actor(sphere._actors, render=render) + self.plotter.remove_actor(sphere._actors, render=False) sphere._actors = None self._spheres.pop(self._spheres.index(sphere)) + if render: + self._renderer._update() self.pick_table.pop(vertex_id) def clear_glyphs(self): @@ -1570,7 +1472,7 @@ self.rms = None self._renderer._update() - def plot_time_course(self, hemi, vertex_id, color): + def plot_time_course(self, hemi, vertex_id, color, update=True): """Plot the vertex time course. Parameters @@ -1581,6 +1483,8 @@ The vertex identifier in the mesh. color : matplotlib color The color of the time course. + update : bool + Force an update of the plot. Defaults to True. Returns ------- @@ -1629,11 +1533,18 @@ lw=1., color=color, zorder=4, + update=update, ) return line - def plot_time_line(self): - """Add the time line to the MPL widget.""" + def plot_time_line(self, update=True): + """Add the time line to the MPL widget. + + Parameters + ---------- + update : bool + Force an update of the plot. Defaults to True. + """ if self.mpl_canvas is None: return if isinstance(self.show_traces, bool) and self.show_traces: @@ -1645,9 +1556,11 @@ label='time', color=self._fg_color, lw=1, + update=update, ) self.time_line.set_xdata(current_time) - self.mpl_canvas.update_plot() + if update: + self.mpl_canvas.update_plot() def _configure_help(self): pairs = [ @@ -1692,19 +1605,14 @@ self.callbacks.clear() # Remove the default key binding if getattr(self, "iren", None) is not None: - try: - # pyvista<0.30.0 - self.plotter._key_press_event_callbacks.clear() - except AttributeError: - # pyvista>=0.30.0 - self.plotter.iren.clear_key_event_callbacks() + self.plotter.iren.clear_key_event_callbacks() def _clear_widgets(self): if not hasattr(self, 'widgets'): return for widget in self.widgets.values(): if widget is not None: - for key in ('triggered', 'valueChanged'): + for key in ('triggered', 'floatValueChanged'): setattr(widget, key, None) self.widgets.clear() @@ -1718,12 +1626,13 @@ """Set the interaction style.""" _validate_type(interaction, str, 'interaction') _check_option('interaction', interaction, ('trackball', 'terrain')) - for ri, ci, _ in self._iter_views('vol'): # will traverse all - self._renderer.subplot(ri, ci) + for _ in self._iter_views('vol'): # will traverse all self._renderer.set_interaction(interaction) def _cortex_colormap(self, cortex): """Return the colormap corresponding to the cortex.""" + from .._3d import _get_cmap + from matplotlib.colors import ListedColormap colormap_map = dict(classic=dict(colormap="Greys", vmin=-1, vmax=2), high_contrast=dict(colormap="Greys", @@ -1733,7 +1642,47 @@ bone=dict(colormap="bone_r", vmin=-.2, vmax=2), ) - return colormap_map[cortex] + _validate_type(cortex, (str, dict, list, tuple), 'cortex') + if isinstance(cortex, str): + if cortex in colormap_map: + cortex = colormap_map[cortex] + else: + cortex = [cortex] * 2 + if isinstance(cortex, (list, tuple)): + _check_option('len(cortex)', len(cortex), (2, 3), + extra='when cortex is a list or tuple') + if len(cortex) == 3: + cortex = [cortex] * 2 + cortex = list(cortex) + for ci, c in enumerate(cortex): + cortex[ci] = _to_rgb(c, name='cortex') + cortex = dict( + colormap=ListedColormap(cortex, name='custom binary'), + vmin=0, vmax=1) + cortex = dict( + vmin=float(cortex['vmin']), + vmax=float(cortex['vmax']), + colormap=_get_cmap(cortex['colormap']), + ) + return cortex + + def _remove(self, item, render=False): + """Remove actors from the rendered scene.""" + if item in self._actors: + logger.debug( + f'Removing {len(self._actors[item])} {item} actor(s)') + for actor in self._actors[item]: + self._renderer.plotter.remove_actor(actor, render=False) + self._actors.pop(item) # remove actor list + if render: + self._renderer._update() + + def _add_actor(self, item, actor): + """Add an actor to the internal register.""" + if item in self._actors: # allows adding more than one + self._actors[item].append(actor) + else: + self._actors[item] = [actor] @verbose def add_data(self, array, fmin=None, fmid=None, fmax=None, @@ -1814,7 +1763,7 @@ Original clim arguments. %(src_volume_options)s colorbar_kwargs : dict | None - Options to pass to :meth:`pyvista.BasePlotter.add_scalar_bar` + Options to pass to :meth:`pyvista.Plotter.add_scalar_bar` (e.g., ``dict(title_font_size=10)``). %(verbose)s @@ -1827,7 +1776,7 @@ in which case only as many smoothing steps are applied until the whole surface is filled with non-zeros. - Due to a Mayavi (or VTK) alpha rendering bug, ``vector_alpha`` is + Due to a VTK alpha rendering bug, ``vector_alpha`` is clamped to be strictly < 1. """ _validate_type(transparent, bool, 'transparent') @@ -1898,7 +1847,7 @@ if smoothing_steps is None: smoothing_steps = 7 elif smoothing_steps == 'nearest': - smoothing_steps = 0 + smoothing_steps = -1 elif isinstance(smoothing_steps, int): if smoothing_steps < 0: raise ValueError('Expected value of `smoothing_steps` is' @@ -1936,14 +1885,14 @@ # 1) add the surfaces first actor = None - for ri, ci, _ in self._iter_views(hemi): - self._renderer.subplot(ri, ci) + for _ in self._iter_views(hemi): if hemi in ('lh', 'rh'): actor = self._layered_meshes[hemi]._actor else: src_vol = src[2:] if src.kind == 'mixed' else src actor, _ = self._add_volume_data(hemi, src_vol, volume_options) assert actor is not None # should have added one + self._add_actor('data', actor) # 2) update time and smoothing properties # set_data_smoothing calls "set_time_point" for us, which will set @@ -1953,10 +1902,9 @@ # 3) add the other actors if colorbar is True: - # botto left by default + # bottom left by default colorbar = (self._subplot_shape[0] - 1, 0) for ri, ci, v in self._iter_views(hemi): - self._renderer.subplot(ri, ci) # Add the time label to the bottommost view do = (ri, ci) == colorbar if not self._time_label_added and time_label is not None and do: @@ -1974,29 +1922,31 @@ bgcolor=self._brain_color[:3]) kwargs.update(colorbar_kwargs or {}) self._scalar_bar = self._renderer.scalarbar(**kwargs) - self._renderer.set_camera(**views_dicts[hemi][v]) + self._renderer.set_camera( + update=False, reset_camera=False, **views_dicts[hemi][v]) # 4) update the scalar bar and opacity self.update_lut(alpha=alpha) + def remove_data(self): + """Remove rendered data from the mesh.""" + self._remove('data', render=True) + def _iter_views(self, hemi): - # which rows and columns each type of visual needs to be added to + """Iterate over rows and columns that need to be added to.""" + hemi_dict = dict(lh=[0], rh=[0], vol=[0]) if self._hemi == 'split': - hemi_dict = dict(lh=[0], rh=[1], vol=[0, 1]) - else: - hemi_dict = dict(lh=[0], rh=[0], vol=[0]) + hemi_dict.update(rh=[1], vol=[0, 1]) for vi, view in enumerate(self._views): + view_dict = dict(lh=[vi], rh=[vi], vol=[vi]) if self._hemi == 'split': - view_dict = dict(lh=[vi], rh=[vi], vol=[vi, vi]) - else: - view_dict = dict(lh=[vi], rh=[vi], vol=[vi]) + view_dict.update(vol=[vi, vi]) if self._view_layout == 'vertical': - rows = view_dict # views are rows - cols = hemi_dict # hemis are columns + rows, cols = view_dict, hemi_dict # views are rows, hemis cols else: - rows = hemi_dict # hemis are rows - cols = view_dict # views are columns + rows, cols = hemi_dict, view_dict # hemis are rows, views cols for ri, ci in zip(rows[hemi], cols[hemi]): + self._renderer.subplot(ri, ci) yield ri, ci, view def remove_labels(self): @@ -2011,12 +1961,15 @@ def remove_annotations(self): """Remove all annotations from the image.""" for hemi in self._hemis: - mesh = self._layered_meshes[hemi] - mesh.remove_overlay(self._annots[hemi]) - self._annots[hemi].clear() + if hemi in self._layered_meshes: + mesh = self._layered_meshes[hemi] + mesh.remove_overlay(self._annots[hemi]) + if hemi in self._annots: + self._annots[hemi].clear() self._renderer._update() def _add_volume_data(self, hemi, src, volume_options): + from ..backends._pyvista import _hide_testing_actor _validate_type(src, SourceSpaces, 'src') _check_option('src.kind', src.kind, ('volume',)) _validate_type( @@ -2098,28 +2051,31 @@ self._data[hemi]['grid_volume_pos'] = volume_pos self._data[hemi]['grid_volume_neg'] = volume_neg actor_pos, _ = self._renderer.plotter.add_actor( - volume_pos, reset_camera=False, name=None, culling=False) + volume_pos, reset_camera=False, name=None, culling=False, + render=False) + actor_neg = actor_mesh = None if volume_neg is not None: actor_neg, _ = self._renderer.plotter.add_actor( - volume_neg, reset_camera=False, name=None, culling=False) - else: - actor_neg = None + volume_neg, reset_camera=False, name=None, culling=False, + render=False) grid_mesh = self._data[hemi]['grid_mesh'] if grid_mesh is not None: - _, prop = self._renderer.plotter.add_actor( + actor_mesh, prop = self._renderer.plotter.add_actor( grid_mesh, reset_camera=False, name=None, culling=False, - pickable=False) + pickable=False, render=False) prop.SetColor(*self._brain_color[:3]) prop.SetOpacity(surface_alpha) if silhouette_alpha > 0 and silhouette_linewidth > 0: - for ri, ci, v in self._iter_views('vol'): - self._renderer.subplot(ri, ci) + for _ in self._iter_views('vol'): self._renderer._silhouette( mesh=grid_mesh.GetInput(), color=self._brain_color[:3], line_width=silhouette_linewidth, alpha=silhouette_alpha, ) + for actor in (actor_pos, actor_neg, actor_mesh): + if actor is not None: + _hide_testing_actor(actor) return actor_pos, actor_neg @@ -2164,7 +2120,6 @@ ----- To remove previously added labels, run Brain.remove_labels(). """ - from matplotlib.colors import colorConverter from ...label import read_label if isinstance(label, str): if color is None: @@ -2220,9 +2175,6 @@ if scalar_thresh is not None: ids = ids[scalars >= scalar_thresh] - scalars = np.zeros(self.geo[hemi].coords.shape[0]) - scalars[ids] = 1 - if self.time_viewer and self.show_traces \ and self.traces_mode == 'label': stc = self._data["stc"] @@ -2238,30 +2190,26 @@ line = None orig_color = color - color = colorConverter.to_rgba(color, alpha) + color = _to_rgb(color, alpha, alpha=True) cmap = np.array([(0, 0, 0, 0,), color]) ctable = np.round(cmap * 255).astype(np.uint8) - for ri, ci, v in self._iter_views(hemi): - self._renderer.subplot(ri, ci) - if borders: - n_vertices = scalars.size - edges = mesh_edges(self.geo[hemi].faces) - edges = edges.tocoo() - border_edges = scalars[edges.row] != scalars[edges.col] - show = np.zeros(n_vertices, dtype=np.int64) - keep_idx = np.unique(edges.row[border_edges]) - if isinstance(borders, int): - for _ in range(borders): - keep_idx = np.in1d( - self.geo[hemi].faces.ravel(), keep_idx) - keep_idx.shape = self.geo[hemi].faces.shape - keep_idx = self.geo[hemi].faces[np.any( - keep_idx, axis=1)] - keep_idx = np.unique(keep_idx) - show[keep_idx] = 1 - scalars *= show - + scalars = np.zeros(self.geo[hemi].coords.shape[0]) + scalars[ids] = 1 + if borders: + keep_idx = _mesh_borders(self.geo[hemi].faces, scalars) + show = np.zeros(scalars.size, dtype=np.int64) + if isinstance(borders, int): + for _ in range(borders): + keep_idx = np.in1d( + self.geo[hemi].faces.ravel(), keep_idx) + keep_idx.shape = self.geo[hemi].faces.shape + keep_idx = self.geo[hemi].faces[np.any( + keep_idx, axis=1)] + keep_idx = np.unique(keep_idx) + show[keep_idx] = 1 + scalars *= show + for _, _, v in self._iter_views(hemi): mesh = self._layered_meshes[hemi] mesh.add_overlay( scalars=scalars, @@ -2271,7 +2219,7 @@ name=label_name, ) if reset_camera: - self._renderer.set_camera(**views_dicts[hemi][v]) + self._renderer.set_camera(update=False, **views_dicts[hemi][v]) if self.time_viewer and self.show_traces \ and self.traces_mode == 'label': label._color = orig_color @@ -2279,6 +2227,271 @@ self._labels[hemi].append(label) self._renderer._update() + @fill_doc + def add_forward(self, fwd, trans, alpha=1, scale=None): + """Add a quiver to render positions of dipoles. + + Parameters + ---------- + %(fwd)s + %(trans_not_none)s + %(alpha)s Default 1. + scale : None | float + The size of the arrow representing the dipoles in + :class:`mne.viz.Brain` units. Default 1.5mm. + + Notes + ----- + .. versionadded:: 1.0 + """ + head_mri_t = _get_trans(trans, 'head', 'mri', allow_none=False)[0] + del trans + if scale is None: + scale = 1.5 if self._units == 'mm' else 1.5e-3 + error_msg = ('Unexpected forward model coordinate frame ' + '{}, must be "head" or "mri"') + if fwd['coord_frame'] in _frame_to_str: + fwd_frame = _frame_to_str[fwd['coord_frame']] + if fwd_frame == 'mri': + fwd_trans = Transform('mri', 'mri') + elif fwd_frame == 'head': + fwd_trans = head_mri_t + else: + raise RuntimeError(error_msg.format(fwd_frame)) + else: + raise RuntimeError(error_msg.format(fwd['coord_frame'])) + for actor in _plot_forward( + self._renderer, fwd, fwd_trans, + fwd_scale=1e3 if self._units == 'mm' else 1, + scale=scale, alpha=alpha): + self._add_actor('forward', actor) + + self._renderer._update() + + def remove_forward(self): + """Remove forward sources from the rendered scene.""" + self._remove('forward', render=True) + + @fill_doc + def add_dipole(self, dipole, trans, colors='red', alpha=1, scales=None): + """Add a quiver to render positions of dipoles. + + Parameters + ---------- + dipole : instance of Dipole + Dipole object containing position, orientation and amplitude of + one or more dipoles or in the forward solution. + %(trans_not_none)s + colors : list | matplotlib-style color | None + A single color or list of anything matplotlib accepts: + string, RGB, hex, etc. Default red. + %(alpha)s Default 1. + scales : list | float | None + The size of the arrow representing the dipole in + :class:`mne.viz.Brain` units. Default 5mm. + + Notes + ----- + .. versionadded:: 1.0 + """ + head_mri_t = _get_trans(trans, 'head', 'mri', allow_none=False)[0] + del trans + n_dipoles = len(dipole) + if not isinstance(colors, (list, tuple)): + colors = [colors] * n_dipoles # make into list + if len(colors) != n_dipoles: + raise ValueError(f'The number of colors ({len(colors)}) ' + f'and dipoles ({n_dipoles}) must match') + colors = [_to_rgb(color, name=f'colors[{ci}]') + for ci, color in enumerate(colors)] + if scales is None: + scales = 5 if self._units == 'mm' else 5e-3 + if not isinstance(scales, (list, tuple)): + scales = [scales] * n_dipoles # make into list + if len(scales) != n_dipoles: + raise ValueError(f'The number of scales ({len(scales)}) ' + f'and dipoles ({n_dipoles}) must match') + pos = apply_trans(head_mri_t, dipole.pos) + pos *= 1e3 if self._units == 'mm' else 1 + for _ in self._iter_views('vol'): + for this_pos, this_ori, color, scale in zip( + pos, dipole.ori, colors, scales): + actor, _ = self._renderer.quiver3d( + *this_pos, *this_ori, color=color, opacity=alpha, + mode='arrow', scale=scale) + self._add_actor('dipole', actor) + + self._renderer._update() + + def remove_dipole(self): + """Remove dipole objects from the rendered scene.""" + self._remove('dipole', render=True) + + @fill_doc + def add_head(self, dense=True, color='gray', alpha=0.5): + """Add a mesh to render the outer head surface. + + Parameters + ---------- + dense : bool + Whether to plot the dense head (``seghead``) or the less dense head + (``head``). + %(color_matplotlib)s + %(alpha)s + + Notes + ----- + .. versionadded:: 0.24 + """ + # load head + surf = _get_head_surface('seghead' if dense else 'head', + self._subject_id, self._subjects_dir) + verts, triangles = surf['rr'], surf['tris'] + verts *= 1e3 if self._units == 'mm' else 1 + color = _to_rgb(color) + + for _ in self._iter_views('vol'): + actor, _ = self._renderer.mesh( + *verts.T, triangles=triangles, color=color, + opacity=alpha, reset_camera=False, render=False) + self._add_actor('head', actor) + + self._renderer._update() + + def remove_head(self): + """Remove head objects from the rendered scene.""" + self._remove('head', render=True) + + @fill_doc + def add_skull(self, outer=True, color='gray', alpha=0.5): + """Add a mesh to render the skull surface. + + Parameters + ---------- + outer : bool + Adds the outer skull if ``True``, otherwise adds the inner skull. + %(color_matplotlib)s + %(alpha)s + + Notes + ----- + .. versionadded:: 0.24 + """ + surf = _get_skull_surface('outer' if outer else 'inner', + self._subject_id, self._subjects_dir) + verts, triangles = surf['rr'], surf['tris'] + verts *= 1e3 if self._units == 'mm' else 1 + color = _to_rgb(color) + + for _ in self._iter_views('vol'): + actor, _ = self._renderer.mesh( + *verts.T, triangles=triangles, color=color, + opacity=alpha, reset_camera=False, render=False) + self._add_actor('skull', actor) + + self._renderer._update() + + def remove_skull(self): + """Remove skull objects from the rendered scene.""" + self._remove('skull', render=True) + + @fill_doc + def add_volume_labels(self, aseg='aparc+aseg', labels=None, colors=None, + alpha=0.5, smooth=0.9, fill_hole_size=None, + legend=None): + """Add labels to the rendering from an anatomical segmentation. + + Parameters + ---------- + %(aseg)s + labels : list + Labeled regions of interest to plot. See + :func:`mne.get_montage_volume_labels` + for one way to determine regions of interest. Regions can also be + chosen from the :term:`FreeSurfer LUT`. + colors : list | matplotlib-style color | None + A list of anything matplotlib accepts: string, RGB, hex, etc. + (default :term:`FreeSurfer LUT` colors). + %(alpha)s + %(smooth)s + fill_hole_size : int | None + The size of holes to remove in the mesh in voxels. Default is None, + no holes are removed. Warning, this dilates the boundaries of the + surface by ``fill_hole_size`` number of voxels so use the minimal + size. + legend : bool | None | dict + Add a legend displaying the names of the ``labels``. Default (None) + is ``True`` if the number of ``labels`` is 10 or fewer. + Can also be a dict of ``kwargs`` to pass to + :meth:`pyvista.Plotter.add_legend`. + + Notes + ----- + .. versionadded:: 0.24 + """ + import nibabel as nib + + # load anatomical segmentation image + if not aseg.endswith('aseg'): + raise RuntimeError( + f'`aseg` file path must end with "aseg", got {aseg}') + aseg = _check_fname(op.join(self._subjects_dir, self._subject_id, + 'mri', aseg + '.mgz'), + overwrite='read', must_exist=True) + aseg_fname = aseg + aseg = nib.load(aseg_fname) + aseg_data = np.asarray(aseg.dataobj) + vox_mri_t = aseg.header.get_vox2ras_tkr() + mult = 1e-3 if self._units == 'm' else 1 + vox_mri_t[:3] *= mult + del aseg + + # read freesurfer lookup table + lut, fs_colors = read_freesurfer_lut() + if labels is None: # assign default ROI labels based on indices + lut_r = {v: k for k, v in lut.items()} + labels = [lut_r[idx] for idx in DEFAULTS['volume_label_indices']] + + _validate_type(fill_hole_size, (int, None), 'fill_hole_size') + _validate_type(legend, (bool, None), 'legend') + if legend is None: + legend = len(labels) < 11 + + if colors is None: + colors = [fs_colors[label] / 255 for label in labels] + elif not isinstance(colors, (list, tuple)): + colors = [colors] * len(labels) # make into list + colors = [_to_rgb(color, name=f'colors[{ci}]') + for ci, color in enumerate(colors)] + surfs = _marching_cubes( + aseg_data, [lut[label] for label in labels], smooth=smooth, + fill_hole_size=fill_hole_size) + for label, color, (verts, triangles) in zip(labels, colors, surfs): + if len(verts) == 0: # not in aseg vals + warn(f'Value {lut[label]} not found for label ' + f'{repr(label)} in: {aseg_fname}') + continue + verts = apply_trans(vox_mri_t, verts) + for _ in self._iter_views('vol'): + actor, _ = self._renderer.mesh( + *verts.T, triangles=triangles, color=color, + opacity=alpha, reset_camera=False, render=False) + self._add_actor('volume_labels', actor) + + if legend or isinstance(legend, dict): + # use empty kwargs for legend = True + legend = legend if isinstance(legend, dict) else dict() + self._renderer.plotter.add_legend( + list(zip(labels, colors)), **legend) + + self._renderer._update() + + def remove_volume_labels(self): + """Remove the volume labels from the rendered scene.""" + self._remove('volume_labels', render=True) + self._renderer.plotter.remove_legend() + + @fill_doc def add_foci(self, coords, coords_as_verts=False, map_surface=None, scale_factor=1, color="white", alpha=1, name=None, hemi=None, resolution=50): @@ -2297,14 +2510,14 @@ vertex ids (with ``coord_as_verts=True``). coords_as_verts : bool Whether the coords parameter should be interpreted as vertex ids. - map_surface : None - Surface to map coordinates through, or None to use raw coords. + map_surface : str | None + Surface to project the coordinates to, or None to use raw coords. + When set to a surface, each foci is positioned at the closest + vertex in the mesh. scale_factor : float Controls the size of the foci spheres (relative to 1cm). - color : matplotlib color code - HTML name, RBG tuple, or hex code. - alpha : float in [0, 1] - Opacity of focus gylphs. + %(color_matplotlib)s + %(alpha)s Default is 1. name : str Internal name to use. hemi : str | None @@ -2314,31 +2527,125 @@ resolution : int The resolution of the spheres. """ - from matplotlib.colors import colorConverter hemi = self._check_hemi(hemi, extras=['vol']) - # those parameters are not supported yet, only None is allowed - _check_option('map_surface', map_surface, [None]) - # Figure out how to interpret the first parameter if coords_as_verts: coords = self.geo[hemi].coords[coords] + map_surface = None + + # Possibly map the foci coords through a surface + if map_surface is not None: + from scipy.spatial.distance import cdist + foci_surf = _Surface(self._subject_id, hemi, map_surface, + self._subjects_dir, offset=0, + units=self._units, x_dir=self._rigid[0, :3]) + foci_surf.load_geometry() + foci_vtxs = np.argmin(cdist(foci_surf.coords, coords), axis=0) + coords = self.geo[hemi].coords[foci_vtxs] # Convert the color code - if not isinstance(color, tuple): - color = colorConverter.to_rgb(color) + color = _to_rgb(color) if self._units == 'm': scale_factor = scale_factor / 1000. - for ri, ci, v in self._iter_views(hemi): - self._renderer.subplot(ri, ci) + + for _, _, v in self._iter_views(hemi): self._renderer.sphere(center=coords, color=color, scale=(10. * scale_factor), opacity=alpha, resolution=resolution) self._renderer.set_camera(**views_dicts[hemi][v]) + # Store the foci in the Brain._data dictionary + data_foci = coords + if 'foci' in self._data.get(hemi, []): + data_foci = np.vstack((self._data[hemi]['foci'], data_foci)) + self._data[hemi] = self._data.get(hemi, dict()) # no data added yet + self._data[hemi]['foci'] = data_foci + + @verbose + def add_sensors(self, info, trans, meg=None, eeg='original', fnirs=True, + ecog=True, seeg=True, dbs=True, verbose=None): + """Add mesh objects to represent sensor positions. + + Parameters + ---------- + %(info_not_none)s + %(trans_not_none)s + %(meg)s + %(eeg)s + %(fnirs)s + %(ecog)s + %(seeg)s + %(dbs)s + %(verbose)s + + Notes + ----- + .. versionadded:: 0.24 + """ + _validate_type(info, Info, 'info') + meg, eeg, fnirs, warn_meg = _handle_sensor_types(meg, eeg, fnirs) + picks = pick_types(info, meg=('sensors' in meg), + ref_meg=('ref' in meg), eeg=(len(eeg) > 0), + ecog=ecog, seeg=seeg, dbs=dbs, + fnirs=(len(fnirs) > 0)) + head_mri_t = _get_trans(trans, 'head', 'mri', allow_none=False)[0] + del trans + # get transforms to "mri"window + to_cf_t = _get_transforms_to_coord_frame( + info, head_mri_t, coord_frame='mri') + if pick_types(info, eeg=True, exclude=()).size > 0 and \ + 'projected' in eeg: + head_surf = _get_head_surface( + 'seghead', self._subject_id, self._subjects_dir) + else: + head_surf = None + # Do the main plotting + for _ in self._iter_views('vol'): + if picks.size > 0: + sensors_actors = _plot_sensors( + self._renderer, info, to_cf_t, picks, meg, eeg, + fnirs, warn_meg, head_surf, self._units) + for item, actors in sensors_actors.items(): + for actor in actors: + self._add_actor(item, actor) + + if 'helmet' in meg and pick_types(info, meg=True).size > 0: + surf = get_meg_helmet_surf(info, head_mri_t) + verts = surf['rr'] * (1 if self._units == 'm' else 1e3) + actor, _ = self._renderer.mesh( + *verts.T, surf['tris'], + color=DEFAULTS['coreg']['helmet_color'], + opacity=0.25, reset_camera=False, render=False) + self._add_actor('helmet', actor) + + self._renderer._update() + + def remove_sensors(self, kind=None): + """Remove sensors from the rendered scene. + + Parameters + ---------- + kind : str | list | None + If None, removes all sensor-related data including the helmet. + Can be "meg", "eeg", "fnirs", "ecog", "seeg", "dbs" or "helmet" + to remove that item. + """ + all_kinds = ('meg', 'eeg', 'fnirs', 'ecog', 'seeg', 'dbs', 'helmet') + if kind is None: + for item in all_kinds: + self._remove(item, render=False) + else: + if isinstance(kind, str): + kind = [kind] + for this_kind in kind: + _check_option('kind', this_kind, all_kinds) + self._remove(this_kind, render=False) + self._renderer._update() + def add_text(self, x, y, text, name=None, color=None, opacity=1.0, - row=-1, col=-1, font_size=None, justification=None): + row=0, col=0, font_size=None, justification=None): """Add a text to the visualization. Parameters @@ -2357,21 +2664,51 @@ background color). opacity : float Opacity of the text (default 1.0). - row : int - Row index of which brain to use. - col : int - Column index of which brain to use. + row : int | None + Row index of which brain to use. Default is the top row. + col : int | None + Column index of which brain to use. Default is the left-most + column. font_size : float | None The font size to use. justification : str | None The text justification. """ - # XXX: support `name` should be added when update_text/remove_text - # are implemented - # _check_option('name', name, [None]) + _validate_type(name, (str, None), 'name') + name = text if name is None else name + if 'text' in self._actors and name in self._actors['text']: + raise ValueError(f'Text with the name {name} already exists') + for ri, ci, _ in self._iter_views('vol'): + if (row is None or row == ri) and (col is None or col == ci): + actor = self._renderer.text2d( + x_window=x, y_window=y, text=text, color=color, + size=font_size, justification=justification) + if 'text' not in self._actors: + self._actors['text'] = dict() + self._actors['text'][name] = actor - self._renderer.text2d(x_window=x, y_window=y, text=text, color=color, - size=font_size, justification=justification) + def remove_text(self, name=None): + """Remove text from the rendered scene. + + Parameters + ---------- + name : str | None + Remove specific text by name. If None, all text will be removed. + """ + _validate_type(name, (str, None), 'name') + if name is None: + for actor in self._actors['text'].values(): + self._renderer.plotter.remove_actor(actor, render=False) + self._actors.pop('text') + else: + names = [None] + if 'text' in self._actors: + names += list(self._actors['text'].keys()) + _check_option('name', name, names) + self._renderer.plotter.remove_actor( + self._actors['text'][name], render=False) + self._actors['text'].pop(name) + self._renderer._update() def _configure_label_time_course(self): from ...label import read_labels_from_annot @@ -2385,7 +2722,7 @@ self.add_annotation(self.annot, color="w", alpha=0.75) # now plot the time line - self.plot_time_line() + self.plot_time_line(update=False) self.mpl_canvas.update_plot() for hemi in self._hemis: @@ -2401,8 +2738,9 @@ for idx, label in enumerate(labels): self._vertex_to_label_id[hemi][label.vertices] = idx + @fill_doc def add_annotation(self, annot, borders=True, alpha=1, hemi=None, - remove_existing=True, color=None, **kwargs): + remove_existing=True, color=None): """Add an annotation file. Parameters @@ -2418,8 +2756,7 @@ Show only label borders. If int, specify the number of steps (away from the true border) along the cortical mesh to include as part of the border definition. - alpha : float in [0, 1] - Alpha level to control opacity. + %(alpha)s Default is 1. hemi : str | None If None, it is assumed to belong to the hemipshere being shown. If two hemispheres are being shown, data must exist @@ -2429,9 +2766,6 @@ color : matplotlib-style color code If used, show all annotations in the same (specified) color. Probably useful only when showing annotation borders. - **kwargs : dict - These are passed to the underlying - ``mayavi.mlab.pipeline.surface`` call. """ from ...label import _read_annot hemis = self._check_hemis(hemi) @@ -2498,13 +2832,11 @@ # Override the cmap when a single color is used if color is not None: - from matplotlib.colors import colorConverter - rgb = np.round(np.multiply(colorConverter.to_rgb(color), 255)) + rgb = np.round(np.multiply(_to_rgb(color), 255)) cmap[:, :3] = rgb.astype(cmap.dtype) ctable = cmap.astype(np.float64) - for ri, ci, _ in self._iter_views(hemi): - self._renderer.subplot(ri, ci) + for _ in self._iter_views(hemi): mesh = self._layered_meshes[hemi] mesh.add_overlay( scalars=ids, @@ -2527,32 +2859,100 @@ def show(self): """Display the window.""" + from ..backends._utils import _qt_app_exec self._renderer.show() + if self._block: + _qt_app_exec(self._renderer.figure.store["app"]) - def show_view(self, view=None, roll=None, distance=None, row=0, col=0, - hemi=None, align=True): - """Orient camera to display view. + @fill_doc + def get_view(self, row=0, col=0): + """Get the camera orientation for a given subplot display. Parameters ---------- - view : str | dict - String view, or a dict with azimuth and elevation. - roll : float | None - The roll. - distance : float | None - The distance. row : int - The row to set. + The row to use, default is the first one. col : int - The column to set. - hemi : str - Which hemi to use for string lookup (when in "both" mode). - align : bool - If True, consider view arguments relative to canonical MRI - directions (closest to MNI for the subject) rather than native MRI - space. This helps when MRIs are not in standard orientation (e.g., - have large rotations). + The column to check, the default is the first one. + + Returns + ------- + %(roll)s + %(distance)s + %(azimuth)s + %(elevation)s + %(focalpoint)s + """ + row = _ensure_int(row, 'row') + col = _ensure_int(col, 'col') + for h in self._hemis: + for ri, ci, _ in self._iter_views(h): + if (row == ri) and (col == ci): + return self._renderer.get_camera() + return (None,) * 5 + + @fill_doc + def show_view(self, view=None, roll=None, distance=None, *, + row=None, col=None, hemi=None, align=True, + azimuth=None, elevation=None, focalpoint=None): + """Orient camera to display view. + + Parameters + ---------- + %(view)s + %(roll)s + %(distance)s + row : int | None + The row to set. Default all rows. + col : int | None + The column to set. Default all columns. + hemi : str | None + Which hemi to use for view lookup (when in "both" mode). + %(align_view)s + %(azimuth)s + %(elevation)s + %(focalpoint)s + + Notes + ----- + The builtin string views are the following perspectives, based on the + :term:`RAS` convention. If not otherwise noted, the view will have the + top of the brain (superior, +Z) in 3D space shown upward in the 2D + perspective: + + ``'lateral'`` + From the left or right side such that the lateral (outside) + surface of the given hemisphere is visible. + ``'medial'`` + From the left or right side such that the medial (inside) + surface of the given hemisphere is visible (at least when in split + or single-hemi mode). + ``'rostral'`` + From the front. + ``'caudal'`` + From the rear. + ``'dorsal'`` + From above, with the front of the brain pointing up. + ``'ventral'`` + From below, with the front of the brain pointing up. + ``'frontal'`` + From the front and slightly lateral, with the brain slightly + tilted forward (yielding a view from slightly above). + ``'parietal'`` + From the rear and slightly lateral, with the brain slightly tilted + backward (yielding a view from slightly above). + ``'axial'`` + From above with the brain pointing up (same as ``'dorsal'``). + ``'sagittal'`` + From the right side. + ``'coronal'`` + From the rear. + + Three letter abbreviations (e.g., ``'lat'``) of all of the above are + also supported. """ + _validate_type(row, ('int-like', None), 'row') + _validate_type(col, ('int-like', None), 'col') hemi = self._hemi if hemi is None else hemi if hemi == 'split': if (self._view_layout == 'vertical' and col == 1 or @@ -2560,23 +2960,25 @@ hemi = 'rh' else: hemi = 'lh' - if isinstance(view, str): - view = views_dicts[hemi].get(view) - view = view.copy() - if roll is not None: - view.update(roll=roll) - if distance is not None: - view.update(distance=distance) - self._renderer.subplot(row, col) + _validate_type(view, (str, None), 'view') + view_params = dict(azimuth=azimuth, elevation=elevation, roll=roll, + distance=distance, focalpoint=focalpoint) + if view is not None: # view_params take precedence + view_params = {param: val for param, val in view_params.items() + if val is not None} # no overwriting with None + view_params = dict(views_dicts[hemi].get(view), **view_params) xfm = self._rigid if align else None - self._renderer.set_camera(**view, reset_camera=False, rigid=xfm) + for h in self._hemis: + for ri, ci, _ in self._iter_views(h): + if (row is None or row == ri) and (col is None or col == ci): + self._renderer.set_camera( + **view_params, reset_camera=False, rigid=xfm) self._renderer._update() def reset_view(self): """Reset the camera.""" for h in self._hemis: - for ri, ci, v in self._iter_views(h): - self._renderer.subplot(ri, ci) + for _, _, v in self._iter_views(h): self._renderer.set_camera(**views_dicts[h][v], reset_camera=False) @@ -2603,13 +3005,14 @@ ---------- mode : str Either 'rgb' or 'rgba' for values to return. - %(brain_screenshot_time_viewer)s + %(time_viewer_brain_screenshot)s Returns ------- screenshot : array Image pixel values. """ + n_channels = 3 if mode == 'rgb' else 4 img = self._renderer.screenshot(mode) logger.debug(f'Got screenshot of size {img.shape}') if time_viewer and self.time_viewer and \ @@ -2641,11 +3044,12 @@ fig.savefig(output, dpi=dpi, format='png', facecolor=self._bg_color, edgecolor='none') output.seek(0) - trace_img = imread(output, format='png')[:, :, :3] + trace_img = imread(output, format='png')[:, :, :n_channels] trace_img = np.clip( np.round(trace_img * 255), 0, 255).astype(np.uint8) - bgcolor = np.array(self._brain_color[:3]) / 255 - img = concatenate_images([img, trace_img], bgcolor=bgcolor) + bgcolor = np.array(self._brain_color[:n_channels]) / 255 + img = concatenate_images([img, trace_img], bgcolor=bgcolor, + n_channels=n_channels) return img @contextlib.contextmanager @@ -2664,8 +3068,7 @@ Parameters ---------- %(fmin_fmid_fmax)s - alpha : float | None - Alpha to use in the update. + %(alpha)s """ args = f'{fmin}, {fmid}, {fmax}, {alpha}' if self._lut_locked is not None: @@ -2727,7 +3130,6 @@ n_steps : int Number of smoothing steps. """ - from scipy import sparse from ...morph import _hemi_morph for hemi in ['lh', 'rh']: hemi_data = self._data.get(hemi) @@ -2740,13 +3142,12 @@ 'len(data) < nvtx (%s < %s): the vertices ' 'parameter must not be None' % (len(hemi_data), self.geo[hemi].x.shape[0])) - morph_n_steps = 'nearest' if n_steps == 0 else n_steps - maps = sparse.eye(len(self.geo[hemi].coords), format='csr') + morph_n_steps = 'nearest' if n_steps == -1 else n_steps with use_log_level(False): smooth_mat = _hemi_morph( self.geo[hemi].orig_faces, np.arange(len(self.geo[hemi].coords)), - vertices, morph_n_steps, maps, warn=False) + vertices, morph_n_steps, maps=None, warn=False) self._data[hemi]['smooth_mat'] = smooth_mat self.set_time_point(self._data['time_idx']) self._data['smoothing_steps'] = n_steps @@ -2766,7 +3167,7 @@ Parameters ---------- - %(brain_time_interpolation)s + %(interpolation_brain_time)s """ self._time_interpolation = _check_option( 'interpolation', @@ -2825,12 +3226,12 @@ values = self._current_act_data['vol'] rng = self._cmap_range fill = 0 if self._data['center'] is not None else rng[0] - grid.cell_arrays['values'].fill(fill) + _cell_data(grid)['values'].fill(fill) # XXX for sided data, we probably actually need two # volumes as composite/MIP needs to look at two # extremes... for now just use abs. Eventually we can add # two volumes if we want. - grid.cell_arrays['values'][vertices] = values + _cell_data(grid)['values'][vertices] = values # interpolate in space smooth_mat = hemi_data.get('smooth_mat') @@ -2892,8 +3293,7 @@ else: add = False count = 0 - for ri, ci, _ in self._iter_views(hemi): - self._renderer.subplot(ri, ci) + for _ in self._iter_views(hemi): if hemi_data['glyph_dataset'] is None: glyph_mapper, glyph_dataset = self._renderer.quiver3d( x, y, z, @@ -2909,14 +3309,14 @@ hemi_data['glyph_mapper'] = glyph_mapper else: glyph_dataset = hemi_data['glyph_dataset'] - glyph_dataset.point_arrays['vec'] = vectors + _point_data(glyph_dataset)['vec'] = vectors glyph_mapper = hemi_data['glyph_mapper'] if add: glyph_actor = self._renderer._actor(glyph_mapper) prop = glyph_actor.GetProperty() prop.SetLineWidth(2.) prop.SetOpacity(vector_alpha) - self._renderer.plotter.add_actor(glyph_actor) + self._renderer.plotter.add_actor(glyph_actor, render=False) hemi_data['glyph_actor'].append(glyph_actor) else: glyph_actor = hemi_data['glyph_actor'][count] @@ -3046,12 +3446,9 @@ self._renderer._window_new_cursor("WaitCursor")) try: - self._save_movie( - filename=filename, - time_dilation=(1. / self.playback_speed), - callback=frame_callback, - **kwargs - ) + self._save_movie(filename, time_dilation, tmin, tmax, + framerate, interpolation, codec, + bitrate, frame_callback, time_viewer, **kwargs) except (Exception, KeyboardInterrupt): warn('Movie saving aborted:\n' + traceback.format_exc()) finally: @@ -3065,9 +3462,8 @@ The movie is created through the :mod:`imageio` module. The format is determined by the extension, and additional options can be specified - through keyword arguments that depend on the format. For available - formats and corresponding parameters see the imageio documentation: - http://imageio.readthedocs.io/en/latest/formats.html#multiple-images + through keyword arguments that depend on the format, see + :doc:`imageio's format page `. .. Warning:: This method assumes that time is specified in seconds when adding @@ -3090,7 +3486,7 @@ Last time point to include (default: all data). framerate : float Framerate of the movie (frames per second, default 24). - %(brain_time_interpolation)s + %(interpolation_brain_time)s If None, it uses the current ``brain.interpolation``, which defaults to ``'nearest'``. Defaults to None. codec : str | None @@ -3101,14 +3497,9 @@ A function to call on each iteration. Useful for status message updates. It will be passed keyword arguments ``frame`` and ``n_frames``. - %(brain_screenshot_time_viewer)s + %(time_viewer_brain_screenshot)s **kwargs : dict Specify additional options for :mod:`imageio`. - - Returns - ------- - dialog : object - The opened dialog is returned for testing purpose only. """ if filename is None: filename = _generate_default_filename(".mp4") @@ -3228,16 +3619,13 @@ def _check_hemi(self, hemi, extras=()): """Check for safe single-hemi input, returns str.""" + _validate_type(hemi, (None, str), 'hemi') if hemi is None: if self._hemi not in ['lh', 'rh']: raise ValueError('hemi must not be None when both ' 'hemispheres are displayed') - else: - hemi = self._hemi - elif hemi not in ['lh', 'rh'] + list(extras): - extra = ' or None' if self._hemi in ['lh', 'rh'] else '' - raise ValueError('hemi must be either "lh" or "rh"' + - extra + ", got " + str(hemi)) + hemi = self._hemi + _check_option('hemi', hemi, ('lh', 'rh') + tuple(extras)) return hemi def _check_hemis(self, hemi): @@ -3278,10 +3666,6 @@ show[keep_idx] = 1 label *= show - def enable_depth_peeling(self): - """Enable depth peeling.""" - self._renderer.enable_depth_peeling() - def get_picked_points(self): """Return the vertices of the picked points. @@ -3295,7 +3679,7 @@ def __hash__(self): """Hash the object.""" - raise NotImplementedError + return self._hash def _safe_interp1d(x, y, kind='linear', axis=-1, assume_sorted=False): diff -Nru python-mne-0.23.4+dfsg/mne/viz/_brain/callback.py python-mne-1.1.0+dfsg/mne/viz/_brain/callback.py --- python-mne-0.23.4+dfsg/mne/viz/_brain/callback.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/_brain/callback.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,6 +3,9 @@ # Guillaume Favelier # # License: Simplified BSD + +import weakref + from ...utils import logger @@ -54,15 +57,18 @@ """Update the LUT.""" def __init__(self, brain=None): - self.brain = brain - self.widgets = {key: list() for key in self.brain.keys} + self.brain = weakref.ref(brain) + self.widgets = {key: list() for key in brain.keys} def __call__(self, fmin=None, fmid=None, fmax=None): """Update the colorbar sliders.""" - self.brain.update_lut(fmin=fmin, fmid=fmid, fmax=fmax) - with self.brain._no_lut_update(f'UpdateLUT {fmin} {fmid} {fmax}'): + brain = self.brain() + if brain is None: + return + brain.update_lut(fmin=fmin, fmid=fmid, fmax=fmax) + with brain._no_lut_update(f'UpdateLUT {fmin} {fmid} {fmax}'): for key in ('fmin', 'fmid', 'fmax'): - value = self.brain._data[key] + value = brain._data[key] logger.debug(f'Updating {key} = {value}') for widget in self.widgets[key]: widget.set_value(value) diff -Nru python-mne-0.23.4+dfsg/mne/viz/_brain/colormap.py python-mne-1.1.0+dfsg/mne/viz/_brain/colormap.py --- python-mne-0.23.4+dfsg/mne/viz/_brain/colormap.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/_brain/colormap.py 2022-08-03 15:48:14.000000000 +0000 @@ -52,9 +52,9 @@ if ind.size > 0: # choose the two colors between which there is the large step ind = ind[0] + 1 - fillcols = np.r_[np.tile(cols[ind, :], (n_fill / 2, 1)), + fillcols = np.r_[np.tile(cols[ind, :], (n_fill // 2, 1)), np.tile(cols[ind + 1, :], - (n_fill - n_fill / 2, 1))] + (n_fill - n_fill // 2, 1))] else: # choose a color from the middle of the colormap fillcols = np.tile(cols[int(cols.shape[0] / 2), :], (n_fill, 1)) @@ -140,7 +140,7 @@ lut_table[:, -1] = np.round(lut_table[:, -1] * alpha) if divergent: - if fmax == fmin: + if np.isclose(fmax, fmin, rtol=1e-6, atol=0): lut_table = np.r_[ lut_table[:1], get_fill_colors( diff -Nru python-mne-0.23.4+dfsg/mne/viz/_brain/_linkviewer.py python-mne-1.1.0+dfsg/mne/viz/_brain/_linkviewer.py --- python-mne-0.23.4+dfsg/mne/viz/_brain/_linkviewer.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/_brain/_linkviewer.py 2022-08-03 15:48:14.000000000 +0000 @@ -29,7 +29,7 @@ self.link_widgets( name="time", callback=self.set_time_point, - signal_type="valueChanged", + signal_type="floatValueChanged", ) # link playback speed sliders @@ -101,7 +101,7 @@ self.link_widgets( name=name, callback=func, - signal_type="valueChanged" + signal_type="floatValueChanged" ) def set_fmin(self, value): @@ -134,7 +134,7 @@ def link_widgets(self, name, callback, signal_type, actions=False): for brain in self.brains: if actions: - widget = brain._renderer.actions[name] + widget = brain._renderer.actions[name]._action else: widget = brain.widgets[name].widget if widget is not None: diff -Nru python-mne-0.23.4+dfsg/mne/viz/_brain/_scraper.py python-mne-1.1.0+dfsg/mne/viz/_brain/_scraper.py --- python-mne-0.23.4+dfsg/mne/viz/_brain/_scraper.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/_brain/_scraper.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,7 @@ import os import os.path as op -from distutils.version import LooseVersion +from ...fixes import _compare_version from ._brain import Brain @@ -70,9 +70,10 @@ # Out to sphinx-gallery: # # 1. A static image but hide it (useful for carousel) - if LooseVersion(matplotlib.__version__) >= \ - LooseVersion('3.3.1') and \ - animation.FFMpegWriter.isAvailable(): + if ( + _compare_version(matplotlib.__version__, '>=', '3.3.1') and + animation.FFMpegWriter.isAvailable() + ): writer = 'ffmpeg' elif animation.ImageMagickWriter.isAvailable(): writer = 'imagemagick' diff -Nru python-mne-0.23.4+dfsg/mne/viz/_brain/surface.py python-mne-1.1.0+dfsg/mne/viz/_brain/surface.py --- python-mne-0.23.4+dfsg/mne/viz/_brain/surface.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/_brain/surface.py 2022-08-03 15:48:14.000000000 +0000 @@ -95,6 +95,9 @@ subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) self.data_path = path.join(subjects_dir, subject_id) + if surf == 'seghead': + raise ValueError('`surf` cannot be seghead, use ' + '`mne.viz.Brain.add_head` to plot the seghead') def load_geometry(self): """Load geometry of the surface. @@ -131,7 +134,8 @@ else: coords -= (np.min(x_) + self.offset) * self.x_dir surf = dict(rr=coords, tris=faces) - complete_surface_info(surf, copy=False, verbose=False) + complete_surface_info( + surf, copy=False, verbose=False, do_neighbor_tri=False) nn = surf['nn'] self.coords = coords self.faces = faces diff -Nru python-mne-0.23.4+dfsg/mne/viz/_brain/tests/test_brain.py python-mne-1.1.0+dfsg/mne/viz/_brain/tests/test_brain.py --- python-mne-0.23.4+dfsg/mne/viz/_brain/tests/test_brain.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/_brain/tests/test_brain.py 2022-08-03 15:48:14.000000000 +0000 @@ -9,7 +9,7 @@ # License: Simplified BSD import os -import os.path as path +import os.path as op import sys import pytest @@ -19,12 +19,14 @@ from mne import (read_source_estimate, read_evokeds, read_cov, read_forward_solution, pick_types_forward, SourceEstimate, MixedSourceEstimate, write_surface, - VolSourceEstimate) + VolSourceEstimate, vertex_to_mni, Dipole) from mne.minimum_norm import apply_inverse, make_inverse_operator -from mne.source_space import (read_source_spaces, vertex_to_mni, +from mne.source_space import (read_source_spaces, setup_volume_source_space) from mne.datasets import testing -from mne.utils import check_version, requires_pysurfer +from mne.fixes import _cell_data +from mne.io import read_info +from mne.utils import check_version from mne.label import read_label from mne.viz._brain import Brain, _LinkViewer, _BrainScraper, _LayeredMesh from mne.viz._brain.colormap import calculate_lut @@ -34,17 +36,16 @@ data_path = testing.data_path(download=False) subject_id = 'sample' -subjects_dir = path.join(data_path, 'subjects') -fname_stc = path.join(data_path, 'MEG/sample/sample_audvis_trunc-meg') -fname_label = path.join(data_path, 'MEG/sample/labels/Vis-lh.label') -fname_cov = path.join( - data_path, 'MEG', 'sample', 'sample_audvis_trunc-cov.fif') -fname_evoked = path.join(data_path, 'MEG', 'sample', - 'sample_audvis_trunc-ave.fif') -fname_fwd = path.join( - data_path, 'MEG', 'sample', 'sample_audvis_trunc-meg-eeg-oct-4-fwd.fif') -src_fname = path.join(data_path, 'subjects', 'sample', 'bem', - 'sample-oct-6-src.fif') +subjects_dir = op.join(data_path, 'subjects') +sample_dir = op.join(data_path, 'MEG', 'sample') +fname_raw_testing = op.join(sample_dir, 'sample_audvis_trunc_raw.fif') +fname_trans = op.join(sample_dir, 'sample_audvis_trunc-trans.fif') +fname_stc = op.join(sample_dir, 'sample_audvis_trunc-meg') +fname_label = op.join(sample_dir, 'labels', 'Vis-lh.label') +fname_cov = op.join(sample_dir, 'sample_audvis_trunc-cov.fif') +fname_evoked = op.join(sample_dir, 'sample_audvis_trunc-ave.fif') +fname_fwd = op.join(sample_dir, 'sample_audvis_trunc-meg-eeg-oct-4-fwd.fif') +src_fname = op.join(subjects_dir, subject_id, 'bem', 'sample-oct-6-src.fif') class _Collection(object): @@ -104,10 +105,10 @@ return np.array(self.GetPickPosition()) - (0, 0, 100) -def test_layered_mesh(renderer_interactive_pyvista): +def test_layered_mesh(renderer_interactive_pyvistaqt): """Test management of scalars/colormap overlay.""" mesh = _LayeredMesh( - renderer=renderer_interactive_pyvista._get_renderer(size=(300, 300)), + renderer=renderer_interactive_pyvistaqt._get_renderer(size=(300, 300)), vertices=np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0]]), triangles=np.array([[0, 1, 2], [1, 2, 3]]), normals=np.array([[0, 0, 1]] * 4), @@ -115,7 +116,8 @@ assert not mesh._is_mapped mesh.map() assert mesh._is_mapped - assert mesh._cache is None + assert mesh._current_colors is None + assert mesh._cached_colors is None mesh.update() assert len(mesh._overlays) == 0 mesh.add_overlay( @@ -123,37 +125,47 @@ colormap=np.array([(1, 1, 1, 1), (0, 0, 0, 0)]), rng=[0, 1], opacity=None, - name='test', + name='test1', ) - assert mesh._cache is not None + assert mesh._current_colors is not None + assert mesh._cached_colors is None + assert len(mesh._overlays) == 1 + assert 'test1' in mesh._overlays + mesh.add_overlay( + scalars=np.array([1, 0, 0, 1]), + colormap=np.array([(1, 1, 1, 1), (0, 0, 0, 0)]), + rng=[0, 1], + opacity=None, + name='test2', + ) + assert mesh._current_colors is not None + assert mesh._cached_colors is not None + assert len(mesh._overlays) == 2 + assert 'test2' in mesh._overlays + mesh.remove_overlay('test2') + assert 'test2' not in mesh._overlays + mesh.update() assert len(mesh._overlays) == 1 - assert 'test' in mesh._overlays - mesh.remove_overlay('test') - assert len(mesh._overlays) == 0 mesh._clean() @testing.requires_testing_data -def test_brain_gc(renderer_pyvista, brain_gc): +def test_brain_gc(renderer_pyvistaqt, brain_gc): """Test that a minimal version of Brain gets GC'ed.""" brain = Brain('fsaverage', 'both', 'inflated', subjects_dir=subjects_dir) brain.close() -@requires_pysurfer @testing.requires_testing_data def test_brain_routines(renderer, brain_gc): """Test backend agnostic Brain routines.""" brain_klass = renderer.get_brain_class() - if renderer.get_3d_backend() == "mayavi": - from surfer import Brain - else: # PyVista - from mne.viz._brain import Brain + from mne.viz._brain import Brain assert brain_klass == Brain @testing.requires_testing_data -def test_brain_init(renderer_pyvista, tmpdir, pixel_ratio, brain_gc): +def test_brain_init(renderer_pyvistaqt, tmp_path, pixel_ratio, brain_gc): """Test initialization of the Brain instance.""" from mne.source_estimate import _BaseSourceEstimate @@ -169,19 +181,29 @@ kwargs = dict(subject_id=subject_id, subjects_dir=subjects_dir) with pytest.raises(ValueError, match='"size" parameter must be'): Brain(hemi=hemi, surf=surf, size=[1, 2, 3], **kwargs) - with pytest.raises(KeyError): + with pytest.raises(ValueError, match='.*hemi.*Allowed values.*'): Brain(hemi='foo', surf=surf, **kwargs) + with pytest.raises(ValueError, match='.*view.*Allowed values.*'): + Brain(hemi='lh', surf=surf, views='foo', **kwargs) with pytest.raises(TypeError, match='figure'): Brain(hemi=hemi, surf=surf, figure='foo', **kwargs) with pytest.raises(TypeError, match='interaction'): Brain(hemi=hemi, surf=surf, interaction=0, **kwargs) with pytest.raises(ValueError, match='interaction'): Brain(hemi=hemi, surf=surf, interaction='foo', **kwargs) - renderer_pyvista.backend._close_all() + with pytest.raises(FileNotFoundError, match=r'lh\.whatever'): + Brain(hemi='lh', surf='whatever', **kwargs) + with pytest.raises(ValueError, match='`surf` cannot be seghead'): + Brain(hemi='lh', surf='seghead', **kwargs) + with pytest.raises(ValueError, match='RGB argument'): + Brain('sample', cortex='badcolor') + Brain(subject_id, hemi=None, surf=None) # test no surfaces + renderer_pyvistaqt.backend._close_all() brain = Brain(hemi=hemi, surf=surf, size=size, title=title, cortex=cortex, units='m', silhouette=dict(decimate=0.95), **kwargs) + assert 'data' not in brain._actors with pytest.raises(TypeError, match='not supported'): brain._check_stc(hemi='lh', array=FakeSTC(), vertices=None) with pytest.raises(ValueError, match='add_data'): @@ -189,10 +211,8 @@ brain._hemi = 'foo' # for testing: hemis with pytest.raises(ValueError, match='not be None'): brain._check_hemi(hemi=None) - with pytest.raises(ValueError, match='either "lh" or "rh"'): + with pytest.raises(ValueError, match='Invalid.*hemi.*Allowed'): brain._check_hemi(hemi='foo') - with pytest.raises(ValueError, match='either "lh" or "rh"'): - brain._check_hemis(hemi='foo') brain._hemi = hemi # end testing: hemis with pytest.raises(ValueError, match='bool or positive'): brain._to_borders(None, None, 'foo') @@ -269,6 +289,10 @@ brain.add_data(hemi_data[:, np.newaxis, np.newaxis], fmin=fmin, hemi=h, fmax=fmax, colormap='hot', vertices=hemi_vertices) + assert len(brain._actors['data']) == 4 + brain.remove_data() + assert 'data' not in brain._actors + # add label label = read_label(fname_label) with pytest.raises(ValueError, match="not a filename"): @@ -295,13 +319,67 @@ brain.add_foci([0], coords_as_verts=True, hemi=hemi, color='blue') + # add head and skull + brain.add_head(color='red', alpha=0.1) + brain.remove_head() + brain.add_skull(outer=True, color='green', alpha=0.1) + brain.remove_skull() + + # add volume labels + brain.add_volume_labels( + aseg='aseg', labels=('Brain-Stem', 'Left-Hippocampus', + 'Left-Amygdala')) + brain.remove_volume_labels() + + # add sensors + info = read_info(fname_raw_testing) + brain.add_sensors(info, trans=fname_trans) + for kind in ('meg', 'eeg', 'fnirs', 'ecog', 'seeg', 'dbs', 'helmet'): + brain.remove_sensors(kind) + brain.add_sensors(info, trans=fname_trans) + brain.remove_sensors() + + info['chs'][0]['coord_frame'] = 99 + with pytest.raises(RuntimeError, match='must be "meg", "head" or "mri"'): + brain.add_sensors(info, trans=fname_trans) + + # add dipole + dip = Dipole(times=[0], pos=[[-0.06439933, 0.00733009, 0.06280205]], + amplitude=[3e-8], ori=[[0, 1, 0]], gof=50) + brain.add_dipole(dip, fname_trans, colors='blue', scales=5, alpha=0.5) + brain.remove_dipole() + + with pytest.raises(ValueError, match='The number of colors'): + brain.add_dipole(dip, fname_trans, colors=['red', 'blue']) + + with pytest.raises(ValueError, match='The number of scales'): + brain.add_dipole(dip, fname_trans, scales=[1, 2]) + + fwd = read_forward_solution(fname_fwd) + brain.add_forward(fwd, fname_trans, alpha=0.5, scale=10) + brain.remove_forward() + + # fake incorrect coordinate frame + fwd['coord_frame'] = 99 + with pytest.raises(RuntimeError, match='must be "head" or "mri"'): + brain.add_forward(fwd, fname_trans) + fwd['coord_frame'] = 2003 + with pytest.raises(RuntimeError, match='must be "head" or "mri"'): + brain.add_forward(fwd, fname_trans) + # add text brain.add_text(x=0, y=0, text='foo') + with pytest.raises(ValueError, match='already exists'): + brain.add_text(x=0, y=0, text='foo') + brain.remove_text('foo') + brain.add_text(x=0, y=0, text='foo') + brain.remove_text() + brain.close() # add annotation - annots = ['aparc', path.join(subjects_dir, 'fsaverage', 'label', - 'lh.PALS_B12_Lobes.annot')] + annots = ['aparc', op.join(subjects_dir, 'fsaverage', 'label', + 'lh.PALS_B12_Lobes.annot')] borders = [True, 2] alphas = [1, 0.5] colors = [None, 'r'] @@ -317,33 +395,45 @@ for a, b, p, color in zip(annots, borders, alphas, colors): brain.add_annotation(a, b, p, color=color) - view_args = dict(view=dict(focalpoint=(1e-5, 1e-5, 1e-5)), - roll=1, distance=500) + view_args = dict(roll=1, distance=500, focalpoint=(1e-5, 1e-5, 1e-5)) cam = brain._renderer.figure.plotter.camera previous_roll = cam.GetRoll() brain.show_view(**view_args) - assert np.allclose(cam.GetFocalPoint(), view_args["view"]["focalpoint"]) - assert np.allclose(cam.GetDistance(), view_args["distance"]) - assert np.allclose(cam.GetRoll(), previous_roll + view_args["roll"]) + assert_allclose(cam.GetFocalPoint(), view_args["focalpoint"]) + assert_allclose(cam.GetDistance(), view_args["distance"]) + assert_allclose(cam.GetRoll(), previous_roll + view_args["roll"]) + + # test get_view + azimuth, elevation = 180., 90. + view_args.update(azimuth=azimuth, elevation=elevation) + brain.show_view(**view_args) + roll, distance, azimuth, elevation, focalpoint = brain.get_view() + assert_allclose(cam.GetRoll(), roll) + assert_allclose(cam.GetDistance(), distance) + assert_allclose(view_args['azimuth'] % 360, azimuth % 360) + assert_allclose(view_args['elevation'] % 180, elevation % 180) + assert_allclose(view_args['focalpoint'], focalpoint) del view_args # image and screenshot - fname = path.join(str(tmpdir), 'test.png') - assert not path.isfile(fname) + fname = op.join(str(tmp_path), 'test.png') + assert not op.isfile(fname) brain.save_image(fname) - assert path.isfile(fname) + assert op.isfile(fname) fp = np.array( brain._renderer.figure.plotter.renderer.ComputeVisiblePropBounds()) fp = (fp[1::2] + fp[::2]) * 0.5 - view_args = dict(azimuth=180., elevation=90., focalpoint='auto') - brain.show_view(view=view_args) - assert np.allclose(brain._renderer.figure._azimuth, view_args["azimuth"]) - assert np.allclose( - brain._renderer.figure._elevation, view_args["elevation"]) - assert np.allclose(cam.GetFocalPoint(), fp) + for view_args in (dict(azimuth=azimuth, elevation=elevation, + focalpoint='auto'), + dict(view='lateral', hemi='lh')): + brain.show_view(**view_args) + assert_allclose(brain._renderer.figure._azimuth % 360, azimuth % 360) + assert_allclose( + brain._renderer.figure._elevation % 180, elevation % 180) + assert_allclose(cam.GetFocalPoint(), fp) del view_args - img = brain.screenshot(mode='rgb') - want_size = np.array([size[0] * pixel_ratio, size[1] * pixel_ratio, 3]) + img = brain.screenshot(mode='rgba') + want_size = np.array([size[0] * pixel_ratio, size[1] * pixel_ratio, 4]) assert_allclose(img.shape, want_size) brain.close() @@ -352,7 +442,7 @@ @pytest.mark.skipif(os.getenv('CI_OS_NAME', '') == 'osx', reason='Unreliable/segfault on macOS CI') @pytest.mark.parametrize('hemi', ('lh', 'rh')) -def test_single_hemi(hemi, renderer_interactive_pyvista, brain_gc): +def test_single_hemi(hemi, renderer_interactive_pyvistaqt, brain_gc): """Test single hemi support.""" stc = read_source_estimate(fname_stc) idx, order = (0, 1) if hemi == 'lh' else (1, -1) @@ -360,7 +450,8 @@ getattr(stc, f'{hemi}_data'), [stc.vertices[idx], []][::order], 0, 1, 'sample') brain = stc.plot( - subjects_dir=subjects_dir, hemi='both', size=300) + subjects_dir=subjects_dir, hemi='both', size=300, + cortex='0.5') # single cortex string arg brain.close() # test skipping when len(vertices) == 0 @@ -372,12 +463,12 @@ @testing.requires_testing_data @pytest.mark.slowtest -def test_brain_save_movie(tmpdir, renderer, brain_gc): +def test_brain_save_movie(tmp_path, renderer, brain_gc): """Test saving a movie of a Brain instance.""" - if renderer._get_3d_backend() == "mayavi": - pytest.skip('Save movie only supported on PyVista') - brain = _create_testing_brain(hemi='lh', time_viewer=False) - filename = str(path.join(tmpdir, "brain_test.mov")) + from imageio_ffmpeg import count_frames_and_secs + brain = _create_testing_brain(hemi='lh', time_viewer=False, + cortex=['r', 'b']) # custom binarized + filename = str(op.join(tmp_path, "brain_test.mov")) for interactive_state in (False, True): # for coverage, we set interactivity if interactive_state: @@ -387,10 +478,16 @@ with pytest.raises(TypeError, match='unexpected keyword argument'): brain.save_movie(filename, time_dilation=1, tmin=1, tmax=1.1, bad_name='blah') - assert not path.isfile(filename) - brain.save_movie(filename, time_dilation=0.1, - interpolation='nearest') - assert path.isfile(filename) + assert not op.isfile(filename) + tmin = 1 + tmax = 5 + duration = np.floor(tmax - tmin) + brain.save_movie(filename, time_dilation=1., tmin=tmin, + tmax=tmax, interpolation='nearest') + assert op.isfile(filename) + _, nsecs = count_frames_and_secs(filename) + assert_allclose(duration, nsecs, atol=0.2) + os.remove(filename) brain.close() @@ -398,27 +495,29 @@ _TINY_SIZE = (350, 300) -def tiny(tmpdir): +def tiny(tmp_path): """Create a tiny fake brain.""" # This is a minimal version of what we need for our viz-with-timeviewer # support currently subject = 'test' - subject_dir = tmpdir.mkdir(subject) - surf_dir = subject_dir.mkdir('surf') + (tmp_path / subject).mkdir() + subject_dir = tmp_path / subject + (subject_dir / 'surf').mkdir() + surf_dir = subject_dir / 'surf' rng = np.random.RandomState(0) rr = rng.randn(4, 3) tris = np.array([[0, 1, 2], [2, 1, 3]]) curv = rng.randn(len(rr)) - with open(surf_dir.join('lh.curv'), 'wb') as fid: + with open(surf_dir / 'lh.curv', 'wb') as fid: fid.write(np.array([255, 255, 255], dtype=np.uint8)) fid.write(np.array([len(rr), 0, 1], dtype='>i4')) fid.write(curv.astype('>f4')) - write_surface(surf_dir.join('lh.white'), rr, tris) - write_surface(surf_dir.join('rh.white'), rr, tris) # needed for vertex tc + write_surface(surf_dir / 'lh.white', rr, tris) + write_surface(surf_dir / 'rh.white', rr, tris) # needed for vertex tc vertices = [np.arange(len(rr)), []] data = rng.randn(len(rr), 10) stc = SourceEstimate(data, vertices, 0, 1, subject) - brain = stc.plot(subjects_dir=tmpdir, hemi='lh', surface='white', + brain = stc.plot(subjects_dir=tmp_path, hemi='lh', surface='white', size=_TINY_SIZE) # in principle this should be sufficient: # @@ -433,12 +532,12 @@ @pytest.mark.filterwarnings('ignore:.*constrained_layout not applied.*:') -def test_brain_screenshot(renderer_interactive_pyvista, tmpdir, brain_gc): +def test_brain_screenshot(renderer_interactive_pyvistaqt, tmp_path, brain_gc): """Test time viewer screenshot.""" # XXX disable for sprint because it's too unreliable if sys.platform == 'darwin' and os.getenv('GITHUB_ACTIONS', '') == 'true': pytest.skip('Test is unreliable on GitHub Actions macOS') - tiny_brain, ratio = tiny(tmpdir) + tiny_brain, ratio = tiny(tmp_path) img_nv = tiny_brain.screenshot(time_viewer=False) want = (_TINY_SIZE[1] * ratio, _TINY_SIZE[0] * ratio, 3) assert img_nv.shape == want @@ -461,7 +560,7 @@ @testing.requires_testing_data @pytest.mark.slowtest -def test_brain_time_viewer(renderer_interactive_pyvista, pixel_ratio, +def test_brain_time_viewer(renderer_interactive_pyvistaqt, pixel_ratio, brain_gc): """Test time viewer primitives.""" with pytest.raises(ValueError, match="between 0 and 1"): @@ -533,7 +632,7 @@ # (it keeps the window size and expands the 3D area when the interface # is toggled off) brain.toggle_interface(value=True) - brain.show_view(view=dict(azimuth=180., elevation=90.)) + brain.show_view(azimuth=180., elevation=90.) img = brain.screenshot(mode='rgb') want_shape = np.array([300 * pixel_ratio, 300 * pixel_ratio, 3]) assert_allclose(img.shape, want_shape) @@ -554,7 +653,7 @@ pytest.param('mixed', marks=pytest.mark.slowtest), ]) @pytest.mark.slowtest -def test_brain_traces(renderer_interactive_pyvista, hemi, src, tmpdir, +def test_brain_traces(renderer_interactive_pyvistaqt, hemi, src, tmp_path, brain_gc): """Test brain traces.""" hemi_str = list() @@ -636,8 +735,10 @@ assert hasattr(brain, "_spheres") assert brain._scalar_bar.GetNumberOfLabels() == 3 - # add foci should work for volumes - brain.add_foci([[0, 0, 0]], hemi='lh' if src == 'surface' else 'vol') + # add foci should work for 'lh', 'rh' and 'vol' + for current_hemi in hemi_str: + brain.add_foci([[0, 0, 0]], hemi=current_hemi) + assert_array_equal(brain._data[current_hemi]['foci'], [[0, 0, 0]]) # test points picked by default picked_points = brain.get_picked_points() @@ -645,10 +746,14 @@ for current_hemi in hemi_str: assert len(picked_points[current_hemi]) == 1 n_spheres = len(hemi_str) + n_actors = n_spheres if hemi == 'split' and src in ('mixed', 'volume'): n_spheres += 1 assert len(spheres) == n_spheres + # test that there are actually enough actors + assert len(brain._actors['data']) == n_actors + # test switching from 'vertex' to 'label' if src == 'surface': brain.widgets["annotation"].set_value('aparc') @@ -666,7 +771,7 @@ if current_hemi == 'vol': current_mesh = brain._data['vol']['grid'] vertices = brain._data['vol']['vertices'] - values = current_mesh.cell_arrays['values'][vertices] + values = _cell_data(current_mesh)['values'][vertices] cell_id = vertices[np.argmax(np.abs(values))] else: current_mesh = brain._layered_meshes[current_hemi]._polydata @@ -721,7 +826,7 @@ check_version('sphinx_gallery')): brain.close() return - fnames = [str(tmpdir.join(f'temp_{ii}.png')) for ii in range(2)] + fnames = [str(tmp_path / f'temp_{ii}.png') for ii in range(2)] block_vars = dict(image_path_iterator=iter(fnames), example_globals=dict(brain=brain)) block = ('code', """ @@ -730,14 +835,14 @@ # interpolation='linear', time_viewer=True) # """, 1) - gallery_conf = dict(src_dir=str(tmpdir), compress_images=[]) + gallery_conf = dict(src_dir=str(tmp_path), compress_images=[]) scraper = _BrainScraper() rst = scraper(block, block_vars, gallery_conf) assert brain.plotter is None # closed gif_0 = fnames[0][:-3] + 'gif' for fname in (gif_0, fnames[1]): - assert path.basename(fname) in rst - assert path.isfile(fname) + assert op.basename(fname) in rst + assert op.isfile(fname) img = image.imread(fname) assert img.shape[1] == screenshot.shape[1] # same width assert img.shape[0] > screenshot.shape[0] # larger height @@ -745,8 +850,37 @@ @testing.requires_testing_data +def test_brain_scraper(renderer_interactive_pyvistaqt, brain_gc, tmp_path): + """Test a simple scraping example.""" + pytest.importorskip('sphinx_gallery') + stc = read_source_estimate(fname_stc, subject='sample') + size = (600, 300) + brain = stc.plot(subjects_dir=subjects_dir, + time_viewer=True, show_traces=True, + hemi='split', size=size, views='lat') + fnames = [str(tmp_path / f'temp_{ii}.png') for ii in range(2)] + block_vars = dict(image_path_iterator=iter(fnames), + example_globals=dict(brain=brain)) + block = ('code', '', 1) + gallery_conf = dict(src_dir=str(tmp_path), compress_images=[]) + scraper = _BrainScraper() + rst = scraper(block, block_vars, gallery_conf) + assert brain.plotter is None # closed + assert brain._cleaned + del brain + fname = fnames[0] + assert op.basename(fname) in rst + assert op.isfile(fname) + img = image.imread(fname) + w = img.shape[1] + w0 = size[0] + assert np.isclose(w, w0, atol=10) or \ + np.isclose(w, w0 * 2, atol=10), f'w ∉ {{{w0}, {2 * w0}}}' # HiDPI + + +@testing.requires_testing_data @pytest.mark.slowtest -def test_brain_linkviewer(renderer_interactive_pyvista, brain_gc): +def test_brain_linkviewer(renderer_interactive_pyvistaqt, brain_gc): """Test _LinkViewer primitives.""" brain1 = _create_testing_brain(hemi='lh', show_traces=False) brain2 = _create_testing_brain(hemi='lh', show_traces='separate') @@ -942,3 +1076,12 @@ clim=clim, src=sample_src, **kwargs) return brain_data + + +def test_foci_mapping(tmp_path, renderer_interactive_pyvistaqt): + """Test mapping foci to the surface.""" + tiny_brain, _ = tiny(tmp_path) + foci_coords = tiny_brain.geo['lh'].coords[:2] + 0.01 + tiny_brain.add_foci(foci_coords, map_surface='white') + assert_array_equal(tiny_brain._data['lh']['foci'], + tiny_brain.geo['lh'].coords[:2]) diff -Nru python-mne-0.23.4+dfsg/mne/viz/_brain/tests/test_notebook.py python-mne-1.1.0+dfsg/mne/viz/_brain/tests/test_notebook.py --- python-mne-0.23.4+dfsg/mne/viz/_brain/tests/test_notebook.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/_brain/tests/test_notebook.py 2022-08-03 15:48:14.000000000 +0000 @@ -20,11 +20,12 @@ def test_notebook_alignment(renderer_notebook, brain_gc, nbexec): """Test plot alignment in a notebook.""" import mne - data_path = mne.datasets.testing.data_path() - raw_fname = data_path + '/MEG/sample/sample_audvis_trunc_raw.fif' - subjects_dir = data_path + '/subjects' + with mne.utils.modified_env(_MNE_FAKE_HOME_DIR=None): + data_path = mne.datasets.testing.data_path(download=False) + raw_fname = data_path / 'MEG' / 'sample' / 'sample_audvis_trunc_raw.fif' + subjects_dir = data_path / 'subjects' subject = 'sample' - trans = data_path + '/MEG/sample/sample_audvis_trunc-trans.fif' + trans = data_path / 'MEG' / 'sample' / 'sample_audvis_trunc-trans.fif' info = mne.io.read_info(raw_fname) mne.viz.set_3d_backend('notebook') fig = mne.viz.plot_alignment( @@ -46,11 +47,13 @@ import matplotlib.pyplot as plt import mne from mne.datasets import testing - data_path = testing.data_path() + with mne.utils.modified_env(_MNE_FAKE_HOME_DIR=None): + data_path = testing.data_path(download=False) sample_dir = os.path.join(data_path, 'MEG', 'sample') subjects_dir = os.path.join(data_path, 'subjects') fname_stc = os.path.join(sample_dir, 'sample_audvis_trunc-meg') stc = mne.read_source_estimate(fname_stc, subject='sample') + stc.crop(0.1, 0.11) initial_time = 0.13 mne.viz.set_3d_backend('notebook') brain_class = mne.viz.get_brain_class() @@ -81,10 +84,13 @@ brain._renderer.actions['screenshot_field'].value = screenshot_path total_number_of_buttons = sum( '_field' not in k for k in brain._renderer.actions.keys()) - number_of_buttons = 0 + assert 'play' in brain._renderer.actions + # play is not a button widget, it does not have a click() method + number_of_buttons = 1 for action in brain._renderer.actions.values(): - if isinstance(action, Button): - action.click() + widget = action._action + if isinstance(widget, Button): + widget.click() number_of_buttons += 1 assert number_of_buttons == total_number_of_buttons assert os.path.isfile(movie_path) @@ -116,8 +122,9 @@ '_field' not in k for k in rend.actions.keys()) number_of_buttons = 0 for action in rend.actions.values(): - if isinstance(action, Button): - action.click() + widget = action._action + if isinstance(widget, Button): + widget.click() number_of_buttons += 1 assert number_of_buttons == total_number_of_buttons assert fig.display is not None diff -Nru python-mne-0.23.4+dfsg/mne/viz/circle.py python-mne-1.1.0+dfsg/mne/viz/circle.py --- python-mne-0.23.4+dfsg/mne/viz/circle.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/circle.py 2022-08-03 15:48:14.000000000 +0000 @@ -13,6 +13,7 @@ import numpy as np from .utils import plt_show +from ..utils import _validate_type def circular_layout(node_names, node_order, start_pos=90, start_between=True, @@ -88,14 +89,14 @@ return node_angles -def _plot_connectivity_circle_onpick(event, fig=None, axes=None, indices=None, +def _plot_connectivity_circle_onpick(event, fig=None, ax=None, indices=None, n_nodes=0, node_angles=None, ylim=[9, 10]): """Isolate connections around a single node when user left clicks a node. On right click, resets all connections. """ - if event.inaxes != axes: + if event.inaxes != ax: return if event.button == 1: # left click @@ -118,111 +119,24 @@ fig.canvas.draw() -def plot_connectivity_circle(con, node_names, indices=None, n_lines=None, - node_angles=None, node_width=None, - node_colors=None, facecolor='black', - textcolor='white', node_edgecolor='black', - linewidth=1.5, colormap='hot', vmin=None, - vmax=None, colorbar=True, title=None, - colorbar_size=0.2, colorbar_pos=(-0.3, 0.1), - fontsize_title=12, fontsize_names=8, - fontsize_colorbar=8, padding=6., - fig=None, subplot=111, interactive=True, - node_linewidth=2., show=True): - """Visualize connectivity as a circular graph. - - Parameters - ---------- - con : array - Connectivity scores. Can be a square matrix, or a 1D array. If a 1D - array is provided, "indices" has to be used to define the connection - indices. - node_names : list of str - Node names. The order corresponds to the order in con. - indices : tuple of array | None - Two arrays with indices of connections for which the connections - strengths are defined in con. Only needed if con is a 1D array. - n_lines : int | None - If not None, only the n_lines strongest connections (strength=abs(con)) - are drawn. - node_angles : array, shape (n_node_names,) | None - Array with node positions in degrees. If None, the nodes are equally - spaced on the circle. See mne.viz.circular_layout. - node_width : float | None - Width of each node in degrees. If None, the minimum angle between any - two nodes is used as the width. - node_colors : list of tuple | list of str - List with the color to use for each node. If fewer colors than nodes - are provided, the colors will be repeated. Any color supported by - matplotlib can be used, e.g., RGBA tuples, named colors. - facecolor : str - Color to use for background. See matplotlib.colors. - textcolor : str - Color to use for text. See matplotlib.colors. - node_edgecolor : str - Color to use for lines around nodes. See matplotlib.colors. - linewidth : float - Line width to use for connections. - colormap : str | instance of matplotlib.colors.LinearSegmentedColormap - Colormap to use for coloring the connections. - vmin : float | None - Minimum value for colormap. If None, it is determined automatically. - vmax : float | None - Maximum value for colormap. If None, it is determined automatically. - colorbar : bool - Display a colorbar or not. - title : str - The figure title. - colorbar_size : float - Size of the colorbar. - colorbar_pos : tuple, shape (2,) - Position of the colorbar. - fontsize_title : int - Font size to use for title. - fontsize_names : int - Font size to use for node names. - fontsize_colorbar : int - Font size to use for colorbar. - padding : float - Space to add around figure to accommodate long labels. - fig : None | instance of matplotlib.figure.Figure - The figure to use. If None, a new figure with the specified background - color will be created. - subplot : int | tuple, shape (3,) - Location of the subplot when creating figures with multiple plots. E.g. - 121 or (1, 2, 1) for 1 row, 2 columns, plot 1. See - matplotlib.pyplot.subplot. - interactive : bool - When enabled, left-click on a node to show only connections to that - node. Right-click shows all connections. - node_linewidth : float - Line with for nodes. - show : bool - Show figure if True. - - Returns - ------- - fig : instance of matplotlib.figure.Figure - The figure handle. - axes : instance of matplotlib.projections.polar.PolarAxes - The subplot handle. - - Notes - ----- - This code is based on a circle graph example by Nicolas P. Rougier - - By default, :func:`matplotlib.pyplot.savefig` does not take ``facecolor`` - into account when saving, even if set when a figure is generated. This - can be addressed via, e.g.:: - - >>> fig.savefig(fname_fig, facecolor='black') # doctest:+SKIP - - If ``facecolor`` is not set via :func:`matplotlib.pyplot.savefig`, the - figure labels, title, and legend may be cut off in the output figure. - """ +def _plot_connectivity_circle(con, node_names, indices=None, n_lines=None, + node_angles=None, node_width=None, + node_height=None, node_colors=None, + facecolor='black', textcolor='white', + node_edgecolor='black', linewidth=1.5, + colormap='hot', vmin=None, vmax=None, + colorbar=True, title=None, + colorbar_size=None, colorbar_pos=None, + fontsize_title=12, fontsize_names=8, + fontsize_colorbar=8, padding=6., + ax=None, interactive=True, + node_linewidth=2., show=True): import matplotlib.pyplot as plt import matplotlib.path as m_path import matplotlib.patches as m_patches + from matplotlib.projections.polar import PolarAxes + + _validate_type(ax, (None, PolarAxes)) n_nodes = len(node_names) @@ -244,6 +158,9 @@ else: node_width = node_width * np.pi / 180 + if node_height is None: + node_height = 1.0 + if node_colors is not None: if len(node_colors) < n_nodes: node_colors = cycle(node_colors) @@ -273,25 +190,23 @@ if isinstance(colormap, str): colormap = plt.get_cmap(colormap) - # Make figure background the same colors as axes - if fig is None: - fig = plt.figure(figsize=(8, 8), facecolor=facecolor) - # Use a polar axes - if not isinstance(subplot, tuple): - subplot = (subplot,) - axes = plt.subplot(*subplot, polar=True) - axes.set_facecolor(facecolor) + if ax is None: + fig = plt.figure(figsize=(8, 8), facecolor=facecolor) + ax = fig.add_subplot(polar=True) + else: + fig = ax.figure + ax.set_facecolor(facecolor) # No ticks, we'll put our own - plt.xticks([]) - plt.yticks([]) + ax.set_xticks([]) + ax.set_yticks([]) # Set y axes limit, add additional space if requested - plt.ylim(0, 10 + padding) + ax.set_ylim(0, 10 + padding) # Remove the black axes border which may obscure the labels - axes.spines['polar'].set_visible(False) + ax.spines['polar'].set_visible(False) # Draw lines between connected nodes, only draw the strongest connections if n_lines is not None and len(con) > n_lines: @@ -373,13 +288,13 @@ # Actual line patch = m_patches.PathPatch(path, fill=False, edgecolor=color, linewidth=linewidth, alpha=1.) - axes.add_patch(patch) + ax.add_patch(patch) # Draw ring with colored nodes - height = np.ones(n_nodes) * 1.0 - bars = axes.bar(node_angles, height, width=node_width, bottom=9, - edgecolor=node_edgecolor, lw=node_linewidth, - facecolor='.9', align='center') + height = np.ones(n_nodes) * node_height + bars = ax.bar(node_angles, height, width=node_width, bottom=9, + edgecolor=node_edgecolor, lw=node_linewidth, + facecolor='.9', align='center') for bar, color in zip(bars, node_colors): bar.set_facecolor(color) @@ -394,22 +309,24 @@ angle_deg += 180 ha = 'right' - axes.text(angle_rad, 10.4, name, size=fontsize_names, - rotation=angle_deg, rotation_mode='anchor', - horizontalalignment=ha, verticalalignment='center', - color=textcolor) + ax.text(angle_rad, 9.4 + node_height, name, size=fontsize_names, + rotation=angle_deg, rotation_mode='anchor', + horizontalalignment=ha, verticalalignment='center', + color=textcolor) if title is not None: - plt.title(title, color=textcolor, fontsize=fontsize_title, - axes=axes) + ax.set_title(title, color=textcolor, fontsize=fontsize_title) if colorbar: sm = plt.cm.ScalarMappable(cmap=colormap, norm=plt.Normalize(vmin, vmax)) sm.set_array(np.linspace(vmin, vmax)) - cb = plt.colorbar(sm, ax=axes, use_gridspec=False, - shrink=colorbar_size, - anchor=colorbar_pos) + colorbar_kwargs = dict() + if colorbar_size is not None: + colorbar_kwargs.update(shrink=colorbar_size) + if colorbar_pos is not None: + colorbar_kwargs.update(anchor=colorbar_pos) + cb = fig.colorbar(sm, ax=ax, **colorbar_kwargs) cb_yticks = plt.getp(cb.ax.axes, 'yticklabels') cb.ax.tick_params(labelsize=fontsize_colorbar) plt.setp(cb_yticks, color=textcolor) @@ -417,10 +334,89 @@ # Add callback for interaction if interactive: callback = partial(_plot_connectivity_circle_onpick, fig=fig, - axes=axes, indices=indices, n_nodes=n_nodes, + ax=ax, indices=indices, n_nodes=n_nodes, node_angles=node_angles) fig.canvas.mpl_connect('button_press_event', callback) plt_show(show) - return fig, axes + return fig, ax + + +def plot_channel_labels_circle(labels, colors=None, picks=None, **kwargs): + """Plot labels for each channel in a circle plot. + + .. note:: This primarily makes sense for sEEG channels where each + channel can be assigned an anatomical label as the electrode + passes through various brain areas. + + Parameters + ---------- + labels : dict + Lists of labels (values) associated with each channel (keys). + colors : dict + The color (value) for each label (key). + picks : list | tuple + The channels to consider. + **kwargs : kwargs + Keyword arguments for + :func:`mne_connectivity.viz.plot_connectivity_circle`. + + Returns + ------- + fig : instance of matplotlib.figure.Figure + The figure handle. + axes : instance of matplotlib.projections.polar.PolarAxes + The subplot handle. + """ + from matplotlib.colors import LinearSegmentedColormap + + _validate_type(labels, dict, 'labels') + _validate_type(colors, (dict, None), 'colors') + _validate_type(picks, (list, tuple, None), 'picks') + if picks is not None: + labels = {k: v for k, v in labels.items() if k in picks} + ch_names = list(labels.keys()) + all_labels = list(set([label for val in labels.values() + for label in val])) + n_labels = len(all_labels) + if colors is not None: + for label in all_labels: + if label not in colors: + raise ValueError(f'No color provided for {label} in `colors`') + # update all_labels, there may be unconnected labels in colors + all_labels = list(colors.keys()) + n_labels = len(all_labels) + # make colormap + label_colors = [colors[label] for label in all_labels] + node_colors = ['black'] * len(ch_names) + label_colors + label_cmap = LinearSegmentedColormap.from_list( + 'label_cmap', label_colors, N=len(label_colors)) + else: + node_colors = None + + node_names = ch_names + all_labels + con = np.zeros((len(node_names), len(node_names))) * np.nan + for idx, ch_name in enumerate(ch_names): + for label in labels[ch_name]: + node_idx = node_names.index(label) + label_color = all_labels.index(label) / n_labels + con[idx, node_idx] = con[node_idx, idx] = label_color # symmetric + # plot + node_order = ch_names + all_labels[::-1] + node_angles = circular_layout(node_names, node_order, start_pos=90, + group_boundaries=[0, len(ch_names)]) + # provide defaults but don't overwrite + if 'node_angles' not in kwargs: + kwargs.update(node_angles=node_angles) + if 'colorbar' not in kwargs: + kwargs.update(colorbar=False) + if 'node_colors' not in kwargs: + kwargs.update(node_colors=node_colors) + if 'vmin' not in kwargs: + kwargs.update(vmin=0) + if 'vmax' not in kwargs: + kwargs.update(vmax=1) + if 'colormap' not in kwargs: + kwargs.update(colormap=label_cmap) + return _plot_connectivity_circle(con, node_names, **kwargs) diff -Nru python-mne-0.23.4+dfsg/mne/viz/conftest.py python-mne-1.1.0+dfsg/mne/viz/conftest.py --- python-mne-0.23.4+dfsg/mne/viz/conftest.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/conftest.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,10 +2,7 @@ # Eric Larson # Alexandre Gramfort # -# License: BSD (3-clause) - -import inspect -from textwrap import dedent +# License: BSD-3-Clause import pytest import numpy as np @@ -14,9 +11,12 @@ from mne import create_info, EvokedArray, events_from_annotations, Epochs from mne.channels import make_standard_montage from mne.datasets.testing import data_path, _pytest_param -from mne.preprocessing.nirs import optical_density, beer_lambert_law from mne.io import read_raw_nirx -from mne.utils import Bunch +from mne.preprocessing.nirs import optical_density, beer_lambert_law + + +fname_nirx = op.join(data_path(download=False), + 'NIRx', 'nirscout', 'nirx_15_2_recording_w_overlap') @pytest.fixture() @@ -37,93 +37,11 @@ @pytest.fixture(params=[_pytest_param()]) def fnirs_epochs(): """Create an fnirs epoch structure.""" - fname = op.join(data_path(download=False), - 'NIRx', 'nirscout', 'nirx_15_2_recording_w_overlap') - raw_intensity = read_raw_nirx(fname, preload=False) + raw_intensity = read_raw_nirx(fname_nirx, preload=False) raw_od = optical_density(raw_intensity) - raw_haemo = beer_lambert_law(raw_od) + raw_haemo = beer_lambert_law(raw_od, ppf=6.) evts, _ = events_from_annotations(raw_haemo, event_id={'1.0': 1}) evts_dct = {'A': 1} tn, tx = -1, 2 epochs = Epochs(raw_haemo, evts, event_id=evts_dct, tmin=tn, tmax=tx) return epochs - - -# Create one nbclient and reuse it -@pytest.fixture(scope='session') -def _nbclient(): - try: - import nbformat - from jupyter_client import AsyncKernelManager - from nbclient import NotebookClient - from ipywidgets import Button # noqa - import ipyvtk_simple # noqa - except Exception as exc: - return pytest.skip(f'Skipping Notebook test: {exc}') - km = AsyncKernelManager(config=None) - nb = nbformat.reads(""" -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata":{}, - "outputs": [], - "source":[] - } - ], - "metadata": { - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version":3}, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}""", as_version=4) - client = NotebookClient(nb, km=km) - yield client - client._cleanup_kernel() - - -@pytest.fixture(scope='function') -def nbexec(_nbclient): - """Execute Python code in a notebook.""" - # Adapted/simplified from nbclient/client.py (BSD 3-clause) - _nbclient._cleanup_kernel() - - def execute(code, reset=False): - _nbclient.reset_execution_trackers() - with _nbclient.setup_kernel(): - assert _nbclient.kc is not None - cell = Bunch(cell_type='code', metadata={}, source=dedent(code)) - _nbclient.execute_cell(cell, 0, execution_count=0) - _nbclient.set_widgets_metadata() - - yield execute - - -def pytest_runtest_call(item): - """Run notebook code written in Python.""" - if 'nbexec' in getattr(item, 'fixturenames', ()): - nbexec = item.funcargs['nbexec'] - code = inspect.getsource(getattr(item.module, item.name.split('[')[0])) - code = code.splitlines() - ci = 0 - for ci, c in enumerate(code): - if c.startswith(' '): # actual content - break - code = '\n'.join(code[ci:]) - - def run(nbexec=nbexec, code=code): - nbexec(code) - - item.runtest = run - return diff -Nru python-mne-0.23.4+dfsg/mne/viz/_dipole.py python-mne-1.1.0+dfsg/mne/viz/_dipole.py --- python-mne-0.23.4+dfsg/mne/viz/_dipole.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/_dipole.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,166 @@ +# -*- coding: utf-8 -*- +"""Dipole viz specific functions.""" + +# Authors: Eric Larson +# +# License: Simplified BSD + +import os.path as op +import numpy as np + +from .utils import plt_show, _validate_if_list_of_axes +from .._freesurfer import _get_head_surface, _estimate_talxfm_rigid +from ..surface import read_surface +from ..transforms import apply_trans, invert_transform, _get_trans +from ..utils import _validate_type, _check_option, get_subjects_dir + + +def _check_concat_dipoles(dipole): + from ..dipole import Dipole, _concatenate_dipoles + if not isinstance(dipole, Dipole): + dipole = _concatenate_dipoles(dipole) + return dipole + + +def _plot_dipole_mri_outlines(dipoles, *, subject, trans, ax, subjects_dir, + color, scale, coord_frame, show, block, + head_source, title, surf, width): + from matplotlib.collections import LineCollection, PatchCollection + from matplotlib.patches import Circle + from scipy.spatial import ConvexHull + import matplotlib.pyplot as plt + extra = 'when mode is "outlines"' + trans = _get_trans(trans, fro='head', to='mri')[0] + _check_option('coord_frame', coord_frame, ['head', 'mri', 'mri_rotated'], + extra=extra) + _validate_type(surf, (str, None), 'surf') + _check_option('surf', surf, ('white', 'pial', None)) + if ax is None: + _, ax = plt.subplots( + 1, 3, figsize=(7, 2.5), squeeze=True, constrained_layout=True) + _validate_if_list_of_axes(ax, 3, name='ax') + dipoles = _check_concat_dipoles(dipoles) + color = 'r' if color is None else color + scale = 0.03 if scale is None else scale + width = 0.015 if width is None else width + fig = ax[0].figure + surfs = dict() + hemis = ('lh', 'rh') + if surf is not None: + for hemi in hemis: + surfs[hemi] = read_surface( + op.join(subjects_dir, subject, 'surf', + f'{hemi}.{surf}'), return_dict=True)[2] + surfs[hemi]['rr'] /= 1000. + subjects_dir = get_subjects_dir(subjects_dir) + surfs['head'] = _get_head_surface(head_source, subject, subjects_dir) + del head_source + mri_trans = head_trans = np.eye(4) + if coord_frame in ('mri', 'mri_rotated'): + head_trans = trans['trans'] + if coord_frame == 'mri_rotated': + rot = _estimate_talxfm_rigid(subject, subjects_dir) + rot[:3, 3] = 0. + head_trans = rot @ head_trans + mri_trans = rot @ mri_trans + else: + assert coord_frame == 'head' + mri_trans = invert_transform(trans)['trans'] + for s in surfs.values(): + s['rr'] = 1000 * apply_trans(mri_trans, s['rr']) + del mri_trans + levels = dict() + if surf is not None: + use_rr = np.concatenate([surfs[key]['rr'] for key in hemis]) + else: + use_rr = surfs['head']['rr'] + views = [('Axial', 'XY'), ('Coronal', 'XZ'), ('Sagittal', 'YZ')] + # axial: 25% up the Z axis + axial = float(np.percentile(use_rr[:, 2], 20.)) + coronal = float(np.percentile(use_rr[:, 1], 55.)) + for key in hemis + ('head',): + levels[key] = dict(Axial=axial, Coronal=coronal) + if surf is not None: + levels['rh']['Sagittal'] = float( + np.percentile(surfs['rh']['rr'][:, 0], 50)) + levels['head']['Sagittal'] = 0. + for ax_, (name, coords) in zip(ax, views): + idx = list(map(dict(X=0, Y=1, Z=2).get, coords)) + miss = np.setdiff1d(np.arange(3), idx)[0] + pos = 1000 * apply_trans(head_trans, dipoles.pos) + ori = 1000 * apply_trans(head_trans, dipoles.ori, move=False) + lims = dict() + for ii, char in enumerate(coords): + lim = surfs['head']['rr'][:, idx[ii]] + lim = np.array([lim.min(), lim.max()]) + lims[char] = lim + ax_.quiver( + pos[:, idx[0]], pos[:, idx[1]], + scale * ori[:, idx[0]], scale * ori[:, idx[1]], + color=color, pivot='middle', zorder=5, + scale_units='xy', angles='xy', scale=1., + width=width, minshaft=0.5, headwidth=2.5, headlength=2.5, + headaxislength=2) + coll = PatchCollection( + [Circle((x, y), radius=scale * 1000 * width * 6) + for x, y in zip(pos[:, idx[0]], pos[:, idx[1]])], + linewidths=0., facecolors=color, zorder=6) + for key, surf in surfs.items(): + try: + level = levels[key][name] + except KeyError: + continue + if key != 'head': + rrs = surf['rr'][:, idx] + tris = ConvexHull(rrs).simplices + segments = LineCollection( + rrs[:, [0, 1]][tris], + linewidths=1, linestyles='-', colors='k', zorder=3, + alpha=0.25) + ax_.add_collection(segments) + ax_.tricontour( + surf['rr'][:, idx[0]], surf['rr'][:, idx[1]], + surf['tris'], surf['rr'][:, miss], + levels=[level], colors='k', linewidths=1.0, linestyles=['-'], + zorder=4, alpha=0.5) + # TODO: this breaks the PatchCollection in MPL + # for coll in h.collections: + # coll.set_clip_on(False) + ax_.add_collection(coll) + ax_.set( + title=name, xlim=lims[coords[0]], ylim=lims[coords[1]], + xlabel=coords[0] + ' (mm)', ylabel=coords[1] + ' (mm)') + for spine in ax_.spines.values(): + spine.set_visible(False) + ax_.grid(True, ls=':', zorder=2) + ax_.set_aspect('equal') + + if title is not None: + fig.suptitle(title) + plt_show(show, block=block) + + return fig + + +def _plot_dipole_3d(dipoles, *, coord_frame, color, fig, trans, scale, mode): + from .backends.renderer import _get_renderer + _check_option('coord_frame', coord_frame, ('head', 'mri')) + color = 'r' if color is None else color + scale = 0.005 if scale is None else scale + renderer = _get_renderer(fig=fig, size=(600, 600)) + pos = dipoles.pos + ori = dipoles.ori + if coord_frame != 'head': + trans = _get_trans(trans, fro='head', to=coord_frame)[0] + pos = apply_trans(trans, pos) + ori = apply_trans(trans, ori) + + renderer.sphere(center=pos, color=color, scale=scale) + if mode == 'arrow': + x, y, z = pos.T + u, v, w = ori.T + renderer.quiver3d(x, y, z, u, v, w, scale=3 * scale, + color=color, mode='arrow') + renderer.show() + fig = renderer.scene() + return fig diff -Nru python-mne-0.23.4+dfsg/mne/viz/epochs.py python-mne-1.1.0+dfsg/mne/viz/epochs.py --- python-mne-0.23.4+dfsg/mne/viz/epochs.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/epochs.py 2022-08-03 15:48:14.000000000 +0000 @@ -24,7 +24,8 @@ from ..io.pick import (_get_channel_types, _picks_to_idx, _DATA_CH_TYPES_SPLIT, _VALID_CHANNEL_TYPES) -from .utils import (tight_layout, _setup_vmin_vmax, plt_show, _check_cov, +from .utils import (tight_layout, _setup_vmin_vmax, plt_show, + _check_cov, _handle_precompute, _compute_scalings, DraggableColorbar, _setup_cmap, _handle_decim, _set_title_multiple_electrodes, _make_combine_callable, _set_window_title, @@ -277,7 +278,8 @@ this_info = create_info(sfreq=epochs.info['sfreq'], ch_names=list(these_ch_names), ch_types=[this_ch_type] * len(these_picks)) - this_info['chs'] = this_ch_info + with this_info._unlock(): + this_info['chs'] = this_ch_info this_epochs = EpochsArray(this_data, this_info, tmin=epochs.times[0]) # apply scalings (only to image, not epochs object), combine channels this_image = combine_func(this_data * scalings[this_ch_type]) @@ -560,8 +562,8 @@ return fig -def plot_drop_log(drop_log, threshold=0, n_max_plot=20, subject='Unknown subj', - color=(0.8, 0.8, 0.8), width=0.8, ignore=('IGNORED',), +def plot_drop_log(drop_log, threshold=0, n_max_plot=20, subject=None, + color='lightgray', width=0.8, ignore=('IGNORED',), show=True): """Show the channel stats based on a drop_log from Epochs. @@ -580,6 +582,9 @@ .. versionchanged:: 0.23 Added support for ``None``. + + .. versionchanged:: 1.0 + Defaults to ``None``. color : tuple | str Color to use for the bars. width : float @@ -601,12 +606,18 @@ logger.info('Percent dropped epochs < supplied threshold; not ' 'plotting drop log.') return + absolute = len([x for x in drop_log if len(x) + if not any(y in ignore for y in x)]) + n_epochs_before_drop = len([x for x in drop_log + if not any(y in ignore for y in x)]) + scores = Counter([ch for d in drop_log for ch in d if ch not in ignore]) ch_names = np.array(list(scores.keys())) counts = np.array(list(scores.values())) # init figure, handle easy case (no drops) fig, ax = plt.subplots() - title = f'{percent:.1f}% of all epochs rejected' + title = (f'{absolute} of {n_epochs_before_drop} epochs removed ' + f'({percent:.1f}%)') if subject is not None: title = f'{subject}: {title}' ax.set_title(title) @@ -625,7 +636,7 @@ ax.set_xticks(x) ax.set_xticklabels(ch_names[order[:n_bars]], rotation=45, size=10, horizontalalignment='right') - ax.set_ylabel('% of epochs rejected') + ax.set_ylabel('% of epochs removed') ax.grid(axis='y') tight_layout(pad=1, fig=fig) plt_show(show) @@ -637,7 +648,9 @@ title=None, events=None, event_color=None, order=None, show=True, block=False, decim='auto', noise_cov=None, butterfly=False, show_scrollbars=True, - epoch_colors=None, event_id=None, group_by='type'): + show_scalebars=True, epoch_colors=None, event_id=None, + group_by='type', precompute=None, use_opengl=None, *, + theme=None, overview_mode=None): """Visualize epochs. Bad epochs can be marked with a left click on top of the epoch. Bad @@ -650,17 +663,7 @@ epochs : instance of Epochs The epochs object. %(picks_good_data)s - scalings : dict | 'auto' | None - Scaling factors for the traces. If any fields in scalings are 'auto', - the scaling factor is set to match the 99.5th percentile of a subset of - the corresponding data. If scalings == 'auto', all scalings fields are - set to 'auto'. If any fields are 'auto' and data is not preloaded, - a subset of epochs up to 100 Mb will be loaded. If None, defaults to:: - - dict(mag=1e-12, grad=4e-11, eeg=20e-6, eog=150e-6, ecg=5e-4, - emg=1e-3, ref_meg=1e-12, misc=1e-3, stim=1, resp=1, chpi=1e-4, - whitened=10.) - + %(scalings)s n_epochs : int The number of epochs per view. Defaults to 20. n_channels : int @@ -715,6 +718,9 @@ .. versionadded:: 0.18.0 %(show_scrollbars)s + %(show_scalebars)s + + .. versionadded:: 0.24.0 epoch_colors : list of (n_epochs) list (of n_channels) | None Colors to use for individual epochs. If None, use default colors. event_id : dict | None @@ -725,12 +731,19 @@ ``epochs.event_id`` when there are overlapping keys. .. versionadded:: 0.20 - %(browse_group_by)s + %(group_by_browse)s + %(precompute)s + %(use_opengl)s + %(theme_pg)s + + .. versionadded:: 1.0 + %(overview_mode)s + + .. versionadded:: 1.1 Returns ------- - fig : instance of matplotlib.figure.Figure - The figure. + %(browser)s Notes ----- @@ -739,16 +752,15 @@ keys, but this depends on the backend matplotlib is configured to use (e.g., mpl.use(``TkAgg``) should work). Full screen mode can be toggled with f11 key. The amount of epochs and channels per view can be adjusted - with home/end and page down/page up keys. These can also be set through - options dialog by pressing ``o`` key. ``h`` key plots a histogram of + with home/end and page down/page up keys. ``h`` key plots a histogram of peak-to-peak values along with the used rejection thresholds. Butterfly - plot can be toggled with ``b`` key. Right mouse click adds a vertical line + plot can be toggled with ``b`` key. Left mouse click adds a vertical line to the plot. Click 'help' button at bottom left corner of the plotter to view all the options. .. versionadded:: 0.10.0 """ - from ._figure import _browse_figure + from ._figure import _get_browser epochs.drop_bad() info = epochs.info.copy() @@ -756,7 +768,8 @@ projs = info['projs'] projs_on = np.full_like(projs, epochs.proj, dtype=bool) if not epochs.proj: - info['projs'] = list() + with info._unlock(): + info['projs'] = list() # handle defaults / check arg validity color = _handle_default('color', None) @@ -795,16 +808,29 @@ # events if events is not None: event_nums = events[:, 2] - boundary_samps = epochs.events[1:, 0] - epochs.time_as_index(0) - event_ixs = np.searchsorted(boundary_samps, events[:, 0], side='right') - event_samp_offsets = (events[:, 0] - epochs.events[:, 0][event_ixs] - + epochs.time_as_index(0)) - event_times = ((np.arange(len(epochs)) * len(epochs.times))[event_ixs] - + event_samp_offsets) / sfreq - # don't show events that are beyond the range of their epoch - mask = event_samp_offsets <= len(epochs.times) - event_times = event_times[mask] - event_nums = event_nums[mask] + event_samps = events[:, 0] + epoch_n_samps = len(epochs.times) + # handle overlapping epochs (each event may show up in multiple places) + boundaries = (epochs.events[:, [0]] + np.array([-1, 1]) + * epochs.time_as_index(0)) + in_bounds = np.logical_and(boundaries[:, [0]] <= event_samps, + event_samps < boundaries[:, [1]]) + event_ixs = [np.nonzero(a)[0] for a in in_bounds.T] + warned = False + event_times = list() + event_numbers = list() + for samp, num, _ixs in zip(event_samps, event_nums, event_ixs): + relevant_epoch_events = epochs.events[:, 0][_ixs] + if len(relevant_epoch_events) > 1 and not warned: + logger.info('You seem to have overlapping epochs. Some event ' + 'lines may be duplicated in the plot.') + warned = True + offsets = samp - relevant_epoch_events + epochs.time_as_index(0) + this_event_times = (_ixs * epoch_n_samps + offsets) / sfreq + event_times.extend(this_event_times) + event_numbers.extend([num] * len(_ixs)) + event_nums = np.array(event_numbers) + event_times = np.array(event_times) else: event_nums = None event_times = None @@ -831,6 +857,7 @@ elif not isinstance(title, str): raise TypeError(f'title must be None or a string, got a {type(title)}') + precompute = _handle_precompute(precompute) params = dict(inst=epochs, info=info, n_epochs=n_epochs, @@ -848,6 +875,7 @@ duration=duration, n_times=n_times, first_time=0, + time_format='float', decim=decim, boundary_times=boundary_times, # events @@ -869,7 +897,7 @@ units=units, unit_scalings=unit_scalings, # colors - ch_color_bad=(0.8, 0.8, 0.8), + ch_color_bad='lightgray', ch_color_dict=color, epoch_color_bad=(1, 0, 0), epoch_colors=epoch_colors, @@ -877,22 +905,17 @@ butterfly=butterfly, clipping=None, scrollbars_visible=show_scrollbars, - scalebars_visible=False, + scalebars_visible=show_scalebars, window_title=title, - xlabel='Epoch number') - fig = _browse_figure(**params) - fig._update_picks() + xlabel='Epoch number', + # pyqtgraph-specific + precompute=precompute, + use_opengl=use_opengl, + theme=theme, + overview_mode=overview_mode) - # make channel selection dialog, if requested (doesn't work well in init) - if group_by in ('selection', 'position'): - fig._create_selection_fig() + fig = _get_browser(show=show, block=block, **params) - fig._update_projector() - fig._update_trace_offsets() - fig._update_data() - fig._draw_traces() - - plt_show(show, block=block) return fig @@ -901,9 +924,9 @@ proj=False, bandwidth=None, adaptive=False, low_bias=True, normalization='length', picks=None, ax=None, color='black', xscale='linear', area_mode='std', area_alpha=0.33, - dB=True, estimate='auto', show=True, n_jobs=1, + dB=True, estimate='auto', show=True, n_jobs=None, average=False, line_alpha=None, spatial_colors=True, - sphere=None, verbose=None): + sphere=None, exclude='bads', verbose=None): """%(plot_psd_doc)s. Parameters @@ -929,25 +952,28 @@ low_bias : bool Only use tapers with more than 90%% spectral concentration within bandwidth. - normalization : str - Either "full" or "length" (default). If "full", the PSD will - be normalized by the sampling rate as well as the length of - the signal (as in nitime). - %(plot_psd_picks_good_data)s + %(normalization)s + %(picks_plot_psd_good_data)s ax : instance of Axes | None Axes to plot into. If None, axes will be created. - %(plot_psd_color)s - %(plot_psd_xscale)s - %(plot_psd_area_mode)s - %(plot_psd_area_alpha)s - %(plot_psd_dB)s - %(plot_psd_estimate)s + %(color_plot_psd)s + %(xscale_plot_psd)s + %(area_mode_plot_psd)s + %(area_alpha_plot_psd)s + %(dB_plot_psd)s + %(estimate_plot_psd)s %(show)s %(n_jobs)s - %(plot_psd_average)s - %(plot_psd_line_alpha)s - %(plot_psd_spatial_colors)s - %(topomap_sphere_auto)s + %(average_plot_psd)s + %(line_alpha_plot_psd)s + %(spatial_colors_plot_psd)s + %(sphere_topomap_auto)s + exclude : list of str | 'bads' + Channels names to exclude from being shown. If 'bads', the bad channels + are excluded. Pass an empty list to plot all channels (including + channels marked "bad", if any). + + .. versionadded:: 0.24.0 %(verbose)s Returns @@ -955,7 +981,7 @@ fig : instance of Figure Figure with frequency spectra of the data channels. """ - from ._figure import _psd_figure + from ._mpl_figure import _psd_figure # generate figure # epochs always use multitaper, not Welch, so no need to allow "window" @@ -967,6 +993,6 @@ line_alpha=line_alpha, area_alpha=area_alpha, color=color, spatial_colors=spatial_colors, n_jobs=n_jobs, bandwidth=bandwidth, adaptive=adaptive, low_bias=low_bias, normalization=normalization, - window='hamming') + window='hamming', exclude=exclude) plt_show(show) return fig diff -Nru python-mne-0.23.4+dfsg/mne/viz/evoked.py python-mne-1.1.0+dfsg/mne/viz/evoked.py --- python-mne-0.23.4+dfsg/mne/viz/evoked.