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Source: python-bumps
Section: science
Priority: optional
Maintainer: Debian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
Uploaders:
Drew Parsons <dparsons@debian.org>,
Stuart Prescott <stuart@debian.org>
Build-Depends:
debhelper-compat (= 13),
dh-python,
pybuild-plugin-pyproject,
libjs-jquery,
libjs-mathjax,
python3-all,
python3-matplotlib,
python3-numpy (>= 1.3.0),
python3-scipy (>= 0.7.0),
python3-periodictable,
python3-pyparsing (>= 1.5.2),
python3-pytest,
python3-pytest-cov,
python3-setuptools,
python3-sklearn,
python3-aiohttp,
python3-blinker,
python3-h5py,
python3-plotly,
python3-socketio,
python3-sphinx,
python3-tk,
python3-wxgtk4.0,
librandom123-dev,
texinfo
Standards-Version: 4.7.2
Homepage: https://github.com/bumps/bumps
Vcs-Git: https://salsa.debian.org/science-team/python-bumps.git
Vcs-Browser: https://salsa.debian.org/science-team/python-bumps
Rules-Requires-Root: no
Package: python3-bumps
Architecture: all
Section: python
Depends:
python3-matplotlib (>= 1.0),
python3-numpy (>= 1.3.0),
python3-scipy (>= 0.7.0),
${misc:Depends},
${python3:Depends},
Recommends:
bumps-private-libs,
python3-wxgtk4.0
Suggests:
python-bumps-doc,
python3-sklearn,
python3-aiohttp,
python3-blinker,
python3-h5py,
python3-plotly,
python3-socketio
Description: 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.
Package: bumps-private-libs
Architecture: any-amd64 any-i386 powerpc arm64
Section: libs
Depends:
${misc:Depends},
${shlibs:Depends}
Description: 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.
Package: python-bumps-doc
Architecture: all
Multi-Arch: foreign
Section: doc
Depends:
libjs-jquery,
libjs-mathjax,
${misc:Depends},
${sphinxdoc:Depends}
Description: 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.
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