Merge pull request #864 from satra/fix/openfmri

Fix/openfmri

Author: satra

examples/fmri_openfmri.py

@@ -87,7 +87,7 @@ def get_subjectinfo(subject_id, base_dir, task_id, model_id):
 
 
 def analyze_openfmri_dataset(data_dir, subject=None, model_id=None,
-                             task_id=None, output_dir=None):
+                             task_id=None, output_dir=None, subj_prefix='*'):
     """Analyzes an open fmri dataset
 
     Parameters
@@ -122,21 +122,21 @@ def analyze_openfmri_dataset(data_dir, subject=None, model_id=None,
     """
 
     subjects = sorted([path.split(os.path.sep)[-1] for path in
-                       glob(os.path.join(data_dir, 'sub*'))])
+                       glob(os.path.join(data_dir, subj_prefix))])
 
     infosource = pe.Node(niu.IdentityInterface(fields=['subject_id',
                                                        'model_id',
                                                        'task_id']),
                          name='infosource')
-    if subject is None:
+    if len(subject) == 0:
         infosource.iterables = [('subject_id', subjects),
                                 ('model_id', [model_id]),
-                                ('task_id', [task_id])]
+                                ('task_id', task_id)]
     else:
         infosource.iterables = [('subject_id',
-                                 [subjects[subjects.index(subject)]]),
+                                 [subjects[subjects.index(subj)] for subj in subject]),
                                 ('model_id', [model_id]),
-                                ('task_id', [task_id])]
+                                ('task_id', task_id)]
 
     subjinfo = pe.Node(niu.Function(input_names=['subject_id', 'base_dir',
                                                  'task_id', 'model_id'],
@@ -156,7 +156,7 @@ def analyze_openfmri_dataset(data_dir, subject=None, model_id=None,
                          name='datasource')
     datasource.inputs.base_directory = data_dir
     datasource.inputs.template = '*'
-    datasource.inputs.field_template = {'anat': '%s/anatomy/highres001.nii.gz',
+    datasource.inputs.field_template = {'anat': '%s/anatomy/T1_001.nii.gz',
                                 'bold': '%s/BOLD/task%03d_r*/bold.nii.gz',
                                 'behav': ('%s/model/model%03d/onsets/task%03d_'
                                           'run%03d/cond*.txt'),
@@ -235,8 +235,19 @@ def get_contrasts(contrast_file, task_id, conds):
                            name="modelspec")
     modelspec.inputs.input_units = 'secs'
 
+    def check_behav_list(behav):
+        out_behav = []
+        if isinstance(behav, basestring):
+            behav = [behav]
+        for val in behav:
+            if not isinstance(val, list):
+                out_behav.append([val])
+            else:
+                out_behav.append(val)
+        return out_behav
+
     wf.connect(subjinfo, 'TR', modelspec, 'time_repetition')
-    wf.connect(datasource, 'behav', modelspec, 'event_files')
+    wf.connect(datasource, ('behav', check_behav_list), modelspec, 'event_files')
     wf.connect(subjinfo, 'TR', modelfit, 'inputspec.interscan_interval')
     wf.connect(subjinfo, 'conds', contrastgen, 'conds')
     wf.connect(datasource, 'contrasts', contrastgen, 'contrast_file')
@@ -338,6 +349,9 @@ def get_subs(subject_id, conds, model_id, task_id):
         subs.append(('task_id_%d/' % task_id, '/task%03d_' % task_id))
         subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_warp_warp',
         'mean'))
+        subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_flirt',
+        'affine'))
+
         for i in range(len(conds)):
             subs.append(('_flameo%d/cope1.' % i, 'cope%02d.' % (i + 1)))
             subs.append(('_flameo%d/varcope1.' % i, 'varcope%02d.' % (i + 1)))
@@ -376,6 +390,8 @@ def get_subs(subject_id, conds, model_id, task_id):
                   ('varcopes', 'varcopes.mni'),
                   ])])
     wf.connect(registration, 'outputspec.transformed_mean', datasink, 'mean.mni')
+    wf.connect(registration, 'outputspec.func2anat_transform', datasink, 'xfm.mean2anat')
+    wf.connect(registration, 'outputspec.anat2target_transform', datasink, 'xfm.anat2target')
 
