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)[source]#

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 | path-like: Override the SUBJECTS_DIR environment variable.
skip_fiducialsbool: Do not scale the MRI fiducials. If False (default), an OSError 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 v1.0.
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.

Examples using `mne.scale_mri`#

Using an automated approach to coregistration

mne.scale_mri#

Examples using mne.scale_mri#

Examples using `mne.scale_mri`#