class MaskSurfaceInputSpec(BaseInterfaceInputSpec):
sxfmout = File(exists=True, desc='original surface', mandatory=True)
hemi = traits.String(exists=True, desc='hemisphere', mandatory=True)
fs = traits.String(exists=True, desc='fsaverage', mandatory=True)
freesurferdir = traits.String(exists=True, desc='freesurfer directory', mandatory=True)
sourcelabels = traits.ListInt(exists=True, desc= 'source labels', mandatory=True)
targetlabels = traits.ListInt(exists=True, desc= 'target labels', mandatory=True)
class MaskVolumeInputSpec(BaseInterfaceInputSpec):
preprocessedfile = File(exists=True,
desc='original volume',
mandatory=True)
regfile = File(exists=True, desc='register .mat file', mandatory=True)
parcfile = File(exists=True,
desc='parcellation/segmentation file',
mandatory=True)
vol_source = traits.ListInt(exists=True,
desc='source labels',
mandatory=True)
vol_target = traits.ListInt(exists=True,
desc='target labels',
mandatory=True)
class WriteXMLFilesInputSpec(BaseInterfaceInputSpec):
# import datetime
# now = datetime.datetime.now()
today_date = 'today' #now.strftime("%Y_%b_%d") # %Y_%b_%dT%Hh%M
type_xml_file = traits.Enum(
'All',
'Def',
'Obj',
usedefault=True,
desc="'Def' to write the deformation xml file. " +
"'Obj' for the object parameter xml file. " +
"'All' to write both files",
mandatory=True)
path = traits.String(
desc=" path where the xml_files folder will be created.")
dkw = traits.Int(desc=' Diffeo Kernel width ', default_value=10)
dkt = traits.String("Exact", desc=' Diffeo Kernel type ', usedefault=True)
dtp = traits.Int(desc=' Diffeo: number of time points', default_value=30)
dsk = traits.Float(desc=' Diffeo: smoothing kernel width ',
default_value=0.5)
dcps = traits.Int(desc="Diffeos: Initial spacing for Control Points",
usedefault=True,
default_value=5)
dcpp = File(
'x',
desc=
"Diffeos: name of a file containing positions of control points. In case of conflict with "
+
"initial-cp-spacing, if a file name is given in initial-cp-position and initial-cp-spacing "
+
"is set, the latter is ignored and control point positions in the file name are used.",
usedefault=True)
dfcp = traits.String('Off',
desc="Diffeos: Freeze the Control Points",
usedefault=True)
dmi = traits.Int(desc="Diffeos : Maximum of descent iterations",
usedefault=True,
default_value=100)
dat = traits.Float(
desc="Diffeos : adaptative tolerence for the gradient descent",
usedefault=True,
default_value=0.00005)
dls = traits.Int(desc="Diffeos: Maximum line search iterations",
usedefault=True,
default_value=20)
ods = traits.ListFloat(
desc="Object: weight of the object in the fidelity-to-data term ",
usedefault=True,
default_value=[0.5])
okw = traits.ListInt(default_value=[3],
desc="Object: Kernel width",
usedefault=True)
ot = traits.List(["NonOrientedSurfaceMesh"],
desc="Object type",
usedefault=True)
xml_diffeo = traits.String(
'parametersDiffeo' + today_date + '.xml',
desc='Name of the xml file containing the diffeo parameters',
usedefault=True)
class PlotCoclassInputSpec(BaseInterfaceInputSpec):
coclass_matrix_file = File(exists=True,
desc='coclass matrix in npy format',
mandatory=True)
labels_file = File(exists=True, desc='labels of nodes', mandatory=False)
list_value_range = traits.ListInt(desc='force the range of the plot',
mandatory=False)
class sortingTimePointsOutputSpec(TraitedSpec):
out_t_from = traits.ListInt(
desc=
'list of time points sorted the same way as the 2 other output files.')
files_trajectory_source = traits.List(
File(exists=True,
desc='list of the source.vtk file sorted by the time points'))
files_xmlDiffeo = traits.List(
File,
desc=
'list of param Diffeo file, to be able to use the CP.txt files generated by '
+ 'the shooting as initial CP position for the subsequent ' +
'SparseMatching3 function.')
