class param2xfmInterfaceInput(CommandLineInputSpec):
out_file = File(position=-1, argstr="%s", desc="resampled image")
transformation = traits.List(desc="Transformation to apply")
class EstimateFODInputSpec(MRTrix3BaseInputSpec):
algorithm = traits.Enum('csd',
'msmt_csd',
argstr='%s',
mandatory=True,
position=0,
desc="Algorithm used for CSD estimation")
in_file = File(exists=True,
argstr='%s',
mandatory=True,
position=-3,
desc='input diffusion weighted images')
response = File(
exists=True,
argstr='%s',
mandatory=True,
position=-2,
desc=('a text file containing the diffusion-weighted signal response '
'function coefficients for a single fibre population'))
out_file = File('fods.mif',
argstr='%s',
mandatory=True,
position=-1,
usedefault=True,
desc=('the output spherical harmonics coefficients'
' image'))
# DW Shell selection parameters
shell = traits.List(traits.Float,
sep=',',
argstr='-shell %s',
desc='specify one or more dw gradient shells')
# Spherical deconvolution parameters
max_sh = traits.Int(8,
argstr='-lmax %d',
desc='maximum harmonic degree of response function')
in_mask = File(exists=True,
argstr='-mask %s',
desc='provide initial mask image')
in_dirs = File(
exists=True,
argstr='-directions %s',
desc=('specify the directions over which to apply the non-negativity '
'constraint (by default, the built-in 300 direction set is '
'used). These should be supplied as a text file containing the '
'[ az el ] pairs for the directions.'))
sh_filter = File(
exists=True,
argstr='-filter %s',
desc=('the linear frequency filtering parameters used for the initial '
'linear spherical deconvolution step (default = [ 1 1 1 0 0 ]). '
'These should be supplied as a text file containing the '
'filtering coefficients for each even harmonic order.'))
neg_lambda = traits.Float(
1.0,
argstr='-neg_lambda %f',
desc=('the regularisation parameter lambda that controls the strength'
' of the non-negativity constraint'))
thres = traits.Float(
0.0,
argstr='-threshold %f',
desc=('the threshold below which the amplitude of the FOD is assumed '
'to be zero, expressed as an absolute amplitude'))
n_iter = traits.Int(50,
argstr='-niter %d',
desc=('the maximum number of iterations '
'to perform for each voxel'))
class FilterListInputSpec(DynamicTraitedSpec, BaseInterfaceInputSpec):
keys = traits.List(traits.Str(), mandatory=True)
pattern = traits.Str(mandatory=True)
class _UnzipJoinedSurfacesInputSpec(BaseInterfaceInputSpec):
in_files = traits.List(
InputMultiPath(File(exists=True),
mandatory=True,
desc='input GIfTI files'))
class ProbTrackXInputSpec(FSLCommandInputSpec):
thsamples = InputMultiPath(File(exists=True), mandatory=True)
phsamples = InputMultiPath(File(exists=True), mandatory=True)
fsamples = InputMultiPath(File(exists=True), mandatory=True)
samples_base_name = traits.Str(
"merged",
desc='the rootname/base_name for samples files',
argstr='--samples=%s',
usedefault=True)
mask = File(exists=True,
desc='bet binary mask file in diffusion space',
argstr='-m %s',
mandatory=True)
seed = traits.Either(
File(exists=True),
traits.List(File(exists=True)),
traits.List(traits.List(traits.Int(), minlen=3, maxlen=3)),
desc='seed volume(s), or voxel(s)' + 'or freesurfer label file',
argstr='--seed=%s',
mandatory=True)
mode = traits.Enum(
"simple",
"two_mask_symm",
"seedmask",
desc=
'options: simple (single seed voxel), seedmask (mask of seed voxels), '
+ 'twomask_symm (two bet binary masks) ',
argstr='--mode=%s',
genfile=True)
target_masks = InputMultiPath(
File(exits=True),
desc='list of target masks - ' +
'required for seeds_to_targets classification',
argstr='--targetmasks=%s')
mask2 = File(
exists=True,
desc='second bet binary mask (in diffusion space) in twomask_symm mode',
argstr='--mask2=%s')
waypoints = File(exists=True,
desc='waypoint mask or ascii list of waypoint masks - ' +
'only keep paths going through ALL the masks',
argstr='--waypoints=%s')
network = traits.Bool(
