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mm_argold.m
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function varargout = mm_arg(typeAnal, argfile)
% Get arguments for MLM/SVD analysis.
% Format varargout = mm_arg(typeAnal, argfile)
% nbsub = Number of subject or space domain.
% Pimg = Beta images for MLM, Data images for SVD.
% Res = estimated residual variance image.
% Mask = analysis mask image indicating which voxels were
% included in the analysis
% gcsd = global result directory.
% cwd = current working directory (dir contening the model)
% csd = current saving directory for each space domain.
% fname = Eigen image basename.
% Xc = Contrast
% gsf = global scaling factor.
% Filter = Sparse temporal smoothing matrix.
% leig = Eigen Images to save.
% paramsAnal = Parameters for the anlysis :
%================================================================================
%- Copyright (C) 1997-2002 CEA
%- This software and supporting documentation were developed by
%- CEA/DSV/SHFJ/UNAF
%- 4 place du General Leclerc
%- 91401 Orsay cedex
%- France
%================================================================================
switch typeAnal
case 'MLM'
[nbsub, Pimg, Res, Mask, Yimg, gcsd, cwd, csd, xC, fname, gsf, Filter, leig, paramsAnal,W] = ...
sf_arg_mlm(argfile);
varargout = {nbsub, Pimg, Res, Mask, Yimg,gcsd, cwd, csd, xC, fname, gsf, Filter, ...
leig, paramsAnal,W};
case 'SVD'
[nbsub, Pimg, Res, Mask, gcsd, cwd, csd, xC, Filter, paramsAnal, fname, leig, gsf,W] = ...
sf_arg_svd(argfile);
varargout = {nbsub, Pimg, Res, Mask, gcsd, cwd, csd, xC, Filter, paramsAnal, ...
fname, leig, gsf,W};
otherwise
error('unknown type of analysis')
end
%=========================================================================================================
function [nbsub, Pimg, Res, Mask, Yimg ,gcsd, cwd, csd, xC, fname, gsf, Filter, leig, paramsAnal,W] = ...
sf_arg_mlm(argfile)
%- if the argfile is not given then the interactive mode is use.
% argfile format is : one line per subject whith 3 variables cwd, csd and d delimited by a semicolon ";".
%
% input argument for the multivariate analysis.
% FORMAT [nbsub, Pimg, Res,Mask] = mm_arg(argfile)
%
% nbsub : number of subject
% Pimg : images filename, input data for the mlm computation
% cwd, csd : SPM.mat directory, directory for saving results.
% argfile : parameter file
% cwd, csd : working and saving directory.
% Xc : contrast.
% paramsAnal : on what is the eigen value decomposition done :
% - divideByRessd = 1 : divide by the residual standard deviation
% - temporalFilter = 1 : apply the temporal filter to data first
% fname : generic name for the eigen images
% leig : liste of number for which the corresponding eigenimages will be constructed
%=========================================================================================================
if isempty(argfile) %- INTERACTIVE MODE
%- Set the number of subjects and gcsd
%--------------------------------------------------------------------
gcsd = ui_arg(mm_load_arg('god'),'god');
nbsub = ui_arg(mm_load_arg('nbsub'),'nbsub');
%- Set working directory for each subject and names for eigenimages
%--------------------------------------------------------------------
for sub=1:nbsub
input = sprintf('for space domain %d ',sub);
arg = mm_load_arg('mod');
arg.mod.input.prompt = ['model ' input];
cwd{sub} = spm_str_manip(ui_arg(arg,'mod'),'H');
if nbsub > 1
arg = mm_load_arg('iod');
arg.iod.input.prompt = ['dir ' input];
csd{sub} = ui_arg(arg,'iod');
else
csd{1} = gcsd;
end
arg = mm_load_arg('neig');arg.neig.input.prompt=[arg.neig.input.prompt input];
fname{sub} = ui_arg(arg,'neig');
arg = mm_load_arg('DefImages');arg.DefImages.input.prompt=['Images ' input];
defImg=ui_arg(arg,'DefImages');
if defImg
load(fullfile(cwd{sub},'SPM.mat'));
Yimg{sub}=char(SPM.xY.VY.fname);
else
arg=mm_load_arg('Image');
arg.image.input.prompt=['Images ' input] ;
Yimg{sub} = ui_arg(arg,'Image');
end
end
% csd = cwd; %- by default working and saving directory are the same.
