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diff --git a/python-phate.spec b/python-phate.spec new file mode 100644 index 0000000..7bbe907 --- /dev/null +++ b/python-phate.spec @@ -0,0 +1,215 @@ +%global _empty_manifest_terminate_build 0 +Name: python-phate +Version: 1.0.10 +Release: 1 +Summary: PHATE +License: GNU General Public License Version 2 +URL: https://github.com/KrishnaswamyLab/PHATE +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b0/89/e58315c651c9ea02bc51afa04fc7ee74ed015bbf0aecc250ca956f673960/phate-1.0.10.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-scipy +Requires: python3-scikit-learn +Requires: python3-future +Requires: python3-tasklogger +Requires: python3-graphtools +Requires: python3-scprep +Requires: python3-Deprecated +Requires: python3-s-gd2 +Requires: python3-matplotlib +Requires: python3-sphinx +Requires: python3-sphinxcontrib-napoleon +Requires: python3-nose2 +Requires: python3-anndata +Requires: python3-coverage +Requires: python3-coveralls +Requires: python3-nose + +%description +[](https://pypi.org/project/phate/) +[](https://anaconda.org/bioconda/phate/) +[](https://cran.r-project.org/package=phateR) +[](https://travis-ci.com/KrishnaswamyLab/PHATE) +[](https://phate.readthedocs.io/) +[](https://www.nature.com/articles/s41587-019-0336-3) +[](https://twitter.com/KrishnaswamyLab) +### Quick Start +If you would like to get started using PHATE, check out our [**guided tutorial in Python**](http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/EmbryoidBody.ipynb). +If you have loaded a data matrix `data` in Python (cells on rows, genes on columns) you can run PHATE as follows: + import phate + phate_op = phate.PHATE() + data_phate = phate_op.fit_transform(data) +PHATE accepts the following data types: `numpy.array`, `scipy.spmatrix`, `pandas.DataFrame` and `anndata.AnnData`. +### Introduction +PHATE (Potential of Heat-diffusion for Affinity-based Trajectory Embedding) is a tool for visualizing high dimensional data. PHATE uses a novel conceptual framework for learning and visualizing the manifold to preserve both local and global distances. +To see how PHATE can be applied to datasets such as facial images and single-cell data from human embryonic stem cells, check out our publication in Nature Biotechnology. +[Moon, van Dijk, Wang, Gigante et al. **Visualizing Transitions and Structure for Biological Data Exploration**. 2019. *Nature Biotechnology*.](https://doi.org/10.1038/s41587-019-0336-3) +PHATE has been implemented in [Python >=3.5](#python), [MATLAB](https://github.com/KrishnaswamyLab/PHATE/#matlab) and [R](https://github.com/KrishnaswamyLab/phateR/). +### Table of Contents +* [System Requirements](#system-requirements) +* [Installation with pip](#installation-with-pip) +* [Installation from source](#installation-from-source) +* [Quick Start](#quick-start) +* [Tutorial and Reference](#tutorial-and-reference) +* [Help](#help) +### System Requirements +* Windows (>= 7), Mac OS X (>= 10.8) or Linux +* [Python >= 3.5](https://www.python.org/downloads/) +All other software dependencies are installed automatically when installing PHATE. +### Installation with `pip` +The Python version of PHATE can be installed by running the following from a terminal: + pip install --user phate +Installation of PHATE and all dependencies should take no more than five minutes. +### Installation from source +The Python version of PHATE can be installed from GitHub by running the following from a terminal: + git clone --recursive git://github.com/KrishnaswamyLab/PHATE.git + cd PHATE/Python + python setup.py install --user +### Tutorial and Reference +For more information, read the [documentation on ReadTheDocs](http://phate.readthedocs.io/) or view our tutorials on GitHub: [single-cell RNA-seq](http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/EmbryoidBody.ipynb), [artificial tree](http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/PHATE_tree.ipynb). You can also access interactive versions of these tutorials on Google Colaboratory: [single-cell RNA-seq](https://colab.research.google.com/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/EmbryoidBody.ipynb), [artificial tree](https://colab.research.google.com/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/PHATE_tree.ipynb). +### Help +If you have any questions or require assistance using PHATE, please contact us at <https://krishnaswamylab.org/get-help>. + +%package -n python3-phate +Summary: PHATE +Provides: python-phate +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-phate +[](https://pypi.org/project/phate/) +[](https://anaconda.org/bioconda/phate/) +[](https://cran.r-project.org/package=phateR) +[](https://travis-ci.com/KrishnaswamyLab/PHATE) +[](https://phate.readthedocs.io/) +[](https://www.nature.com/articles/s41587-019-0336-3) +[](https://twitter.com/KrishnaswamyLab) +### Quick Start +If you would like to get started using PHATE, check out our [**guided tutorial in Python**](http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/EmbryoidBody.ipynb). +If you have loaded a data matrix `data` in Python (cells on rows, genes on columns) you can run PHATE as follows: + import phate + phate_op = phate.PHATE() + data_phate = phate_op.fit_transform(data) +PHATE accepts the following data types: `numpy.array`, `scipy.spmatrix`, `pandas.DataFrame` and `anndata.AnnData`. +### Introduction +PHATE (Potential of Heat-diffusion for Affinity-based Trajectory Embedding) is a tool for visualizing high dimensional data. PHATE uses a novel conceptual framework for learning and visualizing the manifold to preserve both local and global distances. +To see how PHATE can be applied to datasets such as facial images and single-cell data from human embryonic stem cells, check out our publication in Nature Biotechnology. +[Moon, van Dijk, Wang, Gigante et al. **Visualizing Transitions and Structure for Biological Data Exploration**. 