desc='activate network mode - only keep paths going through ' +
'at least one seed mask (required if multiple seed masks)',
argstr='--network')
mesh = File(exists=True,
desc='Freesurfer-type surface descriptor (in ascii format)',
argstr='--mesh=%s')
seed_ref = File(exists=True,
desc='reference vol to define seed space in ' +
'simple mode - diffusion space assumed if absent',
argstr='--seedref=%s')
out_dir = Directory(exists=True,
argstr='--dir=%s',
desc='directory to put the final volumes in',
genfile=True)
force_dir = traits.Bool(
True,
desc='use the actual directory name given - i.e. ' +
'do not add + to make a new directory',
argstr='--forcedir',
usedefault=True)
opd = traits.Bool(True,
desc='outputs path distributions',
argstr='--opd',
usedefault=True)
correct_path_distribution = traits.Bool(
desc='correct path distribution for the length of the pathways',
argstr='--pd')
os2t = traits.Bool(desc='Outputs seeds to targets', argstr='--os2t')
#paths_file = File('nipype_fdtpaths', usedefault=True, argstr='--out=%s',
# desc='produces an output file (default is fdt_paths)')
avoid_mp = File(
exists=True,
desc='reject pathways passing through locations given by this mask',
argstr='--avoid=%s')
stop_mask = File(exists=True,
argstr='--stop=%s',
desc='stop tracking at locations given by this mask file')
xfm = File(
exists=True,
argstr='--xfm=%s',
desc='transformation matrix taking seed space to DTI space ' +
'(either FLIRT matrix or FNIRT warp_field) - default is identity')
inv_xfm = File(
argstr='--invxfm=%s',
desc='transformation matrix taking DTI space to seed' +
' space (compulsory when using a warp_field for seeds_to_dti)')
n_samples = traits.Int(5000,
argstr='--nsamples=%d',
desc='number of samples - default=5000',
usedefault=True)
n_steps = traits.Int(argstr='--nsteps=%d',
desc='number of steps per sample - default=2000')
dist_thresh = traits.Float(argstr='--distthresh=%.3f',
desc='discards samples shorter than ' +
'this threshold (in mm - default=0)')
c_thresh = traits.Float(argstr='--cthr=%.3f',
desc='curvature threshold - default=0.2')
sample_random_points = traits.Bool(
argstr='--sampvox', desc='sample random points within seed voxels')
step_length = traits.Float(argstr='--steplength=%.3f',
desc='step_length in mm - default=0.5')
loop_check = traits.Bool(argstr='--loopcheck',
desc='perform loop_checks on paths -' +
' slower, but allows lower curvature threshold')
use_anisotropy = traits.Bool(argstr='--usef',
desc='use anisotropy to constrain tracking')
rand_fib = traits.Enum(
0,
1,
2,
3,
argstr='--randfib=%d',
desc='options: 0 - default, 1 - to randomly sample' +
' initial fibres (with f > fibthresh), 2 - to sample in ' +
'proportion fibres (with f>fibthresh) to f, 3 - to sample ALL ' +
'populations at random (even if f<fibthresh)')
fibst = traits.Int(
argstr='--fibst=%d',
desc='force a starting fibre for tracking - ' +
'default=1, i.e. first fibre orientation. Only works if randfib==0')
mod_euler = traits.Bool(argstr='--modeuler',
desc='use modified euler streamlining')
random_seed = traits.Bool(argstr='--rseed', desc='random seed')
s2tastext = traits.Bool(argstr='--s2tastext',
desc='output seed-to-target counts as a' +
' text file (useful when seeding from a mesh)')
verbose = traits.Enum(0,
1,
2,
desc="Verbose level, [0-2]." +
"Level 2 is required to output particle files.",
argstr="--verbose=%d")
class SegStatsInputSpec(FSTraitedSpec):
_xor_inputs = ('segmentation_file', 'annot', 'surf_label')
segmentation_file = File(exists=True,
argstr='--seg %s',
xor=_xor_inputs,
mandatory=True,
desc='segmentation volume path')
annot = traits.Tuple(traits.Str,
traits.Enum('lh', 'rh'),
traits.Str,
argstr='--annot %s %s %s',
xor=_xor_inputs,
mandatory=True,
desc='subject hemi parc : use surface parcellation')
surf_label = traits.Tuple(traits.Str,
traits.Enum('lh', 'rh'),
traits.Str,
argstr='--slabel %s %s %s',
xor=_xor_inputs,
mandatory=True,
desc='subject hemi label : use surface label')
