Is rising_edges still empty if you do this?
rising_edges = find(diff(Y > 3) > 0) % use Y not X here
hold(ax,"on");
xline(ax,X(rising_edges))
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Extracting Zoomed in part of curve and finding rising Edges of data
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Hello, I have a plot consisting of "square-ish" TTL pulses and I want to be able to
1: Extract the data thats in the "Zoomed" XLim, and then
2: Find the x-location of each rising edge at a certain y-value (say 3, although I may not have data thats exactly on 3, so the nearest to it)
3: Bin the seperations of each rising edge. As you can see below from the 2nd (zoomed in) plot I am having some drop outs so I want to analyse e.g. if there is any variabity in the drop out seperation as well as the actual rising edge seperation. My actual data consists of thousands of these peaks
(I dont have the signal processing toolbox!)
Heres a sample of my real data
and here's a zoomed in version (this would be what I would want to work with and I obtain it by setting the XLims
So for 1: above, I have this.
ax=app.UIAxes;
newlimits =ax.XLim;
[x,y] = getDataFromGraph(app,ax,1); %See function below
xminidx = find(x >= newlimits(1),1,"first");
xmaxidx = find(x <= newlimits(2),1,"last");
X=x(xminidx:xmaxidx,1); Y=y(xminidx:xmaxidx,1);
D=[X,Y];
head(D,8) %View data
where
function [datax,datay] = getDataFromGraph(app,ax,n)
% ax = axes plot is on
% n is the plot index, 1 is the last plot
b=ax.Children; % b(1) is last object added to ax
datax=(b(n).XData)';
datay=(b(n).YData)';
end
Then for 2, this is where I am struggling, I have tried this:
rising_edges = find(diff(X > 3) > 0)
hold(ax,"on");
xline(ax,X(rising_edges))
But rising_edges is empty.
And for 3 I have no idea how to plot some kind of histogram of the seperations between adjacent pulse
8 Comments
Jason
on 4 Feb 2025
Thanks Vos, Rising edges now seems correct!
So I now have this:
%Find Rising edges
rising_edges = find(diff(Y > 3) > 0)
hold(ax,"on");
%Plot
plot(ax,X(rising_edges),Y(rising_edges),'r*');
%Calc adjacent pulse seperation
Xsep=X(rising_edges);
format shortG
diff(Xsep) % I think this is the adjacent rising edge spacing?
But why when the threshold is 3 are the r* at lower valuesnd not at 3?
Voss
on 4 Feb 2025
"But why when the threshold is 3 are the r* at lower valuesnd not at 3?"
rising_edges is the indices where Y is about to cross above 3 (i.e., the indices ii where Y(ii) <= 3 and Y(ii+1) > 3). Therefore, Y(rising_edges) is an array of numbers that are <= 3.
A simple example with one rising edge:
Y = linspace(0.5,10,7)
Y = 1×7
0.5000 2.0833 3.6667 5.2500 6.8333 8.4167 10.0000
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
rising_edges = find(diff(Y > 3) > 0)
rising_edges = 2
Y(rising_edges)
ans = 2.0833
If you want to plot the markers with a y-coordinate of exactly 3, then:
plot(ax,X(rising_edges),3*ones(numel(rising_edges),1),'r*');
Jason
on 4 Feb 2025
Thanks.
So if i wanted to be strict and compare the seoerstion of rising edges at exactly tbe same y value. Would i need to interp between the Y(ii) <= 3 and Y(ii+1).
Also if i wanted the index to instead be the first value >3, how would I do that?
Voss
on 4 Feb 2025
"Would i need to interp"
You could do that.
"if i wanted the index to instead be the first value >3"
Add 1 to the current definition:
rising_edges = find(diff(Y > 3) > 0) + 1
Steven Lord
on 12 Feb 2025
Just to add one more possible tool to this discussion, you may want to load your data into a live script and use the Find Local Extrema task to interactively experiment with the various options that islocalmax and islocalmin support like min prominence, min separation, and max number of extrema. This will also allow you to see the code that programmatically performs that extrema finding, so you could use the code directly on other data sets after you've interactively tuned the options on one data set.
Jason
on 12 Feb 2025
thanks steven, ultimately I need to use appd esigner, but I would be keen to explore your suggestion. where do I find this "Find Local Extrema task "
thank
Steven Lord
on 12 Feb 2025
The "Open the Task" section on the documentation page to which I linked describes two ways to add the task to a live script.
Accepted Answer
Star Strider
on 4 Feb 2025
T1 = readtable('DS0007_REDUCED.CSV')
T1 = 2522x2 table
Col1 Col2
_______ ____
0.00033 4.72
0.00034 4.8
0.00035 4.72
0.00036 4.68
0.00037 4.72
0.00038 4.72
0.00039 4.72
0.0004 4.72
0.00041 4.8
0.00042 4.8
0.00043 4.72
0.00044 4.72
0.00045 4.8
0.00046 4.72
0.00047 4.72
0.00048 4.72
t = T1.Col1;
s = T1.Col2;
% figure
% plot(t, s)
% grid
Lvs = islocalmax(s, 'flatselection', 'first', MinProminence=0.5);
Lve = islocalmin(s, 'flatselection', 'last', MinProminence=0.5);
figure
plot(t, s)
hold on
plot(t(Lvs), s(Lvs), '|r')
plot(t(Lve), s(Lve), '|m')
hold off
grid
figure
plot(t, s)
hold on
plot(t(Lvs), s(Lvs), '|r')
plot(t(Lve), s(Lve), '|m')
hold off
grid
xlim([0.011 0.025])
ylim('padded')
●
Lvt = t >= 0.01;
Ivs = find(Lvs & Lvt);
Ive = find(Lve & Lvt);
for k = 1:numel(Ivs)
idxrng = Ivs(k) - 10 : Ive(k) + 10;
segments{k} = [t(idxrng) s(idxrng)];
end
figure
tiledlayout(4,2)
for k = 1:numel(segments)
nexttile
plot(segments{k}(:,1), segments{k}(:,2))
hold on
plot(t(Ivs(k)), s(Ivs(k)), 'vr')
plot(t(Ive(k)), s(Ive(k)), '^r')
hold off
grid
title(string(k))
ylim('padded')
end
.
29 Comments
Jason
on 5 Feb 2025
Hi, sorry one quick follow on question. Occasionally I get a double count for a single square pulse - how can I ignore the 2nd one
This 2nd one messes up the pulse seperation as the ditance to the next pulse is considered from the 2nd count on this pulse - so I need to get rid of it
Thanks
Star Strider
on 5 Feb 2025
As always, my pleasure!
With respect to the double counts, experiment with the MinProminence value. You may have to nudge it up a bit. I would start by incrementing it from 0.50 to 0.55 by increments of 0.05 or less, until the double counts disappear. That should not significantly affect the pulses without the double counts.
Depending on what you want to do (and if you have the Signal Processing Toolbox), the risetime and falltime functions may also be useful.
.
Jason
on 5 Feb 2025
Edited: Jason
on 5 Feb 2025
Thanks. I've noticed that when there are more than 8 pulses the line with idxrng errors and I don't understand why.
for k = 1:numel(Ivs)
idxrng = Ivs(k) - 10 : Ive(k) + 10;
segments{k} = [x(idxrng) y(idxrng)];
end
Is it possible to handle the case when there aremany more pulse and for example just take the 1st 8? (Thats a fantastic graphic btw!!)
BTW, I did try changing the prominence parameter but it didn't have any effect. I also have some occurances like this and was wondering if I should just remove any of the max locations that are within a certain distance 'x1' of another max or a certain distance 'x2' of a min
Star Strider
on 5 Feb 2025
Edited: Star Strider
on 5 Feb 2025
I crafted the loop and the following tiledlayout plot series simply to show the individual pulses in detail, since the original plot doesn’t permiit that.
To make ‘idxrng’ more robust, change it to:
idxrng = max(1, Ivs(k) - 5) : min(Ive(k) + 5, numel(t));
That should work. (I didn’t do that initially because I didn’t realise you would want to use that part of my code.)
I also changed the offsets from 10 to 5 for clarity. You can change them back (or to other values) if you want to.
If you’re still having problems after making this change, please post the data so that I can work with it.
EDIT —
With respect to the switching transients, I was going to suggest adding MinSeparation, however the documentation indicates that does not work with 'flatselection' because the function considers the flat selection to be a single point. (The problem with any sort of filtering — using smoothdata or an elliptic filter — is that it risks obscuring the sharp transitions of the pulses.) One option would be to use islocalmax and islocalmin without using 'flatselection' and using MinSeparation to return ponly the peaks (these being the initial overshsoots of the switching transients).
Using the current data, that would work liike this —
T1 = readtable('DS0007_REDUCED.CSV')
T1 = 2522x2 table
Col1 Col2
_______ ____
0.00033 4.72
0.00034 4.8
0.00035 4.72
0.00036 4.68
0.00037 4.72
0.00038 4.72
0.00039 4.72
0.0004 4.72
0.00041 4.8
0.00042 4.8
0.00043 4.72
0.00044 4.72
0.00045 4.8
0.00046 4.72
0.00047 4.72
0.00048 4.72
t = T1.Col1;
s = T1.Col2;
% figure
% plot(t, s)
% grid
% Lvs = islocalmax(s, 'flatselection', 'first', MinProminence=0.5);
% Lve = islocalmin(s, 'flatselection', 'last', MinProminence=0.5);
Lvs = islocalmax(s, MinSeparation=9, MinProminence=0.5); % New Call
Lve = islocalmin(s, MinSeparation=9, MinProminence=0.5); % New Call
figure
plot(t, s)
hold on
plot(t(Lvs), s(Lvs), '|r')
plot(t(Lve), s(Lve), '|m')
hold off
grid
figure
plot(t, s)
hold on
plot(t(Lvs), s(Lvs), '|r')
plot(t(Lve), s(Lve), '|m')
hold off
grid
xlim([0.011 0.025])
ylim('padded')
●
Lvt = t >= 0.01;
Ivs = find(Lvs & Lvt);
Ive = find(Lve & Lvt);
for k = 1:numel(Ivs)
idxrng = max(1, Ivs(k) - 5) : min(Ive(k) + 5, numel(t)); % New Code
segments{k} = [t(idxrng) s(idxrng)];
end
figure
tiledlayout(4,2)
for k = 1:numel(segments)
nexttile
plot(segments{k}(:,1), segments{k}(:,2))
hold on
plot(t(Ivs(k)), s(Ivs(k)), 'vr')
plot(t(Ive(k)), s(Ive(k)), '^r')
hold off
grid
title(string(k))
ylim('padded')
end
You can take advantage of the switching transients this way.
