Automatically save figures to file

RunTests.m

@@ -40,6 +40,8 @@ if plotFigures
     prettyFigures();
 end
 
+rng('shuffle');
+
 %% Set size of problem %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 %Number of times to repeat simulation / test cycle
@@ -247,8 +249,6 @@ end
 
 %% Run the tests %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
-rng('shuffle')
-
 scores = struct();
 for n = 1:params.nRepeats
     
@@ -377,7 +377,7 @@ for n = 1:params.nRepeats
         %    = calculateDecompositionAccuracy( ...
         %        P, icadr_P, A, icadr_A, pcAn, icadr_pcA, params);
     catch
-        warning('MELODIC run failed.')
+        warning('MELODIC run failed.');
     end
     
     %% Run PROFUMO %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -413,7 +413,7 @@ for n = 1:params.nRepeats
         'scores', ...
         'params', 'atlasParams', 'modeParams', ...
         'options', 'atlasOptions', 'modeOptions', ...
-        '-v7.3')
+        '-v7.3');
     
     %% And delete everything we don't need saved %%%%%%%%%%%%%%%%%%%%%%%%%%
     if ~saveData
@@ -424,17 +424,20 @@ end
 
 %% Plot results %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
-fprintf('Finished! You can now run e.g.:\n');
+plotScores(scores, params, baseFileName, plotFigures);
+if plotFigures
+    input('Press return to continue');
+    close all
+end
+
+fprintf('Finished! Figures have been saved to:\n');
+fprintf('    %s/\n', baseFileName);
+fprintf('To replot the results you can now run e.g.:\n');
 fprintf('>> initialiseEnvironment();\n');
 fprintf('>> prettyFigures();\n');
 fprintf('>> load(''%s.mat'');\n', baseFileName);
 fprintf('>> plotScores(scores, params);\n');
-
-if plotFigures
-    plotScores(scores, params);
-    input('Press return to continue')
-    close all
-end
+fprintf('\n');
 
 %% If run from the command line make sure we quit MATLAB %%%%%%%%%%%%%%%%%%
 

Scoring/calculateDecompositionAccuracy.m

@@ -143,7 +143,7 @@ scores.pcA.metric = 'Cosine similarity';
 scores.pcA.range  = [-1.0, 1.0];
 % Each element, across subjects (i.e. behavioural prediction)
 scores.pcA_xs.data   = diag(corr(pcAz', inf_pcAz'));
-scores.pcA_xs.name   = 'Netmats: cross subject corrs';
+scores.pcA_xs.name   = 'Netmats (cross subject corrs)';
 scores.pcA_xs.metric = 'Correlation';
 scores.pcA_xs.range  = [-1.0, 1.0];
 

Visualisation/plotScores.m

-function [  ] = plotScores( scores, params, saveFigures )
+function [  ] = plotScores( scores, params, outputDir, plotFigures )
 %Plots the results from whichever analyses have been run
 
-if nargin == 2
-    saveFigures = false;
+if nargin < 3
+    outputDir = false;
 end
+saveFigures = logical(outputDir);
 
-if saveFigures
-    figPosLong = 200 + [0 0 1300 200];
-    figPosSquare = 200 + [0 0 300 300];
+if nargin < 4
+    plotFigures = true;
+end
+if plotFigures
+    visibility = 'on';
+else
+    visibility = 'off';
 end
-ticks = 0:0.2:1; %Where to place ticks for correlations
 
 %--------------------------------------------------------------------------
 % Get all the metadata sorted
@@ -19,27 +23,55 @@ methods = fieldnames(scores)
 tests = struct2cell(structfun(@fieldnames, scores, 'UniformOutput', false));
 tests = unique(vertcat(tests{:}))
 
+if saveFigures
+    mkdir(outputDir);
+    % [left bottom width height]
+    figPosLong = 200 + [0 0 1000 200];
+    figPosSquare = 200 + [0 0 300 300];
+end
+ticks = 0:0.2:1; %Where to place ticks for correlations
+
 %--------------------------------------------------------------------------
 % Plot all the tests
 
 for test = tests'
     test = test{1};
+    
     results = combineResults(scores, params, test);
-    plotDistributions(results.methods, results.data);
+    
+    % Plot marginals
+    plotDistributions(results.methods, results.data, visibility);
     ylim(results.range + 0.025 * diff(results.range) * [-1; 1]);
     ylabel(results.metric);
-    %if saveFigures
-    %    set(gcf, 'Position', figPosLong)
-    %    export_fig('SimData_SpatialMaps', '-pdf', '-transparent')
-    %end
+    if saveFigures
+       set(gcf(), 'Position', figPosSquare);
+       set(gcf(), 'Color', 'w');
+       export_fig( ...
+           fullfile(outputDir, strrep(results.name, ' ', '_')), ...
+           '-pdf')  % '-transparent'
+    end
     title(results.name);
     
-    plotAllVals(results.methods, results.data, results.range);
+    % And all data
+    plotAllVals(results.methods, results.data, results.range, visibility);
     ylabel(results.metric);
+    if saveFigures
+       set(gcf(), 'Position', figPosLong);
+       set(gcf(), 'Color', 'w');
+       export_fig( ...
+           fullfile(outputDir, [strrep(results.name, ' ', '_') '_all']), ...
+           '-pdf')  % '-transparent'
+    end
     title(results.name);
 end
 
+% Tidy up invisible figures that have been saved
+%if ~plotFigures && saveFigures
+%    close all
+%end
+
 %--------------------------------------------------------------------------
+if false
 
 input('Press return to continue')
 
@@ -105,6 +137,9 @@ for n = 1:length(H)
     end
 end
 
+end
+%--------------------------------------------------------------------------
+
 end
 
 %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -170,7 +205,7 @@ end
 
 %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
-function [  ] = plotDistributions( methods, results )
+function [  ] = plotDistributions( methods, results, visibility )
 %Given a set of methods and results, shows the marginal distributions
 
 %Find max number of data points recorded for any method
@@ -186,7 +221,7 @@ for n = 1:numel(methods)
 end
 
 %Plot results with appropriate labels
-figure(); box on;
+figure('Visible', visibility); box on;
 %h = boxplot(boxmat, 'labels', methods);%, 'labelorientation', 'inline');
 %Change line thicknesses
 %for ih=1:6
@@ -234,12 +269,12 @@ end
 
 %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
-function [ ] = plotAllVals( methods, results, lims )
+function [ ] = plotAllVals( methods, results, lims, visibility )
 %Plots all the results, rather than combining into a single box plot
 
 methodSpacing = 1/2; repeatSpacing = 1/4;
 
-figure; hold on; box on
+figure('Visible', visibility); hold on; box on
 
 xEnd = 0; labelTicks = NaN(numel(methods,1));
 for n = 1:numel(methods)