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/evoked.py 2022-08-03 15:48:14.000000000 +0000 @@ -13,10 +13,12 @@ from copy import deepcopy from functools import partial +from itertools import cycle from numbers import Integral import numpy as np +from ..fixes import _is_last_row from ..io.pick import (channel_type, _VALID_CHANNEL_TYPES, channel_indices_by_type, _DATA_CH_TYPES_SPLIT, _pick_inst, _get_channel_types, @@ -32,7 +34,8 @@ _plot_masked_image, _trim_ticks, _set_window_title, _prop_kw) from ..utils import (logger, _clean_names, warn, _pl, verbose, _validate_type, - _check_if_nan, _check_ch_locs, fill_doc, _is_numeric) + _check_if_nan, _check_ch_locs, fill_doc, _is_numeric, + _to_rgb) from .topo import _plot_evoked_topo from .topomap import (_prepare_topomap_plot, plot_topomap, _get_pos_outlines, @@ -190,16 +193,19 @@ _draw_outlines(ax, outlines) -def _plot_evoked(evoked, picks, exclude, unit, show, ylim, proj, xlim, hline, - units, scalings, titles, axes, plot_type, cmap=None, +def _plot_evoked(evoked, picks=None, exclude='bads', unit=True, show=True, + ylim=None, proj=False, xlim='tight', hline=None, + units=None, scalings=None, titles=None, axes=None, + plot_type='butterfly', cmap=None, gfp=False, window_title=None, spatial_colors=False, selectable=True, zorder='unsorted', noise_cov=None, colorbar=True, mask=None, mask_style=None, mask_cmap=None, mask_alpha=.25, time_unit='s', - show_names=False, group_by=None, sphere=None): + show_names=False, group_by=None, sphere=None, *, + highlight=None, draw=True): """Aux function for plot_evoked and plot_evoked_image (cf. docstrings). - Extra param is: + Extra params are: plot_type : str, value ('butterfly' | 'image') The type of graph to plot: 'butterfly' plots each channel as a line @@ -207,6 +213,8 @@ color depicts the amplitude of each channel at a given time point (x axis: time, y axis: channel). In 'image' mode, the plot is not interactive. + draw : bool + If True, draw at the end. """ import matplotlib.pyplot as plt @@ -232,10 +240,10 @@ "found in `axes`") ax = axes[sel] # the unwieldy dict comp below defaults the title to the sel - titles = ({channel_type(evoked.info, idx): sel - for idx in group_by[sel]} if titles is None else titles) + title = ({channel_type(evoked.info, idx): sel + for idx in group_by[sel]} if titles is None else titles) _plot_evoked(evoked, group_by[sel], exclude, unit, show, ylim, - proj, xlim, hline, units, scalings, titles, + proj, xlim, hline, units, scalings, title, ax, plot_type, cmap=cmap, gfp=gfp, window_title=window_title, selectable=selectable, noise_cov=noise_cov, @@ -243,7 +251,7 @@ mask_style=mask_style, mask_cmap=mask_cmap, mask_alpha=mask_alpha, time_unit=time_unit, show_names=show_names, - sphere=sphere) + sphere=sphere, draw=False) if remove_xlabels and not _is_last_row(ax): ax.set_xticklabels([]) ax.set_xlabel("") @@ -270,6 +278,15 @@ _check_option('gfp', gfp, [True, False, 'only']) + if highlight is not None: + highlight = np.array(highlight, dtype=float) + highlight = np.atleast_2d(highlight) + if highlight.shape[1] != 2: + raise ValueError( + f'"highlight" must be reshapable into a 2D array with shape ' + f'(n, 2). Got {highlight.shape}.' + ) + scalings = _handle_default('scalings', scalings) titles = _handle_default('titles', titles) units = _handle_default('units', units) @@ -348,8 +365,8 @@ units, scalings, hline, gfp, types, zorder, xlim, ylim, times, bad_ch_idx, titles, ch_types_used, selectable, False, line_alpha=1., nave=evoked.nave, - time_unit=time_unit, sphere=sphere) - plt.setp(axes, xlabel='Time (%s)' % time_unit) + time_unit=time_unit, sphere=sphere, highlight=highlight) + plt.setp(axes, xlabel=f'Time ({time_unit})') elif plot_type == 'image': for ai, (ax, this_type) in enumerate(zip(axes, ch_types_used)): @@ -371,22 +388,16 @@ _draw_proj_checkbox(None, params) plt.setp(fig.axes[:len(ch_types_used) - 1], xlabel='') - fig.canvas.draw() # for axes plots update axes. + if draw: + fig.canvas.draw() # for axes plots update axes. plt_show(show) return fig -def _is_last_row(ax): - try: - return ax.get_subplotspec().is_last_row() - except AttributeError: # XXX old mpl - return ax.is_last_row() - - def _plot_lines(data, info, picks, fig, axes, spatial_colors, unit, units, scalings, hline, gfp, types, zorder, xlim, ylim, times, bad_ch_idx, titles, ch_types_used, selectable, psd, - line_alpha, nave, time_unit, sphere): + line_alpha, nave, time_unit, sphere, *, highlight): """Plot data as butterfly plot.""" from matplotlib import patheffects, pyplot as plt from matplotlib.widgets import SpanSelector @@ -440,7 +451,8 @@ if not gfp_only: chs = [info['chs'][i] for i in idx] locs3d = np.array([ch['loc'][:3] for ch in chs]) - if spatial_colors is True and not _check_ch_locs(chs): + if (spatial_colors is True and + not _check_ch_locs(info=info, picks=idx)): warn('Channel locations not available. Disabling spatial ' 'colors.') spatial_colors = selectable = False @@ -481,6 +493,7 @@ linewidth=0.5)[0]) line_list[-1].set_pickradius(3.) + # Plot GFP / RMS if gfp: if gfp in [True, 'only']: if this_type == 'eeg': @@ -533,7 +546,21 @@ for h in hline: c = ('grey' if spatial_colors is True else 'r') ax.axhline(h, linestyle='--', linewidth=2, color=c) + + # Plot highlights + if highlight is not None: + this_ylim = ax.get_ylim() if (ylim is None or this_type not in + ylim.keys()) else ylim[this_type] + for this_highlight in highlight: + ax.fill_betweenx( + this_ylim, this_highlight[0], this_highlight[1], + facecolor='orange', alpha=0.15, zorder=99 + ) + # Put back the y limits as fill_betweenx messes them up + ax.set_ylim(this_ylim) + lines.append(line_list) + if selectable: for ax in np.array(axes)[selectables]: if len(ax.lines) == 1: @@ -630,7 +657,7 @@ ylabel = 'Channels' if show_names else 'Channel (index)' t = titles[this_type] + ' (%d channel%s' % (len(data), _pl(data)) + t_end - ax.set(ylabel=ylabel, xlabel='Time (%s)' % (time_unit,), title=t) + ax.set(ylabel=ylabel, xlabel=f'Time ({time_unit})', title=t) _add_nave(ax, nave) yticks = np.arange(len(picks)) @@ -646,7 +673,7 @@ scalings=None, titles=None, axes=None, gfp=False, window_title=None, spatial_colors=False, zorder='unsorted', selectable=True, noise_cov=None, time_unit='s', sphere=None, - verbose=None): + *, highlight=None, verbose=None): """Plot evoked data using butterfly plots. Left click to a line shows the channel name. Selecting an area by clicking @@ -673,7 +700,7 @@ for each channel equals the pyplot default. xlim : 'tight' | tuple | None X limits for plots. - %(plot_proj)s + %(proj_plot)s hline : list of float | None The values at which to show an horizontal line. units : dict | None @@ -747,11 +774,22 @@ consider using :meth:`mne.Evoked.plot_white`. .. versionadded:: 0.16.0 - time_unit : str - The units for the time axis, can be "ms" or "s" (default). + %(time_unit)s .. versionadded:: 0.16 - %(topomap_sphere_auto)s + %(sphere_topomap_auto)s + highlight : array-like of float, shape(2,) | array-like of float, shape (n, 2) | None + Segments of the data to highlight by means of a light-yellow + background color. Can be used to put visual emphasis on certain + time periods. The time periods must be specified as ``array-like`` + objects in the form of ``(t_start, t_end)`` in the unit given by the + ``time_unit`` parameter. + Multiple time periods can be specified by passing an ``array-like`` + object of individual time periods (e.g., for 3 time periods, the shape + of the passed object would be ``(3, 2)``. If ``None``, no highlighting + is applied. + + .. versionadded:: 1.1 %(verbose)s Returns @@ -762,14 +800,14 @@ See Also -------- mne.viz.plot_evoked_white - """ + """ # noqa: E501 return _plot_evoked( evoked=evoked, picks=picks, exclude=exclude, unit=unit, show=show, ylim=ylim, proj=proj, xlim=xlim, hline=hline, units=units, scalings=scalings, titles=titles, axes=axes, plot_type="butterfly", gfp=gfp, window_title=window_title, spatial_colors=spatial_colors, selectable=selectable, zorder=zorder, noise_cov=noise_cov, - time_unit=time_unit, sphere=sphere) + time_unit=time_unit, sphere=sphere, highlight=highlight) def plot_evoked_topo(evoked, layout=None, layout_scale=0.945, @@ -854,13 +892,11 @@ fig : instance of matplotlib.figure.Figure Images of evoked responses at sensor locations. """ - from matplotlib.colors import colorConverter - if not type(evoked) in (tuple, list): evoked = [evoked] - dark_background = \ - np.mean(colorConverter.to_rgb(background_color)) < 0.5 + background_color = _to_rgb(background_color, name='background_color') + dark_background = np.mean(background_color) < 0.5 if dark_background: fig_facecolor = background_color axis_facecolor = background_color @@ -869,13 +905,25 @@ fig_facecolor = background_color axis_facecolor = background_color font_color = 'k' - if color is None: + + if isinstance(color, (tuple, list)): + if len(color) != len(evoked): + raise ValueError('Lists of evoked objects and colors' + ' must have the same length') + elif color is None: if dark_background: color = ['w'] + _get_color_list() else: color = _get_color_list() color = color * ((len(evoked) % len(color)) + 1) color = color[:len(evoked)] + else: + if not isinstance(color, str): + raise ValueError( + 'color must be of type tuple, list, str, or None.' + ) + color = cycle([color]) + return _plot_evoked_topo(evoked=evoked, layout=layout, layout_scale=layout_scale, color=color, border=border, ylim=ylim, scalings=scalings, @@ -1004,7 +1052,7 @@ group_by=dict(Left_ROI=[1, 2, 3, 4], Right_ROI=[5, 6, 7, 8]) If None, all picked channels are plotted to the same axis. - %(topomap_sphere_auto)s + %(sphere_topomap_auto)s Returns ------- @@ -1058,16 +1106,16 @@ ---------- evoked : instance of mne.Evoked The evoked response. - noise_cov : list | instance of Covariance | str + noise_cov : list | instance of Covariance | path-like The noise covariance. Can be a string to load a covariance from disk. show : bool Show figure if True. - %(rank_None)s + %(rank_none)s time_unit : str The units for the time axis, can be "ms" or "s" (default). .. versionadded:: 0.16 - %(topomap_sphere_auto)s + %(sphere_topomap_auto)s axes : list | None List of axes to plot into. @@ -1106,14 +1154,16 @@ covariance estimation and spatial whitening of MEG and EEG signals, vol. 108, 328-342, NeuroImage. """ - from ..cov import whiten_evoked, read_cov # recursive import + from ..cov import whiten_evoked, Covariance, _ensure_cov import matplotlib.pyplot as plt time_unit, times = _check_time_unit(time_unit, evoked.times) - if isinstance(noise_cov, str): - noise_cov = read_cov(noise_cov) + _validate_type(noise_cov, (list, tuple, Covariance, 'path-like')) if not isinstance(noise_cov, (list, tuple)): noise_cov = [noise_cov] + for ci, c in enumerate(noise_cov): + noise_cov[ci] = _ensure_cov( + noise_cov[ci], f'noise_cov[{ci}]', verbose=False) evoked = evoked.copy() # handle ref meg passive_idx = [idx for idx, proj in enumerate(evoked.info['projs']) @@ -1226,7 +1276,7 @@ label=label if n_columns > 1 else title, color=color if n_columns > 1 else ch_colors[color_ch], lw=0.5) - ax.set(xlabel='Time (%s)' % (time_unit,), ylabel=r'GFP ($\chi^2$)', + ax.set(xlabel=f'Time ({time_unit})', ylabel=r'GFP ($\chi^2$)', xlim=[times[0], times[-1]], ylim=(0, 10)) ax.axhline(1, color='red', linestyle='--', lw=2.) if n_columns > 1: @@ -1388,7 +1438,13 @@ raise ValueError("If one of `ts_args` and `topomap_args` contains " "'axes', the other must, too.") _validate_if_list_of_axes([ts_args["axes"]], 1) - n_topomaps = (3 if times is None else len(times)) + 1 + + if times in (None, 'peaks'): + n_topomaps = 3 + 1 + else: + assert not isinstance(times, str) + n_topomaps = len(times) + 1 + _validate_if_list_of_axes(list(topomap_args["axes"]), n_topomaps) got_axes = True @@ -1456,7 +1512,7 @@ proj=False, hline=None, units=None, scalings=None, titles=None, gfp=False, window_title=None, spatial_colors=True, zorder='std', - sphere=None) + sphere=None, draw=False) ts_args_def.update(ts_args) _plot_evoked(evoked, axes=ts_ax, show=False, plot_type='butterfly', exclude=[], **ts_args_def) @@ -1465,8 +1521,10 @@ # we use a new axis for the title to handle scaling of plots old_title = ts_ax.get_title() ts_ax.set_title('') + + # XXX BUG destroys ax -> fig assignment if title & axes are passed if title is not None: - title_ax = plt.subplot(4, 3, 2) + title_ax = fig.add_subplot(4, 3, 2) if title == '': title = old_title title_ax.text(.5, .5, title, transform=title_ax.transAxes, @@ -1497,6 +1555,7 @@ if topomap_args.get('colorbar', True): from matplotlib import ticker + cbar_ax.grid(False) # auto-removal deprecated as of 2021/10/05 cbar = plt.colorbar(map_ax[0].images[0], cax=cbar_ax) if isinstance(contours, (list, np.ndarray)): cbar.set_ticks(contours) @@ -1846,7 +1905,7 @@ def _draw_axes_pce(ax, ymin, ymax, truncate_yaxis, truncate_xaxis, invert_y, - vlines, tmin, tmax, unit, skip_axlabel=True): + vlines, tmin, tmax, unit, skip_axlabel=True, time_unit='s'): """Position, draw, and truncate axes for plot_compare_evokeds.""" # avoid matplotlib errors if ymin == ymax: @@ -1877,7 +1936,8 @@ raise ValueError('"truncate_yaxis" must be bool or ' '"auto", got {}'.format(truncate_yaxis)) _setup_ax_spines(ax, vlines, tmin, tmax, ybounds[0], ybounds[1], invert_y, - unit, truncate_xaxis, trunc_y, skip_axlabel) + unit, truncate_xaxis, trunc_y, skip_axlabel, + time_unit=time_unit) def _get_data_and_ci(evoked, combine, combine_func, picks, scaling=1, @@ -1977,7 +2037,7 @@ truncate_xaxis=True, ylim=None, invert_y=False, show_sensors=None, legend=True, split_legend=None, axes=None, title=None, show=True, - combine=None, sphere=None): + combine=None, sphere=None, time_unit='s'): """Plot evoked time courses for one or more conditions and/or channels. Parameters @@ -2012,11 +2072,13 @@ indicating steps or percentiles (respectively) along the colormap. If ``cmap`` is ``None``, list elements or dict values of ``colors`` must be :class:`ints ` or valid :doc:`matplotlib colors - `; lists are cycled through sequentially, + `; lists are cycled through + sequentially, while dicts must have keys matching the keys or conditions of an ``evokeds`` dict (see Notes for details). If ``None``, the current - :doc:`matplotlib color cycle ` is - used. Defaults to ``None``. + :doc:`matplotlib color cycle + ` + is used. Defaults to ``None``. linestyles : list | dict | None Styles to use when plotting the ERP/F lines. If a :class:`list` or :class:`dict`, elements must be valid :doc:`matplotlib linestyles @@ -2123,7 +2185,10 @@ unless ``picks`` is a single channel (not channel type) or ``axes='topo'``, in which cases no combining is performed. Defaults to ``None``. - %(topomap_sphere_auto)s + %(sphere_topomap_auto)s + %(time_unit)s + + .. versionadded:: 1.1 Returns ------- @@ -2229,6 +2294,7 @@ times = one_evoked.times info = one_evoked.info sphere = _check_sphere(sphere, info) + time_unit, times = _check_time_unit(time_unit, one_evoked.times) tmin, tmax = times[0], times[-1] # set some defaults if ylim is None: @@ -2242,8 +2308,8 @@ picks, picked_types = _picks_to_idx(info, picks, return_kind=True) # some things that depend on picks: ch_names = np.array(one_evoked.ch_names)[picks].tolist() - ch_types = list(_get_channel_types(info, picks=picks, unique=True) - .intersection(_DATA_CH_TYPES_SPLIT + ('misc',))) # miscICA + ch_types = [t for t in _get_channel_types(info, picks=picks, unique=True) + if t in _DATA_CH_TYPES_SPLIT + ('misc',)] # miscICA picks_by_type = channel_indices_by_type(info, picks) # discard picks from non-data channels (e.g., ref_meg) good_picks = sum([picks_by_type[ch_type] for ch_type in ch_types], []) @@ -2346,14 +2412,49 @@ sphere=sphere) layout = find_layout(info) - # shift everything to the right by 15% of one axes width - layout.pos[:, 0] += layout.pos[0, 2] * .15 - layout.pos[:, 1] += layout.pos[0, 3] * .15 + # make sure everything fits nicely. our figsize is (18, 14) so margins + # of 0.25 inch seem OK + w_margin = 0.25 / 18 + h_margin = 0.25 / 14 + axes_width = layout.pos[0, 2] + axes_height = layout.pos[0, 3] + left_edge = layout.pos[:, 0].min() + right_edge = layout.pos[:, 0].max() + axes_width + bottom_edge = layout.pos[:, 1].min() + top_edge = layout.pos[:, 1].max() + axes_height + # compute scale. Use less of vertical height (leave room for title) + w_scale = (0.95 - 2 * w_margin) / (right_edge - left_edge) + h_scale = (0.9 - 2 * h_margin) / (top_edge - bottom_edge) + # apply transformation + layout.pos[:, 0] = ((layout.pos[:, 0] - left_edge) * w_scale + + w_margin + 0.025) + layout.pos[:, 1] = ((layout.pos[:, 1] - bottom_edge) * h_scale + + h_margin + 0.025) + # make sure there is room for a legend axis (sometimes not if only a + # few channels were picked) + data_lefts = layout.pos[:, 0] + data_bottoms = layout.pos[:, 1] + legend_left = data_lefts.max() + legend_bottom = data_bottoms.min() + overlap = np.any(np.logical_and( + np.logical_and( + data_lefts <= legend_left, + legend_left <= (data_lefts + axes_width)), + np.logical_and( + data_bottoms <= legend_bottom, + legend_bottom <= (data_bottoms + axes_height) + ) + )) + right_edge = legend_left + axes_width + n_columns = (right_edge - data_lefts.min()) / axes_width + scale_factor = n_columns / (n_columns + 1) + if overlap: + layout.pos[:, [0, 2]] *= scale_factor # `axes` will be a list of (axis_object, channel_index) tuples axes = list(iter_topography( info, layout=layout, on_pick=click_func, fig=fig, fig_facecolor='w', axis_facecolor='w', - axis_spinecolor='k', layout_scale=.925, legend=True)) + axis_spinecolor='k', layout_scale=None, legend=True)) picks = list(picks) del info @@ -2367,9 +2468,11 @@ ci_dict = dict() for cond in conditions: this_evokeds = evokeds[cond] - # skip CIs when possible; assign ci_fun first to get arg checking + # assign ci_fun first to get arg checking ci_fun = _get_ci_function_pce(ci, do_topo=do_topo) - ci_fun = ci_fun if len(this_evokeds) > 1 else None + # for bootstrap or parametric CIs, skip when only 1 observation + if not callable(ci): + ci_fun = ci_fun if len(this_evokeds) > 1 else None res = _get_data_and_ci(this_evokeds, combine, c_func, picks=_picks, scaling=scalings, ci_fun=ci_fun) data_dict[cond] = res[0] @@ -2409,12 +2512,13 @@ # draw axes & vlines skip_axlabel = do_topo and (idx != -1) _draw_axes_pce(ax, ymin, ymax, truncate_yaxis, truncate_xaxis, - invert_y, vlines, tmin, tmax, units, skip_axlabel) + invert_y, vlines, tmin, tmax, units, skip_axlabel, + time_unit) # add inset scalp plot showing location of sensors picked if show_sensors: _validate_type(show_sensors, (np.int64, bool, str, type(None)), 'show_sensors', 'numeric, str, None or bool') - if not _check_ch_locs(np.array(one_evoked.info['chs'])[pos_picks]): + if not _check_ch_locs(info=one_evoked.info, picks=pos_picks): warn('Cannot find channel coordinates in the supplied Evokeds. ' 'Not showing channel locations.') else: diff -Nru python-mne-0.23.4+dfsg/mne/viz/_figure.py python-mne-1.1.0+dfsg/mne/viz/_figure.py --- python-mne-0.23.4+dfsg/mne/viz/_figure.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/_figure.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,75 +1,33 @@ # -*- coding: utf-8 -*- -"""Figure classes for MNE-Python's 2D plots. - -Class Hierarchy ---------------- - -MNEFigParams Container object, attached to MNEFigure by default. Sets - close_key='escape' plus whatever other key-value pairs are - passed to its constructor. - -matplotlib.figure.Figure - └ MNEFigure - ├ MNEBrowseFigure Interactive figure for scrollable data. - │ Generated by: - │ - raw.plot() - │ - epochs.plot() - │ - ica.plot_sources(raw) - │ - ica.plot_sources(epochs) - │ - ├ MNEAnnotationFigure GUI for adding annotations to Raw - │ - ├ MNESelectionFigure GUI for spatial channel selection. raw.plot() - │ and epochs.plot() will generate one of these - │ alongside an MNEBrowseFigure when - │ group_by == 'selection' or 'position' - │ - └ MNELineFigure Interactive figure for non-scrollable data. - Generated by: - - raw.plot_psd() - - evoked.plot() TODO Not yet implemented - - evoked.plot_white() TODO Not yet implemented - - evoked.plot_joint() TODO Not yet implemented -""" +"""Base classes and functions for 2D browser backends.""" # Authors: Daniel McCloy +# Martin Schulz # # License: Simplified BSD -from copy import deepcopy +import importlib +from abc import ABC, abstractmethod from collections import OrderedDict from contextlib import contextmanager -from functools import partial +from copy import deepcopy from itertools import cycle -import platform -import warnings import numpy as np -from matplotlib.figure import Figure -from .epochs import plot_epochs_image -from .ica import (_create_properties_layout, _fast_plot_ica_properties, - _prepare_data_ica_properties) -from .utils import (plt_show, plot_sensors, _setup_plot_projector, _events_off, - _set_window_title, _merge_annotations, DraggableLine, - _get_color_list, logger, _validate_if_list_of_axes, - _plot_psd, _prop_kw) -from ..defaults import _handle_default -from ..utils import set_config, _check_option, _check_sphere, Bunch +from .. import verbose, get_config, set_config from ..annotations import _sync_onset -from ..time_frequency import psd_welch, psd_multitaper -from ..io.pick import (pick_types, _picks_to_idx, channel_indices_by_type, - _DATA_CH_TYPES_SPLIT, _DATA_CH_TYPES_ORDER_DEFAULT, - _VALID_CHANNEL_TYPES, _FNIRS_CH_TYPES_SPLIT) - -# CONSTANTS (inches) -ANNOTATION_FIG_PAD = 0.1 -ANNOTATION_FIG_MIN_H = 2.9 # fixed part, not including radio buttons/labels -ANNOTATION_FIG_W = 5.0 -ANNOTATION_FIG_CHECKBOX_COLUMN_W = 0.5 +from ..defaults import _handle_default +from ..utils import logger, _validate_type, _check_option +from ..io.pick import _DATA_CH_TYPES_SPLIT +from .backends._utils import VALID_BROWSE_BACKENDS +from .utils import _get_color_list, _setup_plot_projector, _show_browser +MNE_BROWSER_BACKEND = None +backend = None -class MNEFigParams: - """Container object for MNE figure parameters.""" + +class BrowserParams: + """Container object for 2D browser parameters.""" def __init__(self, **kwargs): # default key to close window @@ -77,256 +35,26 @@ vars(self).update(**kwargs) -class MNEFigure(Figure): - """Base class for 2D figures & dialogs; wraps matplotlib.figure.Figure.""" - - def __init__(self, **kwargs): - from matplotlib import rcParams - # figsize is the only kwarg we pass to matplotlib Figure() - figsize = kwargs.pop('figsize', None) - super().__init__(figsize=figsize) - # things we'll almost always want - defaults = dict(fgcolor=rcParams['axes.edgecolor'], - bgcolor=rcParams['axes.facecolor']) - for key, value in defaults.items(): - if key not in kwargs: - kwargs[key] = value - # add our param object - self.mne = MNEFigParams(**kwargs) - - def _close(self, event): - """Handle close events.""" - # remove references from parent fig to child fig - is_child = getattr(self.mne, 'parent_fig', None) is not None - is_named = getattr(self.mne, 'fig_name', None) is not None - if is_child: - self.mne.parent_fig.mne.child_figs.remove(self) - if is_named: - setattr(self.mne.parent_fig.mne, self.mne.fig_name, None) - - def _keypress(self, event): - """Handle keypress events.""" - if event.key == self.mne.close_key: - from matplotlib.pyplot import close - close(self) - elif event.key == 'f11': # full screen - self.canvas.manager.full_screen_toggle() - - def _buttonpress(self, event): - """Handle buttonpress events.""" - pass - - def _pick(self, event): - """Handle matplotlib pick events.""" - pass - - def _resize(self, event): - """Handle window resize events.""" - pass - - def _add_default_callbacks(self, **kwargs): - """Remove some matplotlib default callbacks and add MNE-Python ones.""" - # Remove matplotlib default keypress catchers - default_callbacks = list( - self.canvas.callbacks.callbacks.get('key_press_event', {})) - for callback in default_callbacks: - self.canvas.callbacks.disconnect(callback) - # add our event callbacks - callbacks = dict(resize_event=self._resize, - key_press_event=self._keypress, - button_press_event=self._buttonpress, - close_event=self._close, - pick_event=self._pick) - callbacks.update(kwargs) - callback_ids = dict() - for event, callback in callbacks.items(): - callback_ids[event] = self.canvas.mpl_connect(event, callback) - # store callback references so they aren't garbage-collected - self.mne._callback_ids = callback_ids - - def _get_dpi_ratio(self): - """Get DPI ratio (to handle hi-DPI screens).""" - dpi_ratio = 1. - for key in ('_dpi_ratio', '_device_scale'): - dpi_ratio = getattr(self.canvas, key, dpi_ratio) - return dpi_ratio - - def _get_size_px(self): - """Get figure size in pixels.""" - dpi_ratio = self._get_dpi_ratio() - return self.get_size_inches() * self.dpi / dpi_ratio - - def _inch_to_rel(self, dim_inches, horiz=True): - """Convert inches to figure-relative distances.""" - fig_w, fig_h = self.get_size_inches() - w_or_h = fig_w if horiz else fig_h - return dim_inches / w_or_h - +class BrowserBase(ABC): + """A base class containing for the 2D browser. -class MNEAnnotationFigure(MNEFigure): - """Interactive dialog figure for annotations.""" - - def _close(self, event): - """Handle close events (via keypress or window [x]).""" - parent = self.mne.parent_fig - # disable span selector - parent.mne.ax_main.selector.active = False - # clear hover line - parent._remove_annotation_hover_line() - # disconnect hover callback - callback_id = parent.mne._callback_ids['motion_notify_event'] - parent.canvas.callbacks.disconnect(callback_id) - # do all the other cleanup activities - super()._close(event) - - def _keypress(self, event): - """Handle keypress events.""" - text = self.label.get_text() - key = event.key - if key == self.mne.close_key: - from matplotlib.pyplot import close - close(self) - elif key == 'backspace': - text = text[:-1] - elif key == 'enter': - self.mne.parent_fig._add_annotation_label(event) - return - elif len(key) > 1 or key == ';': # ignore modifier keys - return - else: - text = text + key - self.label.set_text(text) - self.canvas.draw() - - def _radiopress(self, event): - """Handle Radiobutton clicks for Annotation label selection.""" - # update which button looks active - buttons = self.mne.radio_ax.buttons - labels = [label.get_text() for label in buttons.labels] - idx = labels.index(buttons.value_selected) - self._set_active_button(idx) - # update click-drag rectangle color - color = buttons.circles[idx].get_edgecolor() - selector = self.mne.parent_fig.mne.ax_main.selector - # We need to update this pending - # https://github.com/matplotlib/matplotlib/issues/20618 - with warnings.catch_warnings(record=True): - warnings.simplefilter('ignore', DeprecationWarning) - selector.rect.set_color(color) - selector.rectprops.update(dict(facecolor=color)) - - def _click_override(self, event): - """Override MPL radiobutton click detector to use transData.""" - ax = self.mne.radio_ax - buttons = ax.buttons - if (buttons.ignore(event) or event.button != 1 or event.inaxes != ax): - return - pclicked = ax.transData.inverted().transform((event.x, event.y)) - distances = {} - for i, (p, t) in enumerate(zip(buttons.circles, buttons.labels)): - if (t.get_window_extent().contains(event.x, event.y) - or np.linalg.norm(pclicked - p.center) < p.radius): - distances[i] = np.linalg.norm(pclicked - p.center) - if len(distances) > 0: - closest = min(distances, key=distances.get) - buttons.set_active(closest) - - def _set_active_button(self, idx): - """Set active button in annotation dialog figure.""" - buttons = self.mne.radio_ax.buttons - with _events_off(buttons): - buttons.set_active(idx) - for circle in buttons.circles: - circle.set_facecolor(self.mne.parent_fig.mne.bgcolor) - # active circle gets filled in, partially transparent - color = list(buttons.circles[idx].get_edgecolor()) - color[-1] = 0.5 - buttons.circles[idx].set_facecolor(color) - self.canvas.draw() - - -class MNESelectionFigure(MNEFigure): - """Interactive dialog figure for channel selections.""" - - def _close(self, event): - """Handle close events.""" - from matplotlib.pyplot import close - self.mne.parent_fig.mne.child_figs.remove(self) - self.mne.fig_selection = None - # selection fig & main fig tightly integrated; closing one closes both - close(self.mne.parent_fig) + This class contains all backend-independent attributes and methods. + """ - def _keypress(self, event): - """Handle keypress events.""" - if event.key in ('up', 'down', 'b'): - self.mne.parent_fig._keypress(event) - else: # check for close key - super()._keypress(event) - - def _radiopress(self, event): - """Handle RadioButton clicks for channel selection groups.""" - selections_dict = self.mne.parent_fig.mne.ch_selections - buttons = self.mne.radio_ax.buttons - labels = [label.get_text() for label in buttons.labels] - this_label = buttons.value_selected - parent = self.mne.parent_fig - if this_label == 'Custom' and not len(selections_dict['Custom']): - with _events_off(buttons): - buttons.set_active(self.mne.old_selection) - return - # clicking a selection cancels butterfly mode - if parent.mne.butterfly: - parent._toggle_butterfly() - with _events_off(buttons): - buttons.set_active(labels.index(this_label)) - parent._update_selection() - - def _set_custom_selection(self): - """Set custom selection by lasso selector.""" - chs = self.lasso.selection - parent = self.mne.parent_fig - buttons = self.mne.radio_ax.buttons - if not len(chs): - return - labels = [label.get_text() for label in buttons.labels] - inds = np.in1d(parent.mne.ch_names, chs) - parent.mne.ch_selections['Custom'] = inds.nonzero()[0] - buttons.set_active(labels.index('Custom')) - - def _style_radio_buttons_butterfly(self): - """Handle RadioButton state for keyboard interactions.""" - # Show all radio buttons as selected when in butterfly mode - parent = self.mne.parent_fig - buttons = self.mne.radio_ax.buttons - color = (buttons.activecolor if parent.mne.butterfly else - parent.mne.bgcolor) - for circle in buttons.circles: - circle.set_facecolor(color) - # when leaving butterfly mode, make most-recently-used selection active - if not parent.mne.butterfly: - with _events_off(buttons): - buttons.set_active(self.mne.old_selection) - # update the sensors too - parent._update_highlighted_sensors() - - -class MNEBrowseFigure(MNEFigure): - """Interactive figure with scrollbars, for data browsing.""" - - def __init__(self, inst, figsize, ica=None, xlabel='Time (s)', **kwargs): - from matplotlib.colors import to_rgba_array - from matplotlib.ticker import (FixedLocator, FixedFormatter, - FuncFormatter, NullFormatter) - from matplotlib.patches import Rectangle - from matplotlib.widgets import Button - from matplotlib.transforms import blended_transform_factory - from mpl_toolkits.axes_grid1.axes_size import Fixed - from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable + def __init__(self, **kwargs): from .. import BaseEpochs from ..io import BaseRaw from ..preprocessing import ICA - super().__init__(figsize=figsize, inst=inst, ica=ica, **kwargs) + self.backend_name = None + + self._data = None + self._times = None + + self.mne = BrowserParams(**kwargs) + + inst = kwargs.get('inst', None) + ica = kwargs.get('ica', None) # what kind of data are we dealing with? if isinstance(ica, ICA): @@ -338,6 +66,9 @@ else: raise TypeError('Expected an instance of Raw, Epochs, or ICA, ' f'got {type(inst)}.') + + logger.debug(f'Opening {self.mne.instance_type} browser...') + self.mne.ica_type = None if self.mne.instance_type == 'ica': if isinstance(self.mne.ica_inst, BaseRaw): @@ -350,14 +81,20 @@ # things that always start the same self.mne.ch_start = 0 self.mne.projector = None - self.mne.projs_active = np.array([p['active'] for p in self.mne.projs]) + if hasattr(self.mne, 'projs'): + self.mne.projs_active = np.array([p['active'] + for p in self.mne.projs]) self.mne.whitened_ch_names = list() - self.mne.use_noise_cov = self.mne.noise_cov is not None + if hasattr(self.mne, 'noise_cov'): + self.mne.use_noise_cov = self.mne.noise_cov is not None self.mne.zorder = dict(patch=0, grid=1, ann=2, events=3, bads=4, data=5, mag=6, grad=7, scalebar=8, vline=9) # additional params for epochs (won't affect raw / ICA) self.mne.epoch_traces = list() self.mne.bad_epochs = list() + if inst is not None: + self.mne.sampling_period = (np.diff(inst.times[:2])[0] + / inst.info['sfreq']) # annotations self.mne.annotations = list() self.mne.hscroll_annotations = list() @@ -368,11 +105,15 @@ self.mne.annotation_hover_line = None self.mne.draggable_annotations = False # lines - self.mne.event_lines = None + self.mne.event_lines = list() self.mne.event_texts = list() self.mne.vline_visible = False + # decim + self.mne.decim_times = None + self.mne.decim_data = None # scalings - self.mne.scale_factor = 0.5 if self.mne.butterfly else 1. + if hasattr(self.mne, 'butterfly'): + self.mne.scale_factor = 0.5 if self.mne.butterfly else 1. self.mne.scalebars = dict() self.mne.scalebar_texts = dict() # ancillary child figures @@ -382,938 +123,26 @@ self.mne.fig_histogram = None self.mne.fig_selection = None self.mne.fig_annotation = None - - # MAIN AXES: default sizes (inches) - # XXX simpler with constrained_layout? (when it's no longer "beta") - l_margin = 1. - r_margin = 0.1 - b_margin = 0.45 - t_margin = 0.25 - scroll_width = 0.25 - hscroll_dist = 0.25 - vscroll_dist = 0.1 - help_width = scroll_width * 2 - # MAIN AXES: default margins (figure-relative coordinates) - left = self._inch_to_rel(l_margin - vscroll_dist - help_width) - right = 1 - self._inch_to_rel(r_margin) - bottom = self._inch_to_rel(b_margin, horiz=False) - top = 1 - self._inch_to_rel(t_margin, horiz=False) - width = right - left - height = top - bottom - position = [left, bottom, width, height] - # Main axes must be a subplot for subplots_adjust to work (so user can - # adjust margins). That's why we don't use the Divider class directly. - ax_main = self.add_subplot(1, 1, 1, position=position) - self.subplotpars.update(left=left, bottom=bottom, top=top, right=right) - div = make_axes_locatable(ax_main) - # this only gets shown in zen mode - self.mne.zen_xlabel = ax_main.set_xlabel(xlabel) - self.mne.zen_xlabel.set_visible(not self.mne.scrollbars_visible) - - # SCROLLBARS - ax_hscroll = div.append_axes(position='bottom', - size=Fixed(scroll_width), - pad=Fixed(hscroll_dist)) - ax_vscroll = div.append_axes(position='right', - size=Fixed(scroll_width), - pad=Fixed(vscroll_dist)) - ax_hscroll.get_yaxis().set_visible(False) - ax_hscroll.set_xlabel(xlabel) - ax_vscroll.set_axis_off() - # HORIZONTAL SCROLLBAR PATCHES (FOR MARKING BAD EPOCHS) + # extra attributes for epochs if self.mne.is_epochs: - epoch_nums = self.mne.inst.selection - for ix, _ in enumerate(epoch_nums): - start = self.mne.boundary_times[ix] - width = np.diff(self.mne.boundary_times[ix:ix + 2])[0] - ax_hscroll.add_patch( - Rectangle((start, 0), width, 1, color='none', - zorder=self.mne.zorder['patch'])) # add epoch boundaries & center epoch numbers between boundaries - midpoints = np.convolve(self.mne.boundary_times, np.ones(2), - mode='valid') / 2 - # both axes, major ticks: gridlines - for _ax in (ax_main, ax_hscroll): - _ax.xaxis.set_major_locator( - FixedLocator(self.mne.boundary_times[1:-1])) - _ax.xaxis.set_major_formatter(NullFormatter()) - grid_kwargs = dict(color=self.mne.fgcolor, axis='x', - zorder=self.mne.zorder['grid']) - ax_main.grid(linewidth=2, linestyle='dashed', **grid_kwargs) - ax_hscroll.grid(alpha=0.5, linewidth=0.5, linestyle='solid', - **grid_kwargs) - # main axes, minor ticks: ticklabel (epoch number) for every epoch - ax_main.xaxis.set_minor_locator(FixedLocator(midpoints)) - ax_main.xaxis.set_minor_formatter(FixedFormatter(epoch_nums)) - # hscroll axes, minor ticks: up to 20 ticklabels (epoch numbers) - ax_hscroll.xaxis.set_minor_locator( - FixedLocator(midpoints, nbins=20)) - ax_hscroll.xaxis.set_minor_formatter( - FuncFormatter(lambda x, pos: self._get_epoch_num_from_time(x))) - # hide some ticks - ax_main.tick_params(axis='x', which='major', bottom=False) - ax_hscroll.tick_params(axis='x', which='both', bottom=False) - - # VERTICAL SCROLLBAR PATCHES (COLORED BY CHANNEL TYPE) - ch_order = self.mne.ch_order - for ix, pick in enumerate(ch_order): - this_color = (self.mne.ch_color_bad - if self.mne.ch_names[pick] in self.mne.info['bads'] - else self.mne.ch_color_dict) - if isinstance(this_color, dict): - this_color = this_color[self.mne.ch_types[pick]] - ax_vscroll.add_patch( - Rectangle((0, ix), 1, 1, color=this_color, - zorder=self.mne.zorder['patch'])) - ax_vscroll.set_ylim(len(ch_order), 0) - ax_vscroll.set_visible(not self.mne.butterfly) - # SCROLLBAR VISIBLE SELECTION PATCHES - sel_kwargs = dict(alpha=0.3, linewidth=4, clip_on=False, - edgecolor=self.mne.fgcolor) - vsel_patch = Rectangle((0, 0), 1, self.mne.n_channels, - facecolor=self.mne.bgcolor, **sel_kwargs) - ax_vscroll.add_patch(vsel_patch) - hsel_facecolor = np.average( - np.vstack((to_rgba_array(self.mne.fgcolor), - to_rgba_array(self.mne.bgcolor))), - axis=0, weights=(3, 1)) # 75% foreground, 25% background - hsel_patch = Rectangle((self.mne.t_start, 0), self.mne.duration, 1, - facecolor=hsel_facecolor, **sel_kwargs) - ax_hscroll.add_patch(hsel_patch) - ax_hscroll.set_xlim(self.mne.first_time, self.mne.first_time + - self.mne.n_times / self.mne.info['sfreq']) - # VLINE - vline_color = (0., 0.75, 0.) - vline_kwargs = dict(visible=False, zorder=self.mne.zorder['vline']) - if self.mne.is_epochs: - x = np.arange(self.mne.n_epochs) - vline = ax_main.vlines( - x, 0, 1, colors=vline_color, **vline_kwargs) - vline.set_transform(blended_transform_factory(ax_main.transData, - ax_main.transAxes)) - vline_hscroll = None - else: - vline = ax_main.axvline(0, color=vline_color, **vline_kwargs) - vline_hscroll = ax_hscroll.axvline(0, color=vline_color, - **vline_kwargs) - vline_text = ax_hscroll.text( - self.mne.first_time, 1.2, '', fontsize=10, ha='right', va='bottom', - color=vline_color, **vline_kwargs) - - # HELP BUTTON: initialize in the wrong spot... - ax_help = div.append_axes(position='left', - size=Fixed(help_width), - pad=Fixed(vscroll_dist)) - # HELP BUTTON: ...move it down by changing its locator - loc = div.new_locator(nx=0, ny=0) - ax_help.set_axes_locator(loc) - # HELP BUTTON: make it a proper button - with _patched_canvas(ax_help.figure): - self.mne.button_help = Button(ax_help, 'Help') - # PROJ BUTTON - ax_proj = None - if len(self.mne.projs) and not inst.proj: - proj_button_pos = [ - 1 - self._inch_to_rel(r_margin + scroll_width), # left - self._inch_to_rel(b_margin, horiz=False), # bottom - self._inch_to_rel(scroll_width), # width - self._inch_to_rel(scroll_width, horiz=False) # height - ] - loc = div.new_locator(nx=4, ny=0) - ax_proj = self.add_axes(proj_button_pos) - ax_proj.set_axes_locator(loc) - with _patched_canvas(ax_help.figure): - self.mne.button_proj = Button(ax_proj, 'Prj') - - # INIT TRACES - self.mne.trace_kwargs = dict(antialiased=True, linewidth=0.5) - self.mne.traces = ax_main.plot( - np.full((1, self.mne.n_channels), np.nan), **self.mne.trace_kwargs) - - # SAVE UI ELEMENT HANDLES - vars(self.mne).update( - ax_main=ax_main, ax_help=ax_help, ax_proj=ax_proj, - ax_hscroll=ax_hscroll, ax_vscroll=ax_vscroll, - vsel_patch=vsel_patch, hsel_patch=hsel_patch, vline=vline, - vline_hscroll=vline_hscroll, vline_text=vline_text) - - def _close(self, event): - """Handle close events (via keypress or window [x]).""" - from matplotlib.pyplot import close - # write out bad epochs (after converting epoch numbers to indices) - if self.mne.instance_type == 'epochs': - bad_ixs = np.in1d(self.mne.inst.selection, - self.mne.bad_epochs).nonzero()[0] - self.mne.inst.drop(bad_ixs) - # write bad channels back to instance (don't do this for proj; - # proj checkboxes are for viz only and shouldn't modify the instance) - if self.mne.instance_type in ('raw', 'epochs'): - self.mne.inst.info['bads'] = self.mne.info['bads'] - logger.info( - f"Channels marked as bad: {self.mne.info['bads'] or 'none'}") - # ICA excludes - elif self.mne.instance_type == 'ica': - self.mne.ica.exclude = [self.mne.ica._ica_names.index(ch) - for ch in self.mne.info['bads']] - # write window size to config - size = ','.join(self.get_size_inches().astype(str)) - set_config('MNE_BROWSE_RAW_SIZE', size, set_env=False) - # Clean up child figures (don't pop(), child figs remove themselves) - while len(self.mne.child_figs): - fig = self.mne.child_figs[-1] - close(fig) - - def _resize(self, event): - """Handle resize event for mne_browse-style plots (Raw/Epochs/ICA).""" - old_width, old_height = self.mne.fig_size_px - new_width, new_height = self._get_size_px() - new_margins = _calc_new_margins( - self, old_width, old_height, new_width, new_height) - self.subplots_adjust(**new_margins) - # zen mode bookkeeping - self.mne.zen_w *= old_width / new_width - self.mne.zen_h *= old_height / new_height - self.mne.fig_size_px = (new_width, new_height) - self.canvas.draw_idle() - - def _hover(self, event): - """Handle motion event when annotating.""" - if (event.button is not None or event.xdata is None or - event.inaxes != self.mne.ax_main): - return - if not self.mne.draggable_annotations: - self._remove_annotation_hover_line() - return - from matplotlib.patheffects import Stroke, Normal - for coll in self.mne.annotations: - if coll.contains(event)[0]: - path = coll.get_paths() - assert len(path) == 1 - path = path[0] - color = coll.get_edgecolors()[0] - ylim = self.mne.ax_main.get_ylim() - # are we on the left or right edge? - _l = path.vertices[:, 0].min() - _r = path.vertices[:, 0].max() - x = _l if abs(event.xdata - _l) < abs(event.xdata - _r) else _r - mask = path.vertices[:, 0] == x - - def drag_callback(x0): - path.vertices[mask, 0] = x0 - - # create or update the DraggableLine - hover_line = self.mne.annotation_hover_line - if hover_line is None: - line = self.mne.ax_main.plot([x, x], ylim, color=color, - linewidth=2, pickradius=5.)[0] - hover_line = DraggableLine( - line, self._modify_annotation, drag_callback) - else: - hover_line.set_x(x) - hover_line.drag_callback = drag_callback - # style the line - line = hover_line.line - patheff = [Stroke(linewidth=4, foreground=color, alpha=0.5), - Normal()] - line.set_path_effects(patheff if line.contains(event)[0] else - patheff[1:]) - self.mne.ax_main.selector.active = False - self.mne.annotation_hover_line = hover_line - self.canvas.draw_idle() - return - self._remove_annotation_hover_line() - - def _keypress(self, event): - """Handle keypress events.""" - key = event.key - n_channels = self.mne.n_channels - if self.mne.is_epochs: - last_time = self.mne.n_times / self.mne.info['sfreq'] - else: - last_time = self.mne.inst.times[-1] - # scroll up/down - if key in ('down', 'up', 'shift+down', 'shift+up'): - key = key.split('+')[-1] - direction = -1 if key == 'up' else 1 - # butterfly case - if self.mne.butterfly: - return - # group_by case - elif self.mne.fig_selection is not None: - buttons = self.mne.fig_selection.mne.radio_ax.buttons - labels = [label.get_text() for label in buttons.labels] - current_label = buttons.value_selected - current_idx = labels.index(current_label) - selections_dict = self.mne.ch_selections - penult = current_idx < (len(labels) - 1) - pre_penult = current_idx < (len(labels) - 2) - has_custom = selections_dict.get('Custom', None) is not None - def_custom = len(selections_dict.get('Custom', list())) - up_ok = key == 'up' and current_idx > 0 - down_ok = key == 'down' and ( - pre_penult or - (penult and not has_custom) or - (penult and has_custom and def_custom)) - if up_ok or down_ok: - buttons.set_active(current_idx + direction) - # normal case - else: - ceiling = len(self.mne.ch_order) - n_channels - ch_start = self.mne.ch_start + direction * n_channels - self.mne.ch_start = np.clip(ch_start, 0, ceiling) - self._update_picks() - self._update_vscroll() - self._redraw() - # scroll left/right - elif key in ('right', 'left', 'shift+right', 'shift+left'): - old_t_start = self.mne.t_start - direction = 1 if key.endswith('right') else -1 - if self.mne.is_epochs: - denom = 1 if key.startswith('shift') else self.mne.n_epochs - else: - denom = 1 if key.startswith('shift') else 4 - t_max = last_time - self.mne.duration - t_start = self.mne.t_start + direction * self.mne.duration / denom - self.mne.t_start = np.clip(t_start, self.mne.first_time, t_max) - if self.mne.t_start != old_t_start: - self._update_hscroll() - self._redraw(annotations=True) - # scale traces - elif key in ('=', '+', '-'): - scaler = 1 / 1.1 if key == '-' else 1.1 - self.mne.scale_factor *= scaler - self._redraw(update_data=False) - # change number of visible channels - elif (key in ('pageup', 'pagedown') and - self.mne.fig_selection is None and - not self.mne.butterfly): - new_n_ch = n_channels + (1 if key == 'pageup' else -1) - self.mne.n_channels = np.clip(new_n_ch, 1, len(self.mne.ch_order)) - # add new chs from above if we're at the bottom of the scrollbar - ch_end = self.mne.ch_start + self.mne.n_channels - if ch_end > len(self.mne.ch_order) and self.mne.ch_start > 0: - self.mne.ch_start -= 1 - self._update_vscroll() - # redraw only if changed - if self.mne.n_channels != n_channels: - self._update_picks() - self._update_trace_offsets() - self._redraw(annotations=True) - # change duration - elif key in ('home', 'end'): - dur_delta = 1 if key == 'end' else -1 - if self.mne.is_epochs: - self.mne.n_epochs = np.clip(self.mne.n_epochs + dur_delta, - 1, len(self.mne.inst)) - min_dur = len(self.mne.inst.times) / self.mne.info['sfreq'] - dur_delta *= min_dur - else: - min_dur = 3 * np.diff(self.mne.inst.times[:2])[0] - old_dur = self.mne.duration - new_dur = self.mne.duration + dur_delta - self.mne.duration = np.clip(new_dur, min_dur, last_time) - if self.mne.duration != old_dur: - if self.mne.t_start + self.mne.duration > last_time: - self.mne.t_start = last_time - self.mne.duration - self._update_hscroll() - if key == 'end' and self.mne.vline_visible: # prevent flicker - self._show_vline(None) - self._redraw(annotations=True) - elif key == '?': # help window - self._toggle_help_fig(event) - elif key == 'a': # annotation mode - self._toggle_annotation_fig() - elif key == 'b' and self.mne.instance_type != 'ica': # butterfly mode - self._toggle_butterfly() - elif key == 'd': # DC shift - self.mne.remove_dc = not self.mne.remove_dc - self._redraw() - elif key == 'h' and self.mne.instance_type == 'epochs': # histogram - self._toggle_epoch_histogram() - elif key == 'j' and len(self.mne.projs): # SSP window - self._toggle_proj_fig() - elif key == 'J' and len(self.mne.projs): - self._toggle_proj_checkbox(event, toggle_all=True) - elif key == 'p': # toggle draggable annotations - self._toggle_draggable_annotations(event) - if self.mne.fig_annotation is not None: - checkbox = self.mne.fig_annotation.mne.drag_checkbox - with _events_off(checkbox): - checkbox.set_active(0) - elif key == 's': # scalebars - self._toggle_scalebars(event) - elif key == 'w': # toggle noise cov whitening - if self.mne.noise_cov is not None: - self.mne.use_noise_cov = not self.mne.use_noise_cov - self._update_projector() - self._update_yaxis_labels() # add/remove italics - self._redraw() - elif key == 'z': # zen mode: hide scrollbars and buttons - self._toggle_scrollbars() - self._redraw(update_data=False) - else: # check for close key / fullscreen toggle - super()._keypress(event) - - def _buttonpress(self, event): - """Handle mouse clicks.""" - butterfly = self.mne.butterfly - annotating = self.mne.fig_annotation is not None - ax_main = self.mne.ax_main - inst = self.mne.inst - # ignore middle clicks, scroll wheel events, and clicks outside axes - if event.button not in (1, 3) or event.inaxes is None: - return - elif event.button == 1: # left-click (primary) - # click in main axes - if (event.inaxes == ax_main and not annotating): - if self.mne.instance_type == 'epochs' or not butterfly: - for line in self.mne.traces + self.mne.epoch_traces: - if line.contains(event)[0]: - if self.mne.instance_type == 'epochs': - self._toggle_bad_epoch(event) - else: - idx = self.mne.traces.index(line) - self._toggle_bad_channel(idx) - return - self._show_vline(event.xdata) # butterfly / not on data trace - self._redraw(update_data=False, annotations=False) - return - # click in vertical scrollbar - elif event.inaxes == self.mne.ax_vscroll: - if self.mne.fig_selection is not None: - self._change_selection_vscroll(event) - elif self._check_update_vscroll_clicked(event): - self._redraw() - # click in horizontal scrollbar - elif event.inaxes == self.mne.ax_hscroll: - if self._check_update_hscroll_clicked(event): - self._redraw(annotations=True) - # click on proj button - elif event.inaxes == self.mne.ax_proj: - self._toggle_proj_fig(event) - # click on help button - elif event.inaxes == self.mne.ax_help: - self._toggle_help_fig(event) - else: # right-click (secondary) - if annotating: - if any(c.contains(event)[0] for c in ax_main.collections): - xdata = event.xdata - self.mne.first_time - start = _sync_onset(inst, inst.annotations.onset) - end = start + inst.annotations.duration - ann_idx = np.where((xdata > start) & (xdata < end))[0] - for idx in sorted(ann_idx)[::-1]: - # only remove visible annotation spans - descr = inst.annotations[idx]['description'] - if self.mne.visible_annotations[descr]: - inst.annotations.delete(idx) - self._remove_annotation_hover_line() - self._draw_annotations() - self.canvas.draw_idle() - elif event.inaxes == ax_main: - self._toggle_vline(False) - - def _pick(self, event): - """Handle matplotlib pick events.""" - from matplotlib.text import Text - if self.mne.butterfly: - return - # clicked on channel name - if isinstance(event.artist, Text): - ch_name = event.artist.get_text() - ind = self.mne.ch_names[self.mne.picks].tolist().index(ch_name) - if event.mouseevent.button == 1: # left click - self._toggle_bad_channel(ind) - elif event.mouseevent.button == 3: # right click - self._create_ch_context_fig(ind) - - def _new_child_figure(self, fig_name, **kwargs): - """Instantiate a new MNE dialog figure (with event listeners).""" - fig = _figure(toolbar=False, parent_fig=self, fig_name=fig_name, - **kwargs) - fig._add_default_callbacks() - self.mne.child_figs.append(fig) - if isinstance(fig_name, str): - setattr(self.mne, fig_name, fig) - return fig - - def _create_ch_context_fig(self, idx): - """Show context figure; idx is index of **visible** channels.""" - inst = self.mne.instance_type - pick = self.mne.picks[idx] - if inst == 'raw': - self._create_ch_location_fig(pick) - elif inst == 'ica': - self._create_ica_properties_fig(pick) - else: - self._create_epoch_image_fig(pick) - - def _create_ch_location_fig(self, pick): - """Show channel location figure.""" - from .utils import _channel_type_prettyprint - ch_name = self.mne.ch_names[pick] - ch_type = self.mne.ch_types[pick] - if ch_type not in _DATA_CH_TYPES_SPLIT: - return - # create figure and axes - fig = self._new_child_figure(figsize=(4, 4), fig_name=None, - window_title=f'Location of {ch_name}') - ax = fig.add_subplot(111) - title = f'{ch_name} position ({_channel_type_prettyprint[ch_type]})' - _ = plot_sensors(self.mne.info, ch_type=ch_type, axes=ax, - title=title, kind='select') - # highlight desired channel & disable interactivity - inds = np.in1d(fig.lasso.ch_names, [ch_name]) - fig.lasso.disconnect() - fig.lasso.alpha_other = 0.3 - fig.lasso.linewidth_selected = 3 - fig.lasso.style_sensors(inds) - plt_show(fig=fig) - - def _create_ica_properties_fig(self, idx): - """Show ICA properties for the selected component.""" - ch_name = self.mne.ch_names[idx] - if ch_name not in self.mne.ica._ica_names: # for EOG chans: do nothing - return - pick = self.mne.ica._ica_names.index(ch_name) - fig = self._new_child_figure(figsize=(7, 6), fig_name=None, - window_title=f'{ch_name} properties') - fig, axes = _create_properties_layout(fig=fig) - if not hasattr(self.mne, 'data_ica_properties'): - # Precompute epoch sources only once - self.mne.data_ica_properties = _prepare_data_ica_properties( - self.mne.ica_inst, self.mne.ica) - _fast_plot_ica_properties( - self.mne.ica, self.mne.ica_inst, picks=pick, axes=axes, - precomputed_data=self.mne.data_ica_properties) - - def _create_epoch_image_fig(self, pick): - """Show epochs image for the selected channel.""" - from matplotlib.gridspec import GridSpec - ch_name = self.mne.ch_names[pick] - fig = self._new_child_figure(figsize=(6, 4), fig_name=None, - window_title=f'Epochs image ({ch_name})') - gs = GridSpec(nrows=3, ncols=10) - fig.add_subplot(gs[:2, :9]) - fig.add_subplot(gs[2, :9]) - fig.add_subplot(gs[:2, 9]) - plot_epochs_image(self.mne.inst, picks=pick, fig=fig) - - def _toggle_epoch_histogram(self): - """Show or hide peak-to-peak histogram of channel amplitudes.""" - if self.mne.fig_histogram is None: - self._create_epoch_histogram() - plt_show(fig=self.mne.fig_histogram) - else: - from matplotlib.pyplot import close - close(self.mne.fig_histogram) - - def _create_epoch_histogram(self): - """Create peak-to-peak histogram of channel amplitudes.""" - epochs = self.mne.inst - data = OrderedDict() - ptp = np.ptp(epochs.get_data(), axis=2) - for ch_type in ('eeg', 'mag', 'grad'): - if ch_type in epochs: - data[ch_type] = ptp.T[self.mne.ch_types == ch_type].ravel() - units = _handle_default('units') - titles = _handle_default('titles') - colors = _handle_default('color') - scalings = _handle_default('scalings') - title = 'Histogram of peak-to-peak amplitudes' - figsize = (4, 1 + 1.5 * len(data)) - fig = self._new_child_figure(figsize=figsize, fig_name='fig_histogram', - window_title=title) - for ix, (_ch_type, _data) in enumerate(data.items()): - ax = fig.add_subplot(len(data), 1, ix + 1) - ax.set(title=titles[_ch_type], xlabel=units[_ch_type], - ylabel='Count') - # set histogram bin range based on rejection thresholds - reject = None - _range = None - if epochs.reject is not None and _ch_type in epochs.reject: - reject = epochs.reject[_ch_type] * scalings[_ch_type] - _range = (0., reject * 1.1) - # plot it - ax.hist(_data * scalings[_ch_type], bins=100, - color=colors[_ch_type], range=_range) - if reject is not None: - ax.plot((reject, reject), (0, ax.get_ylim()[1]), color='r') - # finalize - fig.suptitle(title, y=0.99) - kwargs = dict(bottom=fig._inch_to_rel(0.5, horiz=False), - top=1 - fig._inch_to_rel(0.5, horiz=False), - left=fig._inch_to_rel(0.75), - right=1 - fig._inch_to_rel(0.25)) - fig.subplots_adjust(hspace=0.7, **kwargs) - self.mne.fig_histogram = fig - plt_show(fig=fig) + self.mne.midpoints = np.convolve(self.mne.boundary_times, + np.ones(2), mode='valid') / 2 - # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # - # HELP DIALOG - # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # - - def _create_help_fig(self): - """Create help dialog window.""" - text = {key: val for key, val in self._get_help_text().items() - if val is not None} - keys = '' - vals = '' - for key, val in text.items(): - newsection = '\n' if key.startswith('_') else '' - key = key[1:] if key.startswith('_') else key - newlines = '\n' * len(val.split('\n')) # handle multiline values - keys += f'{newsection}{key} {newlines}' - vals += f'{newsection}{val}\n' - # calc figure size - n_lines = len(keys.split('\n')) - longest_key = max(len(k) for k in text.keys()) - longest_val = max(max(len(w) for w in v.split('\n')) if '\n' in v else - len(v) for v in text.values()) - width = (longest_key + longest_val) / 12 - height = (n_lines) / 5 - # create figure and axes - fig = self._new_child_figure(figsize=(width, height), - fig_name='fig_help', - window_title='Help') - ax = fig.add_axes((0.01, 0.01, 0.98, 0.98)) - ax.set_axis_off() - kwargs = dict(va='top', linespacing=1.5, usetex=False) - ax.text(0.42, 1, keys, ma='right', ha='right', **kwargs) - ax.text(0.42, 1, vals, ma='left', ha='left', **kwargs) - - def _toggle_help_fig(self, event): - """Show/hide the help dialog window.""" - if self.mne.fig_help is None: - self._create_help_fig() - plt_show(fig=self.mne.fig_help) - else: - from matplotlib.pyplot import close - close(self.mne.fig_help) - - def _get_help_text(self): - """Generate help dialog text; `None`-valued entries removed later.""" - inst = self.mne.instance_type - is_raw = inst == 'raw' - is_epo = inst == 'epochs' - is_ica = inst == 'ica' - has_proj = bool(len(self.mne.projs)) - # adapt keys to different platforms - is_mac = platform.system() == 'Darwin' - dur_keys = ('fn + ←', 'fn + →') if is_mac else ('Home', 'End') - ch_keys = ('fn + ↑', 'fn + ↓') if is_mac else ('Page up', 'Page down') - # adapt descriptions to different instance types - ch_cmp = 'component' if is_ica else 'channel' - ch_epo = 'epoch' if is_epo else 'channel' - ica_bad = 'Mark/unmark component for exclusion' - dur_vals = ([f'Show {n} epochs' for n in ('fewer', 'more')] - if self.mne.is_epochs else - [f'Show {d} time window' for d in ('shorter', 'longer')]) - ch_vals = [f'{inc_dec} number of visible {ch_cmp}s' for inc_dec in - ('Increase', 'Decrease')] - lclick_data = ica_bad if is_ica else f'Mark/unmark bad {ch_epo}' - lclick_name = (ica_bad if is_ica else 'Mark/unmark bad channel') - rclick_name = dict(ica='Show diagnostics for component', - epochs='Show imageplot for channel', - raw='Show channel location')[inst] - # TODO not yet implemented - # ldrag = ('Show spectrum plot for selected time span;\nor (in ' - # 'annotation mode) add annotation') if inst== 'raw' else None - ldrag = 'add annotation (in annotation mode)' if is_raw else None - noise_cov = (None if self.mne.noise_cov is None else - 'Toggle signal whitening') - scrl = '1 epoch' if self.mne.is_epochs else '¼ window' - # below, value " " is a hack to make "\n".split(value) have length 1 - help_text = OrderedDict([ - ('_NAVIGATION', ' '), - ('→', f'Scroll {scrl} right (scroll full window with Shift + →)'), - ('←', f'Scroll {scrl} left (scroll full window with Shift + ←)'), - (dur_keys[0], dur_vals[0]), - (dur_keys[1], dur_vals[1]), - ('↑', f'Scroll up ({ch_cmp}s)'), - ('↓', f'Scroll down ({ch_cmp}s)'), - (ch_keys[0], ch_vals[0]), - (ch_keys[1], ch_vals[1]), - ('_SIGNAL TRANSFORMATIONS', ' '), - ('+ or =', 'Increase signal scaling'), - ('-', 'Decrease signal scaling'), - ('b', 'Toggle butterfly mode' if not is_ica else None), - ('d', 'Toggle DC removal' if is_raw else None), - ('w', noise_cov), - ('_USER INTERFACE', ' '), - ('a', 'Toggle annotation mode' if is_raw else None), - ('h', 'Toggle peak-to-peak histogram' if is_epo else None), - ('j', 'Toggle SSP projector window' if has_proj else None), - ('shift+j', 'Toggle all SSPs'), - ('p', 'Toggle draggable annotations' if is_raw else None), - ('s', 'Toggle scalebars' if not is_ica else None), - ('z', 'Toggle scrollbars'), - ('F11', 'Toggle fullscreen' if not is_mac else None), - ('?', 'Open this help window'), - ('esc', 'Close focused figure or dialog window'), - ('_MOUSE INTERACTION', ' '), - (f'Left-click {ch_cmp} name', lclick_name), - (f'Left-click {ch_cmp} data', lclick_data), - ('Left-click-and-drag on plot', ldrag), - ('Left-click on plot background', 'Place vertical guide'), - ('Right-click on plot background', 'Clear vertical guide'), - ('Right-click on channel name', rclick_name) - ]) - return help_text + # initialize picks and projectors + self._update_picks() + if not self.mne.instance_type == 'ica': + self._update_projector() # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ANNOTATIONS # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # - def _create_annotation_fig(self): - """Create the annotation dialog window.""" - from matplotlib.widgets import Button, SpanSelector, CheckButtons - from mpl_toolkits.axes_grid1.axes_size import Fixed - from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable - # make figure - labels = np.array(sorted(set(self.mne.inst.annotations.description))) - radio_button_h = self._compute_annotation_figsize(len(labels)) - figsize = (ANNOTATION_FIG_W, ANNOTATION_FIG_MIN_H + radio_button_h) - fig = self._new_child_figure(figsize=figsize, - FigureClass=MNEAnnotationFigure, - fig_name='fig_annotation', - window_title='Annotations') - # make main axes - left = fig._inch_to_rel(ANNOTATION_FIG_PAD) - bottom = fig._inch_to_rel(ANNOTATION_FIG_PAD, horiz=False) - width = 1 - 2 * left - height = 1 - 2 * bottom - fig.mne.radio_ax = fig.add_axes((left, bottom, width, height), - frame_on=False, aspect='equal') - div = make_axes_locatable(fig.mne.radio_ax) - # append show/hide checkboxes at right - fig.mne.show_hide_ax = div.append_axes( - position='right', size=Fixed(ANNOTATION_FIG_CHECKBOX_COLUMN_W), - pad=Fixed(ANNOTATION_FIG_PAD), aspect='equal', - sharey=fig.mne.radio_ax) - # populate w/ radio buttons & labels - self._update_annotation_fig() - # append instructions at top - instructions_ax = div.append_axes(position='top', size=Fixed(1), - pad=Fixed(5 * ANNOTATION_FIG_PAD)) - # XXX when we support a newer matplotlib (something >3.0) the - # instructions can have inline bold formatting: - # instructions = '\n'.join( - # [r'$\mathbf{Left‐click~&~drag~on~plot:}$ create/modify annotation', # noqa E501 - # r'$\mathbf{Right‐click~on~plot~annotation:}$ delete annotation', - # r'$\mathbf{Type~in~annotation~window:}$ modify new label name', - # r'$\mathbf{Enter~(or~click~button):}$ add new label to list', - # r'$\mathbf{Esc:}$ exit annotation mode & close this window']) - instructions = '\n'.join( - ['Left click & drag on plot: create/modify annotation', - 'Right click on annotation highlight: delete annotation', - 'Type in this window: modify new label name', - 'Enter (or click button): add new label to list', - 'Esc: exit annotation mode & close this dialog window']) - instructions_ax.text(0, 1, instructions, va='top', ha='left', - linespacing=1.7, - usetex=False) # force use of MPL mathtext parser - instructions_ax.set_axis_off() - # append text entry axes at bottom - text_entry_ax = div.append_axes(position='bottom', - size=Fixed(3 * ANNOTATION_FIG_PAD), - pad=Fixed(ANNOTATION_FIG_PAD)) - text_entry_ax.text(0.4, 0.5, 'New label:', va='center', ha='right', - weight='bold') - fig.label = text_entry_ax.text(0.5, 0.5, 'BAD_', va='center', - ha='left') - text_entry_ax.set_axis_off() - # append button at bottom - button_ax = div.append_axes(position='bottom', - size=Fixed(3 * ANNOTATION_FIG_PAD), - pad=Fixed(ANNOTATION_FIG_PAD)) - fig.button = Button(button_ax, 'Add new label') - fig.button.on_clicked(self._add_annotation_label) - plt_show(fig=fig) - # add "draggable" checkbox - drag_ax_height = 3 * ANNOTATION_FIG_PAD - drag_ax = div.append_axes('bottom', size=Fixed(drag_ax_height), - pad=Fixed(ANNOTATION_FIG_PAD), - aspect='equal') - checkbox = CheckButtons(drag_ax, labels=('Draggable edges?',), - actives=(self.mne.draggable_annotations,)) - checkbox.on_clicked(self._toggle_draggable_annotations) - fig.mne.drag_checkbox = checkbox - # reposition & resize axes - width_in, height_in = fig.get_size_inches() - width_ax = fig._inch_to_rel(width_in - - ANNOTATION_FIG_CHECKBOX_COLUMN_W - - 3 * ANNOTATION_FIG_PAD) - aspect = width_ax / fig._inch_to_rel(drag_ax_height) - drag_ax.set_xlim(0, aspect) - drag_ax.set_axis_off() - # reposition & resize checkbox & label - rect = checkbox.rectangles[0] - _pad, _size = (0.2, 0.6) - rect.set_bounds(_pad, _pad, _size, _size) - lines = checkbox.lines[0] - for line, direction in zip(lines, (1, -1)): - line.set_xdata((_pad, _pad + _size)[::direction]) - line.set_ydata((_pad, _pad + _size)) - text = checkbox.labels[0] - text.set(position=(3 * _pad + _size, 0.45), va='center') - for artist in lines + (rect, text): - artist.set_transform(drag_ax.transData) - # setup interactivity in plot window - col = ('#ff0000' if len(fig.mne.radio_ax.buttons.circles) < 1 else - fig.mne.radio_ax.buttons.circles[0].get_edgecolor()) - # TODO: we would like useblit=True here, but it behaves oddly when the - # first span is dragged (subsequent spans seem to work OK) - rect_kw = _prop_kw('rect', dict(alpha=0.5, facecolor=col)) - selector = SpanSelector(self.mne.ax_main, self._select_annotation_span, - 'horizontal', minspan=0.1, useblit=False, - **rect_kw) - self.mne.ax_main.selector = selector - self.mne._callback_ids['motion_notify_event'] = \ - self.canvas.mpl_connect('motion_notify_event', self._hover) - - def _toggle_visible_annotations(self, event): - """Enable/disable display of annotations on a per-label basis.""" - checkboxes = self.mne.show_hide_annotation_checkboxes - labels = [t.get_text() for t in checkboxes.labels] - actives = checkboxes.get_status() - self.mne.visible_annotations = dict(zip(labels, actives)) - self._redraw(update_data=False, annotations=True) - - def _toggle_draggable_annotations(self, event): - """Enable/disable draggable annotation edges.""" - self.mne.draggable_annotations = not self.mne.draggable_annotations - def _get_annotation_labels(self): """Get the unique labels in the raw object and added in the UI.""" return sorted(set(self.mne.inst.annotations.description) | set(self.mne.new_annotation_labels)) - def _update_annotation_fig(self): - """Draw or redraw the radio buttons and annotation labels.""" - from matplotlib.widgets import RadioButtons, CheckButtons - # define shorthand variables - fig = self.mne.fig_annotation - ax = fig.mne.radio_ax - labels = self._get_annotation_labels() - # compute new figsize - radio_button_h = self._compute_annotation_figsize(len(labels)) - fig.set_size_inches(ANNOTATION_FIG_W, - ANNOTATION_FIG_MIN_H + radio_button_h, - forward=True) - # populate center axes with labels & radio buttons - ax.clear() - title = 'Existing labels:' if len(labels) else 'No existing labels' - ax.set_title(title, size=None, loc='left') - ax.buttons = RadioButtons(ax, labels) - # adjust xlim to keep equal aspect & full width (keep circles round) - aspect = (ANNOTATION_FIG_W - ANNOTATION_FIG_CHECKBOX_COLUMN_W - - 3 * ANNOTATION_FIG_PAD) / radio_button_h - ax.set_xlim((0, aspect)) - # style the buttons & adjust spacing - radius = 0.15 - circles = ax.buttons.circles - for circle, label in zip(circles, ax.buttons.labels): - circle.set_transform(ax.transData) - center = ax.transData.inverted().transform( - ax.transAxes.transform((0.1, 0))) - # XXX older MPL doesn't have circle.set_center - circle.center = (center[0], circle.center[1]) - circle.set_edgecolor( - self.mne.annotation_segment_colors[label.get_text()]) - circle.set_linewidth(4) - circle.set_radius(radius / len(labels)) - # style the selected button - if len(labels): - fig._set_active_button(0) - # add event listeners - ax.buttons.disconnect_events() # clear MPL default listeners - ax.buttons.on_clicked(fig._radiopress) - ax.buttons.connect_event('button_press_event', fig._click_override) - - # now do the show/hide checkboxes - show_hide_ax = fig.mne.show_hide_ax - show_hide_ax.clear() - show_hide_ax.set_axis_off() - aspect = ANNOTATION_FIG_CHECKBOX_COLUMN_W / radio_button_h - show_hide_ax.set(xlim=(0, aspect), ylim=(0, 1)) - # ensure new labels have checkbox values - check_values = {label: False for label in labels} - check_values.update(self.mne.visible_annotations) # existing checks - actives = [check_values[label] for label in labels] - # regenerate checkboxes - checkboxes = CheckButtons(ax=fig.mne.show_hide_ax, - labels=labels, - actives=actives) - checkboxes.on_clicked(self._toggle_visible_annotations) - # add title, hide labels - show_hide_ax.set_title('show/\nhide ', size=None, loc='right') - for label in checkboxes.labels: - label.set_visible(False) - # fix aspect and right-align - if len(labels) == 1: - bounds = (0.05, 0.375, 0.25, 0.25) # undo MPL special case - checkboxes.rectangles[0].set_bounds(bounds) - for line, step in zip(checkboxes.lines[0], (1, -1)): - line.set_xdata((bounds[0], bounds[0] + bounds[2])) - line.set_ydata((bounds[1], bounds[1] + bounds[3])[::step]) - for rect in checkboxes.rectangles: - rect.set_transform(show_hide_ax.transData) - bbox = rect.get_bbox() - bounds = (aspect, bbox.ymin, -bbox.width, bbox.height) - rect.set_bounds(bounds) - rect.set_clip_on(False) - for line in np.array(checkboxes.lines).ravel(): - line.set_transform(show_hide_ax.transData) - line.set_xdata(aspect + 0.05 - np.array(line.get_xdata())) - # store state - self.mne.visible_annotations = check_values - self.mne.show_hide_annotation_checkboxes = checkboxes - - def _toggle_annotation_fig(self): - """Show/hide the annotation dialog window.""" - if self.mne.fig_annotation is None: - self._create_annotation_fig() - else: - from matplotlib.pyplot import close - close(self.mne.fig_annotation) - - def _compute_annotation_figsize(self, n_labels): - """Adapt size of Annotation UI to accommodate the number of buttons. - - self._create_annotation_fig() implements the following: - - Fixed part of height: - 0.1 top margin - 1.0 instructions - 0.5 padding below instructions - --- (variable-height axis for label list, returned by this method) - 0.1 padding above text entry - 0.3 text entry - 0.1 padding above button - 0.3 button - 0.1 padding above checkbox - 0.3 checkbox - 0.1 bottom margin - ------------------------------------------ - 2.9 total fixed height - """ - return max(ANNOTATION_FIG_PAD, 0.7 * n_labels) - - def _add_annotation_label(self, event): - """Add new annotation description.""" - text = self.mne.fig_annotation.label.get_text() - self.mne.new_annotation_labels.append(text) - self._setup_annotation_colors() - self._update_annotation_fig() - # automatically activate new label's radio button - idx = [label.get_text() for label in - self.mne.fig_annotation.mne.radio_ax.buttons.labels].index(text) - self.mne.fig_annotation._set_active_button(idx) - # simulate a click on the radiobutton → update the span selector color - self.mne.fig_annotation._radiopress(event=None) - # reset the text entry box's text - self.mne.fig_annotation.label.set_text('BAD_') - def _setup_annotation_colors(self): """Set up colors for annotations; init some annotation vars.""" segment_colors = getattr(self.mne, 'annotation_segment_colors', dict()) @@ -1336,96 +165,6 @@ self.mne.visible_annotations = {label: True for label in labels} self.mne.show_hide_annotation_checkboxes = None - def _select_annotation_span(self, vmin, vmax): - """Handle annotation span selector.""" - onset = _sync_onset(self.mne.inst, vmin, True) - self.mne.first_time - duration = vmax - vmin - buttons = self.mne.fig_annotation.mne.radio_ax.buttons - labels = [label.get_text() for label in buttons.labels] - active_idx = labels.index(buttons.value_selected) - _merge_annotations(onset, onset + duration, labels[active_idx], - self.mne.inst.annotations) - # if adding a span with an annotation label that is hidden, show it - if not self.mne.visible_annotations[buttons.value_selected]: - self.mne.show_hide_annotation_checkboxes.set_active(active_idx) - self._redraw(update_data=False, annotations=True) - - def _remove_annotation_hover_line(self): - """Remove annotation line from the plot and reactivate selector.""" - if self.mne.annotation_hover_line is not None: - self.mne.annotation_hover_line.remove() - self.mne.annotation_hover_line = None - self.mne.ax_main.selector.active = True - self.canvas.draw() - - def _modify_annotation(self, old_x, new_x): - """Modify annotation.""" - segment = np.array(np.where(self.mne.annotation_segments == old_x)) - if segment.shape[1] == 0: - return - raw = self.mne.inst - annotations = raw.annotations - first_time = self.mne.first_time - idx = [segment[0][0], segment[1][0]] - onset = _sync_onset(raw, self.mne.annotation_segments[idx[0]][0], True) - ann_idx = np.where(annotations.onset == onset - first_time)[0] - if idx[1] == 0: # start of annotation - onset = _sync_onset(raw, new_x, True) - first_time - duration = annotations.duration[ann_idx] + old_x - new_x - else: # end of annotation - onset = annotations.onset[ann_idx] - duration = _sync_onset(raw, new_x, True) - onset - first_time - if duration < 0: - onset += duration - duration *= -1. - _merge_annotations(onset, onset + duration, - annotations.description[ann_idx], - annotations, ann_idx) - self._draw_annotations() - self._remove_annotation_hover_line() - self.canvas.draw_idle() - - def _clear_annotations(self): - """Clear all annotations from the figure.""" - for annot in list(self.mne.annotations): - annot.remove() - self.mne.annotations.remove(annot) - for annot in list(self.mne.hscroll_annotations): - annot.remove() - self.mne.hscroll_annotations.remove(annot) - for text in list(self.mne.annotation_texts): - text.remove() - self.mne.annotation_texts.remove(text) - - def _draw_annotations(self): - """Draw (or redraw) the annotation spans.""" - self._clear_annotations() - self._update_annotation_segments() - segments = self.mne.annotation_segments - times = self.mne.times - ax = self.mne.ax_main - ylim = ax.get_ylim() - for idx, (start, end) in enumerate(segments): - descr = self.mne.inst.annotations.description[idx] - segment_color = self.mne.annotation_segment_colors[descr] - kwargs = dict(color=segment_color, alpha=0.3, - zorder=self.mne.zorder['ann']) - if self.mne.visible_annotations[descr]: - # draw all segments on ax_hscroll - annot = self.mne.ax_hscroll.fill_betweenx((0, 1), start, end, - **kwargs) - self.mne.hscroll_annotations.append(annot) - # draw only visible segments on ax_main - visible_segment = np.clip([start, end], times[0], times[-1]) - if np.diff(visible_segment) > 0: - annot = ax.fill_betweenx(ylim, *visible_segment, **kwargs) - self.mne.annotations.append(annot) - xy = (visible_segment.mean(), ylim[1]) - text = ax.annotate(descr, xy, xytext=(0, 9), - textcoords='offset points', ha='center', - va='baseline', color=segment_color) - self.mne.annotation_texts.append(text) - def _update_annotation_segments(self): """Update the array of annotation start/end times.""" segments = list() @@ -1439,231 +178,16 @@ self.mne.annotation_segments = np.array(segments) # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # - # CHANNEL SELECTION GUI + # PROJECTOR & BADS # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # - def _create_selection_fig(self): - """Create channel selection dialog window.""" - from matplotlib.colors import to_rgb - from matplotlib.widgets import RadioButtons - from matplotlib.gridspec import GridSpec - # make figure - fig = self._new_child_figure(figsize=(3, 7), - FigureClass=MNESelectionFigure, - fig_name='fig_selection', - window_title='Channel selection') - # XXX when matplotlib 3.3 is min version, replace this with - # XXX gs = fig.add_gridspec(15, 1) - gs = GridSpec(nrows=15, ncols=1) - # add sensor plot at top - fig.mne.sensor_ax = fig.add_subplot(gs[:5]) - plot_sensors(self.mne.info, kind='select', ch_type='all', title='', - axes=fig.mne.sensor_ax, ch_groups=self.mne.group_by, - show=False) - fig.subplots_adjust(bottom=0.01, top=0.99, left=0.01, right=0.99) - # style the sensors so the selection is easier to distinguish - fig.lasso.linewidth_selected = 2 - self._update_highlighted_sensors() - # add radio button axes - radio_ax = fig.add_subplot(gs[5:-3], frame_on=False, aspect='equal') - fig.mne.radio_ax = radio_ax - selections_dict = self.mne.ch_selections - selections_dict.update(Custom=np.array([], dtype=int)) # for lasso - labels = list(selections_dict) - # make & style the radio buttons - activecolor = to_rgb(self.mne.fgcolor) + (0.5,) - radio_ax.buttons = RadioButtons(radio_ax, labels, - activecolor=activecolor) - fig.mne.old_selection = 0 - for circle in radio_ax.buttons.circles: - circle.set_radius(0.25 / len(labels)) - circle.set_linewidth(2) - circle.set_edgecolor(self.mne.fgcolor) - fig._style_radio_buttons_butterfly() - # add instructions at bottom - instructions = ( - 'To use a custom selection, first click-drag on the sensor plot ' - 'to "lasso" the sensors you want to select, or hold Ctrl while ' - 'clicking individual sensors. Holding Ctrl while click-dragging ' - 'allows a lasso selection adding to (rather than replacing) the ' - 'existing selection.') - instructions_ax = fig.add_subplot(gs[-3:], frame_on=False) - instructions_ax.text(0.04, 0.08, instructions, va='bottom', ha='left', - ma='left', wrap=True) - instructions_ax.set_axis_off() - # add event listeners - radio_ax.buttons.on_clicked(fig._radiopress) - fig.canvas.mpl_connect('lasso_event', fig._set_custom_selection) - - def _change_selection_vscroll(self, event): - """Handle clicks on vertical scrollbar when using selections.""" - buttons = self.mne.fig_selection.mne.radio_ax.buttons - labels = [label.get_text() for label in buttons.labels] - offset = 0 - selections_dict = self.mne.ch_selections - for idx, label in enumerate(labels): - offset += len(selections_dict[label]) - if event.ydata < offset: - with _events_off(buttons): - buttons.set_active(idx) - self.mne.fig_selection._radiopress(event) - return - - def _update_selection(self): - """Update visible channels based on selection dialog interaction.""" - selections_dict = self.mne.ch_selections - fig = self.mne.fig_selection - buttons = fig.mne.radio_ax.buttons - label = buttons.value_selected - labels = [_label.get_text() for _label in buttons.labels] - self.mne.fig_selection.mne.old_selection = labels.index(label) - self.mne.picks = selections_dict[label] - self.mne.n_channels = len(self.mne.picks) - self._update_highlighted_sensors() - # if "Vertex" is defined, some channels appear twice, so if - # "Vertex" is selected, ch_start should be the *first* match; - # otherwise it should be the *last* match (since "Vertex" is - # always the first selection group, if it exists). - index = 0 if label == 'Vertex' else -1 - ch_order = np.concatenate(list(selections_dict.values())) - ch_start = np.where(ch_order == self.mne.picks[0])[0][index] - self.mne.ch_start = ch_start - self._update_trace_offsets() - self._update_vscroll() - self._redraw(annotations=True) - - def _make_butterfly_selections_dict(self): - """Make an altered copy of the selections dict for butterfly mode.""" - from ..utils import _get_stim_channel - selections_dict = deepcopy(self.mne.ch_selections) - # remove potential duplicates - for selection_group in ('Vertex', 'Custom'): - selections_dict.pop(selection_group, None) - # if present, remove stim channel from non-misc selection groups - stim_ch = _get_stim_channel(None, self.mne.info, raise_error=False) - if len(stim_ch): - stim_pick = self.mne.ch_names.tolist().index(stim_ch[0]) - for _sel, _picks in selections_dict.items(): - if _sel != 'Misc': - stim_mask = np.in1d(_picks, [stim_pick], invert=True) - selections_dict[_sel] = np.array(_picks)[stim_mask] - return selections_dict - - def _update_highlighted_sensors(self): - """Update the sensor plot to show what is selected.""" - inds = np.in1d(self.mne.fig_selection.lasso.ch_names, - self.mne.ch_names[self.mne.picks]).nonzero()[0] - self.mne.fig_selection.lasso.select_many(inds) - - def _update_bad_sensors(self, pick, mark_bad): - """Update the sensor plot to reflect (un)marked bad channels.""" - # replicate plotting order from plot_sensors(), to get index right - sensor_picks = list() - ch_indices = channel_indices_by_type(self.mne.info) - for this_type in _DATA_CH_TYPES_SPLIT: - if this_type in self.mne.ch_types: - sensor_picks.extend(ch_indices[this_type]) - sensor_idx = np.in1d(sensor_picks, pick).nonzero()[0] - # change the sensor color - fig = self.mne.fig_selection - fig.lasso.ec[sensor_idx, 0] = float(mark_bad) # change R of RGBA array - fig.lasso.collection.set_edgecolors(fig.lasso.ec) - fig.canvas.draw_idle() - - # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # - # PROJECTORS & BAD CHANNELS - # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # - - def _create_proj_fig(self): - """Create the projectors dialog window.""" - from matplotlib.widgets import Button, CheckButtons - - projs = self.mne.projs - labels = [p['desc'] for p in projs] - for ix, active in enumerate(self.mne.projs_active): - if active: - labels[ix] += ' (already applied)' - # make figure - width = max([4.5, max([len(label) for label in labels]) / 8 + 0.5]) - height = (len(projs) + 1) / 6 + 1.5 - fig = self._new_child_figure(figsize=(width, height), - fig_name='fig_proj', - window_title='SSP projection vectors') - # pass through some proj fig keypresses to the parent - fig.canvas.mpl_connect( - 'key_press_event', - lambda ev: self._keypress(ev) if ev.key in 'jJ' else None) - # make axes - offset = (1 / 6 / height) - position = (0, offset, 1, 0.8 - offset) - ax = fig.add_axes(position, frame_on=False, aspect='equal') - # make title - first_line = ('Projectors already applied to the data are dimmed.\n' - if any(self.mne.projs_active) else '') - second_line = 'Projectors marked with "X" are active on the plot.' - ax.set_title(f'{first_line}{second_line}') - # draw checkboxes - checkboxes = CheckButtons(ax, labels=labels, actives=self.mne.projs_on) - # gray-out already applied projectors - for label, rect, lines in zip(checkboxes.labels, - checkboxes.rectangles, - checkboxes.lines): - if label.get_text().endswith('(already applied)'): - label.set_color('0.5') - rect.set_edgecolor('0.7') - [x.set_color('0.7') for x in lines] - rect.set_linewidth(1) - # add "toggle all" button - ax_all = fig.add_axes((0.25, 0.01, 0.5, offset), frame_on=True) - fig.mne.proj_all = Button(ax_all, 'Toggle all') - # add event listeners - checkboxes.on_clicked(self._toggle_proj_checkbox) - fig.mne.proj_all.on_clicked(partial(self._toggle_proj_checkbox, - toggle_all=True)) - # save params - fig.mne.proj_checkboxes = checkboxes - # show figure - self.mne.fig_proj.canvas.draw() - plt_show(fig=self.mne.fig_proj, warn=False) - - def _toggle_proj_fig(self, event=None): - """Show/hide the projectors dialog window.""" - if self.mne.fig_proj is None: - self._create_proj_fig() - else: - from matplotlib.pyplot import close - close(self.mne.fig_proj) - - def _toggle_proj_checkbox(self, event, toggle_all=False): - """Perform operations when proj boxes clicked.""" - on = self.mne.projs_on - applied = self.mne.projs_active - fig = self.mne.fig_proj - new_state = (np.full_like(on, not all(on)) if toggle_all else - np.array(fig.mne.proj_checkboxes.get_status())) - # update Xs when toggling all - if fig is not None: - if toggle_all: - with _events_off(fig.mne.proj_checkboxes): - for ix in np.where(on != new_state)[0]: - fig.mne.proj_checkboxes.set_active(ix) - # don't allow disabling already-applied projs - with _events_off(fig.mne.proj_checkboxes): - for ix in np.where(applied)[0]: - if not new_state[ix]: - fig.mne.proj_checkboxes.set_active(ix) - new_state[applied] = True - # update the data if necessary - if not np.array_equal(on, new_state): - self.mne.projs_on = new_state - self._update_projector() - self._redraw() - def _update_projector(self): """Update the data after projectors (or bads) have changed.""" inds = np.where(self.mne.projs_on)[0] # doesn't include "active" projs # copy projs from full list (self.mne.projs) to info object - self.mne.info['projs'] = [deepcopy(self.mne.projs[ix]) for ix in inds] + with self.mne.info._unlock(): + self.mne.info['projs'] = [deepcopy(self.mne.projs[ix]) + for ix in inds] # compute the projection operator proj, wh_chs = _setup_plot_projector(self.mne.info, self.mne.noise_cov, True, self.mne.use_noise_cov) @@ -1683,22 +207,19 @@ else: while ch_name in bads: # to make sure duplicates are removed bads.remove(ch_name) - color = self.mne.ch_colors[idx] + # Only mpl-backend has ch_colors + if hasattr(self.mne, 'ch_colors'): + color = self.mne.ch_colors[idx] + else: + color = None self.mne.info['bads'] = bads - # update sensor color (if in selection mode) - if self.mne.fig_selection is not None: - self._update_bad_sensors(pick, marked_bad) - # update vscroll color - vscroll_idx = (self.mne.ch_order == pick).nonzero()[0] - for _idx in vscroll_idx: - self.mne.ax_vscroll.patches[_idx].set_color(color) - # redraw + self._update_projector() - self._redraw() - def _toggle_bad_epoch(self, event): - """Mark/unmark bad epochs.""" - epoch_num = self._get_epoch_num_from_time(event.xdata) + return color, pick, marked_bad + + def _toggle_bad_epoch(self, xtime): + epoch_num = self._get_epoch_num_from_time(xtime) epoch_ix = self.mne.inst.selection.tolist().index(epoch_num) if epoch_num in self.mne.bad_epochs: self.mne.bad_epochs.remove(epoch_num) @@ -1707,210 +228,55 @@ self.mne.bad_epochs.append(epoch_num) self.mne.bad_epochs.sort() color = self.mne.epoch_color_bad - self.mne.ax_hscroll.patches[epoch_ix].set_color(color) - self._redraw(update_data=False) - # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # - # SCROLLBARS - # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + return epoch_ix, color - def _update_zen_mode_offsets(self): - """Compute difference between main axes edges and scrollbar edges.""" - self.mne.fig_size_px = self._get_size_px() - self.mne.zen_w = (self.mne.ax_vscroll.get_position().xmax - - self.mne.ax_main.get_position().xmax) - self.mne.zen_h = (self.mne.ax_main.get_position().ymin - - self.mne.ax_hscroll.get_position().ymin) - - def _toggle_scrollbars(self): - """Show or hide scrollbars (A.K.A. zen mode).""" - self._update_zen_mode_offsets() - # grow/shrink main axes to take up space from (or make room for) - # scrollbars. We can't use ax.set_position() because axes are - # locatable, so we use subplots_adjust - should_show = not self.mne.scrollbars_visible - margins = {side: getattr(self.subplotpars, side) - for side in ('left', 'bottom', 'right', 'top')} - # if should_show, bottom margin moves up; right margin moves left - margins['bottom'] += (1 if should_show else -1) * self.mne.zen_h - margins['right'] += (-1 if should_show else 1) * self.mne.zen_w - self.subplots_adjust(**margins) - # handle x-axis label - self.mne.zen_xlabel.set_visible(not should_show) - # show/hide other UI elements - for elem in ('ax_hscroll', 'ax_vscroll', 'ax_proj', 'ax_help'): - if elem == 'ax_vscroll' and self.mne.butterfly: - continue - # sometimes we don't have a proj button (ax_proj) - if getattr(self.mne, elem, None) is not None: - getattr(self.mne, elem).set_visible(should_show) - self.mne.scrollbars_visible = should_show - - def _update_vscroll(self): - """Update the vertical scrollbar (channel) selection indicator.""" - self.mne.vsel_patch.set_xy((0, self.mne.ch_start)) - self.mne.vsel_patch.set_height(self.mne.n_channels) - self._update_yaxis_labels() - - def _update_hscroll(self): - """Update the horizontal scrollbar (time) selection indicator.""" - self.mne.hsel_patch.set_xy((self.mne.t_start, 0)) - self.mne.hsel_patch.set_width(self.mne.duration) - - def _check_update_hscroll_clicked(self, event): - """Handle clicks on horizontal scrollbar.""" - time = event.xdata - self.mne.duration / 2 - max_time = (self.mne.n_times / self.mne.info['sfreq'] + - self.mne.first_time - self.mne.duration) - time = np.clip(time, self.mne.first_time, max_time) - if self.mne.is_epochs: - ix = np.searchsorted(self.mne.boundary_times[1:], time) - time = self.mne.boundary_times[ix] - if self.mne.t_start != time: - self.mne.t_start = time - self._update_hscroll() - return True - return False - - def _check_update_vscroll_clicked(self, event): - """Update vscroll patch on click, return True if location changed.""" - new_ch_start = np.clip( - int(round(event.ydata - self.mne.n_channels / 2)), - 0, len(self.mne.ch_order) - self.mne.n_channels) - if self.mne.ch_start != new_ch_start: - self.mne.ch_start = new_ch_start - self._update_picks() - self._update_vscroll() - return True - return False - - # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # - # SCALEBARS & Y-AXIS LABELS - # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # - - def _show_scalebars(self): - """Add channel scale bars.""" - for offset, pick in zip(self.mne.trace_offsets, self.mne.picks): - this_name = self.mne.ch_names[pick] - this_type = self.mne.ch_types[pick] - if (this_type not in self.mne.scalebars and - this_type != 'stim' and - this_type in self.mne.scalings and - this_type in getattr(self.mne, 'units', {}) and - this_type in getattr(self.mne, 'unit_scalings', {}) and - this_name not in self.mne.info['bads'] and - this_name not in self.mne.whitened_ch_names): - x = (self.mne.times[0] + self.mne.first_time,) * 2 - denom = 4 if self.mne.butterfly else 2 - y = tuple(np.array([-1, 1]) / denom + offset) - self._draw_one_scalebar(x, y, this_type) - - def _hide_scalebars(self): - """Remove channel scale bars.""" - for bar in self.mne.scalebars.values(): - bar.remove() - for text in self.mne.scalebar_texts.values(): - text.remove() - self.mne.scalebars = dict() - self.mne.scalebar_texts = dict() - - def _toggle_scalebars(self, event): - """Show/hide the scalebars.""" - if self.mne.scalebars_visible: - self._hide_scalebars() - else: - self._update_picks() - self._show_scalebars() - # toggle - self.mne.scalebars_visible = not self.mne.scalebars_visible - self._redraw(update_data=False) - - def _draw_one_scalebar(self, x, y, ch_type): - """Draw a scalebar.""" - from .utils import _simplify_float - color = '#AA3377' # purple - kwargs = dict(color=color, zorder=self.mne.zorder['scalebar']) - scaler = 1 if self.mne.butterfly else 2 - inv_norm = (scaler * - self.mne.scalings[ch_type] * - self.mne.unit_scalings[ch_type] / - self.mne.scale_factor) - bar = self.mne.ax_main.plot(x, y, lw=4, **kwargs)[0] - label = f'{_simplify_float(inv_norm)} {self.mne.units[ch_type]} ' - text = self.mne.ax_main.text(x[1], y[1], label, va='baseline', - ha='right', size='xx-small', **kwargs) - self.mne.scalebars[ch_type] = bar - self.mne.scalebar_texts[ch_type] = text - - def _update_yaxis_labels(self): - """Change the y-axis labels.""" - if self.mne.butterfly and self.mne.fig_selection is not None: - exclude = ('Vertex', 'Custom') - ticklabels = list(self.mne.ch_selections) - keep_mask = np.in1d(ticklabels, exclude, invert=True) - ticklabels = [t.replace('Left-', 'L-').replace('Right-', 'R-') - for t in ticklabels] # avoid having to rotate labels - ticklabels = np.array(ticklabels)[keep_mask] - elif self.mne.butterfly: - _, ixs, _ = np.intersect1d(_DATA_CH_TYPES_ORDER_DEFAULT, - self.mne.ch_types, return_indices=True) - ixs.sort() - ticklabels = np.array(_DATA_CH_TYPES_ORDER_DEFAULT)[ixs] - else: - ticklabels = self.mne.ch_names[self.mne.picks] - texts = self.mne.ax_main.set_yticklabels(ticklabels, picker=True) - for text in texts: - sty = ('italic' if text.get_text() in self.mne.whitened_ch_names - else 'normal') - text.set_style(sty) + def _toggle_whitening(self): + if self.mne.noise_cov is not None: + self.mne.use_noise_cov = not self.mne.use_noise_cov + self._update_projector() + self._update_yaxis_labels() # add/remove italics + self._redraw() # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # - # DATA TRACES + # MANAGE TRACES # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # - def _toggle_butterfly(self): - """Enter or leave butterfly mode.""" - self.mne.ax_vscroll.set_visible(self.mne.butterfly) - self.mne.butterfly = not self.mne.butterfly - self.mne.scale_factor *= 0.5 if self.mne.butterfly else 2. - self._update_picks() - self._update_trace_offsets() - self._redraw(annotations=True) - if self.mne.fig_selection is not None: - self.mne.fig_selection._style_radio_buttons_butterfly() - def _update_picks(self): """Compute which channel indices to show.""" if self.mne.butterfly and self.mne.ch_selections is not None: selections_dict = self._make_butterfly_selections_dict() self.mne.picks = np.concatenate(tuple(selections_dict.values())) elif self.mne.butterfly: - self.mne.picks = np.arange(self.mne.ch_names.shape[0]) + self.mne.picks = self.mne.ch_order else: _slice = slice(self.mne.ch_start, self.mne.ch_start + self.mne.n_channels) self.mne.picks = self.mne.ch_order[_slice] self.mne.n_channels = len(self.mne.picks) - def _get_epoch_num_from_time(self, time): - epoch_nums = self.mne.inst.selection - return epoch_nums[np.searchsorted(self.mne.boundary_times[1:], time)] + def _make_butterfly_selections_dict(self): + """Make an altered copy of the selections dict for butterfly mode.""" + from ..utils import _get_stim_channel + selections_dict = deepcopy(self.mne.ch_selections) + # remove potential duplicates + for selection_group in ('Vertex', 'Custom'): + selections_dict.pop(selection_group, None) + # if present, remove stim channel from non-misc selection groups + stim_ch = _get_stim_channel(None, self.mne.info, raise_error=False) + if len(stim_ch): + stim_pick = self.mne.ch_names.tolist().index(stim_ch[0]) + for _sel, _picks in selections_dict.items(): + if _sel != 'Misc': + stim_mask = np.in1d(_picks, [stim_pick], invert=True) + selections_dict[_sel] = np.array(_picks)[stim_mask] + return selections_dict - def _load_data(self, start=None, stop=None): - """Retrieve the bit of data we need for plotting.""" - if 'raw' in (self.mne.instance_type, self.mne.ica_type): - return self.mne.inst[:, start:stop] - else: - ix = np.searchsorted(self.mne.boundary_times, self.mne.t_start) - item = slice(ix, ix + self.mne.n_epochs) - data = np.concatenate(self.mne.inst.get_data(item=item), axis=-1) - times = np.arange(len(self.mne.inst) * len(self.mne.inst.times) - )[start:stop] / self.mne.info['sfreq'] - return data, times + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + # MANAGE DATA + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # - def _update_data(self): - """Update self.mne.data after user interaction.""" - from ..filter import _overlap_add_filter, _filtfilt + def _get_start_stop(self): # update time start_sec = self.mne.t_start - self.mne.first_time stop_sec = start_sec + self.mne.duration @@ -1919,36 +285,75 @@ * self.mne.info['sfreq']).astype(int) else: start, stop = self.mne.inst.time_as_index((start_sec, stop_sec)) - # get the data - data, times = self._load_data(start, stop) + + return start, stop + + def _load_data(self, start=None, stop=None): + """Retrieve the bit of data we need for plotting.""" + if 'raw' in (self.mne.instance_type, self.mne.ica_type): + # Add additional sample to cover the case sfreq!