class RefineBrainMaskInputSpec(BaseInterfaceInputSpec):
in_anat = File(exists=True, mandatory=True,
desc='input anatomical reference (INU corrected)')
in_aseg = File(exists=True, mandatory=True,
desc='input ``aseg`` file, in NifTi format.')
in_ants = File(exists=True, mandatory=True,
desc='brain tissue segmentation generated with antsBrainExtraction.sh')
class ArtifactMaskInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc='File to be plotted')
head_mask = File(exists=True, mandatory=True, desc='head mask')
rot_mask = File(exists=True, desc='a rotation mask')
nasion_post_mask = File(exists=True,
mandatory=True,
desc='nasion to posterior of cerebellum mask')
class ConcatROIsInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc='input file')
in_mask = File(exists=True, mandatory=True, desc='input mask')
ref_header = File(
exists=True,
mandatory=True,
desc='reference NIfTI file with desired output header/affine')
class ComputeQI2InputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc='File to be plotted')
air_msk = File(exists=True,
mandatory=True,
desc='air (without artifacts) mask')
erodemsk = traits.Bool(True, usedefault=True, desc='erode mask')
ncoils = traits.Int(12, usedefault=True, desc='number of coils')
class DemeanImageInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True,
desc='image to be demeaned')
in_mask = File(exists=True, mandatory=True,
desc='mask where median will be calculated')
only_mask = traits.Bool(False, usedefault=True,
desc='demean only within mask')
class PlotContoursInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc='File to be plotted')
in_contours = File(exists=True,
mandatory=True,
desc='file to pick the contours from')
cut_coords = traits.Int(8, usedefault=True, desc='number of slices')
levels = traits.List([.5],
traits.Float,
usedefault=True,
desc='add a contour per level')
colors = traits.List(['r'],
traits.Str,
usedefault=True,
desc='colors to be used for contours')
display_mode = traits.Enum('ortho',
'x',
'y',
'z',
'yx',
'xz',
'yz',
usedefault=True,
desc='visualization mode')
saturate = traits.Bool(False, usedefault=True, desc='saturate background')
out_file = traits.File(exists=False, desc='output file name')
vmin = traits.Float(desc='minimum intensity')
vmax = traits.Float(desc='maximum intensity')
class InvertT1wInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True,
mandatory=True,
desc='Skull-stripped T1w structural image')
ref_file = File(exists=True,
mandatory=True,
desc='Skull-stripped reference image')
class FSDetectInputsOutputSpec(TraitedSpec):
t2w = File(desc='reference T2w image')
use_t2w = traits.Bool(desc='enable use of T2w downstream computation')
flair = File(desc='reference FLAIR image')
use_flair = traits.Bool(desc='enable use of FLAIR downstream computation')
hires = traits.Bool(desc='enable hi-res processing')
mris_inflate = traits.Str(desc='mris_inflate argument')
class SubjectSummaryInputSpec(BaseInterfaceInputSpec):
t1w = InputMultiPath(File(exists=True), desc='T1w structural images')
t2w = InputMultiPath(File(exists=True), desc='T2w structural images')
subjects_dir = Directory(desc='FreeSurfer subjects directory')
subject_id = Str(desc='Subject ID')
bold = traits.List(desc='BOLD functional series')
output_spaces = traits.List(desc='Target spaces')
template = traits.Enum('MNI152NLin2009cAsym', desc='Template space')
class CombineROIsInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiPath(File(exists=True),
mandatory=True,
desc='input list of ROIs')
ref_header = File(
exists=True,
mandatory=True,
desc='reference NIfTI file with desired output header/affine')
class MaskT2SMapInputSpec(BaseInterfaceInputSpec):
image = File(mandatory=True, exists=True, desc='T2* volume to mask')
mask = File(mandatory=True,
exists=True,
desc='skull-stripped mean optimal combination volume')
compress = traits.Bool(True,
usedefault=True,
desc='use gzip compression on .nii output')
class DerivativesDataSinkInputSpec(BaseInterfaceInputSpec):
base_directory = traits.Directory(
desc='Path to the base directory for storing data.')
