From 1e98e3441265377acb63b9cbcd708f065dc5e694 Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Tue, 20 Jun 2023 08:53:58 +0000 Subject: automatic import of python-midr --- python-midr.spec | 591 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 591 insertions(+) create mode 100644 python-midr.spec (limited to 'python-midr.spec') diff --git a/python-midr.spec b/python-midr.spec new file mode 100644 index 0000000..0f480a3 --- /dev/null +++ b/python-midr.spec @@ -0,0 +1,591 @@ +%global _empty_manifest_terminate_build 0 +Name: python-midr +Version: 1.5.1 +Release: 1 +Summary: Compute idr from m NarrowPeak files and a merged NarrowPeak +License: CEA CNRS Inria Logiciel Libre License, version 2.1 (CeCILL-2.1) +URL: https://gitbio.ens-lyon.fr/LBMC/sbdm/midr +Source0: https://mirrors.aliyun.com/pypi/web/packages/0e/65/efcda47b6786746ebd703041c8a54c8898db20b6e16c0265b5592772a299/midr-1.5.1.tar.gz +BuildArch: noarch + + +%description +# IDR + +This tool is designed to compute the Irreproducible Discovery Rate (IDR) +from NarrowPeaks files for two or more replicates. +It’s an implementation of the method described in the following paper using +Gaussian copula. + +> LI, Qunhua, BROWN, James B., HUANG, Haiyan, et al. Measuring reproducibility +> of high-throughput experiments. The annals of applied statistics, 2011, +> vol. 5, no 3, p. 1752-1779. doi:10.1214/11-AOAS466 + +The default method for the IDR computation is the one developped in the +following paper using Archimedean copula. + +> Measuring Reproducibility of High-Throughput Deep-Sequencing Experiments +> Based on Self-adaptive Mixture Copula. Part of the Lecture Notes in Computer +> Science book series (LNCS, volume 7818) PAKDD 2013: Advances in Knowledge +> Discovery and Data Mining p 301-313, isbn: 978-3-642-37453-1 + +All the estimators for multivariate Archimedean copula are implemented from the +two following papers: + +> Likelihood inference for Archimedean copulas in high dimensions under known +> margins, Journal of Multivariate Analysis, 2012, p133-150, +> doi:10.1016/j.jmva.2012.02.019 + +> Archimedean Copulas in High Dimensions: Estimators and Numerical Challenges +> Motivated by Financial Applications | Journal de la Société Française de +> Statistique, Vol. 154 No. 1 (2013), ISSN: 2102-6238 + +## Getting Started + +These instructions will get you a copy of the project up and running on your +local machine for development and testing purposes. + +### Prerequisites + +To run **midr** on your computer you need to have python (>= 3) installed. + +```sh +python3 --version +``` + +### Installing + +To easily install **midr** on your computer using `pip` run the following command: + +``` +pip3 install midr +``` + +Otherwise you can clone this repository: + +``` +git clone git@gitbio.ens-lyon.fr:/LBMC/sbdm/midr.git +cd midr/src/ +python3 setup.py install +``` + +Given a list of peak calls in NarrowPeaks format and the corresponding peak +call for the merged replicate. This tool computes and appends a IDR column to +NarrowPeaks files. + +### Dependencies + +The **idr** package depends on the following python3 library: + +- [scipy>=1.3](https://scipy.org) [DOI:10.1109/MCSE.2007.58](https://doi.org/10.1109/MCSE.2007.58) [DOI:10.1109/MCSE.2011.36](https://doi.org/10.1109/MCSE.2011.36) + +> Travis E. Oliphant. Python for Scientific Computing, Computing in Science & +> Engineering, 9, 10-20 (2007), DOI:10.1109/MCSE.2007.58 + +> K. Jarrod Millman and Michael Aivazis. Python for Scientists and Engineers, +> Computing in Science & Engineering, 13, 9-12 (2011), +> DOI:10.1109/MCSE.2011.36 + + +- [numpy>=1.16](https://numpy.org/) [DOI:10.1109/MCSE.2011.37](https://doi.org/10.1109/MCSE.2010.118) + +> Travis