plotting mutiple curve in the same graph

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Chun Fai Leung am 16 Jul. 2022

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Kommentiert: Star Strider am 29 Jul. 2022

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modifiedDataset.xlsx

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I would like to plot a graph for the table. So I use the following code.

clear
dataset = xlsread('modifiedDataset.xlsx','Sheet1');
x = dataset(:,6);
y = dataset(:,11);
plot(x,y);

But for column G, the number are actually the particle numbers. I would like to plot several curve in the same graph if possible, each curve for each particle respectively. In this case, there are 9 particles. Is there any code that could help me identify the particle number and plot the curves respectively. I'm new to MATLAB, sorry about that. I would much appreciate your help. Thank you.

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Star Strider am 16 Jul. 2022

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https://de.mathworks.com/matlabcentral/answers/1761835-plotting-mutiple-curve-in-the-same-graph#answer_1009015

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Here are two options —

dataset = readmatrix('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1067780/modifiedDataset.xlsx');

x = dataset(:,6);

y = dataset(:,11);

p = dataset(:,7);

up = unique(p);

figure

hold on

for k = 1:numel(up)

Lv = p == up(k);

plot(x(Lv), y(Lv), 'DisplayName',sprintf('Particle %d',up(k)))

end

hold off

grid

legend('Location','best')

NrSP = numel(up);

figure

hold on

for k = 1:numel(up)

subplot(5,2,k)

Lv = p == up(k);

plot(x(Lv), y(Lv))

grid

ylim([0 1.4])

title(sprintf('Particle %d',up(k)))

end

hold off

There are other possibilities as well.

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Chun Fai Leung am 17 Jul. 2022

Bearbeitet: Chun Fai Leung am 17 Jul. 2022

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My apologies for my incomplete description. I am actually calculating the mean square displacement(MSD) for the particle tracking project. From other's matlab coding, I generate datas of some tracked particles and store it in the excel data sheet,which is the below attached excel datasheet. Then, I need to further data treat the excel. At last, plotting the curve according to the datas.

In the excel sheet, column A,B and C are the tracked particles x,y and z coordinates while in column F are the recorded time frames. In column G, it shows the particle numbers, therefore, in this excel sheet, it successfully record trajectories of 9 particles.

In the second steps of the data treatments, i uses this code to calculate the MSD to the empty column K and the maximum number of time frame to the empty column L.

clear
dataset = xlsread("12.35mW-cm22_gray.xlsx",'5')
for i=1:length(dataset)
   dataset(i,11) = ((dataset(i,1)-dataset(1,1))^2 + (dataset(i,2)-dataset(1,2))^2 + (dataset(i,3)-dataset(1,3))^2)/3; % H -> 8, I -> 9, J ->10, K ->11
   dataset(1,12) = max(dataset(:,6));
end
writematrix(dataset,'modifiedDataset.xlsx');

However, all data minus the coordinates of the first frame of the first particles, which is incorrect. The correct ways would be, for first particles data (row 1 to row 153), it should minus the xyz coordinates of the first frame of first particles (row 1) while for the second particles (row 154 to 306), it should minus the xyz coordinates of the first frame of second particles, and continues until all particles. I wonder if it is possible?

Thank you so much for your patience and your time. Really appreciate it.

Star Strider am 17 Jul. 2022

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Try this —

dataset = readmatrix('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1068210/modifiedDataset.xlsx')

dataset = 1376×12

17.7926 433.1585 11.5639 6.4030 -0.1606 1.0000 1.0000 0.3504 -0.0522 -0.0253 0 153.0000 18.1430 433.1062 11.5401 6.7857 -0.1859 2.0000 1.0000 0.3504 -0.0522 -0.0253 0.0420 0 18.6103 432.7898 11.8783 6.6620 -0.2182 3.0000 1.0000 0.4672 -0.3165 -0.0322 0.3011 0 18.9350 432.2805 12.0421 6.5213 -0.2557 4.0000 1.0000 0.3247 -0.5093 -0.0375 0.7682 0 19.0232 431.8207 12.0999 6.9411 -0.3025 5.0000 1.0000 0.0883 -0.4598 -0.0468 1.1971 0 19.0083 431.5381 11.8379 7.2035 -0.3395 6.0000 1.0000 -0.0149 -0.2826 -0.0370 1.3928 0 19.1212 431.3237 12.0590 7.1167 -0.3411 7.0000 1.0000 0.1129 -0.2144 -0.0016 1.7922 0 19.4238 430.9495 12.0646 7.0499 -0.3250 8.0000 1.0000 0.3026 -0.3742 0.0161 2.5970 0 19.7554 430.3502 12.0633 6.9559 -0.3215 9.0000 1.0000 0.3315 -0.5993 0.0034 3.9960 0 19.9394 429.6530 12.2847 6.9449 -0.3330 10.0000 1.0000 0.1840 -0.6972 -0.0115 5.8056 0

p = dataset(:,7);

[up,ix1] = unique(p, 'stable');

for k = 1:numel(up)

