Spectral Coherence in Julia

Hi everyone, I am trying to do the spectral coherence between two signals in julia. Much like mscohere in Matlab.


I guess DSP.jl must have it, but no example is provided.
Any help? :slight_smile:


Hi @joa-quim, wow I did not think about GMT. I have used the coherence for gravity admittance. Do you think it will work for seismic data?

Well, the code just swallows numbers so why not?

In DSP.jl, there are cross power spectrum analyses.

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Hi @rafael.guerra! Thanks, I tried it a bit, but I cannot get it to run, any example around?

That is a great philosophy. Let me try and report back hahaha

What have you tried? Please provide a MWE.

I tried the basic

 mt_cross_power_spectra([data[1,:] data[2,:])

A data matrix in:


Check out the docs that were linked; the input should be a matrix of size n_channels x n_samples.

You should write:


That works great :slight_smile:


A=[data[1,:] data[2,:]]'

But how do I get the spectra from this? I found the power and the frequencies, but not the “spectrum” :s

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Assuming r is the result above, the frequency is r.freq and for two input signals x, y, the x-y off-diagonal entry of the CSD matrix is: r.power[1,2,:]

The r.freq is in sample units I guess. Can I pass a time vector, or sample rate so the freq are in hz?

it assumes the data is regularly sampled, but you can pass the keyword argument fs to set the sampling rate

HAHAH sorry for that dumb question. I found it in the doc after you said so!
Thanks a lot! @ericphanson , @rafael.guerra and @joa-quim.

Maybe an extra question. I am testing several ways to compare these signals. I have used the cross-correlation function, the pearson coeficient and now this spectral (if I understand it measures the frequencies where 2 signals are similar). Any other ideas to do this? I have several signals and I want to classify them, if I can group them in families.

You could try dynamic time warping as a time-domain approach. The README of GitHub - baggepinnen/DynamicAxisWarping.jl: Dynamic Time Warping (DTW) and related algorithms in Julia, at Julia speeds shows how to use that package for clustering for example.


WOW. I did not know about DAW… I see maybe example 1 is close to what I like but I need to read about this. Any paper on the matter you can recommend?

I also thought about Matrix profile, see if I can cat all the signals and find motives. Thoughs?

Hey there :wave:

I’ve contributed to both DynamicAxisWarping.jl and MatrixProfile.jl, and similar to you, my motivation for this came from measuring distances and similarities between spectra.

I implemented several higher-level features in the package

maybe you could find something that would fit you there. The docs and the associated paper should have quite a few examples


Hi! Thanks for the input.

Just to clarify a bit, my particular problem. I have some signals like this ones (19 so far but more on the way):

Usually people use template matching to say “well signal 1 4 and 7 look alike and 3, 8 and 14 look allike too, so this is group and and this is group B”. I want something different to do this clasification. I was looking into MatrixProfile. What would you recommend? Maybe an example that leads me in the right direction :). Thanks in advance

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