class input_spec(TraitedSpec):
experiment = traits.Str()
model = traits.Str()
data_dir = traits.Directory(exists=True)
proc_dir = traits.Directory(exists=True)
subject = traits.Str()
run_tuple = traits.Tuple(traits.Str(), traits.Str())
class prepareDockerPathsInputSpec(BaseInterfaceInputSpec):
local_T2ws_paths = InputMultiPath(
File(desc='input T2ws paths', mandatory=True, exists=True))
local_masks_paths = InputMultiPath(
File(desc='input masks paths', mandatory=True, exists=True))
local_dir = traits.Directory(mandatory=True)
docker_dir = traits.Directory('/fetaldata', mandatory=True)
class input_spec(TraitedSpec):
run = traits.Tuple()
data_dir = traits.Directory(exists=True)
proc_dir = traits.Directory(exists=True)
experiment = traits.Str()
sb_template = traits.Str()
ts_template = traits.Str()
crop_frames = traits.Int(0, usedefault=True)
class input_spec(TraitedSpec):
run = traits.Tuple()
data_dir = traits.Directory(exists=True)
proc_dir = traits.Directory(exists=True)
experiment = traits.Str()
sb_template = traits.Str()
ts_template = traits.Str()
# TODO this default should be defined at the project/experiment level
crop_frames = traits.Int(0, usedefault=True)
class _DerivativesDataSinkInputSpec(DynamicTraitedSpec,
BaseInterfaceInputSpec):
base_directory = traits.Directory(
desc="Path to the base directory for storing data.")
check_hdr = traits.Bool(True,
usedefault=True,
desc="fix headers of NIfTI outputs")
compress = InputMultiObject(
traits.Either(None, traits.Bool),
usedefault=True,
desc=
"whether ``in_file`` should be compressed (True), uncompressed (False) "
"or left unmodified (None, default).",
)
data_dtype = Str(
desc="NumPy datatype to coerce NIfTI data to, or `source` to"
"match the input file dtype")
dismiss_entities = InputMultiObject(
traits.Either(None, Str),
usedefault=True,
desc="a list entities that will not be propagated from the source file",
)
in_file = InputMultiObject(File(exists=True),
mandatory=True,
desc="the object to be saved")
meta_dict = traits.DictStrAny(
desc="an input dictionary containing metadata")
source_file = InputMultiObject(
File(exists=False),
mandatory=True,
desc="the source file(s) to extract entities from")
class _DerivativesDataSinkInputSpec(DynamicTraitedSpec,
BaseInterfaceInputSpec):
base_directory = traits.Directory(
desc='Path to the base directory for storing data.')
check_hdr = traits.Bool(True,
usedefault=True,
desc='fix headers of NIfTI outputs')
compress = traits.Bool(
desc="force compression (True) or uncompression (False)"
" of the output file (default: same as input)")
desc = Str('', usedefault=True, desc='Label for description field')
extra_values = traits.List(Str)
in_file = InputMultiObject(File(exists=True),
mandatory=True,
desc='the object to be saved')
keep_dtype = traits.Bool(False,
usedefault=True,
desc='keep datatype suffix')
meta_dict = traits.DictStrAny(
desc='an input dictionary containing metadata')
source_file = File(exists=False,
mandatory=True,
desc='the input func file')
space = Str('', usedefault=True, desc='Label for space field')
suffix = Str('', usedefault=True, desc='suffix appended to source_file')
class DerivativesDataSinkInputSpec(BaseInterfaceInputSpec):
base_directory = traits.Directory(
desc='Path to the base directory for storing data.')
in_file = File(exists=True, mandatory=True)
source_file = File(exists=False,
mandatory=True,
desc='the input func file')
class BycycleNodeInputSpec(BaseInterfaceInputSpec):
"""Input interface for bycycle."""
