# Authors: Matti Hämäläinen # Alexandre Gramfort # Martin Luessi # Eric Larson # # License: BSD-3-Clause # The computations in this code were primarily derived from Matti Hämäläinen's # C code. from copy import deepcopy from contextlib import contextmanager from pathlib import Path import os import os.path as op import numpy as np from ._compute_forward import _compute_forwards from ..io import read_info, _loc_to_coil_trans, _loc_to_eeg_loc, Info from ..io.compensator import get_current_comp, make_compensator from ..io.pick import _has_kit_refs, pick_types, pick_info from ..io.constants import FIFF, FWD from ..transforms import (_ensure_trans, transform_surface_to, apply_trans, _get_trans, _print_coord_trans, _coord_frame_name, Transform, invert_transform) from ..utils import logger, verbose, warn, _pl, _validate_type, _check_fname from ..source_space import (_ensure_src, _filter_source_spaces, _make_discrete_source_space, _complete_vol_src) from ..source_estimate import VolSourceEstimate from ..surface import _normalize_vectors, _CheckInside from ..bem import read_bem_solution, _bem_find_surface, ConductorModel from .forward import (Forward, _merge_fwds, convert_forward_solution, _FWD_ORDER) _accuracy_dict = dict(normal=FWD.COIL_ACCURACY_NORMAL, accurate=FWD.COIL_ACCURACY_ACCURATE) _extra_coil_def_fname = None @verbose def _read_coil_defs(verbose=None): """Read a coil definition file. Parameters ---------- %(verbose)s Returns ------- res : list of dict The coils. It is a dictionary with valid keys: 'cosmag' | 'coil_class' | 'coord_frame' | 'rmag' | 'type' | 'chname' | 'accuracy'. cosmag contains the direction of the coils and rmag contains the position vector. Notes ----- The global variable "_extra_coil_def_fname" can be used to prepend additional definitions. These are never added to the registry. """ coil_dir = op.join(op.split(__file__)[0], '..', 'data') coils = list() if _extra_coil_def_fname is not None: coils += _read_coil_def_file(_extra_coil_def_fname, use_registry=False) coils += _read_coil_def_file(op.join(coil_dir, 'coil_def.dat')) return coils # Typically we only have 1 or 2 coil def files, but they can end up being # read a lot. Let's keep a list of them and just reuse them: _coil_registry = {} def _read_coil_def_file(fname, use_registry=True): """Read a coil def file.""" if not use_registry or fname not in _coil_registry: big_val = 0.5 coils = list() with open(fname, 'r') as fid: lines = fid.readlines() lines = lines[::-1] while len(lines) > 0: line = lines.pop().strip() if line[0] == '#' and len(line) > 0: continue desc_start = line.find('"') desc_end = len(line) - 1 assert line.strip()[desc_end] == '"' desc = line[desc_start:desc_end] vals = np.fromstring(line[:desc_start].strip(), dtype=float, sep=' ') assert len(vals) == 6 npts = int(vals[3]) coil = dict(coil_type=vals[1], coil_class=vals[0], desc=desc, accuracy=vals[2], size=vals[4], base=vals[5]) # get parameters of each component rmag = list() cosmag = list() w = list() for p in range(npts): # get next non-comment line line = lines.pop() while line[0] == '#': line = lines.pop() vals = np.fromstring(line, sep=' ') if len(vals) != 7: raise RuntimeError( f'Could not interpret line {p + 1} as 7 points:\n' f'{line}') # Read and verify data for each integration point w.append(vals[0]) rmag.append(vals[[1, 2, 3]]) cosmag.append(vals[[4, 5, 6]]) w = np.array(w) rmag = np.array(rmag) cosmag = np.array(cosmag) size = np.sqrt(np.sum(cosmag ** 2, axis=1)) if np.any(np.sqrt(np.sum(rmag ** 2, axis=1)) > big_val): raise RuntimeError('Unreasonable integration point') if np.any(size <= 0): raise RuntimeError('Unreasonable