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@@ -0,0 +1 @@ +/covsirphy-2.28.0.tar.gz diff --git a/python-covsirphy.spec b/python-covsirphy.spec new file mode 100644 index 0000000..723d615 --- /dev/null +++ b/python-covsirphy.spec @@ -0,0 +1,460 @@ +%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"> + +[](https://badge.fury.io/py/covsirphy) +[](https://pepy.tech/project/covsirphy) +[](https://badge.fury.io/py/covsirphy) +[](https://github.com/lisphilar/covid19-sir/blob/master/LICENSE) +[](https://github.com/lisphilar/covid19-sir/actions/workflows/test.yml) +[](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). +[](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"> + +[](https://badge.fury.io/py/covsirphy) +[](https://pepy.tech/project/covsirphy) +[](https://badge.fury.io/py/covsirphy) +[](https://github.com/lisphilar/covid19-sir/blob/master/LICENSE) +[](https://github.com/lisphilar/covid19-sir/actions/workflows/test.yml) +[](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). +[](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"> + +[](https://badge.fury.io/py/covsirphy) +[](https://pepy.tech/project/covsirphy) +[](https://badge.fury.io/py/covsirphy) +[](https://github.com/lisphilar/covid19-sir/blob/master/LICENSE) +[](https://github.com/lisphilar/covid19-sir/actions/workflows/test.yml) +[](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). +[](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 @@ -0,0 +1 @@ +00bad7ea52db7f7dd0cd9e30a9a86ceb covsirphy-2.28.0.tar.gz |