Lv = p == up(k); % Choose Rows

v = dataset(Lv,1:3); % Extract Relevant Rows & Columns

dataset(Lv,11) = mean((v-v(1,:)).^2, 2); % Calculate Mean Squared Differences

dataset(Lv,12) = max(dataset(Lv,6)); % Calculate Relevant Maximum

fprintf('\nParticle # %2d:\n',up(k)) % Label Results (Discard When No Longer Needed)

Check = dataset(Lv,[11 12]) % View Results (Discard When No Longer Needed)

end

Particle # 1:

Check = 153×2

0 153.0000 0.0420 153.0000 0.3011 153.0000 0.7682 153.0000 1.1971 153.0000 1.3928 153.0000 1.7922 153.0000 2.5970 153.0000 3.9960 153.0000 5.8056 153.0000

Particle # 2:

Check = 153×2

0 153.0000 0.0345 153.0000 0.1440 153.0000 0.2916 153.0000 0.3740 153.0000 0.2313 153.0000 0.1576 153.0000 0.2115 153.0000 0.1143 153.0000 0.0822 153.0000

Particle # 3:

Check = 153×2

0 153.0000 0.0084 153.0000 0.0525 153.0000 0.1011 153.0000 0.1494 153.0000 0.2150 153.0000 0.3853 153.0000 0.5693 153.0000 0.8489 153.0000 1.3386 153.0000

Particle # 4:

Check = 153×2

0 153.0000 0.1121 153.0000 0.4015 153.0000 0.8666 153.0000 1.7666 153.0000 3.1850 153.0000 4.6540 153.0000 5.8991 153.0000 7.2790 153.0000 8.9053 153.0000

Particle # 5:

Check = 153×2

0 153.0000 0.1555 153.0000 0.3277 153.0000 0.8220 153.0000 1.2691 153.0000 1.3865 153.0000 1.3936 153.0000 2.1265 153.0000 5.9877 153.0000 7.0238 153.0000

Particle # 6:

Check = 153×2

0 153.0000 0.1065 153.0000 0.6501 153.0000 1.7468 153.0000 2.8957 153.0000 3.7337 153.0000 4.1963 153.0000 4.8106 153.0000 6.0009 153.0000 7.2302 153.0000

Particle # 7:

Check = 153×2

0 153.0000 0.0230 153.0000 0.0945 153.0000 0.4436 153.0000 1.0362 153.0000 2.0825 153.0000 3.6829 153.0000 6.0417 153.0000 9.6684 153.0000 14.5893 153.0000

Particle # 8:

Check = 153×2

0 153.0000 0.0226 153.0000 0.2362 153.0000 0.8242 153.0000 1.3160 153.0000 1.3110 153.0000 0.9586 153.0000 0.7092 153.0000 0.6353 153.0000 0.6979 153.0000

Particle # 9:

Check = 152×2

0 153.0000 0.0097 153.0000 0.1180 153.0000 0.5796 153.0000 1.4487 153.0000 2.4298 153.0000 2.9192 153.0000 2.8464 153.0000 2.5226 153.0000 2.3991 153.0000

x = dataset(:,6);

y = dataset(:,11);

p = dataset(:,7);

up = unique(p);

NrSP = numel(up);

figure

for k = 1:numel(up)

subplot(3,3,k)

Lv = p == up(k);

plot(x(Lv), y(Lv).*(y(Lv)>0))

grid

set(gca, 'YScale','log')

ylim([0 2000])

title(sprintf('Particle %d',up(k)))

end

sgtitle('Particle MSD Vectors (Logarithmic Y-Axis)')

If I understand correctly what you want to do, this should be the desired result. I checked the column 11 calculation here with a result using the procedure in your code, and they were the same. (This code should be more efficient.) The column 12 result simply seems to be the number of observations for each particle.

Chun Fai Leung am 29 Jul. 2022

Bearbeitet: Chun Fai Leung am 29 Jul. 2022

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modifiedDataset.xlsx

@Star Strider Mr Star Strider!! I am so sorry to bother you again. I do the experiment and generate particle video with different light intensity again, therefore having a different datasheet. Then I used the same coding to plot the graph. Just viewing the datasheet, numericals seems normal but by plotting the graph, somehow different particles curve are linked together, is there something wrong? Please kindly advice again.

This is the last datasheet and it worked perfect well. But in the recent datasheet. As you could see, different particles curve are linked together somehow. Could you help me again please?

