Reduces "bayesShadedPlot" to use only the "mean" fit option (#277)

* Removes "fit" option from "bayesShadedPlot"

* Tidies up "bayesShadedPlot"

* Addresses review comments

Author: DrPaulSharp

examples/miscellaneous/convertRasCAL1Project/convertRascal.mlx

@@ -188,7 +188,7 @@
 % ..and plot this out....
 
     figure(30); clf;
-    bayesShadedPlot(problem, results,'fit','average','KeepAxes',true,'interval',65,'q4',false)
+    bayesShadedPlot(problem, results,'KeepAxes',true,'interval',65,'q4',false)
 
     h3 = figure(40); clf
     plotHists(results,h3,'smooth',true)
@@ -271,12 +271,14 @@
 
 % In RAT, there is a useful function called 'shade' that we can use
 % here.....
+fillType = [1 2;2 1];
+fillAlpha = 0.3;
 cols = get(gca,'ColorOrder');
-shade(z,ciSi(1,:),z,ciSi(2,:),'FillColor',cols(1,:),'FillType',[1 2;2 1],'FillAlpha',0.3); 
-shade(z,ciOxide(1,:),z,ciOxide(2,:),'FillColor',cols(2,:),'FillType',[1 2;2 1],'FillAlpha',0.3); 
-shade(z,ciHeadL(1,:),z,ciHeadL(2,:),'FillColor',cols(3,:),'FillType',[1 2;2 1],'FillAlpha',0.3);
-shade(z,ciTails(1,:),z,ciTails(2,:),'FillColor',cols(4,:),'FillType',[1 2;2 1],'FillAlpha',0.3);
-shade(z,ciHeadR(1,:),z,ciHeadR(2,:),'FillColor',cols(5,:),'FillType',[1 2;2 1],'FillAlpha',0.3);
+shade(z,ciSi(1,:),z,ciSi(2,:),'FillColor',cols(1,:),'FillType',fillType,'FillAlpha',fillAlpha); 
+shade(z,ciOxide(1,:),z,ciOxide(2,:),'FillColor',cols(2,:),'FillType',fillType,'FillAlpha',fillAlpha); 
+shade(z,ciHeadL(1,:),z,ciHeadL(2,:),'FillColor',cols(3,:),'FillType',fillType,'FillAlpha',fillAlpha);
+shade(z,ciTails(1,:),z,ciTails(2,:),'FillColor',cols(4,:),'FillType',fillType,'FillAlpha',fillAlpha);
+shade(z,ciHeadR(1,:),z,ciHeadR(2,:),'FillColor',cols(5,:),'FillType',fillType,'FillAlpha',fillAlpha);
 title('Volume Fractions');
 %% 
 % .. and we are done.

examples/normalReflectivity/customXY/customXYDSPCScript.m

@@ -2,7 +2,7 @@ function bayesShadedPlot(problem,result,varargin)
 
 % Plot the shaded reflectivities from Bayes output
 % from RAT
-sf = result.contrastParams.scalefactors;
+
 if isa(problem,'domainsClass')
     isDomains = true;
 else
@@ -11,60 +11,55 @@ function bayesShadedPlot(problem,result,varargin)
 
 %  Parse the input options
 if ~isempty(varargin)
-    defaultq4  = 0;
-    defaultFit = 'mean';
+
+    defaultq4  = false;
     defaultKeep = false;
     defaultInterval = 95;
 
-    
     allIntervals = [65 95];
 
     p = inputParser;
     addOptional(p,  'q4',           defaultq4,          @islogical);
-    addOptional(p,  'fit',          defaultFit,         @(x) any(strcmpi(x,{'mean','average','all'})));
     addOptional(p,  'KeepAxes',     defaultKeep,        @islogical);
     addOptional(p,  'interval',     defaultInterval,    @(x) ismember(x,allIntervals));
 
     parse(p,varargin{:});
     inputBlock = p.Results;
 
     q4 = inputBlock.q4;
-    fit = inputBlock.fit;
     keepAx = inputBlock.KeepAxes;
     interval = inputBlock.interval;
 
 else
+
     q4 = false;
-    fit = 'average';
     keepAx = false;
-    interval = 65;
-end
-
-                   %mean  %Average   % All
-showWhichCurves = [false    false    false];
+    interval = 95;
 
-switch fit
-    case 'mean'
-        showWhichCurves(1) = 1;
-    case 'average'
-        showWhichCurves(2) = 1;
-    case 'all'
-        showWhichCurves(1:2) = 1;
 end
 
-f = gcf;
 
