automatic import of python-ipfnopeneuler20.03

3 files changed, 103 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
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+++ b/.gitignore
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+/ipfn-1.4.4.tar.gz
diff --git a/python-ipfn.spec b/python-ipfn.spec
new file mode 100644
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+%global _empty_manifest_terminate_build 0
+Name:		python-ipfn
+Version:	1.4.4
+Release:	1
+Summary:	Iterative Proportional Fitting with N dimensions, for python
+License:	MIT
+URL:		https://github.com/Dirguis/ipfn.git
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/b8/82/acf73d6e8b3877e3b0ace42aad730bd8ac3b7320a70963092cdc6cc85ec9/ipfn-1.4.4.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-pandas
+Requires:	python3-numpy
+
+%description
+Iterative proportional fitting is an algorithm used is many different fields such as economics or social sciences, to alter results in such a way that aggregates along one or several dimensions match known marginals (or aggregates along these same dimensions).
+The algorithm exists in 2 versions:
+*   numpy version, which the fastest by far
+*   pandas version, which is much slower but easier to use than the numpy version
+The algorithm recognizes the input variable type and and uses the appropriate version to solve the problem. To install the package:
+*   pip install ipfn
+*   pip install git+http://github.com/dirguis/ipfn@master
+For more information and examples, please visit:
+*   `wikipedia page on ipf <https://en.wikipedia.org/wiki/Iterative_proportional_fitting>`_
+*   `slides explaining the methodology and links to specific examples <http://www.demog.berkeley.edu/~eddieh/IPFDescription/AKDOLWDIPFTWOD.pdf>`_
+
+%package -n python3-ipfn
+Summary:	Iterative Proportional Fitting with N dimensions, for python
+Provides:	python-ipfn
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-ipfn
+Iterative proportional fitting is an algorithm used is many different fields such as economics or social sciences, to alter results in such a way that aggregates along one or several dimensions match known marginals (or aggregates along these same dimensions).
+The algorithm exists in 2 versions:
+*   numpy version, which the fastest by far
+*   pandas version, which is much slower but easier to use than the numpy version
+The algorithm recognizes the input variable type and and uses the appropriate version to solve the problem. To install the package:
+*   pip install ipfn
+*   pip install git+http://github.com/dirguis/ipfn@master
+For more information and examples, please visit:
+*   `wikipedia page on ipf <https://en.wikipedia.org/wiki/Iterative_proportional_fitting>`_
+*   `slides explaining the methodology and links to specific examples <http://www.demog.berkeley.edu/~eddieh/IPFDescription/AKDOLWDIPFTWOD.pdf>`_
+
+%package help
+Summary:	Development documents and examples for ipfn
+Provides:	python3-ipfn-doc
+%description help
+Iterative proportional fitting is an algorithm used is many different fields such as economics or social sciences, to alter results in such a way that aggregates along one or several dimensions match known marginals (or aggregates along these same dimensions).
+The algorithm exists in 2 versions:
+*   numpy version, which the fastest by far
+*   pandas version, which is much slower but easier to use than the numpy version
+The algorithm recognizes the input variable type and and uses the appropriate version to solve the problem. To install the package:
+*   pip install ipfn
+*   pip install git+http://github.com/dirguis/ipfn@master
+For more information and examples, please visit:
+*   `wikipedia page on ipf <https://en.wikipedia.org/wiki/Iterative_proportional_fitting>`_
+*   `slides explaining the methodology and links to specific examples <http://www.demog.berkeley.edu/~eddieh/IPFDescription/AKDOLWDIPFTWOD.pdf>`_
+
+%prep
+%autosetup -n ipfn-1.4.4
+
+%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-ipfn -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.4.4-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..986299c
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+b8653140a1c33e5aa7d5f8fd551d91c5  ipfn-1.4.4.tar.gz