class CombinePandasDfsInputSpec(BaseInterfaceInputSpec):
"""Input interface wrapper for CombinePandasDfs"""
ID = traits.Any(mandatory=True)
network = traits.Any(mandatory=False)
net_mets_csv_list = traits.List(mandatory=True)
plot_switch = traits.Bool(False, usedefault=True)
multi_nets = traits.Any(mandatory=False)
multimodal = traits.Bool(False, usedefault=True)
class Export2PandasInputSpec(BaseInterfaceInputSpec):
"""
Input interface wrapper for Export2Pandas
"""
csv_loc = File(exists=True, mandatory=True, desc="")
ID = traits.Any(mandatory=True)
network = traits.Any(mandatory=False)
roi = traits.Any(mandatory=False)
class NetworkAnalysisInputSpec(BaseInterfaceInputSpec):
"""Input interface wrapper for NetworkAnalysis"""
ID = traits.Any(mandatory=True)
est_path = File(exists=True, mandatory=True)
prune = traits.Any(mandatory=False)
norm = traits.Any(mandatory=False)
binary = traits.Bool(False, usedefault=True)
class FilterResultdictsInputSpec(BaseInterfaceInputSpec):
indicts = traits.List(traits.Dict(traits.Str(), traits.Any()), mandatory=True)
filterdicts = traits.List(traits.Any(), desc="filter list")
variabledicts = traits.List(traits.Any(), desc="variable list")
spreadsheet = File(desc="spreadsheet", exists=True)
requireoneofimages = traits.List(
traits.Str(), desc="only keep resultdicts that have at least one of these keys"
)
excludefiles = traits.Str()
class BoldFileReportMetadataInputSpec(TraitedSpec):
basedict = traits.Dict(traits.Str(), traits.Any())
confounds = File(exists=True, mandatory=True)
tsnr_file = File(exists=True, mandatory=True)
dseg = File(exists=True, mandatory=True)
aroma_metadata = traits.Dict(traits.Str(),
traits.Any(),
exists=True,
mandatory=True)
class PerformMLInputSpec(BaseInterfaceInputSpec):
ds_file = File(desc='dataset file for ML to be performed on',
exists=True,
mandatory=True)
classifier = traits.Any(None) #TODO: make this a classifier object
scoring = traits.Any(None) #TODO: make this a scoring object
targets = traits.Any(None) #TODO: make this a string
learning_curve = traits.Bool(
False, desc='whether training curve analysis will be performed')
class CollectPandasDfsInputSpec(BaseInterfaceInputSpec):
"""
Input interface wrapper for CollectPandasDfs
"""
ID = traits.Any(mandatory=True)
network = traits.Any(mandatory=True)
net_pickle_mt_list = traits.List(mandatory=True)
plot_switch = traits.Any(mandatory=True)
multi_nets = traits.Any(mandatory=True)
multimodal = traits.Any(mandatory=True)
class _IndividualClusteringInputSpec(BaseInterfaceInputSpec):
"""Input interface wrapper for IndividualClustering"""
func_file = File(exists=True, mandatory=True)
conf = traits.Any(mandatory=False)
clust_mask = File(exists=True, mandatory=True)
ID = traits.Any(mandatory=True)
k = traits.Any(mandatory=True)
clust_type = traits.Str(mandatory=True)
vox_size = traits.Str('2mm', mandatory=True, usedefault=True)
local_corr = traits.Str('allcorr', mandatory=True, usedefault=True)
mask = traits.Any(mandatory=False)
class _FetchNodesLabelsInputSpec(BaseInterfaceInputSpec):
"""Input interface wrapper for FetchNodesLabels"""
atlas = traits.Any(mandatory=False)
parcellation = traits.Any(mandatory=False)
ref_txt = traits.Any()
in_file = traits.Any(mandatory=True)
parc = traits.Bool(mandatory=True)
use_parcel_naming = traits.Bool(False, usedefault=True)
outdir = traits.Str(mandatory=True)
vox_size = traits.Str("2mm", mandatory=True, usedefault=True)
clustering = traits.Bool(False, usedefault=True)
class _RegisterDWIInputSpec(BaseInterfaceInputSpec):
"""Input interface wrapper for RegisterDWI"""
fa_path = File(exists=True, mandatory=True)
ap_path = File(exists=True, mandatory=True)
B0_mask = File(exists=True, mandatory=True)
anat_file = File(exists=True, mandatory=True)
gtab_file = File(exists=True, mandatory=True)
dwi_file = File(exists=True, mandatory=True)
vox_size = traits.Str('2mm', mandatory=True, usedefault=True)
waymask = traits.Any(mandatory=False)
mask = traits.Any(mandatory=False)
simple = traits.Bool(False, usedefault=True)
overwrite = traits.Bool(True, usedefault=True)
class _ExtractTimeseriesOutputSpec(TraitedSpec):
"""Output interface wrapper for ExtractTimeseries"""
ts_within_nodes = traits.Any(mandatory=True)
node_size = traits.Any(mandatory=True)
smooth = traits.Any(mandatory=True)
dir_path = traits.Str(mandatory=True)
atlas = traits.Any(mandatory=True)
uatlas = traits.Any(mandatory=True)
labels = traits.Any(mandatory=True)
coords = traits.Any(mandatory=True)
c_boot = traits.Any(mandatory=True)
hpass = traits.Any(mandatory=True)
roi = traits.Any(mandatory=True)
class _ExtractTimeseriesOutputSpec(TraitedSpec):
"""Output interface wrapper for ExtractTimeseries"""
ts_within_nodes = traits.Any(mandatory=True)
node_radius = traits.Any(mandatory=True)
smooth = traits.Any(mandatory=True)
dir_path = Directory(exists=True, mandatory=True)
atlas = traits.Any(mandatory=False)
parcellation = traits.Any(mandatory=False)
labels = traits.Any(mandatory=True)
coords = traits.Any(mandatory=True)
hpass = traits.Any(mandatory=True)
roi = traits.Any(mandatory=True)
signal = traits.Any(mandatory=False)
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 RegressOutputSpec(TraitedSpec):
cleaned_path = File(exists=True, mandatory=True,
desc="Cleaned timeseries.")
VE_file_path = File(exists=True, mandatory=True,
desc="Variance explained map from confound regression.")
STD_file_path = File(exists=True, mandatory=True,
desc="Temporal standard deviation map after confound correction, prior to standardization.")
CR_STD_file_path = File(exists=True, mandatory=True,
desc="Temporal standard deviation map after confound correction, prior to standardization.")
frame_mask_file = File(exists=True, mandatory=True,
desc="Frame mask from temporal censoring.")
data_dict = traits.Any(
desc="A dictionary with key outputs.")
aroma_out = traits.Any(
desc="Output directory from ICA-AROMA.")
class LFComputationConnInputSpec(BaseInterfaceInputSpec):
sbj_id = traits.String(desc='subject id', mandatory=True)
sbj_dir = traits.String(exists=True,
desc='Freesurfer main directory',
mandatory=True)
raw_info = traits.Any(desc='raw info', mandatory=True)
raw_fname = traits.String(desc='raw file name', mandatory=True)
spacing = traits.String(desc='spacing to use to setup a source space',
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)
save_mixed_src_space = traits.Bool(False,
desc='if true save src space',
usedefault=True,
mandatory=False)
class _RegisterFuncInputSpec(BaseInterfaceInputSpec):
"""Input interface wrapper for RegisterFunc"""
anat_file = File(exists=True, mandatory=True)
mask = traits.Any(mandatory=False)
vox_size = traits.Str('2mm', mandatory=True, usedefault=True)
simple = traits.Bool(False, usedefault=True)
overwrite = traits.Bool(True, usedefault=True)
class _PlotMatricesInputSpec(BaseInterfaceInputSpec):
run_info = traits.Any(desc="List of regressors of no interest")
mat_file = File(exists=True, desc="Matrix File produced by Generate Model")
con_file = File(exists=True, desc="Contrast File Produces by Generate Model")
