align signal plot close to the origin Y Axis
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Hello, I have some signals that have some issues as you can see in the figure below. Herein, this example shows that the signal does not start at the origin of the Y axis (0 value) and it can have different starts from the origin for the three components (2 horizontals and 1 vertical). This potentially affects the estimation of the maximum peak amplitude for each component (PGA as shown in the figure below). The easiest option to correct this issue is to just to substract the average between the first and the last amplitud of the signal from all the points of the entire signal so that the signal can almost starts from the Y origin. You can suggest another sophisticated way to do this operation. I attached an example of one of the signals that has this issue in the amplitude of the three components. Thank you for your help. By the way, this is operation is someting different for the base line correction of signals that are performed after the filtering process.
The following code was provided by Star Strider and it was deleted by technical issues from the answers:
A1 = readmatrix('example.txt')
A1 = 19800×3
4.9381 -5.3897 -8.5907
4.9415 -5.3906 -8.5912
4.9343 -5.3983 -8.5931
4.9305 -5.3964 -8.5921
4.9291 -5.4107 -8.5983
4.9291 -5.4035 -8.5893
4.9334 -5.3868 -8.5859
4.9243 -5.4030 -8.5945
4.9248 -5.3926 -8.5921
4.9214 -5.3859 -8.5959
Fs = 200; % Signal Sampling Frequency (samples/time unit)
L = size(A1,1);
t = linspace(0, L-1, L).'/Fs;
figure
plot(t, A1)
grid
legend('Column #1','Column #2','Column #3', 'Location','best')
[PeakMax1,idx] = max(abs(A1))
PeakMax1 = 1×3
22.1162 22.4118 17.8099
idx = 1×3
5395 5237 4440
figure
tiledlayout(3,1)
for k = 1:size(A1,2)
nexttile
plot(t, A1(:,k), 'Color',[0.9 0.7 0.1])
hold on
plot(t(idx(k)), A1(idx(k),k), 'pb', 'MarkerFaceColor','b', 'MarkerSize',10)
hold off
grid
axis('padded')
text(t(idx(k)), A1(idx(k),k), sprintf(' \\leftarrow Amplitude = %.3f\n Time = %.3f', A1(idx(k),k),t(idx(k))) )
end
sgtitle('Original')
for k = 1:size(A1,2)
B(:,k) = [t([1 end]) [1;1]] \ A1([1 end],k);
nv(:,k) = [t ones(size(t))] * B(:,k);
end
[PeakMax2,idx] = max( abs(A1 - A1(1,:)) )
PeakMax2 = 1×3
17.8957 18.9390 9.8138
idx = 1×3
5044 5295 5059
figure
tiledlayout(3,1)
for k = 1:size(A1,2)
nexttile
plot(t, A1(:,k) - A1(1,k), 'Color',[0.9 0.7 0.1])
hold on
plot(t(idx(k)), A1(idx(k),k) - A1(1,k), 'pb', 'MarkerFaceColor','b', 'MarkerSize',10)
hold off
grid
axis('padded')
A1k = (A1(idx(k),k) - nv(idx(k),k));
tk = t(idx(k));
text(tk, A1k, sprintf(' \\leftarrow Amplitude = %.3f\n Time = %.3f', A1k, tk) )
end
sgtitle('Centre Normalised')
[PeakMax3,idx] = max( abs(A1 - nv) )
PeakMax3 = 1×3
17.9097 18.9066 9.8223
idx = 1×3
5044 5295 5059
PeakMaxDiff = PeakMax2 - PeakMax3
PeakMaxDiff = 1×3
-0.0140 0.0324 -0.0085
figure
tiledlayout(3,1)
for k = 1:size(A1,2)
nexttile
plot(t, A1(:,k) - nv(:,k), 'Color',[0.9 0.7 0.1])
hold on
plot(t(idx(k)), A1(idx(k),k) - nv(idx(k),k), 'pb', 'MarkerFaceColor','b', 'MarkerSize',10)
hold off
grid
axis('padded')
A1k = (A1(idx(k),k) - nv(idx(k),k));
tk = t(idx(k));
text(tk, A1k, sprintf(' \\leftarrow Amplitude = %.3f\n Time = %.3f', A1k, tk) )
end
sgtitle('Centre Detrended')
5 Commenti
Star Strider
il 3 Gen 2024
I did not keep my code.
Since you did not accept my answer, I have no incentive to follow up on this.
Risposta accettata
Hassaan
il 31 Dic 2023
To address this issue, one approach would be to apply a detrending method to remove any linear trend from the signal, which could re-align the signal closer to the Y-axis origin. This can be done using MATLAB's detrend function, which by default removes the best straight-line fit. Here is a conceptual example of how you might apply it:
% Load your signal data
signal = % your signal data here;
% Apply detrending to remove linear trend
signal_detrended = detrend(signal);
% Now, plot the detrended signal
plot(signal_detrended);
xlabel('Time (s)');
ylabel('Amplitude');
title('Detrended Signal Plot');
This approach assumes that the deviation from the origin is linear over time. If the deviation is more complex, you might need a more sophisticated method like polynomial fitting and subtraction or other types of baseline correction methods that fit the non-linear trends in the data before subtracting them.
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If you find the solution helpful and it resolves your issue, it would be greatly appreciated if you could accept the answer. Also, leaving an upvote and a comment are also wonderful ways to provide feedback.
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