A question about most local maxima

[skyan]

Hi, @ig248 ,

I read the original paper and compared it with your implementation, I am wondering why below code reshapes LSM matrix by LSM[0:l_scale, :] but not by LSM[0:l_scale+1, :]?

pyampd/pyampd/ampd.py

Line 106 in cd24703

pks_logical = np.min(LSM[0:l_scale, :], axis=0)

As the paper said, it reshapes the LSM matrix by removing all elements m_{k,i} for which k > λ holds. So it should lead to new λ ×N matrix. But in your implementation, the new matrix is (λ-1) ×N.

And under certain dataset, the l_scale may equals to 0, then LSM[0:0] will throw an exception...

Looking forward to your response

[OskarHofmann]

Yeah I just downloaded this module and encountered the very same issue as l_scale is 0 for my data.
I also changed it to l_scale+1 and now it works.
The same problem is in line 52 for the original implementation:

pyampd/pyampd/ampd.py

Line 52 in cd24703

pks_logical = np.min(LSM[0:l_scale, :], axis=0)

[yidilozdemir]

Just wanted to add here that, when I got an error message "zero-size array to reduction operation minimum which has no identity" from using find_peaks, implementing the change you suggested fixed my issue.

[code-yuan-shi]

Why when I try to change l_scale to l_scale+1 I get the wrong peak, although this does not report an error anymore.