automatic import of python-cy

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+%global _empty_manifest_terminate_build 0
+Name:		python-cy
+Version:	0.5.8
+Release:	1
+Summary:	Modelling CRISPR dropout data
+License:	BSD License
+URL:		https://github.com/EmanuelGoncalves/crispy
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/01/59/217fe9c5cab3da35afd6a0bfa3ad0ba982014554f26efcac7588ea823bc3/cy-0.5.8.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-scipy
+Requires:	python3-pandas
+Requires:	python3-xlrd
+Requires:	python3-openpyxl
+Requires:	python3-scikit-learn
+Requires:	python3-matplotlib
+Requires:	python3-seaborn
+Requires:	python3-natsort
+Requires:	python3-statsmodels
+Requires:	python3-pybedtools
+Requires:	python3-adjustText
+
+%description
+![Crispy logo](crispy/data/images/logo.png)
+
+[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) [![PyPI version](https://badge.fury.io/py/cy.svg)](https://badge.fury.io/py/cy) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2530755.svg)](https://doi.org/10.5281/zenodo.2530755)
+
+
+Module with utility functions to process CRISPR-based screens and method to correct gene independent copy-number effects.
+
+
+Description
+--
+Crispy uses [Sklearn](http://scikit-learn.org/stable/index.html) implementation of [Gaussian Process Regression](http://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html#sklearn.gaussian_process.GaussianProcessRegressor), fitting each sample independently.
+
+Install
+--
+
+Install [`pybedtools`](https://daler.github.io/pybedtools/main.html#quick-install-via-conda) and then install `Crispy`
+
+```
+conda install -c bioconda pybedtools
+
+pip install cy
+```
+
+Examples
+--
+Support to library imports:
+```python
+from crispy.CRISPRData import Library
+
+# Master Library, standardised assembly of KosukeYusa V1.1, Avana, Brunello and TKOv3 
+# CRISPR-Cas9 libraries.
+master_lib = Library.load_library("MasterLib_v1.csv.gz")
+
+
+# Genome-wide minimal CRISPR-Cas9 library. 
+minimal_lib = Library.load_library("MinLibCas9.csv.gz")
+
+# Some of the most broadly adopted CRISPR-Cas9 libraries:
+# 'Avana_v1.csv.gz', 'Brunello_v1.csv.gz', 'GeCKO_v2.csv.gz', 'Manjunath_Wu_v1.csv.gz', 
+# 'TKOv3.csv.gz', 'Yusa_v1.1.csv.gz'
+brunello_lib = Library.load_library("Brunello_v1.csv.gz")
+```
+
+Select sgRNAs (across multiple CRISPR-Cas9 libraries) for a given gene:
+```python
+from crispy.GuideSelection import GuideSelection
+
+# sgRNA selection class
+gselection = GuideSelection()
+
+# Select 5 optimal sgRNAs for MCL1 across multiple libraries 
+gene_guides = gselection.select_sgrnas(
+    "MCL1", n_guides=5, offtarget=[1, 0], jacks_thres=1, ruleset2_thres=.4
+)
+
+# Perform different rounds of sgRNA selection with increasingly relaxed efficiency thresholds 
+gene_guides = gselection.selection_rounds("TRIM49", n_guides=5, do_amber_round=True, do_red_round=True)
+```
+
+Copy-number correction:
+```python
+import crispy as cy
+import matplotlib.pyplot as plt
+from crispy.CRISPRData import ReadCounts, Library
+
+"""
+Import sample data
+"""
+rawcounts, copynumber = cy.Utils.get_example_data()
+
+"""
+Import CRISPR-Cas9 library
+
+Important:
+      Library has to have the following columns: "Chr", "Start", "End", "Approved_Symbol"
+      Library and segments have to have consistent "Chr" formating: "Chr1" or "chr1" or "1"
+      Gurantee that "Start" and "End" columns are int
+"""
+lib = Library.load_library("Yusa_v1.1.csv.gz")
+
+lib = lib.rename(
+    columns=dict(start="Start", end="End", chr="Chr", Gene="Approved_Symbol")
+).dropna(subset=["Chr", "Start", "End"])
+
+lib["Chr"] = "chr" + lib["Chr"]
+
+lib["Start"] = lib["Start"].astype(int)
+lib["End"] = lib["End"].astype(int)
+
+"""
+Calculate fold-change
+"""
+plasmids = ["ERS717283"]
+rawcounts = ReadCounts(rawcounts).remove_low_counts(plasmids)
+sgrna_fc = rawcounts.norm_rpm().foldchange(plasmids)
+
+"""
+Correct CRISPR-Cas9 sgRNA fold changes
+"""
+crispy = cy.Crispy(
+    sgrna_fc=sgrna_fc.mean(1), copy_number=copynumber, library=lib.loc[sgrna_fc.index]
+)
+
+# Fold-changes and correction integrated funciton.
