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You could draw a lot of parallel lines (or curves) using a color from a gray-scale colormap. The example code below uses a transformation u = t/2 + 0.25, so when t goes from 0 to 1, u would just go between 0.25 and 0.75 to select of specific range from the colormap, avoiding the very dark and very light parts.

I saw that you are using MinGW64, therefore I think you are using CodeBlocks IDE. To add a library in CodeBlocks, go to Settings->Compiler->Global compiler settings-> Search Directories-> and add the include directory from the downloaded folder. Then, do to Linker settings and then add the libraries you need.

You can read an useful article here !

By the way, you can do that using just c++. I think that is more easier to do. That's the code:

Your question is trickier than it seems.

The short answer is: use pywt's scale2freq built-in function to return the frequency associated with a given wavelet at a given scale. For instance, the code below returns the frequency of the Daubechies 4 wavelet, at scale 5 (0.14285714285714285):

import pywt pywt.scale2frequency('db4',5)

You could get to the same result by computing the central frequency of your db4 wavelet (0.7142857142857143) and then dividing by the scale (5)

import pywt pywt.central_frequency('db4')/5

Please note that this is not the actual central frequency of the signal! This quantity is called a pseudo-frequency because it is independent from the signal being analyzed.

In order to recover the central frequency of the signal, you need to divide the pseudo-frequency by the sampling rate of the signal:

import pywt pywt.scale2frequency('db4',5)/dt

Where dt is your sampling rate.

I hope this helps!

PS: I suggest plotting the spectrum of the reconstructed signal to convince yourself that the central frequency matches the value output by the aforementioned analytical formula.

I'm not sure if you can read in an image just using pywt but you can load in a image using OpenCV and then convert it to a usable format for use with pywt

First of all just to be clear, you are creating multiple processes and not threads. If you are specifically interested in threads, change your import to: from multiprocessing.dummy import Pool.

From the multiprocessing introduction:

multiprocessing is a package that supports spawning processes using an API similar to the threading module.

From the multprocessing.dummy section:

multiprocessing.dummy replicates the API of multiprocessing but is no more than a wrapper around the threading module.

Now, I was able to recreate your issue (according to your previous linked question) and indeed the same happened. Running on an interactive shell things simply hanged.

However, interestingly enough, running through the windows cmd, an endless chain of this error appeared on screen:

pywt.cwt() offers the One dimensional Continuous Wavelet Transform. When working with Pandas, you can apply it on a Series, but not on a Dataframe.

Some wavelets do have complex components, and these are typically chosen when both phase and amplitude are desired.

Easiest way to solve your problem is to simply chose a different wavelet.

I find a solution for my problem in the answers to the following question ModuleNotFoundError: No module named 'pandas._libs.tslibs.timedeltas'

I can't explain why but it does solve my problem.

The solution is: After generating the ".spec" file using pyinstaller,add the missing module into the ".spec" file at "hiddenimport=[]",like this:

Just add it to the hidden imports: