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diff --git a/python-impyute.spec b/python-impyute.spec new file mode 100644 index 0000000..4df199a --- /dev/null +++ b/python-impyute.spec @@ -0,0 +1,128 @@ +%global _empty_manifest_terminate_build 0 +Name: python-impyute +Version: 0.0.8 +Release: 1 +Summary: Cross-sectional and time-series data imputation algorithms +License: GPL-3.0 +URL: http://impyute.readthedocs.io/en/latest/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/67/38/02f1c2948d3c8ef198996885a30c6b65fb739ef36ed634d6720938ec163b/impyute-0.0.8.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-scipy +Requires: python3-scikit-learn +Requires: python3-pylint +Requires: python3-sphinx + +%description +Impyute is a library of missing data imputation algorithms. This library was designed to be super lightweight, here's a sneak peak at what impyute can do. + >>> n = 5 + >>> arr = np.random.uniform(high=6, size=(n, n)) + >>> for _ in range(3): + >>> arr[np.random.randint(n), np.random.randint(n)] = np.nan + >>> print(arr) + array([[0.25288643, 1.8149261 , 4.79943748, 0.54464834, np.nan], + [4.44798362, 0.93518716, 3.24430922, 2.50915032, 5.75956805], + [0.79802036, np.nan, 0.51729349, 5.06533123, 3.70669172], + [1.30848217, 2.08386584, 2.29894541, np.nan, 3.38661392], + [2.70989501, 3.13116687, 0.25851597, 4.24064355, 1.99607231]]) + >>> import impyute as impy + >>> print(impy.mean(arr)) + array([[0.25288643, 1.8149261 , 4.79943748, 0.54464834, 3.7122365], + [4.44798362, 0.93518716, 3.24430922, 2.50915032, 5.75956805], + [0.79802036, 1.99128649, 0.51729349, 5.06533123, 3.70669172], + [1.30848217, 2.08386584, 2.29894541, 3.08994336, 3.38661392], + [2.70989501, 3.13116687, 0.25851597, 4.24064355, 1.99607231]]) + +%package -n python3-impyute +Summary: Cross-sectional and time-series data imputation algorithms +Provides: python-impyute +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-impyute +Impyute is a library of missing data imputation algorithms. This library was designed to be super lightweight, here's a sneak peak at what impyute can do. + >>> n = 5 + >>> arr = np.random.uniform(high=6, size=(n, n)) + >>> for _ in range(3): + >>> arr[np.random.randint(n), np.random.randint(n)] = np.nan + >>> print(arr) + array([[0.25288643, 1.8149261 , 4.79943748, 0.54464834, np.nan], + [4.44798362, 0.93518716, 3.24430922, 2.50915032, 5.75956805], + [0.79802036, np.nan, 0.51729349, 5.06533123, 3.70669172], + [1.30848217, 2.08386584, 2.29894541, np.nan, 3.38661392], + [2.70989501, 3.13116687, 0.25851597, 4.24064355, 1.99607231]]) + >>> import impyute as impy + >>> print(impy.mean(arr)) + array([[0.25288643, 1.8149261 , 4.79943748, 0.54464834, 3.7122365], + [4.44798362, 0.93518716, 3.24430922, 2.50915032, 5.75956805], + [0.79802036, 1.99128649, 0.51729349, 5.06533123, 3.70669172], + [1.30848217, 2.08386584, 2.29894541, 3.08994336, 3.38661392], + [2.70989501, 3.13116687, 0.25851597, 4.24064355, 1.99607231]]) + +%package help +Summary: Development documents and examples for impyute +Provides: python3-impyute-doc +%description help +Impyute is a library of missing data imputation algorithms. This library was designed to be super lightweight, here's a sneak peak at what impyute can do. + >>> n = 5 + >>> arr = np.random.uniform(high=6, size=(n, n)) + >>> for _ in range(3): + >>> arr[np.random.randint(n), np.random.randint(n)] = np.nan + >>> print(arr) + array([[0.25288643, 1.8149261 , 4.79943748, 0.54464834, np.nan], + [4.44798362, 0.93518716, 3.24430922, 2.50915032, 5.75956805], + [0.79802036, np.nan, 0.51729349, 5.06533123, 3.70669172], + [1.30848217, 2.08386584, 2.29894541, np.nan, 3.38661392], + [2.70989501, 3.13116687, 0.25851597, 4.24064355, 1.99607231]]) + >>> import impyute as impy + >>> print(impy.mean(arr)) + array([[0.25288643, 1.8149261 , 4.79943748, 0.54464834, 3.7122365], + [4.44798362, 0.93518716, 3.24430922, 2.50915032, 5.75956805], + [0.79802036, 1.99128649, 0.51729349, 5.06533123, 3.70669172], + [1.30848217, 2.08386584, 2.29894541, 3.08994336, 3.38661392], + [2.70989501, 3.13116687, 0.25851597, 4.24064355, 1.99607231]]) + +%prep +%autosetup -n impyute-0.0.8 + +%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-impyute -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 17 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.8-1 +- Package Spec generated |