class _UniformGridInputSpec(BaseInterfaceInputSpec):
in_data = InputMultiObject(
File(exists=True),
mandatory=True,
desc="list of input data",
)
reference = traits.Int(0, usedefault=True, desc="reference index")
class ResponseSDInputSpec(MRTrix3BaseInputSpec):
algorithm = traits.Enum(
"msmt_5tt",
"dhollander",
"tournier",
"tax",
argstr="%s",
position=1,
mandatory=True,
desc="response estimation algorithm (multi-tissue)",
)
in_file = File(
exists=True, argstr="%s", position=-5, mandatory=True, desc="input DWI image"
)
mtt_file = File(argstr="%s", position=-4, desc="input 5tt image")
wm_file = File(
"wm.txt",
argstr="%s",
position=-3,
usedefault=True,
desc="output WM response text file",
)
gm_file = File(argstr="%s", position=-2, desc="output GM response text file")
csf_file = File(argstr="%s", position=-1, desc="output CSF response text file")
in_mask = File(exists=True, argstr="-mask %s", desc="provide initial mask image")
max_sh = InputMultiObject(
traits.Int,
argstr="-lmax %s",
sep=",",
desc=(
"maximum harmonic degree of response function - single value for "
"single-shell response, list for multi-shell response"
),
)
class IntraModalMergeInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiObject(File(exists=True),
mandatory=True,
desc='input files')
hmc = traits.Bool(True, usedefault=True)
zero_based_avg = traits.Bool(True, usedefault=True)
to_lps = traits.Bool(True, usedefault=True)
class Phases2FieldmapInputSpec(BaseInterfaceInputSpec):
phase_files = InputMultiObject(File(exists=True),
mandatory=True,
desc='list of phase1, phase2 files')
metadatas = traits.List(traits.Dict,
mandatory=True,
desc='list of phase1, phase2 metadata dicts')
class ResampleInputSpec(ApplyTransformsInputSpec):
input_space = traits.Either("MNI152NLin6Asym",
"MNI152NLin2009cAsym",
mandatory=True)
reference_space = traits.Either("MNI152NLin6Asym",
"MNI152NLin2009cAsym",
mandatory=True)
reference_res = traits.Int(mandatory=False)
lazy = traits.Bool(default=True,
usedefault=True,
desc="only resample if necessary")
# make not mandatory as these inputs will be computed from other inputs
reference_image = File(
argstr="--reference-image %s",
mandatory=False,
desc="reference image space that you wish to warp INTO",
exists=True,
)
transforms = InputMultiObject(
traits.Either(File(exists=True), "identity"),
argstr="%s",
mandatory=False,
desc="transform files: will be applied in reverse order. For "
"example, the last specified transform will be applied first.",
)
class _TemplateDimensionsInputSpec(BaseInterfaceInputSpec):
t1w_list = InputMultiObject(File(exists=True),
mandatory=True,
desc="input T1w images")
max_scale = traits.Float(3.0,
usedefault=True,
desc="Maximum scaling factor in images to accept")
class _SignalExtractionInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="4-D fMRI nii file")
label_files = InputMultiObject(
File(exists=True),
mandatory=True,
desc="a 3D label image, with 0 denoting "
"background, or a list of 3D probability "
"maps (one per label) or the equivalent 4D "
"file.",
)
prob_thres = traits.Range(
low=0.0,
high=1.0,
value=0.5,
usedefault=True,
desc="If label_files are probability masks, threshold "
"at specified probability.",
)
class_labels = traits.List(
mandatory=True,
desc="Human-readable labels for each segment "
"in the label file, in order. The length of "
"class_labels must be equal to the number of "
"segments (background excluded). This list "
"corresponds to the class labels in label_file "
"in ascending order",
)
out_file = File(
"signals.tsv",
usedefault=True,
exists=False,
desc="The name of the file to output to. "
"signals.tsv by default",
)
class _DerivativesDataSinkInputSpec(DynamicTraitedSpec,
BaseInterfaceInputSpec):
base_directory = traits.Directory(
desc='Path to the base directory for storing data.')
