![]() In the case of a line or an arrow, we specify two x-y pairs, denoting Width from the left and 40% of the height from the bottom. The location is, by default, specified by normalized coordinates between 0 and 1 so that is the point 80% of the Annotations are placed relative to the figure window, not the axes. ![]() We will now add a few annotations to a simple figure. Maintaining all of the graphics object information for future editing. Alternatively, you can save a Matlab figure as a. Source file so that we can regenerate the complete figure at will. Is usually easier to add annotations graphically first, generate the m-code and then add the appropriate lines to the original Once you have added elements, you can see the m-code that generates these objects by going to 'File -> Generate M-File'. We can also inspect and change attributesĪs an alternative to using set() and get(). ![]() Here we can addĪnnotations such as text, arrows, and shapes as you would in a program like powerpoint. This mode can be enteredīy selecting 'view->Figure Palette' or by selecting the appropriate shortcut button on the figure toolbar. Matlab provides an interactive graphical interface for modifying and inspecting existing figures. ![]() P13 = mesh(XI,YI,ZI) % mesh of the interpolated points Annotations and the Plot Editor ZI = griddata(X,Y,Z,XI,YI) % interpolate to obtain ZI = meshgrid(d,d) % create our grid as before Z = cos(X.^2).*exp(X.^2 - Y.^2) % value of data at these points X = randn(100,50) % data captured at these points. Randn( 'state',0) % seed the normal random num generator In such cases, we can use the griddata() function to interpolate along a uniform grid of points for us. Using xlabel(), ylabel(), and zlabel(), change the range of the axes, the font size, etc.ģD plotting in Matlab requires a uniform grid of points but the data we obtain from experiments or measurements may not satisfy Virtually everything we said about customizing 2D plots applies equally to 3D plots. P12 = bar3(convMat) % display a 3d bar plot of the aggregated data 'UniformOutput',false) % apply that function to every blockĬonvMat = cell2mat(convCell) % convert back to a, (smaller) matrix Grouped = mat2cell(Z,6*ones(21,1),6*ones(21,1)) % partition matrix into 6-by-6 blocksĬonvCell=cellfun(fconv,grouped. Note that when the size of the data within cells is different, 'UniformOutput' must be set to false. We can use this same technique to apply any function to arbitrary sized blocks We then use the cellfun() function to replace each block with its mean. We take advantage of the mat2cell() command to partition the Z data into 21x21 6-by-6 blocks, each block stored within a cell. Note, the command view() can be very useful to rotate a plot by 90 degrees, effectively reversing the locations of the x and y axes.īelow we display a 3d bar plot of the same underlying data. View() % change the viewing angle - see doc view R = 1:3:126 % mesh plots look better at lower resolution Set(gca, 'XTick', 'YTick',) % remove all ticks f6 = figure % create a new figure Shading interp % interpolate between the pointsĬamlight(90,0) % add some light - see doc camlightīox on % same as set(gca,'box','on') f5 = figure % create a new figure P9 = surf(X,Y,Z) % plot the surface of the function There are several other 3d plotting functions: plot3() for instance is the 3d generalization of plot(). The colors used in each are specified by the current colormap and canīe changed by using the colormap() command. Here we use surf(), which plots the surface of the function, contourf(), which plots the contour lines of a function and fills the area between them with color, and mesh(), which is similar to surf(), displaying a wire mesh rather than a solid surface. There are several 3d plot types available. ![]() Z1 = f(X(:),Y(:)) % convert X,Y to column vectors and evaluate f After obtaining X and Y from meshgrid(), save X's size, evaluate f by passing in X and Y as column vectors, (using the : operator), and then reshape the output Zīack to the original size and continue as before. Still plot such functions in 3D with a few small changes. The mvnpdf() function for example interprets a matrix of inputs, (say n-by-d), as n, d-dimensional inputs, not n*d 1-by-1 inputs. Some functions, while still vectorized, will only operate on vectors and not matrices. Z = f(X,Y) % evaluate f at every point on grid = meshgrid(d,d) % create a grid of points We use the meshgrid() function to create such a grid, as in this example.į = exp(cos(sqrt(x.^2 + y.^2))) % a function of two variablesĭ = -2*pi:0.1:2*pi % domain for both x,y To graph a function of two variables we need to first evaluate that function over a grid of points, not just a line as in
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