+# Output is a modified/expanded BED formated data-frame with sgRNA and segments information
+#   n_sgrna: represents the minimum number of sgRNAs required per segment to consider in the fit.
+#            Recomended default values range between 4-10.
+bed_df = crispy.correct(n_sgrna=10)
+print(bed_df.head())
+
+# Gaussian Process Regression is stored
+crispy.gpr.plot(x_feature="ratio", y_feature="fold_change")
+plt.show()
+```
+![GPR](crispy/data/images/example_gp_fit.png)
+
+
+Credits and License
+--
+Developed at the [Wellcome Sanger Institue](https://www.sanger.ac.uk/) (2017-2020).
+
+For citation please refer to:
+
+[Gonçalves E, Behan FM, Louzada S, Arnol D, Stronach EA, Yang F, Yusa K, Stegle O, Iorio F, Garnett MJ (2019) Structural 
+rearrangements generate cell-specific, gene-independent CRISPR-Cas9 loss of fitness effects. Genome Biol 20: 27](https://doi.org/10.1186/s13059-019-1637-z)
+
+[Gonçalves E, Thomas M, Behan FM, Picco G, Pacini C, Allen F, Parry-Smith D, Iorio F, Parts L, Yusa K, Garnett MJ (2019) 
+Minimal genome-wide human CRISPR-Cas9 library. bioRxiv](https://www.biorxiv.org/content/10.1101/848895v1)
+
+
+
+
+%package -n python3-cy
+Summary:	Modelling CRISPR dropout data
+Provides:	python-cy
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-cy
+![Crispy logo](crispy/data/images/logo.png)
+
+[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) [![PyPI version](https://badge.fury.io/py/cy.svg)](https://badge.fury.io/py/cy) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2530755.svg)](https://doi.org/10.5281/zenodo.2530755)
+
+
+Module with utility functions to process CRISPR-based screens and method to correct gene independent copy-number effects.
+
+
+Description
+--
+Crispy uses [Sklearn](http://scikit-learn.org/stable/index.html) implementation of [Gaussian Process Regression](http://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html#sklearn.gaussian_process.GaussianProcessRegressor), fitting each sample independently.
+
+Install
+--
+
+Install [`pybedtools`](https://daler.github.io/pybedtools/main.html#quick-install-via-conda) and then install `Crispy`
+
+```
+conda install -c bioconda pybedtools
+
+pip install cy
+```
+
+Examples
+--
+Support to library imports:
+```python
+from crispy.CRISPRData import Library
+
+# Master Library, standardised assembly of KosukeYusa V1.1, Avana, Brunello and TKOv3 
+# CRISPR-Cas9 libraries.
+master_lib = Library.load_library("MasterLib_v1.csv.gz")
+
+
+# Genome-wide minimal CRISPR-Cas9 library. 