check_hdr = traits.Bool(True,
usedefault=True,
desc='fix headers of NIfTI outputs')
compress = traits.Bool(
desc="force compression (True) or uncompression (False)"
" of the output file (default: same as input)")
desc = Str('', usedefault=True, desc='Label for description field')
extra_values = traits.List(Str)
in_file = InputMultiObject(File(exists=True),
mandatory=True,
desc='the object to be saved')
keep_dtype = traits.Bool(False,
usedefault=True,
desc='keep datatype suffix')
meta_dict = traits.DictStrAny(
desc='an input dictionary containing metadata')
source_file = File(exists=False,
mandatory=True,
desc='the input func file')
space = Str('', usedefault=True, desc='Label for space field')
suffix = Str('', usedefault=True, desc='suffix appended to source_file')
class _BSplineApproxInputSpec(BaseInterfaceInputSpec):
in_data = File(exists=True, mandatory=True, desc="path to a fieldmap")
in_mask = File(exists=True, mandatory=True, desc="path to a brain mask")
bs_spacing = InputMultiObject(
[DEFAULT_ZOOMS_MM],
traits.Tuple(traits.Float, traits.Float, traits.Float),
usedefault=True,
desc="spacing between B-Spline control points",
)
ridge_alpha = traits.Float(0.01,
usedefault=True,
desc="controls the regularization")
recenter = traits.Enum(
"mode",
"median",
"mean",
False,
usedefault=True,
desc="strategy to recenter the distribution of the input fieldmap",
)
extrapolate = traits.Bool(
True,
usedefault=True,
desc="generate a field, extrapolated outside the brain mask",
)
class DMRISummaryInputSpec(BaseInterfaceInputSpec):
confounds_file = File(exists=True,
desc="BIDS' _confounds.tsv file")
sliceqc_file = File(exists=True,
desc="output from SliceQC")
sliceqc_mask = File(exists=True, desc='Mask')
str_or_tuple = traits.Either(
traits.Str,
traits.Tuple(traits.Str, traits.Either(None, traits.Str)),
traits.Tuple(traits.Str, traits.Either(None, traits.Str), traits.Either(None, traits.Str)))
confounds_list = traits.List(
str_or_tuple, minlen=1,
desc='list of headers to extract from the confounds_file')
bval_files = InputMultiObject(File(exists=True), desc='bvals files')
orig_bvecs = InputMultiObject(File(exists=True), desc='original bvecs file')
class _MergeSeriesInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiObject(
File(exists=True, mandatory=True, desc="input list of 3d images"))
allow_4D = traits.Bool(
True,
usedefault=True,
desc="whether 4D images are allowed to be concatenated")
class EstimateFODInputSpec(MRTrix3BaseInputSpec):
algorithm = traits.Enum(
'csd',
'msmt_csd',
argstr='%s',
position=-8,
mandatory=True,
desc='FOD algorithm')
in_file = File(
exists=True,
argstr='%s',
position=-7,
mandatory=True,
desc='input DWI image')
wm_txt = File(
argstr='%s', position=-6, mandatory=True, desc='WM response text file')
wm_odf = File(
argstr='%s',
position=-5,
genfile=True,
desc='output WM ODF')
gm_txt = File(
argstr='%s',
position=-4,
requires=['csf_txt'],
desc='GM response text file')
gm_odf = File(
argstr='%s',
position=-3,
genfile=True,
requires=['gm_txt'],
desc='output GM ODF')
csf_txt = File(
argstr='%s', position=-2, desc='CSF response text file')
csf_odf = File(
argstr='%s',
position=-1,
genfile=True,
requires=['csf_txt'],
desc='output CSF ODF')
mask_file = File(exists=True, argstr='-mask %s', desc='mask image')
shell = traits.List(
traits.Float,
sep=',',
argstr='-shell %s',
desc='specify one or more dw gradient shells')
max_sh = InputMultiObject(
traits.Int,
argstr='-lmax %s',
sep=',',
desc='maximum harmonic degree of response function - single value for single-shell '
'response, list for multi-shell response')
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.'))
