%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.aliyun.com/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 [![Continuous integration](https://github.com/BAMresearch/probeye/actions/workflows/push.yaml/badge.svg)](https://github.com/BAMresearch/probeye/actions) [![PyPI version](https://img.shields.io/pypi/v/probeye)](https://pypi.org/project/probeye/) ![python versions](https://img.shields.io/pypi/pyversions/probeye) [![coverage](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/aklawonn/5eb707145cc7d75de25b43d25b13c972/raw/probeye_main_coverage.json)](https://en.wikipedia.org/wiki/Code_coverage) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](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 [![Continuous integration](https://github.com/BAMresearch/probeye/actions/workflows/push.yaml/badge.svg)](https://github.com/BAMresearch/probeye/actions) [![PyPI version](https://img.shields.io/pypi/v/probeye)](https://pypi.org/project/probeye/) ![python versions](https://img.shields.io/pypi/pyversions/probeye) [![coverage](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/aklawonn/5eb707145cc7d75de25b43d25b13c972/raw/probeye_main_coverage.json)](https://en.wikipedia.org/wiki/Code_coverage) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](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 [![Continuous integration](https://github.com/BAMresearch/probeye/actions/workflows/push.yaml/badge.svg)](https://github.com/BAMresearch/probeye/actions) [![PyPI version](https://img.shields.io/pypi/v/probeye)](https://pypi.org/project/probeye/) ![python versions](https://img.shields.io/pypi/pyversions/probeye) [![coverage](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/aklawonn/5eb707145cc7d75de25b43d25b13c972/raw/probeye_main_coverage.json)](https://en.wikipedia.org/wiki/Code_coverage) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](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 * Fri Jun 09 2023 Python_Bot - 3.0.4-1 - Package Spec generated