I originally used 'flatselection' because it seemed the best option, and it usually is for such pulse trains without switching transients, although it is not universally appropriate. The MinProminence name-value pair is still required.
.
Jason
on 5 Feb 2025
Thankyou, please see the attached which is more realistic of the number of peaks I will be looking at (although its not hard coded). You will see some of the double counts, also I do love those individual pulse plots you have, maybe limit it to just 8 somehow or even 4 (two ends and two in the middle?)
thankyou for all your effort
Star Strider
on 5 Feb 2025
As always, my pleasure!
Adapting my code to your new data turned out not to be as straightforward as I thought it would be. There are still problems, most notably when the first oscillation after the switch is higher than the iniitial peak (or valley). This is the situation in pulses 6 and 10 (and perhaps others).
The MinProminence value can only do so much with respect to detecting the ‘correct’ peak, because it then detects (or fails to detect) others that it should not. This causes problems with the indexing. The best solution for this is to increase the sampling frequency (if possible), so that the signal is sampled such that it detects more points and therefore is likely to return the initial peak of the switching transient as always higher than any subsequent peak. Since that information cannot be recovered (or interpolated) from the present data, t+his is likely the best outcome. I encourage you to experiment with the islocalmax MinProminence parameter.
Try this —
T1 = readtable('TESTPULSEDATA.csv')
T1 = 5827x2 table
Col1 Col2
______ ____
9.8917 4.6
9.8917 4.8
9.8918 4.72
9.8918 4.6
9.8918 4.72
9.8918 4.72
9.8918 4.68
9.8918 4.68
9.8918 4.72
9.8918 4.72
9.8918 4.68
9.8918 4.72
9.8918 4.72
9.8919 4.68
9.8919 4.72
9.8919 4.72
Ti = mean(diff(T1.Col1)) % Sampling Interval
Ti = 1.0000e-05
Fs = 1/Ti % Sampling Frequency
Fs = 1.0000e+05
t = T1.Col1;
s = T1.Col2;
% figure
% plot(t, s)
% grid
% Lvs = islocalmax(s, 'flatselection', 'first', MinProminence=0.5);
% Lve = islocalmin(s, 'flatselection', 'last', MinProminence=0.5);
smax = max(s)
smax = 4.8800
Lvm = [0; 0; s > smax*0.9]; % Pulse Peaks
Lvms = strfind(Lvm(:).', [0 0 1]); % Pulse Starts
per = mean(diff(Lvms)) % Pulse Period (Index Units)
per = 189.9333
Lvs = islocalmax(s, MinSeparation=ceil(per/3), MinProminence=0.275); % New Call
Lve = islocalmin(s, MinSeparation=ceil(per/3), MinProminence=0.275); % New Call
figure
plot(t, s)
hold on
plot(t(Lvs), s(Lvs), '|r')
plot(t(Lve), s(Lve), '|m')
hold off
grid
figure
plot(t, s, '.-') % Plot Showing Sampling Points
hold on
plot(t(Lvs), s(Lvs), 'vr')
plot(t(Lve), s(Lve), '^m')
hold off
grid
xlim([min(t)-0.0001 min(t)+0.005])
ylim('padded')
% Lvt = t >= 0.01;
% Ivs = find(Lvs & Lvt);
% Ive = find(Lve & Lvt);
Ivs = find(Lvs); % Start Indices
Ive = find(Lve); % ENd Indices
maxlen = min([numel(Ive) numel(Ivs)])
maxlen = 31
CheckIdx = [Ivs(1:maxlen) Ive(1:maxlen) Ive(1:maxlen)-Ivs(1:maxlen)]; % Check Indexing Results
disp(CheckIdx)
52 51 -1
189 241 52
379 431 52
569 621 52
759 811 52
949 1001 52
1142 1191 49
1329 1381 52
1519 1571 52
1709 1761 52
1902 1951 49
2089 2141 52
2279 2331 52
2469 2521 52
2659 2711 52
2849 2901 52
3039 3091 52
3229 3281 52
3419 3471 52
3609 3661 52
3799 3851 52
3989 4041 52
4179 4231 52
4369 4421 52
4559 4611 52
4749 4801 52
4939 4991 52
5129 5181 52
5319 5371 52
5509 5561 52
5699 5751 52
Lvgt = CheckIdx(:,3) > 10; % ‘Logical Vector Greater Than’
CheckLen = nnz(Lvgt)
CheckLen = 30
Ivs = Ivs(Lvgt);
Ive = Ive(Lvgt);
for k = 1:numel(Ivs)
idxrng = max(1, Ivs(k) - 5) : min(Ive(k) + 5, numel(t)); % New Code (Changed From Previous)
segments{k} = [t(idxrng) s(idxrng)];
end
NrPlots = numel(Ive)
NrPlots = 30
NrCols = 3;
NrRows = ceil(NrPlots/NrCols)
NrRows = 10
figure
tiledlayout(NrRows,NrCols)
for k = 1:numel(segments)
nexttile
plot(segments{k}(:,1), segments{k}(:,2))
hold on
plot(t(Ivs(k)), s(Ivs(k)), 'vr')
plot(t(Ive(k)), s(Ive(k)), '^r')
hold off
grid
title(string(k))
ylim('padded')
end
sgtitle('All Pulses')
figure
tiledlayout(3,2)
for k = 1:ceil(numel(Ive)/6):numel(Ive)
nexttile
plot(segments{k}(:,1), segments{k}(:,2))
hold on
plot(t(Ivs(k)), s(Ivs(k)), 'vr')
plot(t(Ive(k)), s(Ive(k)), '^r')
hold off
grid
title(string(k))
ylim('padded')
end
sgtitle('Selected Pulses')
figure
tiledlayout(2,1)
for k = [6 10]
nexttile
plot(segments{k}(:,1), segments{k}(:,2))
hold on
plot(t(Ivs(k)), s(Ivs(k)), 'vr')
plot(t(Ive(k)), s(Ive(k)), '^r')
hold off
grid
title(string(k))
ylim('padded')
end
This code may still need to be tweaked for other data sets. I made it as robust as I could.
.
Jason
on 5 Feb 2025
Thankyou again, this is beautiful.
Can I ask how the strfind line works?
Lvm = [0; 0; s > smax*0.9]; % Pulse Peaks Lvms = strfind(Lvm(:).', [0 0 1]);
% Pulse Starts per = mean(diff(Lvms)) % Pulse Period (Index Units) per = 189.9333
Star Strider
on 5 Feb 2025
As always, my pleasure!
This is an atypical application of strfind. It generally works to find specific patterns in strings (string array or character vector), however it turns out to also find specific numerical patterns in numeric vectors. In this instance, I’m looking to see where the logical vector matches the [0 0 1] pattern, indicating that it is detecting a transition. (The results are actually offset by 2 because it returns the index of first instance of the pattern, so here the first 0 in the vector. However since I’m then using it to get the differences in the vectors to estimate the period in index units, the offset doesn’t matter. If I wanted the position of the first 1 in the pattern, I would add 2 to the result.)
I’m using the indices here to get the period of the pulse train to use with the MinSeparation name-value pair.
.
Jason
on 8 Feb 2025
Hi Star Strider, is it possible to suggest a way around this issue that I have found. It occurs when a valley is found before a peak, such as:
I thought the following would handle this:
Ivs = find(Lvs); % Start (Peaks) Indices
Ive = find(Lve); % End (Valleys) Indices
Ive(1,1)
Ivs(1,1)
if Ive(1,1)<=Ivs(1,1) % If first valley is before first peak, delete it
disp('TRUE')
Ive(1,1)=[];
end
But I get the following
ans =
261
ans =
6001
TRUE
A null assignment can have only one non-colon index.
Error in PulseAnalysis/FindPeakMaxMinAUTOButtonPushed (line 332)
Ive(1,1)=[];
Jason
on 8 Feb 2025
HA....just found this!
A = rand(10,2) ;
A(1,:) = [] % you can remove a row
A(1,1) = [] % you cannot remove an element
Star Strider
on 8 Feb 2025
Edited: Star Strider
on 8 Feb 2025
I’vee been working on this for a while this morning.
I’m having problems getting an outlier detection approach to work that works in both instances (both data sets). It should be possible.
EDIT — (8 Feb 2025 at 18:14)
I checked my code with respect to both of these data sets, and it turns out that the second version of my code is robust to both of them. I also put the code in a function. At present it returns nothing, however if you want it to return an output, I just need to know what that output is. It should be fairly straightforward to add it.
