class CompIcaInputSpec(BaseInterfaceInputSpec): """Input specification for CompIca.""" fif_file = traits.File(exists=True, desc='filtered raw meg data in fif format', mandatory=True) raw_fif_file = traits.File(exists=True, desc='orignal raw meg data in fif format', mandatory=True) ecg_ch_name = traits.String(desc='name of ecg channel') eog_ch_name = traits.String(desc='name of eog channel') n_components = traits.Float(desc='number of ica components') reject = traits.Dict(desc='rejection parameters', mandatory=False)
def __init__(self): super(ASL_Rescaling, self).__init__() # Inputs self.add_trait("ASL_file", traits.File(output=False, optional=False)) self.add_trait("method", traits.Str('whitepaper', output=False, optional=True)) self.add_trait("M0_image_file", traits.File(output=False, optional=True)) self.add_trait("T1_map_file", traits.File(output=False, optional=True)) self.add_trait("T1_blood", traits.Float(1.65, output=False, optional=True)) # seconds self.add_trait("T2w_blood", traits.Float(0.1, output=False, optional=True)) # seconds self.add_trait("rho_blood", traits.Float(0.8, output=False, optional=True)) self.add_trait("T1_CSF", traits.Float(4.0, output=False, optional=True)) # seconds self.add_trait("T2w_CSF", traits.Float(0.1, output=False, optional=True)) # seconds self.add_trait("rho_CSF", traits.Float(1.0, output=False, optional=True)) self.add_trait("rho_brain", traits.Float(1040, output=False, optional=True)) #kg/m^3 self.add_trait("lambda_", traits.Float(0.0009, output=False, optional=True)) #kg/m^3 self.add_trait("label_eff", traits.Float(output=False, optional=True)) self.add_trait("transit_delay", traits.Float(1.5, output=False, optional=True)) #seconds self.add_trait("tAcq_slice", traits.Float(0.0, output=False, optional=True)) self.add_trait("back_supp", traits.Int(output=False, optional=True)) self.add_trait("TI1", traits.Float(1.8, output=False, optional=True)) self.add_trait("PLD", traits.Float(2.0, output=False, optional=True)) self.add_trait("TRm0", traits.Float(10.0, output=False, optional=True)) self.add_trait("T1t", traits.Float(0.9, output=False, optional=True)) self.add_trait("ASL_mode", traits.Str(output=False, optional=True)) self.add_trait("output_prefix", traits.Str('t', output=False, optional=True)) self.add_trait("output_datatype", traits.Str('int16', output=False, optional=True)) # Outputs self.add_trait("ASL_output", traits.File(output=True)) self.add_trait("M0_output", traits.File(output=True))
class output_spec(TraitedSpec): mask_file = traits.File(exists=True) beta_file = traits.File(exists=True) error_file = traits.File(exists=True) ols_file = traits.File(exists=True) resid_file = traits.File() design_file = traits.File(exists=True) resid_plot = traits.File(exists=True) design_plot = traits.File(exists=True) error_plot = traits.File(exists=True)
class coreg_qc_metricsInput(BaseInterfaceInputSpec): pet = traits.File(exists=True, mandatory=True, desc="Input PET image") t1 = traits.File(exists=True, mandatory=True, desc="Input T1 MRI") t1_brain_mask = traits.File(exists=True, mandatory=True, desc="Input T1 MRI") pet_brain_mask = traits.File(exists=True, mandatory=True, desc="Input T1 MRI") sid = traits.Str(desc="Subject") ses = traits.Str(desc="Session") task = traits.Str(desc="Task") run = traits.Str(desc="Run") rec = traits.Str(desc="Reconstruction") acq = traits.Str(desc="Acquisition") study_prefix = traits.Str(desc="Study Prefix") out_file = traits.File(desc="Output file") clobber = traits.Bool(desc="Overwrite output file", default=False)
class LOGISMOSBPreprocessingInputSpec(BaseInterfaceInputSpec): """This class represents a...""" white_mask = traits.File(exists=True, mandatory=True) erode_mask = traits.Int(default_value=1, usedefault=True) gm_proba = traits.File(exists=True, mandatory=True) wm_proba = traits.File(exists=True, mandatory=True) background_penalty = traits.Int(default_value=100, usedefault=True, desc="Penalty for a zero probability.") proba_scale = traits.Int(default_value=50, usedefault=True, desc="Scale the probabilities.") min_probability = traits.Float(default_value=0.01, usedefault=True)
class CoilCombineInputSpec(QI.InputSpec): in_file = traits.File(desc='Input complex-valued file to coil-combine', argstr='%s', exists=True) composer_file = traits.File( desc='Short Echo-Time reference file for COMPOSER', argstr='--composer=%s', exists=True) hammond_coils = traits.Int(desc='Number of coils for Hammond method', argstr='--coils=%d') hammond_volume = traits.Int(desc='Volume to use for Hammond method', argstr='--vol=%d') hammond_region = traits.Str(desc='Region to use for Hammond method', argstr='--region=%s')
class SlicesInputSpec(BaseInterfaceInputSpec): out_file = traits.File(mandatory=True, desc='Output slice image') base_file = traits.File(exists=True, mandatory=True, desc='Image file to slice') base_map = traits.String('gist_gray', usedefault=True, desc='Color map for base image') base_window = traits.Tuple(minlen=2, maxlen=2, desc='Window for base image') base_scale = traits.Float(1.0, usedefault=True, desc='Scaling factor for base image') base_label = traits.String('', usedefault=True, desc='Label for base image color-bar') mask_file = traits.File(None, usedefault=True, mandatory=True, desc='Mask file') slice_axis = traits.String('z', usedefault=True, desc='Axis to slice') slice_lims = traits.Tuple( (0.1, 0.9), minlen=2, maxlen=2, usedefault=True, desc='Limits of axis to slice, fraction (low, high) default (0.1, 0.9)' ) slice_layout = traits.Tuple((3, 6), minlen=2, maxlen=2, usedefault=True, desc='Slices layout (rows, cols)') volume = traits.Int(0, usedefault=True, desc='Volume for slicing in multi-volume file') figsize = traits.Tuple(minlen=2, maxlen=2, desc='Output figure size') preclinical = traits.Bool(False, usedefault=True, desc='Data is pre-clinical, fix orientation') transpose = traits.Bool(False, usedefault=True, desc='Transpose slice layout') bar_pos = traits.String('bottom', usedefault=True, desc='Color/Alphabar position')
class TestMathInputSpec(TraitedSpec): x = traits.Either(traits.Float(), traits.File(exists=True), traits.List(traits.Float), traits.List(traits.File(exists=True)), desc='first arg') y = traits.Either(traits.Float(), traits.File(exists=True), mandatory=False, desc='second arg') op = traits.Str(mandatory=True, desc='operation') z = traits.File(genfile=True, mandatory=False, desc="Name for output file") as_file = traits.Bool(False, desc="Whether to write as a file", usedefault=True)
class SpectralConnInputSpec(BaseInterfaceInputSpec): """Input specification.""" ts_file = traits.File( exists=True, desc='nodes * time series in .npy format', mandatory=True) sfreq = traits.Float(desc='sampling frequency', mandatory=True) freq_band = traits.List(traits.Float(exists=True), desc='frequency bands', mandatory=True) mode = traits.Enum("multitaper", "cwt_morlet", desc='Mode for computing frequency bands') con_method = traits.Enum("coh", "imcoh", "plv", "pli", "wpli", "pli2_unbiased", "ppc", "cohy", "wpli2_debiased", desc='connectivity measure') epoch_window_length = traits.Float(-1.0, desc='epoched data', mandatory=False) export_to_matlab = traits.Bool( False, desc='If conmat is exported to .mat format as well', usedefault=True) index = traits.Int( 0, desc="What to add to the name of the file", usedefault=True) multi_con = traits.Bool( False, desc='If multiple connectivity matrices are exported', usedefault=True) gathering_method = traits.Enum("mean", "max", "none", desc='gathering_method', usedefault=True)
class MouseCroppingInputSpec(BaseInterfaceInputSpec): ct = InputMultiPath(traits.File(exists=True), desc='Mouse clinical CT image to crop') out_folder = Directory('Cropping_dir', usedefault=True, desc='Folder to store the cropping results.')
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 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([0.5], 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 PowerInputSpec(BaseInterfaceInputSpec): data_file = traits.File(exists=True, desc='File with mne.Epochs or mne.io.Raw or .npy', mandatory=True) fmin = traits.Float(desc='lower psd frequency', mandatory=False) fmax = traits.Float(desc='higher psd frequency', mandatory=False) sfreq = traits.Float(desc='sampling frequency', mandatory=False) nfft = traits.Int(desc='the length of FFT used', mandatory=False) overlap = traits.Float( desc='The number of points of overlap between segments', mandatory=False) method = traits.Enum('welch', 'multitaper', desc='power spectral density computation method') is_epoched = traits.Bool(desc='if true input data are mne.Epochs', mandatory=False) is_sensor_space = traits.Bool(True, usedefault=True, dedesc='True for PSD on sensor space \ False for PSD on source', mandatory=False)
class CollectFeatureFilesOutputSpec(TraitedSpec): """ This class represents a... """ feature_files = traits.Dict(trait=traits.File(exists=True), desc="Output dictionary of feature files")
class CalculateProbabilityFromSamplesInput(TraitedSpec): sample_maps = InputMultiPath(File(exists=True), mandatory=True) stat_map = File(exists=True, mandatory=True) mask = traits.File( exists=True, desc="restrict the fitting only to the region defined by this mask") independent_voxel_nulls = traits.Bool(False, usedefault=True)
class DARTELExistingTemplateInputSpec(SPMCommandInputSpec): image_files = traits.List(traits.List(File(exists=True)), desc="A list of files to be segmented", field='warp1.images', copyfile=False, mandatory=True) regularization_form = traits.Enum('Linear', 'Membrane', 'Bending', field='warp1.settings.rform', desc='Form of regularization energy term') iteration_parameters = traits.List(traits.Tuple(traits.Range(1, 10), traits.Tuple(traits.Float, traits.Float, traits.Float), traits.Range(0, 9), traits.File(exists=True)), minlen=3, maxlen=12, mandatory=True, field='warp1.settings.param', desc="""List of tuples for each iteration - Inner iterations - Regularization parameters - Time points for deformation model - DARTEL template """) optimization_parameters = traits.Tuple(traits.Float, traits.Range(1, 8), traits.Range(1, 8), field='warp1.settings.optim', desc="""Optimization settings a tuple - LM regularization - cycles of multigrid solver - relaxation iterations """)
class SimInputBaseSpec(DynamicTraitedSpec): """ Input specification for tools in simulation mode """ ignore_exception = traits.Bool( False, usedefault=True, nohash=True, deprecated='1.0.0', desc='Print an error message instead of throwing an exception ' 'in case the interface fails to run') args = traits.Str(argstr='%s', desc='Additional parameters to the command') environ = traits.DictStrStr( desc='Environment variables', usedefault=True, nohash=True) # This input does not have a "usedefault=True" so the set_default_terminal_output() # method would work terminal_output = traits.Enum( 'stream', 'allatonce', 'file', 'none', deprecated='1.0.0', desc=('Control terminal output: `stream` - ' 'displays to terminal immediately (default), ' '`allatonce` - waits till command is ' 'finished to display output, `file` - ' 'writes output to file, `none` - output' ' is ignored'), nohash=True) json = traits.File(exists=True, desc='JSON Input file', argstr='--json=%s') noise = traits.Float(desc='Noise level to add to simulation', argstr='--simulate=%f', default_value=0.0, usedefault=True)
class JsonToSifInput(CommandLineInputSpec): out_file = File(argstr="%s", desc="SIF text file with correct time frames.") pet = File(exists=True, argstr="%s", desc="Minc PET image.") pet_header_json = traits.File(exists=True, argstr="%s", desc="PET header file")
class ModifyHeaderInput(CommandLineInputSpec): in_file = File(position=-1, argstr="%s", mandatory=True, desc="Image") out_file = File(desc="Image after centering") sinsert = traits.Bool(argstr="-sinsert", position=-3, default_value=False, desc="Insert a string attribute") dinsert = traits.Bool(argstr="-dinsert", position=-3, default_value=False, desc="Insert a double precision attribute") sappend = traits.Bool(argstr="-sappend", position=-3, default_value=False, desc="Append a string attribute") dappend = traits.Bool(argstr="-dappend", position=-3, default_value=False, desc="Append a double precision attribute") delete = traits.Bool(argstr="-delete", position=-3, default_value=False, desc="Delete an attribute") opt_string = traits.Str(argstr="%s", position=-2, desc="Option defining the infos to print out") header = traits.File( argstr="MINC header for PET image, stored as dictionary")
class MultiLabelDilationOutputSpec(TraitedSpec): """This class represents a... :param TraitedSpec: """ out_file = traits.File()
class SplitWindowsInputSpec(BaseInterfaceInputSpec): ts_file = traits.File( exists=True, desc='nodes * time series in .npy format', mandatory=True) n_windows = traits.List( traits.Tuple, desc='List of start and stop points (tuple of two integers) of temporal windows', mandatory=True)
class SplitLabelsOutputSpec(TraitedSpec): """This class represents a... :param TraitedSpec: """ out_file = traits.File(exists=True)
class CreateEpInputSpec(BaseInterfaceInputSpec): """Input specification for CreateEp.""" fif_file = traits.File(exists=True, desc='raw meg data in fif format', mandatory=True) ep_length = traits.Float(desc='epoch length in seconds')
class SurfaceMaskOutputSpec(TraitedSpec): """This class represents a... :param TraitedSpec: """ out_file = traits.File(desc="Output masked volume.")