subject_id = traits.List(traits.String())
class Labels2ProbMapsInputSpec(CommandLineInputSpec):
output_template = traits.Str(
default='out_{label}.nii.gz',
argstr='%s',
position=0,
desc='Template string for output files. Must contain {label}.')
input_files = InputMultiPath(File(exists=True),
mandatory=True,
argstr='%s',
position=1)
labels = traits.ListInt(minlen=1, mandatory=True, argstr='--labels %s')
bids_labels = traits.Bool(
argstr='--bids_labels',
desc=
'Assume label numbers correspond to then standard anatomical labels in BEP011'
)
class MRDeGibbsInputSpec(MRTrix3BaseInputSpec):
in_file = File(
exists=True,
argstr="%s",
position=-2,
mandatory=True,
desc="input DWI image",
)
axes = traits.ListInt(
default_value=[0, 1],
usedefault=True,
sep=",",
minlen=2,
maxlen=2,
argstr="-axes %s",
desc="indicate the plane in which the data was acquired (axial = 0,1; "
"coronal = 0,2; sagittal = 1,2",
)
nshifts = traits.Int(
default_value=20,
usedefault=True,
argstr="-nshifts %d",
desc="discretization of subpixel spacing (default = 20)",
)
minW = traits.Int(
default_value=1,
usedefault=True,
argstr="-minW %d",
desc="left border of window used for total variation (TV) computation "
"(default = 1)",
)
maxW = traits.Int(
default_value=3,
usedefault=True,
argstr="-maxW %d",
desc="right border of window used for total variation (TV) computation "
"(default = 3)",
)
out_file = File(
name_template="%s_unr",
name_source="in_file",
keep_extension=True,
argstr="%s",
position=-1,
desc="the output unringed DWI image",
genfile=True,
)
class STCParametersInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiPath(
traits.File(
desc="fMRI image file(s) from where to obtain the slice time "
"correction parameters.",
exists=True,
mandatory=True))
num_slices = traits.Int(
desc="Number of slices (depends on acquisition direction).")
slice_order = traits.ListInt(
desc="List of integers with the order in which slices are acquired.")
time_repetition = traits.Float(
desc="The time repetition (TR) of the input dataset in seconds. "
"If left to default will read the TR from the nifti image header.",
default=0)
time_acquisition = traits.Float(
desc=
"Time of volume acquisition. usually calculated as TR-(TR/num_slices)."
)
ref_slice = traits.Int(desc="Index of the slice of reference.")
slice_mode = traits.Enum(
'unknown',
'seq_inc',
'seq_dec',
'alt_inc',
'alt_dec',
'alt_inc2',
'alt_dec2',
desc="Slicing mode of the acquisition. \n"
"Choices: \n"
" 'unknown': auto detect if images are from Siemens and converted with dcm2nii"
" from Nov 2013 or later #kNIFTI_SLICE_UNKNOWN\n"
" 'seq_inc': sequential ascending kNIFTI_SLICE_SEQ_INC = 1; %1,2,3,4\n"
" 'seq_dec': sequential descending kNIFTI_SLICE_SEQ_DEC = 2; %4,3,2,1\n"
" 'alt_inc': Siemens: interleaved ascending with odd number of slices,"
" interleaved for other vendors kNIFTI_SLICE_ALT_INC = 3; %1,3,2,4\n"
" 'alt_dec': descending interleaved kNIFTI_SLICE_ALT_DEC = 4; %4,2,3,1\n"
" 'alt_inc2': Siemens interleaved ascending with even number of slices kNIFTI_SLICE_ALT_INC2 = 5; %2,4,1,3\n"
" 'alt_dec2': Siemens interleaved descending with even number of slices kNIFTI_SLICE_ALT_DEC2 = 6; %3,1,4,2\n"
"If left to default will try to detect the TR from the nifti image header, if it doesn't work"
"an AttributeError exception will be raise.",
default='unknown')
class STCParametersOutputSpec(TraitedSpec):
in_files = InputMultiPath(
traits.File(
desc=
"fMRI image file from where to obtain the slice time correction "
"parameters.",
exists=True,
mandatory=True))
num_slices = traits.Int(
desc="Number of slices (depends on acquisition direction).")
slice_order = traits.ListInt(
desc="List of integers with the order in which slices are acquired.")
time_repetition = traits.Float(
desc="The time repetition (TR) of the input dataset in seconds. "
"If left to default will read the TR from the nifti image header.",
default=0)
time_acquisition = traits.Float(
desc=
"Time of volume acquisition. usually calculated as TR-(TR/num_slices)."
