Updates naming convention for main RAT variables

3rdParty/mcmcstat/%runDram.m

@@ -1,8 +1,8 @@
-function  [problemDef,problem,result,bayesResults] = runDram(problemDef,problemDefCells,problemDefLimits,priors,controls)
+function  [problemStruct,problem,result,bayesResults] = runDram(problemStruct,problemCells,problemLimits,priors,controls)
 debug = 0;
 
 checks = controls.checks;
-[problemDef,fitNames] = packParams(problemDef,problemDefCells,problemDefLimits,checks);
+[problemStruct,fitNames] = packParams(problemStruct,problemCells,problemLimits,checks);
 %fitPriors = packPriors(priors,checks);
 
 % Seed the Random Number Generator
@@ -15,12 +15,12 @@
 %Make uniform priors from the
 %min/max limits for now.
 prior = {};
-lims = problemDef.fitLimits;
+lims = problemStruct.fitLimits;
 % Get the li
 
 for i = 1:length(fitNames)
     name = fitNames{i};
-    value = problemDef.fitParams(i);
+    value = problemStruct.fitParams(i);
     min = lims(i,1);
     max = lims(i,2);
     mu = 0;
@@ -51,7 +51,7 @@
 burnin = controls.burnin;
 adaptint = 100;%controls.adaptint;
 
-problem = {problemDef ; controls ; problemDefLimits ; problemDefCells};
+problem = {problemStruct ; controls ; problemLimits ; problemCells};
 
 res = [];
 output = runBayes(loop,nsimu,burnin,adaptint,params,problem);
@@ -65,9 +65,9 @@
 bayesResults.bestFits = output.bestFits;
 bayesResults.predlims = output.predlims;
 
-problemDef.fitParams = output.bestPars;
-problemDef = unpackParams(problemDef,controls);
-[problem,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
+problemStruct.fitParams = output.bestPars;
+problemStruct = unpackParams(problemStruct,controls);
+[problem,result] = reflectivityCalculation(problemStruct,problemCells,controls);
 
 % Pre-processor directives for Matlab Coder.
 coder.varsize('problem.ssubs',[Inf 1],[1 0]);

3rdParty/mcmcstat/refModel.m

@@ -30,14 +30,14 @@
 
 pars = theta;
 
-problemDef = problem{1};
+problemStruct = problem{1};
 controls = problem{2};
-problemDefLimits = problem{3};
-problemDefCells = problem{4};
+problemLimits = problem{3};
+problemCells = problem{4};
 
-problemDef.fitParams = pars;
-problemDef = unpackParams(problemDef,controls);
-[problem,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
+problemStruct.fitParams = pars;
+problemStruct = unpackParams(problemStruct,controls);
+[problem,result] = reflectivityCalculation(problemStruct,problemCells,controls);
 
 ySim = result{1}{contrast};
 

3rdParty/mcmcstat/refModel_scaled.m

@@ -33,18 +33,18 @@
 
 
 
-problemDef = problem{1};
+problemStruct = problem{1};
 controls = problem{2};
-problemDefLimits = problem{3};
-problemDefCells = problem{4};
+problemLimits = problem{3};
+problemCells = problem{4};
 
 pars = theta;
-constr = problemDef.fitLimits;
+constr = problemStruct.fitLimits;
 pars = unscalePars(pars,constr);
 
-problemDef.fitParams = pars;
-problemDef = unpackParams(problemDef,controls);
-[problem,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
+problemStruct.fitParams = pars;
+problemStruct = unpackParams(problemStruct,controls);
+[problem,result] = reflectivityCalculation(problemStruct,problemCells,controls);
 
 ySim = result{1}{contrast};
 

3rdParty/mcmcstat/reflectivity_fitModel.m

@@ -6,20 +6,16 @@
 pars = theta;                                % Current parameter values from mcmcstat
 problem = data.problem;                   % Struct needed for the calculation
 %allProblem = data{1}.problem;
-problemDef = problem{1};
+problemStruct = problem{1};
 controls = problem{2};
-problemDefLimits = problem{3};
-problemDefCells = problem{4};
+problemLimits = problem{3};
+problemCells = problem{4};
 
 
-problemDef.fitParams = pars;
-problemDef = unpackParams(problemDef,controls);
-%setappdata(0,'problem',problem);
-%problem = reflectivityCalculation(problem);
-[problemDef,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
+problemStruct.fitParams = pars;
+problemStruct = unpackParams(problemStruct,controls);
+[contrastParams,~] = reflectivityCalculation(problemStruct,problemCells,controls);
 
-%problem = getappdata(0,'problem');
-ss = problemDef.calculations.sumChi;
+ss = contrastParams.calculations.sumChi;
 
 end
-

3rdParty/mcmcstat/reflectivity_fitModel_scaled.m

@@ -4,23 +4,20 @@
 % Calls reflectivity calculation and returns the value of chisquared.
 % Scaled version so the parameters need to be unscaled before use.
 
