Remove `TwoColourTufteHist` dependency

New built-in `histogram` function is pretty decent.

DataGeneration/generateBoldSignal.m

@@ -246,8 +246,9 @@ for s = 1:params.S
         PA{s}{r} = PA{s}{r} / mPA;
         
         if plotFigures && (s==1) && (r==1)
-            figure; TwoColourTufteHist(PA{s}{r}(:), 49, 'normalise', ...
-                'xlim', 1.2*[-1 1]); xlim(1.2*[-1 1])
+            figure; histogram(PA{s}{r}(:), linspace(-1.2, 1.2, 50), ...
+                'Normalization', 'pdf'); xlim(1.2*[-1 1]);
+            xlabel('Kurtosis'); ylabel('Probility density');
             title(sprintf('PA distribution pre-saturation (kurtosis: %.2f)', ...
                 kurtosis(PA{s}{r}(:))))
             
@@ -271,8 +272,9 @@ for s = 1:params.S
             legend('Linear', 'Saturating')
             title('Saturating HRF: Comparison with linear HRF')
             
-            figure; TwoColourTufteHist(PA{s}{r}(:), 49, 'normalise', ...
-                'xlim', 1.2*[-1 1]); xlim(1.2*[-1 1])
+            figure; histogram(PA{s}{r}(:), linspace(-1.2, 1.2, 50), ...
+                'Normalization', 'pdf'); xlim(1.2*[-1 1]);
+            xlabel('Kurtosis'); ylabel('Probility density');
             title(sprintf('PA distribution post-saturation (kurtosis: %.2f)', ...
                 kurtosis(PA{s}{r}(:))))
         end

DataGeneration/generateData.m

@@ -137,7 +137,9 @@ end
 %Plot noise distribution
 if plotFigures
     s = randi(params.S,1); r = randi(params.R(s),1);
-    figure; TwoColourTufteHist(noise{s}{r} / std(noise{s}{r}(:)), 'normalise')
+    figure; histogram(noise{s}{r} / std(noise{s}{r}(:)), ...
+        'Normalization', 'pdf');
+    xlabel('Noise'); ylabel('Probility density');
     title(sprintf('Noise distribution (kurtosis: %.2f)', kurtosis(noise{s}{r}(:))))
 end
 
@@ -203,7 +205,9 @@ end
 %Plot noise distribution
 if plotFigures
     s = randi(params.S,1); r = randi(params.R(s),1);
-    figure; TwoColourTufteHist(noise{s}{r} / std(noise{s}{r}(:)), 'normalise')
+    figure; histogram(noise{s}{r} / std(noise{s}{r}(:)), ...
+        'Normalization', 'pdf');
+    xlabel('Noise'); ylabel('Probility density');
     title(sprintf('Noise distribution (kurtosis: %.2f)', kurtosis(noise{s}{r}(:))))
     
     [U,S,V] = svd(noise{s}{r}, 0);

DataGeneration/generateMaps.m

@@ -501,7 +501,8 @@ colorbar; colormap(bluewhitered)
 title('Mean map')
 
 %And the distribution of weights
-figure; TwoColourTufteHist(Pg(:), 'normalise')
+figure; histogram(Pg(:), 'Normalization', 'pdf');
+xlabel('Voxel value'); ylabel('Probility density');
 title(sprintf('Mean map distribution (kurtosis: %.2f)', kurtosis(Pg(:))))
 
 %Spatial correlations
@@ -561,8 +562,9 @@ PsPg = NaN(params.N, params.S); inds = 1:params.N;
 for s = 1:params.S
     PsPg(:,s) = diag(cP( inds, s*params.N+inds ));
 end
-figure; TwoColourTufteHist(PsPg(:), 'xlim', [-1 1], 'normalise');
-xlabel('Correlation'); ylabel('Relative Frequency')
+figure; histogram(PsPg(:), linspace(-1.0, 1.0, 50), ...
+    'Normalization', 'pdf'); xlim([-1.0 1.0]);
+xlabel('Correlation'); ylabel('Probility density');
 title('Subject map - mean map correlations')
 
 %Then to each other
@@ -570,8 +572,9 @@ cP = cP((params.N+1):end, (params.N+1):end); PsPs = [ ];
 for s = 1:(params.S-1)
     PsPs = [PsPs; diag(cP, s*params.N)];
 end
-figure; TwoColourTufteHist(PsPs(:), 'xlim', [-1 1], 'normalise');
-xlabel('Correlation'); ylabel('Relative Frequency')
+figure; histogram(PsPs(:), linspace(-1.0, 1.0, 50), ...
+    'Normalization', 'pdf'); xlim([-1.0 1.0]);
+xlabel('Correlation'); ylabel('Probility density');
 title('Subject map - subject map correlations')
 
 %--------------------------------------------------------------------------

DataGeneration/generateNeuralTimecourses.m

@@ -190,8 +190,8 @@ set(gca, 'YTick', 0); ylabel('FFT')
 title('Frequency content of neuronal signal')
 
 %Plot the distribution of values
-figure; TwoColourTufteHist(An{s}{r}(:), 'normalise')
-xlabel('Neural signal (a.u.)'); ylabel('Relative Frequency')
+figure; histogram(An{s}{r}(:), 'Normalization', 'pdf');
+xlabel('Neural signal (a.u.)'); ylabel('Probility density');
 title('Distribution of neural activity')
 
 %Look at temporal correlations