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I've found this answer, but I can't complete my work. I wanted to plot more precisely the functions I am studying, without overcoloring my function with black ink... meaning reducing the number of mesh lines. I precise that the functions are complex.
I tried to add to my already existing code the work written at the link above.
This is what I've done:
r = (0:0.35:15)'; % create a matrix of complex inputs
theta = pi*(-2:0.04:2);
z = r*exp(1i*theta);
w = z.^2;
figure('Name','Graphique complexe','units','normalized','outerposition',[0.08 0.1 0.8 0.55]);
s = surf(real(z),imag(z),imag(w),real(w)); % visualize the complex function using surf
s.EdgeColor = 'none';
x=s.XData;
y=s.YData;
z=s.ZData;
x=x(1,:);
y=y(:,1);
% Divide the lengths by the number of lines needed
xnumlines = 10; % 10 lines
ynumlines = 10; % 10 partitions
xspacing = round(length(x)/xnumlines);
yspacing = round(length(y)/ynumlines);
hold on
for i = 1:yspacing:length(y)
Y1 = y(i)*ones(size(x)); % a constant vector
Z1 = z(i,:);
plot3(x,Y1,Z1,'-k');
end
% Plotting lines in the Y-Z plane
for i = 1:xspacing:length(x)
X2 = x(i)*ones(size(y)); % a constant vector
Z2 = z(:,i);
plot3(X2,y,Z2,'-k');
end
hold off
But the problem is that the mesh is still invisible. How to fix this? Where is the problem?
And maybe, instead of drawing a grid, perhaps it is possible to draw circles and radiuses like originally on the graph?
I found an old script of mine where I did more or less what you're looking for. I adapted it to the radial plot you have here.
There are two tricks in this script:
The surface plot contains all the data, but because there is no mesh drawn, it is hard to see the details in this surface (your data is quite smooth, this is particularly true for a more bumpy surface, so I added some noise to the data to show this off). To improve the visibility, we use interpolation for the color, and add a light source.
The mesh drawn is a subsampled version of the original data. Because the original data is radial, the XData and YData properties are not a rectangular grid, and therefore one cannot just take the first row and column of these arrays. Instead, we use the full matrices, but subsample rows for drawing the circles and subsample columns for drawing the radii.
% create a matrix of complex inputs
% (similar to OP, but with more data points)
r = linspace(0,15,101).';
theta = linspace(-pi,pi,101);
z = r * exp(1i*theta);
w = z.^2;
figure, hold on
% visualize the complex function using surf
% (similar to OP, but with a little bit of noise added to Z)
s = surf(real(z),imag(z),imag(w)+5*rand(size(w)),real(w));
s.EdgeColor = 'none';
s.FaceColor = 'interp';
% get data back from figure
x = s.XData;
y = s.YData;
z = s.ZData;
% draw circles -- loop written to make sure the outer circle is drawn
for ii=size(x,1):-10:1
plot3(x(ii,:),y(ii,:),z(ii,:),'k-');
end
% draw radii
for ii=1:5:size(x,2)
plot3(x(:,ii),y(:,ii),z(:,ii),'k-');
end
% set axis properties for better 3D viewing of data
set(gca,'box','on','projection','perspective')
set(gca,'DataAspectRatio',[1,1,40])
view(-10,26)
% add lighting
h = camlight('left');
lighting gouraud
material dull
How about this approach?
[X,Y,Z] = peaks(500) ;
surf(X,Y,Z) ;
shading interp ;
colorbar
hold on
miss = 10 ; % enter the number of lines you want to miss
plot3(X(1:miss:end,1:miss:end),Y(1:miss:end,1:miss:end),Z(1:miss:end,1:miss:end),'k') ;
plot3(X(1:miss:end,1:miss:end)',Y(1:miss:end,1:miss:end)',Z(1:miss:end,1:miss:end)','k') ;
I want to set the limit for X axis in this plot from 0 to 325. When i am using xlim to set the limits (commented in the code). It doesn't work properly. When i use xlim, the entire structure of plot changes. Any help will be appreciated.