E, Oliphant. A guide to NumPy, USA: Trelgol Publishing, (2006). + +> Stéfan van der Walt, S. Chris Colbert and Gaël Varoquaux. The NumPy Array: +> A Structure for Efficient Numerical Computation, Computing in Science & +> Engineering, 13, 22-30 (2011), DOI:10.1109/MCSE.2011.37 + +- [matplotlib>=3.1](https://github.com/matplotlib/matplotlib/tree/v3.1.1) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3264781.svg)](https://doi.org/10.5281/zenodo.3264781) + +> J. D. Hunter, "Matplotlib: A 2D Graphics Environment", +> Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007. + +- [pandas>=0.25.0](https://pandas.pydata.org) +> McKinney. Data Structures for Statistical Computing in Python, Proceedings +> of the 9th Python in Science Conference, 51-56 (2010) [(publisher link)](http://conference.scipy.org/proceedings/scipy2010/mckinney.html) + +- [pynverse>=0.1](https://pypi.org/project/pynverse/) + +- [mpmath>=1.1.0](http://mpmath.org/) +- [cython>=0.28.0](https://cython.org/) + +## Usage + +**idr** Takes as input file in the [NarrowPeaks format](https://genome.ucsc.edu/FAQ/FAQformat.html#format12), +and output NarrowPeaks files with an additional *idr* column. + +Computing *IDR* between three replicates + +``` +$ midr -m merged_peak_calling.NarrowPeaks \ + -f replicate1_.NarrowPeaks replicate2.NarrowPeaks replicate3.NarrowPeaks \ + -o results +``` + +Where `replicate1_.NarrowPeaks` is the output of the peak caller on the +alignment file corresponding to the first replicate and +`merged_peak_calling.NarrowPeaks` is the output of the peak caller on the merge +of the replicates alignment files. +`Results` are the directory where we want to output our results. + +Displaying help: + +``` +$ midr -h +usage: midr [-h] [--merged FILE] [--files FILES [FILES ...]] [--output DIR] [--score SCORE_COLUMN] [--threshold THRESHOLD] [--merge_function MERGE_FUNCTION] [--size SIZE_MERGE] [--nodrop] [--method METHOD] + [--cpu CPU] [--debug] [--verbose] [--matrix FILE] + +Compute the Irreproducible Discovery Rate (IDR) from NarrowPeaks files + +Implementation of the IDR methods for two or more replicates. + +LI, Qunhua, BROWN, James B., HUANG, Haiyan, et al. Measuring reproducibility +of high-throughput experiments. The annals of applied statistics, 2011, +vol. 5, no 3, p. 1752-1779. + +Given a list of peak calls in NarrowPeaks format and the corresponding peak +call for the merged replicate. This tool computes and appends a IDR column to +NarrowPeaks files. + +optional arguments: + -h, --help show this help message and exit + +IDR settings: + --merged FILE, -m FILE + file of the merged NarrowPeaks + --files FILES [FILES ...], -f FILES [FILES ...] + list of NarrowPeaks files + --output DIR, -o DIR output directory (default: results) + --score SCORE_COLUMN, -s SCORE_COLUMN + NarrowPeaks score column to compute the IDR on, one of 'score', 'signalValue', 'pValue' or 'qValue' (default: signalValue) + --threshold THRESHOLD, -t THRESHOLD + Threshold value for the precision of the estimators (default: 0.0001) + --merge_function MERGE_FUNCTION, -mf MERGE_FUNCTION + function to determine the score to keep for overlapping peak within a replica ('sum', 'max', 'mean', 'median', 'min') (default: max) + --size SIZE_MERGE, -ws SIZE_MERGE + distance (bp) to add before and after each peak before merging finding match between --merged file and --files files (default: 100) + --nodrop, -nd don't drop peak unmatched in any bed. The score of the absent peak is set to 0.0 (default: True) + --method METHOD, -mt METHOD + copula model to use('archimedean' or 'gaussian' (default: archimedean) + --cpu CPU, -cpu CPU