Metadata-Version: 2.1
Name: emd
Version: 0.2.0
Summary: Empirical Mode Decomposition
Home-page: UNKNOWN
Author: Andrew Quinn <andrew.quinn@psych.ox.ac.uk>
Author-email: andrew.quinn@psych.ox.ac.uk
License: UNKNOWN
Description: A python package for Empirical Mode Decomposition and related spectral analyses.
        
        Please note that this project is in active development for the moment - the API may change relatively quickly between releases!
        
        # Installation
        
        You can install the latest stable release from the PyPI repository
        
        ```
        pip install emd
        ```
        
        or clone and install the source code.
        
        ```
        git clone https://gitlab.com/emd-dev/emd.git
        cd emd
        pip install .
        ```
        
        Requirements are specified in requirements.txt. Main functionality only depends
        on numpy and scipy for computation and matplotlib for visualisation.
        
        # Quick Start
        
        Full documentation can be found at https://emd.readthedocs.org and development/issue tracking at gitlab.com/emd-dev/emd
        
        Import emd
        
        ```python
        import emd
        ```
        
        Define a simulated waveform containing a non-linear wave at 5Hz and a sinusoid at 1Hz.
        
        ```python
        sample_rate = 1000
        seconds = 10
        num_samples = sample_rate*seconds
        
        import numpy as np
        time_vect = np.linspace(0, seconds, num_samples)
        
        freq = 5
        nonlinearity_deg = .25 # change extent of deformation from sinusoidal shape [-1 to 1]
        nonlinearity_phi = -np.pi/4 # change left-right skew of deformation [-pi to pi]
        x = emd.utils.abreu2010( freq, nonlinearity_deg, nonlinearity_phi, sample_rate, seconds )
        x += np.cos( 2*np.pi*1*time_vect )
        ```
        
        Estimate IMFs
        
        ```python
        imf = emd.sift.sift( x )
        ```
        
        Compute instantaneous frequency, phase and amplitude using the Normalised Hilbert Transform Method.
        
        ```python
        IP,IF,IA = emd.spectra.frequency_stats( imf, sample_rate, 'nht' )
        ```
        Compute Hilbert-Huang spectrum
        
        ```python
        freq_edges,freq_bins = emd.spectra.define_hist_bins(0,10,100)
        hht = emd.spectra.hilberthuang( IF, IA, freq_edges )
        ```
        Make a summary plot
        
        ```python
        import matplotlib.pyplot as plt
        plt.figure( figsize=(16,8) )
        plt.subplot(211,frameon=False)
        plt.plot(time_vect,x,'k')
        plt.plot(time_vect,imf[:,0]-4,'r')
        plt.plot(time_vect,imf[:,1]-8,'g')
        plt.plot(time_vect,imf[:,2]-12,'b')
        plt.xlim(time_vect[0], time_vect[-1])
        plt.grid(True)
        plt.subplot(2,1,2)
        plt.pcolormesh( time_vect, freq_bins, hht, cmap='ocean_r' )
        plt.ylabel('Frequency (Hz)')
        plt.xlabel('Time (secs)')
        plt.grid(True)
        plt.show()
        ```
        
        
Keywords: EMD Spectrum Frequency Non-Linear Holospectrum Hilbert-Huang
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: License :: OSI Approved :: GNU General Public License v2 or later (GPLv2+)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Requires-Python: >3.4
Description-Content-Type: text/markdown
Provides-Extra: test