     """
     Set processing parameters
@@ -398,12 +414,15 @@ def get_subs(subject_id, conds, model_id, task_id):
     parser = argparse.ArgumentParser(prog='fmri_openfmri.py',
                                      description=__doc__)
     parser.add_argument('-d', '--datasetdir', required=True)
-    parser.add_argument('-s', '--subject', default=None,
+    parser.add_argument('-s', '--subject', default=[],
+                        nargs='+', type=str,
                         help="Subject name (e.g. 'sub001')")
     parser.add_argument('-m', '--model', default=1,
                         help="Model index" + defstr)
-    parser.add_argument('-t', '--task', default=1,
-                        help="Task index" + defstr)
+    parser.add_argument('-x', '--subjectprefix', default='sub*',
+                        help="Subject prefix" + defstr)
+    parser.add_argument('-t', '--task', default=1, #nargs='+',
+                        type=int, help="Task index" + defstr)
     parser.add_argument("-o", "--output_dir", dest="outdir",
                         help="Output directory base")
     parser.add_argument("-w", "--work_dir", dest="work_dir",
@@ -427,7 +446,8 @@ def get_subs(subject_id, conds, model_id, task_id):
     wf = analyze_openfmri_dataset(data_dir=os.path.abspath(args.datasetdir),
                              subject=args.subject,
                              model_id=int(args.model),
-                             task_id=int(args.task),
+                             task_id=[int(args.task)],
+                             subj_prefix=args.subjectprefix,
                              output_dir=outdir)
     wf.base_dir = work_dir
     if args.plugin_args:

nipype/external/provcopy.py

@@ -346,7 +346,7 @@ def provn_representation(self):
 
     def json_representation(self):
         return {'$': self._uri, 'type': u'xsd:anyURI'}
-    
+
     def rdf_representation(self):
         return URIRef(self.get_uri())
 
@@ -787,7 +787,7 @@ def rdf(self, graph=None, subj=None):
                     pred = attr.rdf_representation()
                 graph.add((subj, pred, obj))
         return graph
-        
+
     def is_asserted(self):
         return self._asserted
 
@@ -802,7 +802,7 @@ def is_relation(self):
 class ProvElement(ProvRecord):
     def is_element(self):
         return True
-    
+
     def rdf(self, graph=None):
         if graph is None:
             graph = Graph()
@@ -1673,15 +1673,15 @@ def rdf(self, graph=None):
             # graph should not None here
             uri = self.get_identifier().rdf_representation()
             graph = Graph(graph.store, uri)
-        
+
         for prefix, namespace in self._namespaces.items():
             graph.bind(prefix, namespace.get_uri())
-        
+
         for record in self._records:
             if record.is_asserted():
                 record.rdf(graph)
         return graph
-    
+
     def get_provjson(self, **kw):
         """Return the `PROV-JSON <http://www.w3.org/Submission/prov-json/>`_ representation for the bundle/document.
 

nipype/interfaces/freesurfer/__init__.py

@@ -9,7 +9,7 @@
                          SynthesizeFLASH)
 from .model import (MRISPreproc, GLMFit, OneSampleTTest, Binarize, Concatenate,
                     SegStats, Label2Vol, MS_LDA)
-from .utils import (SampleToSurface, SurfaceSmooth, SurfaceTransform, Surface2VolTransform, 
+from .utils import (SampleToSurface, SurfaceSmooth, SurfaceTransform, Surface2VolTransform,
                     SurfaceSnapshots,ApplyMask, MRIsConvert, MRITessellate,
                     MRIMarchingCubes, SmoothTessellation, MakeAverageSubject,
                     ExtractMainComponent)

nipype/interfaces/fsl/preprocess.py

@@ -678,17 +678,17 @@ def _list_outputs(self):
                                                       '_variance.ext', cwd=cwd)
             outputs['std_img'] = self._gen_fname(outputs['out_file'] +
                                                  '_sigma.ext', cwd=cwd)
-                                                  
+
         # The mean image created if -stats option is specified ('meanvol')
-        # is missing the top and bottom slices. Therefore we only expose the 
-        # mean image created by -meanvol option ('mean_reg') which isn't 
+        # is missing the top and bottom slices. Therefore we only expose the
+        # mean image created by -meanvol option ('mean_reg') which isn't
         # corrupted.
         # Note that the same problem holds for the std and variance image.
-                                                  
+
         if isdefined(self.inputs.mean_vol) and self.inputs.mean_vol:
             outputs['mean_img'] = self._gen_fname(outputs['out_file'] +
                                                   '_mean_reg.ext', cwd=cwd)
-                                                  
+
         if isdefined(self.inputs.save_mats) and self.inputs.save_mats:
             _, filename = os.path.split(outputs['out_file'])
             matpathname = os.path.join(cwd, filename + '.mat')

nipype/interfaces/mrtrix/tensors.py

@@ -327,10 +327,10 @@ class GenerateDirections(CommandLine):
     input_spec=GenerateDirectionsInputSpec
     output_spec=GenerateDirectionsOutputSpec
 