summary_file = File(argstr='--sum %s',
genfile=True,
desc='Segmentation stats summary table file')
partial_volume_file = File(exists=True,
argstr='--pv %f',
desc='Compensate for partial voluming')
in_file = File(exists=True,
argstr='--i %s',
desc='Use the segmentation to report stats on this volume')
frame = traits.Int(argstr='--frame %d',
desc='Report stats on nth frame of input volume')
multiply = traits.Float(argstr='--mul %f', desc='multiply input by val')
calc_snr = traits.Bool(
argstr='--snr', desc='save mean/std as extra column in output table')
calc_power = traits.Enum(
'sqr',
'sqrt',
argstr='--%s',
desc='Compute either the sqr or the sqrt of the input')
_ctab_inputs = ('color_table_file', 'default_color_table',
'gca_color_table')
color_table_file = File(exists=True,
argstr='--ctab %s',
xor=_ctab_inputs,
desc='color table file with seg id names')
default_color_table = traits.Bool(
argstr='--ctab-default',
xor=_ctab_inputs,
desc='use $FREESURFER_HOME/FreeSurferColorLUT.txt')
gca_color_table = File(exists=True,
argstr='--ctab-gca %s',
xor=_ctab_inputs,
desc='get color table from GCA (CMA)')
segment_id = traits.List(argstr='--id %s...',
desc='Manually specify segmentation ids')
exclude_id = traits.Int(argstr='--excludeid %d',
desc='Exclude seg id from report')
exclude_ctx_gm_wm = traits.Bool(
argstr='--excl-ctxgmwm', desc='exclude cortical gray and white matter')
wm_vol_from_surf = traits.Bool(argstr='--surf-wm-vol',
desc='Compute wm volume from surf')
cortex_vol_from_surf = traits.Bool(argstr='--surf-ctx-vol',
desc='Compute cortex volume from surf')
non_empty_only = traits.Bool(argstr='--nonempty',
desc='Only report nonempty segmentations')
mask_file = File(exists=True,
argstr='--mask %s',
desc='Mask volume (same size as seg')
mask_thresh = traits.Float(argstr='--maskthresh %f',
desc='binarize mask with this threshold <0.5>')
mask_sign = traits.Enum('abs',
'pos',
'neg',
'--masksign %s',
desc='Sign for mask threshold: pos, neg, or abs')
mask_frame = traits.Int(
'--maskframe %d',
requires=['mask_file'],
desc='Mask with this (0 based) frame of the mask volume')
mask_invert = traits.Bool(argstr='--maskinvert',
desc='Invert binarized mask volume')
mask_erode = traits.Int(argstr='--maskerode %d',
desc='Erode mask by some amount')
brain_vol = traits.Enum(
'brain-vol-from-seg',
'brainmask',
'--%s',
desc=
'Compute brain volume either with ``brainmask`` or ``brain-vol-from-seg``'
)
etiv = traits.Bool(argstr='--etiv',
desc='Compute ICV from talairach transform')
etiv_only = traits.Enum(
'etiv',
'old-etiv',
'--%s-only',
desc='Compute etiv and exit. Use ``etiv`` or ``old-etiv``')
avgwf_txt_file = traits.Either(
traits.Bool,
File,
argstr='--avgwf %s',
desc='Save average waveform into file (bool or filename)')
avgwf_file = traits.Either(traits.Bool,
File,
argstr='--avgwfvol %s',
desc='Save as binary volume (bool or filename)')
sf_avg_file = traits.Either(traits.Bool,
File,
argstr='--sfavg %s',
desc='Save mean across space and time')
vox = traits.List(
traits.Int,
argstr='--vox %s',
desc='Replace seg with all 0s except at C R S (three int inputs)')
class ComputeTDIInputSpec(CommandLineInputSpec):
in_file = File(exists=True,
argstr='%s',
mandatory=True,
position=-2,
desc='input tractography')
out_file = File('tdi.mif',
argstr='%s',
usedefault=True,
position=-1,
desc='output TDI file')
reference = File(exists=True,
argstr='-template %s',
desc='a reference'
'image to be used as template')
vox_size = traits.List(traits.Int,
argstr='-vox %s',
sep=',',
desc='voxel dimensions')
data_type = traits.Enum('float',
'unsigned int',
argstr='-datatype %s',
desc='specify output image data type')
use_dec = traits.Bool(argstr='-dec', desc='perform mapping in DEC space')
dixel = File(argstr='-dixel %s',
desc='map streamlines to'
'dixels within each voxel. Directions are stored as'
'azimuth elevation pairs.')
max_tod = traits.Int(argstr='-tod %d',
desc='generate a Track Orientation '
'Distribution (TOD) in each voxel.')
