DOC: fix "joining spaces". Will stay with convention of leading space due to easier detection

Author: yarikoptic

nipype/interfaces/ants/resampling.py

@@ -493,15 +493,15 @@ class ApplyTransformsToPointsInputSpec(ANTSCommandInputSpec):
         argstr='--input %s',
         mandatory=True,
         desc=
-        ("Currently, the only input supported is a csv file with "
-         "columns including x,y (2D), x,y,z (3D) or x,y,z,t,label (4D) column headers."
-         "The points should be defined in physical space."
-         "If in doubt how to convert coordinates from your files to the space"
-         "required by antsApplyTransformsToPoints try creating/drawing a simple"
-         "label volume with only one voxel set to 1 and all others set to 0."
-         "Write down the voxel coordinates. Then use ImageMaths LabelStats to find"
-         "out what coordinates for this voxel antsApplyTransformsToPoints is"
-         "expecting."),
+        ("Currently, the only input supported is a csv file with"
+         " columns including x,y (2D), x,y,z (3D) or x,y,z,t,label (4D) column headers."
+         " The points should be defined in physical space."
+         " If in doubt how to convert coordinates from your files to the space"
+         " required by antsApplyTransformsToPoints try creating/drawing a simple"
+         " label volume with only one voxel set to 1 and all others set to 0."
+         " Write down the voxel coordinates. Then use ImageMaths LabelStats to find"
+         " out what coordinates for this voxel antsApplyTransformsToPoints is"
+         " expecting."),
         exists=True)
     output_file = traits.Str(
         argstr='--output %s',

