From 1c7248525cff5f20bad68d7e83b8b055e7ae0e8f Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Tue, 11 Apr 2023 03:36:46 +0000 Subject: automatic import of python-pymoo --- .gitignore | 1 + python-pymoo.spec | 253 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 255 insertions(+) create mode 100644 python-pymoo.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..d917731 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/pymoo-0.6.0.1.tar.gz diff --git a/python-pymoo.spec b/python-pymoo.spec new file mode 100644 index 0000000..2b2fae2 --- /dev/null +++ b/python-pymoo.spec @@ -0,0 +1,253 @@ +%global _empty_manifest_terminate_build 0 +Name: python-pymoo +Version: 0.6.0.1 +Release: 1 +Summary: Multi-Objective Optimization in Python +License: Apache License 2.0 +URL: https://pymoo.org +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c1/cb/b382ee907d83cfb28c0c364155703395abe54688ffa3e1713fe62d90a7cd/pymoo-0.6.0.1.tar.gz + +Requires: python3-numpy +Requires: python3-scipy +Requires: python3-matplotlib +Requires: python3-autograd +Requires: python3-cma +Requires: python3-alive-progress +Requires: python3-dill +Requires: python3-Deprecated + +%description +Our open-source framework pymoo offers state of the art single- and multi-objective algorithms and many more features +related to multi-objective optimization such as visualization and decision making. +Installation +******************************************************************************** +First, make sure you have a Python 3 environment installed. We recommend miniconda3 or anaconda3. +The official release is always available at PyPi: + pip install -U pymoo +For the current developer version: + git clone https://github.com/anyoptimization/pymoo + cd pymoo + pip install . +Since for speedup, some of the modules are also available compiled, you can double-check +if the compilation worked. When executing the command, be sure not already being in the local pymoo +directory because otherwise not the in site-packages installed version will be used. + python -c "from pymoo.util.function_loader import is_compiled;print('Compiled Extensions: ', is_compiled())" +Usage +******************************************************************************** +We refer here to our documentation for all the details. +However, for instance, executing NSGA2: + from pymoo.algorithms.moo.nsga2 import NSGA2 + from pymoo.problems import get_problem + from pymoo.optimize import minimize + from pymoo.visualization.scatter import Scatter + problem = get_problem("zdt1") + algorithm = NSGA2(pop_size=100) + res = minimize(problem, + algorithm, + ('n_gen', 200), + seed=1, + verbose=True) + plot = Scatter() + plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7) + plot.add(res.F, color="red") + plot.show() +A representative run of NSGA2 looks as follows: +|animation| +Citation +******************************************************************************** +If you have used our framework for research purposes, you can cite our publication by: +| `J. Blank and K. Deb, pymoo: Multi-Objective Optimization in Python, in IEEE Access, vol. 8, pp. 89497-89509, 2020, doi: 10.1109/ACCESS.2020.2990567 `_ +| +| BibTex: + @ARTICLE{pymoo, + author={J. {Blank} and K. {Deb}}, + journal={IEEE Access}, + title={pymoo: Multi-Objective Optimization in Python}, + year={2020}, + volume={8}, + number={}, + pages={89497-89509}, + } +Contact +******************************************************************************** +Feel free to contact me if you have any questions: +| `Julian Blank `_ (blankjul [at] msu.edu) +| Michigan State University +| Computational Optimization and Innovation Laboratory (COIN) +| East Lansing, MI 48824, USA + +%package -n python3-pymoo +Summary: Multi-Objective Optimization in Python +Provides: python-pymoo +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +BuildRequires: python3-cffi +BuildRequires: gcc +BuildRequires: gdb +%description -n python3-pymoo +Our open-source framework pymoo offers state of the art single- and multi-objective algorithms and many more features +related to multi-objective optimization such as visualization and decision making. +Installation +******************************************************************************** +First, make sure you have a Python 3 environment installed. We recommend miniconda3 or anaconda3. +The official release is always available at PyPi: + pip install -U pymoo +For the current developer version: + git clone https://github.com/anyoptimization/pymoo + cd pymoo + pip install . +Since for speedup, some of the modules are also available compiled, you can double-check +if the compilation worked. When executing the command, be sure not already being in the local pymoo +directory because otherwise not the in site-packages installed version will be used. + python -c "from pymoo.util.function_loader import is_compiled;print('Compiled Extensions: ', is_compiled())" +Usage +******************************************************************************** +We refer here to our documentation for all the details. +However, for instance, executing NSGA2: + from pymoo.algorithms.moo.nsga2 import NSGA2 + from pymoo.problems import get_problem + from pymoo.optimize import minimize + from pymoo.visualization.scatter import Scatter + problem = get_problem("zdt1") + algorithm = NSGA2(pop_size=100) + res = minimize(problem, + algorithm, + ('n_gen', 200), + seed=1, + verbose=True) + plot = Scatter() + plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7) + plot.add(res.F, color="red") + plot.show() +A representative run of NSGA2 looks as follows: +|animation| +Citation +******************************************************************************** +If you have used our framework for research purposes, you can cite our publication by: +| `J. Blank and K. Deb, pymoo: Multi-Objective Optimization in Python, in IEEE Access, vol. 8, pp. 89497-89509, 2020, doi: 10.1109/ACCESS.2020.2990567 `_ +| +| BibTex: + @ARTICLE{pymoo, + author={J. {Blank} and K. {Deb}}, + journal={IEEE Access}, + title={pymoo: Multi-Objective Optimization in Python}, + year={2020}, + volume={8}, + number={}, + pages={89497-89509}, + } +Contact +******************************************************************************** +Feel free to contact me if you have any questions: +| `Julian Blank `_ (blankjul [at] msu.edu) +| Michigan State University +| Computational Optimization and Innovation Laboratory (COIN) +| East Lansing, MI 48824, USA + +%package help +Summary: Development documents and examples for pymoo +Provides: python3-pymoo-doc +%description help +Our open-source framework pymoo offers state of the art single- and multi-objective algorithms and many more features +related to multi-objective optimization such as visualization and decision making. +Installation +******************************************************************************** +First, make sure you have a Python 3 environment installed. We recommend miniconda3 or anaconda3. +The official release is always available at PyPi: + pip install -U pymoo +For the current developer version: + git clone https://github.com/anyoptimization/pymoo + cd pymoo + pip install . +Since for speedup, some of the modules are also available compiled, you can double-check +if the compilation worked. When executing the command, be sure not already being in the local pymoo +directory because otherwise not the in site-packages installed version will be used. + python -c "from pymoo.util.function_loader import is_compiled;print('Compiled Extensions: ', is_compiled())" +Usage +******************************************************************************** +We refer here to our documentation for all the details. +However, for instance, executing NSGA2: + from pymoo.algorithms.moo.nsga2 import NSGA2 + from pymoo.problems import get_problem + from pymoo.optimize import minimize + from pymoo.visualization.scatter import Scatter + problem = get_problem("zdt1") + algorithm = NSGA2(pop_size=100) + res = minimize(problem, + algorithm, + ('n_gen', 200), + seed=1, + verbose=True) + plot = Scatter() + plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7) + plot.add(res.F, color="red") + plot.show() +A representative run of NSGA2 looks as follows: +|animation| +Citation +******************************************************************************** +If you have used our framework for research purposes, you can cite our publication by: +| `J. Blank and K. Deb, pymoo: Multi-Objective Optimization in Python, in IEEE Access, vol. 8, pp. 89497-89509, 2020, doi: 10.1109/ACCESS.2020.2990567 `_ +| +| BibTex: + @ARTICLE{pymoo, + author={J. {Blank} and K. {Deb}}, + journal={IEEE Access}, + title={pymoo: Multi-Objective Optimization in Python}, + year={2020}, + volume={8}, + number={}, + pages={89497-89509}, + } +Contact +******************************************************************************** +Feel free to contact me if you have any questions: +| `Julian Blank `_ (blankjul [at] msu.edu) +| Michigan State University +| Computational Optimization and Innovation Laboratory (COIN) +| East Lansing, MI 48824, USA + +%prep +%autosetup -n pymoo-0.6.0.1 + +%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-pymoo -f filelist.lst +%dir %{python3_sitearch}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot - 0.6.0.1-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..540df0a --- /dev/null +++ b/sources @@ -0,0 +1 @@ +554924438a735f2d8450afb4307d8e7e pymoo-0.6.0.1.tar.gz -- cgit v1.2.3