normal') cosmag /= size[:, np.newaxis] coil.update(dict(w=w, cosmag=cosmag, rmag=rmag)) coils.append(coil) if use_registry: _coil_registry[fname] = coils if use_registry: coils = deepcopy(_coil_registry[fname]) logger.info('%d coil definition%s read', len(coils), _pl(coils)) return coils def _create_meg_coil(coilset, ch, acc, do_es): """Create a coil definition using templates, transform if necessary.""" # Also change the coordinate frame if so desired if ch['kind'] not in [FIFF.FIFFV_MEG_CH, FIFF.FIFFV_REF_MEG_CH]: raise RuntimeError('%s is not a MEG channel' % ch['ch_name']) # Simple linear search from the coil definitions for coil in coilset: if coil['coil_type'] == (ch['coil_type'] & 0xFFFF) and \ coil['accuracy'] == acc: break else: raise RuntimeError('Desired coil definition not found ' '(type = %d acc = %d)' % (ch['coil_type'], acc)) # Apply a coordinate transformation if so desired coil_trans = _loc_to_coil_trans(ch['loc']) # Create the result res = dict(chname=ch['ch_name'], coil_class=coil['coil_class'], accuracy=coil['accuracy'], base=coil['base'], size=coil['size'], type=ch['coil_type'], w=coil['w'], desc=coil['desc'], coord_frame=FIFF.FIFFV_COORD_DEVICE, rmag_orig=coil['rmag'], cosmag_orig=coil['cosmag'], coil_trans_orig=coil_trans, r0=coil_trans[:3, 3], rmag=apply_trans(coil_trans, coil['rmag']), cosmag=apply_trans(coil_trans, coil['cosmag'], False)) if do_es: r0_exey = (np.dot(coil['rmag'][:, :2], coil_trans[:3, :2].T) + coil_trans[:3, 3]) res.update(ex=coil_trans[:3, 0], ey=coil_trans[:3, 1], ez=coil_trans[:3, 2], r0_exey=r0_exey) return res def _create_eeg_el(ch, t=None): """Create an electrode definition, transform coords if necessary.""" if ch['kind'] != FIFF.FIFFV_EEG_CH: raise RuntimeError('%s is not an EEG channel. Cannot create an ' 'electrode definition.' % ch['ch_name']) if t is None: t = Transform('head', 'head') # identity, no change if t.from_str != 'head': raise RuntimeError('Inappropriate coordinate transformation') r0ex = _loc_to_eeg_loc(ch['loc']) if r0ex.shape[1] == 1: # no reference w = np.array([1.]) else: # has reference w = np.array([1., -1.]) # Optional coordinate transformation r0ex = apply_trans(t['trans'], r0ex.T) # The electrode location cosmag = r0ex.copy() _normalize_vectors(cosmag) res = dict(chname=ch['ch_name'], coil_class=FWD.COILC_EEG, w=w, accuracy=_accuracy_dict['normal'], type=ch['coil_type'], coord_frame=t['to'], rmag=r0ex, cosmag=cosmag) return res def _create_meg_coils(chs, acc, t=None, coilset=None, do_es=False): """Create a set of MEG coils in the head coordinate frame.""" acc = _accuracy_dict[acc] if isinstance(acc, str) else acc coilset = _read_coil_defs(verbose=False) if coilset is None else coilset coils = [_create_meg_coil(coilset, ch, acc, do_es) for ch in chs] _transform_orig_meg_coils(coils, t, do_es=do_es) return coils def _transform_orig_meg_coils(coils, t, do_es=True): """Transform original (device) MEG coil positions.""" if t is None: return for coil in coils: coil_trans = np.dot(t['trans'], coil['coil_trans_orig']) coil.update( coord_frame=t['to'], r0=coil_trans[:3, 3], rmag=apply_trans(coil_trans, coil['rmag_orig']), cosmag=apply_trans(coil_trans, coil['cosmag_orig'], False)) if do_es: r0_exey = (np.dot(coil['rmag_orig'][:, :2], coil_trans[:3, :2].T) + coil_trans[:3, 3]) coil.update(ex=coil_trans[:3, 0], ey=coil_trans[:3, 1], ez=coil_trans[:3, 2], r0_exey=r0_exey) def _create_eeg_els(chs): """Create a set of EEG electrodes in the head coordinate frame.""" return [_create_eeg_el(ch) for ch in chs] @verbose def _setup_bem(bem, bem_extra, neeg, mri_head_t, allow_none=False, verbose=None): """Set up a BEM for forward computation, making a copy and modifying.""" if allow_none and bem is None: return None logger.info('') _validate_type(bem, ('path-like', ConductorModel), bem) if not isinstance(bem, ConductorModel): logger.info('Setting up the BEM model using %s...