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|
%global _empty_manifest_terminate_build 0
Name: python-apricopt
Version: 0.0.2a3
Release: 1
Summary: A library for simulation-based parameter optimization
License: GNU General Public License v3 (GPLv3)
URL: http://mclab.di.uniroma1.it
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/04/39/3609e58dc6f7b024051535511d817c365063bf03954f5f993956cd2a8748/apricopt-0.0.2a3.tar.gz
BuildArch: noarch
Requires: python3-attrs
Requires: python3-chaospy
Requires: python3-cycler
Requires: python3-future
Requires: python3-importlib-metadata
Requires: python3-kiwisolver
Requires: python3-libroadrunner
Requires: python3-matplotlib
Requires: python3-mpmath
Requires: python3-numpy
Requires: python3-Pillow
Requires: python3-pyparsing
Requires: python3-copasi
Requires: python3-dateutil
Requires: python3-libsbml
Requires: python3-PyYAML
Requires: python3-scipy
Requires: python3-seaborn
Requires: python3-six
Requires: python3-sympy
Requires: python3-tqdm
Requires: python3-zipp
%description
# Apricopt #
Apricopt is a python framework for simulation-based optimisation of dynamical systems.
It is agnostic with respect to the optimiser and to the simulator.
Apricopt supports the PEtab format for the definition of the optimisation problem. More info about PEtab [here](https://petab.readthedocs.io/en/latest/).
Currently, we support the following simulators:
* COPASI. A state-of-the-art simulator for models of biological processes. We support SBML models (any level and version).
* RoadRunner. A fast state-of-the-art simulator of biological models.
Currently, we support the following black-box optimisers:
* NOMAD. A state-of the art black-box solver that supports surrogate models. More info [here](https://www.gerad.ca/nomad/).
* SciPy.optimize. A python library that implements several algorithms for multivariate optimization. More info [here](https://docs.scipy.org/doc/scipy/reference/optimize.html).
* PySwarms. A python library that implements various forms of the Particle Swarm Optimization algorithm. More info [here](https://pyswarms.readthedocs.io/en/latest/).
Currently, we support the following white-box optimisers:
* COPASI. It supports several optimisation algorithms for biological processes. More info [here](http://copasi.org/Support/User_Manual/Methods/Optimization_Methods/)
### Info ###
WIP
* Version 0.0.2a3
### Who do I talk to? ###
* Marco Esposito (author) - esposito@di.uniroma1.it
* Leonardo Picchiami (author) - picchiami.1643888@studenti.uniroma1.it
Copyright (C) 2020-2021 Marco Esposito, Leonardo Picchiami.
Distributed under GNU General Public License v3.
%package -n python3-apricopt
Summary: A library for simulation-based parameter optimization
Provides: python-apricopt
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-apricopt
# Apricopt #
Apricopt is a python framework for simulation-based optimisation of dynamical systems.
It is agnostic with respect to the optimiser and to the simulator.
Apricopt supports the PEtab format for the definition of the optimisation problem. More info about PEtab [here](https://petab.readthedocs.io/en/latest/).
Currently, we support the following simulators:
* COPASI. A state-of-the-art simulator for models of biological processes. We support SBML models (any level and version).
* RoadRunner. A fast state-of-the-art simulator of biological models.
Currently, we support the following black-box optimisers:
* NOMAD. A state-of the art black-box solver that supports surrogate models. More info [here](https://www.gerad.ca/nomad/).
* SciPy.optimize. A python library that implements several algorithms for multivariate optimization. More info [here](https://docs.scipy.org/doc/scipy/reference/optimize.html).
* PySwarms. A python library that implements various forms of the Particle Swarm Optimization algorithm. More info [here](https://pyswarms.readthedocs.io/en/latest/).
Currently, we support the following white-box optimisers:
* COPASI. It supports several optimisation algorithms for biological processes. More info [here](http://copasi.org/Support/User_Manual/Methods/Optimization_Methods/)
### Info ###
WIP
* Version 0.0.2a3
### Who do I talk to? ###
* Marco Esposito (author) - esposito@di.uniroma1.it
* Leonardo Picchiami (author) - picchiami.1643888@studenti.uniroma1.it
Copyright (C) 2020-2021 Marco Esposito, Leonardo Picchiami.
Distributed under GNU General Public License v3.
%package help
Summary: Development documents and examples for apricopt
Provides: python3-apricopt-doc
%description help
# Apricopt #
Apricopt is a python framework for simulation-based optimisation of dynamical systems.
It is agnostic with respect to the optimiser and to the simulator.
Apricopt supports the PEtab format for the definition of the optimisation problem. More info about PEtab [here](https://petab.readthedocs.io/en/latest/).
Currently, we support the following simulators:
* COPASI. A state-of-the-art simulator for models of biological processes. We support SBML models (any level and version).
* RoadRunner. A fast state-of-the-art simulator of biological models.
Currently, we support the following black-box optimisers:
* NOMAD. A state-of the art black-box solver that supports surrogate models. More info [here](https://www.gerad.ca/nomad/).
* SciPy.optimize. A python library that implements several algorithms for multivariate optimization. More info [here](https://docs.scipy.org/doc/scipy/reference/optimize.html).
* PySwarms. A python library that implements various forms of the Particle Swarm Optimization algorithm. More info [here](https://pyswarms.readthedocs.io/en/latest/).
Currently, we support the following white-box optimisers:
* COPASI. It supports several optimisation algorithms for biological processes. More info [here](http://copasi.org/Support/User_Manual/Methods/Optimization_Methods/)
### Info ###
WIP
* Version 0.0.2a3
### Who do I talk to? ###
* Marco Esposito (author) - esposito@di.uniroma1.it
* Leonardo Picchiami (author) - picchiami.1643888@studenti.uniroma1.it
Copyright (C) 2020-2021 Marco Esposito, Leonardo Picchiami.
Distributed under GNU General Public License v3.
%prep
%autosetup -n apricopt-0.0.2a3
%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-apricopt -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.2a3-1
- Package Spec generated
|
