refactor

src/batches/stats/setBatchFactorialDesign.m

@@ -76,7 +76,7 @@
       rfxDir = getRFXdir(opt, nodeName, contrasts{1}, label);
       overwriteDir(rfxDir, opt);
 
-      assert(exist(fullfile(rfxDir, 'SPM.mat'), 'file') == 0);
+      assert(~checkSpmMat(rfxDir, opt));
 
       matlabbatch = returnOneWayAnovaBatch(matlabbatch, rfxDir);
 
@@ -89,16 +89,7 @@
 
         thisGroup = availableGroups{iGroup};
 
-        % grab subjects label from participants.tsv in raw
-        % and only keep those that are part of the requested subjects
-        %
-        % Note that this will lead to different results
-        % depending on the requested subejcts
-        %
-        participants = bids.util.tsvread(fullfile(opt.dir.raw, 'participants.tsv'));
-        subjectsInGroup = strcmp(participants.(groupColumnHdr), thisGroup);
-        subjectsLabel = regexprep(participants.participant_id(subjectsInGroup), '^sub-', '');
-        subjectsLabel = intersect(subjectsLabel, opt.subjects);
+        subjectsLabel = returnSubjectLabelInGroup(opt, groupColumnHdr, thisGroup);
 
         % collect all con images from all subjects
         for iSub = 1:numel(subjectsLabel)
@@ -183,16 +174,7 @@
   for iGroup = 1:numel(availableGroups)
 
     thisGroup = availableGroups{iGroup};
-
-    % grab subjects label from participants.tsv in raw
-    % and only keep those that are part of the requested subjects
-    %
-    % Note that this will lead to different results depending on the requested
-    % subejcts
-    %
-    subjectsInGroup = strcmp(participants.(groupColumnHdr), thisGroup);
-    subjectsLabel = regexprep(participants.participant_id(subjectsInGroup), '^sub-', '');
-    subjectsLabel = intersect(subjectsLabel, opt.subjects);
+    subjectsLabel = returnSubjectLabelInGroup(opt, groupColumnHdr, thisGroup);
 
     % collect all con images from all subjects
     for iSub = 1:numel(subjectsLabel)
@@ -220,6 +202,18 @@
 
 end
 
+function subjectsLabel = returnSubjectLabelInGroup(opt, groupColumnHdr, group)
+  % grab subjects label from participants.tsv in raw
+  % and only keep those that are part of the requested subjects
+  %
+  % Note that this will lead to different results depending on the requested subejcts
+  %
+  participants = bids.util.tsvread(fullfile(opt.dir.raw, 'participants.tsv'));
+  subjectsInGroup = strcmp(participants.(groupColumnHdr), group);
+  subjectsLabel = regexprep(participants.participant_id(subjectsInGroup), '^sub-', '');
+  subjectsLabel = intersect(subjectsLabel, opt.subjects);
+end
+
 function icell = allocateSubjectsContrasts(opt, subjectsLabel, conImages, iCon)
 
   icell(1).scans = {};
@@ -256,7 +250,7 @@
   rfxDir = getRFXdir(opt, nodeName, contrastName, thisGroup);
   overwriteDir(rfxDir, opt);
 
-  assert(exist(fullfile(rfxDir, 'SPM.mat'), 'file') == 0);
+  assert(~checkSpmMat(rfxDir, opt));
 
   icell(1).levels = 1;
 
@@ -342,11 +336,11 @@
   % TODO refactor
   participants = bids.util.tsvread(fullfile(opt.dir.raw, 'participants.tsv'));
 
-  model = opt.model.bm;
+  bm = opt.model.bm;
 
-  status = model.validateGroupBy(nodeName, participants);
+  status = bm.validateGroupBy(nodeName, participants);
 
-  [glmType, groupBy] = model.groupLevelGlmType(nodeName, participants);
+  [glmType, groupBy] = bm.groupLevelGlmType(nodeName, participants);
 
   % only certain type of model supported for now
   if ismember(glmType, {'unknown', 'two_sample_t_test'})
@@ -358,8 +352,8 @@
     return
   end
 
-  datasetLvlContrasts = model.get_contrasts('Name', nodeName);
-  datasetLvlDummyContrasts = model.get_dummy_contrasts('Name', nodeName);
+  datasetLvlContrasts = bm.get_contrasts('Name', nodeName);
+  datasetLvlDummyContrasts = bm.get_dummy_contrasts('Name', nodeName);
 
   if isempty(datasetLvlContrasts) && isempty(datasetLvlDummyContrasts)
     msg = sprintf('No contrast specified %s', commonMsg);

src/batches/stats/setBatchGroupLevelContrasts.m

@@ -26,14 +26,14 @@
 
   printBatchName('group level contrast estimation', opt);
 
