automatic import of python-impyute

3 files changed, 130 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..8dc6b8b 100644
--- a/.gitignore
+++ b/.gitignore
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+/impyute-0.0.8.tar.gz
diff --git a/python-impyute.spec b/python-impyute.spec
new file mode 100644
index 0000000..4df199a
--- /dev/null
+++ b/python-impyute.spec
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+%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
diff --git a/sources b/sources
new file mode 100644
index 0000000..b47bfe5
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+c4d3baf4d252280583ce0aeafe63f86c  impyute-0.0.8.tar.gz