class ParcellateOutputSpec(TraitedSpec):
#roi_files = OutputMultiPath(File(exists=True),desc='Region of Interest files for connectivity mapping')
white_matter_mask_file = File(desc='White matter mask file')
#cc_unknown_file = File(desc='Image file with regions labelled as unknown cortical structures',
# exists=True)
#ribbon_file = File(desc='Image file detailing the cortical ribbon',
# exists=True)
#aseg_file = File(desc='Automated segmentation file converted from Freesurfer "subjects" directory',
# exists=True)
wm_eroded = File(desc="Eroded wm file in original space")
csf_eroded = File(desc="Eroded csf file in original space")
brain_eroded = File(desc="Eroded brain file in original space")
roi_files_in_structural_space = OutputMultiPath(File(exists=True),
desc='ROI image resliced to the dimensions of the original structural image')
T1 = File(desc="T1 image file")
brain = File(desc="Brain-masked T1 image file")
brain_mask = File(desc="Brain mask file")
class RealignUnwarpOutputSpec(TraitedSpec):
mean_image = File(exists=True, desc='Mean image file from the realignment')
modified_scans = traits.Either(traits.List(File(exists=True)),
File(exists=True),
desc=('Copies of all files passed to '
'scans. Headers will have '
'been modified to align all '
'images with the first, or '
'optionally to first do that, '
'extract a mean image, and '
're-align to that mean image.'))
runwarped_files = traits.Either(
traits.List(File(exists=True)),
File(exists=True),
desc='These will be the resliced and unwarped files.')
realignment_parameters = OutputMultiPath(File(exists=True),
desc=('Estimated translation and '
'rotation parameters'))
class ProbTrackXOutputSpec(TraitedSpec):
log = File(exists=True,
desc='path/name of a text record of the command that was run')
fdt_paths = OutputMultiPath(
File(exists=True),
desc='path/name of a 3D image file containing the output ' +
'connectivity distribution to the seed mask')
way_total = File(
exists=True,
desc='path/name of a text file containing a single number ' +
'corresponding to the total number of generated tracts that ' +
'have not been rejected by inclusion/exclusion mask criteria')
targets = traits.List(
File(exists=True),
desc='a list with all generated seeds_to_target files')
particle_files = traits.List(
File(exists=True),
desc='Files describing ' +
'all of the tract samples. Generated only if ' + 'verbose is set to 2')
class NetworkXMetricsOutputSpec(TraitedSpec):
gpickled_network_files = OutputMultiPath(File(desc='Output gpickled network files'))
matlab_matrix_files = OutputMultiPath(File(desc='Output network metrics in MATLAB .mat format'))
global_measures_matlab = File(desc='Output global metrics in MATLAB .mat format')
node_measures_matlab = File(desc='Output node metrics in MATLAB .mat format')
edge_measures_matlab = File(desc='Output edge metrics in MATLAB .mat format')
node_measure_networks = OutputMultiPath(File(desc='Output gpickled network files for all node-based measures'))
edge_measure_networks = OutputMultiPath(File(desc='Output gpickled network files for all edge-based measures'))
k_networks = OutputMultiPath(File(desc='Output gpickled network files for the k-core, k-shell, and k-crust networks'))
k_core = File(desc='Computed k-core network stored as a NetworkX pickle.')
k_shell = File(desc='Computed k-shell network stored as a NetworkX pickle.')
k_crust = File(desc='Computed k-crust network stored as a NetworkX pickle.')
pickled_extra_measures = File(desc='Network measures for the group that return dictionaries, stored as a Pickle.')
matlab_dict_measures = OutputMultiPath(File(desc='Network measures for the group that return dictionaries, stored as matlab matrices.'))
class NiiWranglerOutputSpec(TraitedSpec):
dicom_info = traits.List(
mandatory=True,
desc="one dict for each series in the session, in the order they were\
run. each dict should contain at least the series_num (int) and\
the series_desc (str). NiiWrangler writes nifti location here."
