class spec2(nib.CommandLineInputSpec):
doo = nib.File(exists=True, argstr="%s", position=1)
moo = nib.File(name_source=['doo'],
hash_files=False,
argstr="%s",
position=2,
name_template='%s_mootpl')
poo = nib.File(name_source=['moo'],
hash_files=False,
argstr="%s",
position=3)
class spec2(nib.CommandLineInputSpec):
moo = nib.File(name_source=['doo'],
hash_files=False,
argstr="%s",
position=2)
doo = nib.File(exists=True, argstr="%s", position=1)
goo = traits.Int(argstr="%d", position=4)
poo = nib.File(name_source=['goo'],
hash_files=False,
argstr="%s",
position=3)
class ImageMathInputSpec(ANTSCommandInputSpec):
dimension = traits.Int(3, usedefault=True, position=1, argstr='%d',
desc='dimension of output image')
output_image = base.File(position=2, argstr='%s', name_source=['op1'],
name_template='%s_maths', desc='output image file',
keep_extension=True)
operation = base.Str(mandatory=True, position=3, argstr='%s',
desc='operations and intputs')
op1 = base.File(exists=True, mandatory=True, position=-2, argstr='%s',
desc='first operator')
op2 = traits.Either(base.File(exists=True), base.Str, position=-1,
argstr='%s', desc='second operator')
class ThresholdImageInputSpec(ANTSCommandInputSpec):
dimension = traits.Int(3,
usedefault=True,
position=1,
argstr='%d',
desc='dimension of output image')
input_image = base.File(exists=True,
mandatory=True,
position=2,
argstr='%s',
desc='input image file')
output_image = base.File(position=3,
argstr='%s',
name_source=['input_image'],
name_template='%s_resampled',
desc='output image file',
keep_extension=True)
mode = traits.Enum('Otsu',
'Kmeans',
argstr='%s',
position=4,
requires=['num_thresholds'],
xor=['th_low', 'th_high'],
desc='whether to run Otsu / Kmeans thresholding')
num_thresholds = traits.Int(position=5,
argstr='%d',
desc='number of thresholds')
input_mask = base.File(exists=True,
requires=['num_thresholds'],
argstr='%s',
desc='input mask for Otsu, Kmeans')
th_low = traits.Float(position=4,
argstr='%f',
xor=['mode'],
desc='lower threshold')
th_high = traits.Float(position=5,
argstr='%f',
xor=['mode'],
desc='upper threshold')
inside_value = traits.Float(1,
position=6,
argstr='%f',
requires=['th_low'],
desc='inside value')
outside_value = traits.Float(0,
position=7,
argstr='%f',
requires=['th_low'],
desc='outside value')
class spec3(nib.CommandLineInputSpec):
moo = nib.File(name_source=['doo'],
hash_files=False,
argstr="%s",
position=1,
name_template='%s_mootpl')
poo = nib.File(name_source=['moo'],
hash_files=False,
argstr="%s",
position=2)
doo = nib.File(name_source=['poo'],
hash_files=False,
argstr="%s",
position=3)
class _RDFClassifierInputSpec(base.BaseInterfaceInputSpec):
feature_files = base.traits.List(
desc='List of feature files. Must be in the correct order!',
trait=base.File(exists=True),
mandatory=True)
classifier_file = base.File(
desc='Pickled python object containing the RDF classifier to use.',
mandatory=True,
exists=True)
mask_file = base.File(
desc='Mask file indicating on which voxels to operate',
mandatory=True,
exists=True)
segmentation_file = base.File(desc='the target segmentation file')
probability_file = base.File(desc='the target probability file')
class FilterNumsInputSpec(nib.CommandLineInputSpec):
in_file = nib.File(desc="File",
exists=True,
mandatory=True,
argstr="%s",
position=1)
max_number = nib.traits.Int(desc="Max number",
mandatory=True,
argstr="'($1<%d)'",
position=0)
out_file = nib.File(desc="Filtered file",
name_source=['in_file'],
name_template='%s_filtered',
hash_files=False,
argstr="> %s")
class _MedpyResampleInputSpec(base.CommandLineInputSpec):
in_file = base.File(desc='the input image',
position=0,
exists=True,
mandatory=True,
argstr='%s')
out_file = base.File(desc='the output image',
position=1,
argstr='%s',
genfile=True)
spacing = base.traits.String(