Try this —
csvfiles = dir('*.csv');
csvfiles = [csvfiles; dir('*.CSV')]
csvfiles = 2x1 struct array with fields:
name
folder
date
bytes
isdir
datenum
for k = 1:numel(csvfiles)
analysePulseTrain(csvfiles(k).name)
end
T1 = 5827x2 table
Col1 Col2
______ ____
9.8917 4.6
9.8917 4.8
9.8918 4.72
9.8918 4.6
9.8918 4.72
9.8918 4.72
9.8918 4.68
9.8918 4.68
9.8918 4.72
9.8918 4.72
9.8918 4.68
9.8918 4.72
9.8918 4.72
9.8919 4.68
9.8919 4.72
9.8919 4.72
Ti = 1.0000e-05
Fs = 1.0000e+05
smax = 4.8800
per = 189.9333
maxlen = 31
52 51 -1
189 241 52
379 431 52
569 621 52
759 811 52
949 1001 52
1142 1191 49
1329 1381 52
1519 1571 52
1709 1761 52
1902 1951 49
2089 2141 52
2279 2331 52
2469 2521 52
2659 2711 52
2849 2901 52
3039 3091 52
3229 3281 52
3419 3471 52
3609 3661 52
3799 3851 52
3989 4041 52
4179 4231 52
4369 4421 52
4559 4611 52
4749 4801 52
4939 4991 52
5129 5181 52
5319 5371 52
5509 5561 52
5699 5751 52
CheckLen = 30
NrPlots = 30
NrRows = 10
●
T1 = 2522x2 table
Col1 Col2
_______ ____
0.00033 4.72
0.00034 4.8
0.00035 4.72
0.00036 4.68
0.00037 4.72
0.00038 4.72
0.00039 4.72
0.0004 4.72
0.00041 4.8
0.00042 4.8
0.00043 4.72
0.00044 4.72
0.00045 4.8
0.00046 4.72
0.00047 4.72
0.00048 4.72
Ti = 1.0000e-05
Fs = 1.0000e+05
smax = 5
per = 337.8571
maxlen = 8
21 19 -2
1166 1218 52
1366 1418 52
1566 1618 52
1766 1818 52
1966 2018 52
2166 2218 52
2366 2418 52
CheckLen = 7
NrPlots = 7
NrRows = 3
function analysePulseTrain(filename)
titlestr = string(strrep(filename,"_","\_"));
T1 = readtable(filename)
t = T1{:,1};
s = T1{:,2};
Ti = mean(diff(t)) % Sampling Interval
Fs = 1/Ti % Sampling Frequency
% figure
% plot(t, s)
% grid
smax = max(s)
Lvm = [0; 0; s > smax*0.9]; % Pulse Peaks
Lvms = strfind(Lvm(:).', [0 0 1]); % Pulse Starts
per = mean(diff(Lvms)) % Pulse Period (Index Units)
Lvs = islocalmax(s, MinSeparation=ceil(per/3), MinProminence=0.275); % New Call
Lve = islocalmin(s, MinSeparation=ceil(per/3), MinProminence=0.275); % New Call
figure
plot(t, s)
hold on
plot(t(Lvs), s(Lvs), '|r')
plot(t(Lve), s(Lve), '|m')
hold off
grid
title(titlestr)
figure
plot(t, s, '.-') % Plot Showing Sampling Points
hold on
plot(t(Lvs), s(Lvs), 'vr')
plot(t(Lve), s(Lve), '^m')
hold off
grid
xlim([min(t)-0.0001 min(t)+0.005])
ylim('padded')
title(titlestr)
Ivs = find(Lvs); % Start Indices
Ive = find(Lve); % ENd Indices
maxlen = min([numel(Ive) numel(Ivs)])
CheckIdx = [Ivs(1:maxlen) Ive(1:maxlen) Ive(1:maxlen)-Ivs(1:maxlen)]; % Check Indexing Results
disp(CheckIdx)
Lvgt = CheckIdx(:,3) > 10; % ‘Logical Vector Greater Than’
CheckLen = nnz(Lvgt)
Ivs = Ivs(Lvgt);
Ive = Ive(Lvgt);
for k = 1:numel(Ivs)
idxrng = max(1, Ivs(k) - 5) : min(Ive(k) + 5, numel(t)); % New Code (Changed From Previous)
segments{k} = [t(idxrng) s(idxrng)];
end
NrPlots = numel(Ive)
NrCols = 3;
NrRows = ceil(NrPlots/NrCols)
figure
tiledlayout(NrRows,NrCols)
for k = 1:numel(segments)
nexttile
plot(segments{k}(:,1), segments{k}(:,2))
hold on
plot(t(Ivs(k)), s(Ivs(k)), 'vr')
plot(t(Ive(k)), s(Ive(k)), '^r')
hold off
grid
title(string(k))
ylim('padded')
end
sgtitle(["All Pulses" titlestr])
figure
tiledlayout(3,2)
for k = 1:ceil(numel(Ive)/6):numel(Ive)
nexttile
plot(segments{k}(:,1), segments{k}(:,2))
hold on
plot(t(Ivs(k)), s(Ivs(k)), 'vr')
plot(t(Ive(k)), s(Ive(k)), '^r')
hold off
grid
title(string(k))
ylim('padded')
end
sgtitle(["Selected Pulses" titlestr])
% figure
% tiledlayout(2,1)
% for k = [6 10]
% nexttile
% plot(segments{k}(:,1), segments{k}(:,2))
% hold on
% plot(t(Ivs(k)), s(Ivs(k)), 'vr')
% plot(t(Ive(k)), s(Ive(k)), '^r')
% hold off
% grid
% title(string(k))
% ylim('padded')
% end
end
.
Jason
on 9 Feb 2025
Edited: Jason
on 9 Feb 2025
Thanks so much for all your help!
" At present it returns nothing, however if you want it to return an output, I just need to know what that output is. It should be fairly straightforward to add it. "
As you ask, I want to be able to monitor the pulse seperations and identify any anomalies. The below graphic shows what Im tring to do (so in the pic, I want to draw an xline at the x location on the top plot correspondng to the pulse number picked out in the bottom plot.
So I have added the following to your fantastic code.
% Calc Pulse Seperations, but need to ensure the following
if Ive(1,1)<=Ivs(1,1) %If valley comes before a peak
disp('TRUE')
Ive(1,:)=[]; % Cannot do this: Ive(1,1)=[];
end
f=1000; %y value multiplier (to work in us rather than ms)
sep=diff(x(Ivs));
ax=app.UIAxes2; % Bottom Graph
cl=[0.47 0.25 0.80];
plot(ax,f*sep,'LineWidth',2,'Color',cl); grid(ax,"on");
title(ax,'Pulse Seperations','Color','w','FontSize',14,'FontWeight','normal');
ylim(ax,'padded'); xlabel(ax,'Pulse #'); ylabel(ax,'Period (us)');
ytickformat(ax,'%0.2f');
And the code behind my "Find Pulse Number" is
ax=app.UIAxes; % Top Graph
peaks=app.peaks; % Public property so can access anywhere in GUI
head(peaks,5)
pn=app.PeakNumberEditField.Value;
xloc=peaks(pn)+1
hold(ax,"on");
xline(ax,xloc,'g-'); hold(ax,"off");
(app.peaks) was previously saved in this line of the peak finding routine
Lvs = islocalmax(y, MinSeparation=ceil(per/3), MinProminence=mp); app.peaks=x(Lvs);
Lve = islocalmin(y, MinSeparation=ceil(per/3), MinProminence=mp); app.valleys=x(Lve);
But its not quite showing the xline in the correct location
Star Strider
on 9 Feb 2025
I’m not writing my code in the context of an app, so I’m not sure what changies you’re making to it.
Beyond that, I’m having problems making it robust to everything you want to present to it. (I worked on it for a few hours this morning, then took a break from it.) I’m sure this is possible, just not straightforward.
I’m also running all three (so far) .csv files with it to be certain it works with all of them each time I change it.
Star Strider
on 10 Feb 2025
Edited: Star Strider
on 12 Feb 2025
The significant change here is to get the miidpoint rises and falls of the pulses, and then to use interp1 to find the nearest peak or valley to it (as detected by islocalmax or islocalmin). I have not looked in detaiil at every pulse, however the ones I randomty selected in the plots (that show enough requisite detail) seem to be correct, and the first and last pulse times make sense.
