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+/covsirphy-2.28.0.tar.gz
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+%global _empty_manifest_terminate_build 0
+Name:		python-covsirphy
+Version:	2.28.0
+Release:	1
+Summary:	COVID-19 data analysis with phase-dependent SIR-derived ODE models
+License:	Apache-2.0
+URL:		https://github.com/lisphilar/covid19-sir/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/49/f6/938c4848fbeb067c4e05b2e0d45a65736999f167e09d503ac638679f5e41/covsirphy-2.28.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-optuna
+Requires:	python3-pandas
+Requires:	python3-pyarrow
+Requires:	python3-tabulate
+Requires:	python3-seaborn
+Requires:	python3-scipy
+Requires:	python3-scikit-learn
+Requires:	python3-japanmap
+Requires:	python3-requests
+Requires:	python3-ruptures
+Requires:	python3-matplotlib
+Requires:	python3-country-converter
+Requires:	python3-wbdata
+Requires:	python3-geopandas
+Requires:	python3-Unidecode
+Requires:	python3-lightgbm
+Requires:	python3-AutoTS
+Requires:	python3-p-tqdm
+Requires:	python3-pca
+Requires:	python3-better-exceptions
+Requires:	python3-loguru
+
+%description
+
+<img src="https://raw.githubusercontent.com/lisphilar/covid19-sir/master/docs/logo/covsirphy_headline.png" width="390" alt="CovsirPhy: COVID-19 analysis with phase-dependent SIRs">
+
+[![PyPI version](https://badge.fury.io/py/covsirphy.svg)](https://badge.fury.io/py/covsirphy)
+[![Downloads](https://pepy.tech/badge/covsirphy)](https://pepy.tech/project/covsirphy)
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/covsirphy)](https://badge.fury.io/py/covsirphy)
+[![GitHub license](https://img.shields.io/github/license/lisphilar/covid19-sir)](https://github.com/lisphilar/covid19-sir/blob/master/LICENSE)
+[![Quality Check](https://github.com/lisphilar/covid19-sir/actions/workflows/test.yml/badge.svg)](https://github.com/lisphilar/covid19-sir/actions/workflows/test.yml)
+[![Test Coverage](https://codecov.io/gh/lisphilar/covid19-sir/branch/master/graph/badge.svg?token=9Z8Z1UHY3I)](https://codecov.io/gh/lisphilar/covid19-sir)
+
+# CovsirPhy introduction
+
+[<strong>Documentation</strong>](https://lisphilar.github.io/covid19-sir/index.html)
+| [<strong>Installation</strong>](https://lisphilar.github.io/covid19-sir/markdown/INSTALLATION.html)
+| [<strong>Tutorial</strong>](<https://lisphilar.github.io/covid19-sir/01_data_preparation.html>)
+| [<strong>API reference</strong>](https://lisphilar.github.io/covid19-sir/covsirphy.html)
+| [<strong>GitHub</strong>](https://github.com/lisphilar/covid19-sir)
+| [<strong>Qiita (Japanese)</strong>](https://qiita.com/tags/covsirphy)
+
+<strong>CovsirPhy is a Python library for infectious disease (COVID-19: Coronavirus disease 2019, Monkeypox 2022) data analysis with phase-dependent SIR-derived ODE models. We can download datasets and analyze them easily. Scenario analysis with CovsirPhy enables us to make data-informed decisions. </strong>
+
+## Inspiration
+
+* Monitor the spread of COVID-19/Monkeypox with SIR-derived ODE models
+* Predict the number of cases in each country/province
+* Find the relationship of reproductive number and measures taken by each country
+
+<strong>If you have ideas or need new functionalities, please join this project.
+Any suggestions with [Github Issues](https://github.com/lisphilar/covid19-sir/issues/new/choose) and [Twitter: @lisphilar](https://twitter.com/lisphilar) are always welcomed. Questions are also great. Please refer to [Guideline of contribution](https://lisphilar.github.io/covid19-sir/CONTRIBUTING.html).</strong>
+
+## Installation
+
+The latest stable version of CovsirPhy is available at [PyPI (The Python Package Index): covsirphy](https://pypi.org/project/covsirphy/) and supports Python 3.8 or newer versions. Details are explained in [Documentation: Installation](https://lisphilar.github.io/covid19-sir/INSTALLATION.html).
