## Documentation¶

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:

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


## Reference¶

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].

## Platforms and Compatibility¶

pyunicorn is written in Python 2.7. The software is quite flexible, we have it running on Linux and MacOSX machines, the institute’s IBM iDataPlex cluster and even on Windows.

## Dependencies¶

pyunicorn relies on the following open source or freely available packages which have to be installed on your machine.

Required:
Optional (used only in certain classes and methods):

Numpy, Scipy, Matplotlib, igraph and other packages should be available via a package management system on Linux or MacOSX. All packages can be downloaded, compiled and installed following the instructions on their homepages.

An easy way to go may be a Python distribution like Anaconda or Enthought that already include many libraries.

## Installing¶

Stable release

Via the Python Package Index:

$> pip install pyunicorn  Development version For a simple system-wide installation: $> pip install .


Depending on your system, you may need root privileges. On UNIX-based operating systems (Linux, Mac OS X etc.) this is achieved with sudo.

For development, especially if you want to test pyunicorn from within the source directory:

\$> pip install --user -e .