%global _empty_manifest_terminate_build 0 Name: python-cdlib Version: 0.2.6 Release: 1 Summary: Community Discovery Library License: BSD-Clause-2 URL: https://github.com/GiulioRossetti/cdlib Source0: https://mirrors.nju.edu.cn/pypi/web/packages/eb/6d/97167dce848b65023a272e2ffd04b2e462612efdb3538d16e2b8b2221a15/cdlib-0.2.6.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-future Requires: python3-matplotlib Requires: python3-scikit-learn Requires: python3-tqdm Requires: python3-networkx Requires: python3-demon Requires: python3-louvain Requires: python3-nf1 Requires: python3-scipy Requires: python3-pulp Requires: python3-seaborn Requires: python3-pandas Requires: python3-eva-lcd Requires: python3-bimlpa Requires: python3-markov-clustering Requires: python3-chinese-whispers Requires: python3-igraph Requires: python3-angel-cd Requires: python3-pooch Requires: python3-dynetx Requires: python3-thresholdclustering Requires: python3-pyclustering Requires: python3-cython Requires: python3-Levenshtein Requires: python3-infomap Requires: python3-wurlitzer Requires: python3-GraphRicciCurvature Requires: python3-networkit Requires: python3-pycombo Requires: python3-leidenalg Requires: python3-karateclub %description ``CDlib`` is a meta-library for community discovery in complex networks: it implements algorithms, clustering fitness functions as well as visualization facilities. ``CDlib`` is designed around the ``networkx`` python library: however, when needed, it takes care to automatically convert (from and to) ``igraph`` object so to provide an abstraction on specific algorithm implementations to the final user. ``CDlib`` provides a standardized input/output facilities for several Community Discovery algorithms: whenever possible, to guarantee literature coherent results, implementations of CD algorithms are inherited from their original projects (acknowledged on the [documentation](https://cdlib.readthedocs.io)). If you use ``CDlib`` as support to your research consider citing: > G. Rossetti, L. Milli, R. Cazabet. > **CDlib: a Python Library to Extract, Compare and Evaluate Communities from Complex Networks.** > Applied Network Science Journal. 2019. > [DOI:10.1007/s41109-019-0165-9]() ## Tutorial and Online Environments Check out the official [tutorial](https://colab.research.google.com/github/GiulioRossetti/cdlib/blob/master/docs/CDlib.ipynb) to get started! If you would like to test ``CDlib`` functionalities without installing anything on your machine consider using the preconfigured Jupyter Hub instances offered by [SoBigData++](https://sobigdata.d4science.org/group/sobigdata-gateway/explore?siteId=20371853). ## Installation ``CDlib`` *requires* python>=3.8. To install the latest version of our library just download (or clone) the current project, open a terminal and run the following commands: ```bash pip install -r requirements.txt pip install -r requirements_optional.txt # (Optional) this might not work in Windows systems due to C-based dependencies. pip install . ``` Alternatively use pip ```bash pip install cdlib ``` or conda ```bash conda config --add channels giuliorossetti conda config --add channels conda-forge conda install cdlib ``` ### Optional Dependencies (pip package) ``CDlib`` relies on a few packages calling C code that can be cumbersome to install on Windows machines: to address such issue, the default installation does not try to install set up such requirements. Such a choice has been made to allow (even) non *unix user to install the library and get access to its core functionalities. To integrate the standard installation with you can either: - (Windows) manually install the optional packages (versions details are specified in ``requirements_optional.txt``) following the original projects guidelines, or - (Linux/OSX) run the command: ```bash pip install cdlib[C] ``` Such caveat will install everything that can be easily automated under Linux/OSX. #### (Advanced) ##### Graph-tool The only optional dependency that will remain unsatisfied following the previous procedures will be ``graph-tool`` (used to add SBM models). If you need it up and running, refer to the official [documentation](https://git.skewed.de/count0/graph-tool/wikis/installation-instructions) and install the conda-forge version of the package. ##### ASLPAw Since its 2.1.0 release ``ASLPAw`` relies on ``gmpy2`` whose installation through pip is not easy to automatize due to some C dependencies. To address such issue test the following recipe: ```bash conda install gmpy2 pip install shuffle_graph>=2.1.0 similarity-index-of-label-graph>=2.0.1 ASLPAw>=2.1.0 ``` In case this does not solve the issue, please refer to the official ``gmpy2`` [installation](https://gmpy2.readthedocs.io/en/latest/intro.html#installation) instructions. ### Optional Dependencies (Conda package) ``CDlib`` relies on a few packages not available through conda: to install it please use pip: ```bash pip install pycombo pip install GraphRicciCurvature conda install gmpy2 pip install shuffle_graph>=2.1.0 similarity-index-of-label-graph>=2.0.1 ASLPAw>=2.1.0 ``` In case ASLPAw installation fails, please refer to the official ``gmpy2`` [installation](https://gmpy2.readthedocs.io/en/latest/intro.html#installation) instructions. ## Collaborate with us! ``CDlib`` is an active project, any contribution is welcome! If you like to include your model in CDlib feel free to fork the project, open an issue and contact us. ### How to