# Input/Output
input_dir = traits.Directory(
argstr='%s',
exists=True,
resolve=True,
desc=
'Input directory containing timeseries and/or spectra .npy files to read.',
mandatory=True,
position=0)
output_dir = traits.Directory(
argstr='%s',
exists=False,
resolve=True,
desc='Output directory to write results and BIDS derivatives to write.',
mandatory=True,
position=1)
# Required arguments
sig = traits.File(mandatory=True, usedefault=False)
fs = traits.Float(mandatory=True, usedefault=False)
f_range_bycycle = traits.Tuple(mandatory=True, usedefault=False)
# Optional arguments
center_extrema = traits.Str('peak', mandatory=False, usedefault=True)
burst_method = traits.Str('cycles', mandatory=False, usedefault=True)
amp_fraction_threshold = traits.Float(0.0,
mandatory=False,
usedefault=True)
amp_consistency_threshold = traits.Float(0.5,
mandatory=False,
usedefault=True)
period_consistency_threshold = traits.Float(0.5,
mandatory=False,
usedefault=True)
monotonicity_threshold = traits.Float(0.8,
mandatory=False,
usedefault=True)
min_n_cycles = traits.Int(3, mandatory=False, usedefault=True)
burst_fraction_threshold = traits.Float(1.0,
mandatory=False,
usedefault=True)
axis = traits.Str('None', mandatory=False, usedefault=True)
n_jobs = traits.Int(1, mandatory=False, usedefault=True)
class output_spec(TraitedSpec):
subject = traits.Str()
session = traits.Str()
run = traits.Str()
anat_file = traits.File(exists=True)
mask_file = traits.File(exists=True)
beta_file = traits.File(exists=True)
ols_file = traits.File(exists=True)
error_file = traits.File(exists=True)
output_path = traits.Directory()
class CombineStatsInputSpec(BaseInterfaceInputSpec):
bids_dir = traits.Directory(exists=True, mandatory=True)
validate = traits.Bool(default=True, usedefault=True)
row_keys = traits.ListStr(mandatory=True)
invariants = traits.DictStrAny()
strict = traits.Bool(default=True, usedefault=True)
index = traits.Str()
ignore = traits.Either(traits.ListStr,
traits.Set(trait=traits.Str),
traits.Tuple(trait=traits.Str))
class output_spec(TraitedSpec):
subject = traits.Str()
session = traits.Str()
run = traits.Str()
seg_file = traits.File(exists=True)
surf_file = traits.File(exists=True)
mask_file = traits.File(exists=True)
ts_file = traits.File(exists=True)
noise_file = traits.File(Exists=True)
mc_file = traits.File(exists=True)
output_path = traits.Directory()
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=True, mandatory=True, desc='the input func file')
suffix = traits.Str('',
mandatory=True,
desc='suffix appended to source_file')
class input_spec(TraitedSpec):
subject = traits.Str()
session = traits.Str()
run = traits.Str()
data_dir = traits.Directory(exists=True)
info = traits.Dict()
seg_file = traits.File(exists=True)
surf_file = traits.File(exists=True)
ts_file = traits.File(exists=True)
mask_file = traits.File(exists=True)
noise_file = traits.File(exists=True)
mc_file = traits.File(exists=True)
class output_spec(TraitedSpec):
out_file = traits.File(exists=True)
out_plot = traits.File(exists=True)
mask_file = traits.File(exists=True)
mask_plot = traits.File(exists=True)
noise_file = traits.File(exists=True)
noise_plot = traits.File(exists=True)
mean_file = traits.File(exists=True)
mean_plot = traits.File(exists=True)
tsnr_file = traits.File(exists=True)
tsnr_plot = traits.File(exists=True)
output_path = traits.Directory()
class DerivativesDataSinkInputSpec(BaseInterfaceInputSpec):
base_directory = traits.Directory(
desc='Path to the base directory for storing data.')