class PowerBandInputSpec(BaseInterfaceInputSpec): """Input specification for PowerBand""" psds_file = traits.File(exists=True, desc='psd tensor and frequencies in .npz format', mandatory=True) freq_bands = traits.List(desc='frequency bands', mandatory=True)
class e7emhdrInput(CommandLineInputSpec): #CommandLineInputSpec): in_file = File(argstr="%s", position=-2, desc="Input image.") out_file = File(desc="Output image.") isotope = traits.Str(argstr="isotope_halflife := %s", position=-1, desc="Set isotope half life") header = traits.File(exists=True, argstr="%s", desc="PET header file")
class NewImageInputSpec(QI.InputBaseSpec): # Options img_size = traits.List(minsize=2, maxsize=4, mandatory=True, desc='Image size', argstr='--size=%s', sep=',') voxel_spacing = traits.Float(desc='Voxel spacing', argstr='--spacing=%f') origin = traits.Float(desc='Image origin', argstr='--origin=%f') fill = traits.Float(desc='Fill with value', argstr='--fill=%f') grad_dim = traits.Int(desc='Fill with gradient along dimension', argstr='--grad_dim=%d') grad_vals = traits.Tuple(desc='Gradient start/end values', argstr='--grad_vals=%f,%f') grad_steps = traits.Int(desc='Gradient in N discrete steps', argstr='--steps=%s') wrap = traits.Float(desc='Wrap image values at the given value', argstr='--wrap=%f') # Output file out_file = traits.File(desc='Output file', exists=False, position=-1, argstr='%s', mandatory=True)
class NoiseCovarianceConnInputSpec(BaseInterfaceInputSpec): """Input specification for NoiseCovariance.""" cov_fname_in = traits.File(exists=False, desc='file name for Noise \ Covariance Matrix') raw_filename = traits.File(exists=True, desc='raw data filename') is_epoched = traits.Bool(desc='true if we want to epoch the data', mandatory=False) is_evoked = traits.Bool(desc='true if we want to analyze evoked data', mandatory=False) events_id = traits.Dict(None, desc='the id of all events to consider.', mandatory=False) t_min = traits.Float(None, desc='start time before event', mandatory=False) t_max = traits.Float(None, desc='end time after event', mandatory=False)
class MS_LDAInputSpec(FSTraitedSpec): lda_labels = traits.List(traits.Int(), argstr='-lda %s', mandatory=True, minlen=2, maxlen=2, sep=' ', desc='pair of class labels to optimize') weight_file = traits.File( argstr='-weight %s', mandatory=True, desc='filename for the LDA weights (input or output)') output_synth = traits.File( exists=False, argstr='-synth %s', mandatory=True, desc='filename for the synthesized output volume', deprecated='0.8', new_name='vol_synth_file', xor=['vol_synth_file', 'output_synth']) vol_synth_file = traits.File( exists=False, argstr='-synth %s', mandatory=True, desc='filename for the synthesized output volume', xor=['vol_synth_file', 'output_synth']) label_file = traits.File(exists=True, argstr='-label %s', desc='filename of the label volume') mask_file = traits.File(exists=True, argstr='-mask %s', desc='filename of the brain mask volume') shift = traits.Int( argstr='-shift %d', desc='shift all values equal to the given value to zero') conform = traits.Bool(argstr='-conform', desc=('Conform the input volumes (brain mask ' 'typically already conformed)')) use_weights = traits.Bool(argstr='-W', desc=('Use the weights from a previously ' 'generated weight file')) images = InputMultiPath(File(exists=True), argstr='%s', mandatory=True, copyfile=False, desc='list of input FLASH images', position=-1)
class ConnectivityCorrelationInputSpec(BaseInterfaceInputSpec): in_files = InputMultiPath( traits.File( desc="NifTI image file(s) from where to extract the data. \n" "If more than one (3D volumes), all should be spatially normalized.", exists=True, mandatory=True)) atlas_file = traits.File( desc="Atlas image file defining the connectivity ROIs.\n" "Must be spatially normalized to in_files.", exists=True, mandatory=True) atlas_type = traits.Enum("probabilistic", "labels", desc="The type of atlas.", default="labels") # masker options smoothing_fwhm = traits.Float( desc= "If smoothing_fwhm is defined, it gives the full-width half maximum in " "millimeters of the spatial smoothing to apply to the signal.", ) standardize = traits.Bool( desc="If standardize is True, the time-series are centered and normed: " "their mean is put to 0 and their variance to 1 in the time dimension.", default_value=False) resampling_target = traits.Enum( "mask", "maps", "data", "labels", "", desc="Gives which image gives the final shape/size. " "This depends on the `atlas_type`. " "For 'probabilistic' you must use 'mask', 'maps' or None; while for" "'labels' you must use 'data', 'labels' or None." "Have a look on nilearn docs for more information.") # connectome options kind = traits.Enum("correlation", "partial correlation", "tangent", "covariance", "precision", desc="The connectivity matrix kind.", default='covariance')