Merge branch 'master' into enh/AddCSVRow

Conflicts:
	CHANGES
	nipype/algorithms/misc.py

Merge after updating master to upstream

Author: oesteban

.travis.yml

@@ -12,7 +12,7 @@ before_install:
   - sudo rm -rf /dev/shm
   - sudo ln -s /run/shm /dev/shm
   - bash <(wget -q -O- http://neuro.debian.net/_files/neurodebian-travis.sh)
-  - travis_retry sudo apt-get install -qq --no-install-recommends fsl afni
+  - travis_retry sudo apt-get install -qq --no-install-recommends fsl afni elastix
   - travis_retry sudo apt-get install -qq fsl-atlases
   - source /etc/fsl/fsl.sh
 

CHANGES

@@ -2,10 +2,24 @@ Next Release
 ============
 
 * ENH: New miscelaneous interface: AddCSVRow
-* API: Interfaces to external packages are no longer available in the top-level ``nipype`` namespace, and must be imported directly (e.g. ``from nipype.interfaces import fsl``).
+* API: Interfaces to external packages are no longer available in the top-level
+       ``nipype`` namespace, and must be imported directly (e.g.
+       ``from nipype.interfaces import fsl``).
+* ENH: New FSL interface: ProbTrackX2
+* ENH: New misc algorithm: NormalizeProbabilityMapSet
+* ENH: Support for elastix via a set of new interfaces: Registration, ApplyWarp, 
+       AnalyzeWarp, PointsWarp, and EditTransform
 * ENH: New ANTs interface: ApplyTransformsToPoints
+* ENH: New metrics group in algorithms. Now Distance, Overlap, and FuzzyOverlap
+       are found in nipype.algorithms.metrics instead of misc
+* ENH: New interface in algorithms.metrics: ErrorMap (a voxel-wise diff map).
 * ENH: New FreeSurfer workflow: create_skullstripped_recon_flow()
+* ENH: New data grabbing interface that works over SSH connections, SSHDataGrabber
+* ENH: New color mode for write_graph
+* ENH: You can now force MapNodes to be run serially
+* ENH: New ANTs interface: LaplacianThickness
 * FIX: MRTrix tracking algorithms were ignoring mask parameters.
+* FIX: FNIRT registration pathway and associated OpenFMRI example script
 
 Release 0.9.2 (January 31, 2014)
 ============
@@ -26,7 +40,8 @@ Release 0.9.0 (December 20, 2013)
 * ENH: new tools for defining workflows: JoinNode, synchronize and itersource
 * ENH: W3C PROV support with optional RDF export built into Nipype
 * ENH: Added support for Simple Linux Utility Resource Management (SLURM)
-* ENH: AFNI interfaces refactor, prefix, suffix are replaced by "flexible_%s_templates"
+* ENH: AFNI interfaces refactor, prefix, suffix are replaced by
+       "flexible_%s_templates"
 * ENH: New SPM interfaces:
 		- spm.ResliceToReference,
 		- spm.DicomImport

Vagrantfile

@@ -31,7 +31,7 @@ $HOME/miniconda/bin/conda install --yes pip numpy scipy nose traits networkx
 $HOME/miniconda/bin/conda install --yes dateutil ipython-notebook matplotlib
 $HOME/miniconda/bin/pip install nibabel --use-mirrors
 $HOME/miniconda/bin/pip install https://github.com/RDFLib/rdflib/archive/master.zip
-$HOME/miniconda/bin/pip install https://github.com/satra/prov/archive/enh/rdf.zip
+$HOME/miniconda/bin/pip install https://github.com/trungdong/prov/archive/rdf.zip
 $HOME/miniconda/bin/pip install https://github.com/nipy/nipype/archive/master.zip
 SCRIPT
 

doc/users/install.rst

@@ -10,7 +10,7 @@ Download
 --------
 
 Release 0.9.2: [`zip <https://github.com/nipy/nipype/archive/0.9.2.zip>`__ `tar.gz
-<https://github.com/nipy/nipype/archive/0.9.1.tar.gz>`__]
+<https://github.com/nipy/nipype/archive/0.9.2.tar.gz>`__]
 
 Development: [`zip <http://github.com/nipy/nipype/zipball/master>`__ `tar.gz
 <http://github.com/nipy/nipype/tarball/master>`__]

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
@@ -121,22 +121,22 @@ def analyze_openfmri_dataset(data_dir, subject=None, model_id=None,
     Set up openfmri data specific components
     """
 
