python-bumps 0.9.1-2 source package in Ubuntu
Changelog
python-bumps (0.9.1-2) unstable; urgency=medium
* Exclude unused (?) files from tests to prevent failures with Python 3.12
(Closes: #1056459).
* Add arm64 to list of architectures for compiled modules
(Closes: #1056057).
-- Stuart Prescott <email address hidden> Wed, 06 Dec 2023 17:01:01 +1100
Upload details
- Uploaded by:
- Debian Science Team
- Uploaded to:
- Sid
- Original maintainer:
- Debian Science Team
- Architectures:
- any-amd64 any-i386 all powerpc arm64
- Section:
- misc
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section | |
|---|---|---|---|---|
| Noble | release | universe | misc |
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| python-bumps_0.9.1-2.dsc | 2.6 KiB | a84030213a09d8939bc881b4e3b9ec9a3a2bbfc66a6582abe3406995c3bc68b3 |
| python-bumps_0.9.1.orig.tar.gz | 3.5 MiB | ad79ec1eafda09d5b476453593278b2607004241b7f339936b230656fa1328fb |
| python-bumps_0.9.1-2.debian.tar.xz | 13.2 KiB | 65546c52c4efbdd336b85d0dc4260a27b44d1c71e21cc35da55ed245bf10cbf4 |
Available diffs
- diff from 0.9.1-1 to 0.9.1-2 (828 bytes)
No changes file available.
Binary packages built by this source
- bumps-private-libs: data fitting and Bayesian uncertainty modeling for inverse problems (libraries)
Bumps is a set of routines for curve fitting and uncertainty analysis
from a Bayesian perspective. In addition to traditional optimizers
which search for the best minimum they can find in the search space,
bumps provides uncertainty analysis which explores all viable minima
and finds confidence intervals on the parameters based on uncertainty
in the measured values. Bumps has been used for systems of up to 100
parameters with tight constraints on the parameters. Full uncertainty
analysis requires hundreds of thousands of function evaluations,
which is only feasible for cheap functions, systems with many
processors, or lots of patience.
.
Bumps includes several traditional local optimizers such as
Nelder-Mead simplex, BFGS and differential evolution. Bumps
uncertainty analysis uses Markov chain Monte Carlo to explore the
parameter space. Although it was created for curve fitting problems,
Bumps can explore any probability density function, such as those
defined by PyMC. In particular, the bumps uncertainty analysis works
well with correlated parameters.
.
Bumps can be used as a library within your own applications, or as a
framework for fitting, complete with a graphical user interface to
manage your models.
.
This package installs the compiled libraries used by the Python modules.
- bumps-private-libs-dbgsym: debug symbols for bumps-private-libs
- python-bumps-doc: data fitting and Bayesian uncertainty modeling for inverse problems (docs)
Bumps is a set of routines for curve fitting and uncertainty analysis
from a Bayesian perspective. In addition to traditional optimizers
which search for the best minimum they can find in the search space,
bumps provides uncertainty analysis which explores all viable minima
and finds confidence intervals on the parameters based on uncertainty
in the measured values. Bumps has been used for systems of up to 100
parameters with tight constraints on the parameters. Full uncertainty
analysis requires hundreds of thousands of function evaluations,
which is only feasible for cheap functions, systems with many
processors, or lots of patience.
.
Bumps includes several traditional local optimizers such as
Nelder-Mead simplex, BFGS and differential evolution. Bumps
uncertainty analysis uses Markov chain Monte Carlo to explore the
parameter space. Although it was created for curve fitting problems,
Bumps can explore any probability density function, such as those
defined by PyMC. In particular, the bumps uncertainty analysis works
well with correlated parameters.
.
Bumps can be used as a library within your own applications, or as a
framework for fitting, complete with a graphical user interface to
manage your models.
.
This is the common documentation package.
- python3-bumps: data fitting and Bayesian uncertainty modeling for inverse problems (Python 3)
Bumps is a set of routines for curve fitting and uncertainty analysis
from a Bayesian perspective. In addition to traditional optimizers
which search for the best minimum they can find in the search space,
bumps provides uncertainty analysis which explores all viable minima
and finds confidence intervals on the parameters based on uncertainty
in the measured values. Bumps has been used for systems of up to 100
parameters with tight constraints on the parameters. Full uncertainty
analysis requires hundreds of thousands of function evaluations,
which is only feasible for cheap functions, systems with many
processors, or lots of patience.
.
Bumps includes several traditional local optimizers such as
Nelder-Mead simplex, BFGS and differential evolution. Bumps
uncertainty analysis uses Markov chain Monte Carlo to explore the
parameter space. Although it was created for curve fitting problems,
Bumps can explore any probability density function, such as those
defined by PyMC. In particular, the bumps uncertainty analysis works
well with correlated parameters.
.
Bumps can be used as a library within your own applications, or as a
framework for fitting, complete with a graphical user interface to
manage your models.
.
This package installs the library for Python 3.