desc='the desired voxel spacing in colon-separated values, '
'e.g. 1.2,1.2,5.0',
position=2,
mandatory=True,
argstr='%s')
class _ResampleImageBySpacingInputSpec(ANTSCommandInputSpec):
dimension = traits.Int(3,
usedefault=True,
position=1,
argstr="%d",
desc="dimension of output image")
input_image = base.File(exists=True,
mandatory=True,
position=2,
argstr="%s",
desc="input image file")
output_image = base.File(
position=3,
argstr="%s",
name_source=["input_image"],
name_template="%s_resampled",
desc="output image file",
keep_extension=True,
)
out_spacing = traits.Either(
traits.List(traits.Float, minlen=2, maxlen=3),
traits.Tuple(traits.Float, traits.Float, traits.Float),
traits.Tuple(traits.Float, traits.Float),
position=4,
argstr="%s",
mandatory=True,
desc="output spacing",
)
apply_smoothing = traits.Bool(False,
argstr="%d",
position=5,
desc="smooth before resampling")
addvox = traits.Int(
argstr="%d",
position=6,
requires=["apply_smoothing"],
desc="addvox pads each dimension by addvox",
)
nn_interp = traits.Bool(argstr="%d",
desc="nn interpolation",
position=-1,
requires=["addvox"])
class _ExtractFeatureInputSpec(base.BaseInterfaceInputSpec):
in_file = base.File(desc='The image to extract the feature from',
mandatory=True,
exists=True)
mask_file = base.File(
desc='Image mask, features are only extracted where mask has 1 values',
mandatory=True,
exists=True)
function = base.traits.Function(
desc='The function to use for feature extraction',
mandatory=True,
nohash=True)
kwargs = base.traits.DictStrAny(
desc='A dictionary of keyword arguments that is passed to the feature'
' extraction function')
pass_voxelspacing = base.traits.Bool(
desc='Whether to pass the in_file`s voxel spacing to the feature '
'extraction function or not.')
out_file = base.File(desc='Target file name of the extracted features.',
hash_files=False)
class ResampleImageBySpacingInputSpec(ANTSCommandInputSpec):
dimension = traits.Int(3, usedefault=True, position=1, argstr='%d',
desc='dimension of output image')
input_image = base.File(exists=True, mandatory=True, position=2, argstr='%s',
desc='input image file')
output_image = base.File(position=3, argstr='%s', name_source=['input_image'],
name_template='%s_resampled', desc='output image file',
keep_extension=True)
out_spacing = traits.Either(
traits.List(traits.Float, minlen=2, maxlen=3),
traits.Tuple(traits.Float, traits.Float, traits.Float),
traits.Tuple(traits.Float, traits.Float),
position=4, argstr='%s', mandatory=True, desc='output spacing'
)
apply_smoothing = traits.Bool(False, argstr='%d', position=5,
desc='smooth before resampling')
addvox = traits.Int(argstr='%d', position=6, requires=['apply_smoothing'],
desc='addvox pads each dimension by addvox')
nn_interp = traits.Bool(argstr='%d', desc='nn interpolation',
position=-1, requires=['addvox'])
class _MedpyIntensityRangeStandardizationInputSpec(base.CommandLineInputSpec):
in_file = base.File(desc='The image to transform',
position=-1,
exists=True,
mandatory=True,
argstr='%s')
out_dir = base.Directory(
desc='Save the transformed images under this location.',
mandatory=True,
argstr='--save-images %s',
nohash=True)
mask_file = base.File(
desc='A number binary foreground mask. Alternative to supplying a'
'threshold.',
exists=True,
mandatory=True,
xor=['threshold'],
argstr='--masks %s')
threshold = base.traits.Int(
desc='All voxel with an intensity > threshold are considered as'
'foreground. Supply either this or a mask for each image.',
mandatory=True,
xor=['mask_file'],
argstr='--threshold %d')
lmodel = base.traits.File(
desc='Location of the pickled intensity range model to load. Activated'
'application mode.',
exists=True,
mandatory=True,
argstr='--load-model %s')
ignore = base.traits.Bool(
desc=
'Ignore possible loss of information during intensity transformation.'