+minimal_lib = Library.load_library("MinLibCas9.csv.gz")
+
+# Some of the most broadly adopted CRISPR-Cas9 libraries:
+# 'Avana_v1.csv.gz', 'Brunello_v1.csv.gz', 'GeCKO_v2.csv.gz', 'Manjunath_Wu_v1.csv.gz', 
+# 'TKOv3.csv.gz', 'Yusa_v1.1.csv.gz'
+brunello_lib = Library.load_library("Brunello_v1.csv.gz")
+```
+
+Select sgRNAs (across multiple CRISPR-Cas9 libraries) for a given gene:
+```python
+from crispy.GuideSelection import GuideSelection
+
+# sgRNA selection class
+gselection = GuideSelection()
+
+# Select 5 optimal sgRNAs for MCL1 across multiple libraries 
+gene_guides = gselection.select_sgrnas(
+    "MCL1", n_guides=5, offtarget=[1, 0], jacks_thres=1, ruleset2_thres=.4
+)
+
+# Perform different rounds of sgRNA selection with increasingly relaxed efficiency thresholds 
+gene_guides = gselection.selection_rounds("TRIM49", n_guides=5, do_amber_round=True, do_red_round=True)
+```
+
+Copy-number correction:
+```python
+import crispy as cy
+import matplotlib.pyplot as plt
+from crispy.CRISPRData import ReadCounts, Library
+
+"""
+Import sample data
+"""
+rawcounts, copynumber = cy.Utils.get_example_data()
+
+"""
+Import CRISPR-Cas9 library
+
+Important:
+      Library has to have the following columns: "Chr", "Start", "End", "Approved_Symbol"
+      Library and segments have to have consistent "Chr" formating: "Chr1" or "chr1" or "1"
+      Gurantee that "Start" and "End" columns are int
+"""
+lib = Library.load_library("Yusa_v1.1.csv.gz")
+
+lib = lib.rename(
+    columns=dict(start="Start", end="End", chr="Chr", Gene="Approved_Symbol")
+).dropna(subset=["Chr", "Start", "End"])
+
+lib["Chr"] = "chr" + lib["Chr"]
+
+lib["Start"] = lib["Start"].astype(int)
+lib["End"] = lib["End"].astype(int)
+
+"""
+Calculate fold-change
+"""
+plasmids = ["ERS717283"]
+rawcounts = ReadCounts(rawcounts).remove_low_counts(plasmids)
+sgrna_fc = rawcounts.norm_rpm().foldchange(plasmids)
+
+"""
+Correct CRISPR-Cas9 sgRNA fold changes
+"""
+crispy = cy.Crispy(
+    sgrna_fc=sgrna_fc.mean(1), copy_number=copynumber, library=lib.loc[sgrna_fc.index]
+)
+
+# Fold-changes and correction integrated funciton.
+# Output is a modified/expanded BED formated data-frame with sgRNA and segments information
+#   n_sgrna: represents the minimum number of sgRNAs required per segment to consider in the fit.
+#            Recomended default values range between 4-10.
+bed_df = crispy.correct(n_sgrna=10)
+print(bed_df.head())
+
+# Gaussian Process Regression is stored
+crispy.gpr.plot(x_feature="ratio", y_feature="fold_change")
+plt.show()
+```
+![GPR](crispy/data/images/example_gp_fit.png)
+
+
+Credits and License
+--
+Developed at the [Wellcome Sanger Institue](https://www.sanger.ac.uk/) (2017-2020).
+
+For citation please refer to:
+
+[Gonçalves E, Behan FM, Louzada S, Arnol D, Stronach EA, Yang F, Yusa K, Stegle O, Iorio F, Garnett MJ (2019) Structural 
+rearrangements generate cell-specific, gene-independent CRISPR-Cas9 loss of fitness effects. Genome Biol 20: 27](https://doi.org/10.1186/s13059-019-1637-z)
+
+[Gonçalves E, Thomas M, Behan FM, Picco G, Pacini C, Allen F, Parry-Smith D, Iorio F, Parts L, Yusa K, Garnett MJ (2019) 
+Minimal genome-wide human CRISPR-Cas9 library. bioRxiv](https://www.biorxiv.org/content/10.1101/848895v1)
+
+
+
+
+%package help
+Summary:	Development documents and examples for cy
+Provides:	python3-cy-doc
+%description help
+![Crispy logo](crispy/data/images/logo.png)
+
+[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) [![PyPI version](https://badge.fury.io/py/cy.svg)](https://badge.fury.io/py/cy) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2530755.svg)](https://doi.org/10.5281/zenodo.2530755)
+
+
+Module with utility functions to process CRISPR-based screens and method to correct gene independent copy-number effects.
+
+
+Description
+--
+Crispy uses [Sklearn](http://scikit-learn.org/stable/index.html) implementation of [Gaussian Process Regression](http://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html#sklearn.gaussian_process.GaussianProcessRegressor), fitting each sample independently.
+
+Install
+--
+
+Install [`pybedtools`](https://daler.github.io/pybedtools/main.html#quick-install-via-conda) and then install `Crispy`
+
+```
+conda install -c bioconda pybedtools
+
+pip install cy
+```
+
+Examples
+--
+Support to library imports:
+```python
+from crispy.CRISPRData import Library
+
+# Master Library, standardised assembly of KosukeYusa V1.1, Avana, Brunello and TKOv3 
+# CRISPR-Cas9 libraries.
+master_lib = Library.load_library("MasterLib_v1.csv.gz")
+
+
+# Genome-wide minimal CRISPR-Cas9 library. 
