Browse code

preliminary confidence intervall plot data save+load

git-svn-id: https://svn.discofish.de/MATLAB/spmtoolbox/SVMCrossVal@203 83ab2cfd-5345-466c-8aeb-2b2739fb922d

Christoph Budziszewski authored on05/08/2009 14:12:52
Showing3 changed files
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@@ -16,14 +16,13 @@ SubjectID         = subjectData;
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 nSubjects         = size(SubjectID,2);
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+nTrials           = getNTrials(psth);
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     f = figure;
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     hold on;
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     chanceLevel = 100/nClasses;
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-    goodPredictionLevel = chanceLevel*1.5;
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-    plot([psthStart psthEnd],[chanceLevel chanceLevel],'k:');
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-    plot([psthStart psthEnd],[goodPredictionLevel goodPredictionLevel],'k:');
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+
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     axis([psthStart psthEnd 0 100])
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     xlabel('time [sec]');
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     ylabel('decode performance [%]');
... ...
@@ -41,12 +40,40 @@ nSubjects         = size(SubjectID,2);
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             end
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             plotDecodePerformanceWithSE(frameStart,frameEnd,decodePerformance)
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     end
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+    
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+    plot([psthStart psthEnd],[chanceLevel chanceLevel],'k:');
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+
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+    plotBinConfidenceIntervall(psthStart,psthEnd,nTrials,0.05,chanceLevel/100,'r');
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+    plotBinConfidenceIntervall(psthStart,psthEnd,nTrials,0.01,chanceLevel/100,'g');
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+    
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     hold off;
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 %     setTitle(f,header,decode,subjectData);
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 end
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+function plotBinConfidenceIntervall(pStart,pEnd,nTrials,alpha,limit,color)
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+    [pLevel Z] = rev_ki_bin(nTrials,alpha,limit);
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+    [lower upper proz] = ki_bin(Z,nTrials,alpha);
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+
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+    plot([pStart pEnd],[pLevel*100 pLevel*100],[color ':']);
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+
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+    plot([pStart pEnd],[lower*100 lower*100],[color '-.']);
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+    plot([pStart pEnd],[upper*100 upper*100],[color '-.']);
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+end
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+
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+function n = getNTrials(psth)
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+nSubjects = size(psth,2);
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+
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+n = 0;
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+for ns = 1:nSubjects
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+    nClasses = size(psth{ns},2);
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+    for nc = 1:nClasses
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+        n = n + size(psth{ns}{nc},1);
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+    end
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+end
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+end
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+
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 function plotClassPerformance(frameStart,frameEnd,decodePerformance,nClasses)
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 for c = 1:size(decodePerformance,2)
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     plot(frameStart:frameEnd, decodePerformance(:,c) ,[colorChooser(mod(c,nClasses)+3) '-']);
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@@ -76,6 +103,7 @@ PSTH_AXIS_MAX = 2;
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               for timepoint = 1:size(psth{voxel}{label},2)
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                   psthData = nanmean(psth{voxel}{label});
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               end
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+              PSTH_AXIS_MAX = max(PSTH_AXIS_MAX,nanmax(psthData));
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               plot(psthStart:psthEnd,psthData,[colorChooser(voxel), lineStyleChooser(label)]);
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           end
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       end
... ...
@@ -71,7 +71,11 @@ function ui_main(varargin)
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     % MENUS
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     savemenu = uimenu(frame,'Label','Save ...','Enable','on');
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-        uimenu(savemenu,'Label','Save All','Callback',{@mcb_save,model},'Enable','on');
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+        uimenu(savemenu,'Label','Save Parameter','Callback',{@mcb_save,'PARAMS',model},'Enable','on');
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+        uimenu(savemenu,'Label','Save ImageProcesseding Data','Callback',{@mcb_save,'IMAGEPROCESSING',model},'Enable','on');
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+        uimenu(savemenu,'Label','Load ImageProcesseding Data','Callback',{@mcb_load,'IMAGEPROCESSING',model},'Enable','on');
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+        uimenu(savemenu,'Label','Save Decode Data','Callback',{@mcb_save,'DECODE',model},'Enable','on');
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+        uimenu(savemenu,'Label','Load Decode Data','Callback',{@mcb_load,'DECODE',model},'Enable','on');
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     studySelectMenu = uimenu(frame,'Label','Study ...','Enable','on');
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         uimenu(studySelectMenu,'Label','*new Study*','Callback',{@mcb_new_study,studySelectMenu,model});
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@@ -473,9 +477,47 @@ end
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 %%%%%%%%%% menu callbacks
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-function mcb_save(src,evnt,model)
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-studyID = get(model.txtStudyID,'String');
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-saveStudy(studyID,model);
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+function mcb_save(src,evnt,token,model)
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+switch token
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+    case 'PARAMS'
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+        studyID = get(model.txtStudyID,'String');
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+        saveStudy(studyID,model);
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+    case 'IMAGEPROCESSING'
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+        disp('save img data..');
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+        [FileName,PathName,FilterIndex] = uiputfile('*.mat','Save ImageProcessing Data') ;
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+        if(~strcmp(FileName,''))
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+            evalin('base', sprintf('save(''%s'',''%s'')',fullfile(PathName,FileName),'preprocessedData'));
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+            disp('saved');
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+        end
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+%         [FileName,PathName,FilterIndex] = uigetfile('*.mat','Load ImageProcessing Data') 
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+%         evalin('base', sprintf('load(%s,%s)',fullfile(PathName,FileName),'preprocessedData'));
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+    case 'DECODE'
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+        disp('save decode data..');
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+        [FileName,PathName,FilterIndex] = uiputfile('*.mat','Save Decode Data') ;
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+        if(~strcmp(FileName,''))
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+            evalin('base', sprintf('save(''%s'',''%s'')',fullfile(PathName,FileName),'decode'));
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+            disp('saved');
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+        end
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+end
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+end
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+
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+function mcb_load(src,evnt,token,model)
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+switch token
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+    case 'IMAGEPROCESSING'
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+        disp('load img data..');
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+        [FileName,PathName,FilterIndex] = uigetfile('*.mat','Load ImageProcessing Data') ;
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+        if(~strcmp(FileName,''))
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+            evalin('base', sprintf('load(''%s'',''%s'')',fullfile(PathName,FileName),'preprocessedData'));
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+            disp('done');
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+        end
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+    case 'DECODE'
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+        disp('load decode data..');
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+        [FileName,PathName,FilterIndex] = uigetfile('*.mat','Load Decode Data') ;
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+        if(~strcmp(FileName,''))
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+            evalin('base', sprintf('load(''%s'',''%s'')',fullfile(PathName,FileName),'decode'));
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+            disp('done');
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+        end
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+end
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 end
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 function mcb_update_subjects(src,evnt,model)
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Binary files a/study/default.mat and b/study/default.mat differ