| Name | ts2vg JSON |
| Version |
1.2.4
JSON |
| download |
| home_page | None |
| Summary | Build visibility graphs from time series data. |
| upload_time | 2024-07-13 14:40:07 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2020 Carlos Bergillos Varela Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
graph
network
visibility
time
series
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
.. |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|
.. |pypi| image:: https://img.shields.io/pypi/v/ts2vg.svg
:target: https://pypi.python.org/pypi/ts2vg
.. |pyversions| image:: https://img.shields.io/pypi/pyversions/ts2vg.svg
:target: https://pypi.python.org/pypi/ts2vg
.. |wheel| image:: https://img.shields.io/pypi/wheel/ts2vg.svg
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.. |license| image:: https://img.shields.io/pypi/l/ts2vg.svg
:target: https://pypi.python.org/pypi/ts2vg
|cover|
|
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.. [#Lacasa2008] Lucas Lacasa et al., \"*From time series to complex networks: The visibility graph*\", 2008.\n.. [#Lacasa2009] Lucas Lacasa et al., \"*Horizontal visibility graphs: exact results for random time series*\", 2009.\n.. [#Lan2015] Xin Lan et al., \"*Fast transformation from time series to visibility graphs*\", 2015.\n.. [#Zhou2012] T.T Zhou et al., \"*Limited penetrable visibility graph for establishing complex network from time series*\", 2012.\n.. [#Bezsudnov2014] I.V. Bezsudnov et al., \"*From the time series to the complex networks: The parametric natural visibility graph*\", 2014\n.. [#Xuan2021] Qi Xuan et al., \"*CLPVG: Circular limited penetrable visibility graph as a new network model for time series*\", 2021\n",
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