pyunicorn

Unified Complex Network and Recurrence Analysis Toolbox

https://github.com/pik-copan/pyunicorn

Keywords from Contributors

agent-based-framework agent-based-modeling earth-science earth-system-model networks sustainability

Last synced: 6 months ago · JSON representation ·

Repository

Unified Complex Network and Recurrence Analysis Toolbox

Basic Info

Host: GitHub
Owner: pik-copan
License: other
Language: Python
Default Branch: master
Homepage: http://pik-potsdam.de/~donges/pyunicorn/
Size: 34.4 MB

Statistics

Stars: 212
Watchers: 15
Forks: 88
Open Issues: 38
Releases: 9

Created almost 11 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog License Citation

README.rst

=========
pyunicorn
=========

.. image:: https://app.travis-ci.com/pik-copan/pyunicorn.svg?branch=master
  :target: https://app.travis-ci.com/github/pik-copan/pyunicorn

.. image:: https://codecov.io/gh/pik-copan/pyunicorn/branch/master/graph/badge.svg
  :target: https://codecov.io/gh/pik-copan/pyunicorn

.. image:: https://img.shields.io/pypi/v/pyunicorn
  :target: https://pypi.org/project/pyunicorn/

.. image:: https://img.shields.io/pypi/pyversions/pyunicorn
  :target: https://pypi.org/project/pyunicorn/

.. image:: https://zenodo.org/badge/33720178.svg
  :target: https://zenodo.org/badge/latestdoi/33720178

About
=====
``pyunicorn`` (**Uni**\ fied **Co**\ mplex Network and **R**\ ecurre\ **N**\ ce
analysis toolbox) is an object-oriented Python package for the advanced analysis
and modeling of complex networks. Beyond the standard **measures of complex
network theory** (such as *degree*, *betweenness* and *clustering coefficients*), it
provides some uncommon but interesting statistics like *Newman's random walk
betweenness*. ``pyunicorn`` also provides novel **node-weighted** *(node splitting invariant)*
network statistics, measures for analyzing networks of **interacting/interdependent
networks**, and special tools to model **spatially embedded** complex networks.

Moreover, ``pyunicorn`` allows one 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 time series (or pairs thereof), such as *recurrence
quantification analysis* (RQA), *recurrence network analysis* and *visibility
graphs*.

``pyunicorn`` is **fast**, because all costly computations are performed in
compiled C code. It can handle **large networks** through the
use of sparse data structures. The package can be used interactively, from any
Python script, and even for parallel computations on large cluster architectures.
For information about individual releases,
see our `CHANGELOG `_ and `CONTRIBUTIONS `_.


License
-------
``pyunicorn`` is `BSD-licensed `_ (3 clause).

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].

Funding
-------
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.

Getting Started
===============

Installation
------------
Official releases
.................
`Stable releases `_ can be
installed directly from the `Python Package Index (PyPI)
`_::

    $> pip install pyunicorn

Alternatively, source distributions can be downloaded from the
`GitHub Releases `_.

On **Windows**, please *first* install the latest version of the `Microsoft C++ Build
Tools `_, which is required for
compiling Cython modules.

Current development version
...........................
In order to use a `newer version `_,
please follow the ``pip`` instructions for installing from `version control
`_
or from a `local source tree
`_.

Dependencies
............
``pyunicorn`` is implemented in `Python 3 `_ /
`Cython 3 `_, is `tested
`_ on *Linux*
and *Windows*, and relies on the following packages:

- Required:

  - `numpy `_, `scipy `_
  - `python-igraph `_ (for ``Network``)
  - `h5netcdf `_ (for ``Data``, ``NetCDFDictionary``)
  - `tqdm `_ (for progress bars)

- Optional:

  - `Matplotlib `_,
    `Cartopy `_
    (for plotting features)
  - `mpi4py `_
    (for parallelizing costly computations)
  - `Sphinx `_
    (for generating documentation)
  - `Jupyter Notebook `_
    (for tutorial notebooks)


Documentation
-------------
For extensive HTML documentation, jump right to the `homepage
`_. In a local source tree,
HTML and PDF documentation can be generated using ``Sphinx``::

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

Tutorials
---------

For some example applications look into the
`tutorials `_ provided with the documentation.
They are designed to be self-explanatory, and are set up as Jupyter notebooks.