contribute to this project? Contributing is good, doing it correctly is better! Check out our [rules](https://github.com/GiulioRossetti/cdlib/blob/master/.github/CONTRIBUTING.md), issue a proper [pull request](https://github.com/GiulioRossetti/cdlib/blob/master/.github/PULL_REQUEST_TEMPLATE.md) /[bug report](https://github.com/GiulioRossetti/cdlib/blob/master/.github/ISSUE_TEMPLATE/bug_report.md) / [feature request](https://github.com/GiulioRossetti/cdlib/blob/master/.github/ISSUE_TEMPLATE/feature_request.md). We are a welcoming community... just follow the [Code of Conduct](https://github.com/GiulioRossetti/cdlib/blob/master/.github/CODE_OF_CONDUCT.md). %package -n python3-cdlib Summary: Community Discovery Library Provides: python-cdlib BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-cdlib ``CDlib`` is a meta-library for community discovery in complex networks: it implements algorithms, clustering fitness functions as well as visualization facilities. ``CDlib`` is designed around the ``networkx`` python library: however, when needed, it takes care to automatically convert (from and to) ``igraph`` object so to provide an abstraction on specific algorithm implementations to the final user. ``CDlib`` provides a standardized input/output facilities for several Community Discovery algorithms: whenever possible, to guarantee literature coherent results, implementations of CD algorithms are inherited from their original projects (acknowledged on the [documentation](https://cdlib.readthedocs.io)). If you use ``CDlib`` as support to your research consider citing: > G. Rossetti, L. Milli, R. Cazabet. > **CDlib: a Python Library to Extract, Compare and Evaluate Communities from Complex Networks.** > Applied Network Science Journal. 2019. > [DOI:10.1007/s41109-019-0165-9]() ## Tutorial and Online Environments Check out the official [tutorial](https://colab.research.google.com/github/GiulioRossetti/cdlib/blob/master/docs/CDlib.ipynb) to get started! If you would like to test ``CDlib`` functionalities without installing anything on your machine consider using the preconfigured Jupyter Hub instances offered by [SoBigData++](https://sobigdata.d4science.org/group/sobigdata-gateway/explore?siteId=20371853). ## Installation ``CDlib`` *requires* python>=3.8. To install the latest version of our library just download (or clone) the current project, open a terminal and run the following commands: ```bash pip install -r requirements.txt pip install -r requirements_optional.txt # (Optional) this might not work in Windows systems due to C-based dependencies. pip install . ``` Alternatively use pip ```bash pip install cdlib ``` or conda ```bash conda config --add channels giuliorossetti conda config --add channels conda-forge conda install cdlib ``` ### Optional Dependencies (pip package) ``CDlib`` relies on a few packages calling C code that can be cumbersome to install on Windows machines: to address such issue, the default installation does not try to install set up such requirements. Such a choice has been made to allow (even) non *unix user to install the library and get access to its core functionalities. To integrate the standard installation with you can either: - (Windows) manually install the optional packages (versions details are specified in ``requirements_optional.txt``) following the original projects guidelines, or - (Linux/OSX) run the command: ```bash pip install cdlib[C] ``` Such caveat will install everything that can be easily automated under Linux/OSX. #### (Advanced) ##### Graph-tool The only optional dependency that will remain unsatisfied following the previous procedures will be ``graph-tool`` (used to add SBM models). If you need it up and running, refer to the official [documentation](https://git.skewed.de/count0/graph-tool/wikis/installation-instructions) and install the conda-forge version of the package. ##### ASLPAw Since its 2.1.0 release ``ASLPAw`` relies on ``gmpy2`` whose installation through pip is not easy to automatize due to some C dependencies. To address such issue test the following recipe: ```bash conda install gmpy2 pip install shuffle_graph>=2.1.0 similarity-index-of-label-graph>=2.0.1 ASLPAw>=2.1.0 ``` In case this does not solve the issue, please refer to the official ``gmpy2`` [installation](https://gmpy2.readthedocs.io/en/latest/intro.html#installation) instructions. ### Optional Dependencies (Conda package) ``CDlib`` relies on a few packages not available through conda: to install it please use pip: ```bash pip install pycombo pip install GraphRicciCurvature conda install gmpy2 pip install shuffle_graph>=2.1.0 similarity-index-of-label-graph>=2.0.1 ASLPAw>=2.1.0 ``` In case ASLPAw installation fails, please refer to the official ``gmpy2`` [installation](https://gmpy2.readthedocs.io/en/latest/intro.html#installation) instructions. ## Collaborate with us! ``CDlib`` is an active project, any contribution is welcome! If you like to include your model in CDlib feel free to fork the project, open an issue and contact us. ### How to contribute to this project? Contributing is good, doing it correctly is better! Check out our [rules](https://github.com/GiulioRossetti/cdlib/blob/master/.github/CONTRIBUTING.md), issue a proper [pull request](https://github.com/GiulioRossetti/cdlib/blob/master/.github/PULL_REQUEST_TEMPLATE.md) /[bug report](https://github.com/GiulioRossetti/cdlib/blob/master/.github/ISSUE_TEMPLATE/bug_report.md) / [feature