%- Load the design Matrix, (same for all subjects) and the temporal filter
%--------------------------------------------------------------------
Pmat = fullfile(cwd{1},'SPM.mat');
load(Pmat);
xX=SPM.xX
Filter = SPM.xX.K;
W =SPM.xX.W;
%- Load the contrast
%--------------------------------------------------------------------
%Pcon = ui_arg(mm_load_arg('Cm'),'Cm'); %- get name of xCon matrix
%load(Pcon,'xCon');
arg = mm_load_arg('Ci'); %- Ci : a specific contrast
arg.Ci.input.xX = xX;
arg.Ci.input.xCon = SPM.xCon;
arg = ui_arg(arg,'Ci');
[Ic,xCon] = deal(arg{:});
xC = xCon(Ic);
%try, save(Pcon,'xCon'); catch, fprintf('can not save xCon : check permissions\n'); end;
clear arg;
else %- BATCH MODE
[ggcsd cwd csd d fname] = textread(argfile,'%s %s %s %d %s','commentstyle','matlab');
gcsd=ggcsd{1};
nbsub = size(cwd,1);
load(fullfile(cwd{1},'xCon.mat'),'xCon');
load(fullfile(cwd{1},'SPM.mat'),'xX');
Filter=xX.K;
d = d(1,1);
if isempty(d), d=1; end
xC = xCon(d);
%------ SOME DEFAULTS VALES - TO BE PUT IN A DEFAULT FILE ?
leig = 1:5; % defaults that should
end
%- Set parameters : filtering and divide by std deviation
%--------------------------------------------------------------------
paramsAnal.divideByRessd = 1;
paramsAnal.temporalFilter = 1;
paramsAnal.resContSp = 0;
%- set the parameters: Pimg, Res and Mask filenames.
%--------------------------------------------------------------------
for sub = 1:nbsub
load(fullfile(cwd{sub},'SPM.mat'));
Vbeta=SPM.Vbeta;
Pimg{sub} = [repmat([cwd{sub}, filesep],length(Vbeta),1),char(Vbeta.fname)];
Mask{sub} = fullfile(cwd{sub}, 'mask.img');
VResMS =SPM.VResMS
Res{sub} = fullfile(cwd{sub},char(VResMS.fname));
gsf{sub} = SPM.xGX.gSF;
end
if isempty(argfile) %- NOT IN BATCH MODE
%- Set number of eigenimages
%--------------------------------------------------------------------
arg = mm_load_arg('leig');
arg.leig.input.def=['[1:' num2str(min(5,size(Pimg{1},1))) ']'];
leig = ui_arg(arg,'leig');
end
clear Vbeta,VResMS;
%=========================================================================================================
function [nbsub, Pimg, Res, Mask, gcsd, cwd, csd, xC, Filter, paramsAnal, fname, leig, gsf,W] = ...
sf_arg_svd(argfile);
%
% paramsAnal : on what is the eigen value decomposition done :
% - divideByRessd = 1 : divide by the residual standard deviation
% - temporalFilter = 1 : apply the temporal filter to data first
% fname : generic name for the eigen images
% leig : liste of number for which the corresponding
% eigenimages will be constructed
%
%=========================================================================================================
paramsAnal = struct(...
'divideByRessd', 1, ...
'temporalFilter', 1, ...
'resContSp', 0 ...