' Should only be used when you know what you are doing.',
argstr='--ignore')
verbose = base.traits.Bool(desc='Verbose output', argstr='-v')
debug = base.traits.Bool(desc='Display debug information', argst='-d')
force = base.traits.Bool(desc='Overwrite existing files', argstr='-f')
class _ResampleInputSpec(base.CommandLineInputSpec):
ref_image = base.File(desc='Filename of the reference image',
exists=True,
mandatory=True,
argstr='-ref %s',
position=0)
flo_image = base.File(desc='Filename of the floating image',
exists=True,
mandatory=True,
argstr='-flo %s',
position=1)
# only one option of the following will be taken into account
in_affine = base.File(
desc='Filename which contains an affine transformation '
'(Affine*Reference=floating)',
exists=True,
mandatory=True,
argstr='-aff %s',
xor=['in_affFlirt', 'in_cpp', 'in_def'])
in_affFlirt = base.File(
desc='Filename which contains a radiological flirt affine '
'transformation',
exists=True,
mandatory=True,
argstr='-affFlirt %s',
xor=['in_affine', 'in_cpp', 'in_def'])
in_cpp = base.File(
desc='Filename of the control point grid image (from reg_f3d)',
exists=True,
mandatory=True,
argstr='-cpp %s',
xor=['in_affine', 'in_affFlirt', 'in_def'])
in_def = base.File(
desc='Filename of the deformation field image (from reg_transform)',
exists=True,
mandatory=True,
argstr='-def %s',
xor=['in_affine', 'in_affFlirt', 'in_cpp'])
# output options
result_file = base.File('outputResult.nii',
desc='Filename of the resampled image '
'[outputResult.nii]',
argstr='-res %s',
usedefault=True)
# others
interpolation_order = base.traits.Enum(
'0',
'1',
'3',
'4',
desc='Interpolation order (0, 1, 3, 4)[3] (0=NN, '
'1=LIN; 3=CUB, 4=SINC)',
argstr='-inter %s')
class _ImageMathInputSpec(ANTSCommandInputSpec):
dimension = traits.Int(3,
usedefault=True,
position=1,
argstr="%d",
desc="dimension of output image")
output_image = base.File(
position=2,
argstr="%s",
name_source=["op1"],
name_template="%s_maths",
desc="output image file",
keep_extension=True,
)
operation = base.Str(mandatory=True,
position=3,
argstr="%s",
desc="operations and intputs")
op1 = base.File(exists=True,
mandatory=True,
position=-2,
argstr="%s",
desc="first operator")
op2 = traits.Either(
base.File(exists=True),
base.Str,
position=-1,
argstr="%s",
desc="second operator",
)
copy_header = traits.Bool(
True,
usedefault=True,
desc=
"copy headers of the original image into the output (corrected) file",
)
class spec3(nib.TraitedSpec):
moo = nib.File(exists=True, name_source="doo")
doo = nib.traits.List(nib.File(exists=True))
class spec2(nib.TraitedSpec):
moo = nib.File(exists=True, hash_files=False)
doo = nib.traits.List(nib.File(exists=True))
class MRtrix_mul_OutputSpec(TraitedSpec):
out_file = nibase.File(desc='Multiplication result file')
class MRtrix_mul_InputSpec(CommandLineInputSpec):
input1 = nibase.File(desc='Input1 file',position=1,mandatory=True,exists=True,argstr = "%s")
input2 = nibase.File(desc='Input2 file',position=2,mandatory=True,exists=True,argstr = "%s")
out_filename = traits.Str(desc='out filename',position=3,mandatory=True,argstr = "%s")
class DTB_gfaOutputSpec(TraitedSpec):
out_file = nibase.File(desc='Resulting file')
class DTB_P0InputSpec(CommandLineInputSpec):
dsi_basepath = traits.Str(desc='DSI path/basename (e.g. \"data/dsi_\")',position=1,mandatory=True,argstr = "--dsi %s")
dwi_file = nibase.File(desc='DWI file',position=2,mandatory=True,exists=True,argstr = "--dwi %s")
class InputSpec(nib.TraitedSpec):
foo = nib.traits.Int(desc='a random int')
goo = nib.traits.Int(desc='a random int', mandatory=True)
hoo = nib.traits.Int(desc='a random int', usedefault=True)
zoo = nib.File(desc='a file', copyfile=False)
woo = nib.File(desc='a file', copyfile=True)
class ExtendedDespikeOutputSpec(afni.base.AFNICommandOutputSpec):
spike_file = base.File(desc='spike file', exists=True)
class ExtendedDespikeInputSpec(afni.preprocess.DespikeInputSpec):
spike_file = base.File(name_template="%s_despike_SPIKES",
desc='spike image file name',
argstr='-ssave %s',
name_source="in_file")
class _F3DInputSpec(base.CommandLineInputSpec):
ref_image = base.File(desc='Filename of the reference image',
exists=True,
mandatory=True,
argstr='-ref %s',
position=0)
flo_image = base.File(desc='Filename of the floating image',
exists=True,
mandatory=True,
argstr='-flo %s',
position=1)
# initial transformation options (only one will be considered)
in_affine = base.File(
desc='Filename which contains an affine transformation '
'(Affine*Reference=Floating)',
exists=True,
argstr='-aff %s',
xor=['in_affFlirt', 'in_cpp'])
