# -*- coding: utf-8 -*- """Coregistration between different coordinate frames.""" # Authors: Christian Brodbeck # # License: BSD-3-Clause import configparser import fnmatch from glob import glob, iglob import os import os.path as op import stat import sys import re import shutil from functools import reduce import numpy as np from .io import read_fiducials, write_fiducials, read_info from .io.constants import FIFF from .io.meas_info import Info from .io._digitization import _get_data_as_dict_from_dig # keep get_mni_fiducials for backward compat (no burden to keep in this # namespace, too) from ._freesurfer import (_read_mri_info, get_mni_fiducials, # noqa: F401 estimate_head_mri_t) # noqa: F401 from ._freesurfer import _import_nibabel from .label import read_label, Label from .source_space import (add_source_space_distances, read_source_spaces, # noqa: E501,F401 write_source_spaces) from .surface import (read_surface, write_surface, _normalize_vectors, complete_surface_info, decimate_surface, _DistanceQuery) from .bem import read_bem_surfaces, write_bem_surfaces from .transforms import (rotation, rotation3d, scaling, translation, Transform, _read_fs_xfm, _write_fs_xfm, invert_transform, combine_transforms, _quat_to_euler, _fit_matched_points, apply_trans, rot_to_quat, _angle_between_quats) from .channels import make_dig_montage from .utils import (get_config, get_subjects_dir, logger, pformat, verbose, warn, has_nibabel, fill_doc, _validate_type, _check_subject, _check_option) from .viz._3d import _fiducial_coords # some path templates trans_fname = os.path.join('{raw_dir}', '{subject}-trans.fif') subject_dirname = os.path.join('{subjects_dir}', '{subject}') bem_dirname = os.path.join(subject_dirname, 'bem') mri_dirname = os.path.join(subject_dirname, 'mri') mri_transforms_dirname = os.path.join(subject_dirname, 'mri', 'transforms') surf_dirname = os.path.join(subject_dirname, 'surf') bem_fname = os.path.join(bem_dirname, "{subject}-{name}.fif") head_bem_fname = pformat(bem_fname, name='head') head_sparse_fname = pformat(bem_fname, name='head-sparse') fid_fname = pformat(bem_fname, name='fiducials') fid_fname_general = os.path.join(bem_dirname, "{head}-fiducials.fif") src_fname = os.path.join(bem_dirname, '{subject}-{spacing}-src.fif') _head_fnames = (os.path.join(bem_dirname, 'outer_skin.surf'), head_sparse_fname, head_bem_fname) _high_res_head_fnames = (os.path.join(bem_dirname, '{subject}-head-dense.fif'), os.path.join(surf_dirname, 'lh.seghead'), os.path.join(surf_dirname, 'lh.smseghead')) def _map_fid_name_to_idx(name: str) -> int: """Map a fiducial name to its index in the DigMontage.""" name = name.lower() if name == 'lpa': return 0 elif name == 'nasion': return 1 else: assert name == 'rpa' return 2 def _make_writable(fname): """Make a file writable.""" os.chmod(fname, stat.S_IMODE(os.lstat(fname)[stat.ST_MODE]) | 128) # write def _make_writable_recursive(path): """Recursively set writable.""" if sys.platform.startswith('win'): return # can't safely set perms for root, dirs, files in os.walk(path, topdown=False): for f in dirs + files: _make_writable(os.path.join(root, f)) def _find_head_bem(subject, subjects_dir, high_res=False): """Find a high resolution head.""" # XXX this should be refactored with mne.surface.get_head_surf ... fnames = _high_res_head_fnames if high_res else _head_fnames for fname in fnames: path = fname.format(subjects_dir=subjects_dir, subject=subject) if os.path.exists(path): return path @fill_doc def coregister_fiducials(info, fiducials, tol=0.01): """Create a head-MRI transform by aligning 3 fiducial points. Parameters ---------- %(info_not_none)s fiducials : str | list of dict Fiducials in MRI coordinate space (either path to a ``*-fiducials.fif`` file or list of fiducials as returned by :func:`read_fiducials`. Returns ------- trans : Transform The device-MRI transform. .. note:: The :class:`mne.Info` object fiducials must be in the head coordinate space. """ if isinstance(info, str): info = read_info(info) if isinstance(fiducials, str): fiducials, coord_frame_to = read_fiducials(fiducials) else: coord_frame_to = FIFF.FIFFV_COORD_MRI frames_from = {d['coord_frame'] for d in info['dig']} if len(frames_from) > 1: raise ValueError("info contains fiducials from different coordinate " "frames") else: coord_frame_from = frames_from.pop() coords_from = _fiducial_coords(info['dig']) coords_to = _fiducial_coords(fiducials, coord_frame_to) trans = fit_matched_points(coords_from, coords_to, tol=tol) return Transform(coord_frame_from, coord_frame_to, trans) @verbose def create_default_subject(fs_home=None, update=False, subjects_dir=None, verbose=None): """Create an average brain subject for subjects without structural MRI. Create a copy of fsaverage from the Freesurfer directory in subjects_dir and add auxiliary files from the mne package. Parameters ---------- fs_home : None | str The freesurfer home directory (only needed if FREESURFER_HOME is not specified as environment variable). update : bool In cases where a copy of the fsaverage brain already exists in the subjects_dir, this option allows to only copy files that don't already exist in the fsaverage directory. subjects_dir : None | str Override the SUBJECTS_DIR environment variable (os.environ['SUBJECTS_DIR']) as destination for the new subject. %(verbose)s Notes ----- When no structural MRI is available for a subject, an average brain can be substituted. Freesurfer comes with such an average brain model, and MNE comes with some auxiliary files which make coregistration easier. :py:func:`create_default_subject` copies the relevant files from Freesurfer into the current subjects_dir, and also adds the auxiliary files provided by MNE. """ subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) if fs_home is None: fs_home = get_config('FREESURFER_HOME', fs_home) if fs_home is None: raise ValueError( "FREESURFER_HOME environment variable not found. Please " "specify the fs_home parameter in your call to " "create_default_subject().") # make sure freesurfer files exist fs_src = os.path.join(fs_home, 'subjects', 'fsaverage') if not os.path.exists(fs_src): raise IOError('fsaverage not found at %r. Is fs_home specified ' 'correctly?' % fs_src) for name in ('label', 'mri', 'surf'): dirname = os.path.join(fs_src, name) if not os.path.isdir(dirname): raise IOError("Freesurfer fsaverage seems to be incomplete: No " "directory named %s found in %s" % (name, fs_src)) # make sure destination does not already exist dest = os.path.join(subjects_dir, 'fsaverage') if dest == fs_src: raise IOError( "Your subjects_dir points to the freesurfer subjects_dir (%r). " "The default subject can not be created in the freesurfer " "installation directory; please specify a different " "subjects_dir." % subjects_dir) elif (not update) and os.path.exists(dest): raise IOError( "Can not create fsaverage because %r already exists in " "subjects_dir %r. Delete or rename the existing fsaverage " "subject folder." % ('fsaverage', subjects_dir)) # copy fsaverage from freesurfer logger.info("Copying fsaverage subject from freesurfer directory...") if (not update) or not os.path.exists(dest): shutil.copytree(fs_src, dest) _make_writable_recursive(dest) # copy files from mne source_fname = os.path.join(os.path.dirname(__file__), 'data', 'fsaverage', 'fsaverage-%s.fif') dest_bem = os.path.join(dest, 'bem') if not os.path.exists(dest_bem): os.mkdir(dest_bem) logger.info("Copying auxiliary fsaverage files from mne...") dest_fname = os.path.join(dest_bem, 'fsaverage-%s.fif') _make_writable_recursive(dest_bem) for name in ('fiducials', 'head', 'inner_skull-bem', 'trans'): if not os.path.exists(dest_fname % name): shutil.copy(source_fname % name, dest_bem) def _decimate_points(pts, res=10): """Decimate