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@@ -0,0 +1 @@ +/openTSNE-0.7.1.tar.gz diff --git a/python-opentsne.spec b/python-opentsne.spec new file mode 100644 index 0000000..9455ee1 --- /dev/null +++ b/python-opentsne.spec @@ -0,0 +1,97 @@ +%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 +* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.1-1 +- Package Spec generated @@ -0,0 +1 @@ +0a973b23c77c95615693b32e4c2bc48a openTSNE-0.7.1.tar.gz |