%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.aliyun.com/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
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.8-1
- Package Spec generated