Merge pull request #1 from satra/fix/ants

Fix/ants

nipype/interfaces/ants/segmentation.py

@@ -500,7 +500,7 @@ def _format_arg(self, opt, spec, val):
             if priors_directory is not '':
                 retval = "-p %s/BrainSegmentationPrior%%02d" % priors_directory
             else:
-                retval = "-p BrainSegmentationPrior%02d" 
+                retval = "-p BrainSegmentationPrior%02d"
             retval += ext
             return retval
         return super(ANTSCommand, self)._format_arg(opt, spec, val)

nipype/interfaces/spm/__init__.py

@@ -4,12 +4,11 @@
 
 from .base import (Info, SPMCommand, logger, no_spm, scans_for_fname,
                    scans_for_fnames)
-from .preprocess import (SliceTiming, Realign, Coregister, Normalize, Segment,
-                         Smooth, NewSegment, DARTEL, DARTELNorm2MNI,
-                         CreateWarped, VBMSegment)
+from .preprocess import (SliceTiming, Realign, Coregister, Normalize,
+                         Normalize12, Segment, Smooth, NewSegment, DARTEL,
+                         DARTELNorm2MNI, CreateWarped, VBMSegment)
 from .model import (Level1Design, EstimateModel, EstimateContrast, Threshold,
                     OneSampleTTestDesign, TwoSampleTTestDesign,
                     PairedTTestDesign, MultipleRegressionDesign)
 from .utils import (Analyze2nii, CalcCoregAffine, ApplyTransform, Reslice,
-                    ApplyInverseDeformation,ResliceToReference,DicomImport)
-
+                    ApplyInverseDeformation, ResliceToReference, DicomImport)

nipype/interfaces/spm/base.py

@@ -467,7 +467,7 @@ def _make_matlab_command(self, contents, postscript=None):
         fprintf('SPM path: %s\\n', which('spm'));
         spm('Defaults','fMRI');
 
-        if strcmp(name, 'SPM8') || strcmp(name, 'SPM12b'),
+        if strcmp(name, 'SPM8') || strcmp(name(1:5), 'SPM12'),
            spm_jobman('initcfg');
            spm_get_defaults('cmdline', 1);
         end\n
@@ -502,7 +502,7 @@ def _make_matlab_command(self, contents, postscript=None):
         """
         if self.inputs.use_mcr:
             mscript += """
-        if strcmp(name, 'SPM8') || strcmp(name, 'SPM12b'),
+        if strcmp(name, 'SPM8') || strcmp(name(1:5), 'SPM12'),
             close(\'all\', \'force\');
         end;
             """

nipype/interfaces/spm/model.py

@@ -92,7 +92,7 @@ class Level1DesignOutputSpec(TraitedSpec):
 class Level1Design(SPMCommand):
     """Generate an SPM design matrix
 
-    http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=61
+    http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=59
 
     Examples
     --------
@@ -182,7 +182,7 @@ class EstimateModelOutputSpec(TraitedSpec):
 class EstimateModel(SPMCommand):
     """Use spm_spm to estimate the parameters of a model
 
-    http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=71
+    http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=69
 
     Examples
     --------
@@ -744,7 +744,7 @@ class FactorialDesignOutputSpec(TraitedSpec):
 class FactorialDesign(SPMCommand):
     """Base class for factorial designs
 
-    http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=79
+    http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=77
 
     """
 

nipype/interfaces/spm/tests/test_auto_Normalize.py

@@ -5,16 +5,15 @@
 def test_Normalize_inputs():
     input_map = dict(DCT_period_cutoff=dict(field='eoptions.cutoff',
     ),
-    affine_regularization_type=dict(field='eoptions.regype',
+    affine_regularization_type=dict(field='eoptions.regtype',
     ),
     apply_to_files=dict(copyfile=True,
     field='subj.resample',
     ),
     ignore_exception=dict(nohash=True,
     usedefault=True,
     ),
-    jobtype=dict(usedefault=True,
-    ),
+    jobtype=dict(),
     matlab_cmd=dict(),
     mfile=dict(usedefault=True,
     ),

