Romesh

Fantastic work. I'm also interested in the MCC, and successfully compiled the demo on Mac OS R2011a with only one issue:

Depfun error: 'Method 'getBackground' in class 'GSideBar' uses different access permissions than its super-class 'GTool'.'

The compiled code seems to run fine after I deleted this file. I ran waterloo.m before mcc. I guess I'll need to send some emails to work out exactly what constitutes a derivative work/whether including the source code for just the waterloo part of the project is sufficient (my current impression is that this would be acceptable...?)

Awesome code, I use it all the time and it is fantastic. However, there were a couple of things that are rough around the edges
- Different scaling means the resulting plot has different axis limits so it is hard to overlay data
- Cannot move the plot around using the drag tool
- Sometimes the limits are not what I expect (e.g. there are empty spaces on the plot because the plot is larger than the canvas- I get that this shouldn't happen in theory!)
- Cannot readily modify the number of bins to give a different plot resolution

I've made some changes that I think fix all of these problems (definitely NOT certified bug free though!), and I've also managed to remove the try-catch block because I think I've fixed the error condition that it was catching.

This code is a drop-in replacement for cloudPlot:

function [h,canvas] = cloudPlot(X,Y,axisLimits,useLogScale,bins)
% Display a cloud plot from X and Y data
%
% Only X and Y are mandatory arguments
% useLogScale = 1 to take the log of the data (to visalise data where
    % there are large variations in density)
% axisLimits = [x_min, x_max, y_min, y_max], [] to omit
% bins = [number_x_bins, number_y_bins] (defines resolution of output)
    %
    % e.g. cloudPlot(x,y,[],1,[500 500]) to produce a cloudPlot with
    % 500x500 bins, displaying the log of the density with automatic
    % axis limits
%
% By Romesh Abeysuriya, based heavily on cloudPlot.m by Daniel Armyr
    %
    % (from Daniel Armyr's cloudPlot.m):
    % A cloudplot is in essence a 2 dimensional histogram showing the
    % density distribution of the data described by X and Y.
    % As the plot displays density information, the
    % dimensionality of X and Y are ignored. The only requirement is that X and
    % Y have the same number of elements. Cloudplot is written to visualize
    % large quantities of data and is most appropriate for datasets of 10000
    % elements or more.

% Check the data size
assert (numel(X) == numel(Y),'The number of elements in X and Y must be the same');
if nargin < 4; useLogScale = false; end
    if nargin < 3 | isempty(axisLimits) % Calculate default axis limits
axisLimits = [min(X(:)) max(X(:)), min(Y(:)) max(Y(:))] ;
end

%Remove any nans or Infs in the data
pointSelect = isfinite(X) & isfinite(Y) & X >= axisLimits(1) & X <= axisLimits(2) & Y >= axisLimits(3) & Y <= axisLimits(4);
X = X(pointSelect);
Y = Y(pointSelect);

g = axes('Xlim',[axisLimits(1) axisLimits(2)],'Ylim',[axisLimits(3) axisLimits(4)],'units','pixels');
if nargin < 5 % Calculate the default number of pixels
plotsize = get(g,'Position');
bins = ceil(plotsize(3:4));
end
binSize(2) = diff(axisLimits(3:4))./(bins(2)-1);
binSize(1) = diff(axisLimits(1:2))./(bins(1)-1);
canvas = zeros(bins);

% Calculate bin indices for each of the points in the data arrays
xBinIndex = floor((X - axisLimits(1))/binSize(1))+1;
yBinIndex = floor((Y - axisLimits(3))/binSize(2))+1;

% Collect the indices
for i = 1:numel(xBinIndex);
canvas(xBinIndex(i),yBinIndex(i)) = canvas(xBinIndex(i),yBinIndex(i)) + 1;
end

% Postprocessing
    canvas_display = canvas; % Copy the canvas so we can make changes to the display
                    % without modifying the underlying data
    if useLogScale
canvas_display = log10(canvas_display);
    end
    %canvas_display = canvas_display > 0; % Show binary rather than graded data
%colormap gray % Use grayscale so background is black
%canvas_display = imcomplement(canvas_display); % Display a negative image

image_handle = imagesc([axisLimits(1) axisLimits(2)],[axisLimits(3) axisLimits(4)],canvas_display'); % Show the image
axis('xy') % Reverse the y-axis
set(g,'units','normalized' ); % Enable resizing

if nargout > 0
h = image_handle;
end