Finding Peak Edges from Array of 116 Curves

4 vues (au cours des 30 derniers jours)
Christopher Gordon
Christopher Gordon le 21 Jan 2021
Commenté : Mathieu NOE le 28 Jan 2021
Hello! I want to first say I'm incredibly appreciative of this community and its response to questions. People like me very much appreciate it. Here is my situation:
I have an array of 116 curves:
Each cuve is a single peak:
What I'm trying to do is find the edges of the peaks and the position of those edges:
I have successfully used findpeaks() to find the position, location, and peak width of each of the 116 curves/peaks, but the peak width is limited to either the bandwidth at halfheight or halfprominance. I was wondering if anyone could help me with an approach that could help me find the peak edges and what index/pixel it occurs at. I would greatly appreciate it.

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Mathieu NOE
Mathieu NOE le 22 Jan 2021
hello
I suggest to use the attached crossing function , with the y threshold corresponding the y value of the black line in your picture.
the function allows you to get the positive slope (left crossing) point and the negative slope (right crossing) point
hope it helps
  13 commentaires
Christopher Gordon
Christopher Gordon le 27 Jan 2021
Again, thank you so much for all the help and sorry I keep asking so much. I went through the MatLAB crash course, but they only ever do simple examples and I have trouble expanding them, perhaps using other techniques that I haven't learned yet.
Up until now I have three matricies for this step:
t0_pos (116 x 1) that has the left side of each peak
t0_neg (116 x 1) that has the right side of each peak
AllCurves (1024 x 116) that has each curve
I would like the loop (or if there's a better way I'm open) to go through each curve and set that sections range of t0_pos to t0_neg equal to 0.
Mathieu NOE
Mathieu NOE le 28 Jan 2021
hello Cristopher
no problem, I like to work on (interesting) posts !
One problem I figured out this night is that the t0_pos and t0_neg values are obtained by linear interpolation for maximal precision; this is good , but on the other side , these "new" time values do not belong to the original time vector , and they are different for eah individual curve;
so at the end, I cannot create a common time axis for all curves , unless I do not merge the crossing points coordinates into the original x and y datas
IMO, we should step back to what was the first implementation and add some steps :
  • do a for loop in the main code
  • do the crossing stuff on one y vector at a time
  • merge the x and y original datas with the crossing points coordinates (and sort x in ascending order to avoid zig zags in plot)
  • put the y section between x = t0_pos to t0_neg equal to 0
  • repeat for all curves
  • this implies each curve a a specific x vector
  • plot with hold on

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