Create a random dataset and show the points as trees.

PlotTreeData.matlab

% requires showTree.m

% --- create data
nObj = 100;
rng(3);
scale = 100;
data = rand(nObj,2)*scale;

% --- set up parameters
saveOutput = true;
imgResolution = '-r300';
saveDir = 'output/';

% % --- naively show data
figure();
scatter(data(:,1),data(:,2),25,[0 0 0],'filled');
set(gcf,'Color','w');
axis equal tight;
axScale = axis();
axis off
if saveOutput
    print(gcf,[saveDir, '01_data_sparse','.png'],'-dpng',imgResolution)
end

% % --- show data as trees
figure();
hold on
for i=1:size(data,1)
    showTree(data(i,1),data(i,2),200);
end
set(gcf,'Color','w');
axis equal tight;
axScale = axis();
axis off
if saveOutput
    print(gcf,[saveDir, '01_data_trees','.png'],'-dpng',imgResolution)
end


% % --- show different patterns as trees
nTrees = 64; % has to be a square!

figure();
subplot(2,2,1);
rng(5)
datacompare = rand(nTrees,2)*scale;
hold on
for i=1:size(datacompare,1)
   showTree(datacompare(i,1),datacompare(i,2),200); 
end
axis equal tight;
axis off

subplot(2,2,2);
rng(8)
datacompare = rand(nTrees,2)*scale;
hold on
for i=1:size(datacompare,1)
   showTree(datacompare(i,1),datacompare(i,2),200); 
end
axis equal tight;
axis off

subplot(2,2,3);
rng(10)
[xx yy] = meshgrid(linspace(1,100,sqrt(nTrees)));
datacompare(:,1) = xx(:); datacompare(:,2) = yy(:);
hold on
for i=1:size(datacompare,1)
   showTree(datacompare(i,1),datacompare(i,2),200); 
end
axis equal tight;
axis off

subplot(2,2,4);
rng(15)
datacompare = rand(round(nTrees*0.8),2)*scale/3+scale/3;
datacompare = [datacompare; rand(round(nTrees*0.2),2)*scale];
hold on
for i=1:size(datacompare,1)
   showTree(datacompare(i,1),datacompare(i,2),200); 
end
axis equal tight;
axis off

% decorations
set(gcf,'Color','w');
if saveOutput
     print(gcf,[saveDir, 'trees_collection','.png'],'-dpng',imgResolution)
end


% % --- show different patterns as trees - 2D histogram
nTrees = 64; % has to be a square!
nBins = 8;

figure();
subplot(2,2,1);
rng(5)
datacompare = rand(nTrees,2)*scale;
xgrid = linspace(0,scale,nBins);
ygrid = linspace(0,scale,nBins);
% use interpolation to create a native 2D histogram
xcounts = interp1(xgrid,1:numel(xgrid),datacompare(:,1),'nearest')';
ycounts = interp1(ygrid,1:numel(ygrid),datacompare(:,2),'nearest')';
zcounts = accumarray([xcounts' ycounts'], 1, [nBins nBins]);
% show
surf(xgrid,ygrid,zcounts,'LineStyle','-');
view(-90,90)
axis equal tight;
axis off
cvalue = caxis()

subplot(2,2,2);
rng(8)
datacompare = rand(nTrees,2)*scale;
xgrid = linspace(0,scale,nBins);
ygrid = linspace(0,scale,nBins);
% use interpolation to create a native 2D histogram
xcounts = interp1(xgrid,1:numel(xgrid),datacompare(:,1),'nearest')';
ycounts = interp1(ygrid,1:numel(ygrid),datacompare(:,2),'nearest')';
zcounts = accumarray([xcounts' ycounts'], 1, [nBins nBins]);
% show
surf(xgrid,ygrid,zcounts,'LineStyle','-');
view(-90,90)
axis equal tight;
axis off
caxis(cvalue)