)
ref_slice = traits.Int(desc="Index of the slice of reference.")
class PVProcessInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiPath( File(exists=True), copyfile=False, desc='image, or multi-channel set of images,' \
'used for segmentation', mandatory=True )
in_maps = InputMultiPath(File(exists=True),
copyfile=False,
desc='Resulting tissue probability maps',
mandatory=True)
parameters = File(
exists=True,
desc='CSV file containing parameters for all the classes',
mandatory=True)
pure_tissues = traits.ListInt(
value=[0, 2, 4],
minlen=1,
desc='Identifiers of the pure tissue classes')
dist = traits.String(value="euclidean",
desc="Distance definition to be used")
reorder = traits.Bool(
value=True,
desc=
'Reorder classes if the classification is not ordered by first contrast means'
)
class MRDeGibbsInputSpec(MRTrix3BaseInputSpec):
in_file = File(exists=True,
argstr='%s',
position=-2,
mandatory=True,
desc='input DWI image')
axes = traits.ListInt(
default_value=[0, 1],
usedefault=True,
sep=',',
minlen=2,
maxlen=2,
argstr='-axes %s',
desc='indicate the plane in which the data was acquired (axial = 0,1; '
'coronal = 0,2; sagittal = 1,2')
nshifts = traits.Int(
default_value=20,
usedefault=True,
argstr='-nshifts %d',
desc='discretization of subpixel spacing (default = 20)')
minW = traits.Int(
default_value=1,
usedefault=True,
argstr='-minW %d',
desc='left border of window used for total variation (TV) computation '
'(default = 1)')
maxW = traits.Int(
default_value=3,
usedefault=True,
argstr='-maxW %d',
desc='right border of window used for total variation (TV) computation '
'(default = 3)')
out_file = File(name_template='%s_unr',
name_source='in_file',
keep_extension=True,
argstr='%s',
position=-1,
desc='the output unringed DWI image')
class T1xT2BETInputSpec(FSLCommandInputSpec):
t1_file = File(exists=True,
desc='Whole-head T1w image',
mandatory=True,
position=0,
argstr="-t1 %s")
t2_file = File(exists=True,
desc='Whole-head T2w image',
mandatory=True,
position=1,
argstr="-t2 %s")
opt_os = traits.String(
"_BET",
usedefault=True,
desc="Suffix for the brain masked images (default is _BET)",
argstr="-os %s",
mandatory=False)
aT2 = traits.Bool(
False,
usedefault=True,
argstr="-aT2",
desc="Will coregrister T2w to T1w using flirt. Output will have the\
suffix provided. Will only work for spatially close images.",
mandatory=False)
