Extracting Zoomed in part of curve and finding rising Edges of data

Question

Jason 2025-2-4

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2173653-extracting-zoomed-in-part-of-curve-and-finding-rising-edges-of-data

评论： Star Strider 2025-2-17

采纳的回答： Star Strider

DS0007_REDUCED.CSV

在 MATLAB Online 中打开

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

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Jason 2025-2-12

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 2025-2-12

The "Open the Task" section on the documentation page to which I linked describes two ways to add the task to a live script.

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Answer 1

Star Strider 2025-2-4

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2173653-extracting-zoomed-in-part-of-curve-and-finding-rising-edges-of-data#answer_1559049

在 MATLAB Online 中打开

DS0007_REDUCED.CSV

Depending on the result you want, it might be easier to use islocalmax for 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);

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 个评论
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Star Strider 2025-2-5

编辑：Star Strider 2025-2-5

在 MATLAB Online 中打开

DS0007_REDUCED.CSV

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.

.

Star Strider 2025-2-5

在 MATLAB Online 中打开

TESTPULSEDATA.csv

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.

.

Star Strider 2025-2-8

编辑：Star Strider 2025-2-8

在 MATLAB Online 中打开

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 2025-2-9

编辑：Jason 2025-2-9

在 MATLAB Online 中打开

TESTDATA3.csv

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 2025-2-10

编辑：Star Strider 2025-2-12

在 MATLAB Online 中打开

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

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

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

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.

.

Jason 2025-2-12

编辑：Jason 2025-2-12

在 MATLAB Online 中打开

TestTableData5.csv

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 2025-2-12

在 MATLAB Online 中打开

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 2025-2-12

编辑：Star Strider 2025-2-12

在 MATLAB Online 中打开

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

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

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

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

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 2025-2-12

‘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 2025-2-15

在 MATLAB Online 中打开

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 2025-2-17

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 2025-2-17

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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