subplot(2,2,3);
rng(10)
[xx yy] = meshgrid(linspace(1,100,sqrt(nTrees)));
datacompare(:,1) = xx(:); datacompare(:,2) = yy(:);
xgrid = linspace(0,scale,nBins);
ygrid = linspace(0,scale,nBins);
% use interpolation to create a native 2D histogram
xcounts = interp1(xgrid,1:numel(xgrid),datacompare(:,1),'nearest')';
ycounts = interp1(ygrid,1:numel(ygrid),datacompare(:,2),'nearest')';
zcounts = accumarray([xcounts' ycounts'], 1, [nBins nBins]);
% show
surf(xgrid,ygrid,zcounts,'LineStyle','-');
view(-90,90)
axis equal tight;
axis off
caxis(cvalue)

subplot(2,2,4);
rng(15)
datacompare = rand(round(nTrees*0.8),2)*scale/3+scale/3;
datacompare = [datacompare; rand(round(nTrees*0.2),2)*scale];
xgrid = linspace(0,scale,nBins);
ygrid = linspace(0,scale,nBins);
% use interpolation to create a native 2D histogram
xcounts = interp1(xgrid,1:numel(xgrid),datacompare(:,1),'nearest')';
ycounts = interp1(ygrid,1:numel(ygrid),datacompare(:,2),'nearest')';
zcounts = accumarray([xcounts' ycounts'], 1, [nBins nBins]);
% show
surf(xgrid,ygrid,zcounts,'LineStyle','-');
view(-90,90)
axis equal tight;
axis off
caxis(cvalue)

% decorations
set(gcf,'Color','w');
if saveOutput
     print(gcf,[saveDir, 'trees_collection-histo','.png'],'-dpng',imgResolution)
end



% % --- show different patterns as trees - 1D histogram
nTrees = 64; % has to be a square!
nBins = 8;
histBins = cvalue(1):cvalue(2);

figure();
subplot(2,2,1);
rng(5)
datacompare = rand(nTrees,2)*scale;
xgrid = linspace(0,scale,nBins);
ygrid = linspace(0,scale,nBins);
% use interpolation to create a native 2D histogram
xcounts = interp1(xgrid,1:numel(xgrid),datacompare(:,1),'nearest')';
ycounts = interp1(ygrid,1:numel(ygrid),datacompare(:,2),'nearest')';
zcounts = accumarray([xcounts' ycounts'], 1, [nBins nBins]);
% show
[counts,centers] = hist(zcounts(:),histBins);
bar(centers,counts,'LineWidth',1)

subplot(2,2,2);
rng(8)
datacompare = rand(nTrees,2)*scale;
xgrid = linspace(0,scale,nBins);
ygrid = linspace(0,scale,nBins);
% use interpolation to create a native 2D histogram
xcounts = interp1(xgrid,1:numel(xgrid),datacompare(:,1),'nearest')';
ycounts = interp1(ygrid,1:numel(ygrid),datacompare(:,2),'nearest')';
zcounts = accumarray([xcounts' ycounts'], 1, [nBins nBins]);
% show
[counts,centers] = hist(zcounts(:),histBins);
bar(centers,counts,'LineWidth',1)

subplot(2,2,3);
rng(10)
[xx yy] = meshgrid(linspace(1,100,sqrt(nTrees)));
datacompare(:,1) = xx(:); datacompare(:,2) = yy(:);
xgrid = linspace(0,scale,nBins);
ygrid = linspace(0,scale,nBins);
% use interpolation to create a native 2D histogram
xcounts = interp1(xgrid,1:numel(xgrid),datacompare(:,1),'nearest')';
ycounts = interp1(ygrid,1:numel(ygrid),datacompare(:,2),'nearest')';
zcounts = accumarray([xcounts' ycounts'], 1, [nBins nBins]);
% show
[counts,centers] = hist(zcounts(:),histBins);
bar(centers,counts,'LineWidth',1)

subplot(2,2,4);
rng(15)
datacompare = rand(round(nTrees*0.8),2)*scale/3+scale/3;
datacompare = [datacompare; rand(round(nTrees*0.2),2)*scale];
xgrid = linspace(0,scale,nBins);
ygrid = linspace(0,scale,nBins);
% use interpolation to create a native 2D histogram
xcounts = interp1(xgrid,1:numel(xgrid),datacompare(:,1),'nearest')';
ycounts = interp1(ygrid,1:numel(ygrid),datacompare(:,2),'nearest')';
zcounts = accumarray([xcounts' ycounts'], 1, [nBins nBins]);
% show
[counts,centers] = hist(zcounts(:),histBins);
bar(centers,counts,'LineWidth',1)
% decorations
set(gcf,'Color','w');
if saveOutput
     print(gcf,[saveDir, 'trees_collection-histo-histo','.png'],'-dpng',imgResolution)
end