This version actually seems to be robust to every .csv file available to it —
csvfiles = dir('*.csv');
csvfiles = [csvfiles; dir('*.CSV')]
csvfiles = 3x1 struct array with fields:
name
folder
date
bytes
isdir
datenum
for k = 1:numel(csvfiles)
fn = csvfiles(k).name
analysePulseTrain(csvfiles(k).name)
end
fn = 'TESTDATA3.csv'
T1 = 149250x2 table
Var1 Var2
_______ ____
0.3015 4.76
0.3015 4.76
0.3015 4.76
0.30151 4.76
0.30151 4.76
0.30151 4.68
0.30151 4.76
0.30151 4.76
0.30152 4.76
0.30152 4.76
0.30152 4.76
0.30152 4.68
0.30152 4.76
0.30153 4.76
0.30153 4.76
0.30153 4.76
Ti =
2e-06
Fs =
500000
smin =
-0.52
smax =
5
smdn =
2.24
per =
1016.4
tper =
0.30353
lens = 1×2
147 147
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
maxlen =
147
Relevant Times [First Last] —
0.30173 0.30172
0.3031 0.30362
0.305 0.30552
0.3069 0.30742
0.3088 0.30932
0.3107 0.31122
0.3126 0.31312
0.3145 0.31502
0.3164 0.31692
0.3183 0.31882
0.3202 0.32072
0.3221 0.32262
0.324 0.32452
0.3259 0.32642
0.3278 0.32832
0.3297 0.33022
0.3316 0.33212
0.3335 0.33402
0.3354 0.33592
0.3373 0.33782
0.3392 0.33972
0.3411 0.34162
0.343 0.34352
0.3449 0.34542
0.3468 0.34732
0.3487 0.34922
0.3506 0.35112
0.3525 0.35302
0.3544 0.35492
0.3563 0.35682
0.3582 0.35872
0.3601 0.36062
0.362 0.36252
0.3639 0.36442
0.3658 0.36632
0.3677 0.36822
0.3696 0.37012
0.3715 0.37202
0.3734 0.37392
0.3753 0.37582
0.3772 0.37772
0.3791 0.37962
0.381 0.38152
0.3829 0.38342
0.3848 0.38532
0.3867 0.38722
0.3886 0.38912
0.3905 0.39102
0.3924 0.39292
0.3943 0.39482
0.3962 0.39672
0.3981 0.39862
0.4 0.40052
0.412 0.41252
0.414 0.41452
0.416 0.41652
0.418 0.41852
0.42 0.42052
0.422 0.42252
0.424 0.42452
0.426 0.42652
0.428 0.42852
0.43 0.43052
0.432 0.43252
0.434 0.43452
0.436 0.43652
0.438 0.43852
0.44 0.44052
0.442 0.44252
0.444 0.44452
0.446 0.44652
0.448 0.44852
0.45 0.45052
0.452 0.45252
0.454 0.45452
0.456 0.45652
0.458 0.45852
0.46 0.46052
0.462 0.46252
0.464 0.46452
0.466 0.46652
0.468 0.46852
0.47 0.47052
0.472 0.47252
0.474 0.47452
0.476 0.47652
0.478 0.47852
0.48 0.48052
0.482 0.48252
0.484 0.48452
0.486 0.48652
0.488 0.48852
0.49 0.49052
0.492 0.49252
0.494 0.49452
0.496 0.49652
0.498 0.49852
0.5 0.50052
0.502 0.50252
0.504 0.50452
0.506 0.50652
0.508 0.50852
0.51 0.51052
0.512 0.51252
0.514 0.51452
0.516 0.51652
0.518 0.51852
0.52 0.52052
0.522 0.52252
0.524 0.52452
0.526 0.52652
0.528 0.52852
0.53 0.53052
0.532 0.53252
0.534 0.53452
0.536 0.53652
0.538 0.53852
0.54 0.54052
0.542 0.54252
0.544 0.54452
0.546 0.54652
0.548 0.54852
0.55 0.55052
0.552 0.55252
0.554 0.55452
0.556 0.55652
0.558 0.55852
0.56 0.56052
0.562 0.56252
0.564 0.56452
0.566 0.56652
0.568 0.56852
0.57 0.57052
0.572 0.57252
0.574 0.57452
0.576 0.57652
0.578 0.57852
0.58 0.58052
0.582 0.58252
0.584 0.58452
0.586 0.58652
0.588 0.58852
0.59 0.59052
0.592 0.59252
0.594 0.59452
0.596 0.59652
0.598 0.59852
Number of Pulse Starts = 147
Number of Pulse Ends = 147
Check Index =
114 111 -3
801 1061 260
1751 2011 260
2701 2961 260
3651 3910 259
4601 4861 260
5551 5811 260
6501 6761 260
7451 7711 260
8401 8661 260
9351 9611 260
10301 10561 260
11251 11511 260
12201 12461 260
13151 13411 260
14101 14361 260
15051 15310 259
16001 16261 260
16951 17210 259
17901 18161 260
18851 19111 260
19801 20061 260
20751 21010 259
21701 21961 260
22651 22910 259
23601 23861 260
24551 24811 260
25501 25760 259
26451 26711 260
27401 27661 260
28351 28610 259
29301 29561 260
30251 30511 260
31201 31460 259
32151 32410 259
33101 33361 260
34051 34311 260
35001 35261 260
35951 36211 260
36901 37161 260
37851 38110 259
38801 39061 260
39751 40011 260
40701 40961 260
41650 41911 261
42601 42861 260
43551 43810 259
44501 44761 260
45451 45711 260
46401 46661 260
47351 47611 260
48301 48561 260
49251 49511 260
55251 55510 259
56251 56511 260
57250 57511 261
58251 58510 259
59251 59510 259
60250 60511 261
61251 61510 259
62251 62510 259
63251 63511 260
64250 64510 260
65251 65511 260
66250 66511 261
67251 67510 259
68251 68511 260
69250 69511 261
70251 70511 260
71251 71511 260
72250 72511 261
73250 73510 260
74251 74511 260
75250 75511 261
76251 76511 260
77251 77510 259
78250 78511 261
79250 79511 261
80251 80511 260
81250 81510 260
82250 82510 260
83251 83511 260
84250 84511 261
85250 85510 260
86251 86510 259
87251 87510 259
88250 88511 261
89251 89510 259
90250 90510 260
91251 91511 260
92251 92511 260
93250 93510 260
94250 94510 260
95251 95511 260
96250 96511 261
97250 97510 260
98251 98511 260
99250 99510 260
100250 100511 261
101251 101510 259
102250 102510 260
103250 103511 261
104251 104510 259
105250 105511 261
106250 106511 261
107251 107510 259
108251 108511 260
109250 109511 261
110251 110510 259
111250 111511 261
112250 112510 260
113251 113510 259
114250 114511 261
115250 115510 260
116250 116510 260
117250 117511 261
118250 118510 260
119251 119510 259
120250 120511 261
121250 121511 261
122251 122510 259
123250 123510 260
124250 124511 261
125251 125510 259
126250 126511 261
127250 127510 260
128251 128510 259
129250 129511 261
130250 130510 260
131251 131509 258
132250 132510 260
133250 133511 261
134250 134510 260
135250 135511 261
136250 136511 261
137251 137510 259
138250 138510 260
139250 139510 260
140250 140510 260
141250 141511 261
142250 142510 260
143251 143510 259
144250 144510 260
145250 145510 260
146250 146510 260
147250 147510 260
148250 148510 260
CheckLen =
146
NrPlots =
146
NrRows =
49
fn = 'TESTPULSEDATA.csv'
T1 = 5827x2 table
Col1 Col2
______ ____
9.8917 4.6
9.8917 4.8
9.8918 4.72
9.8918 4.6
9.8918 4.72
9.8918 4.72
9.8918 4.68
9.8918 4.68
9.8918 4.72
9.8918 4.72
9.8918 4.68
9.8918 4.72
9.8918 4.72
9.8919 4.68
9.8919 4.72
9.8919 4.72
Ti =
1e-05
Fs =
1e+05
smin =
-0.56
smax =
4.88
smdn =
2.16
per =
190
tper =
9.8936
lens = 1×2
31 31
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
maxlen =
31
Relevant Times [First Last] —
9.8922 9.8922
9.8936 9.8941
9.8955 9.896
9.8974 9.8979
9.8993 9.8998
9.9012 9.9017
9.9031 9.9036
9.905 9.9055
9.9069 9.9074
9.9088 9.9093
9.9107 9.9112
9.9126 9.9131
9.9145 9.915
9.9164 9.9169
9.9183 9.9188
9.9202 9.9207
9.9221 9.9226
9.924 9.9245
9.9259 9.9264
9.9278 9.9283
9.9297 9.9302
9.9316 9.9321
9.9335 9.934
9.9354 9.9359
9.9373 9.9378
9.9392 9.9397
9.9411 9.9416
9.943 9.9435
9.9449 9.9454
9.9468 9.9473
9.9487 9.9492
Number of Pulse Starts = 31
Number of Pulse Ends = 31
Check Index =
52 51 -1
189 241 52
379 431 52
569 621 52
759 811 52
949 1001 52
1142 1191 49
1329 1381 52
1519 1571 52
1709 1761 52
1902 1951 49
2089 2141 52
2279 2331 52
2469 2521 52
2659 2711 52
2849 2901 52
3039 3091 52
3229 3281 52
3419 3471 52
3609 3661 52
3799 3851 52
3989 4041 52
4179 4231 52
4369 4421 52
4559 4611 52
4749 4801 52
4939 4991 52
5129 5181 52
5319 5371 52
5509 5561 52
5699 5751 52
CheckLen =
30
NrPlots =
30
NrRows =
10
fn = 'DS0007_REDUCED.CSV'
T1 = 2522x2 table
Col1 Col2
_______ ____
0.00033 4.72
0.00034 4.8
0.00035 4.72
0.00036 4.68
0.00037 4.72
0.00038 4.72
0.00039 4.72
0.0004 4.72
0.00041 4.8
0.00042 4.8
0.00043 4.72
0.00044 4.72
0.00045 4.8
0.00046 4.72
0.00047 4.72
0.00048 4.72
Ti =
1e-05
Fs =
1e+05
smin =
-0.6
smax =
5
smdn =
2.2
per =
342.71
tper =
0.00375
lens = 1×2
8 8
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
maxlen =
8
Relevant Times [First Last] —
0.00053 0.00051
0.01198 0.0125
0.01398 0.0145
0.01598 0.0165
0.01798 0.0185
0.01998 0.0205
0.02198 0.0225
0.02398 0.0245
Number of Pulse Starts = 8
Number of Pulse Ends = 8
Check Index =
21 19 -2
1166 1218 52
1366 1418 52
1566 1618 52
1766 1818 52
1966 2018 52
2166 2218 52
2366 2418 52
CheckLen =
7
NrPlots =
7
NrRows =
3
function analysePulseTrain(filename)
titlestr = string(strrep(filename,"_","\_"));
format shortG
