%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.
## INSTALL ``` pip install bigmcl ``` Clone `mcl` [from here](https://github.com/micans/mcl), compile, and add to your path.
## USE Input and go: ``` bigmcl -i -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.
## INSTALL ``` pip install bigmcl ``` Clone `mcl` [from here](https://github.com/micans/mcl), compile, and add to your path.
## USE Input and go: ``` bigmcl -i -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.
## INSTALL ``` pip install bigmcl ``` Clone `mcl` [from here](https://github.com/micans/mcl), compile, and add to your path.
## USE Input and go: ``` bigmcl -i -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 - 0.2b2-1 - Package Spec generated