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+%global _empty_manifest_terminate_build 0
+Name:		python-cimcb-lite
+Version:	1.0.2
+Release:	1
+Summary:	A lite version of the cimcb package containing the necessary tools for the statistical analysis of untargeted and targeted metabolomics data.
+License:	MIT License
+URL:		https://github.com/cimcb/cimcb_lite
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/ff/ab/a02de043255d367e4e7cf18834d3df2aa8220204c910f25cc6a8d482770d/cimcb_lite-1.0.2.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-bokeh
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-scipy
+Requires:	python3-scikit-learn
+Requires:	python3-statsmodels
+Requires:	python3-tqdm
+Requires:	python3-xlrd
+
+%description
+<img src="cimcb_logo.png" alt="drawing" width="400"/>
+
+# cimcb lite 
+cimcb_lite is a lite version of the cimcb package containing a small number of basic tools for the statistical analysis of untargeted and targeted metabolomics data.
+
+## Installation
+
+### Dependencies
+cimcb_lite requires:
+- Python (>=3.5)
+- Bokeh (>=1.0.0)
+- NumPy
+- SciPy
+- scikit-learn
+- Statsmodels
+- tqdm
+
+### User installation
+The recommend way to install cimcb_lite and dependencies is to using ``conda``:
+```console
+conda install -c cimcb cimcb_lite
+```
+or ``pip``:
+```console
+pip install cimcb_lite
+```
+Alternatively, to install directly from github:
+```console
+pip install https://github.com/cimcb/cimcb_lite/archive/master.zip
+```
+
+### API
+For futher detail on the usage refer to the docstring.
+
+#### cimcb_lite.model
+- [PLS_SIMPLS](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/PLS_SIMPLS.py#L14-L36): Partial least-squares regression using the SIMPLS algorithm.
+  - [train](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/PLS_SIMPLS.py#L43-L58): Fit the PLS model, save additional stats (as attributes) and return Y predicted values.
+  - [test](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/PLS_SIMPLS.py#L105-L117): Calculate and return Y predicted value.
+  - [evaluate](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/BaseModel.py#L40-L56): Plots a figure containing a Violin plot, Distribution plot, ROC plot and Binary Metrics statistics.
+  - [calc_bootci](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/BaseModel.py#L191-L201): Calculates bootstrap confidence intervals based on bootlist.
+  - [plot_featureimportance](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/BaseModel.py#L211-L212): Plots feature importance metrics.
+  - [plot_permutation_test](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/BaseModel.py#L253-L254): Plots permutation test figures.
+
+#### cimcb_lite.plot
+- [boxplot](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/boxplot.py#L8-L18): Creates a boxplot using Bokeh.
+- [distribution](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/distribution.py#L6-L16): Creates a distribution plot using Bokeh.
+- [pca](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/pca.py#L10-L17): Creates a PCA scores and loadings plot using Bokeh.
+- [permutation_test](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/permutation_test.py#L13-L27): Creates permutation test plots using Bokeh.
+- [roc_plot](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/roc.py#L11-L24): Creates a rocplot using Bokeh.
+- [scatter](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/scatter.py#L6-L16): Creates a scatterplot using Bokeh.
+- [scatterCI](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/scatterCI.py#L7-L14): Creates a scatterCI plot using Bokeh.
+
+#### cimcb_lite.cross_val
+- [kfold](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/cross_val/kfold.py#L14-L42): Exhaustitive search over param_dict calculating binary metrics.
+
+#### cimcb_lite.bootstrap
+- [Perc](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/bootstrap/Perc.py#L6-L35): Returns bootstrap confidence intervals using the percentile boostrap interval.
+- [BC](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/bootstrap/BC.py#L8-L37): Returns bootstrap confidence intervals using the bias-corrected boostrap interval.
+- [BCA](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/bootstrap/BCA.py#L8-L36): Returns bootstrap confidence intervals using the bias-corrected and accelerated boostrap interval.
