continuous wavelet transform in python

Continuous Wavelet Transform (CWT) is a mathematical tool used for signal analysis across different scales. PyWavelets is a Python library used for various wavelet transform applications including the continuous wavelet transform. Below is a basic code snippet showing how to perform CWT using PyWavelets:

main.py
import pywt
import numpy as np

# generate signal
t = np.linspace(-1, 1, 200, endpoint=False)
sig = np.sin(2 * np.pi * 7 * t) + np.cos(2 * np.pi * 2 * t)

# Define the scales and wavelet to perform the transform
scales = np.arange(1, 64)
waveletname = 'morl'
 
# Compute the CWT matrix 
cwtmatr, freqs = pywt.cwt(sig, scales, waveletname)

# Plot the result
import matplotlib.pyplot as plt
plt.imshow(cwtmatr, extent=[-1, 1, 1, 64], cmap='PRGn', aspect='auto',
           vmax=abs(cwtmatr).max(), vmin=-abs(cwtmatr).max())
plt.show()
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In the above code, we first define a sample signal and then define the scales and wavelet to be used for CWT. We then use the pywt.cwt function to compute the CWT matrix for the given signal and parameters. Finally, we plot the result using the imshow function. This is just a basic example and more customization can be done by adjusting the parameters and plotting options.