# HG changeset patch
# User Christopher Hummersone <c.hummersone@surrey.ac.uk>
# Date 1490790046 -3600
# Node ID 9c2d361e5d42f44779da3f00a9b1ce178ca96798
# Parent  ba256926c559eabde0d9bb52cc4aacd2d94510e9
Merged ‘develop’.
- Updated submodule.
- MASSEFresults: Corrected table output when performing aggregating functions. Added boxPlot() method to automatically plot performance data for each algorithm/estimate.
- MASSEF: iosr.install has been moved to install method. Clear mixture cache before saving in order to reduce file size.

diff -r ba256926c559 -r 9c2d361e5d42 .hgsubstate
--- a/.hgsubstate	Wed Mar 29 09:31:12 2017 +0100
+++ b/.hgsubstate	Wed Mar 29 13:20:46 2017 +0100
@@ -1,1 +1,1 @@
-b98b6559c275b487a42c62b1cc32c92c31147e4e Library
+5eb5d21bd3da71ce11ef0e46fc9b3437afcb47a5 Library
diff -r ba256926c559 -r 9c2d361e5d42 MASSEF.m
--- a/MASSEF.m	Wed Mar 29 09:31:12 2017 +0100
+++ b/MASSEF.m	Wed Mar 29 13:20:46 2017 +0100
@@ -403,6 +403,7 @@
                     'target_ele', mixtures(n).target.elevation, ...
                     'tir', mixtures(n).tir ...
                     );
+                mixtures(n).clearCache();
             end
             
             obj.save();
@@ -561,7 +562,6 @@
 
             SOFAstart(0);
             amtstart;
-            iosr.install;
             
             d = pwd;
             cd([MASSEF.dir filesep 'help_html' filesep 'source'])
diff -r ba256926c559 -r 9c2d361e5d42 MASSEFresults.m
--- a/MASSEFresults.m	Wed Mar 29 09:31:12 2017 +0100
+++ b/MASSEFresults.m	Wed Mar 29 13:20:46 2017 +0100
@@ -35,6 +35,8 @@
 %   MASSEFRESULTS methods:
 %       MASSEFresults       - Create a MASSEFresults object.
 %       algorithmInfo       - Add algorithm information.
+%       boxPlot             - Analyse the results data by plotting notched
+%                             box plots.
 %       filter              - Filter the results set.
 %       input               - Add performance data.
 %       merge               - Combine results array into singular object.
@@ -304,6 +306,33 @@
             
         end
         
+        function bph = boxPlot(obj)
+        %BOXPLOT Analyse the results data by plotting notched box plots
+        %
+        %   R.BOXPLOT() produces a series of box plots, one for each
+        %   metric, plotting the performance of each algorithms/estimate
+        %   aggregated across all mixtures.
+        %
+        %   BPH = R.BOXPLOT() returns an array of IOSR.STATISTICS.BOXPLOT
+        %   objects BPH for the plots.
+            
+            for m = numel(obj.metrics):-1:1
+                
+                tabData = obj.filter('metric', @(x) strcmp(x, obj.metrics{m}), 'channel', 'max');
+                tabData.name = strcat(tabData.algorithmLabel, ':', {' '}, tabData.estTag);
+                
+                [y, x] = iosr.statistics.tab2box(tabData.name, tabData.value);
+                
+                figure
+                bph(m) = iosr.statistics.boxPlot(x, y, 'notch', true);
+                ylabel(obj.metrics{m})
+                xlabel('Algorithm')
+                box on
+                
+            end
+            
+        end
+        
     end % public methods
     
     methods (Hidden)
@@ -331,15 +360,16 @@
                 end
                 % do stats
                 try
-                    dataTable = varfun(fhandle,dataTable,'InputVariables','value',...
+                    filteredTable = varfun(fhandle,dataTable,'InputVariables','value',...
                         'GroupingVariables',group);
                 catch
-                    dataTable = varfun(fhandle,dataTable,'InputVariables','value',...
+                    filteredTable = varfun(fhandle,dataTable,'InputVariables','value',...
                         'GroupingVariables',altgroup);
                 end
                 % rename value column and delete GroupCount column
-                dataTable = obj.findRenameVar(dataTable,'value','value');
-                dataTable.GroupCount = [];
+                filteredTable = obj.findRenameVar(filteredTable,'value','value');
+                filteredTable.GroupCount = [];
+                dataTable = join(filteredTable, dataTable);
             else
                 % normal filter function
                 dataTable = obj.filterRows(dataTable,filterval,col);