request](https://github.com/GiulioRossetti/cdlib/blob/master/.github/ISSUE_TEMPLATE/feature_request.md). We are a welcoming community... just follow the [Code of Conduct](https://github.com/GiulioRossetti/cdlib/blob/master/.github/CODE_OF_CONDUCT.md). %package help Summary: Development documents and examples for cdlib Provides: python3-cdlib-doc %description help ``CDlib`` is a meta-library for community discovery in complex networks: it implements algorithms, clustering fitness functions as well as visualization facilities. ``CDlib`` is designed around the ``networkx`` python library: however, when needed, it takes care to automatically convert (from and to) ``igraph`` object so to provide an abstraction on specific algorithm implementations to the final user. ``CDlib`` provides a standardized input/output facilities for several Community Discovery algorithms: whenever possible, to guarantee literature coherent results, implementations of CD algorithms are inherited from their original projects (acknowledged on the [documentation](https://cdlib.readthedocs.io)). If you use ``CDlib`` as support to your research consider citing: > G. Rossetti, L. Milli, R. Cazabet. > **CDlib: a Python Library to Extract, Compare and Evaluate Communities from Complex Networks.** > Applied Network Science Journal. 2019. > [DOI:10.1007/s41109-019-0165-9]() ## Tutorial and Online Environments Check out the official [tutorial](https://colab.research.google.com/github/GiulioRossetti/cdlib/blob/master/docs/CDlib.ipynb) to get started! If you would like to test ``CDlib`` functionalities without installing anything on your machine consider using the preconfigured Jupyter Hub instances offered by [SoBigData++](https://sobigdata.d4science.org/group/sobigdata-gateway/explore?siteId=20371853). ## Installation ``CDlib`` *requires* python>=3.8. To install the latest version of our library just download (or clone) the current project, open a terminal and run the following commands: ```bash pip install -r requirements.txt pip install -r requirements_optional.txt # (Optional) this might not work in Windows systems due to C-based dependencies. pip install . ``` Alternatively use pip ```bash pip install cdlib ``` or conda ```bash conda config --add channels giuliorossetti conda config --add channels conda-forge conda install cdlib ``` ### Optional Dependencies (pip package) ``CDlib`` relies on a few packages calling C code that can be cumbersome to install on Windows machines: to address such issue, the default installation does not try to install set up such requirements. Such a choice has been made to allow (even) non *unix user to install the library and get access to its core functionalities. To integrate the standard installation with you can either: - (Windows) manually install the optional packages (versions details are specified in ``requirements_optional.txt``) following the original projects guidelines, or - (Linux/OSX) run the command: ```bash pip install cdlib[C] ``` Such caveat will install everything that can be easily automated under Linux/OSX. #### (Advanced) ##### Graph-tool The only optional dependency that will remain unsatisfied following the previous procedures will be ``graph-tool`` (used to add SBM models). If you need it up and running, refer to the official [documentation](https://git.skewed.de/count0/graph-tool/wikis/installation-instructions) and install the conda-forge version of the package. ##### ASLPAw Since its 2.1.0 release ``ASLPAw`` relies on ``gmpy2`` whose installation through pip is not easy to automatize due to some C dependencies. To address such issue test the following recipe: ```bash conda install gmpy2 pip install shuffle_graph>=2.1.0 similarity-index-of-label-graph>=2.0.1 ASLPAw>=2.1.0 ``` In case this does not solve the issue, please refer to the official ``gmpy2`` [installation](https://gmpy2.readthedocs.io/en/latest/intro.html#installation) instructions. ### Optional Dependencies (Conda package) ``CDlib`` relies on a few packages not available through conda: to install it please use pip: ```bash pip install pycombo pip install GraphRicciCurvature conda install gmpy2 pip install shuffle_graph>=2.1.0 similarity-index-of-label-graph>=2.0.1 ASLPAw>=2.1.0 ``` In case ASLPAw installation fails, please refer to the official ``gmpy2`` [installation](https://gmpy2.readthedocs.io/en/latest/intro.html#installation) instructions. ## Collaborate with us! ``CDlib`` is an active project, any contribution is welcome! If you like to include your model in CDlib feel free to fork the project, open an issue and contact us. ### How to contribute to this project? Contributing is good, doing it correctly is better! Check out our [rules](https://github.com/GiulioRossetti/cdlib/blob/master/.github/CONTRIBUTING.md), issue a proper [pull request](https://github.com/GiulioRossetti/cdlib/blob/master/.github/PULL_REQUEST_TEMPLATE.md) /[bug report](https://github.com/GiulioRossetti/cdlib/blob/master/.github/ISSUE_TEMPLATE/bug_report.md) / [feature request](https://github.com/GiulioRossetti/cdlib/blob/master/.github/ISSUE_TEMPLATE/feature_request.md). We are a welcoming community... just follow the [Code of Conduct](https://github.com/GiulioRossetti/cdlib/blob/master/.github/CODE_OF_CONDUCT.md). %prep %autosetup -n cdlib-0.2.6 %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-cdlib -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Wed May 10 2023 Python_Bot - 0.2.6-1 - Package Spec generated