automatic import of python-iops

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+/iops-0.5.1.tar.gz
diff --git a/python-iops.spec b/python-iops.spec
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+%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 <Python_Bot@openeuler.org> - 0.5.1-1
+- Package Spec generated
diff --git a/sources b/sources
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+4e76b95c8c1b74dcafa97f59f50b3d1d  iops-0.5.1.tar.gz