% --- count neighbors, circles
nTrees = 64; % has to be a square!
radiusOfInterest = 25;

figure();
subplot(2,2,1);
rng(5)
datacompare = rand(nTrees,2)*scale;
[idx, dist] = rangesearch(datacompare,datacompare,radiusOfInterest);
neighbors = zeros(size(datacompare,1),1);
for i=1:size(datacompare,1)
   neighbors(i) = numel(cell2mat(idx(i)));
end
% show
viscircles(datacompare,repmat(radiusOfInterest,size(datacompare,1),1),'EdgeColor','k','LineWidth',1);
hold on
scatter(datacompare(:,1),datacompare(:,2),50,neighbors,'filled');
colormap(parula())
axis equal tight;
cvalue = caxis();
cvalue(2) = round(cvalue(2)*1.2);
caxis(cvalue);
axis off


subplot(2,2,2);
rng(8)
datacompare = rand(nTrees,2)*scale;
[idx, dist] = rangesearch(datacompare,datacompare,radiusOfInterest);
neighbors = zeros(size(datacompare,1),1);
for i=1:size(datacompare,1)
   neighbors(i) = numel(cell2mat(idx(i)));
end
% show
viscircles(datacompare,repmat(radiusOfInterest,size(datacompare,1),1),'EdgeColor','k','LineWidth',1);
hold on
scatter(datacompare(:,1),datacompare(:,2),50,neighbors,'filled');
colormap(parula())
axis equal tight;
caxis(cvalue)
axis off

subplot(2,2,3);
rng(10)
[xx yy] = meshgrid(linspace(1,100,sqrt(nTrees)));
datacompare(:,1) = xx(:); datacompare(:,2) = yy(:);
[idx, dist] = rangesearch(datacompare,datacompare,radiusOfInterest);
neighbors = zeros(size(datacompare,1),1);
for i=1:size(datacompare,1)
   neighbors(i) = numel(cell2mat(idx(i)));
end
% show
viscircles(datacompare,repmat(radiusOfInterest,size(datacompare,1),1),'EdgeColor','k','LineWidth',1);
hold on
scatter(datacompare(:,1),datacompare(:,2),50,neighbors,'filled');
colormap(parula())
axis equal tight;
caxis(cvalue)
axis off

subplot(2,2,4);
rng(15)
datacompare = rand(round(nTrees*0.8),2)*scale/3+scale/3;
datacompare = [datacompare; rand(round(nTrees*0.2),2)*scale];
[idx, dist] = rangesearch(datacompare,datacompare,radiusOfInterest);
neighbors = zeros(size(datacompare,1),1);
for i=1:size(datacompare,1)
   neighbors(i) = numel(cell2mat(idx(i)));
end
% show
viscircles(datacompare,repmat(radiusOfInterest,size(datacompare,1),1),'EdgeColor','k','LineWidth',1);
hold on
scatter(datacompare(:,1),datacompare(:,2),50,neighbors,'filled');
colormap(parula())
axis equal tight;
caxis(cvalue)
axis off
% decorations
set(gcf,'Color','w');
if saveOutput
     print(gcf,[saveDir, 'trees_collection-neighbors-circ','.png'],'-dpng',imgResolution)
end


% --- count neighbors, no circles
nTrees = 64; % has to be a square!
radiusOfInterest = 25;

figure();
subplot(2,2,1);
rng(5)
datacompare = rand(nTrees,2)*scale;
[idx, dist] = rangesearch(datacompare,datacompare,radiusOfInterest);
neighbors = zeros(size(datacompare,1),1);
for i=1:size(datacompare,1)
   neighbors(i) = numel(cell2mat(idx(i)));
end
% show
scatter(datacompare(:,1),datacompare(:,2),50,neighbors,'filled');
colormap(parula())
axis equal tight;
caxis(cvalue);
axis off


