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diff --git a/python-bigmcl.spec b/python-bigmcl.spec new file mode 100644 index 0000000..77575f9 --- /dev/null +++ b/python-bigmcl.spec @@ -0,0 +1,264 @@ +%global _empty_manifest_terminate_build 0 +Name: python-bigmcl +Version: 0.2b2 +Release: 1 +Summary: Large scale Markov clustering (MCL) via subgraph extraction +License: BSD License +URL: https://gitlab.com/xonq/bigmcl +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/74/5b/17291f8a5b15f9d0b4b531174adee9a2d379a8ec7d23e8ec4f35327c8f1c/bigmcl-0.2b2.tar.gz +BuildArch: noarch + + +%description +# bigmcl +## Large scale Markov clustering (MCL) via subgraph extraction + +`bigmcl` will isolate disconnected subgraphs from a large graph file and execute +MCL on the subgraphs. bigmcl enables MCL on large, highly disconnected graphs, +such as those used in orthogroup inference. Not recommended for graphs that are +manageable with typical MCL. + +Important to note that the inflation parameter is affected by this approach - +I have noted clusters are more granular if anything. In the future, I plan on +implementing a systematic approach option for identifying ideal inflations for +each subgraph. + +Please cite this git repository and MCL when this software contributes to your analysis. + + +## DISCLAIMER +`bigmcl` is currently in a beta state, and while I appreciate bringing issues to +my attention, I am currently focused on getting things working well for my own +research, so I cannot guarantee timely issue resolution. My hope is `bigmcl` will +be in a longterm stable state by publication 2022. + + +<br /> + +## INSTALL + +``` +pip install bigmcl +``` + +Clone `mcl` [from here](https://github.com/micans/mcl), compile, and add to your path. + +<br /> + +## USE + +Input and go: +``` +bigmcl -i <GRAPH.imx> -I 1.5 +``` + +More elaborate options: +``` +usage: bigmcl.py [-h] -i INPUT -I INFLATION [-s] [-r ROW_FILE] [-m] [-o OUTPUT] [-c CORES] + [-v] + +Isolates disconnected graphs and runs MCL on the subgraphs. Input data must be numerical. + +optional arguments: + -h, --help show this help message and exit + -i INPUT, --input INPUT + MCL graph file in imx format + -I INFLATION, --inflation INFLATION + -s, --symmetric Matrix is symmetric (throughput increase) + -r ROW_FILE, --row_file ROW_FILE + Continue from finished row.txt + -m, --mcl_format Output clusters in MCL format + -o OUTPUT, --output OUTPUT + Alternative output directory + -c CORES, --cores CORES + -v, --verbose +``` + + + +%package -n python3-bigmcl +Summary: Large scale Markov clustering (MCL) via subgraph extraction +Provides: python-bigmcl +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-bigmcl +# bigmcl +## Large scale Markov clustering (MCL) via subgraph extraction + +`bigmcl` will isolate disconnected subgraphs from a large graph file and execute +MCL on the subgraphs. bigmcl enables MCL on large, highly disconnected graphs, +such as those used in orthogroup inference. Not recommended for graphs that are +manageable with typical MCL. + +Important to note that the inflation parameter is affected by this approach - +I have noted clusters are more granular if anything. In the future, I plan on +implementing a systematic approach option for identifying ideal inflations for +each subgraph. + +Please cite this git repository and MCL when this software contributes to your analysis. + + +## DISCLAIMER +`bigmcl` is currently in a beta state, and while I appreciate bringing issues to +my attention, I am currently focused on getting things working well for my own +research, so I cannot guarantee timely issue resolution. My hope is `bigmcl` will +be in a longterm stable state by publication 2022. + + +<br /> + +## INSTALL + +``` +pip install bigmcl +``` + +Clone `mcl` [from here](https://github.com/micans/mcl), compile, and add to your path. + +<br /> + +## USE + +Input and go: +``` +bigmcl -i <GRAPH.imx> -I 1.5 +``` + +More elaborate options: +``` +usage: bigmcl.py [-h] -i INPUT -I INFLATION [-s] [-r ROW_FILE] [-m] [-o OUTPUT] [-c CORES] + [-v] + +Isolates disconnected graphs and runs MCL on the subgraphs. Input data must be numerical. + +optional arguments: + -h, --help show this help message and exit + -i INPUT, --input INPUT + MCL graph file in imx format + -I INFLATION, --inflation INFLATION + -s, --symmetric Matrix is symmetric (throughput increase) + -r ROW_FILE, --row_file ROW_FILE + Continue from finished row.txt + -m, --mcl_format Output clusters in MCL format + -o OUTPUT, --output OUTPUT + Alternative output directory + -c CORES, --cores CORES + -v, --verbose +``` + + + +%package help +Summary: Development documents and examples for bigmcl +Provides: python3-bigmcl-doc +%description help +# bigmcl +## Large scale Markov clustering (MCL) via subgraph extraction + +`bigmcl` will isolate disconnected subgraphs from a large graph file and execute +MCL on the subgraphs. bigmcl enables MCL on large, highly disconnected graphs, +such as those used in orthogroup inference. Not recommended for graphs that are +manageable with typical MCL. + +Important to note that the inflation parameter is affected by this approach - +I have noted clusters are more granular if anything. In the future, I plan on +implementing a systematic approach option for identifying ideal inflations for +each subgraph. + +Please cite this git repository and MCL when this software contributes to your analysis. + + +## DISCLAIMER +`bigmcl` is currently in a beta state, and while I appreciate bringing issues to +my attention, I am currently focused on getting things working well for my own +research, so I cannot guarantee timely issue resolution. My hope is `bigmcl` will +be in a longterm stable state by publication 2022. + + +<br /> + +## INSTALL + +``` +pip install bigmcl +``` + +Clone `mcl` [from here](https://github.com/micans/mcl), compile, and add to your path. + +<br /> + +## USE + +Input and go: +``` +bigmcl -i <GRAPH.imx> -I 1.5 +``` + +More elaborate options: +``` +usage: bigmcl.py [-h] -i INPUT -I INFLATION [-s] [-r ROW_FILE] [-m] [-o OUTPUT] [-c CORES] + [-v] + +Isolates disconnected graphs and runs MCL on the subgraphs. Input data must be numerical. + +optional arguments: + -h, --help show this help message and exit + -i INPUT, --input INPUT + MCL graph file in imx format + -I INFLATION, --inflation INFLATION + -s, --symmetric Matrix is symmetric (throughput increase) + -r ROW_FILE, --row_file ROW_FILE + Continue from finished row.txt + -m, --mcl_format Output clusters in MCL format + -o OUTPUT, --output OUTPUT + Alternative output directory + -c CORES, --cores CORES + -v, --verbose +``` + + + +%prep +%autosetup -n bigmcl-0.2b2 + +%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-bigmcl -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2b2-1 +- Package Spec generated |