.. |ts2vg| replace:: **ts2vg**
.. |cover| image:: https://raw.githubusercontent.com/CarlosBergillos/ts2vg/main/docs/source/images/cover_vg.png
:width: 100 %
:alt: Example plot of a visibility graph
.. _Examples: https://carlosbergillos.github.io/ts2vg/examples.html
.. _API Reference: https://carlosbergillos.github.io/ts2vg/api/index.html
.. sphinx-start
|ts2vg|: Time series to visibility graphs
===========================================
|pypi| |pyversions| |wheel| |license|
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|
The Python |ts2vg| package provides high-performance algorithm
implementations to build visibility graphs from time series data,
as first introduced by Lucas Lacasa et al. in 2008 [#Lacasa2008]_.
The visibility graphs and some of their properties (e.g. degree
distributions) are computed quickly and efficiently even for time
series with millions of observations.
An efficient divide-and-conquer algorithm is used to compute the graphs
whenever possible [#Lan2015]_.
Installation
------------
The latest released |ts2vg| version is available at the `Python Package Index (PyPI)`_
and can be easily installed by running:
.. code:: sh
pip install ts2vg
For other advanced uses, to build |ts2vg| from source Cython is required.
Supported graph types
---------------------
Main graph types
~~~~~~~~~~~~~~~~
- Natural Visibility Graphs (NVG) [#Lacasa2008]_ (``ts2vg.NaturalVG``)
- Horizontal Visibility Graphs (HVG) [#Lacasa2009]_ (``ts2vg.HorizontalVG``)
Available variations
~~~~~~~~~~~~~~~~~~~~
Additionally, the following variations of the previous main graph types are available:
- Weighted Visibility Graphs (via the ``weighted`` parameter)
- Directed Visibility Graphs (via the ``directed`` parameter)
- Parametric Visibility Graphs [#Bezsudnov2014]_ (via the ``min_weight`` and ``max_weight`` parameters)
- Limited Penetrable Visibility Graphs (LPVG) [#Zhou2012]_ [#Xuan2021]_ (via the ``penetrable_limit`` parameter)
.. - Dual Perspective Visibility Graph [*planned, not implemented yet*]
Note that multiple graph variations can be combined and used at the same time.
Documentation
-------------
Usage and reference documentation for |ts2vg| can be found at `carlosbergillos.github.io/ts2vg`_.
Basic usage
-----------
To build a visibility graph from a time series do:
.. code:: python
from ts2vg import NaturalVG
ts = [1.0, 0.5, 0.3, 0.7, 1.0, 0.5, 0.3, 0.8]
vg = NaturalVG()
vg.build(ts)
edges = vg.edges
The time series passed (``ts``) can be any one-dimensional iterable, such as a list or a ``numpy`` 1D array.
By default, the input observations are assumed to be equally spaced in time.
Alternatively, a second 1D iterable (``xs``) can be provided for unevenly spaced time series.
Horizontal visibility graphs can be obtained in a very similar way:
.. code:: python
from ts2vg import HorizontalVG
ts = [1.0, 0.5, 0.3, 0.7, 1.0, 0.5, 0.3, 0.8]
vg = HorizontalVG()
vg.build(ts)
edges = vg.edges
If we are only interested in the degree distribution of the visibility graph
we can pass ``only_degrees=True`` to the ``build`` method.
This will be more efficient in time and memory than storing the whole graph.
.. code:: python
vg = NaturalVG()
vg.build(ts, only_degrees=True)
ks, ps = vg.degree_distribution
Directed graphs can be obtained by using the ``directed`` parameter
and weighted graphs can be obtained by using the ``weighted`` parameter:
.. code:: python
vg1 = NaturalVG(directed="left_to_right")
vg1.build(ts)
vg2 = NaturalVG(weighted="distance")
vg2.build(ts)
vg3 = NaturalVG(directed="left_to_right", weighted="distance")
vg3.build(ts)
vg4 = HorizontalVG(directed="left_to_right", weighted="h_distance")
vg4.build(ts)
.. **For more information and options see:** :ref:`Examples` and :ref:`API Reference`.
For more information and options see: `Examples`_ and `API Reference`_.
Interoperability with other libraries
-------------------------------------
The graphs obtained can be easily converted to graph objects
from other common Python graph libraries such as `igraph`_, `NetworkX`_ and `SNAP`_
for further analysis.