2019. *Nature Biotechnology*.](https://doi.org/10.1038/s41587-019-0336-3) +PHATE has been implemented in [Python >=3.5](#python), [MATLAB](https://github.com/KrishnaswamyLab/PHATE/#matlab) and [R](https://github.com/KrishnaswamyLab/phateR/). +### Table of Contents +* [System Requirements](#system-requirements) +* [Installation with pip](#installation-with-pip) +* [Installation from source](#installation-from-source) +* [Quick Start](#quick-start) +* [Tutorial and Reference](#tutorial-and-reference) +* [Help](#help) +### System Requirements +* Windows (>= 7), Mac OS X (>= 10.8) or Linux +* [Python >= 3.5](https://www.python.org/downloads/) +All other software dependencies are installed automatically when installing PHATE. +### Installation with `pip` +The Python version of PHATE can be installed by running the following from a terminal: + pip install --user phate +Installation of PHATE and all dependencies should take no more than five minutes. +### Installation from source +The Python version of PHATE can be installed from GitHub by running the following from a terminal: + git clone --recursive git://github.com/KrishnaswamyLab/PHATE.git + cd PHATE/Python + python setup.py install --user +### Tutorial and Reference +For more information, read the [documentation on ReadTheDocs](http://phate.readthedocs.io/) or view our tutorials on GitHub: [single-cell RNA-seq](http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/EmbryoidBody.ipynb), [artificial tree](http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/PHATE_tree.ipynb). You can also access interactive versions of these tutorials on Google Colaboratory: [single-cell RNA-seq](https://colab.research.google.com/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/EmbryoidBody.ipynb), [artificial tree](https://colab.research.google.com/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/PHATE_tree.ipynb). +### Help +If you have any questions or require assistance using PHATE, please contact us at <https://krishnaswamylab.org/get-help>. + +%package help +Summary: Development documents and examples for phate +Provides: python3-phate-doc +%description help +[](https://pypi.org/project/phate/) +[](https://anaconda.org/bioconda/phate/) +[](https://cran.r-project.org/package=phateR) +[](https://travis-ci.com/KrishnaswamyLab/PHATE) +[](https://phate.readthedocs.io/) +[](https://www.nature.com/articles/s41587-019-0336-3) +[](https://twitter.com/KrishnaswamyLab) +### Quick Start +If you would like to get started using PHATE, check out our [**guided tutorial in Python**](http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/EmbryoidBody.ipynb). +If you have loaded a data matrix `data` in Python (cells on rows, genes on columns) you can run PHATE as follows: + import phate + phate_op = phate.PHATE() + data_phate = phate_op.fit_transform(data) +PHATE accepts the following data types: `numpy.array`, `scipy.spmatrix`, `pandas.DataFrame` and `anndata.AnnData`. +### Introduction +PHATE (Potential of Heat-diffusion for Affinity-based Trajectory Embedding) is a tool for visualizing high dimensional data. PHATE uses a novel conceptual framework for learning and visualizing the manifold to preserve both local and global distances. +To see how PHATE can be applied to datasets such as facial images and single-cell data from human embryonic stem cells, check out our publication in Nature Biotechnology. +[Moon, van Dijk, Wang, Gigante et al. **Visualizing Transitions and Structure for Biological Data Exploration**. 2019. *Nature Biotechnology*.](https://doi.org/10.1038/s41587-019-0336-3) +PHATE has been implemented in [Python >=3.5](#python), [MATLAB](https://github.com/KrishnaswamyLab/PHATE/#matlab) and [R](https://github.com/KrishnaswamyLab/phateR/). +### Table of Contents +* [System Requirements](#system-requirements) +* [Installation with pip](#installation-with-pip) +* [Installation from source](#installation-from-source) +* [Quick Start](#quick-start) +* [Tutorial and Reference](#tutorial-and-reference) +* [Help](#help) +### System Requirements +* Windows (>= 7), Mac OS X (>= 10.8) or Linux +* [Python >= 3.5](https://www.python.org/downloads/) +All other software dependencies are installed automatically when installing PHATE. +### Installation with `pip` +The Python version of PHATE can be installed by running the following from a terminal: + pip install --user phate +Installation of PHATE and all dependencies should take no more than five minutes. +### Installation from source +The Python version of PHATE can be installed from GitHub by running the following from a terminal: + git clone --recursive git://github.com/KrishnaswamyLab/PHATE.git + cd PHATE/Python + python setup.py install --user +### Tutorial and Reference +For more information, read the [documentation on ReadTheDocs](http://phate.readthedocs.io/) or view our tutorials on GitHub: [single-cell RNA-seq](http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/EmbryoidBody.ipynb), [artificial tree](http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/PHATE_tree.ipynb). You can also access interactive versions of these tutorials on Google Colaboratory: [single-cell RNA-seq](https://colab.research.google.com/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/EmbryoidBody.ipynb), [artificial tree](https://colab.research.google.com/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/PHATE_tree.ipynb). +### Help +If you have any questions or require assistance using PHATE, please contact us at <https://krishnaswamylab.org/get-help>. + +%prep +%autosetup -n phate-1.0.10 + +%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-phate -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.10-1 +- Package Spec generated |