"""Coregistration between different coordinate frames""" # Authors: Christian Brodbeck # # License: BSD (3-clause) from .externals.six.moves import configparser import fnmatch from glob import glob, iglob import os import re import shutil from warnings import warn import numpy as np from numpy import dot from scipy.optimize import leastsq from scipy.spatial.distance import cdist from scipy.linalg import norm from .io.meas_info import read_fiducials, write_fiducials from .label import read_label, Label from .source_space import (add_source_space_distances, read_source_spaces, write_source_spaces) from .surface import (read_surface, write_surface, read_bem_surfaces, write_bem_surface) from .transforms import rotation, rotation3d, scaling, translation from .utils import get_config, get_subjects_dir, logger, pformat from functools import reduce from .externals.six.moves import zip # 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') 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') 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') def create_default_subject(mne_root=None, fs_home=None, update=False, subjects_dir=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 ---------- mne_root : None | str The mne root directory (only needed if MNE_ROOT is not specified as environment variable). 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. 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. The files provided by MNE are listed below and can be found under ``share/mne/mne_analyze/fsaverage`` in the MNE directory (see MNE manual section 7.19 Working with the average brain): fsaverage_head.fif: The approximate head surface triangulation for fsaverage. fsaverage_inner_skull-bem.fif: The approximate inner skull surface for fsaverage. fsaverage-fiducials.fif: The locations of the fiducial points (LPA, RPA, and nasion). fsaverage-trans.fif: Contains a default MEG-MRI coordinate transformation suitable for fsaverage. """ 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: err = ("FREESURFER_HOME environment variable not found. Please " "specify the fs_home parameter in your call to " "create_default_subject().") raise ValueError(err) if mne_root is None: mne_root = get_config('MNE_ROOT', mne_root) if mne_root is None: err = ("MNE_ROOT environment variable not found. Please " "specify the mne_root parameter in your call to " "create_default_subject().") raise ValueError(err) # make sure freesurfer files exist fs_src = os.path.join(fs_home, 'subjects', 'fsaverage') if not os.path.exists(fs_src): err = ('fsaverage not found at %r. Is fs_home specified ' 'correctly?' % fs_src) raise IOError(err) for name in ('label', 'mri', 'surf'): dirname = os.path.join(fs_src, name) if not os.path.isdir(dirname): err = ("Freesurfer fsaverage seems to be incomplete: No directory " "named %s found in %s" % (name, fs_src)) raise IOError(err) # make sure destination does not already exist dest = os.path.join(subjects_dir, 'fsaverage') if dest == fs_src: err = ("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) raise IOError(err) elif (not update) and os.path.exists(dest): err = ("Can not create fsaverage because %r already exists in " "subjects_dir %r. Delete or rename the existing fsaverage " "subject folder." % ('fsaverage', subjects_dir)) raise IOError(err) # make sure mne files exist mne_fname = os.path.join(mne_root, 'share', 'mne', 'mne_analyze', 'fsaverage', 'fsaverage-%s.fif') mne_files = ('fiducials', 'head', 'inner_skull-bem', 'trans') for name in mne_files: fname = mne_fname % name if not os.path.isfile(fname): err = ("MNE fsaverage incomplete: %s file not found at " "%s" % (name, fname)) raise IOError(err) # 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) # add files from mne 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 directory...") dest_fname = os.path.join(dest_bem, 'fsaverage-%s.fif') for name in mne_files: if not os.path.exists(dest_fname % name): shutil.copy(mne_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. """ 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), normed=False) # for each voxel, select one point X, Y, Z = pts.T out = np.empty((np.sum(H > 0), 3)) for i, (xbin, ybin, zbin) in enumerate(zip(*np.nonzero(H))): x = xax[xbin] y = yax[ybin] z = zax[zbin] xi = np.logical_and(X >= x, X < x + res) yi = np.logical_and(Y >= y, Y < y + res) zi = np.logical_and(Z >= z, Z < z + res) idx = np.logical_and(zi, np.logical_and(yi, xi)) ipts = pts[idx] mid = np.array([x, y, z]) + res / 2. dist = cdist(ipts, [mid]) i_min = np.argmin(dist) ipt = ipts[i_min] out[i] = ipt return out def _trans_from_params(param_info, params): """Convert transformation parameters into a transformation matrix Parameters ---------- param_info : tuple, len = 3 Tuple describing the parameters in x (do_translate, do_rotate, do_scale). params : tuple The transformation parameters. Returns ------- trans : array, shape = (4, 4) 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(dot, trans) return trans def fit_matched_points(src_pts, tgt_pts, rotate=True, translate=True, scale=False, tol=None, x0=None, out='trans'): """Find a transform that 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 ------- One of the following, depending on the ``out`` parameter: trans : array, shape = (4, 4) Transformation that, if applied to src_pts, minimizes the squared distance to tgt_pts. params : array, shape = (n_params, ) A single tuple containing the translation, rotation and scaling parameters in that order. """ src_pts = np.atleast_2d(src_pts) tgt_pts = np.atleast_2d(tgt_pts) if src_pts.shape != tgt_pts.shape: err = ("src_pts and tgt_pts must have same shape " "(got {0}, {1})".format(src_pts.shape, tgt_pts.shape)) raise ValueError(err) rotate = bool(rotate) translate = bool(translate) scale = int(scale) if translate: src_pts = np.hstack((src_pts, np.ones((len(src_pts), 1)))) param_info = (rotate, translate, scale) if param_info == (True, False, 0): def error(x): rx, ry, rz = x trans = rotation3d(rx, ry, rz) est = dot(src_pts, trans.T) return (tgt_pts - est).ravel() if x0 is None: x0 = (0, 0, 0) elif param_info == (True, False, 1): def error(x): rx, ry, rz, s = x trans = rotation3d(rx, ry, rz) * s est = dot(src_pts, trans.T) return (tgt_pts - est).ravel() if x0 is None: x0 = (0, 0, 0, 1) elif param_info == (True, True, 0): def error(x): rx, ry, rz, tx, ty, tz = x trans = dot(translation(tx, ty, tz), rotation(rx, ry, rz)) est = dot(src_pts, trans.T) return (tgt_pts - est[:, :3]).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(dot, (translation(tx, ty, tz), rotation(rx, ry, rz), scaling(s, s, s))) est = dot(src_pts, trans.T) return (tgt_pts - est[:, :3]).ravel() if x0 is None: x0 = (0, 0, 0, 0, 0, 0, 1) else: err = ("The specified parameter combination is not implemented: " "rotate=%r, translate=%r, scale=%r" % param_info) raise NotImplementedError(err) x, _, _, _, _ = leastsq(error, x0, full_output=True) # 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: if not translate: src_pts = np.hstack((src_pts, np.ones((len(src_pts), 1)))) est_pts = 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: err = ("Invalid out parameter: %r. Needs to be 'params' or " "'trans'." % out) raise ValueError(err) def get_ras_to_neuromag_trans(nasion, lpa, rpa): """Construct a transformation matrix to the MNE head coordinate system Construct a transformation matrix from an arbitrary RAS coordinate system to the MNE head coordinate system, in which the x axis passes through the two preauricular points, and the y axis passes through the nasion and is normal to the x axis. (see mne manual, pg. 97) Parameters ---------- nasion : array_like, shape = (3,) Nasion point coordinate. lpa : array_like, shape = (3,) Left peri-auricular point coordinate. rpa : array_like, shape = (3,) Right peri-auricular point coordinate. Returns ------- trans : numpy.array, shape = (4, 4) Transformation matrix to MNE head space. """ # check input args nasion = np.asarray(nasion) lpa = np.asarray(lpa) rpa = np.asarray(rpa) for pt in (nasion, lpa, rpa): if pt.ndim != 1 or len(pt) != 3: err = ("Points have to be provided as one dimensional arrays of " "length 3.") raise ValueError(err) right = rpa - lpa right_unit = right / norm(right) origin = lpa + np.dot(nasion - lpa, right_unit) * right_unit anterior = nasion - origin anterior_unit = anterior / norm(anterior) superior_unit = np.cross(right_unit, anterior_unit) x, y, z = -origin origin_trans = translation(x, y, z) trans_l = np.vstack((right_unit, anterior_unit, superior_unit, [0, 0, 0])) trans_r = np.reshape([0, 0, 0, 1], (4, 1)) rot_trans = np.hstack((trans_l, trans_r)) trans = np.dot(rot_trans, origin_trans) return trans def _point_cloud_error(src_pts, tgt_pts): """Find the distance from each source point to its closest target point Parameters ---------- src_pts : array, shape = (n, 3) Source points. tgt_pts : array, shape = (m, 3) Target points. Returns ------- dist : array, shape = (n, ) For each point in ``src_pts``, the distance to the closest point in ``tgt_pts``. """ Y = cdist(src_pts, tgt_pts, 'euclidean') dist = Y.min(axis=1) return dist def _point_cloud_error_balltree(src_pts, tgt_tree): """Find the distance from each source point to its closest target point Uses sklearn.neighbors.BallTree for greater efficiency Parameters ---------- src_pts : array, shape = (n, 3) Source points. tgt_tree : sklearn.neighbors.BallTree BallTree of the target points. Returns ------- dist : array, shape = (n, ) For each point in ``src_pts``, the distance to the closest point in ``tgt_pts``. """ dist, _ = tgt_tree.query(src_pts) return dist.ravel() def fit_point_cloud(src_pts, tgt_pts, rotate=True, translate=True, scale=0, x0=None, leastsq_args={}, out='params'): """Find a transform that minimizes the squared distance from each source point to its closest target point 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 = (m, 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 : 0 | 1 | 3 Number of scaling parameters. With 0, points are not scaled. With 1, points are scaled by the same factor along all axes. With 3, points are scaled by a separate factor along each axis. x0 : None | tuple Initial values for the fit parameters. leastsq_args : dict Additional parameters to submit to :func:`scipy.optimize.leastsq`. 