# as -> opt_as, as is already a part of python keywords...
opt_as = traits.String("-in-T1w",
usedefault=True,
argstr="-as %s",
desc="Suffix for T2w to T1w registration \
(\"-in-T1w\" if not specified)",
mandatory=False)
n = traits.Int(
1,
usedefault=True,
desc='n = the number of iterations BET will be run to find center of \
gravity (n=1 if option -n is absent)',
argstr="-n %d",
mandatory=False)
m = traits.Bool(False,
usedefault=True,
argstr="-m",
desc="Will output the BET mask at the format \
output_prefixT1_mask.nii.gz)",
mandatory=False)
ms = traits.String("_mask",
usedefault=True,
desc="Suffix for the mask (default is _mask)",
argstr="-ms %s",
mandatory=False)
c = traits.Int(
desc='Will crop the inputs & outputs after brain extraction. c is the \
space between the brain and the limits of the crop box expressed \
in percentage of the brain size (eg. if the brain size is 200\
voxels in one dimension and c=10: the sides of the brain in this \
dimension will be 20 voxels away from the borders of the resulting\
crop box in this dimension).',
argstr="-c %d",
mandatory=False)
cs = traits.String(
"_cropped",
usedefault=True,
desc="Suffix for the cropped images (default is _cropped)",
argstr="-cs %s",
mandatory=False)
f = traits.Float(
0.5,
usedefault=True,
desc='-f options of BET: fractional intensity threshold (0->1); \
default=0.5; smaller values give larger brain outline estimates',
argstr="-f %f",
mandatory=False)
g = traits.Float(0.0,
usedefault=True,
desc='-g options of BET:\
vertical gradient in fractional intensity threshold (-1->1);\
default=0; positive values give larger brain outline at\
bottom, smaller at top',
argstr="-g %f",
mandatory=False)
cog = traits.ListInt(
desc='For difficult cases, you can directly provide a center of \
gravity. Only one iteration will be performed.',
argstr="-cog %d %d %d",
mandatory=False)
k = traits.Bool(False,
usedefault=True,
argstr="-k",
desc="Will keep temporary files",
mandatory=False)
p = traits.String(desc="Prefix for running FSL functions\
(can be a path or just a prefix)",
argstr="-p %s",
mandatory=False)
class MergeLabelsInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="Input roi file")
roi_list = traits.ListInt(mandatory=True,
desc="List containing the label index to use")
class _AntsJointFusionInputSpec(ANTSCommandInputSpec):
dimension = traits.Enum(3,
2,
4,
argstr='-d %d',
desc='This option forces the image to be treated '
'as a specified-dimensional image. If not '
'specified, the program tries to infer the '
'dimensionality from the input image.')
target_image = traits.List(base.InputMultiPath(base.File(exists=True)),
argstr='-t %s',
mandatory=True,
desc='The target image (or '
'multimodal target images) assumed to be '
'aligned to a common image domain.')
atlas_image = traits.List(base.InputMultiPath(base.File(exists=True)),
argstr="-g %s...",
mandatory=True,
desc='The atlas image (or '
'multimodal atlas images) assumed to be '
'aligned to a common image domain.')
atlas_segmentation_image = base.InputMultiPath(
base.File(exists=True),
argstr="-l %s...",
mandatory=True,
desc='The atlas segmentation '
'images. For performing label fusion the number '
'of specified segmentations should be identical '
'to the number of atlas image sets.')
alpha = traits.Float(
default_value=0.1,
usedefault=True,
argstr='-a %s',
desc=(
'Regularization '
'term added to matrix Mx for calculating the inverse. Default = 0.1'
))
beta = traits.Float(
default_value=2.0,
usedefault=True,
argstr='-b %s',
desc=('Exponent for mapping '
'intensity difference to the joint error. Default = 2.0'))
retain_label_posterior_images = traits.Bool(
False,
argstr='-r',
usedefault=True,
requires=['atlas_segmentation_image'],
desc=('Retain label posterior probability images. Requires '
'atlas segmentations to be specified. Default = false'))
retain_atlas_voting_images = traits.Bool(
False,
argstr='-f',
usedefault=True,
desc=('Retain atlas voting images. Default = false'))
constrain_nonnegative = traits.Bool(
False,
argstr='-c',
usedefault=True,
desc=('Constrain solution to non-negative weights.'))
patch_radius = traits.ListInt(minlen=3,
maxlen=3,
argstr='-p %s',
desc=('Patch radius for similarity measures.'
'Default: 2x2x2'))
patch_metric = traits.Enum(
'PC',
'MSQ',
argstr='-m %s',
desc=('Metric to be used in determining the most similar '
'neighborhood patch. Options include Pearson\'s '
'correlation (PC) and mean squares (MSQ). Default = '
'PC (Pearson correlation).'))
search_radius = traits.List(
[3, 3, 3],
minlen=1,
maxlen=3,
argstr='-s %s',
usedefault=True,
desc=('Search radius for similarity measures. Default = 3x3x3. '
'One can also specify an image where the value at the '
'voxel specifies the isotropic search radius at that voxel.'))
exclusion_image_label = traits.List(
traits.Str(),
argstr='-e %s',
requires=['exclusion_image'],
desc=('Specify a label for the exclusion region.'))
