Unifies variable naming conventions

3rdParty/mcmcstat/%runDram.m

@@ -15,12 +15,12 @@
 %Make uniform priors from the
 %min/max limits for now.
 prior = {};
-lims = problemDef.fitconstr;
+lims = problemDef.fitLimits;
 % Get the li
 
 for i = 1:length(fitNames)
     name = fitNames{i};
-    value = problemDef.fitpars(i);
+    value = problemDef.fitParams(i);
     min = lims(i,1);
     max = lims(i,2);
     mu = 0;
@@ -65,7 +65,7 @@
 bayesResults.bestFits = output.bestFits;
 bayesResults.predlims = output.predlims;
 
-problemDef.fitpars = output.bestPars;
+problemDef.fitParams = output.bestPars;
 problemDef = unpackParams(problemDef,controls);
 [problem,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
 
@@ -78,8 +78,8 @@
 coder.varsize('problem.bulkOut',[Inf 1],[1 0]);
 coder.varsize('problem.resolutionParams',[Inf 1],[1 0]);
 coder.varsize('problem.ssubs',[Inf 1],[1 0]);
-coder.varsize('problem.calculations.all_chis',[Inf 1],[1 0]);
-coder.varsize('problem.calculations.sum_chi',[1 1],[0 0]);
+coder.varsize('problem.calculations.allChis',[Inf 1],[1 0]);
+coder.varsize('problem.calculations.sumChi',[1 1],[0 0]);
 coder.varsize('problem.allSubRough',[Inf 1],[1 0]);
 
 %Result coder definitions....

3rdParty/mcmcstat/refModel.m

@@ -5,7 +5,7 @@
 % We need to reverse this correction, and actually return the simulation
 % corrected for the scalefactor. We have to do this because the scalefactor 
 % is in itself one of our fitting parameters (no need to correct the data -
-% problem.shifted_data contains the corrected data, but
+% problem.shiftedData contains the corrected data, but
 % problem.data is a record of the original).
 
 if isempty(varargin)
@@ -35,7 +35,7 @@
 problemDefLimits = problem{3};
 problemDefCells = problem{4};
 
-problemDef.fitpars = pars;
+problemDef.fitParams = pars;
 problemDef = unpackParams(problemDef,controls);
 [problem,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
 

3rdParty/mcmcstat/refModel_scaled.m

@@ -5,7 +5,7 @@
 % We need to reverse this correction, and actually return the simulation
 % corrected for the scalefactor. We have to do this because the scalefactor 
 % is in itself one of our fitting parameters (no need to correct the data -
-% problem.shifted_data contains the corrected data, but
+% problem.shiftedData contains the corrected data, but
 % problem.data is a record of the original). Also, in this version the
 % input parameters are scaled, so they need to be unscaled befor the
 % calculation
@@ -39,10 +39,10 @@
 problemDefCells = problem{4};
 
 pars = theta;
-constr = problemDef.fitconstr;
+constr = problemDef.fitLimits;
 pars = unscalePars(pars,constr);
 
-problemDef.fitpars = pars;
+problemDef.fitParams = pars;
 problemDef = unpackParams(problemDef,controls);
 [problem,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
 

3rdParty/mcmcstat/reflectivity_fitModel.m

@@ -12,14 +12,14 @@
 problemDefCells = problem{4};
 
 
-problemDef.fitpars = pars;
+problemDef.fitParams = pars;
 problemDef = unpackParams(problemDef,controls);
 %setappdata(0,'problem',problem);
 %problem = reflectivityCalculation(problem);
 [problemDef,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
 
 %problem = getappdata(0,'problem');
-ss = problemDef.calculations.sum_chi;
+ss = problemDef.calculations.sumChi;
 
 end
 

3rdParty/mcmcstat/reflectivity_fitModel_scaled.m

@@ -10,17 +10,17 @@
 problemDefCells = problem{4};
 
 pars = theta;           % Current parameter values from mcmcstat
-constr = problemDef.fitconstr;
+constr = problemDef.fitLimits;
 pars = unscalePars(pars,constr);
 
-problemDef.fitpars = pars;
+problemDef.fitParams = pars;
 problemDef = unpackParams(problemDef,controls);
 %setappdata(0,'problem',problem);
 %problem = reflectivityCalculation(problem);
 [problemDef,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
 
 %problem = getappdata(0,'problem');
-ss = problemDef.calculations.sum_chi;
+ss = problemDef.calculations.sumChi;
 
 end
 

3rdParty/mcmcstat/runBayes.m

@@ -32,8 +32,8 @@
 
 % Make qcov based on the ranges of the parameters
 qcov = [];
-fitPars = problemDef.fitpars;
-fitConstr = problemDef.fitconstr;
+fitPars = problemDef.fitParams;
+fitConstr = problemDef.fitLimits;
 nPars = length(fitPars);
 
 for i = 1:nPars
@@ -89,7 +89,7 @@
 % out = mcmcpred_compile(results,chain,[],data,problem,500);
 % outSld = mcmcpred_compile_sld(results,chain,[],data,problem,500);
 % 
-% problemDef.fitpars = output.bestPars;
+% problemDef.fitParams = output.bestPars;
 % problemDef = unpackParams(problemDef,controls);
 % [problem,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
 % 

3rdParty/mcmcstat/runDram.m

@@ -15,7 +15,7 @@
 
 %First deal with priors.
 prior = {};
-lims = problemDef.fitconstr;
+lims = problemDef.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.fitpars(i);
+    value = problemDef.fitParams(i);
     min = lims(i,1);
     max = lims(i,2);
 
@@ -155,7 +155,7 @@
 
 [problemDef,outProblem,result,bayesResults] = processBayes(output,problem);
 
-% problemDef.fitpars = bayesResults.bestPars_Mean;
+% problemDef.fitParams = bayesResults.bestPars_Mean;
 
 
 % Post processing of Bayes
@@ -170,20 +170,20 @@
 % bestPars_mean = output.results.mean;
 % 
 % % Calulate Max best fit curves
-% problemDef.fitpars = bestPars_max;
+% problemDef.fitParams = bestPars_max;
 % problemDef = unpackParams(problemDef,controls);
 % [outProblem,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
 % bestFitMax_Ref = result(1);
 % bestFitMax_Sld = result(5);
-% bestFitMax_chi = outProblem.calculations.sum_chi;
+% bestFitMax_chi = outProblem.calculations.sumChi;
 % 
 % % Calculate 'mean' best fit curves
-% problemDef.fitpars = bestPars_mean;
+% problemDef.fitParams = bestPars_mean;
 % problemDef = unpackParams(problemDef,controls);
 % [outProblem,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
 % bestFitMean_Ref = result(1);
 % bestFitMean_Sld = result(5);
-% bestFitMean_chi = outProblem.calculations.sum_chi;
+% bestFitMean_chi = outProblem.calculations.sumChi;
 % 
 % % 2. Reflectivity and SLD shading
 % predIntRef = mcmcpred_compile(output.results,output.chain,[],output.data,problem,500);

3rdParty/mcmcstat/sldModel.m

@@ -30,7 +30,7 @@
 problemDefCells = problem{4};
 controls.calcSldDuringFit = true;
 
-problemDef.fitpars = pars;
+problemDef.fitParams = pars;
 problemDef = unpackParams(problemDef,controls);
 [~,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
 

3rdParty/paramonte/pmLogFunction.m

@@ -17,7 +17,7 @@
         function logFuncVal = get(obj,pars)
 
             problem = obj.problemDef;            
-            problem.fitpars = pars;
+            problem.fitParams = pars;
 
             if obj.scaled
                 problem = unscalePars(problem);
@@ -26,7 +26,7 @@
             problem = unpackParams(problem,obj.controls);
 
             [outProblem,~] = reflectivityCalculation_mex(problem,obj.problemDefCells,obj.controls);
-            chi = outProblem.calculations.sum_chi;
+            chi = outProblem.calculations.sumChi;
             logFuncVal = -chi/2;
 
             % Check to catch any strange NaNs
@@ -37,7 +37,7 @@
             % Now apply the priors where necessary. 
             priorfun = @(th,mu,sig) sum(((th-mu)./sig).^2);
 
-            val = priorfun(problem.fitpars,obj.priors(:,1),obj.priors(:,2));
+            val = priorfun(problem.fitParams,obj.priors(:,1),obj.priors(:,2));
 
             logFuncVal = logFuncVal + val;           
 

3rdParty/paramonte/processParamonteRuns.m

@@ -24,7 +24,7 @@
 % only take last part of chain...
 scaledChain = chain((end-npoints):end,:);
 
-limits = problemDef.fitconstr;
+limits = problemDef.fitLimits;
 rows = size(scaledChain,1);
 
 for i = 1:rows
@@ -49,8 +49,8 @@
 
 result = parseResultToStruct(outProblemStruct,result);
 
-if isfield(problemDef,'fitpars')
-    result.bestFitPars = problemDef.fitpars;
+if isfield(problemDef,'fitParams')
+    result.bestFitPars = problemDef.fitParams;
 end
 
 result = mergeStructs(result,bayesResults);

3rdParty/paramonte/runParamonte.m

@@ -23,8 +23,8 @@
 logFunc = pmLogFunction();
 
 [problemDef,fitNames,fitPriors] = packParamsPriors(problemDef,problemDefCells,problemDefLimits,priors,controls.checks);
-nDims = length(problemDef.fitpars);
-testPars = problemDef.fitpars;
+nDims = length(problemDef.fitParams);
+testPars = problemDef.fitParams;
 
 % Scale the parameters
 problemDef = scalePars(problemDef);
@@ -56,7 +56,7 @@
 % if isfield(pmPars,'stVec')
 %     pmpd.spec.startPointVec = pmPars.stVec;
 % else
-%     pmpd.spec.startPointVec = problemDef.fitpars;   % Maybe dependent on scaling?
+%     pmpd.spec.startPointVec = problemDef.fitParams;   % Maybe dependent on scaling?
 % end
 
 % if isfield(pmPars,'burninAdapt')
@@ -70,8 +70,8 @@
 name = pmPars.name;
 pmpd.reportEnabled = false;
 pmpd.spec.outputFileName = sprintf("./paramonte_out/%s",name);%,datestr(now,30)); 
-pmpd.spec.domainLowerLimitVec = scaledMins;%problemDef.fitconstr(:,1);
-pmpd.spec.domainUpperLimitVec = scaledMaxs;%problemDef.fitconstr(:,2);
+pmpd.spec.domainLowerLimitVec = scaledMins;%problemDef.fitLimits(:,1);
+pmpd.spec.domainUpperLimitVec = scaledMaxs;%problemDef.fitLimits(:,2);
 pmpd.spec.overwriteRequested = true;
 pmpd.spec.scaleFactor = pmPars.scalefactor; %1e-4;
 pmpd.spec.sampleRefinementCount = pmPars.chainSize;
@@ -101,7 +101,7 @@
 % % Get out the chain, and unscale it.....
 % scaledChain = chainTable{2:end,8:end};
 % 
-% limits = problemDef.fitconstr;
+% limits = problemDef.fitLimits;
 % rows = size(scaledChain,1);
 % 
 % for i = 1:rows

3rdParty/paramonte/scalePars.m

@@ -1,11 +1,11 @@
 function problem = scalePars(problem)
 
-limits = problem.fitconstr;
-vals = problem.fitpars;
+limits = problem.fitLimits;
+vals = problem.fitParams;
 
 scaled = (vals(:) - limits(:,1))./(limits(:,2)-limits(:,1));
 