=1000 + # when the shown time-range wouldn't correspond to duration anymore + if stop is None: + return self.mne.inst[:, start:] + else: + return self.mne.inst[:, start:stop + 2] + else: + # subtract one sample from tstart before searchsorted, to make sure + # we land on the left side of the boundary time (avoid precision + # errors) + ix_start = np.searchsorted(self.mne.boundary_times, + self.mne.t_start - + self.mne.sampling_period) + ix_stop = ix_start + self.mne.n_epochs + item = slice(ix_start, ix_stop) + data = np.concatenate(self.mne.inst.get_data(item=item), axis=-1) + times = np.arange(start, stop) / self.mne.info['sfreq'] + return data, times + + def _apply_filter(self, data, start, stop, picks): + """Filter (with same defaults as raw.filter()).""" + from ..filter import _overlap_add_filter, _filtfilt + starts, stops = self.mne.filter_bounds + mask = (starts < stop) & (stops > start) + starts = np.maximum(starts[mask], start) - start + stops = np.minimum(stops[mask], stop) - start + for _start, _stop in zip(starts, stops): + _picks = np.where(np.in1d(picks, self.mne.picks_data))[0] + if len(_picks) == 0: + break + this_data = data[_picks, _start:_stop] + if isinstance(self.mne.filter_coefs, np.ndarray): # FIR + this_data = _overlap_add_filter( + this_data, self.mne.filter_coefs, copy=False) + else: # IIR + this_data = _filtfilt( + this_data, self.mne.filter_coefs, None, 1, False) + data[_picks, _start:_stop] = this_data + + def _process_data(self, data, start, stop, picks, + thread=None): + """Update self.mne.data after user interaction.""" # apply projectors if self.mne.projector is not None: + # thread is the loading-thread only available in Qt-backend + if thread: + thread.processText.emit('Applying Projectors...') data = self.mne.projector @ data # get only the channels we're displaying - picks = self.mne.picks data = data[picks] # remove DC if self.mne.remove_dc: - data -= data.mean(axis=1, keepdims=True) - # filter (with same defaults as raw.filter()) + if thread: + thread.processText.emit('Removing DC...') + data -= np.nanmean(data, axis=1, keepdims=True) + # apply filter if self.mne.filter_coefs is not None: - starts, stops = self.mne.filter_bounds - mask = (starts < stop) & (stops > start) - starts = np.maximum(starts[mask], start) - start - stops = np.minimum(stops[mask], stop) - start - for _start, _stop in zip(starts, stops): - _picks = np.where(np.in1d(picks, self.mne.picks_data))[0] - if len(_picks) == 0: - break - this_data = data[_picks, _start:_stop] - if isinstance(self.mne.filter_coefs, np.ndarray): # FIR - this_data = _overlap_add_filter( - this_data, self.mne.filter_coefs, copy=False) - else: # IIR - this_data = _filtfilt( - this_data, self.mne.filter_coefs, None, 1, False) - data[_picks, _start:_stop] = this_data + if thread: + thread.processText.emit('Apply Filter...') + self._apply_filter(data, start, stop, picks) # scale the data for display in a 1-vertical-axis-unit slot + if thread: + thread.processText.emit('Scale Data...') this_names = self.mne.ch_names[picks] this_types = self.mne.ch_types[picks] stims = this_types == 'stim' @@ -1960,483 +365,442 @@ norms[white] = self.mne.scalings['whitened'] norms[norms == 0] = 1 data /= 2 * norms[:, np.newaxis] + + return data + + def _update_data(self): + start, stop = self._get_start_stop() + # get the data + data, times = self._load_data(start, stop) + # process the data + data = self._process_data(data, start, stop, self.mne.picks) + # set the data as attributes self.mne.data = data self.mne.times = times - def _update_trace_offsets(self): - """Compute viewport height and adjust offsets.""" - # simultaneous selection and butterfly modes - if self.mne.butterfly and self.mne.ch_selections is not None: - self._update_picks() - selections_dict = self._make_butterfly_selections_dict() - n_offsets = len(selections_dict) - sel_order = list(selections_dict) - offsets = np.array([]) - for pick in self.mne.picks: - for sel in sel_order: - if pick in selections_dict[sel]: - offsets = np.append(offsets, sel_order.index(sel)) - # butterfly only - elif self.mne.butterfly: - unique_ch_types = set(self.mne.ch_types) - n_offsets = len(unique_ch_types) - ch_type_order = [_type for _type in _DATA_CH_TYPES_ORDER_DEFAULT - if _type in unique_ch_types] - offsets = np.array([ch_type_order.index(ch_type) - for ch_type in self.mne.ch_types]) - # normal mode - else: - n_offsets = self.mne.n_channels - offsets = np.arange(n_offsets, dtype=float) - # update ylim, ticks, vertline, and scrollbar patch - ylim = (n_offsets - 0.5, -0.5) # inverted y axis → new chs at bottom - self.mne.ax_main.set_ylim(ylim) - self.mne.ax_main.set_yticks(np.unique(offsets)) - self.mne.vsel_patch.set_height(self.mne.n_channels) - # store new offsets, update axis labels - self.mne.trace_offsets = offsets - self._update_yaxis_labels() - - def _draw_traces(self): - """Draw (or redraw) the channel data.""" - from matplotlib.colors import to_rgba_array - from matplotlib.patches import Rectangle - # clear scalebars - if self.mne.scalebars_visible: - self._hide_scalebars() - # get info about currently visible channels - picks = self.mne.picks - ch_names = self.mne.ch_names[picks] - ch_types = self.mne.ch_types[picks] - bad_bool = np.in1d(ch_names, self.mne.info['bads']) - # colors - good_ch_colors = [self.mne.ch_color_dict[_type] for _type in ch_types] - ch_colors = to_rgba_array( - [self.mne.ch_color_bad if _bad else _color - for _bad, _color in zip(bad_bool, good_ch_colors)]) - self.mne.ch_colors = np.array(good_ch_colors) # use for unmarking bads - labels = self.mne.ax_main.yaxis.get_ticklabels() - if self.mne.butterfly: - for label in labels: - label.set_color(self.mne.fgcolor) - else: - for label, color in zip(labels, ch_colors): - label.set_color(color) - # decim - decim = np.ones_like(picks) - data_picks_mask = np.in1d(picks, self.mne.picks_data) - decim[data_picks_mask] = self.mne.decim - # decim can vary by channel type, so compute different `times` vectors - decim_times = {decim_value: - self.mne.times[::decim_value] + self.mne.first_time - for decim_value in set(decim)} - # add more traces if needed - n_picks = len(picks) - if n_picks > len(self.mne.traces): - n_new_chs = n_picks - len(self.mne.traces) - new_traces = self.mne.ax_main.plot(np.full((1, n_new_chs), np.nan), - **self.mne.trace_kwargs) - self.mne.traces.extend(new_traces) - # remove extra traces if needed - extra_traces = self.mne.traces[n_picks:] - for trace in extra_traces: - trace.remove() - self.mne.traces = self.mne.traces[:n_picks] - - # check for bad epochs - time_range = (self.mne.times + self.mne.first_time)[[0, -1]] - if self.mne.instance_type == 'epochs': - epoch_ix = np.searchsorted(self.mne.boundary_times, time_range) - epoch_ix = np.arange(epoch_ix[0], epoch_ix[1]) - epoch_nums = self.mne.inst.selection[epoch_ix[0]:epoch_ix[-1] + 1] - visible_bad_epochs = epoch_nums[ - np.in1d(epoch_nums, self.mne.bad_epochs).nonzero()] - while len(self.mne.epoch_traces): - self.mne.epoch_traces.pop(-1).remove() - # handle custom epoch colors (for autoreject integration) - if self.mne.epoch_colors is None: - # shape: n_traces × RGBA → n_traces × n_epochs × RGBA - custom_colors = np.tile(ch_colors[:, None, :], - (1, self.mne.n_epochs, 1)) - else: - custom_colors = np.empty((len(self.mne.picks), - self.mne.n_epochs, 4)) - for ii, _epoch_ix in enumerate(epoch_ix): - this_colors = self.mne.epoch_colors[_epoch_ix] - custom_colors[:, ii] = to_rgba_array([this_colors[_ch] - for _ch in picks]) - # override custom color on bad epochs - for _bad in visible_bad_epochs: - _ix = epoch_nums.tolist().index(_bad) - _cols = np.array([self.mne.epoch_color_bad, - self.mne.ch_color_bad])[bad_bool.astype(int)] - custom_colors[:, _ix] = to_rgba_array(_cols) - - # update traces - ylim = self.mne.ax_main.get_ylim() - for ii, line in enumerate(self.mne.traces): - this_name = ch_names[ii] - this_type = ch_types[ii] - this_offset = self.mne.trace_offsets[ii] - this_times = decim_times[decim[ii]] - this_data = this_offset - self.mne.data[ii] * self.mne.scale_factor - this_data = this_data[..., ::decim[ii]] - # clip - if self.mne.clipping == 'clamp': - this_data = np.clip(this_data, -0.5, 0.5) - elif self.mne.clipping is not None: - clip = self.mne.clipping * (0.2 if self.mne.butterfly else 1) - bottom = max(this_offset - clip, ylim[1]) - height = min(2 * clip, ylim[0] - bottom) - rect = Rectangle(xy=np.array([time_range[0], bottom]), - width=time_range[1] - time_range[0], - height=height, - transform=self.mne.ax_main.transData) - line.set_clip_path(rect) - # prep z order - is_bad_ch = this_name in self.mne.info['bads'] - this_z = self.mne.zorder['bads' if is_bad_ch else 'data'] - if self.mne.butterfly and not is_bad_ch: - this_z = self.mne.zorder.get(this_type, this_z) - # plot each trace multiple times to get the desired epoch coloring. - # use masked arrays to plot discontinuous epochs that have the same - # color in a single plot() call. - if self.mne.instance_type == 'epochs': - this_colors = custom_colors[ii] - for cix, color in enumerate(np.unique(this_colors, axis=0)): - bool_ixs = (this_colors == color).all(axis=1) - mask = np.zeros_like(this_times, dtype=bool) - _starts = self.mne.boundary_times[epoch_ix][bool_ixs] - _stops = self.mne.boundary_times[epoch_ix + 1][bool_ixs] - for _start, _stop in zip(_starts, _stops): - _mask = np.logical_and(_start < this_times, - this_times <= _stop) - mask = mask | _mask - _times = np.ma.masked_array(this_times, mask=~mask) - # always use the existing traces first - if cix == 0: - line.set_xdata(_times) - line.set_ydata(this_data) - line.set_color(color) - line.set_zorder(this_z) - else: # make new traces as needed - _trace = self.mne.ax_main.plot( - _times, this_data, color=color, zorder=this_z, - **self.mne.trace_kwargs) - self.mne.epoch_traces.extend(_trace) - else: - line.set_xdata(this_times) - line.set_ydata(this_data) - line.set_color(ch_colors[ii]) - line.set_zorder(this_z) - # update xlim - self.mne.ax_main.set_xlim(*time_range) - # draw scalebars maybe - if self.mne.scalebars_visible: - self._show_scalebars() - # redraw event lines - if self.mne.event_times is not None: - self._draw_event_lines() + def _get_epoch_num_from_time(self, time): + epoch_nums = self.mne.inst.selection + return epoch_nums[np.searchsorted(self.mne.boundary_times[1:], time)] def _redraw(self, update_data=True, annotations=False): - """Redraw (convenience method for frequently grouped actions).""" + """Redraws backend if necessary.""" if update_data: self._update_data() - if self.mne.vline_visible and self.mne.is_epochs: - # prevent flickering - _ = self._recompute_epochs_vlines(None) + self._draw_traces() + if annotations and not self.mne.is_epochs: self._draw_annotations() - self.canvas.draw_idle() + + def _close(self, event): + """Handle close events (via keypress or window [x]).""" + from matplotlib.pyplot import close + + logger.debug(f'Closing {self.mne.instance_type} browser...') + # write out bad epochs (after converting epoch numbers to indices) + if self.mne.instance_type == 'epochs': + bad_ixs = np.in1d(self.mne.inst.selection, + self.mne.bad_epochs).nonzero()[0] + self.mne.inst.drop(bad_ixs) + logger.info('The following epochs were marked as bad ' + 'and are dropped:\n' + f'{self.mne.bad_epochs}') + # write bad channels back to instance (don't do this for proj; + # proj checkboxes are for viz only and shouldn't modify the instance) + if self.mne.instance_type in ('raw', 'epochs'): + self.mne.inst.info['bads'] = self.mne.info['bads'] + logger.info( + f"Channels marked as bad:\n" + f"{self.mne.info['bads'] or 'none'}") + # ICA excludes + elif self.mne.instance_type == 'ica': + self.mne.ica.exclude = [self.mne.ica._ica_names.index(ch) + for ch in self.mne.info['bads']] + # write window size to config + str_size = ','.join([str(i) for i in self._get_size()]) + set_config('MNE_BROWSE_RAW_SIZE', str_size, set_env=False) + # Clean up child figures (don't pop(), child figs remove themselves) + while len(self.mne.child_figs): + fig = self.mne.child_figs[-1] + close(fig) + self._close_event(fig) # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # - # EVENT LINES AND MARKER LINES + # CHILD FIGURES # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + @abstractmethod + def _new_child_figure(self, fig_name, **kwargs): + pass - def _draw_event_lines(self): - """Draw the event lines and their labels.""" - from matplotlib.colors import to_rgba_array - from matplotlib.collections import LineCollection - if self.mne.event_nums is not None: - mask = np.logical_and(self.mne.event_times >= self.mne.times[0], - self.mne.event_times <= self.mne.times[-1]) - this_event_times = self.mne.event_times[mask] - this_event_nums = self.mne.event_nums[mask] - n_visible_events = len(this_event_times) - colors = to_rgba_array([self.mne.event_color_dict[n] - for n in this_event_nums]) - # create event lines - ylim = self.mne.ax_main.get_ylim() - xs = np.repeat(this_event_times, 2) - ys = np.tile(ylim, n_visible_events) - segs = np.vstack([xs, ys]).T.reshape(n_visible_events, 2, 2) - event_lines = LineCollection(segs, linewidths=0.5, colors=colors, - zorder=self.mne.zorder['events']) - self.mne.ax_main.add_collection(event_lines) - self.mne.event_lines = event_lines - # create event labels - while len(self.mne.event_texts): - self.mne.event_texts.pop().remove() - for _t, _n, _c in zip(this_event_times, this_event_nums, colors): - label = self.mne.event_id_rev.get(_n, _n) - this_text = self.mne.ax_main.annotate( - label, (_t, ylim[1]), ha='center', va='baseline', - color=self.mne.fgcolor, xytext=(0, 2), - textcoords='offset points', fontsize=8) - self.mne.event_texts.append(this_text) - - def _recompute_epochs_vlines(self, xdata): - """Recompute vline x-coords for epochs plots (after scrolling, etc).""" - # special case: changed view duration w/ "home" or "end" key - # (no click event, hence no xdata) - if xdata is None: - xdata = np.array(self.mne.vline.get_segments())[0, 0, 0] - # compute the (continuous) times for the lines on each epoch - epoch_dur = np.diff(self.mne.boundary_times[:2])[0] - rel_time = xdata % epoch_dur - abs_time = self.mne.times[0] - xs = np.arange(self.mne.n_epochs) * epoch_dur + abs_time + rel_time - segs = np.array(self.mne.vline.get_segments()) - # recreate segs from scratch in case view duration changed - # (i.e., handle case when n_segments != n_epochs) - segs = np.tile([[0.], [1.]], (len(xs), 1, 2)) # y values - segs[..., 0] = np.tile(xs[:, None], 2) # x values - self.mne.vline.set_segments(segs) - return rel_time + def _create_ch_context_fig(self, idx): + """Show context figure; idx is index of **visible** channels.""" + inst = self.mne.instance_type + pick = self.mne.picks[idx] + if inst == 'raw': + fig = self._create_ch_location_fig(pick) + elif inst == 'ica': + fig = self._create_ica_properties_fig(pick) + else: + fig = self._create_epoch_image_fig(pick) - def _show_vline(self, xdata): - """Show the vertical line(s).""" - if self.mne.is_epochs: - # special case: changed view duration w/ "home" or "end" key - # (no click event, hence no xdata) - rel_time = self._recompute_epochs_vlines(xdata) - xdata = rel_time + self.mne.inst.times[0] # for the text + return fig + + def _create_ch_location_fig(self, pick): + """Show channel location figure.""" + from .utils import _channel_type_prettyprint, plot_sensors + ch_name = self.mne.ch_names[pick] + ch_type = self.mne.ch_types[pick] + if ch_type not in _DATA_CH_TYPES_SPLIT: + return + # create figure and axes + title = f'Location of {ch_name}' + fig = self._new_child_figure(figsize=(4, 4), fig_name=None, + window_title=title) + fig.suptitle(title) + ax = fig.add_subplot(111) + title = f'{ch_name} position ({_channel_type_prettyprint[ch_type]})' + _ = plot_sensors(self.mne.info, ch_type=ch_type, axes=ax, + title=title, kind='select', show=False) + # highlight desired channel & disable interactivity + inds = np.in1d(fig.lasso.ch_names, [ch_name]) + fig.lasso.disconnect() + fig.lasso.alpha_other = 0.3 + fig.lasso.linewidth_selected = 3 + fig.lasso.style_sensors(inds) + + return fig + + def _create_ica_properties_fig(self, idx): + """Show ICA properties for the selected component.""" + from mne.viz.ica import (_create_properties_layout, + _prepare_data_ica_properties, + _fast_plot_ica_properties) + + ch_name = self.mne.ch_names[idx] + if ch_name not in self.mne.ica._ica_names: # for EOG chans: do nothing + return + pick = self.mne.ica._ica_names.index(ch_name) + title = f'{ch_name} properties' + fig = self._new_child_figure(figsize=(7, 6), fig_name=None, + window_title=title) + fig.suptitle(title) + fig, axes = _create_properties_layout(fig=fig) + if not hasattr(self.mne, 'data_ica_properties'): + # Precompute epoch sources only once + self.mne.data_ica_properties = _prepare_data_ica_properties( + self.mne.ica_inst, self.mne.ica) + _fast_plot_ica_properties( + self.mne.ica, self.mne.ica_inst, picks=pick, axes=axes, + precomputed_data=self.mne.data_ica_properties, show=False) + + return fig + + def _create_epoch_image_fig(self, pick): + """Show epochs image for the selected channel.""" + from matplotlib.gridspec import GridSpec + from mne.viz import plot_epochs_image + ch_name = self.mne.ch_names[pick] + title = f'Epochs image ({ch_name})' + fig = self._new_child_figure(figsize=(6, 4), fig_name=None, + window_title=title) + fig.suptitle = title + gs = GridSpec(nrows=3, ncols=10) + fig.add_subplot(gs[:2, :9]) + fig.add_subplot(gs[2, :9]) + fig.add_subplot(gs[:2, 9]) + plot_epochs_image(self.mne.inst, picks=pick, fig=fig, show=False) + + return fig + + def _create_epoch_histogram(self): + """Create peak-to-peak histogram of channel amplitudes.""" + epochs = self.mne.inst + data = OrderedDict() + ptp = np.ptp(epochs.get_data(), axis=2) + for ch_type in ('eeg', 'mag', 'grad'): + if ch_type in epochs: + data[ch_type] = ptp.T[self.mne.ch_types == ch_type].ravel() + units = _handle_default('units') + titles = _handle_default('titles') + colors = _handle_default('color') + scalings = _handle_default('scalings') + title = 'Histogram of peak-to-peak amplitudes' + figsize = (4, 1 + 1.5 * len(data)) + fig = self._new_child_figure(figsize=figsize, fig_name='fig_histogram', + window_title=title) + for ix, (_ch_type, _data) in enumerate(data.items()): + ax = fig.add_subplot(len(data), 1, ix + 1) + ax.set(title=titles[_ch_type], xlabel=units[_ch_type], + ylabel='Count') + # set histogram bin range based on rejection thresholds + reject = None + _range = None + if epochs.reject is not None and _ch_type in epochs.reject: + reject = epochs.reject[_ch_type] * scalings[_ch_type] + _range = (0., reject * 1.1) + # plot it + ax.hist(_data * scalings[_ch_type], bins=100, + color=colors[_ch_type], range=_range) + if reject is not None: + ax.plot((reject, reject), (0, ax.get_ylim()[1]), color='r') + # finalize + fig.suptitle(title, y=0.99) + if hasattr(fig, '_inch_to_rel'): + kwargs = dict(bottom=fig._inch_to_rel(0.5, horiz=False), + top=1 - fig._inch_to_rel(0.5, horiz=False), + left=fig._inch_to_rel(0.75), + right=1 - fig._inch_to_rel(0.25)) else: - self.mne.vline.set_xdata(xdata) - self.mne.vline_hscroll.set_xdata(xdata) - self.mne.vline_text.set_text(f'{xdata:0.2f} s ') - self._toggle_vline(True) - - def _toggle_vline(self, visible): - """Show or hide the vertical line(s).""" - for artist in (self.mne.vline, self.mne.vline_hscroll, - self.mne.vline_text): - if artist is not None: - artist.set_visible(visible) - self.draw_artist(artist) - self.mne.vline_visible = visible - - -class MNELineFigure(MNEFigure): - """Interactive figure for non-scrolling line plots.""" - - def __init__(self, inst, n_axes, figsize, **kwargs): - super().__init__(figsize=figsize, inst=inst, **kwargs) - - # AXES: default margins (inches) - l_margin = 0.8 - r_margin = 0.2 - b_margin = 0.65 - t_margin = 0.35 - # AXES: default margins (figure-relative coordinates) - left = self._inch_to_rel(l_margin) - right = 1 - self._inch_to_rel(r_margin) - bottom = self._inch_to_rel(b_margin, horiz=False) - top = 1 - self._inch_to_rel(t_margin, horiz=False) - # AXES: make subplots - axes = [self.add_subplot(n_axes, 1, 1)] - for ix in range(1, n_axes): - axes.append(self.add_subplot(n_axes, 1, ix + 1, sharex=axes[0])) - self.subplotpars.update(left=left, bottom=bottom, top=top, right=right, - hspace=0.4) - # save useful things - self.mne.ax_list = axes - - def _resize(self, event): - """Handle resize event.""" - old_width, old_height = self.mne.fig_size_px - new_width, new_height = self._get_size_px() - new_margins = _calc_new_margins( - self, old_width, old_height, new_width, new_height) - self.subplots_adjust(**new_margins) - self.mne.fig_size_px = (new_width, new_height) - - -def _figure(toolbar=True, FigureClass=MNEFigure, **kwargs): - """Instantiate a new figure.""" - from matplotlib import rc_context - from matplotlib.pyplot import figure - title = kwargs.pop('window_title', None) # extract title before init - rc = dict() if toolbar else dict(toolbar='none') - with rc_context(rc=rc): - fig = figure(FigureClass=FigureClass, **kwargs) - if title is not None: - _set_window_title(fig, title) - # add event callbacks - fig._add_default_callbacks() - return fig + kwargs = dict() + fig.subplots_adjust(hspace=0.7, **kwargs) + self.mne.fig_histogram = fig + + return fig + + def _close_event(self, fig): + """Look at _close_event in mne.fixes.py for why this exists.""" + pass + + def fake_keypress(self, key, fig=None): # noqa: D400 + """Pass a fake keypress to the figure. + Parameters + ---------- + key : str + The key to fake (e.g., ``'a'``). + fig : instance of Figure + The figure to pass the keypress to. + """ + return self._fake_keypress(key, fig=fig) -def _browse_figure(inst, **kwargs): + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + # TEST METHODS + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + + @abstractmethod + def _get_size(self): + pass + + @abstractmethod + def _fake_keypress(self, key, fig): + pass + + @abstractmethod + def _fake_click(self, point, fig, axis, xform, button, kind): + pass + + @abstractmethod + def _click_ch_name(self, ch_index, button): + pass + + @abstractmethod + def _resize_by_factor(self, factor): + pass + + @abstractmethod + def _get_ticklabels(self, orientation): + pass + + @abstractmethod + def _update_yaxis_labels(self): + pass + + +def _load_backend(backend_name): + global backend + if backend_name == 'matplotlib': + backend = importlib.import_module(name='._mpl_figure', + package='mne.viz') + else: + from mne_qt_browser import _pg_figure as backend + + logger.info(f'Using {backend_name} as 2D backend.') + + return backend + + +def _get_browser(show, block, **kwargs): """Instantiate a new MNE browse-style figure.""" from .utils import _get_figsize_from_config - figsize = kwargs.pop('figsize', _get_figsize_from_config()) - fig = _figure(inst=inst, toolbar=False, FigureClass=MNEBrowseFigure, - figsize=figsize, **kwargs) - # initialize zen mode (can't do in __init__ due to get_position() calls) - fig.canvas.draw() - fig._update_zen_mode_offsets() - fig._resize(None) # needed for MPL >=3.4 - # if scrollbars are supposed to start hidden, set to True and then toggle - if not fig.mne.scrollbars_visible: - fig.mne.scrollbars_visible = True - fig._toggle_scrollbars() + figsize = kwargs.setdefault('figsize', _get_figsize_from_config()) + if figsize is None or np.any(np.array(figsize) < 8): + kwargs['figsize'] = (8, 8) + kwargs['splash'] = True if show else False + if kwargs.get('theme', None) is None: + kwargs['theme'] = get_config('MNE_BROWSER_THEME', 'auto') + if kwargs.get('overview_mode', None) is None: + kwargs['overview_mode'] = get_config( + 'MNE_BROWSER_OVERVIEW_MODE', 'channels') + + # Initialize browser backend + backend_name = get_browser_backend() + # Check mne-qt-browser compatibility + if backend_name == 'qt': + import mne_qt_browser + from .. import BaseEpochs + from ..fixes import _compare_version + is_ica = kwargs.get('ica', False) + is_epochs = isinstance(kwargs.get('inst', False), BaseEpochs) + not_compat = _compare_version(mne_qt_browser.__version__, '<', '0.2.0') + inst_str = 'ICA' if is_ica else 'Epochs' + if not_compat and (is_ica or is_epochs): + logger.info(f'You set the browser-backend to "qt" but your' + f' current version {mne_qt_browser.__version__}' + f' of mne-qt-browser is too low for {inst_str}.' + f'Update with pip or conda.' + f'Defaults to matplotlib.') + with use_browser_backend('matplotlib'): + # Initialize Browser + fig = backend._init_browser(**kwargs) + _show_browser(show=show, block=block, fig=fig) + return fig + + # Initialize Browser + fig = backend._init_browser(**kwargs) + _show_browser(show=show, block=block, fig=fig) + return fig -def _line_figure(inst, axes=None, picks=None, **kwargs): - """Instantiate a new line figure.""" - from matplotlib.axes import Axes - # if picks is None, only show data channels - allowed_ch_types = (_DATA_CH_TYPES_SPLIT if picks is None else - _VALID_CHANNEL_TYPES) - # figure out expected number of axes - ch_types = np.array(inst.get_channel_types()) - if picks is not None: - ch_types = ch_types[picks] - n_axes = len(np.intersect1d(ch_types, allowed_ch_types)) - # handle user-provided axes - if axes is not None: - if isinstance(axes, Axes): - axes = [axes] - _validate_if_list_of_axes(axes, n_axes) - fig = axes[0].get_figure() - else: - figsize = kwargs.pop('figsize', (10, 2.5 * n_axes + 1)) - fig = _figure(inst=inst, toolbar=True, FigureClass=MNELineFigure, - figsize=figsize, n_axes=n_axes, **kwargs) - fig.mne.fig_size_px = fig._get_size_px() # can't do in __init__ - axes = fig.mne.ax_list - return fig, axes - - -def _psd_figure(inst, proj, picks, axes, area_mode, tmin, tmax, fmin, fmax, - n_jobs, color, area_alpha, dB, estimate, average, - spatial_colors, xscale, line_alpha, sphere, window, **kwargs): - """Instantiate a new power spectral density figure.""" - from .. import BaseEpochs - from ..io import BaseRaw - # triage kwargs for different PSD methods (raw→welch, epochs→multitaper) - welch_kwargs = ('n_fft', 'n_overlap', 'reject_by_annotation') - multitaper_kwargs = ('bandwidth', 'adaptive', 'low_bias', 'normalization') - psd_kwargs = dict() - for kw in welch_kwargs + multitaper_kwargs: - if kw in kwargs: - psd_kwargs[kw] = kwargs.pop(kw) - if isinstance(inst, BaseRaw): - psd_func = partial(psd_welch, window=window) - elif isinstance(inst, BaseEpochs): - psd_func = psd_multitaper +def _check_browser_backend_name(backend_name): + _validate_type(backend_name, str, 'backend_name') + backend_name = backend_name.lower() + backend_name = 'qt' if backend_name == 'pyqtgraph' else backend_name + _check_option('backend_name', backend_name, VALID_BROWSE_BACKENDS) + return backend_name + + +@verbose +def set_browser_backend(backend_name, verbose=None): + """Set the 2D browser backend for MNE. + + The backend will be set as specified and operations will use + that backend. + + Parameters + ---------- + backend_name : str + The 2D browser backend to select. See Notes for the capabilities + of each backend (``'qt'``, ``'matplotlib'``). The ``'qt'`` browser + requires `mne-qt-browser + `__. + %(verbose)s + + Returns + ------- + old_backend_name : str | None + The old backend that was in use. + + Notes + ----- + This table shows the capabilities of each backend ("✓" for full support, + and "-" for partial support): + + .. table:: + :widths: auto + + +--------------------------------------+------------+----+ + | **2D browser function:** | matplotlib | qt | + +======================================+============+====+ + | :func:`plot_raw` | ✓ | ✓ | + +--------------------------------------+------------+----+ + | :func:`plot_epochs` | ✓ | ✓ | + +--------------------------------------+------------+----+ + | :func:`plot_ica_sources` | ✓ | ✓ | + +--------------------------------------+------------+----+ + +--------------------------------------+------------+----+ + | **Feature:** | + +--------------------------------------+------------+----+ + | Show Events | ✓ | ✓ | + +--------------------------------------+------------+----+ + | Add/Edit/Remove Annotations | ✓ | ✓ | + +--------------------------------------+------------+----+ + | Toggle Projections | ✓ | ✓ | + +--------------------------------------+------------+----+ + | Butterfly Mode | ✓ | ✓ | + +--------------------------------------+------------+----+ + | Selection Mode | ✓ | ✓ | + +--------------------------------------+------------+----+ + | Smooth Scrolling | | ✓ | + +--------------------------------------+------------+----+ + | Overview-Bar (with Z-Score-Mode) | | ✓ | + +--------------------------------------+------------+----+ + + .. versionadded:: 0.24 + """ + global MNE_BROWSER_BACKEND + old_backend_name = MNE_BROWSER_BACKEND + backend_name = _check_browser_backend_name(backend_name) + if MNE_BROWSER_BACKEND != backend_name: + _load_backend(backend_name) + MNE_BROWSER_BACKEND = backend_name + + return old_backend_name + + +def _init_browser_backend(): + global MNE_BROWSER_BACKEND + + # check if MNE_BROWSER_BACKEND is not None and valid or get it from config + loaded_backend = (MNE_BROWSER_BACKEND or + get_config(key='MNE_BROWSER_BACKEND', default=None)) + if loaded_backend is not None: + set_browser_backend(loaded_backend) + return MNE_BROWSER_BACKEND else: - raise TypeError('Expected an instance of Raw or Epochs, got ' - f'{type(inst)}.') - # arg checking - if np.isfinite(fmax) and (fmax > inst.info['sfreq'] / 2): - raise ValueError( - f'Requested fmax ({fmax} Hz) must not exceed ½ the sampling ' - f'frequency of the data ({0.5 * inst.info["sfreq"]}).') - _check_option('area_mode', area_mode, [None, 'std', 'range']) - _check_option('xscale', xscale, ('log', 'linear')) - sphere = _check_sphere(sphere, inst.info) - picks = _picks_to_idx(inst.info, picks) - titles = _handle_default('titles', None) - units = _handle_default('units', None) - scalings = _handle_default('scalings', None) - # containers - picks_list = list() - units_list = list() - titles_list = list() - scalings_list = list() - psd_list = list() - # initialize figure - fig, axes = _line_figure(inst, axes, picks, **kwargs) - # split picks, units, etc, for each subplot - (picks_list, units_list, scalings_list, titles_list - ) = _split_picks_by_type(inst, picks, units, scalings, titles) - del picks - # don't add ylabels & titles if figure has unexpected number of axes - make_label = len(axes) == len(fig.axes) - # Plot Frequency [Hz] xlabel only on the last axis - xlabels_list = [False] * (len(axes) - 1) + [True] - # compute PSDs - for picks in picks_list: - psd, freqs = psd_func(inst, tmin=tmin, tmax=tmax, picks=picks, - fmin=fmin, fmax=fmax, proj=proj, n_jobs=n_jobs, - **psd_kwargs) - if isinstance(inst, BaseEpochs): - psd = np.mean(psd, axis=0) - psd_list.append(psd) - # plot - _plot_psd(inst, fig, freqs, psd_list, picks_list, titles_list, units_list, - scalings_list, axes, make_label, color, area_mode, area_alpha, - dB, estimate, average, spatial_colors, xscale, line_alpha, - sphere, xlabels_list) - return fig + errors = dict() + # Try import of valid browser backends + for name in VALID_BROWSE_BACKENDS: + try: + _load_backend(name) + except ImportError as exc: + errors[name] = str(exc) + else: + MNE_BROWSER_BACKEND = name + break + else: + raise RuntimeError( + 'Could not load any valid 2D backend:\n' + + '\n'.join(f'{key}: {val}' for key, val in errors.items())) + return MNE_BROWSER_BACKEND -def _split_picks_by_type(inst, picks, units, scalings, titles): - """Separate picks, units, etc, for plotting on separate subplots.""" - picks_list = list() - units_list = list() - scalings_list = list() - titles_list = list() - # if picks is None, only show data channels - allowed_ch_types = (_DATA_CH_TYPES_SPLIT if picks is None else - _VALID_CHANNEL_TYPES) - for ch_type in allowed_ch_types: - pick_kwargs = dict(meg=False, ref_meg=False, exclude=[]) - if ch_type in ('mag', 'grad'): - pick_kwargs['meg'] = ch_type - elif ch_type in _FNIRS_CH_TYPES_SPLIT: - pick_kwargs['fnirs'] = ch_type - else: - pick_kwargs[ch_type] = True - these_picks = pick_types(inst.info, **pick_kwargs) - these_picks = np.intersect1d(these_picks, picks) - if len(these_picks) > 0: - picks_list.append(these_picks) - units_list.append(units[ch_type]) - scalings_list.append(scalings[ch_type]) - titles_list.append(titles[ch_type]) - if len(picks_list) == 0: - raise RuntimeError('No data channels found') - return picks_list, units_list, scalings_list, titles_list - - -def _calc_new_margins(fig, old_width, old_height, new_width, new_height): - """Compute new figure-relative values to maintain fixed-size margins.""" - new_margins = dict() - for side in ('left', 'right', 'bottom', 'top'): - ratio = ((old_width / new_width) if side in ('left', 'right') else - (old_height / new_height)) - rel_dim = getattr(fig.subplotpars, side) - if side in ('right', 'top'): - new_margins[side] = 1 - ratio * (1 - rel_dim) - else: - new_margins[side] = ratio * rel_dim - # gh-8304: don't allow resizing too small - if (new_margins['bottom'] < new_margins['top'] and - new_margins['left'] < new_margins['right']): - return(new_margins) + +def get_browser_backend(): + """Return the 2D backend currently used. + + Returns + ------- + backend_used : str | None + The 2D browser backend currently in use. If no backend is found, + returns ``None``. + """ + try: + backend_name = _init_browser_backend() + except RuntimeError as exc: + backend_name = None + logger.info(str(exc)) + return backend_name @contextmanager -def _patched_canvas(fig): - old_canvas = fig.canvas - if fig.canvas is None: # XXX old MPL (at least 3.0.3) does this for Agg - fig.canvas = Bunch(mpl_connect=lambda event, callback: None) +def use_browser_backend(backend_name): + """Create a 2D browser visualization context using the designated backend. + + See :func:`mne.viz.set_browser_backend` for more details on the available + 2D browser backends and their capabilities. + + Parameters + ---------- + backend_name : {'qt', 'matplotlib'} + The 2D browser backend to use in the context. + """ + old_backend = set_browser_backend(backend_name) try: - yield + yield backend finally: - fig.canvas = old_canvas + if old_backend is not None: + try: + set_browser_backend(old_backend) + except Exception: + pass diff -Nru python-mne-0.23.4+dfsg/mne/viz/ica.py python-mne-1.1.0+dfsg/mne/viz/ica.py --- python-mne-0.23.4+dfsg/mne/viz/ica.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/ica.py 2022-08-03 15:48:14.000000000 +0000 @@ -13,10 +13,12 @@ import numpy as np from .utils import (tight_layout, _make_event_color_dict, - plt_show, _convert_psds, _compute_scalings) + plt_show, _convert_psds, _compute_scalings, + _handle_precompute) from .topomap import _plot_ica_topomap from .epochs import plot_epochs_image from .evoked import _butterfly_on_button_press, _butterfly_onpick +from ..channels.channels import _get_ch_type from ..utils import _validate_type, fill_doc from ..defaults import _handle_default from ..io.meas_info import create_info @@ -28,7 +30,9 @@ @fill_doc def plot_ica_sources(ica, inst, picks=None, start=None, stop=None, title=None, show=True, block=False, - show_first_samp=False, show_scrollbars=True): + show_first_samp=False, show_scrollbars=True, + time_format='float', precompute=None, + use_opengl=None, *, theme=None, overview_mode=None): """Plot estimated latent sources given the unmixing matrix. Typical usecases: @@ -43,12 +47,15 @@ The ICA solution. inst : instance of mne.io.Raw, mne.Epochs, mne.Evoked The object to plot the sources from. - %(picks_base)s all sources in the order as fitted. - start : int | None - X-axis start index. If None (default), from the beginning. - stop : int | None - X-axis stop index. If None (default), next 20 are shown, in case of - evoked to the end. + %(picks_ica)s ``None`` (default) will pick all sources in the order fitted. + start, stop : float | int | None + If ``inst`` is a `~mne.io.Raw` or an `~mne.Evoked` object, the first and + last time point (in seconds) of the data to plot. If ``inst`` is a + `~mne.io.Raw` object, ``start=None`` and ``stop=None`` will be + translated into ``start=0.`` and ``stop=3.``, respectively. For + `~mne.Evoked`, ``None`` refers to the beginning and end of the evoked + signal. If ``inst`` is an `~mne.Epochs` object, specifies the index of + the first and last epoch to show. title : str | None The window title. If None a default is provided. show : bool @@ -60,11 +67,19 @@ show_first_samp : bool If True, show time axis relative to the ``raw.first_samp``. %(show_scrollbars)s + %(time_format)s + %(precompute)s + %(use_opengl)s + %(theme_pg)s + + .. versionadded:: 1.0 + %(overview_mode)s + + .. versionadded:: 1.1 Returns ------- - fig : instance of Figure - The figure. + %(browser)s Notes ----- @@ -85,7 +100,10 @@ fig = _plot_sources(ica, inst, picks, exclude, start=start, stop=stop, show=show, title=title, block=block, show_first_samp=show_first_samp, - show_scrollbars=show_scrollbars) + show_scrollbars=show_scrollbars, + time_format=time_format, precompute=precompute, + use_opengl=use_opengl, theme=theme, + overview_mode=overview_mode) elif isinstance(inst, Evoked): if start is not None or stop is not None: inst = inst.copy().crop(start, stop) @@ -122,8 +140,8 @@ def _plot_ica_properties(pick, ica, inst, psds_mean, freqs, n_trials, epoch_var, plot_lowpass_edge, epochs_src, set_title_and_labels, plot_std, psd_ylabel, - spectrum_std, topomap_args, image_args, fig, axes, - kind, dropped_indices): + spectrum_std, log_scale, topomap_args, image_args, + fig, axes, kind, dropped_indices): """Plot ICA properties (helper).""" from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable from scipy.stats import gaussian_kde @@ -134,6 +152,8 @@ # -------- # component topomap _plot_ica_topomap(ica, pick, show=False, axes=topo_ax, **topomap_args) + topo_ax._ch_type = _get_ch_type(ica, ch_type=None, + allow_ref_meg=ica.allow_ref_meg) # image and erp # we create a new epoch with dropped rows @@ -192,8 +212,6 @@ # aesthetics # ---------- - topo_ax.set_title(ica._ica_names[pick]) - set_title_and_labels(image_ax, kind + ' image and ERP/ERF', [], kind) # erp @@ -211,6 +229,13 @@ image_ax.yaxis.set_ticks(yt[1:]) image_ax.set_ylim([-0.5, n_trials + 0.5]) + def _set_scale(ax, scale): + """Set the scale of a matplotlib axis.""" + ax.set_xscale(scale) + ax.set_yscale(scale) + ax.relim() + ax.autoscale() + # spectrum set_title_and_labels(spec_ax, 'Spectrum', 'Frequency (Hz)', psd_ylabel) spec_ax.yaxis.labelpad = 0 @@ -219,6 +244,8 @@ air = np.diff(ylim)[0] * 0.1 spec_ax.set_ylim(ylim[0] - air, ylim[1] + air) image_ax.axhline(0, color='k', linewidth=.5) + if log_scale: + _set_scale(spec_ax, 'log') # epoch variance var_ax_title = 'Dropped segments: %.2f %%' % var_percent @@ -228,6 +255,34 @@ hist_ax.set_yticks([]) set_title_and_labels(hist_ax, None, None, None) + def _plot_ica_properties_on_press(event, ica, pick, topomap_args): + """Handle keypress events for ica properties plot.""" + import matplotlib.pyplot as plt + fig = event.canvas.figure + if event.key == 'escape': + plt.close(fig) + if event.key in ('t', 'l'): + ax_labels = [ax.get_label() for ax in fig.axes] + if event.key == 't': + ax = fig.axes[ax_labels.index('topomap')] + ax.clear() + ch_types = list(set(ica.get_channel_types())) + ch_type = \ + ch_types[(ch_types.index(ax._ch_type) + 1) % len(ch_types)] + _plot_ica_topomap(ica, pick, ch_type=ch_type, show=False, + axes=ax, **topomap_args) + ax._ch_type = ch_type + elif event.key == 'l': + ax = fig.axes[ax_labels.index('spectrum')] + _set_scale(ax, 'linear' if ax.get_xscale() == 'log' else 'log') + del ax + fig.canvas.draw() + + # add keypress event handler + fig.canvas.mpl_connect( + 'key_press_event', lambda event: _plot_ica_properties_on_press( + event, ica, pick, topomap_args)) + return fig @@ -252,9 +307,10 @@ @verbose def plot_ica_properties(ica, inst, picks=None, axes=None, dB=True, - plot_std=True, topomap_args=None, image_args=None, - psd_args=None, figsize=None, show=True, reject='auto', - reject_by_annotation=True, *, verbose=None): + plot_std=True, log_scale=False, topomap_args=None, + image_args=None, psd_args=None, figsize=None, + show=True, reject='auto', reject_by_annotation=True, + *, verbose=None): """Display component properties. Properties include the topography, epochs image, ERP/ERF, power @@ -266,9 +322,14 @@ The ICA solution. inst : instance of Epochs or Raw The data to use in plotting properties. - %(picks_base)s the first five sources. - If more than one components were chosen in the picks, - each one will be plotted in a separate figure. + + .. note:: + You can interactively cycle through topographic maps for different + channel types by pressing :kbd:`T`. + picks : str | list | slice | None + Components to include. Slices and lists of integers will be interpreted + as component indices. ``None`` (default) will use the first five + components. Each component will be plotted in a separate figure. axes : list of Axes | None List of five matplotlib axes to use in plotting: [topomap_axis, image_axis, erp_axis, spectrum_axis, variance_axis]. If None a new @@ -285,6 +346,14 @@ For the ERP/ERF, by default, plot the 95 percent parametric confidence interval is calculated. To change this, use ``ci`` in ``ts_args`` in ``image_args`` (see below). + log_scale : bool + Whether to use a logarithmic frequency axis to plot the spectrum. + Defaults to ``False``. + + .. note:: + You can interactively toggle this setting by pressing :kbd:`L`. + + .. versionadded:: 1.1 topomap_args : dict | None Dictionary of arguments to ``plot_topomap``. If None, doesn't pass any additional arguments. Defaults to None. @@ -320,7 +389,7 @@ .. versionadded:: 0.13 """ return _fast_plot_ica_properties(ica, inst, picks=picks, axes=axes, dB=dB, - plot_std=plot_std, + plot_std=plot_std, log_scale=log_scale, topomap_args=topomap_args, image_args=image_args, psd_args=psd_args, figsize=figsize, show=show, @@ -330,9 +399,10 @@ def _fast_plot_ica_properties(ica, inst, picks=None, axes=None, dB=True, - plot_std=True, topomap_args=None, - image_args=None, psd_args=None, figsize=None, - show=True, reject='auto', precomputed_data=None, + plot_std=True, log_scale=False, + topomap_args=None, image_args=None, + psd_args=None, figsize=None, show=True, + reject='auto', precomputed_data=None, reject_by_annotation=True, *, verbose=None): """Display component properties.""" from ..preprocessing import ICA @@ -374,8 +444,8 @@ ("topomap_args", topomap_args), ("image_args", image_args)): _validate_type(item, dict, item_name, "dictionary") - if dB is not None: - _validate_type(dB, bool, "dB", "bool") + _validate_type(dB, (bool, None), "dB") + _validate_type(log_scale, (bool, None), "log_scale") # calculations # ------------ @@ -433,9 +503,9 @@ # the actual plot fig = _plot_ica_properties( pick, ica, inst, psds_mean, freqs, ica_data.shape[1], - epoch_var, plot_lowpass_edge, - epochs_src, set_title_and_labels, plot_std, psd_ylabel, - spectrum_std, topomap_args, image_args, fig, axes, kind, + epoch_var, plot_lowpass_edge, epochs_src, + set_title_and_labels, plot_std, psd_ylabel, spectrum_std, + log_scale, topomap_args, image_args, fig, axes, kind, dropped_indices) all_fig.append(fig) @@ -567,33 +637,49 @@ if ii in indices: annot.append(this_label) - line_label += (' - ' + ', '.join(annot)) + if annot: + line_label += (' – ' + ', '.join(annot)) # Unicode en-dash exclude_labels.append(line_label) else: exclude_labels.append(None) - + label_props = [('k', '-') if lb is None else ('r', '-') for lb in + exclude_labels] + styles = ['-', '--', ':', '-.'] if labels is not None: - # compute colors only based on label categories - unique_labels = {k.split(' - ')[1] for k in exclude_labels if k} - label_colors = plt.cm.rainbow(np.linspace(0, 1, len(unique_labels))) - label_colors = dict(zip(unique_labels, label_colors)) - else: - label_colors = {k: 'red' for k in exclude_labels} + # differentiate categories by linestyle and components by color + col_lbs = [it for it in exclude_labels if it is not None] + cmap = plt.get_cmap('tab10', len(col_lbs)) + + unique_labels = set() + for label in exclude_labels: + if label is None: + continue + elif ' – ' in label: + unique_labels.add(label.split(' – ')[1]) + else: + unique_labels.add('') + + # Determine up to 4 different styles for n categories + cat_styles = dict(zip(unique_labels, + map(lambda ux: styles[int(ux % len(styles))], + range(len(unique_labels))))) + for label_idx, label in enumerate(exclude_labels): + if label is not None: + color = cmap(col_lbs.index(label)) + if ' – ' in label: + label_name = label.split(' – ')[1] + else: + label_name = '' + style = cat_styles[label_name] + label_props[label_idx] = (color, style) for exc_label, ii in zip(exclude_labels, picks): - if exc_label is not None: - # create look up for color ... - if ' - ' in exc_label: - key = exc_label.split(' - ')[1] - else: - key = exc_label - color = label_colors[key] - # ... but display component number too - lines.extend(ax.plot(times, evoked.data[ii].T, picker=True, - zorder=2, color=color, label=exc_label)) - else: - lines.extend(ax.plot(times, evoked.data[ii].T, picker=True, - color='k', zorder=1)) + color, style = label_props[ii] + # ensure traces of excluded components are plotted on top + zorder = 2 if exc_label is None else 10 + lines.extend(ax.plot(times, evoked.data[ii].T, picker=True, + zorder=zorder, color=color, linestyle=style, + label=exc_label)) lines[-1].set_pickradius(3.) ax.set(title=title, xlim=times[[0, -1]], xlabel='Time (ms)', ylabel='(NA)') @@ -741,7 +827,9 @@ ax.set_xlim(-0.6, len(this_scores) - 0.4) tight_layout(fig=fig) - fig.subplots_adjust(top=0.90) + + adjust_top = 0.8 if len(fig.axes) == 1 else 0.9 + fig.subplots_adjust(top=adjust_top) fig.canvas.draw() plt_show(show) return fig @@ -759,24 +847,23 @@ ica : instance of mne.preprocessing.ICA The ICA object. inst : instance of mne.io.Raw or mne.Evoked - The signals to be compared given the ICA solution. If Raw input, - The raw data are displayed before and after cleaning. In a second - panel the cross channel average will be displayed. Since dipolar - sources will be canceled out this display is sensitive to - artifacts. If evoked input, butterfly plots for clean and raw - signals will be superimposed. + The signal to plot. If `~mne.io.Raw`, the raw data is displayed before + and after cleaning. In a second panel, the cross-channel average will + be displayed. Since dipolar sources will be canceled out, this + representation is sensitive to artifacts. If `~mne.Evoked`, butterfly + traces for signals before and after cleaning will be superimposed. exclude : array-like of int | None (default) - The components marked for exclusion. If None (default), ICA.exclude + The components marked for exclusion. If ``None`` (default), ICA.exclude will be used. %(picks_base)s all channels that were included during fitting. - start : int | None - X-axis start index. If None (default) from the beginning. - stop : int | None - X-axis stop index. If None (default) to 3.0s. - title : str - The figure title. - show : bool - Show figure if True. + start, stop : float | None + The first and last time point (in seconds) of the data to plot. If + ``inst`` is a `~mne.io.Raw` object, ``start=None`` and ``stop=None`` + will be translated into ``start=0.`` and ``stop=3.``, respectively. For + `~mne.Evoked`, ``None`` refers to the beginning and end of the evoked + signal. + %(title_none)s + %(show)s %(n_pca_components_apply)s .. versionadded:: 0.22 @@ -791,6 +878,9 @@ from ..evoked import Evoked from ..preprocessing.ica import _check_start_stop + if ica.current_fit == 'unfitted': + raise RuntimeError('You need to fit the ICA first') + _validate_type(inst, (BaseRaw, Evoked), "inst", "Raw or Evoked") if title is None: title = 'Signals before (red) and after (black) cleaning' @@ -821,7 +911,8 @@ assert isinstance(inst, Evoked) inst = inst.copy().crop(start, stop) if picks is not None: - inst.info['comps'] = [] # can be safely disabled + with inst.info._unlock(): + inst.info['comps'] = [] # can be safely disabled inst.pick_channels([inst.ch_names[p] for p in picks]) evoked_cln = ica.apply(inst.copy(), exclude=exclude, n_pca_components=n_pca_components) @@ -923,9 +1014,10 @@ def _plot_sources(ica, inst, picks, exclude, start, stop, show, title, block, - show_scrollbars, show_first_samp): + show_scrollbars, show_first_samp, time_format, + precompute, use_opengl, *, theme=None, overview_mode=None): """Plot the ICA components as a RawArray or EpochsArray.""" - from ._figure import _browse_figure + from ._figure import _get_browser from .. import EpochsArray, BaseEpochs from ..io import RawArray, BaseRaw @@ -982,7 +1074,8 @@ # create info info = create_info(ch_names_picked, sfreq, ch_types=ch_types) - info['meas_date'] = inst.info['meas_date'] + with info._unlock(): + info['meas_date'] = inst.info['meas_date'] info['bads'] = [ch_names[x] for x in exclude if x in picks] if is_raw: inst_array = RawArray(data, info, inst.first_samp) @@ -1014,8 +1107,9 @@ raise RuntimeError('Stop must be larger than start.') # misc - bad_color = (0.8, 0.8, 0.8) + bad_color = 'lightgray' title = 'ICA components' if title is None else title + precompute = _handle_precompute(precompute) params = dict(inst=inst_array, ica=ica, @@ -1033,6 +1127,7 @@ duration=duration, n_times=inst.n_times if is_raw else n_times, first_time=first_time, + time_format=time_format, decim=1, # events event_times=None if is_raw else event_times, @@ -1056,7 +1151,12 @@ clipping=None, scrollbars_visible=show_scrollbars, scalebars_visible=False, - window_title=title) + window_title=title, + precompute=precompute, + use_opengl=use_opengl, + theme=theme, + overview_mode=overview_mode, + ) if is_epo: params.update(n_epochs=n_epochs, boundary_times=boundary_times, @@ -1066,19 +1166,7 @@ epoch_color_bad=(1, 0, 0), epoch_colors=None, xlabel='Epoch number') - fig = _browse_figure(**params) - fig._update_picks() - - # update data, and plot - fig._update_trace_offsets() - fig._update_data() - fig._draw_traces() - # plot annotations (if any) - if is_raw: - fig._setup_annotation_colors() - fig._update_annotation_segments() - fig._draw_annotations() + fig = _get_browser(show=show, block=block, **params) - plt_show(show, block=block) return fig diff -Nru python-mne-0.23.4+dfsg/mne/viz/__init__.py python-mne-1.1.0+dfsg/mne/viz/__init__.py --- python-mne-0.23.4+dfsg/mne/viz/__init__.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/__init__.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,7 +2,8 @@ from .topomap import (plot_evoked_topomap, plot_projs_topomap, plot_arrowmap, plot_ica_components, plot_tfr_topomap, plot_topomap, - plot_epochs_psd_topomap, plot_layout) + plot_epochs_psd_topomap, plot_layout, + plot_bridged_electrodes, plot_ch_adjacency) from .topo import plot_topo_image_epochs, iter_topography from .utils import (tight_layout, mne_analyze_colormap, compare_fiff, ClickableImage, add_background_image, plot_sensors, @@ -11,24 +12,28 @@ plot_vector_source_estimates, plot_evoked_field, plot_dipole_locations, snapshot_brain_montage, plot_head_positions, plot_alignment, plot_brain_colorbar, - plot_volume_source_estimates, plot_sensors_connectivity, - link_brains, set_3d_options) + plot_volume_source_estimates, link_brains, set_3d_options) from .misc import (plot_cov, plot_csd, plot_bem, plot_events, plot_source_spectrogram, _get_presser, plot_dipole_amplitudes, plot_ideal_filter, plot_filter, - adjust_axes) + adjust_axes, plot_chpi_snr) from .evoked import (plot_evoked, plot_evoked_image, plot_evoked_white, plot_snr_estimate, plot_evoked_topo, plot_evoked_joint, plot_compare_evokeds) -from .circle import plot_connectivity_circle, circular_layout +from .circle import (circular_layout, plot_channel_labels_circle) from .epochs import (plot_drop_log, plot_epochs, plot_epochs_psd, plot_epochs_image) from .raw import plot_raw, plot_raw_psd, plot_raw_psd_topo, _RAW_CLIP_DEF from .ica import (plot_ica_scores, plot_ica_sources, plot_ica_overlay, _plot_sources, plot_ica_properties) from .montage import plot_montage +from .backends._abstract import Figure3D from .backends.renderer import (set_3d_backend, get_3d_backend, use_3d_backend, set_3d_view, set_3d_title, create_3d_figure, + close_3d_figure, close_all_3d_figures, get_brain_class) -from . import backends +from . import backends, _scraper from ._brain import Brain +from ._figure import (get_browser_backend, set_browser_backend, + use_browser_backend) +from ._proj import plot_projs_joint diff -Nru python-mne-0.23.4+dfsg/mne/viz/misc.py python-mne-1.1.0+dfsg/mne/viz/misc.py --- python-mne-0.23.4+dfsg/mne/viz/misc.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/misc.py 2022-08-03 15:48:14.000000000 +0000 @@ -10,36 +10,33 @@ # # License: Simplified BSD -import base64 import copy +import io from glob import glob -from io import BytesIO from itertools import cycle import os.path as op import warnings -from distutils.version import LooseVersion from collections import defaultdict import numpy as np from ..defaults import DEFAULTS -from ..fixes import _get_img_fdata +from .._freesurfer import (_reorient_image, _read_mri_info, _check_mri, + _mri_orientation) from ..rank import compute_rank -from ..source_space import _mri_orientation from ..surface import read_surface from ..io.constants import FIFF from ..io.proj import make_projector from ..io.pick import (_DATA_CH_TYPES_SPLIT, pick_types, pick_info, pick_channels) -from ..source_space import (read_source_spaces, SourceSpaces, _read_mri_info, - _check_mri, _ensure_src) -from ..transforms import invert_transform, apply_trans, _frame_to_str +from ..source_space import read_source_spaces, SourceSpaces, _ensure_src +from ..transforms import apply_trans, _frame_to_str from ..utils import (logger, verbose, warn, _check_option, get_subjects_dir, - _mask_to_onsets_offsets, _pl, _on_missing) + _mask_to_onsets_offsets, _pl, _on_missing, fill_doc) from ..io.pick import _picks_by_type from ..filter import estimate_ringing_samples from .utils import (tight_layout, _get_color_list, _prepare_trellis, plt_show, - _figure_agg) + _figure_agg, _validate_type) def _index_info_cov(info, cov, exclude): @@ -81,8 +78,7 @@ ---------- cov : instance of Covariance The covariance matrix. - info : dict - Measurement info. + %(info_not_none)s exclude : list of str | str List of channels to exclude. If empty do not exclude any channel. If 'bads', exclude info['bads']. @@ -141,6 +137,9 @@ logger.info(' The projection vectors do not apply to these ' 'channels.') + if np.iscomplexobj(C): + C = np.sqrt((C * C.conj()).real) + fig_cov, axes = plt.subplots(1, len(idx_names), squeeze=False, figsize=(3.8 * len(idx_names), 3.7)) for k, (idx, name, _, _, _) in enumerate(idx_names): @@ -154,6 +153,7 @@ from mpl_toolkits.axes_grid1 import make_axes_locatable divider = make_axes_locatable(axes[0, k]) cax = divider.append_axes("right", size="5.5%", pad=0.05) + cax.grid(False) # avoid mpl warning about auto-removal plt.colorbar(im, cax=cax, format='%.0e') fig_cov.subplots_adjust(0.04, 0.0, 0.98, 0.94, 0.2, 0.26) @@ -169,7 +169,8 @@ this_C = Covariance(this_C, [info['ch_names'][ii] for ii in idx], [], [], 0) this_info = pick_info(info, idx) - this_info['projs'] = [] + with this_info._unlock(): + this_info['projs'] = [] this_rank = compute_rank(this_C, info=this_info) # Protect against true zero singular values s[s <= 0] = 1e-10 * s[s > 0].min() @@ -304,32 +305,56 @@ return fig -def _plot_mri_contours(mri_fname, surfaces, src, orientation='coronal', +def _plot_mri_contours(*, mri_fname, surfaces, src, orientation='coronal', slices=None, show=True, show_indices=False, - show_orientation=False, img_output=False, width=512): - """Plot BEM contours on anatomical slices.""" + show_orientation=False, width=512, + slices_as_subplots=True): + """Plot BEM contours on anatomical MRI slices. + + Parameters + ---------- + slices_as_subplots : bool + Whether to add all slices as subplots to a single figure, or to + create a new figure for each slice. If ``False``, return NumPy + arrays instead of Matplotlib figures. + + Returns + ------- + matplotlib.figure.Figure | list of array + The plotted slices. + """ import matplotlib.pyplot as plt from matplotlib import patheffects # For ease of plotting, we will do everything in voxel coordinates. + _validate_type(show_orientation, (bool, str), 'show_orientation') + if isinstance(show_orientation, str): + _check_option('show_orientation', show_orientation, ('always',), + extra='when str') _check_option('orientation', orientation, ('coronal', 'axial', 'sagittal')) # Load the T1 data - _, vox_mri_t, _, _, _, nim = _read_mri_info( + _, _, _, _, _, nim = _read_mri_info( mri_fname, units='mm', return_img=True) - mri_vox_t = invert_transform(vox_mri_t)['trans'] - del vox_mri_t - # plot axes (x, y, z) as data axes - (x, y, z), (flip_x, flip_y, flip_z), order = _mri_orientation( - nim, orientation) - transpose = x < y - - data = _get_img_fdata(nim) - shift_x = data.shape[x] if flip_x < 0 else 0 - shift_y = data.shape[y] if flip_y < 0 else 0 - n_slices = data.shape[z] + data, rasvox_mri_t = _reorient_image(nim) + mri_rasvox_t = np.linalg.inv(rasvox_mri_t) + axis, x, y = _mri_orientation(orientation) + + n_slices = data.shape[axis] + + # if no slices were specified, pick some equally-spaced ones automatically if slices is None: - slices = np.round(np.linspace(0, n_slices - 1, 14)).astype(int)[1:-1] + slices = np.round( + np.linspace( + start=0, + stop=n_slices - 1, + num=14 + ) + ).astype(int) + + # omit first and last one (not much brain visible there anyway…) + slices = slices[1:-1] + slices = np.atleast_1d(slices).copy() slices[slices < 0] += n_slices # allow negative indexing if not np.array_equal(np.sort(slices), slices) or slices.ndim != 1 or \ @@ -338,9 +363,6 @@ raise ValueError('slices must be a sorted 1D array of int with unique ' 'elements, at least one element, and no elements ' 'greater than %d, got %s' % (n_slices - 1, slices)) - if flip_z < 0: - # Proceed in the opposite order to maintain left-to-right / orientation - slices = slices[::-1] # create of list of surfaces surfs = list() @@ -348,7 +370,7 @@ surf = dict() surf['rr'], surf['tris'] = read_surface(file_name) # move surface to voxel coordinate system - surf['rr'] = apply_trans(mri_vox_t, surf['rr']) + surf['rr'] = apply_trans(mri_rasvox_t, surf['rr']) surfs.append((surf, color)) sources = list() @@ -361,45 +383,48 @@ f'{_frame_to_str[src[0]["coord_frame"]]}') for src_ in src: points = src_['rr'][src_['inuse'].astype(bool)] - sources.append(apply_trans(mri_vox_t, points * 1e3)) + sources.append(apply_trans(mri_rasvox_t, points * 1e3)) sources = np.concatenate(sources, axis=0) - if img_output: + # get the figure dimensions right + if slices_as_subplots: + n_col = 4 + fig, axs, _, _ = _prepare_trellis(len(slices), n_col) + fig.set_facecolor('k') + dpi = fig.get_dpi() + n_axes = len(axs) + else: n_col = n_axes = 1 dpi = 96 # 2x standard MRI resolution is probably good enough for the # traces w = width / dpi figsize = (w, w / data.shape[x] * data.shape[y]) - fig = _figure_agg(figsize=figsize, dpi=dpi, facecolor='k') - ax = fig.add_axes([0, 0, 1, 1], frame_on=False, facecolor='k') - axs = [ax] * len(slices) - plt.close(fig) - else: - n_col = 4 - fig, axs, _, _ = _prepare_trellis(len(slices), n_col) - fig.set_facecolor('k') - dpi = fig.get_dpi() - n_axes = len(axs) + bounds = np.concatenate( - [[-np.inf], slices[:-1] + np.diff(slices) / 2., [np.inf]]) # float + [[-np.inf], slices[:-1] + np.diff(slices) / 2., + [np.inf]] + ) # float slicer = [slice(None)] * 3 ori_labels = dict(R='LR', A='PA', S='IS') - xlabels, ylabels = ori_labels[order[0]], ori_labels[order[1]] + xlabels, ylabels = ori_labels['RAS'[x]], ori_labels['RAS'[y]] path_effects = [patheffects.withStroke(linewidth=4, foreground="k", alpha=0.75)] - out = list() if img_output else fig - for ai, (ax, sl, lower, upper) in enumerate(zip( - axs, slices, bounds[:-1], bounds[1:])): + figs = [] + for ai, (sl, lower, upper) in enumerate( + zip(slices, bounds[:-1], bounds[1:]) + ): + if slices_as_subplots: + ax = axs[ai] + else: + fig = _figure_agg(figsize=figsize, dpi=dpi, facecolor='k') + ax = fig.add_axes([0, 0, 1, 1], frame_on=False, facecolor='k') + # adjust the orientations for good view - slicer[z] = sl - dat = data[tuple(slicer)] - dat = dat.T if transpose else dat - dat = dat[::flip_y, ::flip_x] + slicer[axis] = sl + dat = data[tuple(slicer)].T # First plot the anatomical data - if img_output: - ax.clear() ax.imshow(dat, cmap=plt.cm.gray, origin='lower') ax.set_autoscale_on(False) ax.axis('off') @@ -409,16 +434,14 @@ for surf, color in surfs: with warnings.catch_warnings(record=True): # ignore contour warn warnings.simplefilter('ignore') - ax.tricontour(flip_x * surf['rr'][:, x] + shift_x, - flip_y * surf['rr'][:, y] + shift_y, - surf['tris'], surf['rr'][:, z], + ax.tricontour(surf['rr'][:, x], surf['rr'][:, y], + surf['tris'], surf['rr'][:, axis], levels=[sl], colors=color, linewidths=1.0, zorder=1) if len(sources): - in_slice = (sources[:, z] >= lower) & (sources[:, z] < upper) - ax.scatter(flip_x * sources[in_slice, x] + shift_x, - flip_y * sources[in_slice, y] + shift_y, + in_slice = (sources[:, axis] >= lower) & (sources[:, axis] < upper) + ax.scatter(sources[in_slice, x], sources[in_slice, y], marker='.', color='#FF00FF', s=1, zorder=2) if show_indices: ax.text(dat.shape[1] // 8 + 0.5, 0.5, str(sl), @@ -427,47 +450,60 @@ kwargs = dict( color='#66CCEE', fontsize='medium', path_effects=path_effects, family='monospace', clip_on=False, zorder=5, weight='bold') + always = (show_orientation == 'always') if show_orientation: - if ai % n_col == 0: # left + if ai % n_col == 0 or always: # left ax.text(0, dat.shape[0] / 2., xlabels[0], va='center', ha='left', **kwargs) - if ai % n_col == n_col - 1 or ai == n_axes - 1: # right + if ai % n_col == n_col - 1 or ai == n_axes - 1 or always: # right ax.text(dat.shape[1] - 1, dat.shape[0] / 2., xlabels[1], va='center', ha='right', **kwargs) - if ai >= n_axes - n_col: # bottom + if ai >= n_axes - n_col or always: # bottom ax.text(dat.shape[1] / 2., 0, ylabels[0], ha='center', va='bottom', **kwargs) - if ai < n_col or n_col == 1: # top + if ai < n_col or n_col == 1 or always: # top ax.text(dat.shape[1] / 2., dat.shape[0] - 1, ylabels[1], ha='center', va='top', **kwargs) - if img_output: - output = BytesIO() - fig.savefig(output, bbox_inches='tight', - pad_inches=0, format='png', dpi=dpi) - out.append(base64.b64encode(output.getvalue()).decode('ascii')) - - fig.subplots_adjust(left=0., bottom=0., right=1., top=1., wspace=0., - hspace=0.) - plt_show(show, fig=fig) - return out, flip_z + + if not slices_as_subplots: + # convert to NumPy array + with io.BytesIO() as buff: + fig.savefig( + buff, format='raw', bbox_inches='tight', pad_inches=0, + dpi=dpi + ) + w_, h_ = fig.canvas.get_width_height() + plt.close(fig) + buff.seek(0) + fig_array = np.frombuffer(buff.getvalue(), dtype=np.uint8) + + fig = fig_array.reshape((int(h_), int(w_), -1)) + figs.append(fig) + + if slices_as_subplots: + fig.subplots_adjust(left=0., bottom=0., right=1., top=1., wspace=0., + hspace=0.) + plt_show(show, fig=fig) + return fig + else: + return figs -def plot_bem(subject=None, subjects_dir=None, orientation='coronal', +@fill_doc +def plot_bem(subject, subjects_dir=None, orientation='coronal', slices=None, brain_surfaces=None, src=None, show=True, show_indices=True, mri='T1.mgz', show_orientation=True): - """Plot BEM contours on anatomical slices. + """Plot BEM contours on anatomical MRI slices. Parameters ---------- - subject : str - Subject name. - subjects_dir : str | None - Path to the SUBJECTS_DIR. If None, the path is obtained by using - the environment variable SUBJECTS_DIR. + %(subject)s + %(subjects_dir)s orientation : str 'coronal' or 'axial' or 'sagittal'. - slices : list of int - Slice indices. + slices : list of int | None + The indices of the MRI slices to plot. If ``None``, automatically + pick 12 equally-spaced slices. brain_surfaces : None | str | list of str One or more brain surface to plot (optional). Entries should correspond to files in the subject's ``surf`` directory (e.g. ``"white"``). @@ -492,10 +528,15 @@ ``'T1.mgz'``, or a full path to a custom MRI file. .. versionadded:: 0.21 - show_orientation : str + show_orientation : bool | str Show the orientation (L/R, P/A, I/S) of the data slices. + True (default) will only show it on the outside most edges of the + figure, False will never show labels, and "always" will label each + plot. .. versionadded:: 0.21 + .. versionchanged:: 0.24 + Added support for "always". Returns ------- @@ -527,7 +568,7 @@ bem_path = op.join(subjects_dir, subject, 'bem') if not op.isdir(bem_path): - raise IOError('Subject bem directory "%s" does not exist' % bem_path) + raise IOError(f'Subject bem directory "{bem_path}" does not exist') surfaces = _get_bem_plotting_surfaces(bem_path) if brain_surfaces is not None: @@ -559,8 +600,13 @@ 'inner_skull.surf, outer_skull.surf or outer_skin.surf') # Plot the contours - return _plot_mri_contours(mri_fname, surfaces, src, orientation, slices, - show, show_indices, show_orientation)[0] + fig = _plot_mri_contours( + mri_fname=mri_fname, surfaces=surfaces, src=src, + orientation=orientation, slices=slices, show=show, + show_indices=show_indices, show_orientation=show_orientation, + slices_as_subplots=True + ) + return fig def _get_bem_plotting_surfaces(bem_path): @@ -580,12 +626,11 @@ def plot_events(events, sfreq=None, first_samp=0, color=None, event_id=None, axes=None, equal_spacing=True, show=True, on_missing='raise', verbose=None): - """Plot events to get a visual display of the paradigm. + """Plot :term:`events` to get a visual display of the paradigm. Parameters ---------- - events : array, shape (n_events, 3) - The events. + %(events)s sfreq : float | None The sample frequency. If None, data will be displayed in samples (not seconds). @@ -598,7 +643,7 @@ Dictionary of event_id integers as keys and colors as values. If None, colors are automatically drawn from a default list (cycled through if number of events longer than list of default colors). Color can be any - valid :doc:`matplotlib color `. + valid :doc:`matplotlib color `. event_id : dict | None Dictionary of event labels (e.g. 'aud_l') as keys and their associated event_id values. Labels are used to plot a legend. If None, no legend @@ -718,14 +763,10 @@ def _get_presser(fig): """Get our press callback.""" - import matplotlib callbacks = fig.canvas.callbacks.callbacks['button_press_event'] func = None for key, val in callbacks.items(): - if LooseVersion(matplotlib.__version__) >= '3': - func = val() - else: - func = val.func + func = val() if func.__class__.__name__ == 'partial': break else: @@ -841,7 +882,7 @@ def plot_filter(h, sfreq, freq=None, gain=None, title=None, color='#1f77b4', flim=None, fscale='log', alim=_DEFAULT_ALIM, show=True, compensate=False, plot=('time', 'magnitude', 'delay'), - axes=None): + axes=None, *, dlim=None): """Plot properties of a filter. Parameters @@ -894,6 +935,11 @@ Defaults to ``None``. .. versionadded:: 0.21.0 + dlim : None | tuple + The y-axis delay limits (sec) to use (default: + ``(-tmax / 2., tmax / 2.)``). + + .. versionadded:: 1.1.0 Returns ------- @@ -998,8 +1044,9 @@ % (len(axes), len(plot))) t = np.arange(len(h)) - dlim = np.abs(t).max() / 2. - dlim = [-dlim, dlim] + if dlim is None: + dlim = np.abs(t).max() / 2. + dlim = [-dlim, dlim] if compensate: n_shift = (len(h) - 1) // 2 t -= n_shift @@ -1106,7 +1153,7 @@ >>> from mne.viz import plot_ideal_filter >>> freq = [0, 1, 40, 50] >>> gain = [0, 1, 1, 0] - >>> plot_ideal_filter(freq, gain, flim=(0.1, 100)) #doctest: +ELLIPSIS + >>> plot_ideal_filter(freq, gain, flim=(0.1, 100)) #doctest: +SKIP <...Figure...> """ import matplotlib.pyplot as plt @@ -1181,6 +1228,7 @@ return default_colors +@fill_doc def plot_csd(csd, info=None, mode='csd', colorbar=True, cmap=None, n_cols=None, show=True): """Plot CSD matrices. @@ -1192,8 +1240,8 @@ ---------- csd : instance of CrossSpectralDensity The CSD matrix to plot. - info : instance of Info | None - To split the figure by channel-type, provide the measurement info. + %(info)s + Used to split the figure by channel-type, if provided. By default, the CSD matrix is plotted as a whole. mode : 'csd' | 'coh' Whether to plot the cross-spectral density ('csd', the default), or @@ -1308,3 +1356,80 @@ plt_show(show) return figs + + +def plot_chpi_snr(snr_dict, axes=None): + """Plot time-varying SNR estimates of the HPI coils. + + Parameters + ---------- + snr_dict : dict + The dictionary returned by `~mne.chpi.compute_chpi_snr`. Must have keys + ``times``, ``freqs``, ``TYPE_snr``, ``TYPE_power``, and ``TYPE_resid`` + (where ``TYPE`` can be ``mag`` or ``grad`` or both). + axes : None | list of matplotlib.axes.Axes + Figure axes in which to draw the SNR, power, and residual plots. The + number of axes should be 3× the number of MEG sensor types present in + ``snr_dict``. If ``None`` (the default), a new + `~matplotlib.figure.Figure` is created with the required number of + axes. + + Returns + ------- + fig : instance of matplotlib.figure.Figure + A figure with subplots for SNR, power, and residual variance, + separately for magnetometers and/or gradiometers (depending on what is + present in ``snr_dict``). + + Notes + ----- + If you supply a list of existing `~matplotlib.axes.Axes`, then the figure + legend will not be drawn automatically. If you still want it, running + ``fig.legend(loc='right', title='cHPI frequencies')`` will recreate it, + though you may also need to manually adjust the margin to make room for it + (e.g., using ``fig.subplots_adjust(right=0.8)``). + + .. versionadded:: 0.24 + """ + import matplotlib.pyplot as plt + + valid_keys = list(snr_dict)[2:] + titles = dict(snr='SNR', power='cHPI power', resid='Residual variance') + full_names = dict(mag='magnetometers', grad='gradiometers') + axes_was_none = axes is None + if axes_was_none: + fig, axes = plt.subplots(len(valid_keys), 1, sharex=True) + else: + fig = axes[0].get_figure() + if len(axes) != len(valid_keys): + raise ValueError(f'axes must be a list of {len(valid_keys)} axes, got ' + f'length {len(axes)} ({axes}).') + fig.set_size_inches(10, 10) + legend_labels_exist = False + for key, ax in zip(valid_keys, axes): + ch_type, kind = key.split('_') + scaling = 1 if kind == 'snr' else DEFAULTS['scalings'][ch_type] + plot_kwargs = dict(color='k') if kind == 'resid' else dict() + lines = ax.plot(snr_dict['times'], snr_dict[key] * scaling ** 2, + **plot_kwargs) + # the freqs should be the same for all sensor types (and for SNR and + # power subplots), so we only need to label the lines on one axes + # (otherwise we get duplicate legend entries). + if not legend_labels_exist: + for line, freq in zip(lines, snr_dict['freqs']): + line.set_label(f'{freq} Hz') + legend_labels_exist = True + unit = DEFAULTS['units'][ch_type] + unit = f'({unit})' if '/' in unit else unit + set_kwargs = dict(title=f'{titles[kind]}, {full_names[ch_type]}', + ylabel='dB' if kind == 'snr' else f'{unit}²') + if not axes_was_none: + set_kwargs.update(xlabel='Time (s)') + ax.set(**set_kwargs) + if axes_was_none: + ax.set(xlabel='Time (s)') + fig.align_ylabels() + fig.subplots_adjust(left=0.1, right=0.825, bottom=0.075, top=0.95, + hspace=0.7) + fig.legend(loc='right', title='cHPI frequencies') + return fig diff -Nru python-mne-0.23.4+dfsg/mne/viz/montage.py python-mne-1.1.0+dfsg/mne/viz/montage.py --- python-mne-0.23.4+dfsg/mne/viz/montage.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/montage.py 2022-08-03 15:48:14.000000000 +0000 @@ -17,13 +17,14 @@ The montage to visualize. scale_factor : float Determines the size of the points. - show_names : bool - Whether to show the channel names. + show_names : bool | list + Whether to display all channel names. If a list, only the channel + names in the list are shown. Defaults to True. kind : str Whether to plot the montage as '3d' or 'topomap' (default). show : bool Show figure if True. - %(topomap_sphere_auto)s + %(sphere_topomap_auto)s %(verbose)s Returns @@ -33,7 +34,6 @@ """ from scipy.spatial.distance import cdist from ..channels import DigMontage, make_dig_montage - from ..io import RawArray from .. import create_info _check_option('kind', kind, ['topomap', '3d']) @@ -67,8 +67,7 @@ montage = make_dig_montage(ch_pos=ch_pos, **fid) info = create_info(ch_names, sfreq=256, ch_types="eeg") - raw = RawArray(np.zeros((len(ch_names), 1)), info, copy=None) - raw.set_montage(montage, on_missing='ignore') + info.set_montage(montage, on_missing='ignore') fig = plot_sensors(info, kind=kind, show_names=show_names, show=show, title=title, sphere=sphere) collection = fig.axes[0].collections[0] diff -Nru python-mne-0.23.4+dfsg/mne/viz/_mpl_figure.py python-mne-1.1.0+dfsg/mne/viz/_mpl_figure.py --- python-mne-0.23.4+dfsg/mne/viz/_mpl_figure.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/_mpl_figure.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,2285 @@ +# -*- coding: utf-8 -*- +"""Figure classes for MNE-Python's 2D plots. + +Class Hierarchy +--------------- + +MNEFigParams Container object, attached to MNEFigure by default. Sets + close_key='escape' plus whatever other key-value pairs are + passed to its constructor. + +matplotlib.figure.Figure + └ MNEFigure + ├ MNEBrowseFigure Interactive figure for scrollable data. + │ Generated by: + │ - raw.plot() + │ - epochs.plot() + │ - ica.plot_sources(raw) + │ - ica.plot_sources(epochs) + │ + ├ MNEAnnotationFigure GUI for adding annotations to Raw + │ + ├ MNESelectionFigure GUI for spatial channel selection. raw.plot() + │ and epochs.plot() will generate one of these + │ alongside an MNEBrowseFigure when + │ group_by == 'selection' or 'position' + │ + └ MNELineFigure Interactive figure for non-scrollable data. + Generated by: + - raw.plot_psd() + - evoked.plot() TODO Not yet implemented + - evoked.plot_white() TODO Not yet implemented + - evoked.plot_joint() TODO Not yet implemented +""" + +# Authors: Daniel McCloy +# +# License: Simplified BSD + +import datetime +import platform +import warnings +from collections import OrderedDict +from contextlib import contextmanager +from functools import partial + +import matplotlib.pyplot as plt +import numpy as np +from matplotlib import get_backend +from matplotlib.figure import Figure + +from .. import channel_indices_by_type, pick_types +from ..annotations import _sync_onset +from ..defaults import _handle_default +from ..io.pick import (_DATA_CH_TYPES_ORDER_DEFAULT, _DATA_CH_TYPES_SPLIT, + _FNIRS_CH_TYPES_SPLIT, _VALID_CHANNEL_TYPES, + _picks_to_idx) +from ..time_frequency import psd_multitaper, psd_welch +from ..utils import Bunch, _check_option, _check_sphere, _click_ch_name, logger +from . import plot_sensors +from ._figure import BrowserBase +from .utils import (DraggableLine, _events_off, _fake_click, + _merge_annotations, _plot_psd, _prop_kw, _set_window_title, + _validate_if_list_of_axes, plt_show) + +name = 'matplotlib' +plt.ion() +BACKEND = get_backend() +# This ↑↑↑↑↑↑↑↑↑↑↑↑↑ does weird things: +# https://github.com/matplotlib/matplotlib/issues/23298 +# but wrapping it in ion() context makes it go away (can't actually use +# `with plt.ion()` as context manager, though, for compat reasons). +# Moving this bit to a separate function in ../../fixes.py doesn't work. +plt.ioff() + +# CONSTANTS (inches) +ANNOTATION_FIG_PAD = 0.1 +ANNOTATION_FIG_MIN_H = 2.9 # fixed part, not including radio buttons/labels +ANNOTATION_FIG_W = 5.0 +ANNOTATION_FIG_CHECKBOX_COLUMN_W = 0.5 + + +class MNEFigure(Figure): + """Base class for 2D figures & dialogs; wraps matplotlib.figure.Figure.""" + + def __init__(self, **kwargs): + from matplotlib import rcParams + + # figsize is the only kwarg we pass to matplotlib Figure() + figsize = kwargs.pop('figsize', None) + super().__init__(figsize=figsize) + # things we'll almost always want + defaults = dict(fgcolor=rcParams['axes.edgecolor'], + bgcolor=rcParams['axes.facecolor']) + for key, value in defaults.items(): + if key not in kwargs: + kwargs[key] = value + + # add param object if not already added (e.g. by BrowserBase) + if not hasattr(self, 'mne'): + from mne.viz._figure import BrowserParams + self.mne = BrowserParams(**kwargs) + else: + for key in [k for k in kwargs if not hasattr(self.mne, k)]: + setattr(self.mne, key, kwargs[key]) + + def _close(self, event): + """Handle close events.""" + logger.debug(f'Closing {self!r}') + # remove references from parent fig to child fig + is_child = getattr(self.mne, 'parent_fig', None) is not None + is_named = getattr(self.mne, 'fig_name', None) is not None + if is_child: + self.mne.parent_fig.mne.child_figs.remove(self) + if is_named: + setattr(self.mne.parent_fig.mne, self.mne.fig_name, None) + + def _keypress(self, event): + """Handle keypress events.""" + if event.key == self.mne.close_key: + plt.close(self) + elif event.key == 'f11': # full screen + self.canvas.manager.full_screen_toggle() + + def _buttonpress(self, event): + """Handle buttonpress events.""" + pass + + def _pick(self, event): + """Handle matplotlib pick events.""" + pass + + def _resize(self, event): + """Handle window resize events.""" + pass + + def _add_default_callbacks(self, **kwargs): + """Remove some matplotlib default callbacks and add MNE-Python ones.""" + # Remove matplotlib default keypress catchers + default_callbacks = list( + self.canvas.callbacks.callbacks.get('key_press_event', {})) + for callback in default_callbacks: + self.canvas.callbacks.disconnect(callback) + # add our event callbacks + callbacks = dict(resize_event=self._resize, + key_press_event=self._keypress, + button_press_event=self._buttonpress, + close_event=self._close, + pick_event=self._pick) + callbacks.update(kwargs) + callback_ids = dict() + for event, callback in callbacks.items(): + callback_ids[event] = self.canvas.mpl_connect(event, callback) + # store callback references so they aren't garbage-collected + self.mne._callback_ids = callback_ids + + def _get_dpi_ratio(self): + """Get DPI ratio (to handle hi-DPI screens).""" + dpi_ratio = 1. + for key in ('_dpi_ratio', '_device_scale'): + dpi_ratio = getattr(self.canvas, key, dpi_ratio) + return dpi_ratio + + def _get_size_px(self): + """Get figure size in pixels.""" + dpi_ratio = self._get_dpi_ratio() + return self.get_size_inches() * self.dpi / dpi_ratio + + def _inch_to_rel(self, dim_inches, horiz=True): + """Convert inches to figure-relative distances.""" + fig_w, fig_h = self.get_size_inches() + w_or_h = fig_w if horiz else fig_h + return dim_inches / w_or_h + + +class MNEAnnotationFigure(MNEFigure): + """Interactive dialog figure for annotations.""" + + def _close(self, event): + """Handle close events (via keypress or window [x]).""" + parent = self.mne.parent_fig + # disable span selector + parent.mne.ax_main.selector.active = False + # clear hover line + parent._remove_annotation_hover_line() + # disconnect hover callback + callback_id = parent.mne._callback_ids['motion_notify_event'] + parent.canvas.callbacks.disconnect(callback_id) + # do all the other cleanup activities + super()._close(event) + + def _keypress(self, event): + """Handle keypress events.""" + text = self.label.get_text() + key = event.key + if key == self.mne.close_key: + plt.close(self) + elif key == 'backspace': + text = text[:-1] + elif key == 'enter': + self.mne.parent_fig._add_annotation_label(event) + return + elif len(key) > 1 or key == ';': # ignore modifier keys + return + else: + text = text + key + self.label.set_text(text) + self.canvas.draw() + + def _radiopress(self, event): + """Handle Radiobutton clicks for Annotation label selection.""" + # update which button looks active + buttons = self.mne.radio_ax.buttons + labels = [label.get_text() for label in buttons.labels] + idx = labels.index(buttons.value_selected) + self._set_active_button(idx) + # update click-drag rectangle color + color = buttons.circles[idx].get_edgecolor() + selector = self.mne.parent_fig.mne.ax_main.selector + # https://github.com/matplotlib/matplotlib/issues/20618 + # https://github.com/matplotlib/matplotlib/pull/20693 + try: # > 3.4.2 + selector.set_props(color=color, facecolor=color) + except AttributeError: + with warnings.catch_warnings(record=True): + warnings.simplefilter('ignore', DeprecationWarning) + selector.rect.set_color(color) + selector.rectprops.update(dict(facecolor=color)) + + def _click_override(self, event): + """Override MPL radiobutton click detector to use transData.""" + ax = self.mne.radio_ax + buttons = ax.buttons + if (buttons.ignore(event) or event.button != 1 or event.inaxes != ax): + return + pclicked = ax.transData.inverted().transform((event.x, event.y)) + distances = {} + for i, (p, t) in enumerate(zip(buttons.circles, buttons.labels)): + if (t.get_window_extent().contains(event.x, event.y) + or np.linalg.norm(pclicked - p.center) < p.radius): + distances[i] = np.linalg.norm(pclicked - p.center) + if len(distances) > 0: + closest = min(distances, key=distances.get) + buttons.set_active(closest) + + def _set_active_button(self, idx): + """Set active button in annotation dialog figure.""" + buttons = self.mne.radio_ax.buttons + with _events_off(buttons): + buttons.set_active(idx) + for circle in buttons.circles: + circle.set_facecolor(self.mne.parent_fig.mne.bgcolor) + # active circle gets filled in, partially transparent + color = list(buttons.circles[idx].get_edgecolor()) + color[-1] = 0.5 + buttons.circles[idx].set_facecolor(color) + self.canvas.draw() + + +class MNESelectionFigure(MNEFigure): + """Interactive dialog figure for channel selections.""" + + def _close(self, event): + """Handle close events.""" + self.mne.parent_fig.mne.child_figs.remove(self) + self.mne.fig_selection = None + # selection fig & main fig tightly integrated; closing one closes both + plt.close(self.mne.parent_fig) + + def _keypress(self, event): + """Handle keypress events.""" + if event.key in ('up', 'down', 'b'): + self.mne.parent_fig._keypress(event) + else: # check for close key + super()._keypress(event) + + def _radiopress(self, event): + """Handle RadioButton clicks for channel selection groups.""" + selections_dict = self.mne.parent_fig.mne.ch_selections + buttons = self.mne.radio_ax.buttons + labels = [label.get_text() for label in buttons.labels] + this_label = buttons.value_selected + parent = self.mne.parent_fig + if this_label == 'Custom' and not len(selections_dict['Custom']): + with _events_off(buttons): + buttons.set_active(self.mne.old_selection) + return + # clicking a selection cancels butterfly mode + if parent.mne.butterfly: + parent._toggle_butterfly() + with _events_off(buttons): + buttons.set_active(labels.index(this_label)) + parent._update_selection() + + def _set_custom_selection(self): + """Set custom selection by lasso selector.""" + chs = self.lasso.selection + parent = self.mne.parent_fig + buttons = self.mne.radio_ax.buttons + if not len(chs): + return + labels = [label.get_text() for label in buttons.labels] + inds = np.in1d(parent.mne.ch_names, chs) + parent.mne.ch_selections['Custom'] = inds.nonzero()[0] + buttons.set_active(labels.index('Custom')) + + def _style_radio_buttons_butterfly(self): + """Handle RadioButton state for keyboard interactions.""" + # Show all radio buttons as selected when in butterfly mode + parent = self.mne.parent_fig + buttons = self.mne.radio_ax.buttons + color = (buttons.activecolor if parent.mne.butterfly else + parent.mne.bgcolor) + for circle in buttons.circles: + circle.set_facecolor(color) + # when leaving butterfly mode, make most-recently-used selection active + if not parent.mne.butterfly: + with _events_off(buttons): + buttons.set_active(self.mne.old_selection) + # update the sensors too + parent._update_highlighted_sensors() + + +class MNEBrowseFigure(BrowserBase, MNEFigure): + """Interactive figure with scrollbars, for data browsing.""" + + def __init__(self, inst, figsize, ica=None, + xlabel='Time (s)', **kwargs): + from matplotlib.colors import to_rgba_array + from matplotlib.patches import Rectangle + from matplotlib.ticker import (FixedFormatter, FixedLocator, + FuncFormatter, NullFormatter) + from matplotlib.transforms import blended_transform_factory + from matplotlib.widgets import Button + from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable + from mpl_toolkits.axes_grid1.axes_size import Fixed + + self.backend_name = 'matplotlib' + + kwargs.update({'inst': inst, + 'figsize': figsize, + 'ica': ica, + 'xlabel': xlabel}) + + BrowserBase.__init__(self, **kwargs) + MNEFigure.__init__(self, **kwargs) + + # MAIN AXES: default sizes (inches) + # XXX simpler with constrained_layout? (when it's no longer "beta") + l_margin = 1. + r_margin = 0.1 + b_margin = 0.45 + t_margin = 0.25 + scroll_width = 0.25 + hscroll_dist = 0.25 + vscroll_dist = 0.1 + help_width = scroll_width * 2 + # MAIN AXES: default margins (figure-relative coordinates) + left = self._inch_to_rel(l_margin - vscroll_dist - help_width) + right = 1 - self._inch_to_rel(r_margin) + bottom = self._inch_to_rel(b_margin, horiz=False) + top = 1 - self._inch_to_rel(t_margin, horiz=False) + width = right - left + height = top - bottom + position = [left, bottom, width, height] + # Main axes must be a subplot for subplots_adjust to work (so user can + # adjust margins). That's why we don't use the Divider class directly. + ax_main = self.add_subplot(1, 1, 1, position=position) + self.subplotpars.update(left=left, bottom=bottom, top=top, right=right) + div = make_axes_locatable(ax_main) + # this only gets shown in zen mode + self.mne.zen_xlabel = ax_main.set_xlabel(xlabel) + self.mne.zen_xlabel.set_visible(not self.mne.scrollbars_visible) + # make sure background color of the axis is set + if 'bgcolor' in kwargs: + ax_main.set_facecolor(kwargs['bgcolor']) + + # SCROLLBARS + ax_hscroll = div.append_axes(position='bottom', + size=Fixed(scroll_width), + pad=Fixed(hscroll_dist)) + ax_vscroll = div.append_axes(position='right', + size=Fixed(scroll_width), + pad=Fixed(vscroll_dist)) + ax_hscroll.get_yaxis().set_visible(False) + ax_hscroll.set_xlabel(xlabel) + ax_vscroll.set_axis_off() + # HORIZONTAL SCROLLBAR PATCHES (FOR MARKING BAD EPOCHS) + if self.mne.is_epochs: + epoch_nums = self.mne.inst.selection + for ix, _ in enumerate(epoch_nums): + start = self.mne.boundary_times[ix] + width = np.diff(self.mne.boundary_times[:2])[0] + ax_hscroll.add_patch( + Rectangle((start, 0), width, 1, color='none', + zorder=self.mne.zorder['patch'])) + # both axes, major ticks: gridlines + for _ax in (ax_main, ax_hscroll): + _ax.xaxis.set_major_locator( + FixedLocator(self.mne.boundary_times[1:-1])) + _ax.xaxis.set_major_formatter(NullFormatter()) + grid_kwargs = dict(color=self.mne.fgcolor, axis='x', + zorder=self.mne.zorder['grid']) + ax_main.grid(linewidth=2, linestyle='dashed', **grid_kwargs) + ax_hscroll.grid(alpha=0.5, linewidth=0.5, linestyle='solid', + **grid_kwargs) + # main axes, minor ticks: ticklabel (epoch number) for every epoch + ax_main.xaxis.set_minor_locator(FixedLocator(self.mne.midpoints)) + ax_main.xaxis.set_minor_formatter(FixedFormatter(epoch_nums)) + # hscroll axes, minor ticks: up to 20 ticklabels (epoch numbers) + ax_hscroll.xaxis.set_minor_locator( + FixedLocator(self.mne.midpoints, nbins=20)) + ax_hscroll.xaxis.set_minor_formatter( + FuncFormatter(lambda x, pos: self._get_epoch_num_from_time(x))) + # hide some ticks + ax_main.tick_params(axis='x', which='major', bottom=False) + ax_hscroll.tick_params(axis='x', which='both', bottom=False) + else: + # RAW / ICA X-AXIS TICK & LABEL FORMATTING + ax_main.xaxis.set_major_formatter( + FuncFormatter(partial(self._xtick_formatter, + ax_type='main'))) + ax_hscroll.xaxis.set_major_formatter( + FuncFormatter(partial(self._xtick_formatter, + ax_type='hscroll'))) + if self.mne.time_format != 'float': + for _ax in (ax_main, ax_hscroll): + _ax.set_xlabel('Time (HH:MM:SS)') + + # VERTICAL SCROLLBAR PATCHES (COLORED BY CHANNEL TYPE) + ch_order = self.mne.ch_order + for ix, pick in enumerate(ch_order): + this_color = (self.mne.ch_color_bad + if self.mne.ch_names[pick] in self.mne.info['bads'] + else self.mne.ch_color_dict) + if isinstance(this_color, dict): + this_color = this_color[self.mne.ch_types[pick]] + ax_vscroll.add_patch( + Rectangle((0, ix), 1, 1, color=this_color, + zorder=self.mne.zorder['patch'])) + ax_vscroll.set_ylim(len(ch_order), 0) + ax_vscroll.set_visible(not self.mne.butterfly) + # SCROLLBAR VISIBLE SELECTION PATCHES + sel_kwargs = dict(alpha=0.3, linewidth=4, clip_on=False, + edgecolor=self.mne.fgcolor) + vsel_patch = Rectangle((0, 0), 1, self.mne.n_channels, + facecolor=self.mne.bgcolor, **sel_kwargs) + ax_vscroll.add_patch(vsel_patch) + hsel_facecolor = np.average( + np.vstack((to_rgba_array(self.mne.fgcolor), + to_rgba_array(self.mne.bgcolor))), + axis=0, weights=(3, 1)) # 75% foreground, 25% background + hsel_patch = Rectangle((self.mne.t_start, 0), self.mne.duration, 1, + facecolor=hsel_facecolor, **sel_kwargs) + ax_hscroll.add_patch(hsel_patch) + ax_hscroll.set_xlim(self.mne.first_time, self.mne.first_time + + self.mne.n_times / self.mne.info['sfreq']) + # VLINE + vline_color = (0., 0.75, 0.) + vline_kwargs = dict(visible=False, zorder=self.mne.zorder['vline']) + if self.mne.is_epochs: + x = np.arange(self.mne.n_epochs) + vline = ax_main.vlines( + x, 0, 1, colors=vline_color, **vline_kwargs) + vline.set_transform(blended_transform_factory(ax_main.transData, + ax_main.transAxes)) + vline_hscroll = None + else: + vline = ax_main.axvline(0, color=vline_color, **vline_kwargs) + vline_hscroll = ax_hscroll.axvline(0, color=vline_color, + **vline_kwargs) + vline_text = ax_main.annotate( + '', xy=(0, 0), xycoords='axes fraction', xytext=(-2, 0), + textcoords='offset points', fontsize=10, ha='right', va='center', + color=vline_color, **vline_kwargs) + + # HELP BUTTON: initialize in the wrong spot... + ax_help = div.append_axes(position='left', + size=Fixed(help_width), + pad=Fixed(vscroll_dist)) + # HELP BUTTON: ...move it down by changing its locator + loc = div.new_locator(nx=0, ny=0) + ax_help.set_axes_locator(loc) + # HELP BUTTON: make it a proper button + with _patched_canvas(ax_help.figure): + self.mne.button_help = Button(ax_help, 'Help') + # PROJ BUTTON + ax_proj = None + if len(self.mne.projs) and not self.mne.inst.proj: + proj_button_pos = [ + 1 - self._inch_to_rel(r_margin + scroll_width), # left + self._inch_to_rel(b_margin, horiz=False), # bottom + self._inch_to_rel(scroll_width), # width + self._inch_to_rel(scroll_width, horiz=False) # height + ] + loc = div.new_locator(nx=4, ny=0) + ax_proj = self.add_axes(proj_button_pos) + ax_proj.set_axes_locator(loc) + with _patched_canvas(ax_help.figure): + self.mne.button_proj = Button(ax_proj, 'Prj') + + # INIT TRACES + self.mne.trace_kwargs = dict(antialiased=True, linewidth=0.5) + self.mne.traces = ax_main.plot( + np.full((1, self.mne.n_channels), np.nan), **self.mne.trace_kwargs) + + # SAVE UI ELEMENT HANDLES + vars(self.mne).update( + ax_main=ax_main, ax_help=ax_help, ax_proj=ax_proj, + ax_hscroll=ax_hscroll, ax_vscroll=ax_vscroll, + vsel_patch=vsel_patch, hsel_patch=hsel_patch, vline=vline, + vline_hscroll=vline_hscroll, vline_text=vline_text) + + def _get_size(self): + return self.get_size_inches() + + def _resize(self, event): + """Handle resize event for mne_browse-style plots (Raw/Epochs/ICA).""" + old_width, old_height = self.mne.fig_size_px + new_width, new_height = self._get_size_px() + new_margins = _calc_new_margins( + self, old_width, old_height, new_width, new_height) + self.subplots_adjust(**new_margins) + # zen mode bookkeeping + self.mne.zen_w *= old_width / new_width + self.mne.zen_h *= old_height / new_height + self.mne.fig_size_px = (new_width, new_height) + self.canvas.draw_idle() + + def _hover(self, event): + """Handle motion event when annotating.""" + if (event.button is not None or event.xdata is None or + event.inaxes != self.mne.ax_main): + return + if not self.mne.draggable_annotations: + self._remove_annotation_hover_line() + return + from matplotlib.patheffects import Normal, Stroke + for coll in self.mne.annotations: + if coll.contains(event)[0]: + path = coll.get_paths() + assert len(path) == 1 + path = path[0] + color = coll.get_edgecolors()[0] + ylim = self.mne.ax_main.get_ylim() + # are we on the left or right edge? + _l = path.vertices[:, 0].min() + _r = path.vertices[:, 0].max() + x = _l if abs(event.xdata - _l) < abs(event.xdata - _r) else _r + mask = path.vertices[:, 0] == x + + def drag_callback(x0): + path.vertices[mask, 0] = x0 + + # create or update the DraggableLine + hover_line = self.mne.annotation_hover_line + if hover_line is None: + line = self.mne.ax_main.plot([x, x], ylim, color=color, + linewidth=2, pickradius=5.)[0] + hover_line = DraggableLine( + line, self._modify_annotation, drag_callback) + else: + hover_line.set_x(x) + hover_line.drag_callback = drag_callback + # style the line + line = hover_line.line + patheff = [Stroke(linewidth=4, foreground=color, alpha=0.5), + Normal()] + line.set_path_effects(patheff if line.contains(event)[0] else + patheff[1:]) + self.mne.ax_main.selector.active = False + self.mne.annotation_hover_line = hover_line + self.canvas.draw_idle() + return + self._remove_annotation_hover_line() + + def _keypress(self, event): + """Handle keypress events.""" + key = event.key + n_channels = self.mne.n_channels + if self.mne.is_epochs: + last_time = self.mne.n_times / self.mne.info['sfreq'] + else: + last_time = self.mne.inst.times[-1] + # scroll up/down + if key in ('down', 'up', 'shift+down', 'shift+up'): + key = key.split('+')[-1] + direction = -1 if key == 'up' else 1 + # butterfly case + if self.mne.butterfly: + return + # group_by case + elif self.mne.fig_selection is not None: + buttons = self.mne.fig_selection.mne.radio_ax.buttons + labels = [label.get_text() for label in buttons.labels] + current_label = buttons.value_selected + current_idx = labels.index(current_label) + selections_dict = self.mne.ch_selections + penult = current_idx < (len(labels) - 1) + pre_penult = current_idx < (len(labels) - 2) + has_custom = selections_dict.get('Custom', None) is not None + def_custom = len(selections_dict.get('Custom', list())) + up_ok = key == 'up' and current_idx > 0 + down_ok = key == 'down' and ( + pre_penult or + (penult and not has_custom) or + (penult and has_custom and def_custom)) + if up_ok or down_ok: + buttons.set_active(current_idx + direction) + # normal case + else: + ceiling = len(self.mne.ch_order) - n_channels + ch_start = self.mne.ch_start + direction * n_channels + self.mne.ch_start = np.clip(ch_start, 0, ceiling) + self._update_picks() + self._update_vscroll() + self._redraw() + # scroll left/right + elif key in ('right', 'left', 'shift+right', 'shift+left'): + old_t_start = self.mne.t_start + direction = 1 if key.endswith('right') else -1 + if self.mne.is_epochs: + denom = 1 if key.startswith('shift') else self.mne.n_epochs + else: + denom = 1 if key.startswith('shift') else 4 + t_max = last_time - self.mne.duration + t_start = self.mne.t_start + direction * self.mne.duration / denom + self.mne.t_start = np.clip(t_start, self.mne.first_time, t_max) + if self.mne.t_start != old_t_start: + self._update_hscroll() + self._redraw(annotations=True) + # scale traces + elif key in ('=', '+', '-'): + scaler = 1 / 1.1 if key == '-' else 1.1 + self.mne.scale_factor *= scaler + self._redraw(update_data=False) + # change number of visible channels + elif (key in ('pageup', 'pagedown') and + self.mne.fig_selection is None and + not self.mne.butterfly): + new_n_ch = n_channels + (1 if key == 'pageup' else -1) + self.mne.n_channels = np.clip(new_n_ch, 1, len(self.mne.ch_order)) + # add new chs from above if we're at the bottom of the scrollbar + ch_end = self.mne.ch_start + self.mne.n_channels + if ch_end > len(self.mne.ch_order) and self.mne.ch_start > 0: + self.mne.ch_start -= 1 + self._update_vscroll() + # redraw only if changed + if self.mne.n_channels != n_channels: + self._update_picks() + self._update_trace_offsets() + self._redraw(annotations=True) + # change duration + elif key in ('home', 'end'): + old_dur = self.mne.duration + dur_delta = 1 if key == 'end' else -1 + if self.mne.is_epochs: + # prevent from showing zero epochs, or more epochs than we have + self.mne.n_epochs = np.clip(self.mne.n_epochs + dur_delta, + 1, len(self.mne.inst)) + # use the length of one epoch as duration change + min_dur = len(self.mne.inst.times) / self.mne.info['sfreq'] + new_dur = self.mne.duration + dur_delta * min_dur + else: + # never show fewer than 3 samples + min_dur = 3 * np.diff(self.mne.inst.times[:2])[0] + # use multiplicative dur_delta + dur_delta = 5 / 4 if dur_delta > 0 else 4 / 5 + new_dur = self.mne.duration * dur_delta + self.mne.duration = np.clip(new_dur, min_dur, last_time) + if self.mne.duration != old_dur: + if self.mne.t_start + self.mne.duration > last_time: + self.mne.t_start = last_time - self.mne.duration + self._update_hscroll() + self._redraw(annotations=True) + elif key == '?': # help window + self._toggle_help_fig(event) + elif key == 'a': # annotation mode + self._toggle_annotation_fig() + elif key == 'b' and self.mne.instance_type != 'ica': # butterfly mode + self._toggle_butterfly() + elif key == 'd': # DC shift + self.mne.remove_dc = not self.mne.remove_dc + self._redraw() + elif key == 'h': # histogram + self._toggle_epoch_histogram() + elif key == 'j' and len(self.mne.projs): # SSP window + self._toggle_proj_fig() + elif key == 'J' and len(self.mne.projs): + self._toggle_proj_checkbox(event, toggle_all=True) + elif key == 'p': # toggle draggable annotations + self._toggle_draggable_annotations(event) + if self.mne.fig_annotation is not None: + checkbox = self.mne.fig_annotation.mne.drag_checkbox + with _events_off(checkbox): + checkbox.set_active(0) + elif key == 's': # scalebars + self._toggle_scalebars(event) + elif key == 'w': # toggle noise cov whitening + self._toggle_whitening() + elif key == 'z': # zen mode: hide scrollbars and buttons + self._toggle_scrollbars() + self._redraw(update_data=False) + elif key == 't': + self._toggle_time_format() + else: # check for close key / fullscreen toggle + super()._keypress(event) + + def _buttonpress(self, event): + """Handle mouse clicks.""" + butterfly = self.mne.butterfly + annotating = self.mne.fig_annotation is not None + ax_main = self.mne.ax_main + inst = self.mne.inst + # ignore middle clicks, scroll wheel events, and clicks outside axes + if event.button not in (1, 3) or event.inaxes is None: + return + elif event.button == 1: # left-click (primary) + # click in main axes + if (event.inaxes == ax_main and not annotating): + if self.mne.instance_type == 'epochs' or not butterfly: + for line in self.mne.traces + self.mne.epoch_traces: + if line.contains(event)[0]: + if self.mne.instance_type == 'epochs': + self._toggle_bad_epoch(event) + else: + idx = self.mne.traces.index(line) + self._toggle_bad_channel(idx) + return + self._show_vline(event.xdata) # butterfly / not on data trace + self._redraw(update_data=False, annotations=False) + return + # click in vertical scrollbar + elif event.inaxes == self.mne.ax_vscroll: + if self.mne.fig_selection is not None: + self._change_selection_vscroll(event) + elif self._check_update_vscroll_clicked(event): + self._redraw() + # click in horizontal scrollbar + elif event.inaxes == self.mne.ax_hscroll: + if self._check_update_hscroll_clicked(event): + self._redraw(annotations=True) + # click on proj button + elif event.inaxes == self.mne.ax_proj: + self._toggle_proj_fig(event) + # click on help button + elif event.inaxes == self.mne.ax_help: + self._toggle_help_fig(event) + else: # right-click (secondary) + if annotating: + if any(c.contains(event)[0] for c in ax_main.collections): + xdata = event.xdata - self.mne.first_time + start = _sync_onset(inst, inst.annotations.onset) + end = start + inst.annotations.duration + ann_idx = np.where((xdata > start) & (xdata < end))[0] + for idx in sorted(ann_idx)[::-1]: + # only remove visible annotation spans + descr = inst.annotations[idx]['description'] + if self.mne.visible_annotations[descr]: + inst.annotations.delete(idx) + self._remove_annotation_hover_line() + self._draw_annotations() + self.canvas.draw_idle() + elif event.inaxes == ax_main: + self._toggle_vline(False) + + def _pick(self, event): + """Handle matplotlib pick events.""" + from matplotlib.text import Text + if self.mne.butterfly: + return + # clicked on channel name + if isinstance(event.artist, Text): + ch_name = event.artist.get_text() + ind = self.mne.ch_names[self.mne.picks].tolist().index(ch_name) + if event.mouseevent.button == 1: # left click + self._toggle_bad_channel(ind) + elif event.mouseevent.button == 3: # right click + self._create_ch_context_fig(ind) + + def _create_ch_context_fig(self, idx): + fig = super()._create_ch_context_fig(idx) + plt_show(fig=fig) + + def _new_child_figure(self, fig_name, **kwargs): + """Instantiate a new MNE dialog figure (with event listeners).""" + fig = _figure(toolbar=False, parent_fig=self, fig_name=fig_name, + **kwargs) + fig._add_default_callbacks() + self.mne.child_figs.append(fig) + if isinstance(fig_name, str): + setattr(self.mne, fig_name, fig) + return fig + + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + # HELP DIALOG + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + + def _create_help_fig(self): + """Create help dialog window.""" + text = {key: val for key, val in self._get_help_text().items() + if val is not None} + keys = '' + vals = '' + for key, val in text.items(): + newsection = '\n' if key.startswith('_') else '' + key = key[1:] if key.startswith('_') else key + newlines = '\n' * len(val.split('\n')) # handle multiline values + keys += f'{newsection}{key} {newlines}' + vals += f'{newsection}{val}\n' + # calc figure size + n_lines = len(keys.split('\n')) + longest_key = max(len(k) for k in text.keys()) + longest_val = max(max(len(w) for w in v.split('\n')) if '\n' in v else + len(v) for v in text.values()) + width = (longest_key + longest_val) / 12 + height = (n_lines) / 5 + # create figure and axes + fig = self._new_child_figure(figsize=(width, height), + fig_name='fig_help', + window_title='Help') + ax = fig.add_axes((0.01, 0.01, 0.98, 0.98)) + ax.set_axis_off() + kwargs = dict(va='top', linespacing=1.5, usetex=False) + ax.text(0.42, 1, keys, ma='right', ha='right', **kwargs) + ax.text(0.42, 1, vals, ma='left', ha='left', **kwargs) + + def _toggle_help_fig(self, event): + """Show/hide the help dialog window.""" + if self.mne.fig_help is None: + self._create_help_fig() + plt_show(fig=self.mne.fig_help) + else: + plt.close(self.mne.fig_help) + + def _get_help_text(self): + """Generate help dialog text; `None`-valued entries removed later.""" + inst = self.mne.instance_type + is_raw = inst == 'raw' + is_epo = inst == 'epochs' + is_ica = inst == 'ica' + has_proj = bool(len(self.mne.projs)) + # adapt keys to different platforms + is_mac = platform.system() == 'Darwin' + dur_keys = ('fn + ←', 'fn + →') if is_mac else ('Home', 'End') + ch_keys = ('fn + ↑', 'fn + ↓') if is_mac else ('Page up', 'Page down') + # adapt descriptions to different instance types + ch_cmp = 'component' if is_ica else 'channel' + ch_epo = 'epoch' if is_epo else 'channel' + ica_bad = 'Mark/unmark component for exclusion' + dur_vals = ([f'Show {n} epochs' for n in ('fewer', 'more')] + if self.mne.is_epochs else + [f'Show {d} time window' for d in ('shorter', 'longer')]) + ch_vals = [f'{inc_dec} number of visible {ch_cmp}s' for inc_dec in + ('Increase', 'Decrease')] + lclick_data = ica_bad if is_ica else f'Mark/unmark bad {ch_epo}' + lclick_name = (ica_bad if is_ica else 'Mark/unmark bad channel') + rclick_name = dict(ica='Show diagnostics for component', + epochs='Show imageplot for channel', + raw='Show channel location')[inst] + # TODO not yet implemented + # ldrag = ('Show spectrum plot for selected time span;\nor (in ' + # 'annotation mode) add annotation') if inst== 'raw' else None + ldrag = 'add annotation (in annotation mode)' if is_raw else None + noise_cov = (None if self.mne.noise_cov is None else + 'Toggle signal whitening') + scrl = '1 epoch' if self.mne.is_epochs else '¼ window' + # below, value " " is a hack to make "\n".split(value) have length 1 + help_text = OrderedDict([ + ('_NAVIGATION', ' '), + ('→', f'Scroll {scrl} right (scroll full window with Shift + →)'), + ('←', f'Scroll {scrl} left (scroll full window with Shift + ←)'), + (dur_keys[0], dur_vals[0]), + (dur_keys[1], dur_vals[1]), + ('↑', f'Scroll up ({ch_cmp}s)'), + ('↓', f'Scroll down ({ch_cmp}s)'), + (ch_keys[0], ch_vals[0]), + (ch_keys[1], ch_vals[1]), + ('_SIGNAL TRANSFORMATIONS', ' '), + ('+ or =', 'Increase signal scaling'), + ('-', 'Decrease signal scaling'), + ('b', 'Toggle butterfly mode' if not is_ica else None), + ('d', 'Toggle DC removal' if is_raw else None), + ('w', noise_cov), + ('_USER INTERFACE', ' '), + ('a', 'Toggle annotation mode' if is_raw else None), + ('h', 'Toggle peak-to-peak histogram' if is_epo else None), + ('j', 'Toggle SSP projector window' if has_proj else None), + ('shift+j', 'Toggle all SSPs'), + ('p', 'Toggle draggable annotations' if is_raw else None), + ('s', 'Toggle scalebars' if not is_ica else None), + ('z', 'Toggle scrollbars'), + ('t', 'Toggle time format' if not is_epo else None), + ('F11', 'Toggle fullscreen' if not is_mac else None), + ('?', 'Open this help window'), + ('esc', 'Close focused figure or dialog window'), + ('_MOUSE INTERACTION', ' '), + (f'Left-click {ch_cmp} name', lclick_name), + (f'Left-click {ch_cmp} data', lclick_data), + ('Left-click-and-drag on plot', ldrag), + ('Left-click on plot background', 'Place vertical guide'), + ('Right-click on plot background', 'Clear vertical guide'), + ('Right-click on channel name', rclick_name) + ]) + return help_text + + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + # ANNOTATIONS + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + + def _create_annotation_fig(self): + """Create the annotation dialog window.""" + from matplotlib.widgets import Button, CheckButtons, SpanSelector + from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable + from mpl_toolkits.axes_grid1.axes_size import Fixed + + # make figure + labels = np.array(sorted(set(self.mne.inst.annotations.description))) + radio_button_h = self._compute_annotation_figsize(len(labels)) + figsize = (ANNOTATION_FIG_W, ANNOTATION_FIG_MIN_H + radio_button_h) + fig = self._new_child_figure(figsize=figsize, + FigureClass=MNEAnnotationFigure, + fig_name='fig_annotation', + window_title='Annotations') + # make main axes + left = fig._inch_to_rel(ANNOTATION_FIG_PAD) + bottom = fig._inch_to_rel(ANNOTATION_FIG_PAD, horiz=False) + width = 1 - 2 * left + height = 1 - 2 * bottom + fig.mne.radio_ax = fig.add_axes((left, bottom, width, height), + frame_on=False, aspect='equal') + div = make_axes_locatable(fig.mne.radio_ax) + # append show/hide checkboxes at right + fig.mne.show_hide_ax = div.append_axes( + position='right', size=Fixed(ANNOTATION_FIG_CHECKBOX_COLUMN_W), + pad=Fixed(ANNOTATION_FIG_PAD), aspect='equal', + sharey=fig.mne.radio_ax) + # populate w/ radio buttons & labels + self._update_annotation_fig() + # append instructions at top + instructions_ax = div.append_axes(position='top', size=Fixed(1), + pad=Fixed(5 * ANNOTATION_FIG_PAD)) + # XXX when we support a newer matplotlib (something >3.0) the + # instructions can have inline bold formatting: + # instructions = '\n'.join( + # [r'$\mathbf{Left‐click~&~drag~on~plot:}$ create/modify annotation', # noqa E501 + # r'$\mathbf{Right‐click~on~plot~annotation:}$ delete annotation', + # r'$\mathbf{Type~in~annotation~window:}$ modify new label name', + # r'$\mathbf{Enter~(or~click~button):}$ add new label to list', + # r'$\mathbf{Esc:}$ exit annotation mode & close this window']) + instructions = '\n'.join( + ['Left click & drag on plot: create/modify annotation', + 'Right click on annotation highlight: delete annotation', + 'Type in this window: modify new label name', + 'Enter (or click button): add new label to list', + 'Esc: exit annotation mode & close this dialog window']) + instructions_ax.text(0, 1, instructions, va='top', ha='left', + linespacing=1.7, + usetex=False) # force use of MPL mathtext parser + instructions_ax.set_axis_off() + # append text entry axes at bottom + text_entry_ax = div.append_axes(position='bottom', + size=Fixed(3 * ANNOTATION_FIG_PAD), + pad=Fixed(ANNOTATION_FIG_PAD)) + text_entry_ax.text(0.4, 0.5, 'New label:', va='center', ha='right', + weight='bold') + fig.label = text_entry_ax.text(0.5, 0.5, 'BAD_', va='center', + ha='left') + text_entry_ax.set_axis_off() + # append button at bottom + button_ax = div.append_axes(position='bottom', + size=Fixed(3 * ANNOTATION_FIG_PAD), + pad=Fixed(ANNOTATION_FIG_PAD)) + fig.button = Button(button_ax, 'Add new label') + fig.button.on_clicked(self._add_annotation_label) + plt_show(fig=fig) + # add "draggable" checkbox + drag_ax_height = 3 * ANNOTATION_FIG_PAD + drag_ax = div.append_axes('bottom', size=Fixed(drag_ax_height), + pad=Fixed(ANNOTATION_FIG_PAD), + aspect='equal') + checkbox = CheckButtons(drag_ax, labels=('Draggable edges?',), + actives=(self.mne.draggable_annotations,)) + checkbox.on_clicked(self._toggle_draggable_annotations) + fig.mne.drag_checkbox = checkbox + # reposition & resize axes + width_in, height_in = fig.get_size_inches() + width_ax = fig._inch_to_rel(width_in + - ANNOTATION_FIG_CHECKBOX_COLUMN_W + - 3 * ANNOTATION_FIG_PAD) + aspect = width_ax / fig._inch_to_rel(drag_ax_height) + drag_ax.set_xlim(0, aspect) + drag_ax.set_axis_off() + # reposition & resize checkbox & label + rect = checkbox.rectangles[0] + _pad, _size = (0.2, 0.6) + rect.set_bounds(_pad, _pad, _size, _size) + lines = checkbox.lines[0] + for line, direction in zip(lines, (1, -1)): + line.set_xdata((_pad, _pad + _size)[::direction]) + line.set_ydata((_pad, _pad + _size)) + text = checkbox.labels[0] + text.set(position=(3 * _pad + _size, 0.45), va='center') + for artist in lines + (rect, text): + artist.set_transform(drag_ax.transData) + # setup interactivity in plot window + col = ('#ff0000' if len(fig.mne.radio_ax.buttons.circles) < 1 else + fig.mne.radio_ax.buttons.circles[0].get_edgecolor()) + # TODO: we would like useblit=True here, but it behaves oddly when the + # first span is dragged (subsequent spans seem to work OK) + rect_kw = _prop_kw('rect', dict(alpha=0.5, facecolor=col)) + selector = SpanSelector(self.mne.ax_main, self._select_annotation_span, + 'horizontal', minspan=0.1, useblit=False, + **rect_kw) + self.mne.ax_main.selector = selector + self.mne._callback_ids['motion_notify_event'] = \ + self.canvas.mpl_connect('motion_notify_event', self._hover) + + def _toggle_visible_annotations(self, event): + """Enable/disable display of annotations on a per-label basis.""" + checkboxes = self.mne.show_hide_annotation_checkboxes + labels = [t.get_text() for t in checkboxes.labels] + actives = checkboxes.get_status() + self.mne.visible_annotations = dict(zip(labels, actives)) + self._redraw(update_data=False, annotations=True) + + def _toggle_draggable_annotations(self, event): + """Enable/disable draggable annotation edges.""" + self.mne.draggable_annotations = not self.mne.draggable_annotations + + def _update_annotation_fig(self): + """Draw or redraw the radio buttons and annotation labels.""" + from matplotlib.widgets import CheckButtons, RadioButtons + + # define shorthand variables + fig = self.mne.fig_annotation + ax = fig.mne.radio_ax + labels = self._get_annotation_labels() + # compute new figsize + radio_button_h = self._compute_annotation_figsize(len(labels)) + fig.set_size_inches(ANNOTATION_FIG_W, + ANNOTATION_FIG_MIN_H + radio_button_h, + forward=True) + # populate center axes with labels & radio buttons + ax.clear() + title = 'Existing labels:' if len(labels) else 'No existing labels' + ax.set_title(title, size=None, loc='left') + ax.buttons = RadioButtons(ax, labels) + # adjust xlim to keep equal aspect & full width (keep circles round) + aspect = (ANNOTATION_FIG_W - ANNOTATION_FIG_CHECKBOX_COLUMN_W + - 3 * ANNOTATION_FIG_PAD) / radio_button_h + ax.set_xlim((0, aspect)) + # style the buttons & adjust spacing + radius = 0.15 + circles = ax.buttons.circles + for circle, label in zip(circles, ax.buttons.labels): + circle.set_transform(ax.transData) + center = ax.transData.inverted().transform( + ax.transAxes.transform((0.1, 0))) + circle.set_center((center[0], circle.center[1])) + circle.set_edgecolor( + self.mne.annotation_segment_colors[label.get_text()]) + circle.set_linewidth(4) + circle.set_radius(radius / len(labels)) + # style the selected button + if len(labels): + fig._set_active_button(0) + # add event listeners + ax.buttons.disconnect_events() # clear MPL default listeners + ax.buttons.on_clicked(fig._radiopress) + ax.buttons.connect_event('button_press_event', fig._click_override) + + # now do the show/hide checkboxes + show_hide_ax = fig.mne.show_hide_ax + show_hide_ax.clear() + show_hide_ax.set_axis_off() + aspect = ANNOTATION_FIG_CHECKBOX_COLUMN_W / radio_button_h + show_hide_ax.set(xlim=(0, aspect), ylim=(0, 1)) + # ensure new labels have checkbox values + check_values = {label: False for label in labels} + check_values.update(self.mne.visible_annotations) # existing checks + actives = [check_values[label] for label in labels] + # regenerate checkboxes + checkboxes = CheckButtons(ax=fig.mne.show_hide_ax, + labels=labels, + actives=actives) + checkboxes.on_clicked(self._toggle_visible_annotations) + # add title, hide labels + show_hide_ax.set_title('show/\nhide ', size=None, loc='right') + for label in checkboxes.labels: + label.set_visible(False) + # fix aspect and right-align + if len(labels) == 1: + bounds = (0.05, 0.375, 0.25, 0.25) # undo MPL special case + checkboxes.rectangles[0].set_bounds(bounds) + for line, step in zip(checkboxes.lines[0], (1, -1)): + line.set_xdata((bounds[0], bounds[0] + bounds[2])) + line.set_ydata((bounds[1], bounds[1] + bounds[3])[::step]) + for rect in checkboxes.rectangles: + rect.set_transform(show_hide_ax.transData) + bbox = rect.get_bbox() + bounds = (aspect, bbox.ymin, -bbox.width, bbox.height) + rect.set_bounds(bounds) + rect.set_clip_on(False) + for line in np.array(checkboxes.lines).ravel(): + line.set_transform(show_hide_ax.transData) + line.set_xdata(aspect + 0.05 - np.array(line.get_xdata())) + # store state + self.mne.visible_annotations = check_values + self.mne.show_hide_annotation_checkboxes = checkboxes + + def _toggle_annotation_fig(self): + """Show/hide the annotation dialog window.""" + if self.mne.fig_annotation is None and not self.mne.is_epochs: + self._create_annotation_fig() + else: + plt.close(self.mne.fig_annotation) + + def _compute_annotation_figsize(self, n_labels): + """Adapt size of Annotation UI to accommodate the number of buttons. + + self._create_annotation_fig() implements the following: + + Fixed part of height: + 0.1 top margin + 1.0 instructions + 0.5 padding below instructions + --- (variable-height axis for label list, returned by this method) + 0.1 padding above text entry + 0.3 text entry + 0.1 padding above button + 0.3 button + 0.1 padding above checkbox + 0.3 checkbox + 0.1 bottom margin + ------------------------------------------ + 2.9 total fixed height + """ + return max(ANNOTATION_FIG_PAD, 0.7 * n_labels) + + def _add_annotation_label(self, event): + """Add new annotation description.""" + text = self.mne.fig_annotation.label.get_text() + self.mne.new_annotation_labels.append(text) + self._setup_annotation_colors() + self._update_annotation_fig() + # automatically activate new label's radio button + idx = [label.get_text() for label in + self.mne.fig_annotation.mne.radio_ax.buttons.labels].index(text) + self.mne.fig_annotation._set_active_button(idx) + # simulate a click on the radiobutton → update the span selector color + self.mne.fig_annotation._radiopress(event=None) + # reset the text entry box's text + self.mne.fig_annotation.label.set_text('BAD_') + + def _select_annotation_span(self, vmin, vmax): + """Handle annotation span selector.""" + onset = _sync_onset(self.mne.inst, vmin, True) - self.mne.first_time + duration = vmax - vmin + buttons = self.mne.fig_annotation.mne.radio_ax.buttons + labels = [label.get_text() for label in buttons.labels] + if buttons.value_selected is not None: + active_idx = labels.index(buttons.value_selected) + _merge_annotations(onset, onset + duration, labels[active_idx], + self.mne.inst.annotations) + # if adding a span with an annotation label that is hidden, show it + if not self.mne.visible_annotations[buttons.value_selected]: + self.mne.show_hide_annotation_checkboxes.set_active(active_idx) + self._redraw(update_data=False, annotations=True) + else: + logger.warning('No annotation-label exists! ' + 'Add one by typing the name and clicking ' + 'on "Add new label" in the annotation-dialog.') + + def _remove_annotation_hover_line(self): + """Remove annotation line from the plot and reactivate selector.""" + if self.mne.annotation_hover_line is not None: + self.mne.annotation_hover_line.remove() + self.mne.annotation_hover_line = None + self.mne.ax_main.selector.active = True + self.canvas.draw() + + def _modify_annotation(self, old_x, new_x): + """Modify annotation.""" + segment = np.array(np.where(self.mne.annotation_segments == old_x)) + if segment.shape[1] == 0: + return + raw = self.mne.inst + annotations = raw.annotations + first_time = self.mne.first_time + idx = [segment[0][0], segment[1][0]] + onset = _sync_onset(raw, self.mne.annotation_segments[idx[0]][0], True) + ann_idx = np.where(annotations.onset == onset - first_time)[0] + if idx[1] == 0: # start of annotation + onset = _sync_onset(raw, new_x, True) - first_time + duration = annotations.duration[ann_idx] + old_x - new_x + else: # end of annotation + onset = annotations.onset[ann_idx] + duration = _sync_onset(raw, new_x, True) - onset - first_time + if duration < 0: + onset += duration + duration *= -1. + _merge_annotations(onset, onset + duration, + annotations.description[ann_idx], + annotations, ann_idx) + self._draw_annotations() + self._remove_annotation_hover_line() + self.canvas.draw_idle() + + def _clear_annotations(self): + """Clear all annotations from the figure.""" + for annot in list(self.mne.annotations): + annot.remove() + self.mne.annotations.remove(annot) + for annot in list(self.mne.hscroll_annotations): + annot.remove() + self.mne.hscroll_annotations.remove(annot) + for text in list(self.mne.annotation_texts): + text.remove() + self.mne.annotation_texts.remove(text) + + def _draw_annotations(self): + """Draw (or redraw) the annotation spans.""" + self._clear_annotations() + self._update_annotation_segments() + segments = self.mne.annotation_segments + times = self.mne.times + ax = self.mne.ax_main + ylim = ax.get_ylim() + for idx, (start, end) in enumerate(segments): + descr = self.mne.inst.annotations.description[idx] + segment_color = self.mne.annotation_segment_colors[descr] + kwargs = dict(color=segment_color, alpha=0.3, + zorder=self.mne.zorder['ann']) + if self.mne.visible_annotations[descr]: + # draw all segments on ax_hscroll + annot = self.mne.ax_hscroll.fill_betweenx((0, 1), start, end, + **kwargs) + self.mne.hscroll_annotations.append(annot) + # draw only visible segments on ax_main + visible_segment = np.clip([start, end], times[0], times[-1]) + if np.diff(visible_segment) > 0: + annot = ax.fill_betweenx(ylim, *visible_segment, **kwargs) + self.mne.annotations.append(annot) + xy = (visible_segment.mean(), ylim[1]) + text = ax.annotate(descr, xy, xytext=(0, 9), + textcoords='offset points', ha='center', + va='baseline', color=segment_color) + self.mne.annotation_texts.append(text) + + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + # CHANNEL SELECTION GUI + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + + def _create_selection_fig(self): + """Create channel selection dialog window.""" + from matplotlib.colors import to_rgb + from matplotlib.gridspec import GridSpec + from matplotlib.widgets import RadioButtons + + # make figure + fig = self._new_child_figure(figsize=(3, 7), + FigureClass=MNESelectionFigure, + fig_name='fig_selection', + window_title='Channel selection') + # XXX when matplotlib 3.3 is min version, replace this with + # XXX gs = fig.add_gridspec(15, 1) + gs = GridSpec(nrows=15, ncols=1) + # add sensor plot at top + fig.mne.sensor_ax = fig.add_subplot(gs[:5]) + plot_sensors(self.mne.info, kind='select', ch_type='all', title='', + axes=fig.mne.sensor_ax, ch_groups=self.mne.group_by, + show=False) + fig.subplots_adjust(bottom=0.01, top=0.99, left=0.01, right=0.99) + # style the sensors so the selection is easier to distinguish + fig.lasso.linewidth_selected = 2 + self._update_highlighted_sensors() + # add radio button axes + radio_ax = fig.add_subplot(gs[5:-3], frame_on=False, aspect='equal') + fig.mne.radio_ax = radio_ax + selections_dict = self.mne.ch_selections + selections_dict.update(Custom=np.array([], dtype=int)) # for lasso + labels = list(selections_dict) + # make & style the radio buttons + activecolor = to_rgb(self.mne.fgcolor) + (0.5,) + radio_ax.buttons = RadioButtons(radio_ax, labels, + activecolor=activecolor) + fig.mne.old_selection = 0 + for circle in radio_ax.buttons.circles: + circle.set_radius(0.25 / len(labels)) + circle.set_linewidth(2) + circle.set_edgecolor(self.mne.fgcolor) + fig._style_radio_buttons_butterfly() + # add instructions at bottom + instructions = ( + 'To use a custom selection, first click-drag on the sensor plot ' + 'to "lasso" the sensors you want to select, or hold Ctrl while ' + 'clicking individual sensors. Holding Ctrl while click-dragging ' + 'allows a lasso selection adding to (rather than replacing) the ' + 'existing selection.') + instructions_ax = fig.add_subplot(gs[-3:], frame_on=False) + instructions_ax.text(0.04, 0.08, instructions, va='bottom', ha='left', + ma='left', wrap=True) + instructions_ax.set_axis_off() + # add event listeners + radio_ax.buttons.on_clicked(fig._radiopress) + fig.lasso.callbacks.append(fig._set_custom_selection) + + def _change_selection_vscroll(self, event): + """Handle clicks on vertical scrollbar when using selections.""" + buttons = self.mne.fig_selection.mne.radio_ax.buttons + labels = [label.get_text() for label in buttons.labels] + offset = 0 + selections_dict = self.mne.ch_selections + for idx, label in enumerate(labels): + offset += len(selections_dict[label]) + if event.ydata < offset: + with _events_off(buttons): + buttons.set_active(idx) + self.mne.fig_selection._radiopress(event) + return + + def _update_selection(self): + """Update visible channels based on selection dialog interaction.""" + selections_dict = self.mne.ch_selections + fig = self.mne.fig_selection + buttons = fig.mne.radio_ax.buttons + label = buttons.value_selected + labels = [_label.get_text() for _label in buttons.labels] + self.mne.fig_selection.mne.old_selection = labels.index(label) + self.mne.picks = selections_dict[label] + self.mne.n_channels = len(self.mne.picks) + self._update_highlighted_sensors() + # if "Vertex" is defined, some channels appear twice, so if + # "Vertex" is selected, ch_start should be the *first* match; + # otherwise it should be the *last* match (since "Vertex" is + # always the first selection group, if it exists). + index = 0 if label == 'Vertex' else -1 + ch_order = np.concatenate(list(selections_dict.values())) + ch_start = np.where(ch_order == self.mne.picks[0])[0][index] + self.mne.ch_start = ch_start + self._update_trace_offsets() + self._update_vscroll() + self._redraw(annotations=True) + + def _update_highlighted_sensors(self): + """Update the sensor plot to show what is selected.""" + inds = np.in1d(self.mne.fig_selection.lasso.ch_names, + self.mne.ch_names[self.mne.picks]).nonzero()[0] + self.mne.fig_selection.lasso.select_many(inds) + + def _update_bad_sensors(self, pick, mark_bad): + """Update the sensor plot to reflect (un)marked bad channels.""" + # replicate plotting order from plot_sensors(), to get index right + sensor_picks = list() + ch_indices = channel_indices_by_type(self.mne.info) + for this_type in _DATA_CH_TYPES_SPLIT: + if this_type in self.mne.ch_types: + sensor_picks.extend(ch_indices[this_type]) + sensor_idx = np.in1d(sensor_picks, pick).nonzero()[0] + # change the sensor color + fig = self.mne.fig_selection + fig.lasso.ec[sensor_idx, 0] = float(mark_bad) # change R of RGBA array + fig.lasso.collection.set_edgecolors(fig.lasso.ec) + fig.canvas.draw_idle() + + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + # PROJECTORS & BAD CHANNELS + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + + def _create_proj_fig(self): + """Create the projectors dialog window.""" + from matplotlib.widgets import Button, CheckButtons + + projs = self.mne.projs + labels = [p['desc'] for p in projs] + for ix, active in enumerate(self.mne.projs_active): + if active: + labels[ix] += ' (already applied)' + # make figure + width = max([4.5, max([len(label) for label in labels]) / 8 + 0.5]) + height = (len(projs) + 1) / 6 + 1.5 + fig = self._new_child_figure(figsize=(width, height), + fig_name='fig_proj', + window_title='SSP projection vectors') + # pass through some proj fig keypresses to the parent + fig.canvas.mpl_connect( + 'key_press_event', + lambda ev: self._keypress(ev) if ev.key in 'jJ' else None) + # make axes + offset = (1 / 6 / height) + position = (0, offset, 1, 0.8 - offset) + ax = fig.add_axes(position, frame_on=False, aspect='equal') + # make title + first_line = ('Projectors already applied to the data are dimmed.\n' + if any(self.mne.projs_active) else '') + second_line = 'Projectors marked with "X" are active on the plot.' + ax.set_title(f'{first_line}{second_line}') + # draw checkboxes + checkboxes = CheckButtons(ax, labels=labels, actives=self.mne.projs_on) + # gray-out already applied projectors + for label, rect, lines in zip(checkboxes.labels, + checkboxes.rectangles, + checkboxes.lines): + if label.get_text().endswith('(already applied)'): + label.set_color('0.5') + rect.set_edgecolor('0.7') + [x.set_color('0.7') for x in lines] + rect.set_linewidth(1) + # add "toggle all" button + ax_all = fig.add_axes((0.25, 0.01, 0.5, offset), frame_on=True) + fig.mne.proj_all = Button(ax_all, 'Toggle all') + # add event listeners + checkboxes.on_clicked(self._toggle_proj_checkbox) + fig.mne.proj_all.on_clicked(partial(self._toggle_proj_checkbox, + toggle_all=True)) + # save params + fig.mne.proj_checkboxes = checkboxes + # show figure + self.mne.fig_proj.canvas.draw() + plt_show(fig=self.mne.fig_proj, warn=False) + + def _toggle_proj_fig(self, event=None): + """Show/hide the projectors dialog window.""" + if self.mne.fig_proj is None: + self._create_proj_fig() + else: + plt.close(self.mne.fig_proj) + + def _toggle_proj_checkbox(self, event, toggle_all=False): + """Perform operations when proj boxes clicked.""" + on = self.mne.projs_on + applied = self.mne.projs_active + fig = self.mne.fig_proj + new_state = (np.full_like(on, not all(on)) if toggle_all else + np.array(fig.mne.proj_checkboxes.get_status())) + # update Xs when toggling all + if fig is not None: + if toggle_all: + with _events_off(fig.mne.proj_checkboxes): + for ix in np.where(on != new_state)[0]: + fig.mne.proj_checkboxes.set_active(ix) + # don't allow disabling already-applied projs + with _events_off(fig.mne.proj_checkboxes): + for ix in np.where(applied)[0]: + if not new_state[ix]: + fig.mne.proj_checkboxes.set_active(ix) + new_state[applied] = True + # update the data if necessary + if not np.array_equal(on, new_state): + self.mne.projs_on = new_state + self._update_projector() + self._redraw() + + def _toggle_epoch_histogram(self): + """Show or hide peak-to-peak histogram of channel amplitudes.""" + if self.mne.instance_type == 'epochs': + if self.mne.fig_histogram is None: + self._create_epoch_histogram() + else: + plt.close(self.mne.fig_histogram) + + def _toggle_bad_channel(self, idx): + """Mark/unmark bad channels; `idx` is index of *visible* channels.""" + color, pick, marked_bad = super()._toggle_bad_channel(idx) + + # update sensor color (if in selection mode) + if self.mne.fig_selection is not None: + self._update_bad_sensors(pick, marked_bad) + # update vscroll color + vscroll_idx = (self.mne.ch_order == pick).nonzero()[0] + for _idx in vscroll_idx: + self.mne.ax_vscroll.patches[_idx].set_color(color) + # redraw + self._redraw() + + def _toggle_bad_epoch(self, event): + """Mark/unmark bad epochs.""" + epoch_ix, color = super()._toggle_bad_epoch(event.xdata) + self.mne.ax_hscroll.patches[epoch_ix].set_color(color) + self._redraw(update_data=False) + + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + # SCROLLBARS + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + + def _update_zen_mode_offsets(self): + """Compute difference between main axes edges and scrollbar edges.""" + self.mne.fig_size_px = self._get_size_px() + self.mne.zen_w = (self.mne.ax_vscroll.get_position().xmax - + self.mne.ax_main.get_position().xmax) + self.mne.zen_h = (self.mne.ax_main.get_position().ymin - + self.mne.ax_hscroll.get_position().ymin) + + def _toggle_scrollbars(self): + """Show or hide scrollbars (A.K.A. zen mode).""" + self._update_zen_mode_offsets() + # grow/shrink main axes to take up space from (or make room for) + # scrollbars. We can't use ax.set_position() because axes are + # locatable, so we use subplots_adjust + should_show = not self.mne.scrollbars_visible + margins = {side: getattr(self.subplotpars, side) + for side in ('left', 'bottom', 'right', 'top')} + # if should_show, bottom margin moves up; right margin moves left + margins['bottom'] += (1 if should_show else -1) * self.mne.zen_h + margins['right'] += (-1 if should_show else 1) * self.mne.zen_w + self.subplots_adjust(**margins) + # handle x-axis label + self.mne.zen_xlabel.set_visible(not should_show) + # show/hide other UI elements + for elem in ('ax_hscroll', 'ax_vscroll', 'ax_proj', 'ax_help'): + if elem == 'ax_vscroll' and self.mne.butterfly: + continue + # sometimes we don't have a proj button (ax_proj) + if getattr(self.mne, elem, None) is not None: + getattr(self.mne, elem).set_visible(should_show) + self.mne.scrollbars_visible = should_show + + def _update_vscroll(self): + """Update the vertical scrollbar (channel) selection indicator.""" + self.mne.vsel_patch.set_xy((0, self.mne.ch_start)) + self.mne.vsel_patch.set_height(self.mne.n_channels) + self._update_yaxis_labels() + + def _update_hscroll(self): + """Update the horizontal scrollbar (time) selection indicator.""" + self.mne.hsel_patch.set_xy((self.mne.t_start, 0)) + self.mne.hsel_patch.set_width(self.mne.duration) + + def _check_update_hscroll_clicked(self, event): + """Handle clicks on horizontal scrollbar.""" + time = event.xdata - self.mne.duration / 2 + max_time = (self.mne.n_times / self.mne.info['sfreq'] + + self.mne.first_time - self.mne.duration) + time = np.clip(time, self.mne.first_time, max_time) + if self.mne.is_epochs: + ix = np.searchsorted(self.mne.boundary_times[1:], time) + time = self.mne.boundary_times[ix] + if self.mne.t_start != time: + self.mne.t_start = time + self._update_hscroll() + return True + return False + + def _check_update_vscroll_clicked(self, event): + """Update vscroll patch on click, return True if location changed.""" + new_ch_start = np.clip( + int(round(event.ydata - self.mne.n_channels / 2)), + 0, len(self.mne.ch_order) - self.mne.n_channels) + if self.mne.ch_start != new_ch_start: + self.mne.ch_start = new_ch_start + self._update_picks() + self._update_vscroll() + return True + return False + + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + # SCALEBARS & AXIS LABELS + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + + def _show_scalebars(self): + """Add channel scale bars.""" + for offset, pick in zip(self.mne.trace_offsets, self.mne.picks): + this_name = self.mne.ch_names[pick] + this_type = self.mne.ch_types[pick] + if (this_type not in self.mne.scalebars and + this_type != 'stim' and + this_type in self.mne.scalings and + this_type in getattr(self.mne, 'units', {}) and + this_type in getattr(self.mne, 'unit_scalings', {}) and + this_name not in self.mne.info['bads'] and + this_name not in self.mne.whitened_ch_names): + x = (self.mne.times[0] + self.mne.first_time,) * 2 + denom = 4 if self.mne.butterfly else 2 + y = tuple(np.array([-1, 1]) / denom + offset) + self._draw_one_scalebar(x, y, this_type) + if self.mne.is_epochs: + x = (self.mne.times[0], self.mne.times[0] + + self.mne.boundary_times[1] / 2) + y_value = self.mne.n_channels - 0.5 + y = (y_value, y_value) + self._draw_one_scalebar(x, y, 'time') + + def _hide_scalebars(self): + """Remove channel scale bars.""" + for bar in self.mne.scalebars.values(): + bar.remove() + for text in self.mne.scalebar_texts.values(): + text.remove() + self.mne.scalebars = dict() + self.mne.scalebar_texts = dict() + + def _toggle_scalebars(self, event): + """Show/hide the scalebars.""" + if self.mne.scalebars_visible: + self._hide_scalebars() + else: + self._update_picks() + self._show_scalebars() + # toggle + self.mne.scalebars_visible = not self.mne.scalebars_visible + self._redraw(update_data=False) + + def _draw_one_scalebar(self, x, y, ch_type): + """Draw a scalebar.""" + from .utils import _simplify_float + color = '#AA3377' # purple + kwargs = dict(color=color, zorder=self.mne.zorder['scalebar']) + if ch_type == 'time': + label = f'{self.mne.boundary_times[1]/2:.2f} sec' + text = self.mne.ax_main.text(x[0] + .015, y[1] - .05, label, + va='bottom', ha='left', + size='xx-small', **kwargs) + else: + scaler = 1 if self.mne.butterfly else 2 + inv_norm = (scaler * + self.mne.scalings[ch_type] * + self.mne.unit_scalings[ch_type] / + self.mne.scale_factor) + label = f'{_simplify_float(inv_norm)} {self.mne.units[ch_type]} ' + text = self.mne.ax_main.text(x[1], y[1], label, va='baseline', + ha='right', size='xx-small', **kwargs) + bar = self.mne.ax_main.plot(x, y, lw=4, **kwargs)[0] + self.mne.scalebars[ch_type] = bar + self.mne.scalebar_texts[ch_type] = text + + def _update_yaxis_labels(self): + """Change the y-axis labels.""" + if self.mne.butterfly and self.mne.fig_selection is not None: + exclude = ('Vertex', 'Custom') + ticklabels = list(self.mne.ch_selections) + keep_mask = np.in1d(ticklabels, exclude, invert=True) + ticklabels = [t.replace('Left-', 'L-').replace('Right-', 'R-') + for t in ticklabels] # avoid having to rotate labels + ticklabels = np.array(ticklabels)[keep_mask] + elif self.mne.butterfly: + _, ixs, _ = np.intersect1d(_DATA_CH_TYPES_ORDER_DEFAULT, + self.mne.ch_types, return_indices=True) + ixs.sort() + ticklabels = np.array(_DATA_CH_TYPES_ORDER_DEFAULT)[ixs] + else: + ticklabels = self.mne.ch_names[self.mne.picks] + texts = self.mne.ax_main.set_yticklabels(ticklabels, picker=True) + for text in texts: + sty = ('italic' if text.get_text() in self.mne.whitened_ch_names + else 'normal') + text.set_style(sty) + + def _xtick_formatter(self, x, pos=None, ax_type='main'): + """Change the x-axis labels.""" + tickdiff = np.diff(self.mne.ax_main.get_xticks())[0] + digits = np.ceil(-np.log10(tickdiff) + 1).astype(int) + # always show millisecond precision for vline text + if ax_type == 'vline': + digits = 3 + if self.mne.time_format == 'float': + # round to integers when possible ('9.0' → '9') + if int(x) == x: + digits = None + if ax_type == 'vline': + return f'{round(x, digits)} s' + return str(round(x, digits)) + # format as timestamp + meas_date = self.mne.inst.info['meas_date'] + first_time = datetime.timedelta(seconds=self.mne.inst.first_time) + xtime = datetime.timedelta(seconds=x) + xdatetime = meas_date + first_time + xtime + xdtstr = xdatetime.strftime('%H:%M:%S') + if digits and ax_type != 'hscroll' and int(xdatetime.microsecond): + xdtstr += f'{round(xdatetime.microsecond * 1e-6, digits)}'[1:] + return xdtstr + + def _toggle_time_format(self): + if self.mne.time_format == 'float': + self.mne.time_format = 'clock' + x_axis_label = 'Time (HH:MM:SS)' + else: + self.mne.time_format = 'float' + x_axis_label = 'Time (s)' + + # Change x-axis label + for _ax in (self.mne.ax_main, self.mne.ax_hscroll): + _ax.set_xlabel(x_axis_label) + + self._redraw(update_data=False, annotations=False) + + # Update vline-text if displayed + if self.mne.vline is not None and self.mne.vline.get_visible(): + self._show_vline(self.mne.vline.get_xdata()) + + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + # DATA TRACES + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + + def _toggle_butterfly(self): + """Enter or leave butterfly mode.""" + self.mne.ax_vscroll.set_visible(self.mne.butterfly) + self.mne.butterfly = not self.mne.butterfly + self.mne.scale_factor *= 0.5 if self.mne.butterfly else 2. + self._update_picks() + self._update_trace_offsets() + self._redraw(annotations=True) + if self.mne.fig_selection is not None: + self.mne.fig_selection._style_radio_buttons_butterfly() + + def _update_trace_offsets(self): + """Compute viewport height and adjust offsets.""" + # simultaneous selection and butterfly modes + if self.mne.butterfly and self.mne.ch_selections is not None: + self._update_picks() + selections_dict = self._make_butterfly_selections_dict() + n_offsets = len(selections_dict) + sel_order = list(selections_dict) + offsets = np.array([]) + for pick in self.mne.picks: + for sel in sel_order: + if pick in selections_dict[sel]: + offsets = np.append(offsets, sel_order.index(sel)) + # butterfly only + elif self.mne.butterfly: + unique_ch_types = set(self.mne.ch_types) + n_offsets = len(unique_ch_types) + ch_type_order = [_type for _type in _DATA_CH_TYPES_ORDER_DEFAULT + if _type in unique_ch_types] + offsets = np.array([ch_type_order.index(ch_type) + for ch_type in self.mne.ch_types]) + # normal mode + else: + n_offsets = self.mne.n_channels + offsets = np.arange(n_offsets, dtype=float) + # update ylim, ticks, vertline, and scrollbar patch + ylim = (n_offsets - 0.5, -0.5) # inverted y axis → new chs at bottom + self.mne.ax_main.set_ylim(ylim) + self.mne.ax_main.set_yticks(np.unique(offsets)) + self.mne.vsel_patch.set_height(self.mne.n_channels) + # store new offsets, update axis labels + self.mne.trace_offsets = offsets + self._update_yaxis_labels() + + def _draw_traces(self): + """Draw (or redraw) the channel data.""" + from matplotlib.colors import to_rgba_array + from matplotlib.patches import Rectangle + + # clear scalebars + if self.mne.scalebars_visible: + self._hide_scalebars() + # get info about currently visible channels + picks = self.mne.picks + ch_names = self.mne.ch_names[picks] + ch_types = self.mne.ch_types[picks] + offset_ixs = (picks + if self.mne.butterfly and self.mne.ch_selections is None + else slice(None)) + offsets = self.mne.trace_offsets[offset_ixs] + bad_bool = np.in1d(ch_names, self.mne.info['bads']) + # colors + good_ch_colors = [self.mne.ch_color_dict[_type] for _type in ch_types] + ch_colors = to_rgba_array( + [self.mne.ch_color_bad if _bad else _color + for _bad, _color in zip(bad_bool, good_ch_colors)]) + self.mne.ch_colors = np.array(good_ch_colors) # use for unmarking bads + labels = self.mne.ax_main.yaxis.get_ticklabels() + if self.mne.butterfly: + for label in labels: + label.set_color(self.mne.fgcolor) + else: + for label, color in zip(labels, ch_colors): + label.set_color(color) + # decim + decim = np.ones_like(picks) + data_picks_mask = np.in1d(picks, self.mne.picks_data) + decim[data_picks_mask] = self.mne.decim + # decim can vary by channel type, so compute different `times` vectors + decim_times = {decim_value: + self.mne.times[::decim_value] + self.mne.first_time + for decim_value in set(decim)} + # add more traces if needed + n_picks = len(picks) + if n_picks > len(self.mne.traces): + n_new_chs = n_picks - len(self.mne.traces) + new_traces = self.mne.ax_main.plot(np.full((1, n_new_chs), np.nan), + **self.mne.trace_kwargs) + self.mne.traces.extend(new_traces) + # remove extra traces if needed + extra_traces = self.mne.traces[n_picks:] + for trace in extra_traces: + trace.remove() + self.mne.traces = self.mne.traces[:n_picks] + + # check for bad epochs + time_range = (self.mne.times + self.mne.first_time)[[0, -1]] + if self.mne.instance_type == 'epochs': + epoch_ix = np.searchsorted(self.mne.boundary_times, time_range) + epoch_ix = np.arange(epoch_ix[0], epoch_ix[1]) + epoch_nums = self.mne.inst.selection[epoch_ix[0]:epoch_ix[-1] + 1] + visible_bad_epoch_ix, = np.in1d( + epoch_nums, self.mne.bad_epochs).nonzero() + while len(self.mne.epoch_traces): + self.mne.epoch_traces.pop(-1).remove() + # handle custom epoch colors (for autoreject integration) + if self.mne.epoch_colors is None: + # shape: n_traces × RGBA → n_traces × n_epochs × RGBA + custom_colors = np.tile(ch_colors[:, None, :], + (1, self.mne.n_epochs, 1)) + else: + custom_colors = np.empty((len(self.mne.picks), + self.mne.n_epochs, 4)) + for ii, _epoch_ix in enumerate(epoch_ix): + this_colors = self.mne.epoch_colors[_epoch_ix] + custom_colors[:, ii] = to_rgba_array([this_colors[_ch] + for _ch in picks]) + # override custom color on bad epochs + for _ix in visible_bad_epoch_ix: + _cols = np.array([self.mne.epoch_color_bad, + self.mne.ch_color_bad], + dtype=object)[bad_bool.astype(int)] + custom_colors[:, _ix] = to_rgba_array(_cols) + + # update traces + ylim = self.mne.ax_main.get_ylim() + for ii, line in enumerate(self.mne.traces): + this_name = ch_names[ii] + this_type = ch_types[ii] + this_offset = offsets[ii] + this_times = decim_times[decim[ii]] + this_data = this_offset - self.mne.data[ii] * self.mne.scale_factor + this_data = this_data[..., ::decim[ii]] + # clip + if self.mne.clipping == 'clamp': + this_data = np.clip(this_data, -0.5, 0.5) + elif self.mne.clipping is not None: + clip = self.mne.clipping * (0.2 if self.mne.butterfly else 1) + bottom = max(this_offset - clip, ylim[1]) + height = min(2 * clip, ylim[0] - bottom) + rect = Rectangle(xy=np.array([time_range[0], bottom]), + width=time_range[1] - time_range[0], + height=height, + transform=self.mne.ax_main.transData) + line.set_clip_path(rect) + # prep z order + is_bad_ch = this_name in self.mne.info['bads'] + this_z = self.mne.zorder['bads' if is_bad_ch else 'data'] + if self.mne.butterfly and not is_bad_ch: + this_z = self.mne.zorder.get(this_type, this_z) + # plot each trace multiple times to get the desired epoch coloring. + # use masked arrays to plot discontinuous epochs that have the same + # color in a single plot() call. + if self.mne.instance_type == 'epochs': + this_colors = custom_colors[ii] + for cix, color in enumerate(np.unique(this_colors, axis=0)): + bool_ixs = (this_colors == color).all(axis=1) + mask = np.zeros_like(this_times, dtype=bool) + _starts = self.mne.boundary_times[epoch_ix][bool_ixs] + _stops = self.mne.boundary_times[epoch_ix + 1][bool_ixs] + for _start, _stop in zip(_starts, _stops): + _mask = np.logical_and(_start < this_times, + this_times <= _stop) + mask = mask | _mask + _times = np.ma.masked_array(this_times, mask=~mask) + # always use the existing traces first + if cix == 0: + line.set_xdata(_times) + line.set_ydata(this_data) + line.set_color(color) + line.set_zorder(this_z) + else: # make new traces as needed + _trace = self.mne.ax_main.plot( + _times, this_data, color=color, zorder=this_z, + **self.mne.trace_kwargs) + self.mne.epoch_traces.extend(_trace) + else: + line.set_xdata(this_times) + line.set_ydata(this_data) + line.set_color(ch_colors[ii]) + line.set_zorder(this_z) + # update xlim + self.mne.ax_main.set_xlim(*time_range) + # draw scalebars maybe + if self.mne.scalebars_visible: + self._show_scalebars() + # redraw event lines + if self.mne.event_times is not None: + self._draw_event_lines() + + def _redraw(self, update_data=True, annotations=False): + """Redraw (convenience method for frequently grouped actions).""" + super()._redraw(update_data, annotations) + if self.mne.vline_visible and self.mne.is_epochs: + # prevent flickering + _ = self._recompute_epochs_vlines(None) + self.canvas.draw_idle() + + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + # EVENT LINES AND MARKER LINES + # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + + def _draw_event_lines(self): + """Draw the event lines and their labels.""" + from matplotlib.collections import LineCollection + from matplotlib.colors import to_rgba_array + if self.mne.event_nums is not None: + mask = np.logical_and(self.mne.event_times >= self.mne.times[0], + self.mne.event_times <= self.mne.times[-1]) + this_event_times = self.mne.event_times[mask] + this_event_nums = self.mne.event_nums[mask] + n_visible_events = len(this_event_times) + colors = to_rgba_array([self.mne.event_color_dict[n] + for n in this_event_nums]) + # create event lines + ylim = self.mne.ax_main.get_ylim() + xs = np.repeat(this_event_times, 2) + ys = np.tile(ylim, n_visible_events) + segs = np.vstack([xs, ys]).T.reshape(n_visible_events, 2, 2) + event_lines = LineCollection(segs, linewidths=0.5, colors=colors, + zorder=self.mne.zorder['events']) + self.mne.ax_main.add_collection(event_lines) + self.mne.event_lines = event_lines + # create event labels + while len(self.mne.event_texts): + self.mne.event_texts.pop().remove() + for _t, _n, _c in zip(this_event_times, this_event_nums, colors): + label = self.mne.event_id_rev.get(_n, _n) + this_text = self.mne.ax_main.annotate( + label, (_t, ylim[1]), ha='center', va='baseline', + color=self.mne.fgcolor, xytext=(0, 2), + textcoords='offset points', fontsize=8) + self.mne.event_texts.append(this_text) + + def _recompute_epochs_vlines(self, xdata): + """Recompute vline x-coords for epochs plots (after scrolling, etc).""" + # special case: changed view duration w/ "home" or "end" key + # (no click event, hence no xdata) + if xdata is None: + xdata = np.array(self.mne.vline.get_segments())[0, 0, 0] + # compute the (continuous) times for the lines on each epoch + epoch_dur = np.diff(self.mne.boundary_times[:2])[0] + rel_time = xdata % epoch_dur + abs_time = self.mne.times[0] + xs = np.arange(self.mne.n_epochs) * epoch_dur + abs_time + rel_time + segs = np.array(self.mne.vline.get_segments()) + # recreate segs from scratch in case view duration changed + # (i.e., handle case when n_segments != n_epochs) + segs = np.tile([[0.], [1.]], (len(xs), 1, 2)) # y values + segs[..., 0] = np.tile(xs[:, None], 2) # x values + self.mne.vline.set_segments(segs) + return rel_time + + def _show_vline(self, xdata): + """Show the vertical line(s).""" + if self.mne.is_epochs: + # convert xdata to be epoch-relative (for the text) + rel_time = self._recompute_epochs_vlines(xdata) + xdata = rel_time + self.mne.inst.times[0] + else: + self.mne.vline.set_xdata(xdata) + self.mne.vline_hscroll.set_xdata(xdata) + text = self._xtick_formatter(xdata, ax_type='vline')[:12] + self.mne.vline_text.set_text(text) + self._toggle_vline(True) + + def _toggle_vline(self, visible): + """Show or hide the vertical line(s).""" + for artist in (self.mne.vline, self.mne.vline_hscroll, + self.mne.vline_text): + if artist is not None: + artist.set_visible(visible) + self.draw_artist(artist) + self.mne.vline_visible = visible + self.canvas.draw_idle() + + # workaround: plt.close() doesn't spawn close_event on Agg backend + # (check MPL github issue #18609; scheduled to be fixed by MPL 3.6) + def _close_event(self, fig=None): + """Force calling of the MPL figure close event.""" + from ..fixes import _close_event + fig = fig or self + _close_event(fig) + + def _fake_keypress(self, key, fig=None): + fig = fig or self + fig.canvas.key_press_event(key) + + def _fake_click(self, point, add_points=None, fig=None, ax=None, + xform='ax', button=1, kind='press'): + """Fake a click at a relative point within axes.""" + fig = fig or self + ax = ax or self.mne.ax_main + if kind == 'drag' and add_points is not None: + _fake_click(fig=fig, ax=ax, point=point, xform=xform, + button=button, kind='press') + for apoint in add_points: + _fake_click(fig=fig, ax=ax, point=apoint, xform=xform, + button=button, kind='motion') + _fake_click(fig=fig, ax=ax, point=add_points[-1], xform=xform, + button=button, kind='release') + else: + _fake_click(fig=fig, ax=ax, point=point, xform=xform, + button=button, kind=kind) + + def _fake_scroll(self, x, y, step, fig=None): + fig = fig or self + fig.canvas.scroll_event(x, y, step) + + def _click_ch_name(self, ch_index, button): + _click_ch_name(self, ch_index, button) + + def _resize_by_factor(self, factor=None): + size = self.canvas.manager.canvas.get_width_height() + if isinstance(factor, tuple): + size = int(size[0] * factor[0], + size[1] * factor[1]) + else: + size = [int(x * factor) for x in size] + self.canvas.manager.resize(*size) + + def _get_ticklabels(self, orientation): + if orientation == 'x': + labels = self.mne.ax_main.get_xticklabels(minor=self.mne.is_epochs) + elif orientation == 'y': + labels = self.mne.ax_main.get_yticklabels() + label_texts = [lb.get_text() for lb in labels] + + return label_texts + + def _get_scale_bar_texts(self): + texts = tuple(t.get_text().strip() for t in self.mne.ax_main.texts) + # First text is empty because of vline + texts = texts[1:] + + return texts + + +class MNELineFigure(MNEFigure): + """Interactive figure for non-scrolling line plots.""" + + def __init__(self, inst, n_axes, figsize, **kwargs): + super().__init__(figsize=figsize, inst=inst, **kwargs) + + # AXES: default margins (inches) + l_margin = 0.8 + r_margin = 0.2 + b_margin = 0.65 + t_margin = 0.35 + # AXES: default margins (figure-relative coordinates) + left = self._inch_to_rel(l_margin) + right = 1 - self._inch_to_rel(r_margin) + bottom = self._inch_to_rel(b_margin, horiz=False) + top = 1 - self._inch_to_rel(t_margin, horiz=False) + # AXES: make subplots + axes = [self.add_subplot(n_axes, 1, 1)] + for ix in range(1, n_axes): + axes.append(self.add_subplot(n_axes, 1, ix + 1, sharex=axes[0])) + self.subplotpars.update(left=left, bottom=bottom, top=top, right=right, + hspace=0.4) + # save useful things + self.mne.ax_list = axes + + def _resize(self, event): + """Handle resize event.""" + old_width, old_height = self.mne.fig_size_px + new_width, new_height = self._get_size_px() + new_margins = _calc_new_margins( + self, old_width, old_height, new_width, new_height) + self.subplots_adjust(**new_margins) + self.mne.fig_size_px = (new_width, new_height) + + +def _close_all(): + """Close all figures (only used in our tests).""" + plt.close('all') + + +def _get_n_figs(): + return len(plt.get_fignums()) + + +def _figure(toolbar=True, FigureClass=MNEFigure, **kwargs): + """Instantiate a new figure.""" + from matplotlib import rc_context + title = kwargs.pop('window_title', None) # extract title before init + rc = dict() if toolbar else dict(toolbar='none') + with rc_context(rc=rc): + fig = plt.figure(FigureClass=FigureClass, **kwargs) + # BACKEND defined globally at the top of this file + fig.mne.backend = BACKEND + if title is not None: + _set_window_title(fig, title) + # add event callbacks + fig._add_default_callbacks() + return fig + + +def _line_figure(inst, axes=None, picks=None, **kwargs): + """Instantiate a new line figure.""" + from matplotlib.axes import Axes + + # if picks is None, only show data channels + allowed_ch_types = (_DATA_CH_TYPES_SPLIT if picks is None else + _VALID_CHANNEL_TYPES) + # figure out expected number of axes + ch_types = np.array(inst.get_channel_types()) + if picks is not None: + ch_types = ch_types[picks] + n_axes = len(np.intersect1d(ch_types, allowed_ch_types)) + # handle user-provided axes + if axes is not None: + if isinstance(axes, Axes): + axes = [axes] + _validate_if_list_of_axes(axes, n_axes) + fig = axes[0].get_figure() + else: + figsize = kwargs.pop('figsize', (10, 2.5 * n_axes + 1)) + fig = _figure(inst=inst, toolbar=True, FigureClass=MNELineFigure, + figsize=figsize, n_axes=n_axes, **kwargs) + fig.mne.fig_size_px = fig._get_size_px() # can't do in __init__ + axes = fig.mne.ax_list + return fig, axes + + +def _psd_figure(inst, proj, picks, axes, area_mode, tmin, tmax, fmin, fmax, + n_jobs, color, area_alpha, dB, estimate, average, + spatial_colors, xscale, line_alpha, sphere, window, exclude, + **kwargs): + """Instantiate a new power spectral density figure.""" + from .. import BaseEpochs + from ..io import BaseRaw + + # triage kwargs for different PSD methods (raw→welch, epochs→multitaper) + welch_kwargs = ('n_fft', 'n_overlap', 'reject_by_annotation') + multitaper_kwargs = ('bandwidth', 'adaptive', 'low_bias', 'normalization') + psd_kwargs = dict() + for kw in welch_kwargs + multitaper_kwargs: + if kw in kwargs: + psd_kwargs[kw] = kwargs.pop(kw) + if isinstance(inst, BaseRaw): + psd_func = partial(psd_welch, window=window) + elif isinstance(inst, BaseEpochs): + psd_func = psd_multitaper + else: + raise TypeError('Expected an instance of Raw or Epochs, got ' + f'{type(inst)}.') + # arg checking + if np.isfinite(fmax) and (fmax > inst.info['sfreq'] / 2): + raise ValueError( + f'Requested fmax ({fmax} Hz) must not exceed ½ the sampling ' + f'frequency of the data ({0.5 * inst.info["sfreq"]}).') + _check_option('area_mode', area_mode, [None, 'std', 'range']) + _check_option('xscale', xscale, ('log', 'linear')) + sphere = _check_sphere(sphere, inst.info) + picks = _picks_to_idx(inst.info, picks, exclude=exclude) + titles = _handle_default('titles', None) + units = _handle_default('units', None) + scalings = _handle_default('scalings', None) + # containers + picks_list = list() + units_list = list() + titles_list = list() + scalings_list = list() + psd_list = list() + # initialize figure + fig, axes = _line_figure(inst, axes, picks, **kwargs) + # split picks, units, etc, for each subplot + (picks_list, units_list, scalings_list, titles_list + ) = _split_picks_by_type(inst, picks, units, scalings, titles) + del picks + # don't add ylabels & titles if figure has unexpected number of axes + make_label = len(axes) == len(fig.axes) + # Plot Frequency [Hz] xlabel only on the last axis + xlabels_list = [False] * (len(axes) - 1) + [True] + # compute PSDs + for picks in picks_list: + psd, freqs = psd_func(inst, tmin=tmin, tmax=tmax, picks=picks, + fmin=fmin, fmax=fmax, proj=proj, n_jobs=n_jobs, + **psd_kwargs) + if isinstance(inst, BaseEpochs): + psd = np.mean(psd, axis=0) + psd_list.append(psd) + # plot + _plot_psd(inst, fig, freqs, psd_list, picks_list, titles_list, units_list, + scalings_list, axes, make_label, color, area_mode, area_alpha, + dB, estimate, average, spatial_colors, xscale, line_alpha, + sphere, xlabels_list) + fig.subplots_adjust(hspace=0.3) + return fig + + +def _split_picks_by_type(inst, picks, units, scalings, titles): + """Separate picks, units, etc, for plotting on separate subplots.""" + picks_list = list() + units_list = list() + scalings_list = list() + titles_list = list() + # if picks is None, only show data channels + allowed_ch_types = (_DATA_CH_TYPES_SPLIT if picks is None else + _VALID_CHANNEL_TYPES) + for ch_type in allowed_ch_types: + pick_kwargs = dict(meg=False, ref_meg=False, exclude=[]) + if ch_type in ('mag', 'grad'): + pick_kwargs['meg'] = ch_type + elif ch_type in _FNIRS_CH_TYPES_SPLIT: + pick_kwargs['fnirs'] = ch_type + else: + pick_kwargs[ch_type] = True + these_picks = pick_types(inst.info, **pick_kwargs) + these_picks = np.intersect1d(these_picks, picks) + if len(these_picks) > 0: + picks_list.append(these_picks) + units_list.append(units[ch_type]) + scalings_list.append(scalings[ch_type]) + titles_list.append(titles[ch_type]) + if len(picks_list) == 0: + raise RuntimeError('No data channels found') + return picks_list, units_list, scalings_list, titles_list + + +def _calc_new_margins(fig, old_width, old_height, new_width, new_height): + """Compute new figure-relative values to maintain fixed-size margins.""" + new_margins = dict() + for side in ('left', 'right', 'bottom', 'top'): + ratio = ((old_width / new_width) if side in ('left', 'right') else + (old_height / new_height)) + rel_dim = getattr(fig.subplotpars, side) + if side in ('right', 'top'): + new_margins[side] = 1 - ratio * (1 - rel_dim) + else: + new_margins[side] = ratio * rel_dim + # gh-8304: don't allow resizing too small + if (new_margins['bottom'] < new_margins['top'] and + new_margins['left'] < new_margins['right']): + return new_margins + + +@contextmanager +def _patched_canvas(fig): + old_canvas = fig.canvas + if fig.canvas is None: # XXX old MPL (at least 3.0.3) does this for Agg + fig.canvas = Bunch(mpl_connect=lambda event, callback: None) + try: + yield + finally: + fig.canvas = old_canvas + + +def _init_browser(**kwargs): + """Instantiate a new MNE browse-style figure.""" + from mne.io import BaseRaw + fig = _figure(toolbar=False, FigureClass=MNEBrowseFigure, **kwargs) + + # splash is ignored (maybe we could do it for mpl if we get_backend() and + # check if it's Qt... but seems overkill) + + # initialize zen mode + # (can't do in __init__ due to get_position() calls) + fig.canvas.draw() + fig._update_zen_mode_offsets() + fig._resize(None) # needed for MPL >=3.4 + + # if scrollbars are supposed to start hidden, + # set to True and then toggle + if not fig.mne.scrollbars_visible: + fig.mne.scrollbars_visible = True + fig._toggle_scrollbars() + + # Initialize parts of the plot + is_ica = fig.mne.instance_type == 'ica' + + if not is_ica: + # make channel selection dialog, + # if requested (doesn't work well in init) + if fig.mne.group_by in ('selection', 'position'): + fig._create_selection_fig() + + # start with projectors dialog open, if requested + if getattr(fig.mne, 'show_options', False): + fig._toggle_proj_fig() + + # update data, and plot + fig._update_trace_offsets() + fig._redraw(update_data=True, annotations=False) + + if isinstance(fig.mne.inst, BaseRaw): + fig._setup_annotation_colors() + fig._draw_annotations() + + return fig diff -Nru python-mne-0.23.4+dfsg/mne/viz/_proj.py python-mne-1.1.0+dfsg/mne/viz/_proj.py --- python-mne-0.23.4+dfsg/mne/viz/_proj.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/_proj.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,224 @@ +"""Functions for plotting projectors.""" + +# Authors: Eric Larson +# +# License: Simplified BSD + +from copy import deepcopy + +import numpy as np + +from .evoked import _plot_evoked +from .topomap import _plot_projs_topomap +from .utils import plt_show, _check_type_projs +from ..defaults import DEFAULTS +from ..io.pick import _picks_to_idx +from ..utils import _validate_type, warn, _pl, verbose + + +@verbose +def plot_projs_joint(projs, evoked, picks_trace=None, *, topomap_kwargs=None, + show=True, verbose=None): + """Plot projectors and evoked jointly. + + Parameters + ---------- + projs : list of Projection + The projectors to plot. + evoked : instance of Evoked + The data to plot. Typically this is the evoked instance created from + averaging the epochs used to create the projection. + %(picks_plot_projs_joint_trace)s + topomap_kwargs : dict | None + Keyword arguments to pass to :func:`mne.viz.plot_projs_topomap`. + %(show)s + %(verbose)s + + Returns + ------- + fig : instance of matplotlib Figure + The figure. + + Notes + ----- + This function creates a figure with three columns: + + 1. The left shows the evoked data traces before (black) and after (green) + projection. + 2. The center shows the topomaps associated with each of the projectors. + 3. The right again shows the data traces (black), but this time with: + + 1. The data projected onto each projector with a single normalization + factor (solid lines). This is useful for seeing the relative power + in each projection vector. + 2. The data projected onto each projector with individual normalization + factors (dashed lines). This is useful for visualizing each time + course regardless of its power. + 3. Additional data traces from ``picks_trace`` (solid yellow lines). + This is useful for visualizing the "ground truth" of the time + course, e.g. the measured EOG or ECG channel time courses. + + .. versionadded:: 1.1 + """ + import matplotlib.pyplot as plt + from ..evoked import Evoked + _validate_type(evoked, Evoked, 'evoked') + _validate_type(topomap_kwargs, (None, dict), 'topomap_kwargs') + projs = _check_type_projs(projs) + topomap_kwargs = dict() if topomap_kwargs is None else topomap_kwargs + if picks_trace is not None: + picks_trace = _picks_to_idx( + evoked.info, picks_trace, allow_empty=False) + info = evoked.info + ch_types = evoked.get_channel_types(unique=True, only_data_chs=True) + proj_by_type = dict() # will be set up like an enumerate key->[pi, proj] + ch_names_by_type = dict() + used = np.zeros(len(projs), int) + for ch_type in ch_types: + these_picks = _picks_to_idx(info, ch_type, allow_empty=True) + these_chs = [evoked.ch_names[pick] for pick in these_picks] + ch_names_by_type[ch_type] = these_chs + for pi, proj in enumerate(projs): + if not set(these_chs).intersection(proj['data']['col_names']): + continue + if ch_type not in proj_by_type: + proj_by_type[ch_type] = list() + proj_by_type[ch_type].append([pi, deepcopy(proj)]) + used[pi] += 1 + missing = (~used.astype(bool)).sum() + if missing: + warn(f'{missing} projector{_pl(missing)} had no channel names ' + 'present in epochs') + del projs + ch_types = list(proj_by_type) # reduce to number we actually need + # room for legend + max_proj_per_type = max(len(x) for x in proj_by_type.values()) + cs_trace = 3 + cs_topo = 2 + n_col = max_proj_per_type * cs_topo + 2 * cs_trace + n_row = len(ch_types) + shape = (n_row, n_col) + fig = plt.figure(figsize=(n_col * 1.1 + 0.5, n_row * 1.8 + 0.5), + constrained_layout=True) + ri = 0 + # pick some sufficiently distinct colors (6 per proj type, e.g., ECG, + # should be enough hopefully!) + # https://personal.sron.nl/~pault/data/colourschemes.pdf + # "Vibrant" color scheme + proj_colors = [ + '#CC3311', # red + '#009988', # teal + '#0077BB', # blue + '#EE3377', # magenta + '#EE7733', # orange + '#33BBEE', # cyan + ] + trace_color = '#CCBB44' # yellow + after_color, after_name = '#228833', 'green' + type_titles = DEFAULTS['titles'] + last_ax = [None] * 2 + first_ax = dict() + pe_kwargs = dict(show=False, draw=False) + for ch_type, these_projs in proj_by_type.items(): + these_idxs, these_projs = zip(*these_projs) + ch_names = ch_names_by_type[ch_type] + idx = np.where([np.in1d(ch_names, proj['data']['col_names']).all() + for proj in these_projs])[0] + used[idx] += 1 + count = len(these_projs) + for proj in these_projs: + sub_idx = [proj['data']['col_names'].index(name) + for name in ch_names] + proj['data']['data'] = proj['data']['data'][:, sub_idx] + proj['data']['col_names'] = ch_names + ba_ax = plt.subplot2grid( + shape, (ri, 0), colspan=cs_trace, fig=fig) + topo_axes = [ + plt.subplot2grid( + shape, (ri, ci * cs_topo + cs_trace), colspan=cs_topo, fig=fig) + for ci in range(count)] + tr_ax = plt.subplot2grid( + shape, (ri, n_col - cs_trace), colspan=cs_trace, fig=fig) + # topomaps + _plot_projs_topomap(these_projs, info=info, show=False, + axes=topo_axes, **topomap_kwargs) + for idx, proj, ax_ in zip(these_idxs, these_projs, topo_axes): + ax_.set_title('') # could use proj['desc'] but it's long + ax_.set_xlabel(f'projs[{idx}]', fontsize='small') + unit = DEFAULTS['units'][ch_type] + # traces + this_evoked = evoked.copy().pick_channels(ch_names) + p = np.concatenate([p['data']['data'] for p in these_projs]) + assert p.shape == (len(these_projs), len(this_evoked.data)) + traces = np.dot(p, this_evoked.data) + traces *= np.sign(np.mean( + np.dot(this_evoked.data, traces.T), 0))[:, np.newaxis] + if picks_trace is not None: + ch_traces = evoked.data[picks_trace] + ch_traces -= np.mean(ch_traces, axis=1, keepdims=True) + ch_traces /= np.abs(ch_traces).max() + _plot_evoked(this_evoked, picks='all', axes=[tr_ax], **pe_kwargs) + for line in tr_ax.lines: + line.set(lw=0.5, zorder=3) + for t in list(tr_ax.texts): + t.remove() + scale = 0.8 * np.abs(tr_ax.get_ylim()).max() + hs, labels = list(), list() + traces /= np.abs(traces).max() # uniformly scaled + for ti, trace in enumerate(traces): + hs.append(tr_ax.plot( + this_evoked.times, trace * scale, + color=proj_colors[ti % len(proj_colors)], zorder=5)[0]) + labels.append(f'projs[{these_idxs[ti]}]') + traces /= np.abs(traces).max(1, keepdims=True) # independently + for ti, trace in enumerate(traces): + tr_ax.plot( + this_evoked.times, trace * scale, + color=proj_colors[ti % len(proj_colors)], zorder=3.5, + ls='--', lw=1., alpha=0.75) + if picks_trace is not None: + trace_ch = [evoked.ch_names[pick] for pick in picks_trace] + if len(picks_trace) == 1: + trace_ch = trace_ch[0] + hs.append(tr_ax.plot( + this_evoked.times, ch_traces.T * scale, color=trace_color, + lw=3, zorder=4, alpha=0.75)[0]) + labels.append(str(trace_ch)) + tr_ax.set(title='', xlabel='', ylabel='') + # This will steal space from the subplots in a constrained layout + # https://matplotlib.org/3.5.0/tutorials/intermediate/constrainedlayout_guide.html#legends # noqa: E501 + tr_ax.legend( + hs, labels, loc='center left', borderaxespad=0.05, + bbox_to_anchor=[1.05, 0.5]) + last_ax[1] = tr_ax + key = 'Projected time course' + if key not in first_ax: + first_ax[key] = tr_ax + # Before and after traces + _plot_evoked(this_evoked, picks='all', axes=[ba_ax], **pe_kwargs) + for line in ba_ax.lines: + line.set(lw=0.5, zorder=3) + loff = len(ba_ax.lines) + this_proj_evoked = this_evoked.copy().add_proj(these_projs) + # with meg='combined' any existing mag projectors (those already part + # of evoked before we add_proj above) will have greatly + # reduced power, so we ignore the warning about this issue + this_proj_evoked.apply_proj(verbose='error') + _plot_evoked(this_proj_evoked, picks='all', axes=[ba_ax], **pe_kwargs) + for line in ba_ax.lines[loff:]: + line.set(lw=0.5, zorder=4, color=after_color) + for t in list(ba_ax.texts): + t.remove() + ba_ax.set(title='', xlabel='') + ba_ax.set(ylabel=f'{type_titles[ch_type]}\n{unit}') + last_ax[0] = ba_ax + key = f'Before (black) and after ({after_name})' + if key not in first_ax: + first_ax[key] = ba_ax + ri += 1 + for ax in last_ax: + ax.set(xlabel='Time (sec)') + for title, ax in first_ax.items(): + ax.set_title(title, fontsize='medium') + plt_show(show) + return fig diff -Nru python-mne-0.23.4+dfsg/mne/viz/raw.py python-mne-1.1.0+dfsg/mne/viz/raw.py --- python-mne-0.23.4+dfsg/mne/viz/raw.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/raw.py 2022-08-03 15:48:14.000000000 +0000 @@ -19,7 +19,7 @@ from ..defaults import _handle_default from .topo import _plot_topo, _plot_timeseries, _plot_timeseries_unified from .utils import (plt_show, _compute_scalings, _handle_decim, _check_cov, - _shorten_path_from_middle, + _shorten_path_from_middle, _handle_precompute, _get_channel_plotting_order, _make_event_color_dict) _RAW_CLIP_DEF = 1.5 @@ -27,14 +27,16 @@ @verbose def plot_raw(raw, events=None, duration=10.0, start=0.0, n_channels=20, - bgcolor='w', color=None, bad_color=(0.8, 0.8, 0.8), + bgcolor='w', color=None, bad_color='lightgray', event_color='cyan', scalings=None, remove_dc=True, order=None, show_options=False, title=None, show=True, block=False, highpass=None, lowpass=None, filtorder=4, - clipping=_RAW_CLIP_DEF, - show_first_samp=False, proj=True, group_by='type', - butterfly=False, decim='auto', noise_cov=None, event_id=None, - show_scrollbars=True, show_scalebars=True, verbose=None): + clipping=_RAW_CLIP_DEF, show_first_samp=False, + proj=True, group_by='type', butterfly=False, decim='auto', + noise_cov=None, event_id=None, show_scrollbars=True, + show_scalebars=True, time_format='float', + precompute=None, use_opengl=None, *, theme=None, + overview_mode=None, verbose=None): """Plot raw data. Parameters @@ -67,17 +69,7 @@ Color to make bad channels. %(event_color)s Defaults to ``'cyan'``. - scalings : 'auto' | dict | None - Scaling factors for the traces. If any fields in scalings are 'auto', - the scaling factor is set to match the 99.5th percentile of a subset of - the corresponding data. If scalings == 'auto', all scalings fields are - set to 'auto'. If any fields are 'auto' and data is not preloaded, a - subset of times up to 100mb will be loaded. If None, defaults to:: - - dict(mag=1e-12, grad=4e-11, eeg=20e-6, eog=150e-6, ecg=5e-4, - emg=1e-3, ref_meg=1e-12, misc=1e-3, stim=1, - resp=1, chpi=1e-4, whitened=1e2) - + %(scalings)s remove_dc : bool If True remove DC component when plotting data. order : array of int | None @@ -97,6 +89,10 @@ Whether to halt program execution until the figure is closed. Useful for setting bad channels on the fly by clicking on a line. May not work on all systems / platforms. + (Only Qt) If you run from a script, this needs to + be ``True`` or a Qt-eventloop needs to be started somewhere + else in the script (e.g. if you want to implement the browser + inside another Qt-Application). highpass : float | None Highpass to apply when displaying data. lowpass : float | None @@ -130,7 +126,7 @@ Individual projectors can be enabled/disabled interactively (see Notes). This argument only affects the plot; use ``raw.apply_proj()`` to modify the data stored in the Raw object. - %(browse_group_by)s + %(group_by_browse)s butterfly : bool Whether to start in butterfly mode. Defaults to False. decim : int | 'auto' @@ -158,16 +154,23 @@ .. versionadded:: 0.16.0 %(show_scrollbars)s - show_scalebars : bool - Whether or not to show the scale bars. Defaults to True. + %(show_scalebars)s .. versionadded:: 0.20.0 + %(time_format)s + %(precompute)s + %(use_opengl)s + %(theme_pg)s + + .. versionadded:: 1.0 + %(overview_mode)s + + .. versionadded:: 1.1 %(verbose)s Returns ------- - fig : instance of matplotlib.figure.Figure - Raw traces. + %(browser)s Notes ----- @@ -194,9 +197,15 @@ 'b', and whitening mode (when ``noise_cov is not None``) by pressing 'w'. By default, the channel means are removed when ``remove_dc`` is set to ``True``. This flag can be toggled by pressing 'd'. + + .. note:: For the Qt backend to run in IPython with ``block=False`` + you must run the magic command ``%%gui qt5`` first. + .. note:: To report issues with the qt-backend, please use the + `issues `_ + of ``mne-qt-browser``. """ from ..io.base import BaseRaw - from ._figure import _browse_figure + from ._figure import _get_browser info = raw.info.copy() sfreq = info['sfreq'] @@ -205,7 +214,8 @@ projs_on = np.full_like(projs, proj, dtype=bool) # disable projs in info if user doesn't want to see them right away if not proj: - info['projs'] = list() + with info._unlock(): + info['projs'] = list() # handle defaults / check arg validity color = _handle_default('color', color) @@ -295,6 +305,8 @@ raise TypeError(f'title must be None or a string, got a {type(title)}') # gather parameters and initialize figure + _validate_type(use_opengl, (bool, None), 'use_opengl') + precompute = _handle_precompute(precompute) params = dict(inst=raw, info=info, # channels and channel order @@ -311,6 +323,7 @@ duration=duration, n_times=raw.n_times, first_time=first_time, + time_format=time_format, decim=decim, # events event_color_dict=event_color_dict, @@ -337,30 +350,17 @@ clipping=clipping, scrollbars_visible=show_scrollbars, scalebars_visible=show_scalebars, - window_title=title) + window_title=title, + bgcolor=bgcolor, + # Qt-specific + precompute=precompute, + use_opengl=use_opengl, + theme=theme, + overview_mode=overview_mode, + ) - fig = _browse_figure(**params) - fig._update_picks() + fig = _get_browser(show=show, block=block, **params) - # make channel selection dialog, if requested (doesn't work well in init) - if group_by in ('selection', 'position'): - fig._create_selection_fig() - - # update projector and data, and plot - fig._update_projector() - fig._update_trace_offsets() - fig._update_data() - fig._draw_traces() - - # plot annotations (if any) - fig._setup_annotation_colors() - fig._draw_annotations() - - # start with projectors dialog open, if requested - if show_options: - fig._toggle_proj_fig() - - plt_show(show, block=block) return fig @@ -369,9 +369,9 @@ n_fft=None, n_overlap=0, reject_by_annotation=True, picks=None, ax=None, color='black', xscale='linear', area_mode='std', area_alpha=0.33, dB=True, estimate='auto', - show=True, n_jobs=1, average=False, line_alpha=None, + show=True, n_jobs=None, average=False, line_alpha=None, spatial_colors=True, sphere=None, window='hamming', - verbose=None): + exclude='bads', verbose=None): """%(plot_psd_doc)s. Parameters @@ -396,24 +396,30 @@ The number of points of overlap between blocks. The default value is 0 (no overlap). %(reject_by_annotation_raw)s - %(plot_psd_picks_good_data)s + %(picks_plot_psd_good_data)s ax : instance of Axes | None Axes to plot into. If None, axes will be created. - %(plot_psd_color)s - %(plot_psd_xscale)s - %(plot_psd_area_mode)s - %(plot_psd_area_alpha)s - %(plot_psd_dB)s - %(plot_psd_estimate)s + %(color_plot_psd)s + %(xscale_plot_psd)s + %(area_mode_plot_psd)s + %(area_alpha_plot_psd)s + %(dB_plot_psd)s + %(estimate_plot_psd)s %(show)s %(n_jobs)s - %(plot_psd_average)s - %(plot_psd_line_alpha)s - %(plot_psd_spatial_colors)s - %(topomap_sphere_auto)s - %(window-psd)s + %(average_plot_psd)s + %(line_alpha_plot_psd)s + %(spatial_colors_plot_psd)s + %(sphere_topomap_auto)s + %(window_psd)s .. versionadded:: 0.22.0 + exclude : list of str | 'bads' + Channels names to exclude from being shown. If 'bads', the bad channels + are excluded. Pass an empty list to plot all channels (including + channels marked "bad", if any). + + .. versionadded:: 0.24.0 %(verbose)s Returns @@ -421,7 +427,7 @@ fig : instance of Figure Figure with frequency spectra of the data channels. """ - from ._figure import _psd_figure + from ._mpl_figure import _psd_figure # handle FFT if n_fft is None: if tmax is None or not np.isfinite(tmax): @@ -436,7 +442,7 @@ line_alpha=line_alpha, area_alpha=area_alpha, color=color, spatial_colors=spatial_colors, n_jobs=n_jobs, n_fft=n_fft, n_overlap=n_overlap, reject_by_annotation=reject_by_annotation, - window=window) + window=window, exclude=exclude) plt_show(show) return fig @@ -445,7 +451,7 @@ def plot_raw_psd_topo(raw, tmin=0., tmax=None, fmin=0., fmax=100., proj=False, n_fft=2048, n_overlap=0, layout=None, color='w', fig_facecolor='k', axis_facecolor='k', dB=True, - show=True, block=False, n_jobs=1, axes=None, + show=True, block=False, n_jobs=None, axes=None, verbose=None): """Plot channel-wise frequency spectra as topography. diff -Nru python-mne-0.23.4+dfsg/mne/viz/_scraper.py python-mne-1.1.0+dfsg/mne/viz/_scraper.py --- python-mne-0.23.4+dfsg/mne/viz/_scraper.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/_scraper.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,82 @@ +# Authors: Eric Larson +# +# License: Simplified BSD + +from contextlib import contextmanager + +from ..utils import _pl +from .backends._utils import _pixmap_to_ndarray + + +class _MNEQtBrowserScraper: + + def __repr__(self): + return '' + + def __call__(self, block, block_vars, gallery_conf): + import mne_qt_browser + from sphinx_gallery.scrapers import figure_rst + if gallery_conf['builder_name'] != 'html': + return '' + img_fnames = list() + inst = None + n_plot = 0 + for gui in list(mne_qt_browser._browser_instances): + try: + scraped = getattr(gui, '_scraped', False) + except Exception: # super __init__ not called, perhaps stale? + scraped = True + if scraped: + continue + gui._scraped = True # monkey-patch but it's easy enough + n_plot += 1 + img_fnames.append(next(block_vars['image_path_iterator'])) + pixmap, inst = _mne_qt_browser_screenshot(gui, inst) + pixmap.save(img_fnames[-1]) + # child figures + for fig in gui.mne.child_figs: + # For now we only support Selection + if not hasattr(fig, 'channel_fig'): + continue + fig = fig.channel_fig + img_fnames.append(next(block_vars['image_path_iterator'])) + fig.savefig(img_fnames[-1]) + gui.close() + del gui, pixmap + if not len(img_fnames): + return '' + for _ in range(2): + inst.processEvents() + return figure_rst( + img_fnames, gallery_conf['src_dir'], + f'Raw plot{_pl(n_plot)}') + + +@contextmanager +def _screenshot_mode(browser): + browser.mne.toolbar.setVisible(False) + browser.statusBar().setVisible(False) + try: + yield + finally: + browser.mne.toolbar.setVisible(True) + browser.statusBar().setVisible(True) + + +def _mne_qt_browser_screenshot(browser, inst=None, return_type='pixmap'): + from mne_qt_browser._pg_figure import QApplication + if getattr(browser, 'load_thread', None) is not None: + if browser.load_thread.isRunning(): + browser.load_thread.wait(30000) + if inst is None: + inst = QApplication.instance() + # processEvents to make sure our progressBar is updated + with _screenshot_mode(browser): + for _ in range(2): + inst.processEvents() + pixmap = browser.grab() + assert return_type in ('pixmap', 'ndarray') + if return_type == 'ndarray': + return _pixmap_to_ndarray(pixmap) + else: + return pixmap, inst diff -Nru python-mne-0.23.4+dfsg/mne/viz/tests/test_3d_mpl.py python-mne-1.1.0+dfsg/mne/viz/tests/test_3d_mpl.py --- python-mne-0.23.4+dfsg/mne/viz/tests/test_3d_mpl.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/tests/test_3d_mpl.py 2022-08-03 15:48:14.000000000 +0000 @@ -8,6 +8,7 @@ # License: Simplified BSD import os.path as op +import re import numpy as np import pytest @@ -16,7 +17,7 @@ VolVectorSourceEstimate, compute_source_morph) from mne.datasets import testing from mne.utils import (requires_dipy, requires_nibabel, requires_version, - catch_logging) + catch_logging, _record_warnings) from mne.viz import plot_volume_source_estimates from mne.viz.utils import _fake_click @@ -56,7 +57,8 @@ else: assert stype == 's' stc = VolSourceEstimate(data, vertices, 1, 1) - with pytest.warns(None): # sometimes get scalars/index warning + # sometimes get scalars/index warning + with _record_warnings(): with catch_logging() as log: fig = stc.plot( sample_src, subject='sample', subjects_dir=subjects_dir, @@ -75,6 +77,14 @@ with pytest.raises(FileNotFoundError, match='MRI file .* not found'): stc.plot(sample_src, subject='sample', subjects_dir=subjects_dir, mode='stat_map', bg_img='junk.mgz') + use_ax = None + for ax in fig.axes: + if ax.get_xlabel().startswith('Time'): + use_ax = ax + break + assert use_ax is not None + label = use_ax.get_legend().get_texts()[0].get_text() + assert re.match('[0-9]*', label) is not None, label @pytest.mark.slowtest # can be slow on OSX @@ -95,7 +105,8 @@ morph = compute_source_morph(sample_src, 'sample', 'fsaverage', zooms=5, subjects_dir=subjects_dir) initial_pos = (-0.05, -0.01, -0.006) - with pytest.warns(None): # sometimes get scalars/index warning + # sometimes get scalars/index warning + with _record_warnings(): with catch_logging() as log: stc.plot(morph, subjects_dir=subjects_dir, mode='glass_brain', initial_pos=initial_pos, verbose=True) diff -Nru python-mne-0.23.4+dfsg/mne/viz/tests/test_3d.py python-mne-1.1.0+dfsg/mne/viz/tests/test_3d.py --- python-mne-0.23.4+dfsg/mne/viz/tests/test_3d.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/tests/test_3d.py 2022-08-03 15:48:14.000000000 +0000 @@ -9,17 +9,17 @@ import os.path as op from pathlib import Path -import sys import numpy as np from numpy.testing import assert_array_equal, assert_allclose import pytest import matplotlib.pyplot as plt from matplotlib.colors import Colormap +from matplotlib.figure import Figure from mne import (make_field_map, pick_channels_evoked, read_evokeds, read_trans, read_dipole, SourceEstimate, - make_sphere_model, use_coil_def, + make_sphere_model, use_coil_def, pick_types, setup_volume_source_space, read_forward_solution, convert_forward_solution, MixedSourceEstimate) from mne.source_estimate import _BaseVolSourceEstimate @@ -31,14 +31,14 @@ from mne.minimum_norm import apply_inverse from mne.viz import (plot_sparse_source_estimates, plot_source_estimates, snapshot_brain_montage, plot_head_positions, - plot_alignment, plot_sensors_connectivity, + plot_alignment, Figure3D, plot_brain_colorbar, link_brains, mne_analyze_colormap) from mne.viz._3d import _process_clim, _linearize_map, _get_map_ticks from mne.viz.utils import _fake_click -from mne.utils import (requires_nibabel, traits_test, - catch_logging, run_subprocess, modified_env) +from mne.utils import requires_nibabel, catch_logging, _record_warnings from mne.datasets import testing from mne.source_space import read_source_spaces +from mne.transforms import Transform from mne.bem import read_bem_solution, read_bem_surfaces @@ -78,6 +78,11 @@ 0.1250 -0.750e-03 0.750e-03 0.750e-03 0.000 0.000 1.000 0.1250 0.750e-03 -0.750e-03 0.750e-03 0.000 0.000 1.000 0.1250 0.750e-03 0.750e-03 0.750e-03 0.000 0.000 1.000 +1 9998 1 4 3e-03 0.000e+00 "3mm square" + 0.1250 -0.750e-03 -0.750e-03 0.000 0.000 0.000 1.000 + 0.1250 -0.750e-03 0.750e-03 0.000 0.000 0.000 1.000 + 0.1250 0.750e-03 -0.750e-03 0.000 0.000 0.000 1.000 + 0.1250 0.750e-03 0.750e-03 0.000 0.000 0.000 1.000 """ @@ -87,7 +92,7 @@ pos = np.random.RandomState(0).randn(4, 10) pos[:, 0] = np.arange(len(pos)) destination = (0., 0., 0.04) - with pytest.warns(None): # old MPL will cause a warning + with _record_warnings(): # old MPL will cause a warning plot_head_positions(pos) plot_head_positions(pos, mode='field', info=info, destination=destination) @@ -100,7 +105,6 @@ @testing.requires_testing_data -@traits_test @pytest.mark.slowtest def test_plot_sparse_source_estimates(renderer_interactive, brain_gc): """Test plotting of (sparse) source estimates.""" @@ -136,39 +140,85 @@ stc_data[1, 4] = 2. vertices = [vertices[inds], np.empty(0, dtype=np.int64)] stc = SourceEstimate(stc_data, vertices, 1, 1) - surf = plot_sparse_source_estimates(sample_src, stc, bgcolor=(1, 1, 1), - opacity=0.5, high_resolution=False) - if renderer_interactive._get_3d_backend() == 'mayavi': - import mayavi # noqa: F401 analysis:ignore - assert isinstance(surf, mayavi.modules.surface.Surface) + out = plot_sparse_source_estimates( + sample_src, stc, bgcolor=(1, 1, 1), opacity=0.5, + high_resolution=False) + assert isinstance(out, Figure3D) @testing.requires_testing_data -@traits_test @pytest.mark.slowtest def test_plot_evoked_field(renderer): """Test plotting evoked field.""" evoked = read_evokeds(evoked_fname, condition='Left Auditory', baseline=(-0.2, 0.0)) evoked = pick_channels_evoked(evoked, evoked.ch_names[::10]) # speed - for t in ['meg', None]: + for t, n_contours in zip(['meg', None], [21, 0]): with pytest.warns(RuntimeWarning, match='projection'): maps = make_field_map(evoked, trans_fname, subject='sample', - subjects_dir=subjects_dir, n_jobs=1, + subjects_dir=subjects_dir, n_jobs=None, ch_type=t) - fig = evoked.plot_field(maps, time=0.1) - if renderer._get_3d_backend() == 'mayavi': - import mayavi # noqa: F401 analysis:ignore - assert isinstance(fig, mayavi.core.scene.Scene) + evoked.plot_field(maps, time=0.1, n_contours=n_contours) + + +def _assert_n_actors(fig, renderer, n_actors): + __tracebackhide__ = True + assert isinstance(fig, Figure3D) + assert len(fig.plotter.renderer.actors) == n_actors + + +@pytest.mark.slowtest # Slow on Azure +@pytest.mark.parametrize('system', [ + 'Neuromag', + pytest.param('CTF', marks=testing._pytest_mark()), + 'BTi', + 'KIT', +]) +def test_plot_alignment_meg(renderer, system): + """Test plotting of MEG sensors + helmet.""" + if system == 'Neuromag': + this_info = read_info(evoked_fname) + elif system == 'CTF': + this_info = read_raw_ctf(ctf_fname).info + elif system == 'BTi': + this_info = read_raw_bti( + pdf_fname, config_fname, hs_fname, convert=True, + preload=False).info + else: + assert system == 'KIT' + this_info = read_raw_kit(sqd_fname).info + + meg = ['helmet', 'sensors'] + if system == 'KIT': + meg.append('ref') + fig = plot_alignment( + this_info, read_trans(trans_fname), subject='sample', + subjects_dir=subjects_dir, meg=meg, eeg=False) + assert isinstance(fig, Figure3D) + # count the number of objects: should be n_meg_ch + 1 (helmet) + 1 (head) + use_info = pick_info(this_info, pick_types( + this_info, meg=True, eeg=False, ref_meg='ref' in meg, exclude=())) + n_actors = use_info['nchan'] + 2 + _assert_n_actors(fig, renderer, n_actors) + + +@testing.requires_testing_data +def test_plot_alignment_surf(renderer): + """Test plotting of a surface.""" + info = read_info(evoked_fname) + fig = plot_alignment( + info, read_trans(trans_fname), subject='sample', + subjects_dir=subjects_dir, meg=False, eeg=False, dig=False, + surfaces=['white', 'head']) + _assert_n_actors(fig, renderer, 3) # left and right hemis plus head @pytest.mark.slowtest # can be slow on OSX @testing.requires_testing_data -@traits_test -def test_plot_alignment(tmpdir, renderer, mixed_fwd_cov_evoked): +def test_plot_alignment_basic(tmp_path, renderer, mixed_fwd_cov_evoked): """Test plotting of -trans.fif files and MEG sensor layouts.""" # generate fiducials file for testing - tempdir = str(tmpdir) + tempdir = str(tmp_path) fiducials_path = op.join(tempdir, 'fiducials.fif') fid = [{'coord_frame': 5, 'ident': 1, 'kind': 1, 'r': [-0.08061612, -0.02908875, -0.04131077]}, @@ -177,29 +227,10 @@ {'coord_frame': 5, 'ident': 3, 'kind': 1, 'r': [0.08436285, -0.02850276, -0.04127743]}] write_dig(fiducials_path, fid, 5) - - renderer.backend._close_all() evoked = read_evokeds(evoked_fname)[0] + info = evoked.info + sample_src = read_source_spaces(src_fname) - bti = read_raw_bti(pdf_fname, config_fname, hs_fname, convert=True, - preload=False).info - infos = dict( - Neuromag=evoked.info, - CTF=read_raw_ctf(ctf_fname).info, - BTi=bti, - KIT=read_raw_kit(sqd_fname).info, - ) - for system, info in infos.items(): - meg = ['helmet', 'sensors'] - if system == 'KIT': - meg.append('ref') - fig = plot_alignment(info, read_trans(trans_fname), subject='sample', - subjects_dir=subjects_dir, meg=meg) - rend = renderer.backend._Renderer(fig=fig) - rend.close() - # KIT ref sensor coil def is defined - renderer.backend._close_all() - info = infos['Neuromag'] pytest.raises(TypeError, plot_alignment, 'foo', trans_fname, subject='sample', subjects_dir=subjects_dir) pytest.raises(OSError, plot_alignment, info, trans_fname, @@ -212,52 +243,73 @@ # mixed source space mixed_src = mixed_fwd_cov_evoked[0]['src'] assert mixed_src.kind == 'mixed' - plot_alignment(info, meg=['helmet', 'sensors'], dig=True, - coord_frame='head', trans=Path(trans_fname), - subject='sample', mri_fiducials=fiducials_path, - subjects_dir=subjects_dir, src=mixed_src) + fig = plot_alignment( + info, meg=['helmet', 'sensors'], dig=True, + coord_frame='head', trans=Path(trans_fname), + subject='sample', mri_fiducials=fiducials_path, + subjects_dir=subjects_dir, src=mixed_src) + assert isinstance(fig, Figure3D) renderer.backend._close_all() # no-head version renderer.backend._close_all() + # trans required + with pytest.raises(ValueError, match='transformation matrix.*in head'): + plot_alignment(info, trans=None, src=src_fname) + with pytest.raises(ValueError, match='transformation matrix.*in head'): + plot_alignment(info, trans=None, mri_fiducials=True) + with pytest.raises(ValueError, match='transformation matrix.*in head'): + plot_alignment(info, trans=None, surfaces=['brain']) + assert mixed_src[0]['coord_frame'] == FIFF.FIFFV_COORD_HEAD + with pytest.raises(ValueError, match='head-coordinate source space in mr'): + plot_alignment(trans=None, src=mixed_src, coord_frame='mri') # all coord frames plot_alignment(info) # works: surfaces='auto' default for coord_frame in ('meg', 'head', 'mri'): - fig = plot_alignment(info, meg=['helmet', 'sensors'], dig=True, - coord_frame=coord_frame, trans=Path(trans_fname), - subject='sample', mri_fiducials=fiducials_path, - subjects_dir=subjects_dir, src=src_fname) + plot_alignment( + info, meg=['helmet', 'sensors'], dig=True, coord_frame=coord_frame, + trans=Path(trans_fname), subject='sample', src=src_fname, + mri_fiducials=fiducials_path, subjects_dir=subjects_dir) renderer.backend._close_all() # EEG only with strange options evoked_eeg_ecog_seeg = evoked.copy().pick_types(meg=False, eeg=True) - evoked_eeg_ecog_seeg.info['projs'] = [] # "remove" avg proj + with evoked_eeg_ecog_seeg.info._unlock(): + evoked_eeg_ecog_seeg.info['projs'] = [] # "remove" avg proj evoked_eeg_ecog_seeg.set_channel_types({'EEG 001': 'ecog', 'EEG 002': 'seeg'}) - with pytest.warns(RuntimeWarning, match='Cannot plot MEG'): - with catch_logging() as log: - plot_alignment(evoked_eeg_ecog_seeg.info, subject='sample', - trans=trans_fname, subjects_dir=subjects_dir, - surfaces=['white', 'outer_skin', 'outer_skull'], - meg=['helmet', 'sensors'], - eeg=['original', 'projected'], ecog=True, seeg=True, - verbose=True) + with catch_logging() as log: + plot_alignment(evoked_eeg_ecog_seeg.info, subject='sample', + trans=trans_fname, subjects_dir=subjects_dir, + surfaces=['white', 'outer_skin', 'outer_skull'], + meg=['helmet', 'sensors'], + eeg=['original', 'projected'], ecog=True, seeg=True, + verbose=True) log = log.getvalue() - assert '1 ECoG location' in log - assert '1 sEEG location' in log + assert 'ecog: 1' in log + assert 'seeg: 1' in log renderer.backend._close_all() - sphere = make_sphere_model(info=evoked.info, r0='auto', head_radius='auto') + sphere = make_sphere_model(info=info, r0='auto', head_radius='auto') bem_sol = read_bem_solution(op.join(subjects_dir, 'sample', 'bem', 'sample-1280-1280-1280-bem-sol.fif')) bem_surfs = read_bem_surfaces(op.join(subjects_dir, 'sample', 'bem', 'sample-1280-1280-1280-bem.fif')) sample_src[0]['coord_frame'] = 4 # hack for coverage - plot_alignment(info, subject='sample', eeg='projected', - meg='helmet', bem=sphere, dig=True, + plot_alignment(info, trans_fname, subject='sample', + eeg='projected', meg='helmet', bem=sphere, dig=True, surfaces=['brain', 'inner_skull', 'outer_skull', 'outer_skin']) plot_alignment(info, trans_fname, subject='sample', meg='helmet', subjects_dir=subjects_dir, eeg='projected', bem=sphere, surfaces=['head', 'brain'], src=sample_src) + # no trans okay, no mri surfaces + plot_alignment(info, bem=sphere, surfaces=['brain']) + with pytest.raises(ValueError, match='A head surface is required'): + plot_alignment(info, trans=trans_fname, subject='sample', + subjects_dir=subjects_dir, eeg='projected', + surfaces=[]) + with pytest.raises(RuntimeError, match='No brain surface found'): + plot_alignment(info, trans=trans_fname, subject='foo', + subjects_dir=subjects_dir, surfaces=['brain']) assert all(surf['coord_frame'] == FIFF.FIFFV_COORD_MRI for surf in bem_sol['surfs']) plot_alignment(info, trans_fname, subject='sample', meg=[], @@ -281,36 +333,50 @@ log = log.getvalue() assert 'not find the surface for head in the provided BEM model' in log # sphere model - sphere = make_sphere_model('auto', 'auto', evoked.info) + sphere = make_sphere_model('auto', 'auto', info) src = setup_volume_source_space(sphere=sphere) - plot_alignment(info, eeg='projected', meg='helmet', bem=sphere, - src=src, dig=True, surfaces=['brain', 'inner_skull', - 'outer_skull', 'outer_skin']) - sphere = make_sphere_model('auto', None, evoked.info) # one layer + plot_alignment( + info, trans=Transform('head', 'mri'), eeg='projected', + meg='helmet', bem=sphere, src=src, dig=True, + surfaces=['brain', 'inner_skull', 'outer_skull', 'outer_skin']) + sphere = make_sphere_model('auto', None, info) # one layer # if you ask for a brain surface with a 1-layer sphere model it's an error with pytest.raises(RuntimeError, match='Sphere model does not have'): - fig = plot_alignment(subject='sample', subjects_dir=subjects_dir, - surfaces=['brain'], bem=sphere) + plot_alignment( + trans=trans_fname, subject='sample', subjects_dir=subjects_dir, + surfaces=['brain'], bem=sphere) # but you can ask for a specific brain surface, and # no info is permitted - fig = plot_alignment(trans=trans_fname, subject='sample', meg=False, - coord_frame='mri', subjects_dir=subjects_dir, - surfaces=['white'], bem=sphere, show_axes=True) + plot_alignment( + trans=trans_fname, subject='sample', meg=False, coord_frame='mri', + subjects_dir=subjects_dir, surfaces=['white'], bem=sphere, + show_axes=True) renderer.backend._close_all() - if renderer._get_3d_backend() == 'mayavi': - import mayavi # noqa: F401 analysis:ignore - assert isinstance(fig, mayavi.core.scene.Scene) + # TODO: We need to make this class public and document it properly + # assert isinstance(fig, some_public_class) # 3D coil with no defined draw (ConvexHull) - info_cube = pick_info(info, [0]) - info['dig'] = None + info_cube = pick_info(info, np.arange(6)) + with info._unlock(): + info['dig'] = None info_cube['chs'][0]['coil_type'] = 9999 + info_cube['chs'][1]['coil_type'] = 9998 with pytest.raises(RuntimeError, match='coil definition not found'): plot_alignment(info_cube, meg='sensors', surfaces=()) coil_def_fname = op.join(tempdir, 'temp') with open(coil_def_fname, 'w') as fid: fid.write(coil_3d) + # make sure our other OPMs can be plotted, too + for ii, kind in enumerate(('QUSPIN_ZFOPM_MAG', 'QUSPIN_ZFOPM_MAG2', + 'FIELDLINE_OPM_MAG_GEN1', + 'KERNEL_OPM_MAG_GEN1'), 2): + info_cube['chs'][ii]['coil_type'] = getattr( + FIFF, f'FIFFV_COIL_{kind}') with use_coil_def(coil_def_fname): - plot_alignment(info_cube, meg='sensors', surfaces=(), dig=True) + with catch_logging() as log: + plot_alignment(info_cube, meg='sensors', surfaces=(), dig=True, + verbose='debug') + log = log.getvalue() + assert 'planar geometry' in log # one layer bem with skull surfaces: with pytest.raises(RuntimeError, match='Sphere model does not.*boundary'): @@ -356,35 +422,39 @@ plot_alignment(subject='sample', subjects_dir=subjects_dir, trans=trans_fname, fwd=fwd, surfaces='white', coord_frame='head') + fwd['coord_frame'] = FIFF.FIFFV_COORD_MRI # check required to get to MRI + with pytest.raises(ValueError, match='transformation matrix.*in head coo'): + plot_alignment(info, trans=None, fwd=fwd) # surfaces as dict plot_alignment(subject='sample', coord_frame='head', - subjects_dir=subjects_dir, + trans=trans_fname, subjects_dir=subjects_dir, surfaces={'white': 0.4, 'outer_skull': 0.6, 'head': None}) + + +@testing.requires_testing_data +def test_plot_alignment_fnirs(renderer, tmp_path): + """Test fNIRS plotting.""" + # Here we use subjects_dir=tmp_path, since no surfaces should actually + # be loaded! + # fNIRS (default is pairs) info = read_raw_nirx(nirx_fname).info + assert info['nchan'] == 26 + kwargs = dict(trans='fsaverage', subject='fsaverage', surfaces=(), + verbose=True, subjects_dir=tmp_path) with catch_logging() as log: - plot_alignment(info, subject='fsaverage', surfaces=(), verbose=True) - log = log.getvalue() - assert '26 fNIRS pairs' in log - assert '26 fNIRS locations' not in log - assert '26 fNIRS sources' not in log - assert '26 fNIRS detectors' not in log - - with catch_logging() as log: - plot_alignment(info, subject='fsaverage', surfaces=(), verbose=True, - fnirs=['channels', 'sources', 'detectors']) + fig = plot_alignment(info, **kwargs) log = log.getvalue() - assert '26 fNIRS pairs' not in log - assert '26 fNIRS locations' in log - assert '26 fNIRS sources' in log - assert '26 fNIRS detectors' in log + assert f'fnirs_cw_amplitude: {info["nchan"]}' in log + _assert_n_actors(fig, renderer, info['nchan']) - renderer.backend._close_all() + fig = plot_alignment( + info, fnirs=['channels', 'sources', 'detectors'], **kwargs) + _assert_n_actors(fig, renderer, 3) @pytest.mark.slowtest # can be slow on OSX @testing.requires_testing_data -@traits_test def test_process_clim_plot(renderer_interactive, brain_gc): """Test functionality for determining control points with stc.plot.""" sample_src = read_source_spaces(src_fname) @@ -534,6 +604,7 @@ time_unit='ss', subject='sample', backend='matplotlib') +@pytest.mark.slowtest @pytest.mark.timeout(60) # can sometimes take > 60 sec @testing.requires_testing_data @requires_nibabel() @@ -560,25 +631,51 @@ @testing.requires_testing_data +@pytest.mark.parametrize('surf, coord_frame, ax, title', [ + pytest.param('white', 'mri', None, None, marks=pytest.mark.slowtest), + pytest.param(None, 'head', None, None, marks=pytest.mark.slowtest), + (None, 'mri_rotated', 'mpl', 'check'), +]) +def test_plot_dipole_mri_outlines(surf, coord_frame, ax, title): + """Test mpl dipole plotting.""" + dipoles = read_dipole(dip_fname) + trans = read_trans(trans_fname) + if ax is not None: + assert isinstance(ax, str) and ax == 'mpl', ax + _, ax = plt.subplots(3, 1) + ax = list(ax) + with pytest.raises(ValueError, match='but the length is 2'): + dipoles.plot_locations( + trans, 'sample', subjects_dir, ax=ax[:2], mode='outlines') + fig = dipoles.plot_locations( + trans=trans, subject='sample', subjects_dir=subjects_dir, + mode='outlines', coord_frame=coord_frame, surf=surf, ax=ax, + title=title) + assert isinstance(fig, Figure) + + +@testing.requires_testing_data def test_plot_dipole_orientations(renderer): """Test dipole plotting in 3d.""" dipoles = read_dipole(dip_fname) trans = read_trans(trans_fname) for coord_frame, mode in zip(['head', 'mri'], ['arrow', 'sphere']): - dipoles.plot_locations(trans=trans, subject='sample', - subjects_dir=subjects_dir, - mode=mode, coord_frame=coord_frame) + fig = dipoles.plot_locations( + trans=trans, subject='sample', subjects_dir=subjects_dir, + mode=mode, coord_frame=coord_frame) + assert isinstance(fig, Figure3D) renderer.backend._close_all() +@pytest.mark.slowtest # slow on Azure @testing.requires_testing_data -@traits_test def test_snapshot_brain_montage(renderer): """Test snapshot brain montage.""" info = read_info(evoked_fname) fig = plot_alignment( - info, trans=None, subject='sample', subjects_dir=subjects_dir) + info, trans=Transform('head', 'mri'), subject='sample', + subjects_dir=subjects_dir) xyz = np.vstack([ich['loc'][:3] for ich in info['chs']]) ch_names = [ich['ch_name'] for ich in info['chs']] @@ -603,10 +700,9 @@ pick_ori, kind, brain_gc): """Test plotting of scalar and vector source estimates.""" backend = renderer_interactive._get_3d_backend() - is_pyvista = backend != 'mayavi' invs, evoked = all_src_types_inv_evoked inv = invs[kind] - with pytest.warns(None): # PCA mag + with _record_warnings(): # PCA mag stc = apply_inverse(evoked, inv, pick_ori=pick_ori) stc.data[1] *= -1 # make it signed meth_key = 'plot_3d' if isinstance(stc, _BaseVolSourceEstimate) else 'plot' @@ -620,11 +716,6 @@ if pick_ori != 'vector': kwargs['surface'] = 'white' kwargs['backend'] = backend - # Mayavi can't handle non-surface - if kind != 'surface' and not is_pyvista: - with pytest.raises(RuntimeError, match='PyVista'): - meth(**kwargs) - return brain = meth(**kwargs) brain.close() del brain @@ -647,15 +738,12 @@ these_kwargs = kwargs.copy() these_kwargs.update(show_traces=True, time_viewer=False) meth(**these_kwargs) - if not is_pyvista: - with pytest.raises(ValueError, match='view_layout must be'): - meth(view_layout='horizontal', **kwargs) # flatmaps (mostly a lot of error checking) these_kwargs = kwargs.copy() these_kwargs.update(surface='flat', views='auto', hemi='both', verbose='debug') - if kind == 'surface' and pick_ori != 'vector' and is_pyvista: + if kind == 'surface' and pick_ori != 'vector': with catch_logging() as log: with pytest.raises(FileNotFoundError, match='flatmap'): meth(**these_kwargs) # sample does not have them @@ -670,9 +758,6 @@ elif kind != 'surface': with pytest.raises(TypeError, match='SourceEstimate when a flatmap'): flat_meth(**these_kwargs) - elif not is_pyvista: - with pytest.raises(RuntimeError, match='PyVista 3D backend.*flatmap'): - flat_meth(**these_kwargs) else: brain = flat_meth(**these_kwargs) brain.close() @@ -684,7 +769,6 @@ # just test one for speed if kind != 'mixed': return - assert is_pyvista brain = meth( views=['lat', 'med', 'ven'], hemi='lh', view_layout='horizontal', **kwargs) @@ -705,44 +789,6 @@ del brain -@pytest.mark.slowtest -@testing.requires_testing_data -def test_plot_sensors_connectivity(renderer): - """Test plotting of sensors connectivity.""" - from mne import io, pick_types - - data_path = data_dir - raw_fname = op.join(data_path, 'MEG', 'sample', - 'sample_audvis_trunc_raw.fif') - - raw = io.read_raw_fif(raw_fname) - picks = pick_types(raw.info, meg='grad', eeg=False, stim=False, - eog=True, exclude='bads') - n_channels = len(picks) - con = np.random.RandomState(42).randn(n_channels, n_channels) - info = raw.info - with pytest.raises(TypeError, match='must be an instance of Info'): - plot_sensors_connectivity(info='foo', con=con, picks=picks) - with pytest.raises(ValueError, match='does not correspond to the size'): - plot_sensors_connectivity(info=info, con=con[::2, ::2], picks=picks) - - fig = plot_sensors_connectivity(info=info, con=con, picks=picks) - if renderer._get_3d_backend() == 'pyvista': - try: - # pyvista<0.30.0 - title = fig.plotter.scalar_bar.GetTitle() - except AttributeError: - # pyvista>=0.30.0 - title = list(fig.plotter.scalar_bars.values())[0].GetTitle() - else: - assert renderer._get_3d_backend() == 'mayavi' - # the last thing we add is the Tube, so we need to go - # vtkDataSource->Stripper->Tube->ModuleManager - mod_man = fig.children[-1].children[0].children[0].children[0] - title = mod_man.scalar_lut_manager.scalar_bar.title - assert title == 'Connectivity' - - @pytest.mark.parametrize('orientation', ('horizontal', 'vertical')) @pytest.mark.parametrize('diverging', (True, False)) @pytest.mark.parametrize('lims', ([0.5, 1, 10], [0, 1, 10])) @@ -775,7 +821,6 @@ @pytest.mark.slowtest # slow-ish on Travis OSX @testing.requires_testing_data -@traits_test def test_mixed_sources_plot_surface(renderer_interactive): """Test plot_surface() for mixed source space.""" src = read_source_spaces(fwd_fname2) @@ -798,7 +843,6 @@ @testing.requires_testing_data -@traits_test @pytest.mark.slowtest def test_link_brains(renderer_interactive): """Test plotting linked brains.""" @@ -820,23 +864,8 @@ subjects_dir=subjects_dir, colorbar=True, clim='auto' ) - if renderer_interactive._get_3d_backend() == 'mayavi': - with pytest.raises(NotImplementedError, match='backend is pyvista'): - link_brains(brain) - else: - with pytest.raises(ValueError, match='is empty'): - link_brains([]) - with pytest.raises(TypeError, match='type is Brain'): - link_brains('foo') - link_brains(brain, time=True, camera=True) - - -def test_renderer(renderer): - """Test that renderers are available on demand.""" - backend = renderer.get_3d_backend() - cmd = [sys.executable, '-uc', - 'import mne; mne.viz.create_3d_figure((800, 600)); ' - 'backend = mne.viz.get_3d_backend(); ' - 'assert backend == %r, backend' % (backend,)] - with modified_env(MNE_3D_BACKEND=backend): - run_subprocess(cmd) + with pytest.raises(ValueError, match='is empty'): + link_brains([]) + with pytest.raises(TypeError, match='type is Brain'): + link_brains('foo') + link_brains(brain, time=True, camera=True) diff -Nru python-mne-0.23.4+dfsg/mne/viz/tests/test_circle.py python-mne-1.1.0+dfsg/mne/viz/tests/test_circle.py --- python-mne-0.23.4+dfsg/mne/viz/tests/test_circle.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/tests/test_circle.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,85 +5,25 @@ # License: Simplified BSD -import numpy as np import pytest -import matplotlib.pyplot as plt +import matplotlib -from mne.viz import plot_connectivity_circle, circular_layout +from mne.viz import plot_channel_labels_circle -def test_plot_connectivity_circle(): - """Test plotting connectivity circle.""" - node_order = ['frontalpole-lh', 'parsorbitalis-lh', - 'lateralorbitofrontal-lh', 'rostralmiddlefrontal-lh', - 'medialorbitofrontal-lh', 'parstriangularis-lh', - 'rostralanteriorcingulate-lh', 'temporalpole-lh', - 'parsopercularis-lh', 'caudalanteriorcingulate-lh', - 'entorhinal-lh', 'superiorfrontal-lh', 'insula-lh', - 'caudalmiddlefrontal-lh', 'superiortemporal-lh', - 'parahippocampal-lh', 'middletemporal-lh', - 'inferiortemporal-lh', 'precentral-lh', - 'transversetemporal-lh', 'posteriorcingulate-lh', - 'fusiform-lh', 'postcentral-lh', 'bankssts-lh', - 'supramarginal-lh', 'isthmuscingulate-lh', 'paracentral-lh', - 'lingual-lh', 'precuneus-lh', 'inferiorparietal-lh', - 'superiorparietal-lh', 'pericalcarine-lh', - 'lateraloccipital-lh', 'cuneus-lh', 'cuneus-rh', - 'lateraloccipital-rh', 'pericalcarine-rh', - 'superiorparietal-rh', 'inferiorparietal-rh', 'precuneus-rh', - 'lingual-rh', 'paracentral-rh', 'isthmuscingulate-rh', - 'supramarginal-rh', 'bankssts-rh', 'postcentral-rh', - 'fusiform-rh', 'posteriorcingulate-rh', - 'transversetemporal-rh', 'precentral-rh', - 'inferiortemporal-rh', 'middletemporal-rh', - 'parahippocampal-rh', 'superiortemporal-rh', - 'caudalmiddlefrontal-rh', 'insula-rh', 'superiorfrontal-rh', - 'entorhinal-rh', 'caudalanteriorcingulate-rh', - 'parsopercularis-rh', 'temporalpole-rh', - 'rostralanteriorcingulate-rh', 'parstriangularis-rh', - 'medialorbitofrontal-rh', 'rostralmiddlefrontal-rh', - 'lateralorbitofrontal-rh', 'parsorbitalis-rh', - 'frontalpole-rh'] - label_names = ['bankssts-lh', 'bankssts-rh', 'caudalanteriorcingulate-lh', - 'caudalanteriorcingulate-rh', 'caudalmiddlefrontal-lh', - 'caudalmiddlefrontal-rh', 'cuneus-lh', 'cuneus-rh', - 'entorhinal-lh', 'entorhinal-rh', 'frontalpole-lh', - 'frontalpole-rh', 'fusiform-lh', 'fusiform-rh', - 'inferiorparietal-lh', 'inferiorparietal-rh', - 'inferiortemporal-lh', 'inferiortemporal-rh', 'insula-lh', - 'insula-rh', 'isthmuscingulate-lh', 'isthmuscingulate-rh', - 'lateraloccipital-lh', 'lateraloccipital-rh', - 'lateralorbitofrontal-lh', 'lateralorbitofrontal-rh', - 'lingual-lh', 'lingual-rh', 'medialorbitofrontal-lh', - 'medialorbitofrontal-rh', 'middletemporal-lh', - 'middletemporal-rh', 'paracentral-lh', 'paracentral-rh', - 'parahippocampal-lh', 'parahippocampal-rh', - 'parsopercularis-lh', 'parsopercularis-rh', - 'parsorbitalis-lh', 'parsorbitalis-rh', - 'parstriangularis-lh', 'parstriangularis-rh', - 'pericalcarine-lh', 'pericalcarine-rh', 'postcentral-lh', - 'postcentral-rh', 'posteriorcingulate-lh', - 'posteriorcingulate-rh', 'precentral-lh', 'precentral-rh', - 'precuneus-lh', 'precuneus-rh', - 'rostralanteriorcingulate-lh', - 'rostralanteriorcingulate-rh', 'rostralmiddlefrontal-lh', - 'rostralmiddlefrontal-rh', 'superiorfrontal-lh', - 'superiorfrontal-rh', 'superiorparietal-lh', - 'superiorparietal-rh', 'superiortemporal-lh', - 'superiortemporal-rh', 'supramarginal-lh', - 'supramarginal-rh', 'temporalpole-lh', 'temporalpole-rh', - 'transversetemporal-lh', 'transversetemporal-rh'] - - group_boundaries = [0, len(label_names) / 2] - node_angles = circular_layout(label_names, node_order, start_pos=90, - group_boundaries=group_boundaries) - con = np.random.RandomState(0).randn(68, 68) - plot_connectivity_circle(con, label_names, n_lines=300, - node_angles=node_angles, title='test', - ) - - pytest.raises(ValueError, circular_layout, label_names, node_order, - group_boundaries=[-1]) - pytest.raises(ValueError, circular_layout, label_names, node_order, - group_boundaries=[20, 0]) - plt.close('all') +@pytest.mark.filterwarnings('ignore:invalid value encountered in greater_equal' + ':RuntimeWarning') +def test_plot_channel_labels_circle(): + """Test plotting channel labels in a circle.""" + fig, axes = plot_channel_labels_circle( + dict(brain=['big', 'great', 'smart']), + colors=dict(big='r', great='y', smart='b')) + texts = [child.get_text() for child in axes.get_children() + if isinstance(child, matplotlib.text.Text)] + for text in ('brain', 'big', 'great', 'smart'): + assert text in texts + # check inputs + with pytest.raises(ValueError, match='No color provided'): + plot_channel_labels_circle( + dict(brain=['big', 'great', 'smart']), + colors=dict(big='r', great='y')) diff -Nru python-mne-0.23.4+dfsg/mne/viz/tests/test_epochs.py python-mne-1.1.0+dfsg/mne/viz/tests/test_epochs.py --- python-mne-0.23.4+dfsg/mne/viz/tests/test_epochs.