If I understand well, a critical aspect of using the wavelet transform is to i) choose the correct scales, ii) choose appropriate wavelets. I use this page as a reference when doing so: https://pywavelets.readthedocs.io/en/latest/ref/cwt.html . At first, it was a bit confusing. I wonder if it would be possible to:
extend the pywt.scale2frequency function signature by adding a sampling_frequency=1 parameter, so that the user does not need to perform the /dt to get to the real world frequency
also provide a "reciprocal" function, i.e. pywt.frequency2scale(wavelet, list_frequencies, sampling_frequency) that will provide the scales to use directly from the frequencies that the user thinks are useful; this way, the user will not have to go through the hurdle of inverting the pywt.scale2frequency function themselves
If I understand well, a critical aspect of using the wavelet transform is to i) choose the correct scales, ii) choose appropriate wavelets. I use this page as a reference when doing so: https://pywavelets.readthedocs.io/en/latest/ref/cwt.html . At first, it was a bit confusing. I wonder if it would be possible to:
pywt.scale2frequency
function signature by adding asampling_frequency=1
parameter, so that the user does not need to perform the/dt
to get to the real world frequencypywt.frequency2scale(wavelet, list_frequencies, sampling_frequency)
that will provide the scales to use directly from the frequencies that the user thinks are useful; this way, the user will not have to go through the hurdle of inverting thepywt.scale2frequency
function themselvespywt.plot_wavelet(wavelet, scale, sampling_frequency)
, that will plot the wavelet as a function of time