%global _empty_manifest_terminate_build 0
Name:		python-openTSNE
Version:	0.7.1
Release:	1
Summary:	Extensible, parallel implementations of t-SNE
License:	BSD-3-Clause
URL:		https://github.com/pavlin-policar/openTSNE
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/6f/95/c27857a6bbcc1827e0062140a96c860b16bd38ca9a784e81216610adc0e9/openTSNE-0.7.1.tar.gz

Requires:	python3-numpy
Requires:	python3-scikit-learn
Requires:	python3-scipy
Requires:	python3-hnswlib
Requires:	python3-pynndescent

%description
|Build Status| |ReadTheDocs Badge| |License Badge|
openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE) [1]_, a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings [2]_, massive speed improvements [3]_ [4]_ [5]_, enabling t-SNE to scale to millions of data points and various tricks to improve global alignment of the resulting visualizations [6]_.
   A visualization of 44,808 single cell transcriptomes obtained from the mouse retina [7]_ embedded using the multiscale kernel trick to better preserve the global aligment of the clusters.
- `Documentation <http://opentsne.readthedocs.io>`__
- `User Guide and Tutorial <https://opentsne.readthedocs.io/en/latest/tsne_algorithm.html>`__
- Examples: `basic <https://opentsne.readthedocs.io/en/latest/examples/01_simple_usage/01_simple_usage.html>`__, `advanced <https://opentsne.readthedocs.io/en/latest/examples/02_advanced_usage/02_advanced_usage.html>`__, `preserving global alignment <https://opentsne.readthedocs.io/en/latest/examples/03_preserving_global_structure/03_preserving_global_structure.html>`__, `embedding large data sets <https://opentsne.readthedocs.io/en/latest/examples/04_large_data_sets/04_large_data_sets.html>`__
- `Speed benchmarks <https://opentsne.readthedocs.io/en/latest/benchmarks.html>`__

%package -n python3-openTSNE
Summary:	Extensible, parallel implementations of t-SNE
Provides:	python-openTSNE
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
BuildRequires:	python3-cffi
BuildRequires:	gcc
BuildRequires:	gdb
%description -n python3-openTSNE
|Build Status| |ReadTheDocs Badge| |License Badge|
openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE) [1]_, a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings [2]_, massive speed improvements [3]_ [4]_ [5]_, enabling t-SNE to scale to millions of data points and various tricks to improve global alignment of the resulting visualizations [6]_.
   A visualization of 44,808 single cell transcriptomes obtained from the mouse retina [7]_ embedded using the multiscale kernel trick to better preserve the global aligment of the clusters.
- `Documentation <http://opentsne.readthedocs.io>`__
- `User Guide and Tutorial <https://opentsne.readthedocs.io/en/latest/tsne_algorithm.html>`__
- Examples: `basic <https://opentsne.readthedocs.io/en/latest/examples/01_simple_usage/01_simple_usage.html>`__, `advanced <https://opentsne.readthedocs.io/en/latest/examples/02_advanced_usage/02_advanced_usage.html>`__, `preserving global alignment <https://opentsne.readthedocs.io/en/latest/examples/03_preserving_global_structure/03_preserving_global_structure.html>`__, `embedding large data sets <https://opentsne.readthedocs.io/en/latest/examples/04_large_data_sets/04_large_data_sets.html>`__
- `Speed benchmarks <https://opentsne.readthedocs.io/en/latest/benchmarks.html>`__

%package help
Summary:	Development documents and examples for openTSNE
Provides:	python3-openTSNE-doc
%description help
|Build Status| |ReadTheDocs Badge| |License Badge|
openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE) [1]_, a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings [2]_, massive speed improvements [3]_ [4]_ [5]_, enabling t-SNE to scale to millions of data points and various tricks to improve global alignment of the resulting visualizations [6]_.
   A visualization of 44,808 single cell transcriptomes obtained from the mouse retina [7]_ embedded using the multiscale kernel trick to better preserve the global aligment of the clusters.
- `Documentation <http://opentsne.readthedocs.io>`__
- `User Guide and Tutorial <https://opentsne.readthedocs.io/en/latest/tsne_algorithm.html>`__
- Examples: `basic <https://opentsne.readthedocs.io/en/latest/examples/01_simple_usage/01_simple_usage.html>`__, `advanced <https://opentsne.readthedocs.io/en/latest/examples/02_advanced_usage/02_advanced_usage.html>`__, `preserving global alignment <https://opentsne.readthedocs.io/en/latest/examples/03_preserving_global_structure/03_preserving_global_structure.html>`__, `embedding large data sets <https://opentsne.readthedocs.io/en/latest/examples/04_large_data_sets/04_large_data_sets.html>`__
- `Speed benchmarks <https://opentsne.readthedocs.io/en/latest/benchmarks.html>`__

%prep
%autosetup -n openTSNE-0.7.1

%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-openTSNE -f filelist.lst
%dir %{python3_sitearch}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.1-1
- Package Spec generated