T1 = readtable(filename)
t = T1{:,1};
s = T1{:,2};
figure
plot(t,s)
grid
Ti = mean(diff(t)) % Sampling Interval
Fs = 1/Ti % Sampling Frequency
[smin,smax] = bounds(s)
smdn = median([smin smax])
Lvm = [0; 0; s <= smdn]; % Pulse Peaks
Lvms = strfind(Lvm(:).', [0 0 1])-2; % Pulse Starts
per = mean(diff(Lvms)) % Pulse Period (Index Units)
tper = t(ceil(per))
dLvms = [0 diff(Lvms)];
tLvms = t(Lvms).';
Lvme = strfind(Lvm(:).', [1 0 0]); % Pulse Ends
dLvme = [0 diff(Lvme)];
tLvme = t(Lvme).';
Lvs = islocalmax(s, MinSeparation=ceil(per*0.5), MinProminence=0.25); % New Call
Lve = islocalmin(s, MinSeparation=ceil(per*0.5), MinProminence=0.25); % New Call
Ivs = find(Lvs);
Ive = find(Lve);
Ivsi = interp1(Ivs, 1:numel(Ivs), Lvms, 'nearest'); % Interpolate ‘Lvms’ To 'nearest' ‘Ivs’ Index
Ivei = interp1(Ive, 1:numel(Ive), Lvme, 'nearest'); % Interpolate ‘Lvme’ To 'nearest' ‘Ive’ Index
Ivs = Ivs(isfinite(Ivsi));
Ive = Ive(isfinite(Ivei));
figure
plot(t, s)
hold on
% plot(t(Lvs), s(Lvs), '|r')
% plot(t(Lve), s(Lve), '|g')
plot(t(Ivs), s(Ivs), '|r')
plot(t(Ive), s(Ive), '|g')
hold off
grid
title(titlestr)
figure
plot(t, s, '.-') % Plot Showing Sampling Points
hold on
% plot(t(Lvs), s(Lvs), 'vr')
% plot(t(Lve), s(Lve), '^m')
plot(t(Ivs), s(Ivs), 'vr')
plot(t(Ive), s(Ive), '^m')
hold off
grid
xlim([min(t)-0.0001 min(t)+0.015])
ylim('padded')
title(titlestr)
% xline(tLvms, '--r')
% xline(tLvme, '--g')
Ivs = find(Lvs); % Start Indices
Ive = find(Lve); % End Indices
lens = [numel(Ive) numel(Ivs)]
maxlen = min([numel(Ive) numel(Ivs)])
Ivs = Ivs(1:maxlen);
Ive = Ive(1:maxlen);
RelevantTimes = t([Ivs Ive]);
disp("Relevant Times [First Last] —")
disp(RelevantTimes)
disp("Number of Pulse Starts = "+numel(Ivs))
disp("Number of Pulse Ends = "+numel(Ive))
disp("Check Index = ")
CheckIdx = [Ivs(1:maxlen) Ive(1:maxlen) Ive(1:maxlen)-Ivs(1:maxlen)]; % Check Indexing Results
disp(CheckIdx)
Lvgt = CheckIdx(:,3) > 10; % ‘Logical Vector Greater Than’
CheckLen = nnz(Lvgt)
Ivs = Ivs(Lvgt);
Ive = Ive(Lvgt);
for k = 1:numel(Ivs)
idxrng = max(1, Ivs(k) - 5) : min(Ive(k) + 5, numel(t)); % New Code (Changed From Previous)
segments{k} = [t(idxrng) s(idxrng)];
end
NrPlots = numel(Ive)
NrCols = 3;
NrRows = ceil(NrPlots/NrCols)
figure
tiledlayout(NrRows,NrCols)
for k = 1:numel(segments)
nexttile
plot(segments{k}(:,1), segments{k}(:,2))
hold on
plot(t(Ivs(k)), s(Ivs(k)), 'vr')
plot(t(Ive(k)), s(Ive(k)), '^r')
hold off
grid
title(string(k))
ylim('padded')
end
sgtitle(["All Pulses" titlestr])
figure
tiledlayout(3,2)
for k = 1:ceil(numel(Ive)/6):numel(Ive)
nexttile
plot(segments{k}(:,1), segments{k}(:,2))
hold on
plot(t(Ivs(k)), s(Ivs(k)), 'vr')
plot(t(Ive(k)), s(Ive(k)), '^r')
hold off
grid
title(string(k))
ylim('padded')
end
sgtitle(["Selected Pulses" titlestr])
% figure
% tiledlayout(2,1)
% for k = [6 10]
% nexttile
% plot(segments{k}(:,1), segments{k}(:,2))
% hold on
% plot(t(Ivs(k)), s(Ivs(k)), 'vr')
% plot(t(Ive(k)), s(Ive(k)), '^r')
% hold off
% grid
% title(string(k))
% ylim('padded')
% end
end
EDIT — (12 Feb 2025 at 13:22)
Corrected typographical error in code.
.
Star Strider
on 10 Feb 2025
Thank you!
As always, my pleasure!
The ‘m’ is for ‘midpoint’. The ‘s’ and ‘e’ are for ‘start’ and ‘end’ respectively. The initial ‘L’ is for ‘logical’, the ‘I’ for ‘index’ (numerical) and ‘v’ for ‘vector’.
Jason
on 11 Feb 2025
Sorry if its not appropriate here, but im wondering how i should handle my data when it consists of 1M and maybe 10M sampling points from oscilloscope.
As there are just 2 variables can a uitable hold this amount of data?
Star Strider
on 11 Feb 2025
It’s appropriate, although I generally don’t do anything wiith GUIs. I’ve simply never needed to use them (although that might change with a project I’m now working on).
When I checked the uitable documentation, I didn’t see any mention of size limits, so that may only be with respect to available memory. I doubt that table arrays have any such limitation, and they do support tall arrays (see Extended Capabilities for that information). The uitable documentation did not mention their excluding tall arrays (or for that matter, anything at all about tall arrays).
Jason
on 12 Feb 2025
thanks. Im getting into your latst code and was wondering about this line, should it be an equals sign?
Ivs = find(Lvs); % Start Indices
Ive = find(Lve); % End Indices
lens = [numel(Ive) numel(Ivs)]
maxlen = min([numel(Ive) numel(Ivs)])
Ivs = Ivs(1:maxlen);
Ive - Ive(1:maxlen); % *********** THIS LINE **********
RelevantTimes = t([Ivs Ive]);
disp("Relevant Times [First Last] —")
disp(RelevantTimes)
Star Strider
on 12 Feb 2025
As always, my pleasure!
It should be an equal sign, and normally something like that would throw an error. (In that instance, I’d have caught it.)
I changed (fixed) it, and re-ran that code, although that change doesn’t seem to make any difference in the results, probably because it didn’t change ‘Ive’, since ‘Ive’ was already defined as (1:maxlen), fortuantely. If it hadn¹t been, the ‘RelevantTimes’ assignment would have thrown a horzcat error, and I‘d have caught it then.
Good pick-up!
.
Jason
on 12 Feb 2025
Edited: Jason
on 12 Feb 2025
Im still seeing a few hiccups (as I collect new data)
I fully understand if you have already committed enough time for this.
1: I sometimes get this error
tLvms = t(Lvms).';
Array indices must be positive integers or logical values.
Error in PulseAnalysis/FindPeakMaxMinAUTOButtonPushed (line 303)
tLvms = x(Lvms).';
2: And, i've noticed now that when there is a missing peak (this is exactly what I want to analyse!!)
it captures a false valley.
Perhaps it doesn't matter, as ultimately what Im interested in is the seperations between the peaks which allows me to see how consistent the pulse periods are and highlight any missing peaks. But perhaps these mis-called valleys will show up on the subplots showing a selection of pulses
UPDATE: It does matter, the pulse seperation starts becoming negative
head and tail of CheckIndex:
Check Index =
2 51 49
199 251 52
399 451 52
599 651 52
799 851 52
999002 992054 -6948
999202 992254 -6948
999402 992454 -6948
999602 992654 -6948
999802 992854 -6948
CheckLen =
As a quick fix, changing the Min Prominence value to 2 knocked out those false valleys
Lvs = islocalmax(y, MinSeparation=ceil(per*0.5), MinProminence=0.25);
Lve = islocalmin(y, MinSeparation=ceil(per*0.5), MinProminence=2); % Changed Value HERE
Jason
on 12 Feb 2025
Could I also ask about the difference in the counts that I also see in Ive:
(see n1 & n2 below)
vs = find(Lvs);
Ive = find(Lve);
Ivsi = interp1(Ivs, 1:numel(Ivs), Lvms, 'nearest'); % Interpolate ‘Lvms’ To 'nearest' ‘Ivs’ Index
Ivei = interp1(Ive, 1:numel(Ive), Lvme, 'nearest'); % Interpolate ‘Lvme’ To 'nearest' ‘Ive’ Index
Ivs = Ivs(isfinite(Ivsi));
Ive = Ive(isfinite(Ivei));
n1=numel(Ivs) % First Count *************************
and then a bit later:
disp("Check Index = ")
CheckIdx = [Ivs(1:maxlen) Ive(1:maxlen) Ive(1:maxlen)-Ivs(1:maxlen)]; % Check Indexing Results
head(CheckIdx,5)
tail(CheckIdx,5)
%disp(CheckIdx)
Lvgt = CheckIdx(:,3) > 10; % ‘Logical Vector Greater Than’
CheckLen = nnz(Lvgt)
Ivs = Ivs(Lvgt);
Ive = Ive(Lvgt);
n2=numel(Ivs) % Second Count *************************
in some instances Im seeing 628 for n1 and only 146 for n2.
(what exactly is the 2nd chunk of code doing?)
Thanks
Star Strider
on 12 Feb 2025
Edited: Star Strider
on 12 Feb 2025
The offset should have been +2 insteead of -2, since strfind counts from the first index of the pattern. The first detected instance of the pattern returned should then have been 3 rather than 1, and similarly for the rest of them. (I was probably just tired when I wrote that.)
