Metadata-Version: 1.1
Name: EMD-signal
Version: 0.2.1
Summary: Implementation of Empirical Mode Decomposition (EMD) and its variations
Home-page: https://github.com/laszukdawid/PyEMD
Author: Dawid Laszuk
Author-email: laszukdawid@gmail.com
License: UNKNOWN
Description: |codecov| |BuildStatus| |DocStatus|
        
        
        *****
        PyEMD
        *****
        
        *The project is ongoing. This is very limited part of my private
        collection, but before I upload everything I want to make sure it works
        as it should. If there is something you wish to have, do email me as
        there is high chance that I have already done it, but it just sits
        around and waits until I'll have more time. Don't hesitate to contact me
        for anything.*
        
        
        Links
        *****
        - HTML documentation: https://pyemd.readthedocs.org
        - Issue tracker: https://github.com/laszukdawid/pyemd/issues
        - Source code repository: https://github.com/laszukdawid/pyemd
        
        Introduction
        ************
        
        This is yet another Python implementation of Empirical Mode
        Decomposition (EMD). The package contains many EMD variations, like:
            - Ensemble EMD (EEMD),
            - Image decomposotion (EMD2D),
            - different settings and configurations of vanilla EMD.
        
        *PyEMD* allows to use different splines for envelopes, stopping criteria
        and extrema interpolation.
        
        Available splines:
            - Natural cubic [default] 
            - Pointwise cubic 
            - Akima 
            - Linear
        
        Available stopping criteria: 
            - Cauchy convergence [default] 
            - Fixed number of iterations 
            - Number of consecutive proto-imfs
        
        Extrema detection: 
            - Discrete extrema [default] 
            - Parabolic interpolation
        
        Installation
        ************
        
        Recommended
        ===========
        
        Simply download this directory either directly from GitHub, or using command line:
        
            $ git clone https://github.com/laszukdawid/PyEMD
        
        Then go into the downloaded project and run from command line:
        
            $ python setup.py install
        
        
        PyPi
        ====
        Packaged obtained from PyPi is/will be slightly behind this project, so some features might not be the same. However, it seems to be the easiest/nicest way of installing any Python packages, so why not this one?
        
            $ pip install EMD-signal
        
        
        Example
        *******
        
        More detailed examples are included in documentation. 
        
        EMD
        ===
        
        In most cases default settings are enough. Simply
        import ``EMD`` and pass your signal to ``emd()`` method.
        
        .. code:: python
        
            from PyEMD import EMD
            import numpy as np
        
            s = np.random.random(100)
            emd = EMD()
            IMFs = emd.emd(s)
        
        The Figure below was produced with input:
        :math:`S(t) = cos(22 \pi t^2) + 6t^2` 
        
        |simpleExample|
        
        EEMD
        ====
        
        Simplest case of using Esnembld EMD (EEMD) is by importing ``EEMD`` and passing your signal to ``eemd()`` method.
        
        .. code:: python
        
            from PyEMD import EEMD
            import numpy as np
        
            s = np.random.random(100)
            eemd = EEMD()
            eIMFs = eemd.eemd(s)
        
        EMD2D
        =====
        
        Simplest case is to pass image as monochromatic numpy 2D array.
        
        .. code:: python
        
            from PyEMD import EMD2D
            import numpy as np
        
            x, y = np.arange(128), np.arange(128).reshape((-1,1))
            img = np.sin(0.1*x)*np.cos(0.2*y)
            emd2d= EMD2D()
            IMFs_2D = emd2d.emd(img)
        
        Contact
        *******
        
        Feel free to contact me with any questions, requests or simply saying
        *hi*. It's always nice to know that I might have contributed to saving
        someone's time or that I might improve my skills/projects.
        
        Contact me either through gmail ({my\_username}@gmail) or search me
        favourite web search.
        
        
        .. |codecov| image:: https://codecov.io/gh/laszukdawid/PyEMD/branch/master/graph/badge.svg
           :target: https://codecov.io/gh/laszukdawid/PyEMD
        .. |BuildStatus| image:: https://travis-ci.org/laszukdawid/PyEMD.png?branch=master
           :target: https://travis-ci.org/laszukdawid/PyEMD
        .. |DocStatus| image:: https://readthedocs.org/projects/pyemd/badge/?version=latest
           :target: https://pyemd.readthedocs.io/
        .. |simpleExample| image:: https://github.com/laszukdawid/PyEMD/raw/master/PyEMD/example/simple_example.png?raw=true
Keywords: signal decomposition data analysis
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