subplot(2,2,2);
rng(8)
datacompare = rand(nTrees,2)*scale;
[idx, dist] = rangesearch(datacompare,datacompare,radiusOfInterest);
neighbors = zeros(size(datacompare,1),1);
for i=1:size(datacompare,1)
   neighbors(i) = numel(cell2mat(idx(i)));
end
% show
scatter(datacompare(:,1),datacompare(:,2),50,neighbors,'filled');
colormap(parula())
axis equal tight;
caxis(cvalue)
axis off

subplot(2,2,3);
rng(10)
[xx yy] = meshgrid(linspace(1,100,sqrt(nTrees)));
datacompare(:,1) = xx(:); datacompare(:,2) = yy(:);
[idx, dist] = rangesearch(datacompare,datacompare,radiusOfInterest);
neighbors = zeros(size(datacompare,1),1);
for i=1:size(datacompare,1)
   neighbors(i) = numel(cell2mat(idx(i)));
end
% show
scatter(datacompare(:,1),datacompare(:,2),50,neighbors,'filled');
colormap(parula())
axis equal tight;
caxis(cvalue)
axis off

subplot(2,2,4);
rng(15)
datacompare = rand(round(nTrees*0.8),2)*scale/3+scale/3;
datacompare = [datacompare; rand(round(nTrees*0.2),2)*scale];
[idx, dist] = rangesearch(datacompare,datacompare,radiusOfInterest);
neighbors = zeros(size(datacompare,1),1);
for i=1:size(datacompare,1)
   neighbors(i) = numel(cell2mat(idx(i)));
end
% show
scatter(datacompare(:,1),datacompare(:,2),50,neighbors,'filled');
colormap(parula())
axis equal tight;
caxis(cvalue)
axis off
% decorations
set(gcf,'Color','w');
if saveOutput
     print(gcf,[saveDir, 'trees_collection-neighbors-nocirc','.png'],'-dpng',imgResolution)
end

%% neighbors, no circles, histogram of distances
nTrees = 64; % has to be a square!
radiusOfInterest = 25;
histBins = cvalue(1):cvalue(2);

figure();
subplot(2,2,1);
rng(5)
datacompare = rand(nTrees,2)*scale;
[idx, dist] = rangesearch(datacompare,datacompare,radiusOfInterest);
neighbors = zeros(size(datacompare,1),1);
for i=1:size(datacompare,1)
   neighbors(i) = numel(cell2mat(idx(i)));
end
% show
[counts,centers] = hist(neighbors,histBins);
bar(centers,counts,'LineWidth',1)



subplot(2,2,2);
rng(8)
datacompare = rand(nTrees,2)*scale;
[idx, dist] = rangesearch(datacompare,datacompare,radiusOfInterest);
neighbors = zeros(size(datacompare,1),1);
for i=1:size(datacompare,1)
   neighbors(i) = numel(cell2mat(idx(i)));
end
% show
[counts,centers] = hist(neighbors,histBins);
bar(centers,counts,'LineWidth',1)


subplot(2,2,3);
rng(10)
[xx yy] = meshgrid(linspace(1,100,sqrt(nTrees)));
datacompare(:,1) = xx(:); datacompare(:,2) = yy(:);
[idx, dist] = rangesearch(datacompare,datacompare,radiusOfInterest);
neighbors = zeros(size(datacompare,1),1);
for i=1:size(datacompare,1)
   neighbors(i) = numel(cell2mat(idx(i)));
end
% show
[counts,centers] = hist(neighbors,histBins);
bar(centers,counts,'LineWidth',1)

subplot(2,2,4);
rng(15)
datacompare = rand(round(nTrees*0.8),2)*scale/3+scale/3;
datacompare = [datacompare; rand(round(nTrees*0.2),2)*scale];
[idx, dist] = rangesearch(datacompare,datacompare,radiusOfInterest);
neighbors = zeros(size(datacompare,1),1);
for i=1:size(datacompare,1)
   neighbors(i) = numel(cell2mat(idx(i)));
end
% show
[counts,centers] = hist(neighbors,histBins);
bar(centers,counts,'LineWidth',1)
% decorations
set(gcf,'Color','w');
if saveOutput
     print(gcf,[saveDir, 'trees_collection-neighbors-hist','.png'],'-dpng',imgResolution)
end