-if ~ keepAx
+if ~keepAx
     clf; hold on; box on
 end
 
-pLims = result.predictionIntervals;
-refPlims = pLims.reflectivity;
-sldPlims = pLims.sld;
+switch interval
+    case 95
+        vals = [1 5];
+    case 65
+        vals = [2 4];
+end
+
+fillColor = [0.7 0.7 0.7];
+fillType = [1 2;2 1];
+fillAlpha = 0.3;
+
+% Get the reflectivities and SLDs
+bestReflectivity = result.reflectivity;
+bestSld = result.sldProfiles;
 
-% Get the reflectivities for mean...
-bestRefMean = result.reflectivity;
-bestSldMean = result.sldProfiles;
+reflectivityLimits = result.predictionIntervals.reflectivity;
+sldLimits = result.predictionIntervals.sld;
 
 shiftedData = result.shiftedData;
 numberOfContrasts = length(shiftedData);
@@ -76,154 +71,87 @@ function bayesShadedPlot(problem,result,varargin)
 
 for i = 1:numberOfContrasts
 
-    %thisRef = reflect{i};
     thisData = shiftedData{i};
-    thisRefMean = bestRefMean{i};
-    thisSf = sf(i);
+    reflectivity = bestReflectivity{i};
 
+    mult = 2^(4*i);
     switch q4
+        case true
+            thisQ4 = thisData(:,1).^4;
         case false
             if i == 1
                 mult = 1;
-            else
-                mult = 2^(4*i);
             end
-        otherwise
-            mult = 2^(4*i);
-            thisQ4 = thisData(:,1).^4;
     end
 
     % Get the limits and fits
-    theseLims = refPlims{i};
-    
-    switch interval
-        case 95
-            vals = [1 5];
-        case 65
-            vals = [2 4];
-    end
-    
-    thisMin = theseLims(vals(1),:)./mult;
-    thisMax = theseLims(vals(2),:)./mult;
-    
-    thisRefAvg = theseLims(3,:)./mult;
+    limits = reflectivityLimits{i};
 
-    thisRefMean(:,2) = thisRefMean(:,2)./mult;
-    %thisRefMax(:,2) = thisRefMax(:,2)./mult;
+    min = limits(vals(1),:)./mult;
+    max = limits(vals(2),:)./mult;
+        
+    reflectivity(:,2) = reflectivity(:,2)./mult;
 
-    thisDataX = thisData(:,1);
-    thisDataY = thisData(:,2)./mult;
-    thisDataErr = thisData(:,3)./mult;
+    dataX = thisData(:,1);
+    dataY = thisData(:,2)./mult;
+    dataErr = thisData(:,3)./mult;
 
-    thisSimX = result.reflectivity{i}(:,1);
-    thisSimQ4 = thisSimX.^4;
+    refXValues = result.reflectivity{i}(:,1);
+    thisSimQ4 = refXValues.^4;
 
     switch q4
         case true
-            thisMin = thisMin .* thisSimQ4;
-            thisMax = thisMax .* thisSimQ4;
-            thisRefMean(:,2) = thisRefMean(:,2) .* thisQ4;
-            thisRefAvg = thisRefAvg .* thisSimQ4;          
-            thisDataY = thisDataY(:) .* thisQ4;
-            thisDataErr = thisDataErr(:) .* thisQ4;
+            min = min .* thisSimQ4;
+            max = max .* thisSimQ4;
+            reflectivity(:,2) = reflectivity(:,2) .* thisQ4;         
+            dataY = dataY(:) .* thisQ4;
+            dataErr = dataErr(:) .* thisQ4;
     end
 
-    errorbar(thisDataX,thisDataY,thisDataErr,'.');
+    errorbar(dataX,dataY,dataErr,'.');
+    shade(refXValues,min,refXValues,max,'FillColor',fillColor,'FillType',fillType,'FillAlpha',fillAlpha);
+    plot(reflectivity(:,1),reflectivity(:,2),'b-');
 
-    %plot(thisData(:,1),thisMin,'-','color',[0.7 0.7 0.7]);
-    %plot(thisData(:,1),thisMax,'-','color',[0.7 0.7 0.7]);
-    %plot(thisData(:,1),thisRef,'LineWidth',2.0);
-    %fillyy(thisData(:,1),thisMin,thisMax,[0.8 0.8 0.8]);
-    shade(thisSimX,thisMin,thisSimX,thisMax,'FillColor',[0.7 0.7 0.7],'FillType',[1 2;2 1],'FillAlpha',0.3);
-    
-    % Plot the requested fit lines;
-    if showWhichCurves(1)
-        % Plot the mean
-        plot(thisRefMean(:,1),thisRefMean(:,2),'b-');
-    end
-    
-    if showWhichCurves(2)
-        % Plot the average
-       plot(thisDataX,thisRefAvg,'r-');
-    end
-    
 end
 