betaseries_file = File(exists=True,
mandatory=True,
desc='the betaseries file')
in_file = File(exists=True, mandatory=True)
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)
class ReportNodeInputSpec(BaseInterfaceInputSpec):
"""Input interface for reporting."""
output_dir = traits.Directory(
argstr='%s',
exists=False,
resolve=True,
desc='Output directory to write results and BIDS derivatives to write.',
mandatory=True,
position=1)
fms = traits.Any()
df_features = traits.Any()
_fit_args = traits.Any()
class output_spec(TraitedSpec):
run_tuple = traits.Tuple()
subject = traits.Str()
session = traits.Str()
run = traits.Str()
sb_file = traits.File(exists=True)
ts_file = traits.File(exists=True)
ts_frames = traits.List(traits.File(exists=True))
ts_plot = traits.File(exists=True)
reg_file = traits.File(exists=True)
seg_file = traits.File(exists=True)
anat_file = traits.File(exists=True)
mask_file = traits.File(exists=True)
output_path = traits.Directory()
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 original file')
prefix = traits.Str(mandatory=False, desc='prefix for output files')
space = traits.Str('', usedefault=True, desc='Label for space field')
desc = traits.Str('', usedefault=True, desc='Label for description field')
suffix = traits.Str('', usedefault=True, desc='suffix appended to source_file')
keep_dtype = traits.Bool(False, usedefault=True, desc='keep datatype suffix')
extra_values = traits.List(traits.Str)
compress = traits.Bool(desc="force compression (True) or uncompression (False)"
" of the output file (default: same as input)")
extension = traits.Str()
class input_spec(TraitedSpec):
subject = traits.Str()
session = traits.Str()
run = traits.Str()
data_dir = traits.Directory(exists=True)
info = traits.Dict()
seg_file = traits.File(exists=True)
surf_file = traits.File(exists=True)
ts_file = traits.File(exists=True)
mask_file = traits.File(exists=True)
edge_file = traits.File(exists=True)
noise_file = traits.File(exists=True)
mc_file = traits.File(exists=True)
wm_erode = traits.Int(default=2)
csf_erode = traits.Int(default=1)
class input_spec(TraitedSpec):
subject = traits.Str()
session = traits.Str()
run = traits.Str()
data_dir = traits.Directory(exists=True)
exp_info = traits.Dict()
model_info = traits.Dict()
seg_file = traits.File(exists=True)
surf_file = traits.File(exists=True)
ts_file = traits.File(exists=True)
mask_file = traits.File(exists=True)
noise_file = traits.File(exists=True)
mc_file = traits.File(exists=True)
mesh_files = traits.Tuple(traits.File(exists=True),
traits.File(exists=True))
class FOOOFNodeInputSpec(BaseInterfaceInputSpec):
"""Input interface for FOOOF."""
