MATLAB: Automate multiple graph for y values

graph plot

Hi, I am still beginner in MATLAB and I wonder how to plot multiple (hundreds) of rows into one graph? For example if I have

x=[1:5] and the my y values are y = [ 2 3 5 6 4; 4 5 6 2 10; 13 4 12 10 4; … ] -> 100 rows or more

Is it possible to automate the plot? instead of using

plot (x,y(1,:)) hold on plot (x,y(2,:)) plot (x,y(3,:)) … hold off

is there an easy way to plot all of those y values into a single graph? Thank you in advance for the reply

Best Answer

No loop necessary. MATLAB will plot these correctly for you:

x = [1:5];
y = [2 3 5 6 4; 4 5 6 2 10; 13 4 12 10 4];
figure
plot(x, y)
grid

Note that if ‘y’ is a ‘numel(x) x numel(x)’ square matrix, you may have to transpose it to get the correct plot.

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This is what I always do:

x = linspace(1,2*pi,250);
v = 1:5;
y = cos(v'*x);
h = colormap(jet(5));
figure(1)
plot(x,y(1,:),'Color', h(1,:))
hold on
for k1 = 2:5
    plot(x,y(k1,:), 'Color',h(k1,:))
end
hold off
grid

Slightly cumbersome, but automated and effective. Experiment with the colormap to get the result you want.

You probably have to build the marker array yourself.

NOTE — R2014b now cycles the colours without having to do anything special.

MATLAB: Automating colour segmentation script

Ok so I think I've written a function that helps somewhat - slightly different from above, same principle. I now need to work out how to read the next image in a directory, and apply the local function to this image, and output the files as the same as the image name in the folder. Updating in case this code is useful for anyone else.

%% read REFERENCE image to get colour
ant = imread("IMG_4335.GREEN.JPG");
antLAB = rgb2lab(ant); % convert RGB to LAB colour space

antL = antLAB(:,:,1); % luminosity

antA = antLAB(:,:,2); % red-green axis

antB = antLAB(:,:,3); % blue-yellow axis

% Draw shape filled with a paint spot - run this at start of new folder
% to get baseline reference colour
imshow(ant)
roi = drawpolygon % draw shape to capture the colour spot you want, then press enter
% use the shape above (roi), to get pixel values of area
BWant = createMask(roi); % create a mask using the drawn shape
aROI = antA(BWant); %get the pixel values for A channel
bROI = antB(BWant); % get the pixel values for B channel
meanROI = [mean(aROI) mean(bROI)]; % take mean values for each channel
aMean = meanROI(1); % index above, channel a
bMean = meanROI(2); %index above, channel b
%% use local function to get output for reference image
[finalantimage centroids] = createantmask(ant, antA, aMean, bMean);
% function in the format: outputs = functionname(inputs)
%% read a different image, use createantmask function again without
%  having to redraw shape
%  using pixel values from first image as a reference
ant = imread("Y2.JPG");
[finalantimage centroids] = createantmask(ant, antA, aMean, bMean);
%% plot centroids on image, check looks ok
imshow(finalantimage)
hold on
plot(centroids(:,1),centroids(:,2),"b*");
hold off
%% display images side by side, check not missing any blobs etc.
fig = figure();
ax(1) = axes('Units','normalized','Position', [ .1 .1 .4 .8]);
ax(2) = axes('Units','normalized','Position', [ .5 .1 .4 .8]);
imshow(finalantimage, 'Parent', ax(1)) %display mask
imshow(ant, 'Parent', ax(2)) %display original image
linkaxes(ax)
% create local function below, to apply to image named "ant"
% have to run the first section first to get the reference colour pixels
% and required inputs
%%
function [finalantimage centroids] = createantmask(ant, antA, aMean, bMean)
ant = imgaussfilt(ant,2); % introduce gaussian filter, to avoid jagged edges later
antLAB = rgb2lab(ant); % convert RGB to LAB colour space
antL = antLAB(:,:,1); % luminosity
antA = antLAB(:,:,2); % red-green axis
antB = antLAB(:,:,3); % blue-yellow axis
distLab = sqrt((antA - aMean).^2 + (antB - bMean).^2); % distance matrix. 
% difference between average colour and every pixel
% treshold the distance matrix - which pixels are similar to sample colour.
% smaller = stricter treshold, but risk cutting off some blobs
mask = distLab < 10; 
% value not much of an issue when just detecting largest blob only.
% dilate image, if two dots close, should merge them.
se = strel("disk",7); % create disk shaped area to use to dilate image next
dilatedant = imdilate(mask,se); % dilate image - to join blotches that should be one blob
maskfilledholes = imfill(dilatedant,"holes"); % fill in holes
maskcleaned = bwareaopen(maskfilledholes,10); % remove leftover pixels smaller than 10
% filter only largest blob - only for the queen coordinate
% DON'T USE FOR NON-QUEEN IMAGES
% finalantimage = bwpropfilt(maskcleaned,"area",1,"largest");
% find coordinates of centroids, write into text file
antmeasurements = regionprops(finalantimage, "all"); 
centroids = cat(1,antmeasurements.Centroid); % turn centroids into two columns
dlmwrite("antcoordinateS.txt",centroids); % (x and y coordinates)
% save jpg output of the final mask
imwrite(maskcleaned,"outputantimage.jpg");
end

Best Answer

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MATLAB: Automating line markers, colors…

MATLAB: Automating colour segmentation script

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