+
+```Bash
+pip install --upgrade covsirphy
+```
+
+> **Warning**  
+> We cannot use `covsirphy` on Google Colab, which uses Python 3.7. [Binder](https://mybinder.org/) is recommended.
+
+## Demo
+
+Quickest tour of CovsirPhy is here. The following codes analyze the records in Japan.
+
+```Python
+import covsirphy as cs
+# Data preparation,time-series segmentation, parameter estimation with SIR-F model
+snr = cs.ODEScenario.auto_build(geo="Japan", model=cs.SIRFModel)
+# Check actual records
+snr.simulate(name=None);
+# Show the result of time-series segmentation
+snr.to_dynamics(name="Baseline").detect();
+# Perform simulation with estimated ODE parameter values
+snr.simulate(name="Baseline");
+# Predict ODE parameter values (30 days from the last date of actual records)
+snr.build_with_template(name="Predicted", template="Baseline");
+snr.predict(days=30, name="Predicted");
+# Perform simulation with estimated and predicted ODE parameter values
+snr.simulate(name="Predicted");
+# Add a future phase to the baseline (ODE parameters will not be changed)
+snr.append();
+# Show created phases and ODE parameter values
+snr.summary()
+# Compare reproduction number of scenarios (predicted/baseline)
+snr.compare_param("Rt");
+# Compare simulated number of cases
+snr.compare_cases("Confirmed");
+# Describe representative values
+snr.describe()
+```
+
+Output of `snr.simulate(name="Predicted");`
+
+<img src="https://raw.githubusercontent.com/lisphilar/covid19-sir/master/example/output/demo_jpn/04_predicted.png" width="600">
+
+## Tutorial
+
+Tutorials of functionalities are included in the [CovsirPhy documentation](https://lisphilar.github.io/covid19-sir/index.html).
+
+* [Data preparation](https://lisphilar.github.io/covid19-sir/01_data_preparation.html)
+* [Data Engineering](https://lisphilar.github.io/covid19-sir/02_data_engineering.html)
+* [SIR-derived ODE models](https://lisphilar.github.io/covid19-sir/03_ode.html)
+* [Phase-dependent SIR models](https://lisphilar.github.io/covid19-sir/04_phase_dependent.html)
+* [Scenario analysis](https://lisphilar.github.io/covid19-sir/05_scenario_analysis.html)
+* [ODE parameter prediction](https://lisphilar.github.io/covid19-sir/06_prediction.html)
+
+## Release notes
+
+Release notes are [here](https://github.com/lisphilar/covid19-sir/releases). Titles & links of issues are listed with acknowledgement.
+
+We can see the release plan for the next stable version in [milestone page of the GitHub repository](https://github.com/lisphilar/covid19-sir/milestones). If you find a highly urgent matter, please let us know via [issue page](https://github.com/lisphilar/covid19-sir/issues).
+
+## Developers
+
+CovsirPhy library is developed by a community of volunteers. Please see the full list [here](https://github.com/lisphilar/covid19-sir/graphs/contributors).
+
+This project started in Kaggle platform. Hirokazu Takaya ([@lisphilar](<https://www.kaggle.com/lisphilar>)) published [Kaggle Notebook: COVID-19 data with SIR model](https://www.kaggle.com/lisphilar/covid-19-data-with-sir-model) on 12Feb2020 and developed it, discussing with Kaggle community. On 07May2020, "covid19-sir" repository was created. On 10May2020, `covsirphy` version 1.0.0 was published in GitHub. First release in PyPI (version 2.3.0) was on 28Jun2020.
+
+## Support
+
+Please support this project as a developer (or a backer).
+[![Become a backer](https://opencollective.com/covsirphy/tiers/backer.svg?avatarHeight=36&width=600)](https://opencollective.com/covsirphy)
+
+## License: Apache License 2.0
+
+Please refer to [LICENSE](https://github.com/lisphilar/covid19-sir/blob/master/LICENSE) file.
+
+## Citation
+
+Please cite this library as follows with version number (`import covsirphy as cs; cs.__version__`).
+
+**Hirokazu Takaya and CovsirPhy Development Team (2020-2022), CovsirPhy version [version number]: Python library for COVID-19 analysis with phase-dependent SIR-derived ODE models, [https://github.com/lisphilar/covid19-sir](https://github.com/lisphilar/covid19-sir)**
+
+This is the output of `covsirphy.__citation__`.
+
+```Python
+import covsirphy as cs
+cs.__citation__
+```
+
+**We have no original papers the author and contributors wrote, but note that some scientific approaches, including SIR-F model, S-R change point analysis, phase-dependent approach to SIR-derived models, were developed in this project.**
+
+
+%package -n python3-covsirphy
+Summary:	COVID-19 data analysis with phase-dependent SIR-derived ODE models
+Provides:	python-covsirphy
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-covsirphy
+
+<img src="https://raw.githubusercontent.com/lisphilar/covid19-sir/master/docs/logo/covsirphy_headline.png" width="390" alt="CovsirPhy: COVID-19 analysis with phase-dependent SIRs">
+
+[![PyPI version](https://badge.fury.io/py/covsirphy.svg)](https://badge.fury.io/py/covsirphy)
+[![Downloads](https://pepy.tech/badge/covsirphy)](https://pepy.tech/project/covsirphy)
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/covsirphy)](https://badge.fury.io/py/covsirphy)
+[![GitHub license](https://img.shields.io/github/license/lisphilar/covid19-sir)](https://github.com/lisphilar/covid19-sir/blob/master/LICENSE)
+[![Quality Check](https://github.com/lisphilar/covid19-sir/actions/workflows/test.yml/badge.svg)](https://github.com/lisphilar/covid19-sir/actions/workflows/test.yml)
+[![Test Coverage](https://codecov.io/gh/lisphilar/covid19-sir/branch/master/graph/badge.svg?token=9Z8Z1UHY3I)](https://codecov.io/gh/lisphilar/covid19-sir)
+
+# CovsirPhy introduction
+
+[<strong>Documentation</strong>](https://lisphilar.github.io/covid19-sir/index.html)
+| [<strong>Installation</strong>](https://lisphilar.github.io/covid19-sir/markdown/INSTALLATION.html)
+| [<strong>Tutorial</strong>](<https://lisphilar.github.io/covid19-sir/01_data_preparation.html>)
+| [<strong>API reference</strong>](https://lisphilar.github.io/covid19-sir/covsirphy.html)
+| [<strong>GitHub</strong>](https://github.com/lisphilar/covid19-sir)
+| [<strong>Qiita (Japanese)</strong>](https://qiita.com/tags/covsirphy)
+
+<strong>CovsirPhy is a Python library for infectious disease (COVID-19: Coronavirus disease 2019, Monkeypox 2022) data analysis with phase-dependent SIR-derived ODE models. We can download datasets and analyze them easily. Scenario analysis with CovsirPhy enables us to make data-informed decisions. </strong>
+
+## Inspiration
+
+* Monitor the spread of COVID-19/Monkeypox with SIR-derived ODE models
+* Predict the number of cases in each country/province
+* Find the relationship of reproductive number and measures taken by each country
+
+<strong>If you have ideas or need new functionalities, please join this project.
+Any suggestions with [Github Issues](https://github.com/lisphilar/covid19-sir/issues/new/choose) and [Twitter: @lisphilar](https://twitter.com/lisphilar) are always welcomed. Questions are also great. Please refer to [Guideline of contribution](https://lisphilar.github.io/covid19-sir/CONTRIBUTING.html).</strong>
+
+## Installation
+
+The latest stable version of CovsirPhy is available at [PyPI (The Python Package Index): covsirphy](https://pypi.org/project/covsirphy/) and supports Python 3.8 or newer versions. Details are explained in [Documentation: Installation](https://lisphilar.github.io/covid19-sir/INSTALLATION.html).
+
+```Bash
+pip install --upgrade covsirphy
+```
+
+> **Warning**  
+> We cannot use `covsirphy` on Google Colab, which uses Python 3.7. [Binder](https://mybinder.org/) is recommended.
