Ubuntu
pytables package

pytables 3.1.0-1 source package in Ubuntu

Changelog

pytables (3.1.0-1) unstable; urgency=low


  * New upstream release (Closes: #734298)
  * Standard version bumped to 3.9.5 (no changes)
  * New python(3)-tables-lib packages.
    The old python(3)-tables packages have been split into
    python(3)-tables (containing common code for all platforms) and
    python(3)-tables-lib (containing only platform specific extensions).
  * New python-tables-data package including all data files used for
    unit testing
  * Added autopkgtests running testsuite
  * debian/patches
    - removed 0001-Fix-detection-of-platforms-supporting-blosc.patch
      (applied upstream)
    - removed 0003-disable-extended-float-support.patch no more
      necessary
    - remaining patches have been refreshed
    - new patch (0002-Use-system-compression-libs.patch) to use
      system compression libraries instead of internal copies
  * debian/control
    - added build dependencies form liblz4-dev and snappy-dev 
  * temporary disabled tests for python3.x-dbg

 -- Antonio Valentino <email address hidden>  Sun, 02 Mar 2014 15:47:11 +0000

Upload details

Uploaded by:
Debian Science Team
Uploaded to:
Sid
Original maintainer:
Debian Science Team
Architectures:
any all
Section:
python
Urgency:
Low Urgency

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File Size SHA-256 Checksum
pytables_3.1.0-1.dsc 2.2 KiB 406e3d83cea1f1667775efd5294954265e22cc9ce5fed11b1f4304701bf166bd
pytables_3.1.0.orig.tar.gz 3.5 MiB f5cfa0cf1d3c49880a84a5643dce1380c846b2642097267eedc7852a5ffdc20e
pytables_3.1.0-1.debian.tar.xz 14.2 KiB a21c1e012e9a51d9ab70b6241e23770802ad8c988ceec40848ff5c4b92635214

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Binary packages built by this source

python-tables: hierarchical database for Python based on HDF5

 PyTables is a hierarchical database package designed to efficiently
 manage very large amounts of data. PyTables is built on top of the
 HDF5 library and the NumPy package. It features an object-oriented
 interface that, combined with natural naming and C-code generated
 from Pyrex sources, makes it a fast, yet extremely easy to use tool
 for interactively save and retrieve large amounts of data.
 .
  - Compound types (records) can be used entirely from Python (i.e. it
    is not necessary to use C for taking advantage of them).
  - The tables are both enlargeable and compressible.
  - I/O is buffered, so you can get very fast I/O, specially with
    large tables.
  - Very easy to select data through the use of iterators over the
    rows in tables. Extended slicing is supported as well.
  - It supports the complete set of NumPy, Numeric and numarray objects.
 .
 This is the Python 2 version of the package.

python-tables-data: hierarchical database for Python based on HDF5 - test data

 PyTables is a hierarchical database package designed to efficiently
 manage very large amounts of data. PyTables is built on top of the
 HDF5 library and the NumPy package. It features an object-oriented
 interface that, combined with natural naming and C-code generated
 from Pyrex sources, makes it a fast, yet extremely easy to use tool
 for interactively save and retrieve large amounts of data.
 .
 This package includes daya fils used for unit testing.

python-tables-dbg: hierarchical database for Python based on HDF5 (debug extension)

 PyTables is a hierarchical database package designed to efficiently
 manage very large amounts of data. PyTables is built on top of the
 HDF5 library and the NumPy package. It features an object-oriented
 interface that, combined with natural naming and C-code generated
 from Pyrex sources, makes it a fast, yet extremely easy to use tool
 for interactively save and retrieve large amounts of data.
 .
  - Compound types (records) can be used entirely from Python (i.e. it
    is not necessary to use C for taking advantage of them).
  - The tables are both enlargeable and compressible.
  - I/O is buffered, so you can get very fast I/O, specially with
    large tables.
  - Very easy to select data through the use of iterators over the
    rows in tables. Extended slicing is supported as well.
  - It supports the complete set of NumPy, Numeric and numarray objects.
 .
 This package contains the extension built for the Python 2 debug interpreter.