-% Now plot the SLD's
+% Now plot the SLDs
 subplot(1,2,2); hold on; box on
 
 if ~isDomains
-    for i = 1:numberOfContrasts
-
-        thisSldMean = bestSldMean{i};
-        %thisSldMax = bestSld_max{i};
 
-        theseLims = sldPlims{i};
-
-        thisSldX = result.sldProfiles{i}(:,1);
-
-        switch interval
-            case 95
-                vals = [1 5];
-            case 65
-                vals = [2 4];
-        end
+    for i = 1:numberOfContrasts
 
-        thisMin = theseLims(vals(1),:);
-        thisMax = theseLims(vals(2),:);
+        sld = bestSld{i};
+        limits = sldLimits{i};
+        sldXValues = result.sldProfiles{i}(:,1);
 
-        thisSldAvg = theseLims(3,:);
+        min = limits(vals(1),:);
+        max = limits(vals(2),:);
 
-        if showWhichCurves(1)
-            % Plot the mean
-            plot(thisSldMean(:,1),thisSldMean(:,2),'b-');
-        end
+        plot(sld(:,1),sld(:,2),'b-');
+        shade(sldXValues,min,sldXValues,max,'FillColor',fillColor,'FillType',fillType,'FillAlpha',fillAlpha);
 
-        if showWhichCurves(2)
-            % Plot the max
-            plot(thisSldX,thisSldAvg,'r-');
-        end
-
-        shade(thisSldX,thisMin,thisSldX,thisMax,'FillColor',[0.7 0.7 0.7],'FillType',[1 2;2 1],'FillAlpha',0.3);
     end
-else
-    for i = 1:numberOfContrasts
-
-        thisSldMean = bestSldMean(i,:);
-        %thisSldMax = bestSld_max{i};
-
-        theseLims = sldPlims(i,:);
 
-        thisSldX = result.sldProfiles(i,:);
-
-        switch interval
-            case 95
-                vals = [1 5];
-            case 65
-                vals = [2 4];
-        end
+else
 
-        for m = 1:2
-            thisMin = theseLims{m}(vals(1),:);
-            thisMax = theseLims{m}(vals(2),:);
+    for i = 1:numberOfContrasts
 
-            thisDomainSldX = thisSldX{m}(:,1);
-            thisSldAvg = theseLims{m}(3,:);
+        sld = bestSld(i,:);
+        limits = sldLimits(i,:);
+        sldXValues = result.sldProfiles(i,:);
 
-            if showWhichCurves(1)
-                % Plot the mean
-                plot(thisSldMean{m}(:,1),thisSldMean{m}(:,2),'b-');
-            end
+        for j = 1:2
+            min = limits{j}(vals(1),:);
+            max = limits{j}(vals(2),:);
 
-            if showWhichCurves(2)
-                % Plot the max
-                plot(thisDomainSldX,thisSldAvg,'r-');
-            end
+            thisDomainSldXValues = sldXValues{j}(:,1);
 
-            shade(thisDomainSldX,thisMin,thisDomainSldX,thisMax,'FillColor',[0.7 0.7 0.7],'FillType',[1 2;2 1],'FillAlpha',0.3);
+            plot(sld{j}(:,1),sld{j}(:,2),'b-');
+            shade(thisDomainSldXValues,min,thisDomainSldXValues,max,'FillColor',fillColor,'FillType',fillType,'FillAlpha',fillAlpha);
         end
     end
 
 end
 
-end
+end

examples/normalReflectivity/customXY/customXYDSPCSheet.mlx

@@ -3,13 +3,13 @@ function plotBayes(problem, results)
     figure(10); clf; plotRefSLD(problem,results)
 
     figure(30); clf;
-    bayesShadedPlot(problem,results,'fit','mean','KeepAxes',true,'interval',95,'q4',false)
+    bayesShadedPlot(problem,results,'KeepAxes',true,'interval',95,'q4',false)
 
     h3 = figure(40); clf
     plotHists(results,h3,'smooth',true)
 
-%     h4 = figure(5); clf
-%     cornerPlot(results,h4,'smooth',false)
+    % h4 = figure(5); clf
+    % cornerPlot(results,h4,'smooth',false)
 
     figure(60); clf
     mcmcplot(results.chain,[],results.fitNames,'chainpanel');