Quick Start

EMD can be install from PyPI using pip:

pip install emd

and used to decompose and describe non-linear timeseries.:

# Imports
import emd
import numpy as np
import matplotlib.pyplot as plt

# Definitions
sample_rate = 1000
seconds = 3
time_vect = np.linspace(0,seconds,seconds*sample_rate)

# A non-linear oscillation
x = emd.simulate.abreu2010(5, .25, -np.pi/4, sample_rate, seconds)
# ...plus a linear oscillation
x += np.cos(2*np.pi*1*time_vect)

# Sift
imf = emd.sift.sift(x)

# Visualise Intrinsic Mode Functions
emd.plotting.plot_imfs(imf, scale_y=True, cmap=True)

# Compute instantaneous spectral stats
IP,IF,IA = emd.spectra.frequency_transform(imf, sample_rate ,'nht')

# Compute Hilbert-Huang transform
edges,centres = emd.spectra.define_hist_bins(0,10,32)
f, hht = emd.spectra.hilberthuang(IF, IA, edges)

# Visualise time-frequency spectrum
plt.figure()
plt.pcolormesh(time_vect, f, hht, cmap='hot_r')
plt.colorbar()
plt.xlabel('Time (seconds)')
plt.ylabel('Instantaneous  Frequency (Hz)')