contrast = traits.Enum('tdi',
'length',
'invlength',
'scalar_map',
'scalar_map_conut',
'fod_amp',
'curvature',
argstr='-constrast %s',
desc='define the desired '
'form of contrast for the output image')
in_map = File(exists=True,
argstr='-image %s',
desc='provide the'
'scalar image map for generating images with '
'\'scalar_map\' contrasts, or the SHs image for fod_amp')
stat_vox = traits.Enum('sum',
'min',
'mean',
'max',
argstr='-stat_vox %s',
desc='define the statistic for choosing the final'
'voxel intesities for a given contrast')
stat_tck = traits.Enum(
'mean',
'sum',
'min',
'max',
'median',
'mean_nonzero',
'gaussian',
'ends_min',
'ends_mean',
'ends_max',
'ends_prod',
argstr='-stat_tck %s',
desc='define the statistic for choosing '
'the contribution to be made by each streamline as a function of'
' the samples taken along their lengths.')
fwhm_tck = traits.Float(
argstr='-fwhm_tck %f',
desc='define the statistic for choosing the'
' contribution to be made by each streamline as a function of the '
'samples taken along their lengths')
map_zero = traits.Bool(
argstr='-map_zero',
desc='if a streamline has zero contribution based '
'on the contrast & statistic, typically it is not mapped; use this '
'option to still contribute to the map even if this is the case '
'(these non-contributing voxels can then influence the mean value in '
'each voxel of the map)')
upsample = traits.Int(argstr='-upsample %d',
desc='upsample the tracks by'
' some ratio using Hermite interpolation before '
'mappping')
precise = traits.Bool(
argstr='-precise',
desc='use a more precise streamline mapping '
'strategy, that accurately quantifies the length through each voxel '
'(these lengths are then taken into account during TWI calculation)')
ends_only = traits.Bool(argstr='-ends_only',
desc='only map the streamline'
' endpoints to the image')
tck_weights = File(exists=True,
argstr='-tck_weights_in %s',
desc='specify'
' a text scalar file containing the streamline weights')
nthreads = traits.Int(argstr='-nthreads %d',
desc='number of threads. if zero, the number'
' of available cpus will be used',
nohash=True)
class _AddTPMsInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiPath(File(exists=True),
mandatory=True,
desc="input list of ROIs")
indices = traits.List(traits.Int, desc="select specific maps")
class _AddTSVHeaderInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="input file")
columns = traits.List(traits.Str,
mandatory=True,
desc="header for columns")
class MaskCoilsOutputSpec(BaseMaskOutputSpec):
out_files = traits.List(File(exists=True), desc="Output files")
class DialateMaskInputSpec(BaseMaskInputSpec):
in_file = File(exists=True, desc="Mask to dialate")
dialation = traits.List((traits.Float(), traits.Float(), traits.Float),
desc="Size of the dialation")
class MRCalcInputSpec(CommandLineInputSpec):
operands = traits.List(traits.Any(),
argstr='%s',
mandatory=True,
position=-3,
desc="Diffusion weighted images with graident info")
out_file = File(genfile=True,
argstr='%s',
position=-1,
desc="Extracted DW or b-zero images")
out_ext = traits.Str(desc='Extention of the output file.')
operation = traits.Enum('abs',
'neg',
'sqrt',
'exp',
'log',
'log10',
'cos',
'sin',
'tan',
'cosh',
'sinh',
'tanh',
'acos',
'asin',
'atan',
'acosh',
'asinh',
'atanh',
'round',
'ceil',
'floor',
'isnan',
'isinf',
'finite',
'real',
'imag',
'phase',
'conj',
'add',
'subtract',
'multiply',
'divide',
'pow',
'min',
'max',
'lt',
'gt',
'le',
'ge',
'eq',
'neq',
'complex',
'if',
mandatory=True,
argstr='-%s',
position=-2,
desc=("Operation to apply to the files"))
quiet = traits.Bool(mandatory=False,
argstr="-quiet",
desc="Don't display output during operation")
class InverseSolutionConnInputSpec(BaseInterfaceInputSpec):
"""Input specification for InverseSolution."""
