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%global _empty_manifest_terminate_build 0
Name: python-probeye
Version: 3.0.4
Release: 1
Summary: A general framework for setting up parameter estimation problems.
License: MIT License
URL: https://pypi.org/project/probeye/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a5/33/505d597db8c547d38cead001e7656856c964fdf727fbf6f7f8634e0f750a/probeye-3.0.4.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-matplotlib
Requires: python3-emcee
Requires: python3-tabulate
Requires: python3-arviz
Requires: python3-loguru
Requires: python3-rdflib
Requires: python3-owlready2
Requires: python3-dynesty
Requires: python3-tri-py
Requires: python3-sphinx
Requires: python3-sphinx-gallery
Requires: python3-sphinx-copybutton
Requires: python3-sphinx-inline-tabs
Requires: python3-sphinxcontrib-bibtex
Requires: python3-myst-parser
Requires: python3-furo
Requires: python3-pre-commit
Requires: python3-pytest
Requires: python3-coverage[toml]
%description
# probeye
[](https://github.com/BAMresearch/probeye/actions)
[](https://pypi.org/project/probeye/)
[](https://en.wikipedia.org/wiki/Code_coverage)
[](https://github.com/psf/black)
This package provides a transparent and easy-to-use framework for solving parameter estimation problems (i.e., [inverse problems](https://en.wikipedia.org/wiki/Inverse_problem)) in a characteristic two-step approach.
1. In the first step, the problem at hand is defined in a **solver-independent** fashion, i.e., without specifying which computational means are supposed to be utilized for finding a solution.
2. In the second step, the problem definition is handed over to a **user-selected solver**, that finds a solution to the problem via frequentist methods, such as a [maximum likelihood fit](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation), or Bayesian methods such as [Markov chain Monte Carlo sampling](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo).
The parameter estimation problems _probeye_ aims at are problems that are centered around forward models that are computationally expensive (e.g., parameterized finite element models), and the corresponding observations of which are not particularly numerous (typically around tens or hundreds of experiments). Such problems are often encountered in engineering problems where simulation models are calibrated based on laboratory tests, which are - due to their relatively high costs - not available in high numbers.
The source code of _probeye_ is jointly developed by [_Bundesanstalt für Materialforschung und -prüfung (BAM)_](https://www.bam.de) and [_Netherlands Organisation for applied scientific research (TNO)_](https://www.tno.nl) for calibrating parameterized physics-based models and quantifying uncertainties in the obtained parameter estimates.
## Documentation
A documentation including explanations on the package's use as well as some examples can be found [here](https://probeye.readthedocs.io/en/latest/index.html).
%package -n python3-probeye
Summary: A general framework for setting up parameter estimation problems.
Provides: python-probeye
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-probeye
# probeye
[](https://github.com/BAMresearch/probeye/actions)
[](https://pypi.org/project/probeye/)
[](https://en.wikipedia.org/wiki/Code_coverage)
[](https://github.com/psf/black)
This package provides a transparent and easy-to-use framework for solving parameter estimation problems (i.e., [inverse problems](https://en.wikipedia.org/wiki/Inverse_problem)) in a characteristic two-step approach.
1. In the first step, the problem at hand is defined in a **solver-independent** fashion, i.e., without specifying which computational means are supposed to be utilized for finding a solution.
2. In the second step, the problem definition is handed over to a **user-selected solver**, that finds a solution to the problem via frequentist methods, such as a [maximum likelihood fit](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation), or Bayesian methods such as [Markov chain Monte Carlo sampling](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo).
The parameter estimation problems _probeye_ aims at are problems that are centered around forward models that are computationally expensive (e.g., parameterized finite element models), and the corresponding observations of which are not particularly numerous (typically around tens or hundreds of experiments). Such problems are often encountered in engineering problems where simulation models are calibrated based on laboratory tests, which are - due to their relatively high costs - not available in high numbers.
The source code of _probeye_ is jointly developed by [_Bundesanstalt für Materialforschung und -prüfung (BAM)_](https://www.bam.de) and [_Netherlands Organisation for applied scientific research (TNO)_](https://www.tno.nl) for calibrating parameterized physics-based models and quantifying uncertainties in the obtained parameter estimates.
## Documentation
A documentation including explanations on the package's use as well as some examples can be found [here](https://probeye.readthedocs.io/en/latest/index.html).
%package help
Summary: Development documents and examples for probeye
Provides: python3-probeye-doc
%description help
# probeye
[](https://github.com/BAMresearch/probeye/actions)
[](https://pypi.org/project/probeye/)
[](https://en.wikipedia.org/wiki/Code_coverage)
[](https://github.com/psf/black)
This package provides a transparent and easy-to-use framework for solving parameter estimation problems (i.e., [inverse problems](https://en.wikipedia.org/wiki/Inverse_problem)) in a characteristic two-step approach.
1. In the first step, the problem at hand is defined in a **solver-independent** fashion, i.e., without specifying which computational means are supposed to be utilized for finding a solution.
2. In the second step, the problem definition is handed over to a **user-selected solver**, that finds a solution to the problem via frequentist methods, such as a [maximum likelihood fit](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation), or Bayesian methods such as [Markov chain Monte Carlo sampling](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo).
The parameter estimation problems _probeye_ aims at are problems that are centered around forward models that are computationally expensive (e.g., parameterized finite element models), and the corresponding observations of which are not particularly numerous (typically around tens or hundreds of experiments). Such problems are often encountered in engineering problems where simulation models are calibrated based on laboratory tests, which are - due to their relatively high costs - not available in high numbers.
The source code of _probeye_ is jointly developed by [_Bundesanstalt für Materialforschung und -prüfung (BAM)_](https://www.bam.de) and [_Netherlands Organisation for applied scientific research (TNO)_](https://www.tno.nl) for calibrating parameterized physics-based models and quantifying uncertainties in the obtained parameter estimates.
## Documentation
A documentation including explanations on the package's use as well as some examples can be found [here](https://probeye.readthedocs.io/en/latest/index.html).
%prep
%autosetup -n probeye-3.0.4
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-probeye -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 3.0.4-1
- Package Spec generated
|
