%global _empty_manifest_terminate_build 0 Name: python-ads2gephi Version: 0.3.8 Release: 1 Summary: A command line tool for querying and modeling citation networks from the Astrophysical Data System (ADS) in a format compatible with Gephi License: MIT URL: https://github.com/03b8/ads2gephi Source0: https://mirrors.nju.edu.cn/pypi/web/packages/66/93/3d29d926e03b5a06d86aea431c764427e0c881ba4690448e02814682a4ae/ads2gephi-0.3.8.tar.gz BuildArch: noarch Requires: python3-ads Requires: python3-sqlalchemy Requires: python3-configparser Requires: python3-click Requires: python3-igraph Requires: python3-tqdm Requires: python3-yaspin %description [![PyPI version](https://badge.fury.io/py/ads2gephi.svg)](https://badge.fury.io/py/ads2gephi) ![CI](https://github.com/03b8/ads2gephi/actions/workflows/github-ci.yml/badge.svg) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) # ads2gephi is a command line tool for querying and modeling citation networks from the Astrophysical Data System (ADS) in a format compatible with Gephi, a popular network visualization tool. ads2gephi has been developed at the history of science department of TU Berlin as part of [a research project on the history of extragalactic astronomy](https://gepris.dfg.de/gepris/projekt/289438140?language=en) financed by the German Research Foundation DFG (PI Karin Pelte). You can install `ads2gephi` from PyPI: ``` pip install ads2gephi ``` ### Usage When using the tool for the first time to model a network, you will be prompted to enter your ADS API key. Your key will then be stored in a configuration file under ~/.ads2gephi. In order to sample an initial citation network, you need to provide ads2gephi with a plain text file with bibcodes (ADS unique identifiers), one per line, as input. The queried network will be output in a SQLite database stored in the current directory: ``` ads2gephi -c bibcodes_example.txt -d my_fancy_netzwerk.db ``` Afterwards you can extend the queried network by providing the existing database file and using the additional sampling options. For example, you can extend the network by querying all the items cited in every publication previously queried: ``` ads2gephi -s ref -d my_fancy_netzwerk.db ``` Finally you might want to also generate the edges of the network. There are several options for generating edges. For example you can use a semantic similarity measure like bibliographic coupling or co-citation: ``` ads2gephi -e bibcp -d my_fancy_netzwerk.db ``` You can also do everything at once: ``` ads2gephi -c bibcodes_example.txt -s ref -e bibcp -d my_fancy_netzwerk.db ``` All other querying and modelling options are described in the help page: ``` ads2gephi --help ``` Once you've finished querying and modeling, the database file can be directly imported in Gephi for network visualization and analysis. ## Special thanks to * Edwin Henneken %package -n python3-ads2gephi Summary: A command line tool for querying and modeling citation networks from the Astrophysical Data System (ADS) in a format compatible with Gephi Provides: python-ads2gephi BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-ads2gephi [![PyPI version](https://badge.fury.io/py/ads2gephi.svg)](https://badge.fury.io/py/ads2gephi) ![CI](https://github.com/03b8/ads2gephi/actions/workflows/github-ci.yml/badge.svg) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) # ads2gephi is a command line tool for querying and modeling citation networks from the Astrophysical Data System (ADS) in a format compatible with Gephi, a popular network visualization tool. ads2gephi has been developed at the history of science department of TU Berlin as part of [a research project on the history of extragalactic astronomy](https://gepris.dfg.de/gepris/projekt/289438140?language=en) financed by the German Research Foundation DFG (PI Karin Pelte). You can install `ads2gephi` from PyPI: ``` pip install ads2gephi ``` ### Usage When using the tool for the first time to model a network, you will be prompted to enter your ADS API key. Your key will then be stored in a configuration file under ~/.ads2gephi. In order to sample an initial citation network, you need to provide ads2gephi with a plain text file with bibcodes (ADS unique identifiers), one per line, as input. The queried network will be output in a SQLite database stored in the current directory: ``` ads2gephi -c bibcodes_example.txt -d my_fancy_netzwerk.db ``` Afterwards you can extend the queried network by providing the existing database file and using the additional sampling options. For example, you can extend the network by querying all the items cited in every publication previously queried: ``` ads2gephi -s ref -d my_fancy_netzwerk.db ``` Finally you might want to also generate the edges of the network. There are several options for generating edges. For example you can use a semantic similarity measure like bibliographic coupling or co-citation: ``` ads2gephi -e bibcp -d my_fancy_netzwerk.db ``` You can also do everything at once: ``` ads2gephi -c bibcodes_example.txt -s ref -e bibcp -d my_fancy_netzwerk.db ``` All other querying and modelling options are described in the help page: ``` ads2gephi --help ``` Once you've finished querying and modeling, the database file can be directly imported in Gephi for network visualization and analysis. ## Special thanks to * Edwin Henneken %package help Summary: Development documents and examples for ads2gephi Provides: python3-ads2gephi-doc %description help [![PyPI version](https://badge.fury.io/py/ads2gephi.svg)](https://badge.fury.io/py/ads2gephi) ![CI](https://github.com/03b8/ads2gephi/actions/workflows/github-ci.yml/badge.svg) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) # ads2gephi is a command line tool for querying and modeling citation networks from the Astrophysical Data System (ADS) in a format compatible with Gephi, a popular network visualization tool. ads2gephi has been developed at the history of science department of TU Berlin as part of [a research project on the history of extragalactic astronomy](https://gepris.dfg.de/gepris/projekt/289438140?language=en) financed by the German Research Foundation DFG (PI Karin Pelte). You can install `ads2gephi` from PyPI: ``` pip install ads2gephi ``` ### Usage When using the tool for the first time to model a network, you will be prompted to enter your ADS API key. Your key will then be stored in a configuration file under ~/.ads2gephi. In order to sample an initial citation network, you need to provide ads2gephi with a plain text file with bibcodes (ADS unique identifiers), one per line, as input. The queried network will be output in a SQLite database stored in the current directory: ``` ads2gephi -c bibcodes_example.txt -d my_fancy_netzwerk.db ``` Afterwards you can extend the queried network by providing the existing database file and using the additional sampling options. For example, you can extend the network by querying all the items cited in every publication previously queried: ``` ads2gephi -s ref -d my_fancy_netzwerk.db ``` Finally you might want to also generate the edges of the network. There are several options for generating edges. For example you can use a semantic similarity measure like bibliographic coupling or co-citation: ``` ads2gephi -e bibcp -d my_fancy_netzwerk.db ``` You can also do everything at once: ``` ads2gephi -c bibcodes_example.txt -s ref -e bibcp -d my_fancy_netzwerk.db ``` All other querying and modelling options are described in the help page: ``` ads2gephi --help ``` Once you've finished querying and modeling, the database file can be directly imported in Gephi for network visualization and analysis. ## Special thanks to * Edwin Henneken %prep %autosetup -n ads2gephi-0.3.8 %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-ads2gephi -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon May 15 2023 Python_Bot - 0.3.8-1 - Package Spec generated