-  model = opt.model.bm;
+  bm = opt.model.bm;
 
   participants = bids.util.tsvread(fullfile(opt.dir.raw, 'participants.tsv'));
 
-  groupColumnHdr = model.getGroupColumnHdrFromGroupBy(nodeName, participants);
+  groupColumnHdr = bm.getGroupColumnHdrFromGroupBy(nodeName, participants);
   availableGroups = getAvailableGroups(opt, groupColumnHdr);
 
-  [groupGlmType, groupBy] =  model.groupLevelGlmType(nodeName, participants);
+  [groupGlmType, groupBy] =  bm.groupLevelGlmType(nodeName, participants);
   switch groupGlmType
 
     case 'one_sample_t_test'
@@ -80,10 +80,10 @@
       % through the Edge filter.
       % Then generate the between group contrasts.
 
-      edge = model.get_edge('Destination', nodeName);
+      edge = bm.get_edge('Destination', nodeName);
       contrastsList = edge.Filter.contrast;
 
-      thisContrast = model.get_contrasts('Name', nodeName);
+      thisContrast = bm.get_contrasts('Name', nodeName);
 
       for j = 1:numel(contrastsList)
 
@@ -127,13 +127,13 @@
       if any(ismember(designMatrix, fieldnames(participants)))
         % TODO will this ignore the contrasts define at other levels
         % and not passed through the filter ?
-        edge = model.get_edge('Destination', nodeName);
+        edge = bm.get_edge('Destination', nodeName);
         contrastsList = edge.Filter.contrast;
       end
 
       for j = 1:numel(contrastsList)
 
-        thisContrast = model.get_contrasts('Name', nodeName);
+        thisContrast = bm.get_contrasts('Name', nodeName);
 
         spmMatFile = fullfile(getRFXdir(opt, nodeName, contrastsList{j}), 'SPM.mat');
 

src/batches/stats/setBatchSubjectLevelContrasts.m

@@ -28,20 +28,20 @@
   printBatchName('subject level contrasts specification', opt);
 
   spmMatFile = fullfile(getFFXdir(subLabel, opt), 'SPM.mat');
-  if noSPMmat(opt, subLabel, spmMatFile)
+  if ~checkSpmMat(dir, opt)
     return
   end
 
   load(spmMatFile, 'SPM');
 
-  model = opt.model.bm;
-  model.validateConstrasts();
+  bm = opt.model.bm;
+  bm.validateConstrasts();
 
   % Create Contrasts
   if nargin < 4 || isempty(nodeName)
-    contrasts = specifyContrasts(model, SPM);
+    contrasts = specifyContrasts(bm, SPM);
   else
-    contrasts = specifyContrasts(model, SPM, nodeName);
+    contrasts = specifyContrasts(bm, SPM, nodeName);
   end
 
   validateContrasts(contrasts);

src/batches/stats/setBatchSubjectLevelGLMSpec.m

@@ -36,7 +36,7 @@
     logger('ERROR', msg, 'filename', mfilename(), 'id', 'missingRawDir');
   end
 
-  opt.model.bm.getModelType();
+  bm = opt.model.bm;
 
   printBatchName('specify subject level fmri model', opt);
 
@@ -87,11 +87,11 @@
 
   fmri_spec.fact = struct('name', {}, 'levels', {});
 
-  fmri_spec.mthresh = opt.model.bm.getInclusiveMaskThreshold();
+  fmri_spec.mthresh = bm.getInclusiveMaskThreshold();
 
-  fmri_spec.bases.hrf.derivs = opt.model.bm.getHRFderivatives();
+  fmri_spec.bases.hrf.derivs = bm.getHRFderivatives();
 
-  fmri_spec.cvi = opt.model.bm.getSerialCorrelationCorrection();
+  fmri_spec.cvi = bm.getSerialCorrelationCorrection();
 
   %% List scans, onsets, confounds for each task / session / run
   subLabel = regexify(subLabel);
@@ -167,7 +167,7 @@
     % multicondition selection
     fmri_spec.sess(iSpmSess).cond = struct('name', {}, 'onset', {}, 'duration', {});
 
-    fmri_spec.sess(iSpmSess).hpf = opt.model.bm.getHighPassFilter();
+    fmri_spec.sess(iSpmSess).hpf = bm.getHighPassFilter();
 
   end
 
@@ -185,7 +185,7 @@
     matlabbatch{end + 1}.spm.stats.fmri_design = fmri_spec;
 
   else
-    node = opt.model.bm.get_root_node;
+    node = model.get_root_node();
 
     fmri_spec.mask = {getInclusiveMask(opt, node.Name, BIDS, subLabel)};
     matlabbatch{end + 1}.spm.stats.fmri_spec = fmri_spec;

src/batches/stats/setBatchTwoSampleTTest.m

@@ -184,7 +184,9 @@
   %   }
   % }
 
-  edge = opt.model.bm.get_edge('Destination', nodeName);
+  bm = opt.model.bm;
+
+  edge = bm.get_edge('Destination', nodeName);
 
   if isfield(edge, 'Filter') && ...
       isfield(edge.Filter, 'contrast')  && ...
@@ -195,12 +197,12 @@
   else
 
