fix: clean auto tests and white spaces

Author: satra

doc/documentation.rst

@@ -29,7 +29,7 @@ Previous versions:  `0.12.0 <http://nipype.readthedocs.io/en/0.12.0/>`_ `0.11.0
             :maxdepth: 2
 
             users/index
-            
+
          .. toctree::
             :maxdepth: 1
 

doc/users/config_file.rst

@@ -145,7 +145,7 @@ Execution
     crashfiles allow interactive debugging and rerunning of nodes, while text
     crashfiles allow portability across machines and shorter load time.
     (possible values: ``pklz`` and ``txt``; default value: ``pklz``)
-    
+
 Example
 ~~~~~~~
 

nipype/algorithms/confounds.py

@@ -306,28 +306,28 @@ class CompCorInputSpec(BaseInterfaceInputSpec):
     realigned_file = File(exists=True, mandatory=True,
                           desc='already realigned brain image (4D)')
     mask_files = InputMultiPath(File(exists=True),
-                                desc=('One or more mask files that determines ' 
+                                desc=('One or more mask files that determines '
                                       'ROI (3D)'))
     merge_method = traits.Enum('union', 'intersect', 'none', xor=['mask_index'],
                                requires=['mask_files'],
-                               desc=('Merge method if multiple masks are ' 
-                                     'present - `union` aggregates all masks, ' 
-                                     '`intersect` computes the truth value of ' 
-                                     'all masks, `none` performs CompCor on ' 
+                               desc=('Merge method if multiple masks are '
+                                     'present - `union` aggregates all masks, '
+                                     '`intersect` computes the truth value of '
+                                     'all masks, `none` performs CompCor on '
                                      'each mask individually'))
     mask_index = traits.Range(low=0, xor=['merge_method'],
                               requires=['mask_files'],
-                              desc=('Position of mask in `mask_files` to use - ' 
+                              desc=('Position of mask in `mask_files` to use - '
                                     'first is the default'))
     components_file = File('components_file.txt', exists=False, usedefault=True,
                            desc='Filename to store physiological components')
     num_components = traits.Int(6, usedefault=True) # 6 for BOLD, 4 for ASL
     use_regress_poly = traits.Bool(True, usedefault=True,
-                                   desc=('use polynomial regression ' 
+                                   desc=('use polynomial regression '
                                          'pre-component extraction'))
     regress_poly_degree = traits.Range(low=1, default=1, usedefault=True,
                                        desc='the degree polynomial to use')
-    header = traits.Str(desc=('the desired header for the output tsv file (one ' 
+    header = traits.Str(desc=('the desired header for the output tsv file (one '
                               'column). If undefined, will default to '
                               '"CompCor"'))
 
@@ -350,7 +350,7 @@ class CompCor(BaseInterface):
     >>> ccinterface.inputs.num_components = 1
     >>> ccinterface.inputs.use_regress_poly = True
     >>> ccinterface.inputs.regress_poly_degree = 2
-    
+
     """
     input_spec = CompCorInputSpec
     output_spec = CompCorOutputSpec
@@ -473,7 +473,7 @@ class TCompCor(CompCor):
     >>> ccinterface.inputs.use_regress_poly = True
     >>> ccinterface.inputs.regress_poly_degree = 2
     >>> ccinterface.inputs.percentile_threshold = .03
-    
+
     """
 
     input_spec = TCompCorInputSpec
@@ -829,16 +829,16 @@ def regress_poly(degree, data, remove_mean=True, axis=-1):
 
 def combine_mask_files(mask_files, mask_method=None, mask_index=None):
     """Combines input mask files into a single nibabel image
-    
+
     A helper function for CompCor
-    
+
     mask_files: a list
         one or more binary mask files
     mask_method: enum ('union', 'intersect', 'none')
         determines how to combine masks
     mask_index: an integer
         determines which file to return (mutually exclusive with mask_method)
-        
+
     returns: a list of nibabel images
     """
 
@@ -879,16 +879,16 @@ def combine_mask_files(mask_files, mask_method=None, mask_index=None):
 