The following methods are provided:
.. - :meth:`~ts2vg.graph.base.VG.as_igraph`
.. - :meth:`~ts2vg.graph.base.VG.as_networkx`
.. - :meth:`~ts2vg.graph.base.VG.as_snap`
- ``as_igraph()``
- ``as_networkx()``
- ``as_snap()``
For example:
.. code:: python
vg = NaturalVG()
vg.build(ts)
g = vg.as_networkx()
Command line interface
----------------------
|ts2vg| can also be used as a command line program directly from the console:
.. code:: sh
ts2vg ./timeseries.txt -o out.edg
For more help and a list of options run:
.. code:: sh
ts2vg --help
Contributing
------------
|ts2vg| can be found `on GitHub`_.
Pull requests and issue reports are welcome.
License
-------
|ts2vg| is licensed under the terms of the `MIT License`_.
.. _NumPy: https://numpy.org/
.. _Cython: https://cython.org/
.. _Python Package Index (PyPI): https://pypi.org/project/ts2vg
.. _igraph: https://igraph.org/python/
.. _NetworkX: https://networkx.github.io/
.. _SNAP: https://snap.stanford.edu/snappy/
.. _on GitHub: https://github.com/CarlosBergillos/ts2vg
.. _MIT License: https://github.com/CarlosBergillos/ts2vg/blob/main/LICENSE
.. _carlosbergillos.github.io/ts2vg: https://carlosbergillos.github.io/ts2vg/
References
----------
.. [#Lacasa2008] Lucas Lacasa et al., "*From time series to complex networks: The visibility graph*", 2008.
.. [#Lacasa2009] Lucas Lacasa et al., "*Horizontal visibility graphs: exact results for random time series*", 2009.
.. [#Lan2015] Xin Lan et al., "*Fast transformation from time series to visibility graphs*", 2015.
.. [#Zhou2012] T.T Zhou et al., "*Limited penetrable visibility graph for establishing complex network from time series*", 2012.
.. [#Bezsudnov2014] I.V. Bezsudnov et al., "*From the time series to the complex networks: The parametric natural visibility graph*", 2014
.. [#Xuan2021] Qi Xuan et al., "*CLPVG: Circular limited penetrable visibility graph as a new network model for time series*", 2021
Raw data
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"description": ".. |ts2vg| replace:: **ts2vg**\n\n.. |cover| image:: https://raw.githubusercontent.com/CarlosBergillos/ts2vg/main/docs/source/images/cover_vg.png\n :width: 100 %\n :alt: Example plot of a visibility graph\n\n.. _Examples: https://carlosbergillos.github.io/ts2vg/examples.html\n\n.. _API Reference: https://carlosbergillos.github.io/ts2vg/api/index.html\n\n.. sphinx-start\n\n|ts2vg|: Time series to visibility graphs\n===========================================\n\n|pypi| |pyversions| |wheel| |license|\n\n.. |pypi| image:: https://img.shields.io/pypi/v/ts2vg.svg\n :target: https://pypi.python.org/pypi/ts2vg\n\n.. |pyversions| image:: https://img.shields.io/pypi/pyversions/ts2vg.svg\n :target: https://pypi.python.org/pypi/ts2vg\n\n.. |wheel| image:: https://img.shields.io/pypi/wheel/ts2vg.svg\n :target: https://pypi.python.org/pypi/ts2vg\n\n.. |license| image:: https://img.shields.io/pypi/l/ts2vg.svg\n :target: https://pypi.python.org/pypi/ts2vg\n\n|cover|\n\n|\n\nThe Python |ts2vg| package provides high-performance algorithm\nimplementations to build visibility graphs from time series data,\nas first introduced by Lucas Lacasa et al. in 2008 [#Lacasa2008]_.\n\nThe visibility graphs and some of their properties (e.g. degree\ndistributions) are computed quickly and efficiently even for time\nseries with millions of observations.\nAn efficient divide-and-conquer algorithm is used to compute the graphs\nwhenever possible [#Lan2015]_.\n\n \nInstallation\n------------\n\nThe latest released |ts2vg| version is available at the `Python Package Index (PyPI)`_\nand can be easily installed by running:\n\n.. code:: sh\n\n pip install ts2vg\n\nFor other advanced uses, to build |ts2vg| from source Cython is required.