figure
imagesc(transpose(all_area_for_visual));
colormap("jet")
colorbar('Ticks',0:3,'TickLabels',{'Home ','Field','Bad house','Good house'})
xlabel('Time (min)')
tickLocs = round(linspace(1,length(final_plot_mat_missing_part(2:end,1)),8));
timeVector = final_plot_mat_missing_part(2:end,1);
timeForTicks = (timeVector(tickLocs))./60;
xticks(tickLocs);
xticklabels(timeForTicks);
%xlim([0 325]);
ylabel('Car identity')
yticks(1:length(Ucolumnnames_fpm))
yticklabels([Ucolumnnames_fpm(1,:)])
If I get you right, you want to plot only part of the data in all_area_for_visual, given by a condition on tickLocs. So you should first condition the data, and then plot it:
% generate the vector of x values:
tickLocs = round(linspace(1,length(final_plot_mat_missing_part(2:end,1)),8));
% create an index vector (of logicals) that marks the columns to plot from the data matix:
validX = tickLocs(tickLocs<=325);
% plot only the relevant part of the data:
imagesc(transpose(all_area_for_visual(:,validX)));
% generate the correct ticks for the data that was plotted:
timeVector = final_plot_mat_missing_part(2:end,1);
timeForTicks = (timeVector(tickLocs(validX)))./60;
xticks(tickLocs(validX));
% here you continue with setting the labels, colormap and so on...
imagesc puts the data in little rectangles centered around integers 1:width and 1:height by default. You can specify what the x and y locations of each data point by adding two vectors to the call:
imagesc(x,y,transpose(all_area_for_visual));
where x and y are vectors with the locations along the x and y axes you want to place the data.
Note that xlim and xticks don’t change the location of the data, only the region of the axis shown, and the location of tick marks along the axis. With xticklabels you can change what is shown at each tick mark, so you can use that to “fake” the data locations, but the xlim setting still applies to the actual locations, not to the labels assigned to the ticks.
I think it is easier to plot the data in the right locations to start with. Here is an example:
% Fake your data, I'm making a small matrix here for illustration purposes
all_area_for_visual = min(floor(cumsum(rand(20,5)/2)),3);
times = linspace(0,500,20); % These are the locations along the time axis for each matrix element
car_id_names = [4,5,8,15,18]; % These are the labels to put along the y-axis
car_ids = 1:numel(car_id_names); % These are the locations to use along the y-axis
% Replicate your plot
figure
imagesc(times,car_ids,transpose(all_area_for_visual));
% ^^^ ^^^ NOTE! specifying locations
colormap("jet")
colorbar('Ticks',0:3,'TickLabels',{'Home ','Field','Bad house','Good house'})
xlabel('Time (min)')
ylabel('Car identity')
set(gca,'YTick',car_ids,'YTickLabel',car_id_names) % Combine YTICK and YTICKLABEL calls
% Now you can specify your limit, in actual time units (min)
xlim([0 325]);
I have a spectral data (1000 variables on xaxis, and peak intensities as y) and a list of peaks of interest at various specific x locations (a matrix called Peak) which I obtained from a function I made. Here, I would like to draw a line from the maximum value of each peaks to the xaxis - or, eventually, place a vertical arrow above each peaks but I read it is quite troublesome, so just a vertical line is welcome. However, using the following code, I get "Error using line Value must be a vector of numeric type". Any thoughts?
X = spectra;
[Peak,intensity]=PeakDetection(X);
nrow = length(Peak);
Peak2=Peak; % to put inside the real xaxis value
plot(xaxis,X);
hold on
for i = 1 : nbrow
Peak2(:,i) = round(xaxis(:,i)); % to get the real xaxis value and round it
xline = Peak2(:,i);
line('XData',xline,'YData',X,'Color','red','LineWidth',2);
end
hold off
Simple annotation:
Here is a simple way to annotate the peaks:
plot(x,y,x_peak,y_peak+0.1,'v','MarkerFaceColor','r');
where x and y is your data, and x_peak and y_peak is the coordinates of the peaks you want to annotate. The add of 0.1 is just for a better placing of the annotation and should be calibrated for your data.
