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SpKit: Signal Processing toolkit

Project description

Signal Processing toolkit

Github

PyPi - project

Information Theory functions

  • Entropy : Shannon entropy, Rényi entropy of order α, Collision entropy
  • Joint entropy
  • Conditional entropy
  • Mutual Information
  • Cross entropy
  • Kullback–Leibler divergence
  • Computation of optimal bin size for histogram using FD-rule
  • Plot histogram with optimal bin size

Continuase Wavelet Transform and other functions comming soon..

Requirement :

['numpy','matplotlib']

Installation

with pip

pip install spkit

Build from the source

Download the repository or clone it with git, after cd in directory build it from source with

python setup.py install

Example - Information Theory

import numpy as np
import matplotlib.pyplot as plt
import spkit as sp

x = np.random.rand(10000)
y = np.random.randn(10000)

#Shannan entropy
H_x= sp.entropy(x,alpha=1)
H_y= sp.entropy(y,alpha=1)

#Rényi entropy
Hr_x= sp.entropy(x,alpha=2)
Hr_y= sp.entropy(y,alpha=2)

H_xy= sp.entropy_joint(x,y)

H_x1y= sp.entropy_cond(x,y)
H_y1x= sp.entropy_cond(y,x)

I_xy = sp.mutual_Info(x,y)

H_xy_cross= sp.entropy_cross(x,y)

D_xy= sp.entropy_kld(x,y)


print('Shannan entropy')
print('Entropy of x: H(x) = ',H_x)
print('Entropy of y: H(y) = ',H_y)
print('-')
print('Rényi entropy')
print('Entropy of x: H(x) = ',Hr_x)
print('Entropy of y: H(y) = ',Hr_y)
print('-')
print('Mutual Information I(x,y) = ',I_xy)
print('Joint Entropy H(x,y) = ',H_xy)
print('Conditional Entropy of : H(x|y) = ',H_x1y)
print('Conditional Entropy of : H(y|x) = ',H_y1x)
print('-')
print('Cross Entropy of : H(x,y) = :',H_xy_cross)
print('Kullback–Leibler divergence : Dkl(x,y) = :',D_xy)



plt.figure(figsize=(12,5))
plt.subplot(121)
sp.HistPlot(x,show=False)

plt.subplot(122)
sp.HistPlot(y,show=False)
plt.show()

Contacts:

PhD Student: Queen Mary University of London & University of Genoa


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