class _TransformCoefficientsInputSpec(BaseInterfaceInputSpec):
in_coeff = InputMultiObject(File(exist=True),
mandatory=True,
desc="input coefficients file(s)")
fmap_ref = File(exists=True, mandatory=True, desc="the fieldmap reference")
transform = File(exists=True,
mandatory=True,
desc="rigid-body transform file")
class _ConcatenateXFMsInputSpec(BaseInterfaceInputSpec):
in_xfms = InputMultiObject(File(exists=True), desc="input transform piles")
inverse = traits.Bool(False, usedefault=True, desc="generate inverse")
out_fmt = traits.Enum("itk", "fs", usedefault=True, desc="output format")
reference = File(exists=True, desc="reference file (only for writing LTA format, if not "
"concatenating another LTA).")
moving = File(exists=True, desc="moving file (only for writing LTA format, if not "
"concatenating another LTA).")
class _FixTraitApplyTransformsInputSpec(ApplyTransformsInputSpec):
transforms = InputMultiObject(
traits.Either(File(exists=True), 'identity'),
argstr="%s",
mandatory=True,
desc="transform files: will be applied in reverse order. For "
"example, the last specified transform will be applied first.",
)
class ConcatRPESplitsInputSpec(BaseInterfaceInputSpec):
dwi_plus = InputMultiObject(File(exists=True), desc='single volume dwis')
bvec_plus = InputMultiObject(File(exists=True), desc='single volume bvecs')
bval_plus = InputMultiObject(File(exists=True), desc='single volume bvals')
b0_images_plus = InputMultiObject(File(exists=True), desc='just the b0s')
b0_indices_plus = traits.List(desc='list of original indices for each b0 image')
original_images_plus = InputMultiObject(File(exists=True))
dwi_minus = InputMultiObject(File(exists=True), desc='single volume dwis')
bvec_minus = InputMultiObject(File(exists=True), desc='single volume bvecs')
bval_minus = InputMultiObject(File(exists=True), desc='single volume bvals')
b0_images_minus = InputMultiObject(File(exists=True), desc='just the b0s')
b0_indices_minus = traits.List(desc='list of original indices for each b0 image')
original_images_minus = traits.List()
class MergeDWIsInputSpec(BaseInterfaceInputSpec):
dwi_files = InputMultiObject(File(),
mandatory=True,
desc='list of dwi files')
bids_dwi_files = InputMultiObject(File(),
mandatory=True,
desc='list of original (BIDS) dwi files')
bval_files = InputMultiObject(File(exists=True),
mandatory=True,
desc='list of bval files')
bvec_files = InputMultiObject(File(exists=True),
mandatory=True,
desc='list of bvec files')
b0_threshold = traits.Int(100, usedefault=True, desc='Maximum b=0 value')
denoising_confounds = InputMultiObject(
File(exists=True,
desc='list of confound files associated with each input dwi'))
harmonize_b0_intensities = traits.Bool(
True,
usedefault=True,
desc='Force scans to have the same mean b=0 intensity')
raw_concatenated_files = InputMultiObject(
File(), mandatory=False, desc='list of raw concatenated images')
b0_refs = InputMultiObject(File(),
mandatory=False,
desc='list of b=0 reference images')
class _IntraModalMergeInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiObject(File(exists=True),
mandatory=True,
desc="input files")
in_mask = File(exists=True, desc="input mask for grand mean scaling")
hmc = traits.Bool(True, usedefault=True)
zero_based_avg = traits.Bool(True, usedefault=True)
to_ras = traits.Bool(True, usedefault=True)
grand_mean_scaling = traits.Bool(False, usedefault=True)
class BIDSGrabInputSpec(BaseInterfaceInputSpec):
bids_dir = Directory(exists=True,
mandatory=True,
desc='BIDS dataset root directory')
derivatives = traits.Either(traits.Str,
traits.List(Str),
default='fmriprep',
usedefault=True,
mandatory=False,
desc='Specifies which derivatives to to index')
task = InputMultiObject(Str,
mandatory=False,
desc='Names of tasks to denoise')
session = InputMultiObject(Str,
mandatory=False,
desc='Names of sessions to denoise')
subject = InputMultiObject(Str,
mandatory=False,
desc='Labels of subjects to denoise')
ica_aroma = traits.Bool(mandatory=False, desc='ICA-Aroma files')
class _MCFLIRT2ITKInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiObject(
File(exists=True), mandatory=True, desc="list of MAT files from MCFLIRT"
)
in_reference = File(
exists=True, mandatory=True, desc="input image for spatial reference"
)
in_source = File(exists=True, mandatory=True, desc="input image for spatial source")
num_threads = traits.Int(
1, usedefault=True, nohash=True, desc="number of parallel processes"
)
class _aCompCorMasksInputSpec(BaseInterfaceInputSpec):
in_vfs = InputMultiObject(File(exists=True),
desc="Input volume fractions.")
