I have a signal with 262144 samples and "sampled" with 200Msps.

I want to use the cwt (Continuous Wavelet Transform), for making a time-frequency scalogram.

Also, I worked in some scripts in python and Matlab, but it's very hard for the machine for make the process and empty the memory of the computer with 262144 samples.

The codes and examples than I run, only work with 1024 samples... 512 samples, and it's very simple for the computer to work with it. But for 262144 SAMPLES !, a 1D signal, it's impossible to work well and output a great scalogram (It's the target).

The signal has frequencies around 10KHz, 20KHz... 200KHz, 1MHz... 2MHz.

I'm really comfortable with Python language, very powerfull and great for data mining, also Matlab, but not eficient in memory use... I think...

Regards and I waiting any suggestions or code in Python, also toolboxes in Python.


1 Answer 1


Usually this is done by chopping a long signal into shorter windows, doing the transform on each window, then recombining the results. For finding 10 kHz signals from 200 Msps data, you will need either longer windows than 1k samples, or to downsample the data before doing the transform.

  • $\begingroup$ +1 I want to know any script than make this operation. How to recombine it ?. Regards !. $\endgroup$
    – Alejandro
    Aug 13, 2013 at 16:48

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