\n' % bem_extra) bem = read_bem_solution(bem) else: bem = bem.copy() if bem['is_sphere']: logger.info('Using the sphere model.\n') if len(bem['layers']) == 0 and neeg > 0: raise RuntimeError('Spherical model has zero shells, cannot use ' 'with EEG data') if bem['coord_frame'] != FIFF.FIFFV_COORD_HEAD: raise RuntimeError('Spherical model is not in head coordinates') else: if bem['surfs'][0]['coord_frame'] != FIFF.FIFFV_COORD_MRI: raise RuntimeError( 'BEM is in %s coordinates, should be in MRI' % (_coord_frame_name(bem['surfs'][0]['coord_frame']),)) if neeg > 0 and len(bem['surfs']) == 1: raise RuntimeError('Cannot use a homogeneous (1-layer BEM) model ' 'for EEG forward calculations, consider ' 'using a 3-layer BEM instead') logger.info('Employing the head->MRI coordinate transform with the ' 'BEM model.') # fwd_bem_set_head_mri_t: Set the coordinate transformation bem['head_mri_t'] = _ensure_trans(mri_head_t, 'head', 'mri') logger.info('BEM model %s is now set up' % op.split(bem_extra)[1]) logger.info('') return bem @verbose def _prep_meg_channels(info, accuracy='accurate', exclude=(), *, ignore_ref=False, head_frame=True, do_es=False, verbose=None): """Prepare MEG coil definitions for forward calculation.""" # Find MEG channels ref_meg = True if not ignore_ref else False picks = pick_types(info, meg=True, ref_meg=ref_meg, exclude=exclude) # Make sure MEG coils exist if len(picks) <= 0: raise RuntimeError('Could not find any MEG channels') info_meg = pick_info(info, picks) del picks # Get channel info and names for MEG channels logger.info(f'Read {len(info_meg["chs"])} MEG channels from info') # Get MEG compensation channels compensator = post_picks = None ch_names = info_meg['ch_names'] if not ignore_ref: ref_picks = pick_types(info, meg=False, ref_meg=True, exclude=exclude) ncomp = len(ref_picks) if (ncomp > 0): logger.info(f'Read {ncomp} MEG compensation channels from info') # We need to check to make sure these are NOT KIT refs if _has_kit_refs(info, ref_picks): raise NotImplementedError( 'Cannot create forward solution with KIT reference ' 'channels. Consider using "ignore_ref=True" in ' 'calculation') logger.info( f'{len(info["comps"])} compensation data sets in info') # Compose a compensation data set if necessary # adapted from mne_make_ctf_comp() from mne_ctf_comp.c logger.info('Setting up compensation data...') comp_num = get_current_comp(info) if comp_num is None or comp_num == 0: logger.info(' No compensation set. Nothing more to do.') else: compensator = make_compensator( info_meg, 0, comp_num, exclude_comp_chs=False) logger.info( f' Desired compensation data ({comp_num}) found.') logger.info(' All compensation channels found.') logger.info(' Preselector created.') logger.info(' Compensation data matrix created.') logger.info(' Postselector created.') post_picks = pick_types( info_meg, meg=True, ref_meg=False, exclude=exclude) ch_names = [ch_names[pick] for pick in post_picks] # Create coil descriptions with transformation to head or device frame templates = _read_coil_defs() if head_frame: _print_coord_trans(info['dev_head_t']) transform = info['dev_head_t'] else: transform = None megcoils = _create_meg_coils( info_meg['chs'], accuracy, transform, templates, do_es=do_es) # Check that coordinate frame is correct and log it if head_frame: assert megcoils[0]['coord_frame'] == FIFF.FIFFV_COORD_HEAD logger.info('MEG coil definitions created in head coordinates.') else: assert megcoils[0]['coord_frame'] == FIFF.FIFFV_COORD_DEVICE logger.info('MEG coil definitions created in device coordinate.') return dict( defs=megcoils, ch_names=ch_names, compensator=compensator, info=info_meg, post_picks=post_picks) @verbose def _prep_eeg_channels(info, exclude=(), verbose=None): """Prepare EEG electrode definitions for forward calculation.""" info_extra = 'info' # Find EEG electrodes picks = pick_types(info, meg=False, eeg=True, ref_meg=False, exclude=exclude) # Make sure EEG electrodes exist neeg = len(picks) if neeg <= 0: raise RuntimeError('Could not find any EEG channels') # Get channel info and names for EEG channels eegchs = pick_info(info, picks)['chs'] eegnames = [info['ch_names'][p] for p in picks] logger.info('Read %3d EEG channels from %s' % (len(picks), info_extra)) # Create EEG electrode descriptions eegels = _create_eeg_els(eegchs) logger.info('Head coordinate coil definitions created.') return dict(defs=eegels, ch_names=eegnames) @verbose def _prepare_for_forward(src, mri_head_t, info, bem, mindist, n_jobs, bem_extra='', trans='', info_extra='', meg=True, eeg=True, ignore_ref=False, allow_bem_none=False, verbose=None): """Prepare for forward computation. The sensors dict contains keys for each sensor type, e.g. 'meg', 'eeg'. The vale for each of these is a dict that comes from _prep_meg_channels or _prep_eeg_channels. Each dict contains: - defs : a list of dicts (one per channel) with 'rmag', 'cosmag', etc. - ch_names: a list of str channel names corresponding to the defs - compensator (optional): the ndarray compensation matrix to apply - post_picks (optional): the ndarray of indices to pick after applying the compensator """ # Read the source locations logger.info('') # let's make a copy in case we modify something src = _ensure_src(src).copy() nsource = sum(s['nuse'] for s in src) if nsource == 0: raise RuntimeError('No sources are active in these source spaces. ' '"do_all" option should be used.') logger.info('Read %d source spaces a total of %d active source locations' % (len(src), nsource)) # Delete some keys to clean up the source space: for key in ['working_dir', 'command_line']: if key in src.info: del src.info[key] # Read the MRI -> head coordinate transformation logger.info('') _print_coord_trans(mri_head_t) # make a new dict with the relevant information arg_list = [info_extra, trans, src, bem_extra, meg, eeg, mindist, n_jobs, verbose] cmd = 'make_forward_solution(%s)' % (', '.join([str(a) for a in arg_list])) mri_id = dict(machid=np.zeros(2, np.int32), version=0, secs=0, usecs=0) info_trans = str(trans) if isinstance(trans, Path) else trans info = Info(chs=info['chs'], comps=info['comps'], dev_head_t=info['dev_head_t'], mri_file=info_trans, mri_id=mri_id, meas_file=info_extra, meas_id=None, working_dir=os.getcwd(), command_line=cmd, bads=info['bads'], mri_head_t=mri_head_t) info._update_redundant() info._check_consistency() logger.info('') sensors = dict() if meg and len(pick_types(info, meg=True, ref_meg=False, exclude=[])) > 0: sensors['meg'] = _prep_meg_channels(info, ignore_ref=ignore_ref) if eeg and len(pick_types(info, eeg=True, exclude=[])) > 0: sensors['eeg'] = _prep_eeg_channels(info) # Check that some channels were found if len(sensors) == 0: raise RuntimeError('No MEG or EEG channels found.') # pick out final info info = pick_info(info, pick_types(info, meg=meg, eeg=eeg, ref_meg=False, exclude=[])) # Transform the source spaces into the appropriate coordinates # (will either be HEAD or MRI) for s in src: transform_surface_to(s, 'head', mri_head_t) logger.info('Source spaces are now