nipype/interfaces/ants/segmentation.py

@@ -455,19 +455,19 @@ class CorticalThicknessInputSpec(ANTSCommandInputSpec):
         exists=True,
         argstr='-a %s',
         desc=('Structural *intensity* image, typically T1.'
-              'If more than one anatomical image is specified,'
-              'subsequently specified images are used during the'
-              'segmentation process. However, only the first'
-              'image is used in the registration of priors.'
-              'Our suggestion would be to specify the T1'
-              'as the first image.'),
+              ' If more than one anatomical image is specified,'
+              ' subsequently specified images are used during the'
+              ' segmentation process. However, only the first'
+              ' image is used in the registration of priors.'
+              ' Our suggestion would be to specify the T1'
+              ' as the first image.'),
         mandatory=True)
     brain_template = File(
         exists=True,
         argstr='-e %s',
         desc=('Anatomical *intensity* template (possibly created using a'
-              'population data set with buildtemplateparallel.sh in ANTs).'
-              'This template is  *not* skull-stripped.'),
+              ' population data set with buildtemplateparallel.sh in ANTs).'
+              ' This template is  *not* skull-stripped.'),
         mandatory=True)
     brain_probability_mask = File(
         exists=True,
@@ -492,10 +492,10 @@ class CorticalThicknessInputSpec(ANTSCommandInputSpec):
     t1_registration_template = File(
         exists=True,
         desc=('Anatomical *intensity* template'
-              '(assumed to be skull-stripped). A common'
-              'case would be where this would be the same'
-              'template as specified in the -e option which'
-              'is not skull stripped.'),
+              ' (assumed to be skull-stripped). A common'
+              ' case would be where this would be the same'
+              ' template as specified in the -e option which'
+              ' is not skull stripped.'),
         argstr='-t %s',
         mandatory=True)
     extraction_registration_mask = File(
@@ -508,66 +508,64 @@ class CorticalThicknessInputSpec(ANTSCommandInputSpec):
         desc='Keep brain extraction/segmentation warps, etc (default = 0).')
     max_iterations = traits.Int(
         argstr='-i %d',
-        desc=('ANTS registration max iterations'
-              '(default = 100x100x70x20)'))
+        desc=('ANTS registration max iterations (default = 100x100x70x20)'))
     prior_segmentation_weight = traits.Float(
         argstr='-w %f',
         desc=('Atropos spatial prior *probability* weight for'
-              'the segmentation'))
+              ' the segmentation'))
     segmentation_iterations = traits.Int(
         argstr='-n %d',
         desc=('N4 -> Atropos -> N4 iterations during segmentation'
-              '(default = 3)'))
+              ' (default = 3)'))
     posterior_formulation = traits.Str(
         argstr='-b %s',
         desc=('Atropos posterior formulation and whether or not'
-              'to use mixture model proportions.'
-              '''e.g 'Socrates[1]' (default) or 'Aristotle[1]'.'''
-              'Choose the latter if you'
-              'want use the distance priors (see also the -l option'
-              'for label propagation control).'))
+              ' to use mixture model proportions.'
+              ''' e.g 'Socrates[1]' (default) or 'Aristotle[1]'.'''
+              ' Choose the latter if you'
+              ' want use the distance priors (see also the -l option'
+              ' for label propagation control).'))
     use_floatingpoint_precision = traits.Enum(
         0,
         1,
         argstr='-j %d',
-        desc=('Use floating point precision '
-              'in registrations (default = 0)'))
+        desc=('Use floating point precision in registrations (default = 0)'))
     use_random_seeding = traits.Enum(
         0,
         1,
         argstr='-u %d',
         desc=('Use random number generated from system clock in Atropos'
-              '(default = 1)'))
+              ' (default = 1)'))
     b_spline_smoothing = traits.Bool(
         argstr='-v',
         desc=('Use B-spline SyN for registrations and B-spline'
-              'exponential mapping in DiReCT.'))
+              ' exponential mapping in DiReCT.'))
     cortical_label_image = File(
         exists=True, desc='Cortical ROI labels to use as a prior for ATITH.')
     label_propagation = traits.Str(
         argstr='-l %s',
         desc=
         ('Incorporate a distance prior one the posterior formulation.  Should be'
-         '''of the form 'label[lambda,boundaryProbability]' where label'''
-         'is a value of 1,2,3,... denoting label ID.  The label'
-         'probability for anything outside the current label'
-         '  = boundaryProbability * exp( -lambda * distanceFromBoundary )'
-         'Intuitively, smaller lambda values will increase the spatial capture'
-         'range of the distance prior.  To apply to all label values, simply omit'
-         'specifying the label, i.e. -l [lambda,boundaryProbability].'))
+         ''' of the form 'label[lambda,boundaryProbability]' where label'''
+         ' is a value of 1,2,3,... denoting label ID.  The label'
+         ' probability for anything outside the current label'
+         ' = boundaryProbability * exp( -lambda * distanceFromBoundary )'
+         ' Intuitively, smaller lambda values will increase the spatial capture'
+         ' range of the distance prior.  To apply to all label values, simply omit'
+         ' specifying the label, i.e. -l [lambda,boundaryProbability].'))
     quick_registration = traits.Bool(
         argstr='-q 1',
         desc=
         ('If = 1, use antsRegistrationSyNQuick.sh as the basis for registration'
-         'during brain extraction, brain segmentation, and'
-         '(optional) normalization to a template.'
-         'Otherwise use antsRegistrationSyN.sh (default = 0).'))
+         ' during brain extraction, brain segmentation, and'
+         ' (optional) normalization to a template.'
+         ' Otherwise use antsRegistrationSyN.sh (default = 0).'))
     debug = traits.Bool(
         argstr='-z 1',
         desc=(
             'If > 0, runs a faster version of the script.'
-            'Only for testing. Implies -u 0.'
-            'Requires single thread computation for complete reproducibility.'
+            ' Only for testing. Implies -u 0.'
+            ' Requires single thread computation for complete reproducibility.'
         ))
 