is_aseg = traits.Bool(
False,
usedefault=True,
desc="Whether the input volume fractions come from FS' aseg.")
bold_zooms = traits.Tuple(traits.Float,
traits.Float,
traits.Float,
mandatory=True,
desc="BOLD series zooms")
class ComposeTransformsInputSpec(ApplyTransformsInputSpec):
input_image = File(mandatory=False)
dwi_files = InputMultiObject(File(exists=True),
mandatory=True,
desc='list of dwi files')
reference_image = File(exists=True, mandatory=True, desc='output grid')
# Transforms to apply
hmc_affines = InputMultiObject(File(exists=True),
desc='head motion correction affines')
fieldwarps = InputMultiObject(File(exists=True),
mandtory=False,
desc='SDC unwarping transform')
b0_to_intramodal_template_transforms = InputMultiObject(
File(exists=True),
mandtory=False,
desc='list of transforms to register the b=0 to '
'the intramodal template.')
intramodal_template_to_t1_affine = File(
exists=True, desc='affine from the intramodal template to t1')
intramodal_template_to_t1_warp = File(
exists=True, desc='warp from the intramodal template to t1')
hmcsdc_dwi_ref_to_t1w_affine = File(exists=True,
desc='affine from dwi ref to t1w')
t1_2_mni_forward_transform = InputMultiObject(
File(exists=True),
mandatory=False,
desc='composite (h5) transform to mni')
save_cmd = traits.Bool(
True,
usedefault=True,
desc='write a log of command lines that were applied')
copy_dtype = traits.Bool(False,
usedefault=True,
desc='copy dtype from inputs to outputs')
num_threads = traits.Int(1,
usedefault=True,
nohash=True,
desc='number of parallel processes')
transforms = File(mandatory=False)
class Dwi2ResponseInputSpec(ResponseSDInputSpec):
wm_file = File(
argstr='%s',
position=-3,
genfile=True,
desc='output WM response text file')
gm_file = File(
argstr='%s', genfile=True, position=-2, desc='output GM response text file')
csf_file = File(
argstr='%s', position=-1, genfile=True, desc='output CSF response text file')
max_sh = InputMultiObject(
traits.Int,
argstr='-lmax %s',
sep=',',
desc=('maximum harmonic degree of response function - single value for '
'single-shell response, list for multi-shell response'))
class _MultiApplyTransformsInputSpec(_FixTraitApplyTransformsInputSpec):
input_image = InputMultiObject(
File(exists=True),
mandatory=True,
desc="input time-series as a list of volumes after splitting"
" through the fourth dimension",
)
num_threads = traits.Int(
1, usedefault=True, nohash=True, desc="number of parallel processes"
)
save_cmd = traits.Bool(
True, usedefault=True, desc="write a log of command lines that were applied"
)
copy_dtype = traits.Bool(
False, usedefault=True, desc="copy dtype from inputs to outputs"
)
class _TOPUPCoeffReorientInputSpec(BaseInterfaceInputSpec):
in_coeff = InputMultiObject(
File(exist=True), mandatory=True, desc="input coefficients file(s) from TOPUP"
)
fmap_ref = File(exists=True, mandatory=True, desc="the fieldmap reference")
pe_dir = traits.Enum(
"+",
"-",
"i",
"i-",
"j",
"j-",
"k",
"k-",
usedefault=True,
desc="the polarity of the phase-encoding direction corresponding to fmap_ref",
)
def init_default_traits(self):
"""Automatically initialise necessary parameters for nipype or capsul"""
if 'output_directory' not in self.user_traits():
self.add_trait(
"output_directory",
traits.Directory(output=False, optional=True, userlevel=1))
if self.requirement is not None and 'spm' in self.requirement:
if 'use_mcr' not in self.user_traits():
self.add_trait("use_mcr",
traits.Bool(optional=True, userlevel=1))
if 'paths' not in self.user_traits():
self.add_trait(
"paths",
InputMultiObject(traits.Directory(),
optional=True,
userlevel=1))
if 'matlab_cmd' not in self.user_traits():
self.add_trait(
"matlab_cmd",
traits_extension.Str(optional=True, userlevel=1))
if 'mfile' not in self.user_traits():
self.add_trait("mfile", traits.Bool(optional=True,
userlevel=1))
if 'spm_script_file' not in self.user_traits():
spm_script_file_desc = (
'The location of the output SPM matlab '
'script automatically generated at the '
'run step time (a string representing '
'a file).')