in %s coordinates.' % _coord_frame_name(s['coord_frame'])) # Prepare the BEM model eegnames = sensors.get('eeg', dict()).get('ch_names', []) bem = _setup_bem(bem, bem_extra, len(eegnames), mri_head_t, allow_none=allow_bem_none) del eegnames # Circumvent numerical problems by excluding points too close to the skull, # and check that sensors are not inside any BEM surface if bem is not None: if not bem['is_sphere']: check_surface = 'inner skull surface' inner_skull = _bem_find_surface(bem, 'inner_skull') check_inside = _filter_source_spaces( inner_skull, mindist, mri_head_t, src, n_jobs) logger.info('') if len(bem['surfs']) == 3: check_surface = 'scalp surface' check_inside = _CheckInside(_bem_find_surface(bem, 'head')) else: check_surface = 'outermost sphere shell' if len(bem['layers']) == 0: def check_inside(x): return np.zeros(len(x), bool) else: def check_inside(x): return (np.linalg.norm(x - bem['r0'], axis=1) < bem['layers'][-1]['rad']) if 'meg' in sensors: meg_loc = apply_trans( invert_transform(mri_head_t), np.array([coil['r0'] for coil in sensors['meg']['defs']])) n_inside = check_inside(meg_loc).sum() if n_inside: raise RuntimeError( f'Found {n_inside} MEG sensor{_pl(n_inside)} inside the ' f'{check_surface}, perhaps coordinate frames and/or ' 'coregistration must be incorrect') rr = np.concatenate([s['rr'][s['vertno']] for s in src]) if len(rr) < 1: raise RuntimeError('No points left in source space after excluding ' 'points close to inner skull.') # deal with free orientations: source_nn = np.tile(np.eye(3), (len(rr), 1)) update_kwargs = dict(nchan=len(info['ch_names']), nsource=len(rr), info=info, src=src, source_nn=source_nn, source_rr=rr, surf_ori=False, mri_head_t=mri_head_t) return sensors, rr, info, update_kwargs, bem @verbose def make_forward_solution(info, trans, src, bem, meg=True, eeg=True, *, mindist=0.0, ignore_ref=False, n_jobs=None, verbose=None): """Calculate a forward solution for a subject. Parameters ---------- %(info_str)s %(trans)s .. versionchanged:: 0.19 Support for 'fsaverage' argument. src : path-like | instance of SourceSpaces If string, should be a source space filename. Can also be an instance of loaded or generated SourceSpaces. bem : path-like | dict Filename of the BEM (e.g., "sample-5120-5120-5120-bem-sol.fif") to use, or a loaded sphere model (dict). meg : bool If True (Default), include MEG computations. eeg : bool If True (Default), include EEG computations. mindist : float Minimum distance of sources from inner skull surface (in mm). ignore_ref : bool If True, do not include reference channels in compensation. This option should be True for KIT files, since forward computation with reference channels is not currently supported. %(n_jobs)s %(verbose)s Returns ------- fwd : instance of Forward The forward solution. See Also -------- convert_forward_solution Notes ----- The ``--grad`` option from MNE-C (to compute gradients) is not implemented here. To create a fixed-orientation forward solution, use this function followed by :func:`mne.convert_forward_solution`. .. note:: If the BEM solution was computed with :doc:`OpenMEEG ` in :func:`mne.make_bem_solution`, then OpenMEEG will automatically be used to compute the forward solution. .. versionchanged:: 1.2 Added support for OpenMEEG-based forward solution calculations. """ # Currently not (sup)ported: # 1. --grad option (gradients of the field, not used much) # 2. --fixed option (can be computed post-hoc) # 3. --mricoord option (probably not necessary) # read the transformation from MRI to HEAD coordinates # (could also be HEAD to MRI) mri_head_t, trans = _get_trans(trans) if