%global _empty_manifest_terminate_build 0 Name: python-iops Version: 0.5.1 Release: 1 Summary: Open-source Python release of the IO-PS package License: MIT License URL: https://github.com/WoutersResearchGroup/py-IO-PS Source0: https://mirrors.nju.edu.cn/pypi/web/packages/3b/07/05bbfac58aa25ef5e35c281cef8fb0bd3689a8f6abeff19bd2e682b93b49/iops-0.5.1.tar.gz BuildArch: noarch %description # py-IO-PS Public repository of developmental Python code related to research on the input-output product space (IO-PS) [Described in Bam, W., & De Bruyne, K. (2019). Improving Industrial Policy Intervention: The Case of Steel in South Africa. The Journal of Development Studies, 55(11), 2460–2475. https://doi.org/10.1080/00220388.2018.1528354] ## Package ### Installation The package is available from the Python Package Index: https://pypi.org/project/iops/ ```text pip install iops pip install ecomplexity ``` ### Usage CEPII-BACI trade data is a required input (.csv). The BACI data is available at: http://www.cepii.fr/CEPII/fr/bdd_modele/presentation.asp?id=37 Full IO-PS analysis requires a value chain input (.csv). Three columns are required: 'Tier', 'Category' and 'HS Trade Code'. ```python import pandas as pd from iops import main tradedata_df = pd.read_csv('BACI_HSXX_YXXXX_V202001.csv') valuechain_df = pd.read_csv('X_Value_Chain.csv') main.iops(tradedata_df,valuechain_df) ``` ### Value Chain Output Results are generated at tier, category and product level. Results are written to an Excel spreadsheet and headless CSV for each. ```text Tier_Results.csv Tier_Results.xlsx Product_Category_Results.csv Product_Category_Results.xlsx Product_Results.csv Product_Results.xlsx ``` ### Function ```Python def iops(tradedata, valuechain=None, countrycode=710, tradedigit=6, statanorm=False): """ IO-PS calculation function that writes the results to .xls and .csv Arguments: tradedata: pandas dataframe containing raw CEPII-BACI trade data. valuechain: .csv of the value chain the IO-PS will map. columns - 'Tier', 'Category', 'HS Trade Code' default - None countrycode: integer indicating which country the IO-PS will map. default - 710 tradedigit: Integer of 6 or 4 to indicate the raw trade digit summation level. default - 6 statanorm: Boolean indicator of literature based or CID-Harvard STATA normalization. default - False """ ``` ## Future Considerations * User error warnings * Investigate use of ecomplexity package fork * Additional IO-PS metrics * ECI and distance alignment ## References ### IO-PS * Bam, W., & De Bruyne, K. (2017). Location policy and downstream mineral processing: A research agenda. Extractive Industries and Society, 4(3), 443–447. https://doi.org/10.1016/j.exis.2017.06.009 * Marais, M., & Bam, W. (2019). Developmental potential of the aerospace industry: the case of South Africa. In 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 1–9). IEEE. https://doi.org/10.1109/ICE.2019.8792812 ### Economic Complexity and Product Complexity This packages uses a modified copy of the Growth Lab at Harvard's Center for International Development py-ecomplexity package. The ecomplexity package is used to calculate economic complexity indices: https://github.com/cid-harvard/py-ecomplexity %package -n python3-iops Summary: Open-source Python release of the IO-PS package Provides: python-iops BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-iops # py-IO-PS Public repository of developmental Python code related to research on the input-output product space (IO-PS) [Described in Bam, W., & De Bruyne, K. (2019). Improving Industrial Policy Intervention: The Case of Steel in South Africa. The Journal of Development Studies, 55(11), 2460–2475. https://doi.org/10.1080/00220388.2018.1528354] ## Package ### Installation The package is available from the Python Package Index: https://pypi.org/project/iops/ ```text pip install iops pip install ecomplexity ``` ### Usage CEPII-BACI trade data is a required input (.csv). The BACI data is available at: http://www.cepii.fr/CEPII/fr/bdd_modele/presentation.asp?id=37 Full IO-PS analysis requires a value chain input (.csv). Three columns are required: 'Tier', 'Category' and 'HS Trade Code'. ```python import pandas as pd from iops import main tradedata_df = pd.read_csv('BACI_HSXX_YXXXX_V202001.csv') valuechain_df = pd.read_csv('X_Value_Chain.csv') main.iops(tradedata_df,valuechain_df) ``` ### Value Chain Output Results are generated at tier, category and product level. Results are written to an Excel spreadsheet and headless CSV for each. ```text Tier_Results.csv Tier_Results.xlsx Product_Category_Results.csv Product_Category_Results.xlsx Product_Results.csv Product_Results.xlsx ``` ### Function ```Python def iops(tradedata, valuechain=None, countrycode=710, tradedigit=6, statanorm=False): """ IO-PS calculation function that writes the results to .xls and .csv Arguments: tradedata: pandas dataframe containing raw CEPII-BACI trade data. valuechain: .csv of the value chain the IO-PS will map. columns - 'Tier', 'Category', 'HS Trade Code' default - None countrycode: integer indicating which country the IO-PS will map. default - 710 tradedigit: Integer of 6 or 4 to indicate the raw trade digit summation level. default - 6 statanorm: Boolean indicator of literature based or CID-Harvard STATA normalization. default - False """ ``` ## Future Considerations * User error warnings * Investigate use of ecomplexity package fork * Additional IO-PS metrics * ECI and distance alignment ## References ### IO-PS * Bam, W., & De Bruyne, K. (2017). Location policy and downstream mineral processing: A research agenda. Extractive Industries and Society, 4(3), 443–447. https://doi.org/10.1016/j.exis.2017.06.009 * Marais, M., & Bam, W. (2019). Developmental potential of the aerospace industry: the case of South Africa. In 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 1–9). IEEE. https://doi.org/10.1109/ICE.2019.8792812 ### Economic Complexity and Product Complexity This packages uses a modified copy of the Growth Lab at Harvard's Center for International Development py-ecomplexity package. The ecomplexity package is used to calculate economic complexity indices: https://github.com/cid-harvard/py-ecomplexity %package help Summary: Development documents and examples for iops Provides: python3-iops-doc %description help # py-IO-PS Public repository of developmental Python code related to research on the input-output product space (IO-PS) [Described in Bam, W., & De Bruyne, K. (2019). Improving Industrial Policy Intervention: The Case of Steel in South Africa. The Journal of Development Studies, 55(11), 2460–2475. https://doi.org/10.1080/00220388.2018.1528354] ## Package ### Installation The package is available from the Python Package Index: https://pypi.org/project/iops/ ```text pip install iops pip install ecomplexity ``` ### Usage CEPII-BACI trade data is a required input (.csv). The BACI data is available at: http://www.cepii.fr/CEPII/fr/bdd_modele/presentation.asp?id=37 Full IO-PS analysis requires a value chain input (.csv). Three columns are required: 'Tier', 'Category' and 'HS Trade Code'. ```python import pandas as pd from iops import main tradedata_df = pd.read_csv('BACI_HSXX_YXXXX_V202001.csv') valuechain_df = pd.read_csv('X_Value_Chain.csv') main.iops(tradedata_df,valuechain_df) ``` ### Value Chain Output Results are generated at tier, category and product level. Results are written to an Excel spreadsheet and headless CSV for each. ```text Tier_Results.csv Tier_Results.xlsx Product_Category_Results.csv Product_Category_Results.xlsx Product_Results.csv Product_Results.xlsx ``` ### Function ```Python def iops(tradedata, valuechain=None, countrycode=710, tradedigit=6, statanorm=False): """ IO-PS calculation function that writes the results to .xls and .csv Arguments: tradedata: pandas dataframe containing raw CEPII-BACI trade data. valuechain: .csv of the value chain the IO-PS will map. columns - 'Tier', 'Category', 'HS Trade Code' default - None countrycode: integer indicating which country the IO-PS will map. default - 710 tradedigit: Integer of 6 or 4 to indicate the raw trade digit summation level. default - 6 statanorm: Boolean indicator of literature based or CID-Harvard STATA normalization. default - False """ ``` ## Future Considerations * User error warnings * Investigate use of ecomplexity package fork * Additional IO-PS metrics * ECI and distance alignment ## References ### IO-PS * Bam, W., & De Bruyne, K. (2017). Location policy and downstream mineral processing: A research agenda. Extractive Industries and Society, 4(3), 443–447. https://doi.org/10.1016/j.exis.2017.06.009 * Marais, M., & Bam, W. (2019). Developmental potential of the aerospace industry: the case of South Africa. In 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 1–9). IEEE. https://doi.org/10.1109/ICE.2019.8792812 ### Economic Complexity and Product Complexity This packages uses a modified copy of the Growth Lab at Harvard's Center for International Development py-ecomplexity package. The ecomplexity package is used to calculate economic complexity indices: https://github.com/cid-harvard/py-ecomplexity %prep %autosetup -n iops-0.5.1 %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-iops -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon May 29 2023 Python_Bot - 0.5.1-1 - Package Spec generated