sbj_id = traits.String(desc='subject id', mandatory=True)
subjects_dir = traits.Directory(
exists=True,
desc='Freesurfer main directory', # noqa
mandatory=True)
raw_filename = traits.File(exists=True,
desc='raw filename',
mandatory=True)
cov_filename = traits.File(exists=True,
desc='Noise Covariance matrix',
mandatory=True)
fwd_filename = traits.File(exists=True, desc='LF matrix', mandatory=True)
is_epoched = traits.Bool(False,
usedefault=True,
desc='if true raw data will be epoched',
mandatory=False)
is_fixed = traits.Bool(False,
usedefault=True,
desc='if true we use fixed orientation',
mandatory=False)
events_id = traits.Dict({},
desc='the id of all events to consider.',
usedefault=True,
mandatory=False)
condition = traits.List(desc='list of conditions', mandatory=False)
t_min = traits.Float(None, desc='start time before event', mandatory=False)
t_max = traits.Float(None, desc='end time after event', mandatory=False)
is_evoked = traits.Bool(desc='if true if we want to create evoked data',
mandatory=False)
is_ave = traits.Bool(False,
desc='if true if we have already evoked data',
mandatory=False)
inv_method = traits.String('MNE',
desc='possible inverse methods are \
sLORETA, MNE, dSPM',
usedefault=True,
mandatory=True)
snr = traits.Float(1.0,
usedefault=True,
desc='use smaller SNR for \
raw data',
mandatory=False)
parc = traits.String('aparc',
usedefault=True,
desc='the parcellation to use: aparc vs aparc.a2009s',
mandatory=False)
aseg = traits.Bool(desc='if true sub structures will be considered',
mandatory=False)
aseg_labels = traits.List(desc='list of substructures in the src space',
mandatory=False)
all_src_space = traits.Bool(False,
desc='if true compute inverse on all \
source space',
usedefault=True,
mandatory=False)
ROIs_mean = traits.Bool(True,
desc='if true compute mean on ROIs',
usedefault=True,
mandatory=False)
class ResultdictsOutputSpec(TraitedSpec):
resultdicts = traits.List(traits.Dict(traits.Str(), traits.Any()))
class MRISPreprocInputSpec(FSTraitedSpec):
out_file = File(argstr='--out %s', genfile=True, desc='output filename')
target = traits.Str(argstr='--target %s',
mandatory=True,
desc='target subject name')
hemi = traits.Enum('lh',
'rh',
argstr='--hemi %s',
mandatory=True,
desc='hemisphere for source and target')
surf_measure = traits.Str(
argstr='--meas %s',
xor=('surf_measure', 'surf_measure_file', 'surf_area'),
desc='Use subject/surf/hemi.surf_measure as input')
surf_area = traits.Str(
argstr='--area %s',
xor=('surf_measure', 'surf_measure_file', 'surf_area'),
desc=
'Extract vertex area from subject/surf/hemi.surfname to use as input.')
subjects = traits.List(argstr='--s %s...',
xor=('subjects', 'fsgd_file', 'subject_file'),
desc='subjects from who measures are calculated')
fsgd_file = File(exists=True,
argstr='--fsgd %s',
xor=('subjects', 'fsgd_file', 'subject_file'),
desc='specify subjects using fsgd file')
subject_file = File(
exists=True,
argstr='--f %s',
xor=('subjects', 'fsgd_file', 'subject_file'),
desc='file specifying subjects separated by white space')
surf_measure_file = InputMultiPath(
File(exists=True),
argstr='--is %s...',
xor=('surf_measure', 'surf_measure_file', 'surf_area'),
desc='file alternative to surfmeas, still requires list of subjects')
source_format = traits.Str(argstr='--srcfmt %s', desc='source format')
surf_dir = traits.Str(argstr='--surfdir %s',
desc='alternative directory (instead of surf)')
vol_measure_file = InputMultiPath(
traits.Tuple(File(exists=True), File(exists=True)),
argstr='--iv %s %s...',
desc='list of volume measure and reg file tuples')
proj_frac = traits.Float(argstr='--projfrac %s',
desc='projection fraction for vol2surf')
fwhm = traits.Float(argstr='--fwhm %f',
xor=['num_iters'],
desc='smooth by fwhm mm on the target surface')
num_iters = traits.Int(
argstr='--niters %d',
xor=['fwhm'],
desc='niters : smooth by niters on the target surface')
fwhm_source = traits.Float(argstr='--fwhm-src %f',
xor=['num_iters_source'],
desc='smooth by fwhm mm on the source surface')
num_iters_source = traits.Int(
argstr='--niterssrc %d',
xor=['fwhm_source'],
desc='niters : smooth by niters on the source surface')
