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)[source]#

Create a scaled copy of an MRI subject.


Name of the subject providing the MRI.


New subject name for which to save the scaled MRI.

scalefloat | array_like, shape = (3,)

The scaling factor (one or 3 parameters).


If an MRI already exists for subject_to, overwrite it.

subjects_dirNone | str

Override the SUBJECTS_DIR environment variable.


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


Also scale all labels (default True).


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


Add a scaled BEM to a scaled MRI.


Add labels to a scaled MRI.


Add a source space to a scaled MRI.


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

Using an automated approach to coregistration