the number of points using a voxel grid. Create a voxel grid with a specified resolution and retain at most one point per voxel. For each voxel, the point closest to its center is retained. Parameters ---------- pts : array, shape (n_points, 3) The points making up the head shape. res : scalar The resolution of the voxel space (side length of each voxel). Returns ------- pts : array, shape = (n_points, 3) The decimated points. """ from scipy.spatial.distance import cdist pts = np.asarray(pts) # find the bin edges for the voxel space xmin, ymin, zmin = pts.min(0) - res / 2. xmax, ymax, zmax = pts.max(0) + res xax = np.arange(xmin, xmax, res) yax = np.arange(ymin, ymax, res) zax = np.arange(zmin, zmax, res) # find voxels containing one or more point H, _ = np.histogramdd(pts, bins=(xax, yax, zax), density=False) xbins, ybins, zbins = np.nonzero(H) x = xax[xbins] y = yax[ybins] z = zax[zbins] mids = np.c_[x, y, z] + res / 2. # each point belongs to at most one voxel center, so figure those out # (cKDTree faster than BallTree for these small problems) tree = _DistanceQuery(mids, method='cKDTree') _, mid_idx = tree.query(pts) # then figure out which to actually use based on proximity # (take advantage of sorting the mid_idx to get our mapping of # pts to nearest voxel midpoint) sort_idx = np.argsort(mid_idx) bounds = np.cumsum( np.concatenate([[0], np.bincount(mid_idx, minlength=len(mids))])) assert len(bounds) == len(mids) + 1 out = list() for mi, mid in enumerate(mids): # Now we do this: # # use_pts = pts[mid_idx == mi] # # But it's faster for many points than making a big boolean indexer # over and over (esp. since each point can only belong to a single # voxel). use_pts = pts[sort_idx[bounds[mi]:bounds[mi + 1]]] if not len(use_pts): out.append([np.inf] * 3) else: out.append( use_pts[np.argmin(cdist(use_pts, mid[np.newaxis])[:, 0])]) out = np.array(out, float).reshape(-1, 3) out = out[np.abs(out - mids).max(axis=1) < res / 2.] # """ return out def _trans_from_params(param_info, params): """Convert transformation parameters into a transformation matrix.""" do_rotate, do_translate, do_scale = param_info i = 0 trans = [] if do_rotate: x, y, z = params[:3] trans.append(rotation(x, y, z)) i += 3 if do_translate: x, y, z = params[i:i + 3] trans.insert(0, translation(x, y, z)) i += 3 if do_scale == 1: s = params[i] trans.append(scaling(s, s, s)) elif do_scale == 3: x, y, z = params[i:i + 3] trans.append(scaling(x, y, z)) trans = reduce(np.dot, trans) return trans _ALLOW_ANALITICAL = True # XXX this function should be moved out of coreg as used elsewhere def fit_matched_points(src_pts, tgt_pts, rotate=True, translate=True, scale=False, tol=None, x0=None, out='trans', weights=None): """Find a transform between matched sets of points. This minimizes the squared distance between two matching sets of points. Uses :func:`scipy.optimize.leastsq` to find a transformation involving a combination of rotation, translation, and scaling (in that order). Parameters ---------- src_pts : array, shape = (n, 3) Points to which the transform should be applied. tgt_pts : array, shape = (n, 3) Points to which src_pts should be fitted. Each point in tgt_pts should correspond to the point in src_pts with the same index. rotate : bool Allow rotation of the ``src_pts``. translate : bool Allow translation of the ``src_pts``. scale : bool Number of scaling parameters. With False, points are not scaled. With True, points are scaled by the same factor along all axes. tol : scalar | None The error tolerance. If the distance between any of the matched points exceeds this value in the solution, a RuntimeError is raised. With None, no error check is performed. x0 : None | tuple Initial values for the fit parameters. out : 'params' | 'trans' In what format to return the estimate: 'params' returns a tuple with the fit parameters; 'trans' returns a transformation matrix of shape (4, 4). Returns ------- trans : array, shape (4, 4) Transformation that, if applied to src_pts, minimizes the squared distance to tgt_pts. Only returned if out=='trans'. params : array, shape (n_params, ) A single tuple containing the rotation, translation, and scaling parameters in that order (as applicable). """ src_pts = np.atleast_2d(src_pts) tgt_pts = np.atleast_2d(tgt_pts) if src_pts.shape != tgt_pts.shape: raise ValueError("src_pts and tgt_pts must have same shape (got " "{}, {})".format(src_pts.shape, tgt_pts.shape)) if weights is not None: weights = np.asarray(weights, src_pts.dtype) if weights.ndim != 1 or weights.size not in (src_pts.shape[0], 1): raise ValueError("weights (shape=%s) must be None or have shape " "(%s,)" % (weights.shape, src_pts.shape[0],)) weights = weights[:, np.newaxis] param_info = (bool(rotate), bool(translate), int(scale)) del rotate, translate, scale # very common use case, rigid transformation (maybe with one scale factor, # with or without weighted errors) if param_info in ((True, True, 0), (True, True, 1)) and _ALLOW_ANALITICAL: src_pts = np.asarray(src_pts, float) tgt_pts = np.asarray(tgt_pts, float) if weights is not None: weights = np.asarray(weights, float) x, s = _fit_matched_points( src_pts, tgt_pts, weights, bool(param_info[2])) x[:3] = _quat_to_euler(x[:3]) x = np.concatenate((x, [s])) if param_info[2] else x else: x = _generic_fit(src_pts, tgt_pts, param_info, weights, x0) # re-create the final transformation matrix if (tol is not None) or (out == 'trans'): trans = _trans_from_params(param_info, x) # assess the error of the solution if tol is not None: src_pts = np.hstack((src_pts, np.ones((len(src_pts), 1)))) est_pts = np.dot(src_pts, trans.T)[:, :3] err = np.sqrt(np.sum((est_pts - tgt_pts) ** 2, axis=1)) if np.any(err > tol): raise RuntimeError("Error exceeds tolerance. Error = %r" % err) if out == 'params': return x elif out == 'trans': return trans else: raise ValueError("Invalid out parameter: %r. Needs to be 'params' or " "'trans'." % out) def _generic_fit(src_pts, tgt_pts, param_info, weights, x0): from scipy.optimize import leastsq if param_info[1]: # translate src_pts = np.hstack((src_pts, np.ones((len(src_pts), 1)))) if param_info == (True, False, 0): def error(x): rx, ry, rz = x trans = rotation3d(rx, ry, rz) est = np.dot(src_pts, trans.T) d = tgt_pts - est if weights is not None: d *= weights return d.ravel() if x0 is None: x0 = (0, 0, 0) elif param_info == (True, True, 0): def error(x): rx, ry, rz, tx, ty, tz = x trans = np.dot(translation(tx, ty, tz), rotation(rx, ry, rz)) est = np.dot(src_pts, trans.T)[:, :3] d = tgt_pts - est if weights is not None: d *= weights return d.ravel() if x0 is None: x0 = (0, 0, 0, 0, 0, 0) elif param_info == (True, True, 1): def error(x): rx, ry, rz, tx, ty, tz, s = x trans = reduce(np.dot, (translation(tx, ty, tz), rotation(rx, ry, rz), scaling(s, s, s))) est = np.dot(src_pts, trans.T)[:, :3] d = tgt_pts - est if weights is not None: d *= weights return d.ravel() if x0 is None: x0 = (0, 0, 0, 0, 0, 0, 1) elif param_info == (True, True, 3): def error(x): rx, ry, rz, tx, ty, tz, sx, sy, sz = x trans = reduce(np.dot, (translation(tx, ty, tz), rotation(rx, ry, rz), scaling(sx, sy, sz))) est = np.dot(src_pts, trans.T)[:, :3] d = tgt_pts - est if weights is not None: d *= weights return d.ravel() if x0 is None: x0 = (0, 0, 0, 0, 0, 0, 1, 1, 1) else: raise NotImplementedError( "The specified parameter combination is not implemented: " "rotate=%r, translate=%r, scale=%r" % param_info) x, _, _, _, _ = leastsq(error, x0, full_output=True) return x def _find_label_paths(subject='fsaverage', pattern=None, subjects_dir=None): """Find paths to label files in a subject's label directory. Parameters ---------- subject : str Name of the mri subject. pattern : str | None Pattern for finding