Development
===========

Test suite
----------
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 is configured to use system-wide packages
when available. Install the test dependencies as follows::

    $> pip install -e .[tests]

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 style,lint,test,docs

To test individual files::

    $> flake8 src/pyunicorn/core/network.py     # style check
    $> pylint src/pyunicorn/core/network.py     # static code analysis
    $> pytest tests/test_core/test_network.py   # unit tests

Owner

Name: copan
Login: pik-copan
Kind: organization
Email: donges@pik-potsdam.de
Location: Potsdam (Germany)

Website: https://www.pik-potsdam.de/copan
Twitter: pik_copan
Repositories: 6
Profile: https://github.com/pik-copan

Coevolutionary Pathways – collaboration at Potsdam Institute for Climate Impact Research (PIK)

Citation (CITATION.cff)

cff-version: 1.2.0
message: If you use this software, please cite it using the metadata provided below.
type: software
title: 'Pyunicorn: Unified Complex Network and Recurrence Analysis Toolbox'
abstract: pyunicorn (Unified Complex Network and RecurreNce analysis toolbox) is an
  object-oriented Python package for the advanced analysis and modeling of complex networks.
  Beyond the standard measures of complex network theory (such as degree, betweenness and
  clustering coefficients), it provides some uncommon but interesting statistics like
  Newman's random walk betweenness. pyunicorn also provides novel node-weighted (node
  splitting invariant) network statistics, measures for analyzing networks of
  interacting/interdependent networks, and special tools to model spatially embedded complex
  networks.

  Moreover, pyunicorn allows one 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 time series (or pairs thereof), such as recurrence quantification
  analysis (RQA), recurrence network analysis and visibility graphs.

  pyunicorn is fast, because all costly computations are performed in compiled C code. It can
  handle large networks through the use of sparse data structures. The package can be used
  interactively, from any Python script, and even for parallel computations on large cluster
  architectures.
authors:
- family-names: Donges
  given-names: Jonathan
  email: donges@pik-potsdam.de
  orcid: https://orcid.org/0000-0001-5233-7703
- family-names: Heitzig
  given-names: Jobst
  email: jobst.heitzig@pik-potsdam.de
  orcid: https://orcid.org/0000-0002-0442-8077
- family-names: Beronov
  given-names: Boyan
  email: beronov@pik-potsdam.de
  orcid: https://orcid.org/0000-0002-0900-752X
- family-names: Kühlein
  given-names: Fritz
  email: fritzku@pik-potsdam.de
- family-names: Bechthold
  given-names: Max
  email: maxbecht@pik-potsdam.de
  orcid: https://orcid.org/0009-0007-7113-4814
- family-names: Kroenke
  given-names: Jonathan
  email: kroenke@pik-potsdam.de
- family-names: Barfuss
  given-names: Wolfram
  email: barfuss@uni-bonn.de
  orcid: https://orcid.org/0000-0002-9077-5242
- family-names: Harmening
  given-names: Nils
- family-names: Nascimento Silva
  given-names: Filipi
- family-names: Kassel
  given-names: Johannes
- family-names: Ziehbarth
  given-names: Malte
- family-names: Odenweller
  given-names: Adrian
- family-names: Tzinis
  given-names: Efthymios
- family-names: Hotz
  given-names: Ronja

license: GPL-3.0
repository-code: https://github.com/pik-copan/pyunicorn

preferred-citation:
  type: article
  title: 'Unified functional network and nonlinear time series analysis for complex
    systems science: The pyunicorn package'
  authors:
  - family-names: Donges
    given-names: Jonathan
    email: donges@pik-potsdam.de
    orcid: https://orcid.org/0000-0001-5233-7703
  - family-names: Heitzig
    given-names: Jobst
    email: jobst.heitzig@pik-potsdam.de
    orcid: https://orcid.org/0000-0002-0442-8077
  - family-names: Beronov
    given-names: Boyan
    email: beronov@pik-potsdam.de
    orcid: https://orcid.org/0000-0002-0900-752X
  - family-names: Wiedermann
    given-names: Marc
    orcid: https://orcid.org/0000-0001-9869-3789
  - family-names: Runge
    given-names: Jakob
  - family-names: Feng
    given-names: Quing Yi
  - family-names: Tupikina
    given-names: Liubov
  - family-names: Stolbova
    given-names: Veronika
  - family-names: Donner
    given-names: Reik V.
    email: redonner@pik-potsdam.de
    orcid: https://orcid.org/0000-0001-7023-6375
  - family-names: Marwan
    given-names: Norbert
    email: marwan@pik-potsdam.de
    orcid: https://orcid.org/0000-0003-1437-7039
  - family-names: Dijkstra
    given-names: Henk A.
  - family-names: Kurths
    given-names: Jürgen
    email: juergen.kurths@pik-potsdam.de
    orcid: https://orcid.org/0000-0002-5926-4276
  abstract: We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis
    toolbox) open source software package for applying and combining modern methods of data
    analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn
    is a fully object-oriented and easily parallelizable package written in the language Python. It
    allows for the construction of functional networks such as climate networks in climatology or
    functional brain networks in neuroscience representing the structure of statistical
    interrelationships in large data sets of time series and, subsequently, investigating this
    structure using advanced methods of complex network theory such as measures and models for
    spatial networks, networks of interacting networks, node-weighted statistics, or network
    surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex
    systems as recorded in uni- and multivariate time series from a non-traditional perspective by
    means of recurrence quantification analysis, recurrence networks, visibility graphs, and
    construction of surrogate time series. The range of possible applications of the library is
    outlined, drawing on several examples mainly from the field of climatology.
  doi: 10.1063/1.4934554
  journal: Chaos
  month: 11
  issue: 11
  volume: 25
  year: 2015