exclusion_image = traits.List(
base.File(exists=True),
desc=('Specify an exclusion region for the given label.'))
mask_image = base.File(
argstr='-x %s',
exists=True,
desc='If a mask image '
'is specified, fusion is only performed in the mask region.')
out_label_fusion = base.File(argstr="%s",
hash_files=False,
desc='The output label fusion image.')
out_intensity_fusion_name_format = traits.Str(
argstr="",
desc='Optional intensity fusion '
'image file name format. '
'(e.g. "antsJointFusionIntensity_%d.nii.gz")')
out_label_post_prob_name_format = traits.Str(
'antsJointFusionPosterior_%d.nii.gz',
requires=['out_label_fusion', 'out_intensity_fusion_name_format'],
desc='Optional label posterior probability '
'image file name format.')
out_atlas_voting_weight_name_format = traits.Str(
'antsJointFusionVotingWeight_%d.nii.gz',
requires=[
'out_label_fusion', 'out_intensity_fusion_name_format',
'out_label_post_prob_name_format'
],
desc='Optional atlas voting weight image '
'file name format.')
verbose = traits.Bool(False, argstr="-v", desc=('Verbose output.'))
class RealignUnwarpInputSpec(SPMCommandInputSpec):
scans = traits.Either(traits.List(File(exists=True)),
File(exists=True),
mandatory=True,
copyfile=True,
field='data.scans',
desc='scans for this session')
pmscan = File(mandatory=True,
exists=True,
copyfile=False,
field='data.pmscan',
desc='phase map (vdm* file)')
# Estimation Options
quality = traits.Range(low=0.0,
high=1.0,
field='eoptions.quality',
desc='0.1 = fast, 1.0 = precise')
fwhm = traits.Range(low=0.0,
field='eoptions.fwhm',
desc='gaussian smoothing kernel width')
separation = traits.Range(low=0.0,
field='eoptions.sep',
desc='sampling separation in mm')
register_to_mean = traits.Bool(
field='eoptions.rtm',
desc='Indicate whether realignment is done to the mean image')
weight_img = File(exists=True,
field='eoptions.weight',
desc='filename of weighting image')
interp = traits.Range(low=0,
high=7,
field='eoptions.interp',
desc='degree of b-spline used for interpolation')
wrap = traits.List(traits.Int(),
minlen=3,
maxlen=3,
field='eoptions.wrap',
desc='Check if interpolation should wrap in [x,y,z]')
# Unwarp Options
basfcn = traits.List(traits.Int(),
minlen=2,
maxlen=2,
field='uweoptions.basfcn',
desc='basic functions')
regorder = traits.Range(low=0,
high=3,
field='uweoptions.regorder',
desc='regularization')
regfactor = traits.Range(low=0,
field='uweoptions.lambda',
desc='regularization factor')
jm = traits.Bool(field='uweoptions.jm', desc='Jacobian deformations')
fot = traits.List(traits.Int(),
minlen=2,
maxlen=2,
field='uweoptions.fot',
desc='first-order effects')
sot = traits.List(traits.Int(),
minlen=2,
maxlen=2,
field='uweoptions.sot',
desc='second-order effects')
uwfwhm = traits.Range(low=0,
field='uweoptions.uwfwhm',
desc='smoothing for unwarp')
rem = traits.Bool(field='uweoptions.rem', desc='')
noi = traits.Range(low=0,
field='uweoptions.noi',
desc='maximum number of iterations')
expround = traits.Enum('Average',
'First',
'Last',
field='uweoptions.expround',
desc='one of: Average, First, Last')
# Reslice Options
write_which = traits.ListInt([2, 1],
minlen=2,
maxlen=2,
usedefault=True,
field='uwroptions.uwwhich',
desc='determines which images to reslice')
write_interp = traits.Range(
low=0,
high=7,
field='uwroptions.rinterp',
desc='degree of b-spline used for interpolation')
write_wrap = traits.List(
traits.Int(),
minlen=3,
maxlen=3,
field='uwroptions.wrap',
desc='Check if interpolation should wrap in [x,y,z]')
write_mask = traits.Bool(field='uwroptions.mask',
desc='True/False mask output image')
out_prefix = traits.String('u',
usedefault=True,
field='uwroptions.prefix',
desc='realigned output prefix')
