Merge pull request #2 from satra/enh/p2

Enh/p2

Author: JensNRAD

.travis.yml

@@ -27,7 +27,7 @@ install:
   - pip install python-coveralls --use-mirrors
   - pip install nose-cov --use-mirrors
   - pip install https://github.com/RDFLib/rdflib/archive/master.zip
-  - pip install https://github.com/satra/prov/archive/enh/rdf.zip
+  - pip install https://github.com/trungdong/prov/archive/rdf.zip
   - python setup.py install
 
 # Run test

CHANGES

@@ -2,8 +2,15 @@ Next Release
 ============
 
 * ENH: New FSL interface: ProbTrackX2
+* 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 ANTs interface: ApplyTransformsToPoints
+* 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
 * FIX: MRTrix tracking algorithms were ignoring mask parameters.
+* FIX: FNIRT registration pathway and associated OpenFMRI example script
 
 Release 0.9.2 (January 31, 2014)
 ============
@@ -24,7 +31,8 @@ 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>`__]

doc/users/mapnode_and_iterables.rst

@@ -61,7 +61,7 @@ reduce scenario.
 
 You might have also noticed that the iterfield arguments expects a list of input
 names instead of just one name. This suggests that there can be more than one!
-Even thou a bit confusing this is true. You can specify more than one input to 
+Even though a bit confusing this is true. You can specify more than one input to 
 iterate over but the lists that you provide (for all the inputs specified in 
 iterfield) have to have the same length. MapNode will then pair the parameters up
 and run the first instance with first set of parameters and second with second set
@@ -114,7 +114,7 @@ should look like this:
    "A" -> "B3" -> "C3";
 
 Of course you can do it manually by creating copies of all the nodes for 
-different parameter set, but this can be very time consuming, especially when is
+different parameter set, but this can be very time consuming, especially when there
 are more than one node taking inputs from "B". Luckily nipype supports this 
 scenario! Its called iterables and and you use it this way:
 

doc/users/spmmcr.rst

@@ -9,7 +9,7 @@ the following commands are executed at the beginning of your script:
 
 .. testcode::
 
-    from nipype import spm
+    from nipype.interfaces import spm
     matlab_cmd = '/path/to/run_spm8.sh /path/to/Compiler_Runtime/v713/ script'
     spm.SPMCommand.set_mlab_paths(matlab_cmd=matlab_cmd, use_mcr=True)
 

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:

examples/fmri_spm.py

@@ -18,7 +18,7 @@
 from nipype import config
 config.enable_provenance()
 
-from nipype import spm, fsl
+from nipype.interfaces import spm, fsl
 
 # In order to use this example with SPM's matlab common runtime
 # matlab_cmd = ('/Users/satra/Downloads/spm8/run_spm8.sh '

examples/rsfmri_fsl.py

@@ -6,12 +6,12 @@
 rsfMRI: FSL - CSF regressed
 ===========================
 
-A pipeline example that uses intergrates several interfaces to
+A pipeline example that intergrates several interfaces to
 perform a first and second level analysis on a two-subject data
 set.
 
 
-1. Tell python where to find the appropriate functions.
+1. Tell Python where to find the appropriate functions.
 """
 
 import numpy as np

examples/rsfmri_preprocessing.py

@@ -156,7 +156,7 @@ def motion_regressors(motion_params, order=2, derivatives=2):
 
 
 def build_filter1(motion_params, comp_norm, outliers):
-    """Builds a regressor set comprisong motion parameters, composite norm and
+    """Builds a regressor set comparison motion parameters, composite norm and
     outliers
 
     The outliers are added as a single time point column for each outlier
@@ -364,7 +364,7 @@ def create_workflow(files,
     fssource.inputs.subject_id = subject_id
     fssource.inputs.subjects_dir = os.environ['SUBJECTS_DIR']
 
-    # Extract wm+csf, brain masks by eroding freesurfer lables and then
+    # Extract wm+csf, brain masks by eroding freesurfer labels and then
     # transform the masks into the space of the median
     wmcsf = Node(freesurfer.Binarize(), name='wmcsfmask')
     mask = wmcsf.clone('anatmask')

nipype/__init__.py

@@ -75,6 +75,5 @@ def _test_local_install():
 
 
 from pipeline import Node, MapNode, JoinNode, Workflow
-from interfaces import (fsl, spm, freesurfer, afni, ants, slicer, dipy, nipy,
-                        mrtrix, camino, DataGrabber, DataSink, SelectFiles,
+from interfaces import (DataGrabber, DataSink, SelectFiles,
                         IdentityInterface, Rename, Function, Select, Merge)