Try this —
csvfiles = dir('*.csv');
csvfiles = [csvfiles; dir('*.CSV')];
fprintf('\n\nNumber of .csv files in this run: %d\n\n', numel(csvfiles))
Number of .csv files in this run: 4
for k = 1:numel(csvfiles)
fprintf(['\n\n' repmat('=',1,80) '\n'])
fn = csvfiles(k).name
analysePulseTrain(csvfiles(k).name)
end
================================================================================
fn = 'TESTDATA3.csv'
T1 = 149250x2 table
Var1 Var2
_______ ____
0.3015 4.76
0.3015 4.76
0.3015 4.76
0.30151 4.76
0.30151 4.76
0.30151 4.68
0.30151 4.76
0.30151 4.76
0.30152 4.76
0.30152 4.76
0.30152 4.76
0.30152 4.68
0.30152 4.76
0.30153 4.76
0.30153 4.76
0.30153 4.76
Sampling Period = 2.000E-06 s
Sampling Frequency = 5.000E+05 Hz
Amplitude Minimum = -5.200E-01
Amplitude Maximum = 5.000E+00
Amplitude Median = 2.240E+00
lens = 1×2
147 147
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
maxlen =
147
Relevant Times [First Last] —
0.30173 0.30172
0.3031 0.30362
0.305 0.30552
0.3069 0.30742
0.3088 0.30932
0.3107 0.31122
0.3126 0.31312
0.3145 0.31502
0.3164 0.31692
0.3183 0.31882
0.3202 0.32072
0.3221 0.32262
0.324 0.32452
0.3259 0.32642
0.3278 0.32832
0.3297 0.33022
0.3316 0.33212
0.3335 0.33402
0.3354 0.33592
0.3373 0.33782
0.3392 0.33972
0.3411 0.34162
0.343 0.34352
0.3449 0.34542
0.3468 0.34732
0.3487 0.34922
0.3506 0.35112
0.3525 0.35302
0.3544 0.35492
0.3563 0.35682
0.3582 0.35872
0.3601 0.36062
0.362 0.36252
0.3639 0.36442
0.3658 0.36632
0.3677 0.36822
0.3696 0.37012
0.3715 0.37202
0.3734 0.37392
0.3753 0.37582
0.3772 0.37772
0.3791 0.37962
0.381 0.38152
0.3829 0.38342
0.3848 0.38532
0.3867 0.38722
0.3886 0.38912
0.3905 0.39102
0.3924 0.39292
0.3943 0.39482
0.3962 0.39672
0.3981 0.39862
0.4 0.40052
0.412 0.41252
0.414 0.41452
0.416 0.41652
0.418 0.41852
0.42 0.42052
0.422 0.42252
0.424 0.42452
0.426 0.42652
0.428 0.42852
0.43 0.43052
0.432 0.43252
0.434 0.43452
0.436 0.43652
0.438 0.43852
0.44 0.44052
0.442 0.44252
0.444 0.44452
0.446 0.44652
0.448 0.44852
0.45 0.45052
0.452 0.45252
0.454 0.45452
0.456 0.45652
0.458 0.45852
0.46 0.46052
0.462 0.46252
0.464 0.46452
0.466 0.46652
0.468 0.46852
0.47 0.47052
0.472 0.47252
0.474 0.47452
0.476 0.47652
0.478 0.47852
0.48 0.48052
0.482 0.48252
0.484 0.48452
0.486 0.48652
0.488 0.48852
0.49 0.49052
0.492 0.49252
0.494 0.49452
0.496 0.49652
0.498 0.49852
0.5 0.50052
0.502 0.50252
0.504 0.50452
0.506 0.50652
0.508 0.50852
0.51 0.51052
0.512 0.51252
0.514 0.51452
0.516 0.51652
0.518 0.51852
0.52 0.52052
0.522 0.52252
0.524 0.52452
0.526 0.52652
0.528 0.52852
0.53 0.53052
0.532 0.53252
0.534 0.53452
0.536 0.53652
0.538 0.53852
0.54 0.54052
0.542 0.54252
0.544 0.54452
0.546 0.54652
0.548 0.54852
0.55 0.55052
0.552 0.55252
0.554 0.55452
0.556 0.55652
0.558 0.55852
0.56 0.56052
0.562 0.56252
0.564 0.56452
0.566 0.56652
0.568 0.56852
0.57 0.57052
0.572 0.57252
0.574 0.57452
0.576 0.57652
0.578 0.57852
0.58 0.58052
0.582 0.58252
0.584 0.58452
0.586 0.58652
0.588 0.58852
0.59 0.59052
0.592 0.59252
0.594 0.59452
0.596 0.59652
0.598 0.59852
Number of Pulse Starts = 147
Number of Pulse Ends = 147
Check Index =
114 111 -3
801 1061 260
1751 2011 260
2701 2961 260
3651 3910 259
4601 4861 260
5551 5811 260
6501 6761 260
7451 7711 260
8401 8661 260
9351 9611 260
10301 10561 260
11251 11511 260
12201 12461 260
13151 13411 260
14101 14361 260
15051 15310 259
16001 16261 260
16951 17210 259
17901 18161 260
18851 19111 260
19801 20061 260
20751 21010 259
21701 21961 260
22651 22910 259
23601 23861 260
24551 24811 260
25501 25760 259
26451 26711 260
27401 27661 260
28351 28610 259
29301 29561 260
30251 30511 260
31201 31460 259
32151 32410 259
33101 33361 260
34051 34311 260
35001 35261 260
35951 36211 260
36901 37161 260
37851 38110 259
38801 39061 260
39751 40011 260
40701 40961 260
41650 41911 261
42601 42861 260
43551 43810 259
44501 44761 260
45451 45711 260
46401 46661 260
47351 47611 260
48301 48561 260
49251 49511 260
55251 55510 259
56251 56511 260
57250 57511 261
58251 58510 259
59251 59510 259
60250 60511 261
61251 61510 259
62251 62510 259
63251 63511 260
64250 64510 260
65251 65511 260
66250 66511 261
67251 67510 259
68251 68511 260
69250 69511 261
70251 70511 260
71251 71511 260
72250 72511 261
73250 73510 260
74251 74511 260
75250 75511 261
76251 76511 260
77251 77510 259
78250 78511 261
79250 79511 261
80251 80511 260
81250 81510 260
82250 82510 260
83251 83511 260
84250 84511 261
85250 85510 260
86251 86510 259
87251 87510 259
88250 88511 261
89251 89510 259
90250 90510 260
91251 91511 260
92251 92511 260
93250 93510 260
94250 94510 260
95251 95511 260
96250 96511 261
97250 97510 260
98251 98511 260
99250 99510 260
100250 100511 261
101251 101510 259
102250 102510 260
103250 103511 261
104251 104510 259
105250 105511 261
106250 106511 261
107251 107510 259
108251 108511 260
109250 109511 261
110251 110510 259
111250 111511 261
112250 112510 260
113251 113510 259
114250 114511 261
115250 115510 260
116250 116510 260
117250 117511 261
118250 118510 260
119251 119510 259
120250 120511 261
121250 121511 261
122251 122510 259
123250 123510 260
124250 124511 261
125251 125510 259
126250 126511 261
127250 127510 260
128251 128510 259
129250 129511 261
130250 130510 260
131251 131509 258
132250 132510 260
133250 133511 261
134250 134510 260
135250 135511 261
136250 136511 261
137251 137510 259
138250 138510 260
139250 139510 260
140250 140510 260
141250 141511 261
142250 142510 260
143251 143510 259
144250 144510 260
145250 145510 260
146250 146510 260
147250 147510 260
148250 148510 260
CheckLen =
146
NrPlots =
146
NrRows =
49
●
================================================================================
fn = 'TESTPULSEDATA.csv'
T1 = 5827x2 table
Col1 Col2
______ ____
9.8917 4.6
9.8917 4.8
9.8918 4.72
9.8918 4.6
9.8918 4.72
9.8918 4.72
9.8918 4.68
9.8918 4.68
9.8918 4.72
9.8918 4.72
9.8918 4.68
9.8918 4.72
9.8918 4.72
9.8919 4.68
9.8919 4.72
9.8919 4.72
Sampling Period = 1.000E-05 s
Sampling Frequency = 1.000E+05 Hz
Amplitude Minimum = -5.600E-01
Amplitude Maximum = 4.880E+00
Amplitude Median = 2.160E+00
lens = 1×2
31 31
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
maxlen =
31
Relevant Times [First Last] —
9.8922 9.8922
9.8936 9.8941
9.8955 9.896
9.8974 9.8979
9.8993 9.8998
9.9012 9.9017
9.9031 9.9036
9.905 9.9055
9.9069 9.9074
9.9088 9.9093
9.9107 9.9112
9.9126 9.9131
9.9145 9.915
9.9164 9.9169
9.9183 9.9188
9.9202 9.9207
9.9221 9.9226
9.924 9.9245
9.9259 9.9264
9.9278 9.9283
9.9297 9.9302
9.9316 9.9321
9.9335 9.934
9.9354 9.9359
9.9373 9.9378
9.9392 9.9397
9.9411 9.9416
9.943 9.9435
9.9449 9.9454
9.9468 9.9473
9.9487 9.9492
Number of Pulse Starts = 31
Number of Pulse Ends = 31
Check Index =
52 51 -1
189 241 52
379 431 52
569 621 52
759 811 52
949 1001 52
1142 1191 49
1329 1381 52
1519 1571 52
1709 1761 52
1902 1951 49
2089 2141 52
2279 2331 52
2469 2521 52
2659 2711 52
2849 2901 52
3039 3091 52
3229 3281 52
3419 3471 52
3609 3661 52
3799 3851 52
3989 4041 52
4179 4231 52
4369 4421 52
4559 4611 52
4749 4801 52
4939 4991 52
5129 5181 52
5319 5371 52
5509 5561 52
5699 5751 52
CheckLen =
30
NrPlots =
30
NrRows =
10
================================================================================
fn = 'TestTableData5.csv'
T1 = 17396x2 table
Var1 Var2
_______ _____
0.70243 -0.04
0.70244 -0.04
0.70245 -0.04
0.70246 -0.04
0.70247 -0.04
0.70248 -0.04
0.70249 -0.04
0.7025 -0.04
0.70251 -0.04
0.70252 -0.04
0.70253 -0.04
0.70254 -0.04