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/tests/test_epochs.py 2022-08-03 15:48:14.000000000 +0000 @@ -7,121 +7,80 @@ # # License: Simplified BSD -import os.path as op - +import matplotlib.pyplot as plt import numpy as np import pytest -import matplotlib.pyplot as plt -from mne import (read_events, Epochs, pick_types, read_cov, create_info, - EpochsArray) -from mne.channels import read_layout -from mne.io import read_raw_fif, read_raw_ctf -from mne.utils import run_tests_if_main, _click_ch_name, _close_event -from mne.viz import plot_drop_log -from mne.viz.utils import _fake_click +from mne import Epochs, create_info, EpochsArray from mne.datasets import testing from mne.event import make_fixed_length_events +from mne.viz import plot_drop_log -base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data') -evoked_fname = op.join(base_dir, 'test-ave.fif') -raw_fname = op.join(base_dir, 'test_raw.fif') -cov_fname = op.join(base_dir, 'test-cov.fif') -event_name = op.join(base_dir, 'test-eve.fif') -event_id, tmin, tmax = 1, -0.1, 1.0 -layout = read_layout('Vectorview-all') -test_base_dir = testing.data_path(download=False) -ctf_fname = op.join(test_base_dir, 'CTF', 'testdata_ctf.ds') - - -def _get_epochs(stop=5, meg=True, eeg=False, n_chan=20): - """Get epochs.""" - raw = read_raw_fif(raw_fname) - events = read_events(event_name) - picks = pick_types(raw.info, meg=meg, eeg=eeg, stim=False, - ecg=False, eog=False, exclude='bads') - # Use a subset of channels for plotting speed - picks = np.round(np.linspace(0, len(picks) + 1, n_chan)).astype(int) - with pytest.warns(RuntimeWarning, match='projection'): - epochs = Epochs(raw, events[:stop], event_id, tmin, tmax, picks=picks, - proj=False, preload=False) - epochs.info.normalize_proj() # avoid warnings - return epochs - - -@pytest.fixture() -def epochs(): - """Get minimal, pre-loaded epochs data suitable for most tests.""" - return _get_epochs().load_data() - - -def test_plot_epochs_not_preloaded(): +def test_plot_epochs_not_preloaded(epochs_unloaded, browser_backend): """Test plotting non-preloaded epochs.""" - epochs = _get_epochs() - assert epochs._data is None - epochs.plot() - assert epochs._data is None + assert epochs_unloaded._data is None + epochs_unloaded.plot() + assert epochs_unloaded._data is None -def test_plot_epochs_basic(epochs, capsys): +def test_plot_epochs_basic(epochs, epochs_full, noise_cov_io, capsys, + browser_backend): """Test epoch plotting.""" assert len(epochs.events) == 1 - epochs.info['lowpass'] = 10. # allow heavy decim during plotting + with epochs.info._unlock(): + epochs.info['lowpass'] = 10. # allow heavy decim during plotting fig = epochs.plot(scalings=None, title='Epochs') - ticks = [x.get_text() for x in fig.mne.ax_main.get_xticklabels(minor=True)] + ticks = fig._get_ticklabels('x') assert ticks == ['2'] - plt.close('all') + browser_backend._close_all() # covariance / whitening - cov = read_cov(cov_fname) - assert len(cov['names']) == 366 # all channels - assert cov['bads'] == [] + assert len(noise_cov_io['names']) == 366 # all channels + assert noise_cov_io['bads'] == [] assert epochs.info['bads'] == [] # all good with pytest.warns(RuntimeWarning, match='projection'): - epochs.plot(noise_cov=cov) - plt.close('all') + epochs.plot(noise_cov=noise_cov_io) + browser_backend._close_all() # add a channel to the epochs.info['bads'] epochs.info['bads'] = [epochs.ch_names[0]] with pytest.warns(RuntimeWarning, match='projection'): - epochs.plot(noise_cov=cov) - plt.close('all') + epochs.plot(noise_cov=noise_cov_io) + browser_backend._close_all() # add a channel to cov['bads'] - cov['bads'] = [epochs.ch_names[1]] + noise_cov_io['bads'] = [epochs.ch_names[1]] with pytest.warns(RuntimeWarning, match='projection'): - epochs.plot(noise_cov=cov) - plt.close('all') + epochs.plot(noise_cov=noise_cov_io) + browser_backend._close_all() # have a data channel missing from the covariance - cov['names'] = cov['names'][:306] - cov['data'] = cov['data'][:306][:306] + noise_cov_io['names'] = noise_cov_io['names'][:306] + noise_cov_io['data'] = noise_cov_io['data'][:306][:306] with pytest.warns(RuntimeWarning, match='projection'): - epochs.plot(noise_cov=cov) - plt.close('all') + epochs.plot(noise_cov=noise_cov_io) + browser_backend._close_all() # other options fig = epochs[0].plot(picks=[0, 2, 3], scalings=None) - fig.canvas.key_press_event('escape') + fig._fake_keypress('escape') with pytest.raises(ValueError, match='No appropriate channels found'): epochs.plot(picks=[]) # gh-5906 - epochs = _get_epochs(None).load_data() - epochs.load_data() - assert len(epochs) == 7 - epochs.info['bads'] = [epochs.ch_names[0]] + assert len(epochs_full) == 7 + epochs_full.info['bads'] = [epochs_full.ch_names[0]] capsys.readouterr() # test title error handling with pytest.raises(TypeError, match='title must be None or a string, got'): - epochs.plot(title=7) + epochs_full.plot(title=7) # test auto-generated title, and selection mode - epochs.plot(group_by='selection', title='') + epochs_full.plot(group_by='selection', title='') @pytest.mark.parametrize('scalings', (dict(mag=1e-12, grad=1e-11, stim='auto'), None, 'auto')) -def test_plot_epochs_scalings(epochs, scalings): +def test_plot_epochs_scalings(epochs, scalings, browser_backend): """Test the valid options for scalings.""" epochs.plot(scalings=scalings) -def test_plot_epochs_colors(epochs): +def test_plot_epochs_colors(epochs, browser_backend): """Test epoch_colors, for compatibility with autoreject.""" epoch_colors = [['r'] * len(epochs.ch_names) for _ in range(len(epochs.events))] @@ -134,87 +93,105 @@ epochs.plot(event_color='b') -def test_plot_epochs_scale_bar(epochs): +def test_plot_epochs_scale_bar(epochs, browser_backend): """Test scale bar for epochs.""" fig = epochs.plot() - fig.canvas.key_press_event('s') # default is to not show scalebars - ax = fig.mne.ax_main - assert len(ax.texts) == 2 # only mag & grad in this instance - texts = tuple(t.get_text().strip() for t in ax.texts) - wants = ('800.0 fT/cm', '2000.0 fT') + texts = fig._get_scale_bar_texts() + # mag & grad in this instance + if browser_backend.name == 'pyqtgraph': + assert len(texts) == 2 + wants = ('800.0 fT/cm', '2000.0 fT') + elif browser_backend.name == 'matplotlib': + assert len(texts) == 4 + wants = ('800.0 fT/cm', '0.55 sec', '2000.0 fT', '0.55 sec') assert texts == wants -def test_plot_epochs_clicks(epochs, capsys): +def test_plot_epochs_clicks(epochs, epochs_full, capsys, + browser_backend): """Test plot_epochs mouse interaction.""" fig = epochs.plot(events=epochs.events) - data_ax = fig.mne.ax_main x = fig.mne.traces[0].get_xdata()[3] y = fig.mne.traces[0].get_ydata()[3] n_epochs = len(epochs) epoch_num = fig.mne.inst.selection[0] # test (un)marking bad epochs - _fake_click(fig, data_ax, [x, y], xform='data') # mark a bad epoch + fig._fake_click((x, y), xform='data') # mark a bad epoch assert epoch_num in fig.mne.bad_epochs - _fake_click(fig, data_ax, [x, y], xform='data') # unmark it + fig._fake_click((x, y), xform='data') # unmark it assert epoch_num not in fig.mne.bad_epochs - _fake_click(fig, data_ax, [x, y], xform='data') # mark it bad again + fig._fake_click((x, y), xform='data') # mark it bad again assert epoch_num in fig.mne.bad_epochs # test vline - fig.canvas.key_press_event('escape') # close and drop epochs - _close_event(fig) # XXX workaround, MPL Agg doesn't trigger close event - assert(n_epochs - 1 == len(epochs)) + fig._fake_keypress('escape') # close and drop epochs + fig._close_event() # XXX workaround, MPL Agg doesn't trigger close event + assert n_epochs - 1 == len(epochs) # test marking bad channels - epochs = _get_epochs(None).load_data() # need more than 1 epoch this time - fig = epochs.plot(n_epochs=3) - data_ax = fig.mne.ax_main - first_ch = data_ax.get_yticklabels()[0].get_text() + # need more than 1 epoch this time + fig = epochs_full.plot(n_epochs=3) + first_ch = fig._get_ticklabels('y')[0] assert first_ch not in fig.mne.info['bads'] - _click_ch_name(fig, ch_index=0, button=1) # click ch name to mark bad + fig._click_ch_name(ch_index=0, button=1) # click ch name to mark bad assert first_ch in fig.mne.info['bads'] # test clicking scrollbars - _fake_click(fig, fig.mne.ax_vscroll, [0.5, 0.5]) - _fake_click(fig, fig.mne.ax_hscroll, [0.5, 0.5]) + fig._fake_click((0.5, 0.5), ax=fig.mne.ax_vscroll) + fig._fake_click((0.5, 0.5), ax=fig.mne.ax_hscroll) # test moving bad epoch offscreen - fig.canvas.key_press_event('right') # move right + fig._fake_keypress('right') # move right x = fig.mne.traces[0].get_xdata()[-3] y = fig.mne.traces[0].get_ydata()[-3] - _fake_click(fig, data_ax, [x, y], xform='data') # mark a bad epoch - fig.canvas.key_press_event('left') # move back + fig._fake_click((x, y), xform='data') # mark a bad epoch + fig._fake_keypress('left') # move back out, err = capsys.readouterr() assert 'out of bounds' not in out assert 'out of bounds' not in err - fig.canvas.key_press_event('escape') - _close_event(fig) # XXX workaround, MPL Agg doesn't trigger close event - assert len(epochs) == 6 + fig._fake_keypress('escape') + fig._close_event() # XXX workaround, MPL Agg doesn't trigger close event + assert len(epochs_full) == 6 # test rightclick → image plot - fig = epochs.plot() - _click_ch_name(fig, ch_index=0, button=3) # show image plot + fig = epochs_full.plot() + fig._click_ch_name(ch_index=0, button=3) # show image plot assert len(fig.mne.child_figs) == 1 # test scroll wheel - fig.canvas.scroll_event(0.5, 0.5, -0.5) # scroll down - fig.canvas.scroll_event(0.5, 0.5, 0.5) # scroll up + fig._fake_scroll(0.5, 0.5, -0.5) # scroll down + fig._fake_scroll(0.5, 0.5, 0.5) # scroll up -def test_plot_epochs_keypresses(): +def test_plot_epochs_keypresses(epochs_full, browser_backend): """Test plot_epochs keypress interaction.""" - epochs = _get_epochs(stop=15).load_data() # we need more than 1 epoch - epochs.drop_bad(dict(mag=4e-12)) # for histogram plot coverage - fig = epochs.plot(n_epochs=3) - data_ax = fig.mne.ax_main + # we need more than 1 epoch + epochs_full.drop_bad(dict(mag=4e-12)) # for histogram plot coverage + fig = epochs_full.plot(n_epochs=3) # make sure green vlines are visible first (for coverage) - sample_idx = len(epochs.times) // 2 # halfway through the first epoch + sample_idx = len(epochs_full.times) // 2 # halfway through the first epoch x = fig.mne.traces[0].get_xdata()[sample_idx] y = (fig.mne.traces[0].get_ydata()[sample_idx] + fig.mne.traces[1].get_ydata()[sample_idx]) / 2 - _fake_click(fig, data_ax, [x, y], xform='data') # click between traces + fig._fake_click([x, y], xform='data') # click between traces # test keys keys = ('pagedown', 'down', 'up', 'down', 'right', 'left', '-', '+', '=', 'd', 'd', 'pageup', 'home', 'shift+right', 'end', 'shift+left', - 'z', 'z', 's', 's', 'f11', '?', 'h', 'j', 'b') + 'z', 'z', 's', 's', '?', 'h', 'j', 'b') for key in keys * 2: # test twice → once in normal, once in butterfly view - fig.canvas.key_press_event(key) - _fake_click(fig, data_ax, [x, y], xform='data', button=3) # remove vlines + fig._fake_keypress(key) + fig._fake_click([x, y], xform='data', button=3) # remove vlines + + +def test_plot_overlapping_epochs_with_events(browser_backend): + """Test drawing of event lines in overlapping epochs.""" + data = np.zeros(shape=(3, 2, 100)) # 3 epochs, 2 channels, 100 samples + sfreq = 100 + info = create_info( + ch_names=('a', 'b'), ch_types=('misc', 'misc'), sfreq=sfreq) + # 90% overlap, so all 3 events should appear in all 3 epochs when plotted: + events = np.column_stack(([50, 60, 70], [0, 0, 0], [1, 2, 3])) + epochs = EpochsArray(data, info, tmin=-0.5, events=events) + fig = epochs.plot(events=events, picks='misc') + if browser_backend.name == 'matplotlib': + n_event_lines = len(fig.mne.event_lines.get_segments()) + else: + n_event_lines = len(fig.mne.event_lines) + assert n_event_lines == 9 def test_epochs_plot_sensors(epochs): @@ -222,7 +199,7 @@ epochs.plot_sensors() -def test_plot_epochs_nodata(): +def test_plot_epochs_nodata(browser_backend): """Test plotting of epochs when no data channels are present.""" data = np.random.RandomState(0).randn(10, 2, 1000) info = create_info(2, 1000., 'stim') @@ -231,6 +208,7 @@ epochs.plot() +@pytest.mark.slowtest def test_plot_epochs_image(epochs): """Test plotting of epochs image. @@ -327,13 +305,12 @@ plt.close('all') -def test_plot_drop_log(): +def test_plot_drop_log(epochs_unloaded): """Test plotting a drop log.""" - epochs = _get_epochs() # not loaded with pytest.raises(ValueError, match='bad epochs have not yet been'): - epochs.plot_drop_log() - epochs.drop_bad() - epochs.plot_drop_log() + epochs_unloaded.plot_drop_log() + epochs_unloaded.drop_bad() + epochs_unloaded.plot_drop_log() plot_drop_log((('One',), (), ())) plot_drop_log((('One',), ('Two',), ())) plot_drop_log((('One',), ('One', 'Two'), ())) @@ -379,36 +356,36 @@ @testing.requires_testing_data -def test_plot_epochs_ctf(): +def test_plot_epochs_ctf(raw_ctf, browser_backend): """Test of basic CTF plotting.""" - raw = read_raw_ctf(ctf_fname, preload=True) - raw.pick_channels(['UDIO001', 'UPPT001', 'SCLK01-177', - 'BG1-4304', 'MLC11-4304', 'MLC11-4304', - 'EEG058', 'UADC007-4302']) - evts = make_fixed_length_events(raw) - epochs = Epochs(raw, evts, preload=True) + raw_ctf.pick_channels(['UDIO001', 'UPPT001', 'SCLK01-177', + 'BG1-4304', 'MLC11-4304', 'MLC11-4304', + 'EEG058', 'UADC007-4302']) + evts = make_fixed_length_events(raw_ctf) + epochs = Epochs(raw_ctf, evts, preload=True) epochs.plot() - plt.close('all') + browser_backend._close_all() # test butterfly fig = epochs.plot(butterfly=True) + # leave fullscreen testing to Raw / _figure abstraction (too annoying here) keys = ('b', 'b', 'pagedown', 'down', 'up', 'down', 'right', 'left', '-', '+', '=', 'd', 'd', 'pageup', 'home', 'end', 'z', 'z', 's', 's', - 'f11', '?', 'h', 'j') + '?', 'h', 'j') for key in keys: - fig.canvas.key_press_event(key) - fig.canvas.scroll_event(0.5, 0.5, -0.5) # scroll down - fig.canvas.scroll_event(0.5, 0.5, 0.5) # scroll up - fig.canvas.resize_event() - fig.canvas.key_press_event('escape') # close and drop epochs + fig._fake_keypress(key) + fig._fake_scroll(0.5, 0.5, -0.5) # scroll down + fig._fake_scroll(0.5, 0.5, 0.5) # scroll up + fig._resize_by_factor(1) + fig._fake_keypress('escape') # close and drop epochs +@pytest.mark.slowtest @testing.requires_testing_data -def test_plot_psd_epochs_ctf(): +def test_plot_psd_epochs_ctf(raw_ctf): """Test plotting CTF epochs psd (+topomap).""" - raw = read_raw_ctf(ctf_fname, preload=True) - evts = make_fixed_length_events(raw) - epochs = Epochs(raw, evts, preload=True) + evts = make_fixed_length_events(raw_ctf) + epochs = Epochs(raw_ctf, evts, preload=True) pytest.raises(RuntimeError, epochs.plot_psd_topomap, bands=[(0, 0.01, 'foo')]) # no freqs in range epochs.plot_psd_topomap() @@ -421,4 +398,9 @@ epochs.plot_psd(spatial_colors=False, average=False) -run_tests_if_main() +def test_plot_epochs_selection_butterfly(raw, browser_backend): + """Test that using selection and butterfly works.""" + events = make_fixed_length_events(raw)[:1] + epochs = Epochs(raw, events, tmin=0, tmax=0.5, preload=True, baseline=None) + assert len(epochs) == 1 + epochs.plot(group_by='selection', butterfly=True) diff -Nru python-mne-0.23.4+dfsg/mne/viz/tests/test_evoked.py python-mne-1.1.0+dfsg/mne/viz/tests/test_evoked.py --- python-mne-0.23.4+dfsg/mne/viz/tests/test_evoked.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/tests/test_evoked.py 2022-08-03 15:48:14.000000000 +0000 @@ -17,12 +17,13 @@ import matplotlib.pyplot as plt from matplotlib import gridspec from matplotlib.cm import get_cmap +from matplotlib.collections import PolyCollection import mne from mne import (read_events, Epochs, read_cov, compute_covariance, make_fixed_length_events, compute_proj_evoked) from mne.io import read_raw_fif -from mne.utils import catch_logging, requires_version +from mne.utils import catch_logging from mne.viz import plot_compare_evokeds, plot_evoked_white from mne.viz.utils import _fake_click from mne.datasets import testing @@ -37,6 +38,8 @@ event_name = op.join(base_dir, 'test-eve.fif') event_id, tmin, tmax = 1, -0.1, 0.1 +ctf_fname = testing.data_path(download=False) / 'CTF' / 'testdata_ctf.ds' + # Use a subset of channels for plotting speed # make sure we have a magnetometer and a pair of grad pairs for topomap. default_picks = (0, 1, 2, 3, 4, 6, 7, 61, 122, 183, 244, 305, @@ -80,7 +83,7 @@ evoked.plot(noise_cov=cov, time_unit='s') with pytest.raises(TypeError, match='Covariance'): evoked.plot(noise_cov=1., time_unit='s') - with pytest.raises(IOError, match='No such file'): + with pytest.raises(FileNotFoundError, match='File does not exist'): evoked.plot(noise_cov='nonexistent-cov.fif', time_unit='s') raw = read_raw_fif(raw_sss_fname) events = make_fixed_length_events(raw) @@ -120,7 +123,8 @@ evoked_delayed_ssp.apply_proj() pytest.raises(RuntimeError, evoked_delayed_ssp.plot, proj='interactive', time_unit='s') - evoked_delayed_ssp.info['projs'] = [] + with evoked_delayed_ssp.info._unlock(): + evoked_delayed_ssp.info['projs'] = [] pytest.raises(RuntimeError, evoked_delayed_ssp.plot, proj='interactive', time_unit='s') pytest.raises(RuntimeError, evoked_delayed_ssp.plot, @@ -165,8 +169,21 @@ evoked.plot(verbose=True, time_unit='s') assert 'Need more than one' in log_file.getvalue() + # Test highlight + for highlight in [ + (0, 0.1), + [(0, 0.1), (0.1, 0.2)] + ]: + fig = evoked.plot(time_unit='s', highlight=highlight) + for ax in fig.get_axes(): + highlighted_areas = [child for child in ax.get_children() + if isinstance(child, PolyCollection)] + assert len(highlighted_areas) == len(np.atleast_2d(highlight)) + + with pytest.raises(ValueError, match='must be reshapable into a 2D array'): + fig = evoked.plot(time_unit='s', highlight=0.1) + -@requires_version('matplotlib', '2.2') def test_constrained_layout(): """Test that we handle constrained layouts correctly.""" fig, ax = plt.subplots(1, 1, constrained_layout=True) @@ -316,13 +333,15 @@ # Hack to test plotting of maxfiltered data evoked_sss = _get_epochs(picks='meg').average() sss = dict(sss_info=dict(in_order=80, components=np.arange(80))) - evoked_sss.info['proc_history'] = [dict(max_info=sss)] + with evoked_sss.info._unlock(): + evoked_sss.info['proc_history'] = [dict(max_info=sss)] evoked_sss.plot_white(cov, rank={'meg': 64}) with pytest.raises(ValueError, match='When using SSS'): evoked_sss.plot_white(cov, rank={'grad': 201}) evoked_sss.plot_white(cov, time_unit='s') +@pytest.mark.slowtest # slow on Azure def test_plot_compare_evokeds(): """Test plot_compare_evokeds.""" evoked = _get_epochs().average() @@ -358,10 +377,22 @@ assert (yvals < ylim[1]).all() assert (yvals > ylim[0]).all() plt.close('all') + # test other CI args - for _ci in (None, False, 0.5, - lambda x: np.stack([x.mean(axis=0) + 1, x.mean(axis=0) - 1])): - plot_compare_evokeds({'cond': [blue, red, evoked]}, ci=_ci) + def ci_func(array): + return array.mean(axis=0, keepdims=True) * np.array([[0.5], [1.5]]) + + ci_types = (None, False, 0.5, ci_func) + for _ci in ci_types: + fig = plot_compare_evokeds({'cond': [blue, red, evoked]}, ci=_ci)[0] + if _ci in ci_types[2:]: + assert np.any([isinstance(coll, PolyCollection) + for coll in fig.axes[0].collections]) + # make sure we can get a CI even for single conditions + fig = plot_compare_evokeds(evoked, picks='eeg', ci=ci_func)[0] + assert np.any([isinstance(coll, PolyCollection) + for coll in fig.axes[0].collections]) + with pytest.raises(TypeError, match='"ci" must be None, bool, float or'): plot_compare_evokeds(evoked, ci='foo') # test sensor inset, legend location, and axis inversion & truncation @@ -476,10 +507,7 @@ @testing.requires_testing_data def test_plot_ctf(): """Test plotting of CTF evoked.""" - ctf_dir = op.join(testing.data_path(download=False), 'CTF') - raw_fname = op.join(ctf_dir, 'testdata_ctf.ds') - - raw = mne.io.read_raw_ctf(raw_fname, preload=True) + raw = mne.io.read_raw_ctf(ctf_fname, preload=True) events = np.array([[200, 0, 1]]) event_id = 1 tmin, tmax = -0.1, 0.5 # start and end of an epoch in sec. diff -Nru python-mne-0.23.4+dfsg/mne/viz/tests/test_figure.py python-mne-1.1.0+dfsg/mne/viz/tests/test_figure.py --- python-mne-0.23.4+dfsg/mne/viz/tests/test_figure.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/tests/test_figure.py 2022-08-03 15:48:14.000000000 +0000 @@ -3,16 +3,17 @@ # License: Simplified BSD import pytest -from mne.viz._figure import _browse_figure, _psd_figure +from mne.viz._mpl_figure import _psd_figure +from mne.viz._figure import _get_browser def test_browse_figure_constructor(): """Test error handling in MNEBrowseFigure constructor.""" with pytest.raises(TypeError, match='an instance of Raw, Epochs, or ICA'): - _browse_figure('foo') + _get_browser(show=False, block=False, inst='foo') def test_psd_figure_constructor(): """Test error handling in MNELineFigure constructor.""" with pytest.raises(TypeError, match='an instance of Raw or Epochs, got'): - _psd_figure('foo', *((None,) * 19)) + _psd_figure('foo', *((None,) * 20)) diff -Nru python-mne-0.23.4+dfsg/mne/viz/tests/test_ica.py python-mne-1.1.0+dfsg/mne/viz/tests/test_ica.py --- python-mne-0.23.4+dfsg/mne/viz/tests/test_ica.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/tests/test_ica.py 2022-08-03 15:48:14.000000000 +0000 @@ -4,6 +4,7 @@ # License: Simplified BSD import os.path as op +import sys import numpy as np from numpy.testing import assert_equal, assert_array_equal @@ -14,8 +15,7 @@ make_fixed_length_events) from mne.io import read_raw_fif from mne.preprocessing import ICA, create_ecg_epochs, create_eog_epochs -from mne.utils import (requires_sklearn, _click_ch_name, catch_logging, - _close_event) +from mne.utils import (requires_sklearn, catch_logging, _record_warnings) from mne.viz.ica import _create_properties_layout, plot_ica_properties from mne.viz.utils import _fake_click @@ -54,8 +54,6 @@ @requires_sklearn -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') def test_plot_ica_components(): """Test plotting of ICA solutions.""" res = 8 @@ -67,17 +65,17 @@ ica.fit(raw, picks=ica_picks) for components in [0, [0], [0, 1], [0, 1] * 2, None]: - ica.plot_components(components, image_interp='bilinear', + ica.plot_components(components, image_interp='cubic', colorbar=True, **fast_test) plt.close('all') # test interactive mode (passing 'inst' arg) with catch_logging() as log: - ica.plot_components([0, 1], image_interp='bilinear', inst=raw, res=16, + ica.plot_components([0, 1], image_interp='cubic', inst=raw, res=16, verbose='debug', ch_type='grad') log = log.getvalue() assert 'grad data' in log - assert 'Interpolation mode local to mean' in log + assert 'extrapolation mode local to mean' in log fig = plt.gcf() # test title click @@ -106,7 +104,7 @@ topomap_ax = c_fig.axes[labels.index('topomap')] title = topomap_ax.get_title() - assert (lbl == title) + assert (lbl.split(' ')[0] == title.split(' ')[0]) ica.info = None with pytest.raises(RuntimeError, match='fit the ICA'): @@ -119,7 +117,8 @@ """Test plotting of ICA properties.""" raw = _get_raw(preload=True).crop(0, 5) raw.add_proj([], remove_existing=True) - raw.info['highpass'] = 1.0 # fake high-pass filtering + with raw.info._unlock(): + raw.info['highpass'] = 1.0 # fake high-pass filtering events = make_fixed_length_events(raw) picks = _get_picks(raw)[:6] pick_names = [raw.ch_names[k] for k in picks] @@ -145,12 +144,33 @@ ica.plot_properties(raw, picks=0, verbose='debug', **topoargs) log = log.getvalue() assert raw.ch_names[0] == 'MEG 0113' - assert 'Interpolation mode local to mean' in log, log + assert 'extrapolation mode local to mean' in log, log ica.plot_properties(epochs, picks=1, dB=False, plot_std=1.5, **topoargs) - ica.plot_properties(epochs, picks=1, image_args={'sigma': 1.5}, - topomap_args={'res': 4, 'colorbar': True}, - psd_args={'fmax': 65.}, plot_std=False, - figsize=[4.5, 4.5], reject=reject) + fig = ica.plot_properties(epochs, picks=1, image_args={'sigma': 1.5}, + topomap_args={'res': 4, 'colorbar': True}, + psd_args={'fmax': 65.}, plot_std=False, + log_scale=True, figsize=[4.5, 4.5], + reject=reject)[0] + + # test keypresses + ax_labels = [ax.get_label() for ax in fig.axes] + + # test topomap change type + ax = fig.axes[ax_labels.index('topomap')] + assert ax.get_title() == 'ICA001 (mag)' + fig.canvas.key_press_event('t') + assert ax.get_title() == 'ICA001 (grad)' + fig.canvas.key_press_event('t') + assert ax.get_title() == 'ICA001 (mag)' + + # test log scale + ax = fig.axes[ax_labels.index('spectrum')] + assert ax.get_xscale() == 'log' + fig.canvas.key_press_event('l') + assert ax.get_xscale() == 'linear' + fig.canvas.key_press_event('l') + assert ax.get_xscale() == 'log' + plt.close('all') with pytest.raises(TypeError, match='must be an instance'): @@ -190,7 +210,8 @@ with pytest.warns(UserWarning, match='did not converge'): ica.fit(epochs) epochs._data[0] = 0 - with pytest.warns(None): # Usually UserWarning: Infinite value .* for epo + # Usually UserWarning: Infinite value .* for epo + with _record_warnings(): ica.plot_properties(epochs, **topoargs) plt.close('all') @@ -210,11 +231,9 @@ @requires_sklearn -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') -def test_plot_ica_sources(): +def test_plot_ica_sources(raw_orig, browser_backend, monkeypatch): """Test plotting of ICA panel.""" - raw = read_raw_fif(raw_fname).crop(0, 1).load_data() + raw = raw_orig.copy().crop(0, 1) picks = _get_picks(raw) epochs = _get_epochs() raw.pick_channels([raw.ch_names[k] for k in picks]) @@ -223,58 +242,74 @@ ica = ICA(n_components=2) ica.fit(raw, picks=ica_picks) ica.exclude = [1] + if sys.platform == 'darwin': # unknown transformation bug + monkeypatch.setenv('MNE_BROWSE_RAW_SIZE', '20,20') fig = ica.plot_sources(raw) - assert len(plt.get_fignums()) == 1 + assert browser_backend._get_n_figs() == 1 # change which component is in ICA.exclude (click data trace to remove # current one; click name to add other one) - fig.canvas.draw() + fig._redraw() + assert_array_equal(ica.exclude, [1]) + assert fig.mne.info['bads'] == [ica._ica_names[1]] x = fig.mne.traces[1].get_xdata()[5] y = fig.mne.traces[1].get_ydata()[5] - _fake_click(fig, fig.mne.ax_main, (x, y), xform='data') # exclude = [] - _click_ch_name(fig, ch_index=0, button=1) # exclude = [0] - fig.canvas.key_press_event(fig.mne.close_key) - _close_event(fig) - assert len(plt.get_fignums()) == 0 + fig._fake_click((x, y), xform='data') # exclude = [] + assert fig.mne.info['bads'] == [] + assert_array_equal(ica.exclude, [1]) # unchanged + fig._click_ch_name(ch_index=0, button=1) # exclude = [0] + assert fig.mne.info['bads'] == [ica._ica_names[0]] + assert_array_equal(ica.exclude, [1]) + fig._fake_keypress(fig.mne.close_key) + fig._close_event() + assert browser_backend._get_n_figs() == 0 assert_array_equal(ica.exclude, [0]) # test when picks does not include ica.exclude. - fig = ica.plot_sources(raw, picks=[1]) - assert len(plt.get_fignums()) == 1 - plt.close('all') + ica.plot_sources(raw, picks=[1]) + assert browser_backend._get_n_figs() == 1 + browser_backend._close_all() # dtype can change int->np.int64 after load, test it explicitly ica.n_components_ = np.int64(ica.n_components_) # test clicks on y-label (need >2 secs for plot_properties() to work) - long_raw = read_raw_fif(raw_fname).crop(0, 5).load_data() + long_raw = raw_orig.crop(0, 5) fig = ica.plot_sources(long_raw) - assert len(plt.get_fignums()) == 1 - fig.canvas.draw() - _click_ch_name(fig, ch_index=0, button=3) + assert browser_backend._get_n_figs() == 1 + fig._redraw() + fig._click_ch_name(ch_index=0, button=3) assert len(fig.mne.child_figs) == 1 - assert len(plt.get_fignums()) == 2 + assert browser_backend._get_n_figs() == 2 # close child fig directly (workaround for mpl issue #18609) - fig.mne.child_figs[0].canvas.key_press_event('escape') - assert len(plt.get_fignums()) == 1 - fig.canvas.key_press_event(fig.mne.close_key) - assert len(plt.get_fignums()) == 0 + fig._fake_keypress('escape', fig=fig.mne.child_figs[0]) + assert browser_backend._get_n_figs() == 1 + fig._fake_keypress(fig.mne.close_key) + assert browser_backend._get_n_figs() == 0 del long_raw # test with annotations orig_annot = raw.annotations raw.set_annotations(Annotations([0.2], [0.1], 'Test')) fig = ica.plot_sources(raw) - assert len(fig.mne.ax_main.collections) == 1 - assert len(fig.mne.ax_hscroll.collections) == 1 + if browser_backend.name == 'matplotlib': + assert len(fig.mne.ax_main.collections) == 1 + assert len(fig.mne.ax_hscroll.collections) == 1 + else: + assert len(fig.mne.regions) == 1 raw.set_annotations(orig_annot) # test error handling - raw.info['bads'] = ['MEG 0113'] - with pytest.raises(RuntimeError, match="Raw doesn't match fitted data"): - ica.plot_sources(inst=raw) - epochs.info['bads'] = ['MEG 0113'] - with pytest.raises(RuntimeError, match="Epochs don't match fitted data"): - ica.plot_sources(inst=epochs) - epochs.info['bads'] = [] + raw_ = raw.copy().load_data() + raw_.drop_channels('MEG 0113') + with pytest.raises(RuntimeError, match="Raw doesn't match fitted data"), \ + pytest.warns(RuntimeWarning, match='could not be picked'): + ica.plot_sources(inst=raw_) + epochs_ = epochs.copy().load_data() + epochs_.drop_channels('MEG 0113') + with pytest.raises(RuntimeError, match="Epochs don't match fitted data"), \ + pytest.warns(RuntimeWarning, match='could not be picked'): + ica.plot_sources(inst=epochs_) + del raw_ + del epochs_ # test w/ epochs and evokeds ica.plot_sources(epochs) @@ -288,26 +323,35 @@ [line.get_xdata()[0], line.get_ydata()[0]], 'data') _fake_click(fig, ax, [ax.get_xlim()[0], ax.get_ylim()[1]], 'data') + # plot with bad channels excluded ica.exclude = [0] ica.plot_sources(evoked) - ica.labels_ = dict(eog=[0]) - ica.labels_['eog/0/crazy-channel'] = [0] + + # pretend find_bads_eog() yielded some results + ica.labels_ = { + 'eog': [0], + 'eog/0/crazy-channel': [0] + } ica.plot_sources(evoked) # now with labels + + # pass an invalid inst with pytest.raises(ValueError, match='must be of Raw or Epochs type'): ica.plot_sources('meeow') @pytest.mark.slowtest @requires_sklearn -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') def test_plot_ica_overlay(): """Test plotting of ICA cleaning.""" raw = _get_raw(preload=True) - raw.info['highpass'] = 1.0 # fake high-pass filtering + with raw.info._unlock(): + raw.info['highpass'] = 1.0 # fake high-pass filtering picks = _get_picks(raw) ica = ICA(noise_cov=read_cov(cov_fname), n_components=2, random_state=0) + # overlay plotting requires a fitted ICA + with pytest.raises(RuntimeError, match='need to fit'): + ica.plot_overlay(inst=raw) # can't use info.normalize_proj here because of how and when ICA and Epochs # objects do picking of Raw data with pytest.warns(RuntimeWarning, match='projection'): @@ -327,7 +371,8 @@ # smoke test for CTF raw = read_raw_fif(raw_ctf_fname) raw.apply_gradient_compensation(3) - raw.info['highpass'] = 1.0 # fake high-pass filtering + with raw.info._unlock(): + raw.info['highpass'] = 1.0 # fake high-pass filtering picks = pick_types(raw.info, meg=True, ref_meg=False) ica = ICA(n_components=2, ) ica.fit(raw, picks=picks) @@ -344,8 +389,6 @@ @requires_sklearn -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') def test_plot_ica_scores(): """Test plotting of ICA scores.""" raw = _get_raw() @@ -388,9 +431,7 @@ @requires_sklearn -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') -def test_plot_instance_components(): +def test_plot_instance_components(browser_backend): """Test plotting of components as instances of raw and epochs.""" raw = _get_raw() picks = _get_picks(raw) @@ -400,24 +441,23 @@ ica.exclude = [0] fig = ica.plot_sources(raw, title='Components') keys = ('home', 'home', 'end', 'down', 'up', 'right', 'left', '-', '+', - '=', 'd', 'd', 'pageup', 'pagedown', 'z', 'z', 's', 's', 'f11', - 'b') + '=', 'd', 'd', 'pageup', 'pagedown', 'z', 'z', 's', 's', 'b') for key in keys: - fig.canvas.key_press_event(key) - ax = fig.mne.ax_main - line = ax.lines[0] - _fake_click(fig, ax, [line.get_xdata()[0], line.get_ydata()[0]], - 'data') - _fake_click(fig, ax, [-0.1, 0.9]) # click on y-label - fig.canvas.key_press_event('escape') - plt.close('all') + fig._fake_keypress(key) + x = fig.mne.traces[0].get_xdata()[0] + y = fig.mne.traces[0].get_ydata()[0] + fig._fake_click((x, y), xform='data') + fig._click_ch_name(ch_index=0, button=1) + fig._fake_keypress('escape') + browser_backend._close_all() + epochs = _get_epochs() fig = ica.plot_sources(epochs, title='Components') for key in keys: - fig.canvas.key_press_event(key) + fig._fake_keypress(key) # Test a click - ax = fig.get_axes()[0] - line = ax.lines[0] - _fake_click(fig, ax, [line.get_xdata()[0], line.get_ydata()[0]], 'data') - _fake_click(fig, ax, [-0.1, 0.9]) # click on y-label - fig.canvas.key_press_event('escape') + x = fig.mne.traces[0].get_xdata()[0] + y = fig.mne.traces[0].get_ydata()[0] + fig._fake_click((x, y), xform='data') + fig._click_ch_name(ch_index=0, button=1) + fig._fake_keypress('escape') diff -Nru python-mne-0.23.4+dfsg/mne/viz/tests/test_misc.py python-mne-1.1.0+dfsg/mne/viz/tests/test_misc.py --- python-mne-0.23.4+dfsg/mne/viz/tests/test_misc.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/tests/test_misc.py 2022-08-03 15:48:14.000000000 +0000 @@ -10,20 +10,22 @@ import os.path as op import numpy as np +from numpy.testing import assert_array_equal import pytest import matplotlib.pyplot as plt from mne import (read_events, read_cov, read_source_spaces, read_evokeds, read_dipole, SourceEstimate, pick_events) +from mne.chpi import compute_chpi_snr from mne.datasets import testing from mne.filter import create_filter from mne.io import read_raw_fif from mne.minimum_norm import read_inverse_operator from mne.viz import (plot_bem, plot_events, plot_source_spectrogram, - plot_snr_estimate, plot_filter, plot_csd) + plot_snr_estimate, plot_filter, plot_csd, plot_chpi_snr) from mne.viz.misc import _handle_event_colors from mne.viz.utils import _get_color_list -from mne.utils import requires_nibabel, run_tests_if_main +from mne.utils import requires_nibabel from mne.time_frequency import CrossSpectralDensity data_path = testing.data_path(download=False) @@ -32,8 +34,8 @@ inv_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc-meg-eeg-oct-4-meg-inv.fif') evoked_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif') -dip_fname = op.join(data_path, 'MEG', 'sample', - 'sample_audvis_trunc_set1.dip') +dip_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_set1.dip') +chpi_fif_fname = op.join(data_path, 'SSS', 'test_move_anon_raw.fif') base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data') raw_fname = op.join(base_dir, 'test_raw.fif') cov_fname = op.join(base_dir, 'test-cov.fif') @@ -111,7 +113,10 @@ cov = read_cov(cov_fname) with pytest.warns(RuntimeWarning, match='projection'): fig1, fig2 = cov.plot(raw.info, proj=True, exclude=raw.ch_names[6:]) - plt.close('all') + + # test complex numbers + cov['data'] = cov.data * (1 + 1j) + fig1, fig2 = cov.plot(raw.info) @testing.requires_testing_data @@ -201,7 +206,6 @@ event_id=extra_id, on_missing='ignore') with pytest.raises(ValueError, match='No events'): plot_events(np.empty((0, 3))) - plt.close('all') @testing.requires_testing_data @@ -222,7 +226,6 @@ [[1, 2], [3, 4]], tmin=0) pytest.raises(ValueError, plot_source_spectrogram, [stc, stc], [[1, 2], [3, 4]], tmax=7) - plt.close('all') @pytest.mark.slowtest @@ -232,7 +235,6 @@ inv = read_inverse_operator(inv_fname) evoked = read_evokeds(evoked_fname, baseline=(None, 0))[0] plot_snr_estimate(evoked, inv) - plt.close('all') @testing.requires_testing_data @@ -240,7 +242,6 @@ """Test plotting dipole amplitudes.""" dipoles = read_dipole(dip_fname) dipoles.plot_amplitudes(show=False) - plt.close('all') def test_plot_csd(): @@ -250,7 +251,27 @@ tmin=0, tmax=1,) plot_csd(csd, mode='csd') # Plot cross-spectral density plot_csd(csd, mode='coh') # Plot coherence - plt.close('all') -run_tests_if_main() +@pytest.mark.slowtest # Slow on Azure +@testing.requires_testing_data +def test_plot_chpi_snr(): + """Test plotting cHPI SNRs.""" + raw = read_raw_fif(chpi_fif_fname, allow_maxshield='yes') + result = compute_chpi_snr(raw) + # test figure creation + fig = plot_chpi_snr(result) + assert len(fig.axes) == len(result) - 2 + assert len(fig.axes[0].lines) == len(result['freqs']) + assert len(fig.legends) == 1 + texts = [entry.get_text() for entry in fig.legends[0].get_texts()] + assert len(texts) == len(result['freqs']) + freqs = [float(text.split()[0]) for text in texts] + assert_array_equal(freqs, result['freqs']) + # test user-passed axes + _, axs = plt.subplots(2, 3) + _ = plot_chpi_snr(result, axes=axs.ravel()) + # test error + _, axs = plt.subplots(5) + with pytest.raises(ValueError, match='a list of 6 axes, got length 5'): + _ = plot_chpi_snr(result, axes=axs.ravel()) diff -Nru python-mne-0.23.4+dfsg/mne/viz/tests/test_montage.py python-mne-1.1.0+dfsg/mne/viz/tests/test_montage.py --- python-mne-0.23.4+dfsg/mne/viz/tests/test_montage.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/tests/test_montage.py 2022-08-03 15:48:14.000000000 +0000 @@ -36,6 +36,8 @@ plt.close('all') m.plot(kind='topomap', show_names=True) plt.close('all') + m.plot(sphere='eeglab') + plt.close('all') N_HSP, N_HPI = 2, 1 montage = make_dig_montage(nasion=[1, 1, 1], lpa=[2, 2, 2], rpa=[3, 3, 3], diff -Nru python-mne-0.23.4+dfsg/mne/viz/tests/test_proj.py python-mne-1.1.0+dfsg/mne/viz/tests/test_proj.py --- python-mne-0.23.4+dfsg/mne/viz/tests/test_proj.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/tests/test_proj.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,56 @@ +# Authors: Eric Larson +# +# License: Simplified BSD + +import numpy as np +import pytest + +from mne import read_evokeds, pick_types, compute_proj_evoked +from mne.datasets import testing +from mne.viz import plot_projs_joint + +data_dir = testing.data_path(download=False) +sample_dir = data_dir / 'MEG' / 'sample' +evoked_fname = sample_dir / 'sample_audvis-ave.fif' + + +@testing.requires_testing_data +def test_plot_projs_joint(): + """Test plot_projs_joint.""" + evoked = read_evokeds(evoked_fname)[0].apply_baseline((None, 0)) + evoked.info['bads'] = [] + n_mag, n_grad, n_eeg = 9, 10, 11 + n_mag_proj, n_grad_proj, n_eeg_proj = 2, 2, 1 + # We pick in this weird order to ensure our plotting order follows it + picks = np.concatenate([ + pick_types(evoked.info, meg='grad')[:n_grad], + pick_types(evoked.info, meg=False, eeg=True)[:n_eeg], + pick_types(evoked.info, meg='mag')[:n_mag], + ]) + evoked.pick(picks) + assert len(evoked.ch_names) == n_mag + n_grad + n_eeg + assert evoked.get_channel_types(unique=True) == ['grad', 'eeg', 'mag'] + projs = compute_proj_evoked( + evoked, n_mag=n_mag_proj, n_grad=n_grad_proj, n_eeg=n_eeg_proj) + assert len(projs) == 5 + with pytest.warns(RuntimeWarning, match='aliasing'): + evoked.crop(-0.1, 0.1).decimate(10) + topomap_kwargs = dict(res=8, contours=0, sensors=False) + fig = plot_projs_joint( + projs, evoked, topomap_kwargs=topomap_kwargs, verbose='error') + ylab = fig.axes[0].get_ylabel() + assert ylab.startswith('Grad'), ylab + ylab = fig.axes[4].get_ylabel() + assert ylab.startswith('EEG'), ylab + ylab = fig.axes[7].get_ylabel() + assert ylab.startswith('Mag'), ylab + mag_trace_ax_idx = 10 + mag_trace_ax = fig.axes[mag_trace_ax_idx] + assert mag_trace_ax.get_ylabel() == '' + assert len(mag_trace_ax.lines) == n_mag + 2 * n_mag_proj + old_len = len(mag_trace_ax.lines) + assert len(fig.axes) == 11 # 3x4 + fig = plot_projs_joint( + projs, evoked, picks_trace='MEG 0111', topomap_kwargs=topomap_kwargs, + verbose='error') + assert len(fig.axes[mag_trace_ax_idx].lines) == old_len + 1 diff -Nru python-mne-0.23.4+dfsg/mne/viz/tests/test_raw.py python-mne-1.1.0+dfsg/mne/viz/tests/test_raw.py --- python-mne-0.23.4+dfsg/mne/viz/tests/test_raw.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/tests/test_raw.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,54 +2,31 @@ # # License: Simplified BSD -import os.path as op import itertools +import os +from copy import deepcopy -import numpy as np -from numpy.testing import assert_allclose -import pytest import matplotlib import matplotlib.pyplot as plt +import numpy as np +import pytest +from numpy.testing import assert_allclose -from mne import read_events, pick_types, Annotations, create_info -from mne.datasets import testing -from mne.io import read_raw_fif, read_raw_ctf, RawArray -from mne.utils import _dt_to_stamp, _click_ch_name, _close_event -from mne.viz.utils import _fake_click +from mne import Annotations, create_info, pick_types from mne.annotations import _sync_onset +from mne.datasets import testing +from mne.io import RawArray +from mne.utils import _dt_to_stamp, _record_warnings, get_config, set_config from mne.viz import plot_raw, plot_sensors - -ctf_dir = op.join(testing.data_path(download=False), 'CTF') -ctf_fname_continuous = op.join(ctf_dir, 'testdata_ctf.ds') - -base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data') -raw_fname = op.join(base_dir, 'test_raw.fif') -event_name = op.join(base_dir, 'test-eve.fif') -cov_fname = op.join(base_dir, 'test-cov.fif') - - -@pytest.fixture() -def raw(): - """Get raw data.""" - raw = read_raw_fif(raw_fname, preload=True) - # Throws a warning about a changed unit. - with pytest.warns(RuntimeWarning, match='unit'): - raw.set_channel_types({raw.ch_names[0]: 'ias'}) - raw.pick_channels(raw.ch_names[:9]) - raw.info.normalize_proj() # Fix projectors after subselection - return raw - - -def _get_events(): - """Get events.""" - return read_events(event_name) +from mne.viz.utils import _fake_click -def _annotation_helper(raw, events=False): +def _annotation_helper(raw, browse_backend, events=False): """Test interactive annotations.""" + ismpl = browse_backend.name == 'matplotlib' # Some of our checks here require modern mpl to work properly n_anns = len(raw.annotations) - plt.close('all') + browse_backend._close_all() if events: events = np.array([[raw.first_samp + 100, 0, 1], @@ -59,143 +36,209 @@ events = None n_events = 0 fig = raw.plot(events=events) - assert len(plt.get_fignums()) == 1 - data_ax = fig.mne.ax_main - fig.canvas.key_press_event('a') # annotation mode - assert len(plt.get_fignums()) == 2 - # +2 from the scale bars - n_scale = 2 - assert len(data_ax.texts) == n_anns + n_events + n_scale - # modify description to create label "BAD test" + if ismpl: + assert browse_backend._get_n_figs() == 1 + + fig._fake_keypress('a') # annotation mode ann_fig = fig.mne.fig_annotation - for key in ['backspace'] + list(' test;'): # semicolon is ignored - ann_fig.canvas.key_press_event(key) - ann_fig.canvas.key_press_event('enter') - - # change annotation label - for ix in (-1, 0): - xy = ann_fig.mne.radio_ax.buttons.circles[ix].center - _fake_click(ann_fig, ann_fig.mne.radio_ax, xy, xform='data') + if ismpl: + assert browse_backend._get_n_figs() == 2 + # +3 from the scale bars + n_scale = 3 + assert len(fig.mne.ax_main.texts) == n_anns + n_events + n_scale + else: + assert ann_fig.isVisible() + + # modify description to create label "BAD test" + # semicolon is ignored + if ismpl: + for key in ['backspace'] + list(' test;') + ['enter']: + fig._fake_keypress(key, fig=ann_fig) + # change annotation label + for ix in (-1, 0): + xy = ann_fig.mne.radio_ax.buttons.circles[ix].center + fig._fake_click(xy, fig=ann_fig, ax=ann_fig.mne.radio_ax, + xform='data') + else: + # The modal dialogs of the Qt-backend would block the test, + # thus a new description will be added programmatically. + ann_fig._add_description('BAD test') # draw annotation - _fake_click(fig, data_ax, [1., 1.], xform='data', button=1, kind='press') - _fake_click(fig, data_ax, [5., 1.], xform='data', button=1, kind='motion') - _fake_click(fig, data_ax, [5., 1.], xform='data', button=1, kind='release') + fig._fake_click((1., 1.), add_points=[(5., 1.)], xform='data', button=1, + kind='drag') + if ismpl: + assert len(fig.mne.ax_main.texts) == n_anns + 1 + n_events + n_scale + # test hover event + fig._fake_keypress('p') # first turn on draggable mode + assert fig.mne.draggable_annotations + hover_kwargs = dict(xform='data', button=None, kind='motion') + fig._fake_click((4.6, 1.), **hover_kwargs) # well inside ann. + fig._fake_click((4.9, 1.), **hover_kwargs) # almost at edge + assert fig.mne.annotation_hover_line is not None + fig._fake_click((5.5, 1.), **hover_kwargs) # well outside ann. + assert fig.mne.annotation_hover_line is None + # more tests of hover line + fig._fake_click((4.6, 1.), **hover_kwargs) # well inside ann. + fig._fake_click((4.9, 1.), **hover_kwargs) # almost at edge + assert fig.mne.annotation_hover_line is not None + fig._fake_keypress('p') # turn off draggable mode, then move a bit + fig._fake_click((4.95, 1.), **hover_kwargs) + assert fig.mne.annotation_hover_line is None + fig._fake_keypress('p') # turn draggable mode back on assert len(raw.annotations.onset) == n_anns + 1 assert len(raw.annotations.duration) == n_anns + 1 assert len(raw.annotations.description) == n_anns + 1 assert raw.annotations.description[n_anns] == 'BAD test' - assert len(data_ax.texts) == n_anns + 1 + n_events + n_scale onset = raw.annotations.onset[n_anns] want_onset = _sync_onset(raw, 1., inverse=True) - assert_allclose(onset, want_onset) - assert_allclose(raw.annotations.duration[n_anns], 4.) - # test hover event - fig.canvas.key_press_event('p') # first turn on draggable mode - assert fig.mne.draggable_annotations - hover_kwargs = dict(xform='data', button=None, kind='motion') - _fake_click(fig, data_ax, [4.6, 1.], **hover_kwargs) # well inside ann. - _fake_click(fig, data_ax, [4.9, 1.], **hover_kwargs) # almost at edge - assert fig.mne.annotation_hover_line is not None - _fake_click(fig, data_ax, [5.5, 1.], **hover_kwargs) # well outside ann. - assert fig.mne.annotation_hover_line is None - # more tests of hover line - _fake_click(fig, data_ax, [4.6, 1.], **hover_kwargs) # well inside ann. - _fake_click(fig, data_ax, [4.9, 1.], **hover_kwargs) # almost at edge - assert fig.mne.annotation_hover_line is not None - fig.canvas.key_press_event('p') # turn off draggable mode, then move a bit - _fake_click(fig, data_ax, [4.95, 1.], **hover_kwargs) - assert fig.mne.annotation_hover_line is None - fig.canvas.key_press_event('p') # turn draggable mode back on + # pyqtgraph: during the transformation from pixel-coordinates + # to scene-coordinates when the click is simulated on QGraphicsView + # with QTest, there seems to happen a rounding of pixels to integers + # internally. This deviatian also seems to change between runs + # (maybe device-dependent?). + atol = 1e-10 if ismpl else 2e-2 + assert_allclose(onset, want_onset, atol=atol) + assert_allclose(raw.annotations.duration[n_anns], 4., atol=atol) # modify annotation from end (duration 4 → 1.5) - _fake_click(fig, data_ax, [4.9, 1.], xform='data', button=None, - kind='motion') # ease up to it - _fake_click(fig, data_ax, [5., 1.], xform='data', button=1, kind='press') - _fake_click(fig, data_ax, [2.5, 1.], xform='data', button=1, kind='motion') - _fake_click(fig, data_ax, [2.5, 1.], xform='data', button=1, - kind='release') + fig._fake_click((4.9, 1.), xform='data', button=1, + kind='motion') # ease up to it + fig._fake_click((5., 1.), add_points=[(2.5, 1.)], xform='data', + button=1, kind='drag') assert raw.annotations.onset[n_anns] == onset - assert_allclose(raw.annotations.duration[n_anns], 1.5) # 4 → 1.5 + # 4 → 1.5 + assert_allclose(raw.annotations.duration[n_anns], 1.5, atol=atol) # modify annotation from beginning (duration 1.5 → 2.0) - _fake_click(fig, data_ax, [1., 1.], xform='data', button=1, kind='press') - _fake_click(fig, data_ax, [0.5, 1.], xform='data', button=1, kind='motion') - _fake_click(fig, data_ax, [0.5, 1.], xform='data', button=1, - kind='release') - assert_allclose(raw.annotations.onset[n_anns], onset - 0.5, atol=1e-10) - assert_allclose(raw.annotations.duration[n_anns], 2.0) # 1.5 → 2.0 + fig._fake_click((1., 1.), add_points=[(0.5, 1.)], xform='data', button=1, + kind='drag') + assert_allclose(raw.annotations.onset[n_anns], onset - 0.5, atol=atol) + # 1.5 → 2.0 + assert_allclose(raw.annotations.duration[n_anns], 2.0, atol=atol) assert len(raw.annotations.onset) == n_anns + 1 assert len(raw.annotations.duration) == n_anns + 1 assert len(raw.annotations.description) == n_anns + 1 assert raw.annotations.description[n_anns] == 'BAD test' - assert len(fig.axes[0].texts) == n_anns + 1 + n_events + n_scale - fig.canvas.key_press_event('shift+right') - assert len(fig.axes[0].texts) == n_scale - fig.canvas.key_press_event('shift+left') - assert len(fig.axes[0].texts) == n_anns + 1 + n_events + n_scale + if ismpl: + assert len(fig.axes[0].texts) == n_anns + 1 + n_events + n_scale + fig._fake_keypress('shift+right') + assert len(fig.axes[0].texts) == n_scale + fig._fake_keypress('shift+left') + assert len(fig.axes[0].texts) == n_anns + 1 + n_events + n_scale # draw another annotation merging the two - _fake_click(fig, data_ax, [5.5, 1.], xform='data', button=1, kind='press') - _fake_click(fig, data_ax, [2., 1.], xform='data', button=1, kind='motion') - _fake_click(fig, data_ax, [2., 1.], xform='data', button=1, kind='release') + fig._fake_click((5.5, 1.), add_points=[(2., 1.)], + xform='data', button=1, kind='drag') # delete the annotation assert len(raw.annotations.onset) == n_anns + 1 assert len(raw.annotations.duration) == n_anns + 1 assert len(raw.annotations.description) == n_anns + 1 - assert_allclose(raw.annotations.onset[n_anns], onset - 0.5, atol=1e-10) - assert_allclose(raw.annotations.duration[n_anns], 5.0) - assert len(fig.axes[0].texts) == n_anns + 1 + n_events + n_scale + assert_allclose(raw.annotations.onset[n_anns], onset - 0.5, atol=atol) + assert_allclose(raw.annotations.duration[n_anns], 5.0, atol=atol) + if ismpl: + assert len(fig.axes[0].texts) == n_anns + 1 + n_events + n_scale # Delete - _fake_click(fig, data_ax, [1.5, 1.], xform='data', button=3, kind='press') + fig._fake_click((1.5, 1.), xform='data', button=3, kind='press') # exit, re-enter, then exit a different way - fig.canvas.key_press_event('a') # exit - fig.canvas.key_press_event('a') # enter - fig.mne.fig_annotation.canvas.key_press_event('escape') # exit again + fig._fake_keypress('a') # exit + fig._fake_keypress('a') # enter assert len(raw.annotations.onset) == n_anns - assert len(fig.axes[0].texts) == n_anns + n_events + n_scale - fig.canvas.key_press_event('shift+right') - assert len(fig.axes[0].texts) == n_scale - fig.canvas.key_press_event('shift+left') - assert len(fig.axes[0].texts) == n_anns + n_events + n_scale - plt.close('all') + if ismpl: + fig._fake_keypress('escape', fig=fig.mne.fig_annotation) # exit again + assert len(fig.axes[0].texts) == n_anns + n_events + n_scale + fig._fake_keypress('shift+right') + assert len(fig.axes[0].texts) == n_scale + fig._fake_keypress('shift+left') + assert len(fig.axes[0].texts) == n_anns + n_events + n_scale + +def _proj_status(ssp_fig, browse_backend): + if browse_backend.name == 'matplotlib': + ax = ssp_fig.mne.proj_checkboxes.ax + return [line.get_visible() for line + in ax.findobj(matplotlib.lines.Line2D)][::2] + else: + return [chkbx.isChecked() for chkbx in ssp_fig.checkboxes] -def _proj_status(ax): - return [line.get_visible() - for line in ax.findobj(matplotlib.lines.Line2D)][::2] +def _proj_label(ssp_fig, browse_backend): + if browse_backend.name == 'matplotlib': + return [lb.get_text() for lb in ssp_fig.mne.proj_checkboxes.labels] + else: + return [chkbx.text() for chkbx in ssp_fig.checkboxes] -def _child_fig_helper(fig, key, attr): - # Spawn and close child figs of raw.plot() - num_figs = len(plt.get_fignums()) - assert getattr(fig.mne, attr) is None - # spawn - fig.canvas.key_press_event(key) - assert len(fig.mne.child_figs) == 1 - assert len(plt.get_fignums()) == num_figs + 1 + +def _proj_click(idx, fig, browse_backend): + ssp_fig = fig.mne.fig_proj + if browse_backend.name == 'matplotlib': + pos = np.array(ssp_fig.mne.proj_checkboxes. + labels[idx].get_position()) + 0.01 + + fig._fake_click(pos, fig=ssp_fig, ax=ssp_fig.mne.proj_checkboxes.ax, + xform='data') + else: + # _fake_click on QCheckBox is inconsistent across platforms + # (also see comment in test_plot_raw_selection). + ssp_fig._proj_changed(not fig.mne.projs_on[idx], idx) + # Update Checkbox + ssp_fig.checkboxes[idx].setChecked(bool(fig.mne.projs_on[idx])) + + +def _proj_click_all(fig, browse_backend): + ssp_fig = fig.mne.fig_proj + if browse_backend.name == 'matplotlib': + fig._fake_click((0.5, 0.5), fig=ssp_fig, ax=ssp_fig.mne.proj_all.ax) + fig._fake_click((0.5, 0.5), fig=ssp_fig, ax=ssp_fig.mne.proj_all.ax, + kind='release') + else: + # _fake_click on QPushButton is inconsistent across platforms. + ssp_fig.toggle_all() + + +def _spawn_child_fig(fig, attr, browse_backend, key): + # starting state + n_figs = browse_backend._get_n_figs() + n_children = len(fig.mne.child_figs) + # spawn the child fig + fig._fake_keypress(key) + # make sure the figure was actually spawned + assert len(fig.mne.child_figs) == n_children + 1 + assert browse_backend._get_n_figs() == n_figs + 1 + # make sure the parent fig knows the child fig's name child_fig = getattr(fig.mne, attr) assert child_fig is not None - # close via main window toggle - fig.canvas.key_press_event(key) - _close_event(child_fig) - assert len(fig.mne.child_figs) == 0 - assert len(plt.get_fignums()) == num_figs + return child_fig + + +def _destroy_child_fig(fig, child_fig, attr, browse_backend, key, key_target): + # starting state + n_figs = browse_backend._get_n_figs() + n_children = len(fig.mne.child_figs) + # destroy child fig (_close_event is MPL agg backend workaround) + fig._fake_keypress(key, fig=key_target) + fig._close_event(child_fig) + # make sure the figure was actually destroyed + assert len(fig.mne.child_figs) == n_children - 1 + assert browse_backend._get_n_figs() == n_figs - 1 assert getattr(fig.mne, attr) is None - # spawn again - fig.canvas.key_press_event(key) - assert len(fig.mne.child_figs) == 1 - assert len(plt.get_fignums()) == num_figs + 1 - child_fig = getattr(fig.mne, attr) - assert child_fig is not None - # close via child window - child_fig.canvas.key_press_event(child_fig.mne.close_key) - _close_event(child_fig) - assert len(fig.mne.child_figs) == 0 - assert len(plt.get_fignums()) == num_figs + + +def _child_fig_helper(fig, key, attr, browse_backend): + # Spawn and close child figs of raw.plot() assert getattr(fig.mne, attr) is None + # spawn, then close via main window toggle + child_fig = _spawn_child_fig(fig, attr, browse_backend, key) + _destroy_child_fig(fig, child_fig, attr, browse_backend, key, + key_target=fig) + # spawn again, then close via child window's close key + child_fig = _spawn_child_fig(fig, attr, browse_backend, key) + _destroy_child_fig(fig, child_fig, attr, browse_backend, + key=child_fig.mne.close_key, + key_target=child_fig) -def test_scale_bar(): +def test_scale_bar(browser_backend): """Test scale bar for raw.""" + ismpl = browser_backend.name == 'matplotlib' sfreq = 1000. t = np.arange(10000) / sfreq data = np.sin(2 * np.pi * 10. * t) @@ -204,79 +247,113 @@ info = create_info(3, sfreq, ('mag', 'grad', 'eeg')) raw = RawArray(data, info) fig = raw.plot() - ax = fig.mne.ax_main - assert len(ax.texts) == 3 # our labels - texts = tuple(t.get_text().strip() for t in ax.texts) + texts = fig._get_scale_bar_texts() + assert len(texts) == 3 # ch_type scale-bars wants = ('800.0 fT/cm', '2000.0 fT', '40.0 µV') assert texts == wants - assert len(ax.lines) == 7 # 1 green vline, 3 data, 3 scalebars + if ismpl: + # 1 green vline, 3 data, 3 scalebars + assert len(fig.mne.ax_main.lines) == 7 + else: + assert len(fig.mne.scalebars) == 3 for data, bar in zip(fig.mne.traces, fig.mne.scalebars.values()): y = data.get_ydata() y_lims = [y.min(), y.max()] bar_lims = bar.get_ydata() assert_allclose(y_lims, bar_lims, atol=1e-4) - plt.close('all') -def test_plot_raw_selection(raw): +def test_plot_raw_selection(raw, browser_backend): """Test selection mode of plot_raw().""" - raw.info['lowpass'] = 10. # allow heavy decim during plotting - plt.close('all') # ensure all are closed - assert len(plt.get_fignums()) == 0 + ismpl = browser_backend.name == 'matplotlib' + with raw.info._unlock(): + raw.info['lowpass'] = 10. # allow heavy decim during plotting + browser_backend._close_all() # ensure all are closed + assert browser_backend._get_n_figs() == 0 fig = raw.plot(group_by='selection', proj=False) - assert len(plt.get_fignums()) == 2 + assert browser_backend._get_n_figs() == 2 sel_fig = fig.mne.fig_selection - buttons = sel_fig.mne.radio_ax.buttons assert sel_fig is not None # test changing selection with arrow keys sel_dict = fig.mne.ch_selections assert len(fig.mne.traces) == len(sel_dict['Left-temporal']) # 6 - sel_fig.canvas.key_press_event('down') + fig._fake_keypress('down', fig=sel_fig) assert len(fig.mne.traces) == len(sel_dict['Left-frontal']) # 3 - sel_fig.canvas.key_press_event('down') + fig._fake_keypress('down', fig=sel_fig) assert len(fig.mne.traces) == len(sel_dict['Misc']) # 1 - sel_fig.canvas.key_press_event('down') # ignored; no custom sel defined + fig._fake_keypress('down', fig=sel_fig) # ignored; no custom sel defined assert len(fig.mne.traces) == len(sel_dict['Misc']) # 1 # switch to butterfly mode - sel_fig.canvas.key_press_event('b') - assert len(fig.mne.traces) == len(np.concatenate(list(sel_dict.values()))) + fig._fake_keypress('b', fig=sel_fig) + + # ToDo: For Qt-backend the framework around RawTraceItem makes + # it difficult to show the same channel multiple times which is why + # it is currently not implemented. + # This would be relevant if you wanted to plot several selections in + # butterfly-mode which have some channels in common. + sel_picks = len(np.concatenate(list(sel_dict.values()))) + if ismpl: + assert len(fig.mne.traces) == sel_picks + else: + assert len(fig.mne.traces) == sel_picks - 1 assert fig.mne.butterfly # test clicking on radio buttons → should cancel butterfly mode - xy = buttons.circles[0].center - _fake_click(sel_fig, sel_fig.mne.radio_ax, xy, xform='data') + if ismpl: + xy = sel_fig.mne.radio_ax.buttons.circles[0].center + fig._fake_click(xy, fig=sel_fig, ax=sel_fig.mne.radio_ax, xform='data') + else: + # For an unknown reason test-clicking on checkboxes is inconsistent + # across platforms. + # (QTest.mouseClick works isolated on all platforms but somehow + # not in this context. _fake_click isn't working on linux) + sel_fig._chkbx_changed(list(sel_fig.chkbxs.keys())[0]) assert len(fig.mne.traces) == len(sel_dict['Left-temporal']) # 6 assert not fig.mne.butterfly # test clicking on "custom" when not defined: should be no-op - before_state = buttons.value_selected - xy = buttons.circles[-1].center - _fake_click(sel_fig, sel_fig.mne.radio_ax, xy, xform='data') + if ismpl: + before_state = sel_fig.mne.radio_ax.buttons.value_selected + xy = sel_fig.mne.radio_ax.buttons.circles[-1].center + fig._fake_click(xy, fig=sel_fig, ax=sel_fig.mne.radio_ax, xform='data') + lasso = sel_fig.lasso + sensor_ax = sel_fig.mne.sensor_ax + assert sel_fig.mne.radio_ax.buttons.value_selected == before_state + else: + before_state = sel_fig.mne.old_selection + chkbx = sel_fig.chkbxs[list(sel_fig.chkbxs.keys())[-1]] + fig._fake_click((0.5, 0.5), fig=chkbx) + lasso = sel_fig.channel_fig.lasso + sensor_ax = sel_fig.channel_widget + assert before_state == sel_fig.mne.old_selection # unchanged assert len(fig.mne.traces) == len(sel_dict['Left-temporal']) # unchanged - assert buttons.value_selected == before_state # unchanged # test marking bad channel in selection mode → should make sensor red - assert sel_fig.lasso.ec[:, 0].sum() == 0 # R of RGBA zero for all chans - _click_ch_name(fig, ch_index=1, button=1) # mark bad - assert sel_fig.lasso.ec[:, 0].sum() == 1 # one channel red - _click_ch_name(fig, ch_index=1, button=1) # mark good - assert sel_fig.lasso.ec[:, 0].sum() == 0 # all channels black + assert lasso.ec[:, 0].sum() == 0 # R of RGBA zero for all chans + fig._click_ch_name(ch_index=1, button=1) # mark bad + assert lasso.ec[:, 0].sum() == 1 # one channel red + fig._click_ch_name(ch_index=1, button=1) # mark good + assert lasso.ec[:, 0].sum() == 0 # all channels black # test lasso - sel_fig._set_custom_selection() # lasso empty → should do nothing - sensor_ax = sel_fig.mne.sensor_ax - # Lasso with 1 mag/grad sensor unit (upper left) - _fake_click(sel_fig, sensor_ax, (0, 1), xform='ax') - _fake_click(sel_fig, sensor_ax, (0.65, 1), xform='ax', kind='motion') - _fake_click(sel_fig, sensor_ax, (0.65, 0.7), xform='ax', kind='motion') - _fake_click(sel_fig, sensor_ax, (0, 0.7), xform='ax', kind='release') + # Testing lasso-interactivity of sensor-plot within Qt-backend + # with QTest doesn't seem to work. want = ['MEG 0121', 'MEG 0122', 'MEG 0123'] - assert sorted(want) == sorted(sel_fig.lasso.selection) + if ismpl: + sel_fig._set_custom_selection() # lasso empty → should do nothing + # Lasso with 1 mag/grad sensor unit (upper left) + fig._fake_click((0, 1), add_points=[(0.65, 1), (0.65, 0.7), (0, 0.7)], + fig=sel_fig, ax=sensor_ax, xform='ax', kind='drag') + else: + lasso.selection = want + sel_fig._set_custom_selection() + assert sorted(want) == sorted(fig.mne.ch_names[fig.mne.picks]) # test joint closing of selection & data windows - sel_fig.canvas.key_press_event(sel_fig.mne.close_key) - _close_event(sel_fig) - assert len(plt.get_fignums()) == 0 + fig._fake_keypress(sel_fig.mne.close_key, fig=sel_fig) + fig._close_event(sel_fig) + assert browser_backend._get_n_figs() == 0 -def test_plot_raw_ssp_interaction(raw): +def test_plot_raw_ssp_interaction(raw, browser_backend): """Test SSP projector UI of plot_raw().""" - raw.info['lowpass'] = 10. # allow heavy decim during plotting + with raw.info._unlock(): + raw.info['lowpass'] = 10. # allow heavy decim during plotting # apply some (not all) projs to test our proj UI (greyed out applied projs) projs = raw.info['projs'][-2:] raw.del_proj([-2, -1]) @@ -284,141 +361,177 @@ raw.add_proj(projs) fig = raw.plot() # open SSP window - _fake_click(fig, fig.mne.ax_proj, [0.5, 0.5]) - assert len(plt.get_fignums()) == 2 + fig._fake_keypress('j') + assert browser_backend._get_n_figs() == 2 ssp_fig = fig.mne.fig_proj - t = ssp_fig.mne.proj_checkboxes.labels - ax = ssp_fig.mne.proj_checkboxes.ax - assert _proj_status(ax) == [True, True, True] + assert _proj_status(ssp_fig, browser_backend) == [True, True, True] # this should have no effect (proj 0 is already applied) - assert t[0].get_text().endswith('(already applied)') - pos = np.array(t[0].get_position()) + 0.01 - _fake_click(ssp_fig, ssp_fig.get_axes()[0], pos, xform='data') - assert _proj_status(ax) == [True, True, True] + assert _proj_label(ssp_fig, + browser_backend)[0].endswith('(already applied)') + _proj_click(0, fig, browser_backend) + assert _proj_status(ssp_fig, browser_backend) == [True, True, True] # this should work (proj 1 not applied) - pos = np.array(t[1].get_position()) + 0.01 - _fake_click(ssp_fig, ax, pos, xform='data') - assert _proj_status(ax) == [True, False, True] + _proj_click(1, fig, browser_backend) + assert _proj_status(ssp_fig, browser_backend) == [True, False, True] # turn it back on - _fake_click(ssp_fig, ax, pos, xform='data') - assert _proj_status(ax) == [True, True, True] + _proj_click(1, fig, browser_backend) + assert _proj_status(ssp_fig, browser_backend) == [True, True, True] # toggle all off (button axes need both press and release) - _fake_click(ssp_fig, ssp_fig.mne.proj_all.ax, [0.5, 0.5]) - _fake_click(ssp_fig, ssp_fig.mne.proj_all.ax, [0.5, 0.5], kind='release') - assert _proj_status(ax) == [True, False, False] - fig.canvas.key_press_event('J') - assert _proj_status(ax) == [True, True, True] - fig.canvas.key_press_event('J') - assert _proj_status(ax) == [True, False, False] + _proj_click_all(fig, browser_backend) + assert _proj_status(ssp_fig, browser_backend) == [True, False, False] + fig._fake_keypress('J') + assert _proj_status(ssp_fig, browser_backend) == [True, True, True] + fig._fake_keypress('J') + assert _proj_status(ssp_fig, browser_backend) == [True, False, False] # turn all on - _fake_click(ssp_fig, ssp_fig.mne.proj_all.ax, [0.5, 0.5]) # all on - _fake_click(ssp_fig, ssp_fig.mne.proj_all.ax, [0.5, 0.5], kind='release') + _proj_click_all(fig, browser_backend) assert fig.mne.projector is not None # on - assert _proj_status(ax) == [True, True, True] + assert _proj_status(ssp_fig, browser_backend) == [True, True, True] -def test_plot_raw_child_figures(raw): +def test_plot_raw_child_figures(raw, browser_backend): """Test spawning and closing of child figures.""" - raw.info['lowpass'] = 10. # allow heavy decim during plotting - plt.close('all') # make sure we start clean - assert len(plt.get_fignums()) == 0 + ismpl = browser_backend.name == 'matplotlib' + with raw.info._unlock(): + raw.info['lowpass'] = 10. # allow heavy decim during plotting + # make sure we start clean + assert browser_backend._get_n_figs() == 0 fig = raw.plot() - assert len(plt.get_fignums()) == 1 + assert browser_backend._get_n_figs() == 1 # test child fig toggles - _child_fig_helper(fig, '?', 'fig_help') - _child_fig_helper(fig, 'j', 'fig_proj') - _child_fig_helper(fig, 'a', 'fig_annotation') - assert len(fig.mne.child_figs) == 0 # make sure the helper cleaned up - assert len(plt.get_fignums()) == 1 + _child_fig_helper(fig, '?', 'fig_help', browser_backend) + _child_fig_helper(fig, 'j', 'fig_proj', browser_backend) + if ismpl: # in mne-qt-browser, annotation is a dock-widget, not a window + _child_fig_helper(fig, 'a', 'fig_annotation', browser_backend) # test right-click → channel location popup - fig.canvas.draw() - _click_ch_name(fig, ch_index=2, button=3) + fig._redraw() + fig._click_ch_name(ch_index=2, button=3) assert len(fig.mne.child_figs) == 1 - assert len(plt.get_fignums()) == 2 - fig.mne.child_figs[0].canvas.key_press_event('escape') - _close_event(fig.mne.child_figs[0]) - assert len(plt.get_fignums()) == 1 + assert browser_backend._get_n_figs() == 2 + fig._fake_keypress('escape', fig=fig.mne.child_figs[0]) + if ismpl: + fig._close_event(fig.mne.child_figs[0]) + assert len(fig.mne.child_figs) == 0 + assert browser_backend._get_n_figs() == 1 # test right-click on non-data channel ix = raw.get_channel_types().index('ias') # find the shielding channel trace_ix = fig.mne.ch_order.tolist().index(ix) # get its plotting position + fig._redraw() + fig._click_ch_name(ch_index=trace_ix, button=3) # should be no-op assert len(fig.mne.child_figs) == 0 - assert len(plt.get_fignums()) == 1 - fig.canvas.draw() - _click_ch_name(fig, ch_index=trace_ix, button=3) # should be no-op - assert len(fig.mne.child_figs) == 0 - assert len(plt.get_fignums()) == 1 + assert browser_backend._get_n_figs() == 1 # test resize of main window - width, height = fig.canvas.manager.canvas.get_width_height() - fig.canvas.manager.canvas.resize(width // 2, height // 2) - plt.close('all') + fig._resize_by_factor(0.5) -def test_plot_raw_keypresses(raw): +def test_orphaned_annot_fig(raw, browser_backend): + """Test that annotation window is not orphaned (GH #10454).""" + if browser_backend.name != 'matplotlib': + return + assert browser_backend._get_n_figs() == 0 + fig = raw.plot() + _spawn_child_fig(fig, 'fig_annotation', browser_backend, 'a') + fig._fake_keypress(key=fig.mne.close_key) + fig._close_event() + assert len(fig.mne.child_figs) == 0 + assert browser_backend._get_n_figs() == 0 + + +def _monkeypatch_fig(fig, browser_backend): + if browser_backend.name == 'matplotlib': + fig.canvas.manager.full_screen_toggle = lambda: None + else: + # Monkeypatch the Qt methods + def _full(): + fig.isFullScreen = lambda: True + + def _norm(): + fig.isFullScreen = lambda: False + + fig.showFullScreen = _full + fig.showNormal = _norm + + +def test_plot_raw_keypresses(raw, browser_backend, monkeypatch): """Test keypress interactivity of plot_raw().""" - raw.info['lowpass'] = 10. # allow heavy decim during plotting + with raw.info._unlock(): + raw.info['lowpass'] = 10. # allow heavy decim during plotting fig = raw.plot() # test twice → once in normal, once in butterfly view. # NB: keys a, j, and ? are tested in test_plot_raw_child_figures() keys = ('pagedown', 'down', 'up', 'down', 'right', 'left', '-', '+', '=', - 'd', 'd', 'pageup', 'home', 'end', 'z', 'z', 's', 's', 'f11', 'b') + 'd', 'd', 'pageup', 'home', 'end', 'z', 'z', 's', 's', 'f11', 't', + 'b') + # Avoid annoying fullscreen issues by monkey-patching our handlers + _monkeypatch_fig(fig, browser_backend) # test for group_by='original' for key in 2 * keys + ('escape',): - fig.canvas.key_press_event(key) + fig._fake_keypress(key) # test for group_by='selection' fig = plot_raw(raw, group_by='selection') + _monkeypatch_fig(fig, browser_backend) for key in 2 * keys + ('escape',): - fig.canvas.key_press_event(key) + fig._fake_keypress(key) -def test_plot_raw_traces(raw): +def test_plot_raw_traces(raw, events, browser_backend): """Test plotting of raw data.""" - raw.info['lowpass'] = 10. # allow heavy decim during plotting - events = _get_events() - plt.close('all') # ensure all are closed + ismpl = browser_backend.name == 'matplotlib' + with raw.info._unlock(): + raw.info['lowpass'] = 10. # allow heavy decim during plotting fig = raw.plot(events=events, order=[1, 7, 5, 2, 3], n_channels=3, group_by='original') assert hasattr(fig, 'mne') # make sure fig.mne param object is present - assert len(fig.axes) == 5 + if ismpl: + assert len(fig.axes) == 5 # setup x = fig.mne.traces[0].get_xdata()[5] y = fig.mne.traces[0].get_ydata()[5] - data_ax = fig.mne.ax_main hscroll = fig.mne.ax_hscroll vscroll = fig.mne.ax_vscroll # test marking bad channels - label = fig.mne.ax_main.get_yticklabels()[0].get_text() + label = fig._get_ticklabels('y')[0] assert label not in fig.mne.info['bads'] - _fake_click(fig, data_ax, [x, y], xform='data') # click data to mark bad + # click data to mark bad + fig._fake_click((x, y), xform='data') assert label in fig.mne.info['bads'] - _fake_click(fig, data_ax, [x, y], xform='data') # click data to unmark bad + # click data to unmark bad + fig._fake_click((x, y), xform='data') assert label not in fig.mne.info['bads'] - _click_ch_name(fig, ch_index=0, button=1) # click name to mark bad + # click name to mark bad + fig._click_ch_name(ch_index=0, button=1) assert label in fig.mne.info['bads'] # test other kinds of clicks - _fake_click(fig, data_ax, [0.5, 0.999]) # click elsewhere (add vline) - _fake_click(fig, data_ax, [0.5, 0.999], button=3) # remove vline - _fake_click(fig, hscroll, [0.5, 0.5]) # change time - _fake_click(fig, hscroll, [0.5, 0.5]) # shouldn't change time this time + fig._fake_click((0.5, 0.98)) # click elsewhere (add vline) + assert fig.mne.vline_visible is True + fig._fake_click((0.5, 0.98), button=3) # remove vline + assert fig.mne.vline_visible is False + fig._fake_click((0.5, 0.5), ax=hscroll) # change time + t_start = fig.mne.t_start + fig._fake_click((0.5, 0.5), ax=hscroll) # shouldn't change time this time + assert round(t_start, 6) == round(fig.mne.t_start, 6) # test scrolling through channels - labels = [label.get_text() for label in data_ax.get_yticklabels()] + labels = fig._get_ticklabels('y') assert labels == [raw.ch_names[1], raw.ch_names[7], raw.ch_names[5]] - _fake_click(fig, vscroll, [0.5, 0.01]) # change channels to end - labels = [label.get_text() for label in data_ax.get_yticklabels()] + fig._fake_click((0.5, 0.05), ax=vscroll) # change channels to end + labels = fig._get_ticklabels('y') assert labels == [raw.ch_names[5], raw.ch_names[2], raw.ch_names[3]] for _ in (0, 0): # first click changes channels to mid; second time shouldn't change - _fake_click(fig, vscroll, [0.5, 0.5]) - labels = [label.get_text() for label in data_ax.get_yticklabels()] + # This needs to be changed for Qt, because there scrollbars are + # drawn differently (value of slider at lower end, not at middle) + yclick = 0.5 if ismpl else 0.7 + fig._fake_click((0.5, yclick), ax=vscroll) + labels = fig._get_ticklabels('y') assert labels == [raw.ch_names[7], raw.ch_names[5], raw.ch_names[2]] - assert len(plt.get_fignums()) == 1 # test clicking a channel name in butterfly mode bads = fig.mne.info['bads'].copy() - fig.canvas.key_press_event('b') - _click_ch_name(fig, ch_index=0, button=1) # should be no-op + fig._fake_keypress('b') + fig._click_ch_name(ch_index=0, button=1) # should be no-op assert fig.mne.info['bads'] == bads # unchanged - fig.canvas.key_press_event('b') + fig._fake_keypress('b') # test starting up in zen mode fig = plot_raw(raw, show_scrollbars=False) @@ -428,158 +541,190 @@ with pytest.raises(TypeError, match='title must be None or a string, got'): raw.plot(title=1) raw.plot(show_options=True) - plt.close('all') + browser_backend._close_all() + + # annotations outside data range + annot = Annotations([10, 10 + raw.first_samp / raw.info['sfreq']], + [10, 10], ['test', 'test'], raw.info['meas_date']) + with pytest.warns(RuntimeWarning, match='outside data range'): + raw.set_annotations(annot) # Color setting with pytest.raises(KeyError, match='must be strictly positive, or -1'): raw.plot(event_color={0: 'r'}) with pytest.raises(TypeError, match='event_color key must be an int, got'): raw.plot(event_color={'foo': 'r'}) - annot = Annotations([10, 10 + raw.first_samp / raw.info['sfreq']], - [10, 10], ['test', 'test'], raw.info['meas_date']) - with pytest.warns(RuntimeWarning, match='outside data range'): - raw.set_annotations(annot) - fig = plot_raw(raw, events=events, event_color={-1: 'r', 998: 'b'}) - plt.close('all') - for group_by, order in zip(['position', 'selection'], - [np.arange(len(raw.ch_names))[::-3], - [1, 2, 4, 6]]): - fig = raw.plot(group_by=group_by, order=order) - x = fig.get_axes()[0].lines[1].get_xdata()[10] - y = fig.get_axes()[0].lines[1].get_ydata()[10] - _fake_click(fig, data_ax, [x, y], xform='data') # mark bad - fig.canvas.key_press_event('down') # change selection - _fake_click(fig, fig.get_axes()[2], [0.5, 0.5]) # change channels - sel_fig = plt.figure(1) - topo_ax = sel_fig.axes[1] - _fake_click(sel_fig, topo_ax, [-0.425, 0.20223853], - xform='data') - fig.canvas.key_press_event('down') - fig.canvas.key_press_event('up') - fig.canvas.scroll_event(0.5, 0.5, -1) # scroll down - fig.canvas.scroll_event(0.5, 0.5, 1) # scroll up - _fake_click(sel_fig, topo_ax, [-0.5, 0.], xform='data') - _fake_click(sel_fig, topo_ax, [0.5, 0.], xform='data', - kind='motion') - _fake_click(sel_fig, topo_ax, [0.5, 0.5], xform='data', - kind='motion') - _fake_click(sel_fig, topo_ax, [-0.5, 0.5], xform='data', - kind='release') + plot_raw(raw, events=events, event_color={-1: 'r', 998: 'b'}) + + +@pytest.mark.parametrize('group_by', ('position', 'selection')) +def test_plot_raw_groupby(raw, browser_backend, group_by): + """Test group-by plotting of raw data.""" + with raw.info._unlock(): + raw.info['lowpass'] = 10. # allow heavy decim during plotting + order = (np.arange(len(raw.ch_names))[::-3] if group_by == 'position' else + [1, 2, 4, 6]) + fig = raw.plot(group_by=group_by, order=order) + x = fig.mne.traces[0].get_xdata()[10] + y = fig.mne.traces[0].get_ydata()[10] + fig._fake_keypress('down') # change selection + fig._fake_click((x, y), xform='data') # mark bad + fig._fake_click((0.5, 0.5), ax=fig.mne.ax_vscroll) # change channels + if browser_backend.name == 'matplotlib': + # Test lasso-selection + # (test difficult with Qt-backend, set plot_raw_selection) + sel_fig = fig.mne.fig_selection + topo_ax = sel_fig.mne.sensor_ax + fig._fake_click([-0.425, 0.20223853], fig=sel_fig, ax=topo_ax, + xform='data') + fig._fake_click((-0.5, 0.), add_points=[(0.5, 0.), + (0.5, 0.5), + (-0.5, 0.5)], + fig=sel_fig, ax=topo_ax, xform='data', kind='drag') + fig._fake_keypress('down') + fig._fake_keypress('up') + fig._fake_keypress('up') + fig._fake_scroll(0.5, 0.5, -1) # scroll down + fig._fake_scroll(0.5, 0.5, 1) # scroll up - plt.close('all') - # test if meas_date is off + +def test_plot_raw_meas_date(raw, browser_backend): + """Test effect of mismatched meas_date in raw.plot().""" raw.set_meas_date(_dt_to_stamp(raw.info['meas_date'])[0]) - annot = Annotations([1 + raw.first_samp / raw.info['sfreq']], - [5], ['bad']) + annot = Annotations([1 + raw.first_samp / raw.info['sfreq']], [5], ['bad']) with pytest.warns(RuntimeWarning, match='outside data range'): raw.set_annotations(annot) - with pytest.warns(None): # sometimes projection + with _record_warnings(): # sometimes projection raw.plot(group_by='position', order=np.arange(8)) - for fig_num in plt.get_fignums(): - fig = plt.figure(fig_num) - if hasattr(fig, 'radio'): # Get access to selection fig. - break + fig = raw.plot() for key in ['down', 'up', 'escape']: - fig.canvas.key_press_event(key) + fig._fake_keypress(key, fig=fig.mne.fig_selection) + +def test_plot_raw_nan(raw, browser_backend): + """Test plotting all NaNs.""" raw._data[:] = np.nan # this should (at least) not die, the output should pretty clearly show # that there is a problem so probably okay to just plot something blank - with pytest.warns(None): + with _record_warnings(): raw.plot(scalings='auto') - plt.close('all') - @testing.requires_testing_data -def test_plot_raw_white(): +def test_plot_raw_white(raw_orig, noise_cov_io, browser_backend): """Test plotting whitened raw data.""" - raw = read_raw_fif(raw_fname).crop(0, 1).load_data() - fig = raw.plot(noise_cov=cov_fname) + raw_orig.crop(0, 1) + fig = raw_orig.plot(noise_cov=noise_cov_io) # toggle whitening - fig.canvas.key_press_event('w') - fig.canvas.key_press_event('w') - plt.close('all') + fig._fake_keypress('w') + fig._fake_keypress('w') @testing.requires_testing_data -def test_plot_ref_meg(): +def test_plot_ref_meg(raw_ctf, browser_backend): """Test plotting ref_meg.""" - raw_ctf = read_raw_ctf(ctf_fname_continuous).crop(0, 1).load_data() + raw_ctf.crop(0, 1) raw_ctf.plot() - plt.close('all') pytest.raises(ValueError, raw_ctf.plot, group_by='selection') -def test_plot_misc_auto(): +def test_plot_misc_auto(browser_backend): """Test plotting of data with misc auto scaling.""" data = np.random.RandomState(0).randn(1, 1000) raw = RawArray(data, create_info(1, 1000., 'misc')) raw.plot() - plt.close('all') + raw = RawArray(data, create_info(1, 1000., 'dipole')) + raw.plot(order=[0]) # plot, even though it's not "data" + browser_backend._close_all() -def test_plot_annotations(raw): +@pytest.mark.slowtest +def test_plot_annotations(raw, browser_backend): """Test annotation mode of the plotter.""" - raw.info['lowpass'] = 10. - _annotation_helper(raw) - _annotation_helper(raw, events=True) + ismpl = browser_backend.name == 'matplotlib' + with raw.info._unlock(): + raw.info['lowpass'] = 10. + _annotation_helper(raw, browser_backend) + _annotation_helper(raw, browser_backend, events=True) annot = Annotations([42], [1], 'test', raw.info['meas_date']) with pytest.warns(RuntimeWarning, match='expanding outside'): raw.set_annotations(annot) - _annotation_helper(raw) + _annotation_helper(raw, browser_backend) # test annotation visibility toggle fig = raw.plot() - assert len(fig.mne.annotations) == 1 - assert len(fig.mne.annotation_texts) == 1 - fig.canvas.key_press_event('a') # start annotation mode - checkboxes = fig.mne.show_hide_annotation_checkboxes - checkboxes.set_active(0) - assert len(fig.mne.annotations) == 0 - assert len(fig.mne.annotation_texts) == 0 - checkboxes.set_active(0) - assert len(fig.mne.annotations) == 1 - assert len(fig.mne.annotation_texts) == 1 + if ismpl: + assert len(fig.mne.annotations) == 1 + assert len(fig.mne.annotation_texts) == 1 + else: + assert len(fig.mne.regions) == 1 + fig._fake_keypress('a') # start annotation mode + if ismpl: + checkboxes = fig.mne.show_hide_annotation_checkboxes + checkboxes.set_active(0) + assert len(fig.mne.annotations) == 0 + assert len(fig.mne.annotation_texts) == 0 + checkboxes.set_active(0) + assert len(fig.mne.annotations) == 1 + assert len(fig.mne.annotation_texts) == 1 + else: + fig.mne.visible_annotations['test'] = False + fig._update_regions_visible() + assert not fig.mne.regions[0].isVisible() + fig.mne.visible_annotations['test'] = True + fig._update_regions_visible() + assert fig.mne.regions[0].isVisible() @pytest.mark.parametrize('hide_which', ([], [0], [1], [0, 1])) -def test_remove_annotations(raw, hide_which): +def test_remove_annotations(raw, hide_which, browser_backend): """Test that right-click doesn't remove hidden annotation spans.""" + descriptions = ['foo', 'bar'] ann = Annotations(onset=[2, 1], duration=[1, 3], - description=['foo', 'bar']) + description=descriptions) raw.set_annotations(ann) assert len(raw.annotations) == 2 fig = raw.plot() - fig.canvas.key_press_event('a') # start annotation mode - checkboxes = fig.mne.show_hide_annotation_checkboxes - for which in hide_which: - checkboxes.set_active(which) - _fake_click(fig, fig.mne.ax_main, (2.5, 0.1), xform='data', button=3) + fig._fake_keypress('a') # start annotation mode + if browser_backend.name == 'matplotlib': + checkboxes = fig.mne.show_hide_annotation_checkboxes + for which in hide_which: + checkboxes.set_active(which) + else: + for hide_idx in hide_which: + hide_key = descriptions[hide_idx] + fig.mne.visible_annotations[hide_key] = False + fig._update_regions_visible() + fig._fake_click((2.5, 0.1), xform='data', button=3) assert len(raw.annotations) == len(hide_which) @pytest.mark.parametrize('filtorder', (0, 2)) # FIR, IIR -def test_plot_raw_filtered(filtorder, raw): +def test_plot_raw_filtered(filtorder, raw, browser_backend): """Test filtering of raw plots.""" + # Opening that many plots can cause a Segmentation fault + # if multithreading is activated in Qt-backend + pg_kwargs = {'precompute': False} with pytest.raises(ValueError, match='lowpass.*Nyquist'): - raw.plot(lowpass=raw.info['sfreq'] / 2., filtorder=filtorder) + raw.plot(lowpass=raw.info['sfreq'] / 2., filtorder=filtorder, + **pg_kwargs) with pytest.raises(ValueError, match='highpass must be > 0'): - raw.plot(highpass=0, filtorder=filtorder) + raw.plot(highpass=0, filtorder=filtorder, **pg_kwargs) with pytest.raises(ValueError, match='Filter order must be'): - raw.plot(lowpass=1, filtorder=-1) + raw.plot(lowpass=1, filtorder=-1, **pg_kwargs) with pytest.raises(ValueError, match="Invalid value for the 'clipping'"): - raw.plot(clipping='foo') - raw.plot(lowpass=40, clipping='transparent', filtorder=filtorder) - raw.plot(highpass=1, clipping='clamp', filtorder=filtorder) - raw.plot(lowpass=40, butterfly=True, filtorder=filtorder) + raw.plot(clipping='foo', **pg_kwargs) + raw.plot(lowpass=40, clipping='transparent', filtorder=filtorder, + **pg_kwargs) + raw.plot(highpass=1, clipping='clamp', filtorder=filtorder, **pg_kwargs) + raw.plot(lowpass=40, butterfly=True, filtorder=filtorder, **pg_kwargs) # shouldn't break if all shown are non-data RawArray(np.zeros((1, 100)), create_info(1, 20., 'stim')).plot(lowpass=5) -def test_plot_raw_psd(raw): +def test_plot_raw_psd(raw, raw_orig): """Test plotting of raw psds.""" - raw_orig = raw.copy() + raw_unchanged = raw.copy() # normal mode fig = raw.plot_psd(average=False) fig.canvas.resize_event() @@ -600,7 +745,7 @@ plt.close('all') # need 2, got 1 ax = plt.axes() - with pytest.raises(ValueError, match='of length 2, while the length is 1'): + with pytest.raises(ValueError, match='of length 2.*the length is 1'): raw.plot_psd(ax=ax, average=True) plt.close('all') # topo psd @@ -636,7 +781,7 @@ assert title == 'Magnetometers', title assert unit in ylabel, ylabel # test reject_by_annotation - raw = raw_orig + raw = raw_unchanged raw.set_annotations(Annotations([1, 5], [3, 3], ['test', 'test'])) raw.plot_psd(reject_by_annotation=True) raw.plot_psd(reject_by_annotation=False) @@ -651,7 +796,7 @@ raw.plot_psd(xscale='blah') # gh-5046 - raw = read_raw_fif(raw_fname, preload=True).crop(0, 1) + raw = raw_orig.crop(0, 1) picks = pick_types(raw.info, meg=True) raw.plot_psd(picks=picks, average=False) raw.plot_psd(picks=picks, average=True) @@ -725,8 +870,65 @@ with pytest.warns(RuntimeWarning, match='identity'): raw.plot_sensors() + # Test plotting with sphere='eeglab' + info = create_info( + ch_names=['Fpz', 'Oz', 'T7', 'T8'], + sfreq=100, + ch_types='eeg' + ) + data = 1e-6 * np.random.rand(4, 100) + raw_eeg = RawArray(data=data, info=info) + raw_eeg.set_montage('biosemi64') + raw_eeg.plot_sensors(sphere='eeglab') + + # Should work with "FPz" as well + raw_eeg.rename_channels({'Fpz': 'FPz'}) + raw_eeg.plot_sensors(sphere='eeglab') + + # Should still work without Fpz/FPz, as long as we still have Oz + raw_eeg.drop_channels('FPz') + raw_eeg.plot_sensors(sphere='eeglab') + + # Should raise if Oz is missing too, as we cannot reconstruct Fpz anymore + raw_eeg.drop_channels('Oz') + with pytest.raises(ValueError, match='could not find: Fpz'): + raw_eeg.plot_sensors(sphere='eeglab') + + # Should raise if we don't have a montage + chs = deepcopy(raw_eeg.info['chs']) + raw_eeg.set_montage(None) + with raw_eeg.info._unlock(): + raw_eeg.info['chs'] = chs + with pytest.raises(ValueError, match='No montage was set'): + raw_eeg.plot_sensors(sphere='eeglab') + + +@pytest.mark.parametrize('cfg_value', (None, '0.1,0.1')) +def test_min_window_size(raw, cfg_value, browser_backend): + """Test minimum window plot size.""" + old_cfg = get_config('MNE_BROWSE_RAW_SIZE') + set_config('MNE_BROWSE_RAW_SIZE', cfg_value) + fig = raw.plot() + # For an unknown reason, the Windows-CI is a bit off + # (on local Windows 10 the size is exactly as expected). + atol = 0 if not os.name == 'nt' else 0.2 + # 8 × 8 inches is default minimum size. + assert_allclose(fig._get_size(), (8, 8), atol=atol) + set_config('MNE_BROWSE_RAW_SIZE', old_cfg) + -def test_scalings_int(): +def test_scalings_int(browser_backend): """Test that auto scalings access samples using integers.""" raw = RawArray(np.zeros((1, 500)), create_info(1, 1000., 'eeg')) raw.plot(scalings='auto') + + +@pytest.mark.parametrize('dur, n_dec', [(20, 1), (1.8, 2), (0.01, 4)]) +def test_clock_xticks(raw, dur, n_dec, browser_backend): + """Test if decimal seconds of xticks have appropriate length.""" + fig = raw.plot(duration=dur, time_format='clock') + fig._redraw() + tick_texts = fig._get_ticklabels('x') + assert tick_texts[0].startswith('19:01:53') + if len(tick_texts[0].split('.')) > 1: + assert len(tick_texts[0].split('.')[1]) == n_dec diff -Nru python-mne-0.23.4+dfsg/mne/viz/tests/test_scraper.py python-mne-1.1.0+dfsg/mne/viz/tests/test_scraper.py --- python-mne-0.23.4+dfsg/mne/viz/tests/test_scraper.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/tests/test_scraper.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,27 @@ +# Authors: Eric Larson +# +# License: Simplified BSD + +import os.path as op + +import mne +from mne.utils import requires_version + + +@requires_version('sphinx_gallery') +def test_qt_scraper(raw, pg_backend, tmp_path): + """Test sphinx-gallery scraping of the browser.""" + # make sure there is only one to scrape from old tests + fig = raw.plot(group_by='selection') + (tmp_path / '_images').mkdir() + image_paths = [ + str(tmp_path / '_images' / 'temp_{ii}.png') for ii in range(2)] + gallery_conf = dict(builder_name='html', src_dir=str(tmp_path)) + block_vars = dict( + example_globals=dict(fig=fig), + image_path_iterator=iter(image_paths)) + assert not any(op.isfile(image_path) for image_path in image_paths) + assert not getattr(fig, '_scraped', False) + mne.viz._scraper._MNEQtBrowserScraper()(None, block_vars, gallery_conf) + assert all(op.isfile(image_path) for image_path in image_paths) + assert fig._scraped diff -Nru python-mne-0.23.4+dfsg/mne/viz/tests/test_topomap.py python-mne-1.1.0+dfsg/mne/viz/tests/test_topomap.py --- python-mne-0.23.4+dfsg/mne/viz/tests/test_topomap.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/tests/test_topomap.py 2022-08-03 15:48:14.000000000 +0000 @@ -18,21 +18,24 @@ from mne import (read_evokeds, read_proj, make_fixed_length_events, Epochs, compute_proj_evoked, find_layout, pick_types, create_info, - read_cov) + read_cov, EvokedArray) from mne.io.proj import make_eeg_average_ref_proj, Projection from mne.io import read_raw_fif, read_info, RawArray from mne.io.constants import FIFF -from mne.io.pick import pick_info, channel_indices_by_type +from mne.io.pick import pick_info, channel_indices_by_type, _picks_to_idx from mne.io.compensator import get_current_comp -from mne.channels import read_layout, make_dig_montage +from mne.channels import (read_layout, make_dig_montage, make_standard_montage, + find_ch_adjacency) from mne.datasets import testing +from mne.preprocessing import compute_bridged_electrodes from mne.time_frequency.tfr import AverageTFR from mne.viz import plot_evoked_topomap, plot_projs_topomap, topomap from mne.viz.topomap import (_get_pos_outlines, _onselect, plot_topomap, - plot_arrowmap, plot_psds_topomap) + plot_arrowmap, plot_psds_topomap, + plot_bridged_electrodes, plot_ch_adjacency) from mne.viz.utils import _find_peaks, _fake_click -from mne.utils import requires_sklearn +from mne.utils import requires_sklearn, check_version data_dir = testing.data_path(download=False) @@ -54,7 +57,8 @@ """Test interactive topomap projection plotting.""" evoked = read_evokeds(evoked_fname, baseline=(None, 0))[0] evoked.pick_types(meg='mag') - evoked.info['projs'] = [] + with evoked.info._unlock(): + evoked.info['projs'] = [] assert not evoked.proj evoked.add_proj(compute_proj_evoked(evoked, n_mag=1)) @@ -150,13 +154,14 @@ # evoked evoked = read_evokeds(evoked_fname, 'Left Auditory', baseline=(None, 0)) + # Test animation _, anim = evoked.animate_topomap(ch_type='grad', times=[0, 0.1], butterfly=False, time_unit='s', verbose='debug') anim._func(1) # _animate has to be tested separately on 'Agg' backend. out, _ = capsys.readouterr() - assert 'Interpolation mode local to 0' in out + assert 'extrapolation mode local to 0' in out plt.close('all') @@ -166,7 +171,7 @@ fig, anim = fnirs_evoked.animate_topomap(ch_type='hbo', verbose='debug') anim._func(1) # _animate has to be tested separately on 'Agg' backend. out, _ = capsys.readouterr() - assert 'Interpolation mode head to 0' in out + assert 'extrapolation mode head to 0' in out assert len(fig.axes) == 2 plt.close('all') @@ -187,6 +192,9 @@ pytest.raises(ValueError, plt_topomap, times=[-100]) # bad time pytest.raises(ValueError, plt_topomap, times=[[0]]) # bad time + with pytest.raises(RuntimeError, match='`image_interp` must be'): + evoked.plot_topomap([0.1], image_interp='bilinear') + evoked.plot_topomap([0.1], ch_type='eeg', scalings=1, res=res, contours=[-100, 0, 100], time_unit='ms') @@ -295,12 +303,12 @@ plt_topomap(times, ch_type='grad', mask=mask, show_names=True, mask_params={'marker': 'x'}) plt.close('all') - with pytest.raises(ValueError, match='number of seconds; got -'): + with pytest.raises(ValueError, match='number of seconds.* got -'): plt_topomap(times, ch_type='eeg', average=-1e3) - with pytest.raises(TypeError, match='number of seconds; got type'): + with pytest.raises(TypeError, match='number of seconds.* got type'): plt_topomap(times, ch_type='eeg', average='x') - p = plt_topomap(times, ch_type='grad', image_interp='bilinear', + p = plt_topomap(times, ch_type='grad', image_interp='cubic', show_names=lambda x: x.replace('MEG', '')) subplot = [x for x in p.get_children() if 'Subplot' in str(type(x))] assert len(subplot) >= 1, [type(x) for x in p.get_children()] @@ -331,6 +339,40 @@ assert_equal(texts[0], 'Custom') plt.close('all') + # Test averaging with a scalar input + averaging_times = [ev_bad.times[0], times[0], ev_bad.times[-1]] + p = plt_topomap(averaging_times, ch_type='eeg', average=0.01) + + expected_ax_titles = ( + '-0.200 – -0.195 s', # clipped on the left + '0.095 – 0.105 s', # full range + '0.494 – 0.499 s' # clipped on the right + ) + for idx, expected_title in enumerate(expected_ax_titles): + assert p.axes[idx].get_title() == expected_title + + # Test averaging with an array-like input + averaging_durations = [0.01, 0.02, None] + p = plt_topomap( + averaging_times, ch_type='eeg', average=averaging_durations + ) + expected_ax_titles = ( + '-0.200 – -0.195 s', # clipped on the left + '0.090 – 0.110 s', # full range + '0.499 s' # No averaging + ) + for idx, expected_title in enumerate(expected_ax_titles): + assert p.axes[idx].get_title() == expected_title + + # Test averaging with array-like input, but n_times != n_average + averaging_durations = [0.01, 0.02] + with pytest.raises(ValueError, match='3 time points.*2 periods'): + plt_topomap( + averaging_times, ch_type='eeg', average=averaging_durations + ) + + del averaging_times, expected_ax_titles, expected_title + # delaunay triangulation warning plt_topomap(times, ch_type='mag') # projs have already been applied @@ -405,7 +447,8 @@ **fast_test) # Remove digitization points. Now topomap should fail - evoked.info['dig'] = None + with evoked.info._unlock(): + evoked.info['dig'] = None pytest.raises(RuntimeError, plot_evoked_topomap, evoked, times, ch_type='eeg', time_unit='s') plt.close('all') @@ -465,6 +508,13 @@ picks = [93, 94, 96, 97, 21, 22, 24, 25, 129, 130, 315, 316, 2, 5, 8, 11] info = pick_info(raw.info, picks) data = rng.randn(len(picks), n_freqs, len(times)) + + # test complex numbers + tfr = AverageTFR(info, data * (1 + 1j), times, np.arange(n_freqs), nave) + tfr.plot_topomap(ch_type='mag', tmin=0.05, tmax=0.150, fmin=0, fmax=10, + res=res, contours=0) + + # test real numbers tfr = AverageTFR(info, data, times, np.arange(n_freqs), nave) tfr.plot_topomap(ch_type='mag', tmin=0.05, tmax=0.150, fmin=0, fmax=10, res=res, contours=0) @@ -572,6 +622,24 @@ plt.close('all') +def test_plot_topomap_channel_distance(): + """ + Test topomap plotting with spread out channels (gh-9511, gh-9526). + + Test topomap plotting when the distance between channels is greater than + the head radius. + """ + ch_names = ['TP9', 'AF7', 'AF8', 'TP10'] + + info = create_info(ch_names, 100, ch_types='eeg') + evoked = EvokedArray(np.random.randn(4, 10) * 1e-6, info) + ten_five = make_standard_montage("standard_1005") + evoked.set_montage(ten_five) + + evoked.plot_topomap(sphere=0.05, res=8) + plt.close('all') + + def test_plot_topomap_bads_grad(): """Test plotting topomap with bad gradiometer channels (gh-8802).""" import matplotlib.pyplot as plt @@ -593,8 +661,6 @@ @requires_sklearn -@pytest.mark.filterwarnings('ignore:.*max_iter.*will be changed.*:' - 'DeprecationWarning') def test_plot_topomap_nirs_ica(fnirs_epochs): """Test plotting nirs ica topomap.""" from mne.preprocessing import ICA @@ -603,7 +669,8 @@ # fake high-pass filtering and hide the fact that the epochs were # baseline corrected - fnirs_epochs.info['highpass'] = 1.0 + with fnirs_epochs.info._unlock(): + fnirs_epochs.info['highpass'] = 1.0 fnirs_epochs.baseline = None ica = ICA().fit(fnirs_epochs) @@ -619,3 +686,159 @@ cov.plot_topomap(info) cov.plot_topomap(info, noise_cov=cov) plt.close('all') + + +def test_plot_topomap_cnorm(): + """Test colormap normalization.""" + if check_version("matplotlib", "3.2.0"): + from matplotlib.colors import TwoSlopeNorm + else: + from matplotlib.colors import DivergingNorm as TwoSlopeNorm + from matplotlib.colors import PowerNorm + + rng = np.random.default_rng(42) + v = rng.uniform(low=-1, high=2.5, size=64) + v[:3] = [-1, 0, 2.5] + + montage = make_standard_montage("biosemi64") + info = create_info(montage.ch_names, 256, "eeg").set_montage("biosemi64") + cnorm = TwoSlopeNorm(vmin=-1, vcenter=0, vmax=2.5) + + # pass only cnorm, no vmin/vmax + plot_topomap(v, info, cnorm=cnorm) + + # pass cnorm and vmin + msg = "vmin=-1.* is implicitly defined by cnorm, ignoring vmin=-10.*" + with pytest.warns(RuntimeWarning, match=msg): + plot_topomap(v, info, vmin=-10, cnorm=cnorm) + + # pass cnorm and vmax + msg = "vmax=2.5 is implicitly defined by cnorm, ignoring vmax=10.*" + with pytest.warns(RuntimeWarning, match=msg): + plot_topomap(v, info, vmax=10, cnorm=cnorm) + + # try another subclass of mpl.colors.Normalize + plot_topomap(v, info, cnorm=PowerNorm(0.5)) + + +def test_plot_bridged_electrodes(): + """Test plotting of bridged electrodes.""" + rng = np.random.default_rng(42) + montage = make_standard_montage("biosemi64") + info = create_info(montage.ch_names, 256, "eeg").set_montage("biosemi64") + bridged_idx = [(0, 1), (2, 3)] + n_epochs = 10 + ed_matrix = np.zeros((n_epochs, len(info.ch_names), + len(info.ch_names))) * np.nan + triu_idx = np.triu_indices(len(info.ch_names), 1) + for i in range(n_epochs): + ed_matrix[i][triu_idx] = rng.random() + rng.random(triu_idx[0].size) + fig = plot_bridged_electrodes(info, bridged_idx, ed_matrix, + topomap_args=dict(names=info.ch_names, + vmax=1, show_names=True)) + # two bridged lines plus head outlines + assert len(fig.axes[0].lines) == 6 + + with pytest.raises(RuntimeError, match='Expected'): + plot_bridged_electrodes(info, bridged_idx, np.zeros((5, 6, 7))) + + # test with multiple channel types + raw = read_raw_fif(raw_fname, preload=True) + picks = _picks_to_idx(raw.info, "eeg") + raw._data[picks[0]] = raw._data[picks[1]] # artificially bridge electrodes + bridged_idx, ed_matrix = compute_bridged_electrodes(raw) + plot_bridged_electrodes(raw.info, bridged_idx, ed_matrix) + + +def test_plot_ch_adjacency(): + """Test plotting of adjacency matrix.""" + xyz_pos = np.array([[-0.1, 0.1, 0.1], [0.1, 0.1, 0.1], [0., 0., 0.12], + [-0.1, -0.1, 0.1], [0.1, -0.1, 0.1]]) + + info = create_info(list('abcde'), 23, ch_types='eeg') + montage = make_dig_montage( + ch_pos={ch: pos for ch, pos in zip(info.ch_names, xyz_pos)}, + coord_frame='head') + info.set_montage(montage) + + # construct adjacency + adj_sparse, ch_names = find_ch_adjacency(info, 'eeg') + + # plot adjacency + fig = plot_ch_adjacency(info, adj_sparse, ch_names, kind='2d', edit=True) + + # find channel positions + collection = fig.axes[0].collections[0] + pos = collection.get_offsets().data + + # get adjacency lines + lines = fig.axes[0].lines[4:] # (first four lines are head outlines) + + # make sure lines match adjacency relations in the matrix + for line in lines: + x, y = line.get_data() + ch_idx = [np.where((pos == [[x[ix], y[ix]]]).all(axis=1))[0][0] + for ix in range(2)] + assert adj_sparse[ch_idx[0], ch_idx[1]] + + # make sure additional point is generated after clicking a channel + _fake_click(fig, fig.axes[0], pos[0], xform='data') + collections = fig.axes[0].collections + assert len(collections) == 2 + + # make sure the point is green + green = matplotlib.colors.to_rgba('tab:green') + assert (collections[1].get_facecolor() == green).all() + + # make sure adjacency entry is modified after second click on another node + assert adj_sparse[0, 1] + assert adj_sparse[1, 0] + n_lines_before = len(lines) + _fake_click(fig, fig.axes[0], pos[1], xform='data') + + assert not adj_sparse[0, 1] + assert not adj_sparse[1, 0] + + # and there is one line less + lines = fig.axes[0].lines[4:] + n_lines_after = len(lines) + assert n_lines_after == n_lines_before - 1 + + # make sure there is still one green point ... + collections = fig.axes[0].collections + assert len(collections) == 2 + assert (collections[1].get_facecolor() == green).all() + + # ... but its at a different location + point_pos = collections[1].get_offsets().data + assert (point_pos == pos[1]).all() + + # check that clicking again removes the green selection point + _fake_click(fig, fig.axes[0], pos[1], xform='data') + collections = fig.axes[0].collections + assert len(collections) == 1 + + # clicking the points again adds a green line + _fake_click(fig, fig.axes[0], pos[1], xform='data') + _fake_click(fig, fig.axes[0], pos[0], xform='data') + + lines = fig.axes[0].lines[4:] + assert len(lines) == n_lines_after + 1 + assert lines[-1].get_color() == 'tab:green' + + # smoke test for 3d option + adj = adj_sparse.toarray() + fig = plot_ch_adjacency(info, adj, ch_names, kind='3d') + + # test errors + # ----------- + # number of channels in the adjacency matrix and info must match + msg = ("``adjacency`` must have the same number of rows as the number of " + "channels in ``info``") + with pytest.raises(ValueError, match=msg): + plot_ch_adjacency(info, adj_sparse, ch_names[:3], kind='2d') + + # edition mode only available for 2d plot + msg = "Editing a 3d adjacency plot is not supported." + with pytest.raises(ValueError, match=msg): + plot_ch_adjacency(info, adj, ch_names, kind='3d', edit=True) diff -Nru python-mne-0.23.4+dfsg/mne/viz/tests/test_topo.py python-mne-1.1.0+dfsg/mne/viz/tests/test_topo.py --- python-mne-0.23.4+dfsg/mne/viz/tests/test_topo.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/tests/test_topo.py 2022-08-03 15:48:14.000000000 +0000 @@ -6,8 +6,8 @@ # # License: Simplified BSD -import os.path as op from collections import namedtuple +import os.path as op import numpy as np import pytest @@ -19,7 +19,7 @@ from mne.channels import read_layout from mne.io import read_raw_fif from mne.time_frequency.tfr import AverageTFR -from mne.utils import run_tests_if_main +from mne.utils import _record_warnings from mne.viz import (plot_topo_image_epochs, _get_presser, mne_analyze_colormap, plot_evoked_topo) @@ -90,7 +90,7 @@ axes = plt.subplots(nrows=3)[-1].flatten().tolist() evoked.plot_joint(times=[0], picks=[6, 7, 8], ts_args=dict(axes=axes[0]), topomap_args={"axes": axes[1:], "time_unit": "s"}) - with pytest.raises(ValueError, match='array of length 6'): + with pytest.raises(ValueError, match='of length 4'): evoked.plot_joint(picks=[6, 7, 8], ts_args=dict(axes=axes[0]), topomap_args=dict(axes=axes[2:])) plt.close('all') @@ -132,6 +132,13 @@ plot_evoked_topo([evoked, evoked], merge_grads=True, background_color='w') + plot_evoked_topo([evoked, evoked], merge_grads=True, + background_color='w', color='blue') + + with pytest.raises(ValueError, match='must be .*tuple, list, str,.