in_file = InputMultiPath(File(exists=True), mandatory=True,
desc='the object to be saved')
source_file = File(exists=False, mandatory=True, desc='the input func file')
suffix = traits.Str('', mandatory=True, desc='suffix appended to source_file')
extra_values = traits.List(traits.Str)
class FSDetectInputsInputSpec(BaseInterfaceInputSpec):
t1w_list = InputMultiPath(File(exists=True),
mandatory=True,
desc='input file, part of a BIDS tree')
t2w_list = InputMultiPath(File(exists=True),
desc='input file, part of a BIDS tree')
hires_enabled = traits.Bool(True,
usedefault=True,
desc='enable hi-resolution processing')
class MCFLIRT2ITKInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiPath(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 FieldEnhanceInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc='input fieldmap')
in_mask = File(exists=True, desc='brain mask')
in_magnitude = File(exists=True, desc='input magnitude')
unwrap = traits.Bool(False, usedefault=True, desc='run phase unwrap')
despike = traits.Bool(True, usedefault=True, desc='run despike filter')
bspline_smooth = traits.Bool(True, usedefault=True, desc='run 3D bspline smoother')
mask_erode = traits.Int(1, usedefault=True, desc='mask erosion iterations')
despike_threshold = traits.Float(0.2, usedefault=True, desc='mask erosion iterations')
njobs = traits.Int(1, usedefault=True, nohash=True, desc='number of jobs')
class FirstEchoInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiPath(File(exists=True),
mandatory=True,
minlen=2,
desc='multi-echo BOLD EPIs')
ref_imgs = InputMultiPath(File(exists=True),
mandatory=True,
minlen=2,
desc='generated reference image for each '
'multi-echo BOLD EPI')
te_list = traits.List(traits.Float, mandatory=True, desc='echo times')
class PlotBaseInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc='File to be plotted')
title = traits.Str(desc='a title string for the plot')
annotate = traits.Bool(True, usedefault=True, desc='annotate left/right')
figsize = traits.Tuple((11.69, 8.27),
traits.Float,
traits.Float,
usedefault=True,
desc='Figure size')
dpi = traits.Int(300, usedefault=True, desc='Desired DPI of figure')
out_file = File('mosaic.svg', usedefault=True, desc='output file name')
cmap = traits.Str('Greys_r', usedefault=True)
class SpikesInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc='input fMRI dataset')
in_mask = File(exists=True, desc='brain mask')
invert_mask = traits.Bool(False, usedefault=True, desc='invert mask')
no_zscore = traits.Bool(False, usedefault=True, desc='do not zscore')
detrend = traits.Bool(True, usedefault=True, desc='do detrend')
spike_thresh = traits.Float(6., usedefault=True,
desc='z-score to call one timepoint of one axial slice a spike')
skip_frames = traits.Int(0, usedefault=True,
desc='number of frames to skip in the beginning of the time series')
out_tsz = File('spikes_tsz.txt', usedefault=True, desc='output file name')
out_spikes = File(
'spikes_idx.txt', usedefault=True, desc='output file name')
class TPM2ROIInputSpec(BaseInterfaceInputSpec):
in_tpm = File(exists=True, mandatory=True, desc='Tissue probability map file in T1 space')
in_mask = File(exists=True, mandatory=True, desc='Binary mask of skull-stripped T1w image')
mask_erode_mm = traits.Float(xor=['mask_erode_prop'],
desc='erode input mask (kernel width in mm)')
erode_mm = traits.Float(xor=['erode_prop'],
desc='erode output mask (kernel width in mm)')
mask_erode_prop = traits.Float(xor=['mask_erode_mm'],
desc='erode input mask (target volume ratio)')
erode_prop = traits.Float(xor=['erode_mm'],
desc='erode output mask (target volume ratio)')
prob_thresh = traits.Float(0.95, usedefault=True,
desc='threshold for the tissue probability maps')
class TemplateDimensionsOutputSpec(TraitedSpec):
t1w_valid_list = OutputMultiPath(exists=True, desc='valid T1w images')
target_zooms = traits.Tuple(traits.Float, traits.Float, traits.Float,
desc='Target zoom information')
target_shape = traits.Tuple(traits.Int, traits.Int, traits.Int,
desc='Target shape information')
out_report = File(exists=True, desc='conformation report')
class IQMFileSinkInputSpec(DynamicTraitedSpec, BaseInterfaceInputSpec):
subject_id = Str(mandatory=True, desc='the subject id')
