diff --git a/nipype/interfaces/ants/registration.py b/nipype/interfaces/ants/registration.py
index 56cdce10c7..7190816cf3 100644
--- a/nipype/interfaces/ants/registration.py
+++ b/nipype/interfaces/ants/registration.py
@@ -70,8 +70,7 @@ class ANTSInputSpec(ANTSCommandInputSpec):
traits.Int(),
requires=['metric'],
mandatory=True,
- desc='radius of the region (i.e. number of layers'
- ' around a voxel point)'
+ desc='radius of the region (i.e. number of layers around a voxel/pixel)'
' that is used for computing cross correlation')
output_transform_prefix = Str(
@@ -126,8 +125,8 @@ class ANTSOutputSpec(TraitedSpec):
class ANTS(ANTSCommand):
- """
-
+ """ANTS wrapper for registration of images
+ (old, use Registration instead)
Examples
--------
@@ -557,7 +556,8 @@ class RegistrationOutputSpec(TraitedSpec):
class Registration(ANTSCommand):
- """
+ """ANTs Registration command for registration of images
+
`antsRegistration `_ registers a ``moving_image`` to a ``fixed_image``,
using a predefined (sequence of) cost function(s) and transformation operations.
The cost function is defined using one or more 'metrics', specifically
@@ -601,12 +601,12 @@ class Registration(ANTSCommand):
Examples
--------
- Set up a Registation node with some default settings. This Node registers
+ Set up a Registration node with some default settings. This Node registers
'fixed1.nii' to 'moving1.nii' by first fitting a linear 'Affine' transformation, and
then a non-linear 'SyN' transformation, both using the Mutual Information-cost
metric.
- The registration is initailized by first applying the (linear) transform
+ The registration is initialized by first applying the (linear) transform
trans.mat.
>>> import copy, pprint
diff --git a/nipype/interfaces/ants/resampling.py b/nipype/interfaces/ants/resampling.py
index fe730aef78..e26a48ed6a 100644
--- a/nipype/interfaces/ants/resampling.py
+++ b/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',
diff --git a/nipype/interfaces/ants/segmentation.py b/nipype/interfaces/ants/segmentation.py
index f6a3f5da97..6ccfb28ba2 100644
--- a/nipype/interfaces/ants/segmentation.py
+++ b/nipype/interfaces/ants/segmentation.py
@@ -266,10 +266,15 @@ class N4BiasFieldCorrectionInputSpec(ANTSCommandInputSpec):
input_image = File(
argstr='--input-image %s',
mandatory=True,
- desc=('image to apply transformation to (generally a '
- 'coregistered functional)'))
- mask_image = File(argstr='--mask-image %s')
- weight_image = File(argstr='--weight-image %s')
+ desc=('input for bias correction. Negative values or values close to '
+ 'zero should be processed prior to correction'))
+ mask_image = File(
+ argstr='--mask-image %s',
+ desc=('image to specify region to perform final bias correction in'))
+ weight_image = File(
+ argstr='--weight-image %s',
+ desc=('image for relative weighting (e.g. probability map of the white '
+ 'matter) of voxels during the B-spline fitting. '))
output_image = traits.Str(
argstr='--output %s',
desc='output file name',
@@ -284,8 +289,7 @@ class N4BiasFieldCorrectionInputSpec(ANTSCommandInputSpec):
False,
mandatory=True,
usedefault=True,
- desc=('True if the estimated bias should be saved'
- ' to file.'),
+ desc=('True if the estimated bias should be saved to file.'),
xor=['bias_image'])
bias_image = File(
desc='Filename for the estimated bias.', hash_files=False)
@@ -451,19 +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,
@@ -488,10 +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(
@@ -504,66 +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.'
))
@@ -700,25 +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(
@@ -733,7 +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,'
@@ -745,7 +747,7 @@ 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).')