+minimal_lib = Library.load_library("MinLibCas9.csv.gz")
+
+# Some of the most broadly adopted CRISPR-Cas9 libraries:
+# 'Avana_v1.csv.gz', 'Brunello_v1.csv.gz', 'GeCKO_v2.csv.gz', 'Manjunath_Wu_v1.csv.gz', 
+# 'TKOv3.csv.gz', 'Yusa_v1.1.csv.gz'
+brunello_lib = Library.load_library("Brunello_v1.csv.gz")
+```
+
+Select sgRNAs (across multiple CRISPR-Cas9 libraries) for a given gene:
+```python
+from crispy.GuideSelection import GuideSelection
+
+# sgRNA selection class
+gselection = GuideSelection()
+
+# Select 5 optimal sgRNAs for MCL1 across multiple libraries 
+gene_guides = gselection.select_sgrnas(
+    "MCL1", n_guides=5, offtarget=[1, 0], jacks_thres=1, ruleset2_thres=.4
+)
+
+# Perform different rounds of sgRNA selection with increasingly relaxed efficiency thresholds 
+gene_guides = gselection.selection_rounds("TRIM49", n_guides=5, do_amber_round=True, do_red_round=True)
+```
+
+Copy-number correction:
+```python
+import crispy as cy
+import matplotlib.pyplot as plt
+from crispy.CRISPRData import ReadCounts, Library
+
+"""
+Import sample data
+"""
+rawcounts, copynumber = cy.Utils.get_example_data()
+
+"""
+Import CRISPR-Cas9 library
+
+Important:
+      Library has to have the following columns: "Chr", "Start", "End", "Approved_Symbol"
+      Library and segments have to have consistent "Chr" formating: "Chr1" or "chr1" or "1"
+      Gurantee that "Start" and "End" columns are int
+"""
+lib = Library.load_library("Yusa_v1.1.csv.gz")
+
+lib = lib.rename(
+    columns=dict(start="Start", end="End", chr="Chr", Gene="Approved_Symbol")
+).dropna(subset=["Chr", "Start", "End"])
+
+lib["Chr"] = "chr" + lib["Chr"]
+
+lib["Start"] = lib["Start"].astype(int)
+lib["End"] = lib["End"].astype(int)
+
+"""
+Calculate fold-change
+"""
+plasmids = ["ERS717283"]
+rawcounts = ReadCounts(rawcounts).remove_low_counts(plasmids)
+sgrna_fc = rawcounts.norm_rpm().foldchange(plasmids)
+
+"""
+Correct CRISPR-Cas9 sgRNA fold changes
+"""
+crispy = cy.Crispy(
+    sgrna_fc=sgrna_fc.mean(1), copy_number=copynumber, library=lib.loc[sgrna_fc.index]
+)
+
+# Fold-changes and correction integrated funciton.
+# Output is a modified/expanded BED formated data-frame with sgRNA and segments information
+#   n_sgrna: represents the minimum number of sgRNAs required per segment to consider in the fit.
+#            Recomended default values range between 4-10.
+bed_df = crispy.correct(n_sgrna=10)
+print(bed_df.head())
+
+# Gaussian Process Regression is stored
+crispy.gpr.plot(x_feature="ratio", y_feature="fold_change")
+plt.show()
+```
+![GPR](crispy/data/images/example_gp_fit.png)
+
+
+Credits and License
+--
+Developed at the [Wellcome Sanger Institue](https://www.sanger.ac.uk/) (2017-2020).
+
+For citation please refer to:
+
+[Gonçalves E, Behan FM, Louzada S, Arnol D, Stronach EA, Yang F, Yusa K, Stegle O, Iorio F, Garnett MJ (2019) Structural 
+rearrangements generate cell-specific, gene-independent CRISPR-Cas9 loss of fitness effects. Genome Biol 20: 27](https://doi.org/10.1186/s13059-019-1637-z)
+
+[Gonçalves E, Thomas M, Behan FM, Picco G, Pacini C, Allen F, Parry-Smith D, Iorio F, Parts L, Yusa K, Garnett MJ (2019) 
+Minimal genome-wide human CRISPR-Cas9 library. bioRxiv](https://www.biorxiv.org/content/10.1101/848895v1)
+
+
+
+
+%prep
+%autosetup -n cy-0.5.8
+
+%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-cy -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5.8-1
+- Package Spec generated