in_affFlirt = base.File(
desc='Filename which contains a flirt affine transformation (Flirt '
'from the FSL package)',
exists=True,
argstr='-affFlirt %s',
xor=['in_affine', 'in_cpp'])
in_cpp = base.File(
desc='Filename ofl control point grid input. The coarse spacing '
'is defined by this file.',
exists=True,
argstr='-incpp %s',
xor=['in_affine', 'in_affFlirt'])
# output options
cpp_file = base.File(desc='Filename of control point grid [outputCPP.nii]',
argstr='-cpp %s')
result_file = base.File(desc='Filename of the resampled image '
'[outputResult.nii]',
argstr='-res %s')
# input image options (incomplete)
rmask_file = base.File(
desc='Filename of a mask image in the reference space',
exists=True,
argstr='-rmask %s')
smooth_ref = base.traits.Float(
desc='Smooth the reference image using the specified sigma (mm) [0]',
argstr='-smooR %f')
smooth_flo = base.traits.Float(
desc='Smooth the floating image using the specified sigma (mm) [0]',
argstr='-smooF %f')
# spline options (not yet implemented)
# objective function options (not yet implemented)
# optimisation options (not yet implemented)
# F3D_SYM options
symmetric = base.traits.Bool(desc='Use symmetric approach', argstr='-sym')
fmask_file = base.File(desc='Filename of a mask image in the floating '
'space. Only used when symmetric turned on',
exists=True,
argstr='-fmask %s')
inverse_consistency = base.traits.Float(
desc='Weight of the inverse consistency penalty term [0.01]',
argstr='-ic %f')
# F3D2 options (not yet implemented)
# other options (incomplete)
verbose_off = base.traits.Bool(desc='Turn verbose off', argstr='-voff')
class _F3DOutputSpec(base.TraitedSpec):
cpp_file = base.File(desc='Filename of control point grid [outputCPP.nii]')
result_file = base.File(
desc='Filename of the resampled image [outputResult.nii]')
class spec4(nib.TraitedSpec):
moo = nib.File(exists=True)
doo = nib.traits.List(nib.File(exists=True))
class CommandLineInputSpec2(nib.CommandLineInputSpec):
foo = nib.File(argstr='%s', desc='a str', genfile=True)
class _ResampleOutputSpec(base.TraitedSpec):
result_file = base.File('outputResult.nii',
desc='Filename of the resampled image '
'[outputResult.nii]')
class ImageMathOuputSpec(base.TraitedSpec):
output_image = base.File(exists=True, desc='output image file')
class _AladinInputSpec(base.CommandLineInputSpec):
ref_image = base.File(desc='Filename of the reference (target) image',
mandatory=True,
exists=True,
argstr='-ref %s',
position=0)
flo_image = base.File(desc='Filename of the floating (source) image',
mandatory=True,
exists=True,
argstr='-flo %s',
position=1)
symmetric = base.traits.Bool(desc='Use symmetric version of the algorithm',
argstr='-sym')
affine = base.File(desc='Filename which contains the output affine '
'transformation [outputAffine.txt] ',
argstr='-aff %s')
rigid_only = base.traits.Bool(desc='Perform a rigid registration only (r'
'igid+affine by default)',
argstr='-rigOnly')
affine_direct = base.traits.Bool(desc='Directly optimize 12 DoF affine '
'[default: rigid initially then affine]',
argstr='-affDirect')
input_affine = base.File(desc='Filename which contains an input affine '
'transformation (Affine*Reference=Floating)',
exists=True,
argstr='-inaff %s')
input_affine_flirt = base.File(desc='Filename which contains an input '
'affine transformation from Flirt',
exists=True,
argstr='-affFlirt %s')
rmask_file = base.File(desc='Filename of a mask image in the reference '
'space',
exists=True,
argstr='-rmask %s')
fmask_file = base.File(desc='Filename of a mask image in the floating '
'space. Only used when symmetric turned on',
exists=True,
argstr='-fmask %s')
result_file = base.File(desc='Filename of the resampled image '
'[outputResult.nii]',
argstr='-res %s')
max_iterations = base.traits.Int(desc='Number of iterations per level [5]',
argstr='maxit %i')
smooth_ref = base.traits.Float(desc='Smooth the reference image using the '
'specified sigma (mm) [0]',
argstr='-smooR %f')
smooth_flo = base.traits.Float(desc='Smooth the floating image using the '
'specified sigma (mm) [0]',
argstr='-smooR %f')
number_levels = base.traits.Int(desc='Number of levels to perform [3]',
argstr='-ln %i')
perform_levels = base.traits.Int(desc='Only perform the first levels [ln]',
argstr='-lp %i')
nac = base.traits.Bool(desc='Use the nifti header origins to initialise '
'the translation',
argstr='-nac')
block_percentage = base.traits.Int(desc='Percentage of block to use [50]',
argstr='-%%v %i')
inlier_percentage = base.traits.Int(desc='Percentage of inliers for the '
'LTS [50]',
argstr='-%%i %i')