the labels relative to the label directory in the MRI subject directory (e.g., "aparc/*.label" will find all labels in the "subject/label/aparc" directory). With None, find all labels. subjects_dir : None | str Override the SUBJECTS_DIR environment variable (sys.environ['SUBJECTS_DIR']) Returns ------- paths : list List of paths relative to the subject's label directory """ subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) subject_dir = os.path.join(subjects_dir, subject) lbl_dir = os.path.join(subject_dir, 'label') if pattern is None: paths = [] for dirpath, _, filenames in os.walk(lbl_dir): rel_dir = os.path.relpath(dirpath, lbl_dir) for filename in fnmatch.filter(filenames, '*.label'): path = os.path.join(rel_dir, filename) paths.append(path) else: paths = [os.path.relpath(path, lbl_dir) for path in iglob(pattern)] return paths def _find_mri_paths(subject, skip_fiducials, subjects_dir): """Find all files of an mri relevant for source transformation. Parameters ---------- subject : str Name of the mri subject. skip_fiducials : bool Do not scale the MRI fiducials. If False, an IOError will be raised if no fiducials file can be found. subjects_dir : None | str Override the SUBJECTS_DIR environment variable (sys.environ['SUBJECTS_DIR']) Returns ------- paths : dict Dictionary whose keys are relevant file type names (str), and whose values are lists of paths. """ subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) paths = {} # directories to create paths['dirs'] = [bem_dirname, surf_dirname] # surf/ files paths['surf'] = [] surf_fname = os.path.join(surf_dirname, '{name}') surf_names = ('inflated', 'white', 'orig', 'orig_avg', 'inflated_avg', 'inflated_pre', 'pial', 'pial_avg', 'smoothwm', 'white_avg', 'seghead', 'smseghead') if os.getenv('_MNE_FEW_SURFACES', '') == 'true': # for testing surf_names = surf_names[:4] for surf_name in surf_names: for hemi in ('lh.', 'rh.'): name = hemi + surf_name path = surf_fname.format(subjects_dir=subjects_dir, subject=subject, name=name) if os.path.exists(path): paths['surf'].append(pformat(surf_fname, name=name)) surf_fname = os.path.join(bem_dirname, '{name}') surf_names = ('inner_skull.surf', 'outer_skull.surf', 'outer_skin.surf') for surf_name in surf_names: path = surf_fname.format(subjects_dir=subjects_dir, subject=subject, name=surf_name) if os.path.exists(path): paths['surf'].append(pformat(surf_fname, name=surf_name)) del surf_names, surf_name, path, hemi # BEM files paths['bem'] = bem = [] path = head_bem_fname.format(subjects_dir=subjects_dir, subject=subject) if os.path.exists(path): bem.append('head') bem_pattern = pformat(bem_fname, subjects_dir=subjects_dir, subject=subject, name='*-bem') re_pattern = pformat(bem_fname, subjects_dir=subjects_dir, subject=subject, name='(.+)').replace('\\', '\\\\') for path in iglob(bem_pattern): match = re.match(re_pattern, path) name = match.group(1) bem.append(name) del bem, path, bem_pattern, re_pattern # fiducials if skip_fiducials: paths['fid'] = [] else: paths['fid'] = _find_fiducials_files(subject, subjects_dir) # check that we found at least one if len(paths['fid']) == 0: raise IOError("No fiducials file found for %s. The fiducials " "file should be named " "{subject}/bem/{subject}-fiducials.fif. In " "order to scale an MRI without fiducials set " "skip_fiducials=True." % subject) # duplicate files (curvature and some surfaces) paths['duplicate'] = [] path = os.path.join(surf_dirname, '{name}') surf_fname = os.path.join(surf_dirname, '{name}') surf_dup_names = ('curv', 'sphere', 'sphere.reg', 'sphere.reg.avg') for surf_dup_name in surf_dup_names: for hemi in ('lh.', 'rh.'): name = hemi + surf_dup_name path = surf_fname.format(subjects_dir=subjects_dir, subject=subject, name=name) if os.path.exists(path): paths['duplicate'].append(pformat(surf_fname, name=name)) del surf_dup_name, name, path, hemi # transform files (talairach) paths['transforms'] = [] transform_fname = os.path.join(mri_transforms_dirname, 'talairach.xfm') path = transform_fname.format(subjects_dir=subjects_dir, subject=subject) if os.path.exists(path): paths['transforms'].append(transform_fname) del transform_fname, path # find source space files paths['src'] = src = [] bem_dir = bem_dirname.format(subjects_dir=subjects_dir, subject=subject) fnames = fnmatch.filter(os.listdir(bem_dir), '*-src.fif') prefix = subject + '-' for fname in fnames: if fname.startswith(prefix): fname = "{subject}-%s" % fname[len(prefix):] path = os.path.join(bem_dirname, fname) src.append(path) # find MRIs mri_dir = mri_dirname.format(subjects_dir=subjects_dir, subject=subject) fnames = fnmatch.filter(os.listdir(mri_dir), '*.mgz') paths['mri'] = [os.path.join(mri_dir, f) for f in fnames] return paths def _find_fiducials_files(subject, subjects_dir): """Find fiducial files.""" fid = [] # standard fiducials if os.path.exists(fid_fname.format(subjects_dir=subjects_dir, subject=subject)): fid.append(fid_fname) # fiducials with subject name pattern = pformat(fid_fname_general, subjects_dir=subjects_dir, subject=subject, head='*') regex = pformat(fid_fname_general, subjects_dir=subjects_dir, subject=subject, head='(.+)').replace('\\', '\\\\') for path in iglob(pattern): match = re.match(regex, path) head = match.group(1).replace(subject, '{subject}') fid.append(pformat(fid_fname_general, head=head)) return fid def _is_mri_subject(subject, subjects_dir=None): """Check whether a directory in subjects_dir is an mri subject directory. Parameters ---------- subject : str Name of the potential subject/directory. subjects_dir : None | str Override the SUBJECTS_DIR environment variable. Returns ------- is_mri_subject : bool Whether ``subject`` is an mri subject. """ subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) return bool(_find_head_bem(subject, subjects_dir) or _find_head_bem(subject, subjects_dir, high_res=True)) def _is_scaled_mri_subject(subject, subjects_dir=None): """Check whether a directory in subjects_dir is a scaled mri subject. Parameters ---------- subject : str Name of the potential subject/directory. subjects_dir : None | str Override the SUBJECTS_DIR environment variable. Returns ------- is_scaled_mri_subject : bool Whether ``subject`` is a scaled mri subject. """ subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) if not _is_mri_subject(subject, subjects_dir): return False fname = os.path.join(subjects_dir, subject, 'MRI scaling parameters.cfg') return os.path.exists(fname) def _mri_subject_has_bem(subject, subjects_dir=None): """Check whether an mri subject has a file matching the bem pattern. Parameters ---------- subject : str Name of the subject. subjects_dir : None | str Override the SUBJECTS_DIR environment variable. Returns ------- has_bem_file : bool Whether ``subject`` has a bem file. """ subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) pattern = bem_fname.format(subjects_dir=subjects_dir, subject=subject, name='*-bem') fnames = glob(pattern) return bool(len(fnames)) def read_mri_cfg(subject, subjects_dir=None): """Read information from the cfg file of a scaled MRI brain. Parameters ---------- subject : str Name of the scaled MRI subject. subjects_dir : None | str Override the SUBJECTS_DIR environment variable. Returns ------- cfg : dict Dictionary with entries from the MRI's cfg file. """ subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) fname = os.path.join(subjects_dir, subject, 'MRI scaling parameters.cfg') if not os.path.exists(fname): raise IOError("%r does not seem to be a scaled mri subject: %r does " "not exist." % (subject, fname)) logger.info("Reading MRI cfg file %s" % fname) config = configparser.RawConfigParser() config.read(fname) n_params = config.getint("MRI Scaling", 'n_params') if n_params == 1: scale = config.getfloat("MRI Scaling", 'scale') elif n_params == 3: scale_str = config.get("MRI Scaling", 'scale') scale = np.array([float(s) for s in scale_str.split()]) else: raise ValueError("Invalid n_params value in MRI cfg: %i" % n_params) out = {'subject_from': config.get("MRI Scaling", 'subject_from'), 'n_params': n_params, 'scale': scale} return out def _write_mri_config(fname, subject_from, subject_to, scale): """Write the cfg file describing a scaled MRI subject. Parameters ---------- fname : str Target file. subject_from : str Name of the source MRI subject. subject_to : str Name of the scaled MRI subject. scale : float | array_like, shape = (3,) The scaling parameter. """ scale = np.asarray(scale) if np.isscalar(scale) or scale.shape == (): n_params = 1 else: n_params = 3 config = configparser.RawConfigParser() config.add_section("MRI Scaling") config.set("MRI Scaling", 'subject_from', subject_from) config.set("MRI Scaling", 'subject_to', subject_to) config.set("MRI Scaling", 'n_params', str(n_params)) if n_params == 1: config.set("MRI Scaling", 'scale', str(scale)) else: config.set("MRI Scaling", 'scale', ' '.join([str(s) for s in scale])) config.set("MRI Scaling", 'version', '1') with open(fname, 'w') as fid: config.write(fid) def _scale_params(subject_to, subject_from, scale, subjects_dir): """Assemble parameters for scaling. Returns ------- subjects_dir : str Subjects directory. subject_from : str Name of the source subject. scale : array Scaling factor, either shape=() for uniform scaling or shape=(3,) for non-uniform scaling. uniform : bool Whether scaling is uniform. """ subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) if (subject_from is None) != (scale is None): raise TypeError("Need to provide either both subject_from and scale " "parameters, or neither.") if subject_from is None: cfg = read_mri_cfg(subject_to, subjects_dir) subject_from = cfg['subject_from'] n_params = cfg['n_params'] assert n_params in (1, 3) scale = cfg['scale'] scale = np.atleast_1d(scale) if scale.ndim != 1 or scale.shape[0] not in (1, 3): raise ValueError("Invalid shape for scale parameter. Need scalar " "or array of length 3. Got shape %s." % (scale.shape,)) n_params = len(scale) return subjects_dir, subject_from, scale, n_params == 1 @verbose def scale_bem(subject_to, bem_name, subject_from=None, scale=None, subjects_dir=None, *, on_defects='raise', verbose=None): """Scale a bem file. Parameters ---------- subject_to : str Name of the scaled MRI subject (the destination mri subject). bem_name : str Name of the bem file. For example, to scale ``fsaverage-inner_skull-bem.fif``, the bem_name would be "inner_skull-bem". subject_from : None | str The subject from which to read the source space. If None, subject_from is read from subject_to's config file. scale : None | float | array, shape = (3,) Scaling factor. Has to be specified if subjects_from is specified, otherwise it is read from subject_to's config file. subjects_dir : None | str Override the SUBJECTS_DIR environment variable. %(on_defects)s .. versionadded:: 1.0 %(verbose)s """ subjects_dir, subject_from, scale, uniform = \ _scale_params(subject_to, subject_from, scale, subjects_dir) src = bem_fname.format(subjects_dir=subjects_dir, subject=subject_from, name=bem_name) dst = bem_fname.format(subjects_dir=subjects_dir, subject=subject_to, name=bem_name) if os.path.exists(dst): raise IOError("File already exists: %s" % dst) surfs = read_bem_surfaces(src, on_defects=on_defects) for surf in surfs: surf['rr'] *= scale if not uniform: assert len(surf['nn']) > 0 surf['nn'] /= scale _normalize_vectors(surf['nn']) write_bem_surfaces(dst, surfs) def scale_labels(subject_to, pattern=None, overwrite=False, subject_from=None, scale=None, subjects_dir=None): r"""Scale labels to match a brain that was previously created by scaling. Parameters ---------- subject_to : str Name of the scaled MRI subject (the destination brain). pattern : str | None Pattern for finding the labels relative to the label directory in the MRI subject directory (e.g., "lh.BA3a.label" will scale "fsaverage/label/lh.BA3a.label"; "aparc/\*.label" will find all labels in the "fsaverage/label/aparc" directory). With None, scale all labels. overwrite : bool Overwrite any label file that already exists for subject_to (otherwise existing labels are skipped). subject_from : None | str Name of the original MRI subject (the brain that was scaled to create subject_to). If None, the value is read from subject_to's cfg file. scale : None | float | array_like, shape = (3,) Scaling parameter. If None, the value is read from subject_to's cfg file. subjects_dir : None | str Override the SUBJECTS_DIR environment variable. """ subjects_dir, subject_from, scale, _ = _scale_params( subject_to, subject_from, scale, subjects_dir) # find labels paths = _find_label_paths(subject_from, pattern, subjects_dir) if not paths: return subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) src_root = os.path.join(subjects_dir, subject_from, 'label') dst_root = os.path.join(subjects_dir, subject_to, 'label') # scale labels for fname in paths: dst = os.path.join(dst_root, fname) if not overwrite and os.path.exists(dst): continue dirname = os.path.dirname(dst) if not os.path.exists(dirname): os.makedirs(dirname) src = os.path.join(src_root, fname) l_old = read_label(src) pos = l_old.pos * scale l_new = Label(l_old.vertices, pos, l_old.values, l_old.hemi, l_old.comment, subject=subject_to) l_new.save(dst) @verbose def 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_from : str Name of the subject providing the MRI. subject_to : str New subject name for which to save the scaled MRI. scale : float | array_like, shape = (3,) The scaling factor (one or 3 parameters). overwrite : bool If an MRI already exists for subject_to, overwrite it. subjects_dir : None | str Override the SUBJECTS_DIR environment variable. skip_fiducials : bool Do not scale the MRI fiducials. If False (default), an IOError will be raised if no fiducials file can be found. labels : bool Also scale all labels (default True). annot : bool Copy ``*.annot`` files to the new location (default False). %(on_defects)s .. versionadded:: 1.0 %(verbose)s 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 :func:`scale_bem`, :func:`scale_labels`, and :func:`scale_source_space` based on expected filename patterns in the subject directory. """ subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) paths = _find_mri_paths(subject_from, skip_fiducials, subjects_dir) scale = np.atleast_1d(scale) if scale.shape == (3,): if np.isclose(scale[1], scale[0]) and np.isclose(scale[2], scale[0]): scale = scale[0] # speed up scaling conditionals using a singleton elif scale.shape != (1,): raise ValueError('scale must have shape (3,) or (1,), got %s' % (scale.shape,)) # make sure we have an empty target directory dest = subject_dirname.format(subject=subject_to, subjects_dir=subjects_dir) if os.path.exists(dest): if not overwrite: raise IOError("Subject directory for %s already exists: %r" % (subject_to, dest)) shutil.rmtree(dest) logger.debug('create empty directory structure') for dirname in paths['dirs']: dir_ = dirname.format(subject=subject_to, subjects_dir=subjects_dir) os.makedirs(dir_) logger.debug('save MRI scaling parameters') fname = os.path.join(dest, 'MRI scaling parameters.cfg') _write_mri_config(fname, subject_from, subject_to, scale) logger.debug('surf files [in mm]') for fname in paths['surf']: src = fname.format(subject=subject_from, subjects_dir=subjects_dir) src = os.path.realpath(src) dest = fname.format(subject=subject_to, subjects_dir=subjects_dir) pts, tri = read_surface(src) write_surface(dest, pts * scale, tri) logger.debug('BEM files [in m]') for bem_name in