+
+## Demo
+
+Quickest tour of CovsirPhy is here. The following codes analyze the records in Japan.
+
+```Python
+import covsirphy as cs
+# Data preparation,time-series segmentation, parameter estimation with SIR-F model
+snr = cs.ODEScenario.auto_build(geo="Japan", model=cs.SIRFModel)
+# Check actual records
+snr.simulate(name=None);
+# Show the result of time-series segmentation
+snr.to_dynamics(name="Baseline").detect();
+# Perform simulation with estimated ODE parameter values
+snr.simulate(name="Baseline");
+# Predict ODE parameter values (30 days from the last date of actual records)
+snr.build_with_template(name="Predicted", template="Baseline");
+snr.predict(days=30, name="Predicted");
+# Perform simulation with estimated and predicted ODE parameter values
+snr.simulate(name="Predicted");
+# Add a future phase to the baseline (ODE parameters will not be changed)
+snr.append();
+# Show created phases and ODE parameter values
+snr.summary()
+# Compare reproduction number of scenarios (predicted/baseline)
+snr.compare_param("Rt");
+# Compare simulated number of cases
+snr.compare_cases("Confirmed");
+# Describe representative values
+snr.describe()
+```
+
+Output of `snr.simulate(name="Predicted");`
+
+<img src="https://raw.githubusercontent.com/lisphilar/covid19-sir/master/example/output/demo_jpn/04_predicted.png" width="600">
+
+## Tutorial
+
+Tutorials of functionalities are included in the [CovsirPhy documentation](https://lisphilar.github.io/covid19-sir/index.html).
+
+* [Data preparation](https://lisphilar.github.io/covid19-sir/01_data_preparation.html)
+* [Data Engineering](https://lisphilar.github.io/covid19-sir/02_data_engineering.html)
+* [SIR-derived ODE models](https://lisphilar.github.io/covid19-sir/03_ode.html)
+* [Phase-dependent SIR models](https://lisphilar.github.io/covid19-sir/04_phase_dependent.html)
+* [Scenario analysis](https://lisphilar.github.io/covid19-sir/05_scenario_analysis.html)
+* [ODE parameter prediction](https://lisphilar.github.io/covid19-sir/06_prediction.html)
+
+## Release notes
+
+Release notes are [here](https://github.com/lisphilar/covid19-sir/releases). Titles & links of issues are listed with acknowledgement.
+
+We can see the release plan for the next stable version in [milestone page of the GitHub repository](https://github.com/lisphilar/covid19-sir/milestones). If you find a highly urgent matter, please let us know via [issue page](https://github.com/lisphilar/covid19-sir/issues).
+
+## Developers
+
+CovsirPhy library is developed by a community of volunteers. Please see the full list [here](https://github.com/lisphilar/covid19-sir/graphs/contributors).
+
+This project started in Kaggle platform. Hirokazu Takaya ([@lisphilar](<https://www.kaggle.com/lisphilar>)) published [Kaggle Notebook: COVID-19 data with SIR model](https://www.kaggle.com/lisphilar/covid-19-data-with-sir-model) on 12Feb2020 and developed it, discussing with Kaggle community. On 07May2020, "covid19-sir" repository was created. On 10May2020, `covsirphy` version 1.0.0 was published in GitHub. First release in PyPI (version 2.3.0) was on 28Jun2020.
+
+## Support
+
+Please support this project as a developer (or a backer).
+[![Become a backer](https://opencollective.com/covsirphy/tiers/backer.svg?avatarHeight=36&width=600)](https://opencollective.com/covsirphy)
+
+## License: Apache License 2.0
+
+Please refer to [LICENSE](https://github.com/lisphilar/covid19-sir/blob/master/LICENSE) file.
+
+## Citation
+
+Please cite this library as follows with version number (`import covsirphy as cs; cs.__version__`).
+
+**Hirokazu Takaya and CovsirPhy Development Team (2020-2022), CovsirPhy version [version number]: Python library for COVID-19 analysis with phase-dependent SIR-derived ODE models, [https://github.com/lisphilar/covid19-sir](https://github.com/lisphilar/covid19-sir)**
+
+This is the output of `covsirphy.__citation__`.