+
+#### cimcb_lite.utils
+- [binary_metrics](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/binary_metrics.py#L5-L23): Return a dict of binary stats with the following metrics: R2, auc, accuracy, precision, sensitivity, specificity, and F1 score.
+- [ci95_ellipse](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/ci95_ellipse.py#L6-L28): Construct a 95% confidence ellipse using PCA.
+- [knnimpute](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/knnimpute.py#L7-L22): kNN missing value imputation using Euclidean distance.
+- [load_dataXL](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/load_dataXL.py#L7-L29): Loads and validates the DataFile and PeakFile from an excel file.
+- [nested_getattr](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/nested_getattr.py#L4-L5): getattr for nested attributes.
+- [scale](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/scale.py#L4-L42): Scales x (which can include nans) with method: 'auto', 'pareto', 'vast', or 'level'.
+- [table_check](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/table_check.py#L4-L17): Error checking for DataTable and PeakTable (used in load_dataXL).
+- [univariate_2class](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/univariate_2class.py#L8-L35): Creates a table of univariate statistics (2 class).
+- [wmean](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/wmean.py#L4-L19): Returns Weighted Mean. Ignores NaNs and handles infinite weights.
+
+### License
+cimcb_lite is licensed under the MIT license. 
+
+### Authors
+- [Kevin Mendez](https://github.com/KevinMMendez/)
+- [David Broadhurst](https://scholar.google.ca/citations?user=M3_zZwUAAAAJ&hl=en)
+
+### Correspondence
+Professor David Broadhurst, Director of the Centre for Integrative Metabolomics & Computation Biology at Edith Cowan University.
+
+
+
+
+%package -n python3-cimcb-lite
+Summary:	A lite version of the cimcb package containing the necessary tools for the statistical analysis of untargeted and targeted metabolomics data.
+Provides:	python-cimcb-lite
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-cimcb-lite
+<img src="cimcb_logo.png" alt="drawing" width="400"/>
+
+# cimcb lite 
+cimcb_lite is a lite version of the cimcb package containing a small number of basic tools for the statistical analysis of untargeted and targeted metabolomics data.
+
+## Installation
+
+### Dependencies
+cimcb_lite requires:
+- Python (>=3.5)
+- Bokeh (>=1.0.0)
+- NumPy
+- SciPy
+- scikit-learn
+- Statsmodels
+- tqdm
+
+### User installation
+The recommend way to install cimcb_lite and dependencies is to using ``conda``:
+```console
+conda install -c cimcb cimcb_lite
+```
+or ``pip``:
+```console
+pip install cimcb_lite
+```
+Alternatively, to install directly from github:
+```console
+pip install https://github.com/cimcb/cimcb_lite/archive/master.zip
+```
+
+### API
+For futher detail on the usage refer to the docstring.
+
+#### cimcb_lite.model
+- [PLS_SIMPLS](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/PLS_SIMPLS.py#L14-L36): Partial least-squares regression using the SIMPLS algorithm.
+  - [train](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/PLS_SIMPLS.py#L43-L58): Fit the PLS model, save additional stats (as attributes) and return Y predicted values.
+  - [test](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/PLS_SIMPLS.py#L105-L117): Calculate and return Y predicted value.
+  - [evaluate](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/BaseModel.py#L40-L56): Plots a figure containing a Violin plot, Distribution plot, ROC plot and Binary Metrics statistics.
+  - [calc_bootci](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/BaseModel.py#L191-L201): Calculates bootstrap confidence intervals based on bootlist.
+  - [plot_featureimportance](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/BaseModel.py#L211-L212): Plots feature importance metrics.
+  - [plot_permutation_test](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/BaseModel.py#L253-L254): Plots permutation test figures.
+
+#### cimcb_lite.plot
+- [boxplot](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/boxplot.py#L8-L18): Creates a boxplot using Bokeh.
+- [distribution](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/distribution.py#L6-L16): Creates a distribution plot using Bokeh.
+- [pca](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/pca.py#L10-L17): Creates a PCA scores and loadings plot using Bokeh.
