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I don't do Python, I'm an old person sticking to his old Matlab (codes and) habits. However, up to extension/wavelet/border issues, SWT is a discrete equivalent to CWT. And in most versions, the number of samples in approximations or details is the same (which is not the case for the DWT). Hence, you can concatenate 1D rows of details (or approximations) ...


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In the DWT scheme, whether it is the classical $2$-band or the $M$-band wavelet setting, the very same analysis filter bank (lowpass/highpass + subsampling) is used at each level. Under this condition, one can derive the cascade algorithm that provides the spectrum of the scaling function: $$\Phi(\omega)= \prod_{k=1}^\infty \frac {1} {\sqrt 2} H\left( \frac ...


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The first inner products are not easy to compute, especially with wavelets, like Daubechies', that don't have closed form expression. As mentioned, references have been provided in Wavelet transform: How to compute the initial coefficients when only samples are available? In Quantitative Fourier Analysis of Approximation Techniques, it is mentioned that: ...


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Non-linearity and non-stationarity are non-properties. Without more details, they do not say much about the methods that may perform well, and moreover the choice depends a lot on what you really do: analysis, feature extraction, enhancement, filtering, component separation, restoration? What follows are typical sets of tools you could use: Your moving-...


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