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 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 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 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 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 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 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 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 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')
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)
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')
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')
num_threads = traits.Int(1,
usedefault=True,
nohash=True,
desc='number of jobs')
class GenerateCiftiInputSpec(BaseInterfaceInputSpec):
bold_file = File(mandatory=True, exists=True, desc="input BOLD file")
volume_target = traits.Enum("MNI152NLin2009cAsym",
mandatory=True,
usedefault=True,
desc="CIFTI volumetric output space")
surface_target = traits.Enum("fsaverage5",
"fsaverage6",
mandatory=True,
usedefault=True,
desc="CIFTI surface target space")
subjects_dir = Directory(mandatory=True, desc="FreeSurfer SUBJECTS_DIR")
TR = traits.Float(mandatory=True, desc="repetition time")
gifti_files = traits.List(
File(exists=True),
mandatory=True,
desc="list of surface geometry files (length 2 with order [L,R])")
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 ConformInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc='Input image')
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')
class GCORInputSpec(CommandLineInputSpec):
in_file = File(desc='input dataset to compute the GCOR over',
argstr='-input %s',
position=-1,
mandatory=True,
exists=True,
copyfile=False)
mask = File(desc='mask dataset, for restricting the computation',
argstr='-mask %s',
exists=True,
copyfile=False)
nfirst = traits.Int(0,
argstr='-nfirst %d',
desc='specify number of initial TRs to ignore')
no_demean = traits.Bool(False,
argstr='-no_demean',
desc='do not (need to) demean as first step')
class MultiApplyTransformsInputSpec(ApplyTransformsInputSpec):
input_image = InputMultiPath(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 FUGUEvsm2ANTSwarpInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True,
mandatory=True,
desc='input displacements field map')
pe_dir = traits.Enum('i',
'i-',
'j',
'j-',
'k',
'k-',
desc='phase-encoding axis')
class TPM2ROIInputSpec(BaseInterfaceInputSpec):
t1_tpm = File(exists=True,
mandatory=True,
desc='Tissue probability map file in T1 space')
t1_mask = File(exists=True,
mandatory=True,
desc='Binary mask of skull-stripped T1w image')
bold_mask = File(exists=True,
mandatory=True,
desc='Binary mask of skull-stripped BOLD image')
mask_erode_mm = traits.Float(0.0,
usedefault=True,
desc='erode input mask (kernel width in mm)')
erode_mm = traits.Float(0.0,
usedefault=True,
desc='erode output mask (kernel width in mm)')
prob_thresh = traits.Float(
0.95,
usedefault=True,
desc='threshold for the tissue probability maps')
class UploadIQMsInputSpec(BaseInterfaceInputSpec):
in_iqms = File(exists=True,
mandatory=True,
desc='the input IQMs-JSON file')
url = Str(mandatory=True, desc='URL (protocol and name) listening')
port = traits.Int(desc='MRIQCWebAPI service port')
path = Str(desc='MRIQCWebAPI endpoint root path')
email = Str(desc='set sender email')
strict = traits.Bool(False,
usedefault=True,
desc='crash if upload was not succesfull')
class MaskEPIInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiPath(File(exists=True),
mandatory=True,
desc='input EPI or list of files')
lower_cutoff = traits.Float(0.2, usedefault=True)
upper_cutoff = traits.Float(0.85, usedefault=True)
connected = traits.Bool(True, usedefault=True)
opening = traits.Int(2, usedefault=True)
exclude_zeros = traits.Bool(False, usedefault=True)
ensure_finite = traits.Bool(True, usedefault=True)
target_affine = traits.File()
target_shape = traits.File()
class FunctionalQCInputSpec(BaseInterfaceInputSpec):
in_epi = File(exists=True, mandatory=True, desc='input EPI file')
in_hmc = File(exists=True,
mandatory=True,
desc='input motion corrected file')
in_tsnr = File(exists=True, mandatory=True, desc='input tSNR volume')
in_mask = File(exists=True, mandatory=True, desc='input mask')
direction = traits.Enum('all',
'x',
'y',
'-x',
'-y',
usedefault=True,
desc='direction for GSR computation')
in_fd = File(exists=True,
mandatory=True,
desc='motion parameters for FD computation')
fd_thres = traits.Float(0.2,
usedefault=True,
desc='motion threshold for FD computation')
in_dvars = File(exists=True,
mandatory=True,
desc='input file containing DVARS')
in_fwhm = traits.List(traits.Float,
mandatory=True,
desc='smoothness estimated with AFNI')
class StructuralQCOutputSpec(TraitedSpec):
summary = traits.Dict(desc='summary statistics per tissue')
icvs = traits.Dict(desc='intracranial volume (ICV) fractions')
rpve = traits.Dict(desc='partial volume fractions')
size = traits.Dict(desc='image sizes')
spacing = traits.Dict(desc='image sizes')
fwhm = traits.Dict(desc='full width half-maximum measure')
inu = traits.Dict(desc='summary statistics of the bias field')
snr = traits.Dict
snrd = traits.Dict
cnr = traits.Float
fber = traits.Float
efc = traits.Float
qi_1 = traits.Float
wm2max = traits.Float
cjv = traits.Float
out_qc = traits.Dict(desc='output flattened dictionary with all measures')
out_noisefit = File(exists=True,
desc='plot of background noise and chi fitting')
tpm_overlap = traits.Dict
class MaskEPIInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiPath(File(exists=True),
mandatory=True,
desc='input EPI or list of files')
lower_cutoff = traits.Float(0.2, usedefault=True)
upper_cutoff = traits.Float(0.85, usedefault=True)
connected = traits.Bool(True, usedefault=True)
enhance_t2 = traits.Bool(False,
usedefault=True,
desc='enhance T2 contrast on image')
opening = traits.Int(2, usedefault=True)
closing = traits.Bool(True, usedefault=True)
fill_holes = traits.Bool(True, usedefault=True)
exclude_zeros = traits.Bool(False, usedefault=True)
ensure_finite = traits.Bool(True, usedefault=True)
target_affine = traits.Either(None,
traits.File(exists=True),
default=None,
usedefault=True)
target_shape = traits.Either(None,
traits.File(exists=True),
default=None,
usedefault=True)
no_sanitize = traits.Bool(False, usedefault=True)
class SpikesOutputSpec(TraitedSpec):
out_tsz = File(
desc='slice-wise z-scored timeseries (Z x N), inside brainmask')
out_spikes = File(desc='indices of spikes')
num_spikes = traits.Int(desc='number of spikes found (total)')
class MaskT2SMapOutputSpec(TraitedSpec):
masked_t2s = File(exists=True, desc='masked T2* map')
class FirstEchoOutputSpec(TraitedSpec):
first_image = File(exists=True, desc='BOLD EPI series for the first echo')
first_ref_image = File(exists=True,
desc='generated reference image for '
'the first echo')
class T2SMapOutputSpec(TraitedSpec):
t2s_vol = File(exists=True, desc='T2* map')
opt_comb = File(exists=True, desc='optimal combination of echos')
class RotationMaskOutputSpec(TraitedSpec):
out_file = File(exists=True, desc='rotation mask (if any)')