@@ -753,14 +755,13 @@ class BrainExtractionInputSpec(ANTSCommandInputSpec):
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.'
))
@@ -945,21 +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,
@@ -981,14 +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):
@@ -1082,25 +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'],
@@ -1445,8 +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(
@@ -1466,26 +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(
@@ -1516,9 +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",
@@ -1532,7 +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(
diff --git a/nipype/interfaces/fsl/preprocess.py b/nipype/interfaces/fsl/preprocess.py
index 201106502f..dd2c969945 100644
--- a/nipype/interfaces/fsl/preprocess.py
+++ b/nipype/interfaces/fsl/preprocess.py
@@ -113,7 +113,7 @@ class BETOutputSpec(TraitedSpec):
class BET(FSLCommand):
- """Use FSL BET command for skull stripping.
+ """FSL BET wrapper for skull stripping
For complete details, see the `BET Documentation.
`_
@@ -312,7 +312,7 @@ class FASTOutputSpec(TraitedSpec):
class FAST(FSLCommand):
- """ Use FSL FAST for segmenting and bias correction.
+ """FSL FAST wrapper for segmentation and bias correction
For complete details, see the `FAST Documentation.
`_
@@ -635,7 +635,7 @@ class FLIRTOutputSpec(TraitedSpec):
class FLIRT(FSLCommand):
- """Use FSL FLIRT for coregistration.
+ """FSL FLIRT wrapper for coregistration
For complete details, see the `FLIRT Documentation.
`_
@@ -790,7 +790,7 @@ class MCFLIRTOutputSpec(TraitedSpec):
class MCFLIRT(FSLCommand):
- """Use FSL MCFLIRT to do within-modality motion correction.
+ """FSL MCFLIRT wrapper for within-modality motion correction
For complete details, see the `MCFLIRT Documentation.
`_
@@ -1106,7 +1106,7 @@ class FNIRTOutputSpec(TraitedSpec):
class FNIRT(FSLCommand):
- """Use FSL FNIRT for non-linear registration.
+ """FSL FNIRT wrapper for non-linear registration
For complete details, see the `FNIRT Documentation.
`_
@@ -1305,7 +1305,7 @@ class ApplyWarpOutputSpec(TraitedSpec):
class ApplyWarp(FSLCommand):
- """Use FSL's applywarp to apply the results of a FNIRT registration
+ """FSL's applywarp wrapper to apply the results of a FNIRT registration
Examples
--------
@@ -1389,7 +1389,7 @@ class SliceTimerOutputSpec(TraitedSpec):
class SliceTimer(FSLCommand):
- """ use FSL slicetimer to perform slice timing correction.
+ """FSL slicetimer wrapper to perform slice timing correction
Examples
--------
@@ -1476,7 +1476,7 @@ class SUSANOutputSpec(TraitedSpec):
class SUSAN(FSLCommand):
- """ use FSL SUSAN to perform smoothing
+ """FSL SUSAN wrapper to perform smoothing
For complete details, see the `SUSAN Documentation.
`_
@@ -1646,7 +1646,8 @@ class FUGUEOutputSpec(TraitedSpec):
class FUGUE(FSLCommand):
- """
+ """FSL FUGUE set of tools for EPI distortion correction
+
`FUGUE `_ is, most generally,
a set of tools for EPI distortion correction.
@@ -1869,7 +1870,7 @@ class PRELUDEOutputSpec(TraitedSpec):
class PRELUDE(FSLCommand):
- """Use FSL prelude to do phase unwrapping
+ """FSL prelude wrapper for phase unwrapping
Examples
--------
@@ -1979,8 +1980,7 @@ class FIRSTOutputSpec(TraitedSpec):
class FIRST(FSLCommand):
- """
- Use FSL's run_first_all command to segment subcortical volumes
+ """FSL run_first_all wrapper for segmentation of subcortical volumes
http://www.fmrib.ox.ac.uk/fsl/first/index.html