0.70255 -0.04
0.70256 -0.04
0.70257 -0.04
0.70258 3.32
Sampling Period = 1.000E-05 s
Sampling Frequency = 1.000E+05 Hz
Amplitude Minimum = -8.000E-01
Amplitude Maximum = 5.040E+00
Amplitude Median = 2.120E+00
lens = 1×2
88 85
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
maxlen =
85
Relevant Times [First Last] —
0.70259 0.70311
0.70449 0.70501
0.70639 0.70691
0.70829 0.70881
0.71019 0.71071
0.71209 0.71261
0.71399 0.71451
0.71589 0.71641
0.71779 0.71831
0.71969 0.72021
0.72159 0.72211
0.72349 0.72401
0.72539 0.72591
0.72729 0.72781
0.72919 0.72971
0.73109 0.73161
0.73299 0.73351
0.73489 0.73541
0.73679 0.73731
0.73869 0.73921
0.74059 0.74111
0.74249 0.74301
0.74439 0.74491
0.74629 0.74681
0.74819 0.74871
0.75009 0.75061
0.75199 0.75251
0.75389 0.75441
0.75579 0.75631
0.75769 0.75821
0.7596 0.76011
0.76149 0.76201
0.76339 0.76391
0.76529 0.76581
0.76719 0.76771
0.76909 0.76961
0.771 0.77151
0.7729 0.77341
0.7748 0.77531
0.7767 0.77721
0.7786 0.77911
0.78049 0.78101
0.7824 0.78291
0.78429 0.78481
0.7862 0.78671
0.7881 0.78861
0.79 0.79051
0.80198 0.79269
0.80399 0.7959
0.80598 0.79868
0.80799 0.8025
0.80998 0.8045
0.81198 0.8065
0.81399 0.8085
0.81599 0.8105
0.81798 0.8125
0.81999 0.8145
0.82199 0.8165
0.82398 0.8185
0.82599 0.8205
0.82798 0.8225
0.82999 0.8245
0.83199 0.8265
0.83399 0.8285
0.83599 0.8305
0.83799 0.8325
0.83999 0.8345
0.84199 0.8365
0.84399 0.8385
0.84599 0.8405
0.84799 0.8425
0.84999 0.8445
0.85199 0.8465
0.85399 0.8485
0.85599 0.8505
0.85799 0.8525
0.85999 0.8545
0.86199 0.8565
0.86399 0.8585
0.86599 0.8605
0.86799 0.8625
0.86999 0.8645
0.87199 0.8665
0.87399 0.8685
0.87599 0.8705
Number of Pulse Starts = 85
Number of Pulse Ends = 85
Check Index =
17 69 52
207 259 52
397 449 52
587 639 52
777 829 52
967 1019 52
1157 1209 52
1347 1399 52
1537 1589 52
1727 1779 52
1917 1969 52
2107 2159 52
2297 2349 52
2487 2539 52
2677 2729 52
2867 2919 52
3057 3109 52
3247 3299 52
3437 3489 52
3627 3679 52
3817 3869 52
4007 4059 52
4197 4249 52
4387 4439 52
4577 4629 52
4767 4819 52
4957 5009 52
5147 5199 52
5337 5389 52
5527 5579 52
5718 5769 51
5907 5959 52
6097 6149 52
6287 6339 52
6477 6529 52
6667 6719 52
6858 6909 51
7048 7099 51
7238 7289 51
7428 7479 51
7618 7669 51
7807 7859 52
7998 8049 51
8187 8239 52
8378 8429 51
8568 8619 51
8758 8809 51
9956 9027 -929
10157 9348 -809
10356 9626 -730
10557 10008 -549
10756 10208 -548
10956 10408 -548
11157 10608 -549
11357 10808 -549
11556 11008 -548
11757 11208 -549
11957 11408 -549
12156 11608 -548
12357 11808 -549
12556 12008 -548
12757 12208 -549
12957 12408 -549
13157 12608 -549
13357 12808 -549
13557 13008 -549
13757 13208 -549
13957 13408 -549
14157 13608 -549
14357 13808 -549
14557 14008 -549
14757 14208 -549
14957 14408 -549
15157 14608 -549
15357 14808 -549
15557 15008 -549
15757 15208 -549
15957 15408 -549
16157 15608 -549
16357 15808 -549
16557 16008 -549
16757 16208 -549
16957 16408 -549
17157 16608 -549
17357 16808 -549
CheckLen =
47
NrPlots =
47
NrRows =
16
================================================================================
fn = 'DS0007_REDUCED.CSV'
T1 = 2522x2 table
Col1 Col2
_______ ____
0.00033 4.72
0.00034 4.8
0.00035 4.72
0.00036 4.68
0.00037 4.72
0.00038 4.72
0.00039 4.72
0.0004 4.72
0.00041 4.8
0.00042 4.8
0.00043 4.72
0.00044 4.72
0.00045 4.8
0.00046 4.72
0.00047 4.72
0.00048 4.72
Sampling Period = 1.000E-05 s
Sampling Frequency = 1.000E+05 Hz
Amplitude Minimum = -6.000E-01
Amplitude Maximum = 5.000E+00
Amplitude Median = 2.200E+00
lens = 1×2
8 8
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
maxlen =
8
Relevant Times [First Last] —
0.00053 0.00051
0.01198 0.0125
0.01398 0.0145
0.01598 0.0165
0.01798 0.0185
0.01998 0.0205
0.02198 0.0225
0.02398 0.0245
Number of Pulse Starts = 8
Number of Pulse Ends = 8
Check Index =
21 19 -2
1166 1218 52
1366 1418 52
1566 1618 52
1766 1818 52
1966 2018 52
2166 2218 52
2366 2418 52
CheckLen =
7
NrPlots =
7
NrRows =
3
function analysePulseTrain(filename)
titlestr = string(strrep(filename,"_","\_"));
format shortG
T1 = readtable(filename)
t = T1{:,1};
s = T1{:,2};
figure
plot(t,s)
grid
title(titlestr)
Ti = mean(diff(t)); % Sampling Interval
Fs = 1/Ti; % Sampling Frequency
[smin,smax] = bounds(s);
smdn = (smax+smin)/2;
Lvm = [0; 0; s <= smdn]; % Pulse Peaks
Lvms = strfind(Lvm(:).', [0 0 1])+2; % Pulse Starts
per = mean(diff(Lvms)); % Pulse Period (Index Units)
tper = t(ceil(per));
fprintf('\n\nSampling Period \t= %10.3E s\nSampling Frequency \t= %10.3E Hz\nAmplitude Minimum \t= %10.3E\nAmplitude Maximum \t= %10.3E\nAmplitude Median \t= %10.3E\n\n', Ti, Fs, smin, smax, smdn)
dLvms = [0 diff(Lvms)];
tLvms = t(Lvms).';
Lvme = strfind(Lvm(:).', [1 0 0]); % Pulse Ends
dLvme = [0 diff(Lvme)];
tLvme = t(Lvme).';
Lvs = islocalmax(s, MinSeparation=ceil(per*0.5), MinProminence=0.25); % New Call
Lve = islocalmin(s, MinSeparation=ceil(per*0.5), MinProminence=0.25); % New Call
Ivs = find(Lvs);
Ive = find(Lve);
Ivsi = interp1(Ivs, 1:numel(Ivs), Lvms, 'nearest'); % Interpolate ‘Lvms’ To 'nearest' ‘Ivs’ Index
Ivei = interp1(Ive, 1:numel(Ive), Lvme, 'nearest'); % Interpolate ‘Lvme’ To 'nearest' ‘Ive’ Index
Ivs = Ivs(isfinite(Ivsi));
Ive = Ive(isfinite(Ivei));
figure
plot(t, s)
hold on
% plot(t(Lvs), s(Lvs), '|r')
% plot(t(Lve), s(Lve), '|g')
plot(t(Ivs), s(Ivs), '|r')
plot(t(Ive), s(Ive), '|g')
hold off
grid
title(titlestr)
figure
plot(t, s, '.-') % Plot Showing Sampling Points
hold on
% plot(t(Lvs), s(Lvs), 'vr')
% plot(t(Lve), s(Lve), '^m')
plot(t(Ivs), s(Ivs), 'vr')
plot(t(Ive), s(Ive), '^m')
hold off
grid
xlim([min(t)-0.0001 min(t)+0.015])
ylim('padded')
title(titlestr)
% xline(tLvms, '--r')
% xline(tLvme, '--g')
Ivs = find(Lvs); % Start Indices
Ive = find(Lve); % End Indices
lens = [numel(Ive) numel(Ivs)]
maxlen = min([numel(Ive) numel(Ivs)])
Ivs = Ivs(1:maxlen);
Ive = Ive(1:maxlen);
RelevantTimes = t([Ivs Ive]);
disp("Relevant Times [First Last] —")
disp(RelevantTimes)
disp("Number of Pulse Starts = "+numel(Ivs))
disp("Number of Pulse Ends = "+numel(Ive))
disp("Check Index = ")
CheckIdx = [Ivs(1:maxlen) Ive(1:maxlen) Ive(1:maxlen)-Ivs(1:maxlen)]; % Check Indexing Results
disp(CheckIdx)
Lvgt = CheckIdx(:,3) > 10; % ‘Logical Vector Greater Than’
CheckLen = nnz(Lvgt)
Ivs = Ivs(Lvgt);
Ive = Ive(Lvgt);
for k = 1:numel(Ivs)
idxrng = max(1, Ivs(k) - 5) : min(Ive(k) + 5, numel(t)); % New Code (Changed From Previous)
segments{k} = [t(idxrng) s(idxrng)];
end
NrPlots = numel(Ive)
NrCols = 3;
NrRows = ceil(NrPlots/NrCols)
figure
tiledlayout(NrRows,NrCols)
for k = 1:numel(segments)
nexttile
plot(segments{k}(:,1), segments{k}(:,2))
hold on
plot(t(Ivs(k)), s(Ivs(k)), 'vr')
plot(t(Ive(k)), s(Ive(k)), '^r')
hold off
grid
title(string(k))
ylim('padded')
end
sgtitle(["All Pulses" titlestr])
figure
tiledlayout(3,2)
for k = 1:ceil(numel(Ive)/6):numel(Ive)
nexttile
plot(segments{k}(:,1), segments{k}(:,2))
hold on
plot(t(Ivs(k)), s(Ivs(k)), 'vr')
plot(t(Ive(k)), s(Ive(k)), '^r')
hold off
grid
title(string(k))
ylim('padded')
end
sgtitle(["Selected Pulses" titlestr])
% figure
% tiledlayout(3,2)
% for k = numel(segments)-5: numel(segments)
% nexttile
% plot(segments{k}(:,1), segments{k}(:,2))
% hold on
% plot(t(Ivs(k)), s(Ivs(k)), 'vr')
% plot(t(Ive(k)), s(Ive(k)), '^r')
% hold off
% grid
% title(string(k))
% ylim('padded')
% end
end
EDIT — Ran all the files again to be certain that my revised code works with all of them. It seems to. Also, some aesthetic changes.