self.add_trait(
"spm_script_file",
File(output=True,
optional=True,
input_filename=True,
userlevel=1,
desc=spm_script_file_desc))
class DerivativesDataSinkInputSpec(BaseInterfaceInputSpec):
base_directory = traits.Directory(
desc='Path to the base directory for storing data.')
in_file = InputMultiObject(File(exists=True),
mandatory=True,
desc='the object to be saved')
source_file = File(mandatory=True,
desc='the original file or name of merged files')
space = traits.Str('', usedefault=True, desc='Label for space field')
desc = traits.Str('', usedefault=True, desc='Label for description field')
suffix = traits.Str('',
usedefault=True,
desc='suffix appended to source_file')
keep_dtype = traits.Bool(False,
usedefault=True,
desc='keep datatype suffix')
extra_values = traits.List(traits.Str)
compress = traits.Bool(
desc="force compression (True) or uncompression (False)"
" of the output file (default: same as input)")
extension = traits.Str()
class _Coefficients2WarpInputSpec(BaseInterfaceInputSpec):
in_target = File(exist=True,
mandatory=True,
desc="input EPI data to be corrected")
in_coeff = InputMultiObject(
File(exists=True),
mandatory=True,
desc="input coefficients, after alignment to the EPI data",
)
ro_time = traits.Float(mandatory=True, desc="EPI readout time (s).")
pe_dir = traits.Enum(
"i",
"i-",
"j",
"j-",
"k",
"k-",
mandatory=True,
desc="the phase-encoding direction corresponding to in_target",
)
low_mem = traits.Bool(False,
usedefault=True,
desc="perform on low-mem fingerprint regime")
class ResponseSDInputSpec(MRTrix3BaseInputSpec):
algorithm = traits.Enum(
'msmt_5tt',
'dhollander',
'tournier',
'tax',
argstr='%s',
position=1,
mandatory=True,
desc='response estimation algorithm (multi-tissue)')
in_file = File(exists=True,
argstr='%s',
position=-5,
mandatory=True,
desc='input DWI image')
mtt_file = File(argstr='%s', position=-4, desc='input 5tt image')
wm_file = File('wm.txt',
argstr='%s',
position=-3,
usedefault=True,
desc='output WM response text file')
gm_file = File(argstr='%s',
position=-2,
desc='output GM response text file')
csf_file = File(argstr='%s',
position=-1,
desc='output CSF response text file')
in_mask = File(exists=True,
argstr='-mask %s',
desc='provide initial mask image')
max_sh = InputMultiObject(
traits.Int,
argstr='-lmax %s',
sep=',',
desc=(
'maximum harmonic degree of response function - single value for '
'single-shell response, list for multi-shell response'))
class DiffusionSummaryInputSpec(BaseInterfaceInputSpec):
distortion_correction = traits.Str(
desc='Susceptibility distortion correction method', mandatory=True)
pe_direction = traits.Enum(None,
'i',
'i-',
'j',
'j-',
mandatory=True,
desc='Phase-encoding direction detected')
distortion_correction = traits.Str(mandatory=True,
desc='Method used for SDC')
impute_slice_threshold = traits.CFloat(
desc='threshold for imputing a slice')
hmc_transform = traits.Str(mandatory=True,
desc='transform used during HMC')
hmc_model = traits.Str(desc='model used for hmc')
b0_to_t1w_transform = traits.Enum("Rigid",
"Affine",
desc='Transform type for coregistration')
dwi_denoise_window = traits.Int(desc='window size for dwidenoise')
output_spaces = traits.List(desc='Target spaces')
confounds_file = File(exists=True, desc='Confounds file')
validation_reports = InputMultiObject(File(exists=True))