number of thread to use for merging the beds files (default: 1) + --debug, -d enable debugging (default: False) + --verbose, -v log to console (default: False) + --matrix FILE matrix file of the peaks score in raw (tsv format), replace the --merge and --files options if used +``` + + +## Authors + +* **Laurent Modolo** - *Initial work* + +## License + +This project is licensed under the CeCiLL License- see the [LICENSE](LICENSE) file for details. + + + + +%package -n python3-midr +Summary: Compute idr from m NarrowPeak files and a merged NarrowPeak +Provides: python-midr +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-midr +# IDR + +This tool is designed to compute the Irreproducible Discovery Rate (IDR) +from NarrowPeaks files for two or more replicates. +It’s an implementation of the method described in the following paper using +Gaussian copula. + +> LI, Qunhua, BROWN, James B., HUANG, Haiyan, et al. Measuring reproducibility +> of high-throughput experiments. The annals of applied statistics, 2011, +> vol. 5, no 3, p. 1752-1779. doi:10.1214/11-AOAS466 + +The default method for the IDR computation is the one developped in the +following paper using Archimedean copula. + +> Measuring Reproducibility of High-Throughput Deep-Sequencing Experiments +> Based on Self-adaptive Mixture Copula. Part of the Lecture Notes in Computer +> Science book series (LNCS, volume 7818) PAKDD 2013: Advances in Knowledge +> Discovery and Data Mining p 301-313, isbn: 978-3-642-37453-1 + +All the estimators for multivariate Archimedean copula are implemented from the +two following papers: + +> Likelihood inference for Archimedean copulas in high dimensions under known +> margins, Journal of Multivariate Analysis, 2012, p133-150, +> doi:10.1016/j.jmva.2012.02.019 + +> Archimedean Copulas in High Dimensions: Estimators and Numerical Challenges +> Motivated by Financial Applications | Journal de la Société Française de +> Statistique, Vol. 154 No. 1 (2013), ISSN: 2102-6238 + +## Getting Started + +These instructions will get you a copy of the project up and running on your +local machine for development and testing purposes. + +### Prerequisites + +To run **midr** on your computer you need to have python (>= 3) installed. + +```sh +python3 --version +``` + +### Installing + +To easily install **midr** on your computer using `pip` run the following command: + +``` +pip3 install midr +``` + +Otherwise you can clone this repository: + +``` +git clone git@gitbio.ens-lyon.fr:/LBMC/sbdm/midr.git +cd midr/src/ +python3 setup.py install +``` + +Given a list of peak calls in NarrowPeaks format and the corresponding peak +call for the merged replicate. This tool computes and appends a IDR column to +NarrowPeaks files. + +### Dependencies + +The **idr** package depends on the following python3 library: + +- [scipy>=1.3](https://scipy.org) [DOI:10.1109/MCSE.2007.58](https://doi.org/10.1109/MCSE.2007.58) [DOI:10.1109/MCSE.2011.36](https://doi.org/10.1109/MCSE.2011.36) + +> Travis E. Oliphant. Python for Scientific Computing, Computing in Science & +> Engineering, 9, 10-20 (2007), DOI:10.1109/MCSE.2007.58 + +> K. Jarrod Millman and Michael Aivazis. Python for Scientists and Engineers, +> Computing in Science & Engineering, 13, 9-12 (2011), +> DOI:10.1109/MCSE.2011.36 + + +- [numpy>=1.16](https://numpy.org/) [DOI:10.1109/MCSE.2011.37](https://doi.org/10.1109/MCSE.2010.118) + +> Travis E, Oliphant. A guide to NumPy, USA: Trelgol Publishing, (2006). + +> Stéfan van der Walt, S. Chris Colbert and Gaël Varoquaux. The NumPy Array: +> A Structure for Efficient Numerical Computation, Computing in Science & +> Engineering, 13, 22-30 (2011), DOI:10.1109/MCSE.2011.37 + +- [matplotlib>=3.1](https://github.com/matplotlib/matplotlib/tree/v3.1.1) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3264781.svg)](https://doi.org/10.5281/zenodo.3264781) + +> J. D. Hunter, "Matplotlib: A 2D Graphics Environment", +> Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007. + +- [pandas>=0.25.0](https://pandas.pydata.org) +> McKinney. Data Structures for Statistical Computing in Python, Proceedings +> of the 9th Python in Science Conference, 51-56 (2010) [(publisher link)](http://conference.scipy.org/proceedings/scipy2010/mckinney.html) + +- [pynverse>=0.1](https://pypi.org/project/pynverse/) + +- [mpmath>=1.1.0](http://mpmath.org/) +- [cython>=0.28.0](https://cython.org/) + +## Usage + +**idr** Takes as input file in the [NarrowPeaks format](https://genome.ucsc.edu/FAQ/FAQformat.html#format12), +and output NarrowPeaks files with an additional *idr* column. + +Computing *IDR* between three replicates + +``` +$ midr -m merged_peak_calling.NarrowPeaks \ + -f replicate1_.NarrowPeaks replicate2.NarrowPeaks replicate3.NarrowPeaks \ + -o results +``` + +Where `replicate1_.NarrowPeaks` is the output of the peak caller on the +alignment file corresponding to the first replicate and +`merged_peak_calling.NarrowPeaks` is the output of the peak caller on the merge +of the replicates alignment files. +`Results` are the directory where we want to output our results. + +Displaying help: + +``` +$ midr -h +usage: midr [-h] [--merged FILE] [--files FILES [FILES ...]] [--output DIR] [--score SCORE_COLUMN] [--threshold THRESHOLD] [--merge_function MERGE_FUNCTION] [--size SIZE_MERGE] [--nodrop] [--method METHOD] + [--cpu CPU] [--debug] [--verbose] [--matrix FILE] + +Compute the Irreproducible Discovery Rate (IDR) from NarrowPeaks files + +Implementation of the IDR methods for two or more replicates. + +LI, Qunhua, BROWN, James B., HUANG, Haiyan, et al. Measuring reproducibility +of high-throughput experiments. The annals of applied statistics, 2011, +vol. 5, no 3, p. 1752-1779. + +Given a list of peak calls in NarrowPeaks format and the corresponding peak +call for the merged replicate. This tool computes and appends a IDR column to +NarrowPeaks files. + +optional arguments: + -h, --help show this help message and exit + +IDR settings: + --merged FILE, -m FILE + file of the merged NarrowPeaks + --files FILES [FILES ...], -f FILES [FILES ...] + list of NarrowPeaks files + --output DIR, -o DIR output directory (default: results) + --score SCORE_COLUMN, -s SCORE_COLUMN + NarrowPeaks score column to compute the IDR on, one of 'score', 'signalValue', 'pValue' or 'qValue' (default: signalValue) + --threshold THRESHOLD, -t THRESHOLD + Threshold value for the precision of the estimators (default: 0.0001) + --merge_function MERGE_FUNCTION, -mf MERGE_FUNCTION + function to determine the score to keep for overlapping peak within a replica ('sum', 'max', 'mean', 'median', 'min') (default: max) + --size SIZE_MERGE, -ws SIZE_MERGE + distance (bp) to add before and after each peak before merging finding match between --merged file and --files files (default: 100) + --nodrop, -nd don't drop peak unmatched in any bed. The score of the absent peak is set to 0.0 (default: True) + --method METHOD, -mt METHOD + copula model to use('archimedean' or 'gaussian' (default: archimedean) + --cpu CPU, -cpu CPU number of thread to use for merging the beds files (default: 1) + --debug, -d enable debugging (default: False) + --verbose, -v log to console (default: False) + --matrix FILE matrix file of the peaks score in raw (tsv format), replace the --merge and --files options if used +``` + + +## Authors + +* **Laurent