mne.scale_mri

mne.scale_mri(subject_from, subject_to, scale, overwrite=False, subjects_dir=None, skip_fiducials=False, labels=True, annot=False, *, on_defects='raise', verbose=None)

Create a scaled copy of an MRI subject.

Parameters:

subject_fromstr

Name of the subject providing the MRI.

subject_tostr

New subject name for which to save the scaled MRI.

scalefloat | array_like, shape = (3,)

The scaling factor (one or 3 parameters).

overwritebool

If an MRI already exists for subject_to, overwrite it.

subjects_dirNone | str

Override the SUBJECTS_DIR environment variable.

skip_fiducialsbool

Do not scale the MRI fiducials. If False (default), an IOError will be raised if no fiducials file can be found.

labelsbool

Also scale all labels (default True).

annotbool

Copy *.annot files to the new location (default False).

on_defects‘raise’ | ‘warn’ | ‘ignore’

What to do if the surface is found to have topological defects. Can be 'raise' (default) to raise an error, 'warn' to emit a warning, or 'ignore' to ignore when one or more defects are found. Note that a lot of computations in MNE-Python assume the surfaces to be topologically correct, topological defects may still make other computations (e.g., mne.make_bem_model and mne.make_bem_solution) fail irrespective of this parameter.

New in version 1.0.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

See also

scale_bem

Add a scaled BEM to a scaled MRI.

scale_labels

Add labels to a scaled MRI.

scale_source_space

Add a source space to a scaled MRI.

Notes

This function will automatically call scale_bem(), scale_labels(), and scale_source_space() based on expected filename patterns in the subject directory.

Examples using mne.scale_mri

Using an automated approach to coregistration

Using an automated approach to coregistration