-problem.fitpars = scaled;
+problem.fitParams = scaled;
 %problem.unscaledPars = vals;
 
 end

3rdParty/paramonte/unscalePars.m

@@ -1,10 +1,10 @@
 function problem = unscalePars(problem) 
 
-limits = problem.fitconstr;
-scaled = problem.fitpars;
+limits = problem.fitLimits;
+scaled = problem.fitParams;
 
 unscaled = (scaled.*(limits(:,2)-limits(:,1)))+limits(:,1);
 
-problem.fitpars = unscaled;
+problem.fitParams = unscaled;
 
 end

API/RAT.m

@@ -13,7 +13,7 @@
 %Call the main RAT routine...
 
 % If display is not silent print a
-% line confirminf RAT is starting
+% line confirming RAT is starting
 if ~strcmpi(controls.display,'off')
     fprintf('Starting RAT ________________________________________________________________________________________________\n\n');
 end
@@ -37,8 +37,8 @@
 
 result = parseResultToStruct(problem,result);
 
-if isfield(outProblemStruct,'fitpars')
-    result.bestFitPars = outProblemStruct.fitpars;
+if isfield(outProblemStruct,'fitParams')
+    result.bestFitPars = outProblemStruct.fitParams;
 end
 
 if any((strcmpi(controls.procedure,{'bayes','NS','dream'})))

API/RATMain.m

@@ -15,7 +15,7 @@
                  'bulkIn',preAlloc,...
                  'bulkOut',preAlloc,...
                  'resolutionParams',preAlloc,...
-                 'calculations',struct('all_chis',preAlloc,'sum_chi',0),...
+                 'calculations',struct('allChis',preAlloc,'sumChi',0),...
                  'allSubRough',preAlloc,...
                  'resample',preAlloc);
 

API/parseClassToStructs.m

@@ -347,10 +347,10 @@
 end    
 
 % Initialise the lists of fitting parameters    
-problemDef.fitpars = [];
-problemDef.otherpars = [];
-problemDef.fitconstr = [];
-problemDef.otherconstr = [];
+problemDef.fitParams = [];
+problemDef.otherParams = [];
+problemDef.fitLimits = [];
+problemDef.otherLimits = [];
 
 
 %% Now deal with the controls class

API/parseResultToStruct.m

@@ -1,9 +1,9 @@
 function result = parseResultToStruct(problem,results)
 
 result.reflectivity = results{1};   % Reflectivity art points
-result.Simulation = results{2};     % Reflectivity between sim limits    
-result.shifted_data = results{3};   % Data corrected for sfs
-result.layerSlds = results{4};      % Layers if defined (i.e. not custolXY)
+result.simulation = results{2};     % Reflectivity between sim limits    
+result.shiftedData = results{3};    % Data corrected for sfs
+result.layerSlds = results{4};      % Layers if defined (i.e. not customXY)
 result.sldProfiles = results{5};    % Calculated SLD profiles
 result.allLayers = results{6};      % Resampled layers
 

API/projectClass/backgroundsClass.m

@@ -59,8 +59,7 @@
             % in the object. 
             % 
             % names = background.getNames();
-            backsTable = obj.backgrounds.varTable;
-            names = backsTable{:,1};      
+            names = obj.backgrounds.varTable{:,1};      
         end
 
         function obj = addBackground(obj, varargin)

API/projectClass/resolutionsClass.m

@@ -134,7 +134,7 @@ function setResolution(obj, row, varargin)
             % Changes the value of a given resolution in the table. Expects the 
             % index or name of resolution and keyword/value pairs to set. 
             %
-            % resolution.setResolution(1, 'name', 'res 1', 'type', 'constant', 'value1', 'param_name');
+            % resolution.setResolution(1, 'name', 'resolution 1', 'type', 'constant', 'value1', 'param_name');
             if isText(row)
                 row = obj.resolutions.findRowIndex(row, obj.getNames(), 'Unrecognised resolution');
             elseif isnumeric(row)