+
+```Python
+import covsirphy as cs
+cs.__citation__
+```
+
+**We have no original papers the author and contributors wrote, but note that some scientific approaches, including SIR-F model, S-R change point analysis, phase-dependent approach to SIR-derived models, were developed in this project.**
+
+
+%package help
+Summary:	Development documents and examples for covsirphy
+Provides:	python3-covsirphy-doc
+%description help
+
+<img src="https://raw.githubusercontent.com/lisphilar/covid19-sir/master/docs/logo/covsirphy_headline.png" width="390" alt="CovsirPhy: COVID-19 analysis with phase-dependent SIRs">
+
+[![PyPI version](https://badge.fury.io/py/covsirphy.svg)](https://badge.fury.io/py/covsirphy)
+[![Downloads](https://pepy.tech/badge/covsirphy)](https://pepy.tech/project/covsirphy)
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/covsirphy)](https://badge.fury.io/py/covsirphy)
+[![GitHub license](https://img.shields.io/github/license/lisphilar/covid19-sir)](https://github.com/lisphilar/covid19-sir/blob/master/LICENSE)
+[![Quality Check](https://github.com/lisphilar/covid19-sir/actions/workflows/test.yml/badge.svg)](https://github.com/lisphilar/covid19-sir/actions/workflows/test.yml)
+[![Test Coverage](https://codecov.io/gh/lisphilar/covid19-sir/branch/master/graph/badge.svg?token=9Z8Z1UHY3I)](https://codecov.io/gh/lisphilar/covid19-sir)
+
+# CovsirPhy introduction
+
+[<strong>Documentation</strong>](https://lisphilar.github.io/covid19-sir/index.html)
+| [<strong>Installation</strong>](https://lisphilar.github.io/covid19-sir/markdown/INSTALLATION.html)
+| [<strong>Tutorial</strong>](<https://lisphilar.github.io/covid19-sir/01_data_preparation.html>)
+| [<strong>API reference</strong>](https://lisphilar.github.io/covid19-sir/covsirphy.html)
+| [<strong>GitHub</strong>](https://github.com/lisphilar/covid19-sir)
+| [<strong>Qiita (Japanese)</strong>](https://qiita.com/tags/covsirphy)
+
+<strong>CovsirPhy is a Python library for infectious disease (COVID-19: Coronavirus disease 2019, Monkeypox 2022) data analysis with phase-dependent SIR-derived ODE models. We can download datasets and analyze them easily. Scenario analysis with CovsirPhy enables us to make data-informed decisions. </strong>
+
+## Inspiration
+
+* Monitor the spread of COVID-19/Monkeypox with SIR-derived ODE models
+* Predict the number of cases in each country/province
+* Find the relationship of reproductive number and measures taken by each country
+
+<strong>If you have ideas or need new functionalities, please join this project.
+Any suggestions with [Github Issues](https://github.com/lisphilar/covid19-sir/issues/new/choose) and [Twitter: @lisphilar](https://twitter.com/lisphilar) are always welcomed. Questions are also great. Please refer to [Guideline of contribution](https://lisphilar.github.io/covid19-sir/CONTRIBUTING.html).</strong>
+
+## Installation
+
+The latest stable version of CovsirPhy is available at [PyPI (The Python Package Index): covsirphy](https://pypi.org/project/covsirphy/) and supports Python 3.8 or newer versions. Details are explained in [Documentation: Installation](https://lisphilar.github.io/covid19-sir/INSTALLATION.html).
+
+```Bash
+pip install --upgrade covsirphy
+```
+
+> **Warning**  
+> We cannot use `covsirphy` on Google Colab, which uses Python 3.7. [Binder](https://mybinder.org/) is recommended.
+
+## Demo
+
+Quickest tour of CovsirPhy is here. The following codes analyze the records in Japan.
