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self._results.update(results)
return runtime
class ModelResultsInput(LymanInterface):
class input_spec(TraitedSpec):
experiment = traits.Str()
model = traits.Str()
proc_dir = traits.Directory(exists=True)
subject = traits.Str()
run_tuple = traits.Tuple(traits.Str(), traits.Str())
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)
model_file = traits.File(exists=True)
output_path = traits.Directory()
def _run_interface(self, runtime):
subject = self.inputs.subject
session, run = self.inputs.run_tuple
run_key = "{}_{}".format(session, run)
argstr='--input-image-type %d',
desc=('Option specifying the input image '
'type of scalar (default), vector, '
'tensor, or time series.'))
input_image = File(
argstr='--input %s',
mandatory=True,
desc=('image to apply transformation to (generally a '
'coregistered functional)'),
exists=True)
output_image = traits.Str(
argstr='--output %s',
desc='output file name',
genfile=True,
hash_files=False)
out_postfix = traits.Str(
"_trans",
usedefault=True,
desc=('Postfix that is appended to all output '
'files (default = _trans)'))
reference_image = File(
argstr='--reference-image %s',
mandatory=True,
desc='reference image space that you wish to warp INTO',
exists=True)
interpolation = traits.Enum(
'Linear',
'NearestNeighbor',
'CosineWindowedSinc',
'WelchWindowedSinc',
'HammingWindowedSinc',
'LanczosWindowedSinc',
out_file = traits.File(desc="Output file")
class deployDashInput(BaseInterfaceInputSpec):
targetDir = traits.File(mandatory=True, desc="Target directory")
sourceDir = traits.File(mandatory=True, desc="Source directory")
pvc_method = traits.Str(desc="PVC method")
quant_method = traits.Str(desc="TKA method")
analysis_space = traits.Str(desc="Analysis Space")
pet = traits.File(exists=True, mandatory=True, desc="PET image")
pet_space_mri = traits.File(exists=True, mandatory=True, desc="Output PETMRI image")
mri_space_nat = traits.File(exists=True, mandatory=True, desc="Output T1 native space image")
t1_analysis_space = traits.File(exists=True, mandatory=True, desc="Output T1 in analysis space image")
pvc = traits.File(exists=True, desc="Output PVC image")
quant = traits.File(exists=True, desc="Output TKA image")
sid =traits.Str()
cid=traits.Str()
ses=traits.Str()
task=traits.Str()
run=traits.Str()
out_file = traits.File(desc="Output file")
clobber = traits.Bool(desc="Overwrite output file", default=False)
class deployDashCommand(BaseInterface):
input_spec = deployDashInput
output_spec = deployDashOutput
def _gen_output(self):
fname = "nodes.xml"
dname = os.getcwd()
return dname+os.sep+fname
'image file name format.')
out_atlas_voting_weight_name_format = traits.Str(
'antsJointFusionVotingWeight_%d.nii.gz',
requires=[
'out_label_fusion', 'out_intensity_fusion_name_format',
'out_label_post_prob_name_format'
],
desc='Optional atlas voting weight image '
'file name format.')
verbose = traits.Bool(False, argstr="-v", desc=('Verbose output.'))