+- [permutation_test](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/permutation_test.py#L13-L27): Creates permutation test plots using Bokeh.
+- [roc_plot](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/roc.py#L11-L24): Creates a rocplot using Bokeh.
+- [scatter](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/scatter.py#L6-L16): Creates a scatterplot using Bokeh.
+- [scatterCI](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/scatterCI.py#L7-L14): Creates a scatterCI plot using Bokeh.
+
+#### cimcb_lite.cross_val
+- [kfold](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/cross_val/kfold.py#L14-L42): Exhaustitive search over param_dict calculating binary metrics.
+
+#### cimcb_lite.bootstrap
+- [Perc](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/bootstrap/Perc.py#L6-L35): Returns bootstrap confidence intervals using the percentile boostrap interval.
+- [BC](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/bootstrap/BC.py#L8-L37): Returns bootstrap confidence intervals using the bias-corrected boostrap interval.
+- [BCA](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/bootstrap/BCA.py#L8-L36): Returns bootstrap confidence intervals using the bias-corrected and accelerated boostrap interval.
+
+#### cimcb_lite.utils
+- [binary_metrics](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/binary_metrics.py#L5-L23): Return a dict of binary stats with the following metrics: R2, auc, accuracy, precision, sensitivity, specificity, and F1 score.
+- [ci95_ellipse](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/ci95_ellipse.py#L6-L28): Construct a 95% confidence ellipse using PCA.
+- [knnimpute](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/knnimpute.py#L7-L22): kNN missing value imputation using Euclidean distance.
+- [load_dataXL](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/load_dataXL.py#L7-L29): Loads and validates the DataFile and PeakFile from an excel file.
+- [nested_getattr](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/nested_getattr.py#L4-L5): getattr for nested attributes.
+- [scale](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/scale.py#L4-L42): Scales x (which can include nans) with method: 'auto', 'pareto', 'vast', or 'level'.
+- [table_check](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/table_check.py#L4-L17): Error checking for DataTable and PeakTable (used in load_dataXL).
+- [univariate_2class](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/univariate_2class.py#L8-L35): Creates a table of univariate statistics (2 class).
+- [wmean](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/wmean.py#L4-L19): Returns Weighted Mean. Ignores NaNs and handles infinite weights.
+
+### License
+cimcb_lite is licensed under the MIT license. 
+
+### Authors
+- [Kevin Mendez](https://github.com/KevinMMendez/)
+- [David Broadhurst](https://scholar.google.ca/citations?user=M3_zZwUAAAAJ&hl=en)
+
+### Correspondence
+Professor David Broadhurst, Director of the Centre for Integrative Metabolomics & Computation Biology at Edith Cowan University.
+
+
+
+
+%package help
+Summary:	Development documents and examples for cimcb-lite
+Provides:	python3-cimcb-lite-doc
+%description help
+<img src="cimcb_logo.png" alt="drawing" width="400"/>
+
+# cimcb lite 
+cimcb_lite is a lite version of the cimcb package containing a small number of basic tools for the statistical analysis of untargeted and targeted metabolomics data.
+
+## Installation
+
+### Dependencies
+cimcb_lite requires:
+- Python (>=3.5)
+- Bokeh (>=1.0.0)
+- NumPy
+- SciPy
+- scikit-learn
+- Statsmodels
+- tqdm
+
+### User installation
+The recommend way to install cimcb_lite and dependencies is to using ``conda``:
+```console
+conda install -c cimcb cimcb_lite
+```
+or ``pip``:
+```console
+pip install cimcb_lite
+```
+Alternatively, to install directly from github:
+```console
+pip install https://github.com/cimcb/cimcb_lite/archive/master.zip
+```
+
+### API
+For futher detail on the usage refer to the docstring.
+
+#### cimcb_lite.model
+- [PLS_SIMPLS](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/PLS_SIMPLS.py#L14-L36): Partial least-squares regression using the SIMPLS algorithm.
