run black on the preprocess.py file to improve formatting

Author: Fabio Bernardoni

nipype/interfaces/spm/preprocess.py

@@ -46,14 +46,14 @@ class FieldMapInputSpec(SPMCommandInputSpec):
  desc="one of: calculatevdm, applyvdm",
  )
  phase_file = File(
- #mandatory=True,
+ # mandatory=True,
  exists=True,
  copyfile=False,
  field="subj.data.presubphasemag.phase",
  desc="presubstracted phase file",
  )
  magnitude_file = File(
- #mandatory=True,
+ # mandatory=True,
  exists=True,
  copyfile=False,
  field="subj.data.presubphasemag.magnitude",
@@ -62,7 +62,7 @@ class FieldMapInputSpec(SPMCommandInputSpec):
  echo_times = traits.Tuple(
  traits.Float,
  traits.Float,
- #mandatory=True,
+ # mandatory=True,
  field="subj.defaults.defaultsval.et",
  desc="short and long echo times",
  )
@@ -169,7 +169,7 @@ class FieldMapInputSpec(SPMCommandInputSpec):
  epi_file = File(
  copyfile=False,
  exists=True,
- #mandatory=True,
+ # mandatory=True,
  field="subj.session.epi",
  desc="EPI to unwarp",
  )
@@ -196,59 +196,78 @@ class FieldMapInputSpec(SPMCommandInputSpec):
  )
 
  in_files = InputMultiObject(
- traits.Either(ImageFileSPM(exists=True),
- traits.List(ImageFileSPM(exists=True))),
- field='data.scans',mandatory=True,
+ traits.Either(
+ ImageFileSPM(exists=True), traits.List(ImageFileSPM(exists=True))
+ ),
+ field="data.scans",
+ mandatory=True,
  copyfile=True,
- desc='list of filenames to apply the vdm to')
+ desc="list of filenames to apply the vdm to",
+ )
  vdmfile = File(
- field='data.vdmfile',
- desc='Voxel displacement map to use',mandatory=True,
- copyfile=True)
+ field="data.vdmfile",
+ desc="Voxel displacement map to use",
+ mandatory=True,
+ copyfile=True,
+ )
  distortion_direction = traits.Int(
- 2, field='roptions.pedir', desc='phase encode direction input data have been acquired with',
- usedefault=True)
+ 2,
+ field="roptions.pedir",
+ desc="phase encode direction input data have been acquired with",
+ usedefault=True,
+ )
  write_which = traits.ListInt(
  [2, 1],
- field='roptions.which',
+ field="roptions.which",
  minlen=2,
  maxlen=2,
  usedefault=True,
- desc='determines which images to apply vdm to')
+ desc="determines which images to apply vdm to",
+ )
  interpolation = traits.Int(
- 4, field='roptions.rinterp', desc='phase encode direction input data have been acquired with',
- usedefault=True)
+ 4,
+ field="roptions.rinterp",
+ desc="phase encode direction input data have been acquired with",
+ usedefault=True,
+ )
  reslice_interp = traits.Range(
  low=0,
  high=7,
- field='roptions.rinterp',
- desc='degree of b-spline used for interpolation')
+ field="roptions.rinterp",
+ desc="degree of b-spline used for interpolation",
+ )
  write_wrap = traits.List(
  traits.Int(),
  minlen=3,
  maxlen=3,
- field='roptions.wrap',
- desc=('Check if interpolation should wrap in [x,y,z]'))
+ field="roptions.wrap",
+ desc=("Check if interpolation should wrap in [x,y,z]"),
+ )
  write_mask = traits.Bool(
- field='roptions.mask', desc='True/False mask time series images')
+ field="roptions.mask", desc="True/False mask time series images"
+ )
  out_prefix = traits.String(
- 'u',
- field='roptions.prefix',
+ "u",
+ field="roptions.prefix",
  usedefault=True,
- desc='fieldmap corrected output prefix')
+ desc="fieldmap corrected output prefix",
+ )
 
 
 class FieldMapOutputSpec(TraitedSpec):
  vdm = File(exists=True, desc="voxel difference map")
 
  out_files = OutputMultiPath(
  traits.Either(traits.List(File(exists=True)), File(exists=True)),
- desc=('If jobtype is applyvdm, '
- 'these will be the fieldmap corrected files.'
- ' Otherwise, they will be copies '
- 'of in_files that have had their '
- 'headers rewritten.'))
- mean_image = File(exists=True, desc='Mean image')
+ desc=(
+ "If jobtype is applyvdm, "
+ "these will be the fieldmap corrected files."
+ " Otherwise, they will be copies "
+ "of in_files that have had their "
+ "headers rewritten."
+ ),
+ )
+ mean_image = File(exists=True, desc="Mean image")
 
 
 class FieldMap(SPMCommand):
@@ -283,75 +302,94 @@ class FieldMap(SPMCommand):
  def _format_arg(self, opt, spec, val):
  """Convert input to appropriate format for spm"""
 