 
@@ -704,25 +702,25 @@ class BrainExtractionInputSpec(ANTSCommandInputSpec):
         exists=True,
         argstr='-a %s',
         desc=('Structural image, typically T1.  If more than one'
-              'anatomical image is specified, subsequently specified'
-              'images are used during the segmentation process.  However,'
-              'only the first image is used in the registration of priors.'
-              'Our suggestion would be to specify the T1 as the first image.'
-              'Anatomical template created using e.g. LPBA40 data set with'
-              'buildtemplateparallel.sh in ANTs.'),
+              ' anatomical image is specified, subsequently specified'
+              ' images are used during the segmentation process.  However,'
+              ' only the first image is used in the registration of priors.'
+              ' Our suggestion would be to specify the T1 as the first image.'
+              ' Anatomical template created using e.g. LPBA40 data set with'
+              ' buildtemplateparallel.sh in ANTs.'),
         mandatory=True)
     brain_template = File(
         exists=True,
         argstr='-e %s',
         desc=('Anatomical template created using e.g. LPBA40 data set with'
-              'buildtemplateparallel.sh in ANTs.'),
+              ' buildtemplateparallel.sh in ANTs.'),
         mandatory=True)
     brain_probability_mask = File(
         exists=True,
         argstr='-m %s',
         desc=('Brain probability mask created using e.g. LPBA40 data set which'
-              'have brain masks defined, and warped to anatomical template and'
-              'averaged resulting in a probability image.'),
+              ' have brain masks defined, and warped to anatomical template and'
+              ' averaged resulting in a probability image.'),
         copyfile=False,
         mandatory=True)
     out_prefix = traits.Str(
@@ -737,7 +735,7 @@ class BrainExtractionInputSpec(ANTSCommandInputSpec):
         argstr='-f %s',
         desc=('Mask (defined in the template space) used during'
               ' registration for brain extraction.'
-              'To limit the metric computation to a specific region.'))
+              ' To limit the metric computation to a specific region.'))
     image_suffix = traits.Str(
         'nii.gz',
         desc=('any of standard ITK formats,'
@@ -749,22 +747,21 @@ class BrainExtractionInputSpec(ANTSCommandInputSpec):
         1,
         argstr='-u %d',
         desc=('Use random number generated from system clock in Atropos'
-              '(default = 1)'))
+              ' (default = 1)'))
     keep_temporary_files = traits.Int(
         argstr='-k %d',
         desc='Keep brain extraction/segmentation warps, etc (default = 0).')
     use_floatingpoint_precision = traits.Enum(
         0,
         1,
         argstr='-q %d',
-        desc=('Use floating point precision '
-              'in registrations (default = 0)'))
+        desc=('Use floating point precision in registrations (default = 0)'))
     debug = traits.Bool(
         argstr='-z 1',
         desc=(
             'If > 0, runs a faster version of the script.'
-            'Only for testing. Implies -u 0.'
-            'Requires single thread computation for complete reproducibility.'
+            ' Only for testing. Implies -u 0.'
+            ' Requires single thread computation for complete reproducibility.'
         ))
 
 
@@ -949,21 +946,19 @@ class JointFusionInputSpec(ANTSCommandInputSpec):
         default='Joint',
         argstr='-m %s',
         usedefault=True,
-        desc=('Select voting method. Options: Joint (Joint '
-              'Label Fusion). May be followed by optional '
-              'parameters in brackets, e.g., -m Joint[0.1,2]'))
+        desc=('Select voting method. Options: Joint (Joint'
+              ' Label Fusion). May be followed by optional'
+              ' parameters in brackets, e.g., -m Joint[0.1,2]'))
     alpha = traits.Float(
         default=0.1,
         usedefault=True,
         requires=['method'],
-        desc=('Regularization term added to matrix Mx for '
-              'inverse'))
+        desc=('Regularization term added to matrix Mx for inverse'))
     beta = traits.Int(
         default=2,
         usedefault=True,
         requires=['method'],
-        desc=('Exponent for mapping intensity difference to joint'
-              ' error'))
+        desc=('Exponent for mapping intensity difference to joint error'))
     output_label_image = File(
         argstr='%s',
         mandatory=True,
@@ -985,14 +980,12 @@ class JointFusionInputSpec(ANTSCommandInputSpec):
     exclusion_region = File(
         exists=True,
         argstr='-x %s',
-        desc=('Specify an exclusion region for the given '
-              'label.'))
+        desc=('Specify an exclusion region for the given label.'))
     atlas_group_id = traits.ListInt(
         argstr='-gp %d...', desc='Assign a group ID for each atlas')
     atlas_group_weights = traits.ListInt(
         argstr='-gpw %d...',
-        desc=('Assign the voting weights to '
-              'each atlas group'))
+        desc=('Assign the voting weights to each atlas group'))
 
