Merge pull request #50 from SnPM-toolbox/next_release

Preparing SnPM 13.1.07 release

README.txt

@@ -4,6 +4,14 @@ This file describes the bugs that have been reported, along with the appropriate
 Updated versions of appropriate SnPM functions are available from in the snpm13_updates: 
 http://warwick.ac.uk/snpm/distribution/snpm13_updates
 
+--- SnPM 13.1.07 ---
+ * fix: Add GPL licence. 
+ * fix: decrease lower bound on number of subject for approximate perms.
+ * enh: Table: Create TabDat structure expected by spm_list, allowing access to all peak/cluster results, even if there are too many for the SnPM results page. 
+ * enh: Apply mlint suggestions for better compatibility with MATLAB Compiler and Octave. 
+ * test: use latest available ground truth if perfect match for matlab version is not available.
+ * Version 13.1.07
+
 --- SnPM 13.1.06 ---
  * fix: ANOVA between group "all zero"/"all equal" options were flipped in the menu of the matlabbatch.
  * fix: snpm_cp can now handle XYZ locations in a 1D space.

config/snpm_bch_ui_Corr1S.m

@@ -1,4 +1,4 @@
-function snpmui = snpm_bch_ui_Corr1s
+function snpmui = snpm_bch_ui_Corr1S
 % Example script that creates an cfg_exbranch to sum two numbers. The
 % inputs are entered as two single numbers, the output is just a single
 % number.

snpm.m

@@ -56,7 +56,7 @@
 if nargin == 0, Action='Init'; end
 
 
-if strcmp(lower(Action),lower('Init'))
+if strcmpi(Action,'Init')
 %=======================================================================
 global MODALITY
 if isempty(spm_figure('FindWin','Menu'))
@@ -66,7 +66,7 @@
 	clc
 end
 % Add test code to Matlabpath
-if ~exist('test_oneSample', 'file')
+if ~exist('test_oneSample', 'file') && ~isdeployed
     addpath(fullfile(spm_file(which('snpm'), 'path'), 'test'));
     addpath(fullfile(spm_file(which('snpm'), 'path'), 'test', 'common'));
 end
@@ -77,7 +77,7 @@
 spm_help('!Disp','snpm.m','','Graphics',snpm('Ver'))
 snpm('CreateMenuWin')
 
-elseif strcmp(lower(Action),lower('AsciiWelcome'))
+elseif strcmpi(Action,'AsciiWelcome')
 %=======================================================================
 disp( ' ___       ____  __  __                                                  ')
 disp( '/ __) ___ (  _ \(  \/  )  Statistical nonParametric Mapping toolbox      ')
@@ -86,12 +86,12 @@
 fprintf('\n')
 
 
-elseif strcmp(lower(Action),lower('Ver'))
+elseif strcmpi(Action,'Ver')
 %=======================================================================
 % snpm('Ver')
 varargout = {SnPMver};
 
-elseif strcmp(lower(Action),lower('Colour'))
+elseif strcmpi(Action,'Colour')
 %=======================================================================
 % snpm('Colour')
 %-----------------------------------------------------------------------

snpm_P_FDR.m

@@ -84,7 +84,7 @@
   Cnj_n = abs(n);  % Inf on Global Null
 end
 
-if isempty(Z), 
+if isempty(Z)
   AllP = 1;
   Z = Ps;
   STAT = 'P';

snpm_STcalc.m

@@ -121,7 +121,7 @@
   df    = varargin{8};
 
   %- determine whether the user input is a p value or real threshold
-  if (ST_Ut < 1),
+  if (ST_Ut < 1)
     ST_Ut = spm_invTcdf(1-ST_Ut,df);
   end 
 

snpm_abline.m

@@ -20,7 +20,7 @@
 % Id: snpm_abline.m  SnPM13 2013/10/12
 % Thomas Nichols
 
-if (nargin==2) & isstr(b)
+if (nargin==2) && ischar(b)
   b = lower(b);
 else
 
@@ -36,11 +36,11 @@
 XX=get(gca,'Xlim');
 YY=get(gca,'Ylim');
 
-if isstr(b) & (b=='h')
+if ischar(b) && (b=='h')
 
   g=line(XX,[a a],'LineStyle',':',varargin{:});
 
-elseif isstr(b) & (b=='v')
+elseif ischar(b) && (b=='v')
 
   g=line([a a],YY,'LineStyle',':',varargin{:});
 

snpm_check_nperm.m

@@ -1,4 +1,4 @@
-function snpm_check_perm(nPerm,TotPerm)
+function snpm_check_nperm(nPerm,TotPerm)
 %
 % Issue consistent warning messages about number of permutations
 %
@@ -13,11 +13,11 @@ function snpm_check_perm(nPerm,TotPerm)
 % specific warning about variability of Monte Carlo variation.
 
 str='';
-if nPerm<100 & nPerm>=0.9*TotPerm
+if nPerm<100 && nPerm>=0.9*TotPerm
   str = sprintf(['Very few (%d) permutations used, nonparametric P-values are very coarse (but exact).\n'...
 		 'Smallest possible P-value is %0.4f.'],nPerm,1/nPerm);
   id = 'SnPM:VeryFewPermsCoarseExactPValues';
-elseif nPerm<100 & nPerm<0.9*TotPerm
+elseif nPerm<100 && nPerm<0.9*TotPerm
   str = sprintf(['Very few (%d) permutations used, nonparametric P-values are very coarse will vary \n'...
 		 'substantially over repeated re-analyses.'...
 		 'Smallest possible P-value is %0.4f.'],nPerm,1/nPerm);

snpm_combo.m

@@ -24,7 +24,7 @@
 if nargin == 0, Action='Init'; end
 
 
-if strcmp(lower(Action),lower('Init'))
+if strcmpi(Action,'Init')
 %=======================================================================
 global MODALITY
 if isempty(spm_figure('FindWin','Menu'))
@@ -41,7 +41,7 @@
 
 
 
-elseif strcmp(lower(Action),lower('AsciiWelcome'))
+elseif strcmpi(Action,'AsciiWelcome')
 %=======================================================================
 disp( ' SnPM Combo Toolbox                                   ')
 disp( ' -----------------------------------------------------')
@@ -52,7 +52,7 @@
 
 
 
-elseif strcmp(lower(Action),lower('CreateMenuWin'))
+elseif strcmpi(Action,'CreateMenuWin')
 %=======================================================================
 %close(findobj(get(0,'Children'),'Tag','SnPM Menu'))
 
@@ -113,7 +113,7 @@
 
 
 
-elseif strcmp(lower(Action),lower('Colour'))
+elseif strcmpi(Action,'Colour')
 %=======================================================================
 % snpm_combo('Colour')
 %-----------------------------------------------------------------------

snpm_combo_pp.m

@@ -159,7 +159,7 @@ function snpm_combo_pp(CWD, job)
 
 %-Compute critical threshold for level alpha test
 %-----------------------------------------------------------------------
-if alpha < 1;
+if alpha < 1
 	c=ceil((1-alpha)*nPerm);
 	C_MaxT=StMaxT(c);
 else
@@ -175,7 +175,7 @@ function snpm_combo_pp(CWD, job)
 % significant.
 %-bST flags whether spatial extent information was collected.)
 bSpatEx = bST & exist(fullfile(CWD,'SnPM_ST.mat'))==2;
-if bSpatEx & (C_MaxT <= ST_Ut) & (alpha ~= 1)
+if bSpatEx && (C_MaxT <= ST_Ut) && (alpha ~= 1)
   str = 'Voxelwise corrected threshold = %g, which is smaller ';
   str = [str 'than minimum saved suprathreshold information (%g)'];
   str = [str '\nAll results significant voxelwise.'];
@@ -232,7 +232,7 @@ function snpm_combo_pp(CWD, job)
             clear pU_C_MaxT
         end
         clear pU_ST_Ut
-        if (primaryThresh < 1), 
+        if (primaryThresh < 1) 
             primaryThresh = spm_invTcdf(1-primaryThresh,df); 
         end
     end
@@ -328,7 +328,7 @@ function snpm_combo_pp(CWD, job)
 	ClInfo = zeros(8,size(SnPM_ST,2));
 	ClInfoEnd = 0;
 	for i = nPerm:-1:1
-                if (rem(i,10)==0)
+        if (rem(i,10)==0)
 		  fprintf('\b\b\b\b%-4u',i)
 		  drawnow
 		end
@@ -357,7 +357,7 @@ function snpm_combo_pp(CWD, job)
                            % all cluster info
                            tmpCLoc  = (tmp==j);
                            subsubST = subSnPM_ST(:,tmpCLoc);
-                           [tmpPeak tmpPLoc] = max(subsubST(4,:));
+                           [tmpPeak, tmpPLoc] = max(subsubST(4,:));
                            tmpXYZ   = subsubST(1:3,tmpPLoc);
                            tmpMass  = sum((subsubST(4,:)-ST_Ut).^mTheta);
                            tmpVec   = [tmpXYZ; tmpPeak; tmpCS(j); tmpMass; j; i];
@@ -400,12 +400,12 @@ function snpm_combo_pp(CWD, job)
 
