I have to create a carpet plot (or raster plot) with MATLAB. This plot represents the hourly electric consumption along a year.
In my actual implementation I have a m x n matrix (m is the hours in a day, n the days in a year) containing the consumption values. In order to obtain the carpet plot I use the surf function setting view(0,-90) .
The problem is that MATLAB represent each "cell" of the surface with a color that is related with the interpolation of the 4 consumption values around that "cell", whereas I need that each cell of the surface represent a single value of the matrix.
Is there a way to obtain, with MATLAB, what I need?
surf is for making 3-dimensional surface plots.
You want a 2-dimensional heat map - try looking at the output from help imagesc and help image.
Related
I have a T x 2 Matrix, where in the second column I have some daily financial returns and in the first column I have an indicator, which can assume integer values in the interval [1, 9].
I want to extract 9 different conditional distributions of my returns, conditioned on the values assumed by the indicator. At this point, I want to plot the conditional densities through a Gaussian smoothing with the function 'ksdensity' and plot them in the same 3D plot. The final output should be similar to this one: Image
I tried to reach this result by adapting the answer I found at this thread: Function.
Now suppose that x = axis of returns, y = axis of indicator possible values, z = smoothed conditional densities.
My problem is that, while in the example the meshgrid required for all the values of y have the same values of x by construction, I have different values of x (the returns) because of the conditioning.
First split your returns data into 9 vectors, according to the indicator variable. You can use accumarray for that. Then run ksdensity on each vector separately. Then plot those outputs.
For example, I got 3 pairs of 1-D loglog curves and additionally their associated cartesian coordinate points (x,y,z) of one of their ends A, B and C over a mesh surface S (z is positive downwards and linear but coincides in direction with the log(y)-axis from the curves). Is it possible to respresent in a single figure such system of plots in matlab?
Moreover, obtain an interpolated slice from A,B and C?
The images from the question of user3281667 gives an insight of what we are trying to do here:
https://gis.stackexchange.com/questions/252939/interpolating-xyz-data-in-arcgis-3d-analyst
Thanks.
Kind of solved. First we need to know in which format is our data, this case scattered.
I concatenated a nx4 matrix with the preprocessed data A=[X Y Z C].
Then use the right tools, to plot use scatter3: scatter3(A(:,1), A(:,2), A(:,3),30, A(:,4), 'filled' )
Now to interpolate, fisrt generate a grid refinement with meshgrid: [Xm, Ym, Zm] = meshgrid(min(X):2:max(X), min(Y):2:max(Y), min(Z):2:max(Z)) next interpolate using griddata Cm = griddata(X,Y,Z,C,Xm,Ym,Zm);and last plot again.
figure
scatter3(Xm(:), Ym(:), Zm(:), 30, Cm(:), 'filled' )
Thanks to user7431005
i am plotting a convolution in matlab for that purpose i create 2 arrays representing the values of the functions in various points.
x=[1:1000];
c=[1:1000];
t = linspace(-18,18,1000);
for k=1:1000, x(k)=input(t(k));
c(k)=h(t(k));
end;
plot(conv(c,x));
the thing is that it plots the conv against the place of the answer in the array.
i want to plot the conv against the 'n' that will give the value.
plotting against t,c or x from the example above does not give the righ answer. also the plot here is of length 1999.
creating a linspace of length 1999 will plot but it wont give the right answer.
any suggestions?
I finished an SVM training and got data like X, Y. X is the feature matrix only with 2 dimensions, and Y is the classification labels. Because the data is only in two dimensions, so I would like to draw a decision boundary to show the surface of support vectors.
I use contouf in Matlab to do the trick, but really find it hard to understand how to use the function.
I wrote like:
#1 try:
contourf(X);
#2 try:
contourf([X(:,1) X(:,2) Y]);
#3 try:
Z(:,:,1)=X(Y==1,:);
Z(:,:,2)=X(Y==2,:);
contourf(Z);
all these things do not correctly. And I checked the Matlab help files, most of them make Z as a function, so I really do not know how to form the correct Z matrix.
If you're using the svmtrain and svmclassify commands from Bioinformatics Toolbox, you can just use the additional input argument (...'showplot', true), and it will display a scatter plot with a decision boundary and the support vectors highlighted.
If you're using your own SVM, or a third-party tool such as libSVM, what you probably need to do is to:
Create a grid of points in your 2D input feature space using the meshgrid command
Classify those points using your trained SVM
Plot the grid of points and the classifications using contourf.