     % TODO?? can't imagine a 2 sample t-test with dummy contrasts
-    node = opt.model.bm.get_nodes('Name', nodeName);
+    node = bm.get_nodes('Name', nodeName);
 
     % if no specific dummy contrasts mentioned also include all contrasts from previous levels
     % or if contrasts are mentioned we grab them
     if isfield(node, 'Contrasts')
-      tmp = getContrastsList(opt.model.bm, nodeName);
+      tmp = getContrastsList(bm, nodeName);
       for i = 1:numel(tmp)
         contrastsList{end + 1} = tmp{i}.Name;
       end

src/bids_model/getContrastsListForFactorialDesign.m

@@ -17,13 +17,13 @@
   % assuming we want to only average / comparisons at the group level
   participants = bids.util.tsvread(fullfile(opt.dir.raw, 'participants.tsv'));
 
-  model = opt.model.bm;
+  bm = opt.model.bm;
 
-  groupGlmType =  model.groupLevelGlmType(nodeName, participants);
+  groupGlmType =  bm.groupLevelGlmType(nodeName, participants);
 
   if ismember(groupGlmType, {'one_sample_t_test', 'one_way_anova'})
 
-    edge = model.get_edge('Destination', nodeName);
+    edge = bm.get_edge('Destination', nodeName);
 
     if isfield(edge, 'Filter') && ...
         isfield(edge.Filter, 'contrast')  && ...
@@ -34,14 +34,14 @@
     else
 
       % this assumes DummyContrasts exist
-      contrastsList = getDummyContrastsList(opt.model.bm, nodeName, participants);
+      contrastsList = getDummyContrastsList(bm, nodeName, participants);
 
-      node = model.get_nodes('Name', nodeName);
+      node = bm.get_nodes('Name', nodeName);
 
       % if no specific dummy contrasts mentioned also include all contrasts from previous levels
       % or if contrasts are mentioned we grab them
       if ~isfield(node.DummyContrasts, 'Contrasts') || isfield(node, 'Contrasts')
-        tmp = getContrastsList(opt.model.bm, nodeName, columns);
+        tmp = getContrastsList(bm, nodeName, columns);
         for i = 1:numel(tmp)
           contrastsList{end + 1} = tmp{i}.Name;
         end

src/bids_model/getInclusiveMask.m

@@ -13,13 +13,15 @@
 
   % (C) Copyright 2022 bidspm developers
 
+  bm = opt.model.bm;
+
   if nargin < 2
-    [mask, nodeName] = opt.model.bm.getModelMask();
+    [mask, nodeName] = bm.getModelMask();
   else
-    [mask, nodeName] = opt.model.bm.getModelMask('Name', nodeName);
+    [mask, nodeName] = bm.getModelMask('Name', nodeName);
   end
 
-  node = opt.model.bm.get_nodes('Name',  nodeName);
+  node = bm.get_nodes('Name',  nodeName);
 
   % TODO refactor with bidsResults part for checking background for montage
   if isstruct(mask)

src/stats/subject_level/convertOnsetTsvToMat.m

@@ -113,8 +113,10 @@
     opt.model.bm = BidsModel('file', opt.model.file);
   end
 
-  varToConvolve = opt.model.bm.getVariablesToConvolve();
-  designMatrix = opt.model.bm.getBidsDesignMatrix();
+  bm = opt.model.bm;
+
+  varToConvolve = bm.getVariablesToConvolve();
+  designMatrix = bm.getBidsDesignMatrix();
   designMatrix = removeIntercept(designMatrix);
 
   % conditions to be filled according to the conditions present in each run
@@ -125,7 +127,7 @@
   condToModel.idx = 1;
 
   % TODO get / apply transformers from a specific node
-  transformers = opt.model.bm.getBidsTransformers();
+  transformers = bm.getBidsTransformers();
   tsv.content = bids.transformers(transformers, tsv.content);
 
   for iVar = 1:numel(varToConvolve)

src/stats/subject_level/createAndReturnCounfoundMatFile.m

@@ -42,9 +42,11 @@
     return
   end
 
-  designMatrix = opt.model.bm.getBidsDesignMatrix();
+  bm = opt.model.bm;
 
-  transformers = opt.model.bm.getBidsTransformers();
+  designMatrix = bm.getBidsDesignMatrix();
+
+  transformers = bm.getBidsTransformers();
   content = bids.transformers(transformers, content);
 
   [names, R] = createConfounds(content, designMatrix, opt.glm.maxNbVols); %#ok<*ASGLU>