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I am trying to understand the CWT implementation in PyWavelets. As per the definition at a given scale it involves the convolution of the wavefunction at that scale with the signal. I would assume this would imply discretizing the wavefunction for a specified number of sample points to obtain the discrete time filter which is then convolved with the given signal. However the implemention in PyWavelets seems to have an extra step namely integrate the wavefunction before discretizing and convolution. Why is that?
Below is a comparison of the cmor1.5-1.0 wavefunction with its integral along with the discretized filter at scale 1 (real parts)
The text was updated successfully, but these errors were encountered:
karn1986
changed the title
Why is the wavelet function integrated before convolution with the data?
Why is the wavelet function integrated before convolution with the data while computing the CWT?
Mar 5, 2024
pywt/pywt/_cwt.py
Line 126 in 74b4421
I am trying to understand the CWT implementation in PyWavelets. As per the definition at a given scale it involves the convolution of the wavefunction at that scale with the signal. I would assume this would imply discretizing the wavefunction for a specified number of sample points to obtain the discrete time filter which is then convolved with the given signal. However the implemention in PyWavelets seems to have an extra step namely integrate the wavefunction before discretizing and convolution. Why is that?
Below is a comparison of the cmor1.5-1.0 wavefunction with its integral along with the discretized filter at scale 1 (real parts)
The text was updated successfully, but these errors were encountered: