Renames target functions and minimisers (#176)

* Renames "standardTF" to "nonPolarisedTF"

* Tidies up Paramonte functions

* Tidies up code in minimisers

* Renames "NSMain" as "NS"

* Updates paths

* Addresses review comments

3rdParty/paramonte/pmLogFunction.m

@@ -16,21 +16,16 @@
 
         function logFuncVal = get(obj,pars)
 
-            problem = obj.problemDef;
-            control = obj.controls;
-            cells = obj.problemDefCells;
-            limits = obj.problemDefLimits;
-            priors = obj.priors;
-
+            problem = obj.problemDef;            
             problem.fitpars = pars;
 
-            if obj.scaled 
+            if obj.scaled
                 problem = unscalePars(problem);
             end
 
-            problem = unpackparams(problem,control);
+            problem = unpackparams(problem,obj.controls);
 
-            [outProblem,results] = reflectivityCalculation_mex(problem,cells,limits,control);
+            [outProblem,~] = reflectivityCalculation_mex(problem,obj.problemDefCells,obj.controls);
             chi = outProblem.calculations.sum_chi;
             logFuncVal = -chi/2;
 
@@ -42,14 +37,10 @@
             % Now apply the priors where necessary. 
             priorfun = @(th,mu,sig) sum(((th-mu)./sig).^2);
 
-            val = priorfun(problem.fitpars,priors(:,1),priors(:,2));
-
-            logFuncVal = logFuncVal + val;
+            val = priorfun(problem.fitpars,obj.priors(:,1),obj.priors(:,2));
 
-            %end
-
+            logFuncVal = logFuncVal + val;           
 
         end   
     end
 end
-

3rdParty/paramonte/pmPars.m

@@ -1,20 +1,15 @@
 classdef pmPars < handle
 
-
-properties
-
-    covMat = []
-    delayedRejectionCount = []
-    name = ''
-    scalefactor = 1e-3
-    chainSize = 10000
-    chainTrim = 0.25    % Use only the last part of chain...
-    mpi = false;
-
-end
-
-
-
-
+    properties
+
+        covMat = []
+        delayedRejectionCount = []
+        name = ''
+        scalefactor = 1e-3
+        chainSize = 10000
+        chainTrim = 0.25    % Use only the last part of chain...
+        mpi = false;
+
+    end
 
 end

3rdParty/paramonte/processParamonteRuns.m

@@ -1,16 +1,13 @@
-
-function [outProblemDef,result,pmpd] = processFits(problem,controls,chainName,chainTrim)
+function [outProblemDef,result,pmpd] = processParamonteRuns(problem,controls,chainName,chainTrim)
 
 % problem = load(name);
 % problem = problem.twentyMNProblem;
 % 
 % controls = controlsClass();
 
-[problemDef,problemDefCells,problemDefLimits,priors,controls] = parseClassToStructs(problem,controls);
-problemDefInput = problemDef;
+[problemDef,problemDefCells,problemDefLimits,~,controls] = parseClassToStructs(problem,controls);
 
 [problemDef,fitNames] = packparams(problemDef,problemDefCells,problemDefLimits,controls.checks);
-nDims = length(problemDef.fitpars);
 
 pm = paramonte();
 pmpd = pm.ParaDRAM();
@@ -50,7 +47,6 @@
 [problemDef,outProblemStruct,result,bayesResults] = processBayes(bayesOutputs,allProblem);
 bayesResults.chain = unscaledChain;
 
-
 result = parseResultToStruct(outProblemStruct,result);
 
 if isfield(problemDef,'fitpars')

3rdParty/paramonte/runParamonte.m

@@ -40,7 +40,7 @@
 logFunc.NDIM = nDims;
 logFunc.scaled = true;
 
-% Create a paraMonte object
+% Create a paramonte object
 pm = paramonte();
 
 % create a ParaDRAM simulation object

3rdParty/paramonte/scalePars.m

@@ -1,10 +1,11 @@
 function problem = scalePars(problem)
 
-
 limits = problem.fitconstr;
 vals = problem.fitpars;
 
 scaled = (vals(:) - limits(:,1))./(limits(:,2)-limits(:,1));
 
 problem.fitpars = scaled;
-%problem.unscaledPars = vals;
+%problem.unscaledPars = vals;
+
+end

3rdParty/paramonte/unscalePars.m

@@ -5,4 +5,6 @@
 
 unscaled = (scaled.*(limits(:,2)-limits(:,1)))+limits(:,1);
 
-problem.fitpars = unscaled;
+problem.fitpars = unscaled;
+
+end

addPaths.m

@@ -37,7 +37,7 @@
     fullfile(root,'minimisers','DE');
     %fullfile(root,'minimisers','mcmcstat_new');
     %fullfile(root,'minimisers','mcmcstat_new','src');
-    fullfile(root,'minimisers','NSMain');
+    fullfile(root,'minimisers','NS');
 
     fullfile(root,'targetFunctions' );
     fullfile(root,'targetFunctions','common');
@@ -50,7 +50,7 @@
     fullfile(root,'targetFunctions','common','resampleLayers');
     fullfile(root,'targetFunctions','common','resolutionFunctions','simpleGaussian'); 
 
-    fullfile(root,'tests','standardTFReflectivityCalculation');
+    fullfile(root,'tests','nonPolarisedTFReflectivityCalculation');
     fullfile(root,'tests','domainsTFReflectivityCalculation');
     fullfile(root,'tests','testCommonFunctions');
     fullfile(root,'tests','testProjectConversion');

compile/reflectivityCalculation/setCompilePaths.m

@@ -1,4 +1,4 @@
-function setCompilePaths(which)
+function setCompilePaths()
 
 % This function sets the paths for compiling, depending on whether a mex
 % file of source code is being generated.

minimisers/DREAM/diagnostics/raftery.m

@@ -103,7 +103,7 @@
         q = 0.0;
         i1 = find(runs == 0); i2 = find(runs == 1);
         ct1 = size(i1);   ct2 = size(i2);
-        if (ct1+ct2 ~= n), error('raftery needs 0s and 1s in runs'); end;
+        if (ct1+ct2 ~= n), error('raftery needs 0s and 1s in runs'); end
         work = runs;
         q = sum(runs);
         q = q/n;

minimisers/DREAM/functions/DREAM.m

@@ -193,7 +193,7 @@
 
 
         % Store the model simulations (if appropriate)
-        storeDREAMResults ( DREAMPar , fx , Meas_info , 'a+' );
+        storeDREAMResults(DREAMPar,fx,Meas_info,'a+');
     end
 
     % Check whether we update the crossover values

minimisers/DREAM/functions/gelman.m

@@ -25,7 +25,7 @@
     % Step 2: Compute the variance of the various chain
     for zz = 1:DREAMPar.N
         var_chain(zz,:) = var(chain(:,:,zz));
-    end;
+    end
 
     % Step 2: Calculate the average of the within _chainuence variances
     W = mean(var_chain);

minimisers/DREAM/functions/initializeDREAM.m

@@ -50,7 +50,7 @@
 X = [x log_PR_x(:) log_L_x];
 
 % Store the model simulations (if appropriate)
-% storeDREAMResults ( DREAMPar , fx , Meas_info , 'w+' );
+% storeDREAMResults(DREAMPar,fx,Meas_info,'w+');
 
 % Set the first point of each of the DREAMPar.N chain equal to the initial X values
 chain(1,1:DREAMPar.d+2,1:DREAMPar.N) = reshape(X',1,DREAMPar.d+2,DREAMPar.N);

minimisers/DREAM/functions/initializeDREAMForRAT.m

@@ -36,7 +36,7 @@
     otherwise
 
         error('unknown initial sampling method');
-end;
+end
 
 % If specified do boundary handling ( "Bound","Reflect","Fold")
 if isfield(Par_info,'boundhandling')
@@ -54,7 +54,7 @@
 X = [x log_PR_x log_L_x];
 
 % Store the model simulations (if appropriate)
-storeDREAMResults ( DREAMPar , fx , Meas_info , 'w+' );
+storeDREAMResults(DREAMPar,fx,Meas_info,'w+');
 
 % Set the first point of each of the DREAMPar.N chain equal to the initial X values
 chain(1,1:DREAMPar.d+2,1:DREAMPar.N) = reshape(X',1,DREAMPar.d+2,DREAMPar.N);

minimisers/DREAM/functions/multrnd.m

@@ -83,13 +83,11 @@
         m = 1;
     else
         error('You need to input at least two arguments.');
-        return,
     end
 end
 
 if (length(n)~=1) | (fix(n) ~= n) | (n < 0)
    error('n must be a positive integer.');
-   return,
 end
 
 P = sum(p);

minimisers/DREAM/functions/ratDREAM.m

@@ -218,7 +218,7 @@
         % store the distance between the simualted and observed signatures
 
         % Store the model simulations (if appropriate)
-        % storeDREAMResults ( DREAMPar , fx , Meas_info , 'a+' );
+        % storeDREAMResults(DREAMPar,fx,Meas_info,'a+');
     end
 
     % Check whether we update the crossover values

minimisers/DREAM/functions/storeDREAMResults.m

@@ -1,4 +1,4 @@
-function storeDREAMResults ( DREAMPar , fx , Meas_info , id )
+function storeDREAMResults(DREAMPar,fx,Meas_info,id)
 % Stores the results of DREAM to binary files
 