.
Star Strider
on 12 Feb 2025
‘in some instances Im seeing 628 for n1 and only 146 for n2.’
The original data are counted in ‘n1’ however there have to be an equal number of starting and ending indices, annd that is forced in ‘n2’.
My code now seems to be detecting valleys that don’t have a prior peak (or have a very small peak, a small fraction — perhaaps 1% — of the other peak amplitudes in ‘TestTableData5.csv’. The way my code is currently set up, those result in negative pulse lengths for the following pulses and those pulses are eliminated by thresholding the pulse lengths. I’ll have to devise a fix for that, since it didn’t occur in the previous data sets. That will involve either returning those peaks (that might very easily mean returning and filtering a lot of noise, so that might not be practical since those peak amplitudes are similar to the noise amplitude and it may not be possible to distinguish them from the noise) or devising a way of eliminating those valleys from the total. Eliminating them might be easiest.
.
Star Strider
on 15 Feb 2025
I made this adaptable in order to get it to work. (I’ve actually been working on it for several hours a day over the past few days. These data are not easy to work with, part of that being that they’re noisy. I usually deal with that by filtering or smoothing, however that’s not appropiriate here because filtering would destroy the pulse morphology.) It may be necessary for you to make it further adaptable for data that it may still have problems with (and so it hasn’t yet seen), although it works with all the provided files.
The ‘analysePulseTrain’ function now has an output, that being a table with the starting and ending times and indices for each identified pulse in the file.
Try this —
csvfiles = dir('*.csv');
csvfiles = [csvfiles; dir('*.CSV')];
fprintf('\n\nNumber of .csv files in this run: %d\n\n', numel(csvfiles))
Number of .csv files in this run: 4
fprintf(['\n\n' repmat('=',1,80)])
================================================================================
for k = 1:numel(csvfiles)
fprintf(['\n\n' repmat('=',1,80) '\n'])
fn{k} = csvfiles(k).name
PulseTable{k,:} = analysePulseTrain(fn{k});
end
================================================================================
fn = 1x1 cell array
{'TESTDATA3.csv'}
File: TESTDATA3.csv: 149250 Rows, 2 Columns
================================================================================
fn = 1x2 cell array
{'TESTDATA3.csv'} {'TESTPULSEDATA.csv'}
File: TESTPULSEDATA.csv: 5827 Rows, 2 Columns
================================================================================
fn = 1x3 cell array
{'TESTDATA3.csv'} {'TESTPULSEDATA.csv'} {'TestTableData5.csv'}
File: TestTableData5.csv: 17396 Rows, 2 Columns
================================================================================
fn = 1x4 cell array
{'TESTDATA3.csv'} {'TESTPULSEDATA.csv'} {'TestTableData5.csv'} {'DS0007_REDUCED.CSV'}
File: DS0007_REDUCED.CSV: 2522 Rows, 2 Columns
●
for k = 1:numel(PulseTable)
fprintf(['\n\n' repmat('=',1,80) '\n'])
fprintf('\n\nFile: %s\nPulseParameters Size: %5d Rows, %2d Columns\nExcerpt —\n\n',fn{k}, size(PulseTable{k}))
Pulse_Parameters = PulseTable{k}([1:5 end-4:end],:)
end
================================================================================
File: TESTDATA3.csv
PulseParameters Size: 146 Rows, 5 Columns
Excerpt —
Pulse_Parameters = 10x5 table
Pulse Start Time Stop Time Start Index Stop Index
_____ __________ _________ ___________ __________
1 0.3031 0.30362 801 1061
2 0.305 0.30552 1751 2011
3 0.3069 0.30742 2701 2961
4 0.3088 0.30932 3651 3910
5 0.3107 0.31122 4601 4861
142 0.59 0.59052 1.4425e+05 1.4451e+05
143 0.592 0.59252 1.4525e+05 1.4551e+05
144 0.594 0.59452 1.4625e+05 1.4651e+05
145 0.596 0.59652 1.4725e+05 1.4751e+05
146 0.598 0.59852 1.4825e+05 1.4851e+05
================================================================================
File: TESTPULSEDATA.csv
PulseParameters Size: 30 Rows, 5 Columns
Excerpt —
Pulse_Parameters = 10x5 table
Pulse Start Time Stop Time Start Index Stop Index
_____ __________ _________ ___________ __________
1 9.8936 9.8941 189 241
2 9.8955 9.896 379 431
3 9.8974 9.8979 569 621
4 9.8993 9.8998 759 811
5 9.9012 9.9017 949 1001
26 9.9411 9.9416 4939 4991
27 9.943 9.9435 5129 5181
28 9.9449 9.9454 5319 5371
29 9.9468 9.9473 5509 5561
30 9.9487 9.9492 5699 5751
================================================================================
File: TestTableData5.csv
PulseParameters Size: 84 Rows, 5 Columns
Excerpt —
Pulse_Parameters = 10x5 table
Pulse Start Time Stop Time Start Index Stop Index
_____ __________ _________ ___________ __________
1 0.70259 0.70311 17 69
2 0.70449 0.70501 207 259
3 0.70639 0.70691 397 449
4 0.70829 0.70881 587 639
5 0.71019 0.71071 777 829
80 0.86599 0.8665 16357 16408
81 0.86799 0.8685 16557 16608
82 0.86999 0.8705 16757 16808
83 0.87199 0.8725 16957 17008
84 0.87399 0.8745 17157 17208
================================================================================
File: DS0007_REDUCED.CSV
PulseParameters Size: 7 Rows, 5 Columns
Excerpt —
Pulse_Parameters = 10x5 table
Pulse Start Time Stop Time Start Index Stop Index
_____ __________ _________ ___________ __________
1 0.01198 0.0125 1166 1218
2 0.01398 0.0145 1366 1418
3 0.01598 0.0165 1566 1618
4 0.01798 0.0185 1766 1818
5 0.01998 0.0205 1966 2018
3 0.01598 0.0165 1566 1618
4 0.01798 0.0185 1766 1818
5 0.01998 0.0205 1966 2018
6 0.02198 0.0225 2166 2218
7 0.02398 0.0245 2366 2418
function PulseParameters = analysePulseTrain(filename)
PulseParameters = [];
titlestr = string(strrep(filename,"_","\_"));
format shortG
T1 = readtable(filename);
fprintf('\n\nFile: %s: %5d Rows, %2d Columns\n\n',filename, size(T1))
t = T1{:,1};
s = T1{:,2};
% figure
% plot(t,s)
% grid
% title(titlestr+" 1")
[Lx,Px] = islocalmax(s);
[Ln,Pn] = islocalmin(s);
% Q0 = [nnz(Lx) nnz(Ln)]
maxkPx = maxk(Px,5);
maxkPn = maxk(Pn,5);
maxkPxm = mean(maxkPx);
maxkPnm = mean(maxkPn);
Pxmax = maxkPxm*0.85;
Pnmax = maxkPnm*0.85;
MinSep = 150;
Lvs = islocalmax(s, MinProminence=Pxmax, MinSeparation=MinSep);
Lve = islocalmin(s, MinProminence=Pnmax, Minseparation=MinSep);
Ivs1 = find(Lvs);
Ive1 = find(Lve);
Ive1 = Ive1(Ive1>Ivs1(1));
% Q3 = [nnz(Lvs) nnz(Lve)]
if numel(Ive1) < numel(Ivs1)
Lve = islocalmin(s, MinProminence=0.05, Minseparation=MinSep);
end
Ivs = find(Lvs);
Ive = find(Lve);
% Q1 = [numel(Ivs) numel(Ive)]
Ive = Ive(Ive>Ivs(1));
% Q2 = [numel(Ivs) numel(Ive)]
% tStart = t(Ivs(1:min(10,numel(Ivs))));
% Ivsv = Ivs(1:min(10,numel(Ivs)))
% Ivev = Ive(1:min(10,numel(Ive)))
figure
plot(t,s)
hold on
plot(t(Ivs), s(Ivs), '|g', MarkerSize=25)
plot(t(Ive), s(Ive), '|r', MarkerSize=25)
hold off
grid
ylim('padded')
title(titlestr+" 1")
PulseParameters = table((1:numel(Ivs)).', t(Ivs), t(Ive), Ivs, Ive, VariableNames=["Pulse","Start Time","Stop Time","Start Index","Stop Index"]);
end
These are the most challenging data sets I’ve encountered here.
.
Jason
on 17 Feb 2025
Thankyou so much, I wasn't expecting you to spend all your personal time on this - I really am very grateful to you. What is the key features that different here?
Star Strider
on 17 Feb 2025
As always, my pleasure!
I wasn’t going to give up on this until I got a reasonable approach, since it seems to be important to what you’re doing. The key difference is that it is now adaptible. For whatever reason, with the MinProminence value set a a higher value, the last ‘valley’ isn’t detected in some files. With a lower value it is, however with some of the other files, that also identifies a lot of noise. So this approach first uses the higher value, and if then are fewer ‘valleys’ that ‘peaks’ (and all the valleys occurring after the first ‘peak’), it uses the lower value and checks those results. (Thus far it hasn’t needed to add any other adaptations.) With this approach, it results in identifying all the peaks and valleys for each file. It then checks to be sure there are an equal number of beginning and end indices, and calculates and creates the ‘PulseParameters’ table.
It would be difficult to have the results table have the same name as the original file (without the extension), however if you want to store the results files in an identifiable way, create a .mat file with that name:
fn = extractBefore(filename,'.');
save([fn ' PulseParameters.mat'], 'PulseParameters')
In the ‘analysePulseTrain’ function, add these lines after creating the table. That should result in a file with a matching name, for example:
DS0007_REDUCED PulseParameters.mat
I thought of that later.
.
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