Modolo** - *Initial work* + +## License + +This project is licensed under the CeCiLL License- see the [LICENSE](LICENSE) file for details. + + + + +%package help +Summary: Development documents and examples for midr +Provides: python3-midr-doc +%description help +# IDR + +This tool is designed to compute the Irreproducible Discovery Rate (IDR) +from NarrowPeaks files for two or more replicates. +It’s an implementation of the method described in the following paper using +Gaussian copula. + +> LI, Qunhua, BROWN, James B., HUANG, Haiyan, et al. Measuring reproducibility +> of high-throughput experiments. The annals of applied statistics, 2011, +> vol. 5, no 3, p. 1752-1779. doi:10.1214/11-AOAS466 + +The default method for the IDR computation is the one developped in the +following paper using Archimedean copula. + +> Measuring Reproducibility of High-Throughput Deep-Sequencing Experiments +> Based on Self-adaptive Mixture Copula. Part of the Lecture Notes in Computer +> Science book series (LNCS, volume 7818) PAKDD 2013: Advances in Knowledge +> Discovery and Data Mining p 301-313, isbn: 978-3-642-37453-1 + +All the estimators for multivariate Archimedean copula are implemented from the +two following papers: + +> Likelihood inference for Archimedean copulas in high dimensions under known +> margins, Journal of Multivariate Analysis, 2012, p133-150, +> doi:10.1016/j.jmva.2012.02.019 + +> Archimedean Copulas in High Dimensions: Estimators and Numerical Challenges +> Motivated by Financial Applications | Journal de la Société Française de +> Statistique, Vol. 154 No. 1 (2013), ISSN: 2102-6238 + +## Getting Started + +These instructions will get you a copy of the project up and running on your +local machine for development and testing purposes. + +### Prerequisites + +To run **midr** on your computer you need to have python (>= 3) installed. + +```sh +python3 --version +``` + +### Installing + +To easily install **midr** on your computer using `pip` run the following command: + +``` +pip3 install midr +``` + +Otherwise you can clone this repository: + +``` +git clone git@gitbio.ens-lyon.fr:/LBMC/sbdm/midr.git +cd midr/src/ +python3 setup.py install +``` + +Given a list of peak calls in NarrowPeaks format and the corresponding peak +call for the merged replicate. This tool computes and appends a IDR column to +NarrowPeaks files. + +### Dependencies + +The **idr** package depends on the following python3 library: + +- [scipy>=1.3](https://scipy.org) [DOI:10.1109/MCSE.2007.58](https://doi.org/10.1109/MCSE.2007.58) [DOI:10.1109/MCSE.2011.36](https://doi.org/10.1109/MCSE.2011.36) + +> Travis E. Oliphant. Python for Scientific Computing, Computing in Science & +> Engineering, 9, 10-20 (2007), DOI:10.1109/MCSE.2007.58 + +> K. Jarrod Millman and Michael Aivazis. Python for Scientists and Engineers, +> Computing in Science & Engineering, 13, 9-12 (2011), +> DOI:10.1109/MCSE.2011.36 + + +- [numpy>=1.16](https://numpy.org/) [DOI:10.1109/MCSE.2011.37](https://doi.org/10.1109/MCSE.2010.118) + +> Travis E, Oliphant. A guide to NumPy, USA: Trelgol Publishing, (2006). + +> Stéfan van der Walt, S. Chris Colbert and Gaël Varoquaux. The NumPy Array: +> A Structure for Efficient Numerical Computation, Computing in Science & +> Engineering, 13, 22-30 (2011), DOI:10.1109/MCSE.2011.37 + +- [matplotlib>=3.1](https://github.com/matplotlib/matplotlib/tree/v3.1.1) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3264781.svg)](https://doi.org/10.5281/zenodo.3264781) + +> J. D. Hunter, "Matplotlib: A 2D Graphics Environment", +> Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007. + +- [pandas>=0.25.0](https://pandas.pydata.org) +> McKinney. Data