-    
+
 class FindShPeaksInputSpec(CommandLineInputSpec):
     in_file = File(exists=True, argstr='%s', mandatory=True, position=-3, desc='the input image of SH coefficients.')
-    directions_file = File(exists=True, argstr='%s', mandatory=True, position=-2, desc='the set of directions to use as seeds for the peak finding')    
+    directions_file = File(exists=True, argstr='%s', mandatory=True, position=-2, desc='the set of directions to use as seeds for the peak finding')
     peaks_image = File(exists=True, argstr='-peaks %s', desc='the program will try to find the peaks that most closely match those in the image provided')
     num_peaks = traits.Int(argstr='-num %s', desc='the number of peaks to extract (default is 3)')
     peak_directions = traits.List(traits.Float, argstr='-direction %s', sep=' ', minlen=2, maxlen=2,
@@ -342,7 +342,7 @@ class FindShPeaksInputSpec(CommandLineInputSpec):
     display_debug = traits.Bool(argstr='-debug', desc='Display debugging messages.')
     out_file = File(name_template="%s_peak_dirs.mif", keep_extension=False, argstr='%s', hash_files=False, position= -1,
                     desc='the output image. Each volume corresponds to the x, y & z component of each peak direction vector in turn', name_source=["in_file"])
-    
+
 class FindShPeaksOutputSpec(TraitedSpec):
     out_file = File(exists=True, desc='Peak directions image')
 
@@ -368,7 +368,7 @@ class FindShPeaks(CommandLine):
 
 
 class Directions2AmplitudeInputSpec(CommandLineInputSpec):
-    in_file = File(exists=True, argstr='%s', mandatory=True, position=-2, desc='the input directions image. Each volume corresponds to the x, y & z component of each direction vector in turn.')    
+    in_file = File(exists=True, argstr='%s', mandatory=True, position=-2, desc='the input directions image. Each volume corresponds to the x, y & z component of each direction vector in turn.')
     peaks_image = File(exists=True, argstr='-peaks %s', desc='the program will try to find the peaks that most closely match those in the image provided')
     num_peaks = traits.Int(argstr='-num %s', desc='the number of peaks to extract (default is 3)')
     peak_directions = traits.List(traits.Float, argstr='-direction %s', sep=' ', minlen=2, maxlen=2,
@@ -379,7 +379,7 @@ class Directions2AmplitudeInputSpec(CommandLineInputSpec):
     display_debug = traits.Bool(argstr='-debug', desc='Display debugging messages.')
     out_file = File(name_template="%s_amplitudes.mif", keep_extension=False, argstr='%s', hash_files=False, position= -1,
                     desc='the output amplitudes image', name_source=["in_file"])
-    
+
 class Directions2AmplitudeOutputSpec(TraitedSpec):
     out_file = File(exists=True, desc='amplitudes image')
 

nipype/interfaces/mrtrix/tracking.py

@@ -75,22 +75,22 @@ class StreamlineTrackInputSpec(CommandLineInputSpec):
     in_file = File(exists=True, argstr='%s', mandatory=True, position=-2, desc='the image containing the source data.' \
     'The type of data required depends on the type of tracking as set in the preceeding argument. For DT methods, ' \
     'the base DWI are needed. For SD methods, the SH harmonic coefficients of the FOD are needed.')
-    
+
     seed_xor = ['seed_file', 'seed_spec']
     seed_file = File(exists=True, argstr='-seed %s', desc='seed file', xor = seed_xor)
     seed_spec = traits.List(traits.Float, desc='seed specification in mm and radius (x y z r)', position=2,
         argstr='-seed %s', minlen=4, maxlen=4, sep=',', units='mm', xor = seed_xor)
-    
+
     include_xor = ['include_file', 'include_spec']
     include_file = File(exists=True, argstr='-include %s', desc='inclusion file', xor = include_xor)
     include_spec = traits.List(traits.Float, desc='inclusion specification in mm and radius (x y z r)', position=2,
         argstr='-include %s', minlen=4, maxlen=4, sep=',', units='mm', xor = include_xor)
-    
+
     exclude_xor = ['exclude_file', 'exclude_spec']
     exclude_file = File(exists=True, argstr='-exclude %s', desc='exclusion file', xor = exclude_xor)
     exclude_spec = traits.List(traits.Float, desc='exclusion specification in mm and radius (x y z r)', position=2,
         argstr='-exclude %s', minlen=4, maxlen=4, sep=',', units='mm', xor = exclude_xor)
-    
+
     mask_xor = ['mask_file', 'mask_spec']
     mask_file = File(exists=True, argstr='-mask %s', desc='mask file. Only tracks within mask.', xor = mask_xor)
     mask_spec = traits.List(traits.Float, desc='Mask specification in mm and radius (x y z r). Tracks will be terminated when they leave the ROI.', position=2,
@@ -127,7 +127,7 @@ class StreamlineTrackInputSpec(CommandLineInputSpec):
 
