%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 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 [Documentation](https://lisphilar.github.io/covid19-sir/index.html) | [Installation](https://lisphilar.github.io/covid19-sir/markdown/INSTALLATION.html) | [Tutorial]() | [API reference](https://lisphilar.github.io/covid19-sir/covsirphy.html) | [GitHub](https://github.com/lisphilar/covid19-sir) | [Qiita (Japanese)](https://qiita.com/tags/covsirphy) 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. ## 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 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). ## 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");` ## 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]()) 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 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 [Documentation](https://lisphilar.github.io/covid19-sir/index.html) | [Installation](https://lisphilar.github.io/covid19-sir/markdown/INSTALLATION.html) | [Tutorial]() | [API reference](https://lisphilar.github.io/covid19-sir/covsirphy.html) | [GitHub](https://github.com/lisphilar/covid19-sir) | [Qiita (Japanese)](https://qiita.com/tags/covsirphy) 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. ## 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 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). ## 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");` ## 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]()) 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 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 [Documentation](https://lisphilar.github.io/covid19-sir/index.html) | [Installation](https://lisphilar.github.io/covid19-sir/markdown/INSTALLATION.html) | [Tutorial]() | [API reference](https://lisphilar.github.io/covid19-sir/covsirphy.html) | [GitHub](https://github.com/lisphilar/covid19-sir) | [Qiita (Japanese)](https://qiita.com/tags/covsirphy) 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. ## 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 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). ## 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");` ## 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]()) 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 - 2.28.0-1 - Package Spec generated