Structures for Statistical Computing in Python, Proceedings +> of the 9th Python in Science Conference, 51-56 (2010) [(publisher link)](http://conference.scipy.org/proceedings/scipy2010/mckinney.html) + +- [pynverse>=0.1](https://pypi.org/project/pynverse/) + +- [mpmath>=1.1.0](http://mpmath.org/) +- [cython>=0.28.0](https://cython.org/) + +## Usage + +**idr** Takes as input file in the [NarrowPeaks format](https://genome.ucsc.edu/FAQ/FAQformat.html#format12), +and output NarrowPeaks files with an additional *idr* column. + +Computing *IDR* between three replicates + +``` +$ midr -m merged_peak_calling.NarrowPeaks \ + -f replicate1_.NarrowPeaks replicate2.NarrowPeaks replicate3.NarrowPeaks \ + -o results +``` + +Where `replicate1_.NarrowPeaks` is the output of the peak caller on the +alignment file corresponding to the first replicate and +`merged_peak_calling.NarrowPeaks` is the output of the peak caller on the merge +of the replicates alignment files. +`Results` are the directory where we want to output our results. + +Displaying help: + +``` +$ midr -h +usage: midr [-h] [--merged FILE] [--files FILES [FILES ...]] [--output DIR] [--score SCORE_COLUMN] [--threshold THRESHOLD] [--merge_function MERGE_FUNCTION] [--size SIZE_MERGE] [--nodrop] [--method METHOD] + [--cpu CPU] [--debug] [--verbose] [--matrix FILE] + +Compute the Irreproducible Discovery Rate (IDR) from NarrowPeaks files + +Implementation of the IDR methods for two or more replicates. + +LI, Qunhua, BROWN, James B., HUANG, Haiyan, et al. Measuring reproducibility +of high-throughput experiments. The annals of applied statistics, 2011, +vol. 5, no 3, p. 1752-1779. + +Given a list of peak calls in NarrowPeaks format and the corresponding peak +call for the merged replicate. This tool computes and appends a IDR column to +NarrowPeaks files. + +optional arguments: + -h, --help show this help message and exit + +IDR settings: + --merged FILE, -m FILE + file of the merged NarrowPeaks + --files FILES [FILES ...], -f FILES [FILES ...] + list of NarrowPeaks files + --output DIR, -o DIR output directory (default: results) + --score SCORE_COLUMN, -s SCORE_COLUMN + NarrowPeaks score column to compute the IDR on, one of 'score', 'signalValue', 'pValue' or 'qValue' (default: signalValue) + --threshold THRESHOLD, -t THRESHOLD + Threshold value for the precision of the estimators (default: 0.0001) + --merge_function MERGE_FUNCTION, -mf MERGE_FUNCTION + function to determine the score to keep for overlapping peak within a replica ('sum', 'max', 'mean', 'median', 'min') (default: max) + --size SIZE_MERGE, -ws SIZE_MERGE + distance (bp) to add before and after each peak before merging finding match between --merged file and --files files (default: 100) + --nodrop, -nd don't drop peak unmatched in any bed. The score of the absent peak is set to 0.0 (default: True) + --method METHOD, -mt METHOD + copula model to use('archimedean' or 'gaussian' (default: archimedean) + --cpu CPU, -cpu CPU number of thread to use for merging the beds files (default: 1) + --debug, -d enable debugging (default: False) + --verbose, -v log to console (default: False) + --matrix FILE matrix file of the peaks score in raw (tsv format), replace the --merge and --files options if used +``` + + +## Authors + +* **Laurent Modolo** - *Initial work* + +## License + +This project is licensed under the CeCiLL License- see the [LICENSE](LICENSE) file for details. + + + + +%prep +%autosetup -n midr-1.5.1 + +%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-midr -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Jun 20 2023 Python_Bot - 1.5.1-1 +- Package Spec generated -- cgit v1.2.3