Metadata-Version: 2.1
Name: pecuzal-embedding
Version: 1.2.0
Summary: PECUZAL automatic embedding of uni- and multivariate time series
Home-page: https://github.com/hkraemer/PECUZAL_python.git
Author: K.H.Kraemer
Author-email: hkraemer@pik-potsdam.de
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Topic :: Scientific/Engineering :: GIS
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 5 - Production/Stable
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
Requires-Dist: numpy (>=1.17.2)
Requires-Dist: scipy (>=1.3.1)
Requires-Dist: scikit-learn (>=0.21.3)
Requires-Dist: progress (>=1.5)

.. image:: https://travis-ci.org/hkraemer/PECUZAL_python.svg?branch=main
    :target: https://travis-ci.org/hkraemer/PECUZAL_python

.. image:: https://img.shields.io/badge/docs-dev-blue.svg
    :target: https://hkraemer.github.io/PECUZAL_python/


PECUZAL Python
==============

We introduce the PECUZAL automatic embedding of time series method for Python. It is solely based
on the paper [kraemer2020]_ `(Open Source) <https://arxiv.org/abs/2011.07040>`_, where the functionality is explained in detail. Here we
give an introduction to its easy usage in three examples. Enjoy Embedding! 

.. image:: icon.png


Getting started
===============

Install from `PyPI <https://pypi.org/>`_ by simply typing

::

   pip install pecuzal-embedding

in your console.

NOTE
====

This implementation is not profiled well. We recommend to use the implementation
in the `Julia language <https://juliadynamics.github.io/DynamicalSystems.jl/latest/embedding/unified/>`_ or 
in `Matlab <https://github.com/hkraemer/PECUZAL_Matlab>`_,
in order to get fast results, especially in the multivariate case. Moreover,
it is well documented and embedded in the 
`DynamicalSystems.jl <https://juliadynamics.github.io/DynamicalSystems.jl/dev/>`_ ecosystem.
For instance, the compuations made in the `Univariate example <https://hkraemer.github.io/PECUZAL_python/univariate_example.html>`_ 
and the `Multivariate example <https://hkraemer.github.io/PECUZAL_python/multivariate_example.html>`_
in this documentation took approximately `1500s` (approx. 25 mins) and `7500s` (approx. 2 hours!), respectively. In the Julia implementation
the exact same computation took `3s` and `20s`, respectively! (running on a 2.8GHz Quad-Core i7,  16GB 1600 MHz DDR3)


Documentation
=============

There is a `documentation available <https://hkraemer.github.io/PECUZAL_python/>`_ including some basic usage examples.


Citing and reference
====================
If you enjoy this tool and find it valuable for your research please cite

.. [kraemer2020] Kraemer et al., "A unified and automated approach to attractor reconstruction",  `arXiv:2011.07040 [physics.data-an] <https://arxiv.org/abs/2011.07040>`_, 2020.

or as BiBTeX-entry:

::

    @misc{kraemer2020,
    title={A unified and automated approach to attractor reconstruction}, 
    author={K. H. Kraemer and G. Datseris and J. Kurths and I. Z. Kiss and J. L. Ocampo-Espindola and N. Marwan},
    year={2020},
    eprint={2011.07040},
    archivePrefix={arXiv},
    primaryClass={physics.data-an}
    url={https://arxiv.org/abs/2011.07040}
    }


Licence
=======
This is program is free software and runs under `MIT Licence <https://opensource.org/licenses/MIT>`_.