 % Append current model simulations of X to file "fx.bin"
@@ -9,4 +9,4 @@ function storeDREAMResults ( DREAMPar , fx , Meas_info , id )
     fwrite(fid_fx,fx,'double');
     % Now close file
     fclose(fid_fx);
-end;
+end

minimisers/NSMain/calcEllipsoid.m → minimisers/NS/calcEllipsoid.m

@@ -44,7 +44,7 @@
 
 % check condition number of C (eps = 2.2204e-16)
 if rcond(C)<eps || isnan(rcond(C)) 
-    if DEBUG; fprintf('bad condition number!\n'); end;
+    if DEBUG; fprintf('bad condition number!\n'); end
     flag = 1;
     return;
 end

minimisers/NSMain/drawMCMC.m → minimisers/NS/drawMCMC.m

@@ -1,4 +1,4 @@
-function [sample, logL] = drawMcmc(livepoints, cholmat, logLmin, ...
+function [sample, logL] = drawMCMC(livepoints, cholmat, logLmin, ...
     prior, data, likelihood, model, Nmcmc, parnames, extraparvals)
 % This function will draw a multi-dimensional sample from the prior volume
 % for use in the nested sampling algorithm. The new point will have a

minimisers/NSMain/mchol.m → minimisers/NS/mchol.m

@@ -68,34 +68,34 @@
 E=zeros(n);
 theta = zeros(n);
 
-for j=1:n,
+for j=1:n
     bb=[1:j-1];
     ee=[j+1:n];
 
     %
     %  Calculate the jth row of L.  
     %
-    if (j > 1),
+    if (j > 1)
         L(j,bb)=C(j,bb)./diag(D(bb,bb))';
-    end;
+    end
     %
     %  Update the jth column of C.
     %
-    if (j >= 2),
-        if (j < n), 
+    if (j >= 2)
+        if (j < n)
             C(ee,j)=G(ee,j)-(L(j,bb)*C(ee,bb)')';
-        end;
+        end
     else
         C(ee,j)=G(ee,j);
-    end;
+    end
     %
     % Update theta. 
     %
     if (j == n)
         theta(j)=0;
     else
         theta(j)=max(abs(C(ee,j)));
-    end;
+    end
     %
     %  Update D
     %
@@ -119,7 +119,7 @@
     C(ind)=C(ind)-(1/D(j,j))*C(ee,j).^2;
 
 
-end;
+end
 
 %
 % Put 1's on the diagonal of L
@@ -143,7 +143,7 @@
     pneg=L'\rhs;
 else
   pneg=[];
-end;
+end
 
 
 return

minimisers/NSMain/nsIntraFun.m → minimisers/NS/nsIntraFun.m

@@ -2,14 +2,13 @@
 
     problemDef = data{1};
     controls = data{2};
-    problemDefLimits = data{3};
     problemDefCells = data{4};
 
     % Removed use of cells....
     problemDef.fitpars = p;
 
     problemDef = unpackparams(problemDef,controls);
-    [problemDef,result] = reflectivityCalculation(problemDef,problemDefCells,controls);
+    [problemDef,~] = reflectivityCalculation(problemDef,problemDefCells,controls);
 
     fval = (-problemDef.calculations.sum_chi/2); 
 end

minimisers/NSMain/parseBayesResults.m → minimisers/NS/parseBayesResults.m

@@ -1,4 +1,4 @@
-function results = parseBayesResults(chain,problemDef,problemDefCells,problemDefLimits,controls)
+function results = parseBayesResults(chain,problemDef,problemDefCells,controls)
 debug = 1;
 
 numPars = size(chain,2)-1;

minimisers/NSMain/splitEllipsoid.m → minimisers/NS/splitEllipsoid.m

@@ -72,7 +72,7 @@
 
     % check flags
     if flag1 || flag2
-        if DEBUG; fprintf('CANT SPLIT!!\n'); end;
+        if DEBUG; fprintf('CANT SPLIT!!\n'); end
         nosplit = 1;
         break
     end
@@ -137,14 +137,14 @@
 
     end
 
-    u1 = []; u2 = []; mu1 = []; mu2 = [];;
+    u1 = []; u2 = []; mu1 = []; mu2 = [];
     n1 = m1;
     n2 = m2;
 
     u1 = u1new(1:n1,:);
     u2 = u2new(1:n2,:);
 
-    u1new = []; u2new = [];;
+    u1new = []; u2new = [];
 
     % update counter
     counter = counter + 1;
@@ -172,6 +172,6 @@
 VE1 = temp_VE1(idx);
 VE2 = temp_VE2(idx);
 
-if DEBUG; fprintf('SPLIT ELLIPSOID: min F(S) = %f\n', minFS); end;
+if DEBUG; fprintf('SPLIT ELLIPSOID: min F(S) = %f\n', minFS); end
 
 end