- if ((self.inputs.jobtype == "calculatevdm") and (opt in ['phase_file', 'magnitude_file', 'anat_file', 'epi_file'])):
+ if (self.inputs.jobtype == "calculatevdm") and (
+ opt in ["phase_file", "magnitude_file", "anat_file", "epi_file"]
+ ):
  return scans_for_fname(ensure_list(val))
 
- if ((self.inputs.jobtype == "applyvdm") and (opt =='in_files')):
+ if (self.inputs.jobtype == "applyvdm") and (opt == "in_files"):
  return scans_for_fnames(ensure_list(val))
- if ((self.inputs.jobtype == "applyvdm") and (opt =='vdmfile')):
+ if (self.inputs.jobtype == "applyvdm") and (opt == "vdmfile"):
  return scans_for_fname(ensure_list(val))
  return super(FieldMap, self)._format_arg(opt, spec, val)
 
-
  def _parse_inputs(self):
  """validate spm fieldmap options if set to None ignore"""
 
  if self.inputs.jobtype == "applyvdm":
- einputs = (super(FieldMap, self)
- ._parse_inputs(skip=('jobtype','phase_file', 'magnitude_file',
- 'echo_times', 'blip_direction',
- 'total_readout_time','maskbrain',
- 'epifm','jacobian_modulation',
- 'method','unwarp_fwhm','pad','ws',
- 'template','mask_fwhm','nerode','ndilate',
- 'thresh','reg','epi_file','matchvdm',
- 'sessname','writeunwarped',
- 'anat_file','matchanat')))
+ einputs = super(FieldMap, self)._parse_inputs(
+ skip=(
+ "jobtype",
+ "phase_file",
+ "magnitude_file",
+ "echo_times",
+ "blip_direction",
+ "total_readout_time",
+ "maskbrain",
+ "epifm",
+ "jacobian_modulation",
+ "method",
+ "unwarp_fwhm",
+ "pad",
+ "ws",
+ "template",
+ "mask_fwhm",
+ "nerode",
+ "ndilate",
+ "thresh",
+ "reg",
+ "epi_file",
+ "matchvdm",
+ "sessname",
+ "writeunwarped",
+ "anat_file",
+ "matchanat",
+ )
+ )
 
  else:
- einputs = (super(FieldMap, self)
- ._parse_inputs(skip=('jobtype','in_files', 'vdmfile')))
+ einputs = super(FieldMap, self)._parse_inputs(
+ skip=("jobtype", "in_files", "vdmfile")
+ )
  jobtype = self.inputs.jobtype
 
- return [{'%s' % (jobtype): einputs[0]}]
-
+ return [{"%s" % (jobtype): einputs[0]}]
 
  def _list_outputs(self):
  outputs = self._outputs().get()
  jobtype = self.inputs.jobtype
- resliced_all  = self.inputs.write_which[0] > 0
+ resliced_all = self.inputs.write_which[0] > 0
  resliced_mean = self.inputs.write_which[1] > 0
  if jobtype == "calculatevdm":
- outputs['vdm'] = fname_presuffix(self.inputs.phase_file, prefix='vdm5_sc')
+ outputs["vdm"] = fname_presuffix(self.inputs.phase_file, prefix="vdm5_sc")
  elif jobtype == "applyvdm":
  if resliced_mean:
  if isinstance(self.inputs.in_files[0], list):
  first_image = self.inputs.in_files[0][0]
  else:
  first_image = self.inputs.in_files[0]
- outputs['mean_image'] = fname_presuffix(
- first_image, prefix='meanu')
+ outputs["mean_image"] = fname_presuffix(first_image, prefix="meanu")
 
  if resliced_all:
- outputs['out_files'] = []
+ outputs["out_files"] = []
  for idx, imgf in enumerate(ensure_list(self.inputs.in_files)):
  appliedvdm_run = []
  if isinstance(imgf, list):
  for i, inner_imgf in enumerate(ensure_list(imgf)):
- newfile = fname_presuffix(inner_imgf,
- prefix=self.inputs.out_prefix)
+ newfile = fname_presuffix(
+ inner_imgf, prefix=self.inputs.out_prefix
+ )
  appliedvdm_run.append(newfile)
  else:
- appliedvdm_run = fname_presuffix(imgf,
- prefix=self.inputs.out_prefix)
- outputs['out_files'].append(appliedvdm_run)
+ appliedvdm_run = fname_presuffix(
+ imgf, prefix=self.inputs.out_prefix
+ )
+ outputs["out_files"].append(appliedvdm_run)
  return outputs
 
-
  return outputs
 
 
-
 class SliceTimingInputSpec(SPMCommandInputSpec):
  in_files = InputMultiPath(
  traits.Either(