pull from nipy/nipype master

.travis.yml

@@ -24,8 +24,8 @@ before_install:
  fsl afni elastix fsl-atlases; fi &&
  if $INSTALL_DEB_DEPENDECIES; then
  source /etc/fsl/fsl.sh;
- source /etc/afni/afni.sh; fi &&
- export FSLOUTPUTTYPE=NIFTI_GZ; }
+ source /etc/afni/afni.sh; 
+  export FSLOUTPUTTYPE=NIFTI_GZ; fi }
 - travis_retry bef_inst
 install:
 # Add install of vtk and mayavi to test mesh (disabled): conda install -y vtk mayavi &&

nipype/algorithms/confounds.py

@@ -281,8 +281,7 @@ def _list_outputs(self):
 class CompCorInputSpec(BaseInterfaceInputSpec):
  realigned_file = File(exists=True, mandatory=True,
  desc='already realigned brain image (4D)')
- mask_file = File(exists=True,
- desc='mask file that determines ROI (3D)')
+ mask_file = File(exists=True, desc='mask file that determines ROI (3D)')
  components_file = File('components_file.txt', exists=False,
  usedefault=True,
  desc='filename to store physiological components')

nipype/interfaces/fsl/possum.py

@@ -79,6 +79,7 @@ class B0Calc(FSLCommand):
  >>> b0calc = B0Calc()
  >>> b0calc.inputs.in_file = 'tissue+air_map.nii'
  >>> b0calc.inputs.z_b0 = 3.0
+ >>> b0calc.inputs.output_type = "NIFTI_GZ"
  >>> b0calc.cmdline # doctest: +IGNORE_UNICODE
  'b0calc -i tissue+air_map.nii -o tissue+air_map_b0field.nii.gz --b0=3.00'
 

nipype/interfaces/nilearn.py

@@ -38,20 +38,19 @@ class SignalExtractionInputSpec(BaseInterfaceInputSpec):
  'corresponds to the class labels in label_file '
  'in ascending order')
  out_file = File('signals.tsv', usedefault=True, exists=False,
- mandatory=False, desc='The name of the file to output to. '
+ desc='The name of the file to output to. '
  'signals.tsv by default')
- incl_shared_variance = traits.Bool(True, usedefault=True, mandatory=False, desc='By default '
+ incl_shared_variance = traits.Bool(True, usedefault=True, desc='By default '
  '(True), returns simple time series calculated from each '
  'region independently (e.g., for noise regression). If '
  'False, returns unique signals for each region, discarding '
  'shared variance (e.g., for connectivity. Only has effect '
  'with 4D probability maps.')
- include_global = traits.Bool(False, usedefault=True, mandatory=False,
+ include_global = traits.Bool(False, usedefault=True,
  desc='If True, include an extra column '
  'labeled "global", with values calculated from the entire brain '
  '(instead of just regions).')
- detrend = traits.Bool(False, usedefault=True, mandatory=False,
- desc='If True, perform detrending using nilearn.')
+ detrend = traits.Bool(False, usedefault=True, desc='If True, perform detrending using nilearn.')
 
 class SignalExtractionOutputSpec(TraitedSpec):
  out_file = File(exists=True, desc='tsv file containing the computed '

nipype/interfaces/tests/test_auto_SignalExtraction.py

@@ -6,24 +6,20 @@
 def test_SignalExtraction_inputs():
  input_map = dict(class_labels=dict(mandatory=True,
  ),
- detrend=dict(mandatory=False,
- usedefault=True,
+ detrend=dict(usedefault=True,
  ),
  ignore_exception=dict(nohash=True,
  usedefault=True,
  ),
  in_file=dict(mandatory=True,
  ),
- incl_shared_variance=dict(mandatory=False,
- usedefault=True,
+ incl_shared_variance=dict(usedefault=True,
  ),
- include_global=dict(mandatory=False,
- usedefault=True,
+ include_global=dict(usedefault=True,
  ),
  label_files=dict(mandatory=True,
  ),
- out_file=dict(mandatory=False,
- usedefault=True,
+ out_file=dict(usedefault=True,
  ),
  )
  inputs = SignalExtraction.input_spec()