GitHub Events

Total

Create event: 2
Release event: 1
Issues event: 9
Watch event: 16
Delete event: 2
Issue comment event: 3
Push event: 16
Pull request event: 3
Fork event: 2

Last Year

Create event: 2
Release event: 1
Issues event: 9
Watch event: 16
Delete event: 2
Issue comment event: 3
Push event: 16
Pull request event: 3
Fork event: 2

Committers

Last synced: 6 months ago

All Time

Total Commits: 654
Total Committers: 20
Avg Commits per committer: 32.7
Development Distribution Score (DDS): 0.751

Past Year

Commits: 20
Committers: 1
Avg Commits per committer: 20.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
fkuehlein	f**n@s**e	163
ntfrgl	n**l@g**m	141
harmening	n**g@f**e	81
Jonathan Krönke	k**e@p**e	61
MaxBechthold	b**o@g**m	59
harmening	n**g@m**e	32
Jonathan Donges	d**s@p**e	27
lschmidt	8****5	18
wbarfuss	w**s@g**e	16
wbarfuss	w**s@p**e	16
wbarfuss	b**s@p**e	11
Filipi Nascimento Silva	f**o@g**m	9
Jobst Heitzig	h**g@p**e	4
Malte J. Ziebarth	m**h@f**e	4
jakassel	j**l@p**e	4
nils	p**e@g**e	3
Adrian Odenweller	a**r@p**e	2
Efthymios Tzinis	e**s@g**m	1
lenaschmidt	l**t@t**e	1
RonjaHo	1****o	1

Committer Domains (Top 20 + Academic)

pik-potsdam.de: 6 gmx.de: 2 tu-berlin.de: 1 pks.mpg.de: 1 fmvkb.de: 1 mail.de: 1 fu-berlin.de: 1 student.uni-halle.de: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 102
Total pull requests: 54
Average time to close issues: almost 2 years
Average time to close pull requests: about 2 months
Total issue authors: 51
Total pull request authors: 9
Average comments per issue: 3.0
Average comments per pull request: 2.46
Merged pull requests: 50
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 6
Pull requests: 5
Average time to close issues: 19 days
Average time to close pull requests: 8 days
Issue authors: 4
Pull request authors: 1
Average comments per issue: 0.83
Average comments per pull request: 0.8
Merged pull requests: 4
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

fkuehlein (23)
ntfrgl (13)
marcwie (6)
jdonges (4)
rimajj (3)
penguinpee (3)
drkousek (2)
madarax64 (2)
sombuddha-bagchi (2)
keriheuer (2)
sofiafarina (2)
evilzkun (1)
Mospymh (1)
colltoaction (1)
Sumit-Mukherjee (1)

Pull Request Authors

fkuehlein (42)
zugnachpankow (3)
RonjaHo (2)
ntfrgl (2)
filipinascimento (1)
jdonges (1)
jakassel (1)
jkroenke (1)
arnaudon (1)

Top Labels

Issue Labels

maintenance (28) installation (24) bug (20) enhancement (20) duplicate (16) question (6) testing (4)

Pull Request Labels

maintenance (14) enhancement (6) bug (6) testing (3) installation (2)

Packages

Total packages: 2
Total downloads:
- pypi 7,275 last-month

Total dependent packages: 0
(may contain duplicates)
Total dependent repositories: 4
(may contain duplicates)
Total versions: 18
Total maintainers: 2

proxy.golang.org: github.com/pik-copan/pyunicorn

Documentation: https://pkg.go.dev/github.com/pik-copan/pyunicorn#section-documentation
License: other
Latest release: v0.8.2
published 12 months ago

Versions: 9
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.4%

Average: 5.6%

Dependent repos count: 5.8%

Last synced: 6 months ago

pypi.org: pyunicorn

Unified complex network and recurrence analysis toolbox

Homepage: https://www.pik-potsdam.de/members/donges/software-2/software
Documentation: http://www.pik-potsdam.de/~donges/pyunicorn/
License: BSD
Latest release: 0.8.2
published 12 months ago

Versions: 9
Dependent Packages: 0
Dependent Repositories: 4
Downloads: 7,275 Last month

Rankings

Dependent repos count: 7.5%

Average: 9.8%

Dependent packages count: 10.0%

Downloads: 11.8%

Maintainers (2)

pik-copan fkuehlein

Last synced: 6 months ago

pyunicorn

Science Score: 77.0%

Keywords from Contributors

Repository

Basic Info

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Metadata Files

README.rst

Owner

Citation (CITATION.cff)

GitHub Events

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proxy.golang.org: github.com/pik-copan/pyunicorn

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