isinstance(bem, ConductorModel): bem_extra = 'instance of ConductorModel' else: bem_extra = bem _validate_type(info, ('path-like', Info), 'info') if not isinstance(info, Info): info_extra = op.split(info)[1] info = _check_fname(info, must_exist=True, overwrite='read', name='info') info = read_info(info, verbose=False) else: info_extra = 'instance of Info' # Report the setup logger.info('Source space : %s' % src) logger.info('MRI -> head transform : %s' % trans) logger.info('Measurement data : %s' % info_extra) if isinstance(bem, ConductorModel) and bem['is_sphere']: logger.info('Sphere model : origin at %s mm' % (bem['r0'],)) logger.info('Standard field computations') else: logger.info('Conductor model : %s' % bem_extra) logger.info('Accurate field computations') logger.info('Do computations in %s coordinates', _coord_frame_name(FIFF.FIFFV_COORD_HEAD)) logger.info('Free source orientations') # Create MEG coils and EEG electrodes in the head coordinate frame sensors, rr, info, update_kwargs, bem = _prepare_for_forward( src, mri_head_t, info, bem, mindist, n_jobs, bem_extra, trans, info_extra, meg, eeg, ignore_ref) del (src, mri_head_t, trans, info_extra, bem_extra, mindist, meg, eeg, ignore_ref) # Time to do the heavy lifting: MEG first, then EEG fwds = _compute_forwards(rr, bem=bem, sensors=sensors, n_jobs=n_jobs) # merge forwards fwds = {key: _to_forward_dict(fwds[key], sensors[key]['ch_names']) for key in _FWD_ORDER if key in fwds} fwd = _merge_fwds(fwds, verbose=False) del fwds logger.info('') # Don't transform the source spaces back into MRI coordinates (which is # done in the C code) because mne-python assumes forward solution source # spaces are in head coords. fwd.update(**update_kwargs) logger.info('Finished.') return fwd @verbose def make_forward_dipole(dipole, bem, info, trans=None, n_jobs=None, *, verbose=None): """Convert dipole object to source estimate and calculate forward operator. The instance of Dipole is converted to a discrete source space, which is then combined with a BEM or a sphere model and the sensor information in info to form a forward operator. The source estimate object (with the forward operator) can be projected to sensor-space using :func:`mne.simulation.simulate_evoked`. .. note:: If the (unique) time points of the dipole object are unevenly spaced, the first output will be a list of single-timepoint source estimates. Parameters ---------- %(dipole)s bem : str | dict The BEM filename (str) or a loaded sphere model (dict). info : instance of Info The measurement information dictionary. It is sensor-information etc., e.g., from a real data file. trans : str | None The head<->MRI transform filename. Must be provided unless BEM is a sphere model. %(n_jobs)s %(verbose)s Returns ------- fwd : instance of Forward The forward solution corresponding to the source estimate(s). stc : instance of VolSourceEstimate | list of VolSourceEstimate The dipoles converted to a discrete set of points and associated time courses. If the time points of the dipole are unevenly spaced, a list of single-timepoint source estimates are returned. See Also -------- mne.simulation.simulate_evoked Notes ----- .. versionadded:: 0.12.0 """ if isinstance(dipole, list): from ..dipole import _concatenate_dipoles # To avoid circular import dipole = _concatenate_dipoles(dipole) # Make copies to avoid mangling original dipole times = dipole.times.copy() pos = dipole.pos.copy() amplitude = dipole.amplitude.copy() ori = dipole.ori.copy() # Convert positions to discrete source space (allows duplicate rr & nn) # NB information about dipole orientation enters here, then no more sources = dict(rr=pos, nn=ori) # Dipole