I'm doing a wavelet analysis to sensor data, however, I realise that there are a lot of wavelet families to select from. I have read an article that says: "The method firstly uses a criterion of maximum energy-to-Shannon entropy ratio to select the appropriate wavelet base for signal analysis.". So, I would like to know how to calculate the energy-to-Shannon entropy ratio of a sensor signal in python?
Best guess assuming the text meant : np.max(Total Energy/Total Entropy)|wavelet
import pywt
import numpy as np
#series - input data
#wave - current wavelet
data=pywt.wavedec(series,wave)
S=0
Etot=0
for d in data:
E=d**2
P=E/np.sum(E)
S+=-np.sum(P*np.log(P))
Etot+=np.sum(E)
ratio=Etot/S
Then repeated for each candidate wavelet