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 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 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 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 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 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 FunctionalSummaryInputSpec(BaseInterfaceInputSpec): slice_timing = traits.Enum(False, True, 'TooShort', usedefault=True, desc='Slice timing correction used') distortion_correction = traits.Enum( 'epi', 'fieldmap', 'phasediff', 'SyN', 'None', 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 = traits.List(desc='Confounds collected')
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 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 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 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 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 BIDSFreeSurferDirInputSpec(BaseInterfaceInputSpec): derivatives = Directory(exists=True, mandatory=True, desc='BIDS derivatives directory') freesurfer_home = Directory(exists=True, mandatory=True, desc='FreeSurfer installation directory') subjects_dir = traits.Str('freesurfer', usedefault=True, desc='Name of FreeSurfer subjects directory') spaces = traits.List(traits.Str, desc='Set of output spaces to prepare') overwrite_fsaverage = traits.Bool(False, usedefault=True, desc='Overwrite fsaverage directories, if present')
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 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 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') compress = traits.Bool(True, usedefault=True, desc='Use gzip compression on .nii output')
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 FunctionalSummaryInputSpec(BaseInterfaceInputSpec): slice_timing = traits.Bool(False, usedefault=True, desc='Slice timing correction used') distortion_correction = traits.Enum( 'epi', 'fieldmap', 'phasediff', 'SyN', 'None', desc='Susceptibility distortion correction method', mandatory=True) registration = traits.Enum( 'FLIRT', 'bbregister', mandatory=True, desc='Functional/anatomical registration method') output_spaces = traits.List(desc='Target spaces') confounds = traits.List(desc='Confounds collected')
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 ConcatAffinesInputSpec(DynamicTraitedSpec, BaseInterfaceInputSpec): invert = traits.Bool(False, usedefault=True, desc='Invert output transform')
class ICAConfoundsInputSpec(BaseInterfaceInputSpec): in_directory = Directory(mandatory=True, desc='directory where ICA derivatives are found') ignore_aroma_err = traits.Bool(False, usedefault=True, desc='ignore ICA-AROMA errors')
class HarmonizeInputSpec(BaseInterfaceInputSpec): in_file = File(exists=True, mandatory=True, desc='input data (after bias correction)') wm_mask = File(exists=True, mandatory=True, desc='white-matter mask') erodemsk = traits.Bool(True, usedefault=True, desc='erode mask')
class PlotMosaicInputSpec(PlotBaseInputSpec): bbox_mask_file = File(exists=True, desc='brain mask') only_noise = traits.Bool(False, desc='plot only noise')
class ConformImageInputSpec(BaseInterfaceInputSpec): in_file = File(exists=True, mandatory=True, desc='input image') check_ras = traits.Bool(True, usedefault=True, desc='check that orientation is RAS') check_dtype = traits.Bool(True, usedefault=True, desc='check data type')