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SpKit: Signal Processing toolkit

Project description

Signal Processing toolkit


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 :



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 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('Rényi entropy')
print('Entropy of x: H(x) = ',Hr_x)
print('Entropy of y: H(y) = ',Hr_y)
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('Cross Entropy of : H(x,y) = :',H_xy_cross)
print('Kullback–Leibler divergence : Dkl(x,y) = :',D_xy)




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

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