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|
%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
|
