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')