     initial_direction = traits.List(traits.Int, desc='Specify the initial tracking direction as a vector',
         argstr='-initdirection %s', minlen=2, maxlen=2, units='voxels')
-    out_file = File(argstr='%s', position= -1, name_source = ['in_file'], name_template='%s_tracked.tck', 
+    out_file = File(argstr='%s', position= -1, name_source = ['in_file'], name_template='%s_tracked.tck',
                     output_name='tracked', desc='output data file')
 
 class StreamlineTrackOutputSpec(TraitedSpec):

nipype/interfaces/vista/vista.py

@@ -40,8 +40,8 @@ class Vnifti2Image(CommandLine):
     _cmd = 'vnifti2image'
     input_spec=Vnifti2ImageInputSpec
     output_spec=Vnifti2ImageOutputSpec
-    
-    
+
+
 class VtoMatInputSpec(CommandLineInputSpec):
     in_file = File(exists=True, argstr='-in %s', mandatory=True, position=1, desc='in file')
     out_file = File(name_template="%s.mat", keep_extension=False, argstr='-out %s', hash_files=False,

nipype/workflows/smri/freesurfer/recon.py

@@ -26,9 +26,9 @@ def create_skullstripped_recon_flow(name="skullstripped_recon_all"):
 
            outputspec.subject_id : freesurfer subject id
            outputspec.subjects_dir : freesurfer subjects directory
-    """    
+    """
     wf = pe.Workflow(name=name)
-    
+
     inputnode = pe.Node(niu.IdentityInterface(fields=['subject_id',
                                                       'subjects_dir',
                                                       'T1_files']),
@@ -43,34 +43,34 @@ def create_skullstripped_recon_flow(name="skullstripped_recon_all"):
     wf.connect(inputnode, "T1_files", autorecon1, "T1_files")
     wf.connect(inputnode, "subjects_dir", autorecon1, "subjects_dir")
     wf.connect(inputnode, "subject_id", autorecon1, "subject_id")
-    
-    
+
+
     def link_masks(subjects_dir, subject_id):
         import os
         os.symlink(os.path.join(subjects_dir, subject_id, "mri", "T1.mgz"),
                    os.path.join(subjects_dir, subject_id, "mri", "brainmask.auto.mgz"))
         os.symlink(os.path.join(subjects_dir, subject_id, "mri", "brainmask.auto.mgz"),
                    os.path.join(subjects_dir, subject_id, "mri", "brainmask.mgz"))
         return subjects_dir, subject_id
-    
-    masks = pe.Node(niu.Function(input_names=['subjects_dir', 'subject_id'], 
+
+    masks = pe.Node(niu.Function(input_names=['subjects_dir', 'subject_id'],
                                   output_names=['subjects_dir', 'subject_id'],
                                   function=link_masks), name="link_masks")
-    
+
     wf.connect(autorecon1, "subjects_dir", masks, "subjects_dir")
-    wf.connect(autorecon1, "subject_id", masks, "subject_id")   
-    
-    
+    wf.connect(autorecon1, "subject_id", masks, "subject_id")
+
+
     autorecon_resume = pe.Node(fs.ReconAll(), name="autorecon_resume")
     autorecon_resume.plugin_args={'submit_specs': 'request_memory = 2500'}
     autorecon_resume.inputs.args = "-no-isrunning"
     wf.connect(masks, "subjects_dir", autorecon_resume, "subjects_dir")
     wf.connect(masks, "subject_id", autorecon_resume, "subject_id")
-    
+
     outputnode = pe.Node(niu.IdentityInterface(fields=['subject_id',
                                                       'subjects_dir']),
                         name='outputspec')
-    
+
     wf.connect(autorecon_resume, "subjects_dir", outputnode, "subjects_dir")
     wf.connect(autorecon_resume, "subject_id", outputnode, "subject_id")
     return wf