I want to assign vector to a contourf graph, in order to show the direction and magnitude of wind.
For this I am using contourf(A) and quiver(x,y), where as A is a matrix 151x401 and x,y are matrices with the same sizes (151x401) with magnitude and direction respectively.
When I am using large maps i get the position of the arrows but they are to densily placed and that makes the graph look bad.
The final graph has the arrows as desired, but they are to many of them and too close, I would like them to be more scarce and distributed with more gap between them, so as to be able to increase their length and at the same time have the components of the contour map visible.
Can anyone help , any pointers would be helpful
i know its been a long time since the question was asked, but i think i found a way to make it work.
I attach the code in case someone encounters the same issues
[nx,ny]= size(A) % A is the matrix used as base
xx=1:1:ny; % set the x-axis to be equal to the y
yy=1:1:nx; % set the y-axis to be equal to the x
contourf(xx,yy,A)
hold on, delta = 8; %delta is the distance between arrows)
quiver(xx(1:delta:end),yy(1:delta:end),B(1:delta:end,1:delta:end),C(1:delta:end,1:delta:end),1) % the 1 at the end is the size of the arrows
set(gca,'fontsize',12);, hold off
A,B,C are the corresponding matrices ones want to use
Related
Is it possible to make multiple vertical histograms plot in Matlab into one? Much like the excel sheet enclosed ( https://drive.google.com/file/d/1H_mbyrIoln3XrnK1hLajnVNBKn13y_np/view?usp=sharing )
I want to make a plot of many vertical histogram plots into one figure, by importing excel-files, where on the y axis it has the elevation, the x axis the distance between the histogram vertical lines and the length of the histogram bars is the values in the excel sheet. The vertical height of each bar is 5.
Is this even possible? I have to put in a number of conditions for Matlab to know where to plot, but could some one show me the basic methodology?
Help would be very much appreciated!
The problem is that the parent of a Baseline object is the Axis, which prevents us from doing something like
barh(bins1,counts1,'Basevalue',baseline1); hold on;
barh(bins2,counts2,'Basevalue',baseline2); hold off;
because the plots will automatically share the second baseline value set. There might be a workaround for this that I do not know of, so I invite anybody who knows it to show me how its done.
For now, I was able to sort-of replicate the plot you posted a picture of in a much less elegant way. I will post code below, but before I do, I would like to argue against the use of a plot like this. Why? Because I think it is confusing, as the x-axis both relates to the plot number as well as the bin count numbers. You are in fact trying to display a 3-D data set, the three dimensions being bins, bin counts, and 'histogram number'. A plethora of methods exist for displaying 3-D data, and a series of 2-D histograms may not be the best way to go.
That being said, here is a code that more-or-less creates the picture above, as promised. Any changes you may want to make will be more cumbersome than usual :-)
testData = randn(10000,1); % Generate some data
[counts,bins] = hist(testData); % Bin the data
% First histogram
baseline1 = 0;
p1=subplot(1,3,1); barh(bins,counts,'BaseValue',baseline1);
xticks(baseline1); xticklabels({0}); % Graph number on x axis at baseline (0)
box off; % Remove box on right side of plot
ylabel('Property');
% Second histogram
baseline2 = max(counts)*1.2;
sepdist = baseline2-baseline1; % Distance that separates two baselines
counts2 = baseline2 + counts;
p2=subplot(1,3,2); barh(bins,counts2,'BaseValue',baseline2)
xticks(baseline2); xticklabels({1}); % Graph number on x axis at baseline
box off;
Y=gca; Y.YAxis.Visible='off';
p1p=p1.Position; p2p=p2.Position;
p2p(1)=p1p(1)+p1p(3); p2.Position=p2p; % Move subplot so they touch
% Third histogram
baseline3 = baseline2 + sepdist;
counts3 = baseline3+counts;
p3=subplot(1,3,3); barh(bins,counts3,'BaseValue',baseline3)
xticks(baseline3); xticklabels({2});
Y=gca; Y.YAxis.Visible='off';
box off
p3p=p3.Position;
p3p(1)=p2p(1)+p2p(3); p3.Position=p3p;
% Add x-label when you are done:
xl=xlabel('Test xlabel'); xl.Units='normalized';
% Fiddle around with xl.Position(1) until you find a good centering:
xl.Position(1) = -0.49;
Result:
I am trying to write a MATLAB script to give me a contour map. The contour map must be created from inputs that I generated from 100 images.