*'): + plot_evoked_topo([evoked, evoked], merge_grads=True, + color=np.array(["blue", "red"])) + picked_evoked = evoked.copy().pick_channels(evoked.ch_names[:3]) picked_evoked_eeg = evoked.copy().pick_types(meg=False, eeg=True) picked_evoked_eeg.pick_channels(picked_evoked_eeg.ch_names[:3]) @@ -271,6 +278,12 @@ fig = tfr.plot_topo(baseline=(None, 0), mode='ratio', title='Average power', vmin=0., vmax=14.) + # test complex + tfr.data = tfr.data * (1 + 1j) + plt.close('all') + fig = tfr.plot_topo(baseline=(None, 0), mode='ratio', + title='Average power', vmin=0., vmax=14.) + # test opening tfr by clicking num_figures_before = len(plt.get_fignums()) # could use np.reshape(fig.axes[-1].images[0].get_extent(), (2, 2)).mean(1) @@ -291,7 +304,7 @@ # one timesample tfr = AverageTFR(epochs.info, data[:, :, [0]], epochs.times[[1]], freqs, nave) - with pytest.warns(None): # matplotlib equal left/right + with _record_warnings(): # matplotlib equal left/right tfr.plot([4], baseline=None, vmax=14., show=False, yscale='linear') # one frequency bin, log scale required: as it doesn't make sense @@ -314,6 +327,3 @@ None, tfr=data[:, :3, :], freq=these_freqs, x_label=None, y_label=None, colorbar=False, cmap=('RdBu_r', True), yscale='log') - - -run_tests_if_main() diff -Nru python-mne-0.23.4+dfsg/mne/viz/tests/test_utils.py python-mne-1.1.0+dfsg/mne/viz/tests/test_utils.py --- python-mne-0.23.4+dfsg/mne/viz/tests/test_utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/tests/test_utils.py 2022-08-03 15:48:14.000000000 +0000 @@ -8,14 +8,12 @@ from numpy.testing import assert_allclose import pytest import matplotlib.pyplot as plt -import matplotlib.cm as cm from mne.viz.utils import (compare_fiff, _fake_click, _compute_scalings, _validate_if_list_of_axes, _get_color_list, - _setup_vmin_vmax, center_cmap, centers_to_edges, - _make_event_color_dict) + _setup_vmin_vmax, centers_to_edges, + _make_event_color_dict, concatenate_images) from mne.viz import ClickableImage, add_background_image, mne_analyze_colormap -from mne.utils import run_tests_if_main from mne.io import read_raw_fif from mne.event import read_events from mne.epochs import Epochs @@ -145,33 +143,16 @@ _validate_if_list_of_axes(ax_flat, 4) pytest.raises(ValueError, _validate_if_list_of_axes, ax_flat, 5) pytest.raises(ValueError, _validate_if_list_of_axes, ax, 3) - pytest.raises(ValueError, _validate_if_list_of_axes, 'error') - pytest.raises(ValueError, _validate_if_list_of_axes, ['error'] * 2) - pytest.raises(ValueError, _validate_if_list_of_axes, ax[0]) + pytest.raises(TypeError, _validate_if_list_of_axes, 'error') + pytest.raises(TypeError, _validate_if_list_of_axes, ['error'] * 2) + pytest.raises(TypeError, _validate_if_list_of_axes, ax[0]) pytest.raises(ValueError, _validate_if_list_of_axes, ax, 3) ax_flat[2] = 23 - pytest.raises(ValueError, _validate_if_list_of_axes, ax_flat) + pytest.raises(TypeError, _validate_if_list_of_axes, ax_flat) _validate_if_list_of_axes(ax, 4) plt.close('all') -def test_center_cmap(): - """Test centering of colormap.""" - from matplotlib.colors import LinearSegmentedColormap - from matplotlib.pyplot import Normalize - cmap = center_cmap(cm.get_cmap("RdBu"), -5, 10) - - assert isinstance(cmap, LinearSegmentedColormap) - - # get new colors for values -5 (red), 0 (white), and 10 (blue) - new_colors = cmap(Normalize(-5, 10)([-5, 0, 10])) - # get original colors for 0 (red), 0.5 (white), and 1 (blue) - reference = cm.RdBu([0., 0.5, 1.]) - assert_allclose(new_colors, reference) - # new and old colors at 0.5 must be different - assert not np.allclose(cmap(0.5), reference[1]) - - def test_centers_to_edges(): """Test centers_to_edges.""" assert_allclose(centers_to_edges([0, 1, 2])[0], [-0.5, 0.5, 1.5, 2.5]) @@ -201,4 +182,18 @@ _ = _make_event_color_dict({-2: 'r', -1: 'b'}) -run_tests_if_main() +@pytest.mark.parametrize('axis', (0, 1)) +@pytest.mark.parametrize('b_h', (2, 4)) +@pytest.mark.parametrize('b_w', (3, 5)) +@pytest.mark.parametrize('a_h', (2, 4)) +@pytest.mark.parametrize('a_w', (3, 5)) +def test_concatenate_images(a_w, a_h, b_w, b_h, axis): + """Test that concat with arbitrary sizes works.""" + a = np.zeros((a_h, a_w, 3)) + b = np.zeros((b_h, b_w, 3)) + img = concatenate_images([a, b], axis=axis) + if axis == 0: + want_shape = (a_h + b_h, max(a_w, b_w), 3) + else: + want_shape = (max(a_h, b_h), a_w + b_w, 3) + assert img.shape == want_shape diff -Nru python-mne-0.23.4+dfsg/mne/viz/topomap.py python-mne-1.1.0+dfsg/mne/viz/topomap.py --- python-mne-0.23.4+dfsg/mne/viz/topomap.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/topomap.py 2022-08-03 15:48:14.000000000 +0000 @@ -5,6 +5,7 @@ # Martin Luessi # Eric Larson # Robert Luke +# Mikołaj Magnuski # # License: Simplified BSD @@ -20,21 +21,23 @@ from ..channels.channels import _get_ch_type from ..channels.layout import ( _find_topomap_coords, find_layout, _pair_grad_sensors, _merge_ch_data) -from ..defaults import _EXTRAPOLATE_DEFAULT, _BORDER_DEFAULT +from ..defaults import (_INTERPOLATION_DEFAULT, _EXTRAPOLATE_DEFAULT, + _BORDER_DEFAULT) from ..io.pick import (pick_types, _picks_by_type, pick_info, pick_channels, _pick_data_channels, _picks_to_idx, _get_channel_types, _MEG_CH_TYPES_SPLIT) from ..utils import (_clean_names, _time_mask, verbose, logger, fill_doc, - _validate_type, _check_sphere, _check_option, _is_numeric) + _validate_type, _check_sphere, _check_option, _is_numeric, + warn) from .utils import (tight_layout, _setup_vmin_vmax, _prepare_trellis, _check_delayed_ssp, _draw_proj_checkbox, figure_nobar, plt_show, _process_times, DraggableColorbar, - _validate_if_list_of_axes, _setup_cmap, _check_time_unit) + _validate_if_list_of_axes, _setup_cmap, _check_time_unit, + _set_3d_axes_equal, _check_type_projs) from ..time_frequency import psd_multitaper from ..defaults import _handle_default from ..transforms import apply_trans, invert_transform from ..io.meas_info import Info, _simplify_info -from ..io.proj import Projection _fnirs_types = ('hbo', 'hbr', 'fnirs_cw_amplitude', 'fnirs_od') @@ -196,7 +199,8 @@ params['proj_bools'] = bools new_evoked = params['evoked'].copy() - new_evoked.info['projs'] = [] + with new_evoked.info._unlock(): + new_evoked.info['projs'] = [] new_evoked.add_proj(projs) new_evoked.apply_proj() @@ -211,14 +215,12 @@ Zi = interp.set_values(d)() im.set_data(Zi) # must be removed and re-added + color = 'k' if len(cont.collections) > 0: tp = cont.collections[0] visible = tp.get_visible() patch_ = tp.get_clip_path() - try: - color = tp.get_edgecolors() - except AttributeError: # old MPL - color = tp.get_color() + color = tp.get_edgecolors() lw = tp.get_linewidth() for tp in cont.collections: tp.remove() @@ -274,7 +276,8 @@ @fill_doc def plot_projs_topomap(projs, info, cmap=None, sensors=True, colorbar=False, res=64, size=1, show=True, - outlines='head', contours=6, image_interp='bilinear', + outlines='head', contours=6, + image_interp=_INTERPOLATION_DEFAULT, axes=None, vlim=(None, None), sphere=None, extrapolate=_EXTRAPOLATE_DEFAULT, border=_BORDER_DEFAULT): @@ -284,18 +287,17 @@ ---------- projs : list of Projection The projections. - info : instance of Info - The info associated with the channels in the projectors. + %(info_not_none)s Must be associated with the channels in the projectors. .. versionchanged:: 0.20 The positional argument ``layout`` was deprecated and replaced by ``info``. %(proj_topomap_kwargs)s - %(topomap_sphere_auto)s - %(topomap_extrapolate)s + %(sphere_topomap_auto)s + %(extrapolate_topomap)s .. versionadded:: 0.20 - %(topomap_border)s + %(border_topomap)s Returns ------- @@ -306,13 +308,28 @@ ----- .. versionadded:: 0.9.0 """ - import matplotlib.pyplot as plt - sphere = _check_sphere(sphere, info) + fig = _plot_projs_topomap( + projs, info, cmap=cmap, sensors=sensors, colorbar=colorbar, res=res, + size=size, outlines=outlines, contours=contours, + image_interp=image_interp, axes=axes, vlim=vlim, sphere=sphere, + extrapolate=extrapolate, border=border) + with warnings.catch_warnings(record=True): + warnings.simplefilter('ignore') + tight_layout(fig=fig) + plt_show(show) + return fig - # be forgiving if `projs` isn't a list - if isinstance(projs, Projection): - projs = [projs] +def _plot_projs_topomap(projs, info, cmap=None, sensors=True, + colorbar=False, res=64, size=1, show=True, + outlines='head', contours=6, + image_interp=_INTERPOLATION_DEFAULT, + axes=None, vlim=(None, None), + sphere=None, extrapolate=_EXTRAPOLATE_DEFAULT, + border=_BORDER_DEFAULT): + import matplotlib.pyplot as plt + sphere = _check_sphere(sphere, info) + projs = _check_type_projs(projs) _validate_type(info, 'info', 'info') types, datas, poss, spheres, outliness, ch_typess = [], [], [], [], [], [] @@ -398,10 +415,6 @@ _add_colorbar(ax, im, cmap) fig = ax.get_figure() - with warnings.catch_warnings(record=True): - warnings.simplefilter('ignore') - tight_layout(fig=fig) - plt_show(show) return fig @@ -468,8 +481,8 @@ def _get_extra_points(pos, extrapolate, origin, radii): - """Get coordinates of additinal interpolation points.""" - from scipy.spatial.qhull import Delaunay + """Get coordinates of additional interpolation points.""" + from scipy.spatial import Delaunay radii = np.array(radii, float) assert radii.shape == (2,) x, y = origin @@ -576,7 +589,7 @@ else: assert extrapolate == 'head' # return points on the head circle - angle = np.arcsin(distance / np.mean(radii)) + angle = np.arcsin(min(distance / np.mean(radii), 1)) n_pnts = max(12, int(np.round(2 * np.pi / angle))) points_l = np.linspace(0, 2 * np.pi, n_pnts, endpoint=False) use_radii = radii * 1.1 + distance @@ -598,7 +611,10 @@ to be set independently. """ - def __init__(self, pos, extrapolate, origin, radii, border): + def __init__(self, pos, image_interp, extrapolate, origin, radii, border): + from scipy.interpolate import (CloughTocher2DInterpolator, + NearestNDInterpolator, + LinearNDInterpolator) # in principle this works in N dimensions, not just 2 assert pos.ndim == 2 and pos.shape[1] == 2, pos.shape _validate_type(border, ('numeric', str), 'border') @@ -614,6 +630,9 @@ self.mask_pts = mask_pts self.border = border self.tri = tri + self.interp = {'cubic': CloughTocher2DInterpolator, + 'nearest': NearestNDInterpolator, # used only for anim + 'linear': LinearNDInterpolator}[image_interp] def set_values(self, v): """Set the values at interpolation points.""" @@ -624,8 +643,6 @@ # # Eventually we could also do set_values with this class if we want, # see scipy/interpolate/rbf.py, especially the self.nodes one-liner. - from scipy.interpolate import CloughTocher2DInterpolator - if isinstance(self.border, str): # we've already checked that border = 'mean' n_points = v.shape[0] @@ -648,7 +665,7 @@ v_extra = np.full(self.n_extra, self.border, dtype=float) v = np.concatenate((v, v_extra)) - self.interpolator = CloughTocher2DInterpolator(self.tri, v) + self.interpolator = self.interp(self.tri, v) return self def set_locations(self, Xi, Yi): @@ -690,10 +707,10 @@ def plot_topomap(data, pos, vmin=None, vmax=None, cmap=None, sensors=True, res=64, axes=None, names=None, show_names=False, mask=None, mask_params=None, outlines='head', - contours=6, image_interp='bilinear', show=True, + contours=6, image_interp=_INTERPOLATION_DEFAULT, show=True, onselect=None, extrapolate=_EXTRAPOLATE_DEFAULT, sphere=None, border=_BORDER_DEFAULT, - ch_type='eeg'): + ch_type='eeg', cnorm=None): """Plot a topographic map as image. Parameters @@ -703,62 +720,47 @@ pos : array, shape (n_chan, 2) | instance of Info Location information for the data points(/channels). If an array, for each data point, the x and y coordinates. - If an Info object, it must contain only one data type and + If an `~mne.Info` object, it must contain only one data type and exactly ``len(data)`` data channels, and the x/y coordinates will - be inferred from this Info object. - vmin : float | callable | None - The value specifying the lower bound of the color range. - If None, and vmax is None, -vmax is used. Else np.min(data). - If callable, the output equals vmin(data). Defaults to None. - vmax : float | callable | None - The value specifying the upper bound of the color range. - If None, the maximum absolute value is used. If callable, the output - equals vmax(data). Defaults to None. - cmap : matplotlib colormap | None - Colormap to use. If None, 'Reds' is used for all positive data, - otherwise defaults to 'RdBu_r'. + be inferred from the montage applied to the `~mne.Info` object. + %(vmin_vmax_topomap)s + %(cmap_topomap_simple)s sensors : bool | str Add markers for sensor locations to the plot. Accepts matplotlib plot - format string (e.g., 'r+' for red plusses). If True (default), circles - will be used. + format string (e.g., ``'r+'`` for red plusses). If True (default), + circles will be used. res : int - The resolution of the topomap image (n pixels along each side). + The resolution of the topomap image (number of pixels along each side). axes : instance of Axes | None The axes to plot to. If None, the current axes will be used. names : list | None List of channel names. If None, channel names are not plotted. - %(topomap_show_names)s + %(show_names_topomap)s If ``True``, a list of names must be provided (see ``names`` keyword). - mask : ndarray of bool, shape (n_channels, n_times) | None - The channels to be marked as significant at a given time point. - Indices set to ``True`` will be considered. Defaults to None. - mask_params : dict | None - Additional plotting parameters for plotting significant sensors. - Default (None) equals:: - - dict(marker='o', markerfacecolor='w', markeredgecolor='k', - linewidth=0, markersize=4) - %(topomap_outlines)s + %(mask_topomap)s + %(mask_params_topomap)s + %(outlines_topomap)s contours : int | array of float - The number of contour lines to draw. If 0, no contours will be drawn. - If an array, the values represent the levels for the contours. The - values are in µV for EEG, fT for magnetometers and fT/m for - gradiometers. Defaults to 6. - image_interp : str - The image interpolation to be used. All matplotlib options are - accepted. + The number of contour lines to draw. If ``0``, no contours will be + drawn. If an array, the values represent the levels for the contours. + The values are in µV for EEG, fT for magnetometers and fT/m for + gradiometers. Defaults to ``6``. + %(image_interp_topomap)s show : bool Show figure if True. onselect : callable | None Handle for a function that is called when the user selects a set of channels by rectangle selection (matplotlib ``RectangleSelector``). If None interactive selection is disabled. Defaults to None. - %(topomap_extrapolate)s + %(extrapolate_topomap)s .. versionadded:: 0.18 - %(topomap_sphere)s - %(topomap_border)s - %(topomap_ch_type)s + %(sphere_topomap)s + %(border_topomap)s + %(ch_type_topomap)s + %(cnorm)s + + .. versionadded:: 0.24 Returns ------- @@ -766,17 +768,59 @@ The interpolated data. cn : matplotlib.contour.ContourSet The fieldlines. + + Notes + ----- + The ``cnorm`` parameter can be used to implement custom colormap + normalization. By default, a linear mapping from vmin to vmax is used, + which correspond to the first and last colors in the colormap. This might + be undesired when vmin and vmax are not symmetrical around zero (or a value + that can be interpreted as some midpoint). For example, assume we want to + use the RdBu colormap (red to white to blue) for values ranging from -1 to + 3, and 0 should be white. However, white corresponds to the midpoint in the + data by default, i.e. 1. Therefore, we use the following colormap + normalization ``cnorm`` and pass it as the the ``cnorm`` argument: + + .. code-block:: python + + from matplotlib.colors import TwoSlopeNorm + cnorm = TwoSlopeNorm(vmin=-1, vcenter=0, vmax=3) + + Note that because we define ``vmin`` and ``vmax`` in the normalization, + arguments ``vmin`` and ``vmax`` to ``plot_topomap`` will be ignored if a + normalization is provided. See the + :doc:`matplotlib docs ` + for more details on colormap normalization. """ sphere = _check_sphere(sphere) + from matplotlib.colors import Normalize + _validate_type(cnorm, (Normalize, None), 'cnorm') + if cnorm is not None: + if vmin is not None: + warn(f"vmin={cnorm.vmin} is implicitly defined by cnorm, ignoring " + f"vmin={vmin}.") + if vmax is not None: + warn(f"vmax={cnorm.vmax} is implicitly defined by cnorm, ignoring " + f"vmax={vmax}.") return _plot_topomap(data, pos, vmin, vmax, cmap, sensors, res, axes, names, show_names, mask, mask_params, outlines, - contours, image_interp, show, - onselect, extrapolate, sphere=sphere, border=border, - ch_type=ch_type)[:2] - - -def _setup_interp(pos, res, extrapolate, sphere, outlines, border): - logger.debug(f'Interpolation mode {extrapolate} to {border}') + contours, image_interp, show, onselect, extrapolate, + sphere=sphere, border=border, ch_type=ch_type, + cnorm=cnorm)[:2] + + +def _setup_interp(pos, res, image_interp, extrapolate, outlines, border): + if image_interp not in ('cubic', 'linear', 'nearest'): + raise RuntimeError( + '`image_interp` must be `cubic`, `linear` or `nearest`, got ' + f'{image_interp}. Previously, `image_interp` controlled ' + 'the matplotlib image interpolation after an original cubic ' + 'interpolation but this was changed to control the first ' + 'interpolation instead for simplicity. To change the ' + 'matplotlib image interpolation, use ' + '`im.set_interpolation(...)') + logger.debug(f'Interpolation mode {image_interp}, ' + f'extrapolation mode {extrapolate} to {border}') xlim = np.inf, -np.inf, ylim = np.inf, -np.inf, mask_ = np.c_[outlines['mask_pos']] @@ -797,11 +841,47 @@ xi = np.linspace(xmin, xmax, res) yi = np.linspace(ymin, ymax, res) Xi, Yi = np.meshgrid(xi, yi) - interp = _GridData(pos, extrapolate, clip_origin, clip_radius, border) + interp = _GridData(pos, image_interp, extrapolate, + clip_origin, clip_radius, border) extent = (xmin, xmax, ymin, ymax) return extent, Xi, Yi, interp +_VORONOI_CIRCLE_RES = 100 + + +def _voronoi_topomap(data, pos, outlines, ax, cmap, norm, extent, res): + """Make a Voronoi diagram on a topomap.""" + from scipy.spatial import Voronoi + # we need an image axis object so first empty image to plot over + im = ax.imshow(np.zeros((res, res)) * np.nan, cmap=cmap, + origin='lower', aspect='equal', extent=extent, + norm=norm) + rx, ry = outlines['clip_radius'] + cx, cy = outlines.get('clip_origin', (0., 0.)) + # add points on the circle to make boundaries, expand out to + # ensure regions extend to the edge of the topomap + vor = Voronoi(np.concatenate( + [pos, [(rx * 1.5 * np.cos(2 * np.pi / _VORONOI_CIRCLE_RES * t), + ry * 1.5 * np.sin(2 * np.pi / _VORONOI_CIRCLE_RES * t)) + for t in range(_VORONOI_CIRCLE_RES)]])) + for point_idx, region_idx in enumerate( + vor.point_region[:-_VORONOI_CIRCLE_RES]): + if -1 in vor.regions[region_idx]: + continue + polygon = list() + for i in vor.regions[region_idx]: + x, y = vor.vertices[i] + if (x - cx)**2 / rx**2 + (y - cy)**2 / ry**2 < 1: + polygon.append((x, y)) + else: + x *= rx / np.linalg.norm(vor.vertices[i]) + y *= ry / np.linalg.norm(vor.vertices[i]) + polygon.append((x, y)) + ax.fill(*zip(*polygon), color=cmap(norm(data[point_idx]))) + return im + + def _get_patch(outlines, extrapolate, interp, ax): from matplotlib import patches clip_radius = outlines['clip_radius'] @@ -828,10 +908,11 @@ def _plot_topomap(data, pos, vmin=None, vmax=None, cmap=None, sensors=True, res=64, axes=None, names=None, show_names=False, mask=None, - mask_params=None, outlines='head', - contours=6, image_interp='bilinear', show=True, + mask_params=None, outlines='head', contours=6, + image_interp=_INTERPOLATION_DEFAULT, show=True, onselect=None, extrapolate=_EXTRAPOLATE_DEFAULT, sphere=None, - border=_BORDER_DEFAULT, ch_type='eeg'): + border=_BORDER_DEFAULT, ch_type='eeg', cnorm=None): + from matplotlib.colors import Normalize import matplotlib.pyplot as plt from matplotlib.widgets import RectangleSelector data = np.asarray(data) @@ -898,7 +979,9 @@ norm = min(data) >= 0 vmin, vmax = _setup_vmin_vmax(data, vmin, vmax, norm) if cmap is None: - cmap = 'Reds' if norm else 'RdBu_r' + cmap = plt.get_cmap('Reds' if norm else 'RdBu_r') + elif isinstance(cmap, str): + cmap = plt.get_cmap(cmap) outlines = _make_head_outlines(sphere, pos, outlines, (0., 0.)) assert isinstance(outlines, dict) @@ -908,19 +991,26 @@ mask_params = _handle_default('mask_params', mask_params) - # find mask limits + # find mask limits and setup interpolation extent, Xi, Yi, interp = _setup_interp( - pos, res, extrapolate, sphere, outlines, border) + pos, res, image_interp, extrapolate, outlines, border) interp.set_values(data) Zi = interp.set_locations(Xi, Yi)() # plot outline patch_ = _get_patch(outlines, extrapolate, interp, ax) + # get colormap normalization + if cnorm is None: + cnorm = Normalize(vmin=vmin, vmax=vmax) + # plot interpolated map - im = ax.imshow(Zi, cmap=cmap, vmin=vmin, vmax=vmax, origin='lower', - aspect='equal', extent=extent, - interpolation=image_interp) + if image_interp == 'nearest': # plot over with Voronoi, more accurate + im = _voronoi_topomap(data, pos=pos, outlines=outlines, ax=ax, + cmap=cmap, norm=cnorm, extent=extent, res=res) + else: + im = ax.imshow(Zi, cmap=cmap, origin='lower', aspect='equal', + extent=extent, interpolation='bilinear', norm=cnorm) # gh-1432 had a workaround for no contours here, but we'll remove it # because mpl has probably fixed it @@ -943,6 +1033,7 @@ col.set_clip_path(patch_) pos_x, pos_y = pos.T + mask = mask.astype(bool, copy=False) if mask is not None else None if sensors is not False and mask is None: _topomap_plot_sensors(pos_x, pos_y, sensors=sensors, ax=ax) elif sensors and mask is not None: @@ -989,7 +1080,7 @@ def _plot_ica_topomap(ica, idx=0, ch_type=None, res=64, vmin=None, vmax=None, cmap='RdBu_r', colorbar=False, title=None, show=True, outlines='head', contours=6, - image_interp='bilinear', axes=None, + image_interp=_INTERPOLATION_DEFAULT, axes=None, sensors=True, allow_ref_meg=False, extrapolate=_EXTRAPOLATE_DEFAULT, sphere=None, border=_BORDER_DEFAULT): @@ -1017,7 +1108,10 @@ if merge_channels: data, names = _merge_ch_data(data, ch_type, names) - axes.set_title(ica._ica_names[idx], fontsize=12) + topo_title = ica._ica_names[idx] + if len(set(ica.get_channel_types())) > 1: + topo_title += f' ({ch_type})' + axes.set_title(topo_title, fontsize=12) vmin_, vmax_ = _setup_vmin_vmax(data, vmin, vmax) im = plot_topomap( data.ravel(), pos, vmin=vmin_, vmax=vmax_, res=res, axes=axes, @@ -1037,9 +1131,10 @@ vmin=None, vmax=None, cmap='RdBu_r', sensors=True, colorbar=False, title=None, show=True, outlines='head', contours=6, - image_interp='bilinear', - inst=None, plot_std=True, topomap_args=None, - image_args=None, psd_args=None, reject='auto', + image_interp=_INTERPOLATION_DEFAULT, + inst=None, plot_std=True, + topomap_args=None, image_args=None, + psd_args=None, reject='auto', sphere=None, *, verbose=None): """Project mixing matrix on interpolated sensor topography. @@ -1047,22 +1142,14 @@ ---------- ica : instance of mne.preprocessing.ICA The ICA solution. - %(picks_all)s - If None all are plotted in batches of 20. + %(picks_ica)s If ``None``, all components are plotted in batches of 20. ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None The channel type to plot. For 'grad', the gradiometers are collected in pairs and the RMS for each pair is plotted. If None, then channels are chosen in the order given above. res : int The resolution of the topomap image (n pixels along each side). - vmin : float | callable | None - The value specifying the lower bound of the color range. - If None, and vmax is None, -vmax is used. Else np.min(data). - If callable, the output equals vmin(data). Defaults to None. - vmax : float | callable | None - The value specifying the upper bound of the color range. - If None, the maximum absolute value is used. If callable, the output - equals vmax(data). Defaults to None. + %(vmin_vmax_topomap)s cmap : matplotlib colormap | (colormap, bool) | 'interactive' | None Colormap to use. If tuple, the first value indicates the colormap to use and the second value is a boolean defining interactivity. In @@ -1086,16 +1173,14 @@ Title to use. show : bool Show figure if True. - %(topomap_outlines)s + %(outlines_topomap)s contours : int | array of float The number of contour lines to draw. If 0, no contours will be drawn. When an integer, matplotlib ticker locator is used to find suitable values for the contour thresholds (may sometimes be inaccurate, use array for accuracy). If an array, the values represent the levels for the contours. Defaults to 6. - image_interp : str - The image interpolation to be used. All matplotlib options are - accepted. + %(image_interp_topomap)s inst : Raw | Epochs | None To be able to see component properties after clicking on component topomap you need to pass relevant data - instances of Raw or Epochs @@ -1121,13 +1206,17 @@ If None, no rejection is applied. The default is 'auto', which applies the rejection parameters used when fitting the ICA object. - %(topomap_sphere_auto)s + %(sphere_topomap_auto)s %(verbose)s Returns ------- - fig : instance of matplotlib.figure.Figure or list - The figure object(s). + fig : instance of matplotlib.figure.Figure | list of matplotlib.figure.Figure + + The figure object(s). Components are plotted on a grid with maximum + dimensions of 5⨉4. If more than 20 components are plotted, a new figure + will be created for each batch of 20, and a list of those figures + will be returned. Notes ----- @@ -1137,7 +1226,7 @@ also possible to open component properties by clicking on the component topomap (this option is only available when the ``inst`` argument is supplied). - """ + """ # noqa E501 from ..io import BaseRaw from ..epochs import BaseEpochs @@ -1189,7 +1278,10 @@ titles = list() for ii, data_, ax in zip(picks, data, axes): kwargs = dict(color='gray') if ii in ica.exclude else dict() - titles.append(ax.set_title(ica._ica_names[ii], fontsize=12, **kwargs)) + comp_title = ica._ica_names[ii] + if len(set(ica.get_channel_types())) > 1: + comp_title += f' ({ch_type})' + titles.append(ax.set_title(comp_title, fontsize=12, **kwargs)) if merge_channels: data_, names_ = _merge_ch_data(data_, ch_type, names.copy()) vmin_, vmax_ = _setup_vmin_vmax(data_, vmin, vmax) @@ -1221,7 +1313,7 @@ # title was pressed -> identify the IC if title_pressed is not None: label = title_pressed.get_text() - ic = int(label[-3:]) + ic = int(label.split(' ')[0][-3:]) # add or remove IC from exclude depending on current state if ic in ica.exclude: ica.exclude.remove(ic) @@ -1239,7 +1331,7 @@ if event.inaxes is not None: label = event.inaxes.get_label() if label.startswith('ICA'): - ic = int(label[-3:]) + ic = int(label.split(' ')[0][-3:]) ica.plot_properties(inst, picks=ic, show=True, plot_std=plot_std, topomap_args=topomap_args, @@ -1301,15 +1393,7 @@ by the standard deviation of the baseline power ('zlogratio') If None no baseline correction is applied. - vmin : float | callable | None - The value specifying the lower bound of the color range. - If None, and vmax is None, -vmax is used. Else np.min(data) or in case - data contains only positive values 0. If callable, the output equals - vmin(data). Defaults to None. - vmax : float | callable | None - The value specifying the upper bound of the color range. If None, the - maximum value is used. If callable, the output equals vmax(data). - Defaults to None. + %(vmin_vmax_topomap)s cmap : matplotlib colormap | (colormap, bool) | 'interactive' | None Colormap to use. If tuple, the first value indicates the colormap to use and the second value is a boolean defining interactivity. In @@ -1334,14 +1418,14 @@ topomaps at a time). cbar_fmt : str String format for colorbar values. - %(topomap_show_names)s + %(show_names_topomap)s title : str | None Plot title. If None (default), no title is displayed. axes : instance of Axes | None The axes to plot to. If None the axes is defined automatically. show : bool Show figure if True. - %(topomap_outlines)s + %(outlines_topomap)s contours : int | array of float The number of contour lines to draw. If 0, no contours will be drawn. When an integer, matplotlib ticker locator is used to find suitable @@ -1349,7 +1433,7 @@ array for accuracy). If an array, the values represent the levels for the contours. If colorbar=True, the ticks in colorbar correspond to the contour levels. Defaults to 6. - %(topomap_sphere_auto)s + %(sphere_topomap_auto)s Returns ------- @@ -1374,6 +1458,9 @@ data = rescale(data, tfr.times, baseline, mode, copy=True) + if np.iscomplexobj(data): + data = np.sqrt((data * data.conj()).real) + # crop time itmin, itmax = None, None idx = np.where(_time_mask(tfr.times, tmin, tmax))[0] @@ -1417,7 +1504,8 @@ _, contours = _set_contour_locator(vmin, vmax, contours) im, _ = plot_topomap(data[:, 0], pos, vmin=vmin, vmax=vmax, - axes=axes, cmap=cmap[0], image_interp='bilinear', + axes=axes, cmap=cmap[0], + image_interp=_INTERPOLATION_DEFAULT, contours=contours, names=names, show_names=show_names, show=False, onselect=selection_callback, sensors=sensors, res=res, ch_type=ch_type, @@ -1445,7 +1533,7 @@ time_unit='s', time_format=None, proj=False, show=True, show_names=False, title=None, mask=None, mask_params=None, outlines='head', contours=6, - image_interp='bilinear', average=None, + image_interp=_INTERPOLATION_DEFAULT, average=None, axes=None, extrapolate=_EXTRAPOLATE_DEFAULT, sphere=None, border=_BORDER_DEFAULT, nrows=1, ncols='auto'): @@ -1463,16 +1551,16 @@ automatically by checking for local maxima in global field power. If "interactive", the time can be set interactively at run-time by using a slider. - %(topomap_ch_type)s - %(topomap_vmin_vmax)s - %(topomap_cmap)s - %(topomap_sensors)s - %(topomap_colorbar)s - %(topomap_scalings)s - %(topomap_units)s - %(topomap_res)s - %(topomap_size)s - %(topomap_cbar_fmt)s + %(ch_type_evoked_topomap)s + %(vmin_vmax_topomap)s + %(cmap_topomap)s + %(sensors_topomap)s + %(colorbar_topomap)s + %(scalings_topomap)s + %(units_topomap)s + %(res_topomap)s + %(size_topomap)s + %(cbar_fmt_topomap)s time_unit : str The units for the time axis, can be "ms" or "s" (default). @@ -1481,22 +1569,32 @@ String format for topomap values. Defaults (None) to "%%01d ms" if ``time_unit='ms'``, "%%0.3f s" if ``time_unit='s'``, and "%%g" otherwise. Can be an empty string to omit the time label. - %(plot_proj)s + %(proj_plot)s %(show)s - %(topomap_show_names)s - %(title_None)s - %(topomap_mask)s - %(topomap_mask_params)s - %(topomap_outlines)s - %(topomap_contours)s - %(topomap_image_interp)s - %(topomap_average)s - %(topomap_axes)s - %(topomap_extrapolate)s + %(show_names_topomap)s + %(title_none)s + %(mask_evoked_topomap)s + %(mask_params_topomap)s + %(outlines_topomap)s + %(contours_topomap)s + %(image_interp_topomap)s + average : float | array-like of float, shape (n_times,) | None + The time window (in seconds) around a given time point to be used for + averaging. For example, 0.2 would translate into a time window that + starts 0.1 s before and ends 0.1 s after the given time point. If the + time window exceeds the duration of the data, it will be clipped. + Different time windows (one per time point) can be provided by + passing an ``array-like`` object (e.g., ``[0.1, 0.2, 0.3]``). If + ``None`` (default), no averaging will take place. + + .. versionchanged:: 1.1 + Support for ``array-like`` input. + %(axes_topomap)s + %(extrapolate_topomap)s .. versionadded:: 0.18 - %(topomap_sphere_auto)s - %(topomap_border)s + %(sphere_topomap_auto)s + %(border_topomap)s nrows : int | 'auto' The number of rows of topographies to plot. Defaults to 1. If 'auto', obtains the number of rows depending on the amount of times to plot @@ -1520,10 +1618,10 @@ When existing ``axes`` are provided and ``colorbar=True``, note that the colorbar scale will only accurately reflect topomaps that are generated in the same call as the colorbar. Note also that the colorbar will not be - resized automatically when ``axes`` are provided; use matplotlib's + resized automatically when ``axes`` are provided; use Matplotlib's :meth:`axes.set_position() ` method or - :doc:`gridspec ` interface to - adjust the colorbar size yourself. + :doc:`gridspec ` + interface to adjust the colorbar size yourself. """ import matplotlib.pyplot as plt from matplotlib.gridspec import GridSpec @@ -1573,8 +1671,9 @@ data = evoked.data # remove compensation matrices (safe: only plotting & already made copy) - evoked.info['comps'] = [] - evoked = evoked._pick_drop_channels(picks) + with evoked.info._unlock(): + evoked.info['comps'] = [] + evoked = evoked._pick_drop_channels(picks, verbose=False) # determine which times to plot if isinstance(axes, plt.Axes): axes = [axes] @@ -1623,23 +1722,55 @@ sfreq=evoked.info['sfreq']))[0][0] for t in times] # do averaging if requested - avg_err = '"average" must be `None` or a positive number of seconds' + avg_err = ('"average" must be `None`, a positive number of seconds, or ' + 'an array-like object of the previous') + + averaged_times = [] if average is None: + average = np.array([None] * n_times) data = data[np.ix_(picks, time_idx)] - elif not _is_numeric(average): - raise TypeError(f'{avg_err}; got type {type(average)}.') - elif average <= 0: - raise ValueError(f'{avg_err}; got {average}.') else: + if _is_numeric(average): + average = np.array([average] * n_times) + elif np.array(average).ndim == 0: + # It should be an array-like object + raise TypeError(f'{avg_err}; got type: {type(average)}.') + else: + average = np.array(average) + + if len(average) != n_times: + raise ValueError( + f'You requested to plot topographic maps for {n_times} time ' + f'points, but provided {len(average)} periods for ' + f'averaging. The number of time points and averaging periods ' + f'must be equal.' + ) data_ = np.zeros((len(picks), len(time_idx))) - ave_time = average / 2. - iter_times = evoked.times[time_idx] - for ii, (idx, tmin_, tmax_) in enumerate(zip(time_idx, - iter_times - ave_time, - iter_times + ave_time)): - my_range = (tmin_ < evoked.times) & (evoked.times < tmax_) - data_[:, ii] = data[picks][:, my_range].mean(-1) + + for average_idx, (this_average, this_time, this_time_idx) in enumerate( + zip(average, evoked.times[time_idx], time_idx) + ): + if ( + (_is_numeric(this_average) and this_average <= 0) or + (not _is_numeric(this_average) and this_average is not None) + ): + if len(average) == 1: + msg = f'{avg_err}; got {this_average}' + else: + msg = f'{avg_err}; got {this_average} in {average}' + raise ValueError(msg) + + if this_average is None: + data_[:, average_idx] = data[picks][:, this_time_idx] + averaged_times.append([this_time]) + else: + tmin_ = this_time - this_average / 2 + tmax_ = this_time + this_average / 2 + time_mask = (tmin_ < evoked.times) & (evoked.times < tmax_) + data_[:, average_idx] = data[picks][:, time_mask].mean(-1) + averaged_times.append(evoked.times[time_mask]) data = data_ + # apply scalings and merge channels data *= scaling if merge_channels: @@ -1648,6 +1779,7 @@ merge_channels = False # apply mask if requested if mask is not None: + mask = mask.astype(bool, copy=False) if ch_type == 'grad': mask_ = (mask[np.ix_(picks[::2], time_idx)] | mask[np.ix_(picks[1::2], time_idx)]) @@ -1667,22 +1799,36 @@ show_names=show_names, cmap=cmap[0], mask_params=mask_params, outlines=outlines, contours=contours, image_interp=image_interp, show=False, - extrapolate=extrapolate, sphere=sphere, border=border, - ch_type=ch_type) + extrapolate=extrapolate, sphere=sphere, + border=border, ch_type=ch_type) images, contours_ = [], [] # loop over times - for idx, time in enumerate(times): - adjust_for_cbar = colorbar and ncols is not None and idx >= ncols - 1 - ax_idx = idx + 1 if adjust_for_cbar else idx + for average_idx, (time, this_average) in enumerate( + zip(times, average) + ): + adjust_for_cbar = (colorbar and + ncols is not None and + average_idx >= ncols - 1) + ax_idx = average_idx + 1 if adjust_for_cbar else average_idx tp, cn, interp = _plot_topomap( - data[:, idx], pos, axes=axes[ax_idx], - mask=mask_[:, idx] if mask is not None else None, **kwargs) + data[:, average_idx], pos, axes=axes[ax_idx], + mask=mask_[:, average_idx] if mask is not None else None, **kwargs) images.append(tp) if cn is not None: contours_.append(cn) if time_format != '': - axes[ax_idx].set_title(time_format % (time * scaling_time)) + if this_average is None: + axes_title = time_format % (time * scaling_time) + else: + tmin_ = averaged_times[average_idx][0] + tmax_ = averaged_times[average_idx][-1] + from_time = time_format % (tmin_ * scaling_time) + from_time = from_time.split(' ')[0] # Remove unit + to_time = time_format % (tmax_ * scaling_time) + axes_title = f'{from_time} – {to_time}' + del from_time, to_time, tmin_, tmax_ + axes[ax_idx].set_title(axes_title) if interactive: axes.append(plt.subplot(gs[1, :-1])) @@ -1781,9 +1927,9 @@ if title is not None: ax.set_title(title, fontsize=10) im, _ = plot_topomap(data, pos, vmin=vmin, vmax=vmax, axes=ax, - cmap=cmap[0], image_interp='bilinear', contours=0, - outlines=outlines, show=False, sphere=sphere, - ch_type=ch_type) + cmap=cmap[0], image_interp=_INTERPOLATION_DEFAULT, + contours=0, outlines=outlines, show=False, + sphere=sphere, ch_type=ch_type) if colorbar: cbar, cax = _add_colorbar(ax, im, cmap, pad=0.25, title=None, @@ -1799,7 +1945,7 @@ tmin=None, tmax=None, proj=False, bandwidth=None, adaptive=False, low_bias=True, normalization='length', ch_type=None, - cmap=None, agg_fun=None, dB=False, n_jobs=1, + cmap=None, agg_fun=None, dB=False, n_jobs=None, normalize=False, cbar_fmt='auto', outlines='head', axes=None, show=True, sphere=None, vlim=(None, None), verbose=None): @@ -1809,7 +1955,7 @@ ---------- epochs : instance of Epochs The epochs object. - %(psd_topo_bands)s + %(bands_psd_topo)s tmin : float | None Start time to consider. tmax : float | None @@ -1825,27 +1971,24 @@ low_bias : bool Only use tapers with more than 90%% spectral concentration within bandwidth. - normalization : str - Either "full" or "length" (default). If "full", the PSD will - be normalized by the sampling rate as well as the length of - the signal (as in nitime). + %(normalization)s ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None The channel type to plot. For 'grad', the gradiometers are collected in pairs and the mean for each pair is plotted. If None, then first available channel type from order given above is used. Defaults to None. - %(psd_topo_cmap)s - %(psd_topo_agg_fun)s - %(psd_topo_dB)s + %(cmap_psd_topo)s + %(agg_fun_psd_topo)s + %(dB_psd_topo)s %(n_jobs)s - %(psd_topo_normalize)s - %(psd_topo_cbar_fmt)s - %(topomap_outlines)s - %(psd_topo_axes)s + %(normalize_psd_topo)s + %(cbar_fmt_psd_topo)s + %(outlines_topomap)s + %(axes_psd_topo)s show : bool Show figure if True. - %(topomap_sphere_auto)s - %(psd_topo_vlim_joint)s + %(sphere_topomap_auto)s + %(vlim_psd_topo_joint)s %(verbose)s Returns @@ -1855,7 +1998,9 @@ """ ch_type = _get_ch_type(epochs, ch_type) units = _handle_default('units', None) + scalings = _handle_default('scalings', None) unit = units[ch_type] + scaling = scalings[ch_type] picks, pos, merge_channels, names, ch_type, sphere, clip_origin = \ _prepare_topomap_plot(epochs, ch_type, sphere=sphere) @@ -1867,6 +2012,7 @@ normalization=normalization, picks=picks, proj=proj, n_jobs=n_jobs) psds = np.mean(psds, axis=0) + psds *= scaling**2 if merge_channels: psds, names = _merge_ch_data(psds, ch_type, names, method='mean') @@ -1894,22 +2040,22 @@ Frequencies used to compute psds. pos : numpy.ndarray of float, shape (n_sensors, 2) The positions of the sensors. - %(psd_topo_agg_fun)s - %(psd_topo_bands)s - %(psd_topo_cmap)s - %(psd_topo_dB)s - %(psd_topo_normalize)s - %(psd_topo_cbar_fmt)s - %(topomap_outlines)s - %(psd_topo_axes)s + %(agg_fun_psd_topo)s + %(bands_psd_topo)s + %(cmap_psd_topo)s + %(dB_psd_topo)s + %(normalize_psd_topo)s + %(cbar_fmt_psd_topo)s + %(outlines_topomap)s + %(axes_psd_topo)s show : bool Show figure if True. - %(topomap_sphere)s - %(psd_topo_vlim_joint)s + %(sphere_topomap)s + %(vlim_psd_topo_joint)s unit : str | None Measurement unit to be displayed with the colorbar. If ``None``, no unit is displayed (only "power" or "dB" as appropriate). - %(topomap_ch_type)s + %(ch_type_topomap)s Returns ------- @@ -2134,7 +2280,7 @@ @verbose def _init_anim(ax, ax_line, ax_cbar, params, merge_channels, sphere, ch_type, - extrapolate, verbose): + image_interp, extrapolate, verbose): """Initialize animated topomap.""" logger.info('Initializing animation...') data = params['data'] @@ -2161,7 +2307,8 @@ _hide_frame(ax) extent, Xi, Yi, interp = _setup_interp( - params['pos'], 64, extrapolate, sphere, outlines, 0) + pos=params['pos'], res=64, image_interp=image_interp, + extrapolate=extrapolate, outlines=outlines, border=0) patch_ = _get_patch(outlines, extrapolate, interp, ax) @@ -2231,7 +2378,8 @@ patch = params['patch'] im = ax.imshow(Zi, cmap=cmap, vmin=vmin, vmax=vmax, origin='lower', - aspect='equal', extent=extent, interpolation='bilinear') + aspect='equal', extent=extent, + interpolation='bilinear') cont_lims = params['cont_lims'] with warnings.catch_warnings(record=True): warnings.simplefilter('ignore') @@ -2271,7 +2419,8 @@ def _topomap_animation(evoked, ch_type, times, frame_rate, butterfly, blit, - show, time_unit, sphere, extrapolate, *, verbose=None): + show, time_unit, sphere, image_interp, + extrapolate, *, verbose=None): """Make animation of evoked data as topomap timeseries. See mne.evoked.Evoked.animate_topomap. @@ -2322,6 +2471,7 @@ init_func = partial(_init_anim, ax=ax, ax_cbar=ax_cbar, ax_line=ax_line, params=params, merge_channels=merge_channels, sphere=sphere, ch_type=ch_type, + image_interp=image_interp, extrapolate=extrapolate, verbose=verbose) animate_func = partial(_animate, ax=ax, ax_line=ax_line, params=params) pause_func = partial(_pause_anim, params=params) @@ -2400,7 +2550,8 @@ vmin_, vmax_ = _setup_vmin_vmax(data_, None, None) plot_topomap(data_.flatten(), pos, vmin=vmin_, vmax=vmax_, res=64, axes=ax, cmap=cmap, outlines=outlines, - contours=contours, show=False, image_interp='bilinear')[0] + contours=contours, show=False, + image_interp=_INTERPOLATION_DEFAULT)[0] _hide_frame(ax) tight_layout(fig=fig) fig.subplots_adjust(top=0.8) @@ -2424,8 +2575,9 @@ def plot_arrowmap(data, info_from, info_to=None, scale=3e-10, vmin=None, vmax=None, cmap=None, sensors=True, res=64, axes=None, names=None, show_names=False, mask=None, mask_params=None, - outlines='head', contours=6, image_interp='bilinear', - show=True, onselect=None, extrapolate=_EXTRAPOLATE_DEFAULT, + outlines='head', contours=6, + image_interp=_INTERPOLATION_DEFAULT, show=True, + onselect=None, extrapolate=_EXTRAPOLATE_DEFAULT, sphere=None): """Plot arrow map. @@ -2451,17 +2603,8 @@ to be the same as info_from. scale : float, default 3e-10 To scale the arrows. - vmin : float | callable | None - The value specifying the lower bound of the color range. - If None, and vmax is None, -vmax is used. Else np.min(data). - If callable, the output equals vmin(data). Defaults to None. - vmax : float | callable | None - The value specifying the upper bound of the color range. - If None, the maximum absolute value is used. If callable, the output - equals vmax(data). Defaults to None. - cmap : matplotlib colormap | None - Colormap to use. If None, 'Reds' is used for all positive data, - otherwise defaults to 'RdBu_r'. + %(vmin_vmax_topomap)s + %(cmap_topomap_simple)s sensors : bool | str Add markers for sensor locations to the plot. Accepts matplotlib plot format string (e.g., 'r+' for red plusses). If True (default), circles @@ -2472,36 +2615,27 @@ The axes to plot to. If None, a new figure will be created. names : list | None List of channel names. If None, channel names are not plotted. - %(topomap_show_names)s + %(show_names_topomap)s If ``True``, a list of names must be provided (see ``names`` keyword). - mask : ndarray of bool, shape (n_channels, n_times) | None - The channels to be marked as significant at a given time point. - Indices set to ``True`` will be considered. Defaults to None. - mask_params : dict | None - Additional plotting parameters for plotting significant sensors. - Default (None) equals:: - - dict(marker='o', markerfacecolor='w', markeredgecolor='k', - linewidth=0, markersize=4) - %(topomap_outlines)s + %(mask_topomap)s + %(mask_params_topomap)s + %(outlines_topomap)s contours : int | array of float The number of contour lines to draw. If 0, no contours will be drawn. If an array, the values represent the levels for the contours. The values are in µV for EEG, fT for magnetometers and fT/m for gradiometers. Defaults to 6. - image_interp : str - The image interpolation to be used. All matplotlib options are - accepted. + %(image_interp_topomap)s show : bool Show figure if True. onselect : callable | None Handle for a function that is called when the user selects a set of channels by rectangle selection (matplotlib ``RectangleSelector``). If None interactive selection is disabled. Defaults to None. - %(topomap_extrapolate)s + %(extrapolate_topomap)s .. versionadded:: 0.18 - %(topomap_sphere_auto)s + %(sphere_topomap_auto)s Returns ------- @@ -2580,3 +2714,289 @@ plt_show(show) return fig + + +@fill_doc +def plot_bridged_electrodes(info, bridged_idx, ed_matrix, title=None, + topomap_args=None): + """Topoplot electrode distance matrix with bridged electrodes connected. + + Parameters + ---------- + %(info_not_none)s + bridged_idx : list of tuple + The indices of channels marked as bridged with each bridged + pair stored as a tuple. + Can be generated via + :func:`mne.preprocessing.compute_bridged_electrodes`. + ed_matrix : array of float, shape (n_channels, n_channels) + The electrical distance matrix for each pair of EEG electrodes. + Can be generated via + :func:`mne.preprocessing.compute_bridged_electrodes`. + title : str + A title to add to the plot. + topomap_args : dict | None + Arguments to pass to :func:`mne.viz.plot_topomap`. + + Returns + ------- + fig : instance of matplotlib.figure.Figure + The topoplot figure handle. + + See Also + -------- + mne.preprocessing.compute_bridged_electrodes + """ + import matplotlib.pyplot as plt + if topomap_args is None: + topomap_args = dict() + else: + topomap_args = topomap_args.copy() # don't change original + picks = pick_types(info, eeg=True) + topomap_args.setdefault('image_interp', 'nearest') + topomap_args.setdefault('cmap', 'summer_r') + topomap_args.setdefault('names', pick_info(info, picks).ch_names) + topomap_args.setdefault('show_names', True) + topomap_args.setdefault('contours', False) + sphere = topomap_args['sphere'] if 'sphere' in topomap_args \ + else _check_sphere(None) + if 'axes' not in topomap_args: + fig, ax = plt.subplots() + topomap_args['axes'] = ax + else: + fig = None + # handle colorbar here instead of in plot_topomap + colorbar = topomap_args.pop('colorbar') if \ + 'colorbar' in topomap_args else True + if ed_matrix.shape[1:] != (picks.size, picks.size): + raise RuntimeError( + f'Expected {(ed_matrix.shape[0], picks.size, picks.size)} ' + f'shaped `ed_matrix`, got {ed_matrix.shape}') + # fill in lower triangular + ed_matrix = ed_matrix.copy() + tril_idx = np.tril_indices(picks.size) + for epo_idx in range(ed_matrix.shape[0]): + ed_matrix[epo_idx][tril_idx] = ed_matrix[epo_idx].T[tril_idx] + elec_dists = np.median(np.nanmin(ed_matrix, axis=1), axis=0) + im, cn = plot_topomap(elec_dists, pick_info(info, picks), **topomap_args) + fig = im.figure if fig is None else fig + # add bridged connections + for idx0, idx1 in bridged_idx: + pos = _find_topomap_coords(info, [idx0, idx1], sphere=sphere) + im.axes.plot([pos[0, 0], pos[1, 0]], + [pos[0, 1], pos[1, 1]], color='r') + if title is not None: + im.axes.set_title(title) + if colorbar: + cax = fig.colorbar(im) + cax.set_label(r'Electrical Distance ($\mu$$V^2$)') + return fig + + +def plot_ch_adjacency(info, adjacency, ch_names, kind='2d', edit=False): + """Plot channel adjacency. + + Parameters + ---------- + info : instance of Info + Info object with channel locations. + adjacency : array + Array of channels x channels shape. Defines which channels are adjacent + to each other. Note that if you edit adjacencies + (via ``edit=True``), this array will be modified in place. + ch_names : list of str + Names of successive channels in the ``adjacency`` matrix. + kind : str + How to plot the adjacency. Can be either ``'3d'`` or ``'2d'``. + edit : bool + Whether to allow interactive editing of the adjacency matrix via + clicking respective channel pairs. Once clicked, the channel is + "activated" and turns green. Clicking on another channel adds or + removes adjacency relation between the activated and newly clicked + channel (depending on whether the channels are already adjacent or + not); the newly clicked channel now becomes activated. Clicking on + an activated channel deactivates it. Editing is currently only + supported for ``kind='2d'``. + + Returns + ------- + fig : Figure + The :class:`~matplotlib.figure.Figure` instance where the channel + adjacency is plotted. + + See Also + -------- + mne.channels.get_builtin_ch_adjacencies + mne.channels.read_ch_adjacency + mne.channels.find_ch_adjacency + + Notes + ----- + .. versionadded:: 1.1 + """ + from scipy import sparse + import matplotlib as mpl + import matplotlib.pyplot as plt + + from . import plot_sensors + + _validate_type(info, Info, 'info') + _validate_type(adjacency, (np.ndarray, sparse.csr_matrix), 'adjacency') + has_sparse = isinstance(adjacency, sparse.csr_matrix) + + if edit and kind == '3d': + raise ValueError('Editing a 3d adjacency plot is not supported.') + + # select relevant channels + sel = pick_channels(info.ch_names, ch_names, ordered=True) + info = pick_info(info, sel) + + # make sure adjacency is correct size wrt to inst: + n_channels = len(info.ch_names) + if adjacency.shape[0] != n_channels: + raise ValueError('``adjacency`` must have the same number of rows ' + 'as the number of channels in ``info``. Found ' + f'{adjacency.shape[0]} channels for ``adjacency`` and' + f' {n_channels} for ``inst``.') + + if kind == '3d': + with plt.rc_context({'toolbar': 'None'}): + fig = plot_sensors(info, kind=kind, show=False) + _set_3d_axes_equal(fig.axes[0]) + elif kind == '2d': + with plt.rc_context({'toolbar': 'None'}): + fig = plot_sensors(info, kind='topomap', show=False) + fig.axes[0].axis('equal') + + path_collection = fig.axes[0].findobj(mpl.collections.PathCollection) + path_collection[0].set_linewidths(0.) + + if kind == '2d': + path_collection[0].set_alpha(0.7) + pos = path_collection[0].get_offsets() + + # make sure nodes are on top + path_collection[0].set_zorder(10) + + # scale node size with number of connections + n_connections = [np.sum(adjacency[i]) - 1 + for i in range(adjacency.shape[0])] + node_size = [max(x, 3) ** 2.5 for x in n_connections] + path_collection[0].set_sizes(node_size) + else: + # plotting channel positions via mne.viz.plot_sensors(info) and using + # the coordinates from info['chs'][ch_idx]['loc][:3] gives different + # positions. Also .get_offsets gives 2d projections even for 3d points + # so we use the private _offsets3d property... + pos = path_collection[0]._offsets3d + pos = np.stack([pos[0].data, pos[1].data, pos[2]], axis=1) + + ax = fig.axes[0] + lines = dict() + n_channels = adjacency.shape[0] + for ch_idx in range(n_channels): + # make sure we don't repeat channels + row = adjacency[ch_idx, ch_idx + 1:] + if has_sparse: + ch_neighbours = row.nonzero()[1] + else: + ch_neighbours = np.where(row)[0] + + if len(ch_neighbours) == 0: + continue + + ch_neighbours += ch_idx + 1 + + for ngb_idx in ch_neighbours: + this_pos = pos[[ch_idx, ngb_idx], :] + ch_pair = tuple([ch_idx, ngb_idx]) + lines[ch_pair] = ax.plot(*this_pos.T, color=(0.55, 0.55, 0.55), + lw=0.75)[0] + + if edit: + # allow interactivity in 2d plots + highlighted = dict() + this_onpick = partial(_onpick_ch_adjacency, axes=ax, positions=pos, + highlighted=highlighted, line_dict=lines, + adjacency=adjacency, node_size=node_size, + path_collection=path_collection) + fig.canvas.mpl_connect('pick_event', this_onpick) + + return fig + + +def _onpick_ch_adjacency(event, axes=None, positions=None, highlighted=None, + line_dict=None, adjacency=None, node_size=None, + path_collection=None): + """Handle interactivity in plot_ch_adjacency.""" + node_ind = event.ind[0] + + if node_ind in highlighted: + # de-select node, change its color back to normal + highlighted[node_ind].remove() + del highlighted[node_ind] + axes.figure.canvas.draw() + else: + # new node selected + if len(highlighted) == 0: + # no highlighted nodes yet + size = max(node_size[node_ind] * 2, 100) + # add current node + dots = axes.scatter( + *positions[node_ind, :].T, color='tab:green', s=size, + zorder=15) + highlighted[node_ind] = dots + axes.figure.canvas.draw() # make sure it renders + else: + # one previously highlighted - add or remove line + key = list(highlighted.keys())[0] + both_nodes = [key, node_ind] + both_nodes.sort() + both_nodes = tuple(both_nodes) + + if both_nodes in line_dict.keys(): + # remove line + n_conn_change = -1 + line_dict[both_nodes].remove() + # remove line_dict entry + del line_dict[both_nodes] + + # clear adjacency matrix entry + _set_adjacency(adjacency, both_nodes, False) + else: + # add line + n_conn_change = +1 + selected_pos = positions[both_nodes, :] + line = axes.plot(*selected_pos.T, color='tab:green')[0] + # add line to line_dict + line_dict[both_nodes] = line + + # modify adjacency matrix + _set_adjacency(adjacency, both_nodes, True) + + # de-highlight previous + highlighted[key].remove() + del highlighted[key] + + # update node sizes + n_connections = [np.sum(adjacency[idx]) - 1 + n_conn_change + for idx in both_nodes] + for idx, n_conn in zip(both_nodes, n_connections): + node_size[idx] = max(n_conn, 3) ** 2.5 + path_collection[0].set_sizes(node_size) + + # highlight new node + size = max(node_size[node_ind] * 2, 100) + dots = axes.scatter( + *positions[node_ind, :].T, color='tab:green', s=size, + zorder=15) + highlighted[node_ind] = dots + axes.figure.canvas.draw() + + +def _set_adjacency(adjacency, both_nodes, value): + """Set adjacency for given node pair, caching errors for sparse arrays.""" + import warnings + + with warnings.catch_warnings(record=True): + adjacency[both_nodes, both_nodes[::-1]] = value diff -Nru python-mne-0.23.4+dfsg/mne/viz/topo.py python-mne-1.1.0+dfsg/mne/viz/topo.py --- python-mne-0.23.4+dfsg/mne/viz/topo.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/topo.py 2022-08-03 15:48:14.000000000 +0000 @@ -9,20 +9,20 @@ from copy import deepcopy from functools import partial -from itertools import cycle import numpy as np from ..io.pick import channel_type, pick_types -from ..utils import _clean_names, warn, _check_option, Bunch +from ..utils import _clean_names, _check_option, Bunch, fill_doc, _to_rgb from ..channels.layout import _merge_ch_data, _pair_grad_sensors, find_layout from ..defaults import _handle_default -from .utils import (_check_delayed_ssp, _get_color_list, _draw_proj_checkbox, +from .utils import (_check_delayed_ssp, _draw_proj_checkbox, add_background_image, plt_show, _setup_vmin_vmax, DraggableColorbar, _setup_ax_spines, _check_cov, _plot_masked_image) +@fill_doc def iter_topography(info, layout=None, on_pick=None, fig=None, fig_facecolor='k', axis_facecolor='k', axis_spinecolor='k', layout_scale=None, legend=False): @@ -36,8 +36,7 @@ Parameters ---------- - info : instance of Info - The measurement info. + %(info_not_none)s layout : instance of mne.channels.Layout | None The layout to use. If None, layout will be guessed. on_pick : callable | None @@ -81,8 +80,18 @@ """Add a legend axis to the bottom right.""" import matplotlib.pyplot as plt left, bottom = pos[:, 0].max(), pos[:, 1].min() + # check if legend axis overlaps a data axis + overlaps = False + for _pos in pos: + h_overlap = (_pos[0] <= left <= (_pos[0] + _pos[2])) + v_overlap = (_pos[1] <= bottom <= (_pos[1] + _pos[3])) + if h_overlap and v_overlap: + overlaps = True + break + if overlaps: + left += 1.2 * _pos[2] wid, hei = pos[-1, 2:] - return plt.axes([left, bottom + .05, wid, hei]) + return plt.axes([left, bottom, wid, hei]) def _iter_topography(info, layout, on_pick, fig, fig_facecolor='k', @@ -305,7 +314,7 @@ tfr=None, freq=None, x_label=None, y_label=None, colorbar=False, cmap=('RdBu_r', True), yscale='auto', mask=None, mask_style="both", mask_cmap="Greys", - mask_alpha=0.1, is_jointplot=False): + mask_alpha=0.1, is_jointplot=False, cnorm=None): """Show time-frequency map as two-dimensional image.""" from matplotlib import pyplot as plt from matplotlib.widgets import RectangleSelector @@ -318,7 +327,7 @@ img, t_end = _plot_masked_image( ax, tfr[ch_idx], times, mask, yvals=freq, cmap=cmap, vmin=vmin, vmax=vmax, mask_style=mask_style, mask_alpha=mask_alpha, - mask_cmap=mask_cmap, yscale=yscale) + mask_cmap=mask_cmap, yscale=yscale, cnorm=cnorm) if x_label is not None: ax.set_xlabel(x_label) @@ -357,7 +366,6 @@ labels=None): """Show time series on topo split across multiple axes.""" import matplotlib.pyplot as plt - from matplotlib.colors import colorConverter picker_flag = False for data_, color_, times_ in zip(data, color, times): if not picker_flag: @@ -421,11 +429,8 @@ ax._cursorline = None # choose cursor color based on perceived brightness of background - try: - facecol = colorConverter.to_rgb(ax.get_facecolor()) - except AttributeError: # older MPL - facecol = colorConverter.to_rgb(ax.get_axis_bgcolor()) - face_brightness = np.dot(facecol, np.array([299, 587, 114])) + facecol = _to_rgb(ax.get_facecolor()) + face_brightness = np.dot(facecol, [299, 587, 114]) ax._cursorcolor = 'white' if face_brightness < 150 else 'black' plt.connect('motion_notify_event', _cursor_vline) @@ -656,21 +661,6 @@ if not type(evoked) in (tuple, list): evoked = [evoked] - if type(color) in (tuple, list): - if len(color) != len(evoked): - raise ValueError('Lists of evoked objects and colors' - ' must have the same length') - elif color is None: - colors = ['w'] + _get_color_list - stop = (slice(len(evoked)) if len(evoked) < len(colors) - else slice(len(colors))) - color = cycle(colors[stop]) - if len(evoked) > len(colors): - warn('More evoked objects than colors available. You should pass ' - 'a list of unique colors.') - else: - color = cycle([color]) - noise_cov = _check_cov(noise_cov, evoked[0].info) if noise_cov is not None: evoked = [whiten_evoked(e, noise_cov) for e in evoked] @@ -689,10 +679,9 @@ ch = info['chs'][pick] ch['ch_name'] = ch['ch_name'][:-1] + 'X' chs.append(ch) - info['chs'] = chs - info['bads'] = list() # Bads handled by pair_grad_sensors - info._update_redundant() - info._check_consistency() + with info._unlock(update_redundant=True, check_after=True): + info['chs'] = chs + info['bads'] = list() # Bads handled by pair_grad_sensors new_picks = list() for e in evoked: data, _ = _merge_ch_data(e.data[picks], 'grad', []) diff -Nru python-mne-0.23.4+dfsg/mne/viz/utils.py python-mne-1.1.0+dfsg/mne/viz/utils.py --- python-mne-0.23.4+dfsg/mne/viz/utils.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/mne/viz/utils.py 2022-08-03 15:48:14.000000000 +0000 @@ -11,35 +11,37 @@ # Daniel McCloy # # License: Simplified BSD - from collections import defaultdict from contextlib import contextmanager -from functools import partial +from datetime import datetime import difflib -import webbrowser -import tempfile +from functools import partial import math -import numpy as np -from copy import deepcopy -from distutils.version import LooseVersion +import os +import sys +import tempfile +import traceback import warnings -from datetime import datetime +import webbrowser + +from decorator import decorator +import numpy as np from ..defaults import _handle_default -from ..fixes import _get_status, _get_args +from ..fixes import _get_args from ..io import show_fiff, Info from ..io.constants import FIFF +from ..io.meas_info import create_info from ..io.pick import (channel_type, channel_indices_by_type, pick_channels, _pick_data_channels, _DATA_CH_TYPES_SPLIT, _DATA_CH_TYPES_ORDER_DEFAULT, _VALID_CHANNEL_TYPES, pick_info, _picks_by_type, pick_channels_cov, _contains_ch_type) -from ..io.meas_info import create_info +from ..io.proj import setup_proj, Projection from ..rank import compute_rank -from ..io.proj import setup_proj -from ..utils import (verbose, get_config, warn, _check_ch_locs, _check_option, +from ..utils import (verbose, get_config, _check_ch_locs, _check_option, logger, fill_doc, _pl, _check_sphere, _ensure_int, - _validate_type) + _validate_type, _to_rgb, warn) from ..transforms import apply_trans @@ -51,6 +53,15 @@ 'misc': "miscellaneous sensor"} +@decorator +def safe_event(fun, *args, **kwargs): + """Protect against Qt exiting on event-handling errors.""" + try: + return fun(*args, **kwargs) + except Exception: + traceback.print_exc(file=sys.stderr) + + def _setup_vmin_vmax(data, vmin, vmax, norm=False): """Handle vmin and vmax parameters for visualizing topomaps. @@ -103,12 +114,49 @@ **kwargs : dict Extra arguments for :func:`matplotlib.pyplot.show`. """ - from matplotlib import get_backend import matplotlib.pyplot as plt - if show and get_backend() != 'agg': + from matplotlib import get_backend + if hasattr(fig, 'mne') and hasattr(fig.mne, 'backend'): + backend = fig.mne.backend + else: + backend = get_backend() + if show and backend != 'agg': (fig or plt).show(**kwargs) +def _show_browser(show=True, block=True, fig=None, **kwargs): + """Show the browser considering different backends. + + Parameters + ---------- + show : bool + Show the figure. + block : bool + If to block execution on showing. + fig : instance of Figure | None + Needs to be passed for Qt backend, + optional for matplotlib. + **kwargs : dict + Extra arguments for :func:`matplotlib.pyplot.show`. + """ + from ._figure import get_browser_backend + _validate_type(block, bool, 'block') + backend = get_browser_backend() + if os.getenv('_MNE_BROWSER_NO_BLOCK', 'false').lower() == 'true': + block = False + if backend == 'matplotlib': + plt_show(show, block=block, **kwargs) + else: + from qtpy.QtWidgets import QApplication + from .backends._utils import _qt_app_exec + if show: + fig.show() + # If block=False, a Qt-Event-Loop has to be started + # somewhere else in the calling code. + if block: + _qt_app_exec(QApplication.instance()) + + def tight_layout(pad=1.2, h_pad=None, w_pad=None, fig=None): """Adjust subplot parameters to give specified padding. @@ -140,15 +188,19 @@ fig.canvas.draw() constrained = fig.get_constrained_layout() + kwargs = dict(pad=pad, h_pad=h_pad, w_pad=w_pad) if constrained: return # no-op try: # see https://github.com/matplotlib/matplotlib/issues/2654 with warnings.catch_warnings(record=True) as ws: - fig.tight_layout(pad=pad, h_pad=h_pad, w_pad=w_pad) + fig.tight_layout(**kwargs) except Exception: try: with warnings.catch_warnings(record=True) as ws: - fig.set_tight_layout(dict(pad=pad, h_pad=h_pad, w_pad=w_pad)) + if hasattr(fig, 'set_layout_engine'): + fig.set_layout_engine('tight', **kwargs) + else: + fig.set_tight_layout(kwargs) except Exception: warn('Matplotlib function "tight_layout" is not supported.' ' Skipping subplot adjustment.') @@ -169,33 +221,30 @@ raise RuntimeError('No projs found in evoked.') -def _validate_if_list_of_axes(axes, obligatory_len=None): +def _validate_if_list_of_axes(axes, obligatory_len=None, name='axes'): """Validate whether input is a list/array of axes.""" from matplotlib.axes import Axes - if obligatory_len is not None and not isinstance(obligatory_len, int): - raise ValueError('obligatory_len must be None or int, got %d', - 'instead' % type(obligatory_len)) - if not isinstance(axes, (list, np.ndarray)): - raise ValueError('axes must be a list or numpy array of matplotlib ' - 'axes objects, got %s instead.' % type(axes)) + _validate_type(axes, (list, tuple, np.ndarray), name) if isinstance(axes, np.ndarray) and axes.ndim > 1: - raise ValueError('if input is a numpy array, it must be ' - 'one-dimensional. The received numpy array has %d ' - 'dimensions however. Try using ravel or flatten ' - 'method of the array.' % axes.ndim) - is_correct_type = np.array([isinstance(x, Axes) - for x in axes]) - if not np.all(is_correct_type): - first_bad = np.where(np.logical_not(is_correct_type))[0][0] - raise ValueError('axes must be a list or numpy array of matplotlib ' - 'axes objects while one of the list elements is ' - '%s.' % type(axes[first_bad])) - if obligatory_len is not None and not len(axes) == obligatory_len: - raise ValueError('axes must be a list/array of length %d, while the' - ' length is %d' % (obligatory_len, len(axes))) + raise ValueError( + f'if {name} is a numpy array, it must be one-dimensional, but ' + f'the received numpy array has {axes.ndim} dimensions. Try using ' + 'ravel or flatten method of the array.') + wrong_idx = np.where([not isinstance(x, Axes) for x in axes])[0] + if len(wrong_idx): + raise TypeError( + f'{name} must be an array-like of matplotlib axes objects, but ' + f'{name}[{wrong_idx[0]}] is of type {type(axes[wrong_idx[0]])}') + if obligatory_len is not None: + obligatory_len = _ensure_int(obligatory_len, 'obligatory_len', + extra='if not None') + if len(axes) != obligatory_len: + raise ValueError( + f'{name} must be an array-like of length {obligatory_len}, ' + f'but the length is {len(axes)}') -def mne_analyze_colormap(limits=[5, 10, 15], format='mayavi'): +def mne_analyze_colormap(limits=[5, 10, 15], format='vtk'): """Return a colormap similar to that used by mne_analyze. Parameters @@ -205,7 +254,7 @@ 3, or completely specified (and potentially asymmetric) if length 6. format : str Type of colormap to return. If 'matplotlib', will return a - matplotlib.colors.LinearSegmentedColormap. If 'mayavi', will + matplotlib.colors.LinearSegmentedColormap. If 'vtk', will return an RGBA array of shape (256, 4). Returns @@ -263,7 +312,7 @@ (limits[5], 1.0, 1.0)), } return colors.LinearSegmentedColormap('mne_analyze', cdict) - elif format == 'mayavi': + elif format in ('vtk', 'mayavi'): if len(limits) == 3: limits = np.concatenate((-np.flipud(limits), [0], limits)) /\ limits[-1] @@ -279,7 +328,9 @@ for c in [r, g, b, a]]].T return colormap else: - raise ValueError('format must be either matplotlib or mayavi') + # Use this instead of check_option because we have a hidden option + raise ValueError( + f'format must be either matplotlib or vtk, got {repr(format)}') @contextmanager @@ -295,7 +346,7 @@ """Perform operations when proj boxes clicked.""" # read options if possible if 'proj_checks' in params: - bools = _get_status(params['proj_checks']) + bools = list(params['proj_checks'].get_status()) if all_: new_bools = [not all(bools)] * len(bools) with _events_off(params['proj_checks']): @@ -365,7 +416,7 @@ def _prepare_trellis(n_cells, ncols, nrows='auto', title=False, colorbar=False, size=1.3, sharex=False, sharey=False): from matplotlib.gridspec import GridSpec - from ._figure import _figure + from ._mpl_figure import _figure if n_cells == 1: nrows = ncols = 1 @@ -810,13 +861,13 @@ @verbose def plot_sensors(info, kind='topomap', ch_type=None, title=None, show_names=False, ch_groups=None, to_sphere=True, axes=None, - block=False, show=True, sphere=None, verbose=None): + block=False, show=True, sphere=None, pointsize=None, + linewidth=2, verbose=None): """Plot sensors positions. Parameters ---------- - info : instance of Info - Info structure containing the channel locations. + %(info_not_none)s kind : str Whether to plot the sensors as 3d, topomap or as an interactive sensor selection dialog. Available options 'topomap', '3d', 'select'. @@ -860,7 +911,12 @@ .. versionadded:: 0.13.0 show : bool Show figure if True. Defaults to True. - %(topomap_sphere_auto)s + %(sphere_topomap_auto)s + pointsize : float | None + The size of the points. If None (default), will bet set to 75 if + ``kind='3d'``, or 25 otherwise. + linewidth : float + The width of the outline. If 0, the outline will not be drawn. %(verbose)s Returns @@ -877,7 +933,7 @@ Notes ----- This function plots the sensor locations from the info structure using - matplotlib. For drawing the sensors using mayavi see + matplotlib. For drawing the sensors using PyVista see :func:`mne.viz.plot_alignment`. .. versionadded:: 0.12.0 @@ -885,7 +941,7 @@ from .evoked import _rgb _check_option('kind', kind, ['topomap', '3d', 'select']) if not isinstance(info, Info): - raise TypeError('info must be an instance of Info not %s' % type(info)) + raise TypeError(f'info must be an instance of Info not {type(info)}') ch_indices = channel_indices_by_type(info) allowed_types = _DATA_CH_TYPES_SPLIT if ch_type is None: @@ -901,16 +957,17 @@ elif ch_type in allowed_types: picks = ch_indices[ch_type] else: - raise ValueError("ch_type must be one of %s not %s!" % (allowed_types, - ch_type)) + raise ValueError( + f'ch_type must be one of {allowed_types} not {ch_type}!') if len(picks) == 0: - raise ValueError('Could not find any channels of type %s.' % ch_type) + raise ValueError(f'Could not find any channels of type {ch_type}.') - chs = [info['chs'][pick] for pick in picks] - if not _check_ch_locs(chs): + if not _check_ch_locs(info=info, picks=picks): raise RuntimeError('No valid channel positions found') + dev_head_t = info['dev_head_t'] + chs = [info['chs'][pick] for pick in picks] pos = np.empty((len(chs), 3)) for ci, ch in enumerate(chs): pos[ci] = ch['loc'][:3] @@ -969,7 +1026,8 @@ title = 'Sensor positions (%s)' % ch_type if title is None else title fig = _plot_sensors(pos, info, picks, colors, bads, ch_names, title, show_names, axes, show, kind, block, - to_sphere, sphere) + to_sphere, sphere, pointsize=pointsize, + linewidth=linewidth) if kind == 'select': return fig, fig.lasso.selection return fig @@ -994,7 +1052,7 @@ # XXX: Bug in matplotlib won't allow setting the position of existing # text item, so we create a new one. - ax.texts.pop(0) + ax.texts[0].remove() if len(this_pos) == 3: ax.text(this_pos[0], this_pos[1], this_pos[2], ch_name) else: @@ -1009,7 +1067,8 @@ def _plot_sensors(pos, info, picks, colors, bads, ch_names, title, show_names, - ax, show, kind, block, to_sphere, sphere): + ax, show, kind, block, to_sphere, sphere, pointsize=None, + linewidth=2): """Plot sensors.""" from matplotlib import rcParams import matplotlib.pyplot as plt @@ -1031,9 +1090,11 @@ fig = ax.get_figure() if kind == '3d': + pointsize = 75 if pointsize is None else pointsize ax.text(0, 0, 0, '', zorder=1) + ax.scatter(pos[:, 0], pos[:, 1], pos[:, 2], picker=True, c=colors, - s=75, edgecolor=edgecolors, linewidth=2) + s=pointsize, edgecolor=edgecolors, linewidth=linewidth) ax.azim = 90 ax.elev = 0 @@ -1041,13 +1102,15 @@ ax.yaxis.set_label_text('y (m)') ax.zaxis.set_label_text('z (m)') else: # kind in 'select', 'topomap' + pointsize = 25 if pointsize is None else pointsize ax.text(0, 0, '', zorder=1) pos, outlines = _get_pos_outlines(info, picks, sphere, to_sphere=to_sphere) _draw_outlines(ax, outlines) pts = ax.scatter(pos[:, 0], pos[:, 1], picker=True, clip_on=False, - c=colors, edgecolors=edgecolors, s=25, lw=2) + c=colors, edgecolors=edgecolors, s=pointsize, + lw=linewidth) if kind == 'select': fig.lasso = SelectFromCollection(ax, pts, ch_names) else: @@ -1074,6 +1137,16 @@ ax.text(this_pos[0] + 0.0025, this_pos[1], ch_names[idx], ha='left', va='center') connect_picker = (kind == 'select') + # make sure no names go off the edge of the canvas + xmin, ymin, xmax, ymax = fig.get_window_extent().bounds + renderer = fig.canvas.get_renderer() + extents = [x.get_window_extent(renderer=renderer) for x in ax.texts] + xmaxs = np.array([x.max[0] for x in extents]) + bad_xmax_ixs = np.nonzero(xmaxs > xmax)[0] + if len(bad_xmax_ixs): + needed_space = (xmaxs[bad_xmax_ixs] - xmax).max() / xmax + fig.subplots_adjust(right=1 - 1.1 * needed_space) + if connect_picker: picker = partial(_onpick_sensor, fig=fig, ax=ax, pos=pos, ch_names=ch_names, show_names=show_names) @@ -1116,14 +1189,32 @@ """ from ..io.base import BaseRaw from ..epochs import BaseEpochs + scalings = _handle_default('scalings_plot_raw', scalings) if not isinstance(inst, (BaseRaw, BaseEpochs)): raise ValueError('Must supply either Raw or Epochs') + for key, value in scalings.items(): + if not (isinstance(value, str) and value == 'auto'): + try: + scalings[key] = float(value) + except Exception: + raise ValueError( + f'scalings must be "auto" or float, got ' + f'scalings[{key!r}]={value!r} which could not be ' + f'converted to float' + ) + + # If there are no "auto" scalings, we can return early! + if all( + [scalings[ch_type] != 'auto' + for ch_type in inst.get_channel_types(unique=True)] + ): + return scalings + ch_types = channel_indices_by_type(inst.info) ch_types = {i_type: i_ixs for i_type, i_ixs in ch_types.items() if len(i_ixs) != 0} - scalings = deepcopy(scalings) if inst.preload is False: if isinstance(inst, BaseRaw): @@ -1149,15 +1240,7 @@ data = inst._data.swapaxes(0, 1).reshape([len(inst.ch_names), -1]) # Iterate through ch types and update scaling if ' auto' for key, value in scalings.items(): - if key not in ch_types: - continue - if not (isinstance(value, str) and value == 'auto'): - try: - scalings[key] = float(value) - except Exception: - raise ValueError( - f'scalings must be "auto" or float, got scalings[{key!r}]=' - f'{value!r} which could not be converted to float') + if key not in ch_types or value != 'auto': continue this_data = data[ch_types[key]] if remove_dc and (this_data.shape[1] / inst.info["sfreq"] >= duration): @@ -1292,7 +1375,7 @@ self.index = 0 cmap = self.cycle[self.index] self.cbar.mappable.set_cmap(cmap) - self.cbar.draw_all() + _draw_without_rendering(self.cbar) self.mappable.set_cmap(cmap) self._update() @@ -1331,12 +1414,21 @@ from matplotlib.ticker import AutoLocator self.cbar.set_ticks(AutoLocator()) self.cbar.update_ticks() - self.cbar.draw_all() + _draw_without_rendering(self.cbar) self.mappable.set_norm(self.cbar.norm) self.cbar.ax.figure.canvas.draw() -class SelectFromCollection(object): +def _draw_without_rendering(cbar): + # draw_all deprecated in Matplotlib 3.6 + try: + meth = cbar.ax.figure.draw_without_rendering + except AttributeError: + meth = cbar.draw_all + return meth() + + +class SelectFromCollection: """Select channels from a matplotlib collection using ``LassoSelector``. Selected channels are saved in the ``selection`` attribute. This tool @@ -1359,16 +1451,12 @@ Notes ----- This tool selects collection objects based on their *origins* - (i.e., ``offsets``). Emits mpl event 'lasso_event' when selection is ready. + (i.e., ``offsets``). Calls all callbacks in self.callbacks when selection + is ready. """ def __init__(self, ax, collection, ch_names, alpha_other=0.5, linewidth_other=0.5, alpha_selected=1, linewidth_selected=1): - from matplotlib import __version__ - if LooseVersion(__version__) < LooseVersion('1.2.1'): - raise ImportError('Interactive selection not possible for ' - 'matplotlib versions < 1.2.1. Upgrade ' - 'matplotlib.') from matplotlib.widgets import LassoSelector self.canvas = ax.figure.canvas self.collection = collection @@ -1397,6 +1485,7 @@ line_kw = _prop_kw('line', dict(color='red', linewidth=0.5)) self.lasso = LassoSelector(ax, onselect=self.on_select, **line_kw) self.selection = list() + self.callbacks = list() def on_select(self, verts): """Select a subset from the collection.""" @@ -1413,7 +1502,7 @@ self.selection[:] = np.array(self.ch_names)[inds].tolist() self.style_sensors(inds) - self.canvas.callbacks.process('lasso_event') + self.notify() def select_one(self, ind): """Select or deselect one sensor.""" @@ -1425,7 +1514,12 @@ self.selection.append(ch_name) inds = np.in1d(self.ch_names, self.selection).nonzero()[0] self.style_sensors(inds) - self.canvas.callbacks.process('lasso_event') + self.notify() + + def notify(self): + """Notify listeners that a selection has been made.""" + for callback in self.callbacks: + callback() def select_many(self, inds): """Select many sensors using indices (for predefined selections).""" @@ -1599,7 +1693,7 @@ def _setup_ax_spines(axes, vlines, xmin, xmax, ymin, ymax, invert_y=False, unit=None, truncate_xaxis=True, truncate_yaxis=True, - skip_axlabel=False, hline=True): + skip_axlabel=False, hline=True, time_unit='s'): # don't show zero line if it coincides with x-axis (even if hline=True) if hline and ymin != 0.: axes.spines['top'].set_position('zero') @@ -1610,7 +1704,7 @@ # new ticks that are nice round numbers close to (but less extreme than) # xmin and xmax vlines = [] if vlines is None else vlines - xticks = _trim_ticks(axes.get_xticks(), xmin, xmax) + xticks = _trim_ticks(axes.get_xticks(), round(xmin, 2), round(xmax, 2)) xticks = np.array(sorted(set([x for x in xticks] + vlines))) if len(xticks) < 2: def log_fix(tval): @@ -1652,7 +1746,7 @@ else: if unit is not None: axes.set_ylabel(unit, rotation=90) - axes.set_xlabel('Time (s)') + axes.set_xlabel(f'Time ({time_unit})') # plot vertical lines if vlines: _ymin, _ymax = axes.get_ylim() @@ -1670,12 +1764,13 @@ def _handle_decim(info, decim, lowpass): """Handle decim parameter for plotters.""" - from ..evoked import _check_decim + from ..utils.mixin import _check_decim from ..utils import _ensure_int if isinstance(decim, str) and decim == 'auto': lp = info['sfreq'] if info['lowpass'] is None else info['lowpass'] lp = min(lp, info['sfreq'] if lowpass is None else lowpass) - info['lowpass'] = lp + with info._unlock(): + info['lowpass'] = lp decim = max(int(info['sfreq'] / (lp * 3) + 1e-6), 1) decim = _ensure_int(decim, 'decim', must_be='an int or "auto"') if decim <= 0: @@ -1780,7 +1875,8 @@ # we risk the rank estimates being incorrect (i.e., if the projectors # do not match). info_proj = info.copy() - info_proj['projs'] += cov['projs'] + with info_proj._unlock(): + info_proj['projs'] += cov['projs'] this_rank = {} # assemble rank dict for this cov, such that we have meg for ch_type, this_picks in picks_list2: @@ -1806,14 +1902,10 @@ def _check_cov(noise_cov, info): """Check the noise_cov for whitening and issue an SSS warning.""" - from ..cov import read_cov, Covariance + from ..cov import _ensure_cov if noise_cov is None: return None - if isinstance(noise_cov, str): - noise_cov = read_cov(noise_cov) - if not isinstance(noise_cov, Covariance): - raise TypeError('noise_cov must be a str or Covariance, got %s' - % (type(noise_cov),)) + noise_cov = _ensure_cov(noise_cov, name='noise_cov', verbose=False) if _check_sss(info)[2]: # has_sss warn('Data have been processed with SSS, which changes the relative ' 'scaling of magnetometers and gradiometers when viewing data ' @@ -1858,9 +1950,10 @@ def _plot_masked_image(ax, data, times, mask=None, yvals=None, cmap="RdBu_r", vmin=None, vmax=None, ylim=None, mask_style="both", mask_alpha=.25, mask_cmap="Greys", - yscale="linear"): + yscale="linear", cnorm=None): """Plot a potentially masked (evoked, TFR, ...) 2D image.""" - from matplotlib import ticker, __version__ as mpl_version + from matplotlib import ticker + from matplotlib.colors import Normalize if mask_style is None and mask is not None: mask_style = "both" # default @@ -1868,6 +1961,8 @@ draw_contour = mask_style in {"both", "contour"} if cmap is None: mask_cmap = cmap + if cnorm is None: + cnorm = Normalize(vmin=vmin, vmax=vmax) # mask param check and preparation if draw_mask is None: @@ -1916,11 +2011,6 @@ else: yscale = 'linear' - # https://github.com/matplotlib/matplotlib/pull/9477 - if yscale == "log" and mpl_version == "2.1.0": - warn("With matplotlib version 2.1.0, lines may not show up in " - "`AverageTFR.plot_joint`. Upgrade to a more recent version.") - if yscale == "log": # pcolormesh for log scale # compute bounds between time samples time_lims, = centers_to_edges(times) @@ -1933,13 +2023,13 @@ if mask is not None: ax.pcolormesh(time_mesh, yval_mesh, data, cmap=mask_cmap, - vmin=vmin, vmax=vmax, alpha=mask_alpha) + norm=cnorm, alpha=mask_alpha) im = ax.pcolormesh(time_mesh, yval_mesh, np.ma.masked_where(~mask, data), cmap=cmap, - vmin=vmin, vmax=vmax, alpha=1) + norm=cnorm, alpha=1) else: im = ax.pcolormesh(time_mesh, yval_mesh, data, cmap=cmap, - vmin=vmin, vmax=vmax) + norm=cnorm) if ylim is None: ylim = yval_lims[[0, -1]] if yscale == 'log': @@ -1960,16 +2050,16 @@ dy = np.median(np.diff(yvals)) / 2. if len(yvals) > 1 else 0.5 extent = [times[0] - dt, times[-1] + dt, yvals[0] - dy, yvals[-1] + dy] - im_args = dict(interpolation='nearest', origin='lower', - extent=extent, aspect='auto', vmin=vmin, vmax=vmax) - + im_args = dict(interpolation='nearest', origin='lower', extent=extent, + aspect='auto') if draw_mask: - ax.imshow(data, alpha=mask_alpha, cmap=mask_cmap, **im_args) - im = ax.imshow( - np.ma.masked_where(~mask, data), cmap=cmap, **im_args) + ax.imshow(data, alpha=mask_alpha, cmap=mask_cmap, norm=cnorm, + **im_args) + im = ax.imshow(np.ma.masked_where(~mask, data), cmap=cmap, + norm=cnorm, **im_args) else: - ax.imshow(data, cmap=cmap, **im_args) # see #6481 - im = ax.imshow(data, cmap=cmap, **im_args) + ax.imshow(data, cmap=cmap, norm=cnorm, **im_args) # see #6481 + im = ax.imshow(data, cmap=cmap, norm=cnorm, **im_args) if draw_contour and np.unique(mask).size == 2: big_mask = np.kron(mask, np.ones((10, 10))) @@ -2022,51 +2112,6 @@ return combine -def center_cmap(cmap, vmin, vmax, name="cmap_centered"): - """Center given colormap (ranging from vmin to vmax) at value 0. - - Parameters - ---------- - cmap : matplotlib.colors.Colormap - The colormap to center around 0. - vmin : float - Minimum value in the data to map to the lower end of the colormap. - vmax : float - Maximum value in the data to map to the upper end of the colormap. - name : str - Name of the new colormap. Defaults to 'cmap_centered'. - - Returns - ------- - cmap_centered : matplotlib.colors.Colormap - The new colormap centered around 0. - - Notes - ----- - This function can be used in situations where vmin and vmax are not - symmetric around zero. Normally, this results in the value zero not being - mapped to white anymore in many colormaps. Using this function, the value - zero will be mapped to white even for asymmetric positive and negative - value ranges. Note that this could also be achieved by re-normalizing a - given colormap by subclassing matplotlib.colors.Normalize as described - here: - https://matplotlib.org/users/colormapnorms.html#custom-normalization-two-linear-ranges - """ # noqa: E501 - from matplotlib.colors import LinearSegmentedColormap - - vzero = abs(vmin) / float(vmax - vmin) - index_old = np.linspace(0, 1, cmap.N) - index_new = np.hstack([np.linspace(0, vzero, cmap.N // 2, endpoint=False), - np.linspace(vzero, 1, cmap.N // 2)]) - - colors = "red", "green", "blue", "alpha" - cdict = {name: [] for name in colors} - for old, new in zip(index_old, index_new): - for color, name in zip(cmap(old), colors): - cdict[name].append((new, color, color)) - return LinearSegmentedColormap(name, cdict) - - def _convert_psds(psds, dB, estimate, scaling, unit, ch_names=None, first_dim='channel'): """Convert PSDs to dB (if necessary) and appropriate units. @@ -2179,8 +2224,9 @@ # Needed because the data do not match the info anymore. info = create_info([inst.ch_names[p] for p in picks], inst.info['sfreq'], types) - info['chs'] = [inst.info['chs'][p] for p in picks] - info['dev_head_t'] = inst.info['dev_head_t'] + with info._unlock(): + info['chs'] = [inst.info['chs'][p] for p in picks] + info['dev_head_t'] = inst.info['dev_head_t'] ch_types_used = list() for this_type in _VALID_CHANNEL_TYPES: if this_type in types: @@ -2198,7 +2244,7 @@ ylim=None, times=freqs, bad_ch_idx=[], titles=titles, ch_types_used=ch_types_used, selectable=True, psd=True, line_alpha=line_alpha, nave=None, time_unit='ms', - sphere=sphere) + sphere=sphere, highlight=None) for ii, (ax, xlabel) in enumerate(zip(ax_list, xlabels_list)): ax.grid(True, linestyle=':') @@ -2220,8 +2266,11 @@ def _trim_ticks(ticks, _min, _max): """Remove ticks that are more extreme than the given limits.""" - keep = np.where(np.logical_and(ticks >= _min, ticks <= _max)) - return ticks[keep] + if np.isclose(_min, _max): + keep_idx = 0 # ensure we always keep at least one tick + else: + keep_idx = np.where(np.logical_and(ticks >= _min, ticks <= _max)) + return np.atleast_1d(ticks[keep_idx]) def _set_window_title(fig, title): @@ -2293,12 +2342,12 @@ return fig -def _ndarray_to_fig(img): +def _ndarray_to_fig(img, dpi=100): """Convert to MPL figure, adapted from matplotlib.image.imsave.""" - dpi = 100 figsize = np.array(img.shape[:2][::-1]) / dpi - fig = _figure_agg(dpi=dpi, figsize=figsize, frameon=False) - fig.figimage(img, resize=True) + fig = _figure_agg(dpi=dpi, figsize=figsize) + ax = fig.add_axes([0, 0, 1, 1], frame_on=False) + ax.imshow(img) return fig @@ -2308,7 +2357,8 @@ Image.fromarray(img).save(fname) -def concatenate_images(images, axis=0, bgcolor='black', centered=True): +def concatenate_images(images, axis=0, bgcolor='black', centered=True, + n_channels=3): """Concatenate a list of images. Parameters @@ -2324,28 +2374,33 @@ 'black'. centered : bool If True, the images are centered. Defaults to True. + n_channels : int + Number of color channels. Can be 3 or 4. The default value is 3. Returns ------- img : ndarray The concatenated image. """ - from matplotlib.colors import colorConverter - if isinstance(bgcolor, str): - bgcolor = colorConverter.to_rgb(bgcolor) + n_channels = _ensure_int(n_channels, 'n_channels') + axis = _ensure_int(axis) + _check_option('axis', axis, (0, 1)) + _check_option('n_channels', n_channels, (3, 4)) + alpha = True if n_channels == 4 else False + bgcolor = _to_rgb(bgcolor, name='bgcolor', alpha=alpha) bgcolor = np.asarray(bgcolor) * 255 funcs = [np.sum, np.max] ret_shape = np.asarray([ funcs[axis]([image.shape[0] for image in images]), funcs[1 - axis]([image.shape[1] for image in images]), ]) - ret = np.zeros((ret_shape[0], ret_shape[1], 3), dtype=np.uint8) + ret = np.zeros((ret_shape[0], ret_shape[1], n_channels), dtype=np.uint8) ret[:, :, :] = bgcolor ptr = np.array([0, 0]) sec = np.array([0 == axis, 1 == axis]).astype(int) for image in images: shape = image.shape[:-1] - dec = ptr + dec = ptr.copy() dec += ((ret_shape - shape) // 2) * (1 - sec) if centered else 0 ret[dec[0]:dec[0] + shape[0], dec[1]:dec[1] + shape[1], :] = image ptr += shape * sec @@ -2364,3 +2419,60 @@ from matplotlib.widgets import SpanSelector pre = '' if 'props' in _get_args(SpanSelector) else kind return {pre + 'props': val} + + +def _handle_precompute(precompute): + _validate_type(precompute, (bool, str, None), 'precompute') + if precompute is None: + precompute = get_config('MNE_BROWSER_PRECOMPUTE', 'auto').lower() + _check_option('MNE_BROWSER_PRECOMPUTE', + precompute, ('true', 'false', 'auto'), + extra='when precompute=None is used') + precompute = dict(true=True, false=False, auto='auto')[precompute] + return precompute + + +def _set_3d_axes_equal(ax): + """Make axes of 3D plot have equal scale on all dimensions. + + This way spheres appear as actual spheres, cubes as cubes, etc.. + This is one possible solution to Matplotlib's ``ax.set_aspect('equal')`` + and ``ax.axis('equal')`` not working for 3D. + + Parameters + ---------- + ax: matplotlib.axes.Axes + A matplotlib 3d axis to use. + + Notes + ----- + modified from: + https://stackoverflow.com/q/13685386 + + Should no longer be necessary for matplotlib >= 3.3.0: + https://matplotlib.org/stable/users/prev_whats_new/whats_new_3.3.0.html#axes3d-no-longer-distorts-the-3d-plot-to-match-the-2d-aspect-ratio + """ + x_lim, y_lim, z_lim = ax.get_xlim3d(), ax.get_ylim3d(), ax.get_zlim3d() + + def get_range(lim): + return lim[1] - lim[0], np.mean(lim) + x_range, x_mean = get_range(x_lim) + y_range, y_mean = get_range(y_lim) + z_range, z_mean = get_range(z_lim) + + # The plot bounding box is a sphere in the sense of the infinity + # norm, hence I call half the max range the plot radius. + plot_radius = 0.5 * max([x_range, y_range, z_range]) + + ax.set_xlim3d([x_mean - plot_radius, x_mean + plot_radius]) + ax.set_ylim3d([y_mean - plot_radius, y_mean + plot_radius]) + ax.set_zlim3d([z_mean - plot_radius, z_mean + plot_radius]) + + +def _check_type_projs(projs): + _validate_type(projs, (list, tuple, Projection), 'projs') + if isinstance(projs, Projection): + projs = [projs] + for pi, p in enumerate(projs): + _validate_type(p, Projection, f'projs[{pi}]') + return projs diff -Nru python-mne-0.23.4+dfsg/README.rst python-mne-1.1.0+dfsg/README.rst --- python-mne-0.23.4+dfsg/README.rst 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/README.rst 2022-08-03 15:48:14.000000000 +0000 @@ -52,13 +52,14 @@ To install the latest stable version of MNE-Python, you can use pip_ in a terminal: -.. code-block:: bash +.. code-block:: console - pip install -U mne + $ pip install -U mne - MNE-Python 0.17 was the last release to support Python 2.7 - MNE-Python 0.18 requires Python 3.5 or higher - MNE-Python 0.21 requires Python 3.6 or higher +- MNE-Python 0.24 requires Python 3.7 or higher For more complete instructions and more advanced installation methods (e.g. for the latest development version), see the `installation guide`_. @@ -69,15 +70,15 @@ To install the latest version of the code using pip_ open a terminal and type: -.. code-block:: bash +.. code-block:: console - pip install -U https://github.com/mne-tools/mne-python/archive/main.zip + $ pip install -U https://github.com/mne-tools/mne-python/archive/main.zip To get the latest code using `git `__, open a terminal and type: -.. code-block:: bash +.. code-block:: console - git clone git://github.com/mne-tools/mne-python.git + $ git clone https://github.com/mne-tools/mne-python.git Alternatively, you can also download a `zip file of the latest development version `__. @@ -88,24 +89,32 @@ The minimum required dependencies to run MNE-Python are: -- Python >= 3.6 -- NumPy >= 1.15.4 -- SciPy >= 1.1.0 +- Python >= 3.7 +- NumPy >= 1.18.1 +- SciPy >= 1.4.1 +- Matplotlib >= 3.1.0 +- pooch >= 1.5 +- tqdm +- Jinja2 +- decorator For full functionality, some functions require: -- Matplotlib >= 3.0.3 -- Scikit-learn >= 0.20.2 -- Numba >= 0.40 -- NiBabel >= 2.1.0 -- Pandas >= 0.23.4 +- Scikit-learn >= 0.22.0 +- joblib >= 0.15 (for parallelization control) +- Numba >= 0.48.0 +- NiBabel >= 2.5.0 +- Pandas >= 1.0.0 - Picard >= 0.3 -- CuPy >= 4.0 (for NVIDIA CUDA acceleration) -- DIPY >= 0.10.1 +- CuPy >= 7.1.1 (for NVIDIA CUDA acceleration) +- DIPY >= 1.1.0 - Imageio >= 2.6.1 -- PyVista >= 0.24 -- pyvistaqt >= 0.2.0 +- PyVista >= 0.32 +- pyvistaqt >= 0.4 - mffpy >= 0.5.7 +- h5py +- h5io +- pymatreader Contributing to MNE-Python ^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -124,12 +133,12 @@ Licensing ^^^^^^^^^ -MNE-Python is **BSD-licenced** (3 clause): +MNE-Python is **BSD-licenced** (BSD-3-Clause): This software is OSI Certified Open Source Software. OSI Certified is a certification mark of the Open Source Initiative. - Copyright (c) 2011-2019, authors of MNE-Python. + Copyright (c) 2011-2022, authors of MNE-Python. All rights reserved. Redistribution and use in source and binary forms, with or without diff -Nru python-mne-0.23.4+dfsg/requirements_base.txt python-mne-1.1.0+dfsg/requirements_base.txt --- python-mne-0.23.4+dfsg/requirements_base.txt 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/requirements_base.txt 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,9 @@ +# requirements for basic MNE-Python functionality +numpy>=1.15.4 +scipy>=1.1.0 +matplotlib +tqdm +pooch>=1.5 +decorator +packaging +jinja2 diff -Nru python-mne-0.23.4+dfsg/requirements_doc.txt python-mne-1.1.0+dfsg/requirements_doc.txt --- python-mne-0.23.4+dfsg/requirements_doc.txt 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/requirements_doc.txt 2022-08-03 15:48:14.000000000 +0000 @@ -1,12 +1,16 @@ # requirements for building docs -sphinx +sphinx!=4.1.0 https://github.com/numpy/numpydoc/archive/main.zip -pydata-sphinx-theme==0.6.1 +https://github.com/pydata/pydata-sphinx-theme/archive/f8e179403307ea04ea76940ffcbe6f07a82f0dd8.zip https://github.com/sphinx-gallery/sphinx-gallery/archive/master.zip sphinxcontrib-bibtex>=2.1.2 memory_profiler neo seaborn!=0.11.2 sphinx_copybutton -https://github.com/mne-tools/mne-bids/archive/main.zip +sphinx_design +sphinxcontrib-youtube +https://github.com/mne-tools/mne-bids/zipball/main pyxdf +https://github.com/mne-tools/mne-connectivity/archive/main.zip +pytest diff -Nru python-mne-0.23.4+dfsg/requirements_hdf5.txt python-mne-1.1.0+dfsg/requirements_hdf5.txt --- python-mne-0.23.4+dfsg/requirements_hdf5.txt 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/requirements_hdf5.txt 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,3 @@ +# requirements for MNE-Python functions that use HDF5 I/O +h5io +pymatreader diff -Nru python-mne-0.23.4+dfsg/requirements_testing_extra.txt python-mne-1.1.0+dfsg/requirements_testing_extra.txt --- python-mne-0.23.4+dfsg/requirements_testing_extra.txt 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/requirements_testing_extra.txt 2022-08-03 15:48:14.000000000 +0000 @@ -3,3 +3,6 @@ nbclient sphinx-gallery eeglabio +EDFlib-Python +pybv +imageio-ffmpeg diff -Nru python-mne-0.23.4+dfsg/requirements.txt python-mne-1.1.0+dfsg/requirements.txt --- python-mne-0.23.4+dfsg/requirements.txt 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/requirements.txt 2022-08-03 15:48:14.000000000 +0000 @@ -2,15 +2,21 @@ numpy>=1.15.4 scipy>=1.1.0 matplotlib -pyqt5>=5.10,<5.14; platform_system == "Darwin" -pyqt5>=5.10,!=5.15.2,!=5.15.3; platform_system == "Linux" -pyqt5>=5.10,!=5.15.3; platform_system != "Linux" and platform_system != "Darwin" -pyqt5-sip +tqdm +pooch>=1.5 +decorator +h5io +packaging +pymatreader +qtpy +PySide6!=6.3.0 +pyobjc-framework-Cocoa>=5.2.0; platform_system=="Darwin" sip scikit-learn nibabel numba h5py +jinja2 pandas numexpr jupyter @@ -19,17 +25,17 @@ joblib psutil dipy -vtk<8.2; platform_system == "Darwin" -vtk; platform_system != "Darwin" -mayavi -PySurfer[save_movie] +vtk nilearn xlrd imageio>=2.6.1 imageio-ffmpeg>=0.4.1 -pyvista>=0.24 -pyvistaqt>=0.2.0 -tqdm +traitlets +pyvista>=0.32,!=0.35.2 +pyvistaqt>=0.4 mffpy>=0.5.7 ipywidgets -ipyvtk-simple +ipyvtklink +mne-qt-browser +darkdetect +qdarkstyle diff -Nru python-mne-0.23.4+dfsg/SECURITY.md python-mne-1.1.0+dfsg/SECURITY.md --- python-mne-0.23.4+dfsg/SECURITY.md 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/SECURITY.md 2022-08-03 15:48:14.000000000 +0000 @@ -10,9 +10,9 @@ | Version | Supported | | ------- | ------------------------ | -| 0.24.x | :heavy_check_mark: (dev) | -| 0.23.x | :heavy_check_mark: | -| < 0.23 | :x: | +| 1.2.x | :heavy_check_mark: (dev) | +| 1.1.x | :heavy_check_mark: | +| < 1.1 | :x: | ## Reporting a Vulnerability diff -Nru python-mne-0.23.4+dfsg/server_environment.yml python-mne-1.1.0+dfsg/server_environment.yml --- python-mne-0.23.4+dfsg/server_environment.yml 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/server_environment.yml 2022-08-03 15:48:14.000000000 +0000 @@ -1,27 +1,27 @@ -name: base +name: mne channels: - conda-forge/label/vtk_dev - conda-forge dependencies: -- python>=3.7 +- python ==3.8 - pip - mesalib - ffmpeg - vtk -- traits - scipy - numpy - matplotlib-base -- pyvista +- pyvista>=0.32,!=0.35.2 - nilearn - nibabel -- nbformat <5.1 # XXX remove pinning once https://github.com/jupyter/nbformat/issues/206 has been fixed +- nbformat - nbclient - mffpy>=0.5.7 +- pooch - pip: - mne - jupyter - ipympl - ipywidgets - - ipyvtk_simple + - ipyvtklink - jupyter_client!=6.1.5 diff -Nru python-mne-0.23.4+dfsg/setup.cfg python-mne-1.1.0+dfsg/setup.cfg --- python-mne-0.23.4+dfsg/setup.cfg 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/setup.cfg 2022-08-03 15:48:14.000000000 +0000 @@ -12,10 +12,10 @@ # tag_build = .dev [bdist_rpm] -doc-files = doc +doc_files = doc [flake8] -exclude = __init__.py,*externals*,constants.py,fixes.py,resources.py +exclude = __init__.py,constants.py,fixes.py,resources.py,*doc/auto_examples*,*doc/_build* ignore = W503,W504,I100,I101,I201,N806,E201,E202,E221,E222,E241 # We add A for the array-spacing plugin, and ignore the E ones it covers above select = A,E,F,W,C @@ -25,13 +25,14 @@ --durations=20 --doctest-modules -ra --cov-report= --tb=short --doctest-ignore-import-errors --junit-xml=junit-results.xml --ignore=doc --ignore=logo --ignore=examples --ignore=tutorials - --ignore=mne/gui/_*.py --ignore=mne/externals --ignore=mne/icons - --capture=sys + --ignore=mne/gui/_*.py --ignore=mne/icons --ignore=tools + --ignore=mne/report/js_and_css + --color=yes --capture=sys junit_family = xunit2 [pydocstyle] convention = pep257 -match_dir = ^(?!\.|externals|doc|tutorials|examples|logo|icons).*$ +match_dir = ^(?!\.|doc|tutorials|examples|logo|icons).*$ match = (?!tests/__init__\.py|fixes).*\.py add-ignore = D100,D104,D107,D413 add-select = D214,D215,D404,D405,D406,D407,D408,D409,D410,D411 diff -Nru python-mne-0.23.4+dfsg/setup.py python-mne-1.1.0+dfsg/setup.py --- python-mne-0.23.4+dfsg/setup.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/setup.py 2022-08-03 15:48:14.000000000 +0000 @@ -8,6 +8,20 @@ from setuptools import setup + +def parse_requirements_file(fname): + requirements = list() + with open(fname, 'r') as fid: + for line in fid: + req = line.strip() + if req.startswith('#'): + continue + # strip end-of-line comments + req = req.split('#', maxsplit=1)[0].strip() + requirements.append(req) + return requirements + + # get the version (don't import mne here, so dependencies are not needed) version = None with open(op.join('mne', '_version.py'), 'r') as fid: @@ -19,14 +33,12 @@ raise RuntimeError('Could not determine version') -descr = """MNE python project for MEG and EEG data analysis.""" - DISTNAME = 'mne' -DESCRIPTION = descr +DESCRIPTION = 'MNE-Python project for MEG and EEG data analysis.' MAINTAINER = 'Alexandre Gramfort' MAINTAINER_EMAIL = 'alexandre.gramfort@inria.fr' URL = 'https://mne.tools/dev/' -LICENSE = 'BSD (3-clause)' +LICENSE = 'BSD-3-Clause' DOWNLOAD_URL = 'http://github.com/mne-tools/mne-python' VERSION = version @@ -48,15 +60,13 @@ with open('README.rst', 'r') as fid: long_description = fid.read() - hard_dependencies = ('numpy', 'scipy') - install_requires = list() - with open('requirements.txt', 'r') as fid: - for line in fid: - req = line.strip() - for hard_dep in hard_dependencies: - if req.startswith(hard_dep): - install_requires.append(req) - + # data_dependencies is empty, but let's leave them so that we don't break + # people's workflows who did `pip install mne[data]` + install_requires = parse_requirements_file('requirements_base.txt') + data_requires = [] + hdf5_requires = parse_requirements_file('requirements_hdf5.txt') + test_requires = (parse_requirements_file('requirements_testing.txt') + + parse_requirements_file('requirements_testing_extra.txt')) setup(name=DISTNAME, maintainer=MAINTAINER, include_package_data=True, @@ -88,10 +98,16 @@ 'Tracker': 'https://github.com/mne-tools/mne-python/issues/', }, platforms='any', - python_requires='>=3.6', + python_requires='>=3.7', install_requires=install_requires, + extras_require={ + 'data': data_requires, + 'hdf5': hdf5_requires, + 'test': test_requires, + }, packages=package_tree('mne'), package_data={'mne': [ + op.join('data', 'eegbci_checksums.txt'), op.join('data', '*.sel'), op.join('data', 'icos.fif.gz'), op.join('data', 'coil_def*.dat'), @@ -109,9 +125,11 @@ op.join('datasets', 'sleep_physionet', 'SHA1SUMS'), op.join('datasets', '_fsaverage', '*.txt'), op.join('datasets', '_infant', '*.txt'), - op.join('gui', 'help', '*.json'), + op.join('datasets', '_phantom', '*.txt'), op.join('html', '*.js'), op.join('html', '*.css'), + op.join('html_templates', 'repr', '*.jinja'), + op.join('html_templates', 'report', '*.jinja'), op.join('icons', '*.svg'), op.join('icons', '*.png'), op.join('io', 'artemis123', 'resources', '*.csv'), diff -Nru python-mne-0.23.4+dfsg/tools/azure_dependencies.sh python-mne-1.1.0+dfsg/tools/azure_dependencies.sh --- python-mne-0.23.4+dfsg/tools/azure_dependencies.sh 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tools/azure_dependencies.sh 2022-08-03 15:48:14.000000000 +0000 @@ -1,20 +1,29 @@ #!/bin/bash -ef +EXTRA_ARGS="" if [ "${TEST_MODE}" == "pip" ]; then - python -m pip install --upgrade pip setuptools + python -m pip install --upgrade pip setuptools wheel python -m pip install --upgrade --only-binary ":all:" numpy scipy vtk python -m pip install --upgrade --only-binary="numba,llvmlite" -r requirements.txt + # This can be removed once PyVistaQt 0.6 is out (including https://github.com/pyvista/pyvistaqt/pull/127) + python -m pip install --upgrade https://github.com/pyvista/pyvistaqt/zipball/main elif [ "${TEST_MODE}" == "pip-pre" ]; then - python -m pip install --progress-bar off --upgrade pip setuptools - python -m pip install --progress-bar off --upgrade --pre --only-binary ":all:" -i "https://pypi.anaconda.org/scipy-wheels-nightly/simple" --extra-index-url https://www.riverbankcomputing.com/pypi/simple numpy scipy pandas scikit-learn PyQt5 - python -m pip install --progress-bar off --upgrade --pre --only-binary ":all:" -f "https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackcdn.com" h5py Pillow - python -m pip install --progress-bar off --upgrade --pre --only-binary ":all" vtk - python -m pip install --progress-bar off --upgrade --only-binary ":all" matplotlib - python -m pip install --progress-bar off https://github.com/pyvista/pyvista/zipball/master - python -m pip install --progress-bar off https://github.com/pyvista/pyvistaqt/zipball/master - python -m pip install --progress-bar off --upgrade --only-binary="numba,llvmlite" -r requirements.txt + python -m pip install --progress-bar off --upgrade pip setuptools wheel + python -m pip install --progress-bar off --upgrade --pre --only-binary ":all:" python-dateutil pytz joblib threadpoolctl six cycler kiwisolver pyparsing patsy + # XXX: the wheels for PyQt6-sip 13.4 are not available yet from https://www.riverbankcomputing.com/pypi/simple + # python -m pip install --progress-bar off --upgrade --pre --only-binary ":all:" --no-deps --extra-index-url https://www.riverbankcomputing.com/pypi/simple PyQt6 PyQt6-sip PyQt6-Qt6 + python -m pip install --progress-bar off --upgrade --only-binary ":all:" PyQt6 PyQt6-sip PyQt6-Qt6 + # SciPy Windows build is missing from conda nightly builds + python -m pip install --progress-bar off --upgrade --pre --only-binary ":all:" --no-deps -i "https://pypi.anaconda.org/scipy-wheels-nightly/simple" numpy + python -m pip install --progress-bar off --upgrade --pre --only-binary ":all:" --no-deps scipy vtk + python -m pip install --progress-bar off --upgrade --pre --only-binary ":all:" --no-deps --default-timeout=60 -i "https://pypi.anaconda.org/scipy-wheels-nightly/simple" pandas scikit-learn dipy statsmodels + python -m pip install --progress-bar off --upgrade --pre --only-binary ":all:" --no-deps -f "https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackcdn.com" h5py Pillow matplotlib + python -m pip install --progress-bar off https://github.com/pyvista/pyvista/zipball/main + python -m pip install --progress-bar off https://github.com/pyvista/pyvistaqt/zipball/main + python -m pip install --progress-bar off imageio-ffmpeg xlrd mffpy python-picard patsy + EXTRA_ARGS="--pre" else echo "Unknown run type ${TEST_MODE}" exit 1 fi -python -m pip install -r requirements_testing.txt -r requirements_testing_extra.txt codecov +python -m pip install $EXTRA_ARGS .