pyunicorn (Unified Complex Network and RecurreNce analysis toolbox) is a fully object-oriented Python package for the advanced analysis and modeling of complex networks. Above the standard measures of complex network theory such as degree, betweenness and clustering coefficient it provides some uncommon but interesting statistics like Newman's random walk betweenness. pyunicorn features novel node-weighted (node splitting invariant) network statistics as well as measures designed for analyzing networks of interacting/interdependent networks.

Moreover, pyunicorn allows to easily construct networks from uni- and multivariate time series and event data (functional (climate) networks and recurrence networks). This involves linear and nonlinear measures of time series analysis for constructing functional networks from multivariate data (e.g. Pearson correlation, mutual information, event synchronization and event coincidence analysis). pyunicorn also features modern techniques of nonlinear analysis of single and pairs of time series such as recurrence quantification analysis (RQA), recurrence network analysis and visibility graphs.

Please acknowledge and cite the use of this software and its authors when results are used in publications or published elsewhere. You can use the following reference:

J.F. Donges, J. Heitzig, B. Beronov, M. Wiedermann, J. Runge, Q.-Y. Feng, L. Tupikina, V. Stolbova, R.V. Donner, N. Marwan, H.A. Dijkstra, and J. Kurths, Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package, Chaos 25, 113101 (2015), doi:10.1063/1.4934554, Preprint: arxiv.org:1507.01571 [physics.data-an].

The development of pyunicorn has been supported by various funding sources, notably the German Federal Ministry for Education and Research (projects GOTHAM and CoSy-CC2), the Leibniz Association (projects ECONS and DominoES), the German National Academic Foundation, and the Stordalen Foundation via the Planetary Boundary Research Network (PB.net) among others.

pyunicorn is BSD-licensed (3 clause).

Stable releases, Development version

Changelog, Contributions

For extensive HTML documentation, jump right to the pyunicorn homepage. Recent PDF versions are also available.

On a local development version, HTML and PDF documentation can be generated using Sphinx:

$> pip install --user .[docs]
$> cd docs; make clean html latexpdf

pyunicorn is implemented in Python 3. The software is written and tested on Linux and MacOSX, but it is also in active use on Windows. pyunicorn relies on the following open source or freely available packages, which need to be installed on your machine. For exact dependency information, see setup.cfg.

Required at runtime:

• Numpy
• Scipy
• python-igraph

Optional (used only in certain classes and methods):

• PyNGL (for NetCDFDictionary)
• netcdf4-python (for Data and NetCDFDictionary)
• Matplotlib
• Matplotlib Basemap Toolkit (for drawing maps)
• mpi4py (for parallelizing costly computations)
• Sphinx (for generating documentation)

To install these dependencies, please follow the instructions for your system's package manager or consult the libraries' homepages. An easy way to go may be a Python distribution like Anaconda that already includes many libraries.

Before installing pyunicorn itself, we recommend to make sure that the required dependencies are installed using your preferred installation method for Python libraries. Afterwards, the package can be installed in the standard way from the Python Package Index (PyPI).

Linux, MacOSX

With the pip package manager:

$> pip install pyunicorn

On Fedora OS, use:

$> dnf install python3-pyunicorn

Windows

Install the latest version of the Microsoft C++ Build Tools, and then:

$> pip install pyunicorn

Development version

To use a newer version of pyunicorn than the latest official release on PyPI, download the source code from the Github repository and, instead of the above, execute:

$> pip install -e .

Before committing changes or opening a pull request (PR) to the code base, please make sure that all tests pass. The test suite is managed by tox and configured to use system-wide packages when available. Install the test dependencies as follows:

$> pip install .[testing]

The test suite can be run from anywhere in the project tree by issuing:

$> tox

To display the defined test environments and target them individually:

$> tox -l
$> tox -e units,pylint,docs

To test individual files:

$> pytest           tests/test_core/TestNetwork.py   # unit tests
$> pytest --flake8  pyunicorn/core/network.py        # style
$> pylint           pyunicorn/core/network.py        # static code analysis

Not implemented yet.