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Change","sub_category":"Earth and Climate Modeling","monthly_downloads":4656,"total_dependent_repos":4,"total_dependent_packages":0,"readme":"=========\npyunicorn\n=========\n\n.. image:: https://app.travis-ci.com/pik-copan/pyunicorn.svg?branch=master\n  :target: https://app.travis-ci.com/github/pik-copan/pyunicorn\n\n.. image:: https://codecov.io/gh/pik-copan/pyunicorn/branch/master/graph/badge.svg\n  :target: https://codecov.io/gh/pik-copan/pyunicorn\n\n.. image:: https://img.shields.io/pypi/v/pyunicorn\n  :target: https://pypi.org/project/pyunicorn/\n\n.. image:: https://img.shields.io/pypi/pyversions/pyunicorn\n  :target: https://pypi.org/project/pyunicorn/\n\n.. image:: https://zenodo.org/badge/33720178.svg\n  :target: https://zenodo.org/badge/latestdoi/33720178\n\nAbout\n=====\n``pyunicorn`` (**Uni**\\ fied **Co**\\ mplex Network and **R**\\ ecurre\\ **N**\\ ce\nanalysis toolbox) is an object-oriented Python package for the advanced analysis\nand modeling of complex networks. Beyond the standard **measures of complex\nnetwork theory** (such as *degree*, *betweenness* and *clustering coefficients*), it\nprovides some uncommon but interesting statistics like *Newman's random walk\nbetweenness*. ``pyunicorn`` also provides novel **node-weighted** *(node splitting invariant)*\nnetwork statistics, measures for analyzing networks of **interacting/interdependent\nnetworks**, and special tools to model **spatially embedded** complex networks.\n\nMoreover, ``pyunicorn`` allows one to easily *construct networks* from uni- and\nmultivariate time series and event data (**functional/climate networks** and\n**recurrence networks**). This involves linear and nonlinear measures of\n**time series analysis** for constructing functional networks from multivariate data\n(e.g., *Pearson correlation*, *mutual information*, *event synchronization* and *event\ncoincidence analysis*). ``pyunicorn`` also features modern techniques of\nnonlinear analysis of time series (or pairs thereof), such as *recurrence\nquantification analysis* (RQA), *recurrence network analysis* and *visibility\ngraphs*.\n\n``pyunicorn`` is **fast**, because all costly computations are performed in\ncompiled C code. It can handle **large networks** through the\nuse of sparse data structures. The package can be used interactively, from any\nPython script, and even for parallel computations on large cluster architectures.\nFor information about individual releases,\nsee our `CHANGELOG \u003cCHANGELOG.rst\u003e`_ and `CONTRIBUTIONS \u003cCONTRIBUTIONS.rst\u003e`_.\n\n\nLicense\n-------\n``pyunicorn`` is `BSD-licensed \u003cLICENSE.txt\u003e`_ (3 clause).\n\nReference\n---------\n*Please acknowledge and cite the use of this software and its authors when\nresults are used in publications or published elsewhere. You can use the\nfollowing reference:*\n\n    J.F. Donges, J. Heitzig, B. Beronov, M. Wiedermann, J. Runge, Q.-Y. Feng,\n    L. Tupikina, V. Stolbova, R.V. Donner, N. Marwan, H.A. Dijkstra, and J.\n    Kurths.\n    **\"Unified functional network and nonlinear time series analysis for complex\n    systems science: The pyunicorn package\"**.\n    Chaos 25, 113101 (2015), `doi:10.1063/1.4934554\n    \u003chttp://dx.doi.org/10.1063/1.4934554\u003e`_, Preprint: `arxiv.org:1507.01571\n    \u003chttp://arxiv.org/abs/1507.01571\u003e`_ [physics.data-an].\n\nFunding\n-------\nThe development of ``pyunicorn`` has been supported by various funding sources,\nnotably the `German Federal Ministry for Education and Research\n\u003chttps://www.bmbf.de/bmbf/en/home/home_node.html\u003e`_ (projects `GOTHAM\n\u003chttps://www.belmontforum.org/projects\u003e`_ and `CoSy-CC2\n\u003chttp://cosy.pik-potsdam.de/\u003e`_), the `Leibniz Association\n\u003chttps://www.leibniz-gemeinschaft.de/en/\u003e`_ (projects `ECONS\n\u003chttp://econs.pik-potsdam.de/\u003e`_ and `DominoES\n\u003chttps://www.pik-potsdam.de/en/institute/departments/activities/dominoes\u003e`_),\nthe `German National Academic Foundation \u003chttps://www.studienstiftung.de/en/\u003e`_,\nand the `Stordalen Foundation \u003chttp://www.stordalenfoundation.no/\u003e`_ via the\n`Planetary Boundary Research Network\n\u003chttps://web.archive.org/web/20200212214011/http://pb-net.org/\u003e`_ (PB.net) among\nothers.