 
 class JointFusionOutputSpec(TraitedSpec):
@@ -1086,25 +1079,24 @@ class DenoiseImageInputSpec(ANTSCommandInputSpec):
         default_value=1,
         usedefault=True,
         argstr='-s %s',
-        desc=('Running noise correction on large images can '
-              'be time consuming. To lessen computation time, '
-              'the input image can be resampled. The shrink '
-              'factor, specified as a single integer, describes '
-              'this resampling. Shrink factor = 1 is the default.'))
+        desc=('Running noise correction on large images can'
+              ' be time consuming. To lessen computation time,'
+              ' the input image can be resampled. The shrink'
+              ' factor, specified as a single integer, describes'
+              ' this resampling. Shrink factor = 1 is the default.'))
     output_image = File(
         argstr="-o %s",
         name_source=['input_image'],
         hash_files=False,
         keep_extension=True,
         name_template='%s_noise_corrected',
-        desc='The output consists of the noise corrected '
-        'version of the input image.')
+        desc='The output consists of the noise corrected'
+             ' version of the input image.')
     save_noise = traits.Bool(
         False,
         mandatory=True,
         usedefault=True,
-        desc=('True if the estimated noise should be saved '
-              'to file.'),
+        desc=('True if the estimated noise should be saved to file.'),
         xor=['noise_image'])
     noise_image = File(
         name_source=['input_image'],
@@ -1449,8 +1441,8 @@ class KellyKapowskiInputSpec(ANTSCommandInputSpec):
         argstr='--segmentation-image "%s"',
         mandatory=True,
         desc=
-        "A segmentation image must be supplied labeling the gray and white matters.\n"
-        "Default values = 2 and 3, respectively.",
+        "A segmentation image must be supplied labeling the gray and white matters."
+        " Default values = 2 and 3, respectively.",
     )
 
     gray_matter_label = traits.Int(
@@ -1470,26 +1462,26 @@ class KellyKapowskiInputSpec(ANTSCommandInputSpec):
         exists=True,
         argstr='--gray-matter-probability-image "%s"',
         desc=
-        "In addition to the segmentation image, a gray matter probability image can be\n"
-        "used. If no such image is supplied, one is created using the segmentation image\n"
-        "and a variance of 1.0 mm.")
+        "In addition to the segmentation image, a gray matter probability image can be"
+        " used. If no such image is supplied, one is created using the segmentation image"
+        " and a variance of 1.0 mm.")
 
     white_matter_prob_image = File(
         exists=True,
         argstr='--white-matter-probability-image "%s"',
         desc=
-        "In addition to the segmentation image, a white matter probability image can be\n"
-        "used. If no such image is supplied, one is created using the segmentation image\n"
-        "and a variance of 1.0 mm.")
+        "In addition to the segmentation image, a white matter probability image can be"
+        " used. If no such image is supplied, one is created using the segmentation image"
+        " and a variance of 1.0 mm.")
 
     convergence = traits.Str(
         default="[50,0.001,10]",
         argstr='--convergence "%s"',
         usedefault=True,
         desc=
-        "Convergence is determined by fitting a line to the normalized energy profile of\n"
-        "the last N iterations (where N is specified by the window size) and determining\n"
-        "the slope which is then compared with the convergence threshold.",
+        "Convergence is determined by fitting a line to the normalized energy profile of"
+        " the last N iterations (where N is specified by the window size) and determining"
+        " the slope which is then compared with the convergence threshold.",
     )
 
     thickness_prior_estimate = traits.Float(
@@ -1520,9 +1512,9 @@ class KellyKapowskiInputSpec(ANTSCommandInputSpec):
         1.5, usedefault=True,
         argstr="--smoothing-velocity-field-parameter %f",
         desc=
-        "Defines the Gaussian smoothing of the velocity field (default = 1.5).\n"
-        "If the b-spline smoothing option is chosen, then this defines the \n"
-        "isotropic mesh spacing for the smoothing spline (default = 15).")
+        "Defines the Gaussian smoothing of the velocity field (default = 1.5)."
+        " If the b-spline smoothing option is chosen, then this defines the"
+        " isotropic mesh spacing for the smoothing spline (default = 15).")
 
     use_bspline_smoothing = traits.Bool(
         argstr="--use-bspline-smoothing 1",
@@ -1536,7 +1528,7 @@ class KellyKapowskiInputSpec(ANTSCommandInputSpec):
     max_invert_displacement_field_iters = traits.Int(
         20, usedefault=True,
         argstr="--maximum-number-of-invert-displacement-field-iterations %d",
-        desc="Maximum number of iterations for estimating the invert \n"
+        desc="Maximum number of iterations for estimating the invert"
         "displacement field.")
 
     cortical_thickness = File(