)
t1_uni = OutputMultiPath(
traits.List(File(exists=True)),
mandatory=True,
desc="anatomical uni nifti (list in chronological order if repeated)")
t1_inv2 = OutputMultiPath(
traits.List(File(exists=True)),
mandatory=True,
desc="inv2 nifti (list in chronological order if repeated)")
t1_q = OutputMultiPath(
traits.List(File(exists=True)),
mandatory=True,
desc="quantitative t1 nifti (list in chronological order if repeated)"
)
rsfmri = OutputMultiPath(
traits.List(File(exists=True)),
mandatory=True,
desc="rsfmri nifti (list in chronological order if repeated)")
rs_ap = OutputMultiPath(
traits.List(File(exists=True)),
mandatory=True,
desc="rs ap nifti (list in chronological order if repeated)")
rs_pa = OutputMultiPath(
traits.List(File(exists=True)),
mandatory=True,
desc="rs pa (list in chronological order if repeated)")
dwi = traits.List(
traits.Str(),
mandatory=True,
desc="dwi nifti (list in chronological order if repeated).")
dwi_ap = traits.List(
traits.Str(),
mandatory=True,
desc=
"dwi ap nifti for topup (list in chronological order if repeated).")
dwi_pa = traits.List(
traits.Str(),
mandatory=True,
desc=
"dwi pa nifti for topup (list in chronological order if repeated).")
flair = traits.List(
traits.Str(),
mandatory=True,
desc="flair nifti (list in chronological order if repeated).")
class ExecutionModelTourOutputSpec(TraitedSpec):
outputFile1 = File(desc="An output file", exists=True)
image2 = File(desc="An output image", exists=True)
transformOutput = File(desc="A generic output transform", exists=True)
transform2 = File(desc="A linear output transform", exists=True)
transformOutputNonlinear = File(desc="A nonlinear output transform",
exists=True)
transformOutputBspline = File(desc="A bspline output transform",
exists=True)
outputModel = File(desc="Output model", exists=True)
modelSceneFile = OutputMultiPath(
File(exists=True),
desc=
"Generated models, under a model hierarchy node. Models are imported into Slicer under a model hierarchy node. The model hierarchy node must be created before running the model maker, by selecting Create New ModelHierarchy from the Models drop down menu.",
exists=True)
arg1 = File(desc="Second index argument is an image",
position=-1,
exists=True)
outputDT = File(desc="Array of processed (output) Table values",
exists=True)
class FFXReportOutput(TraitedSpec):
summary_files = OutputMultiPath(File(exists=True))
zstat_files = OutputMultiPath(Directory(exists=True))
class FFXSummaryOutput(TraitedSpec):
r2_files = OutputMultiPath(File(exists=True))
tsnr_file = File(exists=True)
mean_file = File(exists=True)
class FFXModelOutput(TraitedSpec):
flame_results = OutputMultiPath(Directory(exists=True))
zstat_files = OutputMultiPath(File(exists=True))
class DerivativesDataSinkOutputSpec(TraitedSpec):
out_file = OutputMultiObject(File(exists=True, desc='written file path'))
compression = OutputMultiPath(
traits.Bool,
desc='whether ``in_file`` was compressed/uncompressed '
'or `it was copied directly.')