objects must be in the head frame src = _complete_vol_src( [_make_discrete_source_space(sources, coord_frame='head')]) # Forward operator created for channels in info (use pick_info to restrict) # Use defaults for most params, including min_dist fwd = make_forward_solution(info, trans, src, bem, n_jobs=n_jobs, verbose=verbose) # Convert from free orientations to fixed (in-place) convert_forward_solution(fwd, surf_ori=False, force_fixed=True, copy=False, use_cps=False, verbose=None) # Check for omissions due to proximity to inner skull in # make_forward_solution, which will result in an exception if fwd['src'][0]['nuse'] != len(pos): inuse = fwd['src'][0]['inuse'].astype(bool) head = ('The following dipoles are outside the inner skull boundary') msg = len(head) * '#' + '\n' + head + '\n' for (t, pos) in zip(times[np.logical_not(inuse)], pos[np.logical_not(inuse)]): msg += ' t={:.0f} ms, pos=({:.0f}, {:.0f}, {:.0f}) mm\n'.\ format(t * 1000., pos[0] * 1000., pos[1] * 1000., pos[2] * 1000.) msg += len(head) * '#' logger.error(msg) raise ValueError('One or more dipoles outside the inner skull.') # multiple dipoles (rr and nn) per time instant allowed # uneven sampling in time returns list timepoints = np.unique(times) if len(timepoints) > 1: tdiff = np.diff(timepoints) if not np.allclose(tdiff, tdiff[0]): warn('Unique time points of dipoles unevenly spaced: returned ' 'stc will be a list, one for each time point.') tstep = -1.0 else: tstep = tdiff[0] elif len(timepoints) == 1: tstep = 0.001 # Build the data matrix, essentially a block-diagonal with # n_rows: number of dipoles in total (dipole.amplitudes) # n_cols: number of unique time points in dipole.times # amplitude with identical value of times go together in one col (others=0) data = np.zeros((len(amplitude), len(timepoints))) # (n_d, n_t) row = 0 for tpind, tp in enumerate(timepoints): amp = amplitude[np.in1d(times, tp)] data[row:row + len(amp), tpind] = amp row += len(amp) if tstep > 0: stc = VolSourceEstimate(data, vertices=[fwd['src'][0]['vertno']], tmin=timepoints[0], tstep=tstep, subject=None) else: # Must return a list of stc, one for each time point stc = [] for col, tp in enumerate(timepoints): stc += [VolSourceEstimate(data[:, col][:, np.newaxis], vertices=[fwd['src'][0]['vertno']], tmin=tp, tstep=0.001, subject=None)] return fwd, stc def _to_forward_dict(fwd, names, fwd_grad=None, coord_frame=FIFF.FIFFV_COORD_HEAD, source_ori=FIFF.FIFFV_MNE_FREE_ORI): """Convert forward solution matrices to dicts.""" assert names is not None sol = dict(data=fwd.T, nrow=fwd.shape[1], ncol=fwd.shape[0], row_names=names, col_names=[]) fwd = Forward(sol=sol, source_ori=source_ori, nsource=sol['ncol'], coord_frame=coord_frame, sol_grad=None, nchan=sol['nrow'], _orig_source_ori=source_ori, _orig_sol=sol['data'].copy(), _orig_sol_grad=None) if fwd_grad is not None: sol_grad = dict(data=fwd_grad.T, nrow=fwd_grad.shape[1], ncol=fwd_grad.shape[0], row_names=names, col_names=[]) fwd.update(dict(sol_grad=sol_grad), _orig_sol_grad=sol_grad['data'].copy()) return fwd @contextmanager def use_coil_def(fname): """Use a custom coil definition file. Parameters ---------- fname : str The filename of the coil definition file. Returns ------- context : contextmanager The context for using the coil definition. Notes ----- This is meant to be used a context manager such as: >>> with use_coil_def(my_fname): # doctest:+SKIP ... make_forward_solution(...) This allows using custom coil definitions with functions that require forward modeling. """ global _extra_coil_def_fname _extra_coil_def_fname = fname try: yield finally: _extra_coil_def_fname = None