Consider renaming ``StructuralReference`` interface

[oesteban]

Coming out from #610 (comment)

Although, I'm still not sure StructuralReference is a bad name. We are indeed calculating "structural" images (as in, with the maximal structural contrast as possible).

If that argument is convincing, we could close this.

[effigies]

I think we should upstream the fix directly into RobustTemplate, though maybe triggered by a short_circuit boolean input, in case anybody depends on the failure condition. Note that this interface exists because mri_robust_template errors if only passed one image, not that it successfully does something we don't like or wastes time.

[oesteban]

Sounds good, a just one comment:

• Add the short_circuit boolean to the outputs too, for the case someone wants to implement different behaviors depending on whether there were one or more images at the input (asking for a friend).

BTW, we should be careful that these tweaks work well with MapNodes. I just discovered that this tweak -

niworkflows/niworkflows/interfaces/fixes.py

Lines 87 to 133 in e2a86a4

	class _FixN4BiasFieldCorrectionOutputSpec(VanillaN4OutputSpec):
	negative_values = traits.Bool(
	False,
	usedefault=True,
	desc="Indicates whether the input was corrected for "
	"nonpositive values by adding a constant offset.",
	)
	class FixN4BiasFieldCorrection(VanillaN4):
	"""Checks and fixes for nonpositive values in the input to ``N4BiasFieldCorrection``."""
	output_spec = _FixN4BiasFieldCorrectionOutputSpec
	def __init__(self, *args, **kwargs):
	"""Add a private property to keep the path to the right input."""
	self._input_image = None
	self._negative_values = False
	super(FixN4BiasFieldCorrection, self).__init__(*args, **kwargs)
	def _format_arg(self, name, trait_spec, value):
	if name == "input_image":
	return trait_spec.argstr % self._input_image
	return super(FixN4BiasFieldCorrection, self)._format_arg(
	name, trait_spec, value
	)
	def _parse_inputs(self, skip=None):
	self._input_image = self.inputs.input_image
	# Check intensities
	input_nii = nb.load(self.inputs.input_image)
	datamin = input_nii.get_fdata().min()
	if datamin < 0:
	self._input_image = fname_presuffix(
	self.inputs.input_image, suffix="_scaled", newpath=os.getcwd()
	)
	data = input_nii.get_fdata() - datamin
	newnii = input_nii.__class__(data, input_nii.affine, input_nii.header)
	newnii.to_filename(self._input_image)
	self._negative_values = True
	return super(FixN4BiasFieldCorrection, self)._parse_inputs(skip=skip)
	def _list_outputs(self):
	outputs = super(FixN4BiasFieldCorrection, self)._list_outputs()
	outputs["negative_values"] = self._negative_values
	return outputs

doesn't work.

[effigies]

• Add the short_circuit boolean to the outputs too, for the case someone wants to implement different behaviors depending on whether there were one or more images at the input (asking for a friend).

So short_circuit on input is whether to enable short_circuiting and on output indicates whether the short circuit was used? e.g.,

outputs["short_circuit"] = self.inputs.short_circuit and len(self.inputs.in_files) == 1

I feel like I would rather not use the same name for two subtly different concepts, but as long as I understand what you want, we can quibble about naming when we go to implement.