pull from nipy/nipype master

Author: Shoshana Berleant

.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()