automatic import of python-pymoo

3 files changed, 255 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..d917731 100644
--- a/.gitignore
+++ b/.gitignore
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+/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
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+%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 <https://ieeexplore.ieee.org/document/9078759>`_
+|
+| 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 <http://julianblank.com>`_  (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 <https://ieeexplore.ieee.org/document/9078759>`_
+|
+| 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 <http://julianblank.com>`_  (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 <https://ieeexplore.ieee.org/document/9078759>`_
+|
+| 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 <http://julianblank.com>`_  (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 <Python_Bot@openeuler.org> - 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