[test,hdf5] codecov diff -Nru python-mne-0.23.4+dfsg/tools/circleci_dependencies.sh python-mne-1.1.0+dfsg/tools/circleci_dependencies.sh --- python-mne-0.23.4+dfsg/tools/circleci_dependencies.sh 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tools/circleci_dependencies.sh 2022-08-03 15:48:14.000000000 +0000 @@ -1,42 +1,19 @@ #!/bin/bash -ef -echo "Working around PyQt5 bugs" -# https://github.com/ContinuumIO/anaconda-issues/issues/9190#issuecomment-386508136 -# https://github.com/golemfactory/golem/issues/1019 -sudo apt update -sudo apt install libosmesa6 libglx-mesa0 libopengl0 libglx0 libdbus-1-3 \ - libxkbcommon-x11-0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 \ - libxcb-render-util0 libxcb-shape0 libxcb-xfixes0 libxcb-xinerama0 \ - graphviz optipng -sudo ln -s /usr/lib/x86_64-linux-gnu/libxcb-util.so.0 /usr/lib/x86_64-linux-gnu/libxcb-util.so.1 - echo "Installing setuptools and sphinx" -python -m pip install --progress-bar off --upgrade "pip!=20.3.0" setuptools wheel +python -m pip install --upgrade "pip!=20.3.0" +python -m pip install --upgrade --progress-bar off setuptools wheel python -m pip install --upgrade --progress-bar off --pre sphinx -if [[ "$CIRCLE_JOB" == "interactive_test" ]]; then - echo "Installing latest dependencies for interactive_test" - python -m pip install --progress-bar off --upgrade --pre --only-binary ":all:" python-dateutil pytz joblib threadpoolctl - python -m pip install --progress-bar off --upgrade --pre --only-binary ":all:" -i "https://pypi.anaconda.org/scipy-wheels-nightly/simple" --extra-index-url https://www.riverbankcomputing.com/pypi/simple numpy scipy pandas scikit-learn PyQt5 - python -m pip install --progress-bar off --upgrade --pre --only-binary ":all:" -f "https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackcdn.com" h5py pillow matplotlib - python -m pip install --progress-bar off --upgrade --pre --only-binary ":all:" numba llvmlite - wget -q https://osf.io/kej3v/download -O vtk-9.0.20201117-cp39-cp39-linux_x86_64.whl - python -m pip install --progress-bar off vtk-9.0.20201117-cp39-cp39-linux_x86_64.whl - python -m pip install --progress-bar off https://github.com/pyvista/pyvista/zipball/main - python -m pip install --progress-bar off https://github.com/pyvista/pyvistaqt/zipball/main - python -m pip install --progress-bar off --upgrade -r requirements_testing.txt -r requirements_testing_extra.txt - python -m pip install -e . -elif [[ "$CIRCLE_JOB" == "linkcheck"* ]]; then +if [[ "$CIRCLE_JOB" == "linkcheck"* ]]; then echo "Installing minimal linkcheck dependencies" - python -m pip install --progress-bar off numpy scipy matplotlib pillow pytest - python -m pip install -e . - python -m pip install --progress-bar off -r requirements_doc.txt + python -m pip install --progress-bar off pillow pytest -r requirements_base.txt -r requirements_doc.txt else # standard doc build echo "Installing doc build dependencies" - python -m pip uninstall -y pydata-sphinx-theme - python -m pip install --upgrade --progress-bar off --only-binary matplotlib -r <(grep -Ev "mayavi|PySurfer" requirements.txt) -r requirements_testing.txt -r requirements_doc.txt - python -m pip install --progress-bar off https://github.com/sphinx-gallery/sphinx-gallery/zipball/master https://github.com/pyvista/pyvista/zipball/main https://github.com/pyvista/pyvistaqt/zipball/main + python -m pip install --upgrade --progress-bar off --only-binary "numpy,scipy,matplotlib,pandas,statsmodels" -r requirements.txt -r requirements_testing.txt -r requirements_doc.txt + python -m pip uninstall -yq sphinx-gallery mne-qt-browser + # TODO: Revert to upstream/main once https://github.com/mne-tools/mne-qt-browser/pull/105 is merged + python -m pip install --upgrade --progress-bar off https://github.com/mne-tools/mne-qt-browser/zipball/main https://github.com/sphinx-gallery/sphinx-gallery/zipball/master # deal with comparisons and escapes (https://app.circleci.com/pipelines/github/mne-tools/mne-python/9686/workflows/3fd32b47-3254-4812-8b9a-8bab0d646d18/jobs/32934) - python -m pip install --upgrade quantities - python -m pip install https://github.com/nilearn/nilearn/zipball/cb5329d89bad6554f036c84634702cb67eb7d5ea - python -m pip install -e . + python -m pip install --upgrade --progress-bar off quantities fi +python -m pip install -e . diff -Nru python-mne-0.23.4+dfsg/tools/circleci_download.sh python-mne-1.1.0+dfsg/tools/circleci_download.sh --- python-mne-0.23.4+dfsg/tools/circleci_download.sh 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tools/circleci_download.sh 2022-08-03 15:48:14.000000000 +0000 @@ -4,7 +4,7 @@ echo "Doing a full dev build"; echo html_dev-memory > build.txt; python -c "import mne; mne.datasets._download_all_example_data()"; -elif [ "$CIRCLE_BRANCH" == "maint/0.23" ]; then +elif [ "$CIRCLE_BRANCH" == "maint/1.1" ]; then echo "Doing a full stable build"; echo html_stable-memory > build.txt; python -c "import mne; mne.datasets._download_all_example_data()"; @@ -12,7 +12,7 @@ echo "Doing a partial build"; FNAMES=$(git diff --name-only $(git merge-base $CIRCLE_BRANCH upstream/main) $CIRCLE_BRANCH); if [[ $(cat gitlog.txt) == *"[circle front]"* ]]; then - FNAMES="tutorials/inverse/30_mne_dspm_loreta.py tutorials/machine-learning/30_strf.py examples/connectivity/mne_inverse_label_connectivity.py tutorials/machine-learning/50_decoding.py tutorials/stats-source-space/20_cluster_1samp_spatiotemporal.py tutorials/evoked/20_visualize_evoked.py "${FNAMES}; + FNAMES="tutorials/inverse/30_mne_dspm_loreta.py tutorials/machine-learning/30_strf.py tutorials/machine-learning/50_decoding.py tutorials/stats-source-space/20_cluster_1samp_spatiotemporal.py tutorials/evoked/20_visualize_evoked.py "${FNAMES}; python -c "import mne; print(mne.datasets.testing.data_path(update_path=True))"; fi; echo FNAMES="$FNAMES"; @@ -39,7 +39,7 @@ python -c "import mne; print(mne.datasets.eegbci.load_data(4, [3], update_path=True))"; fi; if [[ $(cat $FNAME | grep -x ".*datasets.*sleep_physionet.*" | wc -l) -gt 0 ]]; then - python -c "import mne; print(mne.datasets.sleep_physionet.age.fetch_data([0, 1], recording=[1], update_path=True))"; + python -c "import mne; print(mne.datasets.sleep_physionet.age.fetch_data([0, 1], recording=[1]))"; fi; if [[ $(cat $FNAME | grep -x ".*datasets.*hf_sef.*" | wc -l) -gt 0 ]]; then python -c "import mne; print(mne.datasets.hf_sef.data_path(update_path=True))"; @@ -61,7 +61,7 @@ fi; if [[ $(cat $FNAME | grep -x ".*datasets.*hcp_mmp_parcellation.*" | wc -l) -gt 0 ]]; then python -c "import mne; print(mne.datasets.sample.data_path(update_path=True))"; - python -c "import mne; print(mne.datasets.fetch_hcp_mmp_parcellation(subjects_dir=mne.datasets.sample.data_path() + '/subjects', accept=True))"; + python -c "import mne; print(mne.datasets.fetch_hcp_mmp_parcellation(subjects_dir=mne.datasets.sample.data_path() / 'subjects', accept=True))"; fi; if [[ $(cat $FNAME | grep -x ".*datasets.*misc.*" | wc -l) -gt 0 ]]; then python -c "import mne; print(mne.datasets.misc.data_path(update_path=True))"; diff -Nru python-mne-0.23.4+dfsg/tools/generate_codemeta.py python-mne-1.1.0+dfsg/tools/generate_codemeta.py --- python-mne-0.23.4+dfsg/tools/generate_codemeta.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tools/generate_codemeta.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,8 +1,34 @@ import os import subprocess from datetime import date + from mne import __version__ as release_version +# NOTE: ../codemeta.json and ../citation.cff should not be continuously +# updated. Run this script only at release time. + +package_name = 'MNE-Python' +hard_dependencies = ('numpy', 'scipy') +release_date = str(date.today()) +commit = subprocess.run(['git', 'log', '-1', '--pretty=%H'], + capture_output=True, text=True).stdout.strip() + +# KEYWORDS +keywords = ( + 'MEG', + 'magnetoencephalography', + 'EEG', + 'electroencephalography', + 'fNIRS', + 'functional near-infrared spectroscopy', + 'iEEG', + 'intracranial EEG', + 'eCoG', + 'electrocorticography', + 'DBS', + 'deep brain stimulation' +) + # add to these as necessary compound_surnames = ( 'García Alanis', @@ -10,12 +36,13 @@ 'De Santis', 'Dupré la Tour', 'de la Torre', + 'de Montalivet', 'van den Bosch', 'Van den Bossche', 'Van Der Donckt', 'van der Meer', 'van Harmelen', - 'Visconti di Oleggio Castello' + 'Visconti di Oleggio Castello', ) @@ -62,7 +89,7 @@ # CONSTRUCT JSON AUTHORS LIST -authors = [f'''{{ +json_authors = [f'''{{ "@type":"Person", "email":"{email}", "givenName":"{first}", @@ -70,14 +97,13 @@ }}''' for (first, last, email) in all_names] -# GET OUR DEPENDENCIES +# GET OUR DEPENDENCY VERSIONS with open(os.path.join('..', 'setup.py'), 'r') as fid: for line in fid: if line.strip().startswith('python_requires='): version = line.strip().split('=', maxsplit=1)[1].strip("'\",") dependencies = [f'python{version}'] break -hard_dependencies = ('numpy', 'scipy') with open(os.path.join('..', 'requirements.txt'), 'r') as fid: for line in fid: req = line.strip() @@ -87,8 +113,9 @@ # these must be done outside the boilerplate (no \n allowed in f-strings): -authors = ',\n '.join(authors) +json_authors = ',\n '.join(json_authors) dependencies = '",\n "'.join(dependencies) +json_keywords = '",\n "'.join(keywords) # ASSEMBLE COMPLETE JSON @@ -99,22 +126,17 @@ "codeRepository": "git+https://github.com/mne-tools/mne-python.git", "dateCreated": "2010-12-26", "datePublished": "2014-08-04", - "dateModified": "{str(date.today())}", + "dateModified": "{release_date}", "downloadUrl": "https://github.com/mne-tools/mne-python/archive/v{release_version}.zip", "issueTracker": "https://github.com/mne-tools/mne-python/issues", - "name": "MNE-Python", + "name": "{package_name}", "version": "{release_version}", - "description": "MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data. It provides methods for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, and statistics.", + "description": "{package_name} is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data. It provides methods for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, and statistics.", "applicationCategory": "Neuroscience", "developmentStatus": "active", "referencePublication": "https://doi.org/10.3389/fnins.2013.00267", "keywords": [ - "MEG", - "EEG", - "fNIRS", - "ECoG", - "sEEG", - "DBS" + "{json_keywords}" ], "programmingLanguage": [ "Python" @@ -128,7 +150,7 @@ "{dependencies}" ], "author": [ - {authors} + {json_authors} ] }} ''' # noqa E501 @@ -137,3 +159,77 @@ # WRITE TO FILE with open(os.path.join('..', 'codemeta.json'), 'w') as codemeta_file: codemeta_file.write(codemeta_boilerplate) + + +# # # # # # # # # # # # # # # +# GENERATE CITATION.CFF TOO # +# # # # # # # # # # # # # # # +message = ('If you use this software, please cite both the software itself, ' + 'and the paper listed in the preferred-citation field.') + +# in CFF, multi-word keywords need to be wrapped in quotes +cff_keywords = (f'"{kw}"' if ' ' in kw else kw for kw in keywords) +# make into a bulleted list +cff_keywords = '\n'.join(f' - {kw}' for kw in cff_keywords) + +# TODO: someday would be nice to include ORCiD identifiers too +cff_authors = [f' - family-names: {last}\n given-names: {first}' + if first else + f' - name: {last}' + for (first, last, _) in all_names] +cff_authors = '\n'.join(cff_authors) + +# this ↓↓↓ is the meta-DOI that always resolves to the latest release +zenodo_doi = '10.5281/zenodo.592483' + +# ASSEMBLE THE CFF STRING +cff_boilerplate = f'''\ +cff-version: 1.2.0 +title: "{package_name}" +message: "{message}" +version: {release_version} +date-released: "{release_date}" +commit: {commit} +doi: {zenodo_doi} +keywords: +{cff_keywords} +authors: +{cff_authors} +preferred-citation: + title: "MEG and EEG Data Analysis with MNE-Python" + journal: "Frontiers in Neuroscience" + type: article + year: 2013 + volume: 7 + issue: 267 + start: 1 + end: 13 + doi: 10.3389/fnins.2013.00267 + authors: + - family-names: Gramfort + given-names: Alexandre + - family-names: Luessi + given-names: Martin + - family-names: Larson + given-names: Eric + - family-names: Engemann + given-names: Denis A. + - family-names: Strohmeier + given-names: Daniel + - family-names: Brodbeck + given-names: Christian + - family-names: Goj + given-names: Roman + - family-names: Jas + given-names: Mainak + - family-names: Brooks + given-names: Teon + - family-names: Parkkonen + given-names: Lauri + - family-names: Hämäläinen + given-names: Matti S. +''' + +# WRITE TO FILE +with open(os.path.join('..', 'CITATION.cff'), 'w') as cff_file: + cff_file.write(cff_boilerplate) diff -Nru python-mne-0.23.4+dfsg/tools/get_minimal_commands.sh python-mne-1.1.0+dfsg/tools/get_minimal_commands.sh --- python-mne-0.23.4+dfsg/tools/get_minimal_commands.sh 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tools/get_minimal_commands.sh 2022-08-03 15:48:14.000000000 +0000 @@ -21,7 +21,9 @@ fi; if [ "${CI_OS_NAME}" != "osx" ]; then if [ ! -d "${PWD}/minimal_cmds" ]; then - curl -L https://osf.io/g7dzs/download | tar xz + curl -L https://osf.io/g7dzs/download?version=5 | tar xz + else + echo "Minimal commands already downloaded" fi; export LD_LIBRARY_PATH=${MNE_ROOT}/lib:$LD_LIBRARY_PATH export NEUROMAG2FT_ROOT="${PWD}/minimal_cmds/bin" @@ -43,11 +45,14 @@ fi; else if [ ! -d "${PWD}/minimal_cmds" ]; then - curl -L https://osf.io/rjcz4/download | tar xz + curl -L https://osf.io/rjcz4/download?version=2 | tar xz + else + echo "Minimal commands already downloaded" fi; export DYLD_LIBRARY_PATH=${MNE_ROOT}/lib:$DYLD_LIBRARY_PATH if [ "${GITHUB_ACTIONS}" == "true" ]; then echo "DYLD_LIBRARY_PATH=${DYLD_LIBRARY_PATH}" >> "$GITHUB_ENV"; + set -x wget https://github.com/XQuartz/XQuartz/releases/download/XQuartz-2.7.11/XQuartz-2.7.11.dmg sudo hdiutil attach XQuartz-2.7.11.dmg sudo installer -package /Volumes/XQuartz-2.7.11/XQuartz.pkg -target / @@ -62,3 +67,4 @@ fi popd > /dev/null mne_process_raw --version +mri_average --version diff -Nru python-mne-0.23.4+dfsg/tools/get_testing_version.sh python-mne-1.1.0+dfsg/tools/get_testing_version.sh --- python-mne-0.23.4+dfsg/tools/get_testing_version.sh 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tools/get_testing_version.sh 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,6 @@ #!/bin/bash -ef -TESTING_VERSION=`grep -o "testing='[0-9.]\+'" mne/datasets/utils.py | cut -d \' -f 2 | sed "s/\./-/g"` +TESTING_VERSION=`grep -o "testing='[0-9.]\+'" mne/datasets/config.py | cut -d \' -f 2 | sed "s/\./-/g"` # This can be incremented to start fresh when the cache misbehaves, e.g.: # TESTING_VERSION=${TESTING_VERSION}-1 if [ ! -z $GITHUB_ENV ]; then diff -Nru python-mne-0.23.4+dfsg/tools/github_actions_dependencies.sh python-mne-1.1.0+dfsg/tools/github_actions_dependencies.sh --- python-mne-0.23.4+dfsg/tools/github_actions_dependencies.sh 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tools/github_actions_dependencies.sh 2022-08-03 15:48:14.000000000 +0000 @@ -1,28 +1,54 @@ #!/bin/bash -ef +STD_ARGS="--progress-bar off --upgrade" +EXTRA_ARGS="" if [ ! -z "$CONDA_ENV" ]; then pip uninstall -yq mne elif [ ! -z "$CONDA_DEPENDENCIES" ]; then conda install -y $CONDA_DEPENDENCIES -else # pip --pre 3.9 (missing dipy in pre) +else # Changes here should also go in the interactive_test CircleCI job - python -m pip install --progress-bar off --upgrade "pip!=20.3.0" setuptools wheel + python -m pip install $STD_ARGS pip setuptools wheel + echo "Numpy" pip uninstall -yq numpy - pip install --progress-bar off --upgrade --pre --only-binary ":all:" python-dateutil pytz joblib threadpoolctl - pip install --progress-bar off --upgrade --pre --only-binary ":all:" -i "https://pypi.anaconda.org/scipy-wheels-nightly/simple" --extra-index-url https://www.riverbankcomputing.com/pypi/simple numpy scipy pandas scikit-learn PyQt5 - pip install --progress-bar off --upgrade --pre --only-binary ":all:" -f "https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackcdn.com" h5py pillow matplotlib - pip install --progress-bar off --upgrade --pre --only-binary ":all:" numba llvmlite - # built using vtk master branch on an Ubuntu 18.04.5 VM and uploaded to OSF: - wget -q https://osf.io/kej3v/download -O vtk-9.0.20201117-cp39-cp39-linux_x86_64.whl - pip install --progress-bar off vtk-9.0.20201117-cp39-cp39-linux_x86_64.whl + echo "Date utils" + # https://pip.pypa.io/en/latest/user_guide/#possible-ways-to-reduce-backtracking-occurring + pip install $STD_ARGS --pre --only-binary ":all:" python-dateutil pytz joblib threadpoolctl six + echo "PyQt6" + # XXX: the wheels for PyQt6-sip 13.4 are not available yet from https://www.riverbankcomputing.com/pypi/simple + # pip install $STD_ARGS --pre --only-binary ":all:" --no-deps --extra-index-url https://www.riverbankcomputing.com/pypi/simple PyQt6 PyQt6-sip PyQt6-Qt6 + pip install $STD_ARGS --only-binary ":all:" PyQt6 PyQt6-sip PyQt6-Qt6 + echo "NumPy/SciPy/pandas etc." + pip install $STD_ARGS --pre --only-binary ":all:" --no-deps --default-timeout=60 -i "https://pypi.anaconda.org/scipy-wheels-nightly/simple" numpy scipy pandas scikit-learn statsmodels dipy + echo "H5py, pillow, matplotlib" + pip install $STD_ARGS --pre --only-binary ":all:" --no-deps -f "https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackcdn.com" h5py pillow matplotlib + # We don't install Numba here because it forces an old NumPy version + echo "nilearn" + pip install $STD_ARGS --pre https://github.com/nilearn/nilearn/zipball/main + echo "VTK" + # Have to use our own version until VTK releases a 3.10 build + wget -q https://osf.io/ajder/download -O vtk-9.1.20220406.dev0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl + pip install $STD_ARGS --pre --only-binary ":all:" vtk-9.1.20220406.dev0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl + python -c "import vtk" echo "PyVista" pip install --progress-bar off https://github.com/pyvista/pyvista/zipball/main echo "pyvistaqt" pip install --progress-bar off https://github.com/pyvista/pyvistaqt/zipball/main - echo "imageio-ffmpeg, xlrd, mffpy" - pip install --progress-bar off --pre mayavi imageio-ffmpeg xlrd mffpy + echo "imageio-ffmpeg, xlrd, mffpy, python-picard" + pip install --progress-bar off --pre imageio-ffmpeg xlrd mffpy python-picard patsy + if [ "$OSTYPE" == "darwin"* ]; then + echo "pyobjc-framework-Cocoa" + pip install --progress-bar off pyobjc-framework-Cocoa>=5.2.0 + fi + EXTRA_ARGS="--pre" fi -pip install --progress-bar off --upgrade -r requirements_testing.txt +# for compat_minimal and compat_old, we don't want to --upgrade +if [ ! -z "$CONDA_DEPENDENCIES" ]; then + pip install -r requirements_base.txt -r requirements_testing.txt +else + pip install $STD_ARGS $EXTRA_ARGS -r requirements_base.txt -r requirements_testing.txt -r requirements_hdf5.txt +fi + if [ "${DEPS}" != "minimal" ]; then - pip install --progress-bar off --upgrade -r requirements_testing_extra.txt + pip install $STD_ARGS $EXTRA_ARGS -r requirements_testing_extra.txt fi diff -Nru python-mne-0.23.4+dfsg/tools/github_actions_download.sh python-mne-1.1.0+dfsg/tools/github_actions_download.sh --- python-mne-0.23.4+dfsg/tools/github_actions_download.sh 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tools/github_actions_download.sh 2022-08-03 15:48:14.000000000 +0000 @@ -2,4 +2,5 @@ if [ "${DEPS}" != "minimal" ]; then python -c 'import mne; mne.datasets.testing.data_path(verbose=True)'; + python -c "import mne; mne.datasets.misc.data_path(verbose=True)"; fi diff -Nru python-mne-0.23.4+dfsg/tools/github_actions_infos.sh python-mne-1.1.0+dfsg/tools/github_actions_infos.sh --- python-mne-0.23.4+dfsg/tools/github_actions_infos.sh 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tools/github_actions_infos.sh 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,5 @@ #!/bin/bash -ef -mne sys_info +which mne +mne sys_info -pd python -c "import numpy; numpy.show_config()" diff -Nru python-mne-0.23.4+dfsg/tools/github_actions_test.sh python-mne-1.1.0+dfsg/tools/github_actions_test.sh --- python-mne-0.23.4+dfsg/tools/github_actions_test.sh 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tools/github_actions_test.sh 2022-08-03 15:48:14.000000000 +0000 @@ -1,31 +1,10 @@ #!/bin/bash -ef -# Remove numpydoc tests on older Python (builtin docstrings not as good) -if [ "${PYTHON_VERSION}" == "3.6" ]; then - pip uninstall -yq numpydoc; -fi; - -# Test run_tests_if_main -if [ "${DEPS}" == "minimal" ]; then - pip uninstall -yq mne; - pip install -e .; - python mne/tests/test_evoked.py; -fi; - USE_DIRS="mne/" if [ "${CI_OS_NAME}" != "osx" ]; then - CONDITION="not ultraslowtest" + CONDITION="not (ultraslowtest or pgtest)" else - CONDITION="not slowtest" + CONDITION="not (slowtest or pgtest)" fi echo 'pytest -m "${CONDITION}" --tb=short --cov=mne --cov-report xml -vv ${USE_DIRS}' pytest -m "${CONDITION}" --tb=short --cov=mne --cov-report xml -vv ${USE_DIRS} - -# run the minimal one with the testing data as well -if [ "${DEPS}" == "minimal" ]; then - export MNE_SKIP_TESTING_DATASET_TESTS=false; - python -c 'import mne; mne.datasets.testing.data_path(verbose=True)'; -fi; -if [ "${DEPS}" == "minimal" ]; then - pytest -m "${CONDITION}" --tb=short --cov=mne -vv ${USE_DIRS}; -fi; diff -Nru python-mne-0.23.4+dfsg/tools/setup_xvfb.sh python-mne-1.1.0+dfsg/tools/setup_xvfb.sh --- python-mne-0.23.4+dfsg/tools/setup_xvfb.sh 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tools/setup_xvfb.sh 2022-08-03 15:48:14.000000000 +0000 @@ -1,5 +1,15 @@ #!/bin/bash -ef +# this is only relevant for GitHub Actions, but it avoids +# https://github.com/actions/virtual-environments/issues/323 +# via +# https://github.community/t/ubuntu-latest-apt-repository-list-issues/17182/10#M4501 +for apt_file in `grep -lr microsoft /etc/apt/sources.list.d/`; do + echo "Removing $apt_file" + sudo rm $apt_file +done + +# This also includes the libraries necessary for PyQt5/PyQt6 sudo apt update -sudo apt install -yqq libxkbcommon-x11-0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-xinerama0 libxcb-xfixes0 libopengl0 +sudo apt install -yqq xvfb libxkbcommon-x11-0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-xinerama0 libxcb-xfixes0 libopengl0 libegl1 libosmesa6 mesa-utils libxcb-shape0 /sbin/start-stop-daemon --start --quiet --pidfile /tmp/custom_xvfb_99.pid --make-pidfile --background --exec /usr/bin/Xvfb -- :99 -screen 0 1400x900x24 -ac +extension GLX +render -noreset diff -Nru python-mne-0.23.4+dfsg/tutorials/clinical/10_ieeg_localize.py python-mne-1.1.0+dfsg/tutorials/clinical/10_ieeg_localize.py --- python-mne-0.23.4+dfsg/tutorials/clinical/10_ieeg_localize.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/tutorials/clinical/10_ieeg_localize.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,541 @@ +# -*- coding: utf-8 -*- +""" +.. _tut-ieeg-localize: + +======================================== +Locating intracranial electrode contacts +======================================== + +Analysis of intracranial electrophysiology recordings typically involves +finding the position of each contact relative to brain structures. In a +typical setup, the brain and the electrode locations will be in two places +and will have to be aligned; the brain is best visualized by a +pre-implantation magnetic resonance (MR) image whereas the electrode contact +locations are best visualized in a post-implantation computed tomography (CT) +image. The CT image has greater intensity than the background at each of the +electrode contacts and for the skull. Using the skull, the CT can be aligned +to MR-space. This accomplishes our goal of obtaining contact locations in +MR-space (which is where the brain structures are best determined using the +:ref:`tut-freesurfer-reconstruction`). Contact locations in MR-space can also +be warped to a template space such as ``fsaverage`` for group comparisons. +Please note that this tutorial requires ``nibabel``, ``nilearn`` and ``dipy`` +which can be installed using ``pip`` as well as 3D plotting +(see :ref:`manual-install`). + +Support for intracranial electrophysiology analysis in MNE was added after +the original publication, so please cite :footcite:`RockhillEtAl2022` if you +use this module in your analysis to support the addition of new projects to +MNE. +""" + +# Authors: Alex Rockhill +# Eric Larson +# +# License: BSD-3-Clause + +# %% +import numpy as np +import matplotlib.pyplot as plt + +import nibabel as nib +import nilearn.plotting +from dipy.align import resample + +import mne +from mne.datasets import fetch_fsaverage + +# paths to mne datasets: sample sEEG and FreeSurfer's fsaverage subject, +# which is in MNI space +misc_path = mne.datasets.misc.data_path() +sample_path = mne.datasets.sample.data_path() +subjects_dir = sample_path / 'subjects' + +# use mne-python's fsaverage data +fetch_fsaverage(subjects_dir=subjects_dir, verbose=True) # downloads if needed + +# GUI requires pyvista backend +mne.viz.set_3d_backend('pyvistaqt') + +############################################################################### +# Aligning the T1 to ACPC +# ======================= +# +# For intracranial electrophysiology recordings, the Brain Imaging Data +# Structure (BIDS) standard requires that coordinates be aligned to the +# anterior commissure and posterior commissure (ACPC-aligned). Therefore, it is +# recommended that you do this alignment before finding the positions of the +# channels in your recording. Doing this will make the "mri" (aka surface RAS) +# coordinate frame an ACPC coordinate frame. This can be done using +# Freesurfer's freeview: +# +# .. code-block:: console +# +# $ freeview $MISC_PATH/seeg/sample_seeg_T1.mgz +# +# And then interact with the graphical user interface: +# +# First, it is recommended to change the cursor style to long, this can be done +# through the menu options like so: +# +# :menuselection:`Freeview --> Preferences --> General --> Cursor style +# --> Long` +# +# Then, the image needs to be aligned to ACPC to look like the image below. +# This can be done by pulling up the transform popup from the menu like so: +# +# :menuselection:`Tools --> Transform Volume` +# +# .. note:: +# Be sure to set the text entry box labeled RAS (not TkReg RAS) to +# ``0 0 0`` before beginning the transform. +# +# Then translate the image until the crosshairs meet on the AC and +# run through the PC as shown in the plot. The eyes should be in +# the ACPC plane and the image should be rotated until they are symmetrical, +# and the crosshairs should transect the midline of the brain. +# Be sure to use both the rotate and the translate menus and save the volume +# after you're finished using ``Save Volume As`` in the transform popup +# :footcite:`HamiltonEtAl2017`. + +T1 = nib.load(misc_path / 'seeg' / 'sample_seeg' / 'mri' / 'T1.mgz') +viewer = T1.orthoview() +viewer.set_position(0, 9.9, 5.8) +viewer.figs[0].axes[0].annotate( + 'PC', (107, 108), xytext=(10, 75), color='white', + horizontalalignment='center', + arrowprops=dict(facecolor='white', lw=0.5, width=2, headwidth=5)) +viewer.figs[0].axes[0].annotate( + 'AC', (137, 108), xytext=(246, 75), color='white', + horizontalalignment='center', + arrowprops=dict(facecolor='white', lw=0.5, width=2, headwidth=5)) + +# %% +# Freesurfer recon-all +# ==================== +# +# The first step is the most time consuming; the freesurfer reconstruction. +# This process segments out the brain from the rest of the MR image and +# determines which voxels correspond to each brain area based on a template +# deformation. This process takes approximately 8 hours so plan accordingly. +# The example dataset contains the data from completed reconstruction so +# we will proceed using that. +# +# .. code-block:: console +# +# $ export SUBJECT=sample_seeg +# $ export SUBJECTS_DIR=$MY_DATA_DIRECTORY +# $ recon-all -subjid $SUBJECT -sd $SUBJECTS_DIR \ +# -i $MISC_PATH/seeg/sample_seeg_T1.mgz -all -deface +# +# .. note:: +# You may need to include an additional ``-cw256`` flag which can be added +# to the end of the recon-all command if your MR scan is not +# ``256 × 256 × 256`` voxels. +# +# .. note:: +# Using the ``-deface`` flag will create a defaced, anonymized T1 image +# located at ``$MY_DATA_DIRECTORY/$SUBJECT/mri/orig_defaced.mgz``, +# which is helpful for when you publish your data. You can also use +# :func:`mne_bids.write_anat` and pass ``deface=True``. + + +# %% +# Aligning the CT to the MR +# ========================= +# +# Let's load our T1 and CT images and visualize them. You can hardly +# see the CT, it's so misaligned that all you can see is part of the +# stereotactic frame that is anteriolateral to the skull in the middle plot. +# Clearly, we need to align the CT to the T1 image. + +def plot_overlay(image, compare, title, thresh=None): + """Define a helper function for comparing plots.""" + image = nib.orientations.apply_orientation( + np.asarray(image.dataobj), nib.orientations.axcodes2ornt( + nib.orientations.aff2axcodes(image.affine))).astype(np.float32) + compare = nib.orientations.apply_orientation( + np.asarray(compare.dataobj), nib.orientations.axcodes2ornt( + nib.orientations.aff2axcodes(compare.affine))).astype(np.float32) + if thresh is not None: + compare[compare < np.quantile(compare, thresh)] = np.nan + fig, axes = plt.subplots(1, 3, figsize=(12, 4)) + fig.suptitle(title) + for i, ax in enumerate(axes): + ax.imshow(np.take(image, [image.shape[i] // 2], axis=i).squeeze().T, + cmap='gray') + ax.imshow(np.take(compare, [compare.shape[i] // 2], + axis=i).squeeze().T, cmap='gist_heat', alpha=0.5) + ax.invert_yaxis() + ax.axis('off') + fig.tight_layout() + + +CT_orig = nib.load(misc_path / 'seeg' / 'sample_seeg_CT.mgz') + +# resample to T1's definition of world coordinates +CT_resampled = resample(moving=np.asarray(CT_orig.dataobj), + static=np.asarray(T1.dataobj), + moving_affine=CT_orig.affine, + static_affine=T1.affine) +plot_overlay(T1, CT_resampled, 'Unaligned CT Overlaid on T1', thresh=0.95) +del CT_resampled + +# %% +# Now we need to align our CT image to the T1 image. +# +# We want this to be a rigid transformation (just rotation + translation), +# so we don't do a full affine registration (that includes shear) here. +# This takes a while (~10 minutes) to execute so we skip actually running it +# here:: +# +# reg_affine, _ = mne.transforms.compute_volume_registration( +# CT_orig, T1, pipeline='rigids', zooms=dict(translation=5))) +# +# Instead we just hard-code the resulting 4x4 matrix: + +reg_affine = np.array([ + [0.99270756, -0.03243313, 0.11610254, -133.094156], + [0.04374389, 0.99439665, -0.09623816, -97.58320673], + [-0.11233068, 0.10061512, 0.98856381, -84.45551601], + [0., 0., 0., 1.]]) +# use a cval='1%' here to make the values outside the domain of the CT +# the same as the background level during interpolation +CT_aligned = mne.transforms.apply_volume_registration( + CT_orig, T1, reg_affine, cval='1%') +plot_overlay(T1, CT_aligned, 'Aligned CT Overlaid on T1', thresh=0.95) +del CT_orig + +# %% +# .. note:: +# Alignment failures sometimes occur which requires manual pre-alignment. +# Freesurfer's ``freeview`` can be used to to align manually +# +# .. code-block:: console +# +# $ freeview $MISC_PATH/seeg/sample_seeg/mri/T1.mgz \ +# $MISC_PATH/seeg/sample_seeg_CT.mgz:colormap=heat:opacity=0.6 +# +# - Navigate to the upper toolbar, go to +# :menuselection:`Tools --> Transform Volume` +# - Use the rotation and translation slide bars to align the CT +# to the MR (be sure to have the CT selected in the upper left menu) +# - Save the linear transform array (lta) file using the ``Save Reg...`` +# button +# +# Since we really require as much precision as possible for the +# alignment, we should rerun the algorithm starting with the manual +# alignment. This time, we just want to skip to the most exact rigid +# alignment, without smoothing, since the manual alignment is already +# very close. +# +# .. code-block:: python +# +# from dipy.align import affine_registration +# # load transform +# manual_reg_affine_vox = mne.read_lta(op.join( # the path used above +# misc_path, 'seeg', 'sample_seeg_CT_aligned_manual.mgz.lta')) +# # convert from vox->vox to ras->ras +# manual_reg_affine = \ +# CT_orig.affine @ np.linalg.inv(manual_reg_affine_vox) \ +# @ np.linalg.inv(T1.affine) +# CT_aligned_fix_img = affine_registration( +# moving=np.array(CT_orig.dataobj), static=np.array(T1.dataobj), +# moving_affine=CT_orig.affine, static_affine=T1.affine, +# pipeline=['rigid'], starting_affine=manual_reg_affine, +# level_iters=[100], sigmas=[0], factors=[1])[0] +# CT_aligned = nib.MGHImage( +# CT_aligned_fix_img.astype(np.float32), T1.affine) +# +# The rest of the tutorial can then be completed using ``CT_aligned`` +# from this point on. + +# %% +# We can now see how the CT image looks properly aligned to the T1 image. +# +# .. note:: +# The hyperintense skull is actually aligned to the hypointensity between +# the brain and the scalp. The brighter area surrounding the skull in the +# MR is actually subcutaneous fat. + +# make low intensity parts of the CT transparent for easier visualization +CT_data = CT_aligned.get_fdata().copy() +CT_data[CT_data < np.quantile(CT_data, 0.95)] = np.nan +T1_data = np.asarray(T1.dataobj) + +fig, axes = plt.subplots(1, 3, figsize=(12, 6)) +for ax in axes: + ax.axis('off') +axes[0].imshow(T1_data[T1.shape[0] // 2], cmap='gray') +axes[0].set_title('MR') +axes[1].imshow(np.asarray(CT_aligned.dataobj)[CT_aligned.shape[0] // 2], + cmap='gray') +axes[1].set_title('CT') +axes[2].imshow(T1_data[T1.shape[0] // 2], cmap='gray') +axes[2].imshow(CT_data[CT_aligned.shape[0] // 2], cmap='gist_heat', alpha=0.5) +for ax in (axes[0], axes[2]): + ax.annotate('Subcutaneous fat', (110, 52), xytext=(100, 30), + color='white', horizontalalignment='center', + arrowprops=dict(facecolor='white')) +for ax in axes: + ax.annotate('Skull (dark in MR, bright in CT)', (40, 175), + xytext=(120, 246), horizontalalignment='center', + color='white', arrowprops=dict(facecolor='white')) +axes[2].set_title('CT aligned to MR') +fig.tight_layout() +del CT_data, T1 + +# %% +# Now we need to estimate the "head" coordinate transform. +# +# MNE stores digitization montages in a coordinate frame called "head" +# defined by fiducial points (origin is halfway between the LPA and RPA +# see :ref:`tut-source-alignment`). For sEEG, it is convenient to get an +# estimate of the location of the fiducial points for the subject +# using the Talairach transform (see :func:`mne.coreg.get_mni_fiducials`) +# to use to define the coordinate frame so that we don't have to manually +# identify their location. + +# estimate head->mri transform +subj_trans = mne.coreg.estimate_head_mri_t( + 'sample_seeg', misc_path / 'seeg') + +# %% +# Marking the Location of Each Electrode Contact +# ============================================== +# +# Now, the CT and the MR are in the same space, so when you are looking at a +# point in CT space, it is the same point in MR space. So now everything is +# ready to determine the location of each electrode contact in the +# individual subject's anatomical space (T1-space). To do this, we can use the +# MNE intracranial electrode location graphical user interface. +# +# .. note:: The most useful coordinate frame for intracranial electrodes is +# generally the ``surface RAS`` coordinate frame because that is +# the coordinate frame that all the surface and image files that +# Freesurfer outputs are in, see :ref:`tut-freesurfer-mne`. These are +# useful for finding the brain structures nearby each contact and +# plotting the results. +# +# See the following video on how to operate the GUI or follow the steps below: +# +# .. youtube:: 8JWDJhXq0VY +# +# - Click in each image to navigate to each electrode contact +# - Select the contact name in the right panel +# - Press the "Mark" button or the "m" key to associate that +# position with that contact +# - Repeat until each contact is marked, they will both appear as circles +# in the plots and be colored in the sidebar when marked +# +# .. note:: The channel locations are saved to the ``raw`` object every time +# a location is marked or removed so there is no "Save" button. +# +# .. note:: Using the scroll or +/- arrow keys you can zoom in and out, +# and the up/down, left/right and page up/page down keys allow +# you to move one slice in any direction. This information is +# available in the help menu, accessible by pressing the "h" key. +# +# .. note:: If "Snap to Center" is on, this will use the radius so be +# sure to set it properly. + +# sphinx_gallery_thumbnail_number = 5 + +# load electrophysiology data to find channel locations for +# (the channels are already located in the example) + +raw = mne.io.read_raw(misc_path / 'seeg' / 'sample_seeg_ieeg.fif') + +# you may want to add `block=True` to halt execution until you have interacted +# with the GUI to find the channel positions, that way the raw object can +# be used later in the script (e.g. saved with channel positions) +mne.gui.locate_ieeg(raw.info, subj_trans, CT_aligned, + subject='sample_seeg', + subjects_dir=misc_path / 'seeg') +# The `raw` object is modified to contain the channel locations + +# %% +# Let's do a quick sidebar and show what this looks like for ECoG as well. + +T1_ecog = nib.load(misc_path / 'ecog' / 'sample_ecog' / 'mri' / 'T1.mgz') +CT_orig_ecog = nib.load(misc_path / 'ecog' / 'sample_ecog_CT.mgz') + +# pre-computed affine from `mne.transforms.compute_volume_registration` +reg_affine = np.array([ + [0.99982382, -0.00414586, -0.01830679, 0.15413965], + [0.00549597, 0.99721885, 0.07432601, -1.54316131], + [0.01794773, -0.07441352, 0.99706595, -1.84162514], + [0., 0., 0., 1.]]) +# align CT +CT_aligned_ecog = mne.transforms.apply_volume_registration( + CT_orig_ecog, T1_ecog, reg_affine, cval='1%') + +raw_ecog = mne.io.read_raw(misc_path / 'ecog' / 'sample_ecog_ieeg.fif') +# use estimated `trans` which was used when the locations were found previously +subj_trans_ecog = mne.coreg.estimate_head_mri_t( + 'sample_ecog', misc_path / 'ecog') +mne.gui.locate_ieeg(raw_ecog.info, subj_trans_ecog, CT_aligned_ecog, + subject='sample_ecog', + subjects_dir=misc_path / 'ecog') + +# %% +# For ECoG, we typically want to account for "brain shift" or shrinking of the +# brain away from the skull/dura due to changes in pressure during the +# craniotomy +# Note: this requires the BEM surfaces to have been computed e.g. using +# :ref:`mne watershed_bem` or :ref:`mne flash_bem`. +# First, let's plot the localized sensor positions without modification. + +# plot projected sensors +brain_kwargs = dict(cortex='low_contrast', alpha=0.2, background='white') +brain = mne.viz.Brain('sample_ecog', subjects_dir=misc_path / 'ecog', + title='Before Projection', **brain_kwargs) +brain.add_sensors(raw_ecog.info, trans=subj_trans_ecog) +view_kwargs = dict(azimuth=60, elevation=100, distance=350, + focalpoint=(0, 0, -15)) +brain.show_view(**view_kwargs) + +# %% +# Now, let's project the sensors to the brain surface and re-plot them. + +# project sensors to the brain surface +raw_ecog.info = mne.preprocessing.ieeg.project_sensors_onto_brain( + raw_ecog.info, subj_trans_ecog, 'sample_ecog', + subjects_dir=misc_path / 'ecog') + +# plot projected sensors +brain = mne.viz.Brain('sample_ecog', subjects_dir=misc_path / 'ecog', + title='After Projection', **brain_kwargs) +brain.add_sensors(raw_ecog.info, trans=subj_trans_ecog) +brain.show_view(**view_kwargs) + +# %% +# Let's plot the electrode contact locations on the subject's brain. +# +# MNE stores digitization montages in a coordinate frame called "head" +# defined by fiducial points (origin is halfway between the LPA and RPA +# see :ref:`tut-source-alignment`). For sEEG, it is convenient to get an +# estimate of the location of the fiducial points for the subject +# using the Talairach transform (see :func:`mne.coreg.get_mni_fiducials`) +# to use to define the coordinate frame so that we don't have to manually +# identify their location. The estimated head->mri ``trans`` was used +# when the electrode contacts were localized so we need to use it again here. + +# plot the alignment +brain = mne.viz.Brain('sample_seeg', subjects_dir=misc_path / 'seeg', + **brain_kwargs) +brain.add_sensors(raw.info, trans=subj_trans) +brain.show_view(**view_kwargs) + +# %% +# Warping to a Common Atlas +# ========================= +# +# Electrode contact locations are often compared across subjects in a template +# space such as ``fsaverage`` or ``cvs_avg35_inMNI152``. To transform electrode +# contact locations to that space, we need to determine a function that maps +# from the subject's brain to the template brain. We will use the symmetric +# diffeomorphic registration (SDR) implemented by ``Dipy`` to do this. +# +# Before we can make a function to account for individual differences in the +# shape and size of brain areas, we need to fix the alignment of the brains. +# The plot below shows that they are not yet aligned. + +# load the subject's brain and the Freesurfer "fsaverage" template brain +subject_brain = nib.load( + misc_path / 'seeg' / 'sample_seeg' / 'mri' / 'brain.mgz') +template_brain = nib.load( + subjects_dir / 'fsaverage' / 'mri' / 'brain.mgz') + +plot_overlay(template_brain, subject_brain, + 'Alignment with fsaverage before Affine Registration') + +# %% +# Now, we'll register the affine of the subject's brain to the template brain. +# This aligns the two brains, preparing the subject's brain to be warped +# to the template. +# +# .. warning:: Here we use custom ``zooms`` just for speed (this downsamples +# the image resolution), in general we recommend using +# ``zooms=None`` (default) for highest accuracy! + +zooms = dict(translation=10, rigid=10, affine=10, sdr=5) +reg_affine, sdr_morph = mne.transforms.compute_volume_registration( + subject_brain, template_brain, zooms=zooms, verbose=True) +subject_brain_sdr = mne.transforms.apply_volume_registration( + subject_brain, template_brain, reg_affine, sdr_morph) + +# apply the transform to the subject brain to plot it +plot_overlay(template_brain, subject_brain_sdr, + 'Alignment with fsaverage after SDR Registration') + +del subject_brain, template_brain + +# %% +# Finally, we'll apply the registrations to the electrode contact coordinates. +# The brain image is warped to the template but the goal was to warp the +# positions of the electrode contacts. To do that, we'll make an image that is +# a lookup table of the electrode contacts. In this image, the background will +# be ``0`` s all the bright voxels near the location of the first contact will +# be ``1`` s, the second ``2`` s and so on. This image can then be warped by +# the SDR transform. We can finally recover a position by averaging the +# positions of all the voxels that had the contact's lookup number in +# the warped image. + +# first we need our montage but it needs to be converted to "mri" coordinates +# using our ``subj_trans`` +montage = raw.get_montage() +montage.apply_trans(subj_trans) + +montage_warped, elec_image, warped_elec_image = mne.warp_montage_volume( + montage, CT_aligned, reg_affine, sdr_morph, thresh=0.25, + subject_from='sample_seeg', subjects_dir_from=misc_path / 'seeg', + subject_to='fsaverage', subjects_dir_to=subjects_dir) + +fig, axes = plt.subplots(2, 1, figsize=(8, 8)) +nilearn.plotting.plot_glass_brain(elec_image, axes=axes[0], cmap='Dark2') +fig.text(0.1, 0.65, 'Subject T1', rotation='vertical') +nilearn.plotting.plot_glass_brain(warped_elec_image, axes=axes[1], + cmap='Dark2') +fig.text(0.1, 0.25, 'fsaverage', rotation='vertical') +fig.suptitle('Electrodes warped to fsaverage') + +del CT_aligned + +# %% +# We can now plot the result. You can compare this to the plot in +# :ref:`tut-working-with-seeg` to see the difference between this morph, which +# is more complex, and the less-complex, linear Talairach transformation. +# By accounting for the shape of this particular subject's brain using the +# SDR to warp the positions of the electrode contacts, the position in the +# template brain is able to be more accurately estimated. +# +# .. note:: The accuracy of warping to the template has been degraded by +# using ``zooms`` to downsample the image before registration +# which makes some of the contacts inaccurately appear outside +# the brain. + +# first we need to add fiducials so that we can define the "head" coordinate +# frame in terms of them (with the origin at the center between LPA and RPA) +montage_warped.add_estimated_fiducials('fsaverage', subjects_dir) + +# compute the head<->mri ``trans`` now using the fiducials +template_trans = mne.channels.compute_native_head_t(montage_warped) + +# now we can set the montage and, because there are fiducials in the montage, +# the montage will be properly transformed to "head" coordinates when we do +# (this step uses ``template_trans`` but it is recomputed behind the scenes) +raw.set_montage(montage_warped) + +# plot the resulting alignment +brain = mne.viz.Brain('fsaverage', subjects_dir=subjects_dir, **brain_kwargs) +brain.add_sensors(raw.info, trans=template_trans) +brain.show_view(**view_kwargs) + +# %% +# This pipeline was developed based on previous work +# :footcite:`HamiltonEtAl2017`. +# +# References +# ========== +# +# .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/tutorials/clinical/20_seeg.py python-mne-1.1.0+dfsg/tutorials/clinical/20_seeg.py --- python-mne-0.23.4+dfsg/tutorials/clinical/20_seeg.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tutorials/clinical/20_seeg.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,12 +1,13 @@ +# -*- coding: utf-8 -*- """ -.. _tut_working_with_seeg: +.. _tut-working-with-seeg: ====================== Working with sEEG data ====================== -MNE supports working with more than just MEG and EEG data. Here we show some -of the functions that can be used to facilitate working with +MNE-Python supports working with more than just MEG and EEG data. Here we show +some of the functions that can be used to facilitate working with stereoelectroencephalography (sEEG) data. This example shows how to use: @@ -18,116 +19,138 @@ Note that our sample sEEG electrodes are already assumed to be in MNI space. If you want to map positions from your subject MRI space to MNI fsaverage space, you must apply the FreeSurfer's talairach.xfm transform -for your dataset. You can take a look at :ref:`tut-freesurfer-mne` for -more information. +for your dataset. You can take a look at :ref:`tut-freesurfer-mne` for more +information. For an example that involves ECoG data, channel locations in a subject-specific MRI, or projection into a surface, see -:ref:`tut_working_with_ecog`. In the ECoG example, we show +:ref:`tut-working-with-ecog`. In the ECoG example, we show how to visualize surface grid channels on the brain. + +Please note that this tutorial requires 3D plotting dependencies, +see :ref:`manual-install`. """ # Authors: Eric Larson # Adam Li +# Alex Rockhill # -# License: BSD (3-clause) +# License: BSD-3-Clause -import os.path as op +# %% import numpy as np -import pandas as pd import mne from mne.datasets import fetch_fsaverage -print(__doc__) - # paths to mne datasets - sample sEEG and FreeSurfer's fsaverage subject # which is in MNI space misc_path = mne.datasets.misc.data_path() sample_path = mne.datasets.sample.data_path() -subject = 'fsaverage' -subjects_dir = sample_path + '/subjects' +subjects_dir = sample_path / 'subjects' # use mne-python's fsaverage data fetch_fsaverage(subjects_dir=subjects_dir, verbose=True) # downloads if needed -############################################################################### -# Let's load some sEEG electrode locations and names, and turn them into -# a :class:`mne.channels.DigMontage` class. First, use pandas to read in the -# ``.tsv`` file. - -# In mne-python, the electrode coordinates are required to be in meters -elec_df = pd.read_csv(misc_path + '/seeg/sample_seeg_electrodes.tsv', - sep='\t', header=0, index_col=None) -ch_names = elec_df['name'].tolist() -ch_coords = elec_df[['x', 'y', 'z']].to_numpy(dtype=float) - -# the test channel coordinates were in mm, so we convert them to meters -ch_coords = ch_coords / 1000. - -# create dictionary of channels and their xyz coordinates (now in MNI space) -ch_pos = dict(zip(ch_names, ch_coords)) - -# Ideally the nasion/LPA/RPA will also be present from the digitization, here -# we use fiducials estimated from the subject's FreeSurfer MNI transformation: -lpa, nasion, rpa = mne.coreg.get_mni_fiducials( - subject, subjects_dir=subjects_dir) -lpa, nasion, rpa = lpa['r'], nasion['r'], rpa['r'] - -############################################################################### -# Now we make a :class:`mne.channels.DigMontage` stating that the sEEG -# contacts are in the FreeSurfer surface RAS (i.e., MRI) coordinate system -# for the given subject. Keep in mind that ``fsaverage`` is special in that -# it is already in MNI space. - -montage = mne.channels.make_dig_montage( - ch_pos, coord_frame='mri', nasion=nasion, lpa=lpa, rpa=rpa) -print('Created %s channel positions' % len(ch_names)) - -############################################################################### -# Now we get the :term:`trans` that transforms from our MRI coordinate system -# to the head coordinate frame. This transform will be applied to the -# data when applying the montage so that standard plotting functions like -# :func:`mne.viz.plot_evoked_topomap` will be aligned properly. +# %% +# Let's load some sEEG data with channel locations and make epochs. -trans = mne.channels.compute_native_head_t(montage) -print(trans) +raw = mne.io.read_raw(misc_path / 'seeg' / 'sample_seeg_ieeg.fif') + +events, event_id = mne.events_from_annotations(raw) +epochs = mne.Epochs(raw, events, event_id, detrend=1, baseline=None) +epochs = epochs['Response'][0] # just process one epoch of data for speed + +# %% +# Let use the Talairach transform computed in the Freesurfer recon-all +# to apply the Freesurfer surface RAS ('mri') to MNI ('mni_tal') transform. + +montage = epochs.get_montage() + +# first we need a head to mri transform since the data is stored in "head" +# coordinates, let's load the mri to head transform and invert it +this_subject_dir = misc_path / 'seeg' +head_mri_t = mne.coreg.estimate_head_mri_t('sample_seeg', this_subject_dir) +# apply the transform to our montage +montage.apply_trans(head_mri_t) -############################################################################### -# Now that we have our montage, we can load in our corresponding -# time-series data and set the montage to the raw data. - -# first we'll load in the sample dataset -raw = mne.io.read_raw_edf(misc_path + '/seeg/sample_seeg.edf') - -# drop bad channels -raw.info['bads'].extend([ch for ch in raw.ch_names if ch not in ch_names]) -raw.load_data() -raw.drop_channels(raw.info['bads']) -raw.crop(0, 2) # just process 2 sec of data for speed - -# attach montage -raw.set_montage(montage) - -# set channel types to sEEG (instead of EEG) that have actual positions -raw.set_channel_types( - {ch_name: 'seeg' if np.isfinite(ch_pos[ch_name]).all() else 'misc' - for ch_name in raw.ch_names}) +# now let's load our Talairach transform and apply it +mri_mni_t = mne.read_talxfm('sample_seeg', misc_path / 'seeg') +montage.apply_trans(mri_mni_t) # mri to mni_tal (MNI Taliarach) -############################################################################### +# for fsaverage, "mri" and "mni_tal" are equivalent and, since +# we want to plot in fsaverage "mri" space, we need use an identity +# transform to equate these coordinate frames +montage.apply_trans( + mne.transforms.Transform(fro='mni_tal', to='mri', trans=np.eye(4))) + +epochs.set_montage(montage) + +# %% # Let's check to make sure everything is aligned. +# +# .. note:: +# The most rostral electrode in the temporal lobe is outside the +# fsaverage template brain. This is not ideal but it is the best that +# the linear Talairach transform can accomplish. A more complex +# transform is necessary for more accurate warping, see +# :ref:`tut-ieeg-localize`. -fig = mne.viz.plot_alignment(raw.info, trans, 'fsaverage', +# compute the transform to head for plotting +trans = mne.channels.compute_native_head_t(montage) +# note that this is the same as: +# ``mne.transforms.invert_transform( +# mne.transforms.combine_transforms(head_mri_t, mri_mni_t))`` + +fig = mne.viz.plot_alignment(epochs.info, trans, 'fsaverage', subjects_dir=subjects_dir, show_axes=True, - surfaces=["pial", "head"]) + surfaces=['pial', 'head'], coord_frame='mri') + +# %% +# Let's also look at which regions of interest are nearby our electrode +# contacts. + +aseg = 'aparc+aseg' # parcellation/anatomical segmentation atlas +labels, colors = mne.get_montage_volume_labels( + montage, 'fsaverage', subjects_dir=subjects_dir, aseg=aseg) + +# separate by electrodes which have names like LAMY 1 +electrodes = set([''.join([lttr for lttr in ch_name + if not lttr.isdigit() and lttr != ' ']) + for ch_name in montage.ch_names]) +print(f'Electrodes in the dataset: {electrodes}') + +electrodes = ('LPM', 'LSMA') # choose two for this example +for elec in electrodes: + picks = [ch_name for ch_name in epochs.ch_names if elec in ch_name] + fig, ax = mne.viz.plot_channel_labels_circle(labels, colors, picks=picks) + fig.text(0.3, 0.9, 'Anatomical Labels', color='white') + +# %% +# Now, let's the electrodes and a few regions of interest that the contacts +# of the electrode are proximal to. + +picks = [ii for ii, ch_name in enumerate(epochs.ch_names) if + any([elec in ch_name for elec in electrodes])] +labels = ('ctx-lh-caudalmiddlefrontal', 'ctx-lh-precentral', + 'ctx-lh-superiorfrontal', 'Left-Putamen') + +fig = mne.viz.plot_alignment(mne.pick_info(epochs.info, picks), trans, + 'fsaverage', subjects_dir=subjects_dir, + surfaces=[], coord_frame='mri') + +brain = mne.viz.Brain('fsaverage', alpha=0.1, cortex='low_contrast', + subjects_dir=subjects_dir, units='m', figure=fig) +brain.add_volume_labels(aseg='aparc+aseg', labels=labels) +brain.show_view(azimuth=120, elevation=90, distance=0.25) -############################################################################### -# Next, we'll get the raw data and plot its amplitude over time. +# %% +# Next, we'll get the epoch data and plot its amplitude over time. -raw.plot() +epochs.plot() -############################################################################### +# %% # We can visualize this raw data on the ``fsaverage`` brain (in MNI space) as # a heatmap. This works by first creating an ``Evoked`` data structure # from the data of interest (in this example, it is just the raw LFP). @@ -135,18 +158,18 @@ # to visualize source activity on the brain in various different formats. # get standard fsaverage volume (5mm grid) source space -fname_src = op.join(subjects_dir, 'fsaverage', 'bem', - 'fsaverage-vol-5-src.fif') +fname_src = (subjects_dir / 'fsaverage' / 'bem' / + 'fsaverage-vol-5-src.fif') vol_src = mne.read_source_spaces(fname_src) -evoked = mne.EvokedArray(raw.get_data(), raw.info).crop(0, 1) # shorter +evoked = epochs.average() stc = mne.stc_near_sensors( - evoked, trans, subject, subjects_dir=subjects_dir, src=vol_src, + evoked, trans, 'fsaverage', subjects_dir=subjects_dir, src=vol_src, verbose='error') # ignore missing electrode warnings stc = abs(stc) # just look at magnitude clim = dict(kind='value', lims=np.percentile(abs(evoked.data), [10, 50, 75])) -############################################################################### +# %% # Plot 3D source (brain region) visualization: # # By default, `stc.plot_3d() ` will show a time @@ -154,7 +177,7 @@ # In this example, it is simply the source with the largest raw signal value. # Its location is marked on the brain by a small blue sphere. -# sphinx_gallery_thumbnail_number = 4 +# sphinx_gallery_thumbnail_number = 6 brain = stc.plot_3d( src=vol_src, subjects_dir=subjects_dir, @@ -163,10 +186,10 @@ add_data_kwargs=dict(colorbar_kwargs=dict(label_font_size=8))) # You can save a movie like the one on our documentation website with: -# brain.save_movie(time_dilation=3, interpolation='linear', framerate=10, +# brain.save_movie(time_dilation=3, interpolation='linear', framerate=5, # time_viewer=True, filename='./mne-test-seeg.m4') -############################################################################### +# %% # In this tutorial, we used a BEM surface for the ``fsaverage`` subject from # FreeSurfer. # @@ -178,5 +201,5 @@ # :ref:`tut-viz-stcs`. # - For extracting activation within a specific FreeSurfer volume and using # different FreeSurfer volumes, see: :ref:`tut-freesurfer-mne`. -# - For working with BEM surfaces and using FreeSurfer, or mne to generate +# - For working with BEM surfaces and using FreeSurfer, or MNE to generate # them, see: :ref:`tut-forward`. diff -Nru python-mne-0.23.4+dfsg/tutorials/clinical/30_ecog.py python-mne-1.1.0+dfsg/tutorials/clinical/30_ecog.py --- python-mne-0.23.4+dfsg/tutorials/clinical/30_ecog.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tutorials/clinical/30_ecog.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,5 +1,6 @@ +# -*- coding: utf-8 -*- """ -.. _tut_working_with_ecog: +.. _tut-working-with-ecog: ====================== Working with ECoG data @@ -11,13 +12,17 @@ This example shows how to use: -- ECoG data (`available here `_) - from an epilepsy patient during a seizure +- ECoG data (`available here `__) from + an epilepsy patient during a seizure - channel locations in FreeSurfer's ``fsaverage`` MRI space - projection onto a pial surface -For a complementary example that involves sEEG data, channel locations in -MNI space, or projection into a volume, see :ref:`tut_working_with_seeg`. +For a complementary example that involves sEEG data, channel locations in MNI +space, or projection into a volume, see :ref:`tut-working-with-seeg`. + +Please note that this tutorial requires 3D plotting dependencies (see +:ref:`manual-install`) as well as ``mne-bids`` which can be installed using +``pip``. """ # Authors: Eric Larson # Chris Holdgraf @@ -25,9 +30,9 @@ # Alex Rockhill # Liberty Hamilton # -# License: BSD (3-clause) +# License: BSD-3-Clause -import os.path as op +# %% import numpy as np import matplotlib.pyplot as plt @@ -42,15 +47,18 @@ # paths to mne datasets - sample ECoG and FreeSurfer subject bids_root = mne.datasets.epilepsy_ecog.data_path() sample_path = mne.datasets.sample.data_path() -subjects_dir = op.join(sample_path, 'subjects') - +subjects_dir = sample_path / 'subjects' -############################################################################### +# %% # Load in data and perform basic preprocessing # -------------------------------------------- # # Let's load some ECoG electrode data with `mne-bids # `_. +# +# .. note:: +# Downsampling is just to save execution time in this example, you should +# not need to do this in general! # first define the bids path bids_path = BIDSPath(root=bids_root, subject='pt1', session='presurgery', @@ -74,7 +82,17 @@ raw.drop_channels(raw.info['bads']) # the coordinate frame of the montage -print(raw.get_montage().get_positions()['coord_frame']) +montage = raw.get_montage() +print(montage.get_positions()['coord_frame']) + +# add fiducials to montage +montage.add_mni_fiducials(subjects_dir) + +# now with fiducials assigned, the montage will be properly converted +# to "head" which is what MNE requires internally (this is the coordinate +# system with the origin between LPA and RPA whereas MNI has the origin +# at the posterior commissure) +raw.set_montage(montage) # Find the annotated events events, event_id = mne.events_from_annotations(raw) @@ -83,18 +101,12 @@ epoch_length = 25 # seconds epochs = mne.Epochs(raw, events, event_id=event_id['onset'], tmin=13, tmax=13 + epoch_length, baseline=None) +# Make evoked from the one epoch and resample +evoked = epochs.average().resample(200) +del epochs -# And then load data and downsample. -# .. note: This is just to save execution time in this example, you should -# not need to do this in general! -epochs.load_data() -epochs.resample(200) # Hz, will also load the data for us - -# Finally, make evoked from the one epoch -evoked = epochs.average() - -############################################################################### +# %% # Explore the electrodes on a template brain # ------------------------------------------ # @@ -104,14 +116,14 @@ # (along with xy positions of each electrode in the image), so that later # we can plot frequency band power on top of it. -fig = plot_alignment(raw.info, subject='fsaverage', subjects_dir=subjects_dir, - surfaces=['pial'], coord_frame='mri') -az, el, focalpoint = 160, -70, [0.067, -0.040, 0.018] -mne.viz.set_3d_view(fig, azimuth=az, elevation=el, focalpoint=focalpoint) +fig = plot_alignment(raw.info, trans='fsaverage', + subject='fsaverage', subjects_dir=subjects_dir, + surfaces=['pial'], coord_frame='head') +mne.viz.set_3d_view(fig, azimuth=0, elevation=70) xy, im = snapshot_brain_montage(fig, raw.info) -############################################################################### +# %% # Compute frequency features of the data # -------------------------------------- # @@ -123,7 +135,7 @@ envelope=True).resample(sfreq) gamma_info = gamma_power_t.info -############################################################################### +# %% # Visualize the time-evolution of the gamma power on the brain # ------------------------------------------------------------ # @@ -154,7 +166,7 @@ color = cmap(i / xy_pts.shape[0]) ax.plot(x_line + x, gamma_power[i] + y, linewidth=0.5, color=color) -############################################################################### +# %% # We can project gamma power from the sensor data to the nearest locations on # the pial surface and visualize that: # @@ -163,28 +175,24 @@ # this example short time section. This dataset is available using # :func:`mne.datasets.epilepsy_ecog.data_path` for you to examine. -# sphinx_gallery_thumbnail_number = 5 +# sphinx_gallery_thumbnail_number = 3 xyz_pts = np.array([dig['r'] for dig in evoked.info['dig']]) -src = mne.read_source_spaces( - op.join(subjects_dir, 'fsaverage', 'bem', 'fsaverage-ico-5-src.fif')) -trans = None # identity transform -stc = mne.stc_near_sensors(gamma_power_t, trans, 'fsaverage', src=src, - mode='nearest', subjects_dir=subjects_dir, +src = mne.read_source_spaces(subjects_dir / 'fsaverage' / 'bem' / + 'fsaverage-ico-5-src.fif') +stc = mne.stc_near_sensors(gamma_power_t, trans='fsaverage', + subject='fsaverage', subjects_dir=subjects_dir, + src=src, surface='pial', mode='nearest', distance=0.02) vmin, vmid, vmax = np.percentile(gamma_power_t.data, [10, 25, 90]) clim = dict(kind='value', lims=[vmin, vmid, vmax]) brain = stc.plot(surface='pial', hemi='rh', colormap='inferno', colorbar=False, clim=clim, views=['lat', 'med'], subjects_dir=subjects_dir, - size=(250, 250), smoothing_steps=20, time_viewer=False) - -# plot electrode locations -for xyz in xyz_pts: - for subplot in (0, 1): - brain.plotter.subplot(subplot, 0) - brain._renderer.sphere(xyz * 1e3, color='white', scale=2) + size=(250, 250), smoothing_steps='nearest', + time_viewer=False) +brain.add_sensors(raw.info, trans='fsaverage') # You can save a movie like the one on our documentation website with: -# brain.save_movie(time_dilation=1, interpolation='linear', framerate=12, +# brain.save_movie(time_dilation=1, interpolation='linear', framerate=3, # time_viewer=True) diff -Nru python-mne-0.23.4+dfsg/tutorials/clinical/60_sleep.py python-mne-1.1.0+dfsg/tutorials/clinical/60_sleep.py --- python-mne-0.23.4+dfsg/tutorials/clinical/60_sleep.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tutorials/clinical/60_sleep.py 2022-08-03 15:48:14.000000000 +0000 @@ -30,7 +30,9 @@ # Stanislas Chambon # Joan Massich # -# License: BSD Style. +# License: BSD-3-Clause + +# %% import numpy as np import matplotlib.pyplot as plt @@ -76,17 +78,11 @@ [alice_files, bob_files] = fetch_data(subjects=[ALICE, BOB], recording=[1]) -mapping = {'EOG horizontal': 'eog', - 'Resp oro-nasal': 'resp', - 'EMG submental': 'emg', - 'Temp rectal': 'misc', - 'Event marker': 'misc'} - -raw_train = mne.io.read_raw_edf(alice_files[0]) +raw_train = mne.io.read_raw_edf(alice_files[0], stim_channel='Event marker', + misc=['Temp rectal']) annot_train = mne.read_annotations(alice_files[1]) raw_train.set_annotations(annot_train, emit_warning=False) -raw_train.set_channel_types(mapping) # plot some data # scalings were chosen manually to allow for simultaneous visualization of @@ -162,12 +158,12 @@ # Applying the same steps to the test data from Bob # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -raw_test = mne.io.read_raw_edf(bob_files[0]) +raw_test = mne.io.read_raw_edf(bob_files[0], stim_channel='Event marker', + misc=['Temp rectal']) annot_test = mne.read_annotations(bob_files[1]) annot_test.crop(annot_test[1]['onset'] - 30 * 60, annot_test[-2]['onset'] + 30 * 60) raw_test.set_annotations(annot_test, emit_warning=False) -raw_test.set_channel_types(mapping) events_test, _ = mne.events_from_annotations( raw_test, event_id=annotation_desc_2_event_id, chunk_duration=30.) epochs_test = mne.Epochs(raw=raw_test, events=events_test, event_id=event_id, @@ -257,8 +253,8 @@ # # To answer the question of how well can we predict the sleep stages of Bob # from Alice's data and avoid as much boilerplate code as possible, we will -# take advantage of two key features of sckit-learn: -# `Pipeline`_ , and `FunctionTransformer`_. +# take advantage of two key features of sckit-learn: `Pipeline`_ , and +# `FunctionTransformer`_. # # Scikit-learn pipeline composes an estimator as a sequence of transforms # and a final estimator, while the FunctionTransformer converts a python diff -Nru python-mne-0.23.4+dfsg/tutorials/epochs/10_epochs_overview.py python-mne-1.1.0+dfsg/tutorials/epochs/10_epochs_overview.py --- python-mne-0.23.4+dfsg/tutorials/epochs/10_epochs_overview.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tutorials/epochs/10_epochs_overview.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,6 +2,7 @@ """ .. _tut-epochs-class: +============================================= The Epochs data structure: discontinuous data ============================================= @@ -11,15 +12,16 @@ :class:`~mne.Epochs` object. For more information about visualizing :class:`~mne.Epochs` objects, see :ref:`tut-visualize-epochs`. For info on creating an :class:`~mne.Epochs` object from (possibly simulated) data in a -:class:`NumPy array `, see :ref:`tut_creating_data_structures`. +:class:`NumPy array `, see :ref:`tut-creating-data-structures`. As usual we'll start by importing the modules we need: """ -import os +# %% + import mne -############################################################################### +# %% # :class:`~mne.Epochs` objects are a data structure for representing and # analyzing equal-duration chunks of the EEG/MEG signal. :class:`~mne.Epochs` # are most often used to represent data that is time-locked to repeated @@ -71,17 +73,17 @@ # the :class:`~mne.io.Raw` data to save memory: sample_data_folder = mne.datasets.sample.data_path() -sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample', - 'sample_audvis_raw.fif') +sample_data_raw_file = (sample_data_folder / 'MEG' / 'sample' / + 'sample_audvis_raw.fif') raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False).crop(tmax=60) -############################################################################### +# %% # As we saw in the :ref:`tut-events-vs-annotations` tutorial, we can extract an # events array from :class:`~mne.io.Raw` objects using :func:`mne.find_events`: events = mne.find_events(raw, stim_channel='STI 014') -############################################################################### +# %% # .. note:: # # We could also have loaded the events from file, using @@ -107,7 +109,7 @@ epochs = mne.Epochs(raw, events, tmin=-0.3, tmax=0.7) -############################################################################### +# %% # You'll see from the output that: # # - all 320 events were used to create epochs @@ -128,7 +130,7 @@ print(epochs) -############################################################################### +# %% # Notice that the Event IDs are in quotes; since we didn't provide an event # dictionary, the :class:`mne.Epochs` constructor created one automatically and # used the string representation of the integer Event IDs as the dictionary @@ -136,7 +138,7 @@ print(epochs.event_id) -############################################################################### +# %% # This time let's pass ``preload=True`` and provide an event dictionary; our # provided dictionary will get stored as the ``event_id`` attribute and will # make referencing events and pooling across event types easier: @@ -148,7 +150,7 @@ print(epochs.event_id) del raw # we're done with raw, free up some memory -############################################################################### +# %% # Notice that the output now mentions "1 bad epoch dropped". In the tutorial # section :ref:`tut-reject-epochs-section` we saw how you can specify channel # amplitude criteria for rejecting epochs, but here we haven't specified any @@ -160,7 +162,7 @@ print(epochs.drop_log[-4:]) -############################################################################### +# %% # .. note:: # # If you forget to provide the event dictionary to the :class:`~mne.Epochs` @@ -178,7 +180,7 @@ epochs.plot(n_epochs=10) -############################################################################### +# %% # Notice that the individual epochs are sequentially numbered along the bottom # axis and are separated by vertical dashed lines. # Epoch plots are interactive (similar to :meth:`raw.plot() @@ -201,7 +203,7 @@ print(epochs['face']) -############################################################################### +# %% # We can also pool across conditions easily, thanks to how MNE-Python handles # the ``/`` character in epoch labels (using what is sometimes called # "tag-based indexing"): @@ -215,7 +217,7 @@ assert len(epochs['left']) == (len(epochs['auditory/left']) + len(epochs['visual/left'])) -############################################################################### +# %% # You can also pool conditions by passing multiple tags as a list. Note that # MNE-Python will not complain if you ask for tags not present in the object, # as long as it can find *some* match: the below example is parsed as @@ -224,7 +226,7 @@ print(epochs[['right', 'bottom']]) -############################################################################### +# %% # However, if no match is found, an error is returned: try: @@ -232,7 +234,7 @@ except KeyError: print('Tag-based selection with no matches raises a KeyError!') -############################################################################### +# %% # Selecting epochs by index # ~~~~~~~~~~~~~~~~~~~~~~~~~ # @@ -247,7 +249,7 @@ print(epochs['buttonpress'][:4]) # first 4 "buttonpress" epochs print(epochs['buttonpress'][[0, 1, 2, 3]]) # same as previous line -############################################################################### +# %% # Selecting, dropping, and reordering channels # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # @@ -268,11 +270,11 @@ epochs_subset = epochs.copy().reorder_channels(new_order) print(epochs_subset.ch_names) -############################################################################### +# %% del epochs_eeg, epochs_subset -############################################################################### +# %% # Changing channel name and type # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # @@ -287,13 +289,13 @@ epochs.set_channel_types({'EEG 060': 'ecg'}) print(list(zip(epochs.ch_names, epochs.get_channel_types()))[-4:]) -############################################################################### +# %% # let's set them back to the correct values before moving on epochs.rename_channels({'BlinkChannel': 'EOG 061'}) epochs.set_channel_types({'EEG 060': 'eeg'}) -############################################################################### +# %% # Selection in the time domain # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # @@ -306,7 +308,7 @@ print('{} epochs has {} time samples' .format(name, obj.get_data().shape[-1])) -############################################################################### +# %% # Cropping removed part of the baseline. When printing the # cropped :class:`~mne.Epochs`, MNE-Python will inform you about the time # period that was originally used to perform baseline correction by displaying @@ -314,7 +316,7 @@ print(shorter_epochs) -############################################################################### +# %% # However, if you wanted to *expand* the time domain of an :class:`~mne.Epochs` # object, you would need to go back to the :class:`~mne.io.Raw` data and # recreate the :class:`~mne.Epochs` with different values for ``tmin`` and/or @@ -335,11 +337,11 @@ later_epochs.shift_time(tshift=-7, relative=True) print(later_epochs.times[:3]) -############################################################################### +# %% del shorter_epochs, later_epochs -############################################################################### +# %% # Note that although time shifting respects the sampling frequency (the spacing # between samples), it does not enforce the assumption that there is a sample # occurring at exactly time=0. @@ -360,7 +362,7 @@ for name, arr in dict(EOG=eog_data, MEG=meg_data, Slice=channel_4_6_8).items(): print('{} contains {} channels'.format(name, arr.shape[1])) -############################################################################### +# %% # Note that if your analysis requires repeatedly extracting single epochs from # an :class:`~mne.Epochs` object, ``epochs.get_data(item=2)`` will be much # faster than ``epochs[2].get_data()``, because it avoids the step of @@ -385,7 +387,7 @@ del df -############################################################################### +# %% # See the :ref:`tut-epochs-dataframe` tutorial for many more examples of the # :meth:`~mne.Epochs.to_data_frame` method. # @@ -402,7 +404,7 @@ epochs.save('saved-audiovisual-epo.fif', overwrite=True) epochs_from_file = mne.read_epochs('saved-audiovisual-epo.fif', preload=False) -############################################################################### +# %% # The MNE-Python naming convention for epochs files is that the file basename # (the part before the ``.fif`` or ``.fif.gz`` extension) should end with # ``-epo`` or ``_epo``, and a warning will be issued if the filename you @@ -415,7 +417,7 @@ print(type(epochs)) print(type(epochs_from_file)) -############################################################################### +# %% # In almost all cases this will not require changing anything about your code. # However, if you need to do type checking on epochs objects, you can test # against the base class that these classes are derived from: @@ -423,7 +425,7 @@ print(all([isinstance(epochs, mne.BaseEpochs), isinstance(epochs_from_file, mne.BaseEpochs)])) -############################################################################### +# %% # Iterating over ``Epochs`` # ^^^^^^^^^^^^^^^^^^^^^^^^^ # @@ -433,7 +435,7 @@ for epoch in epochs[:3]: print(type(epoch)) -############################################################################### +# %% # If you want to iterate over :class:`~mne.Epochs` objects, you can use an # integer index as the iterator: diff -Nru python-mne-0.23.4+dfsg/tutorials/epochs/15_baseline_regression.py python-mne-1.1.0+dfsg/tutorials/epochs/15_baseline_regression.py --- python-mne-0.23.4+dfsg/tutorials/epochs/15_baseline_regression.py 1970-01-01 00:00:00.000000000 +0000 +++ python-mne-1.1.0+dfsg/tutorials/epochs/15_baseline_regression.py 2022-08-03 15:48:14.000000000 +0000 @@ -0,0 +1,285 @@ +# -*- coding: utf-8 -*- +""" +.. _ex-baseline-regression: + +==================================================================== +Regression-based baseline correction +==================================================================== + +This tutorial compares traditional baseline correction (adding or subtracting a +scalar amount from every timepoint in an epoch) to a regression-based approach +to baseline correction (which allows the effect of the baseline period to vary +by timepoint). Specifically, this tutorial follows the method introduced by +:footcite:t:`Alday2019`. + +There are at least two reasons you might consider using regression-based +baseline correction: + +1. Unlike traditional baseline correction, the regression-based approach does + not assume that the effect of the baseline is equivalent between different + experimental conditions. Thus it is safer against introduced bias. + +2. Assuming that pre-trial baseline signal level is mostly determined by slow + drifts in the data, the further away (in time) you get from the baseline + period, the less likely it is that the signal level is similar in amplitude + to the baseline amplitude. Thus using a time-varying baseline correction is + less likely to introduce signal distortions / spurious effects in the later + spans of long-duration epochs. + +One issue that affects both traditional and regression-based baseline +correction is the question of what time window to choose as the baseline +window. +""" + +# %% + +# Authors: Carina Forster +# Email: carinaforster0611@gmail.com + +# License: BSD-3-Clause + +import numpy as np + +import mne + +# %% +# Load the data +# ============= +# +# We'll start by loading the MNE-Python :ref:`sample dataset ` +# and extracting the experimental events to get trial locations and trial +# types. Since for this tutorial we're only going to look at EEG channels, we +# can drop the other channel types, to speed things up: + +data_path = mne.datasets.sample.data_path() +raw_fname = data_path / 'MEG' / 'sample' / 'sample_audvis_filt-0-40_raw.fif' +raw = mne.io.read_raw_fif(raw_fname, preload=True) + +events = mne.find_events(raw) + +raw.pick_types(meg=False, stim=False, eog=False, eeg=True) + +# %% +# Here we merge visual and auditory events from both hemispheres, and make our +# ``event_id`` dictionary for use during epoching. + +events = mne.merge_events(events, [1, 2], 1) # auditory events will be "1" +events = mne.merge_events(events, [3, 4], 2) # visual events will be "2" +event_id = {'auditory': 1, 'visual': 2} + + +# %% +# Preprocessing +# ============= +# +# Next we'll define some variables needed to epoch and preprocess the +# data. We'll be combining left- and right-side stimuli, so we'll look at a +# single *central* electrode to visualize the difference between auditory and +# visual trials. + +tmin, tmax = -0.2, 0.5 +lowpass, highpass = 40, 0.1 +baseline_tmin, baseline_tmax = None, 0 # None takes the first timepoint +ch = "EEG 021" + +# %% +# We'll do some standard preprocessing (a bandpass filter) and then epoch +# the data. Note that we don't baseline correct the epochs (we specify +# ``baseline=None``); we just minimally clean the data by rejecting channels +# with very high or low amplitudes. Note also that we operate on a *copy* of +# the data so that we can later compare this with traditional baselining. + +raw_filtered = raw.copy().filter(highpass, lowpass) + +epochs = mne.Epochs(raw_filtered, events, event_id, tmin=tmin, tmax=tmax, + reject=dict(eeg=150e-6), flat=dict(eeg=5e-6), + baseline=None, preload=True) + + +# %% +# Traditional baselining +# ---------------------- +# +# First let's baseline correct the data the traditional way. We average epochs +# within each condition, and subtract the condition-specific baseline +# separately for auditory and visual trials. + +baseline = (baseline_tmin, baseline_tmax) +trad_aud = epochs['auditory'].average().apply_baseline(baseline) +trad_vis = epochs['visual'].average().apply_baseline(baseline) + +# %% +# Regression-based baselining +# --------------------------- +# +# Now let's try out the regression-based baseline correction approach. We'll +# use :func:`mne.stats.linear_regression`, which needs a *design matrix* to +# represent the regression predictors. We'll use four predictors: one for each +# experimental condition, one for the effect of baseline, and one that is an +# interaction between the baseline and one of the conditions (to account for +# any heterogeneity of the effect of baseline between the two conditions). Here +# are the first two: + +aud_predictor = epochs.events[:, 2] == epochs.event_id['auditory'] +vis_predictor = epochs.events[:, 2] == epochs.event_id['visual'] + +# %% +# The baseline predictor is a bit trickier to compute: we'll find the average +# value within the baseline period *separately for each epoch*, and use that +# value as our (trial-level) predictor. Here, since we're focused on one +# particular channel, we'll use the baseline value *in that channel* as our +# predictor, but depending on your research question you may want to do this +# seaprately for each channel or combine information across channels. + +baseline_predictor = ( + epochs.copy() + .crop(*baseline) + .pick_channels([ch]) + .get_data() # convert to NumPy array + .mean(axis=-1) # average across timepoints + .squeeze() # only 1 channel, so remove singleton dimension +) +baseline_predictor *= 1e6 # convert V → μV + +# %% +# Note that we converted *just the predictor* (not the epochs data) from Volts +# to microVolts. This is done for regression-model-fitting purposes (very small +# values can make model fitting unstable). +# +# Now we can set up the design matrix, stacking the 1-D predictors as rows, +# then transposing with ``.T`` to make them columns. Combining them into one +# :func:`~numpy.array` will also automatically convert the +# :class:`boolean ` ``aud_predictor`` and ``vis_predictor`` into +# ones and zeros: + +design_matrix = np.vstack([aud_predictor, + vis_predictor, + baseline_predictor, + baseline_predictor * vis_predictor]).T + +# %% +# Finally we fit the regression model: + +reg_model = mne.stats.linear_regression(epochs, design_matrix, + names=["auditory", "visual", + "baseline", + "baseline:visual"]) + +# %% +# The function returns a dictionary of ``mne.stats.regression.lm`` objects, +# which are each a :func:`~collections.namedtuple` with the various estimated +# values stored as if it were an :class:`~mne.Evoked` object. Let's inspect it: + +print(reg_model.keys()) +print(f"model attributes: {reg_model['auditory']._fields}") +print('values are stored in Evoked objects:') +print(reg_model['auditory'].t_val) + +# %% +# Plot the baseline regressor +# =========================== +# +# First let's look at the estimated effect of the baseline period. What we care +# about is the ``beta`` values, which tell us how strongly predictive the +# baseline value is at each timepoint. The model will estimate its +# effectiveness *for every channel* but since we used only one channel to form +# our baseline predictor, let's examine how it looks for that channel only. +# We'll add a horizontal line at β=1 to represent traditional baselining, where +# the effect is assumed to be constant across timepoints: + +effect_of_baseline = reg_model['baseline'].beta +effect_of_baseline.plot(picks=ch, hline=[1.], units=dict(eeg=r'$\beta$ value'), + titles=dict(eeg=ch), selectable=False) + +# %% +# Unsurprisingly, the trend is that the farther away in time we get from the +# baseline period, the weaker the predictive value of the baseline amplitude +# becomes. Put another way, early time points (in this data) should be more +# strongly baseline-corrected than later time points. +# +# Plot the ERPs +# ============= +# +# Now let's look at the ``beta`` values for the two +# conditions (``auditory`` and ``visual``): these are the coefficients that +# represent the "pure" influence of the experimental stimuli on the signal, +# after taking into account the (time-varying!) effect of the baseline. We'll +# plot them together, side-by-side with the traditional baseline approach: + +reg_aud = reg_model['auditory'].beta +reg_vis = reg_model['visual'].beta + +kwargs = dict(picks=ch, show_sensors=False, truncate_yaxis=False) +mne.viz.plot_compare_evokeds(dict(auditory=trad_aud, visual=trad_vis), + title="Traditional", **kwargs) +mne.viz.plot_compare_evokeds(dict(auditory=reg_aud, visual=reg_vis), + title="Regression-based", **kwargs) + +# %% +# They look pretty similar, but there are some subtle differences in how far +# apart the two conditions are (e.g., around 400-500 ms). +# +# Plot the scalp topographies and difference waves +# ================================================ +# +# Now let's compare the +# scalp topographies for the traditional and regression-based approach. We'll +# do this by computing the difference between conditions: + +diff_traditional = mne.combine_evoked([trad_aud, trad_vis], weights=[1, -1]) +diff_regression = mne.combine_evoked([reg_aud, reg_vis], weights=[1, -1]) + +# %% +# Before we plot, let's make sure we get the same color scale for both figures: + +vmin = min(diff_traditional.get_data().min(), + diff_regression.get_data().min()) * 1e6 +vmax = max(diff_traditional.get_data().max(), + diff_regression.get_data().max()) * 1e6 +topo_kwargs = dict(vmin=vmin, vmax=vmax, ch_type='eeg', + times=np.linspace(0.05, 0.45, 9)) + +diff_traditional.plot_topomap(title="Traditional", **topo_kwargs) + +# %% +diff_regression.plot_topomap(title="Regression-based", **topo_kwargs) + +# %% +# We can see that the regression-based approach shows *stronger* difference +# between conditions early on (around 100-150 ms) and *weaker* differences +# later (around 250-350 ms, and again around 450 ms). This is also reflected in +# the difference waves themselves: notice how the regression-based difference +# wave is *further from zero* around 150 ms but *closer to zero* around 250-350 +# ms. + +title = "Difference in evoked potential (auditory minus visual)" +fig = mne.viz.plot_compare_evokeds(dict(Traditional=diff_traditional, + Regression=diff_regression), + title=title, **kwargs) + +# %% +# Examine the interaction term +# ============================ +# +# Finally, let's look at the interaction term from the regression model. This +# tells us whether the effect of the baseline period is different in the visual +# trials versus its effect in the auditory trials. Here we'll add a horizontal +# line at zero, indicating the assumption that there ought to be no difference +# (i.e., baselines should not be systematically higher in one type of trial, +# and there should not be a difference in how long the effect of the baseline +# persists through time in each type of trial). + +interaction_effect = reg_model['baseline:visual'].beta +interaction_effect.plot(picks=ch, hline=[0.], units=dict(eeg=r'$\beta$ value'), + titles=dict(eeg=ch), selectable=False) + + +# %% +# Indeed, the interaction beta weights are rather small and seem to fluctuate +# randomly around zero, suggesting that there is no systematic difference in +# the effect of the baseline on our two trial types. + +# %% +# References +# ========== +# .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/tutorials/epochs/20_visualize_epochs.py python-mne-1.1.0+dfsg/tutorials/epochs/20_visualize_epochs.py --- python-mne-0.23.4+dfsg/tutorials/epochs/20_visualize_epochs.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tutorials/epochs/20_visualize_epochs.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,32 +1,34 @@ +# -*- coding: utf-8 -*- """ .. _tut-visualize-epochs: +======================== Visualizing epoched data ======================== This tutorial shows how to plot epoched data as time series, how to plot the spectral density of epoched data, how to plot epochs as an imagemap, and how to -plot the sensor locations and projectors stored in `~mne.Epochs` -objects. +plot the sensor locations and projectors stored in `~mne.Epochs` objects. We'll start by importing the modules we need, loading the continuous (raw) sample data, and cropping it to save memory: """ -import os +# %% + import mne sample_data_folder = mne.datasets.sample.data_path() -sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample', - 'sample_audvis_raw.fif') +sample_data_raw_file = (sample_data_folder / 'MEG' / 'sample' / + 'sample_audvis_raw.fif') raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False).crop(tmax=120) -############################################################################### +# %% # To create the `~mne.Epochs` data structure, we'll extract the event # IDs stored in the :term:`stim channel`, map those integer event IDs to more # descriptive condition labels using an event dictionary, and pass those to the -# `~mne.Epochs` constructor, along with the `~mne.io.Raw` data -# and the desired temporal limits of our epochs, ``tmin`` and ``tmax`` (for a +# `~mne.Epochs` constructor, along with the `~mne.io.Raw` data and the +# desired temporal limits of our epochs, ``tmin`` and ``tmax`` (for a # detailed explanation of these steps, see :ref:`tut-epochs-class`). events = mne.find_events(raw, stim_channel='STI 014') @@ -36,11 +38,12 @@ preload=True) del raw -############################################################################### +# %% # Plotting ``Epochs`` as time series # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # -# .. sidebar:: Interactivity in pipelines and scripts +# .. admonition:: Interactivity in pipelines and scripts +# :class: sidebar hint # # To use the interactive features of the `~mne.Epochs.plot` method # when running your code non-interactively, pass the ``block=True`` @@ -71,14 +74,14 @@ epochs['face'].plot(events=catch_trials_and_buttonpresses, event_id=event_dict, event_color=dict(button='red', face='blue')) -############################################################################### +# %% # To see all sensors at once, we can use butterfly mode and group by selection: epochs['face'].plot(events=catch_trials_and_buttonpresses, event_id=event_dict, event_color=dict(button='red', face='blue'), group_by='selection', butterfly=True) -############################################################################### +# %% # Plotting projectors from an ``Epochs`` object # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # @@ -86,13 +89,13 @@ # channels, so before we continue let's load ECG projectors from disk and apply # them to the data: -ecg_proj_file = os.path.join(sample_data_folder, 'MEG', 'sample', - 'sample_audvis_ecg-proj.fif') +ecg_proj_file = (sample_data_folder / 'MEG' / 'sample' / + 'sample_audvis_ecg-proj.fif') ecg_projs = mne.read_proj(ecg_proj_file) epochs.add_proj(ecg_projs) epochs.apply_proj() -############################################################################### +# %% # Just as we saw in the :ref:`tut-section-raw-plot-proj` section, we can plot # the projectors present in an `~mne.Epochs` object using the same # `~mne.Epochs.plot_projs_topomap` method. Since the original three @@ -102,7 +105,7 @@ epochs.plot_projs_topomap(vlim='joint') -############################################################################### +# %% # Note that these field maps illustrate aspects of the signal that *have # already been removed* (because projectors in `~mne.io.Raw` data are # applied by default when epoching, and because we called @@ -112,7 +115,7 @@ print(all(proj['active'] for proj in epochs.info['projs'])) -############################################################################### +# %% # Plotting sensor locations # ^^^^^^^^^^^^^^^^^^^^^^^^^ # @@ -123,7 +126,7 @@ epochs.plot_sensors(kind='3d', ch_type='all') epochs.plot_sensors(kind='topomap', ch_type='all') -############################################################################### +# %% # Plotting the power spectrum of ``Epochs`` # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # @@ -133,7 +136,7 @@ epochs['auditory'].plot_psd(picks='eeg') -############################################################################### +# %% # It is also possible to plot spectral estimates across sensors as a scalp # topography, using `~mne.Epochs.plot_psd_topomap`. The default parameters will # plot five frequency bands (δ, θ, α, β, γ), will compute power based on @@ -141,21 +144,21 @@ epochs['visual/right'].plot_psd_topomap() -############################################################################### +# %% # Just like `~mne.Epochs.plot_projs_topomap`, # `~mne.Epochs.plot_psd_topomap` has a ``vlim='joint'`` option for fixing # the colorbar limits jointly across all subplots, to give a better sense of -# the relative magnitude in each band. You can change which channel type is -# used via the ``ch_type`` parameter, and if you want to view different -# frequency bands than the defaults, the ``bands`` parameter takes a list of -# tuples, with each tuple containing either a single frequency and a subplot -# title, or lower/upper frequency limits and a subplot title: +# the relative magnitude in each frequency band. You can change which channel +# type is used via the ``ch_type`` parameter, and if you want to view +# different frequency bands than the defaults, the ``bands`` parameter takes a +# list of tuples, with each tuple containing either a single frequency and a +# subplot title, or lower/upper frequency limits and a subplot title: bands = [(10, '10 Hz'), (15, '15 Hz'), (20, '20 Hz'), (10, 20, '10-20 Hz')] epochs['visual/right'].plot_psd_topomap(bands=bands, vlim='joint', ch_type='grad') -############################################################################### +# %% # If you prefer untransformed power estimates, you can pass ``dB=False``. It is # also possible to normalize the power estimates by dividing by the total power # across all frequencies, by passing ``normalize=True``. See the docstring of @@ -187,7 +190,7 @@ epochs['auditory'].plot_image(picks='mag', combine='mean') -############################################################################### +# %% # To plot image maps for individual sensors or a small group of sensors, use # the ``picks`` parameter. Passing ``combine=None`` (the default) will yield # separate plots for each sensor in ``picks``; passing ``combine='gfp'`` will @@ -198,7 +201,7 @@ epochs['auditory'].plot_image(picks=['MEG 0242', 'MEG 0243']) epochs['auditory'].plot_image(picks=['MEG 0242', 'MEG 0243'], combine='gfp') -############################################################################### +# %% # To plot an image map for *all* sensors, use # `~mne.Epochs.plot_topo_image`, which is optimized for plotting a large # number of image maps simultaneously, and (in interactive sessions) allows you @@ -226,7 +229,7 @@ epochs['auditory/left'].plot_topo_image(layout=layout, fig_facecolor='w', font_color='k', title=title) -############################################################################### +# %% # To plot image maps for all EEG sensors, pass an EEG layout as the ``layout`` # parameter of `~mne.Epochs.plot_topo_image`. Note also here the use of # the ``sigma`` parameter, which smooths each image map along the vertical @@ -240,7 +243,7 @@ epochs['auditory/left'].plot_topo_image(layout=layout, fig_facecolor='w', font_color='k', sigma=1) -############################################################################### +# %% # .. LINKS # # .. _spectral density: https://en.wikipedia.org/wiki/Spectral_density diff -Nru python-mne-0.23.4+dfsg/tutorials/epochs/30_epochs_metadata.py python-mne-1.1.0+dfsg/tutorials/epochs/30_epochs_metadata.py --- python-mne-0.23.4+dfsg/tutorials/epochs/30_epochs_metadata.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tutorials/epochs/30_epochs_metadata.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,8 @@ +# -*- coding: utf-8 -*- """ .. _tut-epochs-metadata: +=========================== Working with Epoch metadata =========================== @@ -16,21 +18,22 @@ need and loading the data: """ -import os +# %% + import numpy as np import pandas as pd import mne kiloword_data_folder = mne.datasets.kiloword.data_path() -kiloword_data_file = os.path.join(kiloword_data_folder, - 'kword_metadata-epo.fif') +kiloword_data_file = kiloword_data_folder / 'kword_metadata-epo.fif' epochs = mne.read_epochs(kiloword_data_file) -############################################################################### +# %% # Viewing ``Epochs`` metadata # ^^^^^^^^^^^^^^^^^^^^^^^^^^^ # -# .. sidebar:: Restrictions on metadata DataFrames +# .. admonition:: Restrictions on metadata DataFrames +# :class: sidebar warning # # Metadata dataframes are less flexible than typical # :class:`Pandas DataFrames `. For example, the allowed @@ -55,7 +58,7 @@ epochs.metadata -############################################################################### +# %% # Viewing the metadata values for a given epoch and metadata variable is done # using any of the :ref:`Pandas indexing ` # methods such as :obj:`~pandas.DataFrame.loc`, @@ -72,7 +75,7 @@ print('\nIndex-based selection with .iloc') print(epochs.metadata.iloc[2:4]) -############################################################################### +# %% # Modifying the metadata # ^^^^^^^^^^^^^^^^^^^^^^ # @@ -88,7 +91,7 @@ epochs.metadata['HighComplexity'] = epochs.metadata['VisualComplexity'] > 65 epochs.metadata.head() -############################################################################### +# %% # Selecting epochs using metadata queries # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # @@ -100,28 +103,28 @@ print(epochs['WORD.str.startswith("dis")']) -############################################################################### +# %% # This capability uses the :meth:`pandas.DataFrame.query` method under the # hood, so you can check out the documentation of that method to learn how to # format query strings. Here's another example: print(epochs['Concreteness > 6 and WordFrequency < 1']) -############################################################################### +# %% # Note also that traditional epochs subselection by condition name still works; # MNE-Python will try the traditional method first before falling back on rich # metadata querying. epochs['solenoid'].plot_psd() -############################################################################### +# %% # One use of the Pandas query string approach is to select specific words for # plotting: words = ['typhoon', 'bungalow', 'colossus', 'drudgery', 'linguist', 'solenoid'] epochs['WORD in {}'.format(words)].plot(n_channels=29) -############################################################################### +# %% # Notice that in this dataset, each "condition" (A.K.A., each word) occurs only # once, whereas with the :ref:`sample-dataset` dataset each condition (e.g., # "auditory/left", "visual/right", etc) occurred dozens of times. This makes @@ -140,7 +143,7 @@ mne.viz.plot_compare_evokeds(evokeds, cmap=('word length', 'viridis'), picks='Pz') -############################################################################### +# %% # Metadata can also be useful for sorting the epochs in an image plot. For # example, here we order the epochs based on word frequency to see if there's a # pattern to the latency or intensity of the response: @@ -148,7 +151,7 @@ sort_order = np.argsort(epochs.metadata['WordFrequency']) epochs.plot_image(order=sort_order, picks='Pz') -############################################################################### +# %% # Although there's no obvious relationship in this case, such analyses may be # useful for metadata variables that more directly index the time course of # stimulus processing (such as reaction time). @@ -166,7 +169,7 @@ epochs.metadata = new_metadata epochs.metadata.head() -############################################################################### +# %% # You can remove metadata from an `~mne.Epochs` object by setting its # metadata to ``None``: diff -Nru python-mne-0.23.4+dfsg/tutorials/epochs/40_autogenerate_metadata.py python-mne-1.1.0+dfsg/tutorials/epochs/40_autogenerate_metadata.py --- python-mne-0.23.4+dfsg/tutorials/epochs/40_autogenerate_metadata.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tutorials/epochs/40_autogenerate_metadata.