For example (with some arbitrary data):
x = 1:1000;
y = sin(0.01*x).*cos(0.05*x);
[y_peak,x_peak] = PeakDetection(y); % this is just a sketch based on your code...
plot(x,y,x_peak,y_peak+0.1,'v','MarkerFaceColor','r');
the result:
Line annotation:
This is just a little bit more complicated because we need 4 values for each line. Again, assuming x_peak and y_peak as before:
plot(x,y);
hold on
ax = gca;
ymin = ax.YLim(1);
plot([x_peak;x_peak],[ymin*ones(1,numel(y_peak));y_peak],'r')
% you could write instead:
% line([x_peak;x_peak],[ymin*ones(1,numel(y_peak));y_peak],'Color','r')
% but I prefer the PLOT function.
hold off
and the result:
Arrow annotation:
If you really want those arrows, then you need to first convert the peak location to the normalized figure units. Here how to do that:
plot(x,y);
ylim([-1.5 1.5]) % only for a better look of the arrows
peaks = [x_peak.' y_peak.'];
ax = gca;
% This prat converts the axis unites to the figure normalized unites
% AX is a handle to the figure
% PEAKS is a n-by-2 matrix, where the first column is the x values and the
% second is the y values
pos = ax.Position;
% NORMPEAKS is a matrix in the same size of PEAKS, but with all the values
% converted to normalized units
normpx = pos(3)*((peaks(:,1)-ax.XLim(1))./range(ax.XLim))+ pos(1);
normpy = pos(4)*((peaks(:,2)-ax.YLim(1))./range(ax.YLim))+ pos(2);
normpeaks = [normpx normpy];
for k = 1:size(normpeaks,1)
annotation('arrow',[normpeaks(k,1) normpeaks(k,1)],...
[normpeaks(k,2)+0.1 normpeaks(k,2)],...
'Color','red','LineWidth',2)
end
and the result:
I am having trouble plotting a histogram of the x-values of my data points together with a line showing the relationship between x and y, mainly because the scale in the y direction of the histogram is not of the same magnitude as the scale in the line plot. For example:
% generate data
rng(1, 'twister')
x = randn(10000,1);
y = x.^2
% plot line, histogram, then histogram and line.
subplot(3,1,1)
scatter(x, y, 1, 'filled')
ax = gca;
maxlim = max(ax.XLim); % store maximum y-value to rescale histogram to this value
subplot(3,1,2)
h = histogram(x, 'FaceAlpha', 0.2)
subplot(3,1,3)
scatter(x, y, 1, 'filled')
hold on
h = histogram(x, 'FaceAlpha', 0.2)
Produces the following:
where the line chart is completely obscured by the histogram.
Now, one might naively try to rescale the histogram using:
h.Values = h.Values/max(h.Values) * maxlim;
which gives
You cannot set the read-only property 'Values' of Histogram.
Alternatively one can get the bin counts using histcounts, but as far as I can tell, the bar function does not allow one to set the face alpha or have other configurability as per the call to histogram.
As discussed in the comments there are several solutions that depend on the version of Matlab you are using. To restate the problem, the histogram function allows you to control many graphics properties like transparency, but only gives you a limited number of options to change the height of the bars. With histcounts you can get the bar heights and rescale them however you want, but you must plot the bars yourself.
First option: use histogram
As you cannot rescale the histogram heights, you must plot them on separate axis.
