Finding Peak Edges from Array of 116 Curves

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Christopher Gordon
Christopher Gordon am 21 Jan. 2021
Kommentiert: Mathieu NOE am 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 am 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
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Christopher Gordon
Christopher Gordon am 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 am 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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