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In CWT code, the wavelet is first integrated: int_psi, x = integrate_wavelet(wavelet, precision=precision)
int_psi, x = integrate_wavelet(wavelet, precision=precision)
then convolved: conv[n, :] = np.convolve(data[n], int_psi_scale)
conv[n, :] = np.convolve(data[n], int_psi_scale)
and then finite difference of convolution is taken: coef = - np.sqrt(scale) * np.diff(conv, axis=-1)
coef = - np.sqrt(scale) * np.diff(conv, axis=-1)
which is equivalent to not integrating the wavelet in the first place.
Why is it done in such a way?
The text was updated successfully, but these errors were encountered:
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In CWT code, the wavelet is first integrated:
int_psi, x = integrate_wavelet(wavelet, precision=precision)then convolved:
conv[n, :] = np.convolve(data[n], int_psi_scale)and then finite difference of convolution is taken:
coef = - np.sqrt(scale) * np.diff(conv, axis=-1)which is equivalent to not integrating the wavelet in the first place.
Why is it done in such a way?
The text was updated successfully, but these errors were encountered: