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)')