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 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 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 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 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 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 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 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 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)')
