wavelets | Python implementation of the wavelet analysis | Dataset library

Based on this paragraph of a paper about brain-computer-interface, I wanna extract time-frequency domain features using discrete wavelet transform and then calculate energy and entropy with the giving equation.

So I've chosen pywt in python, and now I have the below code for getting wavelet and entropy from each frequency band ( for example I'm using D2 ), and here is the link of data:

I am trying to create time-frequency representation of my brain signal. I would like to create the data for frequencies from 0hz - 120hz (so it can cover, delta, theta, alpha, beta, low-gamma and high-gamma frequency bands).

Here is my code:

First, I visualize my brain signal:

I am controlling R trough Python by rpy2 package. Everything works fine except when I have to introduce some function arguments throug a Rlist. Rlists are defined by the same keyword than Python: list; but its content it's very different. Since Python believes I am creating a Python list, instead of a Rlist, an error is always shown. I am using rpy2 to control WaveleComp R package.

Here I show an example in which I try to program legend_params:

I am newbie in Signal Processing, I want find out the frequency rang each of level outputted by a Daubechies wavelet 'db4' transformation. The transformation is done with PyWavelets. I'm working in python and the code below outputs 5 detail levels and 1 approximation however I'm not sure which frequency range each level describes.

I am studying Wavelets and making notes with octave, I wish I could have my own graphs that represent the annotated raw Wavelets, I saw that in MathLab has a function that plots the wavelets, just enter the name of the desired wavelet.

Is there any function in Octave (any of its packages) that does such a plot?

I'm serializing some data and want to make the file size as small as possible without losing the essential details of the data. The first step for me was to save the data in a binary format instead of ASCII and I decided to try Flatbuffers. Previously when the data were stored as text files, they were about 400 mb. Using the schema shown below, the file is about 200 mb. So that's a nice decrease in size, but smaller would of course be better. The data consist of 1 of the ControlParams, 82 of the ControlData, and the intensity vector takes up most of the space, being a matrix with a size of about 128x5000. We are already around the theoretical binary size of 128x5000*82 * 4 bytes per float ~ 200 mb. The matrices are pretty dense in general, but here and there I can see rows that are zero. Can Flatbuffers take advantage of these zeros to reduce the file size further? Perhaps there are other inefficiencies that someone can spot in the schema, since I just am getting started with Flatbuffers?

Another way to go about reducing the file size might be investigating different wavelets to compress the original intensities with. I'm using the Haar transform now because I was able to make a C++ function to do this, and found that a compression of 2x or possibly 4x was possible. I might like to investigate other wavelets, but would like to know if others have tried different wavelets compared to Haar and found they were able to use fewer coefficients with them.