# Input/Output
input_dir = traits.Directory(
argstr='%s',
exists=True,
resolve=True,
desc=
'Input directory containing timeseries and/or spectra .npy files to read.',
mandatory=True,
position=0)
output_dir = traits.Directory(
argstr='%s',
exists=False,
resolve=True,
desc='Output directory to write results and BIDS derivatives to write.',
mandatory=True,
position=1)
# Init params
peak_width_limits = traits.Tuple((0.5, 12.0),
mandatory=False,
usedefault=True)
max_n_peaks = traits.Int(100, mandatory=False, usedefault=True)
min_peak_height = traits.Float(0.0, mandatory=False, usedefault=True)
peak_threshold = traits.Float(2.0, mandatory=False, usedefault=True)
aperiodic_mode = traits.Str('fixed', mandatory=False, usedefault=True)
# Fit params
freqs = traits.File(mandatory=True, usedefault=False)
power_spectrum = traits.File(mandatory=True, usedefault=False)
f_range_fooof = traits.Tuple((-np.inf, np.inf),
mandatory=False,
usedefault=True)
n_jobs = traits.Int(1, mandatory=False, 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 TrainInputSpec(BaseInterfaceInputSpec):
images = traits.Array(mandatory=True,
desc='Images for the training as NumPy array')
masks = traits.Array(mandatory=True,
desc='Masks for training as NumPy array')
model_path = traits.Directory(mandatory=True,
desc='directory where to save the models')
ensemble_parameter = traits.Int(3,
usedefault=True,
desc='ensemble parameter')
verbose = traits.Bool(True, usedefault=True, desc='Verbose')
batch_size = traits.Int(30, usedefault=True, desc='batch size, default 30')
epochs = traits.Int(5, usedefault=True, desc='epochs, default 5')
image_shape = traits.Tuple((traits.Int(), traits.Int(), traits.Int()),
desc='slice shape')
shuffle = traits.Bool(True, desc='shuffle')
class QsiprepAnatomicalIngressInputSpec(BaseInterfaceInputSpec):
recon_input_dir = traits.Directory(exists=True, mandatory=True)
subject_id = traits.Str()
subjects_dir = File(exists=True)
class _BIDSFreeSurferDirOutputSpec(TraitedSpec):
subjects_dir = traits.Directory(exists=True,
desc="FreeSurfer subjects directory")
class InverseSolutionConnInputSpec(BaseInterfaceInputSpec):
"""Input specification for InverseSolution."""
sbj_id = traits.String(desc='subject id', mandatory=True)
subjects_dir = traits.Directory(
exists=True,
desc='Freesurfer main directory', # noqa
mandatory=True)
raw_filename = traits.File(exists=True,
desc='raw filename',
mandatory=True)
cov_filename = traits.File(exists=True,
desc='Noise Covariance matrix',
mandatory=True)
fwd_filename = traits.File(exists=True, desc='LF matrix', mandatory=True)
is_epoched = traits.Bool(False,
usedefault=True,
desc='if true raw data will be epoched',
mandatory=False)
is_fixed = traits.Bool(False,
usedefault=True,
desc='if true we use fixed orientation',
mandatory=False)
events_id = traits.Dict({},
desc='the id of all events to consider.',
usedefault=True,
mandatory=False)
condition = traits.List(desc='list of conditions', 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)
is_evoked = traits.Bool(desc='if true if we want to create evoked data',
mandatory=False)
is_ave = traits.Bool(False,
desc='if true if we have already evoked data',
mandatory=False)
inv_method = traits.String('MNE',
desc='possible inverse methods are \
sLORETA, MNE, dSPM',
usedefault=True,
mandatory=True)
snr = traits.Float(1.0,
usedefault=True,
desc='use smaller SNR for \
raw data',
mandatory=False)
parc = traits.String('aparc',
usedefault=True,
desc='the parcellation to use: aparc vs aparc.a2009s',
mandatory=False)
aseg = traits.Bool(desc='if true sub structures will be considered',
mandatory=False)
aseg_labels = traits.List(desc='list of substructures in the src space',
mandatory=False)
all_src_space = traits.Bool(False,
desc='if true compute inverse on all \
source space',
usedefault=True,
mandatory=False)
ROIs_mean = traits.Bool(True,
desc='if true compute mean on ROIs',
usedefault=True,
mandatory=False)
class input_spec(TraitedSpec):
proc_dir = traits.Directory(exists=True)
subject = traits.Str()
experiment = traits.Str()
model = traits.Str()
class input_spec(TraitedSpec):
session = traits.Tuple()
data_dir = traits.Directory(exists=True)
proc_dir = traits.Directory(exists=True)
fm_template = traits.Str()
phase_encoding = traits.Either("ap", "pa")
class input_spec(TraitedSpec):
subject_id = traits.Str()
data_dir = traits.Directory(exists=True)
in_file = traits.File(exists=True)
cost_file = traits.File(exists=True)
class output_spec(TraitedSpec):
output_path = traits.Directory()
