I have this data with values on the edges of the matrix and other values at evenly spaced interval within the matrix. I want to predict the values of the zero positions from the original values and make a heat map of the new data. Through suggest, I use scatteredInterpolant, ndgrid and interpolant since the data is that interp2 (matlab functions) cannot be used to interpolate the zero elements. Now, this method doe not give me a smooth figure and I am want to know if someone can offer some help. I have attached the figure from my code, the data and the code to this post.Thank you.
[knownrows, knowncolumns, knownvalues] = find(DataGrid); %get location and value of all non-zero points
interpolant = scatteredInterpolant(knownrows, knowncolumns, knownvalues,'linear'); %create interpolant from known values
[queryrows, querycolumns] = ndgrid(1:1:size(DataGrid, 1), 1:1:size(DataGrid, 2)); %create grid of query points
interpolatedj = interpolant(queryrows, querycolumns);
HeatMap(interpolatedj)
https://www.mediafire.com/?pq40x1ljxk8h996
https://www.mediafire.com/?pq40x1ljxk8h996
To plot a smoothed matrix you can use pcolor and set the shading parameter to interp
pcolor(M); %where M is your 2D matrix
shading interp %set the shading to interp
Try
image(M) or imagesc(M) where M is a matrix. pcolor(M) also works. If your matrix is huge then you need to remove edges otherwise figure just looks like blank image.
Related
Hope the title gave an adequate description of my problem. Basically, I am generating a contour plot in MATLAB using the contourf (x,y,z) function, where x and y are vectors of different lengths and z is a matrix of data with dimensions of x times y. The contourf plot is fine, however, I am looking to overlay this plot with the actual data points from the matrix z. I have tried using the scatter function, but I am getting an error message informing me that X and Y must be vectors of the same length - which they're not. Is there any other way to achieve this?
Thanks in advance for any help/suggestions!
I think meshgrid should help you.
z = peaks; %// example 49x49 z data
x = 1:20;
y = 1:49;
z = z(y,x); %// make dimensions not equal so length(x)~=length(y)
[c,h] = contourf(x,y,z);
clabel(c,h); colorbar;
[xx,yy]=meshgrid(x,y); %// this is what you need
hold on;
plot(xx,yy,'k.'); %// overlay points on contourf
Notice plot suffices instead of scatter. If you insist, scatter(xx(:),yy(:),10), for example, does the trick. Although my example isn't particularly interesting, this should hopefully get you started toward whatever you're going for aesthetically.
I want to build a contourf plot of a certain aspect in my Plate. The plate is divided in triangle elements, which I have the coordinates (x,y) of each knot of the triangle.
So, How can I make a meshgrid for my knots so I can make my contourf plot?? I have the coordinates of everything and have the value of my function Z in each knot. (I'm a beginner in Matlab, sorry for this "basic" question)
If your goal is just to visualise the triangles then there is another way that's probably simpler and more robust (see the end of this post).
If you definitely need to generate contours then you will need to interpolate your triangular mesh over a grid. You can use the scatteredInterpolant class for this (documentation here). It takes the X and Y arguments or your triangular vertices (knots), as well as the Z values for each one and creates a 'function' that you can use to evaluate other points. Then you create a grid, interpolate your triangular mesh over the grid and you can use the results for the countour plot.
The inputs to the scatteredInterpolanthave to be linear column vectors, so you will probably need to reshape them using the(:)`notation.
So let's assume you have triangular data like this
X = [1 4; 8 9];
Y = [2 3; 4 5];
Z = [0.3 42; 16 8];
you would work out the upper and lower limits of your range first
xlimits = minmax(X(:));
ylimits = minmax(Y(:));
where the (:) notation serves to line up all the elements of X in a single column.
Then you can create a meshgrid that spans that range. You need to decide how fine that grid should be.
spacing = 1;
xqlinear = xlimits(1):spacing:xlimits(2);
yqlinear = ylimits(1):spacing:ylimits(2);
where linspace makes a vector of values starting at the first one (xlimits(1)) and ending at the third one (xlimits(2)) and separated by spacing. Experiment with this and look at the results, you'll see how it works.
These two vectors specify the grid positions in each dimension. To make an actual meshgrid-style grid you then call meshgrid on them
[XQ, YQ] = meshgrid(xqlinear, yqlinear);
this will produce two matrices of points. XQ holds the x-coordinates of every points in the grid, arranged in the same grid. YQ holds the y-coordinates. The two need to go together. Again experiment with this and look at the results, you'll see how it works.
Then you can put them all together into the interpolation:
F = scatteredInterpolant(X(:), Y(:), Z(:));
ZQ = F(XQ, YQ);
to get the interpolated values ZQ at each of your grid points. You can then send those data to contourf
contourf(XQ, YQ, ZQ);
If the contour is too blocky you will probably need to make the spacing value smaller, which will create more points in your interpolant. If you have lots of data this might cause memory issues, so be aware of that.
If your goal is just to view the triangular mesh then you might find trimesh does what you want or, depending on how your data is already represented, scatter. These will both produce 3D plots with wireframes or point clouds though so if you need contours the interpolation is the way to go.
