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
Description: .. 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>`_.
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