paths['bem']: scale_bem(subject_to, bem_name, subject_from, scale, subjects_dir, on_defects=on_defects, verbose=False) logger.debug('fiducials [in m]') for fname in paths['fid']: src = fname.format(subject=subject_from, subjects_dir=subjects_dir) src = os.path.realpath(src) pts, cframe = read_fiducials(src, verbose=False) for pt in pts: pt['r'] = pt['r'] * scale dest = fname.format(subject=subject_to, subjects_dir=subjects_dir) write_fiducials(dest, pts, cframe, overwrite=True, verbose=False) logger.debug('MRIs [nibabel]') os.mkdir(mri_dirname.format(subjects_dir=subjects_dir, subject=subject_to)) for fname in paths['mri']: mri_name = os.path.basename(fname) _scale_mri(subject_to, mri_name, subject_from, scale, subjects_dir) logger.debug('Transforms') for mri_name in paths['mri']: if mri_name.endswith('T1.mgz'): os.mkdir(mri_transforms_dirname.format(subjects_dir=subjects_dir, subject=subject_to)) for fname in paths['transforms']: xfm_name = os.path.basename(fname) _scale_xfm(subject_to, xfm_name, mri_name, subject_from, scale, subjects_dir) break logger.debug('duplicate files') for fname in paths['duplicate']: src = fname.format(subject=subject_from, subjects_dir=subjects_dir) dest = fname.format(subject=subject_to, subjects_dir=subjects_dir) shutil.copyfile(src, dest) logger.debug('source spaces') for fname in paths['src']: src_name = os.path.basename(fname) scale_source_space(subject_to, src_name, subject_from, scale, subjects_dir, verbose=False) logger.debug('labels [in m]') os.mkdir(os.path.join(subjects_dir, subject_to, 'label')) if labels: scale_labels(subject_to, subject_from=subject_from, scale=scale, subjects_dir=subjects_dir) logger.debug('copy *.annot files') # they don't contain scale-dependent information if annot: src_pattern = os.path.join(subjects_dir, subject_from, 'label', '*.annot') dst_dir = os.path.join(subjects_dir, subject_to, 'label') for src_file in iglob(src_pattern): shutil.copy(src_file, dst_dir) @verbose def scale_source_space(subject_to, src_name, subject_from=None, scale=None, subjects_dir=None, n_jobs=None, verbose=None): """Scale a source space for an mri created with scale_mri(). Parameters ---------- subject_to : str Name of the scaled MRI subject (the destination mri subject). src_name : str Source space name. Can be a spacing parameter (e.g., ``'7'``, ``'ico4'``, ``'oct6'``) or a file name of a source space file relative to the bem directory; if the file name contains the subject name, it should be indicated as "{subject}" in ``src_name`` (e.g., ``"{subject}-my_source_space-src.fif"``). subject_from : None | str The subject from which to read the source space. If None, subject_from is read from subject_to's config file. scale : None | float | array, shape = (3,) Scaling factor. Has to be specified if subjects_from is specified, otherwise it is read from subject_to's config file. subjects_dir : None | str Override the SUBJECTS_DIR environment variable. n_jobs : int Number of jobs to run in parallel if recomputing distances (only applies if scale is an array of length 3, and will not use more cores than there are source spaces). %(verbose)s Notes ----- When scaling volume source spaces, the source (vertex) locations are scaled, but the reference to the MRI volume is left unchanged. Transforms are updated so that source estimates can be plotted on the original MRI volume. """ subjects_dir, subject_from, scale, uniform = \ _scale_params(subject_to, subject_from, scale, subjects_dir) # if n_params==1 scale is a scalar; if n_params==3 scale is a (3,) array # find the source space file names if src_name.isdigit(): spacing = src_name # spacing in mm src_pattern = src_fname else: match = re.match(r"(oct|ico|vol)-?(\d+)$", src_name) if match: spacing = '-'.join(match.groups()) src_pattern = src_fname else: spacing = None src_pattern = os.path.join(bem_dirname, src_name) src = src_pattern.format(subjects_dir=subjects_dir, subject=subject_from, spacing=spacing) dst = src_pattern.format(subjects_dir=subjects_dir, subject=subject_to, spacing=spacing) # read and scale the source space [in m] sss = read_source_spaces(src) logger.info("scaling source space %s: %s -> %s", spacing, subject_from, subject_to) logger.info("Scale factor: %s", scale) add_dist = False for ss in sss: ss['subject_his_id'] = subject_to ss['rr'] *= scale # additional tags for volume source spaces for key in ('vox_mri_t', 'src_mri_t'): # maintain transform to original MRI volume ss['mri_volume_name'] if key in ss: ss[key]['trans'][:3] *= scale[:, np.newaxis] # distances and patch info if uniform: if ss['dist'] is not None: ss['dist'] *= scale[0] # Sometimes this is read-only due to how it's read ss['nearest_dist'] = ss['nearest_dist'] * scale ss['dist_limit'] = ss['dist_limit'] * scale else: # non-uniform scaling ss['nn'] /= scale _normalize_vectors(ss['nn']) if ss['dist'] is not None: add_dist = True dist_limit = float(np.abs(sss[0]['dist_limit'])) elif ss['nearest'] is not None: add_dist = True dist_limit = 0 if add_dist: logger.info("Recomputing distances, this might take a while") add_source_space_distances(sss, dist_limit, n_jobs) write_source_spaces(dst, sss) def _scale_mri(subject_to, mri_fname, subject_from, scale, subjects_dir): """Scale an MRI by setting its affine.""" subjects_dir, subject_from, scale, _ = _scale_params( subject_to, subject_from, scale, subjects_dir) nibabel = _import_nibabel('scale an MRI') fname_from = op.join(mri_dirname.format( subjects_dir=subjects_dir, subject=subject_from), mri_fname) fname_to = op.join(mri_dirname.format( subjects_dir=subjects_dir, subject=subject_to), mri_fname) img = nibabel.load(fname_from) zooms = np.array(img.header.get_zooms()) zooms[[0, 2, 1]] *= scale img.header.set_zooms(zooms) # Hack to fix nibabel problems, see # https://github.com/nipy/nibabel/issues/619 img._affine = img.header.get_affine() # or could use None nibabel.save(img, fname_to) def _scale_xfm(subject_to, xfm_fname, mri_name, subject_from, scale, subjects_dir): """Scale a transform.""" subjects_dir, subject_from, scale, _ = _scale_params( subject_to, subject_from, scale, subjects_dir) # The nibabel warning should already be there in MRI step, if applicable, # as we only get here if T1.mgz is present (and thus a scaling was # attempted) so we can silently return here. if not has_nibabel(): return fname_from = os.path.join( mri_transforms_dirname.format( subjects_dir=subjects_dir, subject=subject_from), xfm_fname) fname_to = op.join( mri_transforms_dirname.format( subjects_dir=subjects_dir, subject=subject_to), xfm_fname) assert op.isfile(fname_from), fname_from assert op.isdir(op.dirname(fname_to)), op.dirname(fname_to) # The "talairach.xfm" file stores the ras_mni transform. # # For "from" subj F, "to" subj T, F->T scaling S, some equivalent vertex # positions F_x and T_x in MRI (Freesurfer RAS) coords, knowing that # we have T_x = S @ F_x, we want to have the same MNI coords computed # for these vertices: # # T_mri_mni @ T_x = F_mri_mni @ F_x # # We need to find the correct T_ras_mni (talaraich.xfm file) that yields # this. So we derive (where † indicates inversion): # # T_mri_mni @ S @ F_x = F_mri_mni @ F_x # T_mri_mni @ S = F_mri_mni # T_ras_mni @ T_mri_ras @ S = F_ras_mni @ F_mri_ras # T_ras_mni @ T_mri_ras = F_ras_mni @ F_mri_ras @ S⁻¹ # T_ras_mni = F_ras_mni @ F_mri_ras @ S⁻¹ @ T_ras_mri # # prepare the scale (S) transform scale = np.atleast_1d(scale) scale = np.tile(scale, 3) if len(scale) == 1 else scale S = Transform('mri', 'mri', scaling(*scale)) # F_mri->T_mri # # Get the necessary transforms of the "from" subject # xfm, kind = _read_fs_xfm(fname_from) assert kind == 'MNI Transform File', kind _, _, F_mri_ras, _, _ = _read_mri_info(mri_name, units='mm') F_ras_mni = Transform('ras', 'mni_tal', xfm) del xfm # # Get the necessary transforms of the "to" subject # mri_name = op.join(mri_dirname.format( subjects_dir=subjects_dir, subject=subject_to), op.basename(mri_name)) _, _, T_mri_ras, _, _ = _read_mri_info(mri_name, units='mm') T_ras_mri = invert_transform(T_mri_ras) del mri_name, T_mri_ras # Finally we construct as above: # # T_ras_mni = F_ras_mni @ F_mri_ras @ S⁻¹ @ T_ras_mri # # By moving right to left through the equation. T_ras_mni = \ combine_transforms( combine_transforms( combine_transforms( T_ras_mri, invert_transform(S), 'ras', 'mri'), F_mri_ras, 'ras', 'ras'), F_ras_mni, 'ras', 'mni_tal') _write_fs_xfm(fname_to, T_ras_mni['trans'], kind) def _read_surface(filename, *, on_defects): bem = dict() if filename is not None and op.exists(filename): if filename.endswith('.fif'): bem = read_bem_surfaces( filename, on_defects=on_defects, verbose=False )[0] else: try: bem = read_surface(filename, return_dict=True)[2] bem['rr'] *= 1e-3 complete_surface_info(bem, copy=False) except Exception: raise ValueError( "Error loading surface from %s (see " "Terminal for details)." % filename) return bem @fill_doc class Coregistration(object): """Class for MRI<->head coregistration. Parameters ---------- info : instance of Info | None The measurement info. %(subject)s %(subjects_dir)s %(fiducials)s %(on_defects)s .. versionadded:: 1.0 Attributes ---------- fiducials : instance of DigMontage A montage containing the MRI fiducials. trans : instance of Transform MRI<->Head coordinate transformation. See Also -------- mne.scale_mri Notes ----- Internal computation quantities parameters are in the following units: - rotation are in radians - translation are in m - scale are in scale proportion If using a scale mode, the :func:`~mne.scale_mri` should be used to create a surrogate MRI subject with the proper scale factors. """ def __init__(self, info, subject, subjects_dir=None, fiducials='auto', *, on_defects='raise'): _validate_type(info, (Info, None), 'info') self._info = info self._subject = _check_subject(subject, subject) self._subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) self._scale_mode = None self._on_defects = on_defects self._rot_trans = None self._default_parameters = \ np.array([0., 0., 0., 0., 0., 0., 1., 1., 1.]) self._rotation = self._default_parameters[:3] self._translation = self._default_parameters[3:6] self._scale = self._default_parameters[6:9] self._icp_iterations = 20 self._icp_angle = 0.2 self._icp_distance = 0.2 self._icp_scale = 0.2 self._icp_fid_matches = ('nearest', 'matched') self._icp_fid_match = self._icp_fid_matches[0] self._lpa_weight = 1. self._nasion_weight = 10. self._rpa_weight = 1. self._hsp_weight = 1. self._eeg_weight = 1. self._hpi_weight = 1. self._extra_points_filter = None self._setup_digs() self._setup_bem() self._fid_filename = None self._setup_fiducials(fiducials) self.reset() def _setup_digs(self): if self._info is None: self._dig_dict = dict( hpi=np.zeros((1, 3)), dig_ch_pos_location=np.zeros((1, 3)), hsp=np.zeros((1, 3)), rpa=np.zeros((1, 3)), nasion=np.zeros((1, 3)), lpa=np.zeros((1, 3)), ) else: self._dig_dict = _get_data_as_dict_from_dig( dig=self._info['dig'], exclude_ref_channel=False ) # adjustments: # set weights to 0 for None input # convert fids to float arrays for k, w_atr in zip(['nasion', 'lpa', 'rpa', 'hsp', 'hpi'], ['_nasion_weight', '_lpa_weight', '_rpa_weight', '_hsp_weight', '_hpi_weight']): if self._dig_dict[k] is None: self._dig_dict[k] = np.zeros((0, 3)) setattr(self, w_atr, 0) elif k in ['rpa', 'nasion', 'lpa']: self._dig_dict[k] = np.array([self._dig_dict[k]], float) def _setup_bem(self): # find high-res head model (if possible) high_res_path = _find_head_bem(self._subject, self._subjects_dir, high_res=True) low_res_path = _find_head_bem(self._subject, self._subjects_dir, high_res=False) if high_res_path is None and low_res_path is None: raise RuntimeError("No standard head model was " f"found for subject {self._subject}") if high_res_path is not None: self._bem_high_res = _read_surface( high_res_path, on_defects=self._on_defects ) logger.info(f'Using high resolution head model in {high_res_path}') else: self._bem_high_res = _read_surface( low_res_path, on_defects=self._on_defects ) logger.info(f'Using low resolution head model in {low_res_path}') if low_res_path is None: # This should be very rare! warn('No low-resolution head found, decimating high resolution ' 'mesh (%d vertices): %s' % (len(self._bem_high_res['rr']), high_res_path,)) # Create one from the high res one, which we know we have rr, tris = decimate_surface(self._bem_high_res['rr'], self._bem_high_res['tris'], n_triangles=5120) # directly set the attributes of bem_low_res self._bem_low_res = complete_surface_info( dict(rr=rr, tris=tris), copy=False, verbose=False) else: self._bem_low_res = _read_surface( low_res_path, on_defects=self._on_defects ) def _setup_fiducials(self, fids): _validate_type(fids, (str, dict, list)) # find fiducials file fid_accurate = None if fids == 'auto': fid_files = _find_fiducials_files(self._subject, self._subjects_dir) if len(fid_files) > 0: # Read fiducials from disk fid_filename = fid_files[0].format( subjects_dir=self._subjects_dir, subject=self._subject) logger.info(f'Using fiducials from: {fid_filename}.') fids, _ = read_fiducials(fid_filename) fid_accurate = True self._fid_filename = fid_filename else: fids = 'estimated' if fids == 'estimated': logger.info('Estimating fiducials from fsaverage.') fid_accurate = False fids = get_mni_fiducials(self._subject, self._subjects_dir) fid_accurate = True if fid_accurate is None else fid_accurate if isinstance(fids, list): fid_coords = _fiducial_coords(fids) else: assert isinstance(fids, dict) fid_coords = np.array([fids['lpa'], fids['nasion'], fids['rpa']], dtype=float) self._fid_points = fid_coords self._fid_accurate = fid_accurate # does not seem to happen by itself ... so hard code it: self._reset_fiducials() def _reset_fiducials(self): dig_montage = make_dig_montage( lpa=self._fid_points[0], nasion=self._fid_points[1], rpa=self._fid_points[2], coord_frame='mri' ) self.fiducials = dig_montage def _update_params(self, rot=None, tra=None, sca=None, force_update=False): if force_update and tra is None: tra = self._translation rot_changed = False if rot is not None: rot_changed = True self._last_rotation = self._rotation.copy() self._rotation = rot tra_changed = False if rot_changed or tra is not None: if tra is None: tra = self._translation tra_changed = True self._last_translation = self._translation.copy() self._translation = tra self._head_mri_t = rotation(*self._rotation).T self._head_mri_t[:3, 3] = \ -np.dot(self._head_mri_t[:3, :3], tra) self._transformed_dig_hpi = \ apply_trans(self._head_mri_t, self._dig_dict['hpi']) self._transformed_dig_eeg = \ apply_trans( self._head_mri_t, self._dig_dict['dig_ch_pos_location']) self._transformed_dig_extra = \ apply_trans(self._head_mri_t, self._filtered_extra_points) self._transformed_orig_dig_extra = \ apply_trans(self._head_mri_t, self._dig_dict['hsp']) self._mri_head_t = rotation(*self._rotation) self._mri_head_t[:3, 3] = np.array(tra) if tra_changed or sca is not None: if sca is None: sca = self._scale self._last_scale = self._scale.copy() self._scale = sca self._mri_trans = np.eye(4) self._mri_trans[:, :3] *= sca self._transformed_high_res_mri_points = \ apply_trans(self._mri_trans, self._processed_high_res_mri_points) self._update_nearest_calc() if tra_changed: self._nearest_transformed_high_res_mri_idx_orig_hsp = \ self._nearest_calc.query(self._transformed_orig_dig_extra)[1] self._nearest_transformed_high_res_mri_idx_hpi = \ self._nearest_calc.query(self._transformed_dig_hpi)[1] self._nearest_transformed_high_res_mri_idx_eeg = \ self._nearest_calc.query(self._transformed_dig_eeg)[1] self._nearest_transformed_high_res_mri_idx_rpa = \ self._nearest_calc.query( apply_trans(self._head_mri_t, self._dig_dict['rpa']))[1] self._nearest_transformed_high_res_mri_idx_nasion = \ self._nearest_calc.query( apply_trans(self._head_mri_t, self._dig_dict['nasion']))[1] self._nearest_transformed_high_res_mri_idx_lpa = \ self._nearest_calc.query( apply_trans(self._head_mri_t, self._dig_dict['lpa']))[1] def set_scale_mode(self, scale_mode): """Select how to fit the scale parameters. Parameters ---------- scale_mode : None | str The scale mode can be 'uniform', '3-axis' or disabled. Defaults to None. * 'uniform': 1 scale factor is recovered. * '3-axis': 3 scale factors are recovered. * None: do not scale the MRI. Returns ------- self : Coregistration The modified Coregistration object. """ self._scale_mode = scale_mode return self def set_grow_hair(self, value): """Compensate for hair on the digitizer head shape. Parameters ---------- value : float Move the back of the MRI head outwards by ``value`` (mm). Returns ------- self : Coregistration The modified Coregistration object. """ self._grow_hair = value self._update_params(force_update=True) return self def set_rotation(self, rot): """Set the rotation parameter. Parameters ---------- rot : array, shape (3,) The rotation parameter (in radians). Returns ------- self : Coregistration The modified Coregistration object. """ self._update_params(rot=np.array(rot)) return self def set_translation(self, tra): """Set the translation parameter. Parameters ---------- tra : array, shape (3,) The translation parameter (in m.). Returns ------- self : Coregistration The modified Coregistration object. """ self._update_params(tra=np.array(tra)) return self def set_scale(self, sca): """Set the scale parameter. Parameters ---------- sca : array, shape (3,) The scale parameter. Returns ------- self : Coregistration The modified Coregistration object. """ self._update_params(sca=np.array(sca)) return self def _update_nearest_calc(self): self._nearest_calc = _DistanceQuery( self._processed_high_res_mri_points * self._scale) @property def _filtered_extra_points(self): if self._extra_points_filter is None: return self._dig_dict['hsp'] else: return self._dig_dict['hsp'][self._extra_points_filter] @property def _parameters(self): return np.concatenate((self._rotation, self._translation, self._scale)) @property def _last_parameters(self): return np.concatenate((self._last_rotation, self._last_translation, self._last_scale)) @property def _changes(self): move = np.linalg.norm(self._last_translation - self._translation) * 1e3 angle = np.rad2deg(_angle_between_quats( rot_to_quat(rotation(*self._rotation)[:3, :3]), rot_to_quat(rotation(*self._last_rotation)[:3, :3]))) percs = 100 * (self._scale - self._last_scale) / self._last_scale return move, angle, percs @property def _nearest_transformed_high_res_mri_idx_hsp(self): return self._nearest_calc.query( apply_trans(self._head_mri_t, self._filtered_extra_points))[1] @property def _has_hsp_data(self): return (self._has_mri_data and len(self._nearest_transformed_high_res_mri_idx_hsp) > 0) @property def _has_hpi_data(self): return (self._has_mri_data and len(self._nearest_transformed_high_res_mri_idx_hpi) > 0) @property def _has_eeg_data(self): return (self._has_mri_data and len(self._nearest_transformed_high_res_mri_idx_eeg) > 0) @property def _has_lpa_data(self): mri_point = self.fiducials.dig[_map_fid_name_to_idx('lpa')] assert mri_point['ident'] == FIFF.FIFFV_POINT_LPA has_mri_data = np.any(mri_point['r']) has_head_data = np.any(self._dig_dict['lpa']) return has_mri_data and has_head_data @property def _has_nasion_data(self): mri_point = self.fiducials.dig[_map_fid_name_to_idx('nasion')] assert mri_point['ident'] == FIFF.FIFFV_POINT_NASION has_mri_data = np.any(mri_point['r']) has_head_data = np.any(self._dig_dict['nasion']) return has_mri_data and has_head_data @property def _has_rpa_data(self): mri_point = self.fiducials.dig[_map_fid_name_to_idx('rpa')] assert mri_point['ident'] == FIFF.FIFFV_POINT_RPA has_mri_data = np.any(mri_point['r']) has_head_data = np.any(self._dig_dict['rpa']) return has_mri_data and has_head_data @property def _processed_high_res_mri_points(self): return self._get_processed_mri_points('high') @property def _processed_low_res_mri_points(self): return self._get_processed_mri_points('low') def _get_processed_mri_points(self, res): bem = self._bem_low_res if res == 'low' else self._bem_high_res points = bem['rr'].copy() if self._grow_hair: assert len(bem['nn']) # should be guaranteed by _read_surface scaled_hair_dist = (1e-3 * self._grow_hair / np.array(self._scale)) hair = points[:, 2] > points[:, 1] points[hair] += bem['nn'][hair] * scaled_hair_dist return points @property def _has_mri_data(self): return len(self._transformed_high_res_mri_points) > 0 @property def _has_dig_data(self): return (self._has_mri_data and len(self._nearest_transformed_high_res_mri_idx_hsp) > 0) @property def _orig_hsp_point_distance(self): mri_points = self._transformed_high_res_mri_points[ self._nearest_transformed_high_res_mri_idx_orig_hsp] hsp_points = self._transformed_orig_dig_extra return np.linalg.norm(mri_points - hsp_points, axis=-1) def _log_dig_mri_distance(self, prefix): errs_nearest = self.compute_dig_mri_distances() logger.info(f'{prefix} median distance: ' f'{np.median(errs_nearest * 1000):6.2f} mm') @property def scale(self): """Get the current scale factor. Returns ------- scale : ndarray, shape (3,) The scale factors. """ return self._scale.copy() @verbose def fit_fiducials(self, lpa_weight=1., nasion_weight=10., rpa_weight=1., verbose=None): """Find rotation and translation to fit all 3 fiducials. Parameters ---------- lpa_weight : float Relative weight for LPA. The default value is 1. nasion_weight : float Relative weight for nasion. The default value is 10. rpa_weight : float Relative weight for RPA. The default value is 1. %(verbose)s Returns ------- self : Coregistration The modified Coregistration object. """ logger.info('Aligning using fiducials') self._log_dig_mri_distance('Start') n_scale_params = self._n_scale_params if n_scale_params == 3: # enforce 1 even for 3-axis here (3 points is not enough) logger.info("Enforcing 1 scaling parameter for fit " "with fiducials.") n_scale_params = 1 self._lpa_weight = lpa_weight self._nasion_weight = nasion_weight self._rpa_weight = rpa_weight head_pts = np.vstack((self._dig_dict['lpa'], self._dig_dict['nasion'], self._dig_dict['rpa'])) mri_pts = np.vstack( (self.fiducials.dig[0]['r'], # LPA self.fiducials.dig[1]['r'], # Nasion self.fiducials.dig[2]['r']) # RPA ) weights = [lpa_weight, nasion_weight, rpa_weight] if n_scale_params == 0: mri_pts *= self._scale # not done in fit_matched_points x0 = self._parameters x0 = x0[:6 + n_scale_params] est = fit_matched_points(mri_pts, head_pts, x0=x0, out='params', scale=n_scale_params, weights=weights) if n_scale_params == 0: self._update_params(rot=est[:3], tra=est[3:6]) else: assert est.size == 7 est = np.concatenate([est, [est[-1]] * 2]) assert est.size == 9 self._update_params(rot=est[:3], tra=est[3:6], sca=est[6:9]) self._log_dig_mri_distance('End ') return self def _setup_icp(self, n_scale_params): head_pts = list() mri_pts = list() weights = list() if self._has_dig_data and self._hsp_weight > 0: # should be true head_pts.append(self._filtered_extra_points) mri_pts.append(self._processed_high_res_mri_points[ self._nearest_transformed_high_res_mri_idx_hsp]) weights.append(np.full(len(head_pts[-1]), self._hsp_weight)) for key in ('lpa', 'nasion', 'rpa'): if getattr(self, f'_has_{key}_data'): head_pts.append(self._dig_dict[key]) if self._icp_fid_match == 'matched': idx = _map_fid_name_to_idx(name=key) p = self.fiducials.dig[idx]['r'].reshape(1, -1) mri_pts.append(p) else: assert self._icp_fid_match == 'nearest' mri_pts.append(self._processed_high_res_mri_points[ getattr( self, '_nearest_transformed_high_res_mri_idx_%s' % (key,))]) weights.append(np.full(len(mri_pts[-1]), getattr(self, '_%s_weight' % key))) if self._has_eeg_data and self._eeg_weight > 0: head_pts.append(self._dig_dict['dig_ch_pos_location']) mri_pts.append(self._processed_high_res_mri_points[ self._nearest_transformed_high_res_mri_idx_eeg]) weights.append(np.full(len(mri_pts[-1]), self._eeg_weight)) if self._has_hpi_data and self._hpi_weight > 0: head_pts.append(self._dig_dict['hpi']) mri_pts.append(self._processed_high_res_mri_points[ self._nearest_transformed_high_res_mri_idx_hpi]) weights.append(np.full(len(mri_pts[-1]), self._hpi_weight)) head_pts = np.concatenate(head_pts) mri_pts = np.concatenate(mri_pts) weights = np.concatenate(weights) if n_scale_params == 0: mri_pts *= self._scale # not done in fit_matched_points return head_pts, mri_pts, weights def set_fid_match(self, match): """Set the strategy for fitting anatomical landmark (fiducial) points. Parameters ---------- match : 'nearest' | 'matched' Alignment strategy; ``'nearest'`` aligns anatomical landmarks to any point on the head surface; ``'matched'`` aligns to the fiducial points in the MRI. Returns ------- self : Coregistration The modified Coregistration object. """ _check_option('match', match, self._icp_fid_matches) self._icp_fid_match = match return self @verbose def fit_icp(self, n_iterations=20, lpa_weight=1., nasion_weight=10., rpa_weight=1., hsp_weight=1., eeg_weight=1., hpi_weight=1., callback=None, verbose=None): """Find MRI scaling, translation, and rotation to match HSP. Parameters ---------- n_iterations : int Maximum number of iterations. lpa_weight : float Relative weight for LPA. The default value is 1. nasion_weight : float Relative weight for nasion. The default value is 10. rpa_weight : float Relative weight for RPA. The default value is 1. hsp_weight : float Relative weight for HSP. The default value is 1. eeg_weight : float Relative weight for EEG. The default value is 1. hpi_weight : float Relative weight for HPI. The default value is 1. callback : callable | None A function to call on each iteration. Useful for status message updates. It will be passed the keyword arguments ``iteration`` and ``n_iterations``. %(verbose)s Returns ------- self : Coregistration The modified Coregistration object. """ logger.info('Aligning using ICP') self._log_dig_mri_distance('Start ') n_scale_params = self._n_scale_params self._lpa_weight = lpa_weight self._nasion_weight = nasion_weight self._rpa_weight = rpa_weight self._hsp_weight = hsp_weight self._eeg_weight = eeg_weight self._hsp_weight = hpi_weight # Initial guess (current state) est = self._parameters est = est[:[6, 7, None, 9][n_scale_params]] # Do the fits, assigning and evaluating at each step for iteration in range(n_iterations): head_pts, mri_pts, weights = self._setup_icp(n_scale_params) est = fit_matched_points(mri_pts, head_pts, scale=n_scale_params, x0=est, out='params', weights=weights) if n_scale_params == 0: self._update_params(rot=est[:3], tra=est[3:6]) elif n_scale_params == 1: est = np.array(list(est) + [est[-1]] * 2) self._update_params(rot=est[:3], tra=est[3:6], sca=est[6:9]) else: self._update_params(rot=est[:3], tra=est[3:6], sca=est[6:9]) angle, move, scale = self._changes self._log_dig_mri_distance(f' ICP {iteration + 1:2d} ') if callback is not None: callback(iteration, n_iterations) if angle <= self._icp_angle and move <= self._icp_distance and \ all(scale <= self._icp_scale): break self._log_dig_mri_distance('End ') return self @property def _n_scale_params(self): if self._scale_mode is None: n_scale_params = 0 elif self._scale_mode == 'uniform': n_scale_params = 1 else: n_scale_params = 3 return n_scale_params def omit_head_shape_points(self, distance): """Exclude head shape points that are far away from the MRI head. Parameters ---------- distance : float Exclude all points that are further away from the MRI head than this distance (in m.). A value of distance <= 0 excludes nothing. Returns ------- self : Coregistration The modified Coregistration object. """ distance = float(distance) if distance <= 0: return # find the new filter mask = self._orig_hsp_point_distance <= distance n_excluded = np.sum(~mask) logger.info("Coregistration: Excluding %i head shape points with " "distance >= %.3f m.", n_excluded, distance) # set the filter self._extra_points_filter = mask self._update_params(force_update=True) return self def compute_dig_mri_distances(self): """Compute distance between head shape points and MRI skin surface. Returns ------- dist : array, shape (n_points,) The distance of the head shape points to the MRI skin surface. See Also -------- mne.dig_mri_distances """ # we don't use `dig_mri_distances` here because it should be much # faster to use our already-determined nearest points hsp_points, mri_points, _ = self._setup_icp(0) hsp_points = apply_trans(self._head_mri_t, hsp_points) return np.linalg.norm(mri_points - hsp_points, axis=-1) @property def trans(self): """The head->mri :class:`~mne.transforms.Transform`.""" return Transform('head', 'mri', self._head_mri_t) def reset(self): """Reset all the parameters affecting the coregistration. Returns ------- self : Coregistration The modified Coregistration object. """ self._grow_hair = 0. self.set_rotation(self._default_parameters[:3]) self.set_translation(self._default_parameters[3:6]) self.set_scale(self._default_parameters[6:9]) self._extra_points_filter = None self._update_nearest_calc() return self def _get_fiducials_distance(self): distance = dict() for key in ('lpa', 'nasion', 'rpa'): idx = _map_fid_name_to_idx(name=key) fid = self.fiducials.dig[idx]['r'].reshape(1, -1) transformed_mri = apply_trans(self._mri_trans, fid) transformed_hsp = apply_trans( self._head_mri_t, self._dig_dict[key]) distance[key] = np.linalg.norm( np.ravel(transformed_mri - transformed_hsp)) return np.array(list(distance.values())) * 1e3 def _get_fiducials_distance_str(self): dists = self._get_fiducials_distance() return f"Fiducials: {dists[0]:.1f}, {dists[1]:.1f}, {dists[2]:.1f} mm" def _get_point_distance(self): mri_points = list() hsp_points = list() if self._hsp_weight > 0 and self._has_hsp_data: mri_points.append(self._transformed_high_res_mri_points[ self._nearest_transformed_high_res_mri_idx_hsp]) hsp_points.append(self._transformed_dig_extra) assert len(mri_points[-1]) == len(hsp_points[-1]) if self._eeg_weight > 0 and self._has_eeg_data: mri_points.append(self._transformed_high_res_mri_points[ self._nearest_transformed_high_res_mri_idx_eeg]) hsp_points.append(self._transformed_dig_eeg) assert len(mri_points[-1]) == len(hsp_points[-1]) if self._hpi_weight > 0 and self._has_hpi_data: mri_points.append(self._transformed_high_res_mri_points[ self._nearest_transformed_high_res_mri_idx_hpi]) hsp_points.append(self._transformed_dig_hpi) assert len(mri_points[-1]) == len(hsp_points[-1]) if all(len(h) == 0 for h in hsp_points): return None mri_points = np.concatenate(mri_points) hsp_points = np.concatenate(hsp_points) return np.linalg.norm(mri_points - hsp_points, axis=-1) def _get_point_distance_str(self): point_distance = self._get_point_distance() if point_distance is None: return "" dists = 1e3 * point_distance av_dist = np.mean(dists) std_dist = np.std(dists) kinds = [kind for kind, check in (('HSP', self._hsp_weight > 0 and self._has_hsp_data), ('EEG', self._eeg_weight > 0 and self._has_eeg_data), ('HPI', self._hpi_weight > 0 and self._has_hpi_data)) if check] kinds = '+'.join(kinds) return f"{len(dists)} {kinds}: {av_dist:.1f} ± {std_dist:.1f} mm"