class StructuralQCOutputSpec(TraitedSpec):
summary = traits.Dict(desc='summary statistics per tissue')
icvs = traits.Dict(desc='intracranial volume (ICV) fractions')
rpve = traits.Dict(desc='partial volume fractions')
size = traits.Dict(desc='image sizes')
spacing = traits.Dict(desc='image sizes')
inu = traits.Dict(desc='summary statistics of the bias field')
snr = traits.Dict
cnr = traits.Float
fber = traits.Float
efc = traits.Float
qi1 = traits.Float
qi2 = traits.Float
wm2max = traits.Float
cjv = traits.Float
out_qc = traits.Dict(desc='output flattened dictionary with all measures')
out_noisefit = File(exists=True,
desc='plot of background noise and chi fitting')
class BetaSeriesInputSpec(BaseInterfaceInputSpec):
bold_file = File(exists=True, mandatory=True, desc="The bold run")
bold_metadata = traits.Dict(
desc='Dictionary containing useful information about'
' the bold_file')
mask_file = File(exists=True,
mandatory=True,
desc="Binarized nifti file indicating the brain")
events_file = File(exists=True,
mandatory=True,
desc="File that contains all events from the bold run")
confounds_file = traits.Either(
None,
File(exists=True),
desc="File that contains all usable confounds")
selected_confounds = traits.Either(
None, traits.List(), desc="Column names of the regressors to include")
hrf_model = traits.String(desc="hemodynamic response model")
smoothing_kernel = traits.Either(
None, traits.Float(), desc="full wide half max smoothing kernel")
high_pass = traits.Float(0.0078125, desc="the high pass filter (Hz)")
class NoiseCovarianceConnInputSpec(BaseInterfaceInputSpec):
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)
def __init__(self, infields=None, outfields=None, **kwargs):
"""
Parameters
----------
infields : list of str
Indicates the input fields to be dynamically created
outfields: list of str
Indicates output fields to be dynamically created
See class examples for usage
"""
if not outfields:
outfields = ['outfiles']
super(DataGrabber, self).__init__(**kwargs)
undefined_traits = {}
# used for mandatory inputs check
self._infields = infields
if infields:
for key in infields:
self.inputs.add_trait(key, traits.Any)
undefined_traits[key] = Undefined
# add ability to insert field specific templates
self.inputs.add_trait(
'field_template',
traits.Dict(traits.Enum(outfields),
desc="arguments that fit into template"))
undefined_traits['field_template'] = Undefined
if not isdefined(self.inputs.template_args):
self.inputs.template_args = {}
for key in outfields:
if not key in self.inputs.template_args:
if infields:
self.inputs.template_args[key] = [infields]
else:
self.inputs.template_args[key] = []
self.inputs.trait_set(trait_change_notify=False, **undefined_traits)
class WMMaskingInputSpec(BaseInterfaceInputSpec):
"""
This class represents a...
:param BaseInterfaceInputSpec:
"""
atlas_file = File(
exists=True, mandatory=True,
desc='Label map used to divide hemispheres')
posterior_files = traits.Dict(mandatory=True, desc='Posterior probability files')
brainlabels_file = File(exists=True, mandatory=True,
desc='BRAINSABC brain labels')
atlas_info = File(exists=True, mandatory=True,
desc='input label information in xml format')
dilation = traits.Int(
default_value=0, desc="""
Parameter to adjust the dilation of the boundary mask (default=0)
A negative value will erode the boundary mask.
""", use_default=True)
csf_threshold = traits.Float(
default_value=0.9, desc="""
Posterior probabilities above this threshold will be considered CSF
""", use_default=True)
hncma_file = File(exists=True, desc="HNCMA atlas is used to define ventricles.")
class LSSBetaSeriesInputSpec(BaseInterfaceInputSpec):
bold_file = traits.Either(
File(exists=True, mandatory=True, desc="The bold run"),
nib.spatialimages.SpatialImage)
bold_metadata = traits.Dict(
desc='Dictionary containing useful information about'
' the bold_file')
mask_file = traits.Either(
File(exists=True,
mandatory=True,
desc="Binarized nifti file indicating the brain"),
nib.spatialimages.SpatialImage)
events_file = File(exists=True,
mandatory=True,
desc="File that contains all events from the bold run")
confounds_file = traits.Either(
None,
File(exists=True),
desc="File that contains all usable confounds")
signal_scaling = traits.Enum(
False,
0,
desc="Whether (0) or not (False) to scale each"
" voxel's timeseries")
selected_confounds = traits.Either(
None, traits.List(), desc="Column names of the regressors to include")
hrf_model = traits.String(desc="hemodynamic response model")
smoothing_kernel = traits.Either(
None, traits.Float(), desc="full wide half max smoothing kernel")
high_pass = traits.Float(0.0078125, desc="the high pass filter (Hz)")
fir_delays = traits.Either(None,
traits.List(traits.Int),
desc="FIR delays (in scans)",
default=None,
usedefault=True)
return_tstat = traits.Bool(
desc="use the T-statistic instead of the raw beta estimates")
class DirectoryDataSinkInputSpec(DynamicTraitedSpec, BaseInterfaceInputSpec):
'''
'''
# Init inputspec data attributes
base_directory = Directory(
desc='Path to the base directory for storing data.',
madatory=True,
exists=True)
_outputs = traits.Dict(Str, value={}, usedefault=True)
remove_dest_dir = traits.Bool(
False, usedefault=True, desc='remove dest directory when copying dirs')
# Set call-able inputs attributes
def __setattr__(self, key, value):
if key not in self.copyable_trait_names():
if not isdefined(value):
super(DirectoryDataSinkInputSpec, self).__setattr__(key, value)
self._outputs[key] = value
else:
if key in self._outputs:
self._outputs[key] = value
super(DirectoryDataSinkInputSpec, self).__setattr__(key, value)
class JSONSinkInputSpec(DynamicTraitedSpec, BaseInterfaceInputSpec):
base_directory = Directory(
desc='Path to the base directory for writing report.')
json_name = traits.Str('QA', usedefault=True)
container = traits.Str(
desc='Folder within base directory in which to store output')
parameterization = traits.Bool(
True, usedefault=True, desc='store output in parametrized structure')
substitutions = InputMultiPath(traits.Tuple(traits.Str, traits.Str),
desc=('List of 2-tuples reflecting string '
'to substitute and string to replace '
'it with'))
regexp_substitutions = InputMultiPath(
traits.Tuple(traits.Str, traits.Str),
desc=('List of 2-tuples reflecting a pair '
'of a Python regexp pattern and a '
'replacement string. Invoked after '
'string `substitutions`'))
_outputs = traits.Dict(traits.Str, value={}, usedefault=True)
_outputs_order = []
remove_dest_dir = traits.Bool(
False, usedefault=True, desc='remove dest directory when copying dirs')
parameterization = traits.Bool(True, usedefault=True)
strip_dir = Directory()
def __setattr__(self, key, value):
if key not in self.copyable_trait_names():
if not isdefined(value):
super(JSONSinkInputSpec, self).__setattr__(key, value)
self._outputs[key] = value
self._outputs_order.append(key)
else:
if key in self._outputs:
self._outputs[key] = value
self._outputs_order.append(key)
super(JSONSinkInputSpec, self).__setattr__(key, value)