nipype/interfaces/spm/tests/test_auto_Normalize12.py

@@ -0,0 +1,71 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from nipype.testing import assert_equal
+from nipype.interfaces.spm.preprocess import Normalize12
+
+def test_Normalize12_inputs():
+    input_map = dict(affine_regularization_type=dict(field='eoptions.affreg',
+    ),
+    apply_to_files=dict(copyfile=True,
+    field='subj.resample',
+    ),
+    bias_fwhm=dict(field='eoptions.biasfwhm',
+    ),
+    bias_regularization=dict(field='eoptions.biasreg',
+    ),
+    deformation_file=dict(copyfile=False,
+    field='subj.def',
+    mandatory=True,
+    xor=['image_to_align', 'tpm'],
+    ),
+    ignore_exception=dict(nohash=True,
+    usedefault=True,
+    ),
+    image_to_align=dict(copyfile=True,
+    field='subj.vol',
+    mandatory=True,
+    xor=['deformation_file'],
+    ),
+    jobtype=dict(),
+    matlab_cmd=dict(),
+    mfile=dict(usedefault=True,
+    ),
+    paths=dict(),
+    sampling_distance=dict(field='eoptions.samp',
+    ),
+    smoothness=dict(field='eoptions.fwhm',
+    ),
+    tpm=dict(copyfile=False,
+    field='eoptions.tpm',
+    mandatory=False,
+    xor=['deformation_file'],
+    ),
+    use_mcr=dict(),
+    use_v8struct=dict(min_ver='8',
+    usedefault=True,
+    ),
+    warping_regularization=dict(field='eoptions.reg',
+    ),
+    write_bounding_box=dict(field='woptions.bb',
+    ),
+    write_interp=dict(field='woptions.interp',
+    ),
+    write_voxel_sizes=dict(field='woptions.vox',
+    ),
+    )
+    inputs = Normalize12.input_spec()
+
+    for key, metadata in input_map.items():
+        for metakey, value in metadata.items():
+            yield assert_equal, getattr(inputs.traits()[key], metakey), value
+
+def test_Normalize12_outputs():
+    output_map = dict(deformation_field=dict(),
+    normalized_files=dict(),
+    normalized_image=dict(),
+    )
+    outputs = Normalize12.output_spec()
+
+    for key, metadata in output_map.items():
+        for metakey, value in metadata.items():
+            yield assert_equal, getattr(outputs.traits()[key], metakey), value
+

nipype/interfaces/spm/tests/test_preprocess.py

@@ -85,10 +85,24 @@ def test_normalize_list_outputs():
     filelist, outdir, cwd = create_files_in_directory()
     norm = spm.Normalize(source=filelist[0])
     yield assert_true, norm._list_outputs()['normalized_source'][0].startswith('w')
-    norm = spm.Normalize(source=filelist[0],apply_to_files=filelist[1])
+    norm = spm.Normalize(source=filelist[0], apply_to_files=filelist[1])
     yield assert_true, norm._list_outputs()['normalized_files'][0].startswith('w')
     clean_directory(outdir, cwd)
 
+def test_normalize12():
+    yield assert_equal, spm.Normalize12._jobtype, 'spatial'
+    yield assert_equal, spm.Normalize12._jobname, 'normalise'
+    yield assert_equal, spm.Normalize12().inputs.jobtype, 'estwrite'
+
+def test_normalize12_list_outputs():
+    filelist, outdir, cwd = create_files_in_directory()
+    norm12 = spm.Normalize12(image_to_align=filelist[0])
+    yield assert_true, norm12._list_outputs()['normalized_image'][0].startswith('w')
+    norm12 = spm.Normalize12(image_to_align=filelist[0],
+                             apply_to_files=filelist[1])
+    yield assert_true, norm12._list_outputs()['normalized_files'][0].startswith('w')
+    clean_directory(outdir, cwd)
+
 @skipif(no_spm)
 def test_segment():
     if spm.Info.version()['name'] == "SPM12":

nipype/interfaces/utility.py

@@ -1,5 +1,14 @@
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
+"""Various utilities
+
+   Change directory to provide relative paths for doctests
+   >>> import os
+   >>> filepath = os.path.dirname( os.path.realpath( __file__ ) )
+   >>> datadir = os.path.realpath(os.path.join(filepath, '../testing/data'))
+   >>> os.chdir(datadir)
+"""
+
 import os
 import re
 from cPickle import dumps, loads

nipype/pipeline/plugins/ipython.py

@@ -22,6 +22,8 @@ def execute_task(pckld_task, node_config, updatehash):
     from nipype import config, logging
     traceback=None
     result=None
+    import os
+    cwd = os.getcwd()
     try:
         config.update_config(node_config)
         logging.update_logging(config)
@@ -31,6 +33,7 @@ def execute_task(pckld_task, node_config, updatehash):
     except:
         traceback = format_exc()
         result = task.result
+    os.chdir(cwd)
     return result, traceback, gethostname()
 
 class IPythonPlugin(DistributedPluginBase):
@@ -72,7 +75,7 @@ def run(self, graph, config, updatehash=False):
 
     def _get_result(self, taskid):
         if taskid not in self.taskmap:
-            raise ValueError('Task %d not in pending list'%taskid)
+            raise ValueError('Task %d not in pending list' % taskid)
         if self.taskmap[taskid].ready():
             result, traceback, hostname = self.taskmap[taskid].get()
             result_out = dict(result=None, traceback=None)

nipype/workflows/dmri/fsl/utils.py

@@ -381,9 +381,11 @@ def recompose_xfm(in_bval, in_xfms):
     return out_files
 
 
-def b0_average(in_dwi, in_bval, out_file=None):
+def b0_average(in_dwi, in_bval, max_b=10.0, out_file=None):
     """
     A function that averages the *b0* volumes from a DWI dataset.
+    As current dMRI data are being acquired with all b-values > 0.0,
+    the *lowb* volumes are selected by specifying the parameter max_b.
 
     .. warning:: *b0* should be already registered (head motion artifact should
       be corrected).
@@ -403,7 +405,7 @@ def b0_average(in_dwi, in_bval, out_file=None):
     imgs = np.array(nb.four_to_three(nb.load(in_dwi)))
     bval = np.loadtxt(in_bval)
     b0s = [im.get_data().astype(np.float32)
-           for im in imgs[np.where(bval == 0)]]
+           for im in imgs[np.where(bval <= max_b)]]
     b0 = np.average(np.array(b0s), axis=0)
 
     hdr = imgs[0].get_header().copy()