smooth_cortex_only = traits.Bool(
argstr='--smooth-cortex-only',
desc='only smooth cortex (ie, exclude medial wall)')
class _JoinTSVColumnsInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="input file")
join_file = File(exists=True, mandatory=True, desc="file to be adjoined")
side = traits.Enum("right", "left", usedefault=True, desc="where to join")
columns = traits.List(traits.Str, desc="header for columns")
class BinarizeInputSpec(FSTraitedSpec):
in_file = File(exists=True,
argstr='--i %s',
mandatory=True,
copyfile=False,
desc='input volume')
min = traits.Float(argstr='--min %f',
xor=['wm_ven_csf'],
desc='min thresh')
max = traits.Float(argstr='--max %f',
xor=['wm_ven_csf'],
desc='max thresh')
rmin = traits.Float(argstr='--rmin %f',
desc='compute min based on rmin*globalmean')
rmax = traits.Float(argstr='--rmax %f',
desc='compute max based on rmax*globalmean')
match = traits.List(traits.Int,
argstr='--match %d...',
desc='match instead of threshold')
wm = traits.Bool(argstr='--wm',
desc='set match vals to 2 and 41 (aseg for cerebral WM)')
ventricles = traits.Bool(
argstr='--ventricles',
desc='set match vals those for aseg ventricles+choroid (not 4th)')
wm_ven_csf = traits.Bool(
argstr='--wm+vcsf',
xor=['min', 'max'],
desc='WM and ventricular CSF, including choroid (not 4th)')
binary_file = File(argstr='--o %s',
genfile=True,
desc='binary output volume')
out_type = traits.Enum('nii',
'nii.gz',
'mgz',
argstr='',
desc='output file type')
count_file = traits.Either(
traits.Bool,
File,
argstr='--count %s',
desc='save number of hits in ascii file (hits, ntotvox, pct)')
bin_val = traits.Int(argstr='--binval %d',
desc='set vox within thresh to val (default is 1)')
bin_val_not = traits.Int(
argstr='--binvalnot %d',
desc='set vox outside range to val (default is 0)')
invert = traits.Bool(argstr='--inv', desc='set binval=0, binvalnot=1')
frame_no = traits.Int(argstr='--frame %s',
desc='use 0-based frame of input (default is 0)')
merge_file = File(exists=True,
argstr='--merge %s',
desc='merge with mergevol')
mask_file = File(exists=True,
argstr='--mask maskvol',
desc='must be within mask')
mask_thresh = traits.Float(argstr='--mask-thresh %f',
desc='set thresh for mask')
abs = traits.Bool(argstr='--abs',
desc='take abs of invol first (ie, make unsigned)')
bin_col_num = traits.Bool(
argstr='--bincol',
desc='set binarized voxel value to its column number')
zero_edges = traits.Bool(argstr='--zero-edges',
desc='zero the edge voxels')
zero_slice_edge = traits.Bool(argstr='--zero-slice-edges',
desc='zero the edge slice voxels')
dilate = traits.Int(argstr='--dilate %d',
desc='niters: dilate binarization in 3D')
erode = traits.Int(
argstr='--erode %d',
desc='nerode: erode binarization in 3D (after any dilation)')
erode2d = traits.Int(
argstr='--erode2d %d',
desc='nerode2d: erode binarization in 2D (after any 3D erosion)')
class _DictMergeInputSpec(BaseInterfaceInputSpec):
in_dicts = traits.List(
traits.Either(traits.Dict, traits.Instance(OrderedDict)),
desc="Dictionaries to be merged. In the event of a collision, values "
"from dictionaries later in the list receive precedence.",
)
class IteratorToFileInputSpec(TraitedSpec):
ids = traits.List(traits.Str(), desc="ID of the iterable")
out_file = File(genfile=True, desc="The name of the generated file")
class ReadSidecarJSONInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc='the input nifti file')
fields = traits.List(traits.Str, desc='get only certain fields')
class ANTSInputSpec(ANTSCommandInputSpec):
dimension = traits.Enum(3,
2,
argstr='%d',
usedefault=False,
position=1,
desc='image dimension (2 or 3)')
fixed_image = InputMultiPath(
File(exists=True),
mandatory=True,
desc=('image to apply transformation to (generally a coregistered '
'functional)'))
moving_image = InputMultiPath(
File(exists=True),
argstr='%s',
mandatory=True,
desc=('image to apply transformation to (generally a coregistered '
'functional)'))
metric = traits.List(traits.Enum('CC', 'MI', 'SMI', 'PR', 'SSD', 'MSQ',
'PSE'),
mandatory=True,
desc='')
metric_weight = traits.List(traits.Float(), requires=['metric'], desc='')
radius = traits.List(traits.Int(), requires=['metric'], desc='')
output_transform_prefix = traits.Str('out',
usedefault=True,
argstr='--output-naming %s',
mandatory=True,
desc='')
transformation_model = traits.Enum('Diff',
'Elast',
'Exp',
'Greedy Exp',
'SyN',
argstr='%s',
mandatory=True,
desc='')
gradient_step_length = traits.Int(requires=['transformation_model'],
desc='')
number_of_time_steps = traits.Int(requires=['gradient_step_length'],
desc='')
delta_time = traits.Float(requires=['number_of_time_steps'], desc='')
symmetry_type = traits.Float(requires=['delta_time'], desc='')
use_histogram_matching = traits.Bool(argstr='%s',
default=True,
usedefault=True)
number_of_iterations = traits.List(traits.Int(),
argstr='--number-of-iterations %s',
sep='x')
smoothing_sigmas = traits.List(traits.Int(),
argstr='--gaussian-smoothing-sigmas %s',
sep='x')
subsampling_factors = traits.List(traits.Int(),
argstr='--subsampling-factors %s',
sep='x')
affine_gradient_descent_option = traits.List(traits.Float(), argstr='%s')
mi_option = traits.List(traits.Int(), argstr='--MI-option %s', sep='x')
regularization = traits.Enum('Gauss', 'DMFFD', argstr='%s', desc='')
regularization_gradient_field_sigma = traits.Float(
requires=['regularization'], desc='')
regularization_deformation_field_sigma = traits.Float(
requires=['regularization'], desc='')
number_of_affine_iterations = traits.List(
traits.Int(), argstr='--number-of-affine-iterations %s', sep='x')
class ScanTimesInfoInputSpec(BaseInterfaceInputSpec):
dicom_infos = traits.List(desc='List of dicoms to calculate the difference'
' between consecutive scan start times.')
class _UnzipJoinedSurfacesOutputSpec(TraitedSpec):
out_files = traits.List(
OutputMultiPath(File(exists=True),
desc='output pointcloud in PLY format'))
surf_keys = traits.List(traits.Str, desc='surface identifier keys')
class Nii2DicomInputSpec(TraitedSpec):
in_file = File(mandatory=True, desc='input nifti file')
reference_dicom = traits.List(mandatory=True, desc='original umap')
class ProjThreshOuputSpec(TraitedSpec):
out_files = traits.List(
File,
exists=True,
desc='path/name of output volume after thresholding')
class LOGISMOSBInputSpec(CommandLineInputSpec):
t1_file = File(exists=True,
desc='T1 scan output by BAW',
argstr='--inputT1 %s',
mandatory=True)
t2_file = File(exists=True,
genfile=True,
desc='T2 scan output by BAW',
argstr='--inputT2 %s',
mandatory=False)
mesh_file = File(
exists=True,
desc=
'final mesh of the white matter surface (must have a genus equal to 0)',
argstr='-m %s',
mandatory=True)
wm_file = File(
exists=True,
desc=
'final binary image of the white matter surface (must have a genus equal to 0)',
argstr='-b %s',
mandatory=True)
atlas_file = File(
exists=True,
desc=
'hcnma atlas to define brain regions. If different atlas is used, thick '
+ 'regions must be defined',
argstr='-z %s',
mandatory=False)
brainlabels_file = File(exists=True,
desc='skullstripped brainlabels file',
argstr='--inputABCLabels %s',
mandatory=True)
smoothnessConstraint = traits.Int(desc='smoothness constraint',
argstr='--smoothnessConstraint %d',
mandatory=True)
nColumns = traits.Int(desc="number of vertices",
argstr="--nColumns %d",
Mandatory=False)
columnChoice = traits.String(desc="some parameter",
argstr="--columnChoice %s",
Mandatory=False)
columnHeight = traits.Int(desc="column height",
argstr="--columnHeight %d",
Mandatory=False)
nodeSpacing = traits.Float(desc="node spacing",
argstr="--nodeSpacing %.2f",
Mandatory=False)
w = traits.Float(desc="w", argstr="-w %.2f", Mandatory=False)
a = traits.Float(desc="a", argstr="-a %.2f", Mandatory=False)
nPropagate = traits.Int(desc="number of propagations",
argstr="--nPropagate %d",
Mandatory=False)
basename = traits.String(desc="basename for output files",
argstr="--outputBase %s",
Mandatory=True)
thick_regions = traits.List(
traits.Int(),
argstr="-r %s",
mandatory=False,
sep=',',
desc="List of regions in the atlas file to that will be thicker")
useHNCMALabels = traits.Bool(
argstr="--useHNCMALabels",
desc="Uses HCNMA label map to define thick regions")
wm_proba_file = File(exist=True,
argstr='--wmProbaMap %s',
desc="White matter pobability map.")
gm_proba_file = File(exist=True,
argstr='--gmProbaMap %s',
desc="Gray matter pobability map.")
class QDecOutputSpec(TraitedSpec):
stats_directory = Directory(desc="stat_tables directory", mandatory=True)
stdout = traits.List(traits.Str(), desc="stdout messages")
stderr = traits.List(traits.Str(), desc="stderr messages")
class MaskCoverageOutputSpec(DynamicTraitedSpec):
out_files = OutputMultiPath(File(exists=True))
coverage = traits.List(traits.Float)
class ResampleScalarVectorDWIVolumeInputSpec(CommandLineInputSpec):
inputVolume = File(position=-2,
desc="Input Volume to be resampled",
exists=True,
argstr="%s")
outputVolume = traits.Either(traits.Bool,
File(),
position=-1,
hash_files=False,
desc="Resampled Volume",
argstr="%s")
Reference = File(desc="Reference Volume (spacing,size,orientation,origin)",
exists=True,
argstr="--Reference %s")
transformationFile = File(exists=True, argstr="--transformationFile %s")
defField = File(
desc=
"File containing the deformation field (3D vector image containing vectors with 3 components)",
exists=True,
argstr="--defField %s")
hfieldtype = traits.Enum("displacement",
"h-Field",
desc="Set if the deformation field is an h-Field",
argstr="--hfieldtype %s")
interpolation = traits.Enum(
"linear",
"nn",
"ws",
"bs",
desc=
"Sampling algorithm (linear or nn (nearest neighborhoor), ws (WindowedSinc), bs (BSpline) )",
argstr="--interpolation %s")
transform_order = traits.Enum(
"input-to-output",
"output-to-input",
desc="Select in what order the transforms are read",
argstr="--transform_order %s")
notbulk = traits.Bool(
desc=
"The transform following the BSpline transform is not set as a bulk transform for the BSpline transform",
argstr="--notbulk ")
spaceChange = traits.Bool(
desc=
"Space Orientation between transform and image is different (RAS/LPS) (warning: if the transform is a Transform Node in Slicer3, do not select)",
argstr="--spaceChange ")
rotation_point = traits.List(
desc=
"Rotation Point in case of rotation around a point (otherwise useless)",
argstr="--rotation_point %s")
centered_transform = traits.Bool(
desc=
"Set the center of the transformation to the center of the input image",
argstr="--centered_transform ")
image_center = traits.Enum(
"input",
"output",
desc=
"Image to use to center the transform (used only if \'Centered Transform\' is selected)",
argstr="--image_center %s")
Inverse_ITK_Transformation = traits.Bool(
desc=
"Inverse the transformation before applying it from output image to input image",
argstr="--Inverse_ITK_Transformation ")
spacing = InputMultiPath(
traits.Float,
desc="Spacing along each dimension (0 means use input spacing)",
sep=",",
argstr="--spacing %s")
size = InputMultiPath(
traits.Float,
desc="Size along each dimension (0 means use input size)",
sep=",",
argstr="--size %s")
origin = traits.List(desc="Origin of the output Image",
argstr="--origin %s")
direction_matrix = InputMultiPath(
traits.Float,
desc=
"9 parameters of the direction matrix by rows (ijk to LPS if LPS transform, ijk to RAS if RAS transform)",
sep=",",
argstr="--direction_matrix %s")
number_of_thread = traits.Int(
desc="Number of thread used to compute the output image",
argstr="--number_of_thread %d")
default_pixel_value = traits.Float(
desc=
"Default pixel value for samples falling outside of the input region",
argstr="--default_pixel_value %f")
window_function = traits.Enum(
"h",
"c",
"w",
"l",
"b",
desc=
"Window Function , h = Hamming , c = Cosine , w = Welch , l = Lanczos , b = Blackman",
argstr="--window_function %s")
spline_order = traits.Int(desc="Spline Order", argstr="--spline_order %d")
transform_matrix = InputMultiPath(
traits.Float,
desc=
"12 parameters of the transform matrix by rows ( --last 3 being translation-- )",
sep=",",
argstr="--transform_matrix %s")
transform = traits.Enum(
"rt",
"a",
desc="Transform algorithm, rt = Rigid Transform, a = Affine Transform",
argstr="--transform %s")
class ContrastsInputSpec(BaseInterfaceInputSpec):
in_list = traits.List(traits.Any, mandatory=True, desc='list of contrasts')
design = File(exists=True, desc="design file to be used in GLM")