python-tables-doc: hierarchical database for Python based on HDF5 - documentation

 PyTables is a hierarchical database package designed to efficiently
 manage very large amounts of data. PyTables is built on top of the
 HDF5 library and the NumPy package. It features an object-oriented
 interface that, combined with natural naming and C-code generated
 from Pyrex sources, makes it a fast, yet extremely easy to use tool
 for interactively save and retrieve large amounts of data.
 .
 This package includes the manual in PDF and HTML formats.

python-tables-lib: hierarchical database for Python based on HDF5 (extension)

 PyTables is a hierarchical database package designed to efficiently
 manage very large amounts of data. PyTables is built on top of the
 HDF5 library and the NumPy package. It features an object-oriented
 interface that, combined with natural naming and C-code generated
 from Pyrex sources, makes it a fast, yet extremely easy to use tool
 for interactively save and retrieve large amounts of data.
 .
  - Compound types (records) can be used entirely from Python (i.e. it
    is not necessary to use C for taking advantage of them).
  - The tables are both enlargeable and compressible.
  - I/O is buffered, so you can get very fast I/O, specially with
    large tables.
  - Very easy to select data through the use of iterators over the
    rows in tables. Extended slicing is supported as well.
  - It supports the complete set of NumPy, Numeric and numarray objects.
 .
 This package contains the extension built for the Python 2 interpreter.

python3-tables: hierarchical database for Python3 based on HDF5

 PyTables is a hierarchical database package designed to efficiently
 manage very large amounts of data. PyTables is built on top of the
 HDF5 library and the NumPy package. It features an object-oriented
 interface that, combined with natural naming and C-code generated
 from Pyrex sources, makes it a fast, yet extremely easy to use tool
 for interactively save and retrieve large amounts of data.
 .
  - Compound types (records) can be used entirely from Python (i.e. it
    is not necessary to use C for taking advantage of them).
  - The tables are both enlargeable and compressible.
  - I/O is buffered, so you can get very fast I/O, specially with
    large tables.
  - Very easy to select data through the use of iterators over the
    rows in tables. Extended slicing is supported as well.
  - It supports the complete set of NumPy, Numeric and numarray objects.
 .
 This is the Python 3 version of the package.

python3-tables-dbg: hierarchical database for Python 3 based on HDF5 (debug extension)

 PyTables is a hierarchical database package designed to efficiently
 manage very large amounts of data. PyTables is built on top of the
 HDF5 library and the NumPy package. It features an object-oriented
 interface that, combined with natural naming and C-code generated
 from Pyrex sources, makes it a fast, yet extremely easy to use tool
 for interactively save and retrieve large amounts of data.
 .
  - Compound types (records) can be used entirely from Python (i.e. it
    is not necessary to use C for taking advantage of them).
  - The tables are both enlargeable and compressible.
  - I/O is buffered, so you can get very fast I/O, specially with
    large tables.
  - Very easy to select data through the use of iterators over the
    rows in tables. Extended slicing is supported as well.
  - It supports the complete set of NumPy, Numeric and numarray objects.
 .
 This package contains the extension built for the Python 3 debug interpreter.

python3-tables-lib: hierarchical database for Python3 based on HDF5 (extension)

 PyTables is a hierarchical database package designed to efficiently
 manage very large amounts of data. PyTables is built on top of the
 HDF5 library and the NumPy package. It features an object-oriented
 interface that, combined with natural naming and C-code generated
 from Pyrex sources, makes it a fast, yet extremely easy to use tool
 for interactively save and retrieve large amounts of data.
 .
  - Compound types (records) can be used entirely from Python (i.e. it
    is not necessary to use C for taking advantage of them).
  - The tables are both enlargeable and compressible.
  - I/O is buffered, so you can get very fast I/O, specially with
    large tables.
  - Very easy to select data through the use of iterators over the
    rows in tables. Extended slicing is supported as well.
  - It supports the complete set of NumPy, Numeric and numarray objects.
 .
 This package contains the extension built for the Python 3 interpreter.