 def compute_noise_components(imgseries, mask_images, degree, num_components):
     """Compute the noise components from the imgseries for each mask
-    
+
     imgseries: a nibabel img
     mask_images: a list of nibabel images
     degree: order of polynomial used to remove trends from the timeseries
     num_components: number of noise components to return
-    
+
     returns:
-    
-    components: a numpy array 
-    
+
+    components: a numpy array
+
     """
     components = None
     for img in mask_images:

nipype/interfaces/afni/preprocess.py

@@ -2417,7 +2417,7 @@ class QwarpPlusMinusOutputSpec(TraitedSpec):
 
 
 class QwarpPlusMinus(CommandLine):
-    """A version of 3dQwarp for performing field susceptibility correction 
+    """A version of 3dQwarp for performing field susceptibility correction
     using two images with opposing phase encoding directions.
 
     For complete details, see the `3dQwarp Documentation.
@@ -2434,7 +2434,7 @@ class QwarpPlusMinus(CommandLine):
     >>> qwarp.cmdline  # doctest: +ALLOW_UNICODE
     '3dQwarp -prefix Qwarp.nii.gz -plusminus -base sub-01_dir-RL_epi.nii.gz -nopadWARP -source sub-01_dir-LR_epi.nii.gz'
     >>> res = warp.run()  # doctest: +SKIP
-    
+
     """
     _cmd = '3dQwarp -prefix Qwarp.nii.gz -plusminus'
     input_spec = QwarpPlusMinusInputSpec

nipype/interfaces/afni/utils.py

@@ -695,7 +695,7 @@ class FWHMx(AFNICommandBase):
     _cmd = '3dFWHMx'
     input_spec = FWHMxInputSpec
     output_spec = FWHMxOutputSpec
-    
+
     references_ = [{'entry': BibTeX('@article{CoxReynoldsTaylor2016,'
                                     'author={R.W. Cox, R.C. Reynolds, and P.A. Taylor},'
                                     'title={AFNI and clustering: false positive rates redux},'

nipype/interfaces/fsl/tests/test_auto_Eddy.py

@@ -88,9 +88,6 @@ def test_Eddy_inputs():
 def test_Eddy_outputs():
     output_map = dict(out_corrected=dict(),
     out_movement_rms=dict(),
-    out_outlier_map=dict(),
-    out_outlier_n_sd_map=dict(),
-    out_outlier_n_sqr_sd_map=dict(),
     out_outlier_report=dict(),
     out_parameter=dict(),
     out_restricted_movement_rms=dict(),

nipype/interfaces/fsl/tests/test_auto_TOPUP.py

@@ -54,10 +54,6 @@ def test_TOPUP_inputs():
     name_source=['in_file'],
     name_template='%s_field',
     ),
-    out_warp_prefix=dict(argstr='--dfout=%s',
-    hash_files=False,
-    usedefault=True,
-    ),
     out_jac_prefix=dict(argstr='--jacout=%s',
     hash_files=False,
     usedefault=True,
@@ -68,6 +64,10 @@ def test_TOPUP_inputs():
     name_source=['in_file'],
     name_template='%s_topup.log',
     ),
+    out_warp_prefix=dict(argstr='--dfout=%s',
+    hash_files=False,
+    usedefault=True,
+    ),
     output_type=dict(),
     readout_times=dict(mandatory=True,
     requires=['encoding_direction'],
@@ -104,10 +104,10 @@ def test_TOPUP_outputs():
     out_enc_file=dict(),
     out_field=dict(),
     out_fieldcoef=dict(),
+    out_jacs=dict(),
     out_logfile=dict(),
     out_movpar=dict(),
     out_warps=dict(),
-    out_jacs=dict(),
     )
     outputs = TOPUP.output_spec()
 

nipype/interfaces/fsl/utils.py

@@ -73,7 +73,7 @@ class RobustFOVInputSpec(FSLCommandInputSpec):
     brainsize = traits.Int(desc=('size of brain in z-dimension (default '
                                  '170mm/150mm)'),
                            argstr='-b %d')
-    out_transform = File(desc=("Transformation matrix in_file to out_roi "  
+    out_transform = File(desc=("Transformation matrix in_file to out_roi "
                                "output name"),
                          argstr="-m %s",
                          name_source=['in_file'], hash_files=False,
@@ -83,17 +83,17 @@ class RobustFOVInputSpec(FSLCommandInputSpec):
 class RobustFOVOutputSpec(TraitedSpec):
     out_roi = File(exists=True, desc="ROI volume output name")
     out_transform = File(exists=True,
-                         desc=("Transformation matrix in_file to out_roi " 
+                         desc=("Transformation matrix in_file to out_roi "
                                "output name"))
 
 
 class RobustFOV(FSLCommand):
     """Automatically crops an image removing lower head and neck.
-    
-    Interface is stable 5.0.0 to 5.0.9, but default brainsize changed from 
+
+    Interface is stable 5.0.0 to 5.0.9, but default brainsize changed from
     150mm to 170mm.
     """
-    
+
     _cmd = 'robustfov'
     input_spec = RobustFOVInputSpec
     output_spec = RobustFOVOutputSpec