class _AntsJointFusionInputSpec(ANTSCommandInputSpec):
dimension = traits.Enum(
3,
2,
4,
argstr="-d %d",
desc="This option forces the image to be treated "
"as a specified-dimensional image. If not "
"specified, the program tries to infer the "
"dimensionality from the input image.",
)
target_image = traits.List(
base.InputMultiPath(base.File(exists=True)),
argstr="-t %s",
mandatory=True,
desc="The target image (or "
"multimodal target images) assumed to be "
"aligned to a common image domain.",
)
atlas_image = traits.List(
base.InputMultiPath(base.File(exists=True)),
argstr="-g %s...",
mandatory=True,
desc="The atlas image (or "
"multimodal atlas images) assumed to be "
"aligned to a common image domain.",
)
atlas_segmentation_image = base.InputMultiPath(
base.File(exists=True),
argstr="-l %s...",
mandatory=True,
desc="The atlas segmentation "
"images. For performing label fusion the number "
"of specified segmentations should be identical "
"to the number of atlas image sets.",
)
alpha = traits.Float(
default_value=0.1,
usedefault=True,
argstr="-a %s",
desc=(
"Regularization "
"term added to matrix Mx for calculating the inverse. Default = 0.1"
),
)
beta = traits.Float(
default_value=2.0,
usedefault=True,
argstr="-b %s",
desc=("Exponent for mapping "
"intensity difference to the joint error. Default = 2.0"),
)
retain_label_posterior_images = traits.Bool(
False,
argstr="-r",
usedefault=True,
requires=["atlas_segmentation_image"],
desc=("Retain label posterior probability images. Requires "
"atlas segmentations to be specified. Default = false"),
)
retain_atlas_voting_images = traits.Bool(
False,
argstr="-f",
usedefault=True,
desc=("Retain atlas voting images. Default = false"),
)
constrain_nonnegative = traits.Bool(
False,
argstr="-c",
usedefault=True,
desc=("Constrain solution to non-negative weights."),
)
patch_radius = traits.ListInt(
minlen=3,
maxlen=3,
argstr="-p %s",
desc=("Patch radius for similarity measures."
"Default: 2x2x2"),
)
patch_metric = traits.Enum(
"PC",
"MSQ",
argstr="-m %s",
desc=("Metric to be used in determining the most similar "
"neighborhood patch. Options include Pearson's "
"correlation (PC) and mean squares (MSQ). Default = "
"PC (Pearson correlation)."),
)
search_radius = traits.List(
[3, 3, 3],
minlen=1,
maxlen=3,
argstr="-s %s",
usedefault=True,
desc=("Search radius for similarity measures. Default = 3x3x3. "
"One can also specify an image where the value at the "
"voxel specifies the isotropic search radius at that voxel."),
)
exclusion_image_label = traits.List(
traits.Str(),
argstr="-e %s",
requires=["exclusion_image"],
desc=("Specify a label for the exclusion region."),
)
exclusion_image = traits.List(
base.File(exists=True),
desc=("Specify an exclusion region for the given label."),
)
mask_image = base.File(
argstr="-x %s",
exists=True,
desc="If a mask image "
"is specified, fusion is only performed in the mask region.",
)
out_label_fusion = base.File(argstr="%s",
hash_files=False,
desc="The output label fusion image.")
out_intensity_fusion_name_format = traits.Str(
argstr="",
desc="Optional intensity fusion "
"image file name format. "
'(e.g. "antsJointFusionIntensity_%d.nii.gz")',
)
out_label_post_prob_name_format = traits.Str(
"antsJointFusionPosterior_%d.nii.gz",
requires=["out_label_fusion", "out_intensity_fusion_name_format"],
desc="Optional label posterior probability "
"image file name format.",
)
out_atlas_voting_weight_name_format = traits.Str(
"antsJointFusionVotingWeight_%d.nii.gz",
requires=[
"out_label_fusion",
"out_intensity_fusion_name_format",
"out_label_post_prob_name_format",
],
desc="Optional atlas voting weight image "
"file name format.",
)
verbose = traits.Bool(False, argstr="-v", desc=("Verbose output."))
