pyUnicorn - UNIfied COmplex Network and Recurrence aNalysis toolbox

(Python package)

pyunicorn

pyunicorn (Uni\ fied Co\ mplex Network and R\ ecurre\ N\ ce
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.

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:

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

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

Dependencies

pyunicorn is written in Python 3.7. The software is quite flexible, we have
it running on Linux and MacOSX machines, the institute's IBM iDataPlex cluster
and even on Windows. It relies on the following open source or freely available
packages which have to be installed on your machine.

Optional (used only in certain classes and methods):
- PyNGL (for class NetCDFDictionary)
- netcdf4-python (for classes
Data and NetCDFDictionary)
- Matplotlib 2.0+
- Matplotlib Basemap Toolkit (for drawing
maps)
- mpi4py (for parallelizing costly
computations)
- Sphinx (for generating documentation)
- Cython 0.27+ (for compiling code during
development)

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.

Development version
For a simple system-wide installation:

$> pip install -r requirements.txt .
nding on your system, you may need root privileges. On UNIX-based
ating systems (Linux, Mac OS X etc.) this is achieved with ``sudo``.
development, especially if you want to test ``pyunicorn`` from within
source directory::
$> pip install -r requirements.txt --user -e .

Test suite

Before committing changes 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. Thus to avoid frequent
waiting, we recommend you to install the current versions of the following
packages:

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, 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

Please respect the copyrights! The content is protected
by the Creative
Commons License. If you use the provided programmes, text or figures,
you have to refer to the given publications and this web site
(tocsy.pik-potsdam.de)
as well.