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%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
[](https://badge.fury.io/py/ads2gephi)
[](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
[](https://badge.fury.io/py/ads2gephi)
[](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
[](https://badge.fury.io/py/ads2gephi)
[](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
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.8-1
- Package Spec generated
|