);
if isempty(argfile) %- INTERACTIVE MODE
%- Set the number of subjects and gcsd
%--------------------------------------------------------------------
gcsd = ui_arg(mm_load_arg('god'),'god');
nbsub = ui_arg(mm_load_arg('nbsub'),'nbsub');
%- Set input and output directories and names for eigenimages - get gsf
%--------------------------------------------------------------------
for sub=1:nbsub
input=sprintf('for space domain %d ',sub);
arg=mm_load_arg('mod');
arg.mod.input.prompt=['model ' input];
cwd{sub} = spm_str_manip(ui_arg(arg,'mod'),'H');
load(fullfile(cwd{sub},'SPM.mat'));
if nbsub > 1
arg=mm_load_arg('iod');arg.iod.input.prompt = ['result directory ' input];
csd{sub} = ui_arg(arg,'iod');
else
csd{1} = gcsd;
end
arg = mm_load_arg('DefImages');arg.DefImages.input.prompt=['Images ' input];
defImg=ui_arg(arg,'DefImages');
if defImg
Pimg{sub}=char(SPM.xY.VY.fname);
else
arg=mm_load_arg('Image');
arg.image.input.prompt=['Images ' input];
Pimg{sub} = ui_arg(arg,'Image');
end
arg = mm_load_arg('neig'); arg.neig.input.prompt=[arg.neig.input.prompt input];
fname{sub} = ui_arg(arg,'neig');
gsf{sub} = SPM.xGX.gSF;
Mask{sub} = fullfile(cwd{sub},'mask.img');
Res{sub} = fullfile(cwd{sub},SPM.VResMS.fname);
end
%- Load design matrix of the first subject
%--------------------------------------------------------------------
load(fullfile(cwd{1},'SPM.mat'));
xX = SPM.xX;
Filter = SPM.xX.K;
W = SPM.xX.W;
%- Load the contrast
%--------------------------------------------------------------------
arg=mm_load_arg('Ci');
arg.Ci.input.xX=SPM.xX;
arg.Ci.input.xCon=SPM.xCon;
arg = ui_arg(arg,'Ci');
[Ic,SPM.xCon] = deal(arg{:});
xC = SPM.xCon(Ic);
save(fullfile(cwd{1},'SPM.mat'),'SPM'),;
clear arg SPM
%- Get images + Mask + ResMS per subject
%- %--------------------------------------------------------------------
%- for sub=1:nbsub
%-
%- Mask{sub} = fullfile(cwd{sub},'mask.img');
%- load(fullfile(cwd{sub},'SPM.mat'),'VResMS');
%- Res{sub} = fullfile(cwd{sub},char(VResMS));
%- end
%-
%- Set number of eigenimages
%--------------------------------------------------------------------
arg = mm_load_arg('leig');
arg.leig.input.def=['[1:' num2str(min(5,size(Pimg{1},1))) ']'];
leig = ui_arg(arg,'leig');
%- Set parameters : filtering and divide by std deviation
%--------------------------------------------------------------------
paramsAnal.resContSp = ui_arg(mm_load_arg('Pres'),'Pres');
paramsAnal.divideByRessd = ui_arg(mm_load_arg('dvres'),'dvres');
paramsAnal.temporalFilter = ui_arg(mm_load_arg('filter'),'filter');
else %- BATCH MODE
[gcsdr cwd csd d fname ImgDir ImgName paramsAnal.temporalFilter paramsAnal.divideByRessd paramsAnal.resContSp ] ...
= textread(argfile,'%s %s %s %d %s %s %s %d %d %d','commentstyle','matlab');
nbsub = size(cwd,1);
gcsd=gcsdr{1}
load(fullfile(cwd{1},'SPM.mat'),'xX');
Filter = xX.K;
load(fullfile(cwd{1},'xCon.mat'),'xCon');
d = d(1,1);
if isempty(d), d=1; end
xC = xCon(d);
clear xX xCon
for sub=1:nbsub
[DIn Dsz] =sf_strsplit(ImgName{sub},';');
for i=1:Dsz
pimg{i}=spm_get('Files',ImgDir{sub},str_clean(DIn{i}));
end
Pimg{sub} = cat(1,pimg{:});
Mask{sub} = fullfile(cwd{sub},'mask.img');
load(fullfile(cwd{sub},'SPM.mat'),'VResMS');
Res{sub} = fullfile(cwd{sub},char(VResMS));
load(fullfile(cwd{sub},'SPMcfg.mat'), 'xGX');
gsf{sub} = xGX.gSF;
clear xGX;
end
leig = 1:5;
end
function [res,n]=sf_strsplit(str,fs)
%- Split the string str into cell array elements a{1}, a{2},
%- a{n}, and return n. The separation will be
%- done with the expression fs
%-
if length(fs) >1
disp('error')
return;
end
id=find(str==fs);
if isempty(id)
n=1;
res{n}=str;
return
end
sz=length(str);
cur=1;
n=1;
for i=1:length(id)
tmp=str(cur:id(i)-1);
if length(tmp)
res{n}=str(cur:id(i)-1);
n=n+1;
end
cur=id(i)+1;
if (id(i)==sz)
break;
end
end
if id(length(id))<sz
res{n}=str(id(length(id))+1:sz);
else n=n-1;
end