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,8 +1,10 @@ +# -*- coding: utf-8 -*- """ .. _tut-autogenerate-metadata: -Auto-generating ``Epochs`` metadata -=================================== +=============================== +Auto-generating Epochs metadata +=============================== This tutorial shows how to auto-generate metadata for `~mne.Epochs`, based on events via `mne.epochs.make_metadata`. @@ -41,6 +43,8 @@ by calling `mne.events_from_annotations`. """ +# %% + from pathlib import Path import matplotlib.pyplot as plt import mne @@ -56,7 +60,7 @@ # extract events all_events, all_event_id = mne.events_from_annotations(raw) -############################################################################### +# %% # Creating metadata from events # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # @@ -92,7 +96,7 @@ # let's look at what we got! metadata -############################################################################### +# %% # Specifying time-locked events # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # @@ -136,7 +140,7 @@ metadata -############################################################################### +# %% # Keeping only the first events of a group # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # @@ -174,7 +178,7 @@ # from the initial event named "response/left" and "response/right" print(metadata['first_response']) -############################################################################### +# %% # We're facing a similar issue with the stimulus events, and now there are not # only two, but **four** different types: ``stimulus/compatible/target_left``, # ``stimulus/compatible/target_right``, ``stimulus/incompatible/target_left``, @@ -187,7 +191,7 @@ metadata['stimulus/compatible/target_right'].notna(), :] -############################################################################### +# %% # This can easily lead to confusion during later stages of processing, so let's # create a column for the first stimulus – which will always be the time-locked # stimulus, as our time interval starts at 0 seconds. We can pass a **list** of @@ -207,7 +211,7 @@ # which events were selected via "keep_first" metadata[['first_stimulus', 'first_response']] -############################################################################### +# %% # Adding new columns to describe stimulation side and response correctness # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # @@ -237,7 +241,7 @@ print(f'Correct responses: {correct_response_count}\n' f'Incorrect responses: {len(metadata) - correct_response_count}') -############################################################################### +# %% # Creating ``Epochs`` with metadata, and visualizing ERPs # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # @@ -255,7 +259,7 @@ events=events, event_id=event_id, metadata=metadata, reject=reject, preload=True) -############################################################################### +# %% # Lastly, let's visualize the ERPs evoked by the visual stimulation, once for # all trials with correct responses, and once for all trials with correct # responses and a response time greater than 0.5 seconds @@ -272,7 +276,7 @@ fig.tight_layout() fig -############################################################################### +# %% # Aside from the fact that the data for the (much fewer) slow responses looks # noisier – which is entirely to be expected – not much of an ERP difference # can be seen. @@ -311,7 +315,7 @@ row_events=row_events, keep_last=keep_last) -############################################################################### +# %% # Exactly like in the previous example, create new columns ``stimulus_side`` # and ``response_correct``. @@ -333,7 +337,7 @@ metadata -############################################################################### +# %% # Now it's already time to epoch the data! When deciding upon the epochs # duration for this specific analysis, we need to ensure we see quite a bit of # signal from before and after the motor response. We also must be aware of @@ -352,7 +356,7 @@ events=events, event_id=event_id, metadata=metadata, preload=True) -############################################################################### +# %% # Let's do a final sanity check: we want to make sure that in every row, we # actually have a stimulus. We use ``epochs.metadata`` (and not ``metadata``) # because when creating the epochs, we passed the ``reject`` parameter, and @@ -361,7 +365,7 @@ epochs.metadata.loc[epochs.metadata['last_stimulus'].isna(), :] -############################################################################### +# %% # Bummer! It seems the very first two responses were recorded before the # first stimulus appeared: the values in the ``stimulus`` column are ``None``. # There is a very simple way to select only those epochs that **do** have a @@ -369,7 +373,7 @@ epochs = epochs['last_stimulus.notna()'] -############################################################################### +# %% # Time to calculate the ERPs for correct and incorrect responses. # For visualization, we'll only look at sensor ``FCz``, which is known to show # the ERN nicely in the given paradigm. We'll also create a topoplot to get an @@ -389,7 +393,7 @@ title='Avg. topography 0–100 ms after ' 'incorrect responses') -############################################################################### +# %% # We can see a strong negative deflection immediately after incorrect # responses, compared to correct responses. The topoplot, too, leaves no doubt: # what we're looking at is, in fact, the ERN. @@ -431,7 +435,7 @@ fig -############################################################################### +# %% # References # ^^^^^^^^^^ # .. footbibliography:: diff -Nru python-mne-0.23.4+dfsg/tutorials/epochs/50_epochs_to_data_frame.py python-mne-1.1.0+dfsg/tutorials/epochs/50_epochs_to_data_frame.py --- python-mne-0.23.4+dfsg/tutorials/epochs/50_epochs_to_data_frame.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tutorials/epochs/50_epochs_to_data_frame.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,6 +1,8 @@ +# -*- coding: utf-8 -*- """ .. _tut-epochs-dataframe: +===================================== Exporting Epochs to Pandas DataFrames ===================================== @@ -14,23 +16,27 @@ applied to its EEG channels. As usual we'll start by importing the modules we need and loading the data: """ -import os + +# %% + +import matplotlib.pyplot as plt import seaborn as sns + import mne sample_data_folder = mne.datasets.sample.data_path() -sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample', - 'sample_audvis_filt-0-40_raw.fif') +sample_data_raw_file = (sample_data_folder / 'MEG' / 'sample' / + 'sample_audvis_filt-0-40_raw.fif') raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False) -############################################################################### +# %% # Next we'll load a list of events from file, map them to condition names with # an event dictionary, set some signal rejection thresholds (cf. # :ref:`tut-reject-epochs-section`), and segment the continuous data into # epochs: -sample_data_events_file = os.path.join(sample_data_folder, 'MEG', 'sample', - 'sample_audvis_filt-0-40_raw-eve.fif') +sample_data_events_file = (sample_data_folder / 'MEG' / 'sample' / + 'sample_audvis_filt-0-40_raw-eve.fif') events = mne.read_events(sample_data_events_file) event_dict = {'auditory/left': 1, 'auditory/right': 2, 'visual/left': 3, @@ -48,7 +54,7 @@ baseline=baseline, reject=reject_criteria, preload=True) del raw -############################################################################### +# %% # Converting an ``Epochs`` object to a ``DataFrame`` # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # @@ -62,7 +68,7 @@ df = epochs.to_data_frame() df.iloc[:5, :10] -############################################################################### +# %% # Scaling time and channel values # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # @@ -82,7 +88,7 @@ scalings=dict(eeg=1, mag=1, grad=1)) df.iloc[:5, :10] -############################################################################### +# %% # Notice that the time values are no longer integers, and the channel values # have changed by several orders of magnitude compared to the earlier # DataFrame. @@ -101,7 +107,7 @@ time_format='timedelta') df.iloc[:5, :10] -############################################################################### +# %% # Wide- versus long-format DataFrames # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # @@ -116,7 +122,7 @@ long_format=True) long_df.head() -############################################################################### +# %% # Generating the :class:`~pandas.DataFrame` in long format can be helpful when # using other Python modules for subsequent analysis or plotting. For example, # here we'll take data from the "auditory/left" condition, pick a couple MEG @@ -124,19 +130,22 @@ # confidence band for each channel, with confidence computed across the epochs # in the chosen condition: +plt.figure() channels = ['MEG 1332', 'MEG 1342'] data = long_df.loc['auditory/left'].query('channel in @channels') # convert channel column (CategoryDtype → string; for a nicer-looking legend) data['channel'] = data['channel'].astype(str) +data.reset_index(drop=True, inplace=True) # speeds things up sns.lineplot(x='time', y='value', hue='channel', data=data) -############################################################################### +# %% # We can also now use all the power of Pandas for grouping and transforming our # data. Here, we find the latency of peak activation of 2 gradiometers (one # near auditory cortex and one near visual cortex), and plot the distribution # of the timing of the peak in each channel as a :func:`~seaborn.violinplot`: # sphinx_gallery_thumbnail_number = 2 +plt.figure() df = epochs.to_data_frame(time_format=None) peak_latency = (df.filter(regex=r'condition|epoch|MEG 1332|MEG 2123') .groupby(['condition', 'epoch']) diff -Nru python-mne-0.23.4+dfsg/tutorials/epochs/60_make_fixed_length_epochs.py python-mne-1.1.0+dfsg/tutorials/epochs/60_make_fixed_length_epochs.py --- python-mne-0.23.4+dfsg/tutorials/epochs/60_make_fixed_length_epochs.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tutorials/epochs/60_make_fixed_length_epochs.py 2022-08-03 15:48:14.000000000 +0000 @@ -2,33 +2,47 @@ """ .. _tut-fixed-length-epochs: -Creating epochs of equal length -=============================== +Divide continuous data into equally-spaced epochs +================================================= -This tutorial shows how to create equal length epochs and briefly demonstrates -an example of their use in connectivity analysis. +This tutorial shows how to segment continuous data into a set of epochs spaced +equidistantly in time. The epochs will not be created based on experimental +events; instead, the continuous data will be "chunked" into consecutive epochs +(which may be temporally overlapping, adjacent, or separated). +We will also briefly demonstrate how to use these epochs in connectivity +analysis. -First, we import necessary modules and read in a sample raw -data set. This data set contains brain activity that is event-related, i.e. +First, we import necessary modules and read in a sample raw data set. +This data set contains brain activity that is event-related, i.e., synchronized to the onset of auditory stimuli. However, rather than creating epochs by segmenting the data around the onset of each stimulus, we will create 30 second epochs that allow us to perform non-event-related analyses of the signal. + +.. note:: + Starting in version 1.0, all functions in the ``mne.connectivity`` + sub-module are housed in a separate package called + :mod:`mne-connectivity `. Download it by running: + + .. code-block:: console + + $ pip install mne-connectivity """ -import os +# %% + import numpy as np import matplotlib.pyplot as plt import mne from mne.preprocessing import compute_proj_ecg +from mne_connectivity import envelope_correlation sample_data_folder = mne.datasets.sample.data_path() -sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample', - 'sample_audvis_raw.fif') - +sample_data_raw_file = (sample_data_folder / 'MEG' / 'sample' / + 'sample_audvis_raw.fif') raw = mne.io.read_raw_fif(sample_data_raw_file) -############################################################################### +# %% # For this tutorial we'll crop and resample the raw data to a manageable size # for our web server to handle, ignore EEG channels, and remove the heartbeat # artifact so we don't get spurious correlations just because of that. @@ -38,7 +52,7 @@ raw.add_proj(ecg_proj) raw.apply_proj() -############################################################################### +# %% # To create fixed length epochs, we simply call the function and provide it # with the appropriate parameters indicating the desired duration of epochs in # seconds, whether or not to preload data, whether or not to reject epochs that @@ -49,7 +63,7 @@ epochs = mne.make_fixed_length_epochs(raw, duration=30, preload=False) -############################################################################### +# %% # Characteristics of Fixed Length Epochs # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # Fixed length epochs are generally unsuitable for event-related analyses. This @@ -59,7 +73,7 @@ event_related_plot = epochs.plot_image(picks=['MEG 1142']) -############################################################################### +# %% # For information about creating epochs for event-related analyses, please see # :ref:`tut-epochs-class`. # @@ -83,14 +97,15 @@ epochs.load_data().filter(l_freq=8, h_freq=12) alpha_data = epochs.get_data() -############################################################################### +# %% # If desired, separate correlation matrices for each epoch can be obtained. -# For envelope correlations, this is done by passing ``combine=None`` to the -# envelope correlations function. +# For envelope correlations, this is the default return if you use +# :meth:`mne-connectivity:mne_connectivity.EpochConnectivity.get_data`: -corr_matrix = mne.connectivity.envelope_correlation(alpha_data, combine=None) +corr_matrix = envelope_correlation(alpha_data).get_data() +print(corr_matrix.shape) -############################################################################### +# %% # Now we can plot correlation matrices. We'll compare the first and last # 30-second epochs of the recording: diff -Nru python-mne-0.23.4+dfsg/tutorials/evoked/10_evoked_overview.py python-mne-1.1.0+dfsg/tutorials/evoked/10_evoked_overview.py --- python-mne-0.23.4+dfsg/tutorials/evoked/10_evoked_overview.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tutorials/evoked/10_evoked_overview.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,41 +1,44 @@ +# -*- coding: utf-8 -*- """ .. _tut-evoked-class: +=============================================== The Evoked data structure: evoked/averaged data =============================================== This tutorial covers the basics of creating and working with :term:`evoked` data. It introduces the :class:`~mne.Evoked` data structure in detail, -including how to load, query, subselect, export, and plot data from an -:class:`~mne.Evoked` object. For info on creating an :class:`~mne.Evoked` +including how to load, query, subset, export, and plot data from an +:class:`~mne.Evoked` object. For details on creating an :class:`~mne.Evoked` object from (possibly simulated) data in a :class:`NumPy array -`, see :ref:`tut_creating_data_structures`. +`, see :ref:`tut-creating-data-structures`. -As usual we'll start by importing the modules we need: +As usual, we'll start by importing the modules we need: """ -import os +# %% + import mne -############################################################################### +# %% # Creating ``Evoked`` objects from ``Epochs`` # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # -# :class:`~mne.Evoked` objects typically store an EEG or MEG signal that has +# :class:`~mne.Evoked` objects typically store EEG or MEG signals that have # been *averaged* over multiple :term:`epochs`, which is a common technique for # estimating stimulus-evoked activity. The data in an :class:`~mne.Evoked` # object are stored in an :class:`array ` of shape # ``(n_channels, n_times)`` (in contrast to an :class:`~mne.Epochs` object, -# which stores data of shape ``(n_epochs, n_channels, n_times)``). Thus to +# which stores data of shape ``(n_epochs, n_channels, n_times)``). Thus, to # create an :class:`~mne.Evoked` object, we'll start by epoching some raw data, # and then averaging together all the epochs from one condition: -sample_data_folder = mne.datasets.sample.data_path() -sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample', - 'sample_audvis_raw.fif') -raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False) +root = mne.datasets.sample.data_path() / 'MEG' / 'sample' +raw_file = root / 'sample_audvis_raw.fif' +raw = mne.io.read_raw_fif(raw_file, verbose=False) + events = mne.find_events(raw, stim_channel='STI 014') -# we'll skip the "face" and "buttonpress" conditions, to save memory: +# we'll skip the "face" and "buttonpress" conditions to save memory event_dict = {'auditory/left': 1, 'auditory/right': 2, 'visual/left': 3, 'visual/right': 4} epochs = mne.Epochs(raw, events, tmin=-0.3, tmax=0.7, event_id=event_dict, @@ -44,20 +47,20 @@ del raw # reduce memory usage -############################################################################### +# %% # You may have noticed that MNE informed us that "baseline correction" has been -# applied. This happened automatically by during creation of the -# `~mne.Epochs` object, but may also be initiated (or disabled!) manually: -# We will discuss this in more detail later. -# -# The information about the baseline period of `~mne.Epochs` is transferred to -# derived `~mne.Evoked` objects to maintain provenance as you process your -# data: +# applied. This happened automatically during creation of the +# :class:`~mne.Epochs` object, but may also be initiated (or disabled) +# manually. We will discuss this in more detail later. +# +# The information about the baseline period of :class:`~mne.Epochs` is +# transferred to derived :class:`~mne.Evoked` objects to maintain provenance as +# you process your data: print(f'Epochs baseline: {epochs.baseline}') print(f'Evoked baseline: {evoked.baseline}') -############################################################################### +# %% # Basic visualization of ``Evoked`` objects # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # @@ -67,49 +70,50 @@ evoked.plot() -############################################################################### +# %% # Like the ``plot()`` methods for :meth:`Raw ` and # :meth:`Epochs ` objects, # :meth:`evoked.plot() ` has many parameters for customizing # the plot output, such as color-coding channel traces by scalp location, or # plotting the :term:`global field power` alongside the channel traces. -# See :ref:`tut-visualize-evoked` for more information about visualizing +# See :ref:`tut-visualize-evoked` for more information on visualizing # :class:`~mne.Evoked` objects. # # -# Subselecting ``Evoked`` data +# Subsetting ``Evoked`` data # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # -# .. sidebar:: Evokeds are not memory-mapped +# .. admonition:: Evokeds are not memory-mapped +# :class: sidebar note # # :class:`~mne.Evoked` objects use a :attr:`~mne.Evoked.data` *attribute* # rather than a :meth:`~mne.Epochs.get_data` *method*; this reflects the fact # that the data in :class:`~mne.Evoked` objects are always loaded into -# memory, never `memory-mapped`_ from their location on disk (because they +# memory and never `memory-mapped`_ from their location on disk (because they # are typically *much* smaller than :class:`~mne.io.Raw` or # :class:`~mne.Epochs` objects). # # # Unlike :class:`~mne.io.Raw` and :class:`~mne.Epochs` objects, # :class:`~mne.Evoked` objects do not support selection by square-bracket -# indexing. Instead, data can be subselected by indexing the +# indexing. Instead, data can be subsetted by indexing the # :attr:`~mne.Evoked.data` attribute: print(evoked.data[:2, :3]) # first 2 channels, first 3 timepoints -############################################################################### +# %% # To select based on time in seconds, the :meth:`~mne.Evoked.time_as_index` # method can be useful, although beware that depending on the sampling # frequency, the number of samples in a span of given duration may not always -# be the same (see the :ref:`time-as-index` section of the -# :ref:`tutorial about Raw data ` for details). +# be the same (see the :ref:`time-as-index` section of the tutorial on +# :ref:`Raw data ` for details). # # # Selecting, dropping, and reordering channels # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # By default, when creating :class:`~mne.Evoked` data from an -# :class:`~mne.Epochs` object, only the "data" channels will be retained: +# :class:`~mne.Epochs` object, only the primary data channels will be retained: # ``eog``, ``ecg``, ``stim``, and ``misc`` channel types will be dropped. You # can control which channel types are retained via the ``picks`` parameter of # :meth:`epochs.average() `, by passing ``'all'`` to @@ -134,7 +138,7 @@ evoked_subset = evoked.copy().reorder_channels(new_order) print(evoked_subset.ch_names) -############################################################################### +# %% # Similarities among the core data structures # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # @@ -143,7 +147,7 @@ # # - They can be loaded from and saved to disk in ``.fif`` format, and their # data can be exported to a :class:`NumPy array ` (but through -# the :attr:`~mne.Evoked.data` attribute, not through a ``get_data()`` +# the :attr:`~mne.Evoked.data` attribute instead of a ``get_data()`` # method). :class:`Pandas DataFrame ` export is also # available through the :meth:`~mne.Evoked.to_data_frame` method. # @@ -181,17 +185,16 @@ # :class:`~mne.Evoked` objects and the other data structures is that multiple # :class:`~mne.Evoked` objects can be saved into a single ``.fif`` file, using # :func:`mne.write_evokeds`. The :ref:`example data ` -# includes just such a ``.fif`` file: the data have already been epoched and +# includes such a ``.fif`` file: the data have already been epoched and # averaged, and the file contains separate :class:`~mne.Evoked` objects for # each experimental condition: -sample_data_evk_file = os.path.join(sample_data_folder, 'MEG', 'sample', - 'sample_audvis-ave.fif') -evokeds_list = mne.read_evokeds(sample_data_evk_file, verbose=False) +evk_file = root / 'sample_audvis-ave.fif' +evokeds_list = mne.read_evokeds(evk_file, verbose=False) print(evokeds_list) print(type(evokeds_list)) -############################################################################### +# %% # Notice that :func:`mne.read_evokeds` returned a :class:`list` of # :class:`~mne.Evoked` objects, and each one has an ``evoked.comment`` # attribute describing the experimental condition that was averaged to @@ -200,45 +203,45 @@ for evok in evokeds_list: print(evok.comment) -############################################################################### +# %% # If you want to load only some of the conditions present in a ``.fif`` file, # :func:`~mne.read_evokeds` has a ``condition`` parameter, which takes either a # string (matched against the comment attribute of the evoked objects on disk), # or an integer selecting the :class:`~mne.Evoked` object based on the order -# it's stored in the file. Passing lists of integers or strings is also +# it is stored in the file. Passing lists of integers or strings is also # possible. If only one object is selected, the :class:`~mne.Evoked` object -# will be returned directly (rather than a length-one list containing it): +# will be returned directly (rather than inside a list of length one): -right_vis = mne.read_evokeds(sample_data_evk_file, condition='Right visual') +right_vis = mne.read_evokeds(evk_file, condition='Right visual') print(right_vis) print(type(right_vis)) -############################################################################### -# Above, when we created an :class:`~mne.Evoked` object by averaging epochs, -# baseline correction was applied by default when we extracted epochs from the -# `~mne.io.Raw` object (the default baseline period is ``(None, 0)``, -# which assured zero mean for times before the stimulus event). In contrast, if +# %% +# Previously, when we created an :class:`~mne.Evoked` object by averaging +# epochs, baseline correction was applied by default when we extracted epochs +# from the `~mne.io.Raw` object (the default baseline period is ``(None, 0)``, +# which ensures zero mean for times before the stimulus event). In contrast, if # we plot the first :class:`~mne.Evoked` object in the list that was loaded # from disk, we'll see that the data have not been baseline-corrected: evokeds_list[0].plot(picks='eeg') -############################################################################### +# %% # This can be remedied by either passing a ``baseline`` parameter to # :func:`mne.read_evokeds`, or by applying baseline correction after loading, # as shown here: -# Original baseline (none set). +# Original baseline (none set) print(f'Baseline after loading: {evokeds_list[0].baseline}') -# Apply a custom baseline correction. +# Apply a custom baseline correction evokeds_list[0].apply_baseline((None, 0)) print(f'Baseline after calling apply_baseline(): {evokeds_list[0].baseline}') -# Visualize the evoked response. +# Visualize the evoked response evokeds_list[0].plot(picks='eeg') -############################################################################### +# %% # Notice that :meth:`~mne.Evoked.apply_baseline` operated in-place. Similarly, # :class:`~mne.Evoked` objects may have been saved to disk with or without # :term:`projectors ` applied; you can pass ``proj=True`` to the @@ -251,13 +254,13 @@ # # One way to pool data across multiple conditions when estimating evoked # responses is to do so *prior to averaging* (recall that MNE-Python can select -# based on partial matching of ``/``-separated epoch labels; see -# :ref:`tut-section-subselect-epochs` for more info): +# based on partial matching of epoch labels separated by ``/``; see +# :ref:`tut-section-subselect-epochs` for more information): left_right_aud = epochs['auditory'].average() print(left_right_aud) -############################################################################### +# %% # This approach will weight each epoch equally and create a single # :class:`~mne.Evoked` object. Notice that the printed representation includes # ``(average, N=145)``, indicating that the :class:`~mne.Evoked` object was @@ -268,45 +271,47 @@ right_aud = epochs['auditory/right'].average() print([evok.nave for evok in (left_aud, right_aud)]) -############################################################################### -# However, this may not always be the case; if for statistical reasons it is +# %% +# However, this may not always be the case. If for statistical reasons it is # important to average *the same number* of epochs from different conditions, # you can use :meth:`~mne.Epochs.equalize_event_counts` prior to averaging. # # Another approach to pooling across conditions is to create separate -# :class:`~mne.Evoked` objects for each condition, and combine them afterward. -# This can be accomplished by the function :func:`mne.combine_evoked`, which +# :class:`~mne.Evoked` objects for each condition, and combine them afterwards. +# This can be accomplished with the function :func:`mne.combine_evoked`, which # computes a weighted sum of the :class:`~mne.Evoked` objects given to it. The # weights can be manually specified as a list or array of float values, or can -# be specified using the keyword ``'equal'`` (weight each `~mne.Evoked` object -# by :math:`\frac{1}{N}`, where :math:`N` is the number of `~mne.Evoked` -# objects given) or the keyword ``'nave'`` (weight each `~mne.Evoked` object -# proportional to the number of epochs averaged together to create it): +# be specified using the keyword ``'equal'`` (weight each :class:`~mne.Evoked` +# object by :math:`\frac{1}{N}`, where :math:`N` is the number of +# :class:`~mne.Evoked` objects given) or the keyword ``'nave'`` (weight each +# :class:`~mne.Evoked` object proportional to the number of epochs averaged +# together to create it): left_right_aud = mne.combine_evoked([left_aud, right_aud], weights='nave') assert left_right_aud.nave == left_aud.nave + right_aud.nave -############################################################################### -# Note that the ``nave`` attribute of the resulting `~mne.Evoked` object will -# reflect the *effective* number of averages, and depends on both the ``nave`` -# attributes of the contributing `~mne.Evoked` objects and the weights at -# which they are combined. Keeping track of effective ``nave`` is important for -# inverse imaging, because ``nave`` is used to scale the noise covariance -# estimate (which in turn affects the magnitude of estimated source activity). -# See :ref:`minimum_norm_estimates` for more information (especially the -# :ref:`whitening_and_scaling` section). Note that `mne.grand_average` does -# *not* adjust ``nave`` to reflect effective number of averaged epochs; rather -# it simply sets ``nave`` to the number of *evokeds* that were averaged -# together. For this reason, it is best to use `mne.combine_evoked` rather than -# `mne.grand_average` if you intend to perform inverse imaging on the resulting -# :class:`~mne.Evoked` object. +# %% +# Note that the ``nave`` attribute of the resulting :class:`~mne.Evoked` object +# will reflect the *effective* number of averages, and depends on both the +# ``nave`` attributes of the contributing :class:`~mne.Evoked` objects and the +# weights with which they are combined. Keeping track of effective ``nave`` is +# important for inverse imaging, because ``nave`` is used to scale the noise +# covariance estimate, which in turn affects the magnitude of estimated source +# activity (see :ref:`minimum_norm_estimates` for more information, especially +# the :ref:`whitening_and_scaling` section). Note that +# :func:`mne.grand_average` does *not* adjust ``nave`` to reflect the effective +# number of averaged epochs; it simply sets ``nave`` to the number of *evokeds* +# that were averaged together. For this reason, it is best to use +# :func:`mne.combine_evoked` rather than :func:`mne.grand_average` if you +# intend to perform inverse imaging on the resulting :class:`~mne.Evoked` +# object. # # # Other uses of ``Evoked`` objects # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # Although the most common use of :class:`~mne.Evoked` objects is to store -# *averages* of epoched data, there are a couple other uses worth noting here. +# *averages* of epoched data, there are a few other uses worth noting here. # First, the method :meth:`epochs.standard_error() ` # will create an :class:`~mne.Evoked` object (just like # :meth:`epochs.average() ` does), but the data in the @@ -320,18 +325,18 @@ # accomplished with the :meth:`epochs.iter_evoked() ` # method, and can be useful for applications where you want to do something # that is only possible for :class:`~mne.Evoked` objects. For example, here -# we use the :meth:`~mne.Evoked.get_peak` method (which isn't available for +# we use the :meth:`~mne.Evoked.get_peak` method (which is not available for # :class:`~mne.Epochs` objects) to get the peak response in each trial: for ix, trial in enumerate(epochs[:3].iter_evoked()): channel, latency, value = trial.get_peak(ch_type='eeg', return_amplitude=True) latency = int(round(latency * 1e3)) # convert to milliseconds - value = int(round(value * 1e6)) # convert to µV + value = int(round(value * 1e6)) # convert to µV print('Trial {}: peak of {} µV at {} ms in channel {}' .format(ix, value, latency, channel)) -############################################################################### +# %% # .. REFERENCES # # .. _`memory-mapped`: https://en.wikipedia.org/wiki/Memory-mapped_file diff -Nru python-mne-0.23.4+dfsg/tutorials/evoked/20_visualize_evoked.py python-mne-1.1.0+dfsg/tutorials/evoked/20_visualize_evoked.py --- python-mne-0.23.4+dfsg/tutorials/evoked/20_visualize_evoked.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tutorials/evoked/20_visualize_evoked.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,42 +1,45 @@ +# -*- coding: utf-8 -*- """ .. _tut-visualize-evoked: +======================= Visualizing Evoked data ======================= This tutorial shows the different visualization methods for -`~mne.Evoked` objects. +:class:`~mne.Evoked` objects. As usual we'll start by importing the modules we need: """ -import os +# %% + import numpy as np + import mne -############################################################################### -# Instead of creating the `~mne.Evoked` object from an `~mne.Epochs` object, -# we'll load an existing `~mne.Evoked` object from disk. Remember, the -# :file:`.fif` format can store multiple `~mne.Evoked` objects, so we'll end up -# with a `list` of `~mne.Evoked` objects after loading. Recall also from the +# %% +# Instead of creating the :class:`~mne.Evoked` object from an +# :class:`~mne.Epochs` object, we'll load an existing :class:`~mne.Evoked` +# object from disk. Remember, the :file:`.fif` format can store multiple +# :class:`~mne.Evoked` objects, so we'll end up with a `list` of +# :class:`~mne.Evoked` objects after loading. Recall also from the # :ref:`tut-section-load-evk` section of :ref:`the introductory Evoked tutorial -# ` that the sample `~mne.Evoked` objects have not been -# baseline-corrected and have unapplied projectors, so we'll take care of that -# when loading: - -sample_data_folder = mne.datasets.sample.data_path() -sample_data_evk_file = os.path.join(sample_data_folder, 'MEG', 'sample', - 'sample_audvis-ave.fif') -evokeds_list = mne.read_evokeds(sample_data_evk_file, baseline=(None, 0), - proj=True, verbose=False) +# ` that the sample :class:`~mne.Evoked` objects have not +# been baseline-corrected and have unapplied projectors, so we'll take care of +# that when loading: + +root = mne.datasets.sample.data_path() / 'MEG' / 'sample' +evk_file = root / 'sample_audvis-ave.fif' +evokeds_list = mne.read_evokeds(evk_file, baseline=(None, 0), proj=True, + verbose=False) -# Show the condition names, and reassure ourselves that baseline correction has -# been applied. +# Show condition names and baseline intervals for e in evokeds_list: print(f'Condition: {e.comment}, baseline: {e.baseline}') -############################################################################### -# To make our life easier, let's convert that list of `~mne.Evoked` +# %% +# To make our life easier, let's convert that list of :class:`~mne.Evoked` # objects into a :class:`dictionary `. We'll use ``/``-separated # dictionary keys to encode the conditions (like is often done when epoching) # because some of the plotting methods can take advantage of that style of @@ -44,21 +47,22 @@ conds = ('aud/left', 'aud/right', 'vis/left', 'vis/right') evks = dict(zip(conds, evokeds_list)) -# ‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾ this is equivalent to: +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ this is equivalent to: # {'aud/left': evokeds_list[0], 'aud/right': evokeds_list[1], # 'vis/left': evokeds_list[2], 'vis/right': evokeds_list[3]} -############################################################################### +# %% # Plotting signal traces # ^^^^^^^^^^^^^^^^^^^^^^ # -# .. sidebar:: Butterfly plots +# .. admonition:: Butterfly plots +# :class: sidebar note # # Plots of superimposed sensor timeseries are called "butterfly plots" # because the positive- and negative-going traces can resemble butterfly # wings. # -# The most basic plot of `~mne.Evoked` objects is a butterfly plot of +# The most basic plot of :class:`~mne.Evoked` objects is a butterfly plot of # each channel type, generated by the `evoked.plot() ` # method. By default, channels marked as "bad" are suppressed, but you can # control this by passing an empty :class:`list` to the ``exclude`` parameter @@ -66,12 +70,12 @@ evks['aud/left'].plot(exclude=[]) -############################################################################### +# %% # Notice the completely flat EEG channel and the noisy gradiometer channel # plotted in red color. Like many MNE-Python plotting functions, # `evoked.plot() ` has a ``picks`` parameter that can -# select channels to plot by name, index, or type. In the next plot we'll show -# only magnetometer channels, and also color-code the channel traces by their +# select channels to plot by name, index, or type. In the next plot, we'll show +# only magnetometer channels and also color-code the channel traces by their # location by passing ``spatial_colors=True``. Finally, we'll superimpose a # trace of the root mean square (RMS) of the signal across channels by # passing ``gfp=True``. This parameter is called ``gfp`` for historical @@ -82,7 +86,19 @@ evks['aud/left'].plot(picks='mag', spatial_colors=True, gfp=True) -############################################################################### +# %% +# Interesting time periods can be highlighted via the ``highlight`` parameter. + +time_ranges_of_interest = [ + (0.05, 0.14), + (0.22, 0.27) +] +evks['aud/left'].plot( + picks='mag', spatial_colors=True, gfp=True, + highlight=time_ranges_of_interest +) + +# %% # Plotting scalp topographies # ^^^^^^^^^^^^^^^^^^^^^^^^^^^ # @@ -90,17 +106,26 @@ # click-dragged to select a time region, which will pop up a map of the average # field distribution over the scalp for the selected time span. You can also # generate scalp topographies at specific times or time spans using the -# `~mne.Evoked.plot_topomap` method: +# :meth:`~mne.Evoked.plot_topomap` method: times = np.linspace(0.05, 0.13, 5) evks['aud/left'].plot_topomap(ch_type='mag', times=times, colorbar=True) -############################################################################### +# %% -fig = evks['aud/left'].plot_topomap(ch_type='mag', times=0.09, average=0.1) -fig.text(0.5, 0.05, 'average from 40-140 ms', ha='center') +fig = evks['aud/left'].plot_topomap(ch_type='mag', times=times, average=0.1) -############################################################################### +# %% +# It is also possible to pass different time durations to average over for each +# time point. Passing a value of ``None`` will disable averaging for that +# time point: + +averaging_durations = [0.01, 0.02, 0.03, None, None] +fig = evks['aud/left'].plot_topomap( + ch_type='mag', times=times, average=averaging_durations +) + +# %% # Additional examples of plotting scalp topographies can be found in # :ref:`ex-evoked-topomap`. # @@ -110,12 +135,12 @@ # # Scalp topographies at a given time point can be augmented with arrows to show # the estimated magnitude and direction of the magnetic field, using the -# function `mne.viz.plot_arrowmap`: +# function :func:`mne.viz.plot_arrowmap`: mags = evks['aud/left'].copy().pick_types(meg='mag') mne.viz.plot_arrowmap(mags.data[:, 175], mags.info, extrapolate='local') -############################################################################### +# %% # Joint plots # ^^^^^^^^^^^ # @@ -125,21 +150,20 @@ # right-visual-field condition; if no ``picks`` are specified we get a separate # figure for each channel type: -# sphinx_gallery_thumbnail_number = 7 evks['vis/right'].plot_joint() -############################################################################### -# Like `~mne.Evoked.plot_topomap` you can specify the ``times`` at which +# %% +# Like :meth:`~mne.Evoked.plot_topomap`, you can specify the ``times`` at which # you want the scalp topographies calculated, and you can customize the plot in -# various other ways as well. See `mne.Evoked.plot_joint` for details. +# various other ways as well. See :meth:`mne.Evoked.plot_joint` for details. # # # Comparing ``Evoked`` objects # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # -# To compare `~mne.Evoked` objects from different experimental -# conditions, the function `mne.viz.plot_compare_evokeds` can take a -# :class:`list` or :class:`dict` of `~mne.Evoked` objects and plot them +# To compare :class:`~mne.Evoked` objects from different experimental +# conditions, the function :func:`mne.viz.plot_compare_evokeds` can take a +# :class:`list` or :class:`dict` of :class:`~mne.Evoked` objects and plot them # all on the same axes. Like most MNE-Python visualization functions, it has a # ``picks`` parameter for selecting channels, but by default will generate one # figure for each channel type, and combine information across channels of the @@ -156,26 +180,30 @@ for combine in ('mean', 'median', 'gfp', custom_func): mne.viz.plot_compare_evokeds(evks, picks='eeg', combine=combine) -############################################################################### -# One nice feature of `~mne.viz.plot_compare_evokeds` is that when +# %% +# One nice feature of :func:`~mne.viz.plot_compare_evokeds` is that when # passing evokeds in a dictionary, it allows specifying plot styles based on # ``/``-separated substrings of the dictionary keys (similar to epoch # selection; see :ref:`tut-section-subselect-epochs`). Here, we specify colors # for "aud" and "vis" conditions, and linestyles for "left" and "right" -# conditions, and the traces and legend are styled accordingly. +# conditions, and the traces and legend are styled accordingly. Here we also +# show the ``time_unit='ms'`` parameter in action. +# sphinx_gallery_thumbnail_number = 15 mne.viz.plot_compare_evokeds(evks, picks='MEG 1811', colors=dict(aud=0, vis=1), - linestyles=dict(left='solid', right='dashed')) + linestyles=dict(left='solid', right='dashed'), + time_unit='ms') -############################################################################### -# The legends generated by `~mne.viz.plot_compare_evokeds` above used the +# %% +# The legends generated by :func:`~mne.viz.plot_compare_evokeds` above used the # dictionary keys provided by the ``evks`` variable. If instead you pass a -# :class:`list` or :class:`tuple` of `~mne.Evoked` objects, the legend keys -# will be generated automatically from the ``comment`` attribute of the -# `~mne.Evoked` objects (or, as sequential integers if the comment attribute is -# empty or ambiguous). To illustrate this, we'll make a list of 5 `~mne.Evoked` -# objects: 2 with identical comments, 2 with empty comments (either an empty -# string or ``None``), and 1 with a unique non-empty comment: +# :class:`list` or :class:`tuple` of :class:`~mne.Evoked` objects, the legend +# keys will be generated automatically from the ``comment`` attribute of the +# :class:`~mne.Evoked` objects (or, as sequential integers if the comment +# attribute is empty or ambiguous). To illustrate this, we'll make a list of +# five :class:`~mne.Evoked` objects: two with identical comments, two with +# empty comments (either an empty string or ``None``), and one with a unique +# non-empty comment: temp_list = list() for idx, _comment in enumerate(('foo', 'foo', '', None, 'bar'), start=1): @@ -186,25 +214,25 @@ mne.viz.plot_compare_evokeds(temp_list, picks='mag') -############################################################################### +# %% # Image plots # ^^^^^^^^^^^ # -# Like `~mne.Epochs`, `~mne.Evoked` objects also have a -# `~mne.Evoked.plot_image` method, but unlike `epochs.plot_image() +# Like :class:`~mne.Epochs`, :class:`~mne.Evoked` objects also have a +# :meth:`~mne.Evoked.plot_image` method, but unlike `epochs.plot_image() # `, `evoked.plot_image() ` # shows one *channel* per row instead of one *epoch* per row. Again, a # ``picks`` parameter is available, as well as several other customization -# options; see `~mne.Evoked.plot_image` for details. +# options; see :meth:`~mne.Evoked.plot_image` for details. evks['vis/right'].plot_image(picks='meg') -############################################################################### +# %% # Topographical subplots # ^^^^^^^^^^^^^^^^^^^^^^ # -# For sensor-level analyses it can be useful to plot the response at each -# sensor in a topographical layout. The `~mne.viz.plot_compare_evokeds` +# For sensor-level analyses, it can be useful to plot the response at each +# sensor in a topographical layout. The :func:`~mne.viz.plot_compare_evokeds` # function can do this if you pass ``axes='topo'``, but it can be quite slow # if the number of sensors is too large, so here we'll plot only the EEG # channels: @@ -214,62 +242,60 @@ axes='topo', styles=dict(aud=dict(linewidth=1), vis=dict(linewidth=1))) -############################################################################### -# For larger numbers of sensors, the method `evoked.plot_topo() -# ` and the function `mne.viz.plot_evoked_topo` -# can both be used. The `~mne.Evoked.plot_topo` method will plot only a -# single condition, while the `~mne.viz.plot_evoked_topo` function can +# %% +# For a larger number of sensors, the method `evoked.plot_topo() +# ` and the function :func:`mne.viz.plot_evoked_topo` +# can both be used. The :meth:`~mne.Evoked.plot_topo` method will plot only a +# single condition, while the :func:`~mne.viz.plot_evoked_topo` function can # plot one or more conditions on the same axes, if passed a list of -# `~mne.Evoked` objects. The legend entries will be automatically drawn -# from the `~mne.Evoked` objects' ``comment`` attribute: +# :class:`~mne.Evoked` objects. The legend entries will be automatically drawn +# from the :class:`~mne.Evoked` objects' ``comment`` attribute: mne.viz.plot_evoked_topo(evokeds_list) -############################################################################### -# By default, `~mne.viz.plot_evoked_topo` will plot all MEG sensors (if +# %% +# By default, :func:`~mne.viz.plot_evoked_topo` will plot all MEG sensors (if # present), so to get EEG sensors you would need to modify the evoked objects # first (e.g., using `mne.pick_types`). # # .. note:: # # In interactive sessions, both approaches to topographical plotting allow -# you to click one of the sensor subplots to pop open a larger version of -# the evoked plot at that sensor. +# you to click one of the sensor subplots to open a larger version of the +# evoked plot at that sensor. # # # 3D Field Maps # ^^^^^^^^^^^^^ # -# The scalp topographies above were all projected into 2-dimensional overhead -# views of the field, but it is also possible to plot field maps in 3D. To do -# this requires a :term:`trans` file to transform locations between the -# coordinate systems of the MEG device and the head surface (based on the MRI). -# You *can* compute 3D field maps without a ``trans`` file, but it will only -# work for calculating the field *on the MEG helmet from the MEG sensors*. - -subjects_dir = os.path.join(sample_data_folder, 'subjects') -sample_data_trans_file = os.path.join(sample_data_folder, 'MEG', 'sample', - 'sample_audvis_raw-trans.fif') +# The scalp topographies above were all projected into two-dimensional overhead +# views of the field, but it is also possible to plot field maps in 3D. This +# requires a :term:`trans` file to transform locations between the coordinate +# systems of the MEG device and the head surface (based on the MRI). You *can* +# compute 3D field maps without a ``trans`` file, but it will only work for +# calculating the field *on the MEG helmet from the MEG sensors*. + +subjects_dir = root.parent.parent / 'subjects' +trans_file = root / 'sample_audvis_raw-trans.fif' -############################################################################### +# %% # By default, MEG sensors will be used to estimate the field on the helmet # surface, while EEG sensors will be used to estimate the field on the scalp. # Once the maps are computed, you can plot them with `evoked.plot_field() # `: -maps = mne.make_field_map(evks['aud/left'], trans=sample_data_trans_file, +maps = mne.make_field_map(evks['aud/left'], trans=str(trans_file), subject='sample', subjects_dir=subjects_dir) evks['aud/left'].plot_field(maps, time=0.1) -############################################################################### +# %% # You can also use MEG sensors to estimate the *scalp* field by passing # ``meg_surf='head'``. By selecting each sensor type in turn, you can compare # the scalp field estimates from each. for ch_type in ('mag', 'grad', 'eeg'): evk = evks['aud/right'].copy().pick(ch_type) - _map = mne.make_field_map(evk, trans=sample_data_trans_file, - subject='sample', subjects_dir=subjects_dir, - meg_surf='head') + _map = mne.make_field_map(evk, trans=str(trans_file), subject='sample', + subjects_dir=subjects_dir, meg_surf='head') fig = evk.plot_field(_map, time=0.1) mne.viz.set_3d_title(fig, ch_type, size=20) diff -Nru python-mne-0.23.4+dfsg/tutorials/evoked/30_eeg_erp.py python-mne-1.1.0+dfsg/tutorials/evoked/30_eeg_erp.py --- python-mne-0.23.4+dfsg/tutorials/evoked/30_eeg_erp.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tutorials/evoked/30_eeg_erp.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,8 +1,10 @@ +# -*- coding: utf-8 -*- """ -.. _tut_erp: +.. _tut-erp: -EEG processing and Event Related Potentials (ERPs) -================================================== +============================================== +EEG analysis - Event-Related Potentials (ERPs) +============================================== This tutorial shows how to perform standard ERP analyses in MNE-Python. Most of the material here is covered in other tutorials too, but for convenience the @@ -12,30 +14,29 @@ As usual we'll start by importing the modules we need and loading some example data. Instead of parsing the events from the raw data's :term:`stim channel` (like we do in :ref:`this tutorial `), we'll load -the events from an external events file. Finally, to speed up computations so -our documentation server can handle them, we'll crop the raw data from ~4.5 -minutes down to 90 seconds. +the events from an external events file. Finally, to speed up computations +we'll crop the raw data from ~4.5 minutes down to 90 seconds. """ -import os +# %% + import numpy as np +import pandas as pd import matplotlib.pyplot as plt import mne -sample_data_folder = mne.datasets.sample.data_path() -sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample', - 'sample_audvis_filt-0-40_raw.fif') -raw = mne.io.read_raw_fif(sample_data_raw_file, preload=False) - -sample_data_events_file = os.path.join(sample_data_folder, 'MEG', 'sample', - 'sample_audvis_filt-0-40_raw-eve.fif') -events = mne.read_events(sample_data_events_file) +root = mne.datasets.sample.data_path() / 'MEG' / 'sample' +raw_file = root / 'sample_audvis_filt-0-40_raw.fif' +raw = mne.io.read_raw_fif(raw_file, preload=False) + +events_file = root / 'sample_audvis_filt-0-40_raw-eve.fif' +events = mne.read_events(events_file) -raw.crop(tmax=90) # in seconds; happens in-place -# discard events >90 seconds (not strictly necessary: avoids some warnings) +raw.crop(tmax=90) # in seconds (happens in-place) +# discard events >90 seconds (not strictly necessary, but avoids some warnings) events = events[events[:, 0] <= raw.last_samp] -############################################################################### +# %% # The file that we loaded has already been partially processed: 3D sensor # locations have been saved as part of the ``.fif`` file, the data have been # low-pass filtered at 40 Hz, and a common average reference is set for the @@ -43,7 +44,7 @@ # :ref:`tut-set-eeg-ref` tutorial for more info about when you may want to do # this). We'll discuss how to do each of these below. # -# Since this is a combined EEG+MEG dataset, let's start by restricting the data +# Since this is a combined EEG/MEG dataset, let's start by restricting the data # to just the EEG and EOG channels. This will cause the other projectors saved # in the file (which apply only to magnetometer channels) to be removed. By # looking at the measurement info we can see that we now have 59 EEG channels @@ -52,78 +53,88 @@ raw.pick(['eeg', 'eog']).load_data() raw.info -############################################################################### +# %% # Channel names and types # ^^^^^^^^^^^^^^^^^^^^^^^ # -# In practice it's quite common to have some channels labelled as EEG that are -# actually EOG channels. `~mne.io.Raw` objects have a -# `~mne.io.Raw.set_channel_types` method that you can use to change a channel -# that is labeled as ``eeg`` into an ``eog`` type. You can also rename channels -# using the `~mne.io.Raw.rename_channels` method. Detailed examples of both of -# these methods can be found in the tutorial :ref:`tut-raw-class`. In this data -# the channel types are all correct already, so for now we'll just rename the -# channels to remove a space and a leading zero in the channel names, and -# convert to lowercase: +# In practice it is quite common to have some channels labeled as EEG that are +# actually EOG channels. :class:`~mne.io.Raw` objects have a +# :meth:`~mne.io.Raw.set_channel_types` method that can be used to change a +# channel that is mislabeled as ``eeg`` to ``eog``. +# +# You can also rename channels using :meth:`~mne.io.Raw.rename_channels`. +# Detailed examples of both of these methods can be found in the tutorial +# :ref:`tut-raw-class`. +# +# In our data set, all channel types are already correct. Therefore, we'll only +# remove a space and a leading zero in the channel names and convert to +# lowercase: channel_renaming_dict = {name: name.replace(' 0', '').lower() for name in raw.ch_names} _ = raw.rename_channels(channel_renaming_dict) # happens in-place -############################################################################### +# %% +# .. note:: +# The assignment to a temporary name ``_`` (the ``_ =`` part) is included +# here to suppress automatic printing of the ``raw`` object. You do not +# have to do this in your interactive analysis. +# # Channel locations # ^^^^^^^^^^^^^^^^^ # -# The tutorial :ref:`tut-sensor-locations` describes MNE-Python's handling of -# sensor positions in great detail. To briefly summarize: MNE-Python -# distinguishes :term:`montages ` (which contain sensor positions in -# 3D: ``x``, ``y``, ``z``, in meters) from :term:`layouts ` (which -# define 2D arrangements of sensors for plotting approximate overhead diagrams -# of sensor positions). Additionally, montages may specify *idealized* sensor -# positions (based on, e.g., an idealized spherical headshape model) or they -# may contain *realistic* sensor positions obtained by digitizing the 3D -# locations of the sensors when placed on the actual subject's head. +# The tutorial :ref:`tut-sensor-locations` describes how sensor locations are +# handled in great detail. To briefly summarize: MNE-Python distinguishes +# :term:`montages ` (which contain 3D sensor locations ``x``, ``y``, +# and ``z``, in meters) from :term:`layouts ` (which define 2D sensor +# arrangements for plotting schematic sensor location diagrams). Additionally, +# montages may specify *idealized* sensor locations (based on, e.g., an +# idealized spherical head model), or they may contain *realistic* sensor +# locations obtained by digitizing the 3D locations of the sensors when placed +# on a real person's head. # # This dataset has realistic digitized 3D sensor locations saved as part of the # ``.fif`` file, so we can view the sensor locations in 2D or 3D using the -# `~mne.io.Raw.plot_sensors` method: +# :meth:`~mne.io.Raw.plot_sensors` method: raw.plot_sensors(show_names=True) fig = raw.plot_sensors('3d') -############################################################################### -# If you're working with a standard montage like the `10-20 `_ -# system, you can add sensor locations to the data like this: -# ``raw.set_montage('standard_1020')``. See :ref:`tut-sensor-locations` for -# info on what other standard montages are built-in to MNE-Python. +# %% +# If you're working with a standard montage like the `10–20 `_ +# system, you can add sensor locations to the data with +# ``raw.set_montage('standard_1020')`` (see :ref:`tut-sensor-locations` for +# information on other standard montages included with MNE-Python). # # If you have digitized realistic sensor locations, there are dedicated -# functions for loading those digitization files into MNE-Python; see +# functions for loading those digitization files into MNE-Python (see # :ref:`reading-dig-montages` for discussion and :ref:`dig-formats` for a list -# of supported formats. Once loaded, the digitized sensor locations can be +# of supported formats). Once loaded, the digitized sensor locations can be # added to the data by passing the loaded montage object to -# ``raw.set_montage()``. +# :meth:`~mne.io.Raw.set_montage`. # # # Setting the EEG reference # ^^^^^^^^^^^^^^^^^^^^^^^^^ # -# As mentioned above, this data already has an EEG common average reference -# added as a :term:`projector`. We can view the effect of this on the raw data -# by plotting with and without the projector applied: +# As mentioned, this data already has an EEG common average reference +# added as a :term:`projector`. We can view the effect of this projector on the +# raw data by plotting it with and without the projector applied: for proj in (False, True): - fig = raw.plot(n_channels=5, proj=proj, scalings=dict(eeg=50e-6)) + with mne.viz.use_browser_backend('matplotlib'): + fig = raw.plot(n_channels=5, proj=proj, scalings=dict(eeg=50e-6), + show_scrollbars=False) fig.subplots_adjust(top=0.9) # make room for title ref = 'Average' if proj else 'No' fig.suptitle(f'{ref} reference', size='xx-large', weight='bold') -############################################################################### +# %% # The referencing scheme can be changed with the function -# `mne.set_eeg_reference` (which by default operates on a *copy* of the data) -# or the `raw.set_eeg_reference() ` method (which -# always modifies the data in-place). The tutorial :ref:`tut-set-eeg-ref` shows -# several examples of this. +# :func:`mne.set_eeg_reference` (which by default operates on a *copy* of the +# data) or the :meth:`raw.set_eeg_reference() ` +# method (which always modifies the data *in-place*). The tutorial +# :ref:`tut-set-eeg-ref` shows several examples. # # # Filtering @@ -137,115 +148,121 @@ raw.filter(l_freq=0.1, h_freq=None) -############################################################################### +# %% # Evoked responses: epoching and averaging # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # The general process for extracting evoked responses from continuous data is -# to use the `~mne.Epochs` constructor, and then average the resulting epochs -# to create an `~mne.Evoked` object. In MNE-Python, events are represented as -# a :class:`NumPy array ` of sample numbers and integer event -# codes. The event codes are stored in the last column of the events array: +# to use the :class:`~mne.Epochs` constructor, and then average the resulting +# epochs to create an :class:`~mne.Evoked` object. In MNE-Python, events are +# represented as a :class:`NumPy array ` containing event +# latencies (in samples) and integer event codes. The event codes are stored in +# the last column of the events array: np.unique(events[:, -1]) -############################################################################### +# %% # The :ref:`tut-event-arrays` tutorial discusses event arrays in more detail. # Integer event codes are mapped to more descriptive text using a Python # :class:`dictionary ` usually called ``event_id``. This mapping is -# determined by your experiment code (i.e., it reflects which event codes you -# chose to use to represent different experimental events or conditions). For -# the :ref:`sample-dataset` data has the following mapping: +# determined by your experiment (i.e., it reflects which event codes you chose +# to represent different experimental events or conditions). The +# :ref:`sample-dataset` data uses the following mapping: event_dict = {'auditory/left': 1, 'auditory/right': 2, 'visual/left': 3, 'visual/right': 4, 'face': 5, 'buttonpress': 32} -############################################################################### -# Now we can extract epochs from the continuous data. An interactive plot -# allows you to click on epochs to mark them as "bad" and drop them from the -# analysis (it is not interactive on the documentation website, but will be +# %% +# Now we can proceed to epoch the continuous data. An interactive plot allows +# us to click on epochs to mark them as "bad" and drop them from the +# analysis (it is not interactive on this documentation website, but will be # when you run `epochs.plot() ` in a Python console). epochs = mne.Epochs(raw, events, event_id=event_dict, tmin=-0.3, tmax=0.7, preload=True) -fig = epochs.plot() +fig = epochs.plot(events=events) -############################################################################### -# It is also possible to automatically drop epochs, when first creating them or -# later on, by providing maximum peak-to-peak signal value thresholds (pass to -# the `~mne.Epochs` constructor as the ``reject`` parameter; see +# %% +# It is also possible to automatically drop epochs (either when first creating +# them or later on) by providing maximum peak-to-peak signal value thresholds +# (passed to :class:`~mne.Epochs` as the ``reject`` parameter; see # :ref:`tut-reject-epochs-section` for details). You can also do this after -# the epochs are already created, using the `~mne.Epochs.drop_bad` method: +# the epochs are already created using :meth:`~mne.Epochs.drop_bad`: + +reject_criteria = dict(eeg=100e-6, eog=200e-6) # 100 µV, 200 µV +epochs.drop_bad(reject=reject_criteria) -reject_criteria = dict(eeg=100e-6, # 100 µV - eog=200e-6) # 200 µV -_ = epochs.drop_bad(reject=reject_criteria) - -############################################################################### -# Next we generate a barplot of which channels contributed most to epochs -# getting rejected. If one channel is responsible for lots of epoch rejections, -# it may be worthwhile to mark that channel as "bad" in the `~mne.io.Raw` -# object and then re-run epoching (fewer channels w/ more good epochs may be -# preferable to keeping all channels but losing many epochs). See -# :ref:`tut-bad-channels` for more info. +# %% +# Next, we generate a barplot of which channels contributed most to epochs +# getting rejected. If one channel is responsible for many epoch rejections, +# it may be worthwhile to mark that channel as "bad" in the +# :class:`~mne.io.Raw` object and then re-run epoching (fewer channels with +# more good epochs may be preferable to keeping all channels but losing many +# epochs). See :ref:`tut-bad-channels` for more information. epochs.plot_drop_log() -############################################################################### -# Another way in which epochs can be automatically dropped is if the -# `~mne.io.Raw` object they're extracted from contains :term:`annotations` that -# begin with either ``bad`` or ``edge`` ("edge" annotations are automatically -# inserted when concatenating two separate `~mne.io.Raw` objects together). See -# :ref:`tut-reject-data-spans` for more information about annotation-based -# epoch rejection. -# -# Now that we've dropped the bad epochs, let's look at our evoked responses for -# some conditions we care about. Here the `~mne.Epochs.average` method will -# create and `~mne.Evoked` object, which we can then plot. Notice that we\ -# select which condition we want to average using the square-bracket indexing -# (like a :class:`dictionary `); that returns a smaller epochs object -# containing just the epochs from that condition, to which we then apply the -# `~mne.Epochs.average` method: +# %% +# Epochs can also be dropped automatically if the event around which the epoch +# is created is too close to the start or end of the :class:`~mne.io.Raw` +# object (e.g., if the epoch would extend past the end of the recording; this +# is the cause for the "TOO_SHORT" entry in the +# :meth:`~mne.Epochs.plot_drop_log` plot). +# +# Epochs may also be dropped automatically if the :class:`~mne.io.Raw` object +# contains :term:`annotations` that begin with either ``bad`` or ``edge`` +# ("edge" annotations are automatically inserted when concatenating two or more +# :class:`~mne.io.Raw` objects). See :ref:`tut-reject-data-spans` for more +# information on annotation-based epoch rejection. +# +# Now that we've dropped all bad epochs, let's look at our evoked responses for +# some conditions we care about. Here, the :meth:`~mne.Epochs.average` method +# will create an :class:`~mne.Evoked` object, which we can then plot. Notice +# that we select which condition we want to average using square-bracket +# indexing (like for a :class:`dictionary `). This returns a subset with +# only the desired epochs, which we then average: l_aud = epochs['auditory/left'].average() l_vis = epochs['visual/left'].average() -############################################################################### -# These `~mne.Evoked` objects have their own interactive plotting method -# (though again, it won't be interactive on the documentation website): -# click-dragging a span of time will generate a scalp field topography for that -# time span. Here we also demonstrate built-in color-coding the channel traces -# by location: +# %% +# These :class:`~mne.Evoked` objects have their own interactive plotting method +# (though again, it won't be interactive on the documentation website). +# Clicking and dragging a span of time will generate a topography of scalp +# potentials for the selected time segment. Here, we also demonstrate built-in +# color-coding the channel traces by location: fig1 = l_aud.plot() fig2 = l_vis.plot(spatial_colors=True) -############################################################################### +# %% # Scalp topographies can also be obtained non-interactively with the -# `~mne.Evoked.plot_topomap` method. Here we display topomaps of the average -# field in 50 ms time windows centered at -200 ms, 100 ms, and 400 ms. +# :meth:`~mne.Evoked.plot_topomap` method. Here, we display topomaps of the +# average evoked potential in 50 ms time windows centered at -200 ms, 100 ms, +# and 400 ms. l_aud.plot_topomap(times=[-0.2, 0.1, 0.4], average=0.05) -############################################################################### +# %% # Considerable customization of these plots is possible, see the docstring of -# `~mne.Evoked.plot_topomap` for details. +# :meth:`~mne.Evoked.plot_topomap` for details. # -# There is also a built-in method for combining "butterfly" plots of the -# signals with scalp topographies, called `~mne.Evoked.plot_joint`. Like -# `~mne.Evoked.plot_topomap` you can specify times for the scalp topographies -# or you can let the method choose times automatically, as is done here: +# There is also a built-in method for combining butterfly plots of the signals +# with scalp topographies called :meth:`~mne.Evoked.plot_joint`. Like in +# :meth:`~mne.Evoked.plot_topomap`, you can specify times for the scalp +# topographies or you can let the method choose times automatically as shown +# here: l_aud.plot_joint() -############################################################################### +# %% # Global field power (GFP) # ^^^^^^^^^^^^^^^^^^^^^^^^ # # Global field power :footcite:`Lehmann1980,Lehmann1984,Murray2008` is, # generally speaking, a measure of agreement of the signals picked up by all # sensors across the entire scalp: if all sensors have the same value at a -# given time point, the GFP will be zero at that time point; if the signals +# given time point, the GFP will be zero at that time point. If the signals # differ, the GFP will be non-zero at that time point. GFP # peaks may reflect "interesting" brain activity, warranting further # investigation. Mathematically, the GFP is the population standard @@ -259,17 +276,17 @@ for evk in (l_aud, l_vis): evk.plot(gfp=True, spatial_colors=True, ylim=dict(eeg=[-12, 12])) -############################################################################### -# To plot the GFP by itself you can pass ``gfp='only'`` (this makes it easier +# %% +# To plot the GFP by itself, you can pass ``gfp='only'`` (this makes it easier # to read off the GFP data values, because the scale is aligned): l_aud.plot(gfp='only') -############################################################################### -# As stated above, the GFP is the population standard deviation of the signal +# %% +# The GFP is the population standard deviation of the signal # across channels. To compute it manually, we can leverage the fact that # `evoked.data ` is a :class:`NumPy array `, -# and verify by plotting it using matplotlib commands: +# and verify by plotting it using plain Matplotlib commands: gfp = l_aud.data.std(axis=0, ddof=0) @@ -279,15 +296,15 @@ ax.fill_between(l_aud.times, gfp * 1e6, color='lime', alpha=0.2) ax.set(xlabel='Time (s)', ylabel='GFP (µV)', title='EEG') -############################################################################### -# Analyzing regions of interest (ROIs): averaging across channels -# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -# -# Since our sample data is responses to left and right auditory and visual -# stimuli, we may want to compare left versus right ROIs. To average across -# channels in a region of interest, we first find the channel indices we want. -# Looking back at the 2D sensor plot above, we might choose the following for -# left and right ROIs: +# %% +# Averaging across channels with regions of interest +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +# +# Since our sample data contains responses to left and right auditory and +# visual stimuli, we may want to compare left versus right regions of interest +# (ROIs). To average across channels in a given ROI, we first find the relevant +# channel indices. Revisiting the 2D sensor plot above, we might choose the +# following channels for left and right ROIs, respectively: left = ['eeg17', 'eeg18', 'eeg25', 'eeg26'] right = ['eeg23', 'eeg24', 'eeg34', 'eeg35'] @@ -295,20 +312,21 @@ left_ix = mne.pick_channels(l_aud.info['ch_names'], include=left) right_ix = mne.pick_channels(l_aud.info['ch_names'], include=right) -############################################################################### -# Now we can create a new Evoked with 2 virtual channels (one for each ROI): +# %% +# Now we can create a new Evoked object with two virtual channels (one for each +# ROI): roi_dict = dict(left_ROI=left_ix, right_ROI=right_ix) roi_evoked = mne.channels.combine_channels(l_aud, roi_dict, method='mean') print(roi_evoked.info['ch_names']) roi_evoked.plot() -############################################################################### +# %% # Comparing conditions # ^^^^^^^^^^^^^^^^^^^^ # -# If we wanted to compare our auditory and visual stimuli, a useful function is -# `mne.viz.plot_compare_evokeds`. By default this will combine all channels in -# each evoked object using global field power (or RMS for MEG channels); here +# If we wanted to contrast auditory to visual stimuli, a useful function is +# :func:`mne.viz.plot_compare_evokeds`. By default, this function will combine +# all channels in each evoked object using GFP (or RMS for MEG channels); here # instead we specify to combine by averaging, and restrict it to a subset of # channels by passing ``picks``: @@ -316,62 +334,311 @@ picks = [f'eeg{n}' for n in range(10, 15)] mne.viz.plot_compare_evokeds(evokeds, picks=picks, combine='mean') -############################################################################### -# We can also easily get confidence intervals by treating each epoch as a -# separate observation using the `~mne.Epochs.iter_evoked` method. A confidence -# interval across subjects could also be obtained, by passing a list of -# `~mne.Evoked` objects (one per subject) to the -# `~mne.viz.plot_compare_evokeds` function. +# %% +# We can also generate confidence intervals by treating each epoch as a +# separate observation using :meth:`~mne.Epochs.iter_evoked`. A confidence +# interval across subjects could also be obtained by passing a list of +# :class:`~mne.Evoked` objects (one per subject) to the +# :func:`~mne.viz.plot_compare_evokeds` function. evokeds = dict(auditory=list(epochs['auditory/left'].iter_evoked()), visual=list(epochs['visual/left'].iter_evoked())) mne.viz.plot_compare_evokeds(evokeds, combine='mean', picks=picks) -############################################################################### -# We can also compare conditions by subtracting one `~mne.Evoked` object from -# another using the `mne.combine_evoked` function (this function also allows -# pooling of epochs without subtraction). +# %% +# We can also compare conditions by subtracting one :class:`~mne.Evoked` object +# from another using the :func:`mne.combine_evoked` function (this function +# also supports pooling of epochs without subtraction). aud_minus_vis = mne.combine_evoked([l_aud, l_vis], weights=[1, -1]) aud_minus_vis.plot_joint() -############################################################################### +# %% # .. warning:: # # The code above yields an **equal-weighted difference**. If you have -# imbalanced trial numbers, you might want to equalize the number of events -# per condition first by using `epochs.equalize_event_counts() -# ` before averaging. +# different numbers of epochs per condition, you might want to equalize the +# number of events per condition first by using +# `epochs.equalize_event_counts() ` +# before averaging. # # # Grand averages # ^^^^^^^^^^^^^^ # # To compute grand averages across conditions (or subjects), you can pass a -# list of `~mne.Evoked` objects to `mne.grand_average`. The result is another -# `~mne.Evoked` object. +# list of :class:`~mne.Evoked` objects to :func:`mne.grand_average`. The result +# is another :class:`~mne.Evoked` object. grand_average = mne.grand_average([l_aud, l_vis]) print(grand_average) -############################################################################### +# %% # For combining *conditions* it is also possible to make use of :term:`HED` # tags in the condition names when selecting which epochs to average. For # example, we have the condition names: list(event_dict) -############################################################################### +# %% # We can select the auditory conditions (left and right together) by passing: epochs['auditory'].average() -############################################################################### -# see :ref:`tut-section-subselect-epochs` for details. +# %% +# See :ref:`tut-section-subselect-epochs` for more details on that. # # The tutorials :ref:`tut-epochs-class` and :ref:`tut-evoked-class` have many -# more details about working with the `~mne.Epochs` and `~mne.Evoked` classes. -# +# more details about working with the :class:`~mne.Epochs` and +# :class:`~mne.Evoked` classes. + +# %% +# Amplitude and latency measures +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +# It is common in ERP research to extract measures of amplitude or latency to +# compare across different conditions. There are many measures that can be +# extracted from ERPs, and many of these are detailed (including the respective +# strengths and weaknesses) in chapter 9 of Luck :footcite:`Luck2014` (also see +# the `Measurement Tool `_ in the ERPLAB Toolbox +# :footcite:`Lopez-CalderonLuck2014`). +# +# This part of the tutorial will demonstrate how to extract three common +# measures: +# +# * Peak latency +# * Peak amplitude +# * Mean amplitude +# +# Peak latency and amplitude +# -------------------------- +# +# The most common measures of amplitude and latency are peak measures. +# Peak measures are basically the maximum amplitude of the signal in a +# specified time window and the time point (or latency) at which the peak +# amplitude occurred. +# +# Peak measures can be obtained using the :meth:`~mne.Evoked.get_peak` method. +# There are two important things to point out about +# :meth:`~mne.Evoked.get_peak`. First, it finds the strongest peak +# looking across **all channels** of the selected type that are available in +# the :class:`~mne.Evoked` object. As a consequence, if you want to restrict +# the search to a group of channels or a single channel, you +# should first use the :meth:`~mne.Evoked.pick` or +# :meth:`~mne.Evoked.pick_channels` methods. Second, the +# :meth:`~mne.Evoked.get_peak` method can find different types of peaks using +# the ``mode`` argument. There are three options: +# +# * ``mode='pos'``: finds the peak with a positive voltage (ignores +# negative voltages) +# * ``mode='neg'``: finds the peak with a negative voltage (ignores +# positive voltages) +# * ``mode='abs'``: finds the peak with the largest absolute voltage +# regardless of sign (positive or negative) +# +# The following example demonstrates how to find the first positive peak in the +# ERP (i.e., the P100) for the left visual condition (i.e., the +# ``l_vis`` :class:`~mne.Evoked` object). The time window used to search for +# the peak ranges from 0.08 to 0.12 s. This time window was selected because it +# is when P100 typically occurs. Note that all ``'eeg'`` channels are submitted +# to the :meth:`~mne.Evoked.get_peak` method. + + +# Define a function to print out the channel (ch) containing the +# peak latency (lat; in msec) and amplitude (amp, in µV), with the +# time range (tmin and tmax) that was searched. +# This function will be used throughout the remainder of the tutorial. +def print_peak_measures(ch, tmin, tmax, lat, amp): + print(f'Channel: {ch}') + print(f'Time Window: {tmin * 1e3:.3f} - {tmax * 1e3:.3f} ms') + print(f'Peak Latency: {lat * 1e3:.3f} ms') + print(f'Peak Amplitude: {amp * 1e6:.3f} µV') + + +# Get peak amplitude and latency from a good time window that contains the peak +good_tmin, good_tmax = 0.08, 0.12 +ch, lat, amp = l_vis.get_peak(ch_type='eeg', tmin=good_tmin, tmax=good_tmax, + mode='pos', return_amplitude=True) + +# Print output from the good time window that contains the peak +print('** PEAK MEASURES FROM A GOOD TIME WINDOW **') +print_peak_measures(ch, good_tmin, good_tmax, lat, amp) + + +# %% +# The output shows that channel ``eeg55`` had the maximum positive peak in +# the chosen time window from all of the ``'eeg'`` channels searched. +# In practice, one might want to pull out the peak for +# an *a priori* region of interest or a single channel depending on the study. +# This can be done by combining the :meth:`~mne.Evoked.pick` +# or :meth:`~mne.Evoked.pick_channels` methods with the +# :meth:`~mne.Evoked.get_peak` method. +# +# Here, let's assume we believe the effects of interest will occur +# at ``eeg59``. + +# Fist, return a copy of l_vis to select the channel from +l_vis_roi = l_vis.copy().pick('eeg59') + +# Get the peak and latency measure from the selected channel +ch_roi, lat_roi, amp_roi = l_vis_roi.get_peak( + tmin=good_tmin, tmax=good_tmax, mode='pos', return_amplitude=True) + +# Print output +print('** PEAK MEASURES FOR ONE CHANNEL FROM A GOOD TIME WINDOW **') +print_peak_measures(ch_roi, good_tmin, good_tmax, lat_roi, amp_roi) + +# %% +# While the peak latencies are the same in channels ``eeg55`` and ``eeg59``, +# the peak amplitudes differ. This approach can also be applied to virtual +# channels created with the :func:`~mne.channels.combine_channels` function and +# difference waves created with the :func:`mne.combine_evoked` function (see +# ``aud_minus_vis`` in section `Comparing conditions`_ above). +# +# Peak measures are very susceptible to high frequency noise in the +# signal (for discussion, see :footcite:`Luck2014`). Specifically, high +# frequency noise positively biases peak amplitude measures. This bias can +# confound comparisons across conditions where ERPs differ in the level of high +# frequency noise, such as when the conditions differ in the number of trials +# contributing to the ERP. One way to avoid this is to apply a non-causal +# low-pass filter to the ERP. Low-pass filters reduce the contribution of high +# frequency noise by smoothing out fast (i.e., high frequency) fluctuations in +# the signal (see :ref:`disc-filtering`). While this can reduce the positive +# bias in peak amplitude measures caused by high frequency noise, low-pass +# filtering the ERP can introduce challenges in interpreting peak latency +# measures for effects of interest :footcite:`Rousselet2012,VanRullen2011`. +# +# If using peak measures, it is critical to visually inspect the data to +# make sure the selected time window actually contains a peak. The +# meth:`~mne.Evoked.get_peak` method detects the maximum or minimum voltage in +# the specified time range and returns the latency and amplitude of this peak. +# There is no guarantee that this method will return an actual peak. Instead, +# it may return a value on the rising or falling edge of a peak we are trying +# to find. +# +# The following example demonstrates why visual inspection is crucial. Below, +# we use a known bad time window (0.095 to 0.135 s) to search for a peak in +# channel ``eeg59``. + +# Get BAD peak measures +bad_tmin, bad_tmax = 0.095, 0.135 +ch_roi, bad_lat_roi, bad_amp_roi = l_vis_roi.get_peak( + mode='pos', tmin=bad_tmin, tmax=bad_tmax, return_amplitude=True) + +# Print output +print('** PEAK MEASURES FOR ONE CHANNEL FROM A BAD TIME WINDOW **') +print_peak_measures(ch_roi, bad_tmin, bad_tmax, bad_lat_roi, bad_amp_roi) + +# %% +# If all we had were the above values, it would be unclear if they are truly +# identifying a peak in the ERP. In fact, the 0.095 to 0.135 s time window +# actually does not contain the true peak, which is shown in the top panel +# below. The bad time window (highlighted in orange) does not contain the true +# peak (the pink star). In contrast, the time window defined initially (0.08 to +# 0.12 s; highlighted in blue) returns an actual peak instead of a just a +# maximum or minimum in the searched time window. Visual inspection will always +# help you to convince yourself that the returned values are actual peaks. + +fig, axs = plt.subplots(nrows=2, ncols=1, layout='tight') +words = (('Bad', 'missing'), ('Good', 'finding')) +times = (np.array([bad_tmin, bad_tmax]), np.array([good_tmin, good_tmax])) +colors = ('C1', 'C0') + +for ix, ax in enumerate(axs): + title = '{} time window {} peak'.format(*words[ix]) + l_vis_roi.plot(axes=ax, time_unit='ms', show=False, titles=title) + ax.plot(lat_roi * 1e3, amp_roi * 1e6, marker='*', color='C6') + ax.axvspan(*(times[ix] * 1e3), facecolor=colors[ix], alpha=0.3) + ax.set_xlim(-50, 150) # Show zoomed in around peak + +# %% +# Mean Amplitude +# -------------- +# +# Another common practice in ERP studies is to define a component (or effect) +# as the mean amplitude within a specified time window. One advantage of this +# approach is that it is less sensitive to high frequency noise (compared to +# peak amplitude measures), because averaging over a time window acts as a +# low-pass filter (see discussion in the previous section +# `Peak latency and amplitude`_). +# +# When using mean amplitude measures, selecting the time window based on +# the effect of interest (e.g., the difference between two conditions) can +# inflate the likelihood of finding false positives in your +# results :footcite:`LuckGaspelin2017`. There are other, and +# better, ways to identify a time window to use for extracting mean amplitude +# measures. First, you can use an *a priori* time window based on prior +# research. +# A second option is to define a time window from an independent condition or +# set of trials not used in the analysis (e.g., a "localizer"). A third +# approach is +# to define a time window using the across-condition grand average. This latter +# approach is not circular because the across-condition mean and condition +# difference are independent of one another. The issues discussed above also +# apply to selecting channels used for analysis. +# +# The following example demonstrates how to pull out the mean amplitude +# from the left visual condition (i.e., the ``l_vis`` :class:`~mne.Evoked` +# object) from selected channels and time windows. Stimulating the +# left visual field increases neural activity of visual cortex in the +# contralateral (i.e., right) hemisphere. We can test this by examining the +# amplitude of the ERP for left visual field stimulation over right +# (contralateral) and left (ipsilateral) channels. The channels used for this +# analysis are ``eeg54`` and ``eeg57`` (left hemisphere), and ``eeg59`` and +# ``eeg55`` (right hemisphere). The time window used is 0.08 (``good_tmin``) +# to 0.12 s (``good_tmax``) as it corresponds to when the P100 typically +# occurs. +# The P100 is sensitive to left and right visual field stimulation. The mean +# amplitude is extracted from the above four channels and stored in a +# :class:`pandas.DataFrame`. + +# Select all of the channels and crop to the time window +channels = ['eeg54', 'eeg57', 'eeg55', 'eeg59'] +hemisphere = ['left', 'left', 'right', 'right'] +l_vis_mean_roi = l_vis.copy().pick(channels).crop( + tmin=good_tmin, tmax=good_tmax) + +# Extract mean amplitude in µV over time +mean_amp_roi = l_vis_mean_roi.data.mean(axis=1) * 1e6 + +# Store the data in a data frame +mean_amp_roi_df = pd.DataFrame({ + 'ch_name': l_vis_mean_roi.ch_names, + 'hemisphere': ['left', 'left', 'right', 'right'], + 'mean_amp': mean_amp_roi +}) + +# Print the data frame +print(mean_amp_roi_df.groupby('hemisphere').mean()) + +# %% +# As demonstrated in this example, the mean amplitude was higher and +# positive in right compared to left hemisphere channels. It should be +# reiterated that both spatial and temporal windows used in the analysis should +# be determined in an independent manner (e.g., defined *a priori* from prior +# research, a "localizer" or another independent condition) and not based +# on the data you will use to test your hypotheses. +# +# The example can be modified to extract the mean amplitude +# from all channels and store the resulting output in a +# :class:`pandas.DataFrame`. This can be useful for statistical analyses +# conducted in other programming languages. + +# Extract mean amplitude for all channels in l_vis (including `eog`) +l_vis_cropped = l_vis.copy().crop(tmin=good_tmin, tmax=good_tmax) +mean_amp_all = l_vis_cropped.data.mean(axis=1) * 1e6 +mean_amp_all_df = pd.DataFrame({ + 'ch_name': l_vis_cropped.info['ch_names'], + 'mean_amp': mean_amp_all +}) +mean_amp_all_df['tmin'] = good_tmin +mean_amp_all_df['tmax'] = good_tmax +mean_amp_all_df['condition'] = 'Left/Visual' +with pd.option_context('display.max_columns', None): + print(mean_amp_all_df.head()) + print(mean_amp_all_df.tail()) + + +# %% # .. _ten_twenty: https://en.wikipedia.org/wiki/10%E2%80%9320_system_(EEG) # # diff -Nru python-mne-0.23.4+dfsg/tutorials/evoked/40_whitened.py python-mne-1.1.0+dfsg/tutorials/evoked/40_whitened.py --- python-mne-0.23.4+dfsg/tutorials/evoked/40_whitened.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tutorials/evoked/40_whitened.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,4 +1,8 @@ +# -*- coding: utf-8 -*- """ +.. _tut-whitened: + +====================== Plotting whitened data ====================== @@ -13,17 +17,19 @@ that we'll consider to be noise. """ +# %% + import mne from mne.datasets import sample -############################################################################### +# %% # Raw data with whitening # ----------------------- # .. note:: In the :meth:`mne.io.Raw.plot` with ``noise_cov`` supplied, # you can press they "w" key to turn whitening on and off. data_path = sample.data_path() -raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' +raw_fname = data_path / 'MEG' / 'sample' / 'sample_audvis_filt-0-40_raw.fif' raw = mne.io.read_raw_fif(raw_fname, preload=True) events = mne.find_events(raw, stim_channel='STI 014') @@ -40,13 +46,13 @@ raw.plot(events=events, butterfly=True) raw.plot(noise_cov=noise_cov, events=events, butterfly=True) -############################################################################### +# %% # Epochs with whitening # --------------------- epochs.plot() epochs.plot(noise_cov=noise_cov) -############################################################################### +# %% # Evoked data with whitening # -------------------------- @@ -54,7 +60,7 @@ evoked.plot(time_unit='s') evoked.plot(noise_cov=noise_cov, time_unit='s') -############################################################################### +# %% # Evoked data with scaled whitening # --------------------------------- # The :meth:`mne.Evoked.plot_white` function takes an additional step of @@ -63,7 +69,7 @@ evoked.plot_white(noise_cov=noise_cov, time_unit='s') -############################################################################### +# %% # Topographic plot with whitening # ------------------------------- diff -Nru python-mne-0.23.4+dfsg/tutorials/forward/10_background_freesurfer.py python-mne-1.1.0+dfsg/tutorials/forward/10_background_freesurfer.py --- python-mne-0.23.4+dfsg/tutorials/forward/10_background_freesurfer.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tutorials/forward/10_background_freesurfer.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,132 +1,145 @@ # -*- coding: utf-8 -*- -r""" +""" .. _tut-freesurfer-reconstruction: ============================= FreeSurfer MRI reconstruction ============================= -FreeSurfer is an open source analysis toolbox for MRI data. It contains several -command line tools and graphical user interfaces. FreeSurfer can be obtained -from https://surfer.nmr.mgh.harvard.edu/ - -In MNE, FreeSurfer is used to provide structural information of various -kinds, for :ref:`source estimation `. Thereby a -subject specific structural MRI will be used to obtain various structural -representations like spherical or inflated brain surfaces. Furthermore features -like curvature as well as various labels for areas of interest (such as V1) are -computed. - -Thus FreeSurfer provides an easy way to shift anatomically related -data between different representations and spaces. See e.g. -:ref:`ch_morph` for information about how to -use FreeSurfer surface representations to allow functional data to morph -between different subjects. +This tutorial covers how to use FreeSurfer alongside MNE-Python, to handle the +structural MRI data that we use to build subject-specific anatomical models of +the scalp, inner/outer skull, and cortical surface. + +FreeSurfer is an open source analysis toolbox for MRI data, available from +https://surfer.nmr.mgh.harvard.edu/. FreeSurfer provides graphical interfaces +for visualizing MRI data, several anatomical parcellations useful for creating +region-of-interest (ROI) labels, template brains such as ``fsaverage``, and +several command-line tools for tasks like finding tissue boundaries or morphing +brains to align analogous anatomical regions across subjects. + +These FreeSurfer capabilities are necessary for MNE-Python to compute the +:term:`forward model ` and set up the corresponding `source space +` (a grid of dipoles located on the cortical surface or +within the brain volume). + First steps =========== -After downloading and installing, the environment needs to be set up correctly. -This can be done by setting the FreeSurfer's root directory correctly and -sourcing the setup file:: +After downloading and installing FreeSurfer, there are a few steps to set up +the environment. First is to define an environment variable ``FREESURFER_HOME`` +and then run the FreeSurfer setup script: + +.. code-block:: console $ export FREESURFER_HOME=/path/to/FreeSurfer $ source $FREESURFER_HOME/SetUpFreeSurfer.sh .. note:: - The FreeSurfer home directory might vary depending on your operating - system. See the `FreeSurfer installation guide - `_ for more. - -Another important step is to define the subject directory correctly. -``SUBJECTS_DIR`` must be defined such, that it contains the individual -subject's reconstructions in separate sub-folders. Those sub-folders will be -created upon the reconstruction of the anatomical data. Nevertheless the parent -directory has to be set beforehand:: + The FreeSurfer home directory will vary depending on your operating + system and choices you made during installation. See the `FreeSurfer + installation guide`_ for more info. + + +Another important step is to tell FreeSurfer where to put the anatomical +reconstructions of your research subjects. This is done with an environment +variable called ``SUBJECTS_DIR``, which will contain the individual subjects' +reconstructions in separate sub-folders. + +.. code-block:: console - $ export SUBJECTS_DIR=~/subjects + $ export SUBJECTS_DIR=/path/to/your/subjects_dir -Again see the `FreeSurfer installation guide -`_ for more. +Again see the `FreeSurfer installation guide`_ for more info. -Once setup correctly, FreeSurfer will create a new subject folder in -``$SUBJECTS_DIR``. Anatomical reconstruction ========================= -MNE-Python works together with FreeSurfer in order to compute the forward model -and setting up the corresponding :class:`source space `. See -:ref:`setting_up_source_space` for more information. Usually a full FreeSurfer -reconstruction is obtained by prompting the following command to a bash -console (e.g. Linux or MacOS Terminal):: +The first processing stage is the creation of various surface reconstructions. +Usually a full FreeSurfer reconstruction is obtained by the following commands: + +.. code-block:: console $ my_subject=sample $ my_NIfTI=/path/to/NIfTI.nii.gz $ recon-all -i $my_NIfTI -s $my_subject -all -where :code:`i` stands for "input" and :code:`s` for "subject". Executing -this, will create the folder "~/subjects/sample", where all -results are stored. - -.. note:: - This compution takes often several hours. Please be patient. - -Within a single subject all the files MNE-Python uses (and some more) are -grouped into meaningful sub-folders (such that "surf" contains surface -representations, "mri" volumetric files, etc.). - -FreeSurfer performs a hemispheric separation and most results are present -in a left and right hemisphere version. This is often indicated by the -prefix ``lh`` or ``rh`` to refer to the aforementioned. For that reason -data representations such as :class:`mne.SourceEstimate` carry two sets of -spatial locations (vertices) for both hemispheres separately. See also -:ref:`tut-source-estimate-class`. +where ``i`` stands for "input" and ``s`` for "subject". Executing this will +create the folder :file:`$SUBJECTS_DIR/sample` and populate it +with several subfolders (``bem``, ``label``, ``mri``, etc). See also the +FreeSurfer wiki's `recommended reconstruction workflow`_ for more detailed +explanation. + +.. warning:: + Anatomical reconstruction can take several hours, even on a fast computer. + +FreeSurfer performs a hemispheric separation so most resulting files have +separate left and right hemisphere versions, indicated by the prefix +``lh`` or ``rh``. This hemispheric separation is preserved by MNE-Python (e.g., +`mne.SourceEstimate` objects store spatial locations (vertices) for the two +hemispheres separately; cf. :ref:`tut-source-estimate-class`). + +Below we show an example of the results of a FreeSurfer reconstruction for the +left hemisphere of the :ref:`sample-dataset` dataset subject, including an +overlay of an anatomical parcellation (in this case, the parcellation from +:footcite:`DestrieuxEtAl2010`). """ +# %% + import mne -subjects_dir = mne.datasets.sample.data_path() + '/subjects' + +sample_data_folder = mne.datasets.sample.data_path() +subjects_dir = sample_data_folder / 'subjects' Brain = mne.viz.get_brain_class() brain = Brain('sample', hemi='lh', surf='pial', subjects_dir=subjects_dir, size=(800, 600)) brain.add_annotation('aparc.a2009s', borders=False) -############################################################################### -# 'fsaverage' -# =========== -# -# During installation, FreeSurfer copies a "default" subject, called -# ``'fsaverage'`` to ``$FREESURFER_HOME/subjects/fsaverage``. It contains all -# data types that a subject reconstruction would yield and is required by -# MNE-Python. -# -# See https://surfer.nmr.mgh.harvard.edu/fswiki/FsAverage for an overview, and -# https://surfer.nmr.mgh.harvard.edu/fswiki/Buckner40Notes for details about -# the included subjects. A copy of 'fsaverage' can be found in the -# :ref:`sample-dataset` dataset and is also distributed as a :ref:`standalone -# dataset `. -# -# When using ``'fsaverage'`` as value for the definition -# of a subject when calling a function, the corresponding data will be read -# (e.g., ``subject='fsaverage'``) from '~/subjects/fsaverage'. This becomes -# especially handy, when attempting statistical analyses on group level, based -# on individual's brain space data. In that case ``'fsaverage'`` will by -# default act as reference space for -# :ref:`source estimate transformations `. -# -# For example, to reproduce a typical header image used by FreeSurfer, we can -# plot the ``aparc`` parcellation: -# +# %% # Use with MNE-Python # =================== # # For source localization analysis to work properly, it is important that the -# FreeSurfer reconstruction has completed beforehand. Furthermore, when using -# related functions, such as :func:`mne.setup_source_space`, ``SUBJECTS_DIR`` -# has to be defined either globally by setting :func:`mne.set_config` or for -# each function separately, by passing the respective keyword argument -# ``subjects_dir='~/subjects'``. -# -# See also :ref:`setting_up_source_space` to get an idea of how this works for -# one particular function, and :ref:`tut-freesurfer-mne` for how the two are -# integrated. +# FreeSurfer reconstruction has completed beforehand. Furthermore, for many +# MNE-Python functions related to inverse imaging (such as +# `mne.setup_source_space`), ``SUBJECTS_DIR`` has to be defined globally (as an +# environment variable or through a call to `mne.set_config`), or specified +# separately in each function call by passing the keyword argument +# ``subjects_dir='/path/to/your/subjects_dir'``. +# +# See :ref:`setting_up_source_space` to get an idea of how this works for one +# particular function, and :ref:`tut-freesurfer-mne` for more details on how +# MNE-Python and FreeSurfer are integrated. +# +# +# 'fsaverage' +# =========== +# +# During installation, FreeSurfer copies a subject called ``'fsaverage'`` to +# ``$FREESURFER_HOME/subjects/fsaverage``. ``fsaverage`` is a template brain +# based on a combination of 40 MRI scans of real brains. The ``fsaverage`` +# subject folder contains all the files that a normal subject reconstruction +# would yield. See https://surfer.nmr.mgh.harvard.edu/fswiki/FsAverage for an +# overview, and https://surfer.nmr.mgh.harvard.edu/fswiki/Buckner40Notes for +# details about the subjects used to create ``fsaverage``. A copy of +# ``fsaverage`` is also provided as part of the :ref:`sample-dataset` dataset +# and is also distributed as a :ref:`standalone dataset `. +# +# One of the most common uses of ``fsaverage`` is as a destination space for +# cortical morphing / :ref:`source estimate transformations `. In +# other words, it is common to morph each individual subject's estimated brain +# activity onto the ``fsaverage`` brain, so that group-level statistical +# comparisons can be made. +# +# +# References +# ========== +# +# .. footbibliography:: +# +# .. _`FreeSurfer installation guide`: +# https://surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall +# .. _`recommended reconstruction workflow`: +# https://surfer.nmr.mgh.harvard.edu/fswiki/RecommendedReconstruction diff -Nru python-mne-0.23.4+dfsg/tutorials/forward/20_source_alignment.py python-mne-1.1.0+dfsg/tutorials/forward/20_source_alignment.py --- python-mne-0.23.4+dfsg/tutorials/forward/20_source_alignment.py 2021-09-09 19:56:26.000000000 +0000 +++ python-mne-1.1.0+dfsg/tutorials/forward/20_source_alignment.py 2022-08-03 15:48:14.000000000 +0000 @@ -1,7 +1,8 @@ # -*- coding: utf-8 -*- """ -.. _plot_source_alignment: +.. _tut-source-alignment: +====================================== Source alignment and coordinate frames ====================================== @@ -12,7 +13,8 @@ Let's start out by loading some data. """ -import os.path as op + +# %% import numpy as np import nibabel as nib @@ -22,22 +24,22 @@ from mne.io.constants import FIFF data_path = mne.datasets.sample.data_path() -subjects_dir = op.join(data_path, 'subjects') -raw_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif') -trans_fname = op.join(data_path, 'MEG', 'sample', - 'sample_audvis_raw-trans.fif') +subjects_dir = data_path / 'subjects' +raw_fname = data_path / 'MEG' / 'sample' / 'sample_audvis_raw.fif' +trans_fname = (data_path / 'MEG' / 'sample' / + 'sample_audvis_raw-trans.fif') raw = mne.io.read_raw_fif(raw_fname) trans = mne.read_trans(trans_fname) -src = mne.read_source_spaces(op.join(subjects_dir, 'sample', 'bem', - 'sample-oct-6-src.fif')) +src = mne.read_source_spaces(subjects_dir / 'sample' / 'bem' / + 'sample-oct-6-src.fif') # Load the T1 file and change the header information to the correct units -t1w = nib.load(op.join(data_path, 'subjects', 'sample', 'mri', 'T1.mgz')) +t1w = nib.load(data_path / 'subjects' / 'sample' / 'mri' / 'T1.mgz') t1w = nib.Nifti1Image(t1w.dataobj, t1w.affine) t1w.header['xyzt_units'] = np.array(10, dtype='uint8') t1_mgh = nib.MGHImage(t1w.dataobj, t1w.affine) -############################################################################### +# %% # .. raw:: html # #