The story is like this:
I have 100 images on which I ran an image processing algorithm for optimization. Now, I got their energy curves. So, I have 100 energy curves. I want to create a contour map that will show me where the points are denser on the plot. (the energy curves are plotted as energy vs. iteration with fixed number of iterations)
The following is my variable:
energy(iteration,numImages)
Hope I explained it well.
Thanks in advance.
I interpret your question to boil down to how can I create a surface plot with colors according to the energy found in energy. I would solve this by using the contour function with a grid generated using meshgrid. If each image is described in 1000 data points with 100 files the plot can be generated as follows:
% using stuff as random junk instead of energy
numPoints = 1000;
numFiles = 100;
stuff = rand(1000,100); % replace with actual information
[X, Y] = meshgrid(1:numFiles, 1:numPoints);
contour(X,Y,stuff);
You can also create a 3D surface plot using surf and the same logic.
From what i see of you graph (and using the comments also), one possible way is to use plot3 to plot a line in 3D for every plot.
For doing so, you can use something like this code:
x=(0:0.01:1)';
aexp=zeros(100,numel(x));
hold on
for ii=1:100;
% aexp(ii,:)=exp((-x+ii/10)); %exponential
aexp(ii,:)=exp(-(x-ii/100).^2); %~gaussian
% aexp(ii,:)= x*ii; %linear increase
plot3(x,aexp(ii,:),ii*ones(1,numel(x)));
end
% set(gca,'yscale','log'); % uncomment if you need logscale.
giving
I have a few options of plot. It always plot from the XY view. I changed by hand, but you can use the view command. Notice that i used a simple counter to make the spacing in the z direction.
In a similar manner, you can plot using the contour. For my code, after the data have been generated in the for loop, remove/comment the plot3 and add:
contour(aexp) %outside the for loop,
giving
Notice that i have not really take care what i'm plotting. You can find more info on contour in the Matlab page .
You commented that the x-axis should be number of iterations, y-axis should be energy and z-axis should be the information containing how many lines are passing through from some areas. For this, make a qq variable, being it qq=number_of_lines(number of iterations,energy) . Make a discrete grid for the energy if you don't have one. Number of iterations is probably discrete anyway. The function is you who need to devise, but i would go for something which checks the number of lines for every energy and every iteration. In this case you will have the z-function that depends on y and x, that is the case to use contour or surface.
My function above make a line for every ii point, to have a 3d function. An edition for another extra loop is not hard. Just remember to have the same regular grid for every point, otherwise you will have trouble.
I am trying to plot x and y velocities using quiver function in MATLAB.
I have x,y,u and v arrays(with their usual meanings) with dimension 100x100
So, the result is my quiver plot is dense and I cannot see the arrows unless I zoom in.
Somewhat like this: quiver not drawing arrows just lots of blue, matlab
Take a look at my plot:
Is there any way to make quiver plot less dense(and with bigger arrows)? I am planning to clip x-axis range to 0-4. But anything apart from that?
I cannot make my mesh less dense for accuracy concerns. I am, however willing to ignore some fine data points if that's required to make the plot look better.
You can plot a reduced number of arrows by plotting, for example, (assuming your data are in arrays)
quiver(x(1:2:end,1:2:end),y(1:2:end,1:2:end),u(1:2:end,1:2:end),v(1:2:end,1:2:end))
where the 2 in this example means we plot only a quarter as many arrows. You can of course change it, as long as you change all of the 2's so that the arrays are all appropriately sized.
If you want to change the length of the arrows there are two options. Firstly, you can use the scale option scale=2 to scale the arrows by the amount specified, or you can normalise the velocities if you want to have all the arrows the same length. You do lose information doing that, because you can't compare the magnitude of the velocity by looking at the arrows, but it may be useful in some situations. You can do this by dividing u and v both by sqrt(u.^2+v.^2) (at the points you wish to plot arrows at.
Hope that helps and sets everything out nicely.
You need to make your interval value a bit larger in order to make your matrix more sparse.
