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
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:
- Optional (used only in certain classes and methods):
- PyNGL
(for
NetCDFDictionary) - netcdf4-python
(for
DataandNetCDFDictionary) - Matplotlib
- Matplotlib Basemap Toolkit (for drawing maps)
- mpi4py (for parallelizing costly computations)
- Sphinx (for generating documentation)
- PyNGL
(for
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.