Enh/spmnormalize

nipype/interfaces/ants/resampling.py

@@ -249,7 +249,7 @@ class ApplyTransformsInputSpec(ANTSCommandInputSpec):
         File(exists=True), argstr='%s', mandatory=True, desc=(''))
     invert_transform_flags = InputMultiPath(traits.Bool())
     default_value = traits.Float(
-        0.0, argstr='--default-value %d', usedefault=True)
+        0.0, argstr='--default-value %g', usedefault=True)
     print_out_composite_warp_file = traits.Enum(
         0, 1, requires=["output_image"], desc=(''))  # TODO: Change to boolean
 

nipype/interfaces/ants/segmentation.py

@@ -490,7 +490,7 @@ 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/ants/tests/test_auto_ApplyTransforms.py

@@ -5,7 +5,7 @@
 def test_ApplyTransforms_inputs():
     input_map = dict(args=dict(argstr='%s',
     ),
-    default_value=dict(argstr='--default-value %d',
+    default_value=dict(argstr='--default-value %g',
     usedefault=True,
     ),
     dimension=dict(argstr='--dimensionality %d',

nipype/interfaces/spm/preprocess.py

@@ -374,8 +374,8 @@ class NormalizeInputSpec(SPMCommandInputSpec):
                             desc='file to normalize to template',
                             xor=['parameter_file'],
                             mandatory=True, copyfile=True)
-    jobtype = traits.Enum('estwrite', 'est', 'write',
-                          desc='one of: est, write, estwrite estwrite')
+    jobtype = traits.Enum('estwrite', 'est', 'write', usedefault=True,
+                          desc='Estimate, Write or do both')
     apply_to_files = InputMultiPath(traits.Either(File(exists=True),
                                                   traits.List(File(exists=True))),
                                     field='subj.resample',
@@ -527,11 +527,11 @@ class Normalize12InputSpec(SPMCommandInputSpec):
                                     copyfile=True)
     deformation_file = File(field='subj.def', mandatory=True,
                             xor=['image_to_align', 'tpm'],
-                            desc=('file y_*.nii containing 3 deformation fields',
+                            desc=('file y_*.nii containing 3 deformation fields '
                                   'for the deformation in x, y and z dimension'),
                             copyfile=False)
-    jobtype = traits.Enum('estwrite', 'est', 'write',
-                          desc='one of: est, write, estwrite estwrite')
+    jobtype = traits.Enum('estwrite', 'est', 'write', usedefault=True,
+                          desc='Estimate, Write or do Both')
     bias_regularization = traits.Enum(0, 0.00001, 0.0001, 0.001, 0.01, 0.1, 1,
                                       10, field='eoptions.biasreg',
                                       desc='no(0) - extremely heavy (10)')
@@ -547,37 +547,37 @@ class Normalize12InputSpec(SPMCommandInputSpec):
                                              desc='mni, size, none')
     warping_regularization = traits.List(traits.Float(), field='eoptions.reg',
                                          minlen=5, maxlen=5,
-                                         desc=('controls balance between',
+                                         desc=('controls balance between '
                                                'parameters and data'))
     smoothness = traits.Float(field='eoptions.fwhm',
-                              desc=('value (in mm) to smooth the data before',
+                              desc=('value (in mm) to smooth the data before '
                                     'normalization'))
     sampling_distance = traits.Float(field='eoptions.samp',
-                                     desc=('Sampling distance on data for',
+                                     desc=('Sampling distance on data for '
                                            'parameter estimation'))
     write_bounding_box = traits.List(traits.List(traits.Float(),
                                                  minlen=3, maxlen=3),
                                      field='woptions.bb', minlen=2, maxlen=2,
-                                     desc=('3x2-element list of lists representing',
+                                     desc=('3x2-element list of lists representing '
                                            'the bounding box (in mm) to be written'))
     write_voxel_sizes = traits.List(traits.Float(), field='woptions.vox',
                                     minlen=3, maxlen=3,
-                                    desc=('3-element list representing the',
-                                          'voxel sizes (in mm) of the written',
+                                    desc=('3-element list representing the '
+                                          'voxel sizes (in mm) of the written '
                                           'normalised images'))
     write_interp = traits.Range(low=0, high=7, field='woptions.interp',
                                 desc='degree of b-spline used for interpolation')
 
 
 class Normalize12OutputSpec(TraitedSpec):
     deformation_field = OutputMultiPath(File(exists=True),
-                                        desc=('NIfTI file containing 3 deformation',
-                                              'fields for the deformation in',
+                                        desc=('NIfTI file containing 3 deformation '
+                                              'fields for the deformation in '
                                               'x, y and z dimension'))
     normalized_image = OutputMultiPath(File(exists=True),
-                                       desc=('Normalized file that needed to',
+                                       desc=('Normalized file that needed to '
                                              'be aligned'))
-    normalized_files = OutputMultiPath(File(exists=True), 
+    normalized_files = OutputMultiPath(File(exists=True),
                                        desc='Normalized other files')
 
 
@@ -621,7 +621,7 @@ def _format_arg(self, opt, spec, val):
                 raise ValueError('%s must have 5 elements' % opt)
         return super(Normalize12, self)._format_arg(opt, spec, val)
 
-    def _parse_inputs(self):
+    def _parse_inputs(self, skip=()):
         """validate spm normalize options if set to None ignore
         """
         einputs = super(Normalize12, self)._parse_inputs(skip=('jobtype',

nipype/interfaces/spm/tests/test_preprocess.py

@@ -85,7 +85,7 @@ 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)
 
@@ -98,7 +98,8 @@ 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])
+    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)
 

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()