class AntsJointFusionOutputSpec(TraitedSpec):
out_label_fusion = File(exists=True)
out_intensity_fusion_name_format = traits.Str()
out_label_post_prob_name_format = traits.Str()
out_atlas_voting_weight_name_format = traits.Str()
class AntsJointFusion(ANTSCommand):
"""
Examples
--------
>>> from nipype.interfaces.ants import AntsJointFusion
>>> antsjointfusion = AntsJointFusion()
>>> antsjointfusion.inputs.out_label_fusion = 'ants_fusion_label_output.nii'
>>> antsjointfusion.inputs.atlas_image = [ ['rc1s1.nii','rc1s2.nii'] ]
>>> antsjointfusion.inputs.atlas_segmentation_image = ['segmentation0.nii.gz']
>>> antsjointfusion.inputs.target_image = ['im1.nii']
>>> antsjointfusion.cmdline
"antsJointFusion -a 0.1 -g ['rc1s1.nii', 'rc1s2.nii'] -l segmentation0.nii.gz \
return outputs
class unu_2opInputSpec(CommandLineInputSpec):
operator = traits.Str(desc="Binary operator", position = 0, argstr="%s")
in1_file = File(desc="First input. Must be a nrrd.", position = 1,
exists=True, argstr="%s")
in2_file = File(desc="Second input. Must be a nrrd.", position = 2,
exists=True, argstr="%s")
in1_scalar = traits.Float(desc="First input. Must be a single value.",
position = 1, argstr="%f")
in2_scalar = traits.Float(desc="Second input. Must be a single value.",
position = 2, argstr="%f")
seed = traits.Int(desc="seed value for RNG for nrand, so that you can get \
repeatable results between runs, or, by not using this option, the RNG \
seeding will be based on the current time", argstr="--seed %d")
type = traits.Str(desc="type to convert all INPUT nrrds to, prior to doing \
operation, useful for doing, for instance, the difference between two \
unsigned char nrrds. This will also determine output type. By default (not \
using this option), the types of the input nrrds are left unchanged.",
argstr="--type %s")
which = traits.Int(desc="Which argument (0 or 1) should be used to \
determine the shape of the output nrrd. By default (not using this \
option), the first non-constant argument is used. (int)",
argstr="--which %d")
output = traits.Either(traits.Bool, File(), hash_files=False,
desc="Output nrrd (string)", argstr="--output %s")
class unu_2opOutputSpec(TraitedSpec):
output = File(desc="Output nrrd (string)", exists=True)
class unu_2op(CommandLine):
_cmd = 'unu 2op '
in_file = Path(self.inputs.in_file)
extension = "".join(in_file.suffixes[-((in_file.suffixes[-1] == ".gz") + 1):])
info = self._pattern.match(in_file.name[: -len(extension)]).groupdict()
self._results["extension"] = f"{info.pop('extprefix', None) or ''}{extension}"
self._results.update(info)
if "hemi" in self._results:
self._results["hemi"] = self._results["hemi"].upper()
return runtime
class _GiftiNameSourceInputSpec(BaseInterfaceInputSpec):
in_file = File(mandatory=True, exists=True, desc="input GIFTI file")
pattern = traits.Str(
mandatory=True, desc='input file name pattern (must capture named group "LR")'
)
template = traits.Str(mandatory=True, desc="output file name template")
template_kwargs = traits.Dict(desc="additional template keyword value pairs")
class _GiftiNameSourceOutputSpec(TraitedSpec):
out_name = traits.Str(desc="(partial) filename formatted according to template")
class GiftiNameSource(SimpleInterface):
r"""
Construct a new filename for a GIFTI file.
Construct a new filename based on an input filename, a matching pattern,
and a related template, with optionally additional keywords.
This interface is intended for use with GIFTI files, to generate names
conforming to Section 9.0 of the `GIFTI Standard`_.
#import pdb
#import cip_convention_manager as cm
# example http://nipy.sourceforge.net/nipype/devel/python_interface_devel.html
class parenchyma_phenotypesInputSpec(BaseInterfaceInputSpec):
in_ct = File(exists=True, desc='Input CT file', mandatory=True)
in_lm = File(exists=True, desc='Input label map containing structures of interest', mandatory=True)
out_csv = File( desc='Output csv file in which to store the computed \
dataframe', mandatory=True)
cid = traits.Str(desc='The database case ID',
mandatory=True)
chest_regions = traits.Str(desc='Chest regions',
mandatory=False)
chest_types = traits.Str(desc='Chest types',
mandatory=False)
pairs = traits.Str(desc='Chest region/type pairs',
mandatory=False)
pheno_names = traits.Str(desc='Phenotype names',
mandatory=False)
out_csv = File( desc='Output csv file in which to store the computed \
dataframe', mandatory=False)
class parenchyma_phenotypesOutputSpec(TraitedSpec):
out_csv = File( desc='Output csv file in which to store the computed \
dataframe', mandatory=False)
class parenchyma_phenotypes(BaseInterface):
input_spec = parenchyma_phenotypesInputSpec
output_spec = parenchyma_phenotypesOutputSpec
def _run_interface(self, runtime):
def _parse_inputs(self, skip=None):
if skip is None:
skip = []
if not isdefined(self.inputs.out_file):
self.inputs.out_file = self._gen_output(self.inputs.in_file)
return super(img2dftCommand, self)._parse_inputs(skip=skip)
class tacunitOutput(TraitedSpec):
in_file = File(argstr=" %s", position=-1, desc="dft image.")
class tacunitInput(BaseInterfaceInputSpec):
in_file= File(exists=True, argstr="%s", position=-1, desc="dft file")
xconv = traits.Str(argstr="-xconv=%s", position=-2, desc="Convert x (time) axis")
yconv = traits.Str(argstr="-yconv=%s", position=-3, desc="Convert y (radioactivity) axis")
x = traits.Bool(argstr="-x", position=-4,desc="Print x (time) axis")
y = traits.Bool(argstr="-y", position=-5,desc="Print y (radioactivity) axis")
class tacunitCommand(CommandLine):
input_spec = tacunitInput
output_spec = tacunitOutput
_cmd = "tacunit"
_file_type = ".dft"
def _list_outputs(self):
outputs = self.output_spec().get()
outputs["in_file"] = self.inputs.in_file
return outputs
def _parse_inputs(self, skip=None):
if skip is None:
skip = []
if not isdefined(self.inputs.out_file):
self.inputs.out_file = self._gen_output(self.inputs.in_file)
return super(img2dftCommand, self)._parse_inputs(skip=skip)
class tacunitOutput(TraitedSpec):
in_file = File(argstr=" %s", position=-1, desc="dft image.")
class tacunitInput(BaseInterfaceInputSpec):
in_file= File(exists=True, argstr="%s", position=-1, desc="dft file")
xconv = traits.Str(argstr="-xconv=%s", position=-2, desc="Convert x (time) axis")
yconv = traits.Str(argstr="-yconv=%s", position=-3, desc="Convert y (radioactivity) axis")
x = traits.Bool(argstr="-x", position=-4,desc="Print x (time) axis")
y = traits.Bool(argstr="-y", position=-5,desc="Print y (radioactivity) axis")
class tacunitCommand(CommandLine):
input_spec = tacunitInput
output_spec = tacunitOutput
_cmd = "tacunit"
_file_type = ".dft"
def _list_outputs(self):
outputs = self.output_spec().get()
outputs["in_file"] = self.inputs.in_file
return outputs
def _parse_inputs(self, skip=None):
)
subjects_dir = Directory(
exists=True,
argstr="-sd %s",
hash_files=False,
desc="path to subjects directory",
genfile=True,
)
flags = InputMultiPath(traits.Str, argstr="%s", desc="additional parameters")
# Expert options
talairach = traits.Str(desc="Flags to pass to talairach commands", xor=["expert"])
mri_normalize = traits.Str(
desc="Flags to pass to mri_normalize commands", xor=["expert"]
)
mri_watershed = traits.Str(
desc="Flags to pass to mri_watershed commands", xor=["expert"]
)
mri_em_register = traits.Str(
desc="Flags to pass to mri_em_register commands", xor=["expert"]
)
mri_ca_normalize = traits.Str(
desc="Flags to pass to mri_ca_normalize commands", xor=["expert"]
)
mri_ca_register = traits.Str(
desc="Flags to pass to mri_ca_register commands", xor=["expert"]
)
mri_remove_neck = traits.Str(
desc="Flags to pass to mri_remove_neck commands", xor=["expert"]
)
mri_ca_label = traits.Str(
desc="Flags to pass to mri_ca_label commands", xor=["expert"]