From release 2016a and onwards, you can use yyaxis left for the scatter plot and yyaxis right for the histogram, see Matlab documentation:
Prior to this one must manually create and set separate y-axis. Although I have not found a good simple example of this, this is perhaps the most relevant answer here: plot two histograms (using the same y-axis) and a line plot (using a different y-axis) on the same figure
Using histcounts and manually creating a bar chart
Using my example, we can get counts as follows:
[Values, Edges] = histcounts(x);
And rescaling:
Values = Values / max(Values) * maxlim;
and finding centres of bars:
bar_centres = 0.5*(Edges(1:end-1) + Edges(2:end));
Up to release 2014a, bar charts had a 'children' property for the patches that allows transparency to be controlled, e.g.:
% plot histogram
b1 = bar(bar_centres,Values);
% change transparency
set(get(b1,'Children'),'FaceAlpha',0.3)
After 2014a bar charts no longer have this property, and to get around it I plot the patches myself using the code from this mathworks q&a, replicated here:
function ptchs = createPatches(x,y,offset,c,FaceAlpha)
%createPatches.m
% This file will create a bar plot with the option for changing the
% FaceAlpha property. It is meant to be able to recreate the functionality
% of bar plots in versions prior to 2014b. It will create the rectangular
% patches with a base centered at the locations in x with a bar width of
% 2*offset and a height of y.
% Ensure x and y are numeric vectors
validateattributes(x,{'numeric'},{'vector'});
validateattributes(y,{'numeric'},{'vector'});
validateattributes(c,{'char'},{'scalar'});
%#TODO Allow use of vector c
% Check size(x) is same as size(y)
assert(all(size(x) == size(y)),'x and y must be same size');
% Default FaceAlpha = 1
if nargin < 5
FaceAlpha = 1;
end
if FaceAlpha > 1 || FaceAlpha <= 0
warning('FaceAlpha has been set to 1, valid range is (0,1]');
FaceAlpha = 1;
end
ptchs = cell(size(x)); % For storing the patch objects
for k = 1:length(x)
leftX = x(k) - offset; % Left Boundary of x
rightX = x(k) + offset; % Right Boundary of x
ptchs{k} = patch([leftX rightX rightX leftX],...
[0 0 y(k) y(k)],c,'FaceAlpha',FaceAlpha, ...
'EdgeColor', 'none');
end
end
I made one change: that is, imposed the no edge condition. Then, it is perfectly fine to use:
createPatches(bin_centres, Values, 1,'k', 0.2)
to create the bars.
I want to plot a confusion matrix in MATLAB. Here's my code;
data = rand(3, 3)
imagesc(data)
colormap(gray)
colorbar
When I run this, a confusion matrix with a color bar is shown. But usually, I have seen confusion matrix in MATLAB will give counts as well as probabilities. How can I get them? How can I change the class labels which will be shown as 1,2,3, etc.?
I want a matrix like this:
If you do not have the neural network toolbox, you can use plotConfMat. It gets you the following result.
I have also included an independent example below without the need for the function:
% sample data
confmat = magic(3);
labels = {'Dog', 'Cat', 'Horse'};
numlabels = size(confmat, 1); % number of labels
% calculate the percentage accuracies
confpercent = 100*confmat./repmat(sum(confmat, 1),numlabels,1);
% plotting the colors
imagesc(confpercent);
title('Confusion Matrix');
ylabel('Output Class'); xlabel('Target Class');
% set the colormap
colormap(flipud(gray));
% Create strings from the matrix values and remove spaces
textStrings = num2str([confpercent(:), confmat(:)], '%.1f%%\n%d\n');
textStrings = strtrim(cellstr(textStrings));
% Create x and y coordinates for the strings and plot them
[x,y] = meshgrid(1:numlabels);
hStrings = text(x(:),y(:),textStrings(:), ...
'HorizontalAlignment','center');
% Get the middle value of the color range
midValue = mean(get(gca,'CLim'));
% Choose white or black for the text color of the strings so
% they can be easily seen over the background color
textColors = repmat(confpercent(:) > midValue,1,3);
set(hStrings,{'Color'},num2cell(textColors,2));
% Setting the axis labels
set(gca,'XTick',1:numlabels,...
'XTickLabel',labels,...
'YTick',1:numlabels,...
'YTickLabel',labels,...
'TickLength',[0 0]);
If you have the neural network toolbox you can use the function plotconfusion. You can create a copy and edit it to customise it further, for example to print custom labels.