+  - [train](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/PLS_SIMPLS.py#L43-L58): Fit the PLS model, save additional stats (as attributes) and return Y predicted values.
+  - [test](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/PLS_SIMPLS.py#L105-L117): Calculate and return Y predicted value.
+  - [evaluate](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/BaseModel.py#L40-L56): Plots a figure containing a Violin plot, Distribution plot, ROC plot and Binary Metrics statistics.
+  - [calc_bootci](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/BaseModel.py#L191-L201): Calculates bootstrap confidence intervals based on bootlist.
+  - [plot_featureimportance](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/BaseModel.py#L211-L212): Plots feature importance metrics.
+  - [plot_permutation_test](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/model/BaseModel.py#L253-L254): Plots permutation test figures.
+
+#### cimcb_lite.plot
+- [boxplot](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/boxplot.py#L8-L18): Creates a boxplot using Bokeh.
+- [distribution](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/distribution.py#L6-L16): Creates a distribution plot using Bokeh.
+- [pca](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/pca.py#L10-L17): Creates a PCA scores and loadings plot using Bokeh.
+- [permutation_test](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/permutation_test.py#L13-L27): Creates permutation test plots using Bokeh.
+- [roc_plot](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/roc.py#L11-L24): Creates a rocplot using Bokeh.
+- [scatter](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/scatter.py#L6-L16): Creates a scatterplot using Bokeh.
+- [scatterCI](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/plot/scatterCI.py#L7-L14): Creates a scatterCI plot using Bokeh.
+
+#### cimcb_lite.cross_val
+- [kfold](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/cross_val/kfold.py#L14-L42): Exhaustitive search over param_dict calculating binary metrics.
+
+#### cimcb_lite.bootstrap
+- [Perc](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/bootstrap/Perc.py#L6-L35): Returns bootstrap confidence intervals using the percentile boostrap interval.
+- [BC](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/bootstrap/BC.py#L8-L37): Returns bootstrap confidence intervals using the bias-corrected boostrap interval.
+- [BCA](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/bootstrap/BCA.py#L8-L36): Returns bootstrap confidence intervals using the bias-corrected and accelerated boostrap interval.
+
+#### cimcb_lite.utils
+- [binary_metrics](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/binary_metrics.py#L5-L23): Return a dict of binary stats with the following metrics: R2, auc, accuracy, precision, sensitivity, specificity, and F1 score.
+- [ci95_ellipse](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/ci95_ellipse.py#L6-L28): Construct a 95% confidence ellipse using PCA.
+- [knnimpute](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/knnimpute.py#L7-L22): kNN missing value imputation using Euclidean distance.
+- [load_dataXL](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/load_dataXL.py#L7-L29): Loads and validates the DataFile and PeakFile from an excel file.
+- [nested_getattr](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/nested_getattr.py#L4-L5): getattr for nested attributes.
+- [scale](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/scale.py#L4-L42): Scales x (which can include nans) with method: 'auto', 'pareto', 'vast', or 'level'.
+- [table_check](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/table_check.py#L4-L17): Error checking for DataTable and PeakTable (used in load_dataXL).
+- [univariate_2class](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/univariate_2class.py#L8-L35): Creates a table of univariate statistics (2 class).
+- [wmean](https://github.com/KevinMMendez/cimcb_lite/blob/master/cimcb_lite/utils/wmean.py#L4-L19): Returns Weighted Mean. Ignores NaNs and handles infinite weights.
+
+### License
+cimcb_lite is licensed under the MIT license. 
+
+### Authors
+- [Kevin Mendez](https://github.com/KevinMMendez/)
+- [David Broadhurst](https://scholar.google.ca/citations?user=M3_zZwUAAAAJ&hl=en)
+
+### Correspondence
+Professor David Broadhurst, Director of the Centre for Integrative Metabolomics & Computation Biology at Edith Cowan University.
+
+
+
+
+%prep
+%autosetup -n cimcb-lite-1.0.2
+
+%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-cimcb-lite -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.2-1
+- Package Spec generated