\n\n\nSupported graph types\n---------------------\n\nMain graph types\n~~~~~~~~~~~~~~~~\n\n- Natural Visibility Graphs (NVG) [#Lacasa2008]_ (``ts2vg.NaturalVG``)\n- Horizontal Visibility Graphs (HVG) [#Lacasa2009]_ (``ts2vg.HorizontalVG``)\n\nAvailable variations\n~~~~~~~~~~~~~~~~~~~~\n\nAdditionally, the following variations of the previous main graph types are available:\n\n- Weighted Visibility Graphs (via the ``weighted`` parameter)\n- Directed Visibility Graphs (via the ``directed`` parameter)\n- Parametric Visibility Graphs [#Bezsudnov2014]_ (via the ``min_weight`` and ``max_weight`` parameters)\n- Limited Penetrable Visibility Graphs (LPVG) [#Zhou2012]_ [#Xuan2021]_ (via the ``penetrable_limit`` parameter)\n\n.. - Dual Perspective Visibility Graph [*planned, not implemented yet*]\n\nNote that multiple graph variations can be combined and used at the same time.\n\n\nDocumentation\n-------------\n\nUsage and reference documentation for |ts2vg| can be found at `carlosbergillos.github.io/ts2vg`_.\n\n\nBasic usage\n-----------\n\nTo build a visibility graph from a time series do:\n\n.. code:: python\n\n from ts2vg import NaturalVG\n\n ts = [1.0, 0.5, 0.3, 0.7, 1.0, 0.5, 0.3, 0.8]\n\n vg = NaturalVG()\n vg.build(ts)\n\n edges = vg.edges\n\n\nThe time series passed (``ts``) can be any one-dimensional iterable, such as a list or a ``numpy`` 1D array.\n\nBy default, the input observations are assumed to be equally spaced in time.\nAlternatively, a second 1D iterable (``xs``) can be provided for unevenly spaced time series.\n\n\nHorizontal visibility graphs can be obtained in a very similar way:\n\n.. code:: python\n\n from ts2vg import HorizontalVG\n\n ts = [1.0, 0.5, 0.3, 0.7, 1.0, 0.5, 0.3, 0.8]\n\n vg = HorizontalVG()\n vg.build(ts)\n\n edges = vg.edges\n\n\nIf we are only interested in the degree distribution of the visibility graph\nwe can pass ``only_degrees=True`` to the ``build`` method.\nThis will be more efficient in time and memory than storing the whole graph.\n\n.. code:: python\n\n vg = NaturalVG()\n vg.build(ts, only_degrees=True)\n\n ks, ps = vg.degree_distribution\n\n\nDirected graphs can be obtained by using the ``directed`` parameter\nand weighted graphs can be obtained by using the ``weighted`` parameter:\n\n.. code:: python\n\n vg1 = NaturalVG(directed=\"left_to_right\")\n vg1.build(ts)\n\n vg2 = NaturalVG(weighted=\"distance\")\n vg2.build(ts)\n\n vg3 = NaturalVG(directed=\"left_to_right\", weighted=\"distance\")\n vg3.build(ts)\n\n vg4 = HorizontalVG(directed=\"left_to_right\", weighted=\"h_distance\")\n vg4.build(ts)\n\n\n.. **For more information and options see:** :ref:`Examples` and :ref:`API Reference`.\n\nFor more information and options see: `Examples`_ and `API Reference`_.\n\n\nInteroperability with other libraries\n-------------------------------------\n\nThe graphs obtained can be easily converted to graph objects\nfrom other common Python graph libraries such as `igraph`_, `NetworkX`_ and `SNAP`_\nfor further analysis.\n\nThe following methods are provided:\n\n.. - :meth:`~ts2vg.graph.base.VG.as_igraph`\n.. - :meth:`~ts2vg.graph.base.VG.as_networkx`\n.. - :meth:`~ts2vg.graph.base.VG.as_snap`\n\n- ``as_igraph()``\n- ``as_networkx()``\n- ``as_snap()``\n\nFor example:\n\n.. code:: python\n\n vg = NaturalVG()\n vg.build(ts)\n \n g = vg.as_networkx()\n\n\nCommand line interface\n----------------------\n\n|ts2vg| can also be used as a command line program directly from the console:\n\n.. code:: sh\n\n ts2vg ./timeseries.txt -o out.edg \n\nFor more help and a list of options run:\n\n.. code:: sh\n\n ts2vg --help\n\n\nContributing\n------------\n\n|ts2vg| can be found `on GitHub`_.\nPull requests and issue reports are welcome.\n\n\nLicense\n-------\n\n|ts2vg| is licensed under the terms of the `MIT License`_.\n\n.. _NumPy: https://numpy.org/\n.. _Cython: https://cython.org/\n.. _Python Package Index (PyPI): https://pypi.org/project/ts2vg\n.. _igraph: https://igraph.org/python/\n.. _NetworkX: https://networkx.github.io/\n.. _SNAP: https://snap.stanford.edu/snappy/\n.. _on GitHub: https://github.com/CarlosBergillos/ts2vg\n.. _MIT License: https://github.com/CarlosBergillos/ts2vg/blob/main/LICENSE\n.. _carlosbergillos.github.io/ts2vg: https://carlosbergillos.github.io/ts2vg/\n\n\nReferences\n----------\n\n.. 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