%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 - 0.0.2a3-1 - Package Spec generated