database_path = Directory(exists=True, desc="Database path for current model")
entities = traits.Dict(desc="Dictionary containing BIDS file entities")
output_dir = Directory(desc="Directory for Output")
class CalcMeanInputSpec(TraitedSpec):
in_file = File(exists=True, mandatory=True)
mask = File(exists=True)
parcellation = File(exists=True)
dseg = File(exists=True)
vals = traits.Dict(traits.Str(), traits.Any())
key = traits.Str()
class _IndividualClusteringOutputSpec(TraitedSpec):
"""Output interface wrapper for IndividualClustering"""
uatlas = File(exists=True)
atlas = traits.Str(mandatory=True)
clustering = traits.Bool(True, usedefault=True)
clust_mask = File(exists=True, mandatory=True)
k = traits.Any(mandatory=True)
clust_type = traits.Str(mandatory=True)
class AtlasTransformInputSpec(TraitedSpec):
nifti = traits.Any(mandatory=True, desc='input nifti')
atlas_name = traits.String(mandatory=True, desc='atlas name')
bids_dir = traits.String(mandatory=True, desc='atlas name')
resolution = traits.Int(mandatory=False,
desc='resolution (for shen atlas)')
number_of_clusters = traits.Int(mandatory=False, desc='for craddock')
similarity_measure = traits.String(mandatory=False, desc='for craddock')
algorithm = traits.String(mandatory=False, desc='for craddock')
class FilterResultdictsInputSpec(BaseInterfaceInputSpec):
indicts = traits.List(traits.Dict(traits.Str(), traits.Any()), mandatory=True)
filterobjs = traits.List(desc="filter list", mandatory=True)
variableobjs = traits.List(desc="variable list")
spreadsheet = traits.File(desc="spreadsheet")
requireoneofkeys = traits.List(
traits.Str(), desc="only keep resultdicts that have at least one of these keys"
)
qualitycheckfile = traits.File()
class ExtractNetStatsInputSpec(BaseInterfaceInputSpec):
ID = traits.Any(mandatory=True)
network = traits.Any(mandatory=False)
thr = traits.Any(mandatory=True)
conn_model = traits.Str(mandatory=True)
est_path = File(exists=True, mandatory=True, desc="")
roi = traits.Any(mandatory=False)
prune = traits.Any(mandatory=False)
node_size = traits.Any(mandatory=False)
smooth = traits.Any(mandatory=False)
c_boot = traits.Any(mandatory=False)
class _FetchNodesLabelsOutputSpec(TraitedSpec):
"""Output interface wrapper for FetchNodesLabels"""
labels = traits.Any(mandatory=True)
coords = traits.Any(mandatory=True)
atlas = traits.Any()
networks_list = traits.Any()
parcels_4d = traits.Any()
par_max = traits.Any()
parcellation = traits.Any()
dir_path = traits.Any()
class CoherenceAnalyzerInputSpec(BaseInterfaceInputSpec):
#Input either csv file, or time-series object and use _xor_inputs to
#discriminate
_xor_inputs = ('in_file', 'in_TS')
in_file = File(desc=('csv file with ROIs on the columns and '
'time-points on the rows. ROI names at the top row'),
exists=True,
requires=('TR', ))
#If you gave just a file name, you need to specify the sampling_rate:
TR = traits.Float(desc=('The TR used to collect the data'
'in your csv file <in_file>'))
in_TS = traits.Any(desc='a nitime TimeSeries object')
NFFT = traits.Range(
low=32,
value=64,
usedefault=True,
desc=('This is the size of the window used for '
'the spectral estimation. Use values between '
'32 and the number of samples in your time-series.'
'(Defaults to 64.)'))
n_overlap = traits.Range(
low=0,
value=0,
usedefault=True,
desc=('The number of samples which overlap'
'between subsequent windows.(Defaults to 0)'))
frequency_range = traits.List(value=[0.02, 0.15],
usedefault=True,
minlen=2,
maxlen=2,
desc=('The range of frequencies over'
'which the analysis will average.'
'[low,high] (Default [0.02,0.15]'))
output_csv_file = File(
desc=
'File to write outputs (coherence,time-delay) with file-names: file_name_ {coherence,timedelay}'
)
output_figure_file = File(
desc=
'File to write output figures (coherence,time-delay) with file-names: file_name_{coherence,timedelay}. Possible formats: .png,.svg,.pdf,.jpg,...'
)
figure_type = traits.Enum('matrix',
'network',
usedefault=True,
desc=("The type of plot to generate, where "
"'matrix' denotes a matrix image and"
"'network' denotes a graph representation."
" Default: 'matrix'"))
class AvScaleOutputSpec(TraitedSpec):
rotation_translation_matrix=traits.Any(desc='Rotation and Translation Matrix')
scales = traits.Any(desc='Scales (x,y,z)')
skews = traits.Any(desc='Skews')
average_scaling = traits.Any(desc='Average Scaling')
determinant = traits.Any(desc='Determinant')
forward_half_transform = traits.Any(desc='Forward Half Transform')
backward_half_transform = traits.Any(desc='Backwards Half Transform')
left_right_orientation_preserved = traits.Bool(desc='True if LR orientation preserved')
class _IndividualClusteringInputSpec(BaseInterfaceInputSpec):
"""Input interface wrapper for IndividualClustering"""
func_file = File(exists=True, mandatory=True)
conf = traits.Any(mandatory=False)
clust_mask = File(exists=True, mandatory=True)
ID = traits.Any(mandatory=True)
k = traits.Any(mandatory=True)
clust_type = traits.Str(mandatory=True)
vox_size = traits.Str("2mm", mandatory=True, usedefault=True)
local_corr = traits.Str("allcorr", mandatory=True, usedefault=True)
mask = traits.Any(mandatory=False)
outdir = traits.Str(mandatory=True)
basedir_path = Directory(exists=True, mandatory=True)
anat_file = File(exists=True, mandatory=True)
t1w_brain = File(exists=True, mandatory=True)
mni2t1w_warp = File(exists=True, mandatory=True)
mni2t1_xfm = File(exists=True, mandatory=True)
template_name = traits.Str("MNI152_T1", mandatory=True, usedefault=True)
simple = traits.Bool(False, usedefault=True)
class _GetRunModelInfoInputSpec(BaseInterfaceInputSpec):
metadata_file = File()
regressor_file = File()
events_file = File()
entities = traits.Dict(mandatory=True)
model = traits.Dict(mandatory=True)
detrend_poly = traits.Any(
default=None,
desc=("Legendre polynomials to regress out"
"for temporal filtering"),
)
class BIDSDataGraberInputSpec(BaseInterfaceInputSpec):
bids_dir = traits.Str(exists=True,
mandatory=True,
desc="BIDS data directory")
suffix = traits.List(exists=True,
mandatory=True,
desc="Suffix to search for")
scan_info = traits.Dict(exists=True,
mandatory=True,
desc="Info required to find the scan")
run = traits.Any(exists=True, desc="Run number")
class RegressInputSpec(BaseInterfaceInputSpec):
bold_file = File(exists=True, mandatory=True,
desc="Timeseries to denoise.")
data_dict = traits.Dict(
exists=True, mandatory=True, desc="Dictionary with extra inputs.")
brain_mask_file = File(exists=True, mandatory=True,
desc="Brain mask.")
CSF_mask_file = File(exists=True, mandatory=True,
desc="CSF mask.")
cr_opts = traits.Any(
exists=True, mandatory=True, desc="Processing specs.")
class PassMetaOutsInputSpec(BaseInterfaceInputSpec):
conn_model_iterlist = traits.Any(mandatory=True)
est_path_iterlist = traits.Any(mandatory=False)
node_size_iterlist = traits.Any(mandatory=True)
thr_iterlist = traits.Str(mandatory=True)
prune_iterlist = File(exists=True, mandatory=True, desc="")
ID_iterlist = traits.Any(mandatory=False)
roi_iterlist = traits.Any(mandatory=False)
norm_iterlist = traits.Any(mandatory=False)
binary_iterlist = traits.Any(mandatory=False)