For example, in kind-of-MATLAB-but-pseudocode, assuming your input features are called X1 and X2:
numPtsInGrid = 100;
x1Range = linspace(x1lower, x1upper, numPtsInGrid);
x2Range = linspace(x2lower, x2upper, numPtsInGrid);
[X1, X2] = meshgrid(x1Range, x2Range);
Z = classifyWithMySVMSomehow([X1(:), X2(:)]);
contourf(X1(:), X2(:), Z(:))
Hope that helps.
I know it's been a while but I will give it a try in case someone else will come up with that issue.
Assume we have a 2D training set so as to train an SVM model, in other words the feature space is a 2D space. We know that a kernel SVM model leads to a score (or decision) function of the form:
f(x) = sumi=1 to N(aiyik(x,xi)) + b
Where N is the number of support vectors, xi is the i -th support vector, ai is the estimated Lagrange multiplier and yi the associated class label. Values(scores) of decision function in way depict the distance of the observation x frοm the decision boundary.
Now assume that for every point (X,Y) in the 2D feature space we can find the corresponding score of the decision function. We can plot the results in the 3D euclidean space, where X corresponds to values of first feature vector f1, Y to values of second feature f2, and Z to the the return of decision function for every point (X,Y). The intersection of this 3D figure with the Z=0 plane gives us the decision boundary into the two-dimensional feature space. In other words, imagine that the decision boundary is formed by the (X,Y) points that have scores equal to 0. Seems logical right?
Now in MATLAB you can easily do that, by first creating a grid in X,Y space:
d = 0.02;
[x1Grid,x2Grid] = meshgrid(minimum_X:d:maximum_X,minimum_Y:d:maximum_Y);
d is selected according to the desired resolution of the grid.
Then for a trained model SVMModel find the scores of every grid's point:
xGrid = [x1Grid(:),x2Grid(:)];
[~,scores] = predict(SVMModel,xGrid);
Finally plot the decision boundary
figure;
contour(x1Grid,x2Grid,reshape(scores(:,2),size(x1Grid)),[0 0],'k');
Contour gives us a 2D graph where information about the 3rd dimension is depicted as solid lines in the 2D plane. These lines implie iso-response values, in other words (X,Y) points with same Z value. In our occasion contour gives us the decision boundary.
Hope I helped to make all that more clear. You can find very useful information and examples in the following links:
MATLAB's example
Representation of decision function in 3D space
I have a time series of temperature profiles that I want to interpolate, I want to ask how to do this if my data is irregularly spaced.
Here are the specifics of the matrix:
The temperature is 30x365
The time is 1x365
Depth is 30x1
Both time and depth are irregularly spaced. I want to ask how I can interpolate them into a regular grid?
I have looked at interp2 and TriScatteredInterp in Matlab, however the problem are the following:
interp2 works only if data is in a regular grid.
TriscatteredInterp works only if the vectors are column vectors. Although time and depth are both column vectors, temperature is not.
Thanks.
Function Interp2 does not require for a regularly spaced measurement grid at all, it only requires a monotonic one. That is, sampling positions stored in vectors depths and times must increase (or decrease) and that's all.
Assuming this is indeed is the situation* and that you want to interpolate at regular positions** stored in vectors rdepths and rtimes, you can do:
[JT, JD] = meshgrid(times, depths); %% The irregular measurement grid
[RT, RD] = meshgrid(rtimes, rdepths); %% The regular interpolation grid
TemperaturesOnRegularGrid = interp2(JT, JD, TemperaturesOnIrregularGrid, RT, RD);
* : If not, you can sort on rows and columns to come back to a monotonic grid.
**: In fact Interp2 has no restriction for output grid (it can be irregular or even non-monotonic).
I would use your data to fit to a spline or polynomial and then re-sample at regular intervals. I would highly recommend the polyfitn function. Actually, anything by this John D'Errico guy is incredible. Aside from that, I have used this function in the past when I had data on a irregularly spaced 3D problem and it worked reasonably well. If your data set has good support, which I suspect it does, this will be a piece of cake. Enjoy! Hope this helps!
Try the GridFit tool on MATLAB central by John D'Errico. To use it, pass in your 2 independent data vectors (time & temperature), the dependent data matrix (depth) along with the regularly spaced X & Y data points to use. By default the tool also does smoothing for overlapping (or nearly) data points. If this is not desired, you can override this (and other options) through a wide range of configuration options. Example code:
%Establish regularly spaced points
num_points = 20;
time_pts = linspace(min(time),max(time),num_points);
depth_pts = linspace(min(depth),max(depth),num_points);
%Run interpolation (with smoothing)
Pest = gridfit(depth, time, temp, time_pts, depth_pts);