modality = Str(mandatory=True, desc='the qc type')
session_id = traits.Either(None, Str, usedefault=True)
task_id = traits.Either(None, Str, usedefault=True)
acq_id = traits.Either(None, Str, usedefault=True)
rec_id = traits.Either(None, Str, usedefault=True)
run_id = traits.Either(None, Str, usedefault=True)
metadata = traits.Dict()
provenance = traits.Dict()
root = traits.Dict(desc='output root dictionary')
out_dir = File(desc='the output directory')
_outputs = traits.Dict(value={}, usedefault=True)
def __setattr__(self, key, value):
if key not in self.copyable_trait_names():
if not isdefined(value):
super(IQMFileSinkInputSpec, self).__setattr__(key, value)
self._outputs[key] = value
else:
if key in self._outputs:
self._outputs[key] = value
super(IQMFileSinkInputSpec, self).__setattr__(key, value)
if key == 'subject_id':
#I can not find where it is set from bids ... dirty fix
if str(value) == '<undefined>':
self.subject_id = 'TODO'
class MergeInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiPath(File(exists=True),
mandatory=True,
desc='input list of files to merge')
dtype = traits.Enum('f4',
'f8',
'u1',
'u2',
'u4',
'i2',
'i4',
usedefault=True,
desc='numpy dtype of output image')
header_source = File(
exists=True,
desc='a Nifti file from which the header should be copied')
class FilledImageLikeInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc='image to be demeaned')
fill_value = traits.Float(1.0, usedefault=True, desc='value to fill')
dtype = traits.Enum('float32',
'uint8',
usedefault=True,
desc='force output data type')
class IntraModalMergeInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiPath(File(exists=True),
mandatory=True,
desc='input files')
hmc = traits.Bool(True, usedefault=True)
zero_based_avg = traits.Bool(True, usedefault=True)
to_ras = traits.Bool(True, usedefault=True)
class TemplateDimensionsInputSpec(BaseInterfaceInputSpec):
t1w_list = InputMultiPath(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 DerivativesDataSinkInputSpec(BaseInterfaceInputSpec):
base_directory = traits.Directory(
desc='Path to the base directory for storing data.')
in_file = InputMultiPath(File(exists=True),
mandatory=True,
desc='the object to be saved')
source_file = File(exists=False,
mandatory=True,
desc='the input func file')
suffix = traits.Str('',
mandatory=True,
desc='suffix appended to source_file')
extra_values = traits.List(traits.Str)
compress = traits.Bool(
desc="force compression (True) or uncompression (False)"
" of the output file (default: same as input)")
class FunctionalSummaryInputSpec(BaseInterfaceInputSpec):
slice_timing = traits.Enum(False,
True,
'TooShort',
usedefault=True,
desc='Slice timing correction used')
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')
registration = traits.Enum(
'FSL',
'FreeSurfer',
mandatory=True,
desc='Functional/anatomical registration method')
fallback = traits.Bool(desc='Boundary-based registration rejected')
registration_dof = traits.Enum(6,
9,
12,
desc='Registration degrees of freedom',
mandatory=True)
output_spaces = traits.List(desc='Target spaces')
confounds_file = File(exists=True, desc='Confounds file')
class GatherConfoundsInputSpec(BaseInterfaceInputSpec):
signals = File(exists=True, desc='input signals')
dvars = File(exists=True, desc='file containing DVARS')
fd = File(exists=True, desc='input framewise displacement')
tcompcor = File(exists=True, desc='input tCompCorr')
acompcor = File(exists=True, desc='input aCompCorr')
cos_basis = File(exists=True, desc='input cosine basis')
motion = File(exists=True, desc='input motion parameters')
aroma = File(exists=True, desc='input ICA-AROMA')
class T2SMapInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiPath(File(exists=True),
mandatory=True,
desc='multi-echo BOLD EPIs')
te_list = traits.List(traits.Float, mandatory=True, desc='echo times')
compress = traits.Bool(True,
usedefault=True,
desc='use gzip compression on .nii output')
def __init__(self, num_affines=0, *args, **kwargs):
super(ConcatAffines, self).__init__(*args, **kwargs)
self._num_affines = num_affines
trait_type = File(exists=True)
if num_affines == 0:
add_traits(self.inputs, ['mat_list'], trait_type)
elif num_affines < 26:
add_traits(self.inputs, self._get_names(num_affines), trait_type)