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 ------- x : array, shape = (n_params, ) Estimated parameters for the transformation. Notes ----- Assumes that the target points form a dense enough point cloud so that the distance of each src_pt to the closest tgt_pt can be used as an estimate of the distance of src_pt to tgt_pts. """ kwargs = {'epsfcn': 0.01} kwargs.update(leastsq_args) # assert correct argument types src_pts = np.atleast_2d(src_pts) tgt_pts = np.atleast_2d(tgt_pts) translate = bool(translate) rotate = bool(rotate) scale = int(scale) if translate: src_pts = np.hstack((src_pts, np.ones((len(src_pts), 1)))) try: from sklearn.neighbors import BallTree tgt_pts = BallTree(tgt_pts) errfunc = _point_cloud_error_balltree except ImportError: warn("Sklearn could not be imported. Fitting points will be slower. " "To improve performance, install the sklearn module.") errfunc = _point_cloud_error # for efficiency, define parameter specific error function param_info = (rotate, translate, scale) if param_info == (True, False, 0): x0 = x0 or (0, 0, 0) def error(x): rx, ry, rz = x trans = rotation3d(rx, ry, rz) est = dot(src_pts, trans.T) err = errfunc(est, tgt_pts) return err elif param_info == (True, False, 1): x0 = x0 or (0, 0, 0, 1) def error(x): rx, ry, rz, s = x trans = rotation3d(rx, ry, rz) * s est = dot(src_pts, trans.T) err = errfunc(est, tgt_pts) return err elif param_info == (True, False, 3): x0 = x0 or (0, 0, 0, 1, 1, 1) def error(x): rx, ry, rz, sx, sy, sz = x trans = rotation3d(rx, ry, rz) * [sx, sy, sz] est = dot(src_pts, trans.T) err = errfunc(est, tgt_pts) return err elif param_info == (True, True, 0): x0 = x0 or (0, 0, 0, 0, 0, 0) def error(x): rx, ry, rz, tx, ty, tz = x trans = dot(translation(tx, ty, tz), rotation(rx, ry, rz)) est = dot(src_pts, trans.T) err = errfunc(est[:, :3], tgt_pts) return err else: err = ("The specified parameter combination is not implemented: " "rotate=%r, translate=%r, scale=%r" % param_info) raise NotImplementedError(err) est, _, info, msg, _ = leastsq(error, x0, full_output=True, **kwargs) logger.debug("fit_point_cloud leastsq (%i calls) info: %s", info['nfev'], msg) if out == 'params': return est elif out == 'trans': return _trans_from_params(param_info, est) else: err = ("Invalid out parameter: %r. Needs to be 'params' or " "'trans'." % out) raise ValueError(err) 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='fsaverage', subjects_dir=None): """Find all files of an mri relevant for source transformation Parameters ---------- subject : str Name of the mri subject. 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 = [] surf_fname = os.path.join(surf_dirname, '{name}') surf_names = ('orig', 'orig_avg', 'inflated', 'inflated_avg', 'inflated_pre', 'pial', 'pial_avg', 'smoothwm', 'white', 'white_avg', 'sphere', 'sphere.reg', 'sphere.reg.avg') for name in surf_names: for hemi in ('lh.', 'rh.'): fname = pformat(surf_fname, name=hemi + name) surf.append(fname) # 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='(.+)') for path in iglob(bem_pattern): match = re.match(re_pattern, path) name = match.group(1) bem.append(name) # fiducials paths['fid'] = [fid_fname] # duplicate curvature files paths['duplicate'] = dup = [] path = os.path.join(surf_dirname, '{name}') for name in ['lh.curv', 'rh.curv']: fname = pformat(path, name=name) dup.append(fname) # check presence of required files for ftype in ['surf', 'fid', 'duplicate']: for fname in paths[ftype]: path = fname.format(subjects_dir=subjects_dir, subject=subject) path = os.path.realpath(path) if not os.path.exists(path): raise IOError("Required file not found: %r" % 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) return paths 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) fname = head_bem_fname.format(subjects_dir=subjects_dir, subject=subject) if not os.path.exists(fname): return False return True 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_elp(fname): """Read point coordinates from a text file Parameters ---------- fname : str Absolute path to laser point file (*.txt). Returns ------- elp_points : array, [n_points x 3] Point coordinates. """ pattern = re.compile(r'(\-?\d+\.\d+)\s+(\-?\d+\.\d+)\s+(\-?\d+\.