-problemDef = problem{1};
+problemStruct = problem{1};
 controls = problem{2};
-problemDefLimits = problem{3};
-problemDefCells = problem{4};
+problemLimits = problem{3};
+problemCells = problem{4};
 
 pars = theta;           % Current parameter values from mcmcstat
-constr = problemDef.fitLimits;
+constr = problemStruct.fitLimits;
 pars = unscalePars(pars,constr);
 
-problemDef.fitParams = pars;
-problemDef = unpackParams(problemDef,controls);
-%setappdata(0,'problem',problem);
-%problem = reflectivityCalculation(problem);
-[problemDef,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
+problemStruct.fitParams = pars;
+problemStruct = unpackParams(problemStruct,controls);
 
-%problem = getappdata(0,'problem');
-ss = problemDef.calculations.sumChi;
+[contrastParams,~] = reflectivityCalculation(problemStruct,problemCells,controls);
 
-end
+ss = contrastParams.calculations.sumChi;
 
+end

3rdParty/mcmcstat/runBayes.m

@@ -1,9 +1,9 @@
 function output = runBayes(loop,nsimu,burnin,adaptint,params,problem,controls)
 
-problemDef = problem{1};
+problemStruct = problem{1};
 controls = problem{2};
-problemDefLimits = problem{3};
-problemDefCells = problem{4};
+problemLimits = problem{3};
+problemCells = problem{4};
 
 display = controls.display;
 
@@ -12,7 +12,7 @@
 % We have the same number of 'batches' as contrasts.
 % Also need to pass problem in order to access this
 % from the subfunctions.
-numberOfContrasts = problemDef.numberOfContrasts;
+numberOfContrasts = problemStruct.numberOfContrasts;
 
 % Pre-allocate data to keep the compiler happy
 % data = cell(1,numberOfContrasts);
@@ -23,7 +23,7 @@
 data.data = cell(1,numberOfContrasts);
 data.problem = problem;
 for i = 1:numberOfContrasts
-    thisData = problemDefCells{2}{i};
+    thisData = problemCells{2}{i};
     if ~isempty(thisData)
         data.data{i} = [thisData(:,:)];
     end
@@ -32,8 +32,8 @@
 
 % Make qcov based on the ranges of the parameters
 qcov = [];
-fitPars = problemDef.fitParams;
-fitConstr = problemDef.fitLimits;
+fitPars = problemStruct.fitParams;
+fitConstr = problemStruct.fitLimits;
 nPars = length(fitPars);
 
 for i = 1:nPars
@@ -89,12 +89,12 @@
 % out = mcmcpred_compile(results,chain,[],data,problem,500);
 % outSld = mcmcpred_compile_sld(results,chain,[],data,problem,500);
 % 
-% problemDef.fitParams = output.bestPars;
-% problemDef = unpackParams(problemDef,controls);
-% [problem,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
+% problemStruct.fitParams = output.bestPars;
+% problemStruct = unpackParams(problemStruct,controls);
+% [problem,result] = reflectivityCalculation(problemStruct,problemCells,controls);
 % 
 % output.bestFits = result{1};
-% output.shiftedData = problemDefCells{2};
+% output.shiftedData = problemCells{2};
 % output.predlims = out;
 
 end

3rdParty/mcmcstat/runDram.m

@@ -1,11 +1,11 @@
-function  [problemDef,outProblem,result,bayesResults] = runDram(problemDef,problemDefCells,problemDefLimits,controls,allPriors)
+function  [problemStruct,outProblem,result,bayesResults] = runDram(problemStruct,problemCells,problemLimits,controls,allPriors)
 
 %#codegen
 
-%coder.varsize('problemDef.contrastBacks',[1 Inf],[0 1]);
+%coder.varsize('problemStruct.contrastBacks',[1 Inf],[0 1]);
 
 checks = controls.checks;
-[problemDef,fitNames] = packParams(problemDef,problemDefCells,problemDefLimits,checks);
+[problemStruct,fitNames] = packParams(problemStruct,problemCells,problemLimits,checks);
 %fitPriors = packPriors(priors,checks);
 
