Matlab sensing the dynamic data changes and automatically plotting

2022 11 1
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更新时间：2022 11 3
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Hello guys,
This is my plot. In the plot, you can see 6 periods where big dynamic data changes happened. I don't want an overall normal conventional plot.
I want the Matlab to sense these 6 periods and generate 6 plots.
load = 'newdata3.csv';
data = readtable(load);
data = sortrows(data,'Var1','ascend');
timetable(data.Var1, data.Var2);
plot(data.Var1,data.Var2)
j1 = find(diff([0; data.Var2]) > 10);
ss = data(j1,:);
plot(ss.Var1,ss.Var2)
This is the plot I got after using your code. But I want to generate 6 plots. Can you help me?

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Star Strider
Star Strider 2022-11-2
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Just the code this time —
data = readtable('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1175778/newdata3.csv');
data.Var1.Format = 'HH:mm:ss.SSS'
data = 126000×2 table
Var1 Var2 ____________ _______ 00:14:54.000 -3.3268 00:14:54.000 -4.1778 00:14:54.000 -4.6337 00:14:54.000 -4.729 00:14:54.000 -4.9851 00:14:54.000 -4.2736 00:14:54.000 -3.2882 00:14:54.000 -2.257 00:14:54.000 -1.5046 00:14:54.000 -1.6617 00:14:54.000 -1.4103 00:14:54.000 -2.4553 00:14:54.000 -3.3668 00:14:54.000 -4.193 00:14:54.000 -5.1044 00:14:54.000 -5.133
TS = sortrows(data,1);
TS.Var1 = TS.Var1 + 0.001*seconds(0:size(TS,1)-1).';
[envh,envl] = envelope(TS.Var2, 130, 'peaks');
% figure
% plot(TS{:,1}, TS{:,2})
% grid
% xlabel('x')
% ylabel('y')
% xline(TS.Var1(1,1)+seconds(0:60:3600))
% xline(TS.Var1(1,1)+seconds(0:300:3600),'-r', 'LineWidth',2)
figure
plot(TS{:,1}, TS{:,2}, 'DisplayName','Data')
hold on
plot(TS{:,1}, envh, '-r', 'DisplayName','Upper Envelope')
plot(TS{:,1}, envl, '-g', 'DisplayName','Lower Envelope')
hold off
grid
xlabel('x')
ylabel('y')
legend('Location','best')
figure
plot(TS{:,1}, TS{:,2}, 'DisplayName','Data')
hold on
plot(TS{:,1}, envh, '-r', 'DisplayName','Upper Envelope')
plot(TS{:,1}, envl, '-g', 'DisplayName','Lower Envelope')
grid
xlabel('x')
ylabel('y')
legend('Location','best')
% xline(TS.Var1(1,1)+seconds(0:60:3600))
% xline(TS.Var1(1,1)+seconds(0:300:3600),'-r', 'LineWidth',2)
xlim([TS.Var1(1,1) TS.Var1(9600,1)])
Lv = envh > 30 & envl < -30;
stidx = strfind([false; Lv].', [0 1])-1;
enidx = strfind([false; Lv].', [1 0]);
s = enidx - stidx;
for k = 1:numel(s)
ixr = stidx(k):enidx(k);
T{k} = TS{ixr,1};
S{k} = TS{ixr,2};
end
figure
hold on
for k = 1:numel(T)
plot(T{k}, S{k})
end
hold off
xlabel('x')
ylabel('y')
.

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uzzi
uzzi 2022-11-3
Dear Star Strider, you saved my day. Thank you very much for your answer. I didn't know the envelope function so I'm learning about it. It was very helpful.
I would like to know how did you determine the X limit for the zoomed graph.
xlim([TS.Var1(1,1) TS.Var1(9600,1)])
Can you explain it to me?
Star Strider
Star Strider 2022-11-3
As always, my pleasure!
That limit was initially from the first figure result (as well as the figure plotted in the commented-out data), then I fine-tuned it to get the interval I wanted. I zoomed it to see the details that are not otherwise visible in the plot of the entire data set, so that I could set the envelope parameters appropriately. (The envelope funciton is quite useful for problems such as this.)
I could not get a combination of the amplitude threshold and time difference logic to work correctly, so I decided to use the envelope function instead.
uzzi
uzzi 2022-11-3
Can you explain me how did you decide to go with 130 peaks?
[envh,envl] = envelope(TS.Var2, 130, 'peaks');
Star Strider
Star Strider 2022-11-3
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I could not figure out how to make the minute values of the time aggregate with my previous code, so I developed and entirely new approach, based on thresholding the absolute amplitudes of the signal and thresholding the time (actually index) differences.
data = readtable('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1175778/newdata3.csv');
data.Var1.Format = 'HH:mm:ss';
TS = sortrows(data,1)
TS = 126000×2 table
Var1 Var2 ________ ________ 00:02:07 0.063494 00:02:07 0.13935 00:02:07 0.11501 00:02:07 0.10228 00:02:07 0.15616 00:02:07 0.094422 00:02:07 0.13998 00:02:07 0.084701 00:02:07 0.16303 00:02:07 0.067169 00:02:07 0.14234 00:02:07 0.088202 00:02:07 0.10777 00:02:07 0.13575 00:02:07 0.1072 00:02:07 0.14045
thidx = abs(TS.Var2) >= 20; % Absolute Amplitude Threshold
idxn = find(thidx); % Index Vector
didxn = diff([0; idxn]) > 150; % Time Difference Threshold
thidxv = [1; idxn(didxn); size(TS,1)]; % Index Vector
for k = 1:numel(thidxv)-1 % Segmentation Loop
idxr = thidxv(k) : thidxv(k+1)-1; % Time Indices
idxre = idxr(abs(TS{idxr,2}) > 20); % Absolute Amplitude Threshold
T{k} = TS{idxre,1}; % Time Segments
S{k} = TS{idxre,2}; % Signal Segments
end
figure
hold on
for k = 1:numel(T)
plot(T{k}, S{k})
end
hold off
grid
xlabel('x')
ylabel('y')
This also appears to be shorter and more efficient.
.
uzzi
uzzi 2022-11-3
编辑：uzzi 2022-11-3
Dear Star Strider,
I am doing this code with another file and I am getting a lot of figures where there is no big data changes occurr. I don't know why. I attached the file here. Can you take a look into it when you are free?
Star Strider
Star Strider 2022-11-3
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These data are not at all like the previous data. I have no idea what to do with them.
Here, the envelope approach would likely be best, although it is going to be difficult as well, because the baselines are not constant between the segments.
The sampling interval is not constant, however resampling it allows digital filtering, and whil that eliminates the wandering baseline, it does not improve the signal itself.
Stopping here with this signal.
Uz = unzip('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1180033/aggff.zip');
T1 = readtable(Uz{1})
T1 = 700880×2 table
tt_agg Var1 ___________ _________ {'51:40.9'} -0.10611 {'51:40.9'} -0.035732 {'51:40.9'} -0.13328 {'51:40.9'} -0.030485 {'51:40.9'} -0.10945 {'51:40.9'} -0.048296 {'51:41.0'} -0.11417 {'51:41.0'} -0.021363 {'51:41.0'} -0.12879 {'51:41.0'} -0.022544 {'51:41.0'} -0.12789 {'51:41.0'} -0.037168 {'51:41.0'} -0.11063 {'51:41.0'} -0.036606 {'51:41.0'} -0.11115 {'51:41.0'} -0.047595
tv = datetime(T1{:,1}, 'InputFormat','mm:ss.S','Format','mm:ss.S');
[tv,sidx] = sort(tv);
dv = T1{sidx,2};
figure
plot(tv, dv)
grid
idx = [373000 : 376000];
figure
plot(tv(idx), dv(idx))
grid
xlim([min(tv(idx)) max(tv(idx))])
title('Zoomed')
idx = [373000 : 376000];
figure
plot(tv(idx), abs(dv(idx)))
grid
xlim([min(tv(idx)) max(tv(idx))])
title('Zoomed Absolute Values')
Ts = mean(diff(tv));
Tsd = std(diff(tv));
ts_min = minutes(Tsd)
ts_min = 0.0030
t_mins = minutes(Ts)
t_mins = 8.5604e-05
% 1/t_mins
Fs = 1E+4;
[dvr,tvr] = resample(dv, tv, Fs);
Fn = Fs/2;
L = numel(dvr)
L = 35999027
NFFT = 2^nextpow2(L)
NFFT = 67108864
FTdvr = fft(dvr-mean(dvr), NFFT)/L;
Fv = linspace(0, 1, NFFT/2+1)*Fn;
Iv = 1:numel(Fv);
figure
semilogy(Fv, abs(FTdvr(Iv))*2)
grid
xlabel('Frequency')
ylabel('Magnitude')
% figure
% semilogy(Fv, abs(FTdvr(Iv))*2)
% grid
% xlim([0 250])
% xlabel('Frequency')
% ylabel('Magnitude')
dvrf = highpass(dvr, 0.1, Fs, 'ImpulseResponse','iir');
figure
plot(tvr, dvrf)
xlabel('Time')
ylabel('Amplitude')
.
uzzi
uzzi 2022-11-3
Thank you very much for helping me. I am figuring it out how to understand that data itself. Your final figure looks like very much well sorted out. Thanks again for helping me.
Star Strider
Star Strider 2022-11-3
As always, my pleasure!
I worked for about an hour to do something with that last file, and could not.
.

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