%global _empty_manifest_terminate_build 0 Name: python3-pingouin Version: 0.5.5 Release: 1 Summary: Pingouin: statistical package for Python License: GPL-3.0 URL: https://pingouin-stats.org/index.html Source0: https://files.pythonhosted.org/packages/0e/3d/14a779790bac2d03a0d8f82c1857fc83c0ef8b1b77542b884808e55ee839/pingouin-0.5.5.tar.gz BuildArch: noarch Requires: (python3-matplotlib) Requires: (python3-numpy) Requires: (python3-pandas>=1.5) Requires: (python3-pandas-flavor) Requires: (python3-scikit-learn>=1.2) Requires: (python3-scipy) Requires: (python3-seaborn) Requires: (python3-statsmodels) Requires: (python3-tabulate) Requires: (python3-sphinx>7.0.0) Requires: (python3-pydata-sphinx-theme) Requires: (python3-numpydoc) Requires: (python3-sphinx-copybutton) Requires: (python3-sphinx-design) Requires: (python3-sphinx-notfound-page) Requires: (python3-pytest>=6) Requires: (python3-pytest-cov) Requires: (python3-codecov) Requires: (python3-openpyxl) Requires: (python3-mpmath) Requires: (python3-coverage[toml]>=5.3) %description **Pingouin** is an open-source statistical package written in Python 3 and based mostly on Pandas and NumPy. Some of its main features are listed below. For a full list of available functions, please refer to the `API documentation `_. 1. ANOVAs: N-ways, repeated measures, mixed, ancova 2. Pairwise post-hocs tests (parametric and non-parametric) and pairwise correlations 3. Robust, partial, distance and repeated measures correlations 4. Linear/logistic regression and mediation analysis 5. Bayes Factors 6. Multivariate tests 7. Reliability and consistency 8. Effect sizes and power analysis 9. Parametric/bootstrapped confidence intervals around an effect size or a correlation coefficient 10. Circular statistics 11. Chi-squared tests 12. Plotting: Bland-Altman plot, Q-Q plot, paired plot, robust correlation... Pingouin is designed for users who want **simple yet exhaustive statistical functions**. For example, the :code:`ttest_ind` function of SciPy returns only the T-value and the p-value. By contrast, the :code:`ttest` function of Pingouin returns the T-value, the p-value, the degrees of freedom, the effect size (Cohen's d), the 95% confidence intervals of the difference in means, the statistical power and the Bayes Factor (BF10) of the test. %package -n python3-pingouin Summary: Pingouin: statistical package for Python Provides: python-pingouin BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip BuildRequires: python3-wheel %description -n python3-pingouin **Pingouin** is an open-source statistical package written in Python 3 and based mostly on Pandas and NumPy. Some of its main features are listed below. For a full list of available functions, please refer to the `API documentation `_. 1. ANOVAs: N-ways, repeated measures, mixed, ancova 2. Pairwise post-hocs tests (parametric and non-parametric) and pairwise correlations 3. Robust, partial, distance and repeated measures correlations 4. Linear/logistic regression and mediation analysis 5. Bayes Factors 6. Multivariate tests 7. Reliability and consistency 8. Effect sizes and power analysis 9. Parametric/bootstrapped confidence intervals around an effect size or a correlation coefficient 10. Circular statistics 11. Chi-squared tests 12. Plotting: Bland-Altman plot, Q-Q plot, paired plot, robust correlation... Pingouin is designed for users who want **simple yet exhaustive statistical functions**. For example, the :code:`ttest_ind` function of SciPy returns only the T-value and the p-value. By contrast, the :code:`ttest` function of Pingouin returns the T-value, the p-value, the degrees of freedom, the effect size (Cohen's d), the 95% confidence intervals of the difference in means, the statistical power and the Bayes Factor (BF10) of the test. %package help Summary: Development documents and examples for pingouin Provides: python3-pingouin-doc %description help **Pingouin** is an open-source statistical package written in Python 3 and based mostly on Pandas and NumPy. Some of its main features are listed below. For a full list of available functions, please refer to the `API documentation `_. 1. ANOVAs: N-ways, repeated measures, mixed, ancova 2. Pairwise post-hocs tests (parametric and non-parametric) and pairwise correlations 3. Robust, partial, distance and repeated measures correlations 4. Linear/logistic regression and mediation analysis 5. Bayes Factors 6. Multivariate tests 7. Reliability and consistency 8. Effect sizes and power analysis 9. Parametric/bootstrapped confidence intervals around an effect size or a correlation coefficient 10. Circular statistics 11. Chi-squared tests 12. Plotting: Bland-Altman plot, Q-Q plot, paired plot, robust correlation... Pingouin is designed for users who want **simple yet exhaustive statistical functions**. For example, the :code:`ttest_ind` function of SciPy returns only the T-value and the p-value. By contrast, the :code:`ttest` function of Pingouin returns the T-value, the p-value, the degrees of freedom, the effect size (Cohen's d), the 95% confidence intervals of the difference in means, the statistical power and the Bayes Factor (BF10) of the test. %prep %autosetup -n pingouin-0.5.5 %build CFLAGS="${CFLAGS:-${RPM_OPT_FLAGS}}" LDFLAGS="${LDFLAGS:-${RPM_LD_FLAGS}}" \ %{__python3} -m pip wheel \ --verbose \ --progress-bar off \ --disable-pip-version-check \ --use-pep517 \ --no-build-isolation \ --no-deps \ --wheel-dir ./build \ . %install WHEEL_FILE=$(find ./build -name "*.whl" | head -n1) if [ -z "$WHEEL_FILE" ]; then echo "ERROR: No .whl file found in ./build!" >&2 exit 1 fi CFLAGS="${CFLAGS:-${RPM_OPT_FLAGS}}" LDFLAGS="${LDFLAGS:-${RPM_LD_FLAGS}}" \ %{__python3} -m pip install \ --verbose \ --progress-bar off \ --disable-pip-version-check \ --root %{buildroot} \ --no-compile \ --ignore-installed \ --no-deps \ --no-index \ --find-links ./build \ "$WHEEL_FILE" 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}%{python3_sitelib} if [ -d "UNKNOWN-0.0.0.dist-info" ]; then mv "UNKNOWN-0.0.0.dist-info" "pingouin-%{version}.dist-info" sed -i "s/^Name: UNKNOWN/Name: pingouin/" "pingouin-%{version}.dist-info/METADATA" 2>/dev/null || true sed -i "s/^Version: 0.0.0/Version: %{version}/" "pingouin-%{version}.dist-info/METADATA" 2>/dev/null || true fi popd pushd %{buildroot} touch filelist.lst 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-pingouin -f filelist.lst %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon Nov 11 2024 Python_Bot - 0.5.5-1 - Package Spec generated