class TrainingSetCreatorOutputSpec(TraitedSpec):
mel_icas_out = OutputMultiPath(Directory(exists=True),
copyfile=False,
desc='Hand labels for noise vs signal',
argstr='%s',
position=-1)
class BRAINSABCOutputSpec(TraitedSpec):
outputDir = Directory(exists=True)
atlasToSubjectTransform = File(exists=True)
outputVolumes = OutputMultiPath(File(exists=True), exists=True)
outputLabels = File(exists=True)
outputDirtyLabels = File(exists=True)
class CleanUpOverlapLabelsOutputSpec(TraitedSpec):
outputBinaryVolumes = OutputMultiPath(
File(exists=True),
desc=
"The output label map images, with integer values in it. Each label value specified in the inputLabels is combined into this output label map volume"
)
class NetworkBasedStatisticOutputSpec(TraitedSpec):
nbs_network = File(exists=True, desc='Output network with edges identified by the NBS')
nbs_pval_network = File(exists=True, desc='Output network with p-values to weight the edges identified by the NBS')
network_files = OutputMultiPath(File(exists=True), desc='Output network with edges identified by the NBS')
class Labels2ProbMapsOutputSpec(TraitedSpec):
output_files = OutputMultiPath(File(exists=True))
class RealignmentReportOutput(TraitedSpec):
realign_report = OutputMultiPath(File(exists=True))
motion_file = File(exists=True)
class mincAtroposOutputSpec(TraitedSpec):
classified_image = File(exists=True)
posteriors = OutputMultiPath(File(exist=True))
class ModelMakerOutputSpec(TraitedSpec):
modelSceneFile = OutputMultiPath(File(exists=True), desc="Generated models, under a model hierarchy node. Models are imported into Slicer under a model hierarchy node, and their colors are set by the color table associated with the input label map volume. The model hierarchy node must be created before running the model maker, by selecting Create New ModelHierarchy from the Models drop down menu. If you're running from the command line, a model hierarchy node in a new mrml scene will be created for you.", exists=True)
class DerivativesDataSinkOutputSpec(TraitedSpec):
out_file = OutputMultiPath(File(exists=True, desc='written file path'))
class MaskCoverageOutputSpec(DynamicTraitedSpec):
out_files = OutputMultiPath(File(exists=True))
coverage = traits.List(traits.Float)
class IntraModalMergeOutputSpec(TraitedSpec):
out_file = File(exists=True, desc='merged image')
out_avg = File(exists=True, desc='average image')
out_mats = OutputMultiPath(exists=True, desc='output matrices')
out_movpar = OutputMultiPath(exists=True, desc='output movement parameters')
class ApplyInverseDeformationOutput(TraitedSpec):
out_files = OutputMultiPath(File(exists=True), desc='Transformed files')
class _IntraModalMergeOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="merged image")
out_avg = File(exists=True, desc="average image")
out_mats = OutputMultiPath(File(exists=True), desc="output matrices")
out_movpar = OutputMultiPath(File(exists=True),
desc="output movement parameters")
class BIDSDataSinkOutputSpec(TraitedSpec):
out_file = OutputMultiPath(File, desc='output file')
class _UnzipJoinedSurfacesOutputSpec(TraitedSpec):
out_files = traits.List(
OutputMultiPath(File(exists=True),
desc='output pointcloud in PLY format'))
surf_keys = traits.List(traits.Str, desc='surface identifier keys')
class AverageNetworksOutputSpec(TraitedSpec):
gpickled_groupavg = File(desc='Average network saved as a NetworkX .pck')
gexf_groupavg = File(desc='Average network saved as a .gexf file')
matlab_groupavgs = OutputMultiPath(File(desc='Average network saved as a .gexf file'))
class BIDSSelectOutputSpec(TraitedSpec):
bold_files = OutputMultiPath(File)
mask_files = OutputMultiPath(traits.Either(File, None))
entities = OutputMultiPath(traits.Dict)
class RemoveEmptyOutput(TraitedSpec):
out_files = OutputMultiPath(File(exists=True))
class ModelSpecLoaderOutputSpec(TraitedSpec):
model_spec = OutputMultiPath(
traits.Dict(), desc='Model specification(s) as Python dictionaries')
class _MultiApplyTransformsOutputSpec(TraitedSpec):
out_files = OutputMultiPath(File(), desc='the output ITKTransform file')
log_cmdline = File(desc='a list of command lines used to apply transforms')