The code I used are the same as before and typed as follow.

clear
dataset = xlsread("12.35mW-cm22_gray.xlsx",'5')
p = dataset(:,7);
[up,ix1] = unique(p, 'stable');
for k = 1:numel(up)
    Lv = p == up(k);                                    % Choose Rows
    v = dataset(Lv,1:3);                                % Extract Relevant Rows & Columns
    dataset(Lv,11) = mean((v-v(1,:)).^2, 2);            % Calculate Mean Squared Differences
    dataset(Lv,12) = max(dataset(Lv,6));                % Calculate Relevant Maximum
    dataset(Lv,13) = max(dataset(Lv,11))
    dataset(1,14) = ((dataset(1,13)/2)/(dataset(1,12)/30))
    fprintf('\nParticle # %2d:\n',up(k))                % Label Results (Discard When No Longer Needed)
    Check = dataset(Lv,[11 12])                         % View Results (Discard When No Longer Needed)
end
writematrix(dataset,'modifiedDataset.xlsx');
clear
dataset = xlsread('modifiedDataset.xlsx','Sheet1');
x = dataset(:,6);
y = dataset(:,11);
p = dataset(:,7);
up = unique(p);
figure
hold on
for k = 1:numel(up)
    Lv = p == up(k);
    plot(x(Lv), y(Lv), 'DisplayName',sprintf('Particle %d',up(k)))
end
hold off
grid
legend('Location','best')

Star Strider am 29 Jul. 2022

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The problem is with the data. Plotting them with a logarithmic y-scale illustrates the problem, that being that some of the data appear to ‘jump’ by several orders of magnitude. One option is to simply eliminate

, such as I did nere.

These are your data, and I am not certain what you want to do with the extreme values, especially since they also apparently have new x-values as well.

dataset = readmatrix('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1082305/modifiedDataset.xlsx')

dataset = 8963×14

18.1950 757.6516 11.8432 7.4063 -1.0773 1.0000 1.0000 0.2939 0.1685 0.0324 0 140.0000 26.1676 2.8037 18.4889 757.8201 11.9827 7.4171 -1.0449 2.0000 1.0000 0.2939 0.1685 0.0324 0.0447 140.0000 26.1676 0 18.7980 758.0051 12.0819 7.5985 -1.0066 3.0000 1.0000 0.3091 0.1851 0.0383 0.1819 140.0000 26.1676 0 19.0629 758.1270 11.9133 7.6209 -0.9832 4.0000 1.0000 0.2650 0.1218 0.0234 0.3281 140.0000 26.1676 0 19.2621 758.1290 12.0798 7.7316 -0.9755 5.0000 1.0000 0.1992 0.0021 0.0077 0.4743 140.0000 26.1676 0 19.3769 758.0750 12.3076 7.8672 -0.9628 6.0000 1.0000 0.1147 -0.0540 0.0127 0.5973 140.0000 26.1676 0 19.3948 758.0286 12.2501 7.9285 -0.9364 7.0000 1.0000 0.0179 -0.0465 0.0264 0.5824 140.0000 26.1676 0 19.3447 757.9728 11.9893 7.9712 -0.9289 8.0000 1.0000 -0.0501 -0.0557 0.0075 0.4821 140.0000 26.1676 0 19.2311 757.8768 12.2711 7.8200 -0.9651 9.0000 1.0000 -0.1136 -0.0960 -0.0361 0.4358 140.0000 26.1676 0 19.1163 757.6586 12.3047 8.0299 -1.0206 10.0000 1.0000 -0.1148 -0.2182 -0.0556 0.3539 140.0000 26.1676 0

% p = dataset(:,7);

% [up,ix1] = unique(p, 'stable');

% for k = 1:numel(up)

% Lv = p == up(k); % Choose Rows

% v = dataset(Lv,1:3); % Extract Relevant Rows & Columns

% dataset(Lv,11) = mean((v-v(1,:)).^2, 2); % Calculate Mean Squared Differences

% dataset(Lv,12) = max(dataset(Lv,6)); % Calculate Relevant Maximum

% dataset(Lv,13) = max(dataset(Lv,11))

% dataset(1,14) = ((dataset(1,13)/2)/(dataset(1,12)/30))

% fprintf('\nParticle # %2d:\n',up(k)) % Label Results (Discard When No Longer Needed)

% Check = dataset(Lv,[11 12]) % View Results (Discard When No Longer Needed)

% end

% writematrix(dataset,'modifiedDataset.xlsx');

% clear

% dataset = xlsread('modifiedDataset.xlsx','Sheet1');

Lz = dataset(:,11) >= 1E5; % Find 'y' Values Greater that 1x10^5

dataset(Lz,11) = NaN; % Set Them To NaN

dataset = rmmissing(dataset); % Remove All Rows With NaN Values In Any Row

% Check = dataset(:,[6 7 11]) % Check Result

x = dataset(:,6);

y = dataset(:,11);

p = dataset(:,7);

up = unique(p);

figure

hold on

for k = 1:numel(up)

Lv = p == up(k);

plot(x(Lv), y(Lv), 'DisplayName',sprintf('Particle %d',up(k)))

end

hold off

grid

% set(gca, 'YScale','log')

legend('Location','eastoutside', 'NumColumns',2)

Warning: Limiting legend entries to 50. Specify a vector of graphics objects to display more than 50 entries.

That is the best I can do.

Chun Fai Leung am 29 Jul. 2022

wow sir. It looks so much better and exactly what I want. I do want to exclude the extreme values and delete the rows of data, apparently you have also done it already.So when I redo the experiment again with different light intensity, should I always check for extreme values and exclude them again. Thank you so much sir.

Star Strider am 29 Jul. 2022

As always, my pleasure!

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plotting mutiple curve in the same graph

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