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Hi Dr. Frey,
Thank you for your article. As your results are amazing I wanted to give Deepinsight a try on my lab data. However, I encouter an issue concerning the preprocessing of the input data.
After running the deepinsight.preprocess.preprocess_input( ) function with our electrophysiology data (which have a 30.000Hz sampling rate), the result of the wavelet transform is unexpected. The frequency bands used to compute the wavelet transform return as follows:
deepinsight.preprocess.preprocess_input(fp_deepinsight, input_data, sampling_rate=sampling_rate, channels=channels)
hdf5_file = h5py.File(fp_deepinsight, mode='r')
frequencies = np.round(hdf5_file['inputs/fourier_frequencies'], 3)
print(list(frequencies))
It returns:
[inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, 58.6, 41.44, 29.3, 20.72, 14.65, 10.36, 7.324, 5.18, 3.662, 2.59]
I assume that the wavelet transform functions automatically determines the best frequency bands to perform the transform, so I do not understand the origin of these "inf" values.
Do you have any idea about what is wrong, or on how to constrain the frequency bands ?
Thank you in advance,
Allan Muller