So I have data in the form [x y z intensity] that I plot on a scatter3 figure with xyz axes. The colour of the data is used to dictate the intensity value. Problem is, using a scatter plot means the data points show up as discrete points. What I need, is a smooth shape - so I guess I need some kind of interpolation between the points?
I've tried using trisurf, but the problem with this one is that it interpolates between points that it shouldn't. So where I should have 'gaps' in my surface, it joins up the edges instead so it fills in the gaps. See the attached pics for clarification.
Does anyone have any suggestions?
The code I use is as below (the commented out scatter3 is what does the scatter plot, the rest does the trisurf):
% Read in data
dataM = csvread('3dDispersion.csv');
% scatter3(dataM(:,1), dataM(:,2), dataM(:,3), 5, dataM(:,4),'filled');
% Plot
hold on;
x = dataM(:,1);
y = dataM(:,2);
freq = dataM(:,3);
tri = delaunay(x,y);
h = trisurf(tri, x, y, freq);
% Make it pretty
% view(-45,30);
view(3);
axis vis3d;
lighting phong;
shading interp;
Use the boundary function in Matlab. This will apply a mesh similar to shrinkwrap over your points. In order to reduce the "gap closers", you will want to increase the "shrink factor".
Try K = boundary(X,Y,Z,0.9)
Where X, Y & Z are the vectors of your data points
https://www.mathworks.com/help/matlab/ref/boundary.html
You can then use trimesh or related surface plotting functions depending on how you want to display it.
I'm trying to draw ROC curves using an existing set of values using the following command
plot(X1,Y1,'--rs',X2,Y2,'-*g');
Where X1,Y1,X2 and Y2 are matrices that have the same size
However, the lines produced by this command are straight ones.
How can I make them curved lines.
Thanks
Aziz
MATLAB by default uses straight line approximation to draw your graph in between control points. If you want, you can interpolate in between the points to produce a more realistic graph. Try using interp1 with the 'spline' option and see how that goes. As such, figure out the minimum and maximum values of both X1 and X2, then define a grid of points in between the minimum and maximum that have finer granularity. Once you do this, throw this into interp1 and plot your curve. Something like:
%// Find dynamic range of domain for both Xs
minX1 = min(X1);
maxX1 = max(X1);
minX2 = min(X2);
maxX2 = max(X2);
%// Generate grid of points for both Xs
x1Vals = linspace(minX1, maxX1, 100);
x2Vals = linspace(minX2, maxX2, 100);
%// Interpolate the curves
y1Vals = interp1(X1, Y1, x1Vals, 'spline');
y2Vals = interp1(X2, Y2, x2Vals, 'spline');
%// Plot the results
plot(x1Vals,y1Vals,'--rs',x2Vals,y2Vals,'-*g');
linspace generates a grid of points from one end to another, and I specified 100 of these points. I then use interp1 in the way we talked about where you specify control points (X1,Y1,X2,Y2), then specify the values I want to interpolate with. I use the output values after interpolation and draw the curve.
What I want to do is very simple, I just cannot seem to get MATLAB to do it. I would like to plot contours using my 2D data set.
My data set is large; 2 x 844240. I can do a scatter plot just fine,
scatter(Data(1,:), Data(2,:));
Reading through the forums I found Scatter plot with density in Matlab, where a hisogram was plotted. This would suffice, however, I would like to overlay the plots.
The issue is that they have different axis, my scatter data has an axis of [0 0.01 0 2500]; whereas the histogram is [0 100 0 100].
Is there a way to change the axis values of the histogram without modifying the image?
Thanks!
If I understand correctly, you are using hist3 to construct a histogram and then using imagesc to plot it. You can use the second output argument of hist3 to get the histogram bin centers, and then pass those on to imagesc, e.g.
nBins_x = 100;
nBins_y = 100;
[counts, bin_centers] = hist3(Data, [nBins_x nBins_y]);
x_bin_centers = bin_centers{1};
y_bin_centers = bin_centers{2};
imagesc(x_bin_centers, y_bin_centers, counts)
A couple other notes:
In your case, you will need to transpose your [2 x N] matrix when passing it to hist3, which expects an [N x 2] matrix.
imagesc puts the first axis (which I've been calling the "x" axis) on the vertical axis and the second on the horizontal axis. If you want to flip it, you can use:
imagesc(y_bin_centers, x_bin_centers, counts')
If you want to specify the histogram bins explicitly (e.g. to match your scatterplot) you can specify that in the arguments to hist3:
x_bin_centers = linspace(0, .01, 100);
y_bin_centers = linspace(0, 2500, 100);
counts = hist3(Data, {x_bin_centers, y_bin_centers};
And if you want a contour plot, you can use (note that contour takes the axes arguments in a different order than imagesc):
contour(x_bin_centers, y_bin_centers, counts');
If you are unhappy with the jaggedness of the contours, you may consider using a kernel density estimate instead of a histogram (check out ksdensity) (oops, looks like ksdensity is 1-D only. But there are File Exchange submissions for bivariate kernel density estimation).