\d+)') with open(fname) as fid: elp_points = pattern.findall(fid.read()) elp_points = np.array(elp_points, dtype=float) if elp_points.shape[1] != 3: err = ("File %r does not contain 3 columns as required; got shape " "%s." % (fname, elp_points.shape)) raise ValueError(err) return elp_points 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): err = ("%r does not seem to be a scaled mri subject: %r does not " "exist." % (subject, fname)) raise IOError(err) 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): subjects_dir = get_subjects_dir(subjects_dir, True) if (subject_from is None) != (scale is None): err = ("Need to provide either both subject_from and scale " "parameters, or neither.") raise TypeError(err) if subject_from is None: cfg = read_mri_cfg(subject_to, subjects_dir) subject_from = cfg['subject_from'] n_params = cfg['n_params'] scale = cfg['scale'] else: scale = np.asarray(scale) if scale.ndim == 0: n_params = 1 elif scale.shape == (3,): n_params = 3 else: err = ("Invalid shape for scale parameer. Need scalar or array of " "length 3. Got %s." % str(scale)) raise ValueError(err) return subjects_dir, subject_from, n_params, scale def scale_bem(subject_to, bem_name, subject_from=None, scale=None, subjects_dir=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. """ subjects_dir, subject_from, _, scale = _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 alredy exists: %s" % dst) surfs = read_bem_surfaces(src) if len(surfs) != 1: err = ("BEM file with more than one surface: %r" % src) raise NotImplementedError(err) surf0 = surfs[0] surf0['rr'] = surf0['rr'] * scale write_bem_surface(dst, surf0) def scale_labels(subject_to, pattern=None, overwrite=False, subject_from=None, scale=None, subjects_dir=None): """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 existsing 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. """ # read parameters from cfg if scale is None or subject_from is None: cfg = read_mri_cfg(subject_to, subjects_dir) if subject_from is None: subject_from = cfg['subject_from'] if scale is None: scale = cfg['scale'] # 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) def scale_mri(subject_from, subject_to, scale, overwrite=False, subjects_dir=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. See Also -------- scale_labels : add labels to a scaled MRI scale_source_space : add a source space to a scaled MRI """ subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) paths = _find_mri_paths(subject_from, subjects_dir=subjects_dir) scale = np.asarray(scale) # 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 overwrite: shutil.rmtree(dest) else: err = ("Subject directory for %s already exists: " "%r" % (subject_to, dest)) raise IOError(err) for dirname in paths['dirs']: dir_ = dirname.format(subject=subject_to, subjects_dir=subjects_dir) os.makedirs(dir_) # save MRI scaling parameters fname = os.path.join(dest, 'MRI scaling parameters.cfg') _write_mri_config(fname, subject_from, subject_to, scale) # 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) # BEM files [in m] for bem_name in paths['bem']: scale_bem(subject_to, bem_name, subject_from, scale, subjects_dir) # 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) for pt in pts: pt['r'] = pt['r'] * scale dest = fname.format(subject=subject_to, subjects_dir=subjects_dir) write_fiducials(dest, pts, cframe) # 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) # 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) # labels [in m] scale_labels(subject_to, subject_from=subject_from, scale=scale, subjects_dir=subjects_dir) def scale_source_space(subject_to, src_name, subject_from=None, scale=None, subjects_dir=None, n_jobs=1): """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). """ subjects_dir, subject_from, n_params, scale = _scale_params(subject_to, subject_from, scale, subjects_dir) # find the source space file names if src_name.isdigit(): spacing = src_name # spacing in mm src_pattern = src_fname else: match = re.match("(oct|ico)-?(\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) # prepare scaling parameters if n_params == 1: norm_scale = None elif n_params == 3: norm_scale = 1. / scale else: err = ("Invalid n_params entry in MRI cfg file: %s" % str(n_params)) raise RuntimeError(err) # 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 # distances and patch info if norm_scale is None: if ss['dist'] is not None: ss['dist'] *= scale ss['nearest_dist'] *= scale ss['dist_limit'] *= scale else: nn = ss['nn'] nn *= norm_scale norm = np.sqrt(np.sum(nn ** 2, 1)) nn /= norm[:, np.newaxis] if ss['dist'] is not None: add_dist = True if add_dist: logger.info("Recomputing distances, this might take a while") dist_limit = np.asscalar(sss[0]['dist_limit']) add_source_space_distances(sss, dist_limit, n_jobs) write_source_spaces(dst, sss)