 % Seed the Random Number Generator
@@ -15,7 +15,7 @@
 
 %First deal with priors.
 prior = {};
-lims = problemDef.fitLimits;
+lims = problemStruct.fitLimits;
 
 % Preallocate params array to keep the compiler happy
 params = cell(length(fitNames),1);
@@ -117,7 +117,7 @@
 for i = 1:length(fitNames)
     coder.varsize('name',[1 Inf],[0 1]);
     name = fitNames{i};
-    value = problemDef.fitParams(i);
+    value = problemStruct.fitParams(i);
     min = lims(i,1);
     max = lims(i,2);
 
@@ -149,13 +149,13 @@
 burnin = controls.burnin;
 adaptint = 100;%controls.adaptint;
 
-problem = {problemDef ; controls ; problemDefLimits ; problemDefCells};
+problem = {problemStruct ; controls ; problemLimits ; problemCells};
 
 output = runBayes(loop,nsimu,burnin,adaptint,params,problem,controls);
 
-[problemDef,outProblem,result,bayesResults] = processBayes(output,problem);
+[problemStruct,outProblem,result,bayesResults] = processBayes(output,problem);
 
-% problemDef.fitParams = bayesResults.bestPars_Mean;
+% problemStruct.fitParams = bayesResults.bestPars_Mean;
 
 
 % Post processing of Bayes
@@ -170,17 +170,17 @@
 % bestPars_mean = output.results.mean;
 % 
 % % Calulate Max best fit curves
-% problemDef.fitParams = bestPars_max;
-% problemDef = unpackParams(problemDef,controls);
-% [outProblem,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
+% problemStruct.fitParams = bestPars_max;
+% problemStruct = unpackParams(problemStruct,controls);
+% [outProblem,result] = reflectivityCalculation(problemStruct,problemCells,controls);
 % bestFitMax_Ref = result(1);
 % bestFitMax_Sld = result(5);
 % bestFitMax_chi = outProblem.calculations.sumChi;
 % 
 % % Calculate 'mean' best fit curves
-% problemDef.fitParams = bestPars_mean;
-% problemDef = unpackParams(problemDef,controls);
-% [outProblem,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
+% problemStruct.fitParams = bestPars_mean;
+% problemStruct = unpackParams(problemStruct,controls);
+% [outProblem,result] = reflectivityCalculation(problemStruct,problemCells,controls);
 % bestFitMean_Ref = result(1);
 % bestFitMean_Sld = result(5);
 % bestFitMean_chi = outProblem.calculations.sumChi;

3rdParty/mcmcstat/sldModel.m

@@ -24,15 +24,15 @@
 
 pars = theta;
 
-problemDef = problem{1};
+problemStruct = problem{1};
 controls = problem{2};
-problemDefLimits = problem{3};
-problemDefCells = problem{4};
+problemLimits = problem{3};
+problemCells = problem{4};
 controls.calcSldDuringFit = true;
 
-problemDef.fitParams = pars;
-problemDef = unpackParams(problemDef,controls);
-[~,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
+problemStruct.fitParams = pars;
+problemStruct = unpackParams(problemStruct,controls);
+[~,result] = reflectivityCalculation(problemStruct,problemCells,controls);
 
 sld = result{5}{contrast};
 

3rdParty/paramonte/pmLogFunction.m

@@ -2,9 +2,9 @@
 
     properties 
 
-        problemDef;
-        problemDefCells;
-        problemDefLimits;
+        problemStruct;
+        problemCells;
+        problemLimits;
         priors;
         controls;
         scaled;
@@ -16,7 +16,7 @@
 
         function logFuncVal = get(obj,pars)
 
-            problem = obj.problemDef;            
+            problem = obj.problemStruct;            
             problem.fitParams = pars;
 
             if obj.scaled
@@ -25,7 +25,7 @@
 
             problem = unpackParams(problem,obj.controls);
 
-            [outProblem,~] = reflectivityCalculation_mex(problem,obj.problemDefCells,obj.controls);
+            [outProblem,~] = reflectivityCalculation_mex(problem,obj.problemCells,obj.controls);
             chi = outProblem.calculations.sumChi;
             logFuncVal = -chi/2;