-    subjects = [path.split(os.path.sep)[-1] for path in
-                glob(os.path.join(data_dir, 'sub*'))]
+    subjects = sorted([path.split(os.path.sep)[-1] for path in
+                       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:
-        infosource.iterables = [('subject_id', subjects[:2]),
+    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'),
@@ -200,13 +200,19 @@ def get_highpass(TR, hpcutoff):
     def get_contrasts(contrast_file, task_id, conds):
         import numpy as np
         contrast_def = np.genfromtxt(contrast_file, dtype=object)
+        if len(contrast_def.shape) == 1:
+            contrast_def = contrast_def[None, :]
         contrasts = []
         for row in contrast_def:
             if row[0] != 'task%03d' % task_id:
                 continue
-            con = [row[1], 'T', ['cond%03d' % i  for i in range(len(conds))],
+            con = [row[1], 'T', ['cond%03d' % (i + 1)  for i in range(len(conds))],
                    row[2:].astype(float).tolist()]
             contrasts.append(con)
+        # add auto contrasts for each column
+        for i, cond in enumerate(conds):
+            con = [cond, 'T', ['cond%03d' % (i + 1)], [1]]
+            contrasts.append(con)
         return contrasts
 
     contrastgen = pe.Node(niu.Function(input_names=['contrast_file',
@@ -229,8 +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')
@@ -288,33 +305,41 @@ def num_copes(files):
     wf.connect(preproc, 'outputspec.mean', registration, 'inputspec.mean_image')
     wf.connect(datasource, 'anat', registration, 'inputspec.anatomical_image')
     registration.inputs.inputspec.target_image = fsl.Info.standard_image('MNI152_T1_2mm.nii.gz')
+    registration.inputs.inputspec.target_image_brain = fsl.Info.standard_image('MNI152_T1_2mm_brain.nii.gz')
+    registration.inputs.inputspec.config_file = 'T1_2_MNI152_2mm'
 
-    def merge_files(copes, varcopes):
+    def merge_files(copes, varcopes, zstats):
         out_files = []
         splits = []
         out_files.extend(copes)
         splits.append(len(copes))
         out_files.extend(varcopes)
         splits.append(len(varcopes))
+        out_files.extend(zstats)
+        splits.append(len(zstats))
         return out_files, splits
 
-    mergefunc = pe.Node(niu.Function(input_names=['copes', 'varcopes'],
+    mergefunc = pe.Node(niu.Function(input_names=['copes', 'varcopes',
+                                                  'zstats'],
                                    output_names=['out_files', 'splits'],
                                    function=merge_files),
                       name='merge_files')
     wf.connect([(fixed_fx.get_node('outputspec'), mergefunc,
                                  [('copes', 'copes'),
                                   ('varcopes', 'varcopes'),
+                                  ('zstats', 'zstats'),
                                   ])])
     wf.connect(mergefunc, 'out_files', registration, 'inputspec.source_files')
 
     def split_files(in_files, splits):
-        copes = in_files[:splits[1]]
-        varcopes = in_files[splits[1]:]
-        return copes, varcopes
+        copes = in_files[:splits[0]]
+        varcopes = in_files[splits[0]:(splits[0] + splits[1])]
+        zstats = in_files[(splits[0] + splits[1]):]
+        return copes, varcopes, zstats
 
     splitfunc = pe.Node(niu.Function(input_names=['in_files', 'splits'],
-                                     output_names=['copes', 'varcopes'],
+                                     output_names=['copes', 'varcopes',
+                                                   'zstats'],
                                      function=split_files),
                       name='split_files')
     wf.connect(mergefunc, 'splits', splitfunc, 'splits')
@@ -330,18 +355,23 @@ def get_subs(subject_id, conds, model_id, task_id):
         subs = [('_subject_id_%s_' % subject_id, '')]
         subs.append(('_model_id_%d' % model_id, 'model%03d' %model_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',
+        subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_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)))
             subs.append(('_flameo%d/zstat1.' % i, 'zstat%02d.' % (i + 1)))
             subs.append(('_flameo%d/tstat1.' % i, 'tstat%02d.' % (i + 1)))
             subs.append(('_flameo%d/res4d.' % i, 'res4d%02d.' % (i + 1)))
-            subs.append(('_warpall%d/cope1_warp_warp.' % i,
+            subs.append(('_warpall%d/cope1_warp.' % i,
                          'cope%02d.' % (i + 1)))
-            subs.append(('_warpall%d/varcope1_warp_warp.' % (len(conds) + i),
+            subs.append(('_warpall%d/varcope1_warp.' % (len(conds) + i),
                          'varcope%02d.' % (i + 1)))
+            subs.append(('_warpall%d/zstat1_warp.' % (2 * len(conds) + i),
+                         'zstat%02d.' % (i + 1)))
         return subs
 
     subsgen = pe.Node(niu.Function(input_names=['subject_id', 'conds',
@@ -368,8 +398,11 @@ def get_subs(subject_id, conds, model_id, task_id):
     wf.connect([(splitfunc, datasink,
                  [('copes', 'copes.mni'),
                   ('varcopes', 'varcopes.mni'),
+                  ('zstats', 'zstats.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
@@ -392,12 +425,15 @@ 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",
@@ -421,7 +457,8 @@ 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: