Matlab sensing the dynamic data changes and automatically plotting

Question

uzzi 2022-11-1

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1840603-matlab-sensing-the-dynamic-data-changes-and-automatically-plotting

评论： Star Strider 2022-11-3

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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Answer 1

Star Strider 2022-11-2

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链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1840603-matlab-sensing-the-dynamic-data-changes-and-automatically-plotting#answer_1090048

在 MATLAB Online 中打开

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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Star Strider 2022-11-3

在 MATLAB Online 中打开

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.

.

Star Strider 2022-11-3

在 MATLAB Online 中打开

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 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 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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