 	fprintf('Calculating corr p-values for each voxel / cluster\n');
 	for ip = 1:(nPerm-1)
-           if rem(ip,50)==0, fprintf('.'), end
-           Qvox = find(ClInfo(4,:)>StMaxT(ip));
-           CorrPs(1,Qvox) = (nPerm-ip)/nPerm;
-           Qcl  = find(ClInfo(5,:)>StMaxSTCS(ip));
-           CorrPs(2,Qcl) = (nPerm-ip)/nPerm;
-        end
+        if rem(ip,50)==0, fprintf('.'), end
+        Qvox = find(ClInfo(4,:)>StMaxT(ip));
+        CorrPs(1,Qvox) = (nPerm-ip)/nPerm;
+        Qcl  = find(ClInfo(5,:)>StMaxSTCS(ip));
+        CorrPs(2,Qcl) = (nPerm-ip)/nPerm;
+    end
 	fprintf('.Done!\n');
 
 	fprintf('Calculating voxel-cluster combining functions\n');
@@ -432,9 +432,9 @@ function snpm_combo_pp(CWD, job)
            end
         end
 
-        [StMaxWf iStMaxWf] = sort(MaxWf);
-        [StMaxWt iStMaxWt] = sort(MaxWt);
-        [StMaxWm iStMaxWm] = sort(MaxWm);
+        [StMaxWf, iStMaxWf] = sort(MaxWf);
+        [StMaxWt, iStMaxWt] = sort(MaxWt);
+        [StMaxWm, iStMaxWm] = sort(MaxWm);
 	C_Wcomb = zeros(4,1);
 	if alpha < 1
 		C_Wcomb(1) = StMaxWf(c);
@@ -481,7 +481,7 @@ function snpm_combo_pp(CWD, job)
 
 
         % Corrected critical meta-statistic
-        [StMaxWa iStMaxWa] = sort(MaxWa);
+        [StMaxWa, iStMaxWa] = sort(MaxWa);
 	if alpha < 1
 		C_Wcomb(4) = StMaxWa(c);
 	else
@@ -524,7 +524,7 @@ function snpm_combo_pp(CWD, job)
 	for i = 1:nSTC
 		tQ = find(STCstats(6,:)==i);
 	     ttQ= find(ClInfo(7,:)==i & ClInfo(8,:)==1);
-		if ( STCS(i) > C_STCS | max(STCstats(4,tQ)) > C_MaxT | ...
+		if ( STCS(i) > C_STCS || max(STCstats(4,tQ)) > C_MaxT || ...
                      max(ComboF(iW,ttQ)) > C_Wcomb(iW))
 			Q        = [Q tQ];
 		end
@@ -560,7 +560,7 @@ function snpm_combo_pp(CWD, job)
 	tmp='voxels'; if bSpatEx, tmp='suprathreshold clusters'; end
 	text(0,0.93,sprintf(...
 		'No %s significant at alpha=%6.4f (corrected)',tmp,alpha));
-	if bSpatEx,
+	if bSpatEx
 	  ShowDist(MaxT,C_MaxT,MaxSTCS,C_STCS);
 	else	   
 	  ShowDist(MaxT,C_MaxT);
@@ -765,7 +765,7 @@ function snpm_combo_pp(CWD, job)
 %-----------------------------------------------------------------------
 r = 1;
 bUsed = zeros(size(STC_SnPMt));
-while max(STC_SnPMt.*(~bUsed)) & (y > 3)
+while max(STC_SnPMt.*(~bUsed)) && (y > 3)
 
 	[null, i] = max(STC_SnPMt.*(~bUsed));	% Largest t value
 	j         = find(STC_r == STC_r(i));	% Maxima in same region
@@ -812,7 +812,7 @@ function snpm_combo_pp(CWD, job)
 	for i = 1:length(k)
 	    d     = j(k(i));
 	    if min( sqrt( sum((STC_XYZ(:,D) - ...
-			STC_XYZ(:,d)*ones(1,size(D,2))).^2) ) ) > 8;
+			STC_XYZ(:,d)*ones(1,size(D,2))).^2) ) ) > 8
 		if length(D) < 3
 
 	text(tCol(5)+0.08,y,sprintf(Fmtst{5},Pt(d)),...