\n\nGetting Started\n===============\n\nInstallation\n------------\nOfficial releases\n.................\n`Stable releases \u003chttps://pypi.org/project/pyunicorn/#history\u003e`_ can be\ninstalled from the Python Package Index, using a Python package manager such as\n`pip \u003chttps://pip.pypa.io/en/latest/user_guide/\u003e`__ or\n`uv \u003chttps://docs.astral.sh/uv/\u003e`__::\n\n    $\u003e pip install pyunicorn\n\nAlternatively, source distributions can be downloaded from the\n`GitHub Releases \u003chttps://github.com/pik-copan/pyunicorn/releases\u003e`_.\n\nOn **Windows**, please *first* install the latest version of the\n`Microsoft C++ Build Tools \u003chttps://wiki.python.org/moin/WindowsCompilers\u003e`_,\nwhich is required for compiling Cython modules.\n\nCurrent development version\n...........................\nIn order to use a `newer version \u003chttps://github.com/pik-copan/pyunicorn\u003e`_,\nplease follow the `pip\n\u003chttps://packaging.python.org/en/latest/tutorials/installing-packages/#installing-from-vcs\u003e`__\nor `uv \u003chttps://docs.astral.sh/uv/concepts/projects/dependencies/#git\u003e`__\ninstructions for installing from version control.\n\nDependencies\n............\n``pyunicorn`` is implemented in `Python 3 \u003chttps://docs.python.org/3/\u003e`_ /\n`Cython 3 \u003chttps://cython.org/\u003e`_, is `tested\n\u003chttps://app.travis-ci.com/github/pik-copan/pyunicorn\u003e`_ on *Linux*\nand *Windows*, and relies on the following packages:\n\n- Required:\n\n  - `numpy \u003chttp://www.numpy.org/\u003e`_, `scipy \u003chttp://www.scipy.org/\u003e`_\n  - `python-igraph \u003chttp://igraph.org/\u003e`_ (for ``Network``)\n  - `h5netcdf \u003chttps://h5netcdf.org/\u003e`_ (for ``Data``, ``NetCDFDictionary``)\n  - `tqdm \u003chttps://tqdm.github.io/\u003e`_ (for progress bars)\n\n- Optional:\n\n  - `Matplotlib \u003chttp://matplotlib.org/\u003e`_,\n    `Cartopy \u003chttps://scitools.org.uk/cartopy/docs/latest/index.html\u003e`_\n    (for plotting features)\n  - `mpi4py \u003chttps://github.com/mpi4py/mpi4py\u003e`_\n    (for parallelizing costly computations)\n  - `Sphinx \u003chttp://sphinx-doc.org/\u003e`_\n    (for generating documentation)\n  - `Jupyter Notebook \u003chttps://jupyter-notebook.readthedocs.io/en/latest/\u003e`_\n    (for tutorial notebooks)\n\n\nDocumentation\n-------------\nFor extensive HTML documentation, jump right to the `homepage\n\u003chttp://www.pik-potsdam.de/~donges/pyunicorn/\u003e`_. In a local source tree,\nHTML and PDF documentation can be generated using ``Sphinx``::\n\n    $\u003e pip install --group docs\n    $\u003e cd docs; make clean html latexpdf\n\nTutorials\n---------\n\nFor some example applications look into the\n`tutorials \u003chttps://github.com/pik-copan/pyunicorn/tree/master/docs/source/examples/tutorials\u003e`_ provided with the documentation.\nThey are designed to be self-explanatory, and are set up as Jupyter notebooks.\n\nDevelopment\n===========\n\nTest suite\n----------\nBefore committing changes or opening a pull request (PR) to the code base,\nplease make sure that all tests pass. The test suite is managed by `tox\n\u003chttps://tox.wiki/\u003e`_ and is configured to use system-wide packages\nwhen available. Install the test dependencies as follows::\n\n    $\u003e pip install --group tests\n\nThe test suite can be run from anywhere in the project tree by issuing::\n\n    $\u003e tox\n\nTo display the defined test environments and target them individually::\n\n    $\u003e tox -l\n    $\u003e tox -e style,lint,test,docs\n\nTo test individual files::\n\n    $\u003e flake8 src/pyunicorn/core/network.py     # style check\n    $\u003e pylint src/pyunicorn/core/network.py     # static code analysis\n    $\u003e pytest tests/test_core/test_network.py   # unit tests\n","funding_links":[],"readme_doi_urls":["http://dx.doi.org/10.1063/1.4934554"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["earth-science","energy-system-model","agent-based-framework","agent-based-modeling","earth-system-model","networks","sustainability"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/194048","html_url":"https://ost.ecosyste.ms/projects/194048"}