This is very dense:
1:0.0001:100
This is very sparse:
1:1:100
EDIT:
If you have the Image Processing Toolkit you can use the imresize function to reduce the matrix resolution:
newMat = imresize(oldMat, newSize);
And if you don't have the Toolbox then you can resize in a similar manner to this example using interp2 Interpolation:
orgY = 1:size(oldMat,1);
orgX = 1:size(oldMat,2);
[orgX,orgY] = meshgrid(orgX ,orgY);
newY = linspace(1,size(mat,1),newHeight);
newX = linspace(1,size(mat,2),newWidth);
[newX,newY] = meshgrid(newX,newY);
newMat = interp2(orgX,orgY,mat,newX,newY);
And thanks to #David, if you want to just strip out some individual points you can simply do:
xPlot=x(1:2:end)
I'm currently plotting 2 separate 3-dimensional amorphous blobs which overlap each other. I have created the blobs by deforming a unit circle (as you can see in the code provided below). My question is: is there an easy way to isolate the overlapping region? I need to isolate the overlapping region and then color it differently (as in turn the region green, for example) to clearly show where the overlap is. My actual program has many shapes that overlap, however for the sake of simplicity, i have produced the following code to illustrate what i am trying to do:
% Create Sphere with 100 points
N = 100; % sphere grid points
[X,Y,Z] = sphere(N); % get x,y,z coordinates for sphere
num=size(X,1)*size(X,2); % get total amount of x-coordinates (m*n)
% Loop through every x-coordinate and apply scaling if applicable
for k=1:num % loop through every coordinate
value=X(k); % store original value of X(k) as value
if value<0 % compare value to 0
X(k)=0.3*value; % if < 0, scale value
end
end
% Loop through every z-coordinate and apply scaling if applicable
for k=1:num % loop through every coordinate
value=Z(k); % store original value of X(k) as value
if value>0 % compare value to 0
Z(k)=0.3*value; % if < 0, scale value
end
end
mesh(X,Y,Z,'facecolor','y','edgecolor','y','facealpha',...
0.2,'edgealpha',0.2);
hold on
mesh(-1*(X-1),Y,Z,'facecolor','r','edgecolor','r','facealpha',...
0.2,'edgealpha',0.2);
hold off
axis equal
I'm not necessarliy looking for code, just an effective algorithm or process to achieve the desired results as I need to adapt this result into the more sophisticated program I have.
Maintain an array (n-dimensional) of integers, as you draw your objects, increment each corresponding point in the array. When done, loop through the array, and each element > 1 has an overlap between two or more objects, use the array coordinate to color the objects based on the number of overlaps.
I have worked in 2D with the MATLAB builtin function inpolygon to find out overlapping areas. However, it does not natively support 3d. I would suggest you try the inhull function which you can find here at file exchange. Please note it only supports convex hulls.
If that doesn´t help you maybe you find some inspiration in this discussion.
If I explain why, this might make more sense
I have a logical matrix (103x3488) output of a photo of a measuring staff having been run through edge detect (1=edge, 0=noedge). Aim- to calculate the distance in pixels between the graduations on the staff. Problem, staff sags in the middle.
Idea: User inputs co-ordinates (using ginput or something) of each end of staff and the midpoint of the sag, then if the edges between these points can be extracted into arrays I can easily find the locations of the edges.
Any way of extracting an array from a matrix in this manner?
Also open to other ideas, only been using matlab for a month, so most functions are unknown to me.
edit:
Link to image
It shows a small area of the matrix, so in this example 1 and 2 are the points I want to sample between, and I'd want to return the points that occur along the red line.
Cheers
Try this
dat=imread('83zlP.png');
figure(1)
pcolor(double(dat))
shading flat
axis equal
% get the line ends
gi=floor(ginput(2))
x=gi(:,1);
y=gi(:,2);
xl=min(x):max(x); % line pixel x coords
yl=floor(interp1(x,y,xl)); % line pixel y coords
pdat=nan(length(xl),1);
for i=1:length(xl)
pdat(i)=dat(yl(i),xl(i));
end
figure(2)
plot(1:length(xl),pdat)
peaks=find(pdat>40); % threshhold for peak detection
bigpeak=peaks(diff(peaks)>10); % threshold for selecting only edge of peak
hold all
plot(xl(bigpeak),pdat(bigpeak),'x')
meanspacex=mean(diff(xl(bigpeak)));
meanspacey=mean(diff(yl(bigpeak)));
meanspace=sqrt(meanspacex^2+meanspacey^2);
The matrix pdat gives the pixels along the line you have selected. The meanspace is edge spacing in pixel units. The thresholds might need fiddling with, depending on the image.
After seeing the image, I'm not sure where the "sagging" you're referring to is taking place. The image is rotated, but you can fix that using imrotate. The degree to which it needs to be rotated should be easy enough; just input the coordinates A and B and use the inverse tangent to find the angle offset from 0 degrees.
Regarding the points, once it's aligned straight, all you need to do is specify a row in the image matrix (it would be a 1 x 3448 vector) and use find to get non-zero vector indexes. As the rotate function may have interpolated the pixels somewhat, you may get more than one index per "line", but they'll be identifiable as being consecutive numbers, and you can just average them to get an approximate value.