+
+```Python
+import covsirphy as cs
+# Data preparation,time-series segmentation, parameter estimation with SIR-F model
+snr = cs.ODEScenario.auto_build(geo="Japan", model=cs.SIRFModel)
+# Check actual records
+snr.simulate(name=None);
+# Show the result of time-series segmentation
+snr.to_dynamics(name="Baseline").detect();
+# Perform simulation with estimated ODE parameter values
+snr.simulate(name="Baseline");
+# Predict ODE parameter values (30 days from the last date of actual records)
+snr.build_with_template(name="Predicted", template="Baseline");
+snr.predict(days=30, name="Predicted");
+# Perform simulation with estimated and predicted ODE parameter values
+snr.simulate(name="Predicted");
+# Add a future phase to the baseline (ODE parameters will not be changed)
+snr.append();
+# Show created phases and ODE parameter values
+snr.summary()
+# Compare reproduction number of scenarios (predicted/baseline)
+snr.compare_param("Rt");
+# Compare simulated number of cases
+snr.compare_cases("Confirmed");
+# Describe representative values
+snr.describe()
+```
+
+Output of `snr.simulate(name="Predicted");`
+
+<img src="https://raw.githubusercontent.com/lisphilar/covid19-sir/master/example/output/demo_jpn/04_predicted.png" width="600">
+
+## Tutorial
+
+Tutorials of functionalities are included in the [CovsirPhy documentation](https://lisphilar.github.io/covid19-sir/index.html).
+
+* [Data preparation](https://lisphilar.github.io/covid19-sir/01_data_preparation.html)
+* [Data Engineering](https://lisphilar.github.io/covid19-sir/02_data_engineering.html)
+* [SIR-derived ODE models](https://lisphilar.github.io/covid19-sir/03_ode.html)
+* [Phase-dependent SIR models](https://lisphilar.github.io/covid19-sir/04_phase_dependent.html)
+* [Scenario analysis](https://lisphilar.github.io/covid19-sir/05_scenario_analysis.html)
+* [ODE parameter prediction](https://lisphilar.github.io/covid19-sir/06_prediction.html)
+
+## Release notes
+
+Release notes are [here](https://github.com/lisphilar/covid19-sir/releases). Titles & links of issues are listed with acknowledgement.
+
+We can see the release plan for the next stable version in [milestone page of the GitHub repository](https://github.com/lisphilar/covid19-sir/milestones). If you find a highly urgent matter, please let us know via [issue page](https://github.com/lisphilar/covid19-sir/issues).
+
+## Developers
+
+CovsirPhy library is developed by a community of volunteers. Please see the full list [here](https://github.com/lisphilar/covid19-sir/graphs/contributors).
+
+This project started in Kaggle platform. Hirokazu Takaya ([@lisphilar](<https://www.kaggle.com/lisphilar>)) published [Kaggle Notebook: COVID-19 data with SIR model](https://www.kaggle.com/lisphilar/covid-19-data-with-sir-model) on 12Feb2020 and developed it, discussing with Kaggle community. On 07May2020, "covid19-sir" repository was created. On 10May2020, `covsirphy` version 1.0.0 was published in GitHub. First release in PyPI (version 2.3.0) was on 28Jun2020.
+
+## Support
+
+Please support this project as a developer (or a backer).
+[![Become a backer](https://opencollective.com/covsirphy/tiers/backer.svg?avatarHeight=36&width=600)](https://opencollective.com/covsirphy)
+
+## License: Apache License 2.0
+
+Please refer to [LICENSE](https://github.com/lisphilar/covid19-sir/blob/master/LICENSE) file.
+
+## Citation
+
+Please cite this library as follows with version number (`import covsirphy as cs; cs.__version__`).
+
+**Hirokazu Takaya and CovsirPhy Development Team (2020-2022), CovsirPhy version [version number]: Python library for COVID-19 analysis with phase-dependent SIR-derived ODE models, [https://github.com/lisphilar/covid19-sir](https://github.com/lisphilar/covid19-sir)**
+
+This is the output of `covsirphy.__citation__`.
+
+```Python
+import covsirphy as cs
+cs.__citation__
+```
+
+**We have no original papers the author and contributors wrote, but note that some scientific approaches, including SIR-F model, S-R change point analysis, phase-dependent approach to SIR-derived models, were developed in this project.**
+
+
+%prep
+%autosetup -n covsirphy-2.28.0
+
+%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-